diff --git a/.travis.yml b/.travis.yml index fef399f..23ed105 100644 --- a/.travis.yml +++ b/.travis.yml @@ -32,9 +32,7 @@ before_install: - sudo tar --wildcards --strip=2 -C /usr/lib/x86_64-linux-gnu/ -xf opencv-3.4-x86_64.pkg.tar.xz usr/lib/libopencv_xfeatures2d.so* usr/lib/libopencv_xphoto.so* usr/lib/libopencv_optflow.so* script: - - qmake -recursive PREFIX=/app - - make - - sudo make install + - qmake -recursive PREFIX=/app && make && sudo make install after_success: - find /app diff --git a/IPL/.qmake.stash b/IPL/.qmake.stash index 1b8387c..17d8eea 100644 --- a/IPL/.qmake.stash +++ b/IPL/.qmake.stash @@ -65,3 +65,34 @@ QMAKE_DEFAULT_LIBDIRS = \ QMAKE_MAC_SDK.macosx10.12.Path = /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.12.sdk QMAKE_MAC_SDK.macosx10.12.PlatformPath = /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform QMAKE_MAC_SDK.macosx10.12.SDKVersion = 10.12 +QMAKE_CXX.INCDIRS = \ + /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/include/c++/v1 \ + /usr/local/include \ + /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/clang/8.0.0/include \ + /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/include \ + /usr/include \ + "/System/Library/Frameworks (framework directory)" \ + "/Library/Frameworks (framework directory)" \ + /usr/include/c++/7 \ + /usr/include/x86_64-linux-gnu/c++/7 \ + /usr/include/c++/7/backward \ + /usr/lib/gcc/x86_64-linux-gnu/7/include \ + /usr/local/include \ + /usr/lib/gcc/x86_64-linux-gnu/7/include-fixed \ + /usr/include/x86_64-linux-gnu \ + /usr/include +QMAKE_CXX.LIBDIRS = \ + /lib \ + /usr/lib \ + /usr/lib/gcc/x86_64-linux-gnu/7 \ + /usr/lib/x86_64-linux-gnu \ + /lib/x86_64-linux-gnu +QMAKE_CXX.QT_COMPILER_STDCXX = 201402L +QMAKE_CXX.QMAKE_GCC_MAJOR_VERSION = 7 +QMAKE_CXX.QMAKE_GCC_MINOR_VERSION = 3 +QMAKE_CXX.QMAKE_GCC_PATCH_VERSION = 0 +QMAKE_CXX.COMPILER_MACROS = \ + QT_COMPILER_STDCXX \ + QMAKE_GCC_MAJOR_VERSION \ + QMAKE_GCC_MINOR_VERSION \ + QMAKE_GCC_PATCH_VERSION diff --git a/IPL/IPL.pro b/IPL/IPL.pro index 0a656ba..d1d291d 100644 --- a/IPL/IPL.pro +++ b/IPL/IPL.pro @@ -114,7 +114,6 @@ macx { LIBS += -L$$PWD/../_lib/opencv/x64/clang/lib/ -lopencv_xfeatures2d.3.1.0 LIBS += -L$$PWD/../_lib/opencv/x64/clang/lib/ -lopencv_photo.3.1.0 LIBS += -L$$PWD/../_lib/opencv/x64/clang/lib/ -lopencv_xphoto.3.1.0 - } linux { diff --git a/IPL/include/IPLCameraIO.h b/IPL/include/IPLCameraIO.h index 34d4322..d012132 100644 --- a/IPL/include/IPLCameraIO.h +++ b/IPL/include/IPLCameraIO.h @@ -23,9 +23,9 @@ #include "IPL_global.h" #include "IPLImage.h" -#include "opencv2/core/core.hpp" -#include "opencv2/imgproc/imgproc.hpp" -#include "opencv2/highgui/highgui.hpp" +#include +#include +#include /** * @brief The IPLCameraIO class diff --git a/IPL/include/IPLImage.h b/IPL/include/IPLImage.h index a3d1e5d..a73cbf9 100644 --- a/IPL/include/IPLImage.h +++ b/IPL/include/IPLImage.h @@ -31,8 +31,8 @@ #include -#include "opencv2/core/core.hpp" -#include "opencv2/imgproc/imgproc.hpp" +#include +#include /** * @brief The IPLImage class diff --git a/IPL/include/opencv/opencv/cv.h b/IPL/include/opencv/opencv/cv.h deleted file mode 100644 index 0aefc6d..0000000 --- a/IPL/include/opencv/opencv/cv.h +++ /dev/null @@ -1,73 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OLD_CV_H__ -#define __OPENCV_OLD_CV_H__ - -#if defined(_MSC_VER) - #define CV_DO_PRAGMA(x) __pragma(x) - #define __CVSTR2__(x) #x - #define __CVSTR1__(x) __CVSTR2__(x) - #define __CVMSVCLOC__ __FILE__ "("__CVSTR1__(__LINE__)") : " - #define CV_MSG_PRAGMA(_msg) CV_DO_PRAGMA(message (__CVMSVCLOC__ _msg)) -#elif defined(__GNUC__) - #define CV_DO_PRAGMA(x) _Pragma (#x) - #define CV_MSG_PRAGMA(_msg) CV_DO_PRAGMA(message (_msg)) -#else - #define CV_DO_PRAGMA(x) - #define CV_MSG_PRAGMA(_msg) -#endif -#define CV_WARNING(x) CV_MSG_PRAGMA("Warning: " #x) - -//CV_WARNING("This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module") - -#include "opencv2/core/core_c.h" -#include "opencv2/imgproc/imgproc_c.h" -#include "opencv2/photo/photo_c.h" -#include "opencv2/video/tracking_c.h" -#include "opencv2/objdetect/objdetect_c.h" - -#if !defined(CV_IMPL) -#define CV_IMPL extern "C" -#endif //CV_IMPL - -#endif // __OPENCV_OLD_CV_H_ diff --git a/IPL/include/opencv/opencv/cv.hpp b/IPL/include/opencv/opencv/cv.hpp deleted file mode 100644 index e498d7a..0000000 --- a/IPL/include/opencv/opencv/cv.hpp +++ /dev/null @@ -1,60 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OLD_CV_HPP__ -#define __OPENCV_OLD_CV_HPP__ - -//#if defined(__GNUC__) -//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" -//#endif - -#include "cv.h" -#include "opencv2/core.hpp" -#include "opencv2/imgproc.hpp" -#include "opencv2/photo.hpp" -#include "opencv2/video.hpp" -#include "opencv2/highgui.hpp" -#include "opencv2/features2d.hpp" -#include "opencv2/calib3d.hpp" -#include "opencv2/objdetect.hpp" - -#endif diff --git a/IPL/include/opencv/opencv/cvaux.h b/IPL/include/opencv/opencv/cvaux.h deleted file mode 100644 index fe86c5d..0000000 --- a/IPL/include/opencv/opencv/cvaux.h +++ /dev/null @@ -1,57 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// Intel License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000, Intel Corporation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of Intel Corporation may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OLD_AUX_H__ -#define __OPENCV_OLD_AUX_H__ - -//#if defined(__GNUC__) -//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" -//#endif - -#include "opencv2/core/core_c.h" -#include "opencv2/imgproc/imgproc_c.h" -#include "opencv2/photo/photo_c.h" -#include "opencv2/video/tracking_c.h" -#include "opencv2/objdetect/objdetect_c.h" - -#endif - -/* End of file. */ diff --git a/IPL/include/opencv/opencv/cvaux.hpp b/IPL/include/opencv/opencv/cvaux.hpp deleted file mode 100644 index b0e60a3..0000000 --- a/IPL/include/opencv/opencv/cvaux.hpp +++ /dev/null @@ -1,52 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// Intel License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000, Intel Corporation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of Intel Corporation may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OLD_AUX_HPP__ -#define __OPENCV_OLD_AUX_HPP__ - -//#if defined(__GNUC__) -//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" -//#endif - -#include "cvaux.h" -#include "opencv2/core/utility.hpp" - -#endif diff --git a/IPL/include/opencv/opencv/cvwimage.h b/IPL/include/opencv/opencv/cvwimage.h deleted file mode 100644 index de89c92..0000000 --- a/IPL/include/opencv/opencv/cvwimage.h +++ /dev/null @@ -1,46 +0,0 @@ -/////////////////////////////////////////////////////////////////////////////// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to -// this license. If you do not agree to this license, do not download, -// install, copy or use the software. -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2008, Google, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of Intel Corporation or contributors may not be used to endorse -// or promote products derived from this software without specific -// prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" -// and any express or implied warranties, including, but not limited to, the -// implied warranties of merchantability and fitness for a particular purpose -// are disclaimed. In no event shall the Intel Corporation or contributors be -// liable for any direct, indirect, incidental, special, exemplary, or -// consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. - - -#ifndef __OPENCV_OLD_WIMAGE_HPP__ -#define __OPENCV_OLD_WIMAGE_HPP__ - -#include "opencv2/core/wimage.hpp" - -#endif diff --git a/IPL/include/opencv/opencv/cxcore.h b/IPL/include/opencv/opencv/cxcore.h deleted file mode 100644 index 0982bd7..0000000 --- a/IPL/include/opencv/opencv/cxcore.h +++ /dev/null @@ -1,52 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OLD_CXCORE_H__ -#define __OPENCV_OLD_CXCORE_H__ - -//#if defined(__GNUC__) -//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" -//#endif - -#include "opencv2/core/core_c.h" - -#endif diff --git a/IPL/include/opencv/opencv/cxcore.hpp b/IPL/include/opencv/opencv/cxcore.hpp deleted file mode 100644 index 9af4ac7..0000000 --- a/IPL/include/opencv/opencv/cxcore.hpp +++ /dev/null @@ -1,53 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OLD_CXCORE_HPP__ -#define __OPENCV_OLD_CXCORE_HPP__ - -//#if defined(__GNUC__) -//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" -//#endif - -#include "cxcore.h" -#include "opencv2/core.hpp" - -#endif diff --git a/IPL/include/opencv/opencv/cxeigen.hpp b/IPL/include/opencv/opencv/cxeigen.hpp deleted file mode 100644 index 1f04d1a..0000000 --- a/IPL/include/opencv/opencv/cxeigen.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OLD_EIGEN_HPP__ -#define __OPENCV_OLD_EIGEN_HPP__ - -#include "opencv2/core/eigen.hpp" - -#endif diff --git a/IPL/include/opencv/opencv/cxmisc.h b/IPL/include/opencv/opencv/cxmisc.h deleted file mode 100644 index 6c93a0c..0000000 --- a/IPL/include/opencv/opencv/cxmisc.h +++ /dev/null @@ -1,8 +0,0 @@ -#ifndef __OPENCV_OLD_CXMISC_H__ -#define __OPENCV_OLD_CXMISC_H__ - -#ifdef __cplusplus -# include "opencv2/core/utility.hpp" -#endif - -#endif diff --git a/IPL/include/opencv/opencv/highgui.h b/IPL/include/opencv/opencv/highgui.h deleted file mode 100644 index 0261029..0000000 --- a/IPL/include/opencv/opencv/highgui.h +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// Intel License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000, Intel Corporation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of Intel Corporation may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OLD_HIGHGUI_H__ -#define __OPENCV_OLD_HIGHGUI_H__ - -#include "opencv2/core/core_c.h" -#include "opencv2/highgui/highgui_c.h" - -#endif diff --git a/IPL/include/opencv/opencv/ml.h b/IPL/include/opencv/opencv/ml.h deleted file mode 100644 index d8e967f..0000000 --- a/IPL/include/opencv/opencv/ml.h +++ /dev/null @@ -1,47 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// Intel License Agreement -// -// Copyright (C) 2000, Intel Corporation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of Intel Corporation may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OLD_ML_H__ -#define __OPENCV_OLD_ML_H__ - -#include "opencv2/core/core_c.h" -#include "opencv2/ml.hpp" - -#endif diff --git a/IPL/include/opencv/opencv2/aruco.hpp b/IPL/include/opencv/opencv2/aruco.hpp deleted file mode 100644 index 3f45dc1..0000000 --- a/IPL/include/opencv/opencv2/aruco.hpp +++ /dev/null @@ -1,513 +0,0 @@ -/* -By downloading, copying, installing or using the software you agree to this -license. If you do not agree to this license, do not download, install, -copy or use the software. - - License Agreement - For Open Source Computer Vision Library - (3-clause BSD License) - -Copyright (C) 2013, OpenCV Foundation, all rights reserved. -Third party copyrights are property of their respective owners. - -Redistribution and use in source and binary forms, with or without modification, -are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. - - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - - * Neither the names of the copyright holders nor the names of the contributors - may be used to endorse or promote products derived from this software - without specific prior written permission. - -This software is provided by the copyright holders and contributors "as is" and -any express or implied warranties, including, but not limited to, the implied -warranties of merchantability and fitness for a particular purpose are -disclaimed. In no event shall copyright holders or contributors be liable for -any direct, indirect, incidental, special, exemplary, or consequential damages -(including, but not limited to, procurement of substitute goods or services; -loss of use, data, or profits; or business interruption) however caused -and on any theory of liability, whether in contract, strict liability, -or tort (including negligence or otherwise) arising in any way out of -the use of this software, even if advised of the possibility of such damage. -*/ - -#ifndef __OPENCV_ARUCO_HPP__ -#define __OPENCV_ARUCO_HPP__ - -#include -#include -#include "opencv2/aruco/dictionary.hpp" - -/** - * @defgroup aruco ArUco Marker Detection - * This module is dedicated to square fiducial markers (also known as Augmented Reality Markers) - * These markers are useful for easy, fast and robust camera pose estimation.ç - * - * The main functionalities are: - * - Detection of markers in a image - * - Pose estimation from a single marker or from a board/set of markers - * - Detection of ChArUco board for high subpixel accuracy - * - Camera calibration from both, ArUco boards and ChArUco boards. - * - Detection of ChArUco diamond markers - * The samples directory includes easy examples of how to use the module. - * - * The implementation is based on the ArUco Library by R. Muñoz-Salinas and S. Garrido-Jurado. - * - * @sa S. Garrido-Jurado, R. Muñoz-Salinas, F. J. Madrid-Cuevas, and M. J. Marín-Jiménez. 2014. - * "Automatic generation and detection of highly reliable fiducial markers under occlusion". - * Pattern Recogn. 47, 6 (June 2014), 2280-2292. DOI=10.1016/j.patcog.2014.01.005 - * - * @sa http://www.uco.es/investiga/grupos/ava/node/26 - * - * This module has been originally developed by Sergio Garrido-Jurado as a project - * for Google Summer of Code 2015 (GSoC 15). - * - * -*/ - -namespace cv { -namespace aruco { - -//! @addtogroup aruco -//! @{ - - - -/** - * @brief Parameters for the detectMarker process: - * - adaptiveThreshWinSizeMin: minimum window size for adaptive thresholding before finding - * contours (default 3). - * - adaptiveThreshWinSizeMax: maximum window size for adaptive thresholding before finding - * contours (default 23). - * - adaptiveThreshWinSizeStep: increments from adaptiveThreshWinSizeMin to adaptiveThreshWinSizeMax - * during the thresholding (default 10). - * - adaptiveThreshConstant: constant for adaptive thresholding before finding contours (default 7) - * - minMarkerPerimeterRate: determine minimum perimeter for marker contour to be detected. This - * is defined as a rate respect to the maximum dimension of the input image (default 0.03). - * - maxMarkerPerimeterRate: determine maximum perimeter for marker contour to be detected. This - * is defined as a rate respect to the maximum dimension of the input image (default 4.0). - * - polygonalApproxAccuracyRate: minimum accuracy during the polygonal approximation process to - * determine which contours are squares. - * - minCornerDistanceRate: minimum distance between corners for detected markers relative to its - * perimeter (default 0.05) - * - minDistanceToBorder: minimum distance of any corner to the image border for detected markers - * (in pixels) (default 3) - * - minMarkerDistanceRate: minimum mean distance beetween two marker corners to be considered - * similar, so that the smaller one is removed. The rate is relative to the smaller perimeter - * of the two markers (default 0.05). - * - doCornerRefinement: do subpixel refinement or not - * - cornerRefinementWinSize: window size for the corner refinement process (in pixels) (default 5). - * - cornerRefinementMaxIterations: maximum number of iterations for stop criteria of the corner - * refinement process (default 30). - * - cornerRefinementMinAccuracy: minimum error for the stop cristeria of the corner refinement - * process (default: 0.1) - * - markerBorderBits: number of bits of the marker border, i.e. marker border width (default 1). - * - perpectiveRemovePixelPerCell: number of bits (per dimension) for each cell of the marker - * when removing the perspective (default 8). - * - perspectiveRemoveIgnoredMarginPerCell: width of the margin of pixels on each cell not - * considered for the determination of the cell bit. Represents the rate respect to the total - * size of the cell, i.e. perpectiveRemovePixelPerCell (default 0.13) - * - maxErroneousBitsInBorderRate: maximum number of accepted erroneous bits in the border (i.e. - * number of allowed white bits in the border). Represented as a rate respect to the total - * number of bits per marker (default 0.35). - * - minOtsuStdDev: minimun standard deviation in pixels values during the decodification step to - * apply Otsu thresholding (otherwise, all the bits are set to 0 or 1 depending on mean higher - * than 128 or not) (default 5.0) - * - errorCorrectionRate error correction rate respect to the maximun error correction capability - * for each dictionary. (default 0.6). - */ -struct CV_EXPORTS_W DetectorParameters { - - DetectorParameters(); - - CV_WRAP static Ptr create(); - - CV_PROP_RW int adaptiveThreshWinSizeMin; - CV_PROP_RW int adaptiveThreshWinSizeMax; - CV_PROP_RW int adaptiveThreshWinSizeStep; - CV_PROP_RW double adaptiveThreshConstant; - CV_PROP_RW double minMarkerPerimeterRate; - CV_PROP_RW double maxMarkerPerimeterRate; - CV_PROP_RW double polygonalApproxAccuracyRate; - CV_PROP_RW double minCornerDistanceRate; - CV_PROP_RW int minDistanceToBorder; - CV_PROP_RW double minMarkerDistanceRate; - CV_PROP_RW bool doCornerRefinement; - CV_PROP_RW int cornerRefinementWinSize; - CV_PROP_RW int cornerRefinementMaxIterations; - CV_PROP_RW double cornerRefinementMinAccuracy; - CV_PROP_RW int markerBorderBits; - CV_PROP_RW int perspectiveRemovePixelPerCell; - CV_PROP_RW double perspectiveRemoveIgnoredMarginPerCell; - CV_PROP_RW double maxErroneousBitsInBorderRate; - CV_PROP_RW double minOtsuStdDev; - CV_PROP_RW double errorCorrectionRate; -}; - - - -/** - * @brief Basic marker detection - * - * @param image input image - * @param dictionary indicates the type of markers that will be searched - * @param corners vector of detected marker corners. For each marker, its four corners - * are provided, (e.g std::vector > ). For N detected markers, - * the dimensions of this array is Nx4. The order of the corners is clockwise. - * @param ids vector of identifiers of the detected markers. The identifier is of type int - * (e.g. std::vector). For N detected markers, the size of ids is also N. - * The identifiers have the same order than the markers in the imgPoints array. - * @param parameters marker detection parameters - * @param rejectedImgPoints contains the imgPoints of those squares whose inner code has not a - * correct codification. Useful for debugging purposes. - * - * Performs marker detection in the input image. Only markers included in the specific dictionary - * are searched. For each detected marker, it returns the 2D position of its corner in the image - * and its corresponding identifier. - * Note that this function does not perform pose estimation. - * @sa estimatePoseSingleMarkers, estimatePoseBoard - * - */ -CV_EXPORTS_W void detectMarkers(InputArray image, Ptr &dictionary, OutputArrayOfArrays corners, - OutputArray ids, const Ptr ¶meters = DetectorParameters::create(), - OutputArrayOfArrays rejectedImgPoints = noArray()); - - - -/** - * @brief Pose estimation for single markers - * - * @param corners vector of already detected markers corners. For each marker, its four corners - * are provided, (e.g std::vector > ). For N detected markers, - * the dimensions of this array should be Nx4. The order of the corners should be clockwise. - * @sa detectMarkers - * @param markerLength the length of the markers' side. The returning translation vectors will - * be in the same unit. Normally, unit is meters. - * @param cameraMatrix input 3x3 floating-point camera matrix - * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ - * @param distCoeffs vector of distortion coefficients - * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements - * @param rvecs array of output rotation vectors (@sa Rodrigues) (e.g. std::vector>). - * Each element in rvecs corresponds to the specific marker in imgPoints. - * @param tvecs array of output translation vectors (e.g. std::vector>). - * Each element in tvecs corresponds to the specific marker in imgPoints. - * - * This function receives the detected markers and returns their pose estimation respect to - * the camera individually. So for each marker, one rotation and translation vector is returned. - * The returned transformation is the one that transforms points from each marker coordinate system - * to the camera coordinate system. - * The marker corrdinate system is centered on the middle of the marker, with the Z axis - * perpendicular to the marker plane. - * The coordinates of the four corners of the marker in its own coordinate system are: - * (-markerLength/2, markerLength/2, 0), (markerLength/2, markerLength/2, 0), - * (markerLength/2, -markerLength/2, 0), (-markerLength/2, -markerLength/2, 0) - */ -CV_EXPORTS_W void estimatePoseSingleMarkers(InputArrayOfArrays corners, float markerLength, - InputArray cameraMatrix, InputArray distCoeffs, - OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs); - - - -/** - * @brief Board of markers - * - * A board is a set of markers in the 3D space with a common cordinate system. - * The common form of a board of marker is a planar (2D) board, however any 3D layout can be used. - * A Board object is composed by: - * - The object points of the marker corners, i.e. their coordinates respect to the board system. - * - The dictionary which indicates the type of markers of the board - * - The identifier of all the markers in the board. - */ -class CV_EXPORTS_W Board { - - public: - // array of object points of all the marker corners in the board - // each marker include its 4 corners, i.e. for M markers, the size is Mx4 - std::vector< std::vector< Point3f > > objPoints; - - // the dictionary of markers employed for this board - Ptr dictionary; - - // vector of the identifiers of the markers in the board (same size than objPoints) - // The identifiers refers to the board dictionary - std::vector< int > ids; -}; - - - -/** - * @brief Planar board with grid arrangement of markers - * More common type of board. All markers are placed in the same plane in a grid arrangment. - * The board can be drawn using drawPlanarBoard() function (@sa drawPlanarBoard) - */ -class CV_EXPORTS_W GridBoard : public Board { - - public: - /** - * @brief Draw a GridBoard - * - * @param outSize size of the output image in pixels. - * @param img output image with the board. The size of this image will be outSize - * and the board will be on the center, keeping the board proportions. - * @param marginSize minimum margins (in pixels) of the board in the output image - * @param borderBits width of the marker borders. - * - * This function return the image of the GridBoard, ready to be printed. - */ - void draw(Size outSize, OutputArray img, int marginSize = 0, int borderBits = 1); - - - /** - * @brief Create a GridBoard object - * - * @param markersX number of markers in X direction - * @param markersY number of markers in Y direction - * @param markerLength marker side length (normally in meters) - * @param markerSeparation separation between two markers (same unit as markerLength) - * @param dictionary dictionary of markers indicating the type of markers - * @param firstMarker id of first marker in dictionary to use on board. - * @return the output GridBoard object - * - * This functions creates a GridBoard object given the number of markers in each direction and - * the marker size and marker separation. - */ - CV_WRAP static Ptr create(int markersX, int markersY, float markerLength, - float markerSeparation, Ptr &dictionary, int firstMarker = 0); - - /** - * - */ - Size getGridSize() const { return Size(_markersX, _markersY); } - - /** - * - */ - float getMarkerLength() const { return _markerLength; } - - /** - * - */ - float getMarkerSeparation() const { return _markerSeparation; } - - - private: - // number of markers in X and Y directions - int _markersX, _markersY; - - // marker side lenght (normally in meters) - float _markerLength; - - // separation between markers in the grid - float _markerSeparation; -}; - - - -/** - * @brief Pose estimation for a board of markers - * - * @param corners vector of already detected markers corners. For each marker, its four corners - * are provided, (e.g std::vector > ). For N detected markers, the - * dimensions of this array should be Nx4. The order of the corners should be clockwise. - * @param ids list of identifiers for each marker in corners - * @param board layout of markers in the board. The layout is composed by the marker identifiers - * and the positions of each marker corner in the board reference system. - * @param cameraMatrix input 3x3 floating-point camera matrix - * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ - * @param distCoeffs vector of distortion coefficients - * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements - * @param rvec Output vector (e.g. cv::Mat) corresponding to the rotation vector of the board - * (@sa Rodrigues). - * @param tvec Output vector (e.g. cv::Mat) corresponding to the translation vector of the board. - * - * This function receives the detected markers and returns the pose of a marker board composed - * by those markers. - * A Board of marker has a single world coordinate system which is defined by the board layout. - * The returned transformation is the one that transforms points from the board coordinate system - * to the camera coordinate system. - * Input markers that are not included in the board layout are ignored. - * The function returns the number of markers from the input employed for the board pose estimation. - * Note that returning a 0 means the pose has not been estimated. - */ -CV_EXPORTS_W int estimatePoseBoard(InputArrayOfArrays corners, InputArray ids, Ptr &board, - InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, - OutputArray tvec); - - - - -/** - * @brief Refind not detected markers based on the already detected and the board layout - * - * @param image input image - * @param board layout of markers in the board. - * @param detectedCorners vector of already detected marker corners. - * @param detectedIds vector of already detected marker identifiers. - * @param rejectedCorners vector of rejected candidates during the marker detection process. - * @param cameraMatrix optional input 3x3 floating-point camera matrix - * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ - * @param distCoeffs optional vector of distortion coefficients - * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements - * @param minRepDistance minimum distance between the corners of the rejected candidate and the - * reprojected marker in order to consider it as a correspondence. - * @param errorCorrectionRate rate of allowed erroneous bits respect to the error correction - * capability of the used dictionary. -1 ignores the error correction step. - * @param checkAllOrders Consider the four posible corner orders in the rejectedCorners array. - * If it set to false, only the provided corner order is considered (default true). - * @param recoveredIdxs Optional array to returns the indexes of the recovered candidates in the - * original rejectedCorners array. - * @param parameters marker detection parameters - * - * This function tries to find markers that were not detected in the basic detecMarkers function. - * First, based on the current detected marker and the board layout, the function interpolates - * the position of the missing markers. Then it tries to find correspondence between the reprojected - * markers and the rejected candidates based on the minRepDistance and errorCorrectionRate - * parameters. - * If camera parameters and distortion coefficients are provided, missing markers are reprojected - * using projectPoint function. If not, missing marker projections are interpolated using global - * homography, and all the marker corners in the board must have the same Z coordinate. - */ -CV_EXPORTS_W void refineDetectedMarkers( - InputArray image, Ptr &board, InputOutputArrayOfArrays detectedCorners, - InputOutputArray detectedIds, InputOutputArray rejectedCorners, - InputArray cameraMatrix = noArray(), InputArray distCoeffs = noArray(), - float minRepDistance = 10.f, float errorCorrectionRate = 3.f, bool checkAllOrders = true, - OutputArray recoveredIdxs = noArray(), const Ptr ¶meters = DetectorParameters::create()); - - - -/** - * @brief Draw detected markers in image - * - * @param image input/output image. It must have 1 or 3 channels. The number of channels is not - * altered. - * @param corners positions of marker corners on input image. - * (e.g std::vector > ). For N detected markers, the dimensions of - * this array should be Nx4. The order of the corners should be clockwise. - * @param ids vector of identifiers for markers in markersCorners . - * Optional, if not provided, ids are not painted. - * @param borderColor color of marker borders. Rest of colors (text color and first corner color) - * are calculated based on this one to improve visualization. - * - * Given an array of detected marker corners and its corresponding ids, this functions draws - * the markers in the image. The marker borders are painted and the markers identifiers if provided. - * Useful for debugging purposes. - */ -CV_EXPORTS_W void drawDetectedMarkers(InputOutputArray image, InputArrayOfArrays corners, - InputArray ids = noArray(), - Scalar borderColor = Scalar(0, 255, 0)); - - - -/** - * @brief Draw coordinate system axis from pose estimation - * - * @param image input/output image. It must have 1 or 3 channels. The number of channels is not - * altered. - * @param cameraMatrix input 3x3 floating-point camera matrix - * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ - * @param distCoeffs vector of distortion coefficients - * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements - * @param rvec rotation vector of the coordinate system that will be drawn. (@sa Rodrigues). - * @param tvec translation vector of the coordinate system that will be drawn. - * @param length length of the painted axis in the same unit than tvec (usually in meters) - * - * Given the pose estimation of a marker or board, this function draws the axis of the world - * coordinate system, i.e. the system centered on the marker/board. Useful for debugging purposes. - */ -CV_EXPORTS_W void drawAxis(InputOutputArray image, InputArray cameraMatrix, InputArray distCoeffs, - InputArray rvec, InputArray tvec, float length); - - - -/** - * @brief Draw a canonical marker image - * - * @param dictionary dictionary of markers indicating the type of markers - * @param id identifier of the marker that will be returned. It has to be a valid id - * in the specified dictionary. - * @param sidePixels size of the image in pixels - * @param img output image with the marker - * @param borderBits width of the marker border. - * - * This function returns a marker image in its canonical form (i.e. ready to be printed) - */ -CV_EXPORTS_W void drawMarker(Ptr &dictionary, int id, int sidePixels, OutputArray img, - int borderBits = 1); - - - -/** - * @brief Draw a planar board - * @sa _drawPlanarBoardImpl - * - * @param board layout of the board that will be drawn. The board should be planar, - * z coordinate is ignored - * @param outSize size of the output image in pixels. - * @param img output image with the board. The size of this image will be outSize - * and the board will be on the center, keeping the board proportions. - * @param marginSize minimum margins (in pixels) of the board in the output image - * @param borderBits width of the marker borders. - * - * This function return the image of a planar board, ready to be printed. It assumes - * the Board layout specified is planar by ignoring the z coordinates of the object points. - */ -CV_EXPORTS_W void drawPlanarBoard(Ptr &board, Size outSize, OutputArray img, - int marginSize = 0, int borderBits = 1); - - - -/** - * @brief Implementation of drawPlanarBoard that accepts a raw Board pointer. - */ -void _drawPlanarBoardImpl(Board *board, Size outSize, OutputArray img, - int marginSize = 0, int borderBits = 1); - - - -/** - * @brief Calibrate a camera using aruco markers - * - * @param corners vector of detected marker corners in all frames. - * The corners should have the same format returned by detectMarkers (@sa detectMarkers). - * @param ids list of identifiers for each marker in corners - * @param counter number of markers in each frame so that corners and ids can be split - * @param board Marker Board layout - * @param imageSize Size of the image used only to initialize the intrinsic camera matrix. - * @param cameraMatrix Output 3x3 floating-point camera matrix - * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS - * and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be - * initialized before calling the function. - * @param distCoeffs Output vector of distortion coefficients - * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements - * @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each board view - * (e.g. std::vector>). That is, each k-th rotation vector together with the corresponding - * k-th translation vector (see the next output parameter description) brings the board pattern - * from the model coordinate space (in which object points are specified) to the world coordinate - * space, that is, a real position of the board pattern in the k-th pattern view (k=0.. *M* -1). - * @param tvecs Output vector of translation vectors estimated for each pattern view. - * @param flags flags Different flags for the calibration process (@sa calibrateCamera) - * @param criteria Termination criteria for the iterative optimization algorithm. - * - * This function calibrates a camera using an Aruco Board. The function receives a list of - * detected markers from several views of the Board. The process is similar to the chessboard - * calibration in calibrateCamera(). The function returns the final re-projection error. - */ -CV_EXPORTS_W double calibrateCameraAruco( - InputArrayOfArrays corners, InputArray ids, InputArray counter, Ptr &board, - Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, - OutputArrayOfArrays rvecs = noArray(), OutputArrayOfArrays tvecs = noArray(), int flags = 0, - TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON)); - - - -//! @} -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/aruco/charuco.hpp b/IPL/include/opencv/opencv2/aruco/charuco.hpp deleted file mode 100644 index ff448ce..0000000 --- a/IPL/include/opencv/opencv2/aruco/charuco.hpp +++ /dev/null @@ -1,323 +0,0 @@ -/* -By downloading, copying, installing or using the software you agree to this -license. If you do not agree to this license, do not download, install, -copy or use the software. - - License Agreement - For Open Source Computer Vision Library - (3-clause BSD License) - -Copyright (C) 2013, OpenCV Foundation, all rights reserved. -Third party copyrights are property of their respective owners. - -Redistribution and use in source and binary forms, with or without modification, -are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. - - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - - * Neither the names of the copyright holders nor the names of the contributors - may be used to endorse or promote products derived from this software - without specific prior written permission. - -This software is provided by the copyright holders and contributors "as is" and -any express or implied warranties, including, but not limited to, the implied -warranties of merchantability and fitness for a particular purpose are -disclaimed. In no event shall copyright holders or contributors be liable for -any direct, indirect, incidental, special, exemplary, or consequential damages -(including, but not limited to, procurement of substitute goods or services; -loss of use, data, or profits; or business interruption) however caused -and on any theory of liability, whether in contract, strict liability, -or tort (including negligence or otherwise) arising in any way out of -the use of this software, even if advised of the possibility of such damage. -*/ - -#ifndef __OPENCV_CHARUCO_HPP__ -#define __OPENCV_CHARUCO_HPP__ - -#include -#include -#include - - -namespace cv { -namespace aruco { - -//! @addtogroup aruco -//! @{ - - -/** - * @brief ChArUco board - * Specific class for ChArUco boards. A ChArUco board is a planar board where the markers are placed - * inside the white squares of a chessboard. The benefits of ChArUco boards is that they provide - * both, ArUco markers versatility and chessboard corner precision, which is important for - * calibration and pose estimation. - * This class also allows the easy creation and drawing of ChArUco boards. - */ -class CV_EXPORTS_W CharucoBoard : public Board { - - public: - // vector of chessboard 3D corners precalculated - std::vector< Point3f > chessboardCorners; - - // for each charuco corner, nearest marker id and nearest marker corner id of each marker - std::vector< std::vector< int > > nearestMarkerIdx; - std::vector< std::vector< int > > nearestMarkerCorners; - - /** - * @brief Draw a ChArUco board - * - * @param outSize size of the output image in pixels. - * @param img output image with the board. The size of this image will be outSize - * and the board will be on the center, keeping the board proportions. - * @param marginSize minimum margins (in pixels) of the board in the output image - * @param borderBits width of the marker borders. - * - * This function return the image of the ChArUco board, ready to be printed. - */ - void draw(Size outSize, OutputArray img, int marginSize = 0, int borderBits = 1); - - - /** - * @brief Create a CharucoBoard object - * - * @param squaresX number of chessboard squares in X direction - * @param squaresY number of chessboard squares in Y direction - * @param squareLength chessboard square side length (normally in meters) - * @param markerLength marker side length (same unit than squareLength) - * @param dictionary dictionary of markers indicating the type of markers. - * The first markers in the dictionary are used to fill the white chessboard squares. - * @return the output CharucoBoard object - * - * This functions creates a CharucoBoard object given the number of squares in each direction - * and the size of the markers and chessboard squares. - */ - CV_WRAP static Ptr create(int squaresX, int squaresY, float squareLength, - float markerLength, Ptr &dictionary); - - /** - * - */ - Size getChessboardSize() const { return Size(_squaresX, _squaresY); } - - /** - * - */ - float getSquareLength() const { return _squareLength; } - - /** - * - */ - float getMarkerLength() const { return _markerLength; } - - private: - void _getNearestMarkerCorners(); - - // number of markers in X and Y directions - int _squaresX, _squaresY; - - // size of chessboard squares side (normally in meters) - float _squareLength; - - // marker side lenght (normally in meters) - float _markerLength; -}; - - - - -/** - * @brief Interpolate position of ChArUco board corners - * @param markerCorners vector of already detected markers corners. For each marker, its four - * corners are provided, (e.g std::vector > ). For N detected markers, the - * dimensions of this array should be Nx4. The order of the corners should be clockwise. - * @param markerIds list of identifiers for each marker in corners - * @param image input image necesary for corner refinement. Note that markers are not detected and - * should be sent in corners and ids parameters. - * @param board layout of ChArUco board. - * @param charucoCorners interpolated chessboard corners - * @param charucoIds interpolated chessboard corners identifiers - * @param cameraMatrix optional 3x3 floating-point camera matrix - * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ - * @param distCoeffs optional vector of distortion coefficients - * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements - * - * This function receives the detected markers and returns the 2D position of the chessboard corners - * from a ChArUco board using the detected Aruco markers. If camera parameters are provided, - * the process is based in an approximated pose estimation, else it is based on local homography. - * Only visible corners are returned. For each corner, its corresponding identifier is - * also returned in charucoIds. - * The function returns the number of interpolated corners. - */ -CV_EXPORTS_W int interpolateCornersCharuco(InputArrayOfArrays markerCorners, InputArray markerIds, - InputArray image, Ptr &board, - OutputArray charucoCorners, OutputArray charucoIds, - InputArray cameraMatrix = noArray(), - InputArray distCoeffs = noArray()); - - - - -/** - * @brief Pose estimation for a ChArUco board given some of their corners - * @param charucoCorners vector of detected charuco corners - * @param charucoIds list of identifiers for each corner in charucoCorners - * @param board layout of ChArUco board. - * @param cameraMatrix input 3x3 floating-point camera matrix - * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ - * @param distCoeffs vector of distortion coefficients - * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements - * @param rvec Output vector (e.g. cv::Mat) corresponding to the rotation vector of the board - * (@sa Rodrigues). - * @param tvec Output vector (e.g. cv::Mat) corresponding to the translation vector of the board. - * - * This function estimates a Charuco board pose from some detected corners. - * The function checks if the input corners are enough and valid to perform pose estimation. - * If pose estimation is valid, returns true, else returns false. - */ -CV_EXPORTS_W bool estimatePoseCharucoBoard(InputArray charucoCorners, InputArray charucoIds, - Ptr &board, InputArray cameraMatrix, - InputArray distCoeffs, OutputArray rvec, OutputArray tvec); - - - - -/** - * @brief Draws a set of Charuco corners - * @param image input/output image. It must have 1 or 3 channels. The number of channels is not - * altered. - * @param charucoCorners vector of detected charuco corners - * @param charucoIds list of identifiers for each corner in charucoCorners - * @param cornerColor color of the square surrounding each corner - * - * This function draws a set of detected Charuco corners. If identifiers vector is provided, it also - * draws the id of each corner. - */ -CV_EXPORTS_W void drawDetectedCornersCharuco(InputOutputArray image, InputArray charucoCorners, - InputArray charucoIds = noArray(), - Scalar cornerColor = Scalar(255, 0, 0)); - - - -/** - * @brief Calibrate a camera using Charuco corners - * - * @param charucoCorners vector of detected charuco corners per frame - * @param charucoIds list of identifiers for each corner in charucoCorners per frame - * @param board Marker Board layout - * @param imageSize input image size - * @param cameraMatrix Output 3x3 floating-point camera matrix - * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS - * and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be - * initialized before calling the function. - * @param distCoeffs Output vector of distortion coefficients - * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements - * @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each board view - * (e.g. std::vector>). That is, each k-th rotation vector together with the corresponding - * k-th translation vector (see the next output parameter description) brings the board pattern - * from the model coordinate space (in which object points are specified) to the world coordinate - * space, that is, a real position of the board pattern in the k-th pattern view (k=0.. *M* -1). - * @param tvecs Output vector of translation vectors estimated for each pattern view. - * @param flags flags Different flags for the calibration process (@sa calibrateCamera) - * @param criteria Termination criteria for the iterative optimization algorithm. - * - * This function calibrates a camera using a set of corners of a Charuco Board. The function - * receives a list of detected corners and its identifiers from several views of the Board. - * The function returns the final re-projection error. - */ -CV_EXPORTS_W double calibrateCameraCharuco( - InputArrayOfArrays charucoCorners, InputArrayOfArrays charucoIds, Ptr &board, - Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, - OutputArrayOfArrays rvecs = noArray(), OutputArrayOfArrays tvecs = noArray(), int flags = 0, - TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON)); - - - - -/** - * @brief Detect ChArUco Diamond markers - * - * @param image input image necessary for corner subpixel. - * @param markerCorners list of detected marker corners from detectMarkers function. - * @param markerIds list of marker ids in markerCorners. - * @param squareMarkerLengthRate rate between square and marker length: - * squareMarkerLengthRate = squareLength/markerLength. The real units are not necessary. - * @param diamondCorners output list of detected diamond corners (4 corners per diamond). The order - * is the same than in marker corners: top left, top right, bottom right and bottom left. Similar - * format than the corners returned by detectMarkers (e.g std::vector > ). - * @param diamondIds ids of the diamonds in diamondCorners. The id of each diamond is in fact of - * type Vec4i, so each diamond has 4 ids, which are the ids of the aruco markers composing the - * diamond. - * @param cameraMatrix Optional camera calibration matrix. - * @param distCoeffs Optional camera distortion coefficients. - * - * This function detects Diamond markers from the previous detected ArUco markers. The diamonds - * are returned in the diamondCorners and diamondIds parameters. If camera calibration parameters - * are provided, the diamond search is based on reprojection. If not, diamond search is based on - * homography. Homography is faster than reprojection but can slightly reduce the detection rate. - */ -CV_EXPORTS_W void detectCharucoDiamond(InputArray image, InputArrayOfArrays markerCorners, - InputArray markerIds, float squareMarkerLengthRate, - OutputArrayOfArrays diamondCorners, OutputArray diamondIds, - InputArray cameraMatrix = noArray(), - InputArray distCoeffs = noArray()); - - - -/** - * @brief Draw a set of detected ChArUco Diamond markers - * - * @param image input/output image. It must have 1 or 3 channels. The number of channels is not - * altered. - * @param diamondCorners positions of diamond corners in the same format returned by - * detectCharucoDiamond(). (e.g std::vector > ). For N detected markers, - * the dimensions of this array should be Nx4. The order of the corners should be clockwise. - * @param diamondIds vector of identifiers for diamonds in diamondCorners, in the same format - * returned by detectCharucoDiamond() (e.g. std::vector). - * Optional, if not provided, ids are not painted. - * @param borderColor color of marker borders. Rest of colors (text color and first corner color) - * are calculated based on this one. - * - * Given an array of detected diamonds, this functions draws them in the image. The marker borders - * are painted and the markers identifiers if provided. - * Useful for debugging purposes. - */ -CV_EXPORTS_W void drawDetectedDiamonds(InputOutputArray image, InputArrayOfArrays diamondCorners, - InputArray diamondIds = noArray(), - Scalar borderColor = Scalar(0, 0, 255)); - - - - -/** - * @brief Draw a ChArUco Diamond marker - * - * @param dictionary dictionary of markers indicating the type of markers. - * @param ids list of 4 ids for each ArUco marker in the ChArUco marker. - * @param squareLength size of the chessboard squares in pixels. - * @param markerLength size of the markers in pixels. - * @param img output image with the marker. The size of this image will be - * 3*squareLength + 2*marginSize,. - * @param marginSize minimum margins (in pixels) of the marker in the output image - * @param borderBits width of the marker borders. - * - * This function return the image of a ChArUco marker, ready to be printed. - */ -// TODO cannot be exported yet; conversion from/to Vec4i is not wrapped in core -CV_EXPORTS void drawCharucoDiamond(Ptr &dictionary, Vec4i ids, int squareLength, - int markerLength, OutputArray img, int marginSize = 0, - int borderBits = 1); - - - - -//! @} -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/aruco/dictionary.hpp b/IPL/include/opencv/opencv2/aruco/dictionary.hpp deleted file mode 100644 index 3ef1a82..0000000 --- a/IPL/include/opencv/opencv2/aruco/dictionary.hpp +++ /dev/null @@ -1,205 +0,0 @@ -/* -By downloading, copying, installing or using the software you agree to this -license. If you do not agree to this license, do not download, install, -copy or use the software. - - License Agreement - For Open Source Computer Vision Library - (3-clause BSD License) - -Copyright (C) 2013, OpenCV Foundation, all rights reserved. -Third party copyrights are property of their respective owners. - -Redistribution and use in source and binary forms, with or without modification, -are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. - - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - - * Neither the names of the copyright holders nor the names of the contributors - may be used to endorse or promote products derived from this software - without specific prior written permission. - -This software is provided by the copyright holders and contributors "as is" and -any express or implied warranties, including, but not limited to, the implied -warranties of merchantability and fitness for a particular purpose are -disclaimed. In no event shall copyright holders or contributors be liable for -any direct, indirect, incidental, special, exemplary, or consequential damages -(including, but not limited to, procurement of substitute goods or services; -loss of use, data, or profits; or business interruption) however caused -and on any theory of liability, whether in contract, strict liability, -or tort (including negligence or otherwise) arising in any way out of -the use of this software, even if advised of the possibility of such damage. -*/ - -#ifndef __OPENCV_DICTIONARY_HPP__ -#define __OPENCV_DICTIONARY_HPP__ - -#include - -namespace cv { -namespace aruco { - -//! @addtogroup aruco -//! @{ - - -/** - * @brief Dictionary/Set of markers. It contains the inner codification - * - * bytesList contains the marker codewords where - * - bytesList.rows is the dictionary size - * - each marker is encoded using `nbytes = ceil(markerSize*markerSize/8.)` - * - each row contains all 4 rotations of the marker, so its length is `4*nbytes` - * - * `bytesList.ptr(i)[k*nbytes + j]` is then the j-th byte of i-th marker, in its k-th rotation. - */ -class CV_EXPORTS_W Dictionary { - - public: - CV_PROP_RW Mat bytesList; // marker code information - CV_PROP_RW int markerSize; // number of bits per dimension - CV_PROP_RW int maxCorrectionBits; // maximum number of bits that can be corrected - - - /** - */ - Dictionary(const Mat &_bytesList = Mat(), int _markerSize = 0, int _maxcorr = 0); - - - /** - Dictionary(const Dictionary &_dictionary); - */ - - - /** - */ - Dictionary(const Ptr &_dictionary); - - - /** - * @see generateCustomDictionary - */ - CV_WRAP_AS(create) static Ptr create(int nMarkers, int markerSize); - - - /** - * @see generateCustomDictionary - */ - CV_WRAP_AS(create_from) static Ptr create(int nMarkers, int markerSize, - Ptr &baseDictionary); - - /** - * @see getPredefinedDictionary - */ - CV_WRAP static Ptr get(int dict); - - /** - * @brief Given a matrix of bits. Returns whether if marker is identified or not. - * It returns by reference the correct id (if any) and the correct rotation - */ - bool identify(const Mat &onlyBits, int &idx, int &rotation, double maxCorrectionRate) const; - - /** - * @brief Returns the distance of the input bits to the specific id. If allRotations is true, - * the four posible bits rotation are considered - */ - int getDistanceToId(InputArray bits, int id, bool allRotations = true) const; - - - /** - * @brief Draw a canonical marker image - */ - void drawMarker(int id, int sidePixels, OutputArray _img, int borderBits = 1) const; - - - /** - * @brief Transform matrix of bits to list of bytes in the 4 rotations - */ - static Mat getByteListFromBits(const Mat &bits); - - - /** - * @brief Transform list of bytes to matrix of bits - */ - static Mat getBitsFromByteList(const Mat &byteList, int markerSize); -}; - - - - -/** - * @brief Predefined markers dictionaries/sets - * Each dictionary indicates the number of bits and the number of markers contained - * - DICT_ARUCO_ORIGINAL: standard ArUco Library Markers. 1024 markers, 5x5 bits, 0 minimum - distance - */ -enum CV_EXPORTS_W_SIMPLE PREDEFINED_DICTIONARY_NAME { - DICT_4X4_50 = 0, - DICT_4X4_100, - DICT_4X4_250, - DICT_4X4_1000, - DICT_5X5_50, - DICT_5X5_100, - DICT_5X5_250, - DICT_5X5_1000, - DICT_6X6_50, - DICT_6X6_100, - DICT_6X6_250, - DICT_6X6_1000, - DICT_7X7_50, - DICT_7X7_100, - DICT_7X7_250, - DICT_7X7_1000, - DICT_ARUCO_ORIGINAL -}; - - -/** - * @brief Returns one of the predefined dictionaries defined in PREDEFINED_DICTIONARY_NAME - */ -CV_EXPORTS Ptr getPredefinedDictionary(PREDEFINED_DICTIONARY_NAME name); - - -/** - * @brief Returns one of the predefined dictionaries referenced by DICT_*. - */ -CV_EXPORTS_W Ptr getPredefinedDictionary(int dict); - - -/** - * @see generateCustomDictionary - */ -CV_EXPORTS_AS(custom_dictionary) Ptr generateCustomDictionary( - int nMarkers, - int markerSize); - - -/** - * @brief Generates a new customizable marker dictionary - * - * @param nMarkers number of markers in the dictionary - * @param markerSize number of bits per dimension of each markers - * @param baseDictionary Include the markers in this dictionary at the beginning (optional) - * - * This function creates a new dictionary composed by nMarkers markers and each markers composed - * by markerSize x markerSize bits. If baseDictionary is provided, its markers are directly - * included and the rest are generated based on them. If the size of baseDictionary is higher - * than nMarkers, only the first nMarkers in baseDictionary are taken and no new marker is added. - */ -CV_EXPORTS_AS(custom_dictionary_from) Ptr generateCustomDictionary( - int nMarkers, - int markerSize, - Ptr &baseDictionary); - - - -//! @} -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/bgsegm.hpp b/IPL/include/opencv/opencv2/bgsegm.hpp deleted file mode 100644 index 5a4ae3f..0000000 --- a/IPL/include/opencv/opencv2/bgsegm.hpp +++ /dev/null @@ -1,194 +0,0 @@ -/* -By downloading, copying, installing or using the software you agree to this -license. If you do not agree to this license, do not download, install, -copy or use the software. - - - License Agreement - For Open Source Computer Vision Library - (3-clause BSD License) - -Copyright (C) 2013, OpenCV Foundation, all rights reserved. -Third party copyrights are property of their respective owners. - -Redistribution and use in source and binary forms, with or without modification, -are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. - - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - - * Neither the names of the copyright holders nor the names of the contributors - may be used to endorse or promote products derived from this software - without specific prior written permission. - -This software is provided by the copyright holders and contributors "as is" and -any express or implied warranties, including, but not limited to, the implied -warranties of merchantability and fitness for a particular purpose are -disclaimed. In no event shall copyright holders or contributors be liable for -any direct, indirect, incidental, special, exemplary, or consequential damages -(including, but not limited to, procurement of substitute goods or services; -loss of use, data, or profits; or business interruption) however caused -and on any theory of liability, whether in contract, strict liability, -or tort (including negligence or otherwise) arising in any way out of -the use of this software, even if advised of the possibility of such damage. -*/ - -#ifndef __OPENCV_BGSEGM_HPP__ -#define __OPENCV_BGSEGM_HPP__ - -#include "opencv2/video.hpp" - -#ifdef __cplusplus - -/** @defgroup bgsegm Improved Background-Foreground Segmentation Methods -*/ - -namespace cv -{ -namespace bgsegm -{ - -//! @addtogroup bgsegm -//! @{ - -/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm. - -The class implements the algorithm described in @cite KB2001 . - */ -class CV_EXPORTS_W BackgroundSubtractorMOG : public BackgroundSubtractor -{ -public: - CV_WRAP virtual int getHistory() const = 0; - CV_WRAP virtual void setHistory(int nframes) = 0; - - CV_WRAP virtual int getNMixtures() const = 0; - CV_WRAP virtual void setNMixtures(int nmix) = 0; - - CV_WRAP virtual double getBackgroundRatio() const = 0; - CV_WRAP virtual void setBackgroundRatio(double backgroundRatio) = 0; - - CV_WRAP virtual double getNoiseSigma() const = 0; - CV_WRAP virtual void setNoiseSigma(double noiseSigma) = 0; -}; - -/** @brief Creates mixture-of-gaussian background subtractor - -@param history Length of the history. -@param nmixtures Number of Gaussian mixtures. -@param backgroundRatio Background ratio. -@param noiseSigma Noise strength (standard deviation of the brightness or each color channel). 0 -means some automatic value. - */ -CV_EXPORTS_W Ptr - createBackgroundSubtractorMOG(int history=200, int nmixtures=5, - double backgroundRatio=0.7, double noiseSigma=0); - - -/** @brief Background Subtractor module based on the algorithm given in @cite Gold2012 . - - Takes a series of images and returns a sequence of mask (8UC1) - images of the same size, where 255 indicates Foreground and 0 represents Background. - This class implements an algorithm described in "Visual Tracking of Human Visitors under - Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere, - A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012. - */ -class CV_EXPORTS_W BackgroundSubtractorGMG : public BackgroundSubtractor -{ -public: - /** @brief Returns total number of distinct colors to maintain in histogram. - */ - CV_WRAP virtual int getMaxFeatures() const = 0; - /** @brief Sets total number of distinct colors to maintain in histogram. - */ - CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0; - - /** @brief Returns the learning rate of the algorithm. - - It lies between 0.0 and 1.0. It determines how quickly features are "forgotten" from - histograms. - */ - CV_WRAP virtual double getDefaultLearningRate() const = 0; - /** @brief Sets the learning rate of the algorithm. - */ - CV_WRAP virtual void setDefaultLearningRate(double lr) = 0; - - /** @brief Returns the number of frames used to initialize background model. - */ - CV_WRAP virtual int getNumFrames() const = 0; - /** @brief Sets the number of frames used to initialize background model. - */ - CV_WRAP virtual void setNumFrames(int nframes) = 0; - - /** @brief Returns the parameter used for quantization of color-space. - - It is the number of discrete levels in each channel to be used in histograms. - */ - CV_WRAP virtual int getQuantizationLevels() const = 0; - /** @brief Sets the parameter used for quantization of color-space - */ - CV_WRAP virtual void setQuantizationLevels(int nlevels) = 0; - - /** @brief Returns the prior probability that each individual pixel is a background pixel. - */ - CV_WRAP virtual double getBackgroundPrior() const = 0; - /** @brief Sets the prior probability that each individual pixel is a background pixel. - */ - CV_WRAP virtual void setBackgroundPrior(double bgprior) = 0; - - /** @brief Returns the kernel radius used for morphological operations - */ - CV_WRAP virtual int getSmoothingRadius() const = 0; - /** @brief Sets the kernel radius used for morphological operations - */ - CV_WRAP virtual void setSmoothingRadius(int radius) = 0; - - /** @brief Returns the value of decision threshold. - - Decision value is the value above which pixel is determined to be FG. - */ - CV_WRAP virtual double getDecisionThreshold() const = 0; - /** @brief Sets the value of decision threshold. - */ - CV_WRAP virtual void setDecisionThreshold(double thresh) = 0; - - /** @brief Returns the status of background model update - */ - CV_WRAP virtual bool getUpdateBackgroundModel() const = 0; - /** @brief Sets the status of background model update - */ - CV_WRAP virtual void setUpdateBackgroundModel(bool update) = 0; - - /** @brief Returns the minimum value taken on by pixels in image sequence. Usually 0. - */ - CV_WRAP virtual double getMinVal() const = 0; - /** @brief Sets the minimum value taken on by pixels in image sequence. - */ - CV_WRAP virtual void setMinVal(double val) = 0; - - /** @brief Returns the maximum value taken on by pixels in image sequence. e.g. 1.0 or 255. - */ - CV_WRAP virtual double getMaxVal() const = 0; - /** @brief Sets the maximum value taken on by pixels in image sequence. - */ - CV_WRAP virtual void setMaxVal(double val) = 0; -}; - -/** @brief Creates a GMG Background Subtractor - -@param initializationFrames number of frames used to initialize the background models. -@param decisionThreshold Threshold value, above which it is marked foreground, else background. - */ -CV_EXPORTS_W Ptr createBackgroundSubtractorGMG(int initializationFrames=120, - double decisionThreshold=0.8); - -//! @} - -} -} - -#endif -#endif diff --git a/IPL/include/opencv/opencv2/bioinspired.hpp b/IPL/include/opencv/opencv2/bioinspired.hpp deleted file mode 100644 index 9c7e23b..0000000 --- a/IPL/include/opencv/opencv2/bioinspired.hpp +++ /dev/null @@ -1,60 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_BIOINSPIRED_HPP__ -#define __OPENCV_BIOINSPIRED_HPP__ - -#include "opencv2/core.hpp" -#include "opencv2/bioinspired/retina.hpp" -#include "opencv2/bioinspired/retinafasttonemapping.hpp" -#include "opencv2/bioinspired/transientareassegmentationmodule.hpp" - -/** @defgroup bioinspired Biologically inspired vision models and derivated tools - -The module provides biological visual systems models (human visual system and others). It also -provides derivated objects that take advantage of those bio-inspired models. - -@ref bioinspired_retina - -*/ - -#endif diff --git a/IPL/include/opencv/opencv2/bioinspired/bioinspired.hpp b/IPL/include/opencv/opencv2/bioinspired/bioinspired.hpp deleted file mode 100644 index 40be285..0000000 --- a/IPL/include/opencv/opencv2/bioinspired/bioinspired.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/bioinspired.hpp" diff --git a/IPL/include/opencv/opencv2/bioinspired/retina.hpp b/IPL/include/opencv/opencv2/bioinspired/retina.hpp deleted file mode 100644 index 4ed6f3a..0000000 --- a/IPL/include/opencv/opencv2/bioinspired/retina.hpp +++ /dev/null @@ -1,460 +0,0 @@ -/*#****************************************************************************** - ** IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. - ** - ** By downloading, copying, installing or using the software you agree to this license. - ** If you do not agree to this license, do not download, install, - ** copy or use the software. - ** - ** - ** bioinspired : interfaces allowing OpenCV users to integrate Human Vision System models. Presented models originate from Jeanny Herault's original research and have been reused and adapted by the author&collaborators for computed vision applications since his thesis with Alice Caplier at Gipsa-Lab. - ** Use: extract still images & image sequences features, from contours details to motion spatio-temporal features, etc. for high level visual scene analysis. Also contribute to image enhancement/compression such as tone mapping. - ** - ** Maintainers : Listic lab (code author current affiliation & applications) and Gipsa Lab (original research origins & applications) - ** - ** Creation - enhancement process 2007-2015 - ** Author: Alexandre Benoit (benoit.alexandre.vision@gmail.com), LISTIC lab, Annecy le vieux, France - ** - ** Theses algorithm have been developped by Alexandre BENOIT since his thesis with Alice Caplier at Gipsa-Lab (www.gipsa-lab.inpg.fr) and the research he pursues at LISTIC Lab (www.listic.univ-savoie.fr). - ** Refer to the following research paper for more information: - ** Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011 - ** This work have been carried out thanks to Jeanny Herault who's research and great discussions are the basis of all this work, please take a look at his book: - ** Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891. - ** - ** The retina filter includes the research contributions of phd/research collegues from which code has been redrawn by the author : - ** _take a look at the retinacolor.hpp module to discover Brice Chaix de Lavarene color mosaicing/demosaicing and the reference paper: - ** ====> B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007 - ** _take a look at imagelogpolprojection.hpp to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions. - ** ====> more informations in the above cited Jeanny Heraults's book. - ** - ** License Agreement - ** For Open Source Computer Vision Library - ** - ** Copyright (C) 2000-2008, Intel Corporation, all rights reserved. - ** Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved. - ** - ** For Human Visual System tools (bioinspired) - ** Copyright (C) 2007-2015, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved. - ** - ** Third party copyrights are property of their respective owners. - ** - ** Redistribution and use in source and binary forms, with or without modification, - ** are permitted provided that the following conditions are met: - ** - ** * Redistributions of source code must retain the above copyright notice, - ** this list of conditions and the following disclaimer. - ** - ** * Redistributions in binary form must reproduce the above copyright notice, - ** this list of conditions and the following disclaimer in the documentation - ** and/or other materials provided with the distribution. - ** - ** * The name of the copyright holders may not be used to endorse or promote products - ** derived from this software without specific prior written permission. - ** - ** This software is provided by the copyright holders and contributors "as is" and - ** any express or implied warranties, including, but not limited to, the implied - ** warranties of merchantability and fitness for a particular purpose are disclaimed. - ** In no event shall the Intel Corporation or contributors be liable for any direct, - ** indirect, incidental, special, exemplary, or consequential damages - ** (including, but not limited to, procurement of substitute goods or services; - ** loss of use, data, or profits; or business interruption) however caused - ** and on any theory of liability, whether in contract, strict liability, - ** or tort (including negligence or otherwise) arising in any way out of - ** the use of this software, even if advised of the possibility of such damage. - *******************************************************************************/ - -#ifndef __OPENCV_BIOINSPIRED_RETINA_HPP__ -#define __OPENCV_BIOINSPIRED_RETINA_HPP__ - -/** -@file -@date Jul 19, 2011 -@author Alexandre Benoit -*/ - -#include "opencv2/core.hpp" // for all OpenCV core functionalities access, including cv::Exception support - - -namespace cv{ -namespace bioinspired{ - -//! @addtogroup bioinspired -//! @{ - -enum { - RETINA_COLOR_RANDOM, //!< each pixel position is either R, G or B in a random choice - RETINA_COLOR_DIAGONAL,//!< color sampling is RGBRGBRGB..., line 2 BRGBRGBRG..., line 3, GBRGBRGBR... - RETINA_COLOR_BAYER//!< standard bayer sampling -}; - - -/** @brief retina model parameters structure - - For better clarity, check explenations on the comments of methods : setupOPLandIPLParvoChannel and setupIPLMagnoChannel - - Here is the default configuration file of the retina module. It gives results such as the first - retina output shown on the top of this page. - - @code{xml} - - - - 1 - 1 - 7.5e-01 - 9.0e-01 - 5.3e-01 - 0.01 - 0.5 - 7. - 7.5e-01 - - 1 - 0. - 0. - 7. - 2.0e+00 - 9.5e-01 - 0. - 7. - - @endcode - - Here is the 'realistic" setup used to obtain the second retina output shown on the top of this page. - - @code{xml} - - - - 1 - 1 - 8.9e-01 - 9.0e-01 - 5.3e-01 - 0.3 - 0.5 - 7. - 8.9e-01 - - 1 - 0. - 0. - 7. - 2.0e+00 - 9.5e-01 - 0. - 7. - - @endcode - */ - struct RetinaParameters{ - //! Outer Plexiform Layer (OPL) and Inner Plexiform Layer Parvocellular (IplParvo) parameters - struct OPLandIplParvoParameters{ - OPLandIplParvoParameters():colorMode(true), - normaliseOutput(true), - photoreceptorsLocalAdaptationSensitivity(0.75f), - photoreceptorsTemporalConstant(0.9f), - photoreceptorsSpatialConstant(0.53f), - horizontalCellsGain(0.01f), - hcellsTemporalConstant(0.5f), - hcellsSpatialConstant(7.f), - ganglionCellsSensitivity(0.75f) { } // default setup - bool colorMode, normaliseOutput; - float photoreceptorsLocalAdaptationSensitivity, photoreceptorsTemporalConstant, photoreceptorsSpatialConstant, horizontalCellsGain, hcellsTemporalConstant, hcellsSpatialConstant, ganglionCellsSensitivity; - }; - //! Inner Plexiform Layer Magnocellular channel (IplMagno) - struct IplMagnoParameters{ - IplMagnoParameters(): - normaliseOutput(true), - parasolCells_beta(0.f), - parasolCells_tau(0.f), - parasolCells_k(7.f), - amacrinCellsTemporalCutFrequency(2.0f), - V0CompressionParameter(0.95f), - localAdaptintegration_tau(0.f), - localAdaptintegration_k(7.f) { } // default setup - bool normaliseOutput; - float parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, V0CompressionParameter, localAdaptintegration_tau, localAdaptintegration_k; - }; - OPLandIplParvoParameters OPLandIplParvo; - IplMagnoParameters IplMagno; - }; - - - -/** @brief class which allows the Gipsa/Listic Labs model to be used with OpenCV. - -This retina model allows spatio-temporal image processing (applied on still images, video sequences). -As a summary, these are the retina model properties: -- It applies a spectral whithening (mid-frequency details enhancement) -- high frequency spatio-temporal noise reduction -- low frequency luminance to be reduced (luminance range compression) -- local logarithmic luminance compression allows details to be enhanced in low light conditions - -USE : this model can be used basically for spatio-temporal video effects but also for : - _using the getParvo method output matrix : texture analysiswith enhanced signal to noise ratio and enhanced details robust against input images luminance ranges - _using the getMagno method output matrix : motion analysis also with the previously cited properties - -for more information, reer to the following papers : -Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011 -Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891. - -The retina filter includes the research contributions of phd/research collegues from which code has been redrawn by the author : -take a look at the retinacolor.hpp module to discover Brice Chaix de Lavarene color mosaicing/demosaicing and the reference paper: -B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007 -take a look at imagelogpolprojection.hpp to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions. -more informations in the above cited Jeanny Heraults's book. - */ -class CV_EXPORTS_W Retina : public Algorithm { - -public: - - - /** @brief Retreive retina input buffer size - @return the retina input buffer size - */ - CV_WRAP virtual Size getInputSize()=0; - - /** @brief Retreive retina output buffer size that can be different from the input if a spatial log - transformation is applied - @return the retina output buffer size - */ - CV_WRAP virtual Size getOutputSize()=0; - - /** @brief Try to open an XML retina parameters file to adjust current retina instance setup - - - if the xml file does not exist, then default setup is applied - - warning, Exceptions are thrown if read XML file is not valid - @param retinaParameterFile the parameters filename - @param applyDefaultSetupOnFailure set to true if an error must be thrown on error - You can retreive the current parameers structure using method Retina::getParameters and update - it before running method Retina::setup - */ - CV_WRAP virtual void setup(String retinaParameterFile="", const bool applyDefaultSetupOnFailure=true)=0; - - /** @overload - @param fs the open Filestorage which contains retina parameters - @param applyDefaultSetupOnFailure set to true if an error must be thrown on error - */ - virtual void setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFailure=true)=0; - - /** @overload - @param newParameters a parameters structures updated with the new target configuration. - */ - virtual void setup(RetinaParameters newParameters)=0; - - /** - @return the current parameters setup - */ - virtual RetinaParameters getParameters()=0; - - /** @brief Outputs a string showing the used parameters setup - @return a string which contains formated parameters information - */ - CV_WRAP virtual const String printSetup()=0; - - /** @brief Write xml/yml formated parameters information - @param fs the filename of the xml file that will be open and writen with formatted parameters - information - */ - CV_WRAP virtual void write( String fs ) const=0; - - /** @overload */ - virtual void write( FileStorage& fs ) const=0; - - /** @brief Setup the OPL and IPL parvo channels (see biologocal model) - - OPL is referred as Outer Plexiform Layer of the retina, it allows the spatio-temporal filtering - which withens the spectrum and reduces spatio-temporal noise while attenuating global luminance - (low frequency energy) IPL parvo is the OPL next processing stage, it refers to a part of the - Inner Plexiform layer of the retina, it allows high contours sensitivity in foveal vision. See - reference papers for more informations. - for more informations, please have a look at the paper Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011 - @param colorMode specifies if (true) color is processed of not (false) to then processing gray - level image - @param normaliseOutput specifies if (true) output is rescaled between 0 and 255 of not (false) - @param photoreceptorsLocalAdaptationSensitivity the photoreceptors sensitivity renage is 0-1 - (more log compression effect when value increases) - @param photoreceptorsTemporalConstant the time constant of the first order low pass filter of - the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is - frames, typical value is 1 frame - @param photoreceptorsSpatialConstant the spatial constant of the first order low pass filter of - the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is - pixels, typical value is 1 pixel - @param horizontalCellsGain gain of the horizontal cells network, if 0, then the mean value of - the output is zero, if the parameter is near 1, then, the luminance is not filtered and is - still reachable at the output, typicall value is 0 - @param HcellsTemporalConstant the time constant of the first order low pass filter of the - horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is - frames, typical value is 1 frame, as the photoreceptors - @param HcellsSpatialConstant the spatial constant of the first order low pass filter of the - horizontal cells, use it to cut low spatial frequencies (local luminance), unit is pixels, - typical value is 5 pixel, this value is also used for local contrast computing when computing - the local contrast adaptation at the ganglion cells level (Inner Plexiform Layer parvocellular - channel model) - @param ganglionCellsSensitivity the compression strengh of the ganglion cells local adaptation - output, set a value between 0.6 and 1 for best results, a high value increases more the low - value sensitivity... and the output saturates faster, recommended value: 0.7 - */ - CV_WRAP virtual void setupOPLandIPLParvoChannel(const bool colorMode=true, const bool normaliseOutput = true, const float photoreceptorsLocalAdaptationSensitivity=0.7f, const float photoreceptorsTemporalConstant=0.5f, const float photoreceptorsSpatialConstant=0.53f, const float horizontalCellsGain=0.f, const float HcellsTemporalConstant=1.f, const float HcellsSpatialConstant=7.f, const float ganglionCellsSensitivity=0.7f)=0; - - /** @brief Set parameters values for the Inner Plexiform Layer (IPL) magnocellular channel - - this channel processes signals output from OPL processing stage in peripheral vision, it allows - motion information enhancement. It is decorrelated from the details channel. See reference - papers for more details. - - @param normaliseOutput specifies if (true) output is rescaled between 0 and 255 of not (false) - @param parasolCells_beta the low pass filter gain used for local contrast adaptation at the - IPL level of the retina (for ganglion cells local adaptation), typical value is 0 - @param parasolCells_tau the low pass filter time constant used for local contrast adaptation - at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical - value is 0 (immediate response) - @param parasolCells_k the low pass filter spatial constant used for local contrast adaptation - at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical - value is 5 - @param amacrinCellsTemporalCutFrequency the time constant of the first order high pass fiter of - the magnocellular way (motion information channel), unit is frames, typical value is 1.2 - @param V0CompressionParameter the compression strengh of the ganglion cells local adaptation - output, set a value between 0.6 and 1 for best results, a high value increases more the low - value sensitivity... and the output saturates faster, recommended value: 0.95 - @param localAdaptintegration_tau specifies the temporal constant of the low pas filter - involved in the computation of the local "motion mean" for the local adaptation computation - @param localAdaptintegration_k specifies the spatial constant of the low pas filter involved - in the computation of the local "motion mean" for the local adaptation computation - */ - CV_WRAP virtual void setupIPLMagnoChannel(const bool normaliseOutput = true, const float parasolCells_beta=0.f, const float parasolCells_tau=0.f, const float parasolCells_k=7.f, const float amacrinCellsTemporalCutFrequency=1.2f, const float V0CompressionParameter=0.95f, const float localAdaptintegration_tau=0.f, const float localAdaptintegration_k=7.f)=0; - - /** @brief Method which allows retina to be applied on an input image, - - after run, encapsulated retina module is ready to deliver its outputs using dedicated - acccessors, see getParvo and getMagno methods - @param inputImage the input Mat image to be processed, can be gray level or BGR coded in any - format (from 8bit to 16bits) - */ - CV_WRAP virtual void run(InputArray inputImage)=0; - - /** @brief Method which processes an image in the aim to correct its luminance correct - backlight problems, enhance details in shadows. - - This method is designed to perform High Dynamic Range image tone mapping (compress \>8bit/pixel - images to 8bit/pixel). This is a simplified version of the Retina Parvocellular model - (simplified version of the run/getParvo methods call) since it does not include the - spatio-temporal filter modelling the Outer Plexiform Layer of the retina that performs spectral - whitening and many other stuff. However, it works great for tone mapping and in a faster way. - - Check the demos and experiments section to see examples and the way to perform tone mapping - using the original retina model and the method. - - @param inputImage the input image to process (should be coded in float format : CV_32F, - CV_32FC1, CV_32F_C3, CV_32F_C4, the 4th channel won't be considered). - @param outputToneMappedImage the output 8bit/channel tone mapped image (CV_8U or CV_8UC3 format). - */ - CV_WRAP virtual void applyFastToneMapping(InputArray inputImage, OutputArray outputToneMappedImage)=0; - - /** @brief Accessor of the details channel of the retina (models foveal vision). - - Warning, getParvoRAW methods return buffers that are not rescaled within range [0;255] while - the non RAW method allows a normalized matrix to be retrieved. - - @param retinaOutput_parvo the output buffer (reallocated if necessary), format can be : - - a Mat, this output is rescaled for standard 8bits image processing use in OpenCV - - RAW methods actually return a 1D matrix (encoding is R1, R2, ... Rn, G1, G2, ..., Gn, B1, - B2, ...Bn), this output is the original retina filter model output, without any - quantification or rescaling. - @see getParvoRAW - */ - CV_WRAP virtual void getParvo(OutputArray retinaOutput_parvo)=0; - - /** @brief Accessor of the details channel of the retina (models foveal vision). - @see getParvo - */ - CV_WRAP virtual void getParvoRAW(OutputArray retinaOutput_parvo)=0; - - /** @brief Accessor of the motion channel of the retina (models peripheral vision). - - Warning, getMagnoRAW methods return buffers that are not rescaled within range [0;255] while - the non RAW method allows a normalized matrix to be retrieved. - @param retinaOutput_magno the output buffer (reallocated if necessary), format can be : - - a Mat, this output is rescaled for standard 8bits image processing use in OpenCV - - RAW methods actually return a 1D matrix (encoding is M1, M2,... Mn), this output is the - original retina filter model output, without any quantification or rescaling. - @see getMagnoRAW - */ - CV_WRAP virtual void getMagno(OutputArray retinaOutput_magno)=0; - - /** @brief Accessor of the motion channel of the retina (models peripheral vision). - @see getMagno - */ - CV_WRAP virtual void getMagnoRAW(OutputArray retinaOutput_magno)=0; - - /** @overload */ - CV_WRAP virtual const Mat getMagnoRAW() const=0; - /** @overload */ - CV_WRAP virtual const Mat getParvoRAW() const=0; - - /** @brief Activate color saturation as the final step of the color demultiplexing process -\> this - saturation is a sigmoide function applied to each channel of the demultiplexed image. - @param saturateColors boolean that activates color saturation (if true) or desactivate (if false) - @param colorSaturationValue the saturation factor : a simple factor applied on the chrominance - buffers - */ - CV_WRAP virtual void setColorSaturation(const bool saturateColors=true, const float colorSaturationValue=4.0f)=0; - - /** @brief Clears all retina buffers - - (equivalent to opening the eyes after a long period of eye close ;o) whatchout the temporal - transition occuring just after this method call. - */ - CV_WRAP virtual void clearBuffers()=0; - - /** @brief Activate/desactivate the Magnocellular pathway processing (motion information extraction), by - default, it is activated - @param activate true if Magnocellular output should be activated, false if not... if activated, - the Magnocellular output can be retrieved using the **getMagno** methods - */ - CV_WRAP virtual void activateMovingContoursProcessing(const bool activate)=0; - - /** @brief Activate/desactivate the Parvocellular pathway processing (contours information extraction), by - default, it is activated - @param activate true if Parvocellular (contours information extraction) output should be - activated, false if not... if activated, the Parvocellular output can be retrieved using the - Retina::getParvo methods - */ - CV_WRAP virtual void activateContoursProcessing(const bool activate)=0; -}; - -//! @relates bioinspired::Retina -//! @{ - -/** @overload */ -CV_EXPORTS_W Ptr createRetina(Size inputSize); -/** @brief Constructors from standardized interfaces : retreive a smart pointer to a Retina instance - -@param inputSize the input frame size -@param colorMode the chosen processing mode : with or without color processing -@param colorSamplingMethod specifies which kind of color sampling will be used : -- cv::bioinspired::RETINA_COLOR_RANDOM: each pixel position is either R, G or B in a random choice -- cv::bioinspired::RETINA_COLOR_DIAGONAL: color sampling is RGBRGBRGB..., line 2 BRGBRGBRG..., line 3, GBRGBRGBR... -- cv::bioinspired::RETINA_COLOR_BAYER: standard bayer sampling -@param useRetinaLogSampling activate retina log sampling, if true, the 2 following parameters can -be used -@param reductionFactor only usefull if param useRetinaLogSampling=true, specifies the reduction -factor of the output frame (as the center (fovea) is high resolution and corners can be -underscaled, then a reduction of the output is allowed without precision leak -@param samplingStrenght only usefull if param useRetinaLogSampling=true, specifies the strenght of -the log scale that is applied - */ -CV_EXPORTS_W Ptr createRetina(Size inputSize, const bool colorMode, int colorSamplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const float reductionFactor=1.0f, const float samplingStrenght=10.0f); - -#ifdef HAVE_OPENCV_OCL -Ptr createRetina_OCL(Size inputSize); -Ptr createRetina_OCL(Size inputSize, const bool colorMode, int colorSamplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const float reductionFactor=1.0f, const float samplingStrenght=10.0f); -#endif - -//! @} - -//! @} - -} -} -#endif /* __OPENCV_BIOINSPIRED_RETINA_HPP__ */ diff --git a/IPL/include/opencv/opencv2/bioinspired/retinafasttonemapping.hpp b/IPL/include/opencv/opencv2/bioinspired/retinafasttonemapping.hpp deleted file mode 100644 index c65709d..0000000 --- a/IPL/include/opencv/opencv2/bioinspired/retinafasttonemapping.hpp +++ /dev/null @@ -1,138 +0,0 @@ - -/*#****************************************************************************** - ** IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. - ** - ** By downloading, copying, installing or using the software you agree to this license. - ** If you do not agree to this license, do not download, install, - ** copy or use the software. - ** - ** - ** bioinspired : interfaces allowing OpenCV users to integrate Human Vision System models. Presented models originate from Jeanny Herault's original research and have been reused and adapted by the author&collaborators for computed vision applications since his thesis with Alice Caplier at Gipsa-Lab. - ** - ** Maintainers : Listic lab (code author current affiliation & applications) and Gipsa Lab (original research origins & applications) - ** - ** Creation - enhancement process 2007-2013 - ** Author: Alexandre Benoit (benoit.alexandre.vision@gmail.com), LISTIC lab, Annecy le vieux, France - ** - ** Theses algorithm have been developped by Alexandre BENOIT since his thesis with Alice Caplier at Gipsa-Lab (www.gipsa-lab.inpg.fr) and the research he pursues at LISTIC Lab (www.listic.univ-savoie.fr). - ** Refer to the following research paper for more information: - ** Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011 - ** This work have been carried out thanks to Jeanny Herault who's research and great discussions are the basis of all this work, please take a look at his book: - ** Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891. - ** - ** - ** - ** - ** - ** This class is based on image processing tools of the author and already used within the Retina class (this is the same code as method retina::applyFastToneMapping, but in an independent class, it is ligth from a memory requirement point of view). It implements an adaptation of the efficient tone mapping algorithm propose by David Alleyson, Sabine Susstruck and Laurence Meylan's work, please cite: - ** -> Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816 - ** - ** - ** License Agreement - ** For Open Source Computer Vision Library - ** - ** Copyright (C) 2000-2008, Intel Corporation, all rights reserved. - ** Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved. - ** - ** For Human Visual System tools (bioinspired) - ** Copyright (C) 2007-2011, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved. - ** - ** Third party copyrights are property of their respective owners. - ** - ** Redistribution and use in source and binary forms, with or without modification, - ** are permitted provided that the following conditions are met: - ** - ** * Redistributions of source code must retain the above copyright notice, - ** this list of conditions and the following disclaimer. - ** - ** * Redistributions in binary form must reproduce the above copyright notice, - ** this list of conditions and the following disclaimer in the documentation - ** and/or other materials provided with the distribution. - ** - ** * The name of the copyright holders may not be used to endorse or promote products - ** derived from this software without specific prior written permission. - ** - ** This software is provided by the copyright holders and contributors "as is" and - ** any express or implied warranties, including, but not limited to, the implied - ** warranties of merchantability and fitness for a particular purpose are disclaimed. - ** In no event shall the Intel Corporation or contributors be liable for any direct, - ** indirect, incidental, special, exemplary, or consequential damages - ** (including, but not limited to, procurement of substitute goods or services; - ** loss of use, data, or profits; or business interruption) however caused - ** and on any theory of liability, whether in contract, strict liability, - ** or tort (including negligence or otherwise) arising in any way out of - ** the use of this software, even if advised of the possibility of such damage. - *******************************************************************************/ - -#ifndef __OPENCV_BIOINSPIRED_RETINAFASTTONEMAPPING_HPP__ -#define __OPENCV_BIOINSPIRED_RETINAFASTTONEMAPPING_HPP__ - -/** -@file -@date May 26, 2013 -@author Alexandre Benoit - */ - -#include "opencv2/core.hpp" // for all OpenCV core functionalities access, including cv::Exception support - -namespace cv{ -namespace bioinspired{ - -//! @addtogroup bioinspired -//! @{ - -/** @brief a wrapper class which allows the tone mapping algorithm of Meylan&al(2007) to be used with OpenCV. - -This algorithm is already implemented in thre Retina class (retina::applyFastToneMapping) but used it does not require all the retina model to be allocated. This allows a light memory use for low memory devices (smartphones, etc. -As a summary, these are the model properties: -- 2 stages of local luminance adaptation with a different local neighborhood for each. -- first stage models the retina photorecetors local luminance adaptation -- second stage models th ganglion cells local information adaptation -- compared to the initial publication, this class uses spatio-temporal low pass filters instead of spatial only filters. - this can help noise robustness and temporal stability for video sequence use cases. - -for more information, read to the following papers : -Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011 -regarding spatio-temporal filter and the bigger retina model : -Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891. -*/ -class CV_EXPORTS_W RetinaFastToneMapping : public Algorithm -{ -public: - - /** @brief applies a luminance correction (initially High Dynamic Range (HDR) tone mapping) - - using only the 2 local adaptation stages of the retina parvocellular channel : photoreceptors - level and ganlion cells level. Spatio temporal filtering is applied but limited to temporal - smoothing and eventually high frequencies attenuation. This is a lighter method than the one - available using the regular retina::run method. It is then faster but it does not include - complete temporal filtering nor retina spectral whitening. Then, it can have a more limited - effect on images with a very high dynamic range. This is an adptation of the original still - image HDR tone mapping algorithm of David Alleyson, Sabine Susstruck and Laurence Meylan's - work, please cite: -> Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local - Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of - America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816 - - @param inputImage the input image to process RGB or gray levels - @param outputToneMappedImage the output tone mapped image - */ - CV_WRAP virtual void applyFastToneMapping(InputArray inputImage, OutputArray outputToneMappedImage)=0; - - /** @brief updates tone mapping behaviors by adjusing the local luminance computation area - - @param photoreceptorsNeighborhoodRadius the first stage local adaptation area - @param ganglioncellsNeighborhoodRadius the second stage local adaptation area - @param meanLuminanceModulatorK the factor applied to modulate the meanLuminance information - (default is 1, see reference paper) - */ - CV_WRAP virtual void setup(const float photoreceptorsNeighborhoodRadius=3.f, const float ganglioncellsNeighborhoodRadius=1.f, const float meanLuminanceModulatorK=1.f)=0; -}; - -//! @relates bioinspired::RetinaFastToneMapping -CV_EXPORTS_W Ptr createRetinaFastToneMapping(Size inputSize); - -//! @} - -} -} -#endif /* __OPENCV_BIOINSPIRED_RETINAFASTTONEMAPPING_HPP__ */ diff --git a/IPL/include/opencv/opencv2/bioinspired/transientareassegmentationmodule.hpp b/IPL/include/opencv/opencv2/bioinspired/transientareassegmentationmodule.hpp deleted file mode 100644 index b11b61d..0000000 --- a/IPL/include/opencv/opencv2/bioinspired/transientareassegmentationmodule.hpp +++ /dev/null @@ -1,205 +0,0 @@ -/*#****************************************************************************** - ** IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. - ** - ** By downloading, copying, installing or using the software you agree to this license. - ** If you do not agree to this license, do not download, install, - ** copy or use the software. - ** - ** - ** bioinspired : interfaces allowing OpenCV users to integrate Human Vision System models. - ** TransientAreasSegmentationModule Use: extract areas that present spatio-temporal changes. - ** => It should be used at the output of the cv::bioinspired::Retina::getMagnoRAW() output that enhances spatio-temporal changes - ** - ** Maintainers : Listic lab (code author current affiliation & applications) - ** - ** Creation - enhancement process 2007-2015 - ** Author: Alexandre Benoit (benoit.alexandre.vision@gmail.com), LISTIC lab, Annecy le vieux, France - ** - ** Theses algorithm have been developped by Alexandre BENOIT since his thesis with Alice Caplier at Gipsa-Lab (www.gipsa-lab.inpg.fr) and the research he pursues at LISTIC Lab (www.listic.univ-savoie.fr). - ** Refer to the following research paper for more information: - ** Strat, S.T.; Benoit, A.; Lambert, P., "Retina enhanced bag of words descriptors for video classification," Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European , vol., no., pp.1307,1311, 1-5 Sept. 2014 (http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6952461&isnumber=6951911) - ** Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011 - ** This work have been carried out thanks to Jeanny Herault who's research and great discussions are the basis of all this work, please take a look at his book: - ** Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891. - ** - ** - ** License Agreement - ** For Open Source Computer Vision Library - ** - ** Copyright (C) 2000-2008, Intel Corporation, all rights reserved. - ** Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved. - ** - ** For Human Visual System tools (bioinspired) - ** Copyright (C) 2007-2015, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved. - ** - ** Third party copyrights are property of their respective owners. - ** - ** Redistribution and use in source and binary forms, with or without modification, - ** are permitted provided that the following conditions are met: - ** - ** * Redistributions of source code must retain the above copyright notice, - ** this list of conditions and the following disclaimer. - ** - ** * Redistributions in binary form must reproduce the above copyright notice, - ** this list of conditions and the following disclaimer in the documentation - ** and/or other materials provided with the distribution. - ** - ** * The name of the copyright holders may not be used to endorse or promote products - ** derived from this software without specific prior written permission. - ** - ** This software is provided by the copyright holders and contributors "as is" and - ** any express or implied warranties, including, but not limited to, the implied - ** warranties of merchantability and fitness for a particular purpose are disclaimed. - ** In no event shall the Intel Corporation or contributors be liable for any direct, - ** indirect, incidental, special, exemplary, or consequential damages - ** (including, but not limited to, procurement of substitute goods or services; - ** loss of use, data, or profits; or business interruption) however caused - ** and on any theory of liability, whether in contract, strict liability, - ** or tort (including negligence or otherwise) arising in any way out of - ** the use of this software, even if advised of the possibility of such damage. - *******************************************************************************/ - -#ifndef SEGMENTATIONMODULE_HPP_ -#define SEGMENTATIONMODULE_HPP_ - -/** -@file -@date 2007-2013 -@author Alexandre BENOIT, benoit.alexandre.vision@gmail.com -*/ - -#include "opencv2/core.hpp" // for all OpenCV core functionalities access, including cv::Exception support - -namespace cv -{ -namespace bioinspired -{ -//! @addtogroup bioinspired -//! @{ - -/** @brief parameter structure that stores the transient events detector setup parameters -*/ -struct SegmentationParameters{ // CV_EXPORTS_W_MAP to export to python native dictionnaries - // default structure instance construction with default values - SegmentationParameters(): - thresholdON(100), - thresholdOFF(100), - localEnergy_temporalConstant(0.5), - localEnergy_spatialConstant(5), - neighborhoodEnergy_temporalConstant(1), - neighborhoodEnergy_spatialConstant(15), - contextEnergy_temporalConstant(1), - contextEnergy_spatialConstant(75){}; - // all properties list - float thresholdON; - float thresholdOFF; - //! the time constant of the first order low pass filter, use it to cut high temporal frequencies (noise or fast motion), unit is frames, typical value is 0.5 frame - float localEnergy_temporalConstant; - //! the spatial constant of the first order low pass filter, use it to cut high spatial frequencies (noise or thick contours), unit is pixels, typical value is 5 pixel - float localEnergy_spatialConstant; - //! local neighborhood energy filtering parameters : the aim is to get information about the energy neighborhood to perform a center surround energy analysis - float neighborhoodEnergy_temporalConstant; - float neighborhoodEnergy_spatialConstant; - //! context neighborhood energy filtering parameters : the aim is to get information about the energy on a wide neighborhood area to filtered out local effects - float contextEnergy_temporalConstant; - float contextEnergy_spatialConstant; -}; - -/** @brief class which provides a transient/moving areas segmentation module - -perform a locally adapted segmentation by using the retina magno input data Based on Alexandre -BENOIT thesis: "Le système visuel humain au secours de la vision par ordinateur" - -3 spatio temporal filters are used: -- a first one which filters the noise and local variations of the input motion energy -- a second (more powerfull low pass spatial filter) which gives the neighborhood motion energy the -segmentation consists in the comparison of these both outputs, if the local motion energy is higher -to the neighborhood otion energy, then the area is considered as moving and is segmented -- a stronger third low pass filter helps decision by providing a smooth information about the -"motion context" in a wider area - */ - -class CV_EXPORTS_W TransientAreasSegmentationModule: public Algorithm -{ -public: - - - /** @brief return the sze of the manage input and output images - */ - CV_WRAP virtual Size getSize()=0; - - /** @brief try to open an XML segmentation parameters file to adjust current segmentation instance setup - - - if the xml file does not exist, then default setup is applied - - warning, Exceptions are thrown if read XML file is not valid - @param segmentationParameterFile : the parameters filename - @param applyDefaultSetupOnFailure : set to true if an error must be thrown on error - */ - CV_WRAP virtual void setup(String segmentationParameterFile="", const bool applyDefaultSetupOnFailure=true)=0; - - /** @brief try to open an XML segmentation parameters file to adjust current segmentation instance setup - - - if the xml file does not exist, then default setup is applied - - warning, Exceptions are thrown if read XML file is not valid - @param fs : the open Filestorage which contains segmentation parameters - @param applyDefaultSetupOnFailure : set to true if an error must be thrown on error - */ - virtual void setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFailure=true)=0; - - /** @brief try to open an XML segmentation parameters file to adjust current segmentation instance setup - - - if the xml file does not exist, then default setup is applied - - warning, Exceptions are thrown if read XML file is not valid - @param newParameters : a parameters structures updated with the new target configuration - */ - virtual void setup(SegmentationParameters newParameters)=0; - - /** @brief return the current parameters setup - */ - virtual SegmentationParameters getParameters()=0; - - /** @brief parameters setup display method - @return a string which contains formatted parameters information - */ - CV_WRAP virtual const String printSetup()=0; - - /** @brief write xml/yml formated parameters information - @param fs : the filename of the xml file that will be open and writen with formatted parameters information - */ - CV_WRAP virtual void write( String fs ) const=0; - - /** @brief write xml/yml formated parameters information - @param fs : a cv::Filestorage object ready to be filled - */ - virtual void write( cv::FileStorage& fs ) const=0; - - /** @brief main processing method, get result using methods getSegmentationPicture() - @param inputToSegment : the image to process, it must match the instance buffer size ! - @param channelIndex : the channel to process in case of multichannel images - */ - CV_WRAP virtual void run(InputArray inputToSegment, const int channelIndex=0)=0; - - /** @brief access function - @return the last segmentation result: a boolean picture which is resampled between 0 and 255 for a display purpose - */ - CV_WRAP virtual void getSegmentationPicture(OutputArray transientAreas)=0; - - /** @brief cleans all the buffers of the instance - */ - CV_WRAP virtual void clearAllBuffers()=0; -}; - -/** @brief allocator -@param inputSize : size of the images input to segment (output will be the same size) -@relates bioinspired::TransientAreasSegmentationModule - */ -CV_EXPORTS_W Ptr createTransientAreasSegmentationModule(Size inputSize); - -//! @} - -}} // namespaces end : cv and bioinspired - - -#endif - - diff --git a/IPL/include/opencv/opencv2/calib3d.hpp b/IPL/include/opencv/opencv2/calib3d.hpp deleted file mode 100644 index e26e8c1..0000000 --- a/IPL/include/opencv/opencv2/calib3d.hpp +++ /dev/null @@ -1,2005 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CALIB3D_HPP__ -#define __OPENCV_CALIB3D_HPP__ - -#include "opencv2/core.hpp" -#include "opencv2/features2d.hpp" -#include "opencv2/core/affine.hpp" - -/** - @defgroup calib3d Camera Calibration and 3D Reconstruction - -The functions in this section use a so-called pinhole camera model. In this model, a scene view is -formed by projecting 3D points into the image plane using a perspective transformation. - -\f[s \; m' = A [R|t] M'\f] - -or - -\f[s \vecthree{u}{v}{1} = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1} -\begin{bmatrix} -r_{11} & r_{12} & r_{13} & t_1 \\ -r_{21} & r_{22} & r_{23} & t_2 \\ -r_{31} & r_{32} & r_{33} & t_3 -\end{bmatrix} -\begin{bmatrix} -X \\ -Y \\ -Z \\ -1 -\end{bmatrix}\f] - -where: - -- \f$(X, Y, Z)\f$ are the coordinates of a 3D point in the world coordinate space -- \f$(u, v)\f$ are the coordinates of the projection point in pixels -- \f$A\f$ is a camera matrix, or a matrix of intrinsic parameters -- \f$(cx, cy)\f$ is a principal point that is usually at the image center -- \f$fx, fy\f$ are the focal lengths expressed in pixel units. - -Thus, if an image from the camera is scaled by a factor, all of these parameters should be scaled -(multiplied/divided, respectively) by the same factor. The matrix of intrinsic parameters does not -depend on the scene viewed. So, once estimated, it can be re-used as long as the focal length is -fixed (in case of zoom lens). The joint rotation-translation matrix \f$[R|t]\f$ is called a matrix of -extrinsic parameters. It is used to describe the camera motion around a static scene, or vice versa, -rigid motion of an object in front of a still camera. That is, \f$[R|t]\f$ translates coordinates of a -point \f$(X, Y, Z)\f$ to a coordinate system, fixed with respect to the camera. The transformation above -is equivalent to the following (when \f$z \ne 0\f$ ): - -\f[\begin{array}{l} -\vecthree{x}{y}{z} = R \vecthree{X}{Y}{Z} + t \\ -x' = x/z \\ -y' = y/z \\ -u = f_x*x' + c_x \\ -v = f_y*y' + c_y -\end{array}\f] - -Real lenses usually have some distortion, mostly radial distortion and slight tangential distortion. -So, the above model is extended as: - -\f[\begin{array}{l} -\vecthree{x}{y}{z} = R \vecthree{X}{Y}{Z} + t \\ -x' = x/z \\ -y' = y/z \\ -x'' = x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2 p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4 \\ -y'' = y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ -\text{where} \quad r^2 = x'^2 + y'^2 \\ -u = f_x*x'' + c_x \\ -v = f_y*y'' + c_y -\end{array}\f] - -\f$k_1\f$, \f$k_2\f$, \f$k_3\f$, \f$k_4\f$, \f$k_5\f$, and \f$k_6\f$ are radial distortion coefficients. \f$p_1\f$ and \f$p_2\f$ are -tangential distortion coefficients. \f$s_1\f$, \f$s_2\f$, \f$s_3\f$, and \f$s_4\f$, are the thin prism distortion -coefficients. Higher-order coefficients are not considered in OpenCV. - -In some cases the image sensor may be tilted in order to focus an oblique plane in front of the -camera (Scheimpfug condition). This can be useful for particle image velocimetry (PIV) or -triangulation with a laser fan. The tilt causes a perspective distortion of \f$x''\f$ and -\f$y''\f$. This distortion can be modelled in the following way, see e.g. @cite Louhichi07. - -\f[\begin{array}{l} -s\vecthree{x'''}{y'''}{1} = -\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}(\tau_x, \tau_y)} -{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} -{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ -u = f_x*x''' + c_x \\ -v = f_y*y''' + c_y -\end{array}\f] - -where the matrix \f$R(\tau_x, \tau_y)\f$ is defined by two rotations with angular parameter \f$\tau_x\f$ -and \f$\tau_y\f$, respectively, - -\f[ -R(\tau_x, \tau_y) = -\vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)} -\vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} = -\vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)} -{0}{\cos(\tau_x)}{\sin(\tau_x)} -{\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}. -\f] - -In the functions below the coefficients are passed or returned as - -\f[(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f] - -vector. That is, if the vector contains four elements, it means that \f$k_3=0\f$ . The distortion -coefficients do not depend on the scene viewed. Thus, they also belong to the intrinsic camera -parameters. And they remain the same regardless of the captured image resolution. If, for example, a -camera has been calibrated on images of 320 x 240 resolution, absolutely the same distortion -coefficients can be used for 640 x 480 images from the same camera while \f$f_x\f$, \f$f_y\f$, \f$c_x\f$, and -\f$c_y\f$ need to be scaled appropriately. - -The functions below use the above model to do the following: - -- Project 3D points to the image plane given intrinsic and extrinsic parameters. -- Compute extrinsic parameters given intrinsic parameters, a few 3D points, and their -projections. -- Estimate intrinsic and extrinsic camera parameters from several views of a known calibration -pattern (every view is described by several 3D-2D point correspondences). -- Estimate the relative position and orientation of the stereo camera "heads" and compute the -*rectification* transformation that makes the camera optical axes parallel. - -@note - - A calibration sample for 3 cameras in horizontal position can be found at - opencv_source_code/samples/cpp/3calibration.cpp - - A calibration sample based on a sequence of images can be found at - opencv_source_code/samples/cpp/calibration.cpp - - A calibration sample in order to do 3D reconstruction can be found at - opencv_source_code/samples/cpp/build3dmodel.cpp - - A calibration sample of an artificially generated camera and chessboard patterns can be - found at opencv_source_code/samples/cpp/calibration_artificial.cpp - - A calibration example on stereo calibration can be found at - opencv_source_code/samples/cpp/stereo_calib.cpp - - A calibration example on stereo matching can be found at - opencv_source_code/samples/cpp/stereo_match.cpp - - (Python) A camera calibration sample can be found at - opencv_source_code/samples/python/calibrate.py - - @{ - @defgroup calib3d_fisheye Fisheye camera model - - Definitions: Let P be a point in 3D of coordinates X in the world reference frame (stored in the - matrix X) The coordinate vector of P in the camera reference frame is: - - \f[Xc = R X + T\f] - - where R is the rotation matrix corresponding to the rotation vector om: R = rodrigues(om); call x, y - and z the 3 coordinates of Xc: - - \f[x = Xc_1 \\ y = Xc_2 \\ z = Xc_3\f] - - The pinehole projection coordinates of P is [a; b] where - - \f[a = x / z \ and \ b = y / z \\ r^2 = a^2 + b^2 \\ \theta = atan(r)\f] - - Fisheye distortion: - - \f[\theta_d = \theta (1 + k_1 \theta^2 + k_2 \theta^4 + k_3 \theta^6 + k_4 \theta^8)\f] - - The distorted point coordinates are [x'; y'] where - - \f[x' = (\theta_d / r) a \\ y' = (\theta_d / r) b \f] - - Finally, conversion into pixel coordinates: The final pixel coordinates vector [u; v] where: - - \f[u = f_x (x' + \alpha y') + c_x \\ - v = f_y y' + c_y\f] - - @defgroup calib3d_c C API - - @} - */ - -namespace cv -{ - -//! @addtogroup calib3d -//! @{ - -//! type of the robust estimation algorithm -enum { LMEDS = 4, //!< least-median algorithm - RANSAC = 8, //!< RANSAC algorithm - RHO = 16 //!< RHO algorithm - }; - -enum { SOLVEPNP_ITERATIVE = 0, - SOLVEPNP_EPNP = 1, //!< EPnP: Efficient Perspective-n-Point Camera Pose Estimation @cite lepetit2009epnp - SOLVEPNP_P3P = 2, //!< Complete Solution Classification for the Perspective-Three-Point Problem @cite gao2003complete - SOLVEPNP_DLS = 3, //!< A Direct Least-Squares (DLS) Method for PnP @cite hesch2011direct - SOLVEPNP_UPNP = 4 //!< Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation @cite penate2013exhaustive - -}; - -enum { CALIB_CB_ADAPTIVE_THRESH = 1, - CALIB_CB_NORMALIZE_IMAGE = 2, - CALIB_CB_FILTER_QUADS = 4, - CALIB_CB_FAST_CHECK = 8 - }; - -enum { CALIB_CB_SYMMETRIC_GRID = 1, - CALIB_CB_ASYMMETRIC_GRID = 2, - CALIB_CB_CLUSTERING = 4 - }; - -enum { CALIB_USE_INTRINSIC_GUESS = 0x00001, - CALIB_FIX_ASPECT_RATIO = 0x00002, - CALIB_FIX_PRINCIPAL_POINT = 0x00004, - CALIB_ZERO_TANGENT_DIST = 0x00008, - CALIB_FIX_FOCAL_LENGTH = 0x00010, - CALIB_FIX_K1 = 0x00020, - CALIB_FIX_K2 = 0x00040, - CALIB_FIX_K3 = 0x00080, - CALIB_FIX_K4 = 0x00800, - CALIB_FIX_K5 = 0x01000, - CALIB_FIX_K6 = 0x02000, - CALIB_RATIONAL_MODEL = 0x04000, - CALIB_THIN_PRISM_MODEL = 0x08000, - CALIB_FIX_S1_S2_S3_S4 = 0x10000, - CALIB_TILTED_MODEL = 0x40000, - CALIB_FIX_TAUX_TAUY = 0x80000, - // only for stereo - CALIB_FIX_INTRINSIC = 0x00100, - CALIB_SAME_FOCAL_LENGTH = 0x00200, - // for stereo rectification - CALIB_ZERO_DISPARITY = 0x00400, - CALIB_USE_LU = (1 << 17), //!< use LU instead of SVD decomposition for solving. much faster but potentially less precise - }; - -//! the algorithm for finding fundamental matrix -enum { FM_7POINT = 1, //!< 7-point algorithm - FM_8POINT = 2, //!< 8-point algorithm - FM_LMEDS = 4, //!< least-median algorithm - FM_RANSAC = 8 //!< RANSAC algorithm - }; - - - -/** @brief Converts a rotation matrix to a rotation vector or vice versa. - -@param src Input rotation vector (3x1 or 1x3) or rotation matrix (3x3). -@param dst Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively. -@param jacobian Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partial -derivatives of the output array components with respect to the input array components. - -\f[\begin{array}{l} \theta \leftarrow norm(r) \\ r \leftarrow r/ \theta \\ R = \cos{\theta} I + (1- \cos{\theta} ) r r^T + \sin{\theta} \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} \end{array}\f] - -Inverse transformation can be also done easily, since - -\f[\sin ( \theta ) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} = \frac{R - R^T}{2}\f] - -A rotation vector is a convenient and most compact representation of a rotation matrix (since any -rotation matrix has just 3 degrees of freedom). The representation is used in the global 3D geometry -optimization procedures like calibrateCamera, stereoCalibrate, or solvePnP . - */ -CV_EXPORTS_W void Rodrigues( InputArray src, OutputArray dst, OutputArray jacobian = noArray() ); - -/** @brief Finds a perspective transformation between two planes. - -@param srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2 -or vector\ . -@param dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or -a vector\ . -@param method Method used to computed a homography matrix. The following methods are possible: -- **0** - a regular method using all the points -- **RANSAC** - RANSAC-based robust method -- **LMEDS** - Least-Median robust method -- **RHO** - PROSAC-based robust method -@param ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier -(used in the RANSAC and RHO methods only). That is, if -\f[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} * \texttt{srcPoints} _i) \| > \texttt{ransacReprojThreshold}\f] -then the point \f$i\f$ is considered an outlier. If srcPoints and dstPoints are measured in pixels, -it usually makes sense to set this parameter somewhere in the range of 1 to 10. -@param mask Optional output mask set by a robust method ( RANSAC or LMEDS ). Note that the input -mask values are ignored. -@param maxIters The maximum number of RANSAC iterations, 2000 is the maximum it can be. -@param confidence Confidence level, between 0 and 1. - -The functions find and return the perspective transformation \f$H\f$ between the source and the -destination planes: - -\f[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\f] - -so that the back-projection error - -\f[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\f] - -is minimized. If the parameter method is set to the default value 0, the function uses all the point -pairs to compute an initial homography estimate with a simple least-squares scheme. - -However, if not all of the point pairs ( \f$srcPoints_i\f$, \f$dstPoints_i\f$ ) fit the rigid perspective -transformation (that is, there are some outliers), this initial estimate will be poor. In this case, -you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different -random subsets of the corresponding point pairs (of four pairs each), estimate the homography matrix -using this subset and a simple least-square algorithm, and then compute the quality/goodness of the -computed homography (which is the number of inliers for RANSAC or the median re-projection error for -LMeDs). The best subset is then used to produce the initial estimate of the homography matrix and -the mask of inliers/outliers. - -Regardless of the method, robust or not, the computed homography matrix is refined further (using -inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the -re-projection error even more. - -The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to -distinguish inliers from outliers. The method LMeDS does not need any threshold but it works -correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the -noise is rather small, use the default method (method=0). - -The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is -determined up to a scale. Thus, it is normalized so that \f$h_{33}=1\f$. Note that whenever an H matrix -cannot be estimated, an empty one will be returned. - -@sa - getAffineTransform, getPerspectiveTransform, estimateRigidTransform, warpPerspective, - perspectiveTransform - -@note - - A example on calculating a homography for image matching can be found at - opencv_source_code/samples/cpp/video_homography.cpp - - */ -CV_EXPORTS_W Mat findHomography( InputArray srcPoints, InputArray dstPoints, - int method = 0, double ransacReprojThreshold = 3, - OutputArray mask=noArray(), const int maxIters = 2000, - const double confidence = 0.995); - -/** @overload */ -CV_EXPORTS Mat findHomography( InputArray srcPoints, InputArray dstPoints, - OutputArray mask, int method = 0, double ransacReprojThreshold = 3 ); - -/** @brief Computes an RQ decomposition of 3x3 matrices. - -@param src 3x3 input matrix. -@param mtxR Output 3x3 upper-triangular matrix. -@param mtxQ Output 3x3 orthogonal matrix. -@param Qx Optional output 3x3 rotation matrix around x-axis. -@param Qy Optional output 3x3 rotation matrix around y-axis. -@param Qz Optional output 3x3 rotation matrix around z-axis. - -The function computes a RQ decomposition using the given rotations. This function is used in -decomposeProjectionMatrix to decompose the left 3x3 submatrix of a projection matrix into a camera -and a rotation matrix. - -It optionally returns three rotation matrices, one for each axis, and the three Euler angles in -degrees (as the return value) that could be used in OpenGL. Note, there is always more than one -sequence of rotations about the three principle axes that results in the same orientation of an -object, eg. see @cite Slabaugh . Returned tree rotation matrices and corresponding three Euler angules -are only one of the possible solutions. - */ -CV_EXPORTS_W Vec3d RQDecomp3x3( InputArray src, OutputArray mtxR, OutputArray mtxQ, - OutputArray Qx = noArray(), - OutputArray Qy = noArray(), - OutputArray Qz = noArray()); - -/** @brief Decomposes a projection matrix into a rotation matrix and a camera matrix. - -@param projMatrix 3x4 input projection matrix P. -@param cameraMatrix Output 3x3 camera matrix K. -@param rotMatrix Output 3x3 external rotation matrix R. -@param transVect Output 4x1 translation vector T. -@param rotMatrixX Optional 3x3 rotation matrix around x-axis. -@param rotMatrixY Optional 3x3 rotation matrix around y-axis. -@param rotMatrixZ Optional 3x3 rotation matrix around z-axis. -@param eulerAngles Optional three-element vector containing three Euler angles of rotation in -degrees. - -The function computes a decomposition of a projection matrix into a calibration and a rotation -matrix and the position of a camera. - -It optionally returns three rotation matrices, one for each axis, and three Euler angles that could -be used in OpenGL. Note, there is always more than one sequence of rotations about the three -principle axes that results in the same orientation of an object, eg. see @cite Slabaugh . Returned -tree rotation matrices and corresponding three Euler angules are only one of the possible solutions. - -The function is based on RQDecomp3x3 . - */ -CV_EXPORTS_W void decomposeProjectionMatrix( InputArray projMatrix, OutputArray cameraMatrix, - OutputArray rotMatrix, OutputArray transVect, - OutputArray rotMatrixX = noArray(), - OutputArray rotMatrixY = noArray(), - OutputArray rotMatrixZ = noArray(), - OutputArray eulerAngles =noArray() ); - -/** @brief Computes partial derivatives of the matrix product for each multiplied matrix. - -@param A First multiplied matrix. -@param B Second multiplied matrix. -@param dABdA First output derivative matrix d(A\*B)/dA of size -\f$\texttt{A.rows*B.cols} \times {A.rows*A.cols}\f$ . -@param dABdB Second output derivative matrix d(A\*B)/dB of size -\f$\texttt{A.rows*B.cols} \times {B.rows*B.cols}\f$ . - -The function computes partial derivatives of the elements of the matrix product \f$A*B\f$ with regard to -the elements of each of the two input matrices. The function is used to compute the Jacobian -matrices in stereoCalibrate but can also be used in any other similar optimization function. - */ -CV_EXPORTS_W void matMulDeriv( InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB ); - -/** @brief Combines two rotation-and-shift transformations. - -@param rvec1 First rotation vector. -@param tvec1 First translation vector. -@param rvec2 Second rotation vector. -@param tvec2 Second translation vector. -@param rvec3 Output rotation vector of the superposition. -@param tvec3 Output translation vector of the superposition. -@param dr3dr1 -@param dr3dt1 -@param dr3dr2 -@param dr3dt2 -@param dt3dr1 -@param dt3dt1 -@param dt3dr2 -@param dt3dt2 Optional output derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and -tvec2, respectively. - -The functions compute: - -\f[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\f] - -where \f$\mathrm{rodrigues}\f$ denotes a rotation vector to a rotation matrix transformation, and -\f$\mathrm{rodrigues}^{-1}\f$ denotes the inverse transformation. See Rodrigues for details. - -Also, the functions can compute the derivatives of the output vectors with regards to the input -vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in -your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a -function that contains a matrix multiplication. - */ -CV_EXPORTS_W void composeRT( InputArray rvec1, InputArray tvec1, - InputArray rvec2, InputArray tvec2, - OutputArray rvec3, OutputArray tvec3, - OutputArray dr3dr1 = noArray(), OutputArray dr3dt1 = noArray(), - OutputArray dr3dr2 = noArray(), OutputArray dr3dt2 = noArray(), - OutputArray dt3dr1 = noArray(), OutputArray dt3dt1 = noArray(), - OutputArray dt3dr2 = noArray(), OutputArray dt3dt2 = noArray() ); - -/** @brief Projects 3D points to an image plane. - -@param objectPoints Array of object points, 3xN/Nx3 1-channel or 1xN/Nx1 3-channel (or -vector\ ), where N is the number of points in the view. -@param rvec Rotation vector. See Rodrigues for details. -@param tvec Translation vector. -@param cameraMatrix Camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$ . -@param distCoeffs Input vector of distortion coefficients -\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of -4, 5, 8, 12 or 14 elements. If the vector is empty, the zero distortion coefficients are assumed. -@param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or -vector\ . -@param jacobian Optional output 2Nx(10+\) jacobian matrix of derivatives of image -points with respect to components of the rotation vector, translation vector, focal lengths, -coordinates of the principal point and the distortion coefficients. In the old interface different -components of the jacobian are returned via different output parameters. -@param aspectRatio Optional "fixed aspect ratio" parameter. If the parameter is not 0, the -function assumes that the aspect ratio (*fx/fy*) is fixed and correspondingly adjusts the jacobian -matrix. - -The function computes projections of 3D points to the image plane given intrinsic and extrinsic -camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of -image points coordinates (as functions of all the input parameters) with respect to the particular -parameters, intrinsic and/or extrinsic. The Jacobians are used during the global optimization in -calibrateCamera, solvePnP, and stereoCalibrate . The function itself can also be used to compute a -re-projection error given the current intrinsic and extrinsic parameters. - -@note By setting rvec=tvec=(0,0,0) or by setting cameraMatrix to a 3x3 identity matrix, or by -passing zero distortion coefficients, you can get various useful partial cases of the function. This -means that you can compute the distorted coordinates for a sparse set of points or apply a -perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup. - */ -CV_EXPORTS_W void projectPoints( InputArray objectPoints, - InputArray rvec, InputArray tvec, - InputArray cameraMatrix, InputArray distCoeffs, - OutputArray imagePoints, - OutputArray jacobian = noArray(), - double aspectRatio = 0 ); - -/** @brief Finds an object pose from 3D-2D point correspondences. - -@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or -1xN/Nx1 3-channel, where N is the number of points. vector\ can be also passed here. -@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, -where N is the number of points. vector\ can be also passed here. -@param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ . -@param distCoeffs Input vector of distortion coefficients -\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of -4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are -assumed. -@param rvec Output rotation vector (see Rodrigues ) that, together with tvec , brings points from -the model coordinate system to the camera coordinate system. -@param tvec Output translation vector. -@param useExtrinsicGuess Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses -the provided rvec and tvec values as initial approximations of the rotation and translation -vectors, respectively, and further optimizes them. -@param flags Method for solving a PnP problem: -- **SOLVEPNP_ITERATIVE** Iterative method is based on Levenberg-Marquardt optimization. In -this case the function finds such a pose that minimizes reprojection error, that is the sum -of squared distances between the observed projections imagePoints and the projected (using -projectPoints ) objectPoints . -- **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang -"Complete Solution Classification for the Perspective-Three-Point Problem". In this case the -function requires exactly four object and image points. -- **SOLVEPNP_EPNP** Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the -paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation". -- **SOLVEPNP_DLS** Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis. -"A Direct Least-Squares (DLS) Method for PnP". -- **SOLVEPNP_UPNP** Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto, -F.Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length -Estimation". In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$ -assuming that both have the same value. Then the cameraMatrix is updated with the estimated -focal length. - -The function estimates the object pose given a set of object points, their corresponding image -projections, as well as the camera matrix and the distortion coefficients. - -@note - - An example of how to use solvePnP for planar augmented reality can be found at - opencv_source_code/samples/python/plane_ar.py - - If you are using Python: - - Numpy array slices won't work as input because solvePnP requires contiguous - arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of - modules/calib3d/src/solvepnp.cpp version 2.4.9) - - The P3P algorithm requires image points to be in an array of shape (N,1,2) due - to its calling of cv::undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) - which requires 2-channel information. - - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of - it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = - np.ascontiguousarray(D[:,:2]).reshape((N,1,2)) - */ -CV_EXPORTS_W bool solvePnP( InputArray objectPoints, InputArray imagePoints, - InputArray cameraMatrix, InputArray distCoeffs, - OutputArray rvec, OutputArray tvec, - bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE ); - -/** @brief Finds an object pose from 3D-2D point correspondences using the RANSAC scheme. - -@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or -1xN/Nx1 3-channel, where N is the number of points. vector\ can be also passed here. -@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, -where N is the number of points. vector\ can be also passed here. -@param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ . -@param distCoeffs Input vector of distortion coefficients -\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of -4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are -assumed. -@param rvec Output rotation vector (see Rodrigues ) that, together with tvec , brings points from -the model coordinate system to the camera coordinate system. -@param tvec Output translation vector. -@param useExtrinsicGuess Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses -the provided rvec and tvec values as initial approximations of the rotation and translation -vectors, respectively, and further optimizes them. -@param iterationsCount Number of iterations. -@param reprojectionError Inlier threshold value used by the RANSAC procedure. The parameter value -is the maximum allowed distance between the observed and computed point projections to consider it -an inlier. -@param confidence The probability that the algorithm produces a useful result. -@param inliers Output vector that contains indices of inliers in objectPoints and imagePoints . -@param flags Method for solving a PnP problem (see solvePnP ). - -The function estimates an object pose given a set of object points, their corresponding image -projections, as well as the camera matrix and the distortion coefficients. This function finds such -a pose that minimizes reprojection error, that is, the sum of squared distances between the observed -projections imagePoints and the projected (using projectPoints ) objectPoints. The use of RANSAC -makes the function resistant to outliers. - -@note - - An example of how to use solvePNPRansac for object detection can be found at - opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/ - */ -CV_EXPORTS_W bool solvePnPRansac( InputArray objectPoints, InputArray imagePoints, - InputArray cameraMatrix, InputArray distCoeffs, - OutputArray rvec, OutputArray tvec, - bool useExtrinsicGuess = false, int iterationsCount = 100, - float reprojectionError = 8.0, double confidence = 0.99, - OutputArray inliers = noArray(), int flags = SOLVEPNP_ITERATIVE ); - -/** @brief Finds an initial camera matrix from 3D-2D point correspondences. - -@param objectPoints Vector of vectors of the calibration pattern points in the calibration pattern -coordinate space. In the old interface all the per-view vectors are concatenated. See -calibrateCamera for details. -@param imagePoints Vector of vectors of the projections of the calibration pattern points. In the -old interface all the per-view vectors are concatenated. -@param imageSize Image size in pixels used to initialize the principal point. -@param aspectRatio If it is zero or negative, both \f$f_x\f$ and \f$f_y\f$ are estimated independently. -Otherwise, \f$f_x = f_y * \texttt{aspectRatio}\f$ . - -The function estimates and returns an initial camera matrix for the camera calibration process. -Currently, the function only supports planar calibration patterns, which are patterns where each -object point has z-coordinate =0. - */ -CV_EXPORTS_W Mat initCameraMatrix2D( InputArrayOfArrays objectPoints, - InputArrayOfArrays imagePoints, - Size imageSize, double aspectRatio = 1.0 ); - -/** @brief Finds the positions of internal corners of the chessboard. - -@param image Source chessboard view. It must be an 8-bit grayscale or color image. -@param patternSize Number of inner corners per a chessboard row and column -( patternSize = cvSize(points_per_row,points_per_colum) = cvSize(columns,rows) ). -@param corners Output array of detected corners. -@param flags Various operation flags that can be zero or a combination of the following values: -- **CV_CALIB_CB_ADAPTIVE_THRESH** Use adaptive thresholding to convert the image to black -and white, rather than a fixed threshold level (computed from the average image brightness). -- **CV_CALIB_CB_NORMALIZE_IMAGE** Normalize the image gamma with equalizeHist before -applying fixed or adaptive thresholding. -- **CV_CALIB_CB_FILTER_QUADS** Use additional criteria (like contour area, perimeter, -square-like shape) to filter out false quads extracted at the contour retrieval stage. -- **CALIB_CB_FAST_CHECK** Run a fast check on the image that looks for chessboard corners, -and shortcut the call if none is found. This can drastically speed up the call in the -degenerate condition when no chessboard is observed. - -The function attempts to determine whether the input image is a view of the chessboard pattern and -locate the internal chessboard corners. The function returns a non-zero value if all of the corners -are found and they are placed in a certain order (row by row, left to right in every row). -Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, -a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black -squares touch each other. The detected coordinates are approximate, and to determine their positions -more accurately, the function calls cornerSubPix. You also may use the function cornerSubPix with -different parameters if returned coordinates are not accurate enough. - -Sample usage of detecting and drawing chessboard corners: : -@code - Size patternsize(8,6); //interior number of corners - Mat gray = ....; //source image - vector corners; //this will be filled by the detected corners - - //CALIB_CB_FAST_CHECK saves a lot of time on images - //that do not contain any chessboard corners - bool patternfound = findChessboardCorners(gray, patternsize, corners, - CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE - + CALIB_CB_FAST_CHECK); - - if(patternfound) - cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1), - TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1)); - - drawChessboardCorners(img, patternsize, Mat(corners), patternfound); -@endcode -@note The function requires white space (like a square-thick border, the wider the better) around -the board to make the detection more robust in various environments. Otherwise, if there is no -border and the background is dark, the outer black squares cannot be segmented properly and so the -square grouping and ordering algorithm fails. - */ -CV_EXPORTS_W bool findChessboardCorners( InputArray image, Size patternSize, OutputArray corners, - int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE ); - -//! finds subpixel-accurate positions of the chessboard corners -CV_EXPORTS bool find4QuadCornerSubpix( InputArray img, InputOutputArray corners, Size region_size ); - -/** @brief Renders the detected chessboard corners. - -@param image Destination image. It must be an 8-bit color image. -@param patternSize Number of inner corners per a chessboard row and column -(patternSize = cv::Size(points_per_row,points_per_column)). -@param corners Array of detected corners, the output of findChessboardCorners. -@param patternWasFound Parameter indicating whether the complete board was found or not. The -return value of findChessboardCorners should be passed here. - -The function draws individual chessboard corners detected either as red circles if the board was not -found, or as colored corners connected with lines if the board was found. - */ -CV_EXPORTS_W void drawChessboardCorners( InputOutputArray image, Size patternSize, - InputArray corners, bool patternWasFound ); - -/** @brief Finds centers in the grid of circles. - -@param image grid view of input circles; it must be an 8-bit grayscale or color image. -@param patternSize number of circles per row and column -( patternSize = Size(points_per_row, points_per_colum) ). -@param centers output array of detected centers. -@param flags various operation flags that can be one of the following values: -- **CALIB_CB_SYMMETRIC_GRID** uses symmetric pattern of circles. -- **CALIB_CB_ASYMMETRIC_GRID** uses asymmetric pattern of circles. -- **CALIB_CB_CLUSTERING** uses a special algorithm for grid detection. It is more robust to -perspective distortions but much more sensitive to background clutter. -@param blobDetector feature detector that finds blobs like dark circles on light background. - -The function attempts to determine whether the input image contains a grid of circles. If it is, the -function locates centers of the circles. The function returns a non-zero value if all of the centers -have been found and they have been placed in a certain order (row by row, left to right in every -row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. - -Sample usage of detecting and drawing the centers of circles: : -@code - Size patternsize(7,7); //number of centers - Mat gray = ....; //source image - vector centers; //this will be filled by the detected centers - - bool patternfound = findCirclesGrid(gray, patternsize, centers); - - drawChessboardCorners(img, patternsize, Mat(centers), patternfound); -@endcode -@note The function requires white space (like a square-thick border, the wider the better) around -the board to make the detection more robust in various environments. - */ -CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize, - OutputArray centers, int flags = CALIB_CB_SYMMETRIC_GRID, - const Ptr &blobDetector = SimpleBlobDetector::create()); - -/** @brief Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. - -@param objectPoints In the new interface it is a vector of vectors of calibration pattern points in -the calibration pattern coordinate space (e.g. std::vector>). The outer -vector contains as many elements as the number of the pattern views. If the same calibration pattern -is shown in each view and it is fully visible, all the vectors will be the same. Although, it is -possible to use partially occluded patterns, or even different patterns in different views. Then, -the vectors will be different. The points are 3D, but since they are in a pattern coordinate system, -then, if the rig is planar, it may make sense to put the model to a XY coordinate plane so that -Z-coordinate of each input object point is 0. -In the old interface all the vectors of object points from different views are concatenated -together. -@param imagePoints In the new interface it is a vector of vectors of the projections of calibration -pattern points (e.g. std::vector>). imagePoints.size() and -objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i. -In the old interface all the vectors of object points from different views are concatenated -together. -@param imageSize Size of the image used only to initialize the intrinsic camera matrix. -@param cameraMatrix Output 3x3 floating-point camera matrix -\f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS -and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be -initialized before calling the function. -@param distCoeffs Output vector of distortion coefficients -\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of -4, 5, 8, 12 or 14 elements. -@param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view -(e.g. std::vector>). That is, each k-th rotation vector together with the corresponding -k-th translation vector (see the next output parameter description) brings the calibration pattern -from the model coordinate space (in which object points are specified) to the world coordinate -space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. *M* -1). -@param tvecs Output vector of translation vectors estimated for each pattern view. -@param flags Different flags that may be zero or a combination of the following values: -- **CV_CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of -fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image -center ( imageSize is used), and focal distances are computed in a least-squares fashion. -Note, that if intrinsic parameters are known, there is no need to use this function just to -estimate extrinsic parameters. Use solvePnP instead. -- **CV_CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global -optimization. It stays at the center or at a different location specified when -CV_CALIB_USE_INTRINSIC_GUESS is set too. -- **CV_CALIB_FIX_ASPECT_RATIO** The functions considers only fy as a free parameter. The -ratio fx/fy stays the same as in the input cameraMatrix . When -CV_CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are -ignored, only their ratio is computed and used further. -- **CV_CALIB_ZERO_TANGENT_DIST** Tangential distortion coefficients \f$(p_1, p_2)\f$ are set -to zeros and stay zero. -- **CV_CALIB_FIX_K1,...,CV_CALIB_FIX_K6** The corresponding radial distortion -coefficient is not changed during the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is -set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. -- **CV_CALIB_RATIONAL_MODEL** Coefficients k4, k5, and k6 are enabled. To provide the -backward compatibility, this extra flag should be explicitly specified to make the -calibration function use the rational model and return 8 coefficients. If the flag is not -set, the function computes and returns only 5 distortion coefficients. -- **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the -backward compatibility, this extra flag should be explicitly specified to make the -calibration function use the thin prism model and return 12 coefficients. If the flag is not -set, the function computes and returns only 5 distortion coefficients. -- **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during -the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the -supplied distCoeffs matrix is used. Otherwise, it is set to 0. -- **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the -backward compatibility, this extra flag should be explicitly specified to make the -calibration function use the tilted sensor model and return 14 coefficients. If the flag is not -set, the function computes and returns only 5 distortion coefficients. -- **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during -the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the -supplied distCoeffs matrix is used. Otherwise, it is set to 0. -@param criteria Termination criteria for the iterative optimization algorithm. - -The function estimates the intrinsic camera parameters and extrinsic parameters for each of the -views. The algorithm is based on @cite Zhang2000 and @cite BouguetMCT . The coordinates of 3D object -points and their corresponding 2D projections in each view must be specified. That may be achieved -by using an object with a known geometry and easily detectable feature points. Such an object is -called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as -a calibration rig (see findChessboardCorners ). Currently, initialization of intrinsic parameters -(when CV_CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration -patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also -be used as long as initial cameraMatrix is provided. - -The algorithm performs the following steps: - -- Compute the initial intrinsic parameters (the option only available for planar calibration - patterns) or read them from the input parameters. The distortion coefficients are all set to - zeros initially unless some of CV_CALIB_FIX_K? are specified. - -- Estimate the initial camera pose as if the intrinsic parameters have been already known. This is - done using solvePnP . - -- Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, - that is, the total sum of squared distances between the observed feature points imagePoints and - the projected (using the current estimates for camera parameters and the poses) object points - objectPoints. See projectPoints for details. - -The function returns the final re-projection error. - -@note - If you use a non-square (=non-NxN) grid and findChessboardCorners for calibration, and - calibrateCamera returns bad values (zero distortion coefficients, an image center very far from - (w/2-0.5,h/2-0.5), and/or large differences between \f$f_x\f$ and \f$f_y\f$ (ratios of 10:1 or more)), - then you have probably used patternSize=cvSize(rows,cols) instead of using - patternSize=cvSize(cols,rows) in findChessboardCorners . - -@sa - findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort - */ -CV_EXPORTS_W double calibrateCamera( InputArrayOfArrays objectPoints, - InputArrayOfArrays imagePoints, Size imageSize, - InputOutputArray cameraMatrix, InputOutputArray distCoeffs, - OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, - int flags = 0, TermCriteria criteria = TermCriteria( - TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ); - -/** @brief Computes useful camera characteristics from the camera matrix. - -@param cameraMatrix Input camera matrix that can be estimated by calibrateCamera or -stereoCalibrate . -@param imageSize Input image size in pixels. -@param apertureWidth Physical width in mm of the sensor. -@param apertureHeight Physical height in mm of the sensor. -@param fovx Output field of view in degrees along the horizontal sensor axis. -@param fovy Output field of view in degrees along the vertical sensor axis. -@param focalLength Focal length of the lens in mm. -@param principalPoint Principal point in mm. -@param aspectRatio \f$f_y/f_x\f$ - -The function computes various useful camera characteristics from the previously estimated camera -matrix. - -@note - Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for - the chessboard pitch (it can thus be any value). - */ -CV_EXPORTS_W void calibrationMatrixValues( InputArray cameraMatrix, Size imageSize, - double apertureWidth, double apertureHeight, - CV_OUT double& fovx, CV_OUT double& fovy, - CV_OUT double& focalLength, CV_OUT Point2d& principalPoint, - CV_OUT double& aspectRatio ); - -/** @brief Calibrates the stereo camera. - -@param objectPoints Vector of vectors of the calibration pattern points. -@param imagePoints1 Vector of vectors of the projections of the calibration pattern points, -observed by the first camera. -@param imagePoints2 Vector of vectors of the projections of the calibration pattern points, -observed by the second camera. -@param cameraMatrix1 Input/output first camera matrix: -\f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If -any of CV_CALIB_USE_INTRINSIC_GUESS , CV_CALIB_FIX_ASPECT_RATIO , -CV_CALIB_FIX_INTRINSIC , or CV_CALIB_FIX_FOCAL_LENGTH are specified, some or all of the -matrix components must be initialized. See the flags description for details. -@param distCoeffs1 Input/output vector of distortion coefficients -\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of -4, 5, 8, 12 or 14 elements. The output vector length depends on the flags. -@param cameraMatrix2 Input/output second camera matrix. The parameter is similar to cameraMatrix1 -@param distCoeffs2 Input/output lens distortion coefficients for the second camera. The parameter -is similar to distCoeffs1 . -@param imageSize Size of the image used only to initialize intrinsic camera matrix. -@param R Output rotation matrix between the 1st and the 2nd camera coordinate systems. -@param T Output translation vector between the coordinate systems of the cameras. -@param E Output essential matrix. -@param F Output fundamental matrix. -@param flags Different flags that may be zero or a combination of the following values: -- **CV_CALIB_FIX_INTRINSIC** Fix cameraMatrix? and distCoeffs? so that only R, T, E , and F -matrices are estimated. -- **CV_CALIB_USE_INTRINSIC_GUESS** Optimize some or all of the intrinsic parameters -according to the specified flags. Initial values are provided by the user. -- **CV_CALIB_FIX_PRINCIPAL_POINT** Fix the principal points during the optimization. -- **CV_CALIB_FIX_FOCAL_LENGTH** Fix \f$f^{(j)}_x\f$ and \f$f^{(j)}_y\f$ . -- **CV_CALIB_FIX_ASPECT_RATIO** Optimize \f$f^{(j)}_y\f$ . Fix the ratio \f$f^{(j)}_x/f^{(j)}_y\f$ -. -- **CV_CALIB_SAME_FOCAL_LENGTH** Enforce \f$f^{(0)}_x=f^{(1)}_x\f$ and \f$f^{(0)}_y=f^{(1)}_y\f$ . -- **CV_CALIB_ZERO_TANGENT_DIST** Set tangential distortion coefficients for each camera to -zeros and fix there. -- **CV_CALIB_FIX_K1,...,CV_CALIB_FIX_K6** Do not change the corresponding radial -distortion coefficient during the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, -the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. -- **CV_CALIB_RATIONAL_MODEL** Enable coefficients k4, k5, and k6. To provide the backward -compatibility, this extra flag should be explicitly specified to make the calibration -function use the rational model and return 8 coefficients. If the flag is not set, the -function computes and returns only 5 distortion coefficients. -- **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the -backward compatibility, this extra flag should be explicitly specified to make the -calibration function use the thin prism model and return 12 coefficients. If the flag is not -set, the function computes and returns only 5 distortion coefficients. -- **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during -the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the -supplied distCoeffs matrix is used. Otherwise, it is set to 0. -- **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the -backward compatibility, this extra flag should be explicitly specified to make the -calibration function use the tilted sensor model and return 14 coefficients. If the flag is not -set, the function computes and returns only 5 distortion coefficients. -- **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during -the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the -supplied distCoeffs matrix is used. Otherwise, it is set to 0. -@param criteria Termination criteria for the iterative optimization algorithm. - -The function estimates transformation between two cameras making a stereo pair. If you have a stereo -camera where the relative position and orientation of two cameras is fixed, and if you computed -poses of an object relative to the first camera and to the second camera, (R1, T1) and (R2, T2), -respectively (this can be done with solvePnP ), then those poses definitely relate to each other. -This means that, given ( \f$R_1\f$,\f$T_1\f$ ), it should be possible to compute ( \f$R_2\f$,\f$T_2\f$ ). You only -need to know the position and orientation of the second camera relative to the first camera. This is -what the described function does. It computes ( \f$R\f$,\f$T\f$ ) so that: - -\f[R_2=R*R_1 -T_2=R*T_1 + T,\f] - -Optionally, it computes the essential matrix E: - -\f[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} *R\f] - -where \f$T_i\f$ are components of the translation vector \f$T\f$ : \f$T=[T_0, T_1, T_2]^T\f$ . And the function -can also compute the fundamental matrix F: - -\f[F = cameraMatrix2^{-T} E cameraMatrix1^{-1}\f] - -Besides the stereo-related information, the function can also perform a full calibration of each of -two cameras. However, due to the high dimensionality of the parameter space and noise in the input -data, the function can diverge from the correct solution. If the intrinsic parameters can be -estimated with high accuracy for each of the cameras individually (for example, using -calibrateCamera ), you are recommended to do so and then pass CV_CALIB_FIX_INTRINSIC flag to the -function along with the computed intrinsic parameters. Otherwise, if all the parameters are -estimated at once, it makes sense to restrict some parameters, for example, pass -CV_CALIB_SAME_FOCAL_LENGTH and CV_CALIB_ZERO_TANGENT_DIST flags, which is usually a -reasonable assumption. - -Similarly to calibrateCamera , the function minimizes the total re-projection error for all the -points in all the available views from both cameras. The function returns the final value of the -re-projection error. - */ -CV_EXPORTS_W double stereoCalibrate( InputArrayOfArrays objectPoints, - InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, - InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, - InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, - Size imageSize, OutputArray R,OutputArray T, OutputArray E, OutputArray F, - int flags = CALIB_FIX_INTRINSIC, - TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) ); - - -/** @brief Computes rectification transforms for each head of a calibrated stereo camera. - -@param cameraMatrix1 First camera matrix. -@param distCoeffs1 First camera distortion parameters. -@param cameraMatrix2 Second camera matrix. -@param distCoeffs2 Second camera distortion parameters. -@param imageSize Size of the image used for stereo calibration. -@param R Rotation matrix between the coordinate systems of the first and the second cameras. -@param T Translation vector between coordinate systems of the cameras. -@param R1 Output 3x3 rectification transform (rotation matrix) for the first camera. -@param R2 Output 3x3 rectification transform (rotation matrix) for the second camera. -@param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first -camera. -@param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second -camera. -@param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ). -@param flags Operation flags that may be zero or CV_CALIB_ZERO_DISPARITY . If the flag is set, -the function makes the principal points of each camera have the same pixel coordinates in the -rectified views. And if the flag is not set, the function may still shift the images in the -horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the -useful image area. -@param alpha Free scaling parameter. If it is -1 or absent, the function performs the default -scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified -images are zoomed and shifted so that only valid pixels are visible (no black areas after -rectification). alpha=1 means that the rectified image is decimated and shifted so that all the -pixels from the original images from the cameras are retained in the rectified images (no source -image pixels are lost). Obviously, any intermediate value yields an intermediate result between -those two extreme cases. -@param newImageSize New image resolution after rectification. The same size should be passed to -initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) -is passed (default), it is set to the original imageSize . Setting it to larger value can help you -preserve details in the original image, especially when there is a big radial distortion. -@param validPixROI1 Optional output rectangles inside the rectified images where all the pixels -are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller -(see the picture below). -@param validPixROI2 Optional output rectangles inside the rectified images where all the pixels -are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller -(see the picture below). - -The function computes the rotation matrices for each camera that (virtually) make both camera image -planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies -the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate -as input. As output, it provides two rotation matrices and also two projection matrices in the new -coordinates. The function distinguishes the following two cases: - -- **Horizontal stereo**: the first and the second camera views are shifted relative to each other - mainly along the x axis (with possible small vertical shift). In the rectified images, the - corresponding epipolar lines in the left and right cameras are horizontal and have the same - y-coordinate. P1 and P2 look like: - - \f[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\f] - - \f[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x*f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f] - - where \f$T_x\f$ is a horizontal shift between the cameras and \f$cx_1=cx_2\f$ if - CV_CALIB_ZERO_DISPARITY is set. - -- **Vertical stereo**: the first and the second camera views are shifted relative to each other - mainly in vertical direction (and probably a bit in the horizontal direction too). The epipolar - lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like: - - \f[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\f] - - \f[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y*f \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f] - - where \f$T_y\f$ is a vertical shift between the cameras and \f$cy_1=cy_2\f$ if CALIB_ZERO_DISPARITY is - set. - -As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera -matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to -initialize the rectification map for each camera. - -See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through -the corresponding image regions. This means that the images are well rectified, which is what most -stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that -their interiors are all valid pixels. - -![image](pics/stereo_undistort.jpg) - */ -CV_EXPORTS_W void stereoRectify( InputArray cameraMatrix1, InputArray distCoeffs1, - InputArray cameraMatrix2, InputArray distCoeffs2, - Size imageSize, InputArray R, InputArray T, - OutputArray R1, OutputArray R2, - OutputArray P1, OutputArray P2, - OutputArray Q, int flags = CALIB_ZERO_DISPARITY, - double alpha = -1, Size newImageSize = Size(), - CV_OUT Rect* validPixROI1 = 0, CV_OUT Rect* validPixROI2 = 0 ); - -/** @brief Computes a rectification transform for an uncalibrated stereo camera. - -@param points1 Array of feature points in the first image. -@param points2 The corresponding points in the second image. The same formats as in -findFundamentalMat are supported. -@param F Input fundamental matrix. It can be computed from the same set of point pairs using -findFundamentalMat . -@param imgSize Size of the image. -@param H1 Output rectification homography matrix for the first image. -@param H2 Output rectification homography matrix for the second image. -@param threshold Optional threshold used to filter out the outliers. If the parameter is greater -than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points -for which \f$|\texttt{points2[i]}^T*\texttt{F}*\texttt{points1[i]}|>\texttt{threshold}\f$ ) are -rejected prior to computing the homographies. Otherwise,all the points are considered inliers. - -The function computes the rectification transformations without knowing intrinsic parameters of the -cameras and their relative position in the space, which explains the suffix "uncalibrated". Another -related difference from stereoRectify is that the function outputs not the rectification -transformations in the object (3D) space, but the planar perspective transformations encoded by the -homography matrices H1 and H2 . The function implements the algorithm @cite Hartley99 . - -@note - While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily - depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, - it would be better to correct it before computing the fundamental matrix and calling this - function. For example, distortion coefficients can be estimated for each head of stereo camera - separately by using calibrateCamera . Then, the images can be corrected using undistort , or - just the point coordinates can be corrected with undistortPoints . - */ -CV_EXPORTS_W bool stereoRectifyUncalibrated( InputArray points1, InputArray points2, - InputArray F, Size imgSize, - OutputArray H1, OutputArray H2, - double threshold = 5 ); - -//! computes the rectification transformations for 3-head camera, where all the heads are on the same line. -CV_EXPORTS_W float rectify3Collinear( InputArray cameraMatrix1, InputArray distCoeffs1, - InputArray cameraMatrix2, InputArray distCoeffs2, - InputArray cameraMatrix3, InputArray distCoeffs3, - InputArrayOfArrays imgpt1, InputArrayOfArrays imgpt3, - Size imageSize, InputArray R12, InputArray T12, - InputArray R13, InputArray T13, - OutputArray R1, OutputArray R2, OutputArray R3, - OutputArray P1, OutputArray P2, OutputArray P3, - OutputArray Q, double alpha, Size newImgSize, - CV_OUT Rect* roi1, CV_OUT Rect* roi2, int flags ); - -/** @brief Returns the new camera matrix based on the free scaling parameter. - -@param cameraMatrix Input camera matrix. -@param distCoeffs Input vector of distortion coefficients -\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of -4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are -assumed. -@param imageSize Original image size. -@param alpha Free scaling parameter between 0 (when all the pixels in the undistorted image are -valid) and 1 (when all the source image pixels are retained in the undistorted image). See -stereoRectify for details. -@param newImgSize Image size after rectification. By default,it is set to imageSize . -@param validPixROI Optional output rectangle that outlines all-good-pixels region in the -undistorted image. See roi1, roi2 description in stereoRectify . -@param centerPrincipalPoint Optional flag that indicates whether in the new camera matrix the -principal point should be at the image center or not. By default, the principal point is chosen to -best fit a subset of the source image (determined by alpha) to the corrected image. -@return new_camera_matrix Output new camera matrix. - -The function computes and returns the optimal new camera matrix based on the free scaling parameter. -By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original -image pixels if there is valuable information in the corners alpha=1 , or get something in between. -When alpha\>0 , the undistortion result is likely to have some black pixels corresponding to -"virtual" pixels outside of the captured distorted image. The original camera matrix, distortion -coefficients, the computed new camera matrix, and newImageSize should be passed to -initUndistortRectifyMap to produce the maps for remap . - */ -CV_EXPORTS_W Mat getOptimalNewCameraMatrix( InputArray cameraMatrix, InputArray distCoeffs, - Size imageSize, double alpha, Size newImgSize = Size(), - CV_OUT Rect* validPixROI = 0, - bool centerPrincipalPoint = false); - -/** @brief Converts points from Euclidean to homogeneous space. - -@param src Input vector of N-dimensional points. -@param dst Output vector of N+1-dimensional points. - -The function converts points from Euclidean to homogeneous space by appending 1's to the tuple of -point coordinates. That is, each point (x1, x2, ..., xn) is converted to (x1, x2, ..., xn, 1). - */ -CV_EXPORTS_W void convertPointsToHomogeneous( InputArray src, OutputArray dst ); - -/** @brief Converts points from homogeneous to Euclidean space. - -@param src Input vector of N-dimensional points. -@param dst Output vector of N-1-dimensional points. - -The function converts points homogeneous to Euclidean space using perspective projection. That is, -each point (x1, x2, ... x(n-1), xn) is converted to (x1/xn, x2/xn, ..., x(n-1)/xn). When xn=0, the -output point coordinates will be (0,0,0,...). - */ -CV_EXPORTS_W void convertPointsFromHomogeneous( InputArray src, OutputArray dst ); - -/** @brief Converts points to/from homogeneous coordinates. - -@param src Input array or vector of 2D, 3D, or 4D points. -@param dst Output vector of 2D, 3D, or 4D points. - -The function converts 2D or 3D points from/to homogeneous coordinates by calling either -convertPointsToHomogeneous or convertPointsFromHomogeneous. - -@note The function is obsolete. Use one of the previous two functions instead. - */ -CV_EXPORTS void convertPointsHomogeneous( InputArray src, OutputArray dst ); - -/** @brief Calculates a fundamental matrix from the corresponding points in two images. - -@param points1 Array of N points from the first image. The point coordinates should be -floating-point (single or double precision). -@param points2 Array of the second image points of the same size and format as points1 . -@param method Method for computing a fundamental matrix. -- **CV_FM_7POINT** for a 7-point algorithm. \f$N = 7\f$ -- **CV_FM_8POINT** for an 8-point algorithm. \f$N \ge 8\f$ -- **CV_FM_RANSAC** for the RANSAC algorithm. \f$N \ge 8\f$ -- **CV_FM_LMEDS** for the LMedS algorithm. \f$N \ge 8\f$ -@param param1 Parameter used for RANSAC. It is the maximum distance from a point to an epipolar -line in pixels, beyond which the point is considered an outlier and is not used for computing the -final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the -point localization, image resolution, and the image noise. -@param param2 Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level -of confidence (probability) that the estimated matrix is correct. -@param mask - -The epipolar geometry is described by the following equation: - -\f[[p_2; 1]^T F [p_1; 1] = 0\f] - -where \f$F\f$ is a fundamental matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the -second images, respectively. - -The function calculates the fundamental matrix using one of four methods listed above and returns -the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point -algorithm, the function may return up to 3 solutions ( \f$9 \times 3\f$ matrix that stores all 3 -matrices sequentially). - -The calculated fundamental matrix may be passed further to computeCorrespondEpilines that finds the -epipolar lines corresponding to the specified points. It can also be passed to -stereoRectifyUncalibrated to compute the rectification transformation. : -@code - // Example. Estimation of fundamental matrix using the RANSAC algorithm - int point_count = 100; - vector points1(point_count); - vector points2(point_count); - - // initialize the points here ... - for( int i = 0; i < point_count; i++ ) - { - points1[i] = ...; - points2[i] = ...; - } - - Mat fundamental_matrix = - findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99); -@endcode - */ -CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2, - int method = FM_RANSAC, - double param1 = 3., double param2 = 0.99, - OutputArray mask = noArray() ); - -/** @overload */ -CV_EXPORTS Mat findFundamentalMat( InputArray points1, InputArray points2, - OutputArray mask, int method = FM_RANSAC, - double param1 = 3., double param2 = 0.99 ); - -/** @brief Calculates an essential matrix from the corresponding points in two images. - -@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should -be floating-point (single or double precision). -@param points2 Array of the second image points of the same size and format as points1 . -@param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . -Note that this function assumes that points1 and points2 are feature points from cameras with the -same camera matrix. -@param method Method for computing a fundamental matrix. -- **RANSAC** for the RANSAC algorithm. -- **MEDS** for the LMedS algorithm. -@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of -confidence (probability) that the estimated matrix is correct. -@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar -line in pixels, beyond which the point is considered an outlier and is not used for computing the -final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the -point localization, image resolution, and the image noise. -@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1 -for the other points. The array is computed only in the RANSAC and LMedS methods. - -This function estimates essential matrix based on the five-point algorithm solver in @cite Nister03 . -@cite SteweniusCFS is also a related. The epipolar geometry is described by the following equation: - -\f[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\f] - -where \f$E\f$ is an essential matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the -second images, respectively. The result of this function may be passed further to -decomposeEssentialMat or recoverPose to recover the relative pose between cameras. - */ -CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2, - InputArray cameraMatrix, int method = RANSAC, - double prob = 0.999, double threshold = 1.0, - OutputArray mask = noArray() ); - -/** @overload -@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should -be floating-point (single or double precision). -@param points2 Array of the second image points of the same size and format as points1 . -@param focal focal length of the camera. Note that this function assumes that points1 and points2 -are feature points from cameras with same focal length and principle point. -@param pp principle point of the camera. -@param method Method for computing a fundamental matrix. -- **RANSAC** for the RANSAC algorithm. -- **LMEDS** for the LMedS algorithm. -@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar -line in pixels, beyond which the point is considered an outlier and is not used for computing the -final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the -point localization, image resolution, and the image noise. -@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of -confidence (probability) that the estimated matrix is correct. -@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1 -for the other points. The array is computed only in the RANSAC and LMedS methods. - -This function differs from the one above that it computes camera matrix from focal length and -principal point: - -\f[K = -\begin{bmatrix} -f & 0 & x_{pp} \\ -0 & f & y_{pp} \\ -0 & 0 & 1 -\end{bmatrix}\f] - */ -CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2, - double focal = 1.0, Point2d pp = Point2d(0, 0), - int method = RANSAC, double prob = 0.999, - double threshold = 1.0, OutputArray mask = noArray() ); - -/** @brief Decompose an essential matrix to possible rotations and translation. - -@param E The input essential matrix. -@param R1 One possible rotation matrix. -@param R2 Another possible rotation matrix. -@param t One possible translation. - -This function decompose an essential matrix E using svd decomposition @cite HartleyZ00 . Generally 4 -possible poses exists for a given E. They are \f$[R_1, t]\f$, \f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$. By -decomposing E, you can only get the direction of the translation, so the function returns unit t. - */ -CV_EXPORTS_W void decomposeEssentialMat( InputArray E, OutputArray R1, OutputArray R2, OutputArray t ); - -/** @brief Recover relative camera rotation and translation from an estimated essential matrix and the -corresponding points in two images, using cheirality check. Returns the number of inliers which pass -the check. - -@param E The input essential matrix. -@param points1 Array of N 2D points from the first image. The point coordinates should be -floating-point (single or double precision). -@param points2 Array of the second image points of the same size and format as points1 . -@param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . -Note that this function assumes that points1 and points2 are feature points from cameras with the -same camera matrix. -@param R Recovered relative rotation. -@param t Recoverd relative translation. -@param mask Input/output mask for inliers in points1 and points2. -: If it is not empty, then it marks inliers in points1 and points2 for then given essential -matrix E. Only these inliers will be used to recover pose. In the output mask only inliers -which pass the cheirality check. -This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible -pose hypotheses by doing cheirality check. The cheirality check basically means that the -triangulated 3D points should have positive depth. Some details can be found in @cite Nister03 . - -This function can be used to process output E and mask from findEssentialMat. In this scenario, -points1 and points2 are the same input for findEssentialMat. : -@code - // Example. Estimation of fundamental matrix using the RANSAC algorithm - int point_count = 100; - vector points1(point_count); - vector points2(point_count); - - // initialize the points here ... - for( int i = 0; i < point_count; i++ ) - { - points1[i] = ...; - points2[i] = ...; - } - - // cametra matrix with both focal lengths = 1, and principal point = (0, 0) - Mat cameraMatrix = Mat::eye(3, 3, CV_64F); - - Mat E, R, t, mask; - - E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask); - recoverPose(E, points1, points2, cameraMatrix, R, t, mask); -@endcode - */ -CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, - InputArray cameraMatrix, OutputArray R, OutputArray t, - InputOutputArray mask = noArray() ); - -/** @overload -@param E The input essential matrix. -@param points1 Array of N 2D points from the first image. The point coordinates should be -floating-point (single or double precision). -@param points2 Array of the second image points of the same size and format as points1 . -@param R Recovered relative rotation. -@param t Recoverd relative translation. -@param focal Focal length of the camera. Note that this function assumes that points1 and points2 -are feature points from cameras with same focal length and principle point. -@param pp Principle point of the camera. -@param mask Input/output mask for inliers in points1 and points2. -: If it is not empty, then it marks inliers in points1 and points2 for then given essential -matrix E. Only these inliers will be used to recover pose. In the output mask only inliers -which pass the cheirality check. - -This function differs from the one above that it computes camera matrix from focal length and -principal point: - -\f[K = -\begin{bmatrix} -f & 0 & x_{pp} \\ -0 & f & y_{pp} \\ -0 & 0 & 1 -\end{bmatrix}\f] - */ -CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, - OutputArray R, OutputArray t, - double focal = 1.0, Point2d pp = Point2d(0, 0), - InputOutputArray mask = noArray() ); - -/** @brief For points in an image of a stereo pair, computes the corresponding epilines in the other image. - -@param points Input points. \f$N \times 1\f$ or \f$1 \times N\f$ matrix of type CV_32FC2 or -vector\ . -@param whichImage Index of the image (1 or 2) that contains the points . -@param F Fundamental matrix that can be estimated using findFundamentalMat or stereoRectify . -@param lines Output vector of the epipolar lines corresponding to the points in the other image. -Each line \f$ax + by + c=0\f$ is encoded by 3 numbers \f$(a, b, c)\f$ . - -For every point in one of the two images of a stereo pair, the function finds the equation of the -corresponding epipolar line in the other image. - -From the fundamental matrix definition (see findFundamentalMat ), line \f$l^{(2)}_i\f$ in the second -image for the point \f$p^{(1)}_i\f$ in the first image (when whichImage=1 ) is computed as: - -\f[l^{(2)}_i = F p^{(1)}_i\f] - -And vice versa, when whichImage=2, \f$l^{(1)}_i\f$ is computed from \f$p^{(2)}_i\f$ as: - -\f[l^{(1)}_i = F^T p^{(2)}_i\f] - -Line coefficients are defined up to a scale. They are normalized so that \f$a_i^2+b_i^2=1\f$ . - */ -CV_EXPORTS_W void computeCorrespondEpilines( InputArray points, int whichImage, - InputArray F, OutputArray lines ); - -/** @brief Reconstructs points by triangulation. - -@param projMatr1 3x4 projection matrix of the first camera. -@param projMatr2 3x4 projection matrix of the second camera. -@param projPoints1 2xN array of feature points in the first image. In case of c++ version it can -be also a vector of feature points or two-channel matrix of size 1xN or Nx1. -@param projPoints2 2xN array of corresponding points in the second image. In case of c++ version -it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1. -@param points4D 4xN array of reconstructed points in homogeneous coordinates. - -The function reconstructs 3-dimensional points (in homogeneous coordinates) by using their -observations with a stereo camera. Projections matrices can be obtained from stereoRectify. - -@note - Keep in mind that all input data should be of float type in order for this function to work. - -@sa - reprojectImageTo3D - */ -CV_EXPORTS_W void triangulatePoints( InputArray projMatr1, InputArray projMatr2, - InputArray projPoints1, InputArray projPoints2, - OutputArray points4D ); - -/** @brief Refines coordinates of corresponding points. - -@param F 3x3 fundamental matrix. -@param points1 1xN array containing the first set of points. -@param points2 1xN array containing the second set of points. -@param newPoints1 The optimized points1. -@param newPoints2 The optimized points2. - -The function implements the Optimal Triangulation Method (see Multiple View Geometry for details). -For each given point correspondence points1[i] \<-\> points2[i], and a fundamental matrix F, it -computes the corrected correspondences newPoints1[i] \<-\> newPoints2[i] that minimize the geometric -error \f$d(points1[i], newPoints1[i])^2 + d(points2[i],newPoints2[i])^2\f$ (where \f$d(a,b)\f$ is the -geometric distance between points \f$a\f$ and \f$b\f$ ) subject to the epipolar constraint -\f$newPoints2^T * F * newPoints1 = 0\f$ . - */ -CV_EXPORTS_W void correctMatches( InputArray F, InputArray points1, InputArray points2, - OutputArray newPoints1, OutputArray newPoints2 ); - -/** @brief Filters off small noise blobs (speckles) in the disparity map - -@param img The input 16-bit signed disparity image -@param newVal The disparity value used to paint-off the speckles -@param maxSpeckleSize The maximum speckle size to consider it a speckle. Larger blobs are not -affected by the algorithm -@param maxDiff Maximum difference between neighbor disparity pixels to put them into the same -blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point -disparity map, where disparity values are multiplied by 16, this scale factor should be taken into -account when specifying this parameter value. -@param buf The optional temporary buffer to avoid memory allocation within the function. - */ -CV_EXPORTS_W void filterSpeckles( InputOutputArray img, double newVal, - int maxSpeckleSize, double maxDiff, - InputOutputArray buf = noArray() ); - -//! computes valid disparity ROI from the valid ROIs of the rectified images (that are returned by cv::stereoRectify()) -CV_EXPORTS_W Rect getValidDisparityROI( Rect roi1, Rect roi2, - int minDisparity, int numberOfDisparities, - int SADWindowSize ); - -//! validates disparity using the left-right check. The matrix "cost" should be computed by the stereo correspondence algorithm -CV_EXPORTS_W void validateDisparity( InputOutputArray disparity, InputArray cost, - int minDisparity, int numberOfDisparities, - int disp12MaxDisp = 1 ); - -/** @brief Reprojects a disparity image to 3D space. - -@param disparity Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit -floating-point disparity image. If 16-bit signed format is used, the values are assumed to have no -fractional bits. -@param _3dImage Output 3-channel floating-point image of the same size as disparity . Each -element of _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity -map. -@param Q \f$4 \times 4\f$ perspective transformation matrix that can be obtained with stereoRectify. -@param handleMissingValues Indicates, whether the function should handle missing values (i.e. -points where the disparity was not computed). If handleMissingValues=true, then pixels with the -minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed -to 3D points with a very large Z value (currently set to 10000). -@param ddepth The optional output array depth. If it is -1, the output image will have CV_32F -depth. ddepth can also be set to CV_16S, CV_32S or CV_32F. - -The function transforms a single-channel disparity map to a 3-channel image representing a 3D -surface. That is, for each pixel (x,y) andthe corresponding disparity d=disparity(x,y) , it -computes: - -\f[\begin{array}{l} [X \; Y \; Z \; W]^T = \texttt{Q} *[x \; y \; \texttt{disparity} (x,y) \; 1]^T \\ \texttt{\_3dImage} (x,y) = (X/W, \; Y/W, \; Z/W) \end{array}\f] - -The matrix Q can be an arbitrary \f$4 \times 4\f$ matrix (for example, the one computed by -stereoRectify). To reproject a sparse set of points {(x,y,d),...} to 3D space, use -perspectiveTransform . - */ -CV_EXPORTS_W void reprojectImageTo3D( InputArray disparity, - OutputArray _3dImage, InputArray Q, - bool handleMissingValues = false, - int ddepth = -1 ); - -/** @brief Calculates the Sampson Distance between two points. - -The function sampsonDistance calculates and returns the first order approximation of the geometric error as: -\f[sd( \texttt{pt1} , \texttt{pt2} )= \frac{(\texttt{pt2}^t \cdot \texttt{F} \cdot \texttt{pt1})^2}{(\texttt{F} \cdot \texttt{pt1})(0) + (\texttt{F} \cdot \texttt{pt1})(1) + (\texttt{F}^t \cdot \texttt{pt2})(0) + (\texttt{F}^t \cdot \texttt{pt2})(1)}\f] -The fundamental matrix may be calculated using the cv::findFundamentalMat function. See HZ 11.4.3 for details. -@param pt1 first homogeneous 2d point -@param pt2 second homogeneous 2d point -@param F fundamental matrix -*/ -CV_EXPORTS_W double sampsonDistance(InputArray pt1, InputArray pt2, InputArray F); - -/** @brief Computes an optimal affine transformation between two 3D point sets. - -@param src First input 3D point set. -@param dst Second input 3D point set. -@param out Output 3D affine transformation matrix \f$3 \times 4\f$ . -@param inliers Output vector indicating which points are inliers. -@param ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as -an inlier. -@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything -between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation -significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. - -The function estimates an optimal 3D affine transformation between two 3D point sets using the -RANSAC algorithm. - */ -CV_EXPORTS_W int estimateAffine3D(InputArray src, InputArray dst, - OutputArray out, OutputArray inliers, - double ransacThreshold = 3, double confidence = 0.99); - -/** @brief Decompose a homography matrix to rotation(s), translation(s) and plane normal(s). - -@param H The input homography matrix between two images. -@param K The input intrinsic camera calibration matrix. -@param rotations Array of rotation matrices. -@param translations Array of translation matrices. -@param normals Array of plane normal matrices. - -This function extracts relative camera motion between two views observing a planar object from the -homography H induced by the plane. The intrinsic camera matrix K must also be provided. The function -may return up to four mathematical solution sets. At least two of the solutions may further be -invalidated if point correspondences are available by applying positive depth constraint (all points -must be in front of the camera). The decomposition method is described in detail in @cite Malis . - */ -CV_EXPORTS_W int decomposeHomographyMat(InputArray H, - InputArray K, - OutputArrayOfArrays rotations, - OutputArrayOfArrays translations, - OutputArrayOfArrays normals); - -/** @brief The base class for stereo correspondence algorithms. - */ -class CV_EXPORTS_W StereoMatcher : public Algorithm -{ -public: - enum { DISP_SHIFT = 4, - DISP_SCALE = (1 << DISP_SHIFT) - }; - - /** @brief Computes disparity map for the specified stereo pair - - @param left Left 8-bit single-channel image. - @param right Right image of the same size and the same type as the left one. - @param disparity Output disparity map. It has the same size as the input images. Some algorithms, - like StereoBM or StereoSGBM compute 16-bit fixed-point disparity map (where each disparity value - has 4 fractional bits), whereas other algorithms output 32-bit floating-point disparity map. - */ - CV_WRAP virtual void compute( InputArray left, InputArray right, - OutputArray disparity ) = 0; - - CV_WRAP virtual int getMinDisparity() const = 0; - CV_WRAP virtual void setMinDisparity(int minDisparity) = 0; - - CV_WRAP virtual int getNumDisparities() const = 0; - CV_WRAP virtual void setNumDisparities(int numDisparities) = 0; - - CV_WRAP virtual int getBlockSize() const = 0; - CV_WRAP virtual void setBlockSize(int blockSize) = 0; - - CV_WRAP virtual int getSpeckleWindowSize() const = 0; - CV_WRAP virtual void setSpeckleWindowSize(int speckleWindowSize) = 0; - - CV_WRAP virtual int getSpeckleRange() const = 0; - CV_WRAP virtual void setSpeckleRange(int speckleRange) = 0; - - CV_WRAP virtual int getDisp12MaxDiff() const = 0; - CV_WRAP virtual void setDisp12MaxDiff(int disp12MaxDiff) = 0; -}; - - -/** @brief Class for computing stereo correspondence using the block matching algorithm, introduced and -contributed to OpenCV by K. Konolige. - */ -class CV_EXPORTS_W StereoBM : public StereoMatcher -{ -public: - enum { PREFILTER_NORMALIZED_RESPONSE = 0, - PREFILTER_XSOBEL = 1 - }; - - CV_WRAP virtual int getPreFilterType() const = 0; - CV_WRAP virtual void setPreFilterType(int preFilterType) = 0; - - CV_WRAP virtual int getPreFilterSize() const = 0; - CV_WRAP virtual void setPreFilterSize(int preFilterSize) = 0; - - CV_WRAP virtual int getPreFilterCap() const = 0; - CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0; - - CV_WRAP virtual int getTextureThreshold() const = 0; - CV_WRAP virtual void setTextureThreshold(int textureThreshold) = 0; - - CV_WRAP virtual int getUniquenessRatio() const = 0; - CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0; - - CV_WRAP virtual int getSmallerBlockSize() const = 0; - CV_WRAP virtual void setSmallerBlockSize(int blockSize) = 0; - - CV_WRAP virtual Rect getROI1() const = 0; - CV_WRAP virtual void setROI1(Rect roi1) = 0; - - CV_WRAP virtual Rect getROI2() const = 0; - CV_WRAP virtual void setROI2(Rect roi2) = 0; - - /** @brief Creates StereoBM object - - @param numDisparities the disparity search range. For each pixel algorithm will find the best - disparity from 0 (default minimum disparity) to numDisparities. The search range can then be - shifted by changing the minimum disparity. - @param blockSize the linear size of the blocks compared by the algorithm. The size should be odd - (as the block is centered at the current pixel). Larger block size implies smoother, though less - accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher - chance for algorithm to find a wrong correspondence. - - The function create StereoBM object. You can then call StereoBM::compute() to compute disparity for - a specific stereo pair. - */ - CV_WRAP static Ptr create(int numDisparities = 0, int blockSize = 21); -}; - -/** @brief The class implements the modified H. Hirschmuller algorithm @cite HH08 that differs from the original -one as follows: - -- By default, the algorithm is single-pass, which means that you consider only 5 directions -instead of 8. Set mode=StereoSGBM::MODE_HH in createStereoSGBM to run the full variant of the -algorithm but beware that it may consume a lot of memory. -- The algorithm matches blocks, not individual pixels. Though, setting blockSize=1 reduces the -blocks to single pixels. -- Mutual information cost function is not implemented. Instead, a simpler Birchfield-Tomasi -sub-pixel metric from @cite BT98 is used. Though, the color images are supported as well. -- Some pre- and post- processing steps from K. Konolige algorithm StereoBM are included, for -example: pre-filtering (StereoBM::PREFILTER_XSOBEL type) and post-filtering (uniqueness -check, quadratic interpolation and speckle filtering). - -@note - - (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found - at opencv_source_code/samples/python/stereo_match.py - */ -class CV_EXPORTS_W StereoSGBM : public StereoMatcher -{ -public: - enum - { - MODE_SGBM = 0, - MODE_HH = 1, - MODE_SGBM_3WAY = 2 - }; - - CV_WRAP virtual int getPreFilterCap() const = 0; - CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0; - - CV_WRAP virtual int getUniquenessRatio() const = 0; - CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0; - - CV_WRAP virtual int getP1() const = 0; - CV_WRAP virtual void setP1(int P1) = 0; - - CV_WRAP virtual int getP2() const = 0; - CV_WRAP virtual void setP2(int P2) = 0; - - CV_WRAP virtual int getMode() const = 0; - CV_WRAP virtual void setMode(int mode) = 0; - - /** @brief Creates StereoSGBM object - - @param minDisparity Minimum possible disparity value. Normally, it is zero but sometimes - rectification algorithms can shift images, so this parameter needs to be adjusted accordingly. - @param numDisparities Maximum disparity minus minimum disparity. The value is always greater than - zero. In the current implementation, this parameter must be divisible by 16. - @param blockSize Matched block size. It must be an odd number \>=1 . Normally, it should be - somewhere in the 3..11 range. - @param P1 The first parameter controlling the disparity smoothness. See below. - @param P2 The second parameter controlling the disparity smoothness. The larger the values are, - the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1 - between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor - pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good - P1 and P2 values are shown (like 8\*number_of_image_channels\*SADWindowSize\*SADWindowSize and - 32\*number_of_image_channels\*SADWindowSize\*SADWindowSize , respectively). - @param disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right - disparity check. Set it to a non-positive value to disable the check. - @param preFilterCap Truncation value for the prefiltered image pixels. The algorithm first - computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval. - The result values are passed to the Birchfield-Tomasi pixel cost function. - @param uniquenessRatio Margin in percentage by which the best (minimum) computed cost function - value should "win" the second best value to consider the found match correct. Normally, a value - within the 5-15 range is good enough. - @param speckleWindowSize Maximum size of smooth disparity regions to consider their noise speckles - and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the - 50-200 range. - @param speckleRange Maximum disparity variation within each connected component. If you do speckle - filtering, set the parameter to a positive value, it will be implicitly multiplied by 16. - Normally, 1 or 2 is good enough. - @param mode Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming - algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and - huge for HD-size pictures. By default, it is set to false . - - The first constructor initializes StereoSGBM with all the default parameters. So, you only have to - set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter - to a custom value. - */ - CV_WRAP static Ptr create(int minDisparity, int numDisparities, int blockSize, - int P1 = 0, int P2 = 0, int disp12MaxDiff = 0, - int preFilterCap = 0, int uniquenessRatio = 0, - int speckleWindowSize = 0, int speckleRange = 0, - int mode = StereoSGBM::MODE_SGBM); -}; - -//! @} calib3d - -/** @brief The methods in this namespace use a so-called fisheye camera model. - @ingroup calib3d_fisheye -*/ -namespace fisheye -{ -//! @addtogroup calib3d_fisheye -//! @{ - - enum{ - CALIB_USE_INTRINSIC_GUESS = 1, - CALIB_RECOMPUTE_EXTRINSIC = 2, - CALIB_CHECK_COND = 4, - CALIB_FIX_SKEW = 8, - CALIB_FIX_K1 = 16, - CALIB_FIX_K2 = 32, - CALIB_FIX_K3 = 64, - CALIB_FIX_K4 = 128, - CALIB_FIX_INTRINSIC = 256 - }; - - /** @brief Projects points using fisheye model - - @param objectPoints Array of object points, 1xN/Nx1 3-channel (or vector\ ), where N is - the number of points in the view. - @param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or - vector\. - @param affine - @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. - @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. - @param alpha The skew coefficient. - @param jacobian Optional output 2Nx15 jacobian matrix of derivatives of image points with respect - to components of the focal lengths, coordinates of the principal point, distortion coefficients, - rotation vector, translation vector, and the skew. In the old interface different components of - the jacobian are returned via different output parameters. - - The function computes projections of 3D points to the image plane given intrinsic and extrinsic - camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of - image points coordinates (as functions of all the input parameters) with respect to the particular - parameters, intrinsic and/or extrinsic. - */ - CV_EXPORTS void projectPoints(InputArray objectPoints, OutputArray imagePoints, const Affine3d& affine, - InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray()); - - /** @overload */ - CV_EXPORTS_W void projectPoints(InputArray objectPoints, OutputArray imagePoints, InputArray rvec, InputArray tvec, - InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray()); - - /** @brief Distorts 2D points using fisheye model. - - @param undistorted Array of object points, 1xN/Nx1 2-channel (or vector\ ), where N is - the number of points in the view. - @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. - @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. - @param alpha The skew coefficient. - @param distorted Output array of image points, 1xN/Nx1 2-channel, or vector\ . - - Note that the function assumes the camera matrix of the undistorted points to be indentity. - This means if you want to transform back points undistorted with undistortPoints() you have to - multiply them with \f$P^{-1}\f$. - */ - CV_EXPORTS_W void distortPoints(InputArray undistorted, OutputArray distorted, InputArray K, InputArray D, double alpha = 0); - - /** @brief Undistorts 2D points using fisheye model - - @param distorted Array of object points, 1xN/Nx1 2-channel (or vector\ ), where N is the - number of points in the view. - @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. - @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. - @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 - 1-channel or 1x1 3-channel - @param P New camera matrix (3x3) or new projection matrix (3x4) - @param undistorted Output array of image points, 1xN/Nx1 2-channel, or vector\ . - */ - CV_EXPORTS_W void undistortPoints(InputArray distorted, OutputArray undistorted, - InputArray K, InputArray D, InputArray R = noArray(), InputArray P = noArray()); - - /** @brief Computes undistortion and rectification maps for image transform by cv::remap(). If D is empty zero - distortion is used, if R or P is empty identity matrixes are used. - - @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. - @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. - @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 - 1-channel or 1x1 3-channel - @param P New camera matrix (3x3) or new projection matrix (3x4) - @param size Undistorted image size. - @param m1type Type of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps() - for details. - @param map1 The first output map. - @param map2 The second output map. - */ - CV_EXPORTS_W void initUndistortRectifyMap(InputArray K, InputArray D, InputArray R, InputArray P, - const cv::Size& size, int m1type, OutputArray map1, OutputArray map2); - - /** @brief Transforms an image to compensate for fisheye lens distortion. - - @param distorted image with fisheye lens distortion. - @param undistorted Output image with compensated fisheye lens distortion. - @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. - @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. - @param Knew Camera matrix of the distorted image. By default, it is the identity matrix but you - may additionally scale and shift the result by using a different matrix. - @param new_size - - The function transforms an image to compensate radial and tangential lens distortion. - - The function is simply a combination of fisheye::initUndistortRectifyMap (with unity R ) and remap - (with bilinear interpolation). See the former function for details of the transformation being - performed. - - See below the results of undistortImage. - - a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, - k_4, k_5, k_6) of distortion were optimized under calibration) - - b\) result of fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, - k_3, k_4) of fisheye distortion were optimized under calibration) - - c\) original image was captured with fisheye lens - - Pictures a) and b) almost the same. But if we consider points of image located far from the center - of image, we can notice that on image a) these points are distorted. - - ![image](pics/fisheye_undistorted.jpg) - */ - CV_EXPORTS_W void undistortImage(InputArray distorted, OutputArray undistorted, - InputArray K, InputArray D, InputArray Knew = cv::noArray(), const Size& new_size = Size()); - - /** @brief Estimates new camera matrix for undistortion or rectification. - - @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. - @param image_size - @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. - @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 - 1-channel or 1x1 3-channel - @param P New camera matrix (3x3) or new projection matrix (3x4) - @param balance Sets the new focal length in range between the min focal length and the max focal - length. Balance is in range of [0, 1]. - @param new_size - @param fov_scale Divisor for new focal length. - */ - CV_EXPORTS_W void estimateNewCameraMatrixForUndistortRectify(InputArray K, InputArray D, const Size &image_size, InputArray R, - OutputArray P, double balance = 0.0, const Size& new_size = Size(), double fov_scale = 1.0); - - /** @brief Performs camera calibaration - - @param objectPoints vector of vectors of calibration pattern points in the calibration pattern - coordinate space. - @param imagePoints vector of vectors of the projections of calibration pattern points. - imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to - objectPoints[i].size() for each i. - @param image_size Size of the image used only to initialize the intrinsic camera matrix. - @param K Output 3x3 floating-point camera matrix - \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If - fisheye::CALIB_USE_INTRINSIC_GUESS/ is specified, some or all of fx, fy, cx, cy must be - initialized before calling the function. - @param D Output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. - @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. - That is, each k-th rotation vector together with the corresponding k-th translation vector (see - the next output parameter description) brings the calibration pattern from the model coordinate - space (in which object points are specified) to the world coordinate space, that is, a real - position of the calibration pattern in the k-th pattern view (k=0.. *M* -1). - @param tvecs Output vector of translation vectors estimated for each pattern view. - @param flags Different flags that may be zero or a combination of the following values: - - **fisheye::CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of - fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image - center ( imageSize is used), and focal distances are computed in a least-squares fashion. - - **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration - of intrinsic optimization. - - **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number. - - **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero. - - **fisheye::CALIB_FIX_K1..4** Selected distortion coefficients are set to zeros and stay - zero. - @param criteria Termination criteria for the iterative optimization algorithm. - */ - CV_EXPORTS_W double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, const Size& image_size, - InputOutputArray K, InputOutputArray D, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0, - TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)); - - /** @brief Stereo rectification for fisheye camera model - - @param K1 First camera matrix. - @param D1 First camera distortion parameters. - @param K2 Second camera matrix. - @param D2 Second camera distortion parameters. - @param imageSize Size of the image used for stereo calibration. - @param R Rotation matrix between the coordinate systems of the first and the second - cameras. - @param tvec Translation vector between coordinate systems of the cameras. - @param R1 Output 3x3 rectification transform (rotation matrix) for the first camera. - @param R2 Output 3x3 rectification transform (rotation matrix) for the second camera. - @param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first - camera. - @param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second - camera. - @param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ). - @param flags Operation flags that may be zero or CV_CALIB_ZERO_DISPARITY . If the flag is set, - the function makes the principal points of each camera have the same pixel coordinates in the - rectified views. And if the flag is not set, the function may still shift the images in the - horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the - useful image area. - @param newImageSize New image resolution after rectification. The same size should be passed to - initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) - is passed (default), it is set to the original imageSize . Setting it to larger value can help you - preserve details in the original image, especially when there is a big radial distortion. - @param balance Sets the new focal length in range between the min focal length and the max focal - length. Balance is in range of [0, 1]. - @param fov_scale Divisor for new focal length. - */ - CV_EXPORTS_W void stereoRectify(InputArray K1, InputArray D1, InputArray K2, InputArray D2, const Size &imageSize, InputArray R, InputArray tvec, - OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2, OutputArray Q, int flags, const Size &newImageSize = Size(), - double balance = 0.0, double fov_scale = 1.0); - - /** @brief Performs stereo calibration - - @param objectPoints Vector of vectors of the calibration pattern points. - @param imagePoints1 Vector of vectors of the projections of the calibration pattern points, - observed by the first camera. - @param imagePoints2 Vector of vectors of the projections of the calibration pattern points, - observed by the second camera. - @param K1 Input/output first camera matrix: - \f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If - any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CV_CALIB_FIX_INTRINSIC are specified, - some or all of the matrix components must be initialized. - @param D1 Input/output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$ of 4 elements. - @param K2 Input/output second camera matrix. The parameter is similar to K1 . - @param D2 Input/output lens distortion coefficients for the second camera. The parameter is - similar to D1 . - @param imageSize Size of the image used only to initialize intrinsic camera matrix. - @param R Output rotation matrix between the 1st and the 2nd camera coordinate systems. - @param T Output translation vector between the coordinate systems of the cameras. - @param flags Different flags that may be zero or a combination of the following values: - - **fisheye::CV_CALIB_FIX_INTRINSIC** Fix K1, K2? and D1, D2? so that only R, T matrices - are estimated. - - **fisheye::CALIB_USE_INTRINSIC_GUESS** K1, K2 contains valid initial values of - fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image - center (imageSize is used), and focal distances are computed in a least-squares fashion. - - **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration - of intrinsic optimization. - - **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number. - - **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero. - - **fisheye::CALIB_FIX_K1..4** Selected distortion coefficients are set to zeros and stay - zero. - @param criteria Termination criteria for the iterative optimization algorithm. - */ - CV_EXPORTS_W double stereoCalibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, - InputOutputArray K1, InputOutputArray D1, InputOutputArray K2, InputOutputArray D2, Size imageSize, - OutputArray R, OutputArray T, int flags = fisheye::CALIB_FIX_INTRINSIC, - TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)); - -//! @} calib3d_fisheye -} - -} // cv - -#ifndef DISABLE_OPENCV_24_COMPATIBILITY -#include "opencv2/calib3d/calib3d_c.h" -#endif - -#endif diff --git a/IPL/include/opencv/opencv2/calib3d/calib3d.hpp b/IPL/include/opencv/opencv2/calib3d/calib3d.hpp deleted file mode 100644 index b3da45e..0000000 --- a/IPL/include/opencv/opencv2/calib3d/calib3d.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/calib3d.hpp" diff --git a/IPL/include/opencv/opencv2/calib3d/calib3d_c.h b/IPL/include/opencv/opencv2/calib3d/calib3d_c.h deleted file mode 100644 index 0e77aa8..0000000 --- a/IPL/include/opencv/opencv2/calib3d/calib3d_c.h +++ /dev/null @@ -1,425 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CALIB3D_C_H__ -#define __OPENCV_CALIB3D_C_H__ - -#include "opencv2/core/core_c.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @addtogroup calib3d_c - @{ - */ - -/****************************************************************************************\ -* Camera Calibration, Pose Estimation and Stereo * -\****************************************************************************************/ - -typedef struct CvPOSITObject CvPOSITObject; - -/* Allocates and initializes CvPOSITObject structure before doing cvPOSIT */ -CVAPI(CvPOSITObject*) cvCreatePOSITObject( CvPoint3D32f* points, int point_count ); - - -/* Runs POSIT (POSe from ITeration) algorithm for determining 3d position of - an object given its model and projection in a weak-perspective case */ -CVAPI(void) cvPOSIT( CvPOSITObject* posit_object, CvPoint2D32f* image_points, - double focal_length, CvTermCriteria criteria, - float* rotation_matrix, float* translation_vector); - -/* Releases CvPOSITObject structure */ -CVAPI(void) cvReleasePOSITObject( CvPOSITObject** posit_object ); - -/* updates the number of RANSAC iterations */ -CVAPI(int) cvRANSACUpdateNumIters( double p, double err_prob, - int model_points, int max_iters ); - -CVAPI(void) cvConvertPointsHomogeneous( const CvMat* src, CvMat* dst ); - -/* Calculates fundamental matrix given a set of corresponding points */ -#define CV_FM_7POINT 1 -#define CV_FM_8POINT 2 - -#define CV_LMEDS 4 -#define CV_RANSAC 8 - -#define CV_FM_LMEDS_ONLY CV_LMEDS -#define CV_FM_RANSAC_ONLY CV_RANSAC -#define CV_FM_LMEDS CV_LMEDS -#define CV_FM_RANSAC CV_RANSAC - -enum -{ - CV_ITERATIVE = 0, - CV_EPNP = 1, // F.Moreno-Noguer, V.Lepetit and P.Fua "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" - CV_P3P = 2, // X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang; "Complete Solution Classification for the Perspective-Three-Point Problem" - CV_DLS = 3 // Joel A. Hesch and Stergios I. Roumeliotis. "A Direct Least-Squares (DLS) Method for PnP" -}; - -CVAPI(int) cvFindFundamentalMat( const CvMat* points1, const CvMat* points2, - CvMat* fundamental_matrix, - int method CV_DEFAULT(CV_FM_RANSAC), - double param1 CV_DEFAULT(3.), double param2 CV_DEFAULT(0.99), - CvMat* status CV_DEFAULT(NULL) ); - -/* For each input point on one of images - computes parameters of the corresponding - epipolar line on the other image */ -CVAPI(void) cvComputeCorrespondEpilines( const CvMat* points, - int which_image, - const CvMat* fundamental_matrix, - CvMat* correspondent_lines ); - -/* Triangulation functions */ - -CVAPI(void) cvTriangulatePoints(CvMat* projMatr1, CvMat* projMatr2, - CvMat* projPoints1, CvMat* projPoints2, - CvMat* points4D); - -CVAPI(void) cvCorrectMatches(CvMat* F, CvMat* points1, CvMat* points2, - CvMat* new_points1, CvMat* new_points2); - - -/* Computes the optimal new camera matrix according to the free scaling parameter alpha: - alpha=0 - only valid pixels will be retained in the undistorted image - alpha=1 - all the source image pixels will be retained in the undistorted image -*/ -CVAPI(void) cvGetOptimalNewCameraMatrix( const CvMat* camera_matrix, - const CvMat* dist_coeffs, - CvSize image_size, double alpha, - CvMat* new_camera_matrix, - CvSize new_imag_size CV_DEFAULT(cvSize(0,0)), - CvRect* valid_pixel_ROI CV_DEFAULT(0), - int center_principal_point CV_DEFAULT(0)); - -/* Converts rotation vector to rotation matrix or vice versa */ -CVAPI(int) cvRodrigues2( const CvMat* src, CvMat* dst, - CvMat* jacobian CV_DEFAULT(0) ); - -/* Finds perspective transformation between the object plane and image (view) plane */ -CVAPI(int) cvFindHomography( const CvMat* src_points, - const CvMat* dst_points, - CvMat* homography, - int method CV_DEFAULT(0), - double ransacReprojThreshold CV_DEFAULT(3), - CvMat* mask CV_DEFAULT(0), - int maxIters CV_DEFAULT(2000), - double confidence CV_DEFAULT(0.995)); - -/* Computes RQ decomposition for 3x3 matrices */ -CVAPI(void) cvRQDecomp3x3( const CvMat *matrixM, CvMat *matrixR, CvMat *matrixQ, - CvMat *matrixQx CV_DEFAULT(NULL), - CvMat *matrixQy CV_DEFAULT(NULL), - CvMat *matrixQz CV_DEFAULT(NULL), - CvPoint3D64f *eulerAngles CV_DEFAULT(NULL)); - -/* Computes projection matrix decomposition */ -CVAPI(void) cvDecomposeProjectionMatrix( const CvMat *projMatr, CvMat *calibMatr, - CvMat *rotMatr, CvMat *posVect, - CvMat *rotMatrX CV_DEFAULT(NULL), - CvMat *rotMatrY CV_DEFAULT(NULL), - CvMat *rotMatrZ CV_DEFAULT(NULL), - CvPoint3D64f *eulerAngles CV_DEFAULT(NULL)); - -/* Computes d(AB)/dA and d(AB)/dB */ -CVAPI(void) cvCalcMatMulDeriv( const CvMat* A, const CvMat* B, CvMat* dABdA, CvMat* dABdB ); - -/* Computes r3 = rodrigues(rodrigues(r2)*rodrigues(r1)), - t3 = rodrigues(r2)*t1 + t2 and the respective derivatives */ -CVAPI(void) cvComposeRT( const CvMat* _rvec1, const CvMat* _tvec1, - const CvMat* _rvec2, const CvMat* _tvec2, - CvMat* _rvec3, CvMat* _tvec3, - CvMat* dr3dr1 CV_DEFAULT(0), CvMat* dr3dt1 CV_DEFAULT(0), - CvMat* dr3dr2 CV_DEFAULT(0), CvMat* dr3dt2 CV_DEFAULT(0), - CvMat* dt3dr1 CV_DEFAULT(0), CvMat* dt3dt1 CV_DEFAULT(0), - CvMat* dt3dr2 CV_DEFAULT(0), CvMat* dt3dt2 CV_DEFAULT(0) ); - -/* Projects object points to the view plane using - the specified extrinsic and intrinsic camera parameters */ -CVAPI(void) cvProjectPoints2( const CvMat* object_points, const CvMat* rotation_vector, - const CvMat* translation_vector, const CvMat* camera_matrix, - const CvMat* distortion_coeffs, CvMat* image_points, - CvMat* dpdrot CV_DEFAULT(NULL), CvMat* dpdt CV_DEFAULT(NULL), - CvMat* dpdf CV_DEFAULT(NULL), CvMat* dpdc CV_DEFAULT(NULL), - CvMat* dpddist CV_DEFAULT(NULL), - double aspect_ratio CV_DEFAULT(0)); - -/* Finds extrinsic camera parameters from - a few known corresponding point pairs and intrinsic parameters */ -CVAPI(void) cvFindExtrinsicCameraParams2( const CvMat* object_points, - const CvMat* image_points, - const CvMat* camera_matrix, - const CvMat* distortion_coeffs, - CvMat* rotation_vector, - CvMat* translation_vector, - int use_extrinsic_guess CV_DEFAULT(0) ); - -/* Computes initial estimate of the intrinsic camera parameters - in case of planar calibration target (e.g. chessboard) */ -CVAPI(void) cvInitIntrinsicParams2D( const CvMat* object_points, - const CvMat* image_points, - const CvMat* npoints, CvSize image_size, - CvMat* camera_matrix, - double aspect_ratio CV_DEFAULT(1.) ); - -#define CV_CALIB_CB_ADAPTIVE_THRESH 1 -#define CV_CALIB_CB_NORMALIZE_IMAGE 2 -#define CV_CALIB_CB_FILTER_QUADS 4 -#define CV_CALIB_CB_FAST_CHECK 8 - -// Performs a fast check if a chessboard is in the input image. This is a workaround to -// a problem of cvFindChessboardCorners being slow on images with no chessboard -// - src: input image -// - size: chessboard size -// Returns 1 if a chessboard can be in this image and findChessboardCorners should be called, -// 0 if there is no chessboard, -1 in case of error -CVAPI(int) cvCheckChessboard(IplImage* src, CvSize size); - - /* Detects corners on a chessboard calibration pattern */ -CVAPI(int) cvFindChessboardCorners( const void* image, CvSize pattern_size, - CvPoint2D32f* corners, - int* corner_count CV_DEFAULT(NULL), - int flags CV_DEFAULT(CV_CALIB_CB_ADAPTIVE_THRESH+CV_CALIB_CB_NORMALIZE_IMAGE) ); - -/* Draws individual chessboard corners or the whole chessboard detected */ -CVAPI(void) cvDrawChessboardCorners( CvArr* image, CvSize pattern_size, - CvPoint2D32f* corners, - int count, int pattern_was_found ); - -#define CV_CALIB_USE_INTRINSIC_GUESS 1 -#define CV_CALIB_FIX_ASPECT_RATIO 2 -#define CV_CALIB_FIX_PRINCIPAL_POINT 4 -#define CV_CALIB_ZERO_TANGENT_DIST 8 -#define CV_CALIB_FIX_FOCAL_LENGTH 16 -#define CV_CALIB_FIX_K1 32 -#define CV_CALIB_FIX_K2 64 -#define CV_CALIB_FIX_K3 128 -#define CV_CALIB_FIX_K4 2048 -#define CV_CALIB_FIX_K5 4096 -#define CV_CALIB_FIX_K6 8192 -#define CV_CALIB_RATIONAL_MODEL 16384 -#define CV_CALIB_THIN_PRISM_MODEL 32768 -#define CV_CALIB_FIX_S1_S2_S3_S4 65536 -#define CV_CALIB_TILTED_MODEL 262144 -#define CV_CALIB_FIX_TAUX_TAUY 524288 - - -/* Finds intrinsic and extrinsic camera parameters - from a few views of known calibration pattern */ -CVAPI(double) cvCalibrateCamera2( const CvMat* object_points, - const CvMat* image_points, - const CvMat* point_counts, - CvSize image_size, - CvMat* camera_matrix, - CvMat* distortion_coeffs, - CvMat* rotation_vectors CV_DEFAULT(NULL), - CvMat* translation_vectors CV_DEFAULT(NULL), - int flags CV_DEFAULT(0), - CvTermCriteria term_crit CV_DEFAULT(cvTermCriteria( - CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,DBL_EPSILON)) ); - -/* Computes various useful characteristics of the camera from the data computed by - cvCalibrateCamera2 */ -CVAPI(void) cvCalibrationMatrixValues( const CvMat *camera_matrix, - CvSize image_size, - double aperture_width CV_DEFAULT(0), - double aperture_height CV_DEFAULT(0), - double *fovx CV_DEFAULT(NULL), - double *fovy CV_DEFAULT(NULL), - double *focal_length CV_DEFAULT(NULL), - CvPoint2D64f *principal_point CV_DEFAULT(NULL), - double *pixel_aspect_ratio CV_DEFAULT(NULL)); - -#define CV_CALIB_FIX_INTRINSIC 256 -#define CV_CALIB_SAME_FOCAL_LENGTH 512 - -/* Computes the transformation from one camera coordinate system to another one - from a few correspondent views of the same calibration target. Optionally, calibrates - both cameras */ -CVAPI(double) cvStereoCalibrate( const CvMat* object_points, const CvMat* image_points1, - const CvMat* image_points2, const CvMat* npoints, - CvMat* camera_matrix1, CvMat* dist_coeffs1, - CvMat* camera_matrix2, CvMat* dist_coeffs2, - CvSize image_size, CvMat* R, CvMat* T, - CvMat* E CV_DEFAULT(0), CvMat* F CV_DEFAULT(0), - int flags CV_DEFAULT(CV_CALIB_FIX_INTRINSIC), - CvTermCriteria term_crit CV_DEFAULT(cvTermCriteria( - CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,1e-6)) ); - -#define CV_CALIB_ZERO_DISPARITY 1024 - -/* Computes 3D rotations (+ optional shift) for each camera coordinate system to make both - views parallel (=> to make all the epipolar lines horizontal or vertical) */ -CVAPI(void) cvStereoRectify( const CvMat* camera_matrix1, const CvMat* camera_matrix2, - const CvMat* dist_coeffs1, const CvMat* dist_coeffs2, - CvSize image_size, const CvMat* R, const CvMat* T, - CvMat* R1, CvMat* R2, CvMat* P1, CvMat* P2, - CvMat* Q CV_DEFAULT(0), - int flags CV_DEFAULT(CV_CALIB_ZERO_DISPARITY), - double alpha CV_DEFAULT(-1), - CvSize new_image_size CV_DEFAULT(cvSize(0,0)), - CvRect* valid_pix_ROI1 CV_DEFAULT(0), - CvRect* valid_pix_ROI2 CV_DEFAULT(0)); - -/* Computes rectification transformations for uncalibrated pair of images using a set - of point correspondences */ -CVAPI(int) cvStereoRectifyUncalibrated( const CvMat* points1, const CvMat* points2, - const CvMat* F, CvSize img_size, - CvMat* H1, CvMat* H2, - double threshold CV_DEFAULT(5)); - - - -/* stereo correspondence parameters and functions */ - -#define CV_STEREO_BM_NORMALIZED_RESPONSE 0 -#define CV_STEREO_BM_XSOBEL 1 - -/* Block matching algorithm structure */ -typedef struct CvStereoBMState -{ - // pre-filtering (normalization of input images) - int preFilterType; // =CV_STEREO_BM_NORMALIZED_RESPONSE now - int preFilterSize; // averaging window size: ~5x5..21x21 - int preFilterCap; // the output of pre-filtering is clipped by [-preFilterCap,preFilterCap] - - // correspondence using Sum of Absolute Difference (SAD) - int SADWindowSize; // ~5x5..21x21 - int minDisparity; // minimum disparity (can be negative) - int numberOfDisparities; // maximum disparity - minimum disparity (> 0) - - // post-filtering - int textureThreshold; // the disparity is only computed for pixels - // with textured enough neighborhood - int uniquenessRatio; // accept the computed disparity d* only if - // SAD(d) >= SAD(d*)*(1 + uniquenessRatio/100.) - // for any d != d*+/-1 within the search range. - int speckleWindowSize; // disparity variation window - int speckleRange; // acceptable range of variation in window - - int trySmallerWindows; // if 1, the results may be more accurate, - // at the expense of slower processing - CvRect roi1, roi2; - int disp12MaxDiff; - - // temporary buffers - CvMat* preFilteredImg0; - CvMat* preFilteredImg1; - CvMat* slidingSumBuf; - CvMat* cost; - CvMat* disp; -} CvStereoBMState; - -#define CV_STEREO_BM_BASIC 0 -#define CV_STEREO_BM_FISH_EYE 1 -#define CV_STEREO_BM_NARROW 2 - -CVAPI(CvStereoBMState*) cvCreateStereoBMState(int preset CV_DEFAULT(CV_STEREO_BM_BASIC), - int numberOfDisparities CV_DEFAULT(0)); - -CVAPI(void) cvReleaseStereoBMState( CvStereoBMState** state ); - -CVAPI(void) cvFindStereoCorrespondenceBM( const CvArr* left, const CvArr* right, - CvArr* disparity, CvStereoBMState* state ); - -CVAPI(CvRect) cvGetValidDisparityROI( CvRect roi1, CvRect roi2, int minDisparity, - int numberOfDisparities, int SADWindowSize ); - -CVAPI(void) cvValidateDisparity( CvArr* disparity, const CvArr* cost, - int minDisparity, int numberOfDisparities, - int disp12MaxDiff CV_DEFAULT(1) ); - -/* Reprojects the computed disparity image to the 3D space using the specified 4x4 matrix */ -CVAPI(void) cvReprojectImageTo3D( const CvArr* disparityImage, - CvArr* _3dImage, const CvMat* Q, - int handleMissingValues CV_DEFAULT(0) ); - -/** @} calib3d_c */ - -#ifdef __cplusplus -} // extern "C" - -////////////////////////////////////////////////////////////////////////////////////////// -class CV_EXPORTS CvLevMarq -{ -public: - CvLevMarq(); - CvLevMarq( int nparams, int nerrs, CvTermCriteria criteria= - cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON), - bool completeSymmFlag=false ); - ~CvLevMarq(); - void init( int nparams, int nerrs, CvTermCriteria criteria= - cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON), - bool completeSymmFlag=false ); - bool update( const CvMat*& param, CvMat*& J, CvMat*& err ); - bool updateAlt( const CvMat*& param, CvMat*& JtJ, CvMat*& JtErr, double*& errNorm ); - - void clear(); - void step(); - enum { DONE=0, STARTED=1, CALC_J=2, CHECK_ERR=3 }; - - cv::Ptr mask; - cv::Ptr prevParam; - cv::Ptr param; - cv::Ptr J; - cv::Ptr err; - cv::Ptr JtJ; - cv::Ptr JtJN; - cv::Ptr JtErr; - cv::Ptr JtJV; - cv::Ptr JtJW; - double prevErrNorm, errNorm; - int lambdaLg10; - CvTermCriteria criteria; - int state; - int iters; - bool completeSymmFlag; - int solveMethod; -}; - -#endif - -#endif /* __OPENCV_CALIB3D_C_H__ */ diff --git a/IPL/include/opencv/opencv2/ccalib.hpp b/IPL/include/opencv/opencv2/ccalib.hpp deleted file mode 100644 index 79df598..0000000 --- a/IPL/include/opencv/opencv2/ccalib.hpp +++ /dev/null @@ -1,157 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// - // - // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. - // - // By downloading, copying, installing or using the software you agree to this license. - // If you do not agree to this license, do not download, install, - // copy or use the software. - // - // - // License Agreement - // For Open Source Computer Vision Library - // - // Copyright (C) 2014, OpenCV Foundation, all rights reserved. - // Third party copyrights are property of their respective owners. - // - // Redistribution and use in source and binary forms, with or without modification, - // are permitted provided that the following conditions are met: - // - // * Redistribution's of source code must retain the above copyright notice, - // this list of conditions and the following disclaimer. - // - // * Redistribution's in binary form must reproduce the above copyright notice, - // this list of conditions and the following disclaimer in the documentation - // and/or other materials provided with the distribution. - // - // * The name of the copyright holders may not be used to endorse or promote products - // derived from this software without specific prior written permission. - // - // This software is provided by the copyright holders and contributors "as is" and - // any express or implied warranties, including, but not limited to, the implied - // warranties of merchantability and fitness for a particular purpose are disclaimed. - // In no event shall the Intel Corporation or contributors be liable for any direct, - // indirect, incidental, special, exemplary, or consequential damages - // (including, but not limited to, procurement of substitute goods or services; - // loss of use, data, or profits; or business interruption) however caused - // and on any theory of liability, whether in contract, strict liability, - // or tort (including negligence or otherwise) arising in any way out of - // the use of this software, even if advised of the possibility of such damage. - // - //M*/ - -#ifndef __OPENCV_CCALIB_HPP__ -#define __OPENCV_CCALIB_HPP__ - -#include -#include -#include -#include - -#include - -/** @defgroup ccalib Custom Calibration Pattern for 3D reconstruction -*/ - -namespace cv{ namespace ccalib{ - -//! @addtogroup ccalib -//! @{ - -class CV_EXPORTS CustomPattern : public Algorithm -{ -public: - CustomPattern(); - virtual ~CustomPattern(); - - bool create(InputArray pattern, const Size2f boardSize, OutputArray output = noArray()); - - bool findPattern(InputArray image, OutputArray matched_features, OutputArray pattern_points, const double ratio = 0.7, - const double proj_error = 8.0, const bool refine_position = false, OutputArray out = noArray(), - OutputArray H = noArray(), OutputArray pattern_corners = noArray()); - - bool isInitialized(); - - void getPatternPoints(OutputArray original_points); - /**< - Returns a vector of the original points. - */ - double getPixelSize(); - /**< - Get the pixel size of the pattern - */ - - bool setFeatureDetector(Ptr featureDetector); - bool setDescriptorExtractor(Ptr extractor); - bool setDescriptorMatcher(Ptr matcher); - - Ptr getFeatureDetector(); - Ptr getDescriptorExtractor(); - Ptr getDescriptorMatcher(); - - double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, - Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, - OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0, - TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON)); - /**< - Calls the calirateCamera function with the same inputs. - */ - - bool findRt(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, - OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE); - bool findRt(InputArray image, InputArray cameraMatrix, InputArray distCoeffs, - OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE); - /**< - Uses solvePnP to find the rotation and translation of the pattern - with respect to the camera frame. - */ - - bool findRtRANSAC(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, - OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int iterationsCount = 100, - float reprojectionError = 8.0, int minInliersCount = 100, OutputArray inliers = noArray(), int flags = SOLVEPNP_ITERATIVE); - bool findRtRANSAC(InputArray image, InputArray cameraMatrix, InputArray distCoeffs, - OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int iterationsCount = 100, - float reprojectionError = 8.0, int minInliersCount = 100, OutputArray inliers = noArray(), int flags = SOLVEPNP_ITERATIVE); - /**< - Uses solvePnPRansac() - */ - - void drawOrientation(InputOutputArray image, InputArray tvec, InputArray rvec, InputArray cameraMatrix, - InputArray distCoeffs, double axis_length = 3, int axis_width = 2); - /**< - pattern_corners -> projected over the image position of the edges of the pattern. - */ - -private: - - Mat img_roi; - std::vector obj_corners; - double pxSize; - - bool initialized; - - Ptr detector; - Ptr descriptorExtractor; - Ptr descriptorMatcher; - - std::vector keypoints; - std::vector points3d; - Mat descriptor; - - bool init(Mat& image, const float pixel_size, OutputArray output = noArray()); - bool findPatternPass(const Mat& image, std::vector& matched_features, std::vector& pattern_points, - Mat& H, std::vector& scene_corners, const double pratio, const double proj_error, - const bool refine_position = false, const Mat& mask = Mat(), OutputArray output = noArray()); - void scaleFoundPoints(const double squareSize, const std::vector& corners, std::vector& pts3d); - void check_matches(std::vector& matched, const std::vector& pattern, std::vector& good, std::vector& pattern_3d, const Mat& H); - - void keypoints2points(const std::vector& in, std::vector& out); - void updateKeypointsPos(std::vector& in, const std::vector& new_pos); - void refinePointsPos(const Mat& img, std::vector& p); - void refineKeypointsPos(const Mat& img, std::vector& kp); -}; - -//! @} - -}} // namespace ccalib, cv - -#endif diff --git a/IPL/include/opencv/opencv2/ccalib/multicalib.hpp b/IPL/include/opencv/opencv2/ccalib/multicalib.hpp deleted file mode 100644 index 686d7a5..0000000 --- a/IPL/include/opencv/opencv2/ccalib/multicalib.hpp +++ /dev/null @@ -1,212 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2015, Baisheng Lai (laibaisheng@gmail.com), Zhejiang University, -// all rights reserved. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_MULTICAMERACALIBRATION_HPP__ -#define __OPENCV_MULTICAMERACALIBRATION_HPP__ - -#include "opencv2/ccalib/randpattern.hpp" -#include "opencv2/ccalib/omnidir.hpp" -#include -#include - -namespace cv { namespace multicalib { - -//! @addtogroup ccalib -//! @{ - -#define HEAD -1 -#define INVALID -2 - -/** @brief Class for multiple camera calibration that supports pinhole camera and omnidirection camera. -For omnidirectional camera model, please refer to omnidir.hpp in ccalib module. -It first calibrate each camera individually, then a bundle adjustment like optimization is applied to -refine extrinsic parameters. So far, it only support "random" pattern for calibration, -see randomPattern.hpp in ccalib module for details. -Images that are used should be named by "cameraIdx-timestamp.*", several images with the same timestamp -means that they are the same pattern that are photographed. cameraIdx should start from 0. - -For more details, please refer to paper - B. Li, L. Heng, K. Kevin and M. Pollefeys, "A Multiple-Camera System - Calibration Toolbox Using A Feature Descriptor-Based Calibration - Pattern", in IROS 2013. -*/ - -class CV_EXPORTS MultiCameraCalibration -{ -public: - enum { - PINHOLE, - OMNIDIRECTIONAL - //FISHEYE - }; - - // an edge connects a camera and pattern - struct edge - { - int cameraVertex; // vertex index for camera in this edge - int photoVertex; // vertex index for pattern in this edge - int photoIndex; // photo index among photos for this camera - Mat transform; // transform from pattern to camera - - edge(int cv, int pv, int pi, Mat trans) - { - cameraVertex = cv; - photoVertex = pv; - photoIndex = pi; - transform = trans; - } - }; - - struct vertex - { - Mat pose; // relative pose to the first camera. For camera vertex, it is the - // transform from the first camera to this camera, for pattern vertex, - // it is the transform from pattern to the first camera - int timestamp; // timestamp of photo, only available for photo vertex - - vertex(Mat po, int ts) - { - pose = po; - timestamp = ts; - } - - vertex() - { - pose = Mat::eye(4, 4, CV_32F); - timestamp = -1; - } - }; - /* @brief Constructor - @param cameraType camera type, PINHOLE or OMNIDIRECTIONAL - @param nCameras number of cameras - @fileName filename of string list that are used for calibration, the file is generated - by imagelist_creator from OpenCv samples. The first one in the list is the pattern filename. - @patternWidth the physical width of pattern, in user defined unit. - @patternHeight the physical height of pattern, in user defined unit. - @showExtration whether show extracted features and feature filtering. - @nMiniMatches minimal number of matched features for a frame. - @flags Calibration flags - @criteria optimization stopping criteria. - @detector feature detector that detect feature points in pattern and images. - @descriptor feature descriptor. - @matcher feature matcher. - */ - MultiCameraCalibration(int cameraType, int nCameras, const std::string& fileName, float patternWidth, - float patternHeight, int verbose = 0, int showExtration = 0, int nMiniMatches = 20, int flags = 0, - TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 200, 1e-7), - Ptr detector = AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 0, 3, 0.006f), - Ptr descriptor = AKAZE::create(AKAZE::DESCRIPTOR_MLDB,0, 3, 0.006f), - Ptr matcher = DescriptorMatcher::create("BruteForce-L1")); - - /* @brief load images - */ - void loadImages(); - - /* @brief initialize multiple camera calibration. It calibrates each camera individually. - */ - void initialize(); - - /* @brief optimization extrinsic parameters - */ - double optimizeExtrinsics(); - - /* @brief run multi-camera camera calibration, it runs loadImage(), initialize() and optimizeExtrinsics() - */ - double run(); - - /* @brief write camera parameters to file. - */ - void writeParameters(const std::string& filename); - -private: - std::vector readStringList(); - - int getPhotoVertex(int timestamp); - - void graphTraverse(const Mat& G, int begin, std::vector& order, std::vector& pre); - - void findRowNonZero(const Mat& row, Mat& idx); - - void computeJacobianExtrinsic(const Mat& extrinsicParams, Mat& JTJ_inv, Mat& JTE); - - void computePhotoCameraJacobian(const Mat& rvecPhoto, const Mat& tvecPhoto, const Mat& rvecCamera, - const Mat& tvecCamera, Mat& rvecTran, Mat& tvecTran, const Mat& objectPoints, const Mat& imagePoints, const Mat& K, - const Mat& distort, const Mat& xi, Mat& jacobianPhoto, Mat& jacobianCamera, Mat& E); - - void compose_motion(InputArray _om1, InputArray _T1, InputArray _om2, InputArray _T2, Mat& om3, Mat& T3, Mat& dom3dom1, - Mat& dom3dT1, Mat& dom3dom2, Mat& dom3dT2, Mat& dT3dom1, Mat& dT3dT1, Mat& dT3dom2, Mat& dT3dT2); - - void JRodriguesMatlab(const Mat& src, Mat& dst); - void dAB(InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB); - - double computeProjectError(Mat& parameters); - - void vector2parameters(const Mat& parameters, std::vector& rvecVertex, std::vector& tvecVertexs); - void parameters2vector(const std::vector& rvecVertex, const std::vector& tvecVertex, Mat& parameters); - - int _camType; //PINHOLE, FISHEYE or OMNIDIRECTIONAL - int _nCamera; - int _nMiniMatches; - int _flags; - int _verbose; - double _error; - float _patternWidth, _patternHeight; - TermCriteria _criteria; - std::string _filename; - int _showExtraction; - Ptr _detector; - Ptr _descriptor; - Ptr _matcher; - - std::vector _edgeList; - std::vector _vertexList; - std::vector > _objectPointsForEachCamera; - std::vector > _imagePointsForEachCamera; - std::vector _cameraMatrix; - std::vector _distortCoeffs; - std::vector _xi; - std::vector > _omEachCamera, _tEachCamera; -}; - -//! @} - -}} // namespace multicalib, cv -#endif \ No newline at end of file diff --git a/IPL/include/opencv/opencv2/ccalib/omnidir.hpp b/IPL/include/opencv/opencv2/ccalib/omnidir.hpp deleted file mode 100644 index 25c41bf..0000000 --- a/IPL/include/opencv/opencv2/ccalib/omnidir.hpp +++ /dev/null @@ -1,312 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2015, Baisheng Lai (laibaisheng@gmail.com), Zhejiang University, -// all rights reserved. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#include -#include - -#ifndef __OPENCV_OMNIDIR_HPP__ -#define __OPENCV_OMNIDIR_HPP__ - -namespace cv -{ -namespace omnidir -{ - //! @addtogroup ccalib - //! @{ - - enum { - CALIB_USE_GUESS = 1, - CALIB_FIX_SKEW = 2, - CALIB_FIX_K1 = 4, - CALIB_FIX_K2 = 8, - CALIB_FIX_P1 = 16, - CALIB_FIX_P2 = 32, - CALIB_FIX_XI = 64, - CALIB_FIX_GAMMA = 128, - CALIB_FIX_CENTER = 256 - }; - - enum{ - RECTIFY_PERSPECTIVE = 1, - RECTIFY_CYLINDRICAL = 2, - RECTIFY_LONGLATI = 3, - RECTIFY_STEREOGRAPHIC = 4 - }; - - enum{ - XYZRGB = 1, - XYZ = 2 - }; -/** - * This module was accepted as a GSoC 2015 project for OpenCV, authored by - * Baisheng Lai, mentored by Bo Li. - */ - - /** @brief Projects points for omnidirectional camera using CMei's model - - @param objectPoints Object points in world coordinate, vector of vector of Vec3f or Mat of - 1xN/Nx1 3-channel of type CV_32F and N is the number of points. 64F is also acceptable. - @param imagePoints Output array of image points, vector of vector of Vec2f or - 1xN/Nx1 2-channel of type CV_32F. 64F is also acceptable. - @param rvec vector of rotation between world coordinate and camera coordinate, i.e., om - @param tvec vector of translation between pattern coordinate and camera coordinate - @param K Camera matrix \f$K = \vecthreethree{f_x}{s}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. - @param D Input vector of distortion coefficients \f$(k_1, k_2, p_1, p_2)\f$. - @param xi The parameter xi for CMei's model - @param jacobian Optional output 2Nx16 of type CV_64F jacobian matrix, contains the derivatives of - image pixel points wrt parameters including \f$om, T, f_x, f_y, s, c_x, c_y, xi, k_1, k_2, p_1, p_2\f$. - This matrix will be used in calibration by optimization. - - The function projects object 3D points of world coordinate to image pixels, parameter by intrinsic - and extrinsic parameters. Also, it optionally compute a by-product: the jacobian matrix containing - contains the derivatives of image pixel points wrt intrinsic and extrinsic parameters. - */ - CV_EXPORTS_W void projectPoints(InputArray objectPoints, OutputArray imagePoints, InputArray rvec, InputArray tvec, - InputArray K, double xi, InputArray D, OutputArray jacobian = noArray()); - - /** @brief Undistort 2D image points for omnidirectional camera using CMei's model - - @param distorted Array of distorted image points, vector of Vec2f - or 1xN/Nx1 2-channel Mat of type CV_32F, 64F depth is also acceptable - @param K Camera matrix \f$K = \vecthreethree{f_x}{s}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. - @param D Distortion coefficients \f$(k_1, k_2, p_1, p_2)\f$. - @param xi The parameter xi for CMei's model - @param R Rotation trainsform between the original and object space : 3x3 1-channel, or vector: 3x1/1x3 - 1-channel or 1x1 3-channel - @param undistorted array of normalized object points, vector of Vec2f/Vec2d or 1xN/Nx1 2-channel Mat with the same - depth of distorted points. - */ - CV_EXPORTS_W void undistortPoints(InputArray distorted, OutputArray undistorted, InputArray K, InputArray D, InputArray xi, InputArray R); - - /** @brief Computes undistortion and rectification maps for omnidirectional camera image transform by a rotation R. - It output two maps that are used for cv::remap(). If D is empty then zero distortion is used, - if R or P is empty then identity matrices are used. - - @param K Camera matrix \f$K = \vecthreethree{f_x}{s}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$, with depth CV_32F or CV_64F - @param D Input vector of distortion coefficients \f$(k_1, k_2, p_1, p_2)\f$, with depth CV_32F or CV_64F - @param xi The parameter xi for CMei's model - @param R Rotation transform between the original and object space : 3x3 1-channel, or vector: 3x1/1x3, with depth CV_32F or CV_64F - @param P New camera matrix (3x3) or new projection matrix (3x4) - @param size Undistorted image size. - @param mltype Type of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps() - for details. - @param map1 The first output map. - @param map2 The second output map. - @param flags Flags indicates the rectification type, RECTIFY_PERSPECTIVE, RECTIFY_CYLINDRICAL, RECTIFY_LONGLATI and RECTIFY_STEREOGRAPHIC - are supported. - */ - CV_EXPORTS_W void initUndistortRectifyMap(InputArray K, InputArray D, InputArray xi, InputArray R, InputArray P, const cv::Size& size, - int mltype, OutputArray map1, OutputArray map2, int flags); - - /** @brief Undistort omnidirectional images to perspective images - - @param distorted The input omnidirectional image. - @param undistorted The output undistorted image. - @param K Camera matrix \f$K = \vecthreethree{f_x}{s}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. - @param D Input vector of distortion coefficients \f$(k_1, k_2, p_1, p_2)\f$. - @param xi The parameter xi for CMei's model. - @param flags Flags indicates the rectification type, RECTIFY_PERSPECTIVE, RECTIFY_CYLINDRICAL, RECTIFY_LONGLATI and RECTIFY_STEREOGRAPHIC - @param Knew Camera matrix of the distorted image. If it is not assigned, it is just K. - @param new_size The new image size. By default, it is the size of distorted. - @param R Rotation matrix between the input and output images. By default, it is identity matrix. - */ - CV_EXPORTS_W void undistortImage(InputArray distorted, OutputArray undistorted, InputArray K, InputArray D, InputArray xi, int flags, - InputArray Knew = cv::noArray(), const Size& new_size = Size(), InputArray R = Mat::eye(3, 3, CV_64F)); - - /** @brief Perform omnidirectional camera calibration, the default depth of outputs is CV_64F. - - @param objectPoints Vector of vector of Vec3f object points in world (pattern) coordinate. - It also can be vector of Mat with size 1xN/Nx1 and type CV_32FC3. Data with depth of 64_F is also acceptable. - @param imagePoints Vector of vector of Vec2f corresponding image points of objectPoints. It must be the same - size and the same type with objectPoints. - @param size Image size of calibration images. - @param K Output calibrated camera matrix. - @param xi Output parameter xi for CMei's model - @param D Output distortion parameters \f$(k_1, k_2, p_1, p_2)\f$ - @param rvecs Output rotations for each calibration images - @param tvecs Output translation for each calibration images - @param flags The flags that control calibrate - @param criteria Termination criteria for optimization - @param idx Indices of images that pass initialization, which are really used in calibration. So the size of rvecs is the - same as idx.total(). - */ - CV_EXPORTS_W double calibrate(InputArray objectPoints, InputArray imagePoints, Size size, - InputOutputArray K, InputOutputArray xi, InputOutputArray D, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, - int flags, TermCriteria criteria, OutputArray idx=noArray()); - - /** @brief Stereo calibration for omnidirectional camera model. It computes the intrinsic parameters for two - cameras and the extrinsic parameters between two cameras. The default depth of outputs is CV_64F. - - @param objectPoints Object points in world (pattern) coordinate. Its type is vector >. - It also can be vector of Mat with size 1xN/Nx1 and type CV_32FC3. Data with depth of 64_F is also acceptable. - @param imagePoints1 The corresponding image points of the first camera, with type vector >. - It must be the same size and the same type as objectPoints. - @param imagePoints2 The corresponding image points of the second camera, with type vector >. - It must be the same size and the same type as objectPoints. - @param imageSize1 Image size of calibration images of the first camera. - @param imageSize2 Image size of calibration images of the second camera. - @param K1 Output camera matrix for the first camera. - @param xi1 Output parameter xi of Mei's model for the first camera - @param D1 Output distortion parameters \f$(k_1, k_2, p_1, p_2)\f$ for the first camera - @param K2 Output camera matrix for the first camera. - @param xi2 Output parameter xi of CMei's model for the second camera - @param D2 Output distortion parameters \f$(k_1, k_2, p_1, p_2)\f$ for the second camera - @param rvec Output rotation between the first and second camera - @param tvec Output translation between the first and second camera - @param rvecsL Output rotation for each image of the first camera - @param tvecsL Output translation for each image of the first camera - @param flags The flags that control stereoCalibrate - @param criteria Termination criteria for optimization - @param idx Indices of image pairs that pass initialization, which are really used in calibration. So the size of rvecs is the - same as idx.total(). - @ - */ - CV_EXPORTS_W double stereoCalibrate(InputOutputArrayOfArrays objectPoints, InputOutputArrayOfArrays imagePoints1, InputOutputArrayOfArrays imagePoints2, - const Size& imageSize1, const Size& imageSize2, InputOutputArray K1, InputOutputArray xi1, InputOutputArray D1, InputOutputArray K2, InputOutputArray xi2, - InputOutputArray D2, OutputArray rvec, OutputArray tvec, OutputArrayOfArrays rvecsL, OutputArrayOfArrays tvecsL, int flags, TermCriteria criteria, OutputArray idx=noArray()); - - /** @brief Stereo rectification for omnidirectional camera model. It computes the rectification rotations for two cameras - - @param R Rotation between the first and second camera - @param T Translation between the first and second camera - @param R1 Output 3x3 rotation matrix for the first camera - @param R2 Output 3x3 rotation matrix for the second camera - */ - CV_EXPORTS_W void stereoRectify(InputArray R, InputArray T, OutputArray R1, OutputArray R2); - - /** @brief Stereo 3D reconstruction from a pair of images - - @param image1 The first input image - @param image2 The second input image - @param K1 Input camera matrix of the first camera - @param D1 Input distortion parameters \f$(k_1, k_2, p_1, p_2)\f$ for the first camera - @param xi1 Input parameter xi for the first camera for CMei's model - @param K2 Input camera matrix of the second camera - @param D2 Input distortion parameters \f$(k_1, k_2, p_1, p_2)\f$ for the second camera - @param xi2 Input parameter xi for the second camera for CMei's model - @param R Rotation between the first and second camera - @param T Translation between the first and second camera - @param flag Flag of rectification type, RECTIFY_PERSPECTIVE or RECTIFY_LONGLATI - @param numDisparities The parameter 'numDisparities' in StereoSGBM, see StereoSGBM for details. - @param SADWindowSize The parameter 'SADWindowSize' in StereoSGBM, see StereoSGBM for details. - @param disparity Disparity map generated by stereo matching - @param image1Rec Rectified image of the first image - @param image2Rec rectified image of the second image - @param newSize Image size of rectified image, see omnidir::undistortImage - @param Knew New camera matrix of rectified image, see omnidir::undistortImage - @param pointCloud Point cloud of 3D reconstruction, with type CV_64FC3 - @param pointType Point cloud type, it can be XYZRGB or XYZ - */ - CV_EXPORTS_W void stereoReconstruct(InputArray image1, InputArray image2, InputArray K1, InputArray D1, InputArray xi1, - InputArray K2, InputArray D2, InputArray xi2, InputArray R, InputArray T, int flag, int numDisparities, int SADWindowSize, - OutputArray disparity, OutputArray image1Rec, OutputArray image2Rec, const Size& newSize = Size(), InputArray Knew = cv::noArray(), - OutputArray pointCloud = cv::noArray(), int pointType = XYZRGB); - -namespace internal -{ - void initializeCalibration(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size size, OutputArrayOfArrays omAll, - OutputArrayOfArrays tAll, OutputArray K, double& xi, OutputArray idx = noArray()); - - void initializeStereoCalibration(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, - const Size& size1, const Size& size2, OutputArray om, OutputArray T, OutputArrayOfArrays omL, OutputArrayOfArrays tL, OutputArray K1, OutputArray D1, OutputArray K2, OutputArray D2, - double &xi1, double &xi2, int flags, OutputArray idx); - - void computeJacobian(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, InputArray parameters, Mat& JTJ_inv, Mat& JTE, int flags, - double epsilon); - - void computeJacobianStereo(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, - InputArray parameters, Mat& JTJ_inv, Mat& JTE, int flags, double epsilon); - - void encodeParameters(InputArray K, InputArrayOfArrays omAll, InputArrayOfArrays tAll, InputArray distoaration, double xi, OutputArray parameters); - - void encodeParametersStereo(InputArray K1, InputArray K2, InputArray om, InputArray T, InputArrayOfArrays omL, InputArrayOfArrays tL, - InputArray D1, InputArray D2, double xi1, double xi2, OutputArray parameters); - - void decodeParameters(InputArray paramsters, OutputArray K, OutputArrayOfArrays omAll, OutputArrayOfArrays tAll, OutputArray distoration, double& xi); - - void decodeParametersStereo(InputArray parameters, OutputArray K1, OutputArray K2, OutputArray om, OutputArray T, OutputArrayOfArrays omL, - OutputArrayOfArrays tL, OutputArray D1, OutputArray D2, double& xi1, double& xi2); - - void estimateUncertainties(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, InputArray parameters, Mat& errors, Vec2d& std_error, double& rms, int flags); - - void estimateUncertaintiesStereo(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, InputArray parameters, Mat& errors, - Vec2d& std_error, double& rms, int flags); - - double computeMeanReproErr(InputArrayOfArrays imagePoints, InputArrayOfArrays proImagePoints); - - double computeMeanReproErr(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, InputArray K, InputArray D, double xi, InputArrayOfArrays omAll, - InputArrayOfArrays tAll); - - double computeMeanReproErrStereo(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, InputArray K1, InputArray K2, - InputArray D1, InputArray D2, double xi1, double xi2, InputArray om, InputArray T, InputArrayOfArrays omL, InputArrayOfArrays TL); - - void checkFixed(Mat &G, int flags, int n); - - void subMatrix(const Mat& src, Mat& dst, const std::vector& cols, const std::vector& rows); - - void flags2idx(int flags, std::vector& idx, int n); - - void flags2idxStereo(int flags, std::vector& idx, int n); - - void fillFixed(Mat&G, int flags, int n); - - void fillFixedStereo(Mat& G, int flags, int n); - - double findMedian(const Mat& row); - - Vec3d findMedian3(InputArray mat); - - void getInterset(InputArray idx1, InputArray idx2, OutputArray inter1, OutputArray inter2, OutputArray inter_ori); - - void compose_motion(InputArray _om1, InputArray _T1, InputArray _om2, InputArray _T2, Mat& om3, Mat& T3, Mat& dom3dom1, - Mat& dom3dT1, Mat& dom3dom2, Mat& dom3dT2, Mat& dT3dom1, Mat& dT3dT1, Mat& dT3dom2, Mat& dT3dT2); - - //void JRodriguesMatlab(const Mat& src, Mat& dst); - - //void dAB(InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB); -} // internal - -//! @} - -} // omnidir - -} //cv -#endif \ No newline at end of file diff --git a/IPL/include/opencv/opencv2/ccalib/randpattern.hpp b/IPL/include/opencv/opencv2/ccalib/randpattern.hpp deleted file mode 100644 index 9fc08f8..0000000 --- a/IPL/include/opencv/opencv2/ccalib/randpattern.hpp +++ /dev/null @@ -1,177 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2015, Baisheng Lai (laibaisheng@gmail.com), Zhejiang University, -// all rights reserved. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_RANDOMPATTERN_HPP__ -#define __OPENCV_RANDOMPATTERN_HPP__ - -#include "opencv2/features2d.hpp" -#include "opencv2/highgui.hpp" - -namespace cv { namespace randpattern { - - -//! @addtogroup ccalib -//! @{ - -/** @brief Class for finding features points and corresponding 3D in world coordinate of -a "random" pattern, which can be to be used in calibration. It is useful when pattern is -partly occluded or only a part of pattern can be observed in multiple cameras calibration. -The pattern can be generated by RandomPatternGenerator class described in this file. - -Please refer to paper - B. Li, L. Heng, K. Kevin and M. Pollefeys, "A Multiple-Camera System - Calibration Toolbox Using A Feature Descriptor-Based Calibration - Pattern", in IROS 2013. -*/ - -class CV_EXPORTS RandomPatternCornerFinder -{ -public: - - /* @brief Construct RandomPatternCornerFinder object - - @param patternWidth the real width of "random" pattern in a user defined unit. - @param patternHeight the real height of "random" pattern in a user defined unit. - @param nMiniMatch number of minimal matches, otherwise that image is abandoned - @depth depth of output objectPoints and imagePoints, set it to be CV_32F or CV_64F. - @showExtraction whether show feature extraction, 0 for no and 1 for yes. - @detector feature detector to detect feature points in pattern and images. - @descriptor feature descriptor. - @matcher feature matcher. - */ - RandomPatternCornerFinder(float patternWidth, float patternHeight, - int nminiMatch = 20, int depth = CV_32F, int verbose = 0, int showExtraction = 0, - Ptr detector = AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 0, 3, 0.005f), - Ptr descriptor = AKAZE::create(AKAZE::DESCRIPTOR_MLDB,0, 3, 0.005f), - Ptr matcher = DescriptorMatcher::create("BruteForce-L1")); - - /* @brief Load pattern image and compute features for pattern - @param patternImage image for "random" pattern generated by RandomPatternGenerator, run it first. - */ - void loadPattern(cv::Mat patternImage); - - /* @brief Compute matched object points and image points which are used for calibration - The objectPoints (3D) and imagePoints (2D) are stored inside the class. Run getObjectPoints() - and getImagePoints() to get them. - - @param inputImages vector of 8-bit grayscale images containing "random" pattern - that are used for calibration. - */ - void computeObjectImagePoints(std::vector inputImages); - - //void computeObjectImagePoints2(std::vector inputImages); - - /* @brief Compute object and image points for a single image. It returns a vector that - the first element stores the imagePoints and the second one stores the objectPoints. - - @param inputImage single input image for calibration - */ - std::vector computeObjectImagePointsForSingle(cv::Mat inputImage); - - /* @brief Get object(3D) points - */ - std::vector getObjectPoints(); - - /* @brief and image(2D) points - */ - std::vector getImagePoints(); - -private: - - std::vector _objectPonits, _imagePoints; - float _patternWidth, _patternHeight; - cv::Size _patternImageSize; - int _nminiMatch; - int _depth; - int _verbose; - - Ptr _detector; - Ptr _descriptor; - Ptr _matcher; - Mat _descriptorPattern; - std::vector _keypointsPattern; - Mat _patternImage; - int _showExtraction; - - void keyPoints2MatchedLocation(const std::vector& imageKeypoints, - const std::vector& patternKeypoints, const std::vector matchces, - cv::Mat& matchedImagelocation, cv::Mat& matchedPatternLocation); - void getFilteredLocation(cv::Mat& imageKeypoints, cv::Mat& patternKeypoints, const cv::Mat mask); - void getObjectImagePoints(const cv::Mat& imageKeypoints, const cv::Mat& patternKeypoints); - void crossCheckMatching( cv::Ptr& descriptorMatcher, - const Mat& descriptors1, const Mat& descriptors2, - std::vector& filteredMatches12, int knn=1 ); - void drawCorrespondence(const Mat& image1, const std::vector keypoint1, - const Mat& image2, const std::vector keypoint2, const std::vector matchces, - const Mat& mask1, const Mat& mask2, const int step); -}; - -/* @brief Class to generate "random" pattern image that are used for RandomPatternCornerFinder -Please refer to paper -B. Li, L. Heng, K. Kevin and M. Pollefeys, "A Multiple-Camera System -Calibration Toolbox Using A Feature Descriptor-Based Calibration -Pattern", in IROS 2013. -*/ -class CV_EXPORTS RandomPatternGenerator -{ -public: - /* @brief Construct RandomPatternGenerator - - @param imageWidth image width of the generated pattern image - @param imageHeight image height of the generated pattern image - */ - RandomPatternGenerator(int imageWidth, int imageHeight); - - /* @brief Generate pattern - */ - void generatePattern(); - /* @brief Get pattern - */ - cv::Mat getPattern(); -private: - cv::Mat _pattern; - int _imageWidth, _imageHeight; -}; - -//! @} - -}} //namespace randpattern, cv -#endif \ No newline at end of file diff --git a/IPL/include/opencv/opencv2/core.hpp b/IPL/include/opencv/opencv2/core.hpp deleted file mode 100644 index 2e47658..0000000 --- a/IPL/include/opencv/opencv2/core.hpp +++ /dev/null @@ -1,3168 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2015, Intel Corporation, all rights reserved. -// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. -// Copyright (C) 2015, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_HPP__ -#define __OPENCV_CORE_HPP__ - -#ifndef __cplusplus -# error core.hpp header must be compiled as C++ -#endif - -#include "opencv2/core/cvdef.h" -#include "opencv2/core/version.hpp" -#include "opencv2/core/base.hpp" -#include "opencv2/core/cvstd.hpp" -#include "opencv2/core/traits.hpp" -#include "opencv2/core/matx.hpp" -#include "opencv2/core/types.hpp" -#include "opencv2/core/mat.hpp" -#include "opencv2/core/persistence.hpp" - -/** -@defgroup core Core functionality -@{ - @defgroup core_basic Basic structures - @defgroup core_c C structures and operations - @{ - @defgroup core_c_glue Connections with C++ - @} - @defgroup core_array Operations on arrays - @defgroup core_xml XML/YAML Persistence - @defgroup core_cluster Clustering - @defgroup core_utils Utility and system functions and macros - @{ - @defgroup core_utils_sse SSE utilities - @defgroup core_utils_neon NEON utilities - @} - @defgroup core_opengl OpenGL interoperability - @defgroup core_ipp Intel IPP Asynchronous C/C++ Converters - @defgroup core_optim Optimization Algorithms - @defgroup core_directx DirectX interoperability - @defgroup core_eigen Eigen support - @defgroup core_opencl OpenCL support - @defgroup core_va_intel Intel VA-API/OpenCL (CL-VA) interoperability - @defgroup core_hal Hardware Acceleration Layer - @{ - @defgroup core_hal_functions Functions - @defgroup core_hal_interface Interface - @defgroup core_hal_intrin Universal intrinsics - @{ - @defgroup core_hal_intrin_impl Private implementation helpers - @} - @} -@} - */ - -namespace cv { - -//! @addtogroup core_utils -//! @{ - -/*! @brief Class passed to an error. - -This class encapsulates all or almost all necessary -information about the error happened in the program. The exception is -usually constructed and thrown implicitly via CV_Error and CV_Error_ macros. -@see error - */ -class CV_EXPORTS Exception : public std::exception -{ -public: - /*! - Default constructor - */ - Exception(); - /*! - Full constructor. Normally the constuctor is not called explicitly. - Instead, the macros CV_Error(), CV_Error_() and CV_Assert() are used. - */ - Exception(int _code, const String& _err, const String& _func, const String& _file, int _line); - virtual ~Exception() throw(); - - /*! - \return the error description and the context as a text string. - */ - virtual const char *what() const throw(); - void formatMessage(); - - String msg; ///< the formatted error message - - int code; ///< error code @see CVStatus - String err; ///< error description - String func; ///< function name. Available only when the compiler supports getting it - String file; ///< source file name where the error has occured - int line; ///< line number in the source file where the error has occured -}; - -/*! @brief Signals an error and raises the exception. - -By default the function prints information about the error to stderr, -then it either stops if cv::setBreakOnError() had been called before or raises the exception. -It is possible to alternate error processing by using cv::redirectError(). -@param exc the exception raisen. -@deprecated drop this version - */ -CV_EXPORTS void error( const Exception& exc ); - -enum SortFlags { SORT_EVERY_ROW = 0, //!< each matrix row is sorted independently - SORT_EVERY_COLUMN = 1, //!< each matrix column is sorted - //!< independently; this flag and the previous one are - //!< mutually exclusive. - SORT_ASCENDING = 0, //!< each matrix row is sorted in the ascending - //!< order. - SORT_DESCENDING = 16 //!< each matrix row is sorted in the - //!< descending order; this flag and the previous one are also - //!< mutually exclusive. - }; - -//! @} core_utils - -//! @addtogroup core -//! @{ - -//! Covariation flags -enum CovarFlags { - /** The output covariance matrix is calculated as: - \f[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...],\f] - The covariance matrix will be nsamples x nsamples. Such an unusual covariance matrix is used - for fast PCA of a set of very large vectors (see, for example, the EigenFaces technique for - face recognition). Eigenvalues of this "scrambled" matrix match the eigenvalues of the true - covariance matrix. The "true" eigenvectors can be easily calculated from the eigenvectors of - the "scrambled" covariance matrix. */ - COVAR_SCRAMBLED = 0, - /**The output covariance matrix is calculated as: - \f[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...] \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T,\f] - covar will be a square matrix of the same size as the total number of elements in each input - vector. One and only one of COVAR_SCRAMBLED and COVAR_NORMAL must be specified.*/ - COVAR_NORMAL = 1, - /** If the flag is specified, the function does not calculate mean from - the input vectors but, instead, uses the passed mean vector. This is useful if mean has been - pre-calculated or known in advance, or if the covariance matrix is calculated by parts. In - this case, mean is not a mean vector of the input sub-set of vectors but rather the mean - vector of the whole set.*/ - COVAR_USE_AVG = 2, - /** If the flag is specified, the covariance matrix is scaled. In the - "normal" mode, scale is 1./nsamples . In the "scrambled" mode, scale is the reciprocal of the - total number of elements in each input vector. By default (if the flag is not specified), the - covariance matrix is not scaled ( scale=1 ).*/ - COVAR_SCALE = 4, - /** If the flag is - specified, all the input vectors are stored as rows of the samples matrix. mean should be a - single-row vector in this case.*/ - COVAR_ROWS = 8, - /** If the flag is - specified, all the input vectors are stored as columns of the samples matrix. mean should be a - single-column vector in this case.*/ - COVAR_COLS = 16 -}; - -//! k-Means flags -enum KmeansFlags { - /** Select random initial centers in each attempt.*/ - KMEANS_RANDOM_CENTERS = 0, - /** Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007].*/ - KMEANS_PP_CENTERS = 2, - /** During the first (and possibly the only) attempt, use the - user-supplied labels instead of computing them from the initial centers. For the second and - further attempts, use the random or semi-random centers. Use one of KMEANS_\*_CENTERS flag - to specify the exact method.*/ - KMEANS_USE_INITIAL_LABELS = 1 -}; - -//! type of line -enum LineTypes { - FILLED = -1, - LINE_4 = 4, //!< 4-connected line - LINE_8 = 8, //!< 8-connected line - LINE_AA = 16 //!< antialiased line -}; - -//! Only a subset of Hershey fonts -//! are supported -enum HersheyFonts { - FONT_HERSHEY_SIMPLEX = 0, //!< normal size sans-serif font - FONT_HERSHEY_PLAIN = 1, //!< small size sans-serif font - FONT_HERSHEY_DUPLEX = 2, //!< normal size sans-serif font (more complex than FONT_HERSHEY_SIMPLEX) - FONT_HERSHEY_COMPLEX = 3, //!< normal size serif font - FONT_HERSHEY_TRIPLEX = 4, //!< normal size serif font (more complex than FONT_HERSHEY_COMPLEX) - FONT_HERSHEY_COMPLEX_SMALL = 5, //!< smaller version of FONT_HERSHEY_COMPLEX - FONT_HERSHEY_SCRIPT_SIMPLEX = 6, //!< hand-writing style font - FONT_HERSHEY_SCRIPT_COMPLEX = 7, //!< more complex variant of FONT_HERSHEY_SCRIPT_SIMPLEX - FONT_ITALIC = 16 //!< flag for italic font -}; - -enum ReduceTypes { REDUCE_SUM = 0, //!< the output is the sum of all rows/columns of the matrix. - REDUCE_AVG = 1, //!< the output is the mean vector of all rows/columns of the matrix. - REDUCE_MAX = 2, //!< the output is the maximum (column/row-wise) of all rows/columns of the matrix. - REDUCE_MIN = 3 //!< the output is the minimum (column/row-wise) of all rows/columns of the matrix. - }; - - -/** @brief Swaps two matrices -*/ -CV_EXPORTS void swap(Mat& a, Mat& b); -/** @overload */ -CV_EXPORTS void swap( UMat& a, UMat& b ); - -//! @} core - -//! @addtogroup core_array -//! @{ - -/** @brief Computes the source location of an extrapolated pixel. - -The function computes and returns the coordinate of a donor pixel corresponding to the specified -extrapolated pixel when using the specified extrapolation border mode. For example, if you use -cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and -want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img , it -looks like: -@code{.cpp} - float val = img.at(borderInterpolate(100, img.rows, cv::BORDER_REFLECT_101), - borderInterpolate(-5, img.cols, cv::BORDER_WRAP)); -@endcode -Normally, the function is not called directly. It is used inside filtering functions and also in -copyMakeBorder. -@param p 0-based coordinate of the extrapolated pixel along one of the axes, likely \<0 or \>= len -@param len Length of the array along the corresponding axis. -@param borderType Border type, one of the cv::BorderTypes, except for cv::BORDER_TRANSPARENT and -cv::BORDER_ISOLATED . When borderType==cv::BORDER_CONSTANT , the function always returns -1, regardless -of p and len. - -@sa copyMakeBorder -*/ -CV_EXPORTS_W int borderInterpolate(int p, int len, int borderType); - -/** @brief Forms a border around an image. - -The function copies the source image into the middle of the destination image. The areas to the -left, to the right, above and below the copied source image will be filled with extrapolated -pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but -what other more complex functions, including your own, may do to simplify image boundary handling. - -The function supports the mode when src is already in the middle of dst . In this case, the -function does not copy src itself but simply constructs the border, for example: - -@code{.cpp} - // let border be the same in all directions - int border=2; - // constructs a larger image to fit both the image and the border - Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth()); - // select the middle part of it w/o copying data - Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows)); - // convert image from RGB to grayscale - cvtColor(rgb, gray, COLOR_RGB2GRAY); - // form a border in-place - copyMakeBorder(gray, gray_buf, border, border, - border, border, BORDER_REPLICATE); - // now do some custom filtering ... - ... -@endcode -@note When the source image is a part (ROI) of a bigger image, the function will try to use the -pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as -if src was not a ROI, use borderType | BORDER_ISOLATED. - -@param src Source image. -@param dst Destination image of the same type as src and the size Size(src.cols+left+right, -src.rows+top+bottom) . -@param top -@param bottom -@param left -@param right Parameter specifying how many pixels in each direction from the source image rectangle -to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs -to be built. -@param borderType Border type. See borderInterpolate for details. -@param value Border value if borderType==BORDER_CONSTANT . - -@sa borderInterpolate -*/ -CV_EXPORTS_W void copyMakeBorder(InputArray src, OutputArray dst, - int top, int bottom, int left, int right, - int borderType, const Scalar& value = Scalar() ); - -/** @brief Calculates the per-element sum of two arrays or an array and a scalar. - -The function add calculates: -- Sum of two arrays when both input arrays have the same size and the same number of channels: -\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f] -- Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of -elements as `src1.channels()`: -\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f] -- Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of -elements as `src2.channels()`: -\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} + \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f] -where `I` is a multi-dimensional index of array elements. In case of multi-channel arrays, each -channel is processed independently. - -The first function in the list above can be replaced with matrix expressions: -@code{.cpp} - dst = src1 + src2; - dst += src1; // equivalent to add(dst, src1, dst); -@endcode -The input arrays and the output array can all have the same or different depths. For example, you -can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit -floating-point array. Depth of the output array is determined by the dtype parameter. In the second -and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can -be set to the default -1. In this case, the output array will have the same depth as the input -array, be it src1, src2 or both. -@note Saturation is not applied when the output array has the depth CV_32S. You may even get -result of an incorrect sign in the case of overflow. -@param src1 first input array or a scalar. -@param src2 second input array or a scalar. -@param dst output array that has the same size and number of channels as the input array(s); the -depth is defined by dtype or src1/src2. -@param mask optional operation mask - 8-bit single channel array, that specifies elements of the -output array to be changed. -@param dtype optional depth of the output array (see the discussion below). -@sa subtract, addWeighted, scaleAdd, Mat::convertTo -*/ -CV_EXPORTS_W void add(InputArray src1, InputArray src2, OutputArray dst, - InputArray mask = noArray(), int dtype = -1); - -/** @brief Calculates the per-element difference between two arrays or array and a scalar. - -The function subtract calculates: -- Difference between two arrays, when both input arrays have the same size and the same number of -channels: - \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f] -- Difference between an array and a scalar, when src2 is constructed from Scalar or has the same -number of elements as `src1.channels()`: - \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f] -- Difference between a scalar and an array, when src1 is constructed from Scalar or has the same -number of elements as `src2.channels()`: - \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} - \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f] -- The reverse difference between a scalar and an array in the case of `SubRS`: - \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src2} - \texttt{src1}(I) ) \quad \texttt{if mask}(I) \ne0\f] -where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each -channel is processed independently. - -The first function in the list above can be replaced with matrix expressions: -@code{.cpp} - dst = src1 - src2; - dst -= src1; // equivalent to subtract(dst, src1, dst); -@endcode -The input arrays and the output array can all have the same or different depths. For example, you -can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of -the output array is determined by dtype parameter. In the second and third cases above, as well as -in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this -case the output array will have the same depth as the input array, be it src1, src2 or both. -@note Saturation is not applied when the output array has the depth CV_32S. You may even get -result of an incorrect sign in the case of overflow. -@param src1 first input array or a scalar. -@param src2 second input array or a scalar. -@param dst output array of the same size and the same number of channels as the input array. -@param mask optional operation mask; this is an 8-bit single channel array that specifies elements -of the output array to be changed. -@param dtype optional depth of the output array -@sa add, addWeighted, scaleAdd, Mat::convertTo - */ -CV_EXPORTS_W void subtract(InputArray src1, InputArray src2, OutputArray dst, - InputArray mask = noArray(), int dtype = -1); - - -/** @brief Calculates the per-element scaled product of two arrays. - -The function multiply calculates the per-element product of two arrays: - -\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I) \cdot \texttt{src2} (I))\f] - -There is also a @ref MatrixExpressions -friendly variant of the first function. See Mat::mul . - -For a not-per-element matrix product, see gemm . - -@note Saturation is not applied when the output array has the depth -CV_32S. You may even get result of an incorrect sign in the case of -overflow. -@param src1 first input array. -@param src2 second input array of the same size and the same type as src1. -@param dst output array of the same size and type as src1. -@param scale optional scale factor. -@param dtype optional depth of the output array -@sa add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare, -Mat::convertTo -*/ -CV_EXPORTS_W void multiply(InputArray src1, InputArray src2, - OutputArray dst, double scale = 1, int dtype = -1); - -/** @brief Performs per-element division of two arrays or a scalar by an array. - -The functions divide divide one array by another: -\f[\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\f] -or a scalar by an array when there is no src1 : -\f[\texttt{dst(I) = saturate(scale/src2(I))}\f] - -When src2(I) is zero, dst(I) will also be zero. Different channels of -multi-channel arrays are processed independently. - -@note Saturation is not applied when the output array has the depth CV_32S. You may even get -result of an incorrect sign in the case of overflow. -@param src1 first input array. -@param src2 second input array of the same size and type as src1. -@param scale scalar factor. -@param dst output array of the same size and type as src2. -@param dtype optional depth of the output array; if -1, dst will have depth src2.depth(), but in -case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth(). -@sa multiply, add, subtract -*/ -CV_EXPORTS_W void divide(InputArray src1, InputArray src2, OutputArray dst, - double scale = 1, int dtype = -1); - -/** @overload */ -CV_EXPORTS_W void divide(double scale, InputArray src2, - OutputArray dst, int dtype = -1); - -/** @brief Calculates the sum of a scaled array and another array. - -The function scaleAdd is one of the classical primitive linear algebra operations, known as DAXPY -or SAXPY in [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms). It calculates -the sum of a scaled array and another array: -\f[\texttt{dst} (I)= \texttt{scale} \cdot \texttt{src1} (I) + \texttt{src2} (I)\f] -The function can also be emulated with a matrix expression, for example: -@code{.cpp} - Mat A(3, 3, CV_64F); - ... - A.row(0) = A.row(1)*2 + A.row(2); -@endcode -@param src1 first input array. -@param alpha scale factor for the first array. -@param src2 second input array of the same size and type as src1. -@param dst output array of the same size and type as src1. -@sa add, addWeighted, subtract, Mat::dot, Mat::convertTo -*/ -CV_EXPORTS_W void scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst); - -/** @brief Calculates the weighted sum of two arrays. - -The function addWeighted calculates the weighted sum of two arrays as follows: -\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} + \texttt{src2} (I)* \texttt{beta} + \texttt{gamma} )\f] -where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each -channel is processed independently. -The function can be replaced with a matrix expression: -@code{.cpp} - dst = src1*alpha + src2*beta + gamma; -@endcode -@note Saturation is not applied when the output array has the depth CV_32S. You may even get -result of an incorrect sign in the case of overflow. -@param src1 first input array. -@param alpha weight of the first array elements. -@param src2 second input array of the same size and channel number as src1. -@param beta weight of the second array elements. -@param gamma scalar added to each sum. -@param dst output array that has the same size and number of channels as the input arrays. -@param dtype optional depth of the output array; when both input arrays have the same depth, dtype -can be set to -1, which will be equivalent to src1.depth(). -@sa add, subtract, scaleAdd, Mat::convertTo -*/ -CV_EXPORTS_W void addWeighted(InputArray src1, double alpha, InputArray src2, - double beta, double gamma, OutputArray dst, int dtype = -1); - -/** @brief Scales, calculates absolute values, and converts the result to 8-bit. - -On each element of the input array, the function convertScaleAbs -performs three operations sequentially: scaling, taking an absolute -value, conversion to an unsigned 8-bit type: -\f[\texttt{dst} (I)= \texttt{saturate\_cast} (| \texttt{src} (I)* \texttt{alpha} + \texttt{beta} |)\f] -In case of multi-channel arrays, the function processes each channel -independently. When the output is not 8-bit, the operation can be -emulated by calling the Mat::convertTo method (or by using matrix -expressions) and then by calculating an absolute value of the result. -For example: -@code{.cpp} - Mat_ A(30,30); - randu(A, Scalar(-100), Scalar(100)); - Mat_ B = A*5 + 3; - B = abs(B); - // Mat_ B = abs(A*5+3) will also do the job, - // but it will allocate a temporary matrix -@endcode -@param src input array. -@param dst output array. -@param alpha optional scale factor. -@param beta optional delta added to the scaled values. -@sa Mat::convertTo, cv::abs(const Mat&) -*/ -CV_EXPORTS_W void convertScaleAbs(InputArray src, OutputArray dst, - double alpha = 1, double beta = 0); - -/** @brief Performs a look-up table transform of an array. - -The function LUT fills the output array with values from the look-up table. Indices of the entries -are taken from the input array. That is, the function processes each element of src as follows: -\f[\texttt{dst} (I) \leftarrow \texttt{lut(src(I) + d)}\f] -where -\f[d = \fork{0}{if \(\texttt{src}\) has depth \(\texttt{CV_8U}\)}{128}{if \(\texttt{src}\) has depth \(\texttt{CV_8S}\)}\f] -@param src input array of 8-bit elements. -@param lut look-up table of 256 elements; in case of multi-channel input array, the table should -either have a single channel (in this case the same table is used for all channels) or the same -number of channels as in the input array. -@param dst output array of the same size and number of channels as src, and the same depth as lut. -@sa convertScaleAbs, Mat::convertTo -*/ -CV_EXPORTS_W void LUT(InputArray src, InputArray lut, OutputArray dst); - -/** @brief Calculates the sum of array elements. - -The functions sum calculate and return the sum of array elements, -independently for each channel. -@param src input array that must have from 1 to 4 channels. -@sa countNonZero, mean, meanStdDev, norm, minMaxLoc, reduce -*/ -CV_EXPORTS_AS(sumElems) Scalar sum(InputArray src); - -/** @brief Counts non-zero array elements. - -The function returns the number of non-zero elements in src : -\f[\sum _{I: \; \texttt{src} (I) \ne0 } 1\f] -@param src single-channel array. -@sa mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix -*/ -CV_EXPORTS_W int countNonZero( InputArray src ); - -/** @brief Returns the list of locations of non-zero pixels - -Given a binary matrix (likely returned from an operation such -as threshold(), compare(), >, ==, etc, return all of -the non-zero indices as a cv::Mat or std::vector (x,y) -For example: -@code{.cpp} - cv::Mat binaryImage; // input, binary image - cv::Mat locations; // output, locations of non-zero pixels - cv::findNonZero(binaryImage, locations); - - // access pixel coordinates - Point pnt = locations.at(i); -@endcode -or -@code{.cpp} - cv::Mat binaryImage; // input, binary image - vector locations; // output, locations of non-zero pixels - cv::findNonZero(binaryImage, locations); - - // access pixel coordinates - Point pnt = locations[i]; -@endcode -@param src single-channel array (type CV_8UC1) -@param idx the output array, type of cv::Mat or std::vector, corresponding to non-zero indices in the input -*/ -CV_EXPORTS_W void findNonZero( InputArray src, OutputArray idx ); - -/** @brief Calculates an average (mean) of array elements. - -The function mean calculates the mean value M of array elements, -independently for each channel, and return it: -\f[\begin{array}{l} N = \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c = \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}\f] -When all the mask elements are 0's, the functions return Scalar::all(0) -@param src input array that should have from 1 to 4 channels so that the result can be stored in -Scalar_ . -@param mask optional operation mask. -@sa countNonZero, meanStdDev, norm, minMaxLoc -*/ -CV_EXPORTS_W Scalar mean(InputArray src, InputArray mask = noArray()); - -/** Calculates a mean and standard deviation of array elements. - -The function meanStdDev calculates the mean and the standard deviation M -of array elements independently for each channel and returns it via the -output parameters: -\f[\begin{array}{l} N = \sum _{I, \texttt{mask} (I) \ne 0} 1 \\ \texttt{mean} _c = \frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \texttt{src} (I)_c}{N} \\ \texttt{stddev} _c = \sqrt{\frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \left ( \texttt{src} (I)_c - \texttt{mean} _c \right )^2}{N}} \end{array}\f] -When all the mask elements are 0's, the functions return -mean=stddev=Scalar::all(0). -@note The calculated standard deviation is only the diagonal of the -complete normalized covariance matrix. If the full matrix is needed, you -can reshape the multi-channel array M x N to the single-channel array -M\*N x mtx.channels() (only possible when the matrix is continuous) and -then pass the matrix to calcCovarMatrix . -@param src input array that should have from 1 to 4 channels so that the results can be stored in -Scalar_ 's. -@param mean output parameter: calculated mean value. -@param stddev output parameter: calculateded standard deviation. -@param mask optional operation mask. -@sa countNonZero, mean, norm, minMaxLoc, calcCovarMatrix -*/ -CV_EXPORTS_W void meanStdDev(InputArray src, OutputArray mean, OutputArray stddev, - InputArray mask=noArray()); - -/** @brief Calculates an absolute array norm, an absolute difference norm, or a -relative difference norm. - -The functions norm calculate an absolute norm of src1 (when there is no -src2 ): - -\f[norm = \forkthree{\|\texttt{src1}\|_{L_{\infty}} = \max _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) } -{ \| \texttt{src1} \| _{L_1} = \sum _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\) } -{ \| \texttt{src1} \| _{L_2} = \sqrt{\sum_I \texttt{src1}(I)^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) }\f] - -or an absolute or relative difference norm if src2 is there: - -\f[norm = \forkthree{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} = \max _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) } -{ \| \texttt{src1} - \texttt{src2} \| _{L_1} = \sum _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\) } -{ \| \texttt{src1} - \texttt{src2} \| _{L_2} = \sqrt{\sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) }\f] - -or - -\f[norm = \forkthree{\frac{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} }{\|\texttt{src2}\|_{L_{\infty}} }}{if \(\texttt{normType} = \texttt{NORM_RELATIVE_INF}\) } -{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_1} }{\|\texttt{src2}\|_{L_1}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE_L1}\) } -{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE_L2}\) }\f] - -The functions norm return the calculated norm. - -When the mask parameter is specified and it is not empty, the norm is -calculated only over the region specified by the mask. - -A multi-channel input arrays are treated as a single-channel, that is, -the results for all channels are combined. - -@param src1 first input array. -@param normType type of the norm (see cv::NormTypes). -@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type. -*/ -CV_EXPORTS_W double norm(InputArray src1, int normType = NORM_L2, InputArray mask = noArray()); - -/** @overload -@param src1 first input array. -@param src2 second input array of the same size and the same type as src1. -@param normType type of the norm (cv::NormTypes). -@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type. -*/ -CV_EXPORTS_W double norm(InputArray src1, InputArray src2, - int normType = NORM_L2, InputArray mask = noArray()); -/** @overload -@param src first input array. -@param normType type of the norm (see cv::NormTypes). -*/ -CV_EXPORTS double norm( const SparseMat& src, int normType ); - -/** @brief computes PSNR image/video quality metric - -see http://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio for details -@todo document - */ -CV_EXPORTS_W double PSNR(InputArray src1, InputArray src2); - -/** @brief naive nearest neighbor finder - -see http://en.wikipedia.org/wiki/Nearest_neighbor_search -@todo document - */ -CV_EXPORTS_W void batchDistance(InputArray src1, InputArray src2, - OutputArray dist, int dtype, OutputArray nidx, - int normType = NORM_L2, int K = 0, - InputArray mask = noArray(), int update = 0, - bool crosscheck = false); - -/** @brief Normalizes the norm or value range of an array. - -The functions normalize scale and shift the input array elements so that -\f[\| \texttt{dst} \| _{L_p}= \texttt{alpha}\f] -(where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that -\f[\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}\f] - -when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be -normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this -sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or -min-max but modify the whole array, you can use norm and Mat::convertTo. - -In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, -the range transformation for sparse matrices is not allowed since it can shift the zero level. - -Possible usage with some positive example data: -@code{.cpp} - vector positiveData = { 2.0, 8.0, 10.0 }; - vector normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax; - - // Norm to probability (total count) - // sum(numbers) = 20.0 - // 2.0 0.1 (2.0/20.0) - // 8.0 0.4 (8.0/20.0) - // 10.0 0.5 (10.0/20.0) - normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1); - - // Norm to unit vector: ||positiveData|| = 1.0 - // 2.0 0.15 - // 8.0 0.62 - // 10.0 0.77 - normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2); - - // Norm to max element - // 2.0 0.2 (2.0/10.0) - // 8.0 0.8 (8.0/10.0) - // 10.0 1.0 (10.0/10.0) - normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF); - - // Norm to range [0.0;1.0] - // 2.0 0.0 (shift to left border) - // 8.0 0.75 (6.0/8.0) - // 10.0 1.0 (shift to right border) - normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX); -@endcode - -@param src input array. -@param dst output array of the same size as src . -@param alpha norm value to normalize to or the lower range boundary in case of the range -normalization. -@param beta upper range boundary in case of the range normalization; it is not used for the norm -normalization. -@param norm_type normalization type (see cv::NormTypes). -@param dtype when negative, the output array has the same type as src; otherwise, it has the same -number of channels as src and the depth =CV_MAT_DEPTH(dtype). -@param mask optional operation mask. -@sa norm, Mat::convertTo, SparseMat::convertTo -*/ -CV_EXPORTS_W void normalize( InputArray src, InputOutputArray dst, double alpha = 1, double beta = 0, - int norm_type = NORM_L2, int dtype = -1, InputArray mask = noArray()); - -/** @overload -@param src input array. -@param dst output array of the same size as src . -@param alpha norm value to normalize to or the lower range boundary in case of the range -normalization. -@param normType normalization type (see cv::NormTypes). -*/ -CV_EXPORTS void normalize( const SparseMat& src, SparseMat& dst, double alpha, int normType ); - -/** @brief Finds the global minimum and maximum in an array. - -The functions minMaxLoc find the minimum and maximum element values and their positions. The -extremums are searched across the whole array or, if mask is not an empty array, in the specified -array region. - -The functions do not work with multi-channel arrays. If you need to find minimum or maximum -elements across all the channels, use Mat::reshape first to reinterpret the array as -single-channel. Or you may extract the particular channel using either extractImageCOI , or -mixChannels , or split . -@param src input single-channel array. -@param minVal pointer to the returned minimum value; NULL is used if not required. -@param maxVal pointer to the returned maximum value; NULL is used if not required. -@param minLoc pointer to the returned minimum location (in 2D case); NULL is used if not required. -@param maxLoc pointer to the returned maximum location (in 2D case); NULL is used if not required. -@param mask optional mask used to select a sub-array. -@sa max, min, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape -*/ -CV_EXPORTS_W void minMaxLoc(InputArray src, CV_OUT double* minVal, - CV_OUT double* maxVal = 0, CV_OUT Point* minLoc = 0, - CV_OUT Point* maxLoc = 0, InputArray mask = noArray()); - - -/** @brief Finds the global minimum and maximum in an array - -The function minMaxIdx finds the minimum and maximum element values and their positions. The -extremums are searched across the whole array or, if mask is not an empty array, in the specified -array region. The function does not work with multi-channel arrays. If you need to find minimum or -maximum elements across all the channels, use Mat::reshape first to reinterpret the array as -single-channel. Or you may extract the particular channel using either extractImageCOI , or -mixChannels , or split . In case of a sparse matrix, the minimum is found among non-zero elements -only. -@note When minIdx is not NULL, it must have at least 2 elements (as well as maxIdx), even if src is -a single-row or single-column matrix. In OpenCV (following MATLAB) each array has at least 2 -dimensions, i.e. single-column matrix is Mx1 matrix (and therefore minIdx/maxIdx will be -(i1,0)/(i2,0)) and single-row matrix is 1xN matrix (and therefore minIdx/maxIdx will be -(0,j1)/(0,j2)). -@param src input single-channel array. -@param minVal pointer to the returned minimum value; NULL is used if not required. -@param maxVal pointer to the returned maximum value; NULL is used if not required. -@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required; -Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element -in each dimension are stored there sequentially. -@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required. -@param mask specified array region -*/ -CV_EXPORTS void minMaxIdx(InputArray src, double* minVal, double* maxVal = 0, - int* minIdx = 0, int* maxIdx = 0, InputArray mask = noArray()); - -/** @overload -@param a input single-channel array. -@param minVal pointer to the returned minimum value; NULL is used if not required. -@param maxVal pointer to the returned maximum value; NULL is used if not required. -@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required; -Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element -in each dimension are stored there sequentially. -@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required. -*/ -CV_EXPORTS void minMaxLoc(const SparseMat& a, double* minVal, - double* maxVal, int* minIdx = 0, int* maxIdx = 0); - -/** @brief Reduces a matrix to a vector. - -The function reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of -1D vectors and performing the specified operation on the vectors until a single row/column is -obtained. For example, the function can be used to compute horizontal and vertical projections of a -raster image. In case of REDUCE_SUM and REDUCE_AVG , the output may have a larger element -bit-depth to preserve accuracy. And multi-channel arrays are also supported in these two reduction -modes. -@param src input 2D matrix. -@param dst output vector. Its size and type is defined by dim and dtype parameters. -@param dim dimension index along which the matrix is reduced. 0 means that the matrix is reduced to -a single row. 1 means that the matrix is reduced to a single column. -@param rtype reduction operation that could be one of cv::ReduceTypes -@param dtype when negative, the output vector will have the same type as the input matrix, -otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()). -@sa repeat -*/ -CV_EXPORTS_W void reduce(InputArray src, OutputArray dst, int dim, int rtype, int dtype = -1); - -/** @brief Creates one multi-channel array out of several single-channel ones. - -The function merge merges several arrays to make a single multi-channel array. That is, each -element of the output array will be a concatenation of the elements of the input arrays, where -elements of i-th input array are treated as mv[i].channels()-element vectors. - -The function cv::split does the reverse operation. If you need to shuffle channels in some other -advanced way, use cv::mixChannels. -@param mv input array of matrices to be merged; all the matrices in mv must have the same -size and the same depth. -@param count number of input matrices when mv is a plain C array; it must be greater than zero. -@param dst output array of the same size and the same depth as mv[0]; The number of channels will -be equal to the parameter count. -@sa mixChannels, split, Mat::reshape -*/ -CV_EXPORTS void merge(const Mat* mv, size_t count, OutputArray dst); - -/** @overload -@param mv input vector of matrices to be merged; all the matrices in mv must have the same -size and the same depth. -@param dst output array of the same size and the same depth as mv[0]; The number of channels will -be the total number of channels in the matrix array. - */ -CV_EXPORTS_W void merge(InputArrayOfArrays mv, OutputArray dst); - -/** @brief Divides a multi-channel array into several single-channel arrays. - -The functions split split a multi-channel array into separate single-channel arrays: -\f[\texttt{mv} [c](I) = \texttt{src} (I)_c\f] -If you need to extract a single channel or do some other sophisticated channel permutation, use -mixChannels . -@param src input multi-channel array. -@param mvbegin output array; the number of arrays must match src.channels(); the arrays themselves are -reallocated, if needed. -@sa merge, mixChannels, cvtColor -*/ -CV_EXPORTS void split(const Mat& src, Mat* mvbegin); - -/** @overload -@param m input multi-channel array. -@param mv output vector of arrays; the arrays themselves are reallocated, if needed. -*/ -CV_EXPORTS_W void split(InputArray m, OutputArrayOfArrays mv); - -/** @brief Copies specified channels from input arrays to the specified channels of -output arrays. - -The function cv::mixChannels provides an advanced mechanism for shuffling image channels. - -cv::split and cv::merge and some forms of cv::cvtColor are partial cases of cv::mixChannels . - -In the example below, the code splits a 4-channel BGRA image into a 3-channel BGR (with B and R -channels swapped) and a separate alpha-channel image: -@code{.cpp} - Mat bgra( 100, 100, CV_8UC4, Scalar(255,0,0,255) ); - Mat bgr( bgra.rows, bgra.cols, CV_8UC3 ); - Mat alpha( bgra.rows, bgra.cols, CV_8UC1 ); - - // forming an array of matrices is a quite efficient operation, - // because the matrix data is not copied, only the headers - Mat out[] = { bgr, alpha }; - // bgra[0] -> bgr[2], bgra[1] -> bgr[1], - // bgra[2] -> bgr[0], bgra[3] -> alpha[0] - int from_to[] = { 0,2, 1,1, 2,0, 3,3 }; - mixChannels( &bgra, 1, out, 2, from_to, 4 ); -@endcode -@note Unlike many other new-style C++ functions in OpenCV (see the introduction section and -Mat::create ), cv::mixChannels requires the output arrays to be pre-allocated before calling the -function. -@param src input array or vector of matrices; all of the matrices must have the same size and the -same depth. -@param nsrcs number of matrices in `src`. -@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and -depth must be the same as in `src[0]`. -@param ndsts number of matrices in `dst`. -@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is -a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in -dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to -src[0].channels()-1, the second input image channels are indexed from src[0].channels() to -src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image -channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is -filled with zero . -@param npairs number of index pairs in `fromTo`. -@sa cv::split, cv::merge, cv::cvtColor -*/ -CV_EXPORTS void mixChannels(const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, - const int* fromTo, size_t npairs); - -/** @overload -@param src input array or vector of matrices; all of the matrices must have the same size and the -same depth. -@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and -depth must be the same as in src[0]. -@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is -a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in -dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to -src[0].channels()-1, the second input image channels are indexed from src[0].channels() to -src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image -channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is -filled with zero . -@param npairs number of index pairs in fromTo. -*/ -CV_EXPORTS void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst, - const int* fromTo, size_t npairs); - -/** @overload -@param src input array or vector of matrices; all of the matrices must have the same size and the -same depth. -@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and -depth must be the same as in src[0]. -@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is -a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in -dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to -src[0].channels()-1, the second input image channels are indexed from src[0].channels() to -src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image -channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is -filled with zero . -*/ -CV_EXPORTS_W void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst, - const std::vector& fromTo); - -/** @brief extracts a single channel from src (coi is 0-based index) -@todo document -*/ -CV_EXPORTS_W void extractChannel(InputArray src, OutputArray dst, int coi); - -/** @brief inserts a single channel to dst (coi is 0-based index) -@todo document -*/ -CV_EXPORTS_W void insertChannel(InputArray src, InputOutputArray dst, int coi); - -/** @brief Flips a 2D array around vertical, horizontal, or both axes. - -The function flip flips the array in one of three different ways (row -and column indices are 0-based): -\f[\texttt{dst} _{ij} = -\left\{ -\begin{array}{l l} -\texttt{src} _{\texttt{src.rows}-i-1,j} & if\; \texttt{flipCode} = 0 \\ -\texttt{src} _{i, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} > 0 \\ -\texttt{src} _{ \texttt{src.rows} -i-1, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} < 0 \\ -\end{array} -\right.\f] -The example scenarios of using the function are the following: -* Vertical flipping of the image (flipCode == 0) to switch between - top-left and bottom-left image origin. This is a typical operation - in video processing on Microsoft Windows\* OS. -* Horizontal flipping of the image with the subsequent horizontal - shift and absolute difference calculation to check for a - vertical-axis symmetry (flipCode \> 0). -* Simultaneous horizontal and vertical flipping of the image with - the subsequent shift and absolute difference calculation to check - for a central symmetry (flipCode \< 0). -* Reversing the order of point arrays (flipCode \> 0 or - flipCode == 0). -@param src input array. -@param dst output array of the same size and type as src. -@param flipCode a flag to specify how to flip the array; 0 means -flipping around the x-axis and positive value (for example, 1) means -flipping around y-axis. Negative value (for example, -1) means flipping -around both axes. -@sa transpose , repeat , completeSymm -*/ -CV_EXPORTS_W void flip(InputArray src, OutputArray dst, int flipCode); - -/** @brief Fills the output array with repeated copies of the input array. - -The function cv::repeat duplicates the input array one or more times along each of the two axes: -\f[\texttt{dst} _{ij}= \texttt{src} _{i\mod src.rows, \; j\mod src.cols }\f] -The second variant of the function is more convenient to use with @ref MatrixExpressions. -@param src input array to replicate. -@param ny Flag to specify how many times the `src` is repeated along the -vertical axis. -@param nx Flag to specify how many times the `src` is repeated along the -horizontal axis. -@param dst output array of the same type as `src`. -@sa cv::reduce -*/ -CV_EXPORTS_W void repeat(InputArray src, int ny, int nx, OutputArray dst); - -/** @overload -@param src input array to replicate. -@param ny Flag to specify how many times the `src` is repeated along the -vertical axis. -@param nx Flag to specify how many times the `src` is repeated along the -horizontal axis. - */ -CV_EXPORTS Mat repeat(const Mat& src, int ny, int nx); - -/** @brief Applies horizontal concatenation to given matrices. - -The function horizontally concatenates two or more cv::Mat matrices (with the same number of rows). -@code{.cpp} - cv::Mat matArray[] = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), - cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), - cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),}; - - cv::Mat out; - cv::hconcat( matArray, 3, out ); - //out: - //[1, 2, 3; - // 1, 2, 3; - // 1, 2, 3; - // 1, 2, 3] -@endcode -@param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth. -@param nsrc number of matrices in src. -@param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src. -@sa cv::vconcat(const Mat*, size_t, OutputArray), @sa cv::vconcat(InputArrayOfArrays, OutputArray) and @sa cv::vconcat(InputArray, InputArray, OutputArray) -*/ -CV_EXPORTS void hconcat(const Mat* src, size_t nsrc, OutputArray dst); -/** @overload - @code{.cpp} - cv::Mat_ A = (cv::Mat_(3, 2) << 1, 4, - 2, 5, - 3, 6); - cv::Mat_ B = (cv::Mat_(3, 2) << 7, 10, - 8, 11, - 9, 12); - - cv::Mat C; - cv::hconcat(A, B, C); - //C: - //[1, 4, 7, 10; - // 2, 5, 8, 11; - // 3, 6, 9, 12] - @endcode - @param src1 first input array to be considered for horizontal concatenation. - @param src2 second input array to be considered for horizontal concatenation. - @param dst output array. It has the same number of rows and depth as the src1 and src2, and the sum of cols of the src1 and src2. - */ -CV_EXPORTS void hconcat(InputArray src1, InputArray src2, OutputArray dst); -/** @overload - @code{.cpp} - std::vector matrices = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), - cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), - cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),}; - - cv::Mat out; - cv::hconcat( matrices, out ); - //out: - //[1, 2, 3; - // 1, 2, 3; - // 1, 2, 3; - // 1, 2, 3] - @endcode - @param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth. - @param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src. -same depth. - */ -CV_EXPORTS_W void hconcat(InputArrayOfArrays src, OutputArray dst); - -/** @brief Applies vertical concatenation to given matrices. - -The function vertically concatenates two or more cv::Mat matrices (with the same number of cols). -@code{.cpp} - cv::Mat matArray[] = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), - cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), - cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; - - cv::Mat out; - cv::vconcat( matArray, 3, out ); - //out: - //[1, 1, 1, 1; - // 2, 2, 2, 2; - // 3, 3, 3, 3] -@endcode -@param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth. -@param nsrc number of matrices in src. -@param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src. -@sa cv::hconcat(const Mat*, size_t, OutputArray), @sa cv::hconcat(InputArrayOfArrays, OutputArray) and @sa cv::hconcat(InputArray, InputArray, OutputArray) -*/ -CV_EXPORTS void vconcat(const Mat* src, size_t nsrc, OutputArray dst); -/** @overload - @code{.cpp} - cv::Mat_ A = (cv::Mat_(3, 2) << 1, 7, - 2, 8, - 3, 9); - cv::Mat_ B = (cv::Mat_(3, 2) << 4, 10, - 5, 11, - 6, 12); - - cv::Mat C; - cv::vconcat(A, B, C); - //C: - //[1, 7; - // 2, 8; - // 3, 9; - // 4, 10; - // 5, 11; - // 6, 12] - @endcode - @param src1 first input array to be considered for vertical concatenation. - @param src2 second input array to be considered for vertical concatenation. - @param dst output array. It has the same number of cols and depth as the src1 and src2, and the sum of rows of the src1 and src2. - */ -CV_EXPORTS void vconcat(InputArray src1, InputArray src2, OutputArray dst); -/** @overload - @code{.cpp} - std::vector matrices = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), - cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), - cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; - - cv::Mat out; - cv::vconcat( matrices, out ); - //out: - //[1, 1, 1, 1; - // 2, 2, 2, 2; - // 3, 3, 3, 3] - @endcode - @param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth - @param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src. -same depth. - */ -CV_EXPORTS_W void vconcat(InputArrayOfArrays src, OutputArray dst); - -/** @brief computes bitwise conjunction of the two arrays (dst = src1 & src2) -Calculates the per-element bit-wise conjunction of two arrays or an -array and a scalar. - -The function calculates the per-element bit-wise logical conjunction for: -* Two arrays when src1 and src2 have the same size: - \f[\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] -* An array and a scalar when src2 is constructed from Scalar or has - the same number of elements as `src1.channels()`: - \f[\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] -* A scalar and an array when src1 is constructed from Scalar or has - the same number of elements as `src2.channels()`: - \f[\texttt{dst} (I) = \texttt{src1} \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] -In case of floating-point arrays, their machine-specific bit -representations (usually IEEE754-compliant) are used for the operation. -In case of multi-channel arrays, each channel is processed -independently. In the second and third cases above, the scalar is first -converted to the array type. -@param src1 first input array or a scalar. -@param src2 second input array or a scalar. -@param dst output array that has the same size and type as the input -arrays. -@param mask optional operation mask, 8-bit single channel array, that -specifies elements of the output array to be changed. -*/ -CV_EXPORTS_W void bitwise_and(InputArray src1, InputArray src2, - OutputArray dst, InputArray mask = noArray()); - -/** @brief Calculates the per-element bit-wise disjunction of two arrays or an -array and a scalar. - -The function calculates the per-element bit-wise logical disjunction for: -* Two arrays when src1 and src2 have the same size: - \f[\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] -* An array and a scalar when src2 is constructed from Scalar or has - the same number of elements as `src1.channels()`: - \f[\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] -* A scalar and an array when src1 is constructed from Scalar or has - the same number of elements as `src2.channels()`: - \f[\texttt{dst} (I) = \texttt{src1} \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] -In case of floating-point arrays, their machine-specific bit -representations (usually IEEE754-compliant) are used for the operation. -In case of multi-channel arrays, each channel is processed -independently. In the second and third cases above, the scalar is first -converted to the array type. -@param src1 first input array or a scalar. -@param src2 second input array or a scalar. -@param dst output array that has the same size and type as the input -arrays. -@param mask optional operation mask, 8-bit single channel array, that -specifies elements of the output array to be changed. -*/ -CV_EXPORTS_W void bitwise_or(InputArray src1, InputArray src2, - OutputArray dst, InputArray mask = noArray()); - -/** @brief Calculates the per-element bit-wise "exclusive or" operation on two -arrays or an array and a scalar. - -The function calculates the per-element bit-wise logical "exclusive-or" -operation for: -* Two arrays when src1 and src2 have the same size: - \f[\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] -* An array and a scalar when src2 is constructed from Scalar or has - the same number of elements as `src1.channels()`: - \f[\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] -* A scalar and an array when src1 is constructed from Scalar or has - the same number of elements as `src2.channels()`: - \f[\texttt{dst} (I) = \texttt{src1} \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] -In case of floating-point arrays, their machine-specific bit -representations (usually IEEE754-compliant) are used for the operation. -In case of multi-channel arrays, each channel is processed -independently. In the 2nd and 3rd cases above, the scalar is first -converted to the array type. -@param src1 first input array or a scalar. -@param src2 second input array or a scalar. -@param dst output array that has the same size and type as the input -arrays. -@param mask optional operation mask, 8-bit single channel array, that -specifies elements of the output array to be changed. -*/ -CV_EXPORTS_W void bitwise_xor(InputArray src1, InputArray src2, - OutputArray dst, InputArray mask = noArray()); - -/** @brief Inverts every bit of an array. - -The function calculates per-element bit-wise inversion of the input -array: -\f[\texttt{dst} (I) = \neg \texttt{src} (I)\f] -In case of a floating-point input array, its machine-specific bit -representation (usually IEEE754-compliant) is used for the operation. In -case of multi-channel arrays, each channel is processed independently. -@param src input array. -@param dst output array that has the same size and type as the input -array. -@param mask optional operation mask, 8-bit single channel array, that -specifies elements of the output array to be changed. -*/ -CV_EXPORTS_W void bitwise_not(InputArray src, OutputArray dst, - InputArray mask = noArray()); - -/** @brief Calculates the per-element absolute difference between two arrays or between an array and a scalar. - -The function absdiff calculates: -* Absolute difference between two arrays when they have the same - size and type: - \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2}(I)|)\f] -* Absolute difference between an array and a scalar when the second - array is constructed from Scalar or has as many elements as the - number of channels in `src1`: - \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2} |)\f] -* Absolute difference between a scalar and an array when the first - array is constructed from Scalar or has as many elements as the - number of channels in `src2`: - \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1} - \texttt{src2}(I) |)\f] - where I is a multi-dimensional index of array elements. In case of - multi-channel arrays, each channel is processed independently. -@note Saturation is not applied when the arrays have the depth CV_32S. -You may even get a negative value in the case of overflow. -@param src1 first input array or a scalar. -@param src2 second input array or a scalar. -@param dst output array that has the same size and type as input arrays. -@sa cv::abs(const Mat&) -*/ -CV_EXPORTS_W void absdiff(InputArray src1, InputArray src2, OutputArray dst); - -/** @brief Checks if array elements lie between the elements of two other arrays. - -The function checks the range as follows: -- For every element of a single-channel input array: - \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0\f] -- For two-channel arrays: - \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0 \land \texttt{lowerb} (I)_1 \leq \texttt{src} (I)_1 \leq \texttt{upperb} (I)_1\f] -- and so forth. - -That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the -specified 1D, 2D, 3D, ... box and 0 otherwise. - -When the lower and/or upper boundary parameters are scalars, the indexes -(I) at lowerb and upperb in the above formulas should be omitted. -@param src first input array. -@param lowerb inclusive lower boundary array or a scalar. -@param upperb inclusive upper boundary array or a scalar. -@param dst output array of the same size as src and CV_8U type. -*/ -CV_EXPORTS_W void inRange(InputArray src, InputArray lowerb, - InputArray upperb, OutputArray dst); - -/** @brief Performs the per-element comparison of two arrays or an array and scalar value. - -The function compares: -* Elements of two arrays when src1 and src2 have the same size: - \f[\texttt{dst} (I) = \texttt{src1} (I) \,\texttt{cmpop}\, \texttt{src2} (I)\f] -* Elements of src1 with a scalar src2 when src2 is constructed from - Scalar or has a single element: - \f[\texttt{dst} (I) = \texttt{src1}(I) \,\texttt{cmpop}\, \texttt{src2}\f] -* src1 with elements of src2 when src1 is constructed from Scalar or - has a single element: - \f[\texttt{dst} (I) = \texttt{src1} \,\texttt{cmpop}\, \texttt{src2} (I)\f] -When the comparison result is true, the corresponding element of output -array is set to 255. The comparison operations can be replaced with the -equivalent matrix expressions: -@code{.cpp} - Mat dst1 = src1 >= src2; - Mat dst2 = src1 < 8; - ... -@endcode -@param src1 first input array or a scalar; when it is an array, it must have a single channel. -@param src2 second input array or a scalar; when it is an array, it must have a single channel. -@param dst output array of type ref CV_8U that has the same size and the same number of channels as - the input arrays. -@param cmpop a flag, that specifies correspondence between the arrays (cv::CmpTypes) -@sa checkRange, min, max, threshold -*/ -CV_EXPORTS_W void compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop); - -/** @brief Calculates per-element minimum of two arrays or an array and a scalar. - -The functions min calculate the per-element minimum of two arrays: -\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))\f] -or array and a scalar: -\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{value} )\f] -@param src1 first input array. -@param src2 second input array of the same size and type as src1. -@param dst output array of the same size and type as src1. -@sa max, compare, inRange, minMaxLoc -*/ -CV_EXPORTS_W void min(InputArray src1, InputArray src2, OutputArray dst); -/** @overload -needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) -*/ -CV_EXPORTS void min(const Mat& src1, const Mat& src2, Mat& dst); -/** @overload -needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) -*/ -CV_EXPORTS void min(const UMat& src1, const UMat& src2, UMat& dst); - -/** @brief Calculates per-element maximum of two arrays or an array and a scalar. - -The functions max calculate the per-element maximum of two arrays: -\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\f] -or array and a scalar: -\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )\f] -@param src1 first input array. -@param src2 second input array of the same size and type as src1 . -@param dst output array of the same size and type as src1. -@sa min, compare, inRange, minMaxLoc, @ref MatrixExpressions -*/ -CV_EXPORTS_W void max(InputArray src1, InputArray src2, OutputArray dst); -/** @overload -needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) -*/ -CV_EXPORTS void max(const Mat& src1, const Mat& src2, Mat& dst); -/** @overload -needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) -*/ -CV_EXPORTS void max(const UMat& src1, const UMat& src2, UMat& dst); - -/** @brief Calculates a square root of array elements. - -The functions sqrt calculate a square root of each input array element. -In case of multi-channel arrays, each channel is processed -independently. The accuracy is approximately the same as of the built-in -std::sqrt . -@param src input floating-point array. -@param dst output array of the same size and type as src. -*/ -CV_EXPORTS_W void sqrt(InputArray src, OutputArray dst); - -/** @brief Raises every array element to a power. - -The function pow raises every element of the input array to power : -\f[\texttt{dst} (I) = \fork{\texttt{src}(I)^{power}}{if \(\texttt{power}\) is integer}{|\texttt{src}(I)|^{power}}{otherwise}\f] - -So, for a non-integer power exponent, the absolute values of input array -elements are used. However, it is possible to get true values for -negative values using some extra operations. In the example below, -computing the 5th root of array src shows: -@code{.cpp} - Mat mask = src < 0; - pow(src, 1./5, dst); - subtract(Scalar::all(0), dst, dst, mask); -@endcode -For some values of power, such as integer values, 0.5 and -0.5, -specialized faster algorithms are used. - -Special values (NaN, Inf) are not handled. -@param src input array. -@param power exponent of power. -@param dst output array of the same size and type as src. -@sa sqrt, exp, log, cartToPolar, polarToCart -*/ -CV_EXPORTS_W void pow(InputArray src, double power, OutputArray dst); - -/** @brief Calculates the exponent of every array element. - -The function exp calculates the exponent of every element of the input -array: -\f[\texttt{dst} [I] = e^{ src(I) }\f] - -The maximum relative error is about 7e-6 for single-precision input and -less than 1e-10 for double-precision input. Currently, the function -converts denormalized values to zeros on output. Special values (NaN, -Inf) are not handled. -@param src input array. -@param dst output array of the same size and type as src. -@sa log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude -*/ -CV_EXPORTS_W void exp(InputArray src, OutputArray dst); - -/** @brief Calculates the natural logarithm of every array element. - -The function log calculates the natural logarithm of the absolute value -of every element of the input array: -\f[\texttt{dst} (I) = \fork{\log |\texttt{src}(I)|}{if \(\texttt{src}(I) \ne 0\) }{\texttt{C}}{otherwise}\f] - -where C is a large negative number (about -700 in the current -implementation). The maximum relative error is about 7e-6 for -single-precision input and less than 1e-10 for double-precision input. -Special values (NaN, Inf) are not handled. -@param src input array. -@param dst output array of the same size and type as src . -@sa exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude -*/ -CV_EXPORTS_W void log(InputArray src, OutputArray dst); - -/** @brief Calculates x and y coordinates of 2D vectors from their magnitude and angle. - -The function polarToCart calculates the Cartesian coordinates of each 2D -vector represented by the corresponding elements of magnitude and angle: -\f[\begin{array}{l} \texttt{x} (I) = \texttt{magnitude} (I) \cos ( \texttt{angle} (I)) \\ \texttt{y} (I) = \texttt{magnitude} (I) \sin ( \texttt{angle} (I)) \\ \end{array}\f] - -The relative accuracy of the estimated coordinates is about 1e-6. -@param magnitude input floating-point array of magnitudes of 2D vectors; -it can be an empty matrix (=Mat()), in this case, the function assumes -that all the magnitudes are =1; if it is not empty, it must have the -same size and type as angle. -@param angle input floating-point array of angles of 2D vectors. -@param x output array of x-coordinates of 2D vectors; it has the same -size and type as angle. -@param y output array of y-coordinates of 2D vectors; it has the same -size and type as angle. -@param angleInDegrees when true, the input angles are measured in -degrees, otherwise, they are measured in radians. -@sa cartToPolar, magnitude, phase, exp, log, pow, sqrt -*/ -CV_EXPORTS_W void polarToCart(InputArray magnitude, InputArray angle, - OutputArray x, OutputArray y, bool angleInDegrees = false); - -/** @brief Calculates the magnitude and angle of 2D vectors. - -The function cartToPolar calculates either the magnitude, angle, or both -for every 2D vector (x(I),y(I)): -\f[\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\f] - -The angles are calculated with accuracy about 0.3 degrees. For the point -(0,0), the angle is set to 0. -@param x array of x-coordinates; this must be a single-precision or -double-precision floating-point array. -@param y array of y-coordinates, that must have the same size and same type as x. -@param magnitude output array of magnitudes of the same size and type as x. -@param angle output array of angles that has the same size and type as -x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees). -@param angleInDegrees a flag, indicating whether the angles are measured -in radians (which is by default), or in degrees. -@sa Sobel, Scharr -*/ -CV_EXPORTS_W void cartToPolar(InputArray x, InputArray y, - OutputArray magnitude, OutputArray angle, - bool angleInDegrees = false); - -/** @brief Calculates the rotation angle of 2D vectors. - -The function phase calculates the rotation angle of each 2D vector that -is formed from the corresponding elements of x and y : -\f[\texttt{angle} (I) = \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))\f] - -The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 , -the corresponding angle(I) is set to 0. -@param x input floating-point array of x-coordinates of 2D vectors. -@param y input array of y-coordinates of 2D vectors; it must have the -same size and the same type as x. -@param angle output array of vector angles; it has the same size and -same type as x . -@param angleInDegrees when true, the function calculates the angle in -degrees, otherwise, they are measured in radians. -*/ -CV_EXPORTS_W void phase(InputArray x, InputArray y, OutputArray angle, - bool angleInDegrees = false); - -/** @brief Calculates the magnitude of 2D vectors. - -The function magnitude calculates the magnitude of 2D vectors formed -from the corresponding elements of x and y arrays: -\f[\texttt{dst} (I) = \sqrt{\texttt{x}(I)^2 + \texttt{y}(I)^2}\f] -@param x floating-point array of x-coordinates of the vectors. -@param y floating-point array of y-coordinates of the vectors; it must -have the same size as x. -@param magnitude output array of the same size and type as x. -@sa cartToPolar, polarToCart, phase, sqrt -*/ -CV_EXPORTS_W void magnitude(InputArray x, InputArray y, OutputArray magnitude); - -/** @brief Checks every element of an input array for invalid values. - -The functions checkRange check that every array element is neither NaN nor infinite. When minVal \> --DBL_MAX and maxVal \< DBL_MAX, the functions also check that each value is between minVal and -maxVal. In case of multi-channel arrays, each channel is processed independently. If some values -are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the -functions either return false (when quiet=true) or throw an exception. -@param a input array. -@param quiet a flag, indicating whether the functions quietly return false when the array elements -are out of range or they throw an exception. -@param pos optional output parameter, when not NULL, must be a pointer to array of src.dims -elements. -@param minVal inclusive lower boundary of valid values range. -@param maxVal exclusive upper boundary of valid values range. -*/ -CV_EXPORTS_W bool checkRange(InputArray a, bool quiet = true, CV_OUT Point* pos = 0, - double minVal = -DBL_MAX, double maxVal = DBL_MAX); - -/** @brief converts NaN's to the given number -*/ -CV_EXPORTS_W void patchNaNs(InputOutputArray a, double val = 0); - -/** @brief Performs generalized matrix multiplication. - -The function performs generalized matrix multiplication similar to the -gemm functions in BLAS level 3. For example, -`gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)` -corresponds to -\f[\texttt{dst} = \texttt{alpha} \cdot \texttt{src1} ^T \cdot \texttt{src2} + \texttt{beta} \cdot \texttt{src3} ^T\f] - -In case of complex (two-channel) data, performed a complex matrix -multiplication. - -The function can be replaced with a matrix expression. For example, the -above call can be replaced with: -@code{.cpp} - dst = alpha*src1.t()*src2 + beta*src3.t(); -@endcode -@param src1 first multiplied input matrix that could be real(CV_32FC1, -CV_64FC1) or complex(CV_32FC2, CV_64FC2). -@param src2 second multiplied input matrix of the same type as src1. -@param alpha weight of the matrix product. -@param src3 third optional delta matrix added to the matrix product; it -should have the same type as src1 and src2. -@param beta weight of src3. -@param dst output matrix; it has the proper size and the same type as -input matrices. -@param flags operation flags (cv::GemmFlags) -@sa mulTransposed , transform -*/ -CV_EXPORTS_W void gemm(InputArray src1, InputArray src2, double alpha, - InputArray src3, double beta, OutputArray dst, int flags = 0); - -/** @brief Calculates the product of a matrix and its transposition. - -The function mulTransposed calculates the product of src and its -transposition: -\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\f] -if aTa=true , and -\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\f] -otherwise. The function is used to calculate the covariance matrix. With -zero delta, it can be used as a faster substitute for general matrix -product A\*B when B=A' -@param src input single-channel matrix. Note that unlike gemm, the -function can multiply not only floating-point matrices. -@param dst output square matrix. -@param aTa Flag specifying the multiplication ordering. See the -description below. -@param delta Optional delta matrix subtracted from src before the -multiplication. When the matrix is empty ( delta=noArray() ), it is -assumed to be zero, that is, nothing is subtracted. If it has the same -size as src , it is simply subtracted. Otherwise, it is "repeated" (see -repeat ) to cover the full src and then subtracted. Type of the delta -matrix, when it is not empty, must be the same as the type of created -output matrix. See the dtype parameter description below. -@param scale Optional scale factor for the matrix product. -@param dtype Optional type of the output matrix. When it is negative, -the output matrix will have the same type as src . Otherwise, it will be -type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F . -@sa calcCovarMatrix, gemm, repeat, reduce -*/ -CV_EXPORTS_W void mulTransposed( InputArray src, OutputArray dst, bool aTa, - InputArray delta = noArray(), - double scale = 1, int dtype = -1 ); - -/** @brief Transposes a matrix. - -The function transpose transposes the matrix src : -\f[\texttt{dst} (i,j) = \texttt{src} (j,i)\f] -@note No complex conjugation is done in case of a complex matrix. It it -should be done separately if needed. -@param src input array. -@param dst output array of the same type as src. -*/ -CV_EXPORTS_W void transpose(InputArray src, OutputArray dst); - -/** @brief Performs the matrix transformation of every array element. - -The function transform performs the matrix transformation of every -element of the array src and stores the results in dst : -\f[\texttt{dst} (I) = \texttt{m} \cdot \texttt{src} (I)\f] -(when m.cols=src.channels() ), or -\f[\texttt{dst} (I) = \texttt{m} \cdot [ \texttt{src} (I); 1]\f] -(when m.cols=src.channels()+1 ) - -Every element of the N -channel array src is interpreted as N -element -vector that is transformed using the M x N or M x (N+1) matrix m to -M-element vector - the corresponding element of the output array dst . - -The function may be used for geometrical transformation of -N -dimensional points, arbitrary linear color space transformation (such -as various kinds of RGB to YUV transforms), shuffling the image -channels, and so forth. -@param src input array that must have as many channels (1 to 4) as -m.cols or m.cols-1. -@param dst output array of the same size and depth as src; it has as -many channels as m.rows. -@param m transformation 2x2 or 2x3 floating-point matrix. -@sa perspectiveTransform, getAffineTransform, estimateRigidTransform, warpAffine, warpPerspective -*/ -CV_EXPORTS_W void transform(InputArray src, OutputArray dst, InputArray m ); - -/** @brief Performs the perspective matrix transformation of vectors. - -The function perspectiveTransform transforms every element of src by -treating it as a 2D or 3D vector, in the following way: -\f[(x, y, z) \rightarrow (x'/w, y'/w, z'/w)\f] -where -\f[(x', y', z', w') = \texttt{mat} \cdot \begin{bmatrix} x & y & z & 1 \end{bmatrix}\f] -and -\f[w = \fork{w'}{if \(w' \ne 0\)}{\infty}{otherwise}\f] - -Here a 3D vector transformation is shown. In case of a 2D vector -transformation, the z component is omitted. - -@note The function transforms a sparse set of 2D or 3D vectors. If you -want to transform an image using perspective transformation, use -warpPerspective . If you have an inverse problem, that is, you want to -compute the most probable perspective transformation out of several -pairs of corresponding points, you can use getPerspectiveTransform or -findHomography . -@param src input two-channel or three-channel floating-point array; each -element is a 2D/3D vector to be transformed. -@param dst output array of the same size and type as src. -@param m 3x3 or 4x4 floating-point transformation matrix. -@sa transform, warpPerspective, getPerspectiveTransform, findHomography -*/ -CV_EXPORTS_W void perspectiveTransform(InputArray src, OutputArray dst, InputArray m ); - -/** @brief Copies the lower or the upper half of a square matrix to another half. - -The function completeSymm copies the lower half of a square matrix to -its another half. The matrix diagonal remains unchanged: -* \f$\texttt{mtx}_{ij}=\texttt{mtx}_{ji}\f$ for \f$i > j\f$ if - lowerToUpper=false -* \f$\texttt{mtx}_{ij}=\texttt{mtx}_{ji}\f$ for \f$i < j\f$ if - lowerToUpper=true -@param mtx input-output floating-point square matrix. -@param lowerToUpper operation flag; if true, the lower half is copied to -the upper half. Otherwise, the upper half is copied to the lower half. -@sa flip, transpose -*/ -CV_EXPORTS_W void completeSymm(InputOutputArray mtx, bool lowerToUpper = false); - -/** @brief Initializes a scaled identity matrix. - -The function setIdentity initializes a scaled identity matrix: -\f[\texttt{mtx} (i,j)= \fork{\texttt{value}}{ if \(i=j\)}{0}{otherwise}\f] - -The function can also be emulated using the matrix initializers and the -matrix expressions: -@code - Mat A = Mat::eye(4, 3, CV_32F)*5; - // A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]] -@endcode -@param mtx matrix to initialize (not necessarily square). -@param s value to assign to diagonal elements. -@sa Mat::zeros, Mat::ones, Mat::setTo, Mat::operator= -*/ -CV_EXPORTS_W void setIdentity(InputOutputArray mtx, const Scalar& s = Scalar(1)); - -/** @brief Returns the determinant of a square floating-point matrix. - -The function determinant calculates and returns the determinant of the -specified matrix. For small matrices ( mtx.cols=mtx.rows\<=3 ), the -direct method is used. For larger matrices, the function uses LU -factorization with partial pivoting. - -For symmetric positively-determined matrices, it is also possible to use -eigen decomposition to calculate the determinant. -@param mtx input matrix that must have CV_32FC1 or CV_64FC1 type and -square size. -@sa trace, invert, solve, eigen, @ref MatrixExpressions -*/ -CV_EXPORTS_W double determinant(InputArray mtx); - -/** @brief Returns the trace of a matrix. - -The function trace returns the sum of the diagonal elements of the -matrix mtx . -\f[\mathrm{tr} ( \texttt{mtx} ) = \sum _i \texttt{mtx} (i,i)\f] -@param mtx input matrix. -*/ -CV_EXPORTS_W Scalar trace(InputArray mtx); - -/** @brief Finds the inverse or pseudo-inverse of a matrix. - -The function invert inverts the matrix src and stores the result in dst -. When the matrix src is singular or non-square, the function calculates -the pseudo-inverse matrix (the dst matrix) so that norm(src\*dst - I) is -minimal, where I is an identity matrix. - -In case of the DECOMP_LU method, the function returns non-zero value if -the inverse has been successfully calculated and 0 if src is singular. - -In case of the DECOMP_SVD method, the function returns the inverse -condition number of src (the ratio of the smallest singular value to the -largest singular value) and 0 if src is singular. The SVD method -calculates a pseudo-inverse matrix if src is singular. - -Similarly to DECOMP_LU, the method DECOMP_CHOLESKY works only with -non-singular square matrices that should also be symmetrical and -positively defined. In this case, the function stores the inverted -matrix in dst and returns non-zero. Otherwise, it returns 0. - -@param src input floating-point M x N matrix. -@param dst output matrix of N x M size and the same type as src. -@param flags inversion method (cv::DecompTypes) -@sa solve, SVD -*/ -CV_EXPORTS_W double invert(InputArray src, OutputArray dst, int flags = DECOMP_LU); - -/** @brief Solves one or more linear systems or least-squares problems. - -The function solve solves a linear system or least-squares problem (the -latter is possible with SVD or QR methods, or by specifying the flag -DECOMP_NORMAL ): -\f[\texttt{dst} = \arg \min _X \| \texttt{src1} \cdot \texttt{X} - \texttt{src2} \|\f] - -If DECOMP_LU or DECOMP_CHOLESKY method is used, the function returns 1 -if src1 (or \f$\texttt{src1}^T\texttt{src1}\f$ ) is non-singular. Otherwise, -it returns 0. In the latter case, dst is not valid. Other methods find a -pseudo-solution in case of a singular left-hand side part. - -@note If you want to find a unity-norm solution of an under-defined -singular system \f$\texttt{src1}\cdot\texttt{dst}=0\f$ , the function solve -will not do the work. Use SVD::solveZ instead. - -@param src1 input matrix on the left-hand side of the system. -@param src2 input matrix on the right-hand side of the system. -@param dst output solution. -@param flags solution (matrix inversion) method (cv::DecompTypes) -@sa invert, SVD, eigen -*/ -CV_EXPORTS_W bool solve(InputArray src1, InputArray src2, - OutputArray dst, int flags = DECOMP_LU); - -/** @brief Sorts each row or each column of a matrix. - -The function sort sorts each matrix row or each matrix column in -ascending or descending order. So you should pass two operation flags to -get desired behaviour. If you want to sort matrix rows or columns -lexicographically, you can use STL std::sort generic function with the -proper comparison predicate. - -@param src input single-channel array. -@param dst output array of the same size and type as src. -@param flags operation flags, a combination of cv::SortFlags -@sa sortIdx, randShuffle -*/ -CV_EXPORTS_W void sort(InputArray src, OutputArray dst, int flags); - -/** @brief Sorts each row or each column of a matrix. - -The function sortIdx sorts each matrix row or each matrix column in the -ascending or descending order. So you should pass two operation flags to -get desired behaviour. Instead of reordering the elements themselves, it -stores the indices of sorted elements in the output array. For example: -@code - Mat A = Mat::eye(3,3,CV_32F), B; - sortIdx(A, B, SORT_EVERY_ROW + SORT_ASCENDING); - // B will probably contain - // (because of equal elements in A some permutations are possible): - // [[1, 2, 0], [0, 2, 1], [0, 1, 2]] -@endcode -@param src input single-channel array. -@param dst output integer array of the same size as src. -@param flags operation flags that could be a combination of cv::SortFlags -@sa sort, randShuffle -*/ -CV_EXPORTS_W void sortIdx(InputArray src, OutputArray dst, int flags); - -/** @brief Finds the real roots of a cubic equation. - -The function solveCubic finds the real roots of a cubic equation: -- if coeffs is a 4-element vector: -\f[\texttt{coeffs} [0] x^3 + \texttt{coeffs} [1] x^2 + \texttt{coeffs} [2] x + \texttt{coeffs} [3] = 0\f] -- if coeffs is a 3-element vector: -\f[x^3 + \texttt{coeffs} [0] x^2 + \texttt{coeffs} [1] x + \texttt{coeffs} [2] = 0\f] - -The roots are stored in the roots array. -@param coeffs equation coefficients, an array of 3 or 4 elements. -@param roots output array of real roots that has 1 or 3 elements. -*/ -CV_EXPORTS_W int solveCubic(InputArray coeffs, OutputArray roots); - -/** @brief Finds the real or complex roots of a polynomial equation. - -The function solvePoly finds real and complex roots of a polynomial equation: -\f[\texttt{coeffs} [n] x^{n} + \texttt{coeffs} [n-1] x^{n-1} + ... + \texttt{coeffs} [1] x + \texttt{coeffs} [0] = 0\f] -@param coeffs array of polynomial coefficients. -@param roots output (complex) array of roots. -@param maxIters maximum number of iterations the algorithm does. -*/ -CV_EXPORTS_W double solvePoly(InputArray coeffs, OutputArray roots, int maxIters = 300); - -/** @brief Calculates eigenvalues and eigenvectors of a symmetric matrix. - -The functions eigen calculate just eigenvalues, or eigenvalues and eigenvectors of the symmetric -matrix src: -@code - src*eigenvectors.row(i).t() = eigenvalues.at(i)*eigenvectors.row(i).t() -@endcode -@note in the new and the old interfaces different ordering of eigenvalues and eigenvectors -parameters is used. -@param src input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical -(src ^T^ == src). -@param eigenvalues output vector of eigenvalues of the same type as src; the eigenvalues are stored -in the descending order. -@param eigenvectors output matrix of eigenvectors; it has the same size and type as src; the -eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding -eigenvalues. -@sa completeSymm , PCA -*/ -CV_EXPORTS_W bool eigen(InputArray src, OutputArray eigenvalues, - OutputArray eigenvectors = noArray()); - -/** @brief Calculates the covariance matrix of a set of vectors. - -The functions calcCovarMatrix calculate the covariance matrix and, optionally, the mean vector of -the set of input vectors. -@param samples samples stored as separate matrices -@param nsamples number of samples -@param covar output covariance matrix of the type ctype and square size. -@param mean input or output (depending on the flags) array as the average value of the input vectors. -@param flags operation flags as a combination of cv::CovarFlags -@param ctype type of the matrixl; it equals 'CV_64F' by default. -@sa PCA, mulTransposed, Mahalanobis -@todo InputArrayOfArrays -*/ -CV_EXPORTS void calcCovarMatrix( const Mat* samples, int nsamples, Mat& covar, Mat& mean, - int flags, int ctype = CV_64F); - -/** @overload -@note use cv::COVAR_ROWS or cv::COVAR_COLS flag -@param samples samples stored as rows/columns of a single matrix. -@param covar output covariance matrix of the type ctype and square size. -@param mean input or output (depending on the flags) array as the average value of the input vectors. -@param flags operation flags as a combination of cv::CovarFlags -@param ctype type of the matrixl; it equals 'CV_64F' by default. -*/ -CV_EXPORTS_W void calcCovarMatrix( InputArray samples, OutputArray covar, - InputOutputArray mean, int flags, int ctype = CV_64F); - -/** wrap PCA::operator() */ -CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean, - OutputArray eigenvectors, int maxComponents = 0); - -/** wrap PCA::operator() */ -CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean, - OutputArray eigenvectors, double retainedVariance); - -/** wrap PCA::project */ -CV_EXPORTS_W void PCAProject(InputArray data, InputArray mean, - InputArray eigenvectors, OutputArray result); - -/** wrap PCA::backProject */ -CV_EXPORTS_W void PCABackProject(InputArray data, InputArray mean, - InputArray eigenvectors, OutputArray result); - -/** wrap SVD::compute */ -CV_EXPORTS_W void SVDecomp( InputArray src, OutputArray w, OutputArray u, OutputArray vt, int flags = 0 ); - -/** wrap SVD::backSubst */ -CV_EXPORTS_W void SVBackSubst( InputArray w, InputArray u, InputArray vt, - InputArray rhs, OutputArray dst ); - -/** @brief Calculates the Mahalanobis distance between two vectors. - -The function Mahalanobis calculates and returns the weighted distance between two vectors: -\f[d( \texttt{vec1} , \texttt{vec2} )= \sqrt{\sum_{i,j}{\texttt{icovar(i,j)}\cdot(\texttt{vec1}(I)-\texttt{vec2}(I))\cdot(\texttt{vec1(j)}-\texttt{vec2(j)})} }\f] -The covariance matrix may be calculated using the cv::calcCovarMatrix function and then inverted using -the invert function (preferably using the cv::DECOMP_SVD method, as the most accurate). -@param v1 first 1D input vector. -@param v2 second 1D input vector. -@param icovar inverse covariance matrix. -*/ -CV_EXPORTS_W double Mahalanobis(InputArray v1, InputArray v2, InputArray icovar); - -/** @brief Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array. - -The function performs one of the following: -- Forward the Fourier transform of a 1D vector of N elements: - \f[Y = F^{(N)} \cdot X,\f] - where \f$F^{(N)}_{jk}=\exp(-2\pi i j k/N)\f$ and \f$i=\sqrt{-1}\f$ -- Inverse the Fourier transform of a 1D vector of N elements: - \f[\begin{array}{l} X'= \left (F^{(N)} \right )^{-1} \cdot Y = \left (F^{(N)} \right )^* \cdot y \\ X = (1/N) \cdot X, \end{array}\f] - where \f$F^*=\left(\textrm{Re}(F^{(N)})-\textrm{Im}(F^{(N)})\right)^T\f$ -- Forward the 2D Fourier transform of a M x N matrix: - \f[Y = F^{(M)} \cdot X \cdot F^{(N)}\f] -- Inverse the 2D Fourier transform of a M x N matrix: - \f[\begin{array}{l} X'= \left (F^{(M)} \right )^* \cdot Y \cdot \left (F^{(N)} \right )^* \\ X = \frac{1}{M \cdot N} \cdot X' \end{array}\f] - -In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input -spectrum of the inverse Fourier transform can be represented in a packed format called *CCS* -(complex-conjugate-symmetrical). It was borrowed from IPL (Intel\* Image Processing Library). Here -is how 2D *CCS* spectrum looks: -\f[\begin{bmatrix} Re Y_{0,0} & Re Y_{0,1} & Im Y_{0,1} & Re Y_{0,2} & Im Y_{0,2} & \cdots & Re Y_{0,N/2-1} & Im Y_{0,N/2-1} & Re Y_{0,N/2} \\ Re Y_{1,0} & Re Y_{1,1} & Im Y_{1,1} & Re Y_{1,2} & Im Y_{1,2} & \cdots & Re Y_{1,N/2-1} & Im Y_{1,N/2-1} & Re Y_{1,N/2} \\ Im Y_{1,0} & Re Y_{2,1} & Im Y_{2,1} & Re Y_{2,2} & Im Y_{2,2} & \cdots & Re Y_{2,N/2-1} & Im Y_{2,N/2-1} & Im Y_{1,N/2} \\ \hdotsfor{9} \\ Re Y_{M/2-1,0} & Re Y_{M-3,1} & Im Y_{M-3,1} & \hdotsfor{3} & Re Y_{M-3,N/2-1} & Im Y_{M-3,N/2-1}& Re Y_{M/2-1,N/2} \\ Im Y_{M/2-1,0} & Re Y_{M-2,1} & Im Y_{M-2,1} & \hdotsfor{3} & Re Y_{M-2,N/2-1} & Im Y_{M-2,N/2-1}& Im Y_{M/2-1,N/2} \\ Re Y_{M/2,0} & Re Y_{M-1,1} & Im Y_{M-1,1} & \hdotsfor{3} & Re Y_{M-1,N/2-1} & Im Y_{M-1,N/2-1}& Re Y_{M/2,N/2} \end{bmatrix}\f] - -In case of 1D transform of a real vector, the output looks like the first row of the matrix above. - -So, the function chooses an operation mode depending on the flags and size of the input array: -- If DFT_ROWS is set or the input array has a single row or single column, the function - performs a 1D forward or inverse transform of each row of a matrix when DFT_ROWS is set. - Otherwise, it performs a 2D transform. -- If the input array is real and DFT_INVERSE is not set, the function performs a forward 1D or - 2D transform: - - When DFT_COMPLEX_OUTPUT is set, the output is a complex matrix of the same size as - input. - - When DFT_COMPLEX_OUTPUT is not set, the output is a real matrix of the same size as - input. In case of 2D transform, it uses the packed format as shown above. In case of a - single 1D transform, it looks like the first row of the matrix above. In case of - multiple 1D transforms (when using the DFT_ROWS flag), each row of the output matrix - looks like the first row of the matrix above. -- If the input array is complex and either DFT_INVERSE or DFT_REAL_OUTPUT are not set, the - output is a complex array of the same size as input. The function performs a forward or - inverse 1D or 2D transform of the whole input array or each row of the input array - independently, depending on the flags DFT_INVERSE and DFT_ROWS. -- When DFT_INVERSE is set and the input array is real, or it is complex but DFT_REAL_OUTPUT - is set, the output is a real array of the same size as input. The function performs a 1D or 2D - inverse transformation of the whole input array or each individual row, depending on the flags - DFT_INVERSE and DFT_ROWS. - -If DFT_SCALE is set, the scaling is done after the transformation. - -Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed -efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the -current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize -method. - -The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays: -@code - void convolveDFT(InputArray A, InputArray B, OutputArray C) - { - // reallocate the output array if needed - C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type()); - Size dftSize; - // calculate the size of DFT transform - dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1); - dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1); - - // allocate temporary buffers and initialize them with 0's - Mat tempA(dftSize, A.type(), Scalar::all(0)); - Mat tempB(dftSize, B.type(), Scalar::all(0)); - - // copy A and B to the top-left corners of tempA and tempB, respectively - Mat roiA(tempA, Rect(0,0,A.cols,A.rows)); - A.copyTo(roiA); - Mat roiB(tempB, Rect(0,0,B.cols,B.rows)); - B.copyTo(roiB); - - // now transform the padded A & B in-place; - // use "nonzeroRows" hint for faster processing - dft(tempA, tempA, 0, A.rows); - dft(tempB, tempB, 0, B.rows); - - // multiply the spectrums; - // the function handles packed spectrum representations well - mulSpectrums(tempA, tempB, tempA); - - // transform the product back from the frequency domain. - // Even though all the result rows will be non-zero, - // you need only the first C.rows of them, and thus you - // pass nonzeroRows == C.rows - dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows); - - // now copy the result back to C. - tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C); - - // all the temporary buffers will be deallocated automatically - } -@endcode -To optimize this sample, consider the following approaches: -- Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to - the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole - tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols) - rightmost columns of the matrices. -- This DFT-based convolution does not have to be applied to the whole big arrays, especially if B - is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts. - To do this, you need to split the output array C into multiple tiles. For each tile, estimate - which parts of A and B are required to calculate convolution in this tile. If the tiles in C are - too small, the speed will decrease a lot because of repeated work. In the ultimate case, when - each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution - algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and - there is also a slowdown because of bad cache locality. So, there is an optimal tile size - somewhere in the middle. -- If different tiles in C can be calculated in parallel and, thus, the convolution is done by - parts, the loop can be threaded. - -All of the above improvements have been implemented in matchTemplate and filter2D . Therefore, by -using them, you can get the performance even better than with the above theoretically optimal -implementation. Though, those two functions actually calculate cross-correlation, not convolution, -so you need to "flip" the second convolution operand B vertically and horizontally using flip . -@note -- An example using the discrete fourier transform can be found at - opencv_source_code/samples/cpp/dft.cpp -- (Python) An example using the dft functionality to perform Wiener deconvolution can be found - at opencv_source/samples/python/deconvolution.py -- (Python) An example rearranging the quadrants of a Fourier image can be found at - opencv_source/samples/python/dft.py -@param src input array that could be real or complex. -@param dst output array whose size and type depends on the flags . -@param flags transformation flags, representing a combination of the cv::DftFlags -@param nonzeroRows when the parameter is not zero, the function assumes that only the first -nonzeroRows rows of the input array (DFT_INVERSE is not set) or only the first nonzeroRows of the -output array (DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the -rows more efficiently and save some time; this technique is very useful for calculating array -cross-correlation or convolution using DFT. -@sa dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar , -magnitude , phase -*/ -CV_EXPORTS_W void dft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0); - -/** @brief Calculates the inverse Discrete Fourier Transform of a 1D or 2D array. - -idft(src, dst, flags) is equivalent to dft(src, dst, flags | DFT_INVERSE) . -@note None of dft and idft scales the result by default. So, you should pass DFT_SCALE to one of -dft or idft explicitly to make these transforms mutually inverse. -@sa dft, dct, idct, mulSpectrums, getOptimalDFTSize -@param src input floating-point real or complex array. -@param dst output array whose size and type depend on the flags. -@param flags operation flags (see dft and cv::DftFlags). -@param nonzeroRows number of dst rows to process; the rest of the rows have undefined content (see -the convolution sample in dft description. -*/ -CV_EXPORTS_W void idft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0); - -/** @brief Performs a forward or inverse discrete Cosine transform of 1D or 2D array. - -The function dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D -floating-point array: -- Forward Cosine transform of a 1D vector of N elements: - \f[Y = C^{(N)} \cdot X\f] - where - \f[C^{(N)}_{jk}= \sqrt{\alpha_j/N} \cos \left ( \frac{\pi(2k+1)j}{2N} \right )\f] - and - \f$\alpha_0=1\f$, \f$\alpha_j=2\f$ for *j \> 0*. -- Inverse Cosine transform of a 1D vector of N elements: - \f[X = \left (C^{(N)} \right )^{-1} \cdot Y = \left (C^{(N)} \right )^T \cdot Y\f] - (since \f$C^{(N)}\f$ is an orthogonal matrix, \f$C^{(N)} \cdot \left(C^{(N)}\right)^T = I\f$ ) -- Forward 2D Cosine transform of M x N matrix: - \f[Y = C^{(N)} \cdot X \cdot \left (C^{(N)} \right )^T\f] -- Inverse 2D Cosine transform of M x N matrix: - \f[X = \left (C^{(N)} \right )^T \cdot X \cdot C^{(N)}\f] - -The function chooses the mode of operation by looking at the flags and size of the input array: -- If (flags & DCT_INVERSE) == 0 , the function does a forward 1D or 2D transform. Otherwise, it - is an inverse 1D or 2D transform. -- If (flags & DCT_ROWS) != 0 , the function performs a 1D transform of each row. -- If the array is a single column or a single row, the function performs a 1D transform. -- If none of the above is true, the function performs a 2D transform. - -@note Currently dct supports even-size arrays (2, 4, 6 ...). For data analysis and approximation, you -can pad the array when necessary. -Also, the function performance depends very much, and not monotonically, on the array size (see -getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT -of a vector of size N/2 . Thus, the optimal DCT size N1 \>= N can be calculated as: -@code - size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); } - N1 = getOptimalDCTSize(N); -@endcode -@param src input floating-point array. -@param dst output array of the same size and type as src . -@param flags transformation flags as a combination of cv::DftFlags (DCT_*) -@sa dft , getOptimalDFTSize , idct -*/ -CV_EXPORTS_W void dct(InputArray src, OutputArray dst, int flags = 0); - -/** @brief Calculates the inverse Discrete Cosine Transform of a 1D or 2D array. - -idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE). -@param src input floating-point single-channel array. -@param dst output array of the same size and type as src. -@param flags operation flags. -@sa dct, dft, idft, getOptimalDFTSize -*/ -CV_EXPORTS_W void idct(InputArray src, OutputArray dst, int flags = 0); - -/** @brief Performs the per-element multiplication of two Fourier spectrums. - -The function mulSpectrums performs the per-element multiplication of the two CCS-packed or complex -matrices that are results of a real or complex Fourier transform. - -The function, together with dft and idft , may be used to calculate convolution (pass conjB=false ) -or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are -simply multiplied (per element) with an optional conjugation of the second-array elements. When the -arrays are real, they are assumed to be CCS-packed (see dft for details). -@param a first input array. -@param b second input array of the same size and type as src1 . -@param c output array of the same size and type as src1 . -@param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that -each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value. -@param conjB optional flag that conjugates the second input array before the multiplication (true) -or not (false). -*/ -CV_EXPORTS_W void mulSpectrums(InputArray a, InputArray b, OutputArray c, - int flags, bool conjB = false); - -/** @brief Returns the optimal DFT size for a given vector size. - -DFT performance is not a monotonic function of a vector size. Therefore, when you calculate -convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to -pad the input data with zeros to get a bit larger array that can be transformed much faster than the -original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, ...) are the fastest to process. -Though, the arrays whose size is a product of 2's, 3's, and 5's (for example, 300 = 5\*5\*3\*2\*2) -are also processed quite efficiently. - -The function getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize -so that the DFT of a vector of size N can be processed efficiently. In the current implementation N -= 2 ^p^ \* 3 ^q^ \* 5 ^r^ for some integer p, q, r. - -The function returns a negative number if vecsize is too large (very close to INT_MAX ). - -While the function cannot be used directly to estimate the optimal vector size for DCT transform -(since the current DCT implementation supports only even-size vectors), it can be easily processed -as getOptimalDFTSize((vecsize+1)/2)\*2. -@param vecsize vector size. -@sa dft , dct , idft , idct , mulSpectrums -*/ -CV_EXPORTS_W int getOptimalDFTSize(int vecsize); - -/** @brief Returns the default random number generator. - -The function theRNG returns the default random number generator. For each thread, there is a -separate random number generator, so you can use the function safely in multi-thread environments. -If you just need to get a single random number using this generator or initialize an array, you can -use randu or randn instead. But if you are going to generate many random numbers inside a loop, it -is much faster to use this function to retrieve the generator and then use RNG::operator _Tp() . -@sa RNG, randu, randn -*/ -CV_EXPORTS RNG& theRNG(); - -/** @brief Generates a single uniformly-distributed random number or an array of random numbers. - -Non-template variant of the function fills the matrix dst with uniformly-distributed -random numbers from the specified range: -\f[\texttt{low} _c \leq \texttt{dst} (I)_c < \texttt{high} _c\f] -@param dst output array of random numbers; the array must be pre-allocated. -@param low inclusive lower boundary of the generated random numbers. -@param high exclusive upper boundary of the generated random numbers. -@sa RNG, randn, theRNG -*/ -CV_EXPORTS_W void randu(InputOutputArray dst, InputArray low, InputArray high); - -/** @brief Fills the array with normally distributed random numbers. - -The function randn fills the matrix dst with normally distributed random numbers with the specified -mean vector and the standard deviation matrix. The generated random numbers are clipped to fit the -value range of the output array data type. -@param dst output array of random numbers; the array must be pre-allocated and have 1 to 4 channels. -@param mean mean value (expectation) of the generated random numbers. -@param stddev standard deviation of the generated random numbers; it can be either a vector (in -which case a diagonal standard deviation matrix is assumed) or a square matrix. -@sa RNG, randu -*/ -CV_EXPORTS_W void randn(InputOutputArray dst, InputArray mean, InputArray stddev); - -/** @brief Shuffles the array elements randomly. - -The function randShuffle shuffles the specified 1D array by randomly choosing pairs of elements and -swapping them. The number of such swap operations will be dst.rows\*dst.cols\*iterFactor . -@param dst input/output numerical 1D array. -@param iterFactor scale factor that determines the number of random swap operations (see the details -below). -@param rng optional random number generator used for shuffling; if it is zero, theRNG () is used -instead. -@sa RNG, sort -*/ -CV_EXPORTS_W void randShuffle(InputOutputArray dst, double iterFactor = 1., RNG* rng = 0); - -/** @brief Principal Component Analysis - -The class is used to calculate a special basis for a set of vectors. The -basis will consist of eigenvectors of the covariance matrix calculated -from the input set of vectors. The class %PCA can also transform -vectors to/from the new coordinate space defined by the basis. Usually, -in this new coordinate system, each vector from the original set (and -any linear combination of such vectors) can be quite accurately -approximated by taking its first few components, corresponding to the -eigenvectors of the largest eigenvalues of the covariance matrix. -Geometrically it means that you calculate a projection of the vector to -a subspace formed by a few eigenvectors corresponding to the dominant -eigenvalues of the covariance matrix. And usually such a projection is -very close to the original vector. So, you can represent the original -vector from a high-dimensional space with a much shorter vector -consisting of the projected vector's coordinates in the subspace. Such a -transformation is also known as Karhunen-Loeve Transform, or KLT. -See http://en.wikipedia.org/wiki/Principal_component_analysis - -The sample below is the function that takes two matrices. The first -function stores a set of vectors (a row per vector) that is used to -calculate PCA. The second function stores another "test" set of vectors -(a row per vector). First, these vectors are compressed with PCA, then -reconstructed back, and then the reconstruction error norm is computed -and printed for each vector. : - -@code{.cpp} -using namespace cv; - -PCA compressPCA(const Mat& pcaset, int maxComponents, - const Mat& testset, Mat& compressed) -{ - PCA pca(pcaset, // pass the data - Mat(), // we do not have a pre-computed mean vector, - // so let the PCA engine to compute it - PCA::DATA_AS_ROW, // indicate that the vectors - // are stored as matrix rows - // (use PCA::DATA_AS_COL if the vectors are - // the matrix columns) - maxComponents // specify, how many principal components to retain - ); - // if there is no test data, just return the computed basis, ready-to-use - if( !testset.data ) - return pca; - CV_Assert( testset.cols == pcaset.cols ); - - compressed.create(testset.rows, maxComponents, testset.type()); - - Mat reconstructed; - for( int i = 0; i < testset.rows; i++ ) - { - Mat vec = testset.row(i), coeffs = compressed.row(i), reconstructed; - // compress the vector, the result will be stored - // in the i-th row of the output matrix - pca.project(vec, coeffs); - // and then reconstruct it - pca.backProject(coeffs, reconstructed); - // and measure the error - printf("%d. diff = %g\n", i, norm(vec, reconstructed, NORM_L2)); - } - return pca; -} -@endcode -@sa calcCovarMatrix, mulTransposed, SVD, dft, dct -*/ -class CV_EXPORTS PCA -{ -public: - enum Flags { DATA_AS_ROW = 0, //!< indicates that the input samples are stored as matrix rows - DATA_AS_COL = 1, //!< indicates that the input samples are stored as matrix columns - USE_AVG = 2 //! - }; - - /** @brief default constructor - - The default constructor initializes an empty %PCA structure. The other - constructors initialize the structure and call PCA::operator()(). - */ - PCA(); - - /** @overload - @param data input samples stored as matrix rows or matrix columns. - @param mean optional mean value; if the matrix is empty (@c noArray()), - the mean is computed from the data. - @param flags operation flags; currently the parameter is only used to - specify the data layout (PCA::Flags) - @param maxComponents maximum number of components that %PCA should - retain; by default, all the components are retained. - */ - PCA(InputArray data, InputArray mean, int flags, int maxComponents = 0); - - /** @overload - @param data input samples stored as matrix rows or matrix columns. - @param mean optional mean value; if the matrix is empty (noArray()), - the mean is computed from the data. - @param flags operation flags; currently the parameter is only used to - specify the data layout (PCA::Flags) - @param retainedVariance Percentage of variance that PCA should retain. - Using this parameter will let the PCA decided how many components to - retain but it will always keep at least 2. - */ - PCA(InputArray data, InputArray mean, int flags, double retainedVariance); - - /** @brief performs %PCA - - The operator performs %PCA of the supplied dataset. It is safe to reuse - the same PCA structure for multiple datasets. That is, if the structure - has been previously used with another dataset, the existing internal - data is reclaimed and the new eigenvalues, @ref eigenvectors , and @ref - mean are allocated and computed. - - The computed eigenvalues are sorted from the largest to the smallest and - the corresponding eigenvectors are stored as eigenvectors rows. - - @param data input samples stored as the matrix rows or as the matrix - columns. - @param mean optional mean value; if the matrix is empty (noArray()), - the mean is computed from the data. - @param flags operation flags; currently the parameter is only used to - specify the data layout. (Flags) - @param maxComponents maximum number of components that PCA should - retain; by default, all the components are retained. - */ - PCA& operator()(InputArray data, InputArray mean, int flags, int maxComponents = 0); - - /** @overload - @param data input samples stored as the matrix rows or as the matrix - columns. - @param mean optional mean value; if the matrix is empty (noArray()), - the mean is computed from the data. - @param flags operation flags; currently the parameter is only used to - specify the data layout. (PCA::Flags) - @param retainedVariance Percentage of variance that %PCA should retain. - Using this parameter will let the %PCA decided how many components to - retain but it will always keep at least 2. - */ - PCA& operator()(InputArray data, InputArray mean, int flags, double retainedVariance); - - /** @brief Projects vector(s) to the principal component subspace. - - The methods project one or more vectors to the principal component - subspace, where each vector projection is represented by coefficients in - the principal component basis. The first form of the method returns the - matrix that the second form writes to the result. So the first form can - be used as a part of expression while the second form can be more - efficient in a processing loop. - @param vec input vector(s); must have the same dimensionality and the - same layout as the input data used at %PCA phase, that is, if - DATA_AS_ROW are specified, then `vec.cols==data.cols` - (vector dimensionality) and `vec.rows` is the number of vectors to - project, and the same is true for the PCA::DATA_AS_COL case. - */ - Mat project(InputArray vec) const; - - /** @overload - @param vec input vector(s); must have the same dimensionality and the - same layout as the input data used at PCA phase, that is, if - DATA_AS_ROW are specified, then `vec.cols==data.cols` - (vector dimensionality) and `vec.rows` is the number of vectors to - project, and the same is true for the PCA::DATA_AS_COL case. - @param result output vectors; in case of PCA::DATA_AS_COL, the - output matrix has as many columns as the number of input vectors, this - means that `result.cols==vec.cols` and the number of rows match the - number of principal components (for example, `maxComponents` parameter - passed to the constructor). - */ - void project(InputArray vec, OutputArray result) const; - - /** @brief Reconstructs vectors from their PC projections. - - The methods are inverse operations to PCA::project. They take PC - coordinates of projected vectors and reconstruct the original vectors. - Unless all the principal components have been retained, the - reconstructed vectors are different from the originals. But typically, - the difference is small if the number of components is large enough (but - still much smaller than the original vector dimensionality). As a - result, PCA is used. - @param vec coordinates of the vectors in the principal component - subspace, the layout and size are the same as of PCA::project output - vectors. - */ - Mat backProject(InputArray vec) const; - - /** @overload - @param vec coordinates of the vectors in the principal component - subspace, the layout and size are the same as of PCA::project output - vectors. - @param result reconstructed vectors; the layout and size are the same as - of PCA::project input vectors. - */ - void backProject(InputArray vec, OutputArray result) const; - - /** @brief write and load PCA matrix - -*/ - void write(FileStorage& fs ) const; - void read(const FileNode& fs); - - Mat eigenvectors; //!< eigenvectors of the covariation matrix - Mat eigenvalues; //!< eigenvalues of the covariation matrix - Mat mean; //!< mean value subtracted before the projection and added after the back projection -}; - -/** @example pca.cpp - An example using %PCA for dimensionality reduction while maintaining an amount of variance - */ - -/** - @brief Linear Discriminant Analysis - @todo document this class - */ -class CV_EXPORTS LDA -{ -public: - /** @brief constructor - Initializes a LDA with num_components (default 0). - */ - explicit LDA(int num_components = 0); - - /** Initializes and performs a Discriminant Analysis with Fisher's - Optimization Criterion on given data in src and corresponding labels - in labels. If 0 (or less) number of components are given, they are - automatically determined for given data in computation. - */ - LDA(InputArrayOfArrays src, InputArray labels, int num_components = 0); - - /** Serializes this object to a given filename. - */ - void save(const String& filename) const; - - /** Deserializes this object from a given filename. - */ - void load(const String& filename); - - /** Serializes this object to a given cv::FileStorage. - */ - void save(FileStorage& fs) const; - - /** Deserializes this object from a given cv::FileStorage. - */ - void load(const FileStorage& node); - - /** destructor - */ - ~LDA(); - - /** Compute the discriminants for data in src (row aligned) and labels. - */ - void compute(InputArrayOfArrays src, InputArray labels); - - /** Projects samples into the LDA subspace. - src may be one or more row aligned samples. - */ - Mat project(InputArray src); - - /** Reconstructs projections from the LDA subspace. - src may be one or more row aligned projections. - */ - Mat reconstruct(InputArray src); - - /** Returns the eigenvectors of this LDA. - */ - Mat eigenvectors() const { return _eigenvectors; } - - /** Returns the eigenvalues of this LDA. - */ - Mat eigenvalues() const { return _eigenvalues; } - - static Mat subspaceProject(InputArray W, InputArray mean, InputArray src); - static Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src); - -protected: - bool _dataAsRow; // unused, but needed for 3.0 ABI compatibility. - int _num_components; - Mat _eigenvectors; - Mat _eigenvalues; - void lda(InputArrayOfArrays src, InputArray labels); -}; - -/** @brief Singular Value Decomposition - -Class for computing Singular Value Decomposition of a floating-point -matrix. The Singular Value Decomposition is used to solve least-square -problems, under-determined linear systems, invert matrices, compute -condition numbers, and so on. - -If you want to compute a condition number of a matrix or an absolute value of -its determinant, you do not need `u` and `vt`. You can pass -flags=SVD::NO_UV|... . Another flag SVD::FULL_UV indicates that full-size u -and vt must be computed, which is not necessary most of the time. - -@sa invert, solve, eigen, determinant -*/ -class CV_EXPORTS SVD -{ -public: - enum Flags { - /** allow the algorithm to modify the decomposed matrix; it can save space and speed up - processing. currently ignored. */ - MODIFY_A = 1, - /** indicates that only a vector of singular values `w` is to be processed, while u and vt - will be set to empty matrices */ - NO_UV = 2, - /** when the matrix is not square, by default the algorithm produces u and vt matrices of - sufficiently large size for the further A reconstruction; if, however, FULL_UV flag is - specified, u and vt will be full-size square orthogonal matrices.*/ - FULL_UV = 4 - }; - - /** @brief the default constructor - - initializes an empty SVD structure - */ - SVD(); - - /** @overload - initializes an empty SVD structure and then calls SVD::operator() - @param src decomposed matrix. - @param flags operation flags (SVD::Flags) - */ - SVD( InputArray src, int flags = 0 ); - - /** @brief the operator that performs SVD. The previously allocated u, w and vt are released. - - The operator performs the singular value decomposition of the supplied - matrix. The u,`vt` , and the vector of singular values w are stored in - the structure. The same SVD structure can be reused many times with - different matrices. Each time, if needed, the previous u,`vt` , and w - are reclaimed and the new matrices are created, which is all handled by - Mat::create. - @param src decomposed matrix. - @param flags operation flags (SVD::Flags) - */ - SVD& operator ()( InputArray src, int flags = 0 ); - - /** @brief decomposes matrix and stores the results to user-provided matrices - - The methods/functions perform SVD of matrix. Unlike SVD::SVD constructor - and SVD::operator(), they store the results to the user-provided - matrices: - - @code{.cpp} - Mat A, w, u, vt; - SVD::compute(A, w, u, vt); - @endcode - - @param src decomposed matrix - @param w calculated singular values - @param u calculated left singular vectors - @param vt transposed matrix of right singular values - @param flags operation flags - see SVD::SVD. - */ - static void compute( InputArray src, OutputArray w, - OutputArray u, OutputArray vt, int flags = 0 ); - - /** @overload - computes singular values of a matrix - @param src decomposed matrix - @param w calculated singular values - @param flags operation flags - see SVD::Flags. - */ - static void compute( InputArray src, OutputArray w, int flags = 0 ); - - /** @brief performs back substitution - */ - static void backSubst( InputArray w, InputArray u, - InputArray vt, InputArray rhs, - OutputArray dst ); - - /** @brief solves an under-determined singular linear system - - The method finds a unit-length solution x of a singular linear system - A\*x = 0. Depending on the rank of A, there can be no solutions, a - single solution or an infinite number of solutions. In general, the - algorithm solves the following problem: - \f[dst = \arg \min _{x: \| x \| =1} \| src \cdot x \|\f] - @param src left-hand-side matrix. - @param dst found solution. - */ - static void solveZ( InputArray src, OutputArray dst ); - - /** @brief performs a singular value back substitution. - - The method calculates a back substitution for the specified right-hand - side: - - \f[\texttt{x} = \texttt{vt} ^T \cdot diag( \texttt{w} )^{-1} \cdot \texttt{u} ^T \cdot \texttt{rhs} \sim \texttt{A} ^{-1} \cdot \texttt{rhs}\f] - - Using this technique you can either get a very accurate solution of the - convenient linear system, or the best (in the least-squares terms) - pseudo-solution of an overdetermined linear system. - - @param rhs right-hand side of a linear system (u\*w\*v')\*dst = rhs to - be solved, where A has been previously decomposed. - - @param dst found solution of the system. - - @note Explicit SVD with the further back substitution only makes sense - if you need to solve many linear systems with the same left-hand side - (for example, src ). If all you need is to solve a single system - (possibly with multiple rhs immediately available), simply call solve - add pass DECOMP_SVD there. It does absolutely the same thing. - */ - void backSubst( InputArray rhs, OutputArray dst ) const; - - /** @todo document */ - template static - void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt ); - - /** @todo document */ - template static - void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w ); - - /** @todo document */ - template static - void backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, Matx<_Tp, n, nb>& dst ); - - Mat u, w, vt; -}; - -/** @brief Random Number Generator - -Random number generator. It encapsulates the state (currently, a 64-bit -integer) and has methods to return scalar random values and to fill -arrays with random values. Currently it supports uniform and Gaussian -(normal) distributions. The generator uses Multiply-With-Carry -algorithm, introduced by G. Marsaglia ( - ). -Gaussian-distribution random numbers are generated using the Ziggurat -algorithm ( ), -introduced by G. Marsaglia and W. W. Tsang. -*/ -class CV_EXPORTS RNG -{ -public: - enum { UNIFORM = 0, - NORMAL = 1 - }; - - /** @brief constructor - - These are the RNG constructors. The first form sets the state to some - pre-defined value, equal to 2\*\*32-1 in the current implementation. The - second form sets the state to the specified value. If you passed state=0 - , the constructor uses the above default value instead to avoid the - singular random number sequence, consisting of all zeros. - */ - RNG(); - /** @overload - @param state 64-bit value used to initialize the RNG. - */ - RNG(uint64 state); - /**The method updates the state using the MWC algorithm and returns the - next 32-bit random number.*/ - unsigned next(); - - /**Each of the methods updates the state using the MWC algorithm and - returns the next random number of the specified type. In case of integer - types, the returned number is from the available value range for the - specified type. In case of floating-point types, the returned value is - from [0,1) range. - */ - operator uchar(); - /** @overload */ - operator schar(); - /** @overload */ - operator ushort(); - /** @overload */ - operator short(); - /** @overload */ - operator unsigned(); - /** @overload */ - operator int(); - /** @overload */ - operator float(); - /** @overload */ - operator double(); - - /** @brief returns a random integer sampled uniformly from [0, N). - - The methods transform the state using the MWC algorithm and return the - next random number. The first form is equivalent to RNG::next . The - second form returns the random number modulo N , which means that the - result is in the range [0, N) . - */ - unsigned operator ()(); - /** @overload - @param N upper non-inclusive boundary of the returned random number. - */ - unsigned operator ()(unsigned N); - - /** @brief returns uniformly distributed integer random number from [a,b) range - - The methods transform the state using the MWC algorithm and return the - next uniformly-distributed random number of the specified type, deduced - from the input parameter type, from the range [a, b) . There is a nuance - illustrated by the following sample: - - @code{.cpp} - RNG rng; - - // always produces 0 - double a = rng.uniform(0, 1); - - // produces double from [0, 1) - double a1 = rng.uniform((double)0, (double)1); - - // produces float from [0, 1) - double b = rng.uniform(0.f, 1.f); - - // produces double from [0, 1) - double c = rng.uniform(0., 1.); - - // may cause compiler error because of ambiguity: - // RNG::uniform(0, (int)0.999999)? or RNG::uniform((double)0, 0.99999)? - double d = rng.uniform(0, 0.999999); - @endcode - - The compiler does not take into account the type of the variable to - which you assign the result of RNG::uniform . The only thing that - matters to the compiler is the type of a and b parameters. So, if you - want a floating-point random number, but the range boundaries are - integer numbers, either put dots in the end, if they are constants, or - use explicit type cast operators, as in the a1 initialization above. - @param a lower inclusive boundary of the returned random numbers. - @param b upper non-inclusive boundary of the returned random numbers. - */ - int uniform(int a, int b); - /** @overload */ - float uniform(float a, float b); - /** @overload */ - double uniform(double a, double b); - - /** @brief Fills arrays with random numbers. - - @param mat 2D or N-dimensional matrix; currently matrices with more than - 4 channels are not supported by the methods, use Mat::reshape as a - possible workaround. - @param distType distribution type, RNG::UNIFORM or RNG::NORMAL. - @param a first distribution parameter; in case of the uniform - distribution, this is an inclusive lower boundary, in case of the normal - distribution, this is a mean value. - @param b second distribution parameter; in case of the uniform - distribution, this is a non-inclusive upper boundary, in case of the - normal distribution, this is a standard deviation (diagonal of the - standard deviation matrix or the full standard deviation matrix). - @param saturateRange pre-saturation flag; for uniform distribution only; - if true, the method will first convert a and b to the acceptable value - range (according to the mat datatype) and then will generate uniformly - distributed random numbers within the range [saturate(a), saturate(b)), - if saturateRange=false, the method will generate uniformly distributed - random numbers in the original range [a, b) and then will saturate them, - it means, for example, that - theRNG().fill(mat_8u, RNG::UNIFORM, -DBL_MAX, DBL_MAX) will likely - produce array mostly filled with 0's and 255's, since the range (0, 255) - is significantly smaller than [-DBL_MAX, DBL_MAX). - - Each of the methods fills the matrix with the random values from the - specified distribution. As the new numbers are generated, the RNG state - is updated accordingly. In case of multiple-channel images, every - channel is filled independently, which means that RNG cannot generate - samples from the multi-dimensional Gaussian distribution with - non-diagonal covariance matrix directly. To do that, the method - generates samples from multi-dimensional standard Gaussian distribution - with zero mean and identity covariation matrix, and then transforms them - using transform to get samples from the specified Gaussian distribution. - */ - void fill( InputOutputArray mat, int distType, InputArray a, InputArray b, bool saturateRange = false ); - - /** @brief Returns the next random number sampled from the Gaussian distribution - @param sigma standard deviation of the distribution. - - The method transforms the state using the MWC algorithm and returns the - next random number from the Gaussian distribution N(0,sigma) . That is, - the mean value of the returned random numbers is zero and the standard - deviation is the specified sigma . - */ - double gaussian(double sigma); - - uint64 state; -}; - -/** @brief Mersenne Twister random number generator - -Inspired by http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.c -@todo document - */ -class CV_EXPORTS RNG_MT19937 -{ -public: - RNG_MT19937(); - RNG_MT19937(unsigned s); - void seed(unsigned s); - - unsigned next(); - - operator int(); - operator unsigned(); - operator float(); - operator double(); - - unsigned operator ()(unsigned N); - unsigned operator ()(); - - /** @brief returns uniformly distributed integer random number from [a,b) range - -*/ - int uniform(int a, int b); - /** @brief returns uniformly distributed floating-point random number from [a,b) range - -*/ - float uniform(float a, float b); - /** @brief returns uniformly distributed double-precision floating-point random number from [a,b) range - -*/ - double uniform(double a, double b); - -private: - enum PeriodParameters {N = 624, M = 397}; - unsigned state[N]; - int mti; -}; - -//! @} core_array - -//! @addtogroup core_cluster -//! @{ - -/** @example kmeans.cpp - An example on K-means clustering -*/ - -/** @brief Finds centers of clusters and groups input samples around the clusters. - -The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters -and groups the input samples around the clusters. As an output, \f$\texttt{labels}_i\f$ contains a -0-based cluster index for the sample stored in the \f$i^{th}\f$ row of the samples matrix. - -@note -- (Python) An example on K-means clustering can be found at - opencv_source_code/samples/python/kmeans.py -@param data Data for clustering. An array of N-Dimensional points with float coordinates is needed. -Examples of this array can be: -- Mat points(count, 2, CV_32F); -- Mat points(count, 1, CV_32FC2); -- Mat points(1, count, CV_32FC2); -- std::vector\ points(sampleCount); -@param K Number of clusters to split the set by. -@param bestLabels Input/output integer array that stores the cluster indices for every sample. -@param criteria The algorithm termination criteria, that is, the maximum number of iterations and/or -the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster -centers moves by less than criteria.epsilon on some iteration, the algorithm stops. -@param attempts Flag to specify the number of times the algorithm is executed using different -initial labellings. The algorithm returns the labels that yield the best compactness (see the last -function parameter). -@param flags Flag that can take values of cv::KmeansFlags -@param centers Output matrix of the cluster centers, one row per each cluster center. -@return The function returns the compactness measure that is computed as -\f[\sum _i \| \texttt{samples} _i - \texttt{centers} _{ \texttt{labels} _i} \| ^2\f] -after every attempt. The best (minimum) value is chosen and the corresponding labels and the -compactness value are returned by the function. Basically, you can use only the core of the -function, set the number of attempts to 1, initialize labels each time using a custom algorithm, -pass them with the ( flags = KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best -(most-compact) clustering. -*/ -CV_EXPORTS_W double kmeans( InputArray data, int K, InputOutputArray bestLabels, - TermCriteria criteria, int attempts, - int flags, OutputArray centers = noArray() ); - -//! @} core_cluster - -//! @addtogroup core_basic -//! @{ - -/////////////////////////////// Formatted output of cv::Mat /////////////////////////// - -/** @todo document */ -class CV_EXPORTS Formatted -{ -public: - virtual const char* next() = 0; - virtual void reset() = 0; - virtual ~Formatted(); -}; - -/** @todo document */ -class CV_EXPORTS Formatter -{ -public: - enum { FMT_DEFAULT = 0, - FMT_MATLAB = 1, - FMT_CSV = 2, - FMT_PYTHON = 3, - FMT_NUMPY = 4, - FMT_C = 5 - }; - - virtual ~Formatter(); - - virtual Ptr format(const Mat& mtx) const = 0; - - virtual void set32fPrecision(int p = 8) = 0; - virtual void set64fPrecision(int p = 16) = 0; - virtual void setMultiline(bool ml = true) = 0; - - static Ptr get(int fmt = FMT_DEFAULT); - -}; - -static inline -String& operator << (String& out, Ptr fmtd) -{ - fmtd->reset(); - for(const char* str = fmtd->next(); str; str = fmtd->next()) - out += cv::String(str); - return out; -} - -static inline -String& operator << (String& out, const Mat& mtx) -{ - return out << Formatter::get()->format(mtx); -} - -//////////////////////////////////////// Algorithm //////////////////////////////////// - -class CV_EXPORTS Algorithm; - -template struct ParamType {}; - - -/** @brief This is a base class for all more or less complex algorithms in OpenCV - -especially for classes of algorithms, for which there can be multiple implementations. The examples -are stereo correspondence (for which there are algorithms like block matching, semi-global block -matching, graph-cut etc.), background subtraction (which can be done using mixture-of-gaussians -models, codebook-based algorithm etc.), optical flow (block matching, Lucas-Kanade, Horn-Schunck -etc.). - -Here is example of SIFT use in your application via Algorithm interface: -@code - #include "opencv2/opencv.hpp" - #include "opencv2/xfeatures2d.hpp" - using namespace cv::xfeatures2d; - - Ptr sift = SIFT::create(); - FileStorage fs("sift_params.xml", FileStorage::READ); - if( fs.isOpened() ) // if we have file with parameters, read them - { - sift->read(fs["sift_params"]); - fs.release(); - } - else // else modify the parameters and store them; user can later edit the file to use different parameters - { - sift->setContrastThreshold(0.01f); // lower the contrast threshold, compared to the default value - { - WriteStructContext ws(fs, "sift_params", CV_NODE_MAP); - sift->write(fs); - } - } - Mat image = imread("myimage.png", 0), descriptors; - vector keypoints; - sift->detectAndCompute(image, noArray(), keypoints, descriptors); -@endcode - */ -class CV_EXPORTS_W Algorithm -{ -public: - Algorithm(); - virtual ~Algorithm(); - - /** @brief Clears the algorithm state - */ - CV_WRAP virtual void clear() {} - - /** @brief Stores algorithm parameters in a file storage - */ - virtual void write(FileStorage& fs) const { (void)fs; } - - /** @brief Reads algorithm parameters from a file storage - */ - virtual void read(const FileNode& fn) { (void)fn; } - - /** @brief Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read - */ - virtual bool empty() const { return false; } - - /** @brief Reads algorithm from the file node - - This is static template method of Algorithm. It's usage is following (in the case of SVM): - @code - Ptr svm = Algorithm::read(fn); - @endcode - In order to make this method work, the derived class must overwrite Algorithm::read(const - FileNode& fn) and also have static create() method without parameters - (or with all the optional parameters) - */ - template static Ptr<_Tp> read(const FileNode& fn) - { - Ptr<_Tp> obj = _Tp::create(); - obj->read(fn); - return !obj->empty() ? obj : Ptr<_Tp>(); - } - - /** @brief Loads algorithm from the file - - @param filename Name of the file to read. - @param objname The optional name of the node to read (if empty, the first top-level node will be used) - - This is static template method of Algorithm. It's usage is following (in the case of SVM): - @code - Ptr svm = Algorithm::load("my_svm_model.xml"); - @endcode - In order to make this method work, the derived class must overwrite Algorithm::read(const - FileNode& fn). - */ - template static Ptr<_Tp> load(const String& filename, const String& objname=String()) - { - FileStorage fs(filename, FileStorage::READ); - FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname]; - Ptr<_Tp> obj = _Tp::create(); - obj->read(fn); - return !obj->empty() ? obj : Ptr<_Tp>(); - } - - /** @brief Loads algorithm from a String - - @param strModel The string variable containing the model you want to load. - @param objname The optional name of the node to read (if empty, the first top-level node will be used) - - This is static template method of Algorithm. It's usage is following (in the case of SVM): - @code - Ptr svm = Algorithm::loadFromString(myStringModel); - @endcode - */ - template static Ptr<_Tp> loadFromString(const String& strModel, const String& objname=String()) - { - FileStorage fs(strModel, FileStorage::READ + FileStorage::MEMORY); - FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname]; - Ptr<_Tp> obj = _Tp::create(); - obj->read(fn); - return !obj->empty() ? obj : Ptr<_Tp>(); - } - - /** Saves the algorithm to a file. - In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */ - CV_WRAP virtual void save(const String& filename) const; - - /** Returns the algorithm string identifier. - This string is used as top level xml/yml node tag when the object is saved to a file or string. */ - CV_WRAP virtual String getDefaultName() const; -}; - -struct Param { - enum { INT=0, BOOLEAN=1, REAL=2, STRING=3, MAT=4, MAT_VECTOR=5, ALGORITHM=6, FLOAT=7, - UNSIGNED_INT=8, UINT64=9, UCHAR=11 }; -}; - - - -template<> struct ParamType -{ - typedef bool const_param_type; - typedef bool member_type; - - enum { type = Param::BOOLEAN }; -}; - -template<> struct ParamType -{ - typedef int const_param_type; - typedef int member_type; - - enum { type = Param::INT }; -}; - -template<> struct ParamType -{ - typedef double const_param_type; - typedef double member_type; - - enum { type = Param::REAL }; -}; - -template<> struct ParamType -{ - typedef const String& const_param_type; - typedef String member_type; - - enum { type = Param::STRING }; -}; - -template<> struct ParamType -{ - typedef const Mat& const_param_type; - typedef Mat member_type; - - enum { type = Param::MAT }; -}; - -template<> struct ParamType > -{ - typedef const std::vector& const_param_type; - typedef std::vector member_type; - - enum { type = Param::MAT_VECTOR }; -}; - -template<> struct ParamType -{ - typedef const Ptr& const_param_type; - typedef Ptr member_type; - - enum { type = Param::ALGORITHM }; -}; - -template<> struct ParamType -{ - typedef float const_param_type; - typedef float member_type; - - enum { type = Param::FLOAT }; -}; - -template<> struct ParamType -{ - typedef unsigned const_param_type; - typedef unsigned member_type; - - enum { type = Param::UNSIGNED_INT }; -}; - -template<> struct ParamType -{ - typedef uint64 const_param_type; - typedef uint64 member_type; - - enum { type = Param::UINT64 }; -}; - -template<> struct ParamType -{ - typedef uchar const_param_type; - typedef uchar member_type; - - enum { type = Param::UCHAR }; -}; - -//! @} core_basic - -} //namespace cv - -#include "opencv2/core/operations.hpp" -#include "opencv2/core/cvstd.inl.hpp" -#include "opencv2/core/utility.hpp" -#include "opencv2/core/optim.hpp" - -#endif /*__OPENCV_CORE_HPP__*/ diff --git a/IPL/include/opencv/opencv2/core/affine.hpp b/IPL/include/opencv/opencv2/core/affine.hpp deleted file mode 100644 index 7f8deb5..0000000 --- a/IPL/include/opencv/opencv2/core/affine.hpp +++ /dev/null @@ -1,517 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_AFFINE3_HPP__ -#define __OPENCV_CORE_AFFINE3_HPP__ - -#ifdef __cplusplus - -#include - -namespace cv -{ - -//! @addtogroup core -//! @{ - - /** @brief Affine transform - @todo document - */ - template - class Affine3 - { - public: - typedef T float_type; - typedef Matx Mat3; - typedef Matx Mat4; - typedef Vec Vec3; - - Affine3(); - - //! Augmented affine matrix - Affine3(const Mat4& affine); - - //! Rotation matrix - Affine3(const Mat3& R, const Vec3& t = Vec3::all(0)); - - //! Rodrigues vector - Affine3(const Vec3& rvec, const Vec3& t = Vec3::all(0)); - - //! Combines all contructors above. Supports 4x4, 4x3, 3x3, 1x3, 3x1 sizes of data matrix - explicit Affine3(const Mat& data, const Vec3& t = Vec3::all(0)); - - //! From 16th element array - explicit Affine3(const float_type* vals); - - //! Create identity transform - static Affine3 Identity(); - - //! Rotation matrix - void rotation(const Mat3& R); - - //! Rodrigues vector - void rotation(const Vec3& rvec); - - //! Combines rotation methods above. Suports 3x3, 1x3, 3x1 sizes of data matrix; - void rotation(const Mat& data); - - void linear(const Mat3& L); - void translation(const Vec3& t); - - Mat3 rotation() const; - Mat3 linear() const; - Vec3 translation() const; - - //! Rodrigues vector - Vec3 rvec() const; - - Affine3 inv(int method = cv::DECOMP_SVD) const; - - //! a.rotate(R) is equivalent to Affine(R, 0) * a; - Affine3 rotate(const Mat3& R) const; - - //! a.rotate(rvec) is equivalent to Affine(rvec, 0) * a; - Affine3 rotate(const Vec3& rvec) const; - - //! a.translate(t) is equivalent to Affine(E, t) * a; - Affine3 translate(const Vec3& t) const; - - //! a.concatenate(affine) is equivalent to affine * a; - Affine3 concatenate(const Affine3& affine) const; - - template operator Affine3() const; - - template Affine3 cast() const; - - Mat4 matrix; - -#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H - Affine3(const Eigen::Transform& affine); - Affine3(const Eigen::Transform& affine); - operator Eigen::Transform() const; - operator Eigen::Transform() const; -#endif - }; - - template static - Affine3 operator*(const Affine3& affine1, const Affine3& affine2); - - template static - V operator*(const Affine3& affine, const V& vector); - - typedef Affine3 Affine3f; - typedef Affine3 Affine3d; - - static Vec3f operator*(const Affine3f& affine, const Vec3f& vector); - static Vec3d operator*(const Affine3d& affine, const Vec3d& vector); - - template class DataType< Affine3<_Tp> > - { - public: - typedef Affine3<_Tp> value_type; - typedef Affine3::work_type> work_type; - typedef _Tp channel_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = 16, - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) - }; - - typedef Vec vec_type; - }; - -//! @} core - -} - -//! @cond IGNORED - -/////////////////////////////////////////////////////////////////////////////////// -// Implementaiton - -template inline -cv::Affine3::Affine3() - : matrix(Mat4::eye()) -{} - -template inline -cv::Affine3::Affine3(const Mat4& affine) - : matrix(affine) -{} - -template inline -cv::Affine3::Affine3(const Mat3& R, const Vec3& t) -{ - rotation(R); - translation(t); - matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; - matrix.val[15] = 1; -} - -template inline -cv::Affine3::Affine3(const Vec3& _rvec, const Vec3& t) -{ - rotation(_rvec); - translation(t); - matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; - matrix.val[15] = 1; -} - -template inline -cv::Affine3::Affine3(const cv::Mat& data, const Vec3& t) -{ - CV_Assert(data.type() == cv::DataType::type); - - if (data.cols == 4 && data.rows == 4) - { - data.copyTo(matrix); - return; - } - else if (data.cols == 4 && data.rows == 3) - { - rotation(data(Rect(0, 0, 3, 3))); - translation(data(Rect(3, 0, 1, 3))); - return; - } - - rotation(data); - translation(t); - matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; - matrix.val[15] = 1; -} - -template inline -cv::Affine3::Affine3(const float_type* vals) : matrix(vals) -{} - -template inline -cv::Affine3 cv::Affine3::Identity() -{ - return Affine3(cv::Affine3::Mat4::eye()); -} - -template inline -void cv::Affine3::rotation(const Mat3& R) -{ - linear(R); -} - -template inline -void cv::Affine3::rotation(const Vec3& _rvec) -{ - double theta = norm(_rvec); - - if (theta < DBL_EPSILON) - rotation(Mat3::eye()); - else - { - double c = std::cos(theta); - double s = std::sin(theta); - double c1 = 1. - c; - double itheta = (theta != 0) ? 1./theta : 0.; - - Point3_ r = _rvec*itheta; - - Mat3 rrt( r.x*r.x, r.x*r.y, r.x*r.z, r.x*r.y, r.y*r.y, r.y*r.z, r.x*r.z, r.y*r.z, r.z*r.z ); - Mat3 r_x( 0, -r.z, r.y, r.z, 0, -r.x, -r.y, r.x, 0 ); - - // R = cos(theta)*I + (1 - cos(theta))*r*rT + sin(theta)*[r_x] - // where [r_x] is [0 -rz ry; rz 0 -rx; -ry rx 0] - Mat3 R = c*Mat3::eye() + c1*rrt + s*r_x; - - rotation(R); - } -} - -//Combines rotation methods above. Suports 3x3, 1x3, 3x1 sizes of data matrix; -template inline -void cv::Affine3::rotation(const cv::Mat& data) -{ - CV_Assert(data.type() == cv::DataType::type); - - if (data.cols == 3 && data.rows == 3) - { - Mat3 R; - data.copyTo(R); - rotation(R); - } - else if ((data.cols == 3 && data.rows == 1) || (data.cols == 1 && data.rows == 3)) - { - Vec3 _rvec; - data.reshape(1, 3).copyTo(_rvec); - rotation(_rvec); - } - else - CV_Assert(!"Input marix can be 3x3, 1x3 or 3x1"); -} - -template inline -void cv::Affine3::linear(const Mat3& L) -{ - matrix.val[0] = L.val[0]; matrix.val[1] = L.val[1]; matrix.val[ 2] = L.val[2]; - matrix.val[4] = L.val[3]; matrix.val[5] = L.val[4]; matrix.val[ 6] = L.val[5]; - matrix.val[8] = L.val[6]; matrix.val[9] = L.val[7]; matrix.val[10] = L.val[8]; -} - -template inline -void cv::Affine3::translation(const Vec3& t) -{ - matrix.val[3] = t[0]; matrix.val[7] = t[1]; matrix.val[11] = t[2]; -} - -template inline -typename cv::Affine3::Mat3 cv::Affine3::rotation() const -{ - return linear(); -} - -template inline -typename cv::Affine3::Mat3 cv::Affine3::linear() const -{ - typename cv::Affine3::Mat3 R; - R.val[0] = matrix.val[0]; R.val[1] = matrix.val[1]; R.val[2] = matrix.val[ 2]; - R.val[3] = matrix.val[4]; R.val[4] = matrix.val[5]; R.val[5] = matrix.val[ 6]; - R.val[6] = matrix.val[8]; R.val[7] = matrix.val[9]; R.val[8] = matrix.val[10]; - return R; -} - -template inline -typename cv::Affine3::Vec3 cv::Affine3::translation() const -{ - return Vec3(matrix.val[3], matrix.val[7], matrix.val[11]); -} - -template inline -typename cv::Affine3::Vec3 cv::Affine3::rvec() const -{ - cv::Vec3d w; - cv::Matx33d u, vt, R = rotation(); - cv::SVD::compute(R, w, u, vt, cv::SVD::FULL_UV + cv::SVD::MODIFY_A); - R = u * vt; - - double rx = R.val[7] - R.val[5]; - double ry = R.val[2] - R.val[6]; - double rz = R.val[3] - R.val[1]; - - double s = std::sqrt((rx*rx + ry*ry + rz*rz)*0.25); - double c = (R.val[0] + R.val[4] + R.val[8] - 1) * 0.5; - c = c > 1.0 ? 1.0 : c < -1.0 ? -1.0 : c; - double theta = acos(c); - - if( s < 1e-5 ) - { - if( c > 0 ) - rx = ry = rz = 0; - else - { - double t; - t = (R.val[0] + 1) * 0.5; - rx = std::sqrt(std::max(t, 0.0)); - t = (R.val[4] + 1) * 0.5; - ry = std::sqrt(std::max(t, 0.0)) * (R.val[1] < 0 ? -1.0 : 1.0); - t = (R.val[8] + 1) * 0.5; - rz = std::sqrt(std::max(t, 0.0)) * (R.val[2] < 0 ? -1.0 : 1.0); - - if( fabs(rx) < fabs(ry) && fabs(rx) < fabs(rz) && (R.val[5] > 0) != (ry*rz > 0) ) - rz = -rz; - theta /= std::sqrt(rx*rx + ry*ry + rz*rz); - rx *= theta; - ry *= theta; - rz *= theta; - } - } - else - { - double vth = 1/(2*s); - vth *= theta; - rx *= vth; ry *= vth; rz *= vth; - } - - return cv::Vec3d(rx, ry, rz); -} - -template inline -cv::Affine3 cv::Affine3::inv(int method) const -{ - return matrix.inv(method); -} - -template inline -cv::Affine3 cv::Affine3::rotate(const Mat3& R) const -{ - Mat3 Lc = linear(); - Vec3 tc = translation(); - Mat4 result; - result.val[12] = result.val[13] = result.val[14] = 0; - result.val[15] = 1; - - for(int j = 0; j < 3; ++j) - { - for(int i = 0; i < 3; ++i) - { - float_type value = 0; - for(int k = 0; k < 3; ++k) - value += R(j, k) * Lc(k, i); - result(j, i) = value; - } - - result(j, 3) = R.row(j).dot(tc.t()); - } - return result; -} - -template inline -cv::Affine3 cv::Affine3::rotate(const Vec3& _rvec) const -{ - return rotate(Affine3f(_rvec).rotation()); -} - -template inline -cv::Affine3 cv::Affine3::translate(const Vec3& t) const -{ - Mat4 m = matrix; - m.val[ 3] += t[0]; - m.val[ 7] += t[1]; - m.val[11] += t[2]; - return m; -} - -template inline -cv::Affine3 cv::Affine3::concatenate(const Affine3& affine) const -{ - return (*this).rotate(affine.rotation()).translate(affine.translation()); -} - -template template inline -cv::Affine3::operator Affine3() const -{ - return Affine3(matrix); -} - -template template inline -cv::Affine3 cv::Affine3::cast() const -{ - return Affine3(matrix); -} - -template inline -cv::Affine3 cv::operator*(const cv::Affine3& affine1, const cv::Affine3& affine2) -{ - return affine2.concatenate(affine1); -} - -template inline -V cv::operator*(const cv::Affine3& affine, const V& v) -{ - const typename Affine3::Mat4& m = affine.matrix; - - V r; - r.x = m.val[0] * v.x + m.val[1] * v.y + m.val[ 2] * v.z + m.val[ 3]; - r.y = m.val[4] * v.x + m.val[5] * v.y + m.val[ 6] * v.z + m.val[ 7]; - r.z = m.val[8] * v.x + m.val[9] * v.y + m.val[10] * v.z + m.val[11]; - return r; -} - -static inline -cv::Vec3f cv::operator*(const cv::Affine3f& affine, const cv::Vec3f& v) -{ - const cv::Matx44f& m = affine.matrix; - cv::Vec3f r; - r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3]; - r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7]; - r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11]; - return r; -} - -static inline -cv::Vec3d cv::operator*(const cv::Affine3d& affine, const cv::Vec3d& v) -{ - const cv::Matx44d& m = affine.matrix; - cv::Vec3d r; - r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3]; - r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7]; - r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11]; - return r; -} - - - -#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H - -template inline -cv::Affine3::Affine3(const Eigen::Transform& affine) -{ - cv::Mat(4, 4, cv::DataType::type, affine.matrix().data()).copyTo(matrix); -} - -template inline -cv::Affine3::Affine3(const Eigen::Transform& affine) -{ - Eigen::Transform a = affine; - cv::Mat(4, 4, cv::DataType::type, a.matrix().data()).copyTo(matrix); -} - -template inline -cv::Affine3::operator Eigen::Transform() const -{ - Eigen::Transform r; - cv::Mat hdr(4, 4, cv::DataType::type, r.matrix().data()); - cv::Mat(matrix, false).copyTo(hdr); - return r; -} - -template inline -cv::Affine3::operator Eigen::Transform() const -{ - return this->operator Eigen::Transform(); -} - -#endif /* defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H */ - -//! @endcond - -#endif /* __cplusplus */ - -#endif /* __OPENCV_CORE_AFFINE3_HPP__ */ diff --git a/IPL/include/opencv/opencv2/core/base.hpp b/IPL/include/opencv/opencv2/core/base.hpp deleted file mode 100644 index ed633f5..0000000 --- a/IPL/include/opencv/opencv2/core/base.hpp +++ /dev/null @@ -1,689 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2014, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_BASE_HPP__ -#define __OPENCV_CORE_BASE_HPP__ - -#ifndef __cplusplus -# error base.hpp header must be compiled as C++ -#endif - -#include -#include - -#include "opencv2/core/cvdef.h" -#include "opencv2/core/cvstd.hpp" - -namespace cv -{ - -//! @addtogroup core_utils -//! @{ - -namespace Error { -//! error codes -enum Code { - StsOk= 0, //!< everithing is ok - StsBackTrace= -1, //!< pseudo error for back trace - StsError= -2, //!< unknown /unspecified error - StsInternal= -3, //!< internal error (bad state) - StsNoMem= -4, //!< insufficient memory - StsBadArg= -5, //!< function arg/param is bad - StsBadFunc= -6, //!< unsupported function - StsNoConv= -7, //!< iter. didn't converge - StsAutoTrace= -8, //!< tracing - HeaderIsNull= -9, //!< image header is NULL - BadImageSize= -10, //!< image size is invalid - BadOffset= -11, //!< offset is invalid - BadDataPtr= -12, //!< - BadStep= -13, //!< - BadModelOrChSeq= -14, //!< - BadNumChannels= -15, //!< - BadNumChannel1U= -16, //!< - BadDepth= -17, //!< - BadAlphaChannel= -18, //!< - BadOrder= -19, //!< - BadOrigin= -20, //!< - BadAlign= -21, //!< - BadCallBack= -22, //!< - BadTileSize= -23, //!< - BadCOI= -24, //!< - BadROISize= -25, //!< - MaskIsTiled= -26, //!< - StsNullPtr= -27, //!< null pointer - StsVecLengthErr= -28, //!< incorrect vector length - StsFilterStructContentErr= -29, //!< incorr. filter structure content - StsKernelStructContentErr= -30, //!< incorr. transform kernel content - StsFilterOffsetErr= -31, //!< incorrect filter ofset value - StsBadSize= -201, //!< the input/output structure size is incorrect - StsDivByZero= -202, //!< division by zero - StsInplaceNotSupported= -203, //!< in-place operation is not supported - StsObjectNotFound= -204, //!< request can't be completed - StsUnmatchedFormats= -205, //!< formats of input/output arrays differ - StsBadFlag= -206, //!< flag is wrong or not supported - StsBadPoint= -207, //!< bad CvPoint - StsBadMask= -208, //!< bad format of mask (neither 8uC1 nor 8sC1) - StsUnmatchedSizes= -209, //!< sizes of input/output structures do not match - StsUnsupportedFormat= -210, //!< the data format/type is not supported by the function - StsOutOfRange= -211, //!< some of parameters are out of range - StsParseError= -212, //!< invalid syntax/structure of the parsed file - StsNotImplemented= -213, //!< the requested function/feature is not implemented - StsBadMemBlock= -214, //!< an allocated block has been corrupted - StsAssert= -215, //!< assertion failed - GpuNotSupported= -216, - GpuApiCallError= -217, - OpenGlNotSupported= -218, - OpenGlApiCallError= -219, - OpenCLApiCallError= -220, - OpenCLDoubleNotSupported= -221, - OpenCLInitError= -222, - OpenCLNoAMDBlasFft= -223 -}; -} //Error - -//! @} core_utils - -//! @addtogroup core_array -//! @{ - -//! matrix decomposition types -enum DecompTypes { - /** Gaussian elimination with the optimal pivot element chosen. */ - DECOMP_LU = 0, - /** singular value decomposition (SVD) method; the system can be over-defined and/or the matrix - src1 can be singular */ - DECOMP_SVD = 1, - /** eigenvalue decomposition; the matrix src1 must be symmetrical */ - DECOMP_EIG = 2, - /** Cholesky \f$LL^T\f$ factorization; the matrix src1 must be symmetrical and positively - defined */ - DECOMP_CHOLESKY = 3, - /** QR factorization; the system can be over-defined and/or the matrix src1 can be singular */ - DECOMP_QR = 4, - /** while all the previous flags are mutually exclusive, this flag can be used together with - any of the previous; it means that the normal equations - \f$\texttt{src1}^T\cdot\texttt{src1}\cdot\texttt{dst}=\texttt{src1}^T\texttt{src2}\f$ are - solved instead of the original system - \f$\texttt{src1}\cdot\texttt{dst}=\texttt{src2}\f$ */ - DECOMP_NORMAL = 16 -}; - -/** norm types -- For one array: -\f[norm = \forkthree{\|\texttt{src1}\|_{L_{\infty}} = \max _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) } -{ \| \texttt{src1} \| _{L_1} = \sum _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\) } -{ \| \texttt{src1} \| _{L_2} = \sqrt{\sum_I \texttt{src1}(I)^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) }\f] - -- Absolute norm for two arrays -\f[norm = \forkthree{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} = \max _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) } -{ \| \texttt{src1} - \texttt{src2} \| _{L_1} = \sum _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\) } -{ \| \texttt{src1} - \texttt{src2} \| _{L_2} = \sqrt{\sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) }\f] - -- Relative norm for two arrays -\f[norm = \forkthree{\frac{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} }{\|\texttt{src2}\|_{L_{\infty}} }}{if \(\texttt{normType} = \texttt{NORM_RELATIVE_INF}\) } -{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_1} }{\|\texttt{src2}\|_{L_1}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE_L1}\) } -{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE_L2}\) }\f] - -As example for one array consider the function \f$r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\f$. -The \f$ L_{1}, L_{2} \f$ and \f$ L_{\infty} \f$ norm for the sample value \f$r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\f$ -is calculated as follows -\f{align*} - \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\ - \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\ - \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2 -\f} -and for \f$r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\f$ the calculation is -\f{align*} - \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\ - \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\ - \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5. -\f} -The following graphic shows all values for the three norm functions \f$\| r(x) \|_{L_1}, \| r(x) \|_{L_2}\f$ and \f$\| r(x) \|_{L_\infty}\f$. -It is notable that the \f$ L_{1} \f$ norm forms the upper and the \f$ L_{\infty} \f$ norm forms the lower border for the example function \f$ r(x) \f$. -![Graphs for the different norm functions from the above example](pics/NormTypes_OneArray_1-2-INF.png) - */ -enum NormTypes { NORM_INF = 1, - NORM_L1 = 2, - NORM_L2 = 4, - NORM_L2SQR = 5, - NORM_HAMMING = 6, - NORM_HAMMING2 = 7, - NORM_TYPE_MASK = 7, - NORM_RELATIVE = 8, //!< flag - NORM_MINMAX = 32 //!< flag - }; - -//! comparison types -enum CmpTypes { CMP_EQ = 0, //!< src1 is equal to src2. - CMP_GT = 1, //!< src1 is greater than src2. - CMP_GE = 2, //!< src1 is greater than or equal to src2. - CMP_LT = 3, //!< src1 is less than src2. - CMP_LE = 4, //!< src1 is less than or equal to src2. - CMP_NE = 5 //!< src1 is unequal to src2. - }; - -//! generalized matrix multiplication flags -enum GemmFlags { GEMM_1_T = 1, //!< transposes src1 - GEMM_2_T = 2, //!< transposes src2 - GEMM_3_T = 4 //!< transposes src3 - }; - -enum DftFlags { - /** performs an inverse 1D or 2D transform instead of the default forward - transform. */ - DFT_INVERSE = 1, - /** scales the result: divide it by the number of array elements. Normally, it is - combined with DFT_INVERSE. */ - DFT_SCALE = 2, - /** performs a forward or inverse transform of every individual row of the input - matrix; this flag enables you to transform multiple vectors simultaneously and can be used to - decrease the overhead (which is sometimes several times larger than the processing itself) to - perform 3D and higher-dimensional transformations and so forth.*/ - DFT_ROWS = 4, - /** performs a forward transformation of 1D or 2D real array; the result, - though being a complex array, has complex-conjugate symmetry (*CCS*, see the function - description below for details), and such an array can be packed into a real array of the same - size as input, which is the fastest option and which is what the function does by default; - however, you may wish to get a full complex array (for simpler spectrum analysis, and so on) - - pass the flag to enable the function to produce a full-size complex output array. */ - DFT_COMPLEX_OUTPUT = 16, - /** performs an inverse transformation of a 1D or 2D complex array; the - result is normally a complex array of the same size, however, if the input array has - conjugate-complex symmetry (for example, it is a result of forward transformation with - DFT_COMPLEX_OUTPUT flag), the output is a real array; while the function itself does not - check whether the input is symmetrical or not, you can pass the flag and then the function - will assume the symmetry and produce the real output array (note that when the input is packed - into a real array and inverse transformation is executed, the function treats the input as a - packed complex-conjugate symmetrical array, and the output will also be a real array). */ - DFT_REAL_OUTPUT = 32, - /** performs an inverse 1D or 2D transform instead of the default forward transform. */ - DCT_INVERSE = DFT_INVERSE, - /** performs a forward or inverse transform of every individual row of the input - matrix. This flag enables you to transform multiple vectors simultaneously and can be used to - decrease the overhead (which is sometimes several times larger than the processing itself) to - perform 3D and higher-dimensional transforms and so forth.*/ - DCT_ROWS = DFT_ROWS -}; - -//! Various border types, image boundaries are denoted with `|` -//! @see borderInterpolate, copyMakeBorder -enum BorderTypes { - BORDER_CONSTANT = 0, //!< `iiiiii|abcdefgh|iiiiiii` with some specified `i` - BORDER_REPLICATE = 1, //!< `aaaaaa|abcdefgh|hhhhhhh` - BORDER_REFLECT = 2, //!< `fedcba|abcdefgh|hgfedcb` - BORDER_WRAP = 3, //!< `cdefgh|abcdefgh|abcdefg` - BORDER_REFLECT_101 = 4, //!< `gfedcb|abcdefgh|gfedcba` - BORDER_TRANSPARENT = 5, //!< `uvwxyz|absdefgh|ijklmno` - - BORDER_REFLECT101 = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101 - BORDER_DEFAULT = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101 - BORDER_ISOLATED = 16 //!< do not look outside of ROI -}; - -//! @} core_array - -//! @addtogroup core_utils -//! @{ - -//! @cond IGNORED - -//////////////// static assert ///////////////// -#define CVAUX_CONCAT_EXP(a, b) a##b -#define CVAUX_CONCAT(a, b) CVAUX_CONCAT_EXP(a,b) - -#if defined(__clang__) -# ifndef __has_extension -# define __has_extension __has_feature /* compatibility, for older versions of clang */ -# endif -# if __has_extension(cxx_static_assert) -# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) -# elif __has_extension(c_static_assert) -# define CV_StaticAssert(condition, reason) _Static_assert((condition), reason " " #condition) -# endif -#elif defined(__GNUC__) -# if (defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L) -# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) -# endif -#elif defined(_MSC_VER) -# if _MSC_VER >= 1600 /* MSVC 10 */ -# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) -# endif -#endif -#ifndef CV_StaticAssert -# if !defined(__clang__) && defined(__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 302) -# define CV_StaticAssert(condition, reason) ({ extern int __attribute__((error("CV_StaticAssert: " reason " " #condition))) CV_StaticAssert(); ((condition) ? 0 : CV_StaticAssert()); }) -# else - template struct CV_StaticAssert_failed; - template <> struct CV_StaticAssert_failed { enum { val = 1 }; }; - template struct CV_StaticAssert_test {}; -# define CV_StaticAssert(condition, reason)\ - typedef cv::CV_StaticAssert_test< sizeof(cv::CV_StaticAssert_failed< static_cast(condition) >) > CVAUX_CONCAT(CV_StaticAssert_failed_at_, __LINE__) -# endif -#endif - -// Suppress warning "-Wdeprecated-declarations" / C4996 -#if defined(_MSC_VER) - #define CV_DO_PRAGMA(x) __pragma(x) -#elif defined(__GNUC__) - #define CV_DO_PRAGMA(x) _Pragma (#x) -#else - #define CV_DO_PRAGMA(x) -#endif - -#ifdef _MSC_VER -#define CV_SUPPRESS_DEPRECATED_START \ - CV_DO_PRAGMA(warning(push)) \ - CV_DO_PRAGMA(warning(disable: 4996)) -#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(warning(pop)) -#elif defined (__clang__) || ((__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 405)) -#define CV_SUPPRESS_DEPRECATED_START \ - CV_DO_PRAGMA(GCC diagnostic push) \ - CV_DO_PRAGMA(GCC diagnostic ignored "-Wdeprecated-declarations") -#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(GCC diagnostic pop) -#else -#define CV_SUPPRESS_DEPRECATED_START -#define CV_SUPPRESS_DEPRECATED_END -#endif -#define CV_UNUSED(name) (void)name -//! @endcond - -/*! @brief Signals an error and raises the exception. - -By default the function prints information about the error to stderr, -then it either stops if setBreakOnError() had been called before or raises the exception. -It is possible to alternate error processing by using redirectError(). -@param _code - error code (Error::Code) -@param _err - error description -@param _func - function name. Available only when the compiler supports getting it -@param _file - source file name where the error has occured -@param _line - line number in the source file where the error has occured -@see CV_Error, CV_Error_, CV_ErrorNoReturn, CV_ErrorNoReturn_, CV_Assert, CV_DbgAssert - */ -CV_EXPORTS void error(int _code, const String& _err, const char* _func, const char* _file, int _line); - -#ifdef __GNUC__ -# if defined __clang__ || defined __APPLE__ -# pragma GCC diagnostic push -# pragma GCC diagnostic ignored "-Winvalid-noreturn" -# endif -#endif - -/** same as cv::error, but does not return */ -CV_INLINE CV_NORETURN void errorNoReturn(int _code, const String& _err, const char* _func, const char* _file, int _line) -{ - error(_code, _err, _func, _file, _line); -#ifdef __GNUC__ -# if !defined __clang__ && !defined __APPLE__ - // this suppresses this warning: "noreturn" function does return [enabled by default] - __builtin_trap(); - // or use infinite loop: for (;;) {} -# endif -#endif -} -#ifdef __GNUC__ -# if defined __clang__ || defined __APPLE__ -# pragma GCC diagnostic pop -# endif -#endif - -#if defined __GNUC__ -#define CV_Func __func__ -#elif defined _MSC_VER -#define CV_Func __FUNCTION__ -#else -#define CV_Func "" -#endif - -/** @brief Call the error handler. - -Currently, the error handler prints the error code and the error message to the standard -error stream `stderr`. In the Debug configuration, it then provokes memory access violation, so that -the execution stack and all the parameters can be analyzed by the debugger. In the Release -configuration, the exception is thrown. - -@param code one of Error::Code -@param msg error message -*/ -#define CV_Error( code, msg ) cv::error( code, msg, CV_Func, __FILE__, __LINE__ ) - -/** @brief Call the error handler. - -This macro can be used to construct an error message on-fly to include some dynamic information, -for example: -@code - // note the extra parentheses around the formatted text message - CV_Error_( CV_StsOutOfRange, - ("the value at (%d, %d)=%g is out of range", badPt.x, badPt.y, badValue)); -@endcode -@param code one of Error::Code -@param args printf-like formatted error message in parentheses -*/ -#define CV_Error_( code, args ) cv::error( code, cv::format args, CV_Func, __FILE__, __LINE__ ) - -/** @brief Checks a condition at runtime and throws exception if it fails - -The macros CV_Assert (and CV_DbgAssert(expr)) evaluate the specified expression. If it is 0, the macros -raise an error (see cv::error). The macro CV_Assert checks the condition in both Debug and Release -configurations while CV_DbgAssert is only retained in the Debug configuration. -*/ -#define CV_Assert( expr ) if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ) - -/** same as CV_Error(code,msg), but does not return */ -#define CV_ErrorNoReturn( code, msg ) cv::errorNoReturn( code, msg, CV_Func, __FILE__, __LINE__ ) - -/** same as CV_Error_(code,args), but does not return */ -#define CV_ErrorNoReturn_( code, args ) cv::errorNoReturn( code, cv::format args, CV_Func, __FILE__, __LINE__ ) - -/** replaced with CV_Assert(expr) in Debug configuration */ -#ifdef _DEBUG -# define CV_DbgAssert(expr) CV_Assert(expr) -#else -# define CV_DbgAssert(expr) -#endif - -/* - * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor - * bit count of A exclusive XOR'ed with B - */ -struct CV_EXPORTS Hamming -{ - enum { normType = NORM_HAMMING }; - typedef unsigned char ValueType; - typedef int ResultType; - - /** this will count the bits in a ^ b - */ - ResultType operator()( const unsigned char* a, const unsigned char* b, int size ) const; -}; - -typedef Hamming HammingLUT; - -/////////////////////////////////// inline norms //////////////////////////////////// - -template inline _Tp cv_abs(_Tp x) { return std::abs(x); } -inline int cv_abs(uchar x) { return x; } -inline int cv_abs(schar x) { return std::abs(x); } -inline int cv_abs(ushort x) { return x; } -inline int cv_abs(short x) { return std::abs(x); } - -template static inline -_AccTp normL2Sqr(const _Tp* a, int n) -{ - _AccTp s = 0; - int i=0; -#if CV_ENABLE_UNROLLED - for( ; i <= n - 4; i += 4 ) - { - _AccTp v0 = a[i], v1 = a[i+1], v2 = a[i+2], v3 = a[i+3]; - s += v0*v0 + v1*v1 + v2*v2 + v3*v3; - } -#endif - for( ; i < n; i++ ) - { - _AccTp v = a[i]; - s += v*v; - } - return s; -} - -template static inline -_AccTp normL1(const _Tp* a, int n) -{ - _AccTp s = 0; - int i = 0; -#if CV_ENABLE_UNROLLED - for(; i <= n - 4; i += 4 ) - { - s += (_AccTp)cv_abs(a[i]) + (_AccTp)cv_abs(a[i+1]) + - (_AccTp)cv_abs(a[i+2]) + (_AccTp)cv_abs(a[i+3]); - } -#endif - for( ; i < n; i++ ) - s += cv_abs(a[i]); - return s; -} - -template static inline -_AccTp normInf(const _Tp* a, int n) -{ - _AccTp s = 0; - for( int i = 0; i < n; i++ ) - s = std::max(s, (_AccTp)cv_abs(a[i])); - return s; -} - -template static inline -_AccTp normL2Sqr(const _Tp* a, const _Tp* b, int n) -{ - _AccTp s = 0; - int i= 0; -#if CV_ENABLE_UNROLLED - for(; i <= n - 4; i += 4 ) - { - _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]); - s += v0*v0 + v1*v1 + v2*v2 + v3*v3; - } -#endif - for( ; i < n; i++ ) - { - _AccTp v = _AccTp(a[i] - b[i]); - s += v*v; - } - return s; -} - -static inline float normL2Sqr(const float* a, const float* b, int n) -{ - float s = 0.f; - for( int i = 0; i < n; i++ ) - { - float v = a[i] - b[i]; - s += v*v; - } - return s; -} - -template static inline -_AccTp normL1(const _Tp* a, const _Tp* b, int n) -{ - _AccTp s = 0; - int i= 0; -#if CV_ENABLE_UNROLLED - for(; i <= n - 4; i += 4 ) - { - _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]); - s += std::abs(v0) + std::abs(v1) + std::abs(v2) + std::abs(v3); - } -#endif - for( ; i < n; i++ ) - { - _AccTp v = _AccTp(a[i] - b[i]); - s += std::abs(v); - } - return s; -} - -inline float normL1(const float* a, const float* b, int n) -{ - float s = 0.f; - for( int i = 0; i < n; i++ ) - { - s += std::abs(a[i] - b[i]); - } - return s; -} - -inline int normL1(const uchar* a, const uchar* b, int n) -{ - int s = 0; - for( int i = 0; i < n; i++ ) - { - s += std::abs(a[i] - b[i]); - } - return s; -} - -template static inline -_AccTp normInf(const _Tp* a, const _Tp* b, int n) -{ - _AccTp s = 0; - for( int i = 0; i < n; i++ ) - { - _AccTp v0 = a[i] - b[i]; - s = std::max(s, std::abs(v0)); - } - return s; -} - -/** @brief Computes the cube root of an argument. - - The function cubeRoot computes \f$\sqrt[3]{\texttt{val}}\f$. Negative arguments are handled correctly. - NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for - single-precision data. - @param val A function argument. - */ -CV_EXPORTS_W float cubeRoot(float val); - -/** @brief Calculates the angle of a 2D vector in degrees. - - The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured - in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees. - @param x x-coordinate of the vector. - @param y y-coordinate of the vector. - */ -CV_EXPORTS_W float fastAtan2(float y, float x); - -/** proxy for hal::LU */ -CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n); -/** proxy for hal::LU */ -CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n); -/** proxy for hal::Cholesky */ -CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n); -/** proxy for hal::Cholesky */ -CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n); - -////////////////// forward declarations for important OpenCV types ////////////////// - -//! @cond IGNORED - -template class Vec; -template class Matx; - -template class Complex; -template class Point_; -template class Point3_; -template class Size_; -template class Rect_; -template class Scalar_; - -class CV_EXPORTS RotatedRect; -class CV_EXPORTS Range; -class CV_EXPORTS TermCriteria; -class CV_EXPORTS KeyPoint; -class CV_EXPORTS DMatch; -class CV_EXPORTS RNG; - -class CV_EXPORTS Mat; -class CV_EXPORTS MatExpr; - -class CV_EXPORTS UMat; - -class CV_EXPORTS SparseMat; -typedef Mat MatND; - -template class Mat_; -template class SparseMat_; - -class CV_EXPORTS MatConstIterator; -class CV_EXPORTS SparseMatIterator; -class CV_EXPORTS SparseMatConstIterator; -template class MatIterator_; -template class MatConstIterator_; -template class SparseMatIterator_; -template class SparseMatConstIterator_; - -namespace ogl -{ - class CV_EXPORTS Buffer; - class CV_EXPORTS Texture2D; - class CV_EXPORTS Arrays; -} - -namespace cuda -{ - class CV_EXPORTS GpuMat; - class CV_EXPORTS HostMem; - class CV_EXPORTS Stream; - class CV_EXPORTS Event; -} - -namespace cudev -{ - template class GpuMat_; -} - -namespace ipp -{ -CV_EXPORTS int getIppFeatures(); -CV_EXPORTS void setIppStatus(int status, const char * const funcname = NULL, const char * const filename = NULL, - int line = 0); -CV_EXPORTS int getIppStatus(); -CV_EXPORTS String getIppErrorLocation(); -CV_EXPORTS bool useIPP(); -CV_EXPORTS void setUseIPP(bool flag); - -} // ipp - -//! @endcond - -//! @} core_utils - - - - -} // cv - -#include "opencv2/core/neon_utils.hpp" - -#endif //__OPENCV_CORE_BASE_HPP__ diff --git a/IPL/include/opencv/opencv2/core/bufferpool.hpp b/IPL/include/opencv/opencv2/core/bufferpool.hpp deleted file mode 100644 index 76df2d2..0000000 --- a/IPL/include/opencv/opencv2/core/bufferpool.hpp +++ /dev/null @@ -1,31 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. -// -// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved. - -#ifndef __OPENCV_CORE_BUFFER_POOL_HPP__ -#define __OPENCV_CORE_BUFFER_POOL_HPP__ - -namespace cv -{ - -//! @addtogroup core -//! @{ - -class BufferPoolController -{ -protected: - ~BufferPoolController() { } -public: - virtual size_t getReservedSize() const = 0; - virtual size_t getMaxReservedSize() const = 0; - virtual void setMaxReservedSize(size_t size) = 0; - virtual void freeAllReservedBuffers() = 0; -}; - -//! @} - -} - -#endif // __OPENCV_CORE_BUFFER_POOL_HPP__ diff --git a/IPL/include/opencv/opencv2/core/core.hpp b/IPL/include/opencv/opencv2/core/core.hpp deleted file mode 100644 index 4389183..0000000 --- a/IPL/include/opencv/opencv2/core/core.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/core.hpp" diff --git a/IPL/include/opencv/opencv2/core/core_c.h b/IPL/include/opencv/opencv2/core/core_c.h deleted file mode 100644 index a0ed632..0000000 --- a/IPL/include/opencv/opencv2/core/core_c.h +++ /dev/null @@ -1,3152 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - - -#ifndef __OPENCV_CORE_C_H__ -#define __OPENCV_CORE_C_H__ - -#include "opencv2/core/types_c.h" - -#ifdef __cplusplus -# ifdef _MSC_VER -/* disable warning C4190: 'function' has C-linkage specified, but returns UDT 'typename' - which is incompatible with C - - It is OK to disable it because we only extend few plain structures with - C++ construrtors for simpler interoperability with C++ API of the library -*/ -# pragma warning(disable:4190) -# elif defined __clang__ && __clang_major__ >= 3 -# pragma GCC diagnostic ignored "-Wreturn-type-c-linkage" -# endif -#endif - -#ifdef __cplusplus -extern "C" { -#endif - -/** @addtogroup core_c - @{ -*/ - -/****************************************************************************************\ -* Array allocation, deallocation, initialization and access to elements * -\****************************************************************************************/ - -/** `malloc` wrapper. - If there is no enough memory, the function - (as well as other OpenCV functions that call cvAlloc) - raises an error. */ -CVAPI(void*) cvAlloc( size_t size ); - -/** `free` wrapper. - Here and further all the memory releasing functions - (that all call cvFree) take double pointer in order to - to clear pointer to the data after releasing it. - Passing pointer to NULL pointer is Ok: nothing happens in this case -*/ -CVAPI(void) cvFree_( void* ptr ); -#define cvFree(ptr) (cvFree_(*(ptr)), *(ptr)=0) - -/** @brief Creates an image header but does not allocate the image data. - -@param size Image width and height -@param depth Image depth (see cvCreateImage ) -@param channels Number of channels (see cvCreateImage ) - */ -CVAPI(IplImage*) cvCreateImageHeader( CvSize size, int depth, int channels ); - -/** @brief Initializes an image header that was previously allocated. - -The returned IplImage\* points to the initialized header. -@param image Image header to initialize -@param size Image width and height -@param depth Image depth (see cvCreateImage ) -@param channels Number of channels (see cvCreateImage ) -@param origin Top-left IPL_ORIGIN_TL or bottom-left IPL_ORIGIN_BL -@param align Alignment for image rows, typically 4 or 8 bytes - */ -CVAPI(IplImage*) cvInitImageHeader( IplImage* image, CvSize size, int depth, - int channels, int origin CV_DEFAULT(0), - int align CV_DEFAULT(4)); - -/** @brief Creates an image header and allocates the image data. - -This function call is equivalent to the following code: -@code - header = cvCreateImageHeader(size, depth, channels); - cvCreateData(header); -@endcode -@param size Image width and height -@param depth Bit depth of image elements. See IplImage for valid depths. -@param channels Number of channels per pixel. See IplImage for details. This function only creates -images with interleaved channels. - */ -CVAPI(IplImage*) cvCreateImage( CvSize size, int depth, int channels ); - -/** @brief Deallocates an image header. - -This call is an analogue of : -@code - if(image ) - { - iplDeallocate(*image, IPL_IMAGE_HEADER | IPL_IMAGE_ROI); - *image = 0; - } -@endcode -but it does not use IPL functions by default (see the CV_TURN_ON_IPL_COMPATIBILITY macro). -@param image Double pointer to the image header - */ -CVAPI(void) cvReleaseImageHeader( IplImage** image ); - -/** @brief Deallocates the image header and the image data. - -This call is a shortened form of : -@code - if(*image ) - { - cvReleaseData(*image); - cvReleaseImageHeader(image); - } -@endcode -@param image Double pointer to the image header -*/ -CVAPI(void) cvReleaseImage( IplImage** image ); - -/** Creates a copy of IPL image (widthStep may differ) */ -CVAPI(IplImage*) cvCloneImage( const IplImage* image ); - -/** @brief Sets the channel of interest in an IplImage. - -If the ROI is set to NULL and the coi is *not* 0, the ROI is allocated. Most OpenCV functions do -*not* support the COI setting, so to process an individual image/matrix channel one may copy (via -cvCopy or cvSplit) the channel to a separate image/matrix, process it and then copy the result -back (via cvCopy or cvMerge) if needed. -@param image A pointer to the image header -@param coi The channel of interest. 0 - all channels are selected, 1 - first channel is selected, -etc. Note that the channel indices become 1-based. - */ -CVAPI(void) cvSetImageCOI( IplImage* image, int coi ); - -/** @brief Returns the index of the channel of interest. - -Returns the channel of interest of in an IplImage. Returned values correspond to the coi in -cvSetImageCOI. -@param image A pointer to the image header - */ -CVAPI(int) cvGetImageCOI( const IplImage* image ); - -/** @brief Sets an image Region Of Interest (ROI) for a given rectangle. - -If the original image ROI was NULL and the rect is not the whole image, the ROI structure is -allocated. - -Most OpenCV functions support the use of ROI and treat the image rectangle as a separate image. For -example, all of the pixel coordinates are counted from the top-left (or bottom-left) corner of the -ROI, not the original image. -@param image A pointer to the image header -@param rect The ROI rectangle - */ -CVAPI(void) cvSetImageROI( IplImage* image, CvRect rect ); - -/** @brief Resets the image ROI to include the entire image and releases the ROI structure. - -This produces a similar result to the following, but in addition it releases the ROI structure. : -@code - cvSetImageROI(image, cvRect(0, 0, image->width, image->height )); - cvSetImageCOI(image, 0); -@endcode -@param image A pointer to the image header - */ -CVAPI(void) cvResetImageROI( IplImage* image ); - -/** @brief Returns the image ROI. - -If there is no ROI set, cvRect(0,0,image-\>width,image-\>height) is returned. -@param image A pointer to the image header - */ -CVAPI(CvRect) cvGetImageROI( const IplImage* image ); - -/** @brief Creates a matrix header but does not allocate the matrix data. - -The function allocates a new matrix header and returns a pointer to it. The matrix data can then be -allocated using cvCreateData or set explicitly to user-allocated data via cvSetData. -@param rows Number of rows in the matrix -@param cols Number of columns in the matrix -@param type Type of the matrix elements, see cvCreateMat - */ -CVAPI(CvMat*) cvCreateMatHeader( int rows, int cols, int type ); - -#define CV_AUTOSTEP 0x7fffffff - -/** @brief Initializes a pre-allocated matrix header. - -This function is often used to process raw data with OpenCV matrix functions. For example, the -following code computes the matrix product of two matrices, stored as ordinary arrays: -@code - double a[] = { 1, 2, 3, 4, - 5, 6, 7, 8, - 9, 10, 11, 12 }; - - double b[] = { 1, 5, 9, - 2, 6, 10, - 3, 7, 11, - 4, 8, 12 }; - - double c[9]; - CvMat Ma, Mb, Mc ; - - cvInitMatHeader(&Ma, 3, 4, CV_64FC1, a); - cvInitMatHeader(&Mb, 4, 3, CV_64FC1, b); - cvInitMatHeader(&Mc, 3, 3, CV_64FC1, c); - - cvMatMulAdd(&Ma, &Mb, 0, &Mc); - // the c array now contains the product of a (3x4) and b (4x3) -@endcode -@param mat A pointer to the matrix header to be initialized -@param rows Number of rows in the matrix -@param cols Number of columns in the matrix -@param type Type of the matrix elements, see cvCreateMat . -@param data Optional: data pointer assigned to the matrix header -@param step Optional: full row width in bytes of the assigned data. By default, the minimal -possible step is used which assumes there are no gaps between subsequent rows of the matrix. - */ -CVAPI(CvMat*) cvInitMatHeader( CvMat* mat, int rows, int cols, - int type, void* data CV_DEFAULT(NULL), - int step CV_DEFAULT(CV_AUTOSTEP) ); - -/** @brief Creates a matrix header and allocates the matrix data. - -The function call is equivalent to the following code: -@code - CvMat* mat = cvCreateMatHeader(rows, cols, type); - cvCreateData(mat); -@endcode -@param rows Number of rows in the matrix -@param cols Number of columns in the matrix -@param type The type of the matrix elements in the form -CV_\\C\ , where S=signed, U=unsigned, F=float. For -example, CV _ 8UC1 means the elements are 8-bit unsigned and the there is 1 channel, and CV _ -32SC2 means the elements are 32-bit signed and there are 2 channels. - */ -CVAPI(CvMat*) cvCreateMat( int rows, int cols, int type ); - -/** @brief Deallocates a matrix. - -The function decrements the matrix data reference counter and deallocates matrix header. If the data -reference counter is 0, it also deallocates the data. : -@code - if(*mat ) - cvDecRefData(*mat); - cvFree((void**)mat); -@endcode -@param mat Double pointer to the matrix - */ -CVAPI(void) cvReleaseMat( CvMat** mat ); - -/** @brief Decrements an array data reference counter. - -The function decrements the data reference counter in a CvMat or CvMatND if the reference counter - -pointer is not NULL. If the counter reaches zero, the data is deallocated. In the current -implementation the reference counter is not NULL only if the data was allocated using the -cvCreateData function. The counter will be NULL in other cases such as: external data was assigned -to the header using cvSetData, header is part of a larger matrix or image, or the header was -converted from an image or n-dimensional matrix header. -@param arr Pointer to an array header - */ -CV_INLINE void cvDecRefData( CvArr* arr ) -{ - if( CV_IS_MAT( arr )) - { - CvMat* mat = (CvMat*)arr; - mat->data.ptr = NULL; - if( mat->refcount != NULL && --*mat->refcount == 0 ) - cvFree( &mat->refcount ); - mat->refcount = NULL; - } - else if( CV_IS_MATND( arr )) - { - CvMatND* mat = (CvMatND*)arr; - mat->data.ptr = NULL; - if( mat->refcount != NULL && --*mat->refcount == 0 ) - cvFree( &mat->refcount ); - mat->refcount = NULL; - } -} - -/** @brief Increments array data reference counter. - -The function increments CvMat or CvMatND data reference counter and returns the new counter value if -the reference counter pointer is not NULL, otherwise it returns zero. -@param arr Array header - */ -CV_INLINE int cvIncRefData( CvArr* arr ) -{ - int refcount = 0; - if( CV_IS_MAT( arr )) - { - CvMat* mat = (CvMat*)arr; - if( mat->refcount != NULL ) - refcount = ++*mat->refcount; - } - else if( CV_IS_MATND( arr )) - { - CvMatND* mat = (CvMatND*)arr; - if( mat->refcount != NULL ) - refcount = ++*mat->refcount; - } - return refcount; -} - - -/** Creates an exact copy of the input matrix (except, may be, step value) */ -CVAPI(CvMat*) cvCloneMat( const CvMat* mat ); - - -/** @brief Returns matrix header corresponding to the rectangular sub-array of input image or matrix. - -The function returns header, corresponding to a specified rectangle of the input array. In other - -words, it allows the user to treat a rectangular part of input array as a stand-alone array. ROI is -taken into account by the function so the sub-array of ROI is actually extracted. -@param arr Input array -@param submat Pointer to the resultant sub-array header -@param rect Zero-based coordinates of the rectangle of interest - */ -CVAPI(CvMat*) cvGetSubRect( const CvArr* arr, CvMat* submat, CvRect rect ); -#define cvGetSubArr cvGetSubRect - -/** @brief Returns array row or row span. - -The functions return the header, corresponding to a specified row/row span of the input array. -cvGetRow(arr, submat, row) is a shortcut for cvGetRows(arr, submat, row, row+1). -@param arr Input array -@param submat Pointer to the resulting sub-array header -@param start_row Zero-based index of the starting row (inclusive) of the span -@param end_row Zero-based index of the ending row (exclusive) of the span -@param delta_row Index step in the row span. That is, the function extracts every delta_row -th -row from start_row and up to (but not including) end_row . - */ -CVAPI(CvMat*) cvGetRows( const CvArr* arr, CvMat* submat, - int start_row, int end_row, - int delta_row CV_DEFAULT(1)); - -/** @overload -@param arr Input array -@param submat Pointer to the resulting sub-array header -@param row Zero-based index of the selected row -*/ -CV_INLINE CvMat* cvGetRow( const CvArr* arr, CvMat* submat, int row ) -{ - return cvGetRows( arr, submat, row, row + 1, 1 ); -} - - -/** @brief Returns one of more array columns. - -The functions return the header, corresponding to a specified column span of the input array. That - -is, no data is copied. Therefore, any modifications of the submatrix will affect the original array. -If you need to copy the columns, use cvCloneMat. cvGetCol(arr, submat, col) is a shortcut for -cvGetCols(arr, submat, col, col+1). -@param arr Input array -@param submat Pointer to the resulting sub-array header -@param start_col Zero-based index of the starting column (inclusive) of the span -@param end_col Zero-based index of the ending column (exclusive) of the span - */ -CVAPI(CvMat*) cvGetCols( const CvArr* arr, CvMat* submat, - int start_col, int end_col ); - -/** @overload -@param arr Input array -@param submat Pointer to the resulting sub-array header -@param col Zero-based index of the selected column -*/ -CV_INLINE CvMat* cvGetCol( const CvArr* arr, CvMat* submat, int col ) -{ - return cvGetCols( arr, submat, col, col + 1 ); -} - -/** @brief Returns one of array diagonals. - -The function returns the header, corresponding to a specified diagonal of the input array. -@param arr Input array -@param submat Pointer to the resulting sub-array header -@param diag Index of the array diagonal. Zero value corresponds to the main diagonal, -1 -corresponds to the diagonal above the main, 1 corresponds to the diagonal below the main, and so -forth. - */ -CVAPI(CvMat*) cvGetDiag( const CvArr* arr, CvMat* submat, - int diag CV_DEFAULT(0)); - -/** low-level scalar <-> raw data conversion functions */ -CVAPI(void) cvScalarToRawData( const CvScalar* scalar, void* data, int type, - int extend_to_12 CV_DEFAULT(0) ); - -CVAPI(void) cvRawDataToScalar( const void* data, int type, CvScalar* scalar ); - -/** @brief Creates a new matrix header but does not allocate the matrix data. - -The function allocates a header for a multi-dimensional dense array. The array data can further be -allocated using cvCreateData or set explicitly to user-allocated data via cvSetData. -@param dims Number of array dimensions -@param sizes Array of dimension sizes -@param type Type of array elements, see cvCreateMat - */ -CVAPI(CvMatND*) cvCreateMatNDHeader( int dims, const int* sizes, int type ); - -/** @brief Creates the header and allocates the data for a multi-dimensional dense array. - -This function call is equivalent to the following code: -@code - CvMatND* mat = cvCreateMatNDHeader(dims, sizes, type); - cvCreateData(mat); -@endcode -@param dims Number of array dimensions. This must not exceed CV_MAX_DIM (32 by default, but can be -changed at build time). -@param sizes Array of dimension sizes. -@param type Type of array elements, see cvCreateMat . - */ -CVAPI(CvMatND*) cvCreateMatND( int dims, const int* sizes, int type ); - -/** @brief Initializes a pre-allocated multi-dimensional array header. - -@param mat A pointer to the array header to be initialized -@param dims The number of array dimensions -@param sizes An array of dimension sizes -@param type Type of array elements, see cvCreateMat -@param data Optional data pointer assigned to the matrix header - */ -CVAPI(CvMatND*) cvInitMatNDHeader( CvMatND* mat, int dims, const int* sizes, - int type, void* data CV_DEFAULT(NULL) ); - -/** @brief Deallocates a multi-dimensional array. - -The function decrements the array data reference counter and releases the array header. If the -reference counter reaches 0, it also deallocates the data. : -@code - if(*mat ) - cvDecRefData(*mat); - cvFree((void**)mat); -@endcode -@param mat Double pointer to the array - */ -CV_INLINE void cvReleaseMatND( CvMatND** mat ) -{ - cvReleaseMat( (CvMat**)mat ); -} - -/** Creates a copy of CvMatND (except, may be, steps) */ -CVAPI(CvMatND*) cvCloneMatND( const CvMatND* mat ); - -/** @brief Creates sparse array. - -The function allocates a multi-dimensional sparse array. Initially the array contain no elements, -that is PtrND and other related functions will return 0 for every index. -@param dims Number of array dimensions. In contrast to the dense matrix, the number of dimensions is -practically unlimited (up to \f$2^{16}\f$ ). -@param sizes Array of dimension sizes -@param type Type of array elements. The same as for CvMat - */ -CVAPI(CvSparseMat*) cvCreateSparseMat( int dims, const int* sizes, int type ); - -/** @brief Deallocates sparse array. - -The function releases the sparse array and clears the array pointer upon exit. -@param mat Double pointer to the array - */ -CVAPI(void) cvReleaseSparseMat( CvSparseMat** mat ); - -/** Creates a copy of CvSparseMat (except, may be, zero items) */ -CVAPI(CvSparseMat*) cvCloneSparseMat( const CvSparseMat* mat ); - -/** @brief Initializes sparse array elements iterator. - -The function initializes iterator of sparse array elements and returns pointer to the first element, -or NULL if the array is empty. -@param mat Input array -@param mat_iterator Initialized iterator - */ -CVAPI(CvSparseNode*) cvInitSparseMatIterator( const CvSparseMat* mat, - CvSparseMatIterator* mat_iterator ); - -/** @brief Returns the next sparse matrix element - -The function moves iterator to the next sparse matrix element and returns pointer to it. In the -current version there is no any particular order of the elements, because they are stored in the -hash table. The sample below demonstrates how to iterate through the sparse matrix: -@code - // print all the non-zero sparse matrix elements and compute their sum - double sum = 0; - int i, dims = cvGetDims(sparsemat); - CvSparseMatIterator it; - CvSparseNode* node = cvInitSparseMatIterator(sparsemat, &it); - - for(; node != 0; node = cvGetNextSparseNode(&it)) - { - int* idx = CV_NODE_IDX(array, node); - float val = *(float*)CV_NODE_VAL(array, node); - printf("M"); - for(i = 0; i < dims; i++ ) - printf("[%d]", idx[i]); - printf("=%g\n", val); - - sum += val; - } - - printf("nTotal sum = %g\n", sum); -@endcode -@param mat_iterator Sparse array iterator - */ -CV_INLINE CvSparseNode* cvGetNextSparseNode( CvSparseMatIterator* mat_iterator ) -{ - if( mat_iterator->node->next ) - return mat_iterator->node = mat_iterator->node->next; - else - { - int idx; - for( idx = ++mat_iterator->curidx; idx < mat_iterator->mat->hashsize; idx++ ) - { - CvSparseNode* node = (CvSparseNode*)mat_iterator->mat->hashtable[idx]; - if( node ) - { - mat_iterator->curidx = idx; - return mat_iterator->node = node; - } - } - return NULL; - } -} - - -#define CV_MAX_ARR 10 - -/** matrix iterator: used for n-ary operations on dense arrays */ -typedef struct CvNArrayIterator -{ - int count; /**< number of arrays */ - int dims; /**< number of dimensions to iterate */ - CvSize size; /**< maximal common linear size: { width = size, height = 1 } */ - uchar* ptr[CV_MAX_ARR]; /**< pointers to the array slices */ - int stack[CV_MAX_DIM]; /**< for internal use */ - CvMatND* hdr[CV_MAX_ARR]; /**< pointers to the headers of the - matrices that are processed */ -} -CvNArrayIterator; - -#define CV_NO_DEPTH_CHECK 1 -#define CV_NO_CN_CHECK 2 -#define CV_NO_SIZE_CHECK 4 - -/** initializes iterator that traverses through several arrays simulteneously - (the function together with cvNextArraySlice is used for - N-ari element-wise operations) */ -CVAPI(int) cvInitNArrayIterator( int count, CvArr** arrs, - const CvArr* mask, CvMatND* stubs, - CvNArrayIterator* array_iterator, - int flags CV_DEFAULT(0) ); - -/** returns zero value if iteration is finished, non-zero (slice length) otherwise */ -CVAPI(int) cvNextNArraySlice( CvNArrayIterator* array_iterator ); - - -/** @brief Returns type of array elements. - -The function returns type of the array elements. In the case of IplImage the type is converted to -CvMat-like representation. For example, if the image has been created as: -@code - IplImage* img = cvCreateImage(cvSize(640, 480), IPL_DEPTH_8U, 3); -@endcode -The code cvGetElemType(img) will return CV_8UC3. -@param arr Input array - */ -CVAPI(int) cvGetElemType( const CvArr* arr ); - -/** @brief Return number of array dimensions - -The function returns the array dimensionality and the array of dimension sizes. In the case of -IplImage or CvMat it always returns 2 regardless of number of image/matrix rows. For example, the -following code calculates total number of array elements: -@code - int sizes[CV_MAX_DIM]; - int i, total = 1; - int dims = cvGetDims(arr, size); - for(i = 0; i < dims; i++ ) - total *= sizes[i]; -@endcode -@param arr Input array -@param sizes Optional output vector of the array dimension sizes. For 2d arrays the number of rows -(height) goes first, number of columns (width) next. - */ -CVAPI(int) cvGetDims( const CvArr* arr, int* sizes CV_DEFAULT(NULL) ); - - -/** @brief Returns array size along the specified dimension. - -@param arr Input array -@param index Zero-based dimension index (for matrices 0 means number of rows, 1 means number of -columns; for images 0 means height, 1 means width) - */ -CVAPI(int) cvGetDimSize( const CvArr* arr, int index ); - - -/** @brief Return pointer to a particular array element. - -The functions return a pointer to a specific array element. Number of array dimension should match -to the number of indices passed to the function except for cvPtr1D function that can be used for -sequential access to 1D, 2D or nD dense arrays. - -The functions can be used for sparse arrays as well - if the requested node does not exist they -create it and set it to zero. - -All these as well as other functions accessing array elements ( cvGetND , cvGetRealND , cvSet -, cvSetND , cvSetRealND ) raise an error in case if the element index is out of range. -@param arr Input array -@param idx0 The first zero-based component of the element index -@param type Optional output parameter: type of matrix elements - */ -CVAPI(uchar*) cvPtr1D( const CvArr* arr, int idx0, int* type CV_DEFAULT(NULL)); -/** @overload */ -CVAPI(uchar*) cvPtr2D( const CvArr* arr, int idx0, int idx1, int* type CV_DEFAULT(NULL) ); -/** @overload */ -CVAPI(uchar*) cvPtr3D( const CvArr* arr, int idx0, int idx1, int idx2, - int* type CV_DEFAULT(NULL)); -/** @overload -@param arr Input array -@param idx Array of the element indices -@param type Optional output parameter: type of matrix elements -@param create_node Optional input parameter for sparse matrices. Non-zero value of the parameter -means that the requested element is created if it does not exist already. -@param precalc_hashval Optional input parameter for sparse matrices. If the pointer is not NULL, -the function does not recalculate the node hash value, but takes it from the specified location. -It is useful for speeding up pair-wise operations (TODO: provide an example) -*/ -CVAPI(uchar*) cvPtrND( const CvArr* arr, const int* idx, int* type CV_DEFAULT(NULL), - int create_node CV_DEFAULT(1), - unsigned* precalc_hashval CV_DEFAULT(NULL)); - -/** @brief Return a specific array element. - -The functions return a specific array element. In the case of a sparse array the functions return 0 -if the requested node does not exist (no new node is created by the functions). -@param arr Input array -@param idx0 The first zero-based component of the element index - */ -CVAPI(CvScalar) cvGet1D( const CvArr* arr, int idx0 ); -/** @overload */ -CVAPI(CvScalar) cvGet2D( const CvArr* arr, int idx0, int idx1 ); -/** @overload */ -CVAPI(CvScalar) cvGet3D( const CvArr* arr, int idx0, int idx1, int idx2 ); -/** @overload -@param arr Input array -@param idx Array of the element indices -*/ -CVAPI(CvScalar) cvGetND( const CvArr* arr, const int* idx ); - -/** @brief Return a specific element of single-channel 1D, 2D, 3D or nD array. - -Returns a specific element of a single-channel array. If the array has multiple channels, a runtime -error is raised. Note that Get?D functions can be used safely for both single-channel and -multiple-channel arrays though they are a bit slower. - -In the case of a sparse array the functions return 0 if the requested node does not exist (no new -node is created by the functions). -@param arr Input array. Must have a single channel. -@param idx0 The first zero-based component of the element index - */ -CVAPI(double) cvGetReal1D( const CvArr* arr, int idx0 ); -/** @overload */ -CVAPI(double) cvGetReal2D( const CvArr* arr, int idx0, int idx1 ); -/** @overload */ -CVAPI(double) cvGetReal3D( const CvArr* arr, int idx0, int idx1, int idx2 ); -/** @overload -@param arr Input array. Must have a single channel. -@param idx Array of the element indices -*/ -CVAPI(double) cvGetRealND( const CvArr* arr, const int* idx ); - -/** @brief Change the particular array element. - -The functions assign the new value to a particular array element. In the case of a sparse array the -functions create the node if it does not exist yet. -@param arr Input array -@param idx0 The first zero-based component of the element index -@param value The assigned value - */ -CVAPI(void) cvSet1D( CvArr* arr, int idx0, CvScalar value ); -/** @overload */ -CVAPI(void) cvSet2D( CvArr* arr, int idx0, int idx1, CvScalar value ); -/** @overload */ -CVAPI(void) cvSet3D( CvArr* arr, int idx0, int idx1, int idx2, CvScalar value ); -/** @overload -@param arr Input array -@param idx Array of the element indices -@param value The assigned value -*/ -CVAPI(void) cvSetND( CvArr* arr, const int* idx, CvScalar value ); - -/** @brief Change a specific array element. - -The functions assign a new value to a specific element of a single-channel array. If the array has -multiple channels, a runtime error is raised. Note that the Set\*D function can be used safely for -both single-channel and multiple-channel arrays, though they are a bit slower. - -In the case of a sparse array the functions create the node if it does not yet exist. -@param arr Input array -@param idx0 The first zero-based component of the element index -@param value The assigned value - */ -CVAPI(void) cvSetReal1D( CvArr* arr, int idx0, double value ); -/** @overload */ -CVAPI(void) cvSetReal2D( CvArr* arr, int idx0, int idx1, double value ); -/** @overload */ -CVAPI(void) cvSetReal3D( CvArr* arr, int idx0, - int idx1, int idx2, double value ); -/** @overload -@param arr Input array -@param idx Array of the element indices -@param value The assigned value -*/ -CVAPI(void) cvSetRealND( CvArr* arr, const int* idx, double value ); - -/** clears element of ND dense array, - in case of sparse arrays it deletes the specified node */ -CVAPI(void) cvClearND( CvArr* arr, const int* idx ); - -/** @brief Returns matrix header for arbitrary array. - -The function returns a matrix header for the input array that can be a matrix - CvMat, an image - -IplImage, or a multi-dimensional dense array - CvMatND (the third option is allowed only if -allowND != 0) . In the case of matrix the function simply returns the input pointer. In the case of -IplImage\* or CvMatND it initializes the header structure with parameters of the current image ROI -and returns &header. Because COI is not supported by CvMat, it is returned separately. - -The function provides an easy way to handle both types of arrays - IplImage and CvMat using the same -code. Input array must have non-zero data pointer, otherwise the function will report an error. - -@note If the input array is IplImage with planar data layout and COI set, the function returns the -pointer to the selected plane and COI == 0. This feature allows user to process IplImage structures -with planar data layout, even though OpenCV does not support such images. -@param arr Input array -@param header Pointer to CvMat structure used as a temporary buffer -@param coi Optional output parameter for storing COI -@param allowND If non-zero, the function accepts multi-dimensional dense arrays (CvMatND\*) and -returns 2D matrix (if CvMatND has two dimensions) or 1D matrix (when CvMatND has 1 dimension or -more than 2 dimensions). The CvMatND array must be continuous. -@sa cvGetImage, cvarrToMat. - */ -CVAPI(CvMat*) cvGetMat( const CvArr* arr, CvMat* header, - int* coi CV_DEFAULT(NULL), - int allowND CV_DEFAULT(0)); - -/** @brief Returns image header for arbitrary array. - -The function returns the image header for the input array that can be a matrix (CvMat) or image -(IplImage). In the case of an image the function simply returns the input pointer. In the case of -CvMat it initializes an image_header structure with the parameters of the input matrix. Note that -if we transform IplImage to CvMat using cvGetMat and then transform CvMat back to IplImage using -this function, we will get different headers if the ROI is set in the original image. -@param arr Input array -@param image_header Pointer to IplImage structure used as a temporary buffer - */ -CVAPI(IplImage*) cvGetImage( const CvArr* arr, IplImage* image_header ); - - -/** @brief Changes the shape of a multi-dimensional array without copying the data. - -The function is an advanced version of cvReshape that can work with multi-dimensional arrays as -well (though it can work with ordinary images and matrices) and change the number of dimensions. - -Below are the two samples from the cvReshape description rewritten using cvReshapeMatND: -@code - IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3); - IplImage gray_img_hdr, *gray_img; - gray_img = (IplImage*)cvReshapeMatND(color_img, sizeof(gray_img_hdr), &gray_img_hdr, 1, 0, 0); - ... - int size[] = { 2, 2, 2 }; - CvMatND* mat = cvCreateMatND(3, size, CV_32F); - CvMat row_header, *row; - row = (CvMat*)cvReshapeMatND(mat, sizeof(row_header), &row_header, 0, 1, 0); -@endcode -In C, the header file for this function includes a convenient macro cvReshapeND that does away with -the sizeof_header parameter. So, the lines containing the call to cvReshapeMatND in the examples -may be replaced as follow: -@code - gray_img = (IplImage*)cvReshapeND(color_img, &gray_img_hdr, 1, 0, 0); - ... - row = (CvMat*)cvReshapeND(mat, &row_header, 0, 1, 0); -@endcode -@param arr Input array -@param sizeof_header Size of output header to distinguish between IplImage, CvMat and CvMatND -output headers -@param header Output header to be filled -@param new_cn New number of channels. new_cn = 0 means that the number of channels remains -unchanged. -@param new_dims New number of dimensions. new_dims = 0 means that the number of dimensions -remains the same. -@param new_sizes Array of new dimension sizes. Only new_dims-1 values are used, because the -total number of elements must remain the same. Thus, if new_dims = 1, new_sizes array is not -used. - */ -CVAPI(CvArr*) cvReshapeMatND( const CvArr* arr, - int sizeof_header, CvArr* header, - int new_cn, int new_dims, int* new_sizes ); - -#define cvReshapeND( arr, header, new_cn, new_dims, new_sizes ) \ - cvReshapeMatND( (arr), sizeof(*(header)), (header), \ - (new_cn), (new_dims), (new_sizes)) - -/** @brief Changes shape of matrix/image without copying data. - -The function initializes the CvMat header so that it points to the same data as the original array -but has a different shape - different number of channels, different number of rows, or both. - -The following example code creates one image buffer and two image headers, the first is for a -320x240x3 image and the second is for a 960x240x1 image: -@code - IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3); - CvMat gray_mat_hdr; - IplImage gray_img_hdr, *gray_img; - cvReshape(color_img, &gray_mat_hdr, 1); - gray_img = cvGetImage(&gray_mat_hdr, &gray_img_hdr); -@endcode -And the next example converts a 3x3 matrix to a single 1x9 vector: -@code - CvMat* mat = cvCreateMat(3, 3, CV_32F); - CvMat row_header, *row; - row = cvReshape(mat, &row_header, 0, 1); -@endcode -@param arr Input array -@param header Output header to be filled -@param new_cn New number of channels. 'new_cn = 0' means that the number of channels remains -unchanged. -@param new_rows New number of rows. 'new_rows = 0' means that the number of rows remains -unchanged unless it needs to be changed according to new_cn value. -*/ -CVAPI(CvMat*) cvReshape( const CvArr* arr, CvMat* header, - int new_cn, int new_rows CV_DEFAULT(0) ); - -/** Repeats source 2d array several times in both horizontal and - vertical direction to fill destination array */ -CVAPI(void) cvRepeat( const CvArr* src, CvArr* dst ); - -/** @brief Allocates array data - -The function allocates image, matrix or multi-dimensional dense array data. Note that in the case of -matrix types OpenCV allocation functions are used. In the case of IplImage they are used unless -CV_TURN_ON_IPL_COMPATIBILITY() has been called before. In the latter case IPL functions are used -to allocate the data. -@param arr Array header - */ -CVAPI(void) cvCreateData( CvArr* arr ); - -/** @brief Releases array data. - -The function releases the array data. In the case of CvMat or CvMatND it simply calls -cvDecRefData(), that is the function can not deallocate external data. See also the note to -cvCreateData . -@param arr Array header - */ -CVAPI(void) cvReleaseData( CvArr* arr ); - -/** @brief Assigns user data to the array header. - -The function assigns user data to the array header. Header should be initialized before using -cvCreateMatHeader, cvCreateImageHeader, cvCreateMatNDHeader, cvInitMatHeader, -cvInitImageHeader or cvInitMatNDHeader. -@param arr Array header -@param data User data -@param step Full row length in bytes - */ -CVAPI(void) cvSetData( CvArr* arr, void* data, int step ); - -/** @brief Retrieves low-level information about the array. - -The function fills output variables with low-level information about the array data. All output - -parameters are optional, so some of the pointers may be set to NULL. If the array is IplImage with -ROI set, the parameters of ROI are returned. - -The following example shows how to get access to array elements. It computes absolute values of the -array elements : -@code - float* data; - int step; - CvSize size; - - cvGetRawData(array, (uchar**)&data, &step, &size); - step /= sizeof(data[0]); - - for(int y = 0; y < size.height; y++, data += step ) - for(int x = 0; x < size.width; x++ ) - data[x] = (float)fabs(data[x]); -@endcode -@param arr Array header -@param data Output pointer to the whole image origin or ROI origin if ROI is set -@param step Output full row length in bytes -@param roi_size Output ROI size - */ -CVAPI(void) cvGetRawData( const CvArr* arr, uchar** data, - int* step CV_DEFAULT(NULL), - CvSize* roi_size CV_DEFAULT(NULL)); - -/** @brief Returns size of matrix or image ROI. - -The function returns number of rows (CvSize::height) and number of columns (CvSize::width) of the -input matrix or image. In the case of image the size of ROI is returned. -@param arr array header - */ -CVAPI(CvSize) cvGetSize( const CvArr* arr ); - -/** @brief Copies one array to another. - -The function copies selected elements from an input array to an output array: - -\f[\texttt{dst} (I)= \texttt{src} (I) \quad \text{if} \quad \texttt{mask} (I) \ne 0.\f] - -If any of the passed arrays is of IplImage type, then its ROI and COI fields are used. Both arrays -must have the same type, the same number of dimensions, and the same size. The function can also -copy sparse arrays (mask is not supported in this case). -@param src The source array -@param dst The destination array -@param mask Operation mask, 8-bit single channel array; specifies elements of the destination array -to be changed - */ -CVAPI(void) cvCopy( const CvArr* src, CvArr* dst, - const CvArr* mask CV_DEFAULT(NULL) ); - -/** @brief Sets every element of an array to a given value. - -The function copies the scalar value to every selected element of the destination array: -\f[\texttt{arr} (I)= \texttt{value} \quad \text{if} \quad \texttt{mask} (I) \ne 0\f] -If array arr is of IplImage type, then is ROI used, but COI must not be set. -@param arr The destination array -@param value Fill value -@param mask Operation mask, 8-bit single channel array; specifies elements of the destination -array to be changed - */ -CVAPI(void) cvSet( CvArr* arr, CvScalar value, - const CvArr* mask CV_DEFAULT(NULL) ); - -/** @brief Clears the array. - -The function clears the array. In the case of dense arrays (CvMat, CvMatND or IplImage), -cvZero(array) is equivalent to cvSet(array,cvScalarAll(0),0). In the case of sparse arrays all the -elements are removed. -@param arr Array to be cleared - */ -CVAPI(void) cvSetZero( CvArr* arr ); -#define cvZero cvSetZero - - -/** Splits a multi-channel array into the set of single-channel arrays or - extracts particular [color] plane */ -CVAPI(void) cvSplit( const CvArr* src, CvArr* dst0, CvArr* dst1, - CvArr* dst2, CvArr* dst3 ); - -/** Merges a set of single-channel arrays into the single multi-channel array - or inserts one particular [color] plane to the array */ -CVAPI(void) cvMerge( const CvArr* src0, const CvArr* src1, - const CvArr* src2, const CvArr* src3, - CvArr* dst ); - -/** Copies several channels from input arrays to - certain channels of output arrays */ -CVAPI(void) cvMixChannels( const CvArr** src, int src_count, - CvArr** dst, int dst_count, - const int* from_to, int pair_count ); - -/** @brief Converts one array to another with optional linear transformation. - -The function has several different purposes, and thus has several different names. It copies one -array to another with optional scaling, which is performed first, and/or optional type conversion, -performed after: - -\f[\texttt{dst} (I) = \texttt{scale} \texttt{src} (I) + ( \texttt{shift} _0, \texttt{shift} _1,...)\f] - -All the channels of multi-channel arrays are processed independently. - -The type of conversion is done with rounding and saturation, that is if the result of scaling + -conversion can not be represented exactly by a value of the destination array element type, it is -set to the nearest representable value on the real axis. -@param src Source array -@param dst Destination array -@param scale Scale factor -@param shift Value added to the scaled source array elements - */ -CVAPI(void) cvConvertScale( const CvArr* src, CvArr* dst, - double scale CV_DEFAULT(1), - double shift CV_DEFAULT(0) ); -#define cvCvtScale cvConvertScale -#define cvScale cvConvertScale -#define cvConvert( src, dst ) cvConvertScale( (src), (dst), 1, 0 ) - - -/** Performs linear transformation on every source array element, - stores absolute value of the result: - dst(x,y,c) = abs(scale*src(x,y,c)+shift). - destination array must have 8u type. - In other cases one may use cvConvertScale + cvAbsDiffS */ -CVAPI(void) cvConvertScaleAbs( const CvArr* src, CvArr* dst, - double scale CV_DEFAULT(1), - double shift CV_DEFAULT(0) ); -#define cvCvtScaleAbs cvConvertScaleAbs - - -/** checks termination criteria validity and - sets eps to default_eps (if it is not set), - max_iter to default_max_iters (if it is not set) -*/ -CVAPI(CvTermCriteria) cvCheckTermCriteria( CvTermCriteria criteria, - double default_eps, - int default_max_iters ); - -/****************************************************************************************\ -* Arithmetic, logic and comparison operations * -\****************************************************************************************/ - -/** dst(mask) = src1(mask) + src2(mask) */ -CVAPI(void) cvAdd( const CvArr* src1, const CvArr* src2, CvArr* dst, - const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(mask) = src(mask) + value */ -CVAPI(void) cvAddS( const CvArr* src, CvScalar value, CvArr* dst, - const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(mask) = src1(mask) - src2(mask) */ -CVAPI(void) cvSub( const CvArr* src1, const CvArr* src2, CvArr* dst, - const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(mask) = src(mask) - value = src(mask) + (-value) */ -CV_INLINE void cvSubS( const CvArr* src, CvScalar value, CvArr* dst, - const CvArr* mask CV_DEFAULT(NULL)) -{ - cvAddS( src, cvScalar( -value.val[0], -value.val[1], -value.val[2], -value.val[3]), - dst, mask ); -} - -/** dst(mask) = value - src(mask) */ -CVAPI(void) cvSubRS( const CvArr* src, CvScalar value, CvArr* dst, - const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(idx) = src1(idx) * src2(idx) * scale - (scaled element-wise multiplication of 2 arrays) */ -CVAPI(void) cvMul( const CvArr* src1, const CvArr* src2, - CvArr* dst, double scale CV_DEFAULT(1) ); - -/** element-wise division/inversion with scaling: - dst(idx) = src1(idx) * scale / src2(idx) - or dst(idx) = scale / src2(idx) if src1 == 0 */ -CVAPI(void) cvDiv( const CvArr* src1, const CvArr* src2, - CvArr* dst, double scale CV_DEFAULT(1)); - -/** dst = src1 * scale + src2 */ -CVAPI(void) cvScaleAdd( const CvArr* src1, CvScalar scale, - const CvArr* src2, CvArr* dst ); -#define cvAXPY( A, real_scalar, B, C ) cvScaleAdd(A, cvRealScalar(real_scalar), B, C) - -/** dst = src1 * alpha + src2 * beta + gamma */ -CVAPI(void) cvAddWeighted( const CvArr* src1, double alpha, - const CvArr* src2, double beta, - double gamma, CvArr* dst ); - -/** @brief Calculates the dot product of two arrays in Euclidean metrics. - -The function calculates and returns the Euclidean dot product of two arrays. - -\f[src1 \bullet src2 = \sum _I ( \texttt{src1} (I) \texttt{src2} (I))\f] - -In the case of multiple channel arrays, the results for all channels are accumulated. In particular, -cvDotProduct(a,a) where a is a complex vector, will return \f$||\texttt{a}||^2\f$. The function can -process multi-dimensional arrays, row by row, layer by layer, and so on. -@param src1 The first source array -@param src2 The second source array - */ -CVAPI(double) cvDotProduct( const CvArr* src1, const CvArr* src2 ); - -/** dst(idx) = src1(idx) & src2(idx) */ -CVAPI(void) cvAnd( const CvArr* src1, const CvArr* src2, - CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(idx) = src(idx) & value */ -CVAPI(void) cvAndS( const CvArr* src, CvScalar value, - CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(idx) = src1(idx) | src2(idx) */ -CVAPI(void) cvOr( const CvArr* src1, const CvArr* src2, - CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(idx) = src(idx) | value */ -CVAPI(void) cvOrS( const CvArr* src, CvScalar value, - CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(idx) = src1(idx) ^ src2(idx) */ -CVAPI(void) cvXor( const CvArr* src1, const CvArr* src2, - CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(idx) = src(idx) ^ value */ -CVAPI(void) cvXorS( const CvArr* src, CvScalar value, - CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(idx) = ~src(idx) */ -CVAPI(void) cvNot( const CvArr* src, CvArr* dst ); - -/** dst(idx) = lower(idx) <= src(idx) < upper(idx) */ -CVAPI(void) cvInRange( const CvArr* src, const CvArr* lower, - const CvArr* upper, CvArr* dst ); - -/** dst(idx) = lower <= src(idx) < upper */ -CVAPI(void) cvInRangeS( const CvArr* src, CvScalar lower, - CvScalar upper, CvArr* dst ); - -#define CV_CMP_EQ 0 -#define CV_CMP_GT 1 -#define CV_CMP_GE 2 -#define CV_CMP_LT 3 -#define CV_CMP_LE 4 -#define CV_CMP_NE 5 - -/** The comparison operation support single-channel arrays only. - Destination image should be 8uC1 or 8sC1 */ - -/** dst(idx) = src1(idx) _cmp_op_ src2(idx) */ -CVAPI(void) cvCmp( const CvArr* src1, const CvArr* src2, CvArr* dst, int cmp_op ); - -/** dst(idx) = src1(idx) _cmp_op_ value */ -CVAPI(void) cvCmpS( const CvArr* src, double value, CvArr* dst, int cmp_op ); - -/** dst(idx) = min(src1(idx),src2(idx)) */ -CVAPI(void) cvMin( const CvArr* src1, const CvArr* src2, CvArr* dst ); - -/** dst(idx) = max(src1(idx),src2(idx)) */ -CVAPI(void) cvMax( const CvArr* src1, const CvArr* src2, CvArr* dst ); - -/** dst(idx) = min(src(idx),value) */ -CVAPI(void) cvMinS( const CvArr* src, double value, CvArr* dst ); - -/** dst(idx) = max(src(idx),value) */ -CVAPI(void) cvMaxS( const CvArr* src, double value, CvArr* dst ); - -/** dst(x,y,c) = abs(src1(x,y,c) - src2(x,y,c)) */ -CVAPI(void) cvAbsDiff( const CvArr* src1, const CvArr* src2, CvArr* dst ); - -/** dst(x,y,c) = abs(src(x,y,c) - value(c)) */ -CVAPI(void) cvAbsDiffS( const CvArr* src, CvArr* dst, CvScalar value ); -#define cvAbs( src, dst ) cvAbsDiffS( (src), (dst), cvScalarAll(0)) - -/****************************************************************************************\ -* Math operations * -\****************************************************************************************/ - -/** Does cartesian->polar coordinates conversion. - Either of output components (magnitude or angle) is optional */ -CVAPI(void) cvCartToPolar( const CvArr* x, const CvArr* y, - CvArr* magnitude, CvArr* angle CV_DEFAULT(NULL), - int angle_in_degrees CV_DEFAULT(0)); - -/** Does polar->cartesian coordinates conversion. - Either of output components (magnitude or angle) is optional. - If magnitude is missing it is assumed to be all 1's */ -CVAPI(void) cvPolarToCart( const CvArr* magnitude, const CvArr* angle, - CvArr* x, CvArr* y, - int angle_in_degrees CV_DEFAULT(0)); - -/** Does powering: dst(idx) = src(idx)^power */ -CVAPI(void) cvPow( const CvArr* src, CvArr* dst, double power ); - -/** Does exponention: dst(idx) = exp(src(idx)). - Overflow is not handled yet. Underflow is handled. - Maximal relative error is ~7e-6 for single-precision input */ -CVAPI(void) cvExp( const CvArr* src, CvArr* dst ); - -/** Calculates natural logarithms: dst(idx) = log(abs(src(idx))). - Logarithm of 0 gives large negative number(~-700) - Maximal relative error is ~3e-7 for single-precision output -*/ -CVAPI(void) cvLog( const CvArr* src, CvArr* dst ); - -/** Fast arctangent calculation */ -CVAPI(float) cvFastArctan( float y, float x ); - -/** Fast cubic root calculation */ -CVAPI(float) cvCbrt( float value ); - -#define CV_CHECK_RANGE 1 -#define CV_CHECK_QUIET 2 -/** Checks array values for NaNs, Infs or simply for too large numbers - (if CV_CHECK_RANGE is set). If CV_CHECK_QUIET is set, - no runtime errors is raised (function returns zero value in case of "bad" values). - Otherwise cvError is called */ -CVAPI(int) cvCheckArr( const CvArr* arr, int flags CV_DEFAULT(0), - double min_val CV_DEFAULT(0), double max_val CV_DEFAULT(0)); -#define cvCheckArray cvCheckArr - -#define CV_RAND_UNI 0 -#define CV_RAND_NORMAL 1 - -/** @brief Fills an array with random numbers and updates the RNG state. - -The function fills the destination array with uniformly or normally distributed random numbers. -@param rng CvRNG state initialized by cvRNG -@param arr The destination array -@param dist_type Distribution type -> - **CV_RAND_UNI** uniform distribution -> - **CV_RAND_NORMAL** normal or Gaussian distribution -@param param1 The first parameter of the distribution. In the case of a uniform distribution it is -the inclusive lower boundary of the random numbers range. In the case of a normal distribution it -is the mean value of the random numbers. -@param param2 The second parameter of the distribution. In the case of a uniform distribution it -is the exclusive upper boundary of the random numbers range. In the case of a normal distribution -it is the standard deviation of the random numbers. -@sa randu, randn, RNG::fill. - */ -CVAPI(void) cvRandArr( CvRNG* rng, CvArr* arr, int dist_type, - CvScalar param1, CvScalar param2 ); - -CVAPI(void) cvRandShuffle( CvArr* mat, CvRNG* rng, - double iter_factor CV_DEFAULT(1.)); - -#define CV_SORT_EVERY_ROW 0 -#define CV_SORT_EVERY_COLUMN 1 -#define CV_SORT_ASCENDING 0 -#define CV_SORT_DESCENDING 16 - -CVAPI(void) cvSort( const CvArr* src, CvArr* dst CV_DEFAULT(NULL), - CvArr* idxmat CV_DEFAULT(NULL), - int flags CV_DEFAULT(0)); - -/** Finds real roots of a cubic equation */ -CVAPI(int) cvSolveCubic( const CvMat* coeffs, CvMat* roots ); - -/** Finds all real and complex roots of a polynomial equation */ -CVAPI(void) cvSolvePoly(const CvMat* coeffs, CvMat *roots2, - int maxiter CV_DEFAULT(20), int fig CV_DEFAULT(100)); - -/****************************************************************************************\ -* Matrix operations * -\****************************************************************************************/ - -/** @brief Calculates the cross product of two 3D vectors. - -The function calculates the cross product of two 3D vectors: -\f[\texttt{dst} = \texttt{src1} \times \texttt{src2}\f] -or: -\f[\begin{array}{l} \texttt{dst} _1 = \texttt{src1} _2 \texttt{src2} _3 - \texttt{src1} _3 \texttt{src2} _2 \\ \texttt{dst} _2 = \texttt{src1} _3 \texttt{src2} _1 - \texttt{src1} _1 \texttt{src2} _3 \\ \texttt{dst} _3 = \texttt{src1} _1 \texttt{src2} _2 - \texttt{src1} _2 \texttt{src2} _1 \end{array}\f] -@param src1 The first source vector -@param src2 The second source vector -@param dst The destination vector - */ -CVAPI(void) cvCrossProduct( const CvArr* src1, const CvArr* src2, CvArr* dst ); - -/** Matrix transform: dst = A*B + C, C is optional */ -#define cvMatMulAdd( src1, src2, src3, dst ) cvGEMM( (src1), (src2), 1., (src3), 1., (dst), 0 ) -#define cvMatMul( src1, src2, dst ) cvMatMulAdd( (src1), (src2), NULL, (dst)) - -#define CV_GEMM_A_T 1 -#define CV_GEMM_B_T 2 -#define CV_GEMM_C_T 4 -/** Extended matrix transform: - dst = alpha*op(A)*op(B) + beta*op(C), where op(X) is X or X^T */ -CVAPI(void) cvGEMM( const CvArr* src1, const CvArr* src2, double alpha, - const CvArr* src3, double beta, CvArr* dst, - int tABC CV_DEFAULT(0)); -#define cvMatMulAddEx cvGEMM - -/** Transforms each element of source array and stores - resultant vectors in destination array */ -CVAPI(void) cvTransform( const CvArr* src, CvArr* dst, - const CvMat* transmat, - const CvMat* shiftvec CV_DEFAULT(NULL)); -#define cvMatMulAddS cvTransform - -/** Does perspective transform on every element of input array */ -CVAPI(void) cvPerspectiveTransform( const CvArr* src, CvArr* dst, - const CvMat* mat ); - -/** Calculates (A-delta)*(A-delta)^T (order=0) or (A-delta)^T*(A-delta) (order=1) */ -CVAPI(void) cvMulTransposed( const CvArr* src, CvArr* dst, int order, - const CvArr* delta CV_DEFAULT(NULL), - double scale CV_DEFAULT(1.) ); - -/** Tranposes matrix. Square matrices can be transposed in-place */ -CVAPI(void) cvTranspose( const CvArr* src, CvArr* dst ); -#define cvT cvTranspose - -/** Completes the symmetric matrix from the lower (LtoR=0) or from the upper (LtoR!=0) part */ -CVAPI(void) cvCompleteSymm( CvMat* matrix, int LtoR CV_DEFAULT(0) ); - -/** Mirror array data around horizontal (flip=0), - vertical (flip=1) or both(flip=-1) axises: - cvFlip(src) flips images vertically and sequences horizontally (inplace) */ -CVAPI(void) cvFlip( const CvArr* src, CvArr* dst CV_DEFAULT(NULL), - int flip_mode CV_DEFAULT(0)); -#define cvMirror cvFlip - - -#define CV_SVD_MODIFY_A 1 -#define CV_SVD_U_T 2 -#define CV_SVD_V_T 4 - -/** Performs Singular Value Decomposition of a matrix */ -CVAPI(void) cvSVD( CvArr* A, CvArr* W, CvArr* U CV_DEFAULT(NULL), - CvArr* V CV_DEFAULT(NULL), int flags CV_DEFAULT(0)); - -/** Performs Singular Value Back Substitution (solves A*X = B): - flags must be the same as in cvSVD */ -CVAPI(void) cvSVBkSb( const CvArr* W, const CvArr* U, - const CvArr* V, const CvArr* B, - CvArr* X, int flags ); - -#define CV_LU 0 -#define CV_SVD 1 -#define CV_SVD_SYM 2 -#define CV_CHOLESKY 3 -#define CV_QR 4 -#define CV_NORMAL 16 - -/** Inverts matrix */ -CVAPI(double) cvInvert( const CvArr* src, CvArr* dst, - int method CV_DEFAULT(CV_LU)); -#define cvInv cvInvert - -/** Solves linear system (src1)*(dst) = (src2) - (returns 0 if src1 is a singular and CV_LU method is used) */ -CVAPI(int) cvSolve( const CvArr* src1, const CvArr* src2, CvArr* dst, - int method CV_DEFAULT(CV_LU)); - -/** Calculates determinant of input matrix */ -CVAPI(double) cvDet( const CvArr* mat ); - -/** Calculates trace of the matrix (sum of elements on the main diagonal) */ -CVAPI(CvScalar) cvTrace( const CvArr* mat ); - -/** Finds eigen values and vectors of a symmetric matrix */ -CVAPI(void) cvEigenVV( CvArr* mat, CvArr* evects, CvArr* evals, - double eps CV_DEFAULT(0), - int lowindex CV_DEFAULT(-1), - int highindex CV_DEFAULT(-1)); - -///* Finds selected eigen values and vectors of a symmetric matrix */ -//CVAPI(void) cvSelectedEigenVV( CvArr* mat, CvArr* evects, CvArr* evals, -// int lowindex, int highindex ); - -/** Makes an identity matrix (mat_ij = i == j) */ -CVAPI(void) cvSetIdentity( CvArr* mat, CvScalar value CV_DEFAULT(cvRealScalar(1)) ); - -/** Fills matrix with given range of numbers */ -CVAPI(CvArr*) cvRange( CvArr* mat, double start, double end ); - -/** @anchor core_c_CovarFlags -@name Flags for cvCalcCovarMatrix -@see cvCalcCovarMatrix - @{ -*/ - -/** flag for cvCalcCovarMatrix, transpose([v1-avg, v2-avg,...]) * [v1-avg,v2-avg,...] */ -#define CV_COVAR_SCRAMBLED 0 - -/** flag for cvCalcCovarMatrix, [v1-avg, v2-avg,...] * transpose([v1-avg,v2-avg,...]) */ -#define CV_COVAR_NORMAL 1 - -/** flag for cvCalcCovarMatrix, do not calc average (i.e. mean vector) - use the input vector instead - (useful for calculating covariance matrix by parts) */ -#define CV_COVAR_USE_AVG 2 - -/** flag for cvCalcCovarMatrix, scale the covariance matrix coefficients by number of the vectors */ -#define CV_COVAR_SCALE 4 - -/** flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its rows */ -#define CV_COVAR_ROWS 8 - -/** flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its columns */ -#define CV_COVAR_COLS 16 - -/** @} */ - -/** Calculates covariation matrix for a set of vectors -@see @ref core_c_CovarFlags "flags" -*/ -CVAPI(void) cvCalcCovarMatrix( const CvArr** vects, int count, - CvArr* cov_mat, CvArr* avg, int flags ); - -#define CV_PCA_DATA_AS_ROW 0 -#define CV_PCA_DATA_AS_COL 1 -#define CV_PCA_USE_AVG 2 -CVAPI(void) cvCalcPCA( const CvArr* data, CvArr* mean, - CvArr* eigenvals, CvArr* eigenvects, int flags ); - -CVAPI(void) cvProjectPCA( const CvArr* data, const CvArr* mean, - const CvArr* eigenvects, CvArr* result ); - -CVAPI(void) cvBackProjectPCA( const CvArr* proj, const CvArr* mean, - const CvArr* eigenvects, CvArr* result ); - -/** Calculates Mahalanobis(weighted) distance */ -CVAPI(double) cvMahalanobis( const CvArr* vec1, const CvArr* vec2, const CvArr* mat ); -#define cvMahalonobis cvMahalanobis - -/****************************************************************************************\ -* Array Statistics * -\****************************************************************************************/ - -/** Finds sum of array elements */ -CVAPI(CvScalar) cvSum( const CvArr* arr ); - -/** Calculates number of non-zero pixels */ -CVAPI(int) cvCountNonZero( const CvArr* arr ); - -/** Calculates mean value of array elements */ -CVAPI(CvScalar) cvAvg( const CvArr* arr, const CvArr* mask CV_DEFAULT(NULL) ); - -/** Calculates mean and standard deviation of pixel values */ -CVAPI(void) cvAvgSdv( const CvArr* arr, CvScalar* mean, CvScalar* std_dev, - const CvArr* mask CV_DEFAULT(NULL) ); - -/** Finds global minimum, maximum and their positions */ -CVAPI(void) cvMinMaxLoc( const CvArr* arr, double* min_val, double* max_val, - CvPoint* min_loc CV_DEFAULT(NULL), - CvPoint* max_loc CV_DEFAULT(NULL), - const CvArr* mask CV_DEFAULT(NULL) ); - -/** @anchor core_c_NormFlags - @name Flags for cvNorm and cvNormalize - @{ -*/ -#define CV_C 1 -#define CV_L1 2 -#define CV_L2 4 -#define CV_NORM_MASK 7 -#define CV_RELATIVE 8 -#define CV_DIFF 16 -#define CV_MINMAX 32 - -#define CV_DIFF_C (CV_DIFF | CV_C) -#define CV_DIFF_L1 (CV_DIFF | CV_L1) -#define CV_DIFF_L2 (CV_DIFF | CV_L2) -#define CV_RELATIVE_C (CV_RELATIVE | CV_C) -#define CV_RELATIVE_L1 (CV_RELATIVE | CV_L1) -#define CV_RELATIVE_L2 (CV_RELATIVE | CV_L2) -/** @} */ - -/** Finds norm, difference norm or relative difference norm for an array (or two arrays) -@see ref core_c_NormFlags "flags" -*/ -CVAPI(double) cvNorm( const CvArr* arr1, const CvArr* arr2 CV_DEFAULT(NULL), - int norm_type CV_DEFAULT(CV_L2), - const CvArr* mask CV_DEFAULT(NULL) ); - -/** @see ref core_c_NormFlags "flags" */ -CVAPI(void) cvNormalize( const CvArr* src, CvArr* dst, - double a CV_DEFAULT(1.), double b CV_DEFAULT(0.), - int norm_type CV_DEFAULT(CV_L2), - const CvArr* mask CV_DEFAULT(NULL) ); - -/** @anchor core_c_ReduceFlags - @name Flags for cvReduce - @{ -*/ -#define CV_REDUCE_SUM 0 -#define CV_REDUCE_AVG 1 -#define CV_REDUCE_MAX 2 -#define CV_REDUCE_MIN 3 -/** @} */ - -/** @see @ref core_c_ReduceFlags "flags" */ -CVAPI(void) cvReduce( const CvArr* src, CvArr* dst, int dim CV_DEFAULT(-1), - int op CV_DEFAULT(CV_REDUCE_SUM) ); - -/****************************************************************************************\ -* Discrete Linear Transforms and Related Functions * -\****************************************************************************************/ - -/** @anchor core_c_DftFlags - @name Flags for cvDFT, cvDCT and cvMulSpectrums - @{ - */ -#define CV_DXT_FORWARD 0 -#define CV_DXT_INVERSE 1 -#define CV_DXT_SCALE 2 /**< divide result by size of array */ -#define CV_DXT_INV_SCALE (CV_DXT_INVERSE + CV_DXT_SCALE) -#define CV_DXT_INVERSE_SCALE CV_DXT_INV_SCALE -#define CV_DXT_ROWS 4 /**< transform each row individually */ -#define CV_DXT_MUL_CONJ 8 /**< conjugate the second argument of cvMulSpectrums */ -/** @} */ - -/** Discrete Fourier Transform: - complex->complex, - real->ccs (forward), - ccs->real (inverse) -@see core_c_DftFlags "flags" -*/ -CVAPI(void) cvDFT( const CvArr* src, CvArr* dst, int flags, - int nonzero_rows CV_DEFAULT(0) ); -#define cvFFT cvDFT - -/** Multiply results of DFTs: DFT(X)*DFT(Y) or DFT(X)*conj(DFT(Y)) -@see core_c_DftFlags "flags" -*/ -CVAPI(void) cvMulSpectrums( const CvArr* src1, const CvArr* src2, - CvArr* dst, int flags ); - -/** Finds optimal DFT vector size >= size0 */ -CVAPI(int) cvGetOptimalDFTSize( int size0 ); - -/** Discrete Cosine Transform -@see core_c_DftFlags "flags" -*/ -CVAPI(void) cvDCT( const CvArr* src, CvArr* dst, int flags ); - -/****************************************************************************************\ -* Dynamic data structures * -\****************************************************************************************/ - -/** Calculates length of sequence slice (with support of negative indices). */ -CVAPI(int) cvSliceLength( CvSlice slice, const CvSeq* seq ); - - -/** Creates new memory storage. - block_size == 0 means that default, - somewhat optimal size, is used (currently, it is 64K) */ -CVAPI(CvMemStorage*) cvCreateMemStorage( int block_size CV_DEFAULT(0)); - - -/** Creates a memory storage that will borrow memory blocks from parent storage */ -CVAPI(CvMemStorage*) cvCreateChildMemStorage( CvMemStorage* parent ); - - -/** Releases memory storage. All the children of a parent must be released before - the parent. A child storage returns all the blocks to parent when it is released */ -CVAPI(void) cvReleaseMemStorage( CvMemStorage** storage ); - - -/** Clears memory storage. This is the only way(!!!) (besides cvRestoreMemStoragePos) - to reuse memory allocated for the storage - cvClearSeq,cvClearSet ... - do not free any memory. - A child storage returns all the blocks to the parent when it is cleared */ -CVAPI(void) cvClearMemStorage( CvMemStorage* storage ); - -/** Remember a storage "free memory" position */ -CVAPI(void) cvSaveMemStoragePos( const CvMemStorage* storage, CvMemStoragePos* pos ); - -/** Restore a storage "free memory" position */ -CVAPI(void) cvRestoreMemStoragePos( CvMemStorage* storage, CvMemStoragePos* pos ); - -/** Allocates continuous buffer of the specified size in the storage */ -CVAPI(void*) cvMemStorageAlloc( CvMemStorage* storage, size_t size ); - -/** Allocates string in memory storage */ -CVAPI(CvString) cvMemStorageAllocString( CvMemStorage* storage, const char* ptr, - int len CV_DEFAULT(-1) ); - -/** Creates new empty sequence that will reside in the specified storage */ -CVAPI(CvSeq*) cvCreateSeq( int seq_flags, size_t header_size, - size_t elem_size, CvMemStorage* storage ); - -/** Changes default size (granularity) of sequence blocks. - The default size is ~1Kbyte */ -CVAPI(void) cvSetSeqBlockSize( CvSeq* seq, int delta_elems ); - - -/** Adds new element to the end of sequence. Returns pointer to the element */ -CVAPI(schar*) cvSeqPush( CvSeq* seq, const void* element CV_DEFAULT(NULL)); - - -/** Adds new element to the beginning of sequence. Returns pointer to it */ -CVAPI(schar*) cvSeqPushFront( CvSeq* seq, const void* element CV_DEFAULT(NULL)); - - -/** Removes the last element from sequence and optionally saves it */ -CVAPI(void) cvSeqPop( CvSeq* seq, void* element CV_DEFAULT(NULL)); - - -/** Removes the first element from sequence and optioanally saves it */ -CVAPI(void) cvSeqPopFront( CvSeq* seq, void* element CV_DEFAULT(NULL)); - - -#define CV_FRONT 1 -#define CV_BACK 0 -/** Adds several new elements to the end of sequence */ -CVAPI(void) cvSeqPushMulti( CvSeq* seq, const void* elements, - int count, int in_front CV_DEFAULT(0) ); - -/** Removes several elements from the end of sequence and optionally saves them */ -CVAPI(void) cvSeqPopMulti( CvSeq* seq, void* elements, - int count, int in_front CV_DEFAULT(0) ); - -/** Inserts a new element in the middle of sequence. - cvSeqInsert(seq,0,elem) == cvSeqPushFront(seq,elem) */ -CVAPI(schar*) cvSeqInsert( CvSeq* seq, int before_index, - const void* element CV_DEFAULT(NULL)); - -/** Removes specified sequence element */ -CVAPI(void) cvSeqRemove( CvSeq* seq, int index ); - - -/** Removes all the elements from the sequence. The freed memory - can be reused later only by the same sequence unless cvClearMemStorage - or cvRestoreMemStoragePos is called */ -CVAPI(void) cvClearSeq( CvSeq* seq ); - - -/** Retrieves pointer to specified sequence element. - Negative indices are supported and mean counting from the end - (e.g -1 means the last sequence element) */ -CVAPI(schar*) cvGetSeqElem( const CvSeq* seq, int index ); - -/** Calculates index of the specified sequence element. - Returns -1 if element does not belong to the sequence */ -CVAPI(int) cvSeqElemIdx( const CvSeq* seq, const void* element, - CvSeqBlock** block CV_DEFAULT(NULL) ); - -/** Initializes sequence writer. The new elements will be added to the end of sequence */ -CVAPI(void) cvStartAppendToSeq( CvSeq* seq, CvSeqWriter* writer ); - - -/** Combination of cvCreateSeq and cvStartAppendToSeq */ -CVAPI(void) cvStartWriteSeq( int seq_flags, int header_size, - int elem_size, CvMemStorage* storage, - CvSeqWriter* writer ); - -/** Closes sequence writer, updates sequence header and returns pointer - to the resultant sequence - (which may be useful if the sequence was created using cvStartWriteSeq)) -*/ -CVAPI(CvSeq*) cvEndWriteSeq( CvSeqWriter* writer ); - - -/** Updates sequence header. May be useful to get access to some of previously - written elements via cvGetSeqElem or sequence reader */ -CVAPI(void) cvFlushSeqWriter( CvSeqWriter* writer ); - - -/** Initializes sequence reader. - The sequence can be read in forward or backward direction */ -CVAPI(void) cvStartReadSeq( const CvSeq* seq, CvSeqReader* reader, - int reverse CV_DEFAULT(0) ); - - -/** Returns current sequence reader position (currently observed sequence element) */ -CVAPI(int) cvGetSeqReaderPos( CvSeqReader* reader ); - - -/** Changes sequence reader position. It may seek to an absolute or - to relative to the current position */ -CVAPI(void) cvSetSeqReaderPos( CvSeqReader* reader, int index, - int is_relative CV_DEFAULT(0)); - -/** Copies sequence content to a continuous piece of memory */ -CVAPI(void*) cvCvtSeqToArray( const CvSeq* seq, void* elements, - CvSlice slice CV_DEFAULT(CV_WHOLE_SEQ) ); - -/** Creates sequence header for array. - After that all the operations on sequences that do not alter the content - can be applied to the resultant sequence */ -CVAPI(CvSeq*) cvMakeSeqHeaderForArray( int seq_type, int header_size, - int elem_size, void* elements, int total, - CvSeq* seq, CvSeqBlock* block ); - -/** Extracts sequence slice (with or without copying sequence elements) */ -CVAPI(CvSeq*) cvSeqSlice( const CvSeq* seq, CvSlice slice, - CvMemStorage* storage CV_DEFAULT(NULL), - int copy_data CV_DEFAULT(0)); - -CV_INLINE CvSeq* cvCloneSeq( const CvSeq* seq, CvMemStorage* storage CV_DEFAULT(NULL)) -{ - return cvSeqSlice( seq, CV_WHOLE_SEQ, storage, 1 ); -} - -/** Removes sequence slice */ -CVAPI(void) cvSeqRemoveSlice( CvSeq* seq, CvSlice slice ); - -/** Inserts a sequence or array into another sequence */ -CVAPI(void) cvSeqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr ); - -/** a < b ? -1 : a > b ? 1 : 0 */ -typedef int (CV_CDECL* CvCmpFunc)(const void* a, const void* b, void* userdata ); - -/** Sorts sequence in-place given element comparison function */ -CVAPI(void) cvSeqSort( CvSeq* seq, CvCmpFunc func, void* userdata CV_DEFAULT(NULL) ); - -/** Finds element in a [sorted] sequence */ -CVAPI(schar*) cvSeqSearch( CvSeq* seq, const void* elem, CvCmpFunc func, - int is_sorted, int* elem_idx, - void* userdata CV_DEFAULT(NULL) ); - -/** Reverses order of sequence elements in-place */ -CVAPI(void) cvSeqInvert( CvSeq* seq ); - -/** Splits sequence into one or more equivalence classes using the specified criteria */ -CVAPI(int) cvSeqPartition( const CvSeq* seq, CvMemStorage* storage, - CvSeq** labels, CvCmpFunc is_equal, void* userdata ); - -/************ Internal sequence functions ************/ -CVAPI(void) cvChangeSeqBlock( void* reader, int direction ); -CVAPI(void) cvCreateSeqBlock( CvSeqWriter* writer ); - - -/** Creates a new set */ -CVAPI(CvSet*) cvCreateSet( int set_flags, int header_size, - int elem_size, CvMemStorage* storage ); - -/** Adds new element to the set and returns pointer to it */ -CVAPI(int) cvSetAdd( CvSet* set_header, CvSetElem* elem CV_DEFAULT(NULL), - CvSetElem** inserted_elem CV_DEFAULT(NULL) ); - -/** Fast variant of cvSetAdd */ -CV_INLINE CvSetElem* cvSetNew( CvSet* set_header ) -{ - CvSetElem* elem = set_header->free_elems; - if( elem ) - { - set_header->free_elems = elem->next_free; - elem->flags = elem->flags & CV_SET_ELEM_IDX_MASK; - set_header->active_count++; - } - else - cvSetAdd( set_header, NULL, &elem ); - return elem; -} - -/** Removes set element given its pointer */ -CV_INLINE void cvSetRemoveByPtr( CvSet* set_header, void* elem ) -{ - CvSetElem* _elem = (CvSetElem*)elem; - assert( _elem->flags >= 0 /*&& (elem->flags & CV_SET_ELEM_IDX_MASK) < set_header->total*/ ); - _elem->next_free = set_header->free_elems; - _elem->flags = (_elem->flags & CV_SET_ELEM_IDX_MASK) | CV_SET_ELEM_FREE_FLAG; - set_header->free_elems = _elem; - set_header->active_count--; -} - -/** Removes element from the set by its index */ -CVAPI(void) cvSetRemove( CvSet* set_header, int index ); - -/** Returns a set element by index. If the element doesn't belong to the set, - NULL is returned */ -CV_INLINE CvSetElem* cvGetSetElem( const CvSet* set_header, int idx ) -{ - CvSetElem* elem = (CvSetElem*)(void *)cvGetSeqElem( (CvSeq*)set_header, idx ); - return elem && CV_IS_SET_ELEM( elem ) ? elem : 0; -} - -/** Removes all the elements from the set */ -CVAPI(void) cvClearSet( CvSet* set_header ); - -/** Creates new graph */ -CVAPI(CvGraph*) cvCreateGraph( int graph_flags, int header_size, - int vtx_size, int edge_size, - CvMemStorage* storage ); - -/** Adds new vertex to the graph */ -CVAPI(int) cvGraphAddVtx( CvGraph* graph, const CvGraphVtx* vtx CV_DEFAULT(NULL), - CvGraphVtx** inserted_vtx CV_DEFAULT(NULL) ); - - -/** Removes vertex from the graph together with all incident edges */ -CVAPI(int) cvGraphRemoveVtx( CvGraph* graph, int index ); -CVAPI(int) cvGraphRemoveVtxByPtr( CvGraph* graph, CvGraphVtx* vtx ); - - -/** Link two vertices specifed by indices or pointers if they - are not connected or return pointer to already existing edge - connecting the vertices. - Functions return 1 if a new edge was created, 0 otherwise */ -CVAPI(int) cvGraphAddEdge( CvGraph* graph, - int start_idx, int end_idx, - const CvGraphEdge* edge CV_DEFAULT(NULL), - CvGraphEdge** inserted_edge CV_DEFAULT(NULL) ); - -CVAPI(int) cvGraphAddEdgeByPtr( CvGraph* graph, - CvGraphVtx* start_vtx, CvGraphVtx* end_vtx, - const CvGraphEdge* edge CV_DEFAULT(NULL), - CvGraphEdge** inserted_edge CV_DEFAULT(NULL) ); - -/** Remove edge connecting two vertices */ -CVAPI(void) cvGraphRemoveEdge( CvGraph* graph, int start_idx, int end_idx ); -CVAPI(void) cvGraphRemoveEdgeByPtr( CvGraph* graph, CvGraphVtx* start_vtx, - CvGraphVtx* end_vtx ); - -/** Find edge connecting two vertices */ -CVAPI(CvGraphEdge*) cvFindGraphEdge( const CvGraph* graph, int start_idx, int end_idx ); -CVAPI(CvGraphEdge*) cvFindGraphEdgeByPtr( const CvGraph* graph, - const CvGraphVtx* start_vtx, - const CvGraphVtx* end_vtx ); -#define cvGraphFindEdge cvFindGraphEdge -#define cvGraphFindEdgeByPtr cvFindGraphEdgeByPtr - -/** Remove all vertices and edges from the graph */ -CVAPI(void) cvClearGraph( CvGraph* graph ); - - -/** Count number of edges incident to the vertex */ -CVAPI(int) cvGraphVtxDegree( const CvGraph* graph, int vtx_idx ); -CVAPI(int) cvGraphVtxDegreeByPtr( const CvGraph* graph, const CvGraphVtx* vtx ); - - -/** Retrieves graph vertex by given index */ -#define cvGetGraphVtx( graph, idx ) (CvGraphVtx*)cvGetSetElem((CvSet*)(graph), (idx)) - -/** Retrieves index of a graph vertex given its pointer */ -#define cvGraphVtxIdx( graph, vtx ) ((vtx)->flags & CV_SET_ELEM_IDX_MASK) - -/** Retrieves index of a graph edge given its pointer */ -#define cvGraphEdgeIdx( graph, edge ) ((edge)->flags & CV_SET_ELEM_IDX_MASK) - -#define cvGraphGetVtxCount( graph ) ((graph)->active_count) -#define cvGraphGetEdgeCount( graph ) ((graph)->edges->active_count) - -#define CV_GRAPH_VERTEX 1 -#define CV_GRAPH_TREE_EDGE 2 -#define CV_GRAPH_BACK_EDGE 4 -#define CV_GRAPH_FORWARD_EDGE 8 -#define CV_GRAPH_CROSS_EDGE 16 -#define CV_GRAPH_ANY_EDGE 30 -#define CV_GRAPH_NEW_TREE 32 -#define CV_GRAPH_BACKTRACKING 64 -#define CV_GRAPH_OVER -1 - -#define CV_GRAPH_ALL_ITEMS -1 - -/** flags for graph vertices and edges */ -#define CV_GRAPH_ITEM_VISITED_FLAG (1 << 30) -#define CV_IS_GRAPH_VERTEX_VISITED(vtx) \ - (((CvGraphVtx*)(vtx))->flags & CV_GRAPH_ITEM_VISITED_FLAG) -#define CV_IS_GRAPH_EDGE_VISITED(edge) \ - (((CvGraphEdge*)(edge))->flags & CV_GRAPH_ITEM_VISITED_FLAG) -#define CV_GRAPH_SEARCH_TREE_NODE_FLAG (1 << 29) -#define CV_GRAPH_FORWARD_EDGE_FLAG (1 << 28) - -typedef struct CvGraphScanner -{ - CvGraphVtx* vtx; /* current graph vertex (or current edge origin) */ - CvGraphVtx* dst; /* current graph edge destination vertex */ - CvGraphEdge* edge; /* current edge */ - - CvGraph* graph; /* the graph */ - CvSeq* stack; /* the graph vertex stack */ - int index; /* the lower bound of certainly visited vertices */ - int mask; /* event mask */ -} -CvGraphScanner; - -/** Creates new graph scanner. */ -CVAPI(CvGraphScanner*) cvCreateGraphScanner( CvGraph* graph, - CvGraphVtx* vtx CV_DEFAULT(NULL), - int mask CV_DEFAULT(CV_GRAPH_ALL_ITEMS)); - -/** Releases graph scanner. */ -CVAPI(void) cvReleaseGraphScanner( CvGraphScanner** scanner ); - -/** Get next graph element */ -CVAPI(int) cvNextGraphItem( CvGraphScanner* scanner ); - -/** Creates a copy of graph */ -CVAPI(CvGraph*) cvCloneGraph( const CvGraph* graph, CvMemStorage* storage ); - - -/** Does look-up transformation. Elements of the source array - (that should be 8uC1 or 8sC1) are used as indexes in lutarr 256-element table */ -CVAPI(void) cvLUT( const CvArr* src, CvArr* dst, const CvArr* lut ); - - -/******************* Iteration through the sequence tree *****************/ -typedef struct CvTreeNodeIterator -{ - const void* node; - int level; - int max_level; -} -CvTreeNodeIterator; - -CVAPI(void) cvInitTreeNodeIterator( CvTreeNodeIterator* tree_iterator, - const void* first, int max_level ); -CVAPI(void*) cvNextTreeNode( CvTreeNodeIterator* tree_iterator ); -CVAPI(void*) cvPrevTreeNode( CvTreeNodeIterator* tree_iterator ); - -/** Inserts sequence into tree with specified "parent" sequence. - If parent is equal to frame (e.g. the most external contour), - then added contour will have null pointer to parent. */ -CVAPI(void) cvInsertNodeIntoTree( void* node, void* parent, void* frame ); - -/** Removes contour from tree (together with the contour children). */ -CVAPI(void) cvRemoveNodeFromTree( void* node, void* frame ); - -/** Gathers pointers to all the sequences, - accessible from the `first`, to the single sequence */ -CVAPI(CvSeq*) cvTreeToNodeSeq( const void* first, int header_size, - CvMemStorage* storage ); - -/** The function implements the K-means algorithm for clustering an array of sample - vectors in a specified number of classes */ -#define CV_KMEANS_USE_INITIAL_LABELS 1 -CVAPI(int) cvKMeans2( const CvArr* samples, int cluster_count, CvArr* labels, - CvTermCriteria termcrit, int attempts CV_DEFAULT(1), - CvRNG* rng CV_DEFAULT(0), int flags CV_DEFAULT(0), - CvArr* _centers CV_DEFAULT(0), double* compactness CV_DEFAULT(0) ); - -/****************************************************************************************\ -* System functions * -\****************************************************************************************/ - -/** Loads optimized functions from IPP, MKL etc. or switches back to pure C code */ -CVAPI(int) cvUseOptimized( int on_off ); - -typedef IplImage* (CV_STDCALL* Cv_iplCreateImageHeader) - (int,int,int,char*,char*,int,int,int,int,int, - IplROI*,IplImage*,void*,IplTileInfo*); -typedef void (CV_STDCALL* Cv_iplAllocateImageData)(IplImage*,int,int); -typedef void (CV_STDCALL* Cv_iplDeallocate)(IplImage*,int); -typedef IplROI* (CV_STDCALL* Cv_iplCreateROI)(int,int,int,int,int); -typedef IplImage* (CV_STDCALL* Cv_iplCloneImage)(const IplImage*); - -/** @brief Makes OpenCV use IPL functions for allocating IplImage and IplROI structures. - -Normally, the function is not called directly. Instead, a simple macro -CV_TURN_ON_IPL_COMPATIBILITY() is used that calls cvSetIPLAllocators and passes there pointers -to IPL allocation functions. : -@code - ... - CV_TURN_ON_IPL_COMPATIBILITY() - ... -@endcode -@param create_header pointer to a function, creating IPL image header. -@param allocate_data pointer to a function, allocating IPL image data. -@param deallocate pointer to a function, deallocating IPL image. -@param create_roi pointer to a function, creating IPL image ROI (i.e. Region of Interest). -@param clone_image pointer to a function, cloning an IPL image. - */ -CVAPI(void) cvSetIPLAllocators( Cv_iplCreateImageHeader create_header, - Cv_iplAllocateImageData allocate_data, - Cv_iplDeallocate deallocate, - Cv_iplCreateROI create_roi, - Cv_iplCloneImage clone_image ); - -#define CV_TURN_ON_IPL_COMPATIBILITY() \ - cvSetIPLAllocators( iplCreateImageHeader, iplAllocateImage, \ - iplDeallocate, iplCreateROI, iplCloneImage ) - -/****************************************************************************************\ -* Data Persistence * -\****************************************************************************************/ - -/********************************** High-level functions ********************************/ - -/** @brief Opens file storage for reading or writing data. - -The function opens file storage for reading or writing data. In the latter case, a new file is -created or an existing file is rewritten. The type of the read or written file is determined by the -filename extension: .xml for XML and .yml or .yaml for YAML. The function returns a pointer to the -CvFileStorage structure. If the file cannot be opened then the function returns NULL. -@param filename Name of the file associated with the storage -@param memstorage Memory storage used for temporary data and for -: storing dynamic structures, such as CvSeq or CvGraph . If it is NULL, a temporary memory - storage is created and used. -@param flags Can be one of the following: -> - **CV_STORAGE_READ** the storage is open for reading -> - **CV_STORAGE_WRITE** the storage is open for writing -@param encoding - */ -CVAPI(CvFileStorage*) cvOpenFileStorage( const char* filename, CvMemStorage* memstorage, - int flags, const char* encoding CV_DEFAULT(NULL) ); - -/** @brief Releases file storage. - -The function closes the file associated with the storage and releases all the temporary structures. -It must be called after all I/O operations with the storage are finished. -@param fs Double pointer to the released file storage - */ -CVAPI(void) cvReleaseFileStorage( CvFileStorage** fs ); - -/** returns attribute value or 0 (NULL) if there is no such attribute */ -CVAPI(const char*) cvAttrValue( const CvAttrList* attr, const char* attr_name ); - -/** @brief Starts writing a new structure. - -The function starts writing a compound structure (collection) that can be a sequence or a map. After -all the structure fields, which can be scalars or structures, are written, cvEndWriteStruct should -be called. The function can be used to group some objects or to implement the write function for a -some user object (see CvTypeInfo). -@param fs File storage -@param name Name of the written structure. The structure can be accessed by this name when the -storage is read. -@param struct_flags A combination one of the following values: -- **CV_NODE_SEQ** the written structure is a sequence (see discussion of CvFileStorage ), - that is, its elements do not have a name. -- **CV_NODE_MAP** the written structure is a map (see discussion of CvFileStorage ), that - is, all its elements have names. -One and only one of the two above flags must be specified -- **CV_NODE_FLOW** the optional flag that makes sense only for YAML streams. It means that - the structure is written as a flow (not as a block), which is more compact. It is - recommended to use this flag for structures or arrays whose elements are all scalars. -@param type_name Optional parameter - the object type name. In - case of XML it is written as a type_id attribute of the structure opening tag. In the case of - YAML it is written after a colon following the structure name (see the example in - CvFileStorage description). Mainly it is used with user objects. When the storage is read, the - encoded type name is used to determine the object type (see CvTypeInfo and cvFindType ). -@param attributes This parameter is not used in the current implementation - */ -CVAPI(void) cvStartWriteStruct( CvFileStorage* fs, const char* name, - int struct_flags, const char* type_name CV_DEFAULT(NULL), - CvAttrList attributes CV_DEFAULT(cvAttrList())); - -/** @brief Finishes writing to a file node collection. -@param fs File storage -@sa cvStartWriteStruct. - */ -CVAPI(void) cvEndWriteStruct( CvFileStorage* fs ); - -/** @brief Writes an integer value. - -The function writes a single integer value (with or without a name) to the file storage. -@param fs File storage -@param name Name of the written value. Should be NULL if and only if the parent structure is a -sequence. -@param value The written value - */ -CVAPI(void) cvWriteInt( CvFileStorage* fs, const char* name, int value ); - -/** @brief Writes a floating-point value. - -The function writes a single floating-point value (with or without a name) to file storage. Special -values are encoded as follows: NaN (Not A Number) as .NaN, infinity as +.Inf or -.Inf. - -The following example shows how to use the low-level writing functions to store custom structures, -such as termination criteria, without registering a new type. : -@code - void write_termcriteria( CvFileStorage* fs, const char* struct_name, - CvTermCriteria* termcrit ) - { - cvStartWriteStruct( fs, struct_name, CV_NODE_MAP, NULL, cvAttrList(0,0)); - cvWriteComment( fs, "termination criteria", 1 ); // just a description - if( termcrit->type & CV_TERMCRIT_ITER ) - cvWriteInteger( fs, "max_iterations", termcrit->max_iter ); - if( termcrit->type & CV_TERMCRIT_EPS ) - cvWriteReal( fs, "accuracy", termcrit->epsilon ); - cvEndWriteStruct( fs ); - } -@endcode -@param fs File storage -@param name Name of the written value. Should be NULL if and only if the parent structure is a -sequence. -@param value The written value -*/ -CVAPI(void) cvWriteReal( CvFileStorage* fs, const char* name, double value ); - -/** @brief Writes a text string. - -The function writes a text string to file storage. -@param fs File storage -@param name Name of the written string . Should be NULL if and only if the parent structure is a -sequence. -@param str The written text string -@param quote If non-zero, the written string is put in quotes, regardless of whether they are -required. Otherwise, if the flag is zero, quotes are used only when they are required (e.g. when -the string starts with a digit or contains spaces). - */ -CVAPI(void) cvWriteString( CvFileStorage* fs, const char* name, - const char* str, int quote CV_DEFAULT(0) ); - -/** @brief Writes a comment. - -The function writes a comment into file storage. The comments are skipped when the storage is read. -@param fs File storage -@param comment The written comment, single-line or multi-line -@param eol_comment If non-zero, the function tries to put the comment at the end of current line. -If the flag is zero, if the comment is multi-line, or if it does not fit at the end of the current -line, the comment starts a new line. - */ -CVAPI(void) cvWriteComment( CvFileStorage* fs, const char* comment, - int eol_comment ); - -/** @brief Writes an object to file storage. - -The function writes an object to file storage. First, the appropriate type info is found using -cvTypeOf. Then, the write method associated with the type info is called. - -Attributes are used to customize the writing procedure. The standard types support the following -attributes (all the dt attributes have the same format as in cvWriteRawData): - --# CvSeq - - **header_dt** description of user fields of the sequence header that follow CvSeq, or - CvChain (if the sequence is a Freeman chain) or CvContour (if the sequence is a contour or - point sequence) - - **dt** description of the sequence elements. - - **recursive** if the attribute is present and is not equal to "0" or "false", the whole - tree of sequences (contours) is stored. --# CvGraph - - **header_dt** description of user fields of the graph header that follows CvGraph; - - **vertex_dt** description of user fields of graph vertices - - **edge_dt** description of user fields of graph edges (note that the edge weight is - always written, so there is no need to specify it explicitly) - -Below is the code that creates the YAML file shown in the CvFileStorage description: -@code - #include "cxcore.h" - - int main( int argc, char** argv ) - { - CvMat* mat = cvCreateMat( 3, 3, CV_32F ); - CvFileStorage* fs = cvOpenFileStorage( "example.yml", 0, CV_STORAGE_WRITE ); - - cvSetIdentity( mat ); - cvWrite( fs, "A", mat, cvAttrList(0,0) ); - - cvReleaseFileStorage( &fs ); - cvReleaseMat( &mat ); - return 0; - } -@endcode -@param fs File storage -@param name Name of the written object. Should be NULL if and only if the parent structure is a -sequence. -@param ptr Pointer to the object -@param attributes The attributes of the object. They are specific for each particular type (see -the discussion below). - */ -CVAPI(void) cvWrite( CvFileStorage* fs, const char* name, const void* ptr, - CvAttrList attributes CV_DEFAULT(cvAttrList())); - -/** @brief Starts the next stream. - -The function finishes the currently written stream and starts the next stream. In the case of XML -the file with multiple streams looks like this: -@code{.xml} - - - - - - - ... -@endcode -The YAML file will look like this: -@code{.yaml} - %YAML:1.0 - # stream #1 data - ... - --- - # stream #2 data -@endcode -This is useful for concatenating files or for resuming the writing process. -@param fs File storage - */ -CVAPI(void) cvStartNextStream( CvFileStorage* fs ); - -/** @brief Writes multiple numbers. - -The function writes an array, whose elements consist of single or multiple numbers. The function -call can be replaced with a loop containing a few cvWriteInt and cvWriteReal calls, but a single -call is more efficient. Note that because none of the elements have a name, they should be written -to a sequence rather than a map. -@param fs File storage -@param src Pointer to the written array -@param len Number of the array elements to write -@param dt Specification of each array element, see @ref format_spec "format specification" - */ -CVAPI(void) cvWriteRawData( CvFileStorage* fs, const void* src, - int len, const char* dt ); - -/** @brief Returns a unique pointer for a given name. - -The function returns a unique pointer for each particular file node name. This pointer can be then -passed to the cvGetFileNode function that is faster than cvGetFileNodeByName because it compares -text strings by comparing pointers rather than the strings' content. - -Consider the following example where an array of points is encoded as a sequence of 2-entry maps: -@code - points: - - { x: 10, y: 10 } - - { x: 20, y: 20 } - - { x: 30, y: 30 } - # ... -@endcode -Then, it is possible to get hashed "x" and "y" pointers to speed up decoding of the points. : -@code - #include "cxcore.h" - - int main( int argc, char** argv ) - { - CvFileStorage* fs = cvOpenFileStorage( "points.yml", 0, CV_STORAGE_READ ); - CvStringHashNode* x_key = cvGetHashedNode( fs, "x", -1, 1 ); - CvStringHashNode* y_key = cvGetHashedNode( fs, "y", -1, 1 ); - CvFileNode* points = cvGetFileNodeByName( fs, 0, "points" ); - - if( CV_NODE_IS_SEQ(points->tag) ) - { - CvSeq* seq = points->data.seq; - int i, total = seq->total; - CvSeqReader reader; - cvStartReadSeq( seq, &reader, 0 ); - for( i = 0; i < total; i++ ) - { - CvFileNode* pt = (CvFileNode*)reader.ptr; - #if 1 // faster variant - CvFileNode* xnode = cvGetFileNode( fs, pt, x_key, 0 ); - CvFileNode* ynode = cvGetFileNode( fs, pt, y_key, 0 ); - assert( xnode && CV_NODE_IS_INT(xnode->tag) && - ynode && CV_NODE_IS_INT(ynode->tag)); - int x = xnode->data.i; // or x = cvReadInt( xnode, 0 ); - int y = ynode->data.i; // or y = cvReadInt( ynode, 0 ); - #elif 1 // slower variant; does not use x_key & y_key - CvFileNode* xnode = cvGetFileNodeByName( fs, pt, "x" ); - CvFileNode* ynode = cvGetFileNodeByName( fs, pt, "y" ); - assert( xnode && CV_NODE_IS_INT(xnode->tag) && - ynode && CV_NODE_IS_INT(ynode->tag)); - int x = xnode->data.i; // or x = cvReadInt( xnode, 0 ); - int y = ynode->data.i; // or y = cvReadInt( ynode, 0 ); - #else // the slowest yet the easiest to use variant - int x = cvReadIntByName( fs, pt, "x", 0 ); - int y = cvReadIntByName( fs, pt, "y", 0 ); - #endif - CV_NEXT_SEQ_ELEM( seq->elem_size, reader ); - printf(" - } - } - cvReleaseFileStorage( &fs ); - return 0; - } -@endcode -Please note that whatever method of accessing a map you are using, it is still much slower than -using plain sequences; for example, in the above example, it is more efficient to encode the points -as pairs of integers in a single numeric sequence. -@param fs File storage -@param name Literal node name -@param len Length of the name (if it is known apriori), or -1 if it needs to be calculated -@param create_missing Flag that specifies, whether an absent key should be added into the hash table -*/ -CVAPI(CvStringHashNode*) cvGetHashedKey( CvFileStorage* fs, const char* name, - int len CV_DEFAULT(-1), - int create_missing CV_DEFAULT(0)); - -/** @brief Retrieves one of the top-level nodes of the file storage. - -The function returns one of the top-level file nodes. The top-level nodes do not have a name, they -correspond to the streams that are stored one after another in the file storage. If the index is out -of range, the function returns a NULL pointer, so all the top-level nodes can be iterated by -subsequent calls to the function with stream_index=0,1,..., until the NULL pointer is returned. -This function can be used as a base for recursive traversal of the file storage. -@param fs File storage -@param stream_index Zero-based index of the stream. See cvStartNextStream . In most cases, -there is only one stream in the file; however, there can be several. - */ -CVAPI(CvFileNode*) cvGetRootFileNode( const CvFileStorage* fs, - int stream_index CV_DEFAULT(0) ); - -/** @brief Finds a node in a map or file storage. - -The function finds a file node. It is a faster version of cvGetFileNodeByName (see -cvGetHashedKey discussion). Also, the function can insert a new node, if it is not in the map yet. -@param fs File storage -@param map The parent map. If it is NULL, the function searches a top-level node. If both map and -key are NULLs, the function returns the root file node - a map that contains top-level nodes. -@param key Unique pointer to the node name, retrieved with cvGetHashedKey -@param create_missing Flag that specifies whether an absent node should be added to the map - */ -CVAPI(CvFileNode*) cvGetFileNode( CvFileStorage* fs, CvFileNode* map, - const CvStringHashNode* key, - int create_missing CV_DEFAULT(0) ); - -/** @brief Finds a node in a map or file storage. - -The function finds a file node by name. The node is searched either in map or, if the pointer is -NULL, among the top-level file storage nodes. Using this function for maps and cvGetSeqElem (or -sequence reader) for sequences, it is possible to navigate through the file storage. To speed up -multiple queries for a certain key (e.g., in the case of an array of structures) one may use a -combination of cvGetHashedKey and cvGetFileNode. -@param fs File storage -@param map The parent map. If it is NULL, the function searches in all the top-level nodes -(streams), starting with the first one. -@param name The file node name - */ -CVAPI(CvFileNode*) cvGetFileNodeByName( const CvFileStorage* fs, - const CvFileNode* map, - const char* name ); - -/** @brief Retrieves an integer value from a file node. - -The function returns an integer that is represented by the file node. If the file node is NULL, the -default_value is returned (thus, it is convenient to call the function right after cvGetFileNode -without checking for a NULL pointer). If the file node has type CV_NODE_INT, then node-\>data.i is -returned. If the file node has type CV_NODE_REAL, then node-\>data.f is converted to an integer -and returned. Otherwise the error is reported. -@param node File node -@param default_value The value that is returned if node is NULL - */ -CV_INLINE int cvReadInt( const CvFileNode* node, int default_value CV_DEFAULT(0) ) -{ - return !node ? default_value : - CV_NODE_IS_INT(node->tag) ? node->data.i : - CV_NODE_IS_REAL(node->tag) ? cvRound(node->data.f) : 0x7fffffff; -} - -/** @brief Finds a file node and returns its value. - -The function is a simple superposition of cvGetFileNodeByName and cvReadInt. -@param fs File storage -@param map The parent map. If it is NULL, the function searches a top-level node. -@param name The node name -@param default_value The value that is returned if the file node is not found - */ -CV_INLINE int cvReadIntByName( const CvFileStorage* fs, const CvFileNode* map, - const char* name, int default_value CV_DEFAULT(0) ) -{ - return cvReadInt( cvGetFileNodeByName( fs, map, name ), default_value ); -} - -/** @brief Retrieves a floating-point value from a file node. - -The function returns a floating-point value that is represented by the file node. If the file node -is NULL, the default_value is returned (thus, it is convenient to call the function right after -cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_REAL , -then node-\>data.f is returned. If the file node has type CV_NODE_INT , then node-:math:\>data.f -is converted to floating-point and returned. Otherwise the result is not determined. -@param node File node -@param default_value The value that is returned if node is NULL - */ -CV_INLINE double cvReadReal( const CvFileNode* node, double default_value CV_DEFAULT(0.) ) -{ - return !node ? default_value : - CV_NODE_IS_INT(node->tag) ? (double)node->data.i : - CV_NODE_IS_REAL(node->tag) ? node->data.f : 1e300; -} - -/** @brief Finds a file node and returns its value. - -The function is a simple superposition of cvGetFileNodeByName and cvReadReal . -@param fs File storage -@param map The parent map. If it is NULL, the function searches a top-level node. -@param name The node name -@param default_value The value that is returned if the file node is not found - */ -CV_INLINE double cvReadRealByName( const CvFileStorage* fs, const CvFileNode* map, - const char* name, double default_value CV_DEFAULT(0.) ) -{ - return cvReadReal( cvGetFileNodeByName( fs, map, name ), default_value ); -} - -/** @brief Retrieves a text string from a file node. - -The function returns a text string that is represented by the file node. If the file node is NULL, -the default_value is returned (thus, it is convenient to call the function right after -cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_STR , then -node-:math:\>data.str.ptr is returned. Otherwise the result is not determined. -@param node File node -@param default_value The value that is returned if node is NULL - */ -CV_INLINE const char* cvReadString( const CvFileNode* node, - const char* default_value CV_DEFAULT(NULL) ) -{ - return !node ? default_value : CV_NODE_IS_STRING(node->tag) ? node->data.str.ptr : 0; -} - -/** @brief Finds a file node by its name and returns its value. - -The function is a simple superposition of cvGetFileNodeByName and cvReadString . -@param fs File storage -@param map The parent map. If it is NULL, the function searches a top-level node. -@param name The node name -@param default_value The value that is returned if the file node is not found - */ -CV_INLINE const char* cvReadStringByName( const CvFileStorage* fs, const CvFileNode* map, - const char* name, const char* default_value CV_DEFAULT(NULL) ) -{ - return cvReadString( cvGetFileNodeByName( fs, map, name ), default_value ); -} - - -/** @brief Decodes an object and returns a pointer to it. - -The function decodes a user object (creates an object in a native representation from the file -storage subtree) and returns it. The object to be decoded must be an instance of a registered type -that supports the read method (see CvTypeInfo). The type of the object is determined by the type -name that is encoded in the file. If the object is a dynamic structure, it is created either in -memory storage and passed to cvOpenFileStorage or, if a NULL pointer was passed, in temporary -memory storage, which is released when cvReleaseFileStorage is called. Otherwise, if the object is -not a dynamic structure, it is created in a heap and should be released with a specialized function -or by using the generic cvRelease. -@param fs File storage -@param node The root object node -@param attributes Unused parameter - */ -CVAPI(void*) cvRead( CvFileStorage* fs, CvFileNode* node, - CvAttrList* attributes CV_DEFAULT(NULL)); - -/** @brief Finds an object by name and decodes it. - -The function is a simple superposition of cvGetFileNodeByName and cvRead. -@param fs File storage -@param map The parent map. If it is NULL, the function searches a top-level node. -@param name The node name -@param attributes Unused parameter - */ -CV_INLINE void* cvReadByName( CvFileStorage* fs, const CvFileNode* map, - const char* name, CvAttrList* attributes CV_DEFAULT(NULL) ) -{ - return cvRead( fs, cvGetFileNodeByName( fs, map, name ), attributes ); -} - - -/** @brief Initializes the file node sequence reader. - -The function initializes the sequence reader to read data from a file node. The initialized reader -can be then passed to cvReadRawDataSlice. -@param fs File storage -@param src The file node (a sequence) to read numbers from -@param reader Pointer to the sequence reader - */ -CVAPI(void) cvStartReadRawData( const CvFileStorage* fs, const CvFileNode* src, - CvSeqReader* reader ); - -/** @brief Initializes file node sequence reader. - -The function reads one or more elements from the file node, representing a sequence, to a -user-specified array. The total number of read sequence elements is a product of total and the -number of components in each array element. For example, if dt=2if, the function will read total\*3 -sequence elements. As with any sequence, some parts of the file node sequence can be skipped or read -repeatedly by repositioning the reader using cvSetSeqReaderPos. -@param fs File storage -@param reader The sequence reader. Initialize it with cvStartReadRawData . -@param count The number of elements to read -@param dst Pointer to the destination array -@param dt Specification of each array element. It has the same format as in cvWriteRawData . - */ -CVAPI(void) cvReadRawDataSlice( const CvFileStorage* fs, CvSeqReader* reader, - int count, void* dst, const char* dt ); - -/** @brief Reads multiple numbers. - -The function reads elements from a file node that represents a sequence of scalars. -@param fs File storage -@param src The file node (a sequence) to read numbers from -@param dst Pointer to the destination array -@param dt Specification of each array element. It has the same format as in cvWriteRawData . - */ -CVAPI(void) cvReadRawData( const CvFileStorage* fs, const CvFileNode* src, - void* dst, const char* dt ); - -/** @brief Writes a file node to another file storage. - -The function writes a copy of a file node to file storage. Possible applications of the function are -merging several file storages into one and conversion between XML and YAML formats. -@param fs Destination file storage -@param new_node_name New name of the file node in the destination file storage. To keep the -existing name, use cvcvGetFileNodeName -@param node The written node -@param embed If the written node is a collection and this parameter is not zero, no extra level of -hierarchy is created. Instead, all the elements of node are written into the currently written -structure. Of course, map elements can only be embedded into another map, and sequence elements -can only be embedded into another sequence. - */ -CVAPI(void) cvWriteFileNode( CvFileStorage* fs, const char* new_node_name, - const CvFileNode* node, int embed ); - -/** @brief Returns the name of a file node. - -The function returns the name of a file node or NULL, if the file node does not have a name or if -node is NULL. -@param node File node - */ -CVAPI(const char*) cvGetFileNodeName( const CvFileNode* node ); - -/*********************************** Adding own types ***********************************/ - -/** @brief Registers a new type. - -The function registers a new type, which is described by info . The function creates a copy of the -structure, so the user should delete it after calling the function. -@param info Type info structure - */ -CVAPI(void) cvRegisterType( const CvTypeInfo* info ); - -/** @brief Unregisters the type. - -The function unregisters a type with a specified name. If the name is unknown, it is possible to -locate the type info by an instance of the type using cvTypeOf or by iterating the type list, -starting from cvFirstType, and then calling cvUnregisterType(info-\>typeName). -@param type_name Name of an unregistered type - */ -CVAPI(void) cvUnregisterType( const char* type_name ); - -/** @brief Returns the beginning of a type list. - -The function returns the first type in the list of registered types. Navigation through the list can -be done via the prev and next fields of the CvTypeInfo structure. - */ -CVAPI(CvTypeInfo*) cvFirstType(void); - -/** @brief Finds a type by its name. - -The function finds a registered type by its name. It returns NULL if there is no type with the -specified name. -@param type_name Type name - */ -CVAPI(CvTypeInfo*) cvFindType( const char* type_name ); - -/** @brief Returns the type of an object. - -The function finds the type of a given object. It iterates through the list of registered types and -calls the is_instance function/method for every type info structure with that object until one of -them returns non-zero or until the whole list has been traversed. In the latter case, the function -returns NULL. -@param struct_ptr The object pointer - */ -CVAPI(CvTypeInfo*) cvTypeOf( const void* struct_ptr ); - -/** @brief Releases an object. - -The function finds the type of a given object and calls release with the double pointer. -@param struct_ptr Double pointer to the object - */ -CVAPI(void) cvRelease( void** struct_ptr ); - -/** @brief Makes a clone of an object. - -The function finds the type of a given object and calls clone with the passed object. Of course, if -you know the object type, for example, struct_ptr is CvMat\*, it is faster to call the specific -function, like cvCloneMat. -@param struct_ptr The object to clone - */ -CVAPI(void*) cvClone( const void* struct_ptr ); - -/** @brief Saves an object to a file. - -The function saves an object to a file. It provides a simple interface to cvWrite . -@param filename File name -@param struct_ptr Object to save -@param name Optional object name. If it is NULL, the name will be formed from filename . -@param comment Optional comment to put in the beginning of the file -@param attributes Optional attributes passed to cvWrite - */ -CVAPI(void) cvSave( const char* filename, const void* struct_ptr, - const char* name CV_DEFAULT(NULL), - const char* comment CV_DEFAULT(NULL), - CvAttrList attributes CV_DEFAULT(cvAttrList())); - -/** @brief Loads an object from a file. - -The function loads an object from a file. It basically reads the specified file, find the first -top-level node and calls cvRead for that node. If the file node does not have type information or -the type information can not be found by the type name, the function returns NULL. After the object -is loaded, the file storage is closed and all the temporary buffers are deleted. Thus, to load a -dynamic structure, such as a sequence, contour, or graph, one should pass a valid memory storage -destination to the function. -@param filename File name -@param memstorage Memory storage for dynamic structures, such as CvSeq or CvGraph . It is not used -for matrices or images. -@param name Optional object name. If it is NULL, the first top-level object in the storage will be -loaded. -@param real_name Optional output parameter that will contain the name of the loaded object -(useful if name=NULL ) - */ -CVAPI(void*) cvLoad( const char* filename, - CvMemStorage* memstorage CV_DEFAULT(NULL), - const char* name CV_DEFAULT(NULL), - const char** real_name CV_DEFAULT(NULL) ); - -/*********************************** Measuring Execution Time ***************************/ - -/** helper functions for RNG initialization and accurate time measurement: - uses internal clock counter on x86 */ -CVAPI(int64) cvGetTickCount( void ); -CVAPI(double) cvGetTickFrequency( void ); - -/*********************************** CPU capabilities ***********************************/ - -CVAPI(int) cvCheckHardwareSupport(int feature); - -/*********************************** Multi-Threading ************************************/ - -/** retrieve/set the number of threads used in OpenMP implementations */ -CVAPI(int) cvGetNumThreads( void ); -CVAPI(void) cvSetNumThreads( int threads CV_DEFAULT(0) ); -/** get index of the thread being executed */ -CVAPI(int) cvGetThreadNum( void ); - - -/********************************** Error Handling **************************************/ - -/** Get current OpenCV error status */ -CVAPI(int) cvGetErrStatus( void ); - -/** Sets error status silently */ -CVAPI(void) cvSetErrStatus( int status ); - -#define CV_ErrModeLeaf 0 /* Print error and exit program */ -#define CV_ErrModeParent 1 /* Print error and continue */ -#define CV_ErrModeSilent 2 /* Don't print and continue */ - -/** Retrives current error processing mode */ -CVAPI(int) cvGetErrMode( void ); - -/** Sets error processing mode, returns previously used mode */ -CVAPI(int) cvSetErrMode( int mode ); - -/** Sets error status and performs some additonal actions (displaying message box, - writing message to stderr, terminating application etc.) - depending on the current error mode */ -CVAPI(void) cvError( int status, const char* func_name, - const char* err_msg, const char* file_name, int line ); - -/** Retrieves textual description of the error given its code */ -CVAPI(const char*) cvErrorStr( int status ); - -/** Retrieves detailed information about the last error occured */ -CVAPI(int) cvGetErrInfo( const char** errcode_desc, const char** description, - const char** filename, int* line ); - -/** Maps IPP error codes to the counterparts from OpenCV */ -CVAPI(int) cvErrorFromIppStatus( int ipp_status ); - -typedef int (CV_CDECL *CvErrorCallback)( int status, const char* func_name, - const char* err_msg, const char* file_name, int line, void* userdata ); - -/** Assigns a new error-handling function */ -CVAPI(CvErrorCallback) cvRedirectError( CvErrorCallback error_handler, - void* userdata CV_DEFAULT(NULL), - void** prev_userdata CV_DEFAULT(NULL) ); - -/** Output nothing */ -CVAPI(int) cvNulDevReport( int status, const char* func_name, const char* err_msg, - const char* file_name, int line, void* userdata ); - -/** Output to console(fprintf(stderr,...)) */ -CVAPI(int) cvStdErrReport( int status, const char* func_name, const char* err_msg, - const char* file_name, int line, void* userdata ); - -/** Output to MessageBox(WIN32) */ -CVAPI(int) cvGuiBoxReport( int status, const char* func_name, const char* err_msg, - const char* file_name, int line, void* userdata ); - -#define OPENCV_ERROR(status,func,context) \ -cvError((status),(func),(context),__FILE__,__LINE__) - -#define OPENCV_ASSERT(expr,func,context) \ -{if (! (expr)) \ -{OPENCV_ERROR(CV_StsInternal,(func),(context));}} - -#define OPENCV_CALL( Func ) \ -{ \ -Func; \ -} - - -/** CV_FUNCNAME macro defines icvFuncName constant which is used by CV_ERROR macro */ -#ifdef CV_NO_FUNC_NAMES -#define CV_FUNCNAME( Name ) -#define cvFuncName "" -#else -#define CV_FUNCNAME( Name ) \ -static char cvFuncName[] = Name -#endif - - -/** - CV_ERROR macro unconditionally raises error with passed code and message. - After raising error, control will be transferred to the exit label. - */ -#define CV_ERROR( Code, Msg ) \ -{ \ - cvError( (Code), cvFuncName, Msg, __FILE__, __LINE__ ); \ - __CV_EXIT__; \ -} - -/** - CV_CHECK macro checks error status after CV (or IPL) - function call. If error detected, control will be transferred to the exit - label. - */ -#define CV_CHECK() \ -{ \ - if( cvGetErrStatus() < 0 ) \ - CV_ERROR( CV_StsBackTrace, "Inner function failed." ); \ -} - - -/** - CV_CALL macro calls CV (or IPL) function, checks error status and - signals a error if the function failed. Useful in "parent node" - error procesing mode - */ -#define CV_CALL( Func ) \ -{ \ - Func; \ - CV_CHECK(); \ -} - - -/** Runtime assertion macro */ -#define CV_ASSERT( Condition ) \ -{ \ - if( !(Condition) ) \ - CV_ERROR( CV_StsInternal, "Assertion: " #Condition " failed" ); \ -} - -#define __CV_BEGIN__ { -#define __CV_END__ goto exit; exit: ; } -#define __CV_EXIT__ goto exit - -/** @} core_c */ - -#ifdef __cplusplus -} // extern "C" -#endif - -#ifdef __cplusplus - -//! @addtogroup core_c_glue -//! @{ - -//! class for automatic module/RTTI data registration/unregistration -struct CV_EXPORTS CvType -{ - CvType( const char* type_name, - CvIsInstanceFunc is_instance, CvReleaseFunc release=0, - CvReadFunc read=0, CvWriteFunc write=0, CvCloneFunc clone=0 ); - ~CvType(); - CvTypeInfo* info; - - static CvTypeInfo* first; - static CvTypeInfo* last; -}; - -//! @} - -#include "opencv2/core/utility.hpp" - -namespace cv -{ - -//! @addtogroup core_c_glue -//! @{ - -/////////////////////////////////////////// glue /////////////////////////////////////////// - -//! converts array (CvMat or IplImage) to cv::Mat -CV_EXPORTS Mat cvarrToMat(const CvArr* arr, bool copyData=false, - bool allowND=true, int coiMode=0, - AutoBuffer* buf=0); - -static inline Mat cvarrToMatND(const CvArr* arr, bool copyData=false, int coiMode=0) -{ - return cvarrToMat(arr, copyData, true, coiMode); -} - - -//! extracts Channel of Interest from CvMat or IplImage and makes cv::Mat out of it. -CV_EXPORTS void extractImageCOI(const CvArr* arr, OutputArray coiimg, int coi=-1); -//! inserts single-channel cv::Mat into a multi-channel CvMat or IplImage -CV_EXPORTS void insertImageCOI(InputArray coiimg, CvArr* arr, int coi=-1); - - - -////// specialized implementations of DefaultDeleter::operator() for classic OpenCV types ////// - -template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMat* obj) const; -template<> CV_EXPORTS void DefaultDeleter::operator ()(IplImage* obj) const; -template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMatND* obj) const; -template<> CV_EXPORTS void DefaultDeleter::operator ()(CvSparseMat* obj) const; -template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMemStorage* obj) const; - -////////////// convenient wrappers for operating old-style dynamic structures ////////////// - -template class SeqIterator; - -typedef Ptr MemStorage; - -/*! - Template Sequence Class derived from CvSeq - - The class provides more convenient access to sequence elements, - STL-style operations and iterators. - - \note The class is targeted for simple data types, - i.e. no constructors or destructors - are called for the sequence elements. -*/ -template class Seq -{ -public: - typedef SeqIterator<_Tp> iterator; - typedef SeqIterator<_Tp> const_iterator; - - //! the default constructor - Seq(); - //! the constructor for wrapping CvSeq structure. The real element type in CvSeq should match _Tp. - Seq(const CvSeq* seq); - //! creates the empty sequence that resides in the specified storage - Seq(MemStorage& storage, int headerSize = sizeof(CvSeq)); - //! returns read-write reference to the specified element - _Tp& operator [](int idx); - //! returns read-only reference to the specified element - const _Tp& operator[](int idx) const; - //! returns iterator pointing to the beginning of the sequence - SeqIterator<_Tp> begin() const; - //! returns iterator pointing to the element following the last sequence element - SeqIterator<_Tp> end() const; - //! returns the number of elements in the sequence - size_t size() const; - //! returns the type of sequence elements (CV_8UC1 ... CV_64FC(CV_CN_MAX) ...) - int type() const; - //! returns the depth of sequence elements (CV_8U ... CV_64F) - int depth() const; - //! returns the number of channels in each sequence element - int channels() const; - //! returns the size of each sequence element - size_t elemSize() const; - //! returns index of the specified sequence element - size_t index(const _Tp& elem) const; - //! appends the specified element to the end of the sequence - void push_back(const _Tp& elem); - //! appends the specified element to the front of the sequence - void push_front(const _Tp& elem); - //! appends zero or more elements to the end of the sequence - void push_back(const _Tp* elems, size_t count); - //! appends zero or more elements to the front of the sequence - void push_front(const _Tp* elems, size_t count); - //! inserts the specified element to the specified position - void insert(int idx, const _Tp& elem); - //! inserts zero or more elements to the specified position - void insert(int idx, const _Tp* elems, size_t count); - //! removes element at the specified position - void remove(int idx); - //! removes the specified subsequence - void remove(const Range& r); - - //! returns reference to the first sequence element - _Tp& front(); - //! returns read-only reference to the first sequence element - const _Tp& front() const; - //! returns reference to the last sequence element - _Tp& back(); - //! returns read-only reference to the last sequence element - const _Tp& back() const; - //! returns true iff the sequence contains no elements - bool empty() const; - - //! removes all the elements from the sequence - void clear(); - //! removes the first element from the sequence - void pop_front(); - //! removes the last element from the sequence - void pop_back(); - //! removes zero or more elements from the beginning of the sequence - void pop_front(_Tp* elems, size_t count); - //! removes zero or more elements from the end of the sequence - void pop_back(_Tp* elems, size_t count); - - //! copies the whole sequence or the sequence slice to the specified vector - void copyTo(std::vector<_Tp>& vec, const Range& range=Range::all()) const; - //! returns the vector containing all the sequence elements - operator std::vector<_Tp>() const; - - CvSeq* seq; -}; - - -/*! - STL-style Sequence Iterator inherited from the CvSeqReader structure -*/ -template class SeqIterator : public CvSeqReader -{ -public: - //! the default constructor - SeqIterator(); - //! the constructor setting the iterator to the beginning or to the end of the sequence - SeqIterator(const Seq<_Tp>& seq, bool seekEnd=false); - //! positions the iterator within the sequence - void seek(size_t pos); - //! reports the current iterator position - size_t tell() const; - //! returns reference to the current sequence element - _Tp& operator *(); - //! returns read-only reference to the current sequence element - const _Tp& operator *() const; - //! moves iterator to the next sequence element - SeqIterator& operator ++(); - //! moves iterator to the next sequence element - SeqIterator operator ++(int) const; - //! moves iterator to the previous sequence element - SeqIterator& operator --(); - //! moves iterator to the previous sequence element - SeqIterator operator --(int) const; - - //! moves iterator forward by the specified offset (possibly negative) - SeqIterator& operator +=(int); - //! moves iterator backward by the specified offset (possibly negative) - SeqIterator& operator -=(int); - - // this is index of the current element module seq->total*2 - // (to distinguish between 0 and seq->total) - int index; -}; - - - -// bridge C++ => C Seq API -CV_EXPORTS schar* seqPush( CvSeq* seq, const void* element=0); -CV_EXPORTS schar* seqPushFront( CvSeq* seq, const void* element=0); -CV_EXPORTS void seqPop( CvSeq* seq, void* element=0); -CV_EXPORTS void seqPopFront( CvSeq* seq, void* element=0); -CV_EXPORTS void seqPopMulti( CvSeq* seq, void* elements, - int count, int in_front=0 ); -CV_EXPORTS void seqRemove( CvSeq* seq, int index ); -CV_EXPORTS void clearSeq( CvSeq* seq ); -CV_EXPORTS schar* getSeqElem( const CvSeq* seq, int index ); -CV_EXPORTS void seqRemoveSlice( CvSeq* seq, CvSlice slice ); -CV_EXPORTS void seqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr ); - -template inline Seq<_Tp>::Seq() : seq(0) {} -template inline Seq<_Tp>::Seq( const CvSeq* _seq ) : seq((CvSeq*)_seq) -{ - CV_Assert(!_seq || _seq->elem_size == sizeof(_Tp)); -} - -template inline Seq<_Tp>::Seq( MemStorage& storage, - int headerSize ) -{ - CV_Assert(headerSize >= (int)sizeof(CvSeq)); - seq = cvCreateSeq(DataType<_Tp>::type, headerSize, sizeof(_Tp), storage); -} - -template inline _Tp& Seq<_Tp>::operator [](int idx) -{ return *(_Tp*)getSeqElem(seq, idx); } - -template inline const _Tp& Seq<_Tp>::operator [](int idx) const -{ return *(_Tp*)getSeqElem(seq, idx); } - -template inline SeqIterator<_Tp> Seq<_Tp>::begin() const -{ return SeqIterator<_Tp>(*this); } - -template inline SeqIterator<_Tp> Seq<_Tp>::end() const -{ return SeqIterator<_Tp>(*this, true); } - -template inline size_t Seq<_Tp>::size() const -{ return seq ? seq->total : 0; } - -template inline int Seq<_Tp>::type() const -{ return seq ? CV_MAT_TYPE(seq->flags) : 0; } - -template inline int Seq<_Tp>::depth() const -{ return seq ? CV_MAT_DEPTH(seq->flags) : 0; } - -template inline int Seq<_Tp>::channels() const -{ return seq ? CV_MAT_CN(seq->flags) : 0; } - -template inline size_t Seq<_Tp>::elemSize() const -{ return seq ? seq->elem_size : 0; } - -template inline size_t Seq<_Tp>::index(const _Tp& elem) const -{ return cvSeqElemIdx(seq, &elem); } - -template inline void Seq<_Tp>::push_back(const _Tp& elem) -{ cvSeqPush(seq, &elem); } - -template inline void Seq<_Tp>::push_front(const _Tp& elem) -{ cvSeqPushFront(seq, &elem); } - -template inline void Seq<_Tp>::push_back(const _Tp* elem, size_t count) -{ cvSeqPushMulti(seq, elem, (int)count, 0); } - -template inline void Seq<_Tp>::push_front(const _Tp* elem, size_t count) -{ cvSeqPushMulti(seq, elem, (int)count, 1); } - -template inline _Tp& Seq<_Tp>::back() -{ return *(_Tp*)getSeqElem(seq, -1); } - -template inline const _Tp& Seq<_Tp>::back() const -{ return *(const _Tp*)getSeqElem(seq, -1); } - -template inline _Tp& Seq<_Tp>::front() -{ return *(_Tp*)getSeqElem(seq, 0); } - -template inline const _Tp& Seq<_Tp>::front() const -{ return *(const _Tp*)getSeqElem(seq, 0); } - -template inline bool Seq<_Tp>::empty() const -{ return !seq || seq->total == 0; } - -template inline void Seq<_Tp>::clear() -{ if(seq) clearSeq(seq); } - -template inline void Seq<_Tp>::pop_back() -{ seqPop(seq); } - -template inline void Seq<_Tp>::pop_front() -{ seqPopFront(seq); } - -template inline void Seq<_Tp>::pop_back(_Tp* elem, size_t count) -{ seqPopMulti(seq, elem, (int)count, 0); } - -template inline void Seq<_Tp>::pop_front(_Tp* elem, size_t count) -{ seqPopMulti(seq, elem, (int)count, 1); } - -template inline void Seq<_Tp>::insert(int idx, const _Tp& elem) -{ seqInsert(seq, idx, &elem); } - -template inline void Seq<_Tp>::insert(int idx, const _Tp* elems, size_t count) -{ - CvMat m = cvMat(1, count, DataType<_Tp>::type, elems); - seqInsertSlice(seq, idx, &m); -} - -template inline void Seq<_Tp>::remove(int idx) -{ seqRemove(seq, idx); } - -template inline void Seq<_Tp>::remove(const Range& r) -{ seqRemoveSlice(seq, cvSlice(r.start, r.end)); } - -template inline void Seq<_Tp>::copyTo(std::vector<_Tp>& vec, const Range& range) const -{ - size_t len = !seq ? 0 : range == Range::all() ? seq->total : range.end - range.start; - vec.resize(len); - if( seq && len ) - cvCvtSeqToArray(seq, &vec[0], range); -} - -template inline Seq<_Tp>::operator std::vector<_Tp>() const -{ - std::vector<_Tp> vec; - copyTo(vec); - return vec; -} - -template inline SeqIterator<_Tp>::SeqIterator() -{ memset(this, 0, sizeof(*this)); } - -template inline SeqIterator<_Tp>::SeqIterator(const Seq<_Tp>& _seq, bool seekEnd) -{ - cvStartReadSeq(_seq.seq, this); - index = seekEnd ? _seq.seq->total : 0; -} - -template inline void SeqIterator<_Tp>::seek(size_t pos) -{ - cvSetSeqReaderPos(this, (int)pos, false); - index = pos; -} - -template inline size_t SeqIterator<_Tp>::tell() const -{ return index; } - -template inline _Tp& SeqIterator<_Tp>::operator *() -{ return *(_Tp*)ptr; } - -template inline const _Tp& SeqIterator<_Tp>::operator *() const -{ return *(const _Tp*)ptr; } - -template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator ++() -{ - CV_NEXT_SEQ_ELEM(sizeof(_Tp), *this); - if( ++index >= seq->total*2 ) - index = 0; - return *this; -} - -template inline SeqIterator<_Tp> SeqIterator<_Tp>::operator ++(int) const -{ - SeqIterator<_Tp> it = *this; - ++*this; - return it; -} - -template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator --() -{ - CV_PREV_SEQ_ELEM(sizeof(_Tp), *this); - if( --index < 0 ) - index = seq->total*2-1; - return *this; -} - -template inline SeqIterator<_Tp> SeqIterator<_Tp>::operator --(int) const -{ - SeqIterator<_Tp> it = *this; - --*this; - return it; -} - -template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator +=(int delta) -{ - cvSetSeqReaderPos(this, delta, 1); - index += delta; - int n = seq->total*2; - if( index < 0 ) - index += n; - if( index >= n ) - index -= n; - return *this; -} - -template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator -=(int delta) -{ - return (*this += -delta); -} - -template inline ptrdiff_t operator - (const SeqIterator<_Tp>& a, - const SeqIterator<_Tp>& b) -{ - ptrdiff_t delta = a.index - b.index, n = a.seq->total; - if( delta > n || delta < -n ) - delta += delta < 0 ? n : -n; - return delta; -} - -template inline bool operator == (const SeqIterator<_Tp>& a, - const SeqIterator<_Tp>& b) -{ - return a.seq == b.seq && a.index == b.index; -} - -template inline bool operator != (const SeqIterator<_Tp>& a, - const SeqIterator<_Tp>& b) -{ - return !(a == b); -} - -//! @} - -} // cv - -#endif - -#endif diff --git a/IPL/include/opencv/opencv2/core/cuda.hpp b/IPL/include/opencv/opencv2/core/cuda.hpp deleted file mode 100644 index 64bc53e..0000000 --- a/IPL/include/opencv/opencv2/core/cuda.hpp +++ /dev/null @@ -1,846 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_CUDA_HPP__ -#define __OPENCV_CORE_CUDA_HPP__ - -#ifndef __cplusplus -# error cuda.hpp header must be compiled as C++ -#endif - -#include "opencv2/core.hpp" -#include "opencv2/core/cuda_types.hpp" - -/** - @defgroup cuda CUDA-accelerated Computer Vision - @{ - @defgroup cudacore Core part - @{ - @defgroup cudacore_init Initalization and Information - @defgroup cudacore_struct Data Structures - @} - @} - */ - -namespace cv { namespace cuda { - -//! @addtogroup cudacore_struct -//! @{ - -//=================================================================================== -// GpuMat -//=================================================================================== - -/** @brief Base storage class for GPU memory with reference counting. - -Its interface matches the Mat interface with the following limitations: - -- no arbitrary dimensions support (only 2D) -- no functions that return references to their data (because references on GPU are not valid for - CPU) -- no expression templates technique support - -Beware that the latter limitation may lead to overloaded matrix operators that cause memory -allocations. The GpuMat class is convertible to cuda::PtrStepSz and cuda::PtrStep so it can be -passed directly to the kernel. - -@note In contrast with Mat, in most cases GpuMat::isContinuous() == false . This means that rows are -aligned to a size depending on the hardware. Single-row GpuMat is always a continuous matrix. - -@note You are not recommended to leave static or global GpuMat variables allocated, that is, to rely -on its destructor. The destruction order of such variables and CUDA context is undefined. GPU memory -release function returns error if the CUDA context has been destroyed before. - -@sa Mat - */ -class CV_EXPORTS GpuMat -{ -public: - class CV_EXPORTS Allocator - { - public: - virtual ~Allocator() {} - - // allocator must fill data, step and refcount fields - virtual bool allocate(GpuMat* mat, int rows, int cols, size_t elemSize) = 0; - virtual void free(GpuMat* mat) = 0; - }; - - //! default allocator - static Allocator* defaultAllocator(); - static void setDefaultAllocator(Allocator* allocator); - - //! default constructor - explicit GpuMat(Allocator* allocator = defaultAllocator()); - - //! constructs GpuMat of the specified size and type - GpuMat(int rows, int cols, int type, Allocator* allocator = defaultAllocator()); - GpuMat(Size size, int type, Allocator* allocator = defaultAllocator()); - - //! constucts GpuMat and fills it with the specified value _s - GpuMat(int rows, int cols, int type, Scalar s, Allocator* allocator = defaultAllocator()); - GpuMat(Size size, int type, Scalar s, Allocator* allocator = defaultAllocator()); - - //! copy constructor - GpuMat(const GpuMat& m); - - //! constructor for GpuMat headers pointing to user-allocated data - GpuMat(int rows, int cols, int type, void* data, size_t step = Mat::AUTO_STEP); - GpuMat(Size size, int type, void* data, size_t step = Mat::AUTO_STEP); - - //! creates a GpuMat header for a part of the bigger matrix - GpuMat(const GpuMat& m, Range rowRange, Range colRange); - GpuMat(const GpuMat& m, Rect roi); - - //! builds GpuMat from host memory (Blocking call) - explicit GpuMat(InputArray arr, Allocator* allocator = defaultAllocator()); - - //! destructor - calls release() - ~GpuMat(); - - //! assignment operators - GpuMat& operator =(const GpuMat& m); - - //! allocates new GpuMat data unless the GpuMat already has specified size and type - void create(int rows, int cols, int type); - void create(Size size, int type); - - //! decreases reference counter, deallocate the data when reference counter reaches 0 - void release(); - - //! swaps with other smart pointer - void swap(GpuMat& mat); - - //! pefroms upload data to GpuMat (Blocking call) - void upload(InputArray arr); - - //! pefroms upload data to GpuMat (Non-Blocking call) - void upload(InputArray arr, Stream& stream); - - //! pefroms download data from device to host memory (Blocking call) - void download(OutputArray dst) const; - - //! pefroms download data from device to host memory (Non-Blocking call) - void download(OutputArray dst, Stream& stream) const; - - //! returns deep copy of the GpuMat, i.e. the data is copied - GpuMat clone() const; - - //! copies the GpuMat content to device memory (Blocking call) - void copyTo(OutputArray dst) const; - - //! copies the GpuMat content to device memory (Non-Blocking call) - void copyTo(OutputArray dst, Stream& stream) const; - - //! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Blocking call) - void copyTo(OutputArray dst, InputArray mask) const; - - //! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Non-Blocking call) - void copyTo(OutputArray dst, InputArray mask, Stream& stream) const; - - //! sets some of the GpuMat elements to s (Blocking call) - GpuMat& setTo(Scalar s); - - //! sets some of the GpuMat elements to s (Non-Blocking call) - GpuMat& setTo(Scalar s, Stream& stream); - - //! sets some of the GpuMat elements to s, according to the mask (Blocking call) - GpuMat& setTo(Scalar s, InputArray mask); - - //! sets some of the GpuMat elements to s, according to the mask (Non-Blocking call) - GpuMat& setTo(Scalar s, InputArray mask, Stream& stream); - - //! converts GpuMat to another datatype (Blocking call) - void convertTo(OutputArray dst, int rtype) const; - - //! converts GpuMat to another datatype (Non-Blocking call) - void convertTo(OutputArray dst, int rtype, Stream& stream) const; - - //! converts GpuMat to another datatype with scaling (Blocking call) - void convertTo(OutputArray dst, int rtype, double alpha, double beta = 0.0) const; - - //! converts GpuMat to another datatype with scaling (Non-Blocking call) - void convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const; - - //! converts GpuMat to another datatype with scaling (Non-Blocking call) - void convertTo(OutputArray dst, int rtype, double alpha, double beta, Stream& stream) const; - - void assignTo(GpuMat& m, int type=-1) const; - - //! returns pointer to y-th row - uchar* ptr(int y = 0); - const uchar* ptr(int y = 0) const; - - //! template version of the above method - template _Tp* ptr(int y = 0); - template const _Tp* ptr(int y = 0) const; - - template operator PtrStepSz<_Tp>() const; - template operator PtrStep<_Tp>() const; - - //! returns a new GpuMat header for the specified row - GpuMat row(int y) const; - - //! returns a new GpuMat header for the specified column - GpuMat col(int x) const; - - //! ... for the specified row span - GpuMat rowRange(int startrow, int endrow) const; - GpuMat rowRange(Range r) const; - - //! ... for the specified column span - GpuMat colRange(int startcol, int endcol) const; - GpuMat colRange(Range r) const; - - //! extracts a rectangular sub-GpuMat (this is a generalized form of row, rowRange etc.) - GpuMat operator ()(Range rowRange, Range colRange) const; - GpuMat operator ()(Rect roi) const; - - //! creates alternative GpuMat header for the same data, with different - //! number of channels and/or different number of rows - GpuMat reshape(int cn, int rows = 0) const; - - //! locates GpuMat header within a parent GpuMat - void locateROI(Size& wholeSize, Point& ofs) const; - - //! moves/resizes the current GpuMat ROI inside the parent GpuMat - GpuMat& adjustROI(int dtop, int dbottom, int dleft, int dright); - - //! returns true iff the GpuMat data is continuous - //! (i.e. when there are no gaps between successive rows) - bool isContinuous() const; - - //! returns element size in bytes - size_t elemSize() const; - - //! returns the size of element channel in bytes - size_t elemSize1() const; - - //! returns element type - int type() const; - - //! returns element type - int depth() const; - - //! returns number of channels - int channels() const; - - //! returns step/elemSize1() - size_t step1() const; - - //! returns GpuMat size : width == number of columns, height == number of rows - Size size() const; - - //! returns true if GpuMat data is NULL - bool empty() const; - - /*! includes several bit-fields: - - the magic signature - - continuity flag - - depth - - number of channels - */ - int flags; - - //! the number of rows and columns - int rows, cols; - - //! a distance between successive rows in bytes; includes the gap if any - size_t step; - - //! pointer to the data - uchar* data; - - //! pointer to the reference counter; - //! when GpuMat points to user-allocated data, the pointer is NULL - int* refcount; - - //! helper fields used in locateROI and adjustROI - uchar* datastart; - const uchar* dataend; - - //! allocator - Allocator* allocator; -}; - -/** @brief Creates a continuous matrix. - -@param rows Row count. -@param cols Column count. -@param type Type of the matrix. -@param arr Destination matrix. This parameter changes only if it has a proper type and area ( -\f$\texttt{rows} \times \texttt{cols}\f$ ). - -Matrix is called continuous if its elements are stored continuously, that is, without gaps at the -end of each row. - */ -CV_EXPORTS void createContinuous(int rows, int cols, int type, OutputArray arr); - -/** @brief Ensures that the size of a matrix is big enough and the matrix has a proper type. - -@param rows Minimum desired number of rows. -@param cols Minimum desired number of columns. -@param type Desired matrix type. -@param arr Destination matrix. - -The function does not reallocate memory if the matrix has proper attributes already. - */ -CV_EXPORTS void ensureSizeIsEnough(int rows, int cols, int type, OutputArray arr); - -//! BufferPool management (must be called before Stream creation) -CV_EXPORTS void setBufferPoolUsage(bool on); -CV_EXPORTS void setBufferPoolConfig(int deviceId, size_t stackSize, int stackCount); - -//=================================================================================== -// HostMem -//=================================================================================== - -/** @brief Class with reference counting wrapping special memory type allocation functions from CUDA. - -Its interface is also Mat-like but with additional memory type parameters. - -- **PAGE_LOCKED** sets a page locked memory type used commonly for fast and asynchronous - uploading/downloading data from/to GPU. -- **SHARED** specifies a zero copy memory allocation that enables mapping the host memory to GPU - address space, if supported. -- **WRITE_COMBINED** sets the write combined buffer that is not cached by CPU. Such buffers are - used to supply GPU with data when GPU only reads it. The advantage is a better CPU cache - utilization. - -@note Allocation size of such memory types is usually limited. For more details, see *CUDA 2.2 -Pinned Memory APIs* document or *CUDA C Programming Guide*. - */ -class CV_EXPORTS HostMem -{ -public: - enum AllocType { PAGE_LOCKED = 1, SHARED = 2, WRITE_COMBINED = 4 }; - - static MatAllocator* getAllocator(AllocType alloc_type = PAGE_LOCKED); - - explicit HostMem(AllocType alloc_type = PAGE_LOCKED); - - HostMem(const HostMem& m); - - HostMem(int rows, int cols, int type, AllocType alloc_type = PAGE_LOCKED); - HostMem(Size size, int type, AllocType alloc_type = PAGE_LOCKED); - - //! creates from host memory with coping data - explicit HostMem(InputArray arr, AllocType alloc_type = PAGE_LOCKED); - - ~HostMem(); - - HostMem& operator =(const HostMem& m); - - //! swaps with other smart pointer - void swap(HostMem& b); - - //! returns deep copy of the matrix, i.e. the data is copied - HostMem clone() const; - - //! allocates new matrix data unless the matrix already has specified size and type. - void create(int rows, int cols, int type); - void create(Size size, int type); - - //! creates alternative HostMem header for the same data, with different - //! number of channels and/or different number of rows - HostMem reshape(int cn, int rows = 0) const; - - //! decrements reference counter and released memory if needed. - void release(); - - //! returns matrix header with disabled reference counting for HostMem data. - Mat createMatHeader() const; - - /** @brief Maps CPU memory to GPU address space and creates the cuda::GpuMat header without reference counting - for it. - - This can be done only if memory was allocated with the SHARED flag and if it is supported by the - hardware. Laptops often share video and CPU memory, so address spaces can be mapped, which - eliminates an extra copy. - */ - GpuMat createGpuMatHeader() const; - - // Please see cv::Mat for descriptions - bool isContinuous() const; - size_t elemSize() const; - size_t elemSize1() const; - int type() const; - int depth() const; - int channels() const; - size_t step1() const; - Size size() const; - bool empty() const; - - // Please see cv::Mat for descriptions - int flags; - int rows, cols; - size_t step; - - uchar* data; - int* refcount; - - uchar* datastart; - const uchar* dataend; - - AllocType alloc_type; -}; - -/** @brief Page-locks the memory of matrix and maps it for the device(s). - -@param m Input matrix. - */ -CV_EXPORTS void registerPageLocked(Mat& m); - -/** @brief Unmaps the memory of matrix and makes it pageable again. - -@param m Input matrix. - */ -CV_EXPORTS void unregisterPageLocked(Mat& m); - -//=================================================================================== -// Stream -//=================================================================================== - -/** @brief This class encapsulates a queue of asynchronous calls. - -@note Currently, you may face problems if an operation is enqueued twice with different data. Some -functions use the constant GPU memory, and next call may update the memory before the previous one -has been finished. But calling different operations asynchronously is safe because each operation -has its own constant buffer. Memory copy/upload/download/set operations to the buffers you hold are -also safe. : - */ -class CV_EXPORTS Stream -{ - typedef void (Stream::*bool_type)() const; - void this_type_does_not_support_comparisons() const {} - -public: - typedef void (*StreamCallback)(int status, void* userData); - - //! creates a new asynchronous stream - Stream(); - - /** @brief Returns true if the current stream queue is finished. Otherwise, it returns false. - */ - bool queryIfComplete() const; - - /** @brief Blocks the current CPU thread until all operations in the stream are complete. - */ - void waitForCompletion(); - - /** @brief Makes a compute stream wait on an event. - */ - void waitEvent(const Event& event); - - /** @brief Adds a callback to be called on the host after all currently enqueued items in the stream have - completed. - - @note Callbacks must not make any CUDA API calls. Callbacks must not perform any synchronization - that may depend on outstanding device work or other callbacks that are not mandated to run earlier. - Callbacks without a mandated order (in independent streams) execute in undefined order and may be - serialized. - */ - void enqueueHostCallback(StreamCallback callback, void* userData); - - //! return Stream object for default CUDA stream - static Stream& Null(); - - //! returns true if stream object is not default (!= 0) - operator bool_type() const; - - class Impl; - -private: - Ptr impl_; - Stream(const Ptr& impl); - - friend struct StreamAccessor; - friend class BufferPool; - friend class DefaultDeviceInitializer; -}; - -class CV_EXPORTS Event -{ -public: - enum CreateFlags - { - DEFAULT = 0x00, /**< Default event flag */ - BLOCKING_SYNC = 0x01, /**< Event uses blocking synchronization */ - DISABLE_TIMING = 0x02, /**< Event will not record timing data */ - INTERPROCESS = 0x04 /**< Event is suitable for interprocess use. DisableTiming must be set */ - }; - - explicit Event(CreateFlags flags = DEFAULT); - - //! records an event - void record(Stream& stream = Stream::Null()); - - //! queries an event's status - bool queryIfComplete() const; - - //! waits for an event to complete - void waitForCompletion(); - - //! computes the elapsed time between events - static float elapsedTime(const Event& start, const Event& end); - - class Impl; - -private: - Ptr impl_; - Event(const Ptr& impl); - - friend struct EventAccessor; -}; - -//! @} cudacore_struct - -//=================================================================================== -// Initialization & Info -//=================================================================================== - -//! @addtogroup cudacore_init -//! @{ - -/** @brief Returns the number of installed CUDA-enabled devices. - -Use this function before any other CUDA functions calls. If OpenCV is compiled without CUDA support, -this function returns 0. - */ -CV_EXPORTS int getCudaEnabledDeviceCount(); - -/** @brief Sets a device and initializes it for the current thread. - -@param device System index of a CUDA device starting with 0. - -If the call of this function is omitted, a default device is initialized at the fist CUDA usage. - */ -CV_EXPORTS void setDevice(int device); - -/** @brief Returns the current device index set by cuda::setDevice or initialized by default. - */ -CV_EXPORTS int getDevice(); - -/** @brief Explicitly destroys and cleans up all resources associated with the current device in the current -process. - -Any subsequent API call to this device will reinitialize the device. - */ -CV_EXPORTS void resetDevice(); - -/** @brief Enumeration providing CUDA computing features. - */ -enum FeatureSet -{ - FEATURE_SET_COMPUTE_10 = 10, - FEATURE_SET_COMPUTE_11 = 11, - FEATURE_SET_COMPUTE_12 = 12, - FEATURE_SET_COMPUTE_13 = 13, - FEATURE_SET_COMPUTE_20 = 20, - FEATURE_SET_COMPUTE_21 = 21, - FEATURE_SET_COMPUTE_30 = 30, - FEATURE_SET_COMPUTE_32 = 32, - FEATURE_SET_COMPUTE_35 = 35, - FEATURE_SET_COMPUTE_50 = 50, - - GLOBAL_ATOMICS = FEATURE_SET_COMPUTE_11, - SHARED_ATOMICS = FEATURE_SET_COMPUTE_12, - NATIVE_DOUBLE = FEATURE_SET_COMPUTE_13, - WARP_SHUFFLE_FUNCTIONS = FEATURE_SET_COMPUTE_30, - DYNAMIC_PARALLELISM = FEATURE_SET_COMPUTE_35 -}; - -//! checks whether current device supports the given feature -CV_EXPORTS bool deviceSupports(FeatureSet feature_set); - -/** @brief Class providing a set of static methods to check what NVIDIA\* card architecture the CUDA module was -built for. - -According to the CUDA C Programming Guide Version 3.2: "PTX code produced for some specific compute -capability can always be compiled to binary code of greater or equal compute capability". - */ -class CV_EXPORTS TargetArchs -{ -public: - /** @brief The following method checks whether the module was built with the support of the given feature: - - @param feature_set Features to be checked. See :ocvcuda::FeatureSet. - */ - static bool builtWith(FeatureSet feature_set); - - /** @brief There is a set of methods to check whether the module contains intermediate (PTX) or binary CUDA - code for the given architecture(s): - - @param major Major compute capability version. - @param minor Minor compute capability version. - */ - static bool has(int major, int minor); - static bool hasPtx(int major, int minor); - static bool hasBin(int major, int minor); - - static bool hasEqualOrLessPtx(int major, int minor); - static bool hasEqualOrGreater(int major, int minor); - static bool hasEqualOrGreaterPtx(int major, int minor); - static bool hasEqualOrGreaterBin(int major, int minor); -}; - -/** @brief Class providing functionality for querying the specified GPU properties. - */ -class CV_EXPORTS DeviceInfo -{ -public: - //! creates DeviceInfo object for the current GPU - DeviceInfo(); - - /** @brief The constructors. - - @param device_id System index of the CUDA device starting with 0. - - Constructs the DeviceInfo object for the specified device. If device_id parameter is missed, it - constructs an object for the current device. - */ - DeviceInfo(int device_id); - - /** @brief Returns system index of the CUDA device starting with 0. - */ - int deviceID() const; - - //! ASCII string identifying device - const char* name() const; - - //! global memory available on device in bytes - size_t totalGlobalMem() const; - - //! shared memory available per block in bytes - size_t sharedMemPerBlock() const; - - //! 32-bit registers available per block - int regsPerBlock() const; - - //! warp size in threads - int warpSize() const; - - //! maximum pitch in bytes allowed by memory copies - size_t memPitch() const; - - //! maximum number of threads per block - int maxThreadsPerBlock() const; - - //! maximum size of each dimension of a block - Vec3i maxThreadsDim() const; - - //! maximum size of each dimension of a grid - Vec3i maxGridSize() const; - - //! clock frequency in kilohertz - int clockRate() const; - - //! constant memory available on device in bytes - size_t totalConstMem() const; - - //! major compute capability - int majorVersion() const; - - //! minor compute capability - int minorVersion() const; - - //! alignment requirement for textures - size_t textureAlignment() const; - - //! pitch alignment requirement for texture references bound to pitched memory - size_t texturePitchAlignment() const; - - //! number of multiprocessors on device - int multiProcessorCount() const; - - //! specified whether there is a run time limit on kernels - bool kernelExecTimeoutEnabled() const; - - //! device is integrated as opposed to discrete - bool integrated() const; - - //! device can map host memory with cudaHostAlloc/cudaHostGetDevicePointer - bool canMapHostMemory() const; - - enum ComputeMode - { - ComputeModeDefault, /**< default compute mode (Multiple threads can use cudaSetDevice with this device) */ - ComputeModeExclusive, /**< compute-exclusive-thread mode (Only one thread in one process will be able to use cudaSetDevice with this device) */ - ComputeModeProhibited, /**< compute-prohibited mode (No threads can use cudaSetDevice with this device) */ - ComputeModeExclusiveProcess /**< compute-exclusive-process mode (Many threads in one process will be able to use cudaSetDevice with this device) */ - }; - - //! compute mode - ComputeMode computeMode() const; - - //! maximum 1D texture size - int maxTexture1D() const; - - //! maximum 1D mipmapped texture size - int maxTexture1DMipmap() const; - - //! maximum size for 1D textures bound to linear memory - int maxTexture1DLinear() const; - - //! maximum 2D texture dimensions - Vec2i maxTexture2D() const; - - //! maximum 2D mipmapped texture dimensions - Vec2i maxTexture2DMipmap() const; - - //! maximum dimensions (width, height, pitch) for 2D textures bound to pitched memory - Vec3i maxTexture2DLinear() const; - - //! maximum 2D texture dimensions if texture gather operations have to be performed - Vec2i maxTexture2DGather() const; - - //! maximum 3D texture dimensions - Vec3i maxTexture3D() const; - - //! maximum Cubemap texture dimensions - int maxTextureCubemap() const; - - //! maximum 1D layered texture dimensions - Vec2i maxTexture1DLayered() const; - - //! maximum 2D layered texture dimensions - Vec3i maxTexture2DLayered() const; - - //! maximum Cubemap layered texture dimensions - Vec2i maxTextureCubemapLayered() const; - - //! maximum 1D surface size - int maxSurface1D() const; - - //! maximum 2D surface dimensions - Vec2i maxSurface2D() const; - - //! maximum 3D surface dimensions - Vec3i maxSurface3D() const; - - //! maximum 1D layered surface dimensions - Vec2i maxSurface1DLayered() const; - - //! maximum 2D layered surface dimensions - Vec3i maxSurface2DLayered() const; - - //! maximum Cubemap surface dimensions - int maxSurfaceCubemap() const; - - //! maximum Cubemap layered surface dimensions - Vec2i maxSurfaceCubemapLayered() const; - - //! alignment requirements for surfaces - size_t surfaceAlignment() const; - - //! device can possibly execute multiple kernels concurrently - bool concurrentKernels() const; - - //! device has ECC support enabled - bool ECCEnabled() const; - - //! PCI bus ID of the device - int pciBusID() const; - - //! PCI device ID of the device - int pciDeviceID() const; - - //! PCI domain ID of the device - int pciDomainID() const; - - //! true if device is a Tesla device using TCC driver, false otherwise - bool tccDriver() const; - - //! number of asynchronous engines - int asyncEngineCount() const; - - //! device shares a unified address space with the host - bool unifiedAddressing() const; - - //! peak memory clock frequency in kilohertz - int memoryClockRate() const; - - //! global memory bus width in bits - int memoryBusWidth() const; - - //! size of L2 cache in bytes - int l2CacheSize() const; - - //! maximum resident threads per multiprocessor - int maxThreadsPerMultiProcessor() const; - - //! gets free and total device memory - void queryMemory(size_t& totalMemory, size_t& freeMemory) const; - size_t freeMemory() const; - size_t totalMemory() const; - - /** @brief Provides information on CUDA feature support. - - @param feature_set Features to be checked. See cuda::FeatureSet. - - This function returns true if the device has the specified CUDA feature. Otherwise, it returns false - */ - bool supports(FeatureSet feature_set) const; - - /** @brief Checks the CUDA module and device compatibility. - - This function returns true if the CUDA module can be run on the specified device. Otherwise, it - returns false . - */ - bool isCompatible() const; - -private: - int device_id_; -}; - -CV_EXPORTS void printCudaDeviceInfo(int device); -CV_EXPORTS void printShortCudaDeviceInfo(int device); - -//! @} cudacore_init - -}} // namespace cv { namespace cuda { - - -#include "opencv2/core/cuda.inl.hpp" - -#endif /* __OPENCV_CORE_CUDA_HPP__ */ diff --git a/IPL/include/opencv/opencv2/core/cuda.inl.hpp b/IPL/include/opencv/opencv2/core/cuda.inl.hpp deleted file mode 100644 index 01dc6d7..0000000 --- a/IPL/include/opencv/opencv2/core/cuda.inl.hpp +++ /dev/null @@ -1,631 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_CUDAINL_HPP__ -#define __OPENCV_CORE_CUDAINL_HPP__ - -#include "opencv2/core/cuda.hpp" - -//! @cond IGNORED - -namespace cv { namespace cuda { - -//=================================================================================== -// GpuMat -//=================================================================================== - -inline -GpuMat::GpuMat(Allocator* allocator_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) -{} - -inline -GpuMat::GpuMat(int rows_, int cols_, int type_, Allocator* allocator_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) -{ - if (rows_ > 0 && cols_ > 0) - create(rows_, cols_, type_); -} - -inline -GpuMat::GpuMat(Size size_, int type_, Allocator* allocator_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) -{ - if (size_.height > 0 && size_.width > 0) - create(size_.height, size_.width, type_); -} - -inline -GpuMat::GpuMat(int rows_, int cols_, int type_, Scalar s_, Allocator* allocator_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) -{ - if (rows_ > 0 && cols_ > 0) - { - create(rows_, cols_, type_); - setTo(s_); - } -} - -inline -GpuMat::GpuMat(Size size_, int type_, Scalar s_, Allocator* allocator_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) -{ - if (size_.height > 0 && size_.width > 0) - { - create(size_.height, size_.width, type_); - setTo(s_); - } -} - -inline -GpuMat::GpuMat(const GpuMat& m) - : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), allocator(m.allocator) -{ - if (refcount) - CV_XADD(refcount, 1); -} - -inline -GpuMat::GpuMat(InputArray arr, Allocator* allocator_) : - flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) -{ - upload(arr); -} - -inline -GpuMat::~GpuMat() -{ - release(); -} - -inline -GpuMat& GpuMat::operator =(const GpuMat& m) -{ - if (this != &m) - { - GpuMat temp(m); - swap(temp); - } - - return *this; -} - -inline -void GpuMat::create(Size size_, int type_) -{ - create(size_.height, size_.width, type_); -} - -inline -void GpuMat::swap(GpuMat& b) -{ - std::swap(flags, b.flags); - std::swap(rows, b.rows); - std::swap(cols, b.cols); - std::swap(step, b.step); - std::swap(data, b.data); - std::swap(datastart, b.datastart); - std::swap(dataend, b.dataend); - std::swap(refcount, b.refcount); - std::swap(allocator, b.allocator); -} - -inline -GpuMat GpuMat::clone() const -{ - GpuMat m; - copyTo(m); - return m; -} - -inline -void GpuMat::copyTo(OutputArray dst, InputArray mask) const -{ - copyTo(dst, mask, Stream::Null()); -} - -inline -GpuMat& GpuMat::setTo(Scalar s) -{ - return setTo(s, Stream::Null()); -} - -inline -GpuMat& GpuMat::setTo(Scalar s, InputArray mask) -{ - return setTo(s, mask, Stream::Null()); -} - -inline -void GpuMat::convertTo(OutputArray dst, int rtype) const -{ - convertTo(dst, rtype, Stream::Null()); -} - -inline -void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, double beta) const -{ - convertTo(dst, rtype, alpha, beta, Stream::Null()); -} - -inline -void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const -{ - convertTo(dst, rtype, alpha, 0.0, stream); -} - -inline -void GpuMat::assignTo(GpuMat& m, int _type) const -{ - if (_type < 0) - m = *this; - else - convertTo(m, _type); -} - -inline -uchar* GpuMat::ptr(int y) -{ - CV_DbgAssert( (unsigned)y < (unsigned)rows ); - return data + step * y; -} - -inline -const uchar* GpuMat::ptr(int y) const -{ - CV_DbgAssert( (unsigned)y < (unsigned)rows ); - return data + step * y; -} - -template inline -_Tp* GpuMat::ptr(int y) -{ - return (_Tp*)ptr(y); -} - -template inline -const _Tp* GpuMat::ptr(int y) const -{ - return (const _Tp*)ptr(y); -} - -template inline -GpuMat::operator PtrStepSz() const -{ - return PtrStepSz(rows, cols, (T*)data, step); -} - -template inline -GpuMat::operator PtrStep() const -{ - return PtrStep((T*)data, step); -} - -inline -GpuMat GpuMat::row(int y) const -{ - return GpuMat(*this, Range(y, y+1), Range::all()); -} - -inline -GpuMat GpuMat::col(int x) const -{ - return GpuMat(*this, Range::all(), Range(x, x+1)); -} - -inline -GpuMat GpuMat::rowRange(int startrow, int endrow) const -{ - return GpuMat(*this, Range(startrow, endrow), Range::all()); -} - -inline -GpuMat GpuMat::rowRange(Range r) const -{ - return GpuMat(*this, r, Range::all()); -} - -inline -GpuMat GpuMat::colRange(int startcol, int endcol) const -{ - return GpuMat(*this, Range::all(), Range(startcol, endcol)); -} - -inline -GpuMat GpuMat::colRange(Range r) const -{ - return GpuMat(*this, Range::all(), r); -} - -inline -GpuMat GpuMat::operator ()(Range rowRange_, Range colRange_) const -{ - return GpuMat(*this, rowRange_, colRange_); -} - -inline -GpuMat GpuMat::operator ()(Rect roi) const -{ - return GpuMat(*this, roi); -} - -inline -bool GpuMat::isContinuous() const -{ - return (flags & Mat::CONTINUOUS_FLAG) != 0; -} - -inline -size_t GpuMat::elemSize() const -{ - return CV_ELEM_SIZE(flags); -} - -inline -size_t GpuMat::elemSize1() const -{ - return CV_ELEM_SIZE1(flags); -} - -inline -int GpuMat::type() const -{ - return CV_MAT_TYPE(flags); -} - -inline -int GpuMat::depth() const -{ - return CV_MAT_DEPTH(flags); -} - -inline -int GpuMat::channels() const -{ - return CV_MAT_CN(flags); -} - -inline -size_t GpuMat::step1() const -{ - return step / elemSize1(); -} - -inline -Size GpuMat::size() const -{ - return Size(cols, rows); -} - -inline -bool GpuMat::empty() const -{ - return data == 0; -} - -static inline -GpuMat createContinuous(int rows, int cols, int type) -{ - GpuMat m; - createContinuous(rows, cols, type, m); - return m; -} - -static inline -void createContinuous(Size size, int type, OutputArray arr) -{ - createContinuous(size.height, size.width, type, arr); -} - -static inline -GpuMat createContinuous(Size size, int type) -{ - GpuMat m; - createContinuous(size, type, m); - return m; -} - -static inline -void ensureSizeIsEnough(Size size, int type, OutputArray arr) -{ - ensureSizeIsEnough(size.height, size.width, type, arr); -} - -static inline -void swap(GpuMat& a, GpuMat& b) -{ - a.swap(b); -} - -//=================================================================================== -// HostMem -//=================================================================================== - -inline -HostMem::HostMem(AllocType alloc_type_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) -{ -} - -inline -HostMem::HostMem(const HostMem& m) - : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), alloc_type(m.alloc_type) -{ - if( refcount ) - CV_XADD(refcount, 1); -} - -inline -HostMem::HostMem(int rows_, int cols_, int type_, AllocType alloc_type_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) -{ - if (rows_ > 0 && cols_ > 0) - create(rows_, cols_, type_); -} - -inline -HostMem::HostMem(Size size_, int type_, AllocType alloc_type_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) -{ - if (size_.height > 0 && size_.width > 0) - create(size_.height, size_.width, type_); -} - -inline -HostMem::HostMem(InputArray arr, AllocType alloc_type_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) -{ - arr.getMat().copyTo(*this); -} - -inline -HostMem::~HostMem() -{ - release(); -} - -inline -HostMem& HostMem::operator =(const HostMem& m) -{ - if (this != &m) - { - HostMem temp(m); - swap(temp); - } - - return *this; -} - -inline -void HostMem::swap(HostMem& b) -{ - std::swap(flags, b.flags); - std::swap(rows, b.rows); - std::swap(cols, b.cols); - std::swap(step, b.step); - std::swap(data, b.data); - std::swap(datastart, b.datastart); - std::swap(dataend, b.dataend); - std::swap(refcount, b.refcount); - std::swap(alloc_type, b.alloc_type); -} - -inline -HostMem HostMem::clone() const -{ - HostMem m(size(), type(), alloc_type); - createMatHeader().copyTo(m); - return m; -} - -inline -void HostMem::create(Size size_, int type_) -{ - create(size_.height, size_.width, type_); -} - -inline -Mat HostMem::createMatHeader() const -{ - return Mat(size(), type(), data, step); -} - -inline -bool HostMem::isContinuous() const -{ - return (flags & Mat::CONTINUOUS_FLAG) != 0; -} - -inline -size_t HostMem::elemSize() const -{ - return CV_ELEM_SIZE(flags); -} - -inline -size_t HostMem::elemSize1() const -{ - return CV_ELEM_SIZE1(flags); -} - -inline -int HostMem::type() const -{ - return CV_MAT_TYPE(flags); -} - -inline -int HostMem::depth() const -{ - return CV_MAT_DEPTH(flags); -} - -inline -int HostMem::channels() const -{ - return CV_MAT_CN(flags); -} - -inline -size_t HostMem::step1() const -{ - return step / elemSize1(); -} - -inline -Size HostMem::size() const -{ - return Size(cols, rows); -} - -inline -bool HostMem::empty() const -{ - return data == 0; -} - -static inline -void swap(HostMem& a, HostMem& b) -{ - a.swap(b); -} - -//=================================================================================== -// Stream -//=================================================================================== - -inline -Stream::Stream(const Ptr& impl) - : impl_(impl) -{ -} - -//=================================================================================== -// Event -//=================================================================================== - -inline -Event::Event(const Ptr& impl) - : impl_(impl) -{ -} - -//=================================================================================== -// Initialization & Info -//=================================================================================== - -inline -bool TargetArchs::has(int major, int minor) -{ - return hasPtx(major, minor) || hasBin(major, minor); -} - -inline -bool TargetArchs::hasEqualOrGreater(int major, int minor) -{ - return hasEqualOrGreaterPtx(major, minor) || hasEqualOrGreaterBin(major, minor); -} - -inline -DeviceInfo::DeviceInfo() -{ - device_id_ = getDevice(); -} - -inline -DeviceInfo::DeviceInfo(int device_id) -{ - CV_Assert( device_id >= 0 && device_id < getCudaEnabledDeviceCount() ); - device_id_ = device_id; -} - -inline -int DeviceInfo::deviceID() const -{ - return device_id_; -} - -inline -size_t DeviceInfo::freeMemory() const -{ - size_t _totalMemory = 0, _freeMemory = 0; - queryMemory(_totalMemory, _freeMemory); - return _freeMemory; -} - -inline -size_t DeviceInfo::totalMemory() const -{ - size_t _totalMemory = 0, _freeMemory = 0; - queryMemory(_totalMemory, _freeMemory); - return _totalMemory; -} - -inline -bool DeviceInfo::supports(FeatureSet feature_set) const -{ - int version = majorVersion() * 10 + minorVersion(); - return version >= feature_set; -} - - -}} // namespace cv { namespace cuda { - -//=================================================================================== -// Mat -//=================================================================================== - -namespace cv { - -inline -Mat::Mat(const cuda::GpuMat& m) - : flags(0), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows) -{ - m.download(*this); -} - -} - -//! @endcond - -#endif // __OPENCV_CORE_CUDAINL_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/block.hpp b/IPL/include/opencv/opencv2/core/cuda/block.hpp deleted file mode 100644 index 0c6f063..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/block.hpp +++ /dev/null @@ -1,211 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_DEVICE_BLOCK_HPP__ -#define __OPENCV_CUDA_DEVICE_BLOCK_HPP__ - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - struct Block - { - static __device__ __forceinline__ unsigned int id() - { - return blockIdx.x; - } - - static __device__ __forceinline__ unsigned int stride() - { - return blockDim.x * blockDim.y * blockDim.z; - } - - static __device__ __forceinline__ void sync() - { - __syncthreads(); - } - - static __device__ __forceinline__ int flattenedThreadId() - { - return threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x; - } - - template - static __device__ __forceinline__ void fill(It beg, It end, const T& value) - { - int STRIDE = stride(); - It t = beg + flattenedThreadId(); - - for(; t < end; t += STRIDE) - *t = value; - } - - template - static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value) - { - int STRIDE = stride(); - int tid = flattenedThreadId(); - value += tid; - - for(OutIt t = beg + tid; t < end; t += STRIDE, value += STRIDE) - *t = value; - } - - template - static __device__ __forceinline__ void copy(InIt beg, InIt end, OutIt out) - { - int STRIDE = stride(); - InIt t = beg + flattenedThreadId(); - OutIt o = out + (t - beg); - - for(; t < end; t += STRIDE, o += STRIDE) - *o = *t; - } - - template - static __device__ __forceinline__ void transfrom(InIt beg, InIt end, OutIt out, UnOp op) - { - int STRIDE = stride(); - InIt t = beg + flattenedThreadId(); - OutIt o = out + (t - beg); - - for(; t < end; t += STRIDE, o += STRIDE) - *o = op(*t); - } - - template - static __device__ __forceinline__ void transfrom(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op) - { - int STRIDE = stride(); - InIt1 t1 = beg1 + flattenedThreadId(); - InIt2 t2 = beg2 + flattenedThreadId(); - OutIt o = out + (t1 - beg1); - - for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, o += STRIDE) - *o = op(*t1, *t2); - } - - template - static __device__ __forceinline__ void reduce(volatile T* buffer, BinOp op) - { - int tid = flattenedThreadId(); - T val = buffer[tid]; - - if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); } - if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); } - if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); } - if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); } - - if (tid < 32) - { - if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); } - if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); } - if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); } - if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); } - if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); } - if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); } - } - } - - template - static __device__ __forceinline__ T reduce(volatile T* buffer, T init, BinOp op) - { - int tid = flattenedThreadId(); - T val = buffer[tid] = init; - __syncthreads(); - - if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); } - if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); } - if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); } - if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); } - - if (tid < 32) - { - if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); } - if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); } - if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); } - if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); } - if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); } - if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); } - } - __syncthreads(); - return buffer[0]; - } - - template - static __device__ __forceinline__ void reduce_n(T* data, unsigned int n, BinOp op) - { - int ftid = flattenedThreadId(); - int sft = stride(); - - if (sft < n) - { - for (unsigned int i = sft + ftid; i < n; i += sft) - data[ftid] = op(data[ftid], data[i]); - - __syncthreads(); - - n = sft; - } - - while (n > 1) - { - unsigned int half = n/2; - - if (ftid < half) - data[ftid] = op(data[ftid], data[n - ftid - 1]); - - __syncthreads(); - - n = n - half; - } - } - }; -}}} - -//! @endcond - -#endif /* __OPENCV_CUDA_DEVICE_BLOCK_HPP__ */ diff --git a/IPL/include/opencv/opencv2/core/cuda/border_interpolate.hpp b/IPL/include/opencv/opencv2/core/cuda/border_interpolate.hpp deleted file mode 100644 index a204155..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/border_interpolate.hpp +++ /dev/null @@ -1,722 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_BORDER_INTERPOLATE_HPP__ -#define __OPENCV_CUDA_BORDER_INTERPOLATE_HPP__ - -#include "saturate_cast.hpp" -#include "vec_traits.hpp" -#include "vec_math.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - ////////////////////////////////////////////////////////////// - // BrdConstant - - template struct BrdRowConstant - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdRowConstant(int width_, const D& val_ = VecTraits::all(0)) : width(width_), val(val_) {} - - template __device__ __forceinline__ D at_low(int x, const T* data) const - { - return x >= 0 ? saturate_cast(data[x]) : val; - } - - template __device__ __forceinline__ D at_high(int x, const T* data) const - { - return x < width ? saturate_cast(data[x]) : val; - } - - template __device__ __forceinline__ D at(int x, const T* data) const - { - return (x >= 0 && x < width) ? saturate_cast(data[x]) : val; - } - - int width; - D val; - }; - - template struct BrdColConstant - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdColConstant(int height_, const D& val_ = VecTraits::all(0)) : height(height_), val(val_) {} - - template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const - { - return y >= 0 ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; - } - - template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const - { - return y < height ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; - } - - template __device__ __forceinline__ D at(int y, const T* data, size_t step) const - { - return (y >= 0 && y < height) ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; - } - - int height; - D val; - }; - - template struct BrdConstant - { - typedef D result_type; - - __host__ __device__ __forceinline__ BrdConstant(int height_, int width_, const D& val_ = VecTraits::all(0)) : height(height_), width(width_), val(val_) - { - } - - template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const - { - return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(((const T*)((const uchar*)data + y * step))[x]) : val; - } - - template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const - { - return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(src(y, x)) : val; - } - - int height; - int width; - D val; - }; - - ////////////////////////////////////////////////////////////// - // BrdReplicate - - template struct BrdRowReplicate - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdRowReplicate(int width) : last_col(width - 1) {} - template __host__ __device__ __forceinline__ BrdRowReplicate(int width, U) : last_col(width - 1) {} - - __device__ __forceinline__ int idx_col_low(int x) const - { - return ::max(x, 0); - } - - __device__ __forceinline__ int idx_col_high(int x) const - { - return ::min(x, last_col); - } - - __device__ __forceinline__ int idx_col(int x) const - { - return idx_col_low(idx_col_high(x)); - } - - template __device__ __forceinline__ D at_low(int x, const T* data) const - { - return saturate_cast(data[idx_col_low(x)]); - } - - template __device__ __forceinline__ D at_high(int x, const T* data) const - { - return saturate_cast(data[idx_col_high(x)]); - } - - template __device__ __forceinline__ D at(int x, const T* data) const - { - return saturate_cast(data[idx_col(x)]); - } - - int last_col; - }; - - template struct BrdColReplicate - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdColReplicate(int height) : last_row(height - 1) {} - template __host__ __device__ __forceinline__ BrdColReplicate(int height, U) : last_row(height - 1) {} - - __device__ __forceinline__ int idx_row_low(int y) const - { - return ::max(y, 0); - } - - __device__ __forceinline__ int idx_row_high(int y) const - { - return ::min(y, last_row); - } - - __device__ __forceinline__ int idx_row(int y) const - { - return idx_row_low(idx_row_high(y)); - } - - template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const - { - return saturate_cast(*(const T*)((const char*)data + idx_row_low(y) * step)); - } - - template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const - { - return saturate_cast(*(const T*)((const char*)data + idx_row_high(y) * step)); - } - - template __device__ __forceinline__ D at(int y, const T* data, size_t step) const - { - return saturate_cast(*(const T*)((const char*)data + idx_row(y) * step)); - } - - int last_row; - }; - - template struct BrdReplicate - { - typedef D result_type; - - __host__ __device__ __forceinline__ BrdReplicate(int height, int width) : last_row(height - 1), last_col(width - 1) {} - template __host__ __device__ __forceinline__ BrdReplicate(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} - - __device__ __forceinline__ int idx_row_low(int y) const - { - return ::max(y, 0); - } - - __device__ __forceinline__ int idx_row_high(int y) const - { - return ::min(y, last_row); - } - - __device__ __forceinline__ int idx_row(int y) const - { - return idx_row_low(idx_row_high(y)); - } - - __device__ __forceinline__ int idx_col_low(int x) const - { - return ::max(x, 0); - } - - __device__ __forceinline__ int idx_col_high(int x) const - { - return ::min(x, last_col); - } - - __device__ __forceinline__ int idx_col(int x) const - { - return idx_col_low(idx_col_high(x)); - } - - template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const - { - return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); - } - - template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const - { - return saturate_cast(src(idx_row(y), idx_col(x))); - } - - int last_row; - int last_col; - }; - - ////////////////////////////////////////////////////////////// - // BrdReflect101 - - template struct BrdRowReflect101 - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdRowReflect101(int width) : last_col(width - 1) {} - template __host__ __device__ __forceinline__ BrdRowReflect101(int width, U) : last_col(width - 1) {} - - __device__ __forceinline__ int idx_col_low(int x) const - { - return ::abs(x) % (last_col + 1); - } - - __device__ __forceinline__ int idx_col_high(int x) const - { - return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1); - } - - __device__ __forceinline__ int idx_col(int x) const - { - return idx_col_low(idx_col_high(x)); - } - - template __device__ __forceinline__ D at_low(int x, const T* data) const - { - return saturate_cast(data[idx_col_low(x)]); - } - - template __device__ __forceinline__ D at_high(int x, const T* data) const - { - return saturate_cast(data[idx_col_high(x)]); - } - - template __device__ __forceinline__ D at(int x, const T* data) const - { - return saturate_cast(data[idx_col(x)]); - } - - int last_col; - }; - - template struct BrdColReflect101 - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdColReflect101(int height) : last_row(height - 1) {} - template __host__ __device__ __forceinline__ BrdColReflect101(int height, U) : last_row(height - 1) {} - - __device__ __forceinline__ int idx_row_low(int y) const - { - return ::abs(y) % (last_row + 1); - } - - __device__ __forceinline__ int idx_row_high(int y) const - { - return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1); - } - - __device__ __forceinline__ int idx_row(int y) const - { - return idx_row_low(idx_row_high(y)); - } - - template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); - } - - template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); - } - - template __device__ __forceinline__ D at(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); - } - - int last_row; - }; - - template struct BrdReflect101 - { - typedef D result_type; - - __host__ __device__ __forceinline__ BrdReflect101(int height, int width) : last_row(height - 1), last_col(width - 1) {} - template __host__ __device__ __forceinline__ BrdReflect101(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} - - __device__ __forceinline__ int idx_row_low(int y) const - { - return ::abs(y) % (last_row + 1); - } - - __device__ __forceinline__ int idx_row_high(int y) const - { - return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1); - } - - __device__ __forceinline__ int idx_row(int y) const - { - return idx_row_low(idx_row_high(y)); - } - - __device__ __forceinline__ int idx_col_low(int x) const - { - return ::abs(x) % (last_col + 1); - } - - __device__ __forceinline__ int idx_col_high(int x) const - { - return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1); - } - - __device__ __forceinline__ int idx_col(int x) const - { - return idx_col_low(idx_col_high(x)); - } - - template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const - { - return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); - } - - template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const - { - return saturate_cast(src(idx_row(y), idx_col(x))); - } - - int last_row; - int last_col; - }; - - ////////////////////////////////////////////////////////////// - // BrdReflect - - template struct BrdRowReflect - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdRowReflect(int width) : last_col(width - 1) {} - template __host__ __device__ __forceinline__ BrdRowReflect(int width, U) : last_col(width - 1) {} - - __device__ __forceinline__ int idx_col_low(int x) const - { - return (::abs(x) - (x < 0)) % (last_col + 1); - } - - __device__ __forceinline__ int idx_col_high(int x) const - { - return ::abs(last_col - ::abs(last_col - x) + (x > last_col)) % (last_col + 1); - } - - __device__ __forceinline__ int idx_col(int x) const - { - return idx_col_high(::abs(x) - (x < 0)); - } - - template __device__ __forceinline__ D at_low(int x, const T* data) const - { - return saturate_cast(data[idx_col_low(x)]); - } - - template __device__ __forceinline__ D at_high(int x, const T* data) const - { - return saturate_cast(data[idx_col_high(x)]); - } - - template __device__ __forceinline__ D at(int x, const T* data) const - { - return saturate_cast(data[idx_col(x)]); - } - - int last_col; - }; - - template struct BrdColReflect - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdColReflect(int height) : last_row(height - 1) {} - template __host__ __device__ __forceinline__ BrdColReflect(int height, U) : last_row(height - 1) {} - - __device__ __forceinline__ int idx_row_low(int y) const - { - return (::abs(y) - (y < 0)) % (last_row + 1); - } - - __device__ __forceinline__ int idx_row_high(int y) const - { - return ::abs(last_row - ::abs(last_row - y) + (y > last_row)) % (last_row + 1); - } - - __device__ __forceinline__ int idx_row(int y) const - { - return idx_row_high(::abs(y) - (y < 0)); - } - - template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); - } - - template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); - } - - template __device__ __forceinline__ D at(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); - } - - int last_row; - }; - - template struct BrdReflect - { - typedef D result_type; - - __host__ __device__ __forceinline__ BrdReflect(int height, int width) : last_row(height - 1), last_col(width - 1) {} - template __host__ __device__ __forceinline__ BrdReflect(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} - - __device__ __forceinline__ int idx_row_low(int y) const - { - return (::abs(y) - (y < 0)) % (last_row + 1); - } - - __device__ __forceinline__ int idx_row_high(int y) const - { - return /*::abs*/(last_row - ::abs(last_row - y) + (y > last_row)) /*% (last_row + 1)*/; - } - - __device__ __forceinline__ int idx_row(int y) const - { - return idx_row_low(idx_row_high(y)); - } - - __device__ __forceinline__ int idx_col_low(int x) const - { - return (::abs(x) - (x < 0)) % (last_col + 1); - } - - __device__ __forceinline__ int idx_col_high(int x) const - { - return (last_col - ::abs(last_col - x) + (x > last_col)); - } - - __device__ __forceinline__ int idx_col(int x) const - { - return idx_col_low(idx_col_high(x)); - } - - template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const - { - return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); - } - - template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const - { - return saturate_cast(src(idx_row(y), idx_col(x))); - } - - int last_row; - int last_col; - }; - - ////////////////////////////////////////////////////////////// - // BrdWrap - - template struct BrdRowWrap - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdRowWrap(int width_) : width(width_) {} - template __host__ __device__ __forceinline__ BrdRowWrap(int width_, U) : width(width_) {} - - __device__ __forceinline__ int idx_col_low(int x) const - { - return (x >= 0) * x + (x < 0) * (x - ((x - width + 1) / width) * width); - } - - __device__ __forceinline__ int idx_col_high(int x) const - { - return (x < width) * x + (x >= width) * (x % width); - } - - __device__ __forceinline__ int idx_col(int x) const - { - return idx_col_high(idx_col_low(x)); - } - - template __device__ __forceinline__ D at_low(int x, const T* data) const - { - return saturate_cast(data[idx_col_low(x)]); - } - - template __device__ __forceinline__ D at_high(int x, const T* data) const - { - return saturate_cast(data[idx_col_high(x)]); - } - - template __device__ __forceinline__ D at(int x, const T* data) const - { - return saturate_cast(data[idx_col(x)]); - } - - int width; - }; - - template struct BrdColWrap - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdColWrap(int height_) : height(height_) {} - template __host__ __device__ __forceinline__ BrdColWrap(int height_, U) : height(height_) {} - - __device__ __forceinline__ int idx_row_low(int y) const - { - return (y >= 0) * y + (y < 0) * (y - ((y - height + 1) / height) * height); - } - - __device__ __forceinline__ int idx_row_high(int y) const - { - return (y < height) * y + (y >= height) * (y % height); - } - - __device__ __forceinline__ int idx_row(int y) const - { - return idx_row_high(idx_row_low(y)); - } - - template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); - } - - template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); - } - - template __device__ __forceinline__ D at(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); - } - - int height; - }; - - template struct BrdWrap - { - typedef D result_type; - - __host__ __device__ __forceinline__ BrdWrap(int height_, int width_) : - height(height_), width(width_) - { - } - template - __host__ __device__ __forceinline__ BrdWrap(int height_, int width_, U) : - height(height_), width(width_) - { - } - - __device__ __forceinline__ int idx_row_low(int y) const - { - return (y >= 0) ? y : (y - ((y - height + 1) / height) * height); - } - - __device__ __forceinline__ int idx_row_high(int y) const - { - return (y < height) ? y : (y % height); - } - - __device__ __forceinline__ int idx_row(int y) const - { - return idx_row_high(idx_row_low(y)); - } - - __device__ __forceinline__ int idx_col_low(int x) const - { - return (x >= 0) ? x : (x - ((x - width + 1) / width) * width); - } - - __device__ __forceinline__ int idx_col_high(int x) const - { - return (x < width) ? x : (x % width); - } - - __device__ __forceinline__ int idx_col(int x) const - { - return idx_col_high(idx_col_low(x)); - } - - template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const - { - return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); - } - - template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const - { - return saturate_cast(src(idx_row(y), idx_col(x))); - } - - int height; - int width; - }; - - ////////////////////////////////////////////////////////////// - // BorderReader - - template struct BorderReader - { - typedef typename B::result_type elem_type; - typedef typename Ptr2D::index_type index_type; - - __host__ __device__ __forceinline__ BorderReader(const Ptr2D& ptr_, const B& b_) : ptr(ptr_), b(b_) {} - - __device__ __forceinline__ elem_type operator ()(index_type y, index_type x) const - { - return b.at(y, x, ptr); - } - - Ptr2D ptr; - B b; - }; - - // under win32 there is some bug with templated types that passed as kernel parameters - // with this specialization all works fine - template struct BorderReader< Ptr2D, BrdConstant > - { - typedef typename BrdConstant::result_type elem_type; - typedef typename Ptr2D::index_type index_type; - - __host__ __device__ __forceinline__ BorderReader(const Ptr2D& src_, const BrdConstant& b) : - src(src_), height(b.height), width(b.width), val(b.val) - { - } - - __device__ __forceinline__ D operator ()(index_type y, index_type x) const - { - return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(src(y, x)) : val; - } - - Ptr2D src; - int height; - int width; - D val; - }; -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // __OPENCV_CUDA_BORDER_INTERPOLATE_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/color.hpp b/IPL/include/opencv/opencv2/core/cuda/color.hpp deleted file mode 100644 index 6faf8c9..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/color.hpp +++ /dev/null @@ -1,309 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_COLOR_HPP__ -#define __OPENCV_CUDA_COLOR_HPP__ - -#include "detail/color_detail.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - // All OPENCV_CUDA_IMPLEMENT_*_TRAITS(ColorSpace1_to_ColorSpace2, ...) macros implements - // template class ColorSpace1_to_ColorSpace2_traits - // { - // typedef ... functor_type; - // static __host__ __device__ functor_type create_functor(); - // }; - - OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgb, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_bgra, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgba, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_bgr, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgb, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgba, 4, 4, 2) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr555, 3, 0, 5) - OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr565, 3, 0, 6) - OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr555, 3, 2, 5) - OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr565, 3, 2, 6) - OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr555, 4, 0, 5) - OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr565, 4, 0, 6) - OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr555, 4, 2, 5) - OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr565, 4, 2, 6) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgb, 3, 2, 5) - OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgb, 3, 2, 6) - OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgr, 3, 0, 5) - OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgr, 3, 0, 6) - OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgba, 4, 2, 5) - OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgba, 4, 2, 6) - OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgra, 4, 0, 5) - OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgra, 4, 0, 6) - - #undef OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgr, 3) - OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgra, 4) - - #undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr555, 5) - OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr565, 6) - - #undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr555_to_gray, 5) - OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr565_to_gray, 6) - - #undef OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgb_to_gray, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgr_to_gray, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgba_to_gray, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgra_to_gray, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv4, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv4, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv4, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv4, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS - - OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgb, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgba, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgb, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgba, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgr, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgra, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgr, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgra, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb4, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb4, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb4, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb4, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS - - OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgb, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgba, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgb, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgba, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgr, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgra, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgr, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgra, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz4, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz4, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz4, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz4, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS - - OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgb, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgb, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgba, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgba, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgr, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgr, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgra, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgra, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv4, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv4, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv4, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv4, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS - - OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgb, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgba, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgb, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgba, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgr, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgra, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgr, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgra, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls4, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls4, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls4, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls4, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS - - OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgb, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgba, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgb, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgba, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgr, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgra, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgr, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgra, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab, 3, 3, true, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab, 4, 3, true, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab4, 3, 4, true, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab4, 4, 4, true, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab, 3, 3, true, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab, 4, 3, true, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab4, 3, 4, true, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab4, 4, 4, true, 0) - - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab, 3, 3, false, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab, 4, 3, false, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab4, 3, 4, false, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab4, 4, 4, false, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab, 3, 3, false, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab, 4, 3, false, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab4, 3, 4, false, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab4, 4, 4, false, 0) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS - - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgb, 3, 3, true, 2) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgb, 4, 3, true, 2) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgba, 3, 4, true, 2) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgba, 4, 4, true, 2) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgr, 3, 3, true, 0) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgr, 4, 3, true, 0) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgra, 3, 4, true, 0) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgra, 4, 4, true, 0) - - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgb, 3, 3, false, 2) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgb, 4, 3, false, 2) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgba, 3, 4, false, 2) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgba, 4, 4, false, 2) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgr, 3, 3, false, 0) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgr, 4, 3, false, 0) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgra, 3, 4, false, 0) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgra, 4, 4, false, 0) - - #undef OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv, 3, 3, true, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv, 4, 3, true, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv4, 3, 4, true, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv4, 4, 4, true, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv, 3, 3, true, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv, 4, 3, true, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv4, 3, 4, true, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv4, 4, 4, true, 0) - - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv, 3, 3, false, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv, 4, 3, false, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv4, 3, 4, false, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv4, 4, 4, false, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv, 3, 3, false, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv, 4, 3, false, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv4, 3, 4, false, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv4, 4, 4, false, 0) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS - - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgb, 3, 3, true, 2) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgb, 4, 3, true, 2) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgba, 3, 4, true, 2) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgba, 4, 4, true, 2) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgr, 3, 3, true, 0) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgr, 4, 3, true, 0) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgra, 3, 4, true, 0) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgra, 4, 4, true, 0) - - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgb, 3, 3, false, 2) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgb, 4, 3, false, 2) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgba, 3, 4, false, 2) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgba, 4, 4, false, 2) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgr, 3, 3, false, 0) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgr, 4, 3, false, 0) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgra, 3, 4, false, 0) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgra, 4, 4, false, 0) - - #undef OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // __OPENCV_CUDA_BORDER_INTERPOLATE_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/common.hpp b/IPL/include/opencv/opencv2/core/cuda/common.hpp deleted file mode 100644 index b93c3ef..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/common.hpp +++ /dev/null @@ -1,109 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_COMMON_HPP__ -#define __OPENCV_CUDA_COMMON_HPP__ - -#include -#include "opencv2/core/cuda_types.hpp" -#include "opencv2/core/cvdef.h" -#include "opencv2/core/base.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -#ifndef CV_PI_F - #ifndef CV_PI - #define CV_PI_F 3.14159265f - #else - #define CV_PI_F ((float)CV_PI) - #endif -#endif - -namespace cv { namespace cuda { - static inline void checkCudaError(cudaError_t err, const char* file, const int line, const char* func) - { - if (cudaSuccess != err) - cv::error(cv::Error::GpuApiCallError, cudaGetErrorString(err), func, file, line); - } -}} - -#ifndef cudaSafeCall - #define cudaSafeCall(expr) cv::cuda::checkCudaError(expr, __FILE__, __LINE__, CV_Func) -#endif - -namespace cv { namespace cuda -{ - template static inline bool isAligned(const T* ptr, size_t size) - { - return reinterpret_cast(ptr) % size == 0; - } - - static inline bool isAligned(size_t step, size_t size) - { - return step % size == 0; - } -}} - -namespace cv { namespace cuda -{ - namespace device - { - __host__ __device__ __forceinline__ int divUp(int total, int grain) - { - return (total + grain - 1) / grain; - } - - template inline void bindTexture(const textureReference* tex, const PtrStepSz& img) - { - cudaChannelFormatDesc desc = cudaCreateChannelDesc(); - cudaSafeCall( cudaBindTexture2D(0, tex, img.ptr(), &desc, img.cols, img.rows, img.step) ); - } - } -}} - -//! @endcond - -#endif // __OPENCV_CUDA_COMMON_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/datamov_utils.hpp b/IPL/include/opencv/opencv2/core/cuda/datamov_utils.hpp deleted file mode 100644 index bb02cf9..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/datamov_utils.hpp +++ /dev/null @@ -1,113 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_DATAMOV_UTILS_HPP__ -#define __OPENCV_CUDA_DATAMOV_UTILS_HPP__ - -#include "common.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 200 - - // for Fermi memory space is detected automatically - template struct ForceGlob - { - __device__ __forceinline__ static void Load(const T* ptr, int offset, T& val) { val = ptr[offset]; } - }; - - #else // __CUDA_ARCH__ >= 200 - - #if defined(_WIN64) || defined(__LP64__) - // 64-bit register modifier for inlined asm - #define OPENCV_CUDA_ASM_PTR "l" - #else - // 32-bit register modifier for inlined asm - #define OPENCV_CUDA_ASM_PTR "r" - #endif - - template struct ForceGlob; - - #define OPENCV_CUDA_DEFINE_FORCE_GLOB(base_type, ptx_type, reg_mod) \ - template <> struct ForceGlob \ - { \ - __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \ - { \ - asm("ld.global."#ptx_type" %0, [%1];" : "="#reg_mod(val) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \ - } \ - }; - - #define OPENCV_CUDA_DEFINE_FORCE_GLOB_B(base_type, ptx_type) \ - template <> struct ForceGlob \ - { \ - __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \ - { \ - asm("ld.global."#ptx_type" %0, [%1];" : "=r"(*reinterpret_cast(&val)) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \ - } \ - }; - - OPENCV_CUDA_DEFINE_FORCE_GLOB_B(uchar, u8) - OPENCV_CUDA_DEFINE_FORCE_GLOB_B(schar, s8) - OPENCV_CUDA_DEFINE_FORCE_GLOB_B(char, b8) - OPENCV_CUDA_DEFINE_FORCE_GLOB (ushort, u16, h) - OPENCV_CUDA_DEFINE_FORCE_GLOB (short, s16, h) - OPENCV_CUDA_DEFINE_FORCE_GLOB (uint, u32, r) - OPENCV_CUDA_DEFINE_FORCE_GLOB (int, s32, r) - OPENCV_CUDA_DEFINE_FORCE_GLOB (float, f32, f) - OPENCV_CUDA_DEFINE_FORCE_GLOB (double, f64, d) - - #undef OPENCV_CUDA_DEFINE_FORCE_GLOB - #undef OPENCV_CUDA_DEFINE_FORCE_GLOB_B - #undef OPENCV_CUDA_ASM_PTR - - #endif // __CUDA_ARCH__ >= 200 -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // __OPENCV_CUDA_DATAMOV_UTILS_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/detail/color_detail.hpp b/IPL/include/opencv/opencv2/core/cuda/detail/color_detail.hpp deleted file mode 100644 index 1151806..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/detail/color_detail.hpp +++ /dev/null @@ -1,1980 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_COLOR_DETAIL_HPP__ -#define __OPENCV_CUDA_COLOR_DETAIL_HPP__ - -#include "../common.hpp" -#include "../vec_traits.hpp" -#include "../saturate_cast.hpp" -#include "../limits.hpp" -#include "../functional.hpp" - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - #ifndef CV_DESCALE - #define CV_DESCALE(x, n) (((x) + (1 << ((n)-1))) >> (n)) - #endif - - namespace color_detail - { - template struct ColorChannel - { - typedef float worktype_f; - static __device__ __forceinline__ T max() { return numeric_limits::max(); } - static __device__ __forceinline__ T half() { return (T)(max()/2 + 1); } - }; - - template<> struct ColorChannel - { - typedef float worktype_f; - static __device__ __forceinline__ float max() { return 1.f; } - static __device__ __forceinline__ float half() { return 0.5f; } - }; - - template static __device__ __forceinline__ void setAlpha(typename TypeVec::vec_type& vec, T val) - { - } - - template static __device__ __forceinline__ void setAlpha(typename TypeVec::vec_type& vec, T val) - { - vec.w = val; - } - - template static __device__ __forceinline__ T getAlpha(const typename TypeVec::vec_type& vec) - { - return ColorChannel::max(); - } - - template static __device__ __forceinline__ T getAlpha(const typename TypeVec::vec_type& vec) - { - return vec.w; - } - - enum - { - yuv_shift = 14, - xyz_shift = 12, - R2Y = 4899, - G2Y = 9617, - B2Y = 1868, - BLOCK_SIZE = 256 - }; - } - -////////////////// Various 3/4-channel to 3/4-channel RGB transformations ///////////////// - - namespace color_detail - { - template struct RGB2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - dst.x = (&src.x)[bidx]; - dst.y = src.y; - dst.z = (&src.x)[bidx^2]; - setAlpha(dst, getAlpha(src)); - - return dst; - } - - __host__ __device__ __forceinline__ RGB2RGB() {} - __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {} - }; - - template <> struct RGB2RGB : unary_function - { - __device__ uint operator()(uint src) const - { - uint dst = 0; - - dst |= (0xffu & (src >> 16)); - dst |= (0xffu & (src >> 8)) << 8; - dst |= (0xffu & (src)) << 16; - dst |= (0xffu & (src >> 24)) << 24; - - return dst; - } - - __host__ __device__ __forceinline__ RGB2RGB() {} - __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -/////////// Transforming 16-bit (565 or 555) RGB to/from 24/32-bit (888[8]) RGB ////////// - - namespace color_detail - { - template struct RGB2RGB5x5Converter; - template struct RGB2RGB5x5Converter<6, bidx> - { - static __device__ __forceinline__ ushort cvt(const uchar3& src) - { - return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~3) << 3) | (((&src.x)[bidx^2] & ~7) << 8)); - } - - static __device__ __forceinline__ ushort cvt(uint src) - { - uint b = 0xffu & (src >> (bidx * 8)); - uint g = 0xffu & (src >> 8); - uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); - return (ushort)((b >> 3) | ((g & ~3) << 3) | ((r & ~7) << 8)); - } - }; - - template struct RGB2RGB5x5Converter<5, bidx> - { - static __device__ __forceinline__ ushort cvt(const uchar3& src) - { - return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~7) << 2) | (((&src.x)[bidx^2] & ~7) << 7)); - } - - static __device__ __forceinline__ ushort cvt(uint src) - { - uint b = 0xffu & (src >> (bidx * 8)); - uint g = 0xffu & (src >> 8); - uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); - uint a = 0xffu & (src >> 24); - return (ushort)((b >> 3) | ((g & ~7) << 2) | ((r & ~7) << 7) | (a * 0x8000)); - } - }; - - template struct RGB2RGB5x5; - - template struct RGB2RGB5x5<3, bidx,green_bits> : unary_function - { - __device__ __forceinline__ ushort operator()(const uchar3& src) const - { - return RGB2RGB5x5Converter::cvt(src); - } - - __host__ __device__ __forceinline__ RGB2RGB5x5() {} - __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {} - }; - - template struct RGB2RGB5x5<4, bidx,green_bits> : unary_function - { - __device__ __forceinline__ ushort operator()(uint src) const - { - return RGB2RGB5x5Converter::cvt(src); - } - - __host__ __device__ __forceinline__ RGB2RGB5x5() {} - __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(name, scn, bidx, green_bits) \ - struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2RGB5x5 functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - template struct RGB5x52RGBConverter; - - template struct RGB5x52RGBConverter<5, bidx> - { - static __device__ __forceinline__ void cvt(uint src, uchar3& dst) - { - (&dst.x)[bidx] = src << 3; - dst.y = (src >> 2) & ~7; - (&dst.x)[bidx ^ 2] = (src >> 7) & ~7; - } - - static __device__ __forceinline__ void cvt(uint src, uint& dst) - { - dst = 0; - - dst |= (0xffu & (src << 3)) << (bidx * 8); - dst |= (0xffu & ((src >> 2) & ~7)) << 8; - dst |= (0xffu & ((src >> 7) & ~7)) << ((bidx ^ 2) * 8); - dst |= ((src & 0x8000) * 0xffu) << 24; - } - }; - - template struct RGB5x52RGBConverter<6, bidx> - { - static __device__ __forceinline__ void cvt(uint src, uchar3& dst) - { - (&dst.x)[bidx] = src << 3; - dst.y = (src >> 3) & ~3; - (&dst.x)[bidx ^ 2] = (src >> 8) & ~7; - } - - static __device__ __forceinline__ void cvt(uint src, uint& dst) - { - dst = 0xffu << 24; - - dst |= (0xffu & (src << 3)) << (bidx * 8); - dst |= (0xffu &((src >> 3) & ~3)) << 8; - dst |= (0xffu & ((src >> 8) & ~7)) << ((bidx ^ 2) * 8); - } - }; - - template struct RGB5x52RGB; - - template struct RGB5x52RGB<3, bidx, green_bits> : unary_function - { - __device__ __forceinline__ uchar3 operator()(ushort src) const - { - uchar3 dst; - RGB5x52RGBConverter::cvt(src, dst); - return dst; - } - __host__ __device__ __forceinline__ RGB5x52RGB() {} - __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {} - - }; - - template struct RGB5x52RGB<4, bidx, green_bits> : unary_function - { - __device__ __forceinline__ uint operator()(ushort src) const - { - uint dst; - RGB5x52RGBConverter::cvt(src, dst); - return dst; - } - __host__ __device__ __forceinline__ RGB5x52RGB() {} - __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(name, dcn, bidx, green_bits) \ - struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB5x52RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -///////////////////////////////// Grayscale to Color //////////////////////////////// - - namespace color_detail - { - template struct Gray2RGB : unary_function::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator()(T src) const - { - typename TypeVec::vec_type dst; - - dst.z = dst.y = dst.x = src; - setAlpha(dst, ColorChannel::max()); - - return dst; - } - __host__ __device__ __forceinline__ Gray2RGB() {} - __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {} - }; - - template <> struct Gray2RGB : unary_function - { - __device__ __forceinline__ uint operator()(uint src) const - { - uint dst = 0xffu << 24; - - dst |= src; - dst |= src << 8; - dst |= src << 16; - - return dst; - } - __host__ __device__ __forceinline__ Gray2RGB() {} - __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(name, dcn) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::Gray2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - template struct Gray2RGB5x5Converter; - template<> struct Gray2RGB5x5Converter<6> - { - static __device__ __forceinline__ ushort cvt(uint t) - { - return (ushort)((t >> 3) | ((t & ~3) << 3) | ((t & ~7) << 8)); - } - }; - - template<> struct Gray2RGB5x5Converter<5> - { - static __device__ __forceinline__ ushort cvt(uint t) - { - t >>= 3; - return (ushort)(t | (t << 5) | (t << 10)); - } - }; - - template struct Gray2RGB5x5 : unary_function - { - __device__ __forceinline__ ushort operator()(uint src) const - { - return Gray2RGB5x5Converter::cvt(src); - } - - __host__ __device__ __forceinline__ Gray2RGB5x5() {} - __host__ __device__ __forceinline__ Gray2RGB5x5(const Gray2RGB5x5&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(name, green_bits) \ - struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::Gray2RGB5x5 functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -///////////////////////////////// Color to Grayscale //////////////////////////////// - - namespace color_detail - { - template struct RGB5x52GrayConverter; - template <> struct RGB5x52GrayConverter<6> - { - static __device__ __forceinline__ uchar cvt(uint t) - { - return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 3) & 0xfc) * G2Y + ((t >> 8) & 0xf8) * R2Y, yuv_shift); - } - }; - - template <> struct RGB5x52GrayConverter<5> - { - static __device__ __forceinline__ uchar cvt(uint t) - { - return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 2) & 0xf8) * G2Y + ((t >> 7) & 0xf8) * R2Y, yuv_shift); - } - }; - - template struct RGB5x52Gray : unary_function - { - __device__ __forceinline__ uchar operator()(uint src) const - { - return RGB5x52GrayConverter::cvt(src); - } - __host__ __device__ __forceinline__ RGB5x52Gray() {} - __host__ __device__ __forceinline__ RGB5x52Gray(const RGB5x52Gray&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(name, green_bits) \ - struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB5x52Gray functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - template static __device__ __forceinline__ T RGB2GrayConvert(const T* src) - { - return (T)CV_DESCALE((unsigned)(src[bidx] * B2Y + src[1] * G2Y + src[bidx^2] * R2Y), yuv_shift); - } - - template static __device__ __forceinline__ uchar RGB2GrayConvert(uint src) - { - uint b = 0xffu & (src >> (bidx * 8)); - uint g = 0xffu & (src >> 8); - uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); - return CV_DESCALE((uint)(b * B2Y + g * G2Y + r * R2Y), yuv_shift); - } - - template static __device__ __forceinline__ float RGB2GrayConvert(const float* src) - { - return src[bidx] * 0.114f + src[1] * 0.587f + src[bidx^2] * 0.299f; - } - - template struct RGB2Gray : unary_function::vec_type, T> - { - __device__ __forceinline__ T operator()(const typename TypeVec::vec_type& src) const - { - return RGB2GrayConvert(&src.x); - } - __host__ __device__ __forceinline__ RGB2Gray() {} - __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {} - }; - - template struct RGB2Gray : unary_function - { - __device__ __forceinline__ uchar operator()(uint src) const - { - return RGB2GrayConvert(src); - } - __host__ __device__ __forceinline__ RGB2Gray() {} - __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(name, scn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2Gray functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -///////////////////////////////////// RGB <-> YUV ////////////////////////////////////// - - namespace color_detail - { - __constant__ float c_RGB2YUVCoeffs_f[5] = { 0.114f, 0.587f, 0.299f, 0.492f, 0.877f }; - __constant__ int c_RGB2YUVCoeffs_i[5] = { B2Y, G2Y, R2Y, 8061, 14369 }; - - template static __device__ void RGB2YUVConvert(const T* src, D& dst) - { - const int delta = ColorChannel::half() * (1 << yuv_shift); - - const int Y = CV_DESCALE(src[0] * c_RGB2YUVCoeffs_i[bidx^2] + src[1] * c_RGB2YUVCoeffs_i[1] + src[2] * c_RGB2YUVCoeffs_i[bidx], yuv_shift); - const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YUVCoeffs_i[3] + delta, yuv_shift); - const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YUVCoeffs_i[4] + delta, yuv_shift); - - dst.x = saturate_cast(Y); - dst.y = saturate_cast(Cr); - dst.z = saturate_cast(Cb); - } - - template static __device__ __forceinline__ void RGB2YUVConvert(const float* src, D& dst) - { - dst.x = src[0] * c_RGB2YUVCoeffs_f[bidx^2] + src[1] * c_RGB2YUVCoeffs_f[1] + src[2] * c_RGB2YUVCoeffs_f[bidx]; - dst.y = (src[bidx^2] - dst.x) * c_RGB2YUVCoeffs_f[3] + ColorChannel::half(); - dst.z = (src[bidx] - dst.x) * c_RGB2YUVCoeffs_f[4] + ColorChannel::half(); - } - - template struct RGB2YUV - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - RGB2YUVConvert(&src.x, dst); - return dst; - } - __host__ __device__ __forceinline__ RGB2YUV() {} - __host__ __device__ __forceinline__ RGB2YUV(const RGB2YUV&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2YUV functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - __constant__ float c_YUV2RGBCoeffs_f[5] = { 2.032f, -0.395f, -0.581f, 1.140f }; - __constant__ int c_YUV2RGBCoeffs_i[5] = { 33292, -6472, -9519, 18678 }; - - template static __device__ void YUV2RGBConvert(const T& src, D* dst) - { - const int b = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift); - - const int g = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[2] - + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift); - - const int r = src.x + CV_DESCALE((src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift); - - dst[bidx] = saturate_cast(b); - dst[1] = saturate_cast(g); - dst[bidx^2] = saturate_cast(r); - } - - template static __device__ uint YUV2RGBConvert(uint src) - { - const int x = 0xff & (src); - const int y = 0xff & (src >> 8); - const int z = 0xff & (src >> 16); - - const int b = x + CV_DESCALE((z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift); - - const int g = x + CV_DESCALE((z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[2] - + (y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift); - - const int r = x + CV_DESCALE((y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift); - - uint dst = 0xffu << 24; - - dst |= saturate_cast(b) << (bidx * 8); - dst |= saturate_cast(g) << 8; - dst |= saturate_cast(r) << ((bidx ^ 2) * 8); - - return dst; - } - - template static __device__ __forceinline__ void YUV2RGBConvert(const T& src, float* dst) - { - dst[bidx] = src.x + (src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_f[3]; - - dst[1] = src.x + (src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_f[2] - + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_f[1]; - - dst[bidx^2] = src.x + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_f[0]; - } - - template struct YUV2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - YUV2RGBConvert(src, &dst.x); - setAlpha(dst, ColorChannel::max()); - - return dst; - } - __host__ __device__ __forceinline__ YUV2RGB() {} - __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {} - }; - - template struct YUV2RGB : unary_function - { - __device__ __forceinline__ uint operator ()(uint src) const - { - return YUV2RGBConvert(src); - } - __host__ __device__ __forceinline__ YUV2RGB() {} - __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::YUV2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -///////////////////////////////////// RGB <-> YCrCb ////////////////////////////////////// - - namespace color_detail - { - __constant__ float c_RGB2YCrCbCoeffs_f[5] = {0.299f, 0.587f, 0.114f, 0.713f, 0.564f}; - __constant__ int c_RGB2YCrCbCoeffs_i[5] = {R2Y, G2Y, B2Y, 11682, 9241}; - - template static __device__ void RGB2YCrCbConvert(const T* src, D& dst) - { - const int delta = ColorChannel::half() * (1 << yuv_shift); - - const int Y = CV_DESCALE(src[0] * c_RGB2YCrCbCoeffs_i[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_i[1] + src[2] * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift); - const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift); - const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift); - - dst.x = saturate_cast(Y); - dst.y = saturate_cast(Cr); - dst.z = saturate_cast(Cb); - } - - template static __device__ uint RGB2YCrCbConvert(uint src) - { - const int delta = ColorChannel::half() * (1 << yuv_shift); - - const int Y = CV_DESCALE((0xffu & src) * c_RGB2YCrCbCoeffs_i[bidx^2] + (0xffu & (src >> 8)) * c_RGB2YCrCbCoeffs_i[1] + (0xffu & (src >> 16)) * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift); - const int Cr = CV_DESCALE(((0xffu & (src >> ((bidx ^ 2) * 8))) - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift); - const int Cb = CV_DESCALE(((0xffu & (src >> (bidx * 8))) - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift); - - uint dst = 0; - - dst |= saturate_cast(Y); - dst |= saturate_cast(Cr) << 8; - dst |= saturate_cast(Cb) << 16; - - return dst; - } - - template static __device__ __forceinline__ void RGB2YCrCbConvert(const float* src, D& dst) - { - dst.x = src[0] * c_RGB2YCrCbCoeffs_f[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_f[1] + src[2] * c_RGB2YCrCbCoeffs_f[bidx]; - dst.y = (src[bidx^2] - dst.x) * c_RGB2YCrCbCoeffs_f[3] + ColorChannel::half(); - dst.z = (src[bidx] - dst.x) * c_RGB2YCrCbCoeffs_f[4] + ColorChannel::half(); - } - - template struct RGB2YCrCb - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - RGB2YCrCbConvert(&src.x, dst); - return dst; - } - __host__ __device__ __forceinline__ RGB2YCrCb() {} - __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {} - }; - - template struct RGB2YCrCb : unary_function - { - __device__ __forceinline__ uint operator ()(uint src) const - { - return RGB2YCrCbConvert(src); - } - - __host__ __device__ __forceinline__ RGB2YCrCb() {} - __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2YCrCb functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - __constant__ float c_YCrCb2RGBCoeffs_f[5] = {1.403f, -0.714f, -0.344f, 1.773f}; - __constant__ int c_YCrCb2RGBCoeffs_i[5] = {22987, -11698, -5636, 29049}; - - template static __device__ void YCrCb2RGBConvert(const T& src, D* dst) - { - const int b = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift); - const int g = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[2] + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift); - const int r = src.x + CV_DESCALE((src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift); - - dst[bidx] = saturate_cast(b); - dst[1] = saturate_cast(g); - dst[bidx^2] = saturate_cast(r); - } - - template static __device__ uint YCrCb2RGBConvert(uint src) - { - const int x = 0xff & (src); - const int y = 0xff & (src >> 8); - const int z = 0xff & (src >> 16); - - const int b = x + CV_DESCALE((z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift); - const int g = x + CV_DESCALE((z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[2] + (y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift); - const int r = x + CV_DESCALE((y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift); - - uint dst = 0xffu << 24; - - dst |= saturate_cast(b) << (bidx * 8); - dst |= saturate_cast(g) << 8; - dst |= saturate_cast(r) << ((bidx ^ 2) * 8); - - return dst; - } - - template __device__ __forceinline__ void YCrCb2RGBConvert(const T& src, float* dst) - { - dst[bidx] = src.x + (src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[3]; - dst[1] = src.x + (src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[2] + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[1]; - dst[bidx^2] = src.x + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[0]; - } - - template struct YCrCb2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - YCrCb2RGBConvert(src, &dst.x); - setAlpha(dst, ColorChannel::max()); - - return dst; - } - __host__ __device__ __forceinline__ YCrCb2RGB() {} - __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {} - }; - - template struct YCrCb2RGB : unary_function - { - __device__ __forceinline__ uint operator ()(uint src) const - { - return YCrCb2RGBConvert(src); - } - __host__ __device__ __forceinline__ YCrCb2RGB() {} - __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::YCrCb2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -////////////////////////////////////// RGB <-> XYZ /////////////////////////////////////// - - namespace color_detail - { - __constant__ float c_RGB2XYZ_D65f[9] = { 0.412453f, 0.357580f, 0.180423f, 0.212671f, 0.715160f, 0.072169f, 0.019334f, 0.119193f, 0.950227f }; - __constant__ int c_RGB2XYZ_D65i[9] = { 1689, 1465, 739, 871, 2929, 296, 79, 488, 3892 }; - - template static __device__ __forceinline__ void RGB2XYZConvert(const T* src, D& dst) - { - dst.z = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[6] + src[1] * c_RGB2XYZ_D65i[7] + src[bidx] * c_RGB2XYZ_D65i[8], xyz_shift)); - dst.x = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[0] + src[1] * c_RGB2XYZ_D65i[1] + src[bidx] * c_RGB2XYZ_D65i[2], xyz_shift)); - dst.y = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[3] + src[1] * c_RGB2XYZ_D65i[4] + src[bidx] * c_RGB2XYZ_D65i[5], xyz_shift)); - } - - template static __device__ __forceinline__ uint RGB2XYZConvert(uint src) - { - const uint b = 0xffu & (src >> (bidx * 8)); - const uint g = 0xffu & (src >> 8); - const uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); - - const uint x = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[0] + g * c_RGB2XYZ_D65i[1] + b * c_RGB2XYZ_D65i[2], xyz_shift)); - const uint y = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[3] + g * c_RGB2XYZ_D65i[4] + b * c_RGB2XYZ_D65i[5], xyz_shift)); - const uint z = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[6] + g * c_RGB2XYZ_D65i[7] + b * c_RGB2XYZ_D65i[8], xyz_shift)); - - uint dst = 0; - - dst |= x; - dst |= y << 8; - dst |= z << 16; - - return dst; - } - - template static __device__ __forceinline__ void RGB2XYZConvert(const float* src, D& dst) - { - dst.x = src[bidx^2] * c_RGB2XYZ_D65f[0] + src[1] * c_RGB2XYZ_D65f[1] + src[bidx] * c_RGB2XYZ_D65f[2]; - dst.y = src[bidx^2] * c_RGB2XYZ_D65f[3] + src[1] * c_RGB2XYZ_D65f[4] + src[bidx] * c_RGB2XYZ_D65f[5]; - dst.z = src[bidx^2] * c_RGB2XYZ_D65f[6] + src[1] * c_RGB2XYZ_D65f[7] + src[bidx] * c_RGB2XYZ_D65f[8]; - } - - template struct RGB2XYZ - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - RGB2XYZConvert(&src.x, dst); - - return dst; - } - __host__ __device__ __forceinline__ RGB2XYZ() {} - __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {} - }; - - template struct RGB2XYZ : unary_function - { - __device__ __forceinline__ uint operator()(uint src) const - { - return RGB2XYZConvert(src); - } - __host__ __device__ __forceinline__ RGB2XYZ() {} - __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2XYZ functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - __constant__ float c_XYZ2sRGB_D65f[9] = { 3.240479f, -1.53715f, -0.498535f, -0.969256f, 1.875991f, 0.041556f, 0.055648f, -0.204043f, 1.057311f }; - __constant__ int c_XYZ2sRGB_D65i[9] = { 13273, -6296, -2042, -3970, 7684, 170, 228, -836, 4331 }; - - template static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, D* dst) - { - dst[bidx^2] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[0] + src.y * c_XYZ2sRGB_D65i[1] + src.z * c_XYZ2sRGB_D65i[2], xyz_shift)); - dst[1] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[3] + src.y * c_XYZ2sRGB_D65i[4] + src.z * c_XYZ2sRGB_D65i[5], xyz_shift)); - dst[bidx] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[6] + src.y * c_XYZ2sRGB_D65i[7] + src.z * c_XYZ2sRGB_D65i[8], xyz_shift)); - } - - template static __device__ __forceinline__ uint XYZ2RGBConvert(uint src) - { - const int x = 0xff & src; - const int y = 0xff & (src >> 8); - const int z = 0xff & (src >> 16); - - const uint r = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[0] + y * c_XYZ2sRGB_D65i[1] + z * c_XYZ2sRGB_D65i[2], xyz_shift)); - const uint g = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[3] + y * c_XYZ2sRGB_D65i[4] + z * c_XYZ2sRGB_D65i[5], xyz_shift)); - const uint b = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[6] + y * c_XYZ2sRGB_D65i[7] + z * c_XYZ2sRGB_D65i[8], xyz_shift)); - - uint dst = 0xffu << 24; - - dst |= b << (bidx * 8); - dst |= g << 8; - dst |= r << ((bidx ^ 2) * 8); - - return dst; - } - - template static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, float* dst) - { - dst[bidx^2] = src.x * c_XYZ2sRGB_D65f[0] + src.y * c_XYZ2sRGB_D65f[1] + src.z * c_XYZ2sRGB_D65f[2]; - dst[1] = src.x * c_XYZ2sRGB_D65f[3] + src.y * c_XYZ2sRGB_D65f[4] + src.z * c_XYZ2sRGB_D65f[5]; - dst[bidx] = src.x * c_XYZ2sRGB_D65f[6] + src.y * c_XYZ2sRGB_D65f[7] + src.z * c_XYZ2sRGB_D65f[8]; - } - - template struct XYZ2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - XYZ2RGBConvert(src, &dst.x); - setAlpha(dst, ColorChannel::max()); - - return dst; - } - __host__ __device__ __forceinline__ XYZ2RGB() {} - __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {} - }; - - template struct XYZ2RGB : unary_function - { - __device__ __forceinline__ uint operator()(uint src) const - { - return XYZ2RGBConvert(src); - } - __host__ __device__ __forceinline__ XYZ2RGB() {} - __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::XYZ2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -////////////////////////////////////// RGB <-> HSV /////////////////////////////////////// - - namespace color_detail - { - __constant__ int c_HsvDivTable [256] = {0, 1044480, 522240, 348160, 261120, 208896, 174080, 149211, 130560, 116053, 104448, 94953, 87040, 80345, 74606, 69632, 65280, 61440, 58027, 54973, 52224, 49737, 47476, 45412, 43520, 41779, 40172, 38684, 37303, 36017, 34816, 33693, 32640, 31651, 30720, 29842, 29013, 28229, 27486, 26782, 26112, 25475, 24869, 24290, 23738, 23211, 22706, 22223, 21760, 21316, 20890, 20480, 20086, 19707, 19342, 18991, 18651, 18324, 18008, 17703, 17408, 17123, 16846, 16579, 16320, 16069, 15825, 15589, 15360, 15137, 14921, 14711, 14507, 14308, 14115, 13926, 13743, 13565, 13391, 13221, 13056, 12895, 12738, 12584, 12434, 12288, 12145, 12006, 11869, 11736, 11605, 11478, 11353, 11231, 11111, 10995, 10880, 10768, 10658, 10550, 10445, 10341, 10240, 10141, 10043, 9947, 9854, 9761, 9671, 9582, 9495, 9410, 9326, 9243, 9162, 9082, 9004, 8927, 8852, 8777, 8704, 8632, 8561, 8492, 8423, 8356, 8290, 8224, 8160, 8097, 8034, 7973, 7913, 7853, 7795, 7737, 7680, 7624, 7569, 7514, 7461, 7408, 7355, 7304, 7253, 7203, 7154, 7105, 7057, 7010, 6963, 6917, 6872, 6827, 6782, 6739, 6695, 6653, 6611, 6569, 6528, 6487, 6447, 6408, 6369, 6330, 6292, 6254, 6217, 6180, 6144, 6108, 6073, 6037, 6003, 5968, 5935, 5901, 5868, 5835, 5803, 5771, 5739, 5708, 5677, 5646, 5615, 5585, 5556, 5526, 5497, 5468, 5440, 5412, 5384, 5356, 5329, 5302, 5275, 5249, 5222, 5196, 5171, 5145, 5120, 5095, 5070, 5046, 5022, 4998, 4974, 4950, 4927, 4904, 4881, 4858, 4836, 4813, 4791, 4769, 4748, 4726, 4705, 4684, 4663, 4642, 4622, 4601, 4581, 4561, 4541, 4522, 4502, 4483, 4464, 4445, 4426, 4407, 4389, 4370, 4352, 4334, 4316, 4298, 4281, 4263, 4246, 4229, 4212, 4195, 4178, 4161, 4145, 4128, 4112, 4096}; - __constant__ int c_HsvDivTable180[256] = {0, 122880, 61440, 40960, 30720, 24576, 20480, 17554, 15360, 13653, 12288, 11171, 10240, 9452, 8777, 8192, 7680, 7228, 6827, 6467, 6144, 5851, 5585, 5343, 5120, 4915, 4726, 4551, 4389, 4237, 4096, 3964, 3840, 3724, 3614, 3511, 3413, 3321, 3234, 3151, 3072, 2997, 2926, 2858, 2793, 2731, 2671, 2614, 2560, 2508, 2458, 2409, 2363, 2318, 2276, 2234, 2194, 2156, 2119, 2083, 2048, 2014, 1982, 1950, 1920, 1890, 1862, 1834, 1807, 1781, 1755, 1731, 1707, 1683, 1661, 1638, 1617, 1596, 1575, 1555, 1536, 1517, 1499, 1480, 1463, 1446, 1429, 1412, 1396, 1381, 1365, 1350, 1336, 1321, 1307, 1293, 1280, 1267, 1254, 1241, 1229, 1217, 1205, 1193, 1182, 1170, 1159, 1148, 1138, 1127, 1117, 1107, 1097, 1087, 1078, 1069, 1059, 1050, 1041, 1033, 1024, 1016, 1007, 999, 991, 983, 975, 968, 960, 953, 945, 938, 931, 924, 917, 910, 904, 897, 890, 884, 878, 871, 865, 859, 853, 847, 842, 836, 830, 825, 819, 814, 808, 803, 798, 793, 788, 783, 778, 773, 768, 763, 759, 754, 749, 745, 740, 736, 731, 727, 723, 719, 714, 710, 706, 702, 698, 694, 690, 686, 683, 679, 675, 671, 668, 664, 661, 657, 654, 650, 647, 643, 640, 637, 633, 630, 627, 624, 621, 617, 614, 611, 608, 605, 602, 599, 597, 594, 591, 588, 585, 582, 580, 577, 574, 572, 569, 566, 564, 561, 559, 556, 554, 551, 549, 546, 544, 541, 539, 537, 534, 532, 530, 527, 525, 523, 521, 518, 516, 514, 512, 510, 508, 506, 504, 502, 500, 497, 495, 493, 492, 490, 488, 486, 484, 482}; - __constant__ int c_HsvDivTable256[256] = {0, 174763, 87381, 58254, 43691, 34953, 29127, 24966, 21845, 19418, 17476, 15888, 14564, 13443, 12483, 11651, 10923, 10280, 9709, 9198, 8738, 8322, 7944, 7598, 7282, 6991, 6722, 6473, 6242, 6026, 5825, 5638, 5461, 5296, 5140, 4993, 4855, 4723, 4599, 4481, 4369, 4263, 4161, 4064, 3972, 3884, 3799, 3718, 3641, 3567, 3495, 3427, 3361, 3297, 3236, 3178, 3121, 3066, 3013, 2962, 2913, 2865, 2819, 2774, 2731, 2689, 2648, 2608, 2570, 2533, 2497, 2461, 2427, 2394, 2362, 2330, 2300, 2270, 2241, 2212, 2185, 2158, 2131, 2106, 2081, 2056, 2032, 2009, 1986, 1964, 1942, 1920, 1900, 1879, 1859, 1840, 1820, 1802, 1783, 1765, 1748, 1730, 1713, 1697, 1680, 1664, 1649, 1633, 1618, 1603, 1589, 1574, 1560, 1547, 1533, 1520, 1507, 1494, 1481, 1469, 1456, 1444, 1432, 1421, 1409, 1398, 1387, 1376, 1365, 1355, 1344, 1334, 1324, 1314, 1304, 1295, 1285, 1276, 1266, 1257, 1248, 1239, 1231, 1222, 1214, 1205, 1197, 1189, 1181, 1173, 1165, 1157, 1150, 1142, 1135, 1128, 1120, 1113, 1106, 1099, 1092, 1085, 1079, 1072, 1066, 1059, 1053, 1046, 1040, 1034, 1028, 1022, 1016, 1010, 1004, 999, 993, 987, 982, 976, 971, 966, 960, 955, 950, 945, 940, 935, 930, 925, 920, 915, 910, 906, 901, 896, 892, 887, 883, 878, 874, 869, 865, 861, 857, 853, 848, 844, 840, 836, 832, 828, 824, 820, 817, 813, 809, 805, 802, 798, 794, 791, 787, 784, 780, 777, 773, 770, 767, 763, 760, 757, 753, 750, 747, 744, 741, 737, 734, 731, 728, 725, 722, 719, 716, 713, 710, 708, 705, 702, 699, 696, 694, 691, 688, 685}; - - template static __device__ void RGB2HSVConvert(const uchar* src, D& dst) - { - const int hsv_shift = 12; - const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256; - - int b = src[bidx], g = src[1], r = src[bidx^2]; - int h, s, v = b; - int vmin = b, diff; - int vr, vg; - - v = ::max(v, g); - v = ::max(v, r); - vmin = ::min(vmin, g); - vmin = ::min(vmin, r); - - diff = v - vmin; - vr = (v == r) * -1; - vg = (v == g) * -1; - - s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift; - h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff)))); - h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift; - h += (h < 0) * hr; - - dst.x = saturate_cast(h); - dst.y = (uchar)s; - dst.z = (uchar)v; - } - - template static __device__ uint RGB2HSVConvert(uint src) - { - const int hsv_shift = 12; - const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256; - - const int b = 0xff & (src >> (bidx * 8)); - const int g = 0xff & (src >> 8); - const int r = 0xff & (src >> ((bidx ^ 2) * 8)); - - int h, s, v = b; - int vmin = b, diff; - int vr, vg; - - v = ::max(v, g); - v = ::max(v, r); - vmin = ::min(vmin, g); - vmin = ::min(vmin, r); - - diff = v - vmin; - vr = (v == r) * -1; - vg = (v == g) * -1; - - s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift; - h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff)))); - h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift; - h += (h < 0) * hr; - - uint dst = 0; - - dst |= saturate_cast(h); - dst |= (0xffu & s) << 8; - dst |= (0xffu & v) << 16; - - return dst; - } - - template static __device__ void RGB2HSVConvert(const float* src, D& dst) - { - const float hscale = hr * (1.f / 360.f); - - float b = src[bidx], g = src[1], r = src[bidx^2]; - float h, s, v; - - float vmin, diff; - - v = vmin = r; - v = fmax(v, g); - v = fmax(v, b); - vmin = fmin(vmin, g); - vmin = fmin(vmin, b); - - diff = v - vmin; - s = diff / (float)(::fabs(v) + numeric_limits::epsilon()); - diff = (float)(60. / (diff + numeric_limits::epsilon())); - - h = (v == r) * (g - b) * diff; - h += (v != r && v == g) * ((b - r) * diff + 120.f); - h += (v != r && v != g) * ((r - g) * diff + 240.f); - h += (h < 0) * 360.f; - - dst.x = h * hscale; - dst.y = s; - dst.z = v; - } - - template struct RGB2HSV - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - RGB2HSVConvert(&src.x, dst); - - return dst; - } - __host__ __device__ __forceinline__ RGB2HSV() {} - __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {} - }; - - template struct RGB2HSV : unary_function - { - __device__ __forceinline__ uint operator()(uint src) const - { - return RGB2HSVConvert(src); - } - __host__ __device__ __forceinline__ RGB2HSV() {} - __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template struct name ## _full_traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template <> struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template <> struct name ## _full_traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - __constant__ int c_HsvSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} }; - - template static __device__ void HSV2RGBConvert(const T& src, float* dst) - { - const float hscale = 6.f / hr; - - float h = src.x, s = src.y, v = src.z; - float b = v, g = v, r = v; - - if (s != 0) - { - h *= hscale; - - if( h < 0 ) - do h += 6; while( h < 0 ); - else if( h >= 6 ) - do h -= 6; while( h >= 6 ); - - int sector = __float2int_rd(h); - h -= sector; - - if ( (unsigned)sector >= 6u ) - { - sector = 0; - h = 0.f; - } - - float tab[4]; - tab[0] = v; - tab[1] = v * (1.f - s); - tab[2] = v * (1.f - s * h); - tab[3] = v * (1.f - s * (1.f - h)); - - b = tab[c_HsvSectorData[sector][0]]; - g = tab[c_HsvSectorData[sector][1]]; - r = tab[c_HsvSectorData[sector][2]]; - } - - dst[bidx] = b; - dst[1] = g; - dst[bidx^2] = r; - } - - template static __device__ void HSV2RGBConvert(const T& src, uchar* dst) - { - float3 buf; - - buf.x = src.x; - buf.y = src.y * (1.f / 255.f); - buf.z = src.z * (1.f / 255.f); - - HSV2RGBConvert(buf, &buf.x); - - dst[0] = saturate_cast(buf.x * 255.f); - dst[1] = saturate_cast(buf.y * 255.f); - dst[2] = saturate_cast(buf.z * 255.f); - } - - template static __device__ uint HSV2RGBConvert(uint src) - { - float3 buf; - - buf.x = src & 0xff; - buf.y = ((src >> 8) & 0xff) * (1.f/255.f); - buf.z = ((src >> 16) & 0xff) * (1.f/255.f); - - HSV2RGBConvert(buf, &buf.x); - - uint dst = 0xffu << 24; - - dst |= saturate_cast(buf.x * 255.f); - dst |= saturate_cast(buf.y * 255.f) << 8; - dst |= saturate_cast(buf.z * 255.f) << 16; - - return dst; - } - - template struct HSV2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - HSV2RGBConvert(src, &dst.x); - setAlpha(dst, ColorChannel::max()); - - return dst; - } - __host__ __device__ __forceinline__ HSV2RGB() {} - __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {} - }; - - template struct HSV2RGB : unary_function - { - __device__ __forceinline__ uint operator()(uint src) const - { - return HSV2RGBConvert(src); - } - __host__ __device__ __forceinline__ HSV2RGB() {} - __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template struct name ## _full_traits \ - { \ - typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template <> struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template <> struct name ## _full_traits \ - { \ - typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -/////////////////////////////////////// RGB <-> HLS //////////////////////////////////////// - - namespace color_detail - { - template static __device__ void RGB2HLSConvert(const float* src, D& dst) - { - const float hscale = hr * (1.f / 360.f); - - float b = src[bidx], g = src[1], r = src[bidx^2]; - float h = 0.f, s = 0.f, l; - float vmin, vmax, diff; - - vmax = vmin = r; - vmax = fmax(vmax, g); - vmax = fmax(vmax, b); - vmin = fmin(vmin, g); - vmin = fmin(vmin, b); - - diff = vmax - vmin; - l = (vmax + vmin) * 0.5f; - - if (diff > numeric_limits::epsilon()) - { - s = (l < 0.5f) * diff / (vmax + vmin); - s += (l >= 0.5f) * diff / (2.0f - vmax - vmin); - - diff = 60.f / diff; - - h = (vmax == r) * (g - b) * diff; - h += (vmax != r && vmax == g) * ((b - r) * diff + 120.f); - h += (vmax != r && vmax != g) * ((r - g) * diff + 240.f); - h += (h < 0.f) * 360.f; - } - - dst.x = h * hscale; - dst.y = l; - dst.z = s; - } - - template static __device__ void RGB2HLSConvert(const uchar* src, D& dst) - { - float3 buf; - - buf.x = src[0] * (1.f / 255.f); - buf.y = src[1] * (1.f / 255.f); - buf.z = src[2] * (1.f / 255.f); - - RGB2HLSConvert(&buf.x, buf); - - dst.x = saturate_cast(buf.x); - dst.y = saturate_cast(buf.y*255.f); - dst.z = saturate_cast(buf.z*255.f); - } - - template static __device__ uint RGB2HLSConvert(uint src) - { - float3 buf; - - buf.x = (0xff & src) * (1.f / 255.f); - buf.y = (0xff & (src >> 8)) * (1.f / 255.f); - buf.z = (0xff & (src >> 16)) * (1.f / 255.f); - - RGB2HLSConvert(&buf.x, buf); - - uint dst = 0xffu << 24; - - dst |= saturate_cast(buf.x); - dst |= saturate_cast(buf.y * 255.f) << 8; - dst |= saturate_cast(buf.z * 255.f) << 16; - - return dst; - } - - template struct RGB2HLS - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - RGB2HLSConvert(&src.x, dst); - - return dst; - } - __host__ __device__ __forceinline__ RGB2HLS() {} - __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {} - }; - - template struct RGB2HLS : unary_function - { - __device__ __forceinline__ uint operator()(uint src) const - { - return RGB2HLSConvert(src); - } - __host__ __device__ __forceinline__ RGB2HLS() {} - __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template struct name ## _full_traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template <> struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template <> struct name ## _full_traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - __constant__ int c_HlsSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} }; - - template static __device__ void HLS2RGBConvert(const T& src, float* dst) - { - const float hscale = 6.0f / hr; - - float h = src.x, l = src.y, s = src.z; - float b = l, g = l, r = l; - - if (s != 0) - { - float p2 = (l <= 0.5f) * l * (1 + s); - p2 += (l > 0.5f) * (l + s - l * s); - float p1 = 2 * l - p2; - - h *= hscale; - - if( h < 0 ) - do h += 6; while( h < 0 ); - else if( h >= 6 ) - do h -= 6; while( h >= 6 ); - - int sector; - sector = __float2int_rd(h); - - h -= sector; - - float tab[4]; - tab[0] = p2; - tab[1] = p1; - tab[2] = p1 + (p2 - p1) * (1 - h); - tab[3] = p1 + (p2 - p1) * h; - - b = tab[c_HlsSectorData[sector][0]]; - g = tab[c_HlsSectorData[sector][1]]; - r = tab[c_HlsSectorData[sector][2]]; - } - - dst[bidx] = b; - dst[1] = g; - dst[bidx^2] = r; - } - - template static __device__ void HLS2RGBConvert(const T& src, uchar* dst) - { - float3 buf; - - buf.x = src.x; - buf.y = src.y * (1.f / 255.f); - buf.z = src.z * (1.f / 255.f); - - HLS2RGBConvert(buf, &buf.x); - - dst[0] = saturate_cast(buf.x * 255.f); - dst[1] = saturate_cast(buf.y * 255.f); - dst[2] = saturate_cast(buf.z * 255.f); - } - - template static __device__ uint HLS2RGBConvert(uint src) - { - float3 buf; - - buf.x = 0xff & src; - buf.y = (0xff & (src >> 8)) * (1.f / 255.f); - buf.z = (0xff & (src >> 16)) * (1.f / 255.f); - - HLS2RGBConvert(buf, &buf.x); - - uint dst = 0xffu << 24; - - dst |= saturate_cast(buf.x * 255.f); - dst |= saturate_cast(buf.y * 255.f) << 8; - dst |= saturate_cast(buf.z * 255.f) << 16; - - return dst; - } - - template struct HLS2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - HLS2RGBConvert(src, &dst.x); - setAlpha(dst, ColorChannel::max()); - - return dst; - } - __host__ __device__ __forceinline__ HLS2RGB() {} - __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {} - }; - - template struct HLS2RGB : unary_function - { - __device__ __forceinline__ uint operator()(uint src) const - { - return HLS2RGBConvert(src); - } - __host__ __device__ __forceinline__ HLS2RGB() {} - __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template struct name ## _full_traits \ - { \ - typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template <> struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template <> struct name ## _full_traits \ - { \ - typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -///////////////////////////////////// RGB <-> Lab ///////////////////////////////////// - - namespace color_detail - { - enum - { - LAB_CBRT_TAB_SIZE = 1024, - GAMMA_TAB_SIZE = 1024, - lab_shift = xyz_shift, - gamma_shift = 3, - lab_shift2 = (lab_shift + gamma_shift), - LAB_CBRT_TAB_SIZE_B = (256 * 3 / 2 * (1 << gamma_shift)) - }; - - __constant__ ushort c_sRGBGammaTab_b[] = {0,1,1,2,2,3,4,4,5,6,6,7,8,8,9,10,11,11,12,13,14,15,16,17,19,20,21,22,24,25,26,28,29,31,33,34,36,38,40,41,43,45,47,49,51,54,56,58,60,63,65,68,70,73,75,78,81,83,86,89,92,95,98,101,105,108,111,115,118,121,125,129,132,136,140,144,147,151,155,160,164,168,172,176,181,185,190,194,199,204,209,213,218,223,228,233,239,244,249,255,260,265,271,277,282,288,294,300,306,312,318,324,331,337,343,350,356,363,370,376,383,390,397,404,411,418,426,433,440,448,455,463,471,478,486,494,502,510,518,527,535,543,552,560,569,578,586,595,604,613,622,631,641,650,659,669,678,688,698,707,717,727,737,747,757,768,778,788,799,809,820,831,842,852,863,875,886,897,908,920,931,943,954,966,978,990,1002,1014,1026,1038,1050,1063,1075,1088,1101,1113,1126,1139,1152,1165,1178,1192,1205,1218,1232,1245,1259,1273,1287,1301,1315,1329,1343,1357,1372,1386,1401,1415,1430,1445,1460,1475,1490,1505,1521,1536,1551,1567,1583,1598,1614,1630,1646,1662,1678,1695,1711,1728,1744,1761,1778,1794,1811,1828,1846,1863,1880,1897,1915,1933,1950,1968,1986,2004,2022,2040}; - - __device__ __forceinline__ int LabCbrt_b(int i) - { - float x = i * (1.f / (255.f * (1 << gamma_shift))); - return (1 << lab_shift2) * (x < 0.008856f ? x * 7.787f + 0.13793103448275862f : ::cbrtf(x)); - } - - template - __device__ __forceinline__ void RGB2LabConvert_b(const T& src, D& dst) - { - const int Lscale = (116 * 255 + 50) / 100; - const int Lshift = -((16 * 255 * (1 << lab_shift2) + 50) / 100); - - int B = blueIdx == 0 ? src.x : src.z; - int G = src.y; - int R = blueIdx == 0 ? src.z : src.x; - - if (srgb) - { - B = c_sRGBGammaTab_b[B]; - G = c_sRGBGammaTab_b[G]; - R = c_sRGBGammaTab_b[R]; - } - else - { - B <<= 3; - G <<= 3; - R <<= 3; - } - - int fX = LabCbrt_b(CV_DESCALE(B * 778 + G * 1541 + R * 1777, lab_shift)); - int fY = LabCbrt_b(CV_DESCALE(B * 296 + G * 2929 + R * 871, lab_shift)); - int fZ = LabCbrt_b(CV_DESCALE(B * 3575 + G * 448 + R * 73, lab_shift)); - - int L = CV_DESCALE(Lscale * fY + Lshift, lab_shift2); - int a = CV_DESCALE(500 * (fX - fY) + 128 * (1 << lab_shift2), lab_shift2); - int b = CV_DESCALE(200 * (fY - fZ) + 128 * (1 << lab_shift2), lab_shift2); - - dst.x = saturate_cast(L); - dst.y = saturate_cast(a); - dst.z = saturate_cast(b); - } - - __device__ __forceinline__ float splineInterpolate(float x, const float* tab, int n) - { - int ix = ::min(::max(int(x), 0), n-1); - x -= ix; - tab += ix * 4; - return ((tab[3] * x + tab[2]) * x + tab[1]) * x + tab[0]; - } - - __constant__ float c_sRGBGammaTab[] = {0,7.55853e-05,0.,-7.51331e-13,7.55853e-05,7.55853e-05,-2.25399e-12,3.75665e-12,0.000151171,7.55853e-05,9.01597e-12,-6.99932e-12,0.000226756,7.55853e-05,-1.1982e-11,2.41277e-12,0.000302341,7.55853e-05,-4.74369e-12,1.19001e-11,0.000377927,7.55853e-05,3.09568e-11,-2.09095e-11,0.000453512,7.55853e-05,-3.17718e-11,1.35303e-11,0.000529097,7.55853e-05,8.81905e-12,-4.10782e-12,0.000604683,7.55853e-05,-3.50439e-12,2.90097e-12,0.000680268,7.55853e-05,5.19852e-12,-7.49607e-12,0.000755853,7.55853e-05,-1.72897e-11,2.70833e-11,0.000831439,7.55854e-05,6.39602e-11,-4.26295e-11,0.000907024,7.55854e-05,-6.39282e-11,2.70193e-11,0.000982609,7.55853e-05,1.71298e-11,-7.24017e-12,0.00105819,7.55853e-05,-4.59077e-12,1.94137e-12,0.00113378,7.55853e-05,1.23333e-12,-5.25291e-13,0.00120937,7.55853e-05,-3.42545e-13,1.59799e-13,0.00128495,7.55853e-05,1.36852e-13,-1.13904e-13,0.00136054,7.55853e-05,-2.04861e-13,2.95818e-13,0.00143612,7.55853e-05,6.82594e-13,-1.06937e-12,0.00151171,7.55853e-05,-2.52551e-12,3.98166e-12,0.00158729,7.55853e-05,9.41946e-12,-1.48573e-11,0.00166288,7.55853e-05,-3.51523e-11,5.54474e-11,0.00173846,7.55854e-05,1.3119e-10,-9.0517e-11,0.00181405,7.55854e-05,-1.40361e-10,7.37899e-11,0.00188963,7.55853e-05,8.10085e-11,-8.82272e-11,0.00196522,7.55852e-05,-1.83673e-10,1.62704e-10,0.0020408,7.55853e-05,3.04438e-10,-2.13341e-10,0.00211639,7.55853e-05,-3.35586e-10,2.25e-10,0.00219197,7.55853e-05,3.39414e-10,-2.20997e-10,0.00226756,7.55853e-05,-3.23576e-10,1.93326e-10,0.00234315,7.55853e-05,2.564e-10,-8.66446e-11,0.00241873,7.55855e-05,-3.53328e-12,-7.9578e-11,0.00249432,7.55853e-05,-2.42267e-10,1.72126e-10,0.0025699,7.55853e-05,2.74111e-10,-1.43265e-10,0.00264549,7.55854e-05,-1.55683e-10,-6.47292e-11,0.00272107,7.55849e-05,-3.4987e-10,8.67842e-10,0.00279666,7.55868e-05,2.25366e-09,-3.8723e-09,0.00287224,7.55797e-05,-9.36325e-09,1.5087e-08,0.00294783,7.56063e-05,3.58978e-08,-5.69415e-08,0.00302341,7.55072e-05,-1.34927e-07,2.13144e-07,0.003099,7.58768e-05,5.04507e-07,1.38713e-07,0.00317552,7.7302e-05,9.20646e-07,-1.55186e-07,0.00325359,7.86777e-05,4.55087e-07,4.26813e-08,0.00333276,7.97159e-05,5.83131e-07,-1.06495e-08,0.00341305,8.08502e-05,5.51182e-07,3.87467e-09,0.00349446,8.19642e-05,5.62806e-07,-1.92586e-10,0.00357698,8.30892e-05,5.62228e-07,1.0866e-09,0.00366063,8.4217e-05,5.65488e-07,5.02818e-10,0.00374542,8.53494e-05,5.66997e-07,8.60211e-10,0.00383133,8.6486e-05,5.69577e-07,7.13044e-10,0.00391839,8.76273e-05,5.71716e-07,4.78527e-10,0.00400659,8.87722e-05,5.73152e-07,1.09818e-09,0.00409594,8.99218e-05,5.76447e-07,2.50964e-10,0.00418644,9.10754e-05,5.772e-07,1.15762e-09,0.00427809,9.22333e-05,5.80672e-07,2.40865e-10,0.0043709,9.33954e-05,5.81395e-07,1.13854e-09,0.00446488,9.45616e-05,5.84811e-07,3.27267e-10,0.00456003,9.57322e-05,5.85792e-07,8.1197e-10,0.00465635,9.69062e-05,5.88228e-07,6.15823e-10,0.00475384,9.80845e-05,5.90076e-07,9.15747e-10,0.00485252,9.92674e-05,5.92823e-07,3.778e-10,0.00495238,0.000100454,5.93956e-07,8.32623e-10,0.00505343,0.000101645,5.96454e-07,4.82695e-10,0.00515567,0.000102839,5.97902e-07,9.61904e-10,0.00525911,0.000104038,6.00788e-07,3.26281e-10,0.00536375,0.00010524,6.01767e-07,9.926e-10,0.00546959,0.000106447,6.04745e-07,3.59933e-10,0.00557664,0.000107657,6.05824e-07,8.2728e-10,0.0056849,0.000108871,6.08306e-07,5.21898e-10,0.00579438,0.00011009,6.09872e-07,8.10492e-10,0.00590508,0.000111312,6.12303e-07,4.27046e-10,0.00601701,0.000112538,6.13585e-07,7.40878e-10,0.00613016,0.000113767,6.15807e-07,8.00469e-10,0.00624454,0.000115001,6.18209e-07,2.48178e-10,0.00636016,0.000116238,6.18953e-07,1.00073e-09,0.00647702,0.000117479,6.21955e-07,4.05654e-10,0.00659512,0.000118724,6.23172e-07,6.36192e-10,0.00671447,0.000119973,6.25081e-07,7.74927e-10,0.00683507,0.000121225,6.27406e-07,4.54975e-10,0.00695692,0.000122481,6.28771e-07,6.64841e-10,0.00708003,0.000123741,6.30765e-07,6.10972e-10,0.00720441,0.000125004,6.32598e-07,6.16543e-10,0.00733004,0.000126271,6.34448e-07,6.48204e-10,0.00745695,0.000127542,6.36392e-07,5.15835e-10,0.00758513,0.000128816,6.3794e-07,5.48103e-10,0.00771458,0.000130094,6.39584e-07,1.01706e-09,0.00784532,0.000131376,6.42635e-07,4.0283e-11,0.00797734,0.000132661,6.42756e-07,6.84471e-10,0.00811064,0.000133949,6.4481e-07,9.47144e-10,0.00824524,0.000135241,6.47651e-07,1.83472e-10,0.00838112,0.000136537,6.48201e-07,1.11296e-09,0.00851831,0.000137837,6.5154e-07,2.13163e-11,0.0086568,0.00013914,6.51604e-07,6.64462e-10,0.00879659,0.000140445,6.53598e-07,1.04613e-09,0.00893769,0.000141756,6.56736e-07,-1.92377e-10,0.0090801,0.000143069,6.56159e-07,1.58601e-09,0.00922383,0.000144386,6.60917e-07,-5.63754e-10,0.00936888,0.000145706,6.59226e-07,1.60033e-09,0.00951524,0.000147029,6.64027e-07,-2.49543e-10,0.00966294,0.000148356,6.63278e-07,1.26043e-09,0.00981196,0.000149687,6.67059e-07,-1.35572e-10,0.00996231,0.00015102,6.66653e-07,1.14458e-09,0.010114,0.000152357,6.70086e-07,2.13864e-10,0.010267,0.000153698,6.70728e-07,7.93856e-10,0.0104214,0.000155042,6.73109e-07,3.36077e-10,0.0105771,0.000156389,6.74118e-07,6.55765e-10,0.0107342,0.000157739,6.76085e-07,7.66211e-10,0.0108926,0.000159094,6.78384e-07,4.66116e-12,0.0110524,0.000160451,6.78398e-07,1.07775e-09,0.0112135,0.000161811,6.81631e-07,3.41023e-10,0.011376,0.000163175,6.82654e-07,3.5205e-10,0.0115398,0.000164541,6.8371e-07,1.04473e-09,0.0117051,0.000165912,6.86844e-07,1.25757e-10,0.0118717,0.000167286,6.87222e-07,3.14818e-10,0.0120396,0.000168661,6.88166e-07,1.40886e-09,0.012209,0.000170042,6.92393e-07,-3.62244e-10,0.0123797,0.000171425,6.91306e-07,9.71397e-10,0.0125518,0.000172811,6.9422e-07,2.02003e-10,0.0127253,0.0001742,6.94826e-07,1.01448e-09,0.0129002,0.000175593,6.97869e-07,3.96653e-10,0.0130765,0.00017699,6.99059e-07,1.92927e-10,0.0132542,0.000178388,6.99638e-07,6.94305e-10,0.0134333,0.00017979,7.01721e-07,7.55108e-10,0.0136138,0.000181195,7.03986e-07,1.05918e-11,0.0137957,0.000182603,7.04018e-07,1.06513e-09,0.013979,0.000184015,7.07214e-07,3.85512e-10,0.0141637,0.00018543,7.0837e-07,1.86769e-10,0.0143499,0.000186848,7.0893e-07,7.30116e-10,0.0145374,0.000188268,7.11121e-07,6.17983e-10,0.0147264,0.000189692,7.12975e-07,5.23282e-10,0.0149168,0.000191119,7.14545e-07,8.28398e-11,0.0151087,0.000192549,7.14793e-07,1.0081e-09,0.0153019,0.000193981,7.17817e-07,5.41244e-10,0.0154966,0.000195418,7.19441e-07,-3.7907e-10,0.0156928,0.000196856,7.18304e-07,1.90641e-09,0.0158903,0.000198298,7.24023e-07,-7.27387e-10,0.0160893,0.000199744,7.21841e-07,1.00317e-09,0.0162898,0.000201191,7.24851e-07,4.39949e-10,0.0164917,0.000202642,7.2617e-07,9.6234e-10,0.0166951,0.000204097,7.29057e-07,-5.64019e-10,0.0168999,0.000205554,7.27365e-07,1.29374e-09,0.0171062,0.000207012,7.31247e-07,9.77025e-10,0.017314,0.000208478,7.34178e-07,-1.47651e-09,0.0175232,0.000209942,7.29748e-07,3.06636e-09,0.0177338,0.00021141,7.38947e-07,-1.47573e-09,0.017946,0.000212884,7.3452e-07,9.7386e-10,0.0181596,0.000214356,7.37442e-07,1.30562e-09,0.0183747,0.000215835,7.41358e-07,-6.08376e-10,0.0185913,0.000217315,7.39533e-07,1.12785e-09,0.0188093,0.000218798,7.42917e-07,-1.77711e-10,0.0190289,0.000220283,7.42384e-07,1.44562e-09,0.0192499,0.000221772,7.46721e-07,-1.68825e-11,0.0194724,0.000223266,7.4667e-07,4.84533e-10,0.0196964,0.000224761,7.48124e-07,-5.85298e-11,0.0199219,0.000226257,7.47948e-07,1.61217e-09,0.0201489,0.000227757,7.52785e-07,-8.02136e-10,0.0203775,0.00022926,7.50378e-07,1.59637e-09,0.0206075,0.000230766,7.55167e-07,4.47168e-12,0.020839,0.000232276,7.55181e-07,2.48387e-10,0.021072,0.000233787,7.55926e-07,8.6474e-10,0.0213066,0.000235302,7.5852e-07,1.78299e-11,0.0215426,0.000236819,7.58573e-07,9.26567e-10,0.0217802,0.000238339,7.61353e-07,1.34529e-12,0.0220193,0.000239862,7.61357e-07,9.30659e-10,0.0222599,0.000241387,7.64149e-07,1.34529e-12,0.0225021,0.000242915,7.64153e-07,9.26567e-10,0.0227458,0.000244447,7.66933e-07,1.76215e-11,0.022991,0.00024598,7.66986e-07,8.65536e-10,0.0232377,0.000247517,7.69582e-07,2.45677e-10,0.023486,0.000249057,7.70319e-07,1.44193e-11,0.0237358,0.000250598,7.70363e-07,1.55918e-09,0.0239872,0.000252143,7.7504e-07,-6.63173e-10,0.0242401,0.000253691,7.73051e-07,1.09357e-09,0.0244946,0.000255241,7.76331e-07,1.41919e-11,0.0247506,0.000256793,7.76374e-07,7.12248e-10,0.0250082,0.000258348,7.78511e-07,8.62049e-10,0.0252673,0.000259908,7.81097e-07,-4.35061e-10,0.025528,0.000261469,7.79792e-07,8.7825e-10,0.0257902,0.000263031,7.82426e-07,6.47181e-10,0.0260541,0.000264598,7.84368e-07,2.58448e-10,0.0263194,0.000266167,7.85143e-07,1.81558e-10,0.0265864,0.000267738,7.85688e-07,8.78041e-10,0.0268549,0.000269312,7.88322e-07,3.15102e-11,0.027125,0.000270889,7.88417e-07,8.58525e-10,0.0273967,0.000272468,7.90992e-07,2.59812e-10,0.02767,0.000274051,7.91772e-07,-3.5224e-11,0.0279448,0.000275634,7.91666e-07,1.74377e-09,0.0282212,0.000277223,7.96897e-07,-1.35196e-09,0.0284992,0.000278813,7.92841e-07,1.80141e-09,0.0287788,0.000280404,7.98246e-07,-2.65629e-10,0.0290601,0.000281999,7.97449e-07,1.12374e-09,0.0293428,0.000283598,8.0082e-07,-5.04106e-10,0.0296272,0.000285198,7.99308e-07,8.92764e-10,0.0299132,0.000286799,8.01986e-07,6.58379e-10,0.0302008,0.000288405,8.03961e-07,1.98971e-10,0.0304901,0.000290014,8.04558e-07,4.08382e-10,0.0307809,0.000291624,8.05783e-07,3.01839e-11,0.0310733,0.000293236,8.05874e-07,1.33343e-09,0.0313673,0.000294851,8.09874e-07,2.2419e-10,0.031663,0.000296472,8.10547e-07,-3.67606e-10,0.0319603,0.000298092,8.09444e-07,1.24624e-09,0.0322592,0.000299714,8.13182e-07,-8.92025e-10,0.0325597,0.000301338,8.10506e-07,2.32183e-09,0.0328619,0.000302966,8.17472e-07,-9.44719e-10,0.0331657,0.000304598,8.14638e-07,1.45703e-09,0.0334711,0.000306232,8.19009e-07,-1.15805e-09,0.0337781,0.000307866,8.15535e-07,3.17507e-09,0.0340868,0.000309507,8.2506e-07,-4.09161e-09,0.0343971,0.000311145,8.12785e-07,5.74079e-09,0.0347091,0.000312788,8.30007e-07,-3.97034e-09,0.0350227,0.000314436,8.18096e-07,2.68985e-09,0.035338,0.00031608,8.26166e-07,6.61676e-10,0.0356549,0.000317734,8.28151e-07,-1.61123e-09,0.0359734,0.000319386,8.23317e-07,2.05786e-09,0.0362936,0.000321038,8.29491e-07,8.30388e-10,0.0366155,0.0003227,8.31982e-07,-1.65424e-09,0.036939,0.000324359,8.27019e-07,2.06129e-09,0.0372642,0.000326019,8.33203e-07,8.59719e-10,0.0375911,0.000327688,8.35782e-07,-1.77488e-09,0.0379196,0.000329354,8.30458e-07,2.51464e-09,0.0382498,0.000331023,8.38002e-07,-8.33135e-10,0.0385817,0.000332696,8.35502e-07,8.17825e-10,0.0389152,0.00033437,8.37956e-07,1.28718e-09,0.0392504,0.00033605,8.41817e-07,-2.2413e-09,0.0395873,0.000337727,8.35093e-07,3.95265e-09,0.0399258,0.000339409,8.46951e-07,-2.39332e-09,0.0402661,0.000341095,8.39771e-07,1.89533e-09,0.040608,0.000342781,8.45457e-07,-1.46271e-09,0.0409517,0.000344467,8.41069e-07,3.95554e-09,0.041297,0.000346161,8.52936e-07,-3.18369e-09,0.041644,0.000347857,8.43385e-07,1.32873e-09,0.0419927,0.000349548,8.47371e-07,1.59402e-09,0.0423431,0.000351248,8.52153e-07,-2.54336e-10,0.0426952,0.000352951,8.5139e-07,-5.76676e-10,0.043049,0.000354652,8.4966e-07,2.56114e-09,0.0434045,0.000356359,8.57343e-07,-2.21744e-09,0.0437617,0.000358067,8.50691e-07,2.58344e-09,0.0441206,0.000359776,8.58441e-07,-6.65826e-10,0.0444813,0.000361491,8.56444e-07,7.99218e-11,0.0448436,0.000363204,8.56684e-07,3.46063e-10,0.0452077,0.000364919,8.57722e-07,2.26116e-09,0.0455734,0.000366641,8.64505e-07,-1.94005e-09,0.045941,0.000368364,8.58685e-07,1.77384e-09,0.0463102,0.000370087,8.64007e-07,-1.43005e-09,0.0466811,0.000371811,8.59717e-07,3.94634e-09,0.0470538,0.000373542,8.71556e-07,-3.17946e-09,0.0474282,0.000375276,8.62017e-07,1.32104e-09,0.0478043,0.000377003,8.6598e-07,1.62045e-09,0.0481822,0.00037874,8.70842e-07,-3.52297e-10,0.0485618,0.000380481,8.69785e-07,-2.11211e-10,0.0489432,0.00038222,8.69151e-07,1.19716e-09,0.0493263,0.000383962,8.72743e-07,-8.52026e-10,0.0497111,0.000385705,8.70187e-07,2.21092e-09,0.0500977,0.000387452,8.76819e-07,-5.41339e-10,0.050486,0.000389204,8.75195e-07,-4.5361e-11,0.0508761,0.000390954,8.75059e-07,7.22669e-10,0.0512679,0.000392706,8.77227e-07,8.79936e-10,0.0516615,0.000394463,8.79867e-07,-5.17048e-10,0.0520568,0.000396222,8.78316e-07,1.18833e-09,0.0524539,0.000397982,8.81881e-07,-5.11022e-10,0.0528528,0.000399744,8.80348e-07,8.55683e-10,0.0532534,0.000401507,8.82915e-07,8.13562e-10,0.0536558,0.000403276,8.85356e-07,-3.84603e-10,0.05406,0.000405045,8.84202e-07,7.24962e-10,0.0544659,0.000406816,8.86377e-07,1.20986e-09,0.0548736,0.000408592,8.90006e-07,-1.83896e-09,0.0552831,0.000410367,8.84489e-07,2.42071e-09,0.0556944,0.000412143,8.91751e-07,-3.93413e-10,0.0561074,0.000413925,8.90571e-07,-8.46967e-10,0.0565222,0.000415704,8.8803e-07,3.78122e-09,0.0569388,0.000417491,8.99374e-07,-3.1021e-09,0.0573572,0.000419281,8.90068e-07,1.17658e-09,0.0577774,0.000421064,8.93597e-07,2.12117e-09,0.0581993,0.000422858,8.99961e-07,-2.21068e-09,0.0586231,0.000424651,8.93329e-07,2.9961e-09,0.0590486,0.000426447,9.02317e-07,-2.32311e-09,0.059476,0.000428244,8.95348e-07,2.57122e-09,0.0599051,0.000430043,9.03062e-07,-5.11098e-10,0.0603361,0.000431847,9.01528e-07,-5.27166e-10,0.0607688,0.000433649,8.99947e-07,2.61984e-09,0.0612034,0.000435457,9.07806e-07,-2.50141e-09,0.0616397,0.000437265,9.00302e-07,3.66045e-09,0.0620779,0.000439076,9.11283e-07,-4.68977e-09,0.0625179,0.000440885,8.97214e-07,7.64783e-09,0.0629597,0.000442702,9.20158e-07,-7.27499e-09,0.0634033,0.000444521,8.98333e-07,6.55113e-09,0.0638487,0.000446337,9.17986e-07,-4.02844e-09,0.0642959,0.000448161,9.05901e-07,2.11196e-09,0.064745,0.000449979,9.12236e-07,3.03125e-09,0.0651959,0.000451813,9.2133e-07,-6.78648e-09,0.0656486,0.000453635,9.00971e-07,9.21375e-09,0.0661032,0.000455464,9.28612e-07,-7.71684e-09,0.0665596,0.000457299,9.05462e-07,6.7522e-09,0.0670178,0.00045913,9.25718e-07,-4.3907e-09,0.0674778,0.000460968,9.12546e-07,3.36e-09,0.0679397,0.000462803,9.22626e-07,-1.59876e-09,0.0684034,0.000464644,9.1783e-07,3.0351e-09,0.068869,0.000466488,9.26935e-07,-3.09101e-09,0.0693364,0.000468333,9.17662e-07,1.8785e-09,0.0698057,0.000470174,9.23298e-07,3.02733e-09,0.0702768,0.00047203,9.3238e-07,-6.53722e-09,0.0707497,0.000473875,9.12768e-07,8.22054e-09,0.0712245,0.000475725,9.37429e-07,-3.99325e-09,0.0717012,0.000477588,9.2545e-07,3.01839e-10,0.0721797,0.00047944,9.26355e-07,2.78597e-09,0.0726601,0.000481301,9.34713e-07,-3.99507e-09,0.0731423,0.000483158,9.22728e-07,5.7435e-09,0.0736264,0.000485021,9.39958e-07,-4.07776e-09,0.0741123,0.000486888,9.27725e-07,3.11695e-09,0.0746002,0.000488753,9.37076e-07,-9.39394e-10,0.0750898,0.000490625,9.34258e-07,6.4055e-10,0.0755814,0.000492495,9.3618e-07,-1.62265e-09,0.0760748,0.000494363,9.31312e-07,5.84995e-09,0.0765701,0.000496243,9.48861e-07,-6.87601e-09,0.0770673,0.00049812,9.28233e-07,6.75296e-09,0.0775664,0.000499997,9.48492e-07,-5.23467e-09,0.0780673,0.000501878,9.32788e-07,6.73523e-09,0.0785701,0.000503764,9.52994e-07,-6.80514e-09,0.0790748,0.000505649,9.32578e-07,5.5842e-09,0.0795814,0.000507531,9.49331e-07,-6.30583e-10,0.0800899,0.000509428,9.47439e-07,-3.0618e-09,0.0806003,0.000511314,9.38254e-07,5.4273e-09,0.0811125,0.000513206,9.54536e-07,-3.74627e-09,0.0816267,0.000515104,9.43297e-07,2.10713e-09,0.0821427,0.000516997,9.49618e-07,2.76839e-09,0.0826607,0.000518905,9.57924e-07,-5.73006e-09,0.0831805,0.000520803,9.40733e-07,5.25072e-09,0.0837023,0.0005227,9.56486e-07,-3.71718e-10,0.084226,0.000524612,9.5537e-07,-3.76404e-09,0.0847515,0.000526512,9.44078e-07,7.97735e-09,0.085279,0.000528424,9.6801e-07,-5.79367e-09,0.0858084,0.000530343,9.50629e-07,2.96268e-10,0.0863397,0.000532245,9.51518e-07,4.6086e-09,0.0868729,0.000534162,9.65344e-07,-3.82947e-09,0.087408,0.000536081,9.53856e-07,3.25861e-09,0.087945,0.000537998,9.63631e-07,-1.7543e-09,0.088484,0.00053992,9.58368e-07,3.75849e-09,0.0890249,0.000541848,9.69644e-07,-5.82891e-09,0.0895677,0.00054377,9.52157e-07,4.65593e-09,0.0901124,0.000545688,9.66125e-07,2.10643e-09,0.0906591,0.000547627,9.72444e-07,-5.63099e-09,0.0912077,0.000549555,9.55551e-07,5.51627e-09,0.0917582,0.000551483,9.721e-07,-1.53292e-09,0.0923106,0.000553422,9.67501e-07,6.15311e-10,0.092865,0.000555359,9.69347e-07,-9.28291e-10,0.0934213,0.000557295,9.66562e-07,3.09774e-09,0.0939796,0.000559237,9.75856e-07,-4.01186e-09,0.0945398,0.000561177,9.6382e-07,5.49892e-09,0.095102,0.000563121,9.80317e-07,-3.08258e-09,0.0956661,0.000565073,9.71069e-07,-6.19176e-10,0.0962321,0.000567013,9.69212e-07,5.55932e-09,0.0968001,0.000568968,9.8589e-07,-6.71704e-09,0.09737,0.00057092,9.65738e-07,6.40762e-09,0.0979419,0.00057287,9.84961e-07,-4.0122e-09,0.0985158,0.000574828,9.72925e-07,2.19059e-09,0.0990916,0.000576781,9.79496e-07,2.70048e-09,0.0996693,0.000578748,9.87598e-07,-5.54193e-09,0.100249,0.000580706,9.70972e-07,4.56597e-09,0.100831,0.000582662,9.8467e-07,2.17923e-09,0.101414,0.000584638,9.91208e-07,-5.83232e-09,0.102,0.000586603,9.73711e-07,6.24884e-09,0.102588,0.000588569,9.92457e-07,-4.26178e-09,0.103177,0.000590541,9.79672e-07,3.34781e-09,0.103769,0.00059251,9.89715e-07,-1.67904e-09,0.104362,0.000594485,9.84678e-07,3.36839e-09,0.104958,0.000596464,9.94783e-07,-4.34397e-09,0.105555,0.000598441,9.81751e-07,6.55696e-09,0.106155,0.000600424,1.00142e-06,-6.98272e-09,0.106756,0.000602406,9.80474e-07,6.4728e-09,0.107359,0.000604386,9.99893e-07,-4.00742e-09,0.107965,0.000606374,9.8787e-07,2.10654e-09,0.108572,0.000608356,9.9419e-07,3.0318e-09,0.109181,0.000610353,1.00329e-06,-6.7832e-09,0.109793,0.00061234,9.82936e-07,9.1998e-09,0.110406,0.000614333,1.01054e-06,-7.6642e-09,0.111021,0.000616331,9.87543e-07,6.55579e-09,0.111639,0.000618326,1.00721e-06,-3.65791e-09,0.112258,0.000620329,9.96236e-07,6.25467e-10,0.112879,0.000622324,9.98113e-07,1.15593e-09,0.113503,0.000624323,1.00158e-06,2.20158e-09,0.114128,0.000626333,1.00819e-06,-2.51191e-09,0.114755,0.000628342,1.00065e-06,3.95517e-10,0.115385,0.000630345,1.00184e-06,9.29807e-10,0.116016,0.000632351,1.00463e-06,3.33599e-09,0.116649,0.00063437,1.01463e-06,-6.82329e-09,0.117285,0.000636379,9.94163e-07,9.05595e-09,0.117922,0.000638395,1.02133e-06,-7.04862e-09,0.118562,0.000640416,1.00019e-06,4.23737e-09,0.119203,0.000642429,1.0129e-06,-2.45033e-09,0.119847,0.000644448,1.00555e-06,5.56395e-09,0.120492,0.000646475,1.02224e-06,-4.9043e-09,0.121139,0.000648505,1.00753e-06,-8.47952e-10,0.121789,0.000650518,1.00498e-06,8.29622e-09,0.122441,0.000652553,1.02987e-06,-9.98538e-09,0.123094,0.000654582,9.99914e-07,9.2936e-09,0.12375,0.00065661,1.02779e-06,-4.83707e-09,0.124407,0.000658651,1.01328e-06,2.60411e-09,0.125067,0.000660685,1.0211e-06,-5.57945e-09,0.125729,0.000662711,1.00436e-06,1.22631e-08,0.126392,0.000664756,1.04115e-06,-1.36704e-08,0.127058,0.000666798,1.00014e-06,1.26161e-08,0.127726,0.000668836,1.03798e-06,-6.99155e-09,0.128396,0.000670891,1.01701e-06,4.48836e-10,0.129068,0.000672926,1.01836e-06,5.19606e-09,0.129742,0.000674978,1.03394e-06,-6.3319e-09,0.130418,0.000677027,1.01495e-06,5.2305e-09,0.131096,0.000679073,1.03064e-06,3.11123e-10,0.131776,0.000681135,1.03157e-06,-6.47511e-09,0.132458,0.000683179,1.01215e-06,1.06882e-08,0.133142,0.000685235,1.04421e-06,-6.47519e-09,0.133829,0.000687304,1.02479e-06,3.11237e-10,0.134517,0.000689355,1.02572e-06,5.23035e-09,0.135207,0.000691422,1.04141e-06,-6.3316e-09,0.1359,0.000693486,1.02242e-06,5.19484e-09,0.136594,0.000695546,1.038e-06,4.53497e-10,0.137291,0.000697623,1.03936e-06,-7.00891e-09,0.137989,0.000699681,1.01834e-06,1.2681e-08,0.13869,0.000701756,1.05638e-06,-1.39128e-08,0.139393,0.000703827,1.01464e-06,1.31679e-08,0.140098,0.000705896,1.05414e-06,-8.95659e-09,0.140805,0.000707977,1.02727e-06,7.75742e-09,0.141514,0.000710055,1.05055e-06,-7.17182e-09,0.142225,0.000712135,1.02903e-06,6.02862e-09,0.142938,0.000714211,1.04712e-06,-2.04163e-09,0.143653,0.000716299,1.04099e-06,2.13792e-09,0.144371,0.000718387,1.04741e-06,-6.51009e-09,0.14509,0.000720462,1.02787e-06,9.00123e-09,0.145812,0.000722545,1.05488e-06,3.07523e-10,0.146535,0.000724656,1.0558e-06,-1.02312e-08,0.147261,0.000726737,1.02511e-06,1.0815e-08,0.147989,0.000728819,1.05755e-06,-3.22681e-09,0.148719,0.000730925,1.04787e-06,2.09244e-09,0.14945,0.000733027,1.05415e-06,-5.143e-09,0.150185,0.00073512,1.03872e-06,3.57844e-09,0.150921,0.000737208,1.04946e-06,5.73027e-09,0.151659,0.000739324,1.06665e-06,-1.15983e-08,0.152399,0.000741423,1.03185e-06,1.08605e-08,0.153142,0.000743519,1.06443e-06,-2.04106e-09,0.153886,0.000745642,1.05831e-06,-2.69642e-09,0.154633,0.00074775,1.05022e-06,-2.07425e-09,0.155382,0.000749844,1.044e-06,1.09934e-08,0.156133,0.000751965,1.07698e-06,-1.20972e-08,0.156886,0.000754083,1.04069e-06,7.59288e-09,0.157641,0.000756187,1.06347e-06,-3.37305e-09,0.158398,0.000758304,1.05335e-06,5.89921e-09,0.159158,0.000760428,1.07104e-06,-5.32248e-09,0.159919,0.000762554,1.05508e-06,4.8927e-10,0.160683,0.000764666,1.05654e-06,3.36547e-09,0.161448,0.000766789,1.06664e-06,9.50081e-10,0.162216,0.000768925,1.06949e-06,-7.16568e-09,0.162986,0.000771043,1.04799e-06,1.28114e-08,0.163758,0.000773177,1.08643e-06,-1.42774e-08,0.164533,0.000775307,1.0436e-06,1.44956e-08,0.165309,0.000777438,1.08708e-06,-1.39025e-08,0.166087,0.00077957,1.04538e-06,1.13118e-08,0.166868,0.000781695,1.07931e-06,-1.54224e-09,0.167651,0.000783849,1.07468e-06,-5.14312e-09,0.168436,0.000785983,1.05925e-06,7.21381e-09,0.169223,0.000788123,1.0809e-06,-8.81096e-09,0.170012,0.000790259,1.05446e-06,1.31289e-08,0.170803,0.000792407,1.09385e-06,-1.39022e-08,0.171597,0.000794553,1.05214e-06,1.26775e-08,0.172392,0.000796695,1.09018e-06,-7.00557e-09,0.17319,0.000798855,1.06916e-06,4.43796e-10,0.17399,0.000800994,1.07049e-06,5.23031e-09,0.174792,0.000803151,1.08618e-06,-6.46397e-09,0.175596,0.000805304,1.06679e-06,5.72444e-09,0.176403,0.000807455,1.08396e-06,-1.53254e-09,0.177211,0.000809618,1.07937e-06,4.05673e-10,0.178022,0.000811778,1.08058e-06,-9.01916e-11,0.178835,0.000813939,1.08031e-06,-4.49821e-11,0.17965,0.000816099,1.08018e-06,2.70234e-10,0.180467,0.00081826,1.08099e-06,-1.03603e-09,0.181286,0.000820419,1.07788e-06,3.87392e-09,0.182108,0.000822587,1.0895e-06,4.41522e-10,0.182932,0.000824767,1.09083e-06,-5.63997e-09,0.183758,0.000826932,1.07391e-06,7.21707e-09,0.184586,0.000829101,1.09556e-06,-8.32718e-09,0.185416,0.000831267,1.07058e-06,1.11907e-08,0.186248,0.000833442,1.10415e-06,-6.63336e-09,0.187083,0.00083563,1.08425e-06,4.41484e-10,0.187919,0.0008378,1.08557e-06,4.86754e-09,0.188758,0.000839986,1.10017e-06,-5.01041e-09,0.189599,0.000842171,1.08514e-06,2.72811e-10,0.190443,0.000844342,1.08596e-06,3.91916e-09,0.191288,0.000846526,1.09772e-06,-1.04819e-09,0.192136,0.000848718,1.09457e-06,2.73531e-10,0.192985,0.000850908,1.0954e-06,-4.58916e-11,0.193837,0.000853099,1.09526e-06,-9.01158e-11,0.194692,0.000855289,1.09499e-06,4.06506e-10,0.195548,0.00085748,1.09621e-06,-1.53595e-09,0.196407,0.000859668,1.0916e-06,5.73717e-09,0.197267,0.000861869,1.10881e-06,-6.51164e-09,0.19813,0.000864067,1.08928e-06,5.40831e-09,0.198995,0.000866261,1.1055e-06,-2.20401e-10,0.199863,0.000868472,1.10484e-06,-4.52652e-09,0.200732,0.000870668,1.09126e-06,3.42508e-09,0.201604,0.000872861,1.10153e-06,5.72762e-09,0.202478,0.000875081,1.11872e-06,-1.14344e-08,0.203354,0.000877284,1.08441e-06,1.02076e-08,0.204233,0.000879484,1.11504e-06,4.06355e-10,0.205113,0.000881715,1.11626e-06,-1.18329e-08,0.205996,0.000883912,1.08076e-06,1.71227e-08,0.206881,0.000886125,1.13213e-06,-1.19546e-08,0.207768,0.000888353,1.09626e-06,8.93465e-10,0.208658,0.000890548,1.09894e-06,8.38062e-09,0.209549,0.000892771,1.12408e-06,-4.61353e-09,0.210443,0.000895006,1.11024e-06,-4.82756e-09,0.211339,0.000897212,1.09576e-06,9.02245e-09,0.212238,0.00089943,1.12283e-06,-1.45997e-09,0.213138,0.000901672,1.11845e-06,-3.18255e-09,0.214041,0.000903899,1.1089e-06,-7.11073e-10,0.214946,0.000906115,1.10677e-06,6.02692e-09,0.215853,0.000908346,1.12485e-06,-8.49548e-09,0.216763,0.00091057,1.09936e-06,1.30537e-08,0.217675,0.000912808,1.13852e-06,-1.3917e-08,0.218588,0.000915044,1.09677e-06,1.28121e-08,0.219505,0.000917276,1.13521e-06,-7.5288e-09,0.220423,0.000919523,1.11262e-06,2.40205e-09,0.221344,0.000921756,1.11983e-06,-2.07941e-09,0.222267,0.000923989,1.11359e-06,5.91551e-09,0.223192,0.000926234,1.13134e-06,-6.68149e-09,0.224119,0.000928477,1.11129e-06,5.90929e-09,0.225049,0.000930717,1.12902e-06,-2.05436e-09,0.22598,0.000932969,1.12286e-06,2.30807e-09,0.226915,0.000935222,1.12978e-06,-7.17796e-09,0.227851,0.00093746,1.10825e-06,1.15028e-08,0.228789,0.000939711,1.14276e-06,-9.03083e-09,0.22973,0.000941969,1.11566e-06,9.71932e-09,0.230673,0.00094423,1.14482e-06,-1.49452e-08,0.231619,0.000946474,1.09998e-06,2.02591e-08,0.232566,0.000948735,1.16076e-06,-2.13879e-08,0.233516,0.000950993,1.0966e-06,2.05888e-08,0.234468,0.000953247,1.15837e-06,-1.62642e-08,0.235423,0.000955515,1.10957e-06,1.46658e-08,0.236379,0.000957779,1.15357e-06,-1.25966e-08,0.237338,0.000960048,1.11578e-06,5.91793e-09,0.238299,0.000962297,1.13353e-06,3.82602e-09,0.239263,0.000964576,1.14501e-06,-6.3208e-09,0.240229,0.000966847,1.12605e-06,6.55613e-09,0.241197,0.000969119,1.14572e-06,-5.00268e-09,0.242167,0.000971395,1.13071e-06,-1.44659e-09,0.243139,0.000973652,1.12637e-06,1.07891e-08,0.244114,0.000975937,1.15874e-06,-1.19073e-08,0.245091,0.000978219,1.12302e-06,7.03782e-09,0.246071,0.000980486,1.14413e-06,-1.34276e-09,0.247052,0.00098277,1.1401e-06,-1.66669e-09,0.248036,0.000985046,1.1351e-06,8.00935e-09,0.249022,0.00098734,1.15913e-06,-1.54694e-08,0.250011,0.000989612,1.11272e-06,2.4066e-08,0.251002,0.000991909,1.18492e-06,-2.11901e-08,0.251995,0.000994215,1.12135e-06,1.08973e-09,0.25299,0.000996461,1.12462e-06,1.68311e-08,0.253988,0.000998761,1.17511e-06,-8.8094e-09,0.254987,0.00100109,1.14868e-06,-1.13958e-08,0.25599,0.00100335,1.1145e-06,2.45902e-08,0.256994,0.00100565,1.18827e-06,-2.73603e-08,0.258001,0.00100795,1.10618e-06,2.52464e-08,0.25901,0.00101023,1.18192e-06,-1.40207e-08,0.260021,0.00101256,1.13986e-06,1.03387e-09,0.261035,0.00101484,1.14296e-06,9.8853e-09,0.262051,0.00101715,1.17262e-06,-1.07726e-08,0.263069,0.00101947,1.1403e-06,3.40272e-09,0.26409,0.00102176,1.15051e-06,-2.83827e-09,0.265113,0.00102405,1.142e-06,7.95039e-09,0.266138,0.00102636,1.16585e-06,8.39047e-10,0.267166,0.00102869,1.16836e-06,-1.13066e-08,0.268196,0.00103099,1.13444e-06,1.4585e-08,0.269228,0.00103331,1.1782e-06,-1.72314e-08,0.270262,0.00103561,1.1265e-06,2.45382e-08,0.271299,0.00103794,1.20012e-06,-2.13166e-08,0.272338,0.00104028,1.13617e-06,1.12364e-09,0.273379,0.00104255,1.13954e-06,1.68221e-08,0.274423,0.00104488,1.19001e-06,-8.80736e-09,0.275469,0.00104723,1.16358e-06,-1.13948e-08,0.276518,0.00104953,1.1294e-06,2.45839e-08,0.277568,0.00105186,1.20315e-06,-2.73361e-08,0.278621,0.00105418,1.12114e-06,2.51559e-08,0.279677,0.0010565,1.19661e-06,-1.36832e-08,0.280734,0.00105885,1.15556e-06,-2.25706e-10,0.281794,0.00106116,1.15488e-06,1.45862e-08,0.282857,0.00106352,1.19864e-06,-2.83167e-08,0.283921,0.00106583,1.11369e-06,3.90759e-08,0.284988,0.00106817,1.23092e-06,-3.85801e-08,0.286058,0.00107052,1.11518e-06,2.58375e-08,0.287129,0.00107283,1.19269e-06,-5.16498e-09,0.288203,0.0010752,1.1772e-06,-5.17768e-09,0.28928,0.00107754,1.16167e-06,-3.92671e-09,0.290358,0.00107985,1.14988e-06,2.08846e-08,0.29144,0.00108221,1.21254e-06,-2.00072e-08,0.292523,0.00108458,1.15252e-06,-4.60659e-10,0.293609,0.00108688,1.15114e-06,2.18499e-08,0.294697,0.00108925,1.21669e-06,-2.73343e-08,0.295787,0.0010916,1.13468e-06,2.78826e-08,0.29688,0.00109395,1.21833e-06,-2.45915e-08,0.297975,0.00109632,1.14456e-06,1.08787e-08,0.299073,0.00109864,1.17719e-06,1.08788e-08,0.300172,0.00110102,1.20983e-06,-2.45915e-08,0.301275,0.00110337,1.13605e-06,2.78828e-08,0.302379,0.00110573,1.2197e-06,-2.73348e-08,0.303486,0.00110808,1.1377e-06,2.18518e-08,0.304595,0.00111042,1.20325e-06,-4.67556e-10,0.305707,0.00111283,1.20185e-06,-1.99816e-08,0.306821,0.00111517,1.14191e-06,2.07891e-08,0.307937,0.00111752,1.20427e-06,-3.57026e-09,0.309056,0.00111992,1.19356e-06,-6.50797e-09,0.310177,0.00112228,1.17404e-06,-2.00165e-10,0.3113,0.00112463,1.17344e-06,7.30874e-09,0.312426,0.001127,1.19536e-06,7.67424e-10,0.313554,0.00112939,1.19767e-06,-1.03784e-08,0.314685,0.00113176,1.16653e-06,1.09437e-08,0.315818,0.00113412,1.19936e-06,-3.59406e-09,0.316953,0.00113651,1.18858e-06,3.43251e-09,0.318091,0.0011389,1.19888e-06,-1.0136e-08,0.319231,0.00114127,1.16847e-06,7.30915e-09,0.320374,0.00114363,1.1904e-06,1.07018e-08,0.321518,0.00114604,1.2225e-06,-2.03137e-08,0.322666,0.00114842,1.16156e-06,1.09484e-08,0.323815,0.00115078,1.19441e-06,6.32224e-09,0.324967,0.00115319,1.21337e-06,-6.43509e-09,0.326122,0.00115559,1.19407e-06,-1.03842e-08,0.327278,0.00115795,1.16291e-06,1.81697e-08,0.328438,0.00116033,1.21742e-06,-2.6901e-09,0.329599,0.00116276,1.20935e-06,-7.40939e-09,0.330763,0.00116515,1.18713e-06,2.52533e-09,0.331929,0.00116754,1.1947e-06,-2.69191e-09,0.333098,0.00116992,1.18663e-06,8.24218e-09,0.334269,0.00117232,1.21135e-06,-4.74377e-10,0.335443,0.00117474,1.20993e-06,-6.34471e-09,0.336619,0.00117714,1.1909e-06,-3.94922e-09,0.337797,0.00117951,1.17905e-06,2.21417e-08,0.338978,0.00118193,1.24547e-06,-2.50128e-08,0.340161,0.00118435,1.17043e-06,1.8305e-08,0.341346,0.00118674,1.22535e-06,-1.84048e-08,0.342534,0.00118914,1.17013e-06,2.55121e-08,0.343725,0.00119156,1.24667e-06,-2.40389e-08,0.344917,0.00119398,1.17455e-06,1.10389e-08,0.346113,0.00119636,1.20767e-06,9.68574e-09,0.34731,0.0011988,1.23673e-06,-1.99797e-08,0.34851,0.00120122,1.17679e-06,1.06284e-08,0.349713,0.0012036,1.20867e-06,7.26868e-09,0.350917,0.00120604,1.23048e-06,-9.90072e-09,0.352125,0.00120847,1.20078e-06,2.53177e-09,0.353334,0.00121088,1.20837e-06,-2.26199e-10,0.354546,0.0012133,1.20769e-06,-1.62705e-09,0.355761,0.00121571,1.20281e-06,6.73435e-09,0.356978,0.00121813,1.22302e-06,4.49207e-09,0.358197,0.00122059,1.23649e-06,-2.47027e-08,0.359419,0.00122299,1.16238e-06,3.47142e-08,0.360643,0.00122542,1.26653e-06,-2.47472e-08,0.36187,0.00122788,1.19229e-06,4.66965e-09,0.363099,0.00123028,1.20629e-06,6.06872e-09,0.36433,0.00123271,1.2245e-06,8.57729e-10,0.365564,0.00123516,1.22707e-06,-9.49952e-09,0.366801,0.00123759,1.19858e-06,7.33792e-09,0.36804,0.00124001,1.22059e-06,9.95025e-09,0.369281,0.00124248,1.25044e-06,-1.73366e-08,0.370525,0.00124493,1.19843e-06,-2.08464e-10,0.371771,0.00124732,1.1978e-06,1.81704e-08,0.373019,0.00124977,1.25232e-06,-1.28683e-08,0.37427,0.00125224,1.21371e-06,3.50042e-09,0.375524,0.00125468,1.22421e-06,-1.1335e-09,0.37678,0.00125712,1.22081e-06,1.03345e-09,0.378038,0.00125957,1.22391e-06,-3.00023e-09,0.379299,0.00126201,1.21491e-06,1.09676e-08,0.380562,0.00126447,1.24781e-06,-1.10676e-08,0.381828,0.00126693,1.21461e-06,3.50042e-09,0.383096,0.00126937,1.22511e-06,-2.93403e-09,0.384366,0.00127181,1.21631e-06,8.23574e-09,0.385639,0.00127427,1.24102e-06,-2.06607e-10,0.386915,0.00127675,1.2404e-06,-7.40935e-09,0.388193,0.00127921,1.21817e-06,4.1761e-11,0.389473,0.00128165,1.21829e-06,7.24223e-09,0.390756,0.0012841,1.24002e-06,7.91564e-10,0.392042,0.00128659,1.2424e-06,-1.04086e-08,0.393329,0.00128904,1.21117e-06,1.10405e-08,0.39462,0.0012915,1.24429e-06,-3.951e-09,0.395912,0.00129397,1.23244e-06,4.7634e-09,0.397208,0.00129645,1.24673e-06,-1.51025e-08,0.398505,0.0012989,1.20142e-06,2.58443e-08,0.399805,0.00130138,1.27895e-06,-2.86702e-08,0.401108,0.00130385,1.19294e-06,2.92318e-08,0.402413,0.00130632,1.28064e-06,-2.86524e-08,0.403721,0.0013088,1.19468e-06,2.57731e-08,0.405031,0.00131127,1.272e-06,-1.48355e-08,0.406343,0.00131377,1.2275e-06,3.76652e-09,0.407658,0.00131623,1.23879e-06,-2.30784e-10,0.408976,0.00131871,1.2381e-06,-2.84331e-09,0.410296,0.00132118,1.22957e-06,1.16041e-08,0.411618,0.00132367,1.26438e-06,-1.37708e-08,0.412943,0.00132616,1.22307e-06,1.36768e-08,0.41427,0.00132865,1.2641e-06,-1.1134e-08,0.4156,0.00133114,1.2307e-06,1.05714e-09,0.416933,0.00133361,1.23387e-06,6.90538e-09,0.418267,0.00133609,1.25459e-06,1.12372e-09,0.419605,0.00133861,1.25796e-06,-1.14002e-08,0.420945,0.00134109,1.22376e-06,1.46747e-08,0.422287,0.00134358,1.26778e-06,-1.7496e-08,0.423632,0.00134606,1.21529e-06,2.5507e-08,0.424979,0.00134857,1.29182e-06,-2.49272e-08,0.426329,0.00135108,1.21703e-06,1.45972e-08,0.427681,0.00135356,1.26083e-06,-3.65935e-09,0.429036,0.00135607,1.24985e-06,4.00178e-11,0.430393,0.00135857,1.24997e-06,3.49917e-09,0.431753,0.00136108,1.26047e-06,-1.40366e-08,0.433116,0.00136356,1.21836e-06,2.28448e-08,0.43448,0.00136606,1.28689e-06,-1.77378e-08,0.435848,0.00136858,1.23368e-06,1.83043e-08,0.437218,0.0013711,1.28859e-06,-2.56769e-08,0.43859,0.0013736,1.21156e-06,2.47987e-08,0.439965,0.0013761,1.28595e-06,-1.39133e-08,0.441342,0.00137863,1.24421e-06,1.05202e-09,0.442722,0.00138112,1.24737e-06,9.70507e-09,0.444104,0.00138365,1.27649e-06,-1.00698e-08,0.445489,0.00138617,1.24628e-06,7.72123e-10,0.446877,0.00138867,1.24859e-06,6.98132e-09,0.448267,0.00139118,1.26954e-06,1.10477e-09,0.449659,0.00139373,1.27285e-06,-1.14003e-08,0.451054,0.00139624,1.23865e-06,1.4694e-08,0.452452,0.00139876,1.28273e-06,-1.75734e-08,0.453852,0.00140127,1.23001e-06,2.5797e-08,0.455254,0.00140381,1.3074e-06,-2.60097e-08,0.456659,0.00140635,1.22937e-06,1.86371e-08,0.458067,0.00140886,1.28529e-06,-1.8736e-08,0.459477,0.00141137,1.22908e-06,2.65048e-08,0.46089,0.00141391,1.30859e-06,-2.76784e-08,0.462305,0.00141645,1.22556e-06,2.46043e-08,0.463722,0.00141897,1.29937e-06,-1.11341e-08,0.465143,0.00142154,1.26597e-06,-9.87033e-09,0.466565,0.00142404,1.23636e-06,2.08131e-08,0.467991,0.00142657,1.2988e-06,-1.37773e-08,0.469419,0.00142913,1.25746e-06,4.49378e-09,0.470849,0.00143166,1.27094e-06,-4.19781e-09,0.472282,0.00143419,1.25835e-06,1.22975e-08,0.473717,0.00143674,1.29524e-06,-1.51902e-08,0.475155,0.00143929,1.24967e-06,1.86608e-08,0.476596,0.00144184,1.30566e-06,-2.96506e-08,0.478039,0.00144436,1.2167e-06,4.03368e-08,0.479485,0.00144692,1.33771e-06,-4.22896e-08,0.480933,0.00144947,1.21085e-06,3.94148e-08,0.482384,0.00145201,1.32909e-06,-2.59626e-08,0.483837,0.00145459,1.2512e-06,4.83124e-09,0.485293,0.0014571,1.2657e-06,6.63757e-09,0.486751,0.00145966,1.28561e-06,-1.57911e-09,0.488212,0.00146222,1.28087e-06,-3.21468e-10,0.489676,0.00146478,1.27991e-06,2.86517e-09,0.491142,0.00146735,1.2885e-06,-1.11392e-08,0.49261,0.00146989,1.25508e-06,1.18893e-08,0.494081,0.00147244,1.29075e-06,-6.61574e-09,0.495555,0.001475,1.27091e-06,1.45736e-08,0.497031,0.00147759,1.31463e-06,-2.18759e-08,0.49851,0.00148015,1.249e-06,1.33252e-08,0.499992,0.00148269,1.28897e-06,-1.62277e-09,0.501476,0.00148526,1.28411e-06,-6.83421e-09,0.502962,0.00148781,1.2636e-06,2.89596e-08,0.504451,0.00149042,1.35048e-06,-4.93997e-08,0.505943,0.00149298,1.20228e-06,4.94299e-08,0.507437,0.00149553,1.35057e-06,-2.91107e-08,0.508934,0.00149814,1.26324e-06,7.40848e-09,0.510434,0.00150069,1.28547e-06,-5.23187e-10,0.511936,0.00150326,1.2839e-06,-5.31585e-09,0.51344,0.00150581,1.26795e-06,2.17866e-08,0.514947,0.00150841,1.33331e-06,-2.22257e-08,0.516457,0.00151101,1.26663e-06,7.51178e-09,0.517969,0.00151357,1.28917e-06,-7.82128e-09,0.519484,0.00151613,1.2657e-06,2.37733e-08,0.521002,0.00151873,1.33702e-06,-2.76674e-08,0.522522,0.00152132,1.25402e-06,2.72917e-08,0.524044,0.00152391,1.3359e-06,-2.18949e-08,0.525569,0.00152652,1.27021e-06,6.83372e-10,0.527097,0.00152906,1.27226e-06,1.91613e-08,0.528628,0.00153166,1.32974e-06,-1.77241e-08,0.53016,0.00153427,1.27657e-06,-7.86963e-09,0.531696,0.0015368,1.25296e-06,4.92027e-08,0.533234,0.00153945,1.40057e-06,-6.9732e-08,0.534775,0.00154204,1.19138e-06,5.09114e-08,0.536318,0.00154458,1.34411e-06,-1.4704e-08,0.537864,0.00154722,1.3e-06,7.9048e-09,0.539413,0.00154984,1.32371e-06,-1.69152e-08,0.540964,0.00155244,1.27297e-06,1.51355e-10,0.542517,0.00155499,1.27342e-06,1.63099e-08,0.544074,0.00155758,1.32235e-06,-5.78647e-09,0.545633,0.00156021,1.30499e-06,6.83599e-09,0.547194,0.00156284,1.3255e-06,-2.15575e-08,0.548758,0.00156543,1.26083e-06,1.97892e-08,0.550325,0.00156801,1.32019e-06,2.00525e-09,0.551894,0.00157065,1.32621e-06,-2.78103e-08,0.553466,0.00157322,1.24278e-06,4.96314e-08,0.555041,0.00157586,1.39167e-06,-5.1506e-08,0.556618,0.00157849,1.23716e-06,3.71835e-08,0.558198,0.00158107,1.34871e-06,-3.76233e-08,0.55978,0.00158366,1.23584e-06,5.37052e-08,0.561365,0.00158629,1.39695e-06,-5.79884e-08,0.562953,0.00158891,1.22299e-06,5.90392e-08,0.564543,0.00159153,1.4001e-06,-5.89592e-08,0.566136,0.00159416,1.22323e-06,5.7588e-08,0.567731,0.00159678,1.39599e-06,-5.21835e-08,0.569329,0.00159941,1.23944e-06,3.19369e-08,0.57093,0.00160199,1.33525e-06,-1.59594e-08,0.572533,0.00160461,1.28737e-06,3.19006e-08,0.574139,0.00160728,1.38307e-06,-5.20383e-08,0.575748,0.00160989,1.22696e-06,5.70431e-08,0.577359,0.00161251,1.39809e-06,-5.69247e-08,0.578973,0.00161514,1.22731e-06,5.14463e-08,0.580589,0.00161775,1.38165e-06,-2.9651e-08,0.582208,0.00162042,1.2927e-06,7.55339e-09,0.58383,0.00162303,1.31536e-06,-5.62636e-10,0.585455,0.00162566,1.31367e-06,-5.30281e-09,0.587081,0.00162827,1.29776e-06,2.17738e-08,0.588711,0.00163093,1.36309e-06,-2.21875e-08,0.590343,0.00163359,1.29652e-06,7.37164e-09,0.591978,0.00163621,1.31864e-06,-7.29907e-09,0.593616,0.00163882,1.29674e-06,2.18247e-08,0.595256,0.00164148,1.36221e-06,-2.03952e-08,0.596899,0.00164414,1.30103e-06,1.51241e-10,0.598544,0.00164675,1.30148e-06,1.97902e-08,0.600192,0.00164941,1.36085e-06,-1.97074e-08,0.601843,0.00165207,1.30173e-06,-5.65175e-10,0.603496,0.00165467,1.30004e-06,2.1968e-08,0.605152,0.00165734,1.36594e-06,-2.77024e-08,0.606811,0.00165999,1.28283e-06,2.92369e-08,0.608472,0.00166264,1.37054e-06,-2.96407e-08,0.610136,0.00166529,1.28162e-06,2.97215e-08,0.611803,0.00166795,1.37079e-06,-2.96408e-08,0.613472,0.0016706,1.28186e-06,2.92371e-08,0.615144,0.00167325,1.36957e-06,-2.77031e-08,0.616819,0.00167591,1.28647e-06,2.19708e-08,0.618496,0.00167855,1.35238e-06,-5.75407e-10,0.620176,0.00168125,1.35065e-06,-1.9669e-08,0.621858,0.00168389,1.29164e-06,1.96468e-08,0.623544,0.00168653,1.35058e-06,6.86403e-10,0.625232,0.00168924,1.35264e-06,-2.23924e-08,0.626922,0.00169187,1.28547e-06,2.92788e-08,0.628615,0.00169453,1.3733e-06,-3.51181e-08,0.630311,0.00169717,1.26795e-06,5.15889e-08,0.63201,0.00169987,1.42272e-06,-5.2028e-08,0.633711,0.00170255,1.26663e-06,3.73139e-08,0.635415,0.0017052,1.37857e-06,-3.76227e-08,0.637121,0.00170784,1.2657e-06,5.35722e-08,0.63883,0.00171054,1.42642e-06,-5.74567e-08,0.640542,0.00171322,1.25405e-06,5.70456e-08,0.642257,0.0017159,1.42519e-06,-5.15163e-08,0.643974,0.00171859,1.27064e-06,2.98103e-08,0.645694,0.00172122,1.36007e-06,-8.12016e-09,0.647417,0.00172392,1.33571e-06,2.67039e-09,0.649142,0.0017266,1.34372e-06,-2.56152e-09,0.65087,0.00172928,1.33604e-06,7.57571e-09,0.6526,0.00173197,1.35876e-06,-2.77413e-08,0.654334,0.00173461,1.27554e-06,4.3785e-08,0.65607,0.00173729,1.40689e-06,-2.81896e-08,0.657808,0.00174002,1.32233e-06,9.36893e-09,0.65955,0.00174269,1.35043e-06,-9.28617e-09,0.661294,0.00174536,1.32257e-06,2.77757e-08,0.66304,0.00174809,1.4059e-06,-4.2212e-08,0.66479,0.00175078,1.27926e-06,2.1863e-08,0.666542,0.0017534,1.34485e-06,1.43648e-08,0.668297,0.00175613,1.38795e-06,-1.97177e-08,0.670054,0.00175885,1.3288e-06,4.90115e-09,0.671814,0.00176152,1.3435e-06,1.13232e-10,0.673577,0.00176421,1.34384e-06,-5.3542e-09,0.675343,0.00176688,1.32778e-06,2.13035e-08,0.677111,0.0017696,1.39169e-06,-2.02553e-08,0.678882,0.00177232,1.33092e-06,1.13005e-10,0.680656,0.00177499,1.33126e-06,1.98031e-08,0.682432,0.00177771,1.39067e-06,-1.97211e-08,0.684211,0.00178043,1.33151e-06,-5.2349e-10,0.685993,0.00178309,1.32994e-06,2.18151e-08,0.687777,0.00178582,1.39538e-06,-2.71325e-08,0.689564,0.00178853,1.31398e-06,2.71101e-08,0.691354,0.00179124,1.39531e-06,-2.17035e-08,0.693147,0.00179396,1.3302e-06,9.92865e-11,0.694942,0.00179662,1.3305e-06,2.13063e-08,0.69674,0.00179935,1.39442e-06,-2.57198e-08,0.698541,0.00180206,1.31726e-06,2.19682e-08,0.700344,0.00180476,1.38317e-06,-2.54852e-09,0.70215,0.00180752,1.37552e-06,-1.17741e-08,0.703959,0.00181023,1.3402e-06,-9.95999e-09,0.705771,0.00181288,1.31032e-06,5.16141e-08,0.707585,0.00181566,1.46516e-06,-7.72869e-08,0.709402,0.00181836,1.2333e-06,7.87197e-08,0.711222,0.00182106,1.46946e-06,-5.87781e-08,0.713044,0.00182382,1.29312e-06,3.71834e-08,0.714869,0.00182652,1.40467e-06,-3.03511e-08,0.716697,0.00182924,1.31362e-06,2.46161e-08,0.718528,0.00183194,1.38747e-06,-8.5087e-09,0.720361,0.00183469,1.36194e-06,9.41892e-09,0.722197,0.00183744,1.3902e-06,-2.91671e-08,0.724036,0.00184014,1.3027e-06,4.76448e-08,0.725878,0.00184288,1.44563e-06,-4.22028e-08,0.727722,0.00184565,1.31902e-06,1.95682e-09,0.729569,0.00184829,1.3249e-06,3.43754e-08,0.731419,0.00185104,1.42802e-06,-2.0249e-08,0.733271,0.00185384,1.36727e-06,-1.29838e-08,0.735126,0.00185654,1.32832e-06,1.25794e-08,0.736984,0.00185923,1.36606e-06,2.22711e-08,0.738845,0.00186203,1.43287e-06,-4.20594e-08,0.740708,0.00186477,1.3067e-06,2.67571e-08,0.742574,0.00186746,1.38697e-06,-5.36424e-09,0.744443,0.00187022,1.37087e-06,-5.30023e-09,0.746315,0.00187295,1.35497e-06,2.65653e-08,0.748189,0.00187574,1.43467e-06,-4.13564e-08,0.750066,0.00187848,1.3106e-06,1.9651e-08,0.751946,0.00188116,1.36955e-06,2.23572e-08,0.753828,0.00188397,1.43663e-06,-4.9475e-08,0.755714,0.00188669,1.2882e-06,5.63335e-08,0.757602,0.00188944,1.4572e-06,-5.66499e-08,0.759493,0.00189218,1.28725e-06,5.10567e-08,0.761386,0.00189491,1.44042e-06,-2.83677e-08,0.763283,0.00189771,1.35532e-06,2.80962e-09,0.765182,0.00190042,1.36375e-06,1.71293e-08,0.767083,0.0019032,1.41513e-06,-1.17221e-08,0.768988,0.001906,1.37997e-06,-2.98453e-08,0.770895,0.00190867,1.29043e-06,7.14987e-08,0.772805,0.00191146,1.50493e-06,-7.73354e-08,0.774718,0.00191424,1.27292e-06,5.90292e-08,0.776634,0.00191697,1.45001e-06,-3.9572e-08,0.778552,0.00191975,1.33129e-06,3.9654e-08,0.780473,0.00192253,1.45026e-06,-5.94395e-08,0.782397,0.00192525,1.27194e-06,7.88945e-08,0.784324,0.00192803,1.50862e-06,-7.73249e-08,0.786253,0.00193082,1.27665e-06,5.15913e-08,0.788185,0.00193352,1.43142e-06,-9.83099e-09,0.79012,0.00193636,1.40193e-06,-1.22672e-08,0.792058,0.00193912,1.36513e-06,-7.05275e-10,0.793999,0.00194185,1.36301e-06,1.50883e-08,0.795942,0.00194462,1.40828e-06,-4.33147e-11,0.797888,0.00194744,1.40815e-06,-1.49151e-08,0.799837,0.00195021,1.3634e-06,9.93244e-11,0.801788,0.00195294,1.3637e-06,1.45179e-08,0.803743,0.00195571,1.40725e-06,1.43363e-09,0.8057,0.00195853,1.41155e-06,-2.02525e-08,0.80766,0.00196129,1.35079e-06,1.99718e-08,0.809622,0.00196405,1.41071e-06,-3.01649e-11,0.811588,0.00196687,1.41062e-06,-1.9851e-08,0.813556,0.00196964,1.35107e-06,1.98296e-08,0.815527,0.0019724,1.41056e-06,1.37485e-10,0.817501,0.00197522,1.41097e-06,-2.03796e-08,0.819477,0.00197798,1.34983e-06,2.17763e-08,0.821457,0.00198074,1.41516e-06,-7.12085e-09,0.823439,0.00198355,1.3938e-06,6.70707e-09,0.825424,0.00198636,1.41392e-06,-1.97074e-08,0.827412,0.00198913,1.35479e-06,1.25179e-08,0.829402,0.00199188,1.39235e-06,2.92405e-08,0.831396,0.00199475,1.48007e-06,-6.98755e-08,0.833392,0.0019975,1.27044e-06,7.14477e-08,0.835391,0.00200026,1.48479e-06,-3.71014e-08,0.837392,0.00200311,1.37348e-06,1.73533e-08,0.839397,0.00200591,1.42554e-06,-3.23118e-08,0.841404,0.00200867,1.32861e-06,5.2289e-08,0.843414,0.00201148,1.48547e-06,-5.76348e-08,0.845427,0.00201428,1.31257e-06,5.9041e-08,0.847443,0.00201708,1.48969e-06,-5.93197e-08,0.849461,0.00201988,1.31173e-06,5.90289e-08,0.851482,0.00202268,1.48882e-06,-5.75864e-08,0.853507,0.00202549,1.31606e-06,5.21075e-08,0.855533,0.00202828,1.47238e-06,-3.16344e-08,0.857563,0.00203113,1.37748e-06,1.48257e-08,0.859596,0.00203393,1.42196e-06,-2.76684e-08,0.861631,0.00203669,1.33895e-06,3.62433e-08,0.863669,0.00203947,1.44768e-06,1.90463e-09,0.86571,0.00204237,1.45339e-06,-4.38617e-08,0.867754,0.00204515,1.32181e-06,5.43328e-08,0.8698,0.00204796,1.48481e-06,-5.42603e-08,0.87185,0.00205076,1.32203e-06,4.34989e-08,0.873902,0.00205354,1.45252e-06,-5.26029e-10,0.875957,0.00205644,1.45095e-06,-4.13949e-08,0.878015,0.00205922,1.32676e-06,4.68962e-08,0.880075,0.00206201,1.46745e-06,-2.69807e-08,0.882139,0.00206487,1.38651e-06,1.42181e-09,0.884205,0.00206764,1.39077e-06,2.12935e-08,0.886274,0.00207049,1.45465e-06,-2.69912e-08,0.888346,0.00207332,1.37368e-06,2.70664e-08,0.890421,0.00207615,1.45488e-06,-2.16698e-08,0.892498,0.00207899,1.38987e-06,8.14756e-12,0.894579,0.00208177,1.38989e-06,2.16371e-08,0.896662,0.00208462,1.45481e-06,-2.6952e-08,0.898748,0.00208744,1.37395e-06,2.65663e-08,0.900837,0.00209027,1.45365e-06,-1.97084e-08,0.902928,0.00209312,1.39452e-06,-7.33731e-09,0.905023,0.00209589,1.37251e-06,4.90578e-08,0.90712,0.00209878,1.51968e-06,-6.96845e-08,0.90922,0.00210161,1.31063e-06,5.08664e-08,0.911323,0.00210438,1.46323e-06,-1.45717e-08,0.913429,0.00210727,1.41952e-06,7.42038e-09,0.915538,0.00211013,1.44178e-06,-1.51097e-08,0.917649,0.00211297,1.39645e-06,-6.58618e-09,0.919764,0.00211574,1.37669e-06,4.14545e-08,0.921881,0.00211862,1.50105e-06,-4.00222e-08,0.924001,0.0021215,1.38099e-06,-5.7518e-10,0.926124,0.00212426,1.37926e-06,4.23229e-08,0.92825,0.00212714,1.50623e-06,-4.9507e-08,0.930378,0.00213001,1.35771e-06,3.64958e-08,0.93251,0.00213283,1.4672e-06,-3.68713e-08,0.934644,0.00213566,1.35658e-06,5.13848e-08,0.936781,0.00213852,1.51074e-06,-4.94585e-08,0.938921,0.0021414,1.36236e-06,2.72399e-08,0.941064,0.0021442,1.44408e-06,1.0372e-10,0.943209,0.00214709,1.44439e-06,-2.76547e-08,0.945358,0.0021499,1.36143e-06,5.09106e-08,0.947509,0.00215277,1.51416e-06,-5.67784e-08,0.949663,0.00215563,1.34382e-06,5.69935e-08,0.95182,0.00215849,1.5148e-06,-5.19861e-08,0.95398,0.00216136,1.35885e-06,3.17417e-08,0.956143,0.00216418,1.45407e-06,-1.53758e-08,0.958309,0.00216704,1.40794e-06,2.97615e-08,0.960477,0.00216994,1.49723e-06,-4.40657e-08,0.962649,0.00217281,1.36503e-06,2.72919e-08,0.964823,0.00217562,1.44691e-06,-5.49729e-09,0.967,0.0021785,1.43041e-06,-5.30273e-09,0.96918,0.00218134,1.41451e-06,2.67084e-08,0.971363,0.00218425,1.49463e-06,-4.19265e-08,0.973548,0.00218711,1.36885e-06,2.17881e-08,0.975737,0.00218992,1.43422e-06,1.43789e-08,0.977928,0.00219283,1.47735e-06,-1.96989e-08,0.980122,0.00219572,1.41826e-06,4.81221e-09,0.98232,0.00219857,1.43269e-06,4.50048e-10,0.98452,0.00220144,1.43404e-06,-6.61237e-09,0.986722,0.00220429,1.41421e-06,2.59993e-08,0.988928,0.0022072,1.4922e-06,-3.77803e-08,0.991137,0.00221007,1.37886e-06,5.9127e-09,0.993348,0.00221284,1.3966e-06,1.33339e-07,0.995563,0.00221604,1.79662e-06,-5.98872e-07,0.99778,0.00222015,0.,0.}; - - template - __device__ __forceinline__ void RGB2LabConvert_f(const T& src, D& dst) - { - const float _1_3 = 1.0f / 3.0f; - const float _a = 16.0f / 116.0f; - - float B = blueIdx == 0 ? src.x : src.z; - float G = src.y; - float R = blueIdx == 0 ? src.z : src.x; - - if (srgb) - { - B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); - G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); - R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); - } - - float X = B * 0.189828f + G * 0.376219f + R * 0.433953f; - float Y = B * 0.072169f + G * 0.715160f + R * 0.212671f; - float Z = B * 0.872766f + G * 0.109477f + R * 0.017758f; - - float FX = X > 0.008856f ? ::powf(X, _1_3) : (7.787f * X + _a); - float FY = Y > 0.008856f ? ::powf(Y, _1_3) : (7.787f * Y + _a); - float FZ = Z > 0.008856f ? ::powf(Z, _1_3) : (7.787f * Z + _a); - - float L = Y > 0.008856f ? (116.f * FY - 16.f) : (903.3f * Y); - float a = 500.f * (FX - FY); - float b = 200.f * (FY - FZ); - - dst.x = L; - dst.y = a; - dst.z = b; - } - - template struct RGB2Lab; - template - struct RGB2Lab - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - RGB2LabConvert_b(src, dst); - - return dst; - } - __host__ __device__ __forceinline__ RGB2Lab() {} - __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {} - }; - template - struct RGB2Lab - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - RGB2LabConvert_f(src, dst); - - return dst; - } - __host__ __device__ __forceinline__ RGB2Lab() {} - __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(name, scn, dcn, srgb, blueIdx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2Lab functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - __constant__ float c_sRGBInvGammaTab[] = {0,0.0126255,0.,-8.33961e-06,0.0126172,0.0126005,-2.50188e-05,4.1698e-05,0.0252344,0.0126756,0.000100075,-0.000158451,0.0378516,0.0124004,-0.000375277,-0.000207393,0.0496693,0.0110276,-0.000997456,0.00016837,0.0598678,0.00953783,-0.000492346,2.07235e-05,0.068934,0.00861531,-0.000430176,3.62876e-05,0.0771554,0.00786382,-0.000321313,1.87625e-05,0.0847167,0.00727748,-0.000265025,1.53594e-05,0.0917445,0.00679351,-0.000218947,1.10545e-05,0.0983301,0.00638877,-0.000185784,8.66984e-06,0.104542,0.00604322,-0.000159774,6.82996e-06,0.110432,0.00574416,-0.000139284,5.51008e-06,0.116042,0.00548212,-0.000122754,4.52322e-06,0.121406,0.00525018,-0.000109184,3.75557e-06,0.126551,0.00504308,-9.79177e-05,3.17134e-06,0.131499,0.00485676,-8.84037e-05,2.68469e-06,0.13627,0.004688,-8.03496e-05,2.31725e-06,0.14088,0.00453426,-7.33978e-05,2.00868e-06,0.145343,0.00439349,-6.73718e-05,1.74775e-06,0.149671,0.00426399,-6.21286e-05,1.53547e-06,0.153875,0.00414434,-5.75222e-05,1.364e-06,0.157963,0.00403338,-5.34301e-05,1.20416e-06,0.161944,0.00393014,-4.98177e-05,1.09114e-06,0.165825,0.00383377,-4.65443e-05,9.57987e-07,0.169613,0.00374356,-4.36703e-05,8.88359e-07,0.173314,0.00365888,-4.10052e-05,7.7849e-07,0.176933,0.00357921,-3.86697e-05,7.36254e-07,0.180474,0.00350408,-3.6461e-05,6.42534e-07,0.183942,0.00343308,-3.45334e-05,6.12614e-07,0.187342,0.00336586,-3.26955e-05,5.42894e-07,0.190675,0.00330209,-3.10669e-05,5.08967e-07,0.193947,0.00324149,-2.954e-05,4.75977e-07,0.197159,0.00318383,-2.8112e-05,4.18343e-07,0.200315,0.00312887,-2.6857e-05,4.13651e-07,0.203418,0.00307639,-2.5616e-05,3.70847e-07,0.206469,0.00302627,-2.45035e-05,3.3813e-07,0.209471,0.00297828,-2.34891e-05,3.32999e-07,0.212426,0.0029323,-2.24901e-05,2.96826e-07,0.215336,0.00288821,-2.15996e-05,2.82736e-07,0.218203,0.00284586,-2.07514e-05,2.70961e-07,0.221029,0.00280517,-1.99385e-05,2.42744e-07,0.223814,0.00276602,-1.92103e-05,2.33277e-07,0.226561,0.0027283,-1.85105e-05,2.2486e-07,0.229271,0.00269195,-1.78359e-05,2.08383e-07,0.231945,0.00265691,-1.72108e-05,1.93305e-07,0.234585,0.00262307,-1.66308e-05,1.80687e-07,0.237192,0.00259035,-1.60888e-05,1.86632e-07,0.239766,0.00255873,-1.55289e-05,1.60569e-07,0.24231,0.00252815,-1.50472e-05,1.54566e-07,0.244823,0.00249852,-1.45835e-05,1.59939e-07,0.247307,0.00246983,-1.41037e-05,1.29549e-07,0.249763,0.00244202,-1.3715e-05,1.41429e-07,0.252191,0.00241501,-1.32907e-05,1.39198e-07,0.254593,0.00238885,-1.28731e-05,1.06444e-07,0.256969,0.00236342,-1.25538e-05,1.2048e-07,0.25932,0.00233867,-1.21924e-05,1.26892e-07,0.261647,0.00231467,-1.18117e-05,8.72084e-08,0.26395,0.00229131,-1.15501e-05,1.20323e-07,0.26623,0.00226857,-1.11891e-05,8.71514e-08,0.268487,0.00224645,-1.09276e-05,9.73165e-08,0.270723,0.00222489,-1.06357e-05,8.98259e-08,0.272937,0.00220389,-1.03662e-05,7.98218e-08,0.275131,0.00218339,-1.01267e-05,9.75254e-08,0.277304,0.00216343,-9.83416e-06,6.65195e-08,0.279458,0.00214396,-9.63461e-06,8.34313e-08,0.281592,0.00212494,-9.38431e-06,7.65919e-08,0.283708,0.00210641,-9.15454e-06,5.7236e-08,0.285805,0.00208827,-8.98283e-06,8.18939e-08,0.287885,0.00207055,-8.73715e-06,6.2224e-08,0.289946,0.00205326,-8.55047e-06,5.66388e-08,0.291991,0.00203633,-8.38056e-06,6.88491e-08,0.294019,0.00201978,-8.17401e-06,5.53955e-08,0.296031,0.00200359,-8.00782e-06,6.71971e-08,0.298027,0.00198778,-7.80623e-06,3.34439e-08,0.300007,0.00197227,-7.7059e-06,6.7248e-08,0.301971,0.00195706,-7.50416e-06,5.51915e-08,0.303921,0.00194221,-7.33858e-06,3.98124e-08,0.305856,0.00192766,-7.21915e-06,5.37795e-08,0.307776,0.00191338,-7.05781e-06,4.30919e-08,0.309683,0.00189939,-6.92853e-06,4.20744e-08,0.311575,0.00188566,-6.80231e-06,5.68321e-08,0.313454,0.00187223,-6.63181e-06,2.86195e-08,0.31532,0.00185905,-6.54595e-06,3.73075e-08,0.317172,0.00184607,-6.43403e-06,6.05684e-08,0.319012,0.00183338,-6.25233e-06,1.84426e-08,0.320839,0.00182094,-6.197e-06,4.44757e-08,0.322654,0.00180867,-6.06357e-06,4.20729e-08,0.324456,0.00179667,-5.93735e-06,2.56511e-08,0.326247,0.00178488,-5.8604e-06,3.41368e-08,0.328026,0.00177326,-5.75799e-06,4.64177e-08,0.329794,0.00176188,-5.61874e-06,1.86107e-08,0.33155,0.0017507,-5.5629e-06,2.81511e-08,0.333295,0.00173966,-5.47845e-06,4.75987e-08,0.335029,0.00172884,-5.33565e-06,1.98726e-08,0.336753,0.00171823,-5.27604e-06,2.19226e-08,0.338466,0.00170775,-5.21027e-06,4.14483e-08,0.340169,0.00169745,-5.08592e-06,2.09017e-08,0.341861,0.00168734,-5.02322e-06,2.39561e-08,0.343543,0.00167737,-4.95135e-06,3.22852e-08,0.345216,0.00166756,-4.85449e-06,2.57173e-08,0.346878,0.00165793,-4.77734e-06,1.38569e-08,0.348532,0.00164841,-4.73577e-06,3.80634e-08,0.350175,0.00163906,-4.62158e-06,1.27043e-08,0.35181,0.00162985,-4.58347e-06,3.03279e-08,0.353435,0.00162078,-4.49249e-06,1.49961e-08,0.355051,0.00161184,-4.4475e-06,2.88977e-08,0.356659,0.00160303,-4.3608e-06,1.84241e-08,0.358257,0.00159436,-4.30553e-06,1.6616e-08,0.359848,0.0015858,-4.25568e-06,3.43218e-08,0.361429,0.00157739,-4.15272e-06,-4.89172e-09,0.363002,0.00156907,-4.16739e-06,4.48498e-08,0.364567,0.00156087,-4.03284e-06,4.30676e-09,0.366124,0.00155282,-4.01992e-06,2.73303e-08,0.367673,0.00154486,-3.93793e-06,5.58036e-09,0.369214,0.001537,-3.92119e-06,3.97554e-08,0.370747,0.00152928,-3.80193e-06,-1.55904e-08,0.372272,0.00152163,-3.8487e-06,5.24081e-08,0.37379,0.00151409,-3.69147e-06,-1.52272e-08,0.375301,0.00150666,-3.73715e-06,3.83028e-08,0.376804,0.0014993,-3.62225e-06,1.10278e-08,0.378299,0.00149209,-3.58916e-06,6.99326e-09,0.379788,0.00148493,-3.56818e-06,2.06038e-08,0.381269,0.00147786,-3.50637e-06,2.98009e-08,0.382744,0.00147093,-3.41697e-06,-2.05978e-08,0.384211,0.00146404,-3.47876e-06,5.25899e-08,0.385672,0.00145724,-3.32099e-06,-1.09471e-08,0.387126,0.00145056,-3.35383e-06,2.10009e-08,0.388573,0.00144392,-3.29083e-06,1.63501e-08,0.390014,0.00143739,-3.24178e-06,3.00641e-09,0.391448,0.00143091,-3.23276e-06,3.12282e-08,0.392875,0.00142454,-3.13908e-06,-8.70932e-09,0.394297,0.00141824,-3.16521e-06,3.34114e-08,0.395712,0.00141201,-3.06497e-06,-5.72754e-09,0.397121,0.00140586,-3.08215e-06,1.9301e-08,0.398524,0.00139975,-3.02425e-06,1.7931e-08,0.39992,0.00139376,-2.97046e-06,-1.61822e-09,0.401311,0.00138781,-2.97531e-06,1.83442e-08,0.402696,0.00138192,-2.92028e-06,1.76485e-08,0.404075,0.00137613,-2.86733e-06,4.68617e-10,0.405448,0.00137039,-2.86593e-06,1.02794e-08,0.406816,0.00136469,-2.83509e-06,1.80179e-08,0.408178,0.00135908,-2.78104e-06,7.05594e-09,0.409534,0.00135354,-2.75987e-06,1.33633e-08,0.410885,0.00134806,-2.71978e-06,-9.04568e-10,0.41223,0.00134261,-2.72249e-06,2.0057e-08,0.41357,0.00133723,-2.66232e-06,1.00841e-08,0.414905,0.00133194,-2.63207e-06,-7.88835e-10,0.416234,0.00132667,-2.63444e-06,2.28734e-08,0.417558,0.00132147,-2.56582e-06,-1.29785e-09,0.418877,0.00131633,-2.56971e-06,1.21205e-08,0.420191,0.00131123,-2.53335e-06,1.24202e-08,0.421499,0.0013062,-2.49609e-06,-2.19681e-09,0.422803,0.0013012,-2.50268e-06,2.61696e-08,0.424102,0.00129628,-2.42417e-06,-1.30747e-08,0.425396,0.00129139,-2.46339e-06,2.6129e-08,0.426685,0.00128654,-2.38501e-06,-2.03454e-09,0.427969,0.00128176,-2.39111e-06,1.18115e-08,0.429248,0.00127702,-2.35567e-06,1.43932e-08,0.430523,0.00127235,-2.31249e-06,-9.77965e-09,0.431793,0.00126769,-2.34183e-06,2.47253e-08,0.433058,0.00126308,-2.26766e-06,2.85278e-10,0.434319,0.00125855,-2.2668e-06,3.93614e-09,0.435575,0.00125403,-2.25499e-06,1.37722e-08,0.436827,0.00124956,-2.21368e-06,5.79803e-10,0.438074,0.00124513,-2.21194e-06,1.37112e-08,0.439317,0.00124075,-2.1708e-06,4.17973e-09,0.440556,0.00123642,-2.15826e-06,-6.27703e-10,0.44179,0.0012321,-2.16015e-06,2.81332e-08,0.44302,0.00122787,-2.07575e-06,-2.24985e-08,0.444246,0.00122365,-2.14324e-06,3.20586e-08,0.445467,0.00121946,-2.04707e-06,-1.6329e-08,0.446685,0.00121532,-2.09605e-06,3.32573e-08,0.447898,0.00121122,-1.99628e-06,-2.72927e-08,0.449107,0.00120715,-2.07816e-06,4.6111e-08,0.450312,0.00120313,-1.93983e-06,-3.79416e-08,0.451514,0.00119914,-2.05365e-06,4.60507e-08,0.452711,0.00119517,-1.9155e-06,-2.7052e-08,0.453904,0.00119126,-1.99666e-06,3.23551e-08,0.455093,0.00118736,-1.89959e-06,-1.29613e-08,0.456279,0.00118352,-1.93848e-06,1.94905e-08,0.45746,0.0011797,-1.88e-06,-5.39588e-09,0.458638,0.00117593,-1.89619e-06,2.09282e-09,0.459812,0.00117214,-1.88991e-06,2.68267e-08,0.460982,0.00116844,-1.80943e-06,-1.99925e-08,0.462149,0.00116476,-1.86941e-06,2.3341e-08,0.463312,0.00116109,-1.79939e-06,-1.37674e-08,0.464471,0.00115745,-1.84069e-06,3.17287e-08,0.465627,0.00115387,-1.7455e-06,-2.37407e-08,0.466779,0.00115031,-1.81673e-06,3.34315e-08,0.467927,0.00114677,-1.71643e-06,-2.05786e-08,0.469073,0.00114328,-1.77817e-06,1.90802e-08,0.470214,0.00113978,-1.72093e-06,3.86247e-09,0.471352,0.00113635,-1.70934e-06,-4.72759e-09,0.472487,0.00113292,-1.72352e-06,1.50478e-08,0.473618,0.00112951,-1.67838e-06,4.14108e-09,0.474746,0.00112617,-1.66595e-06,-1.80986e-09,0.47587,0.00112283,-1.67138e-06,3.09816e-09,0.476991,0.0011195,-1.66209e-06,1.92198e-08,0.478109,0.00111623,-1.60443e-06,-2.03726e-08,0.479224,0.00111296,-1.66555e-06,3.2468e-08,0.480335,0.00110973,-1.56814e-06,-2.00922e-08,0.481443,0.00110653,-1.62842e-06,1.80983e-08,0.482548,0.00110333,-1.57413e-06,7.30362e-09,0.48365,0.0011002,-1.55221e-06,-1.75107e-08,0.484749,0.00109705,-1.60475e-06,3.29373e-08,0.485844,0.00109393,-1.50594e-06,-2.48315e-08,0.486937,0.00109085,-1.58043e-06,3.65865e-08,0.488026,0.0010878,-1.47067e-06,-3.21078e-08,0.489112,0.00108476,-1.56699e-06,3.22397e-08,0.490195,0.00108172,-1.47027e-06,-7.44391e-09,0.491276,0.00107876,-1.49261e-06,-2.46428e-09,0.492353,0.00107577,-1.5e-06,1.73011e-08,0.493427,0.00107282,-1.4481e-06,-7.13552e-09,0.494499,0.0010699,-1.4695e-06,1.1241e-08,0.495567,0.001067,-1.43578e-06,-8.02637e-09,0.496633,0.0010641,-1.45986e-06,2.08645e-08,0.497695,0.00106124,-1.39726e-06,-1.58271e-08,0.498755,0.0010584,-1.44475e-06,1.26415e-08,0.499812,0.00105555,-1.40682e-06,2.48655e-08,0.500866,0.00105281,-1.33222e-06,-5.24988e-08,0.501918,0.00104999,-1.48972e-06,6.59206e-08,0.502966,0.00104721,-1.29196e-06,-3.237e-08,0.504012,0.00104453,-1.38907e-06,3.95479e-09,0.505055,0.00104176,-1.3772e-06,1.65509e-08,0.506096,0.00103905,-1.32755e-06,-1.05539e-08,0.507133,0.00103637,-1.35921e-06,2.56648e-08,0.508168,0.00103373,-1.28222e-06,-3.25007e-08,0.509201,0.00103106,-1.37972e-06,4.47336e-08,0.51023,0.00102844,-1.24552e-06,-2.72245e-08,0.511258,0.00102587,-1.32719e-06,4.55952e-09,0.512282,0.00102323,-1.31352e-06,8.98645e-09,0.513304,0.00102063,-1.28656e-06,1.90992e-08,0.514323,0.00101811,-1.22926e-06,-2.57786e-08,0.51534,0.00101557,-1.30659e-06,2.44104e-08,0.516355,0.00101303,-1.23336e-06,-1.22581e-08,0.517366,0.00101053,-1.27014e-06,2.4622e-08,0.518376,0.00100806,-1.19627e-06,-2.66253e-08,0.519383,0.00100559,-1.27615e-06,2.22744e-08,0.520387,0.00100311,-1.20932e-06,-2.8679e-09,0.521389,0.00100068,-1.21793e-06,-1.08029e-08,0.522388,0.000998211,-1.25034e-06,4.60795e-08,0.523385,0.000995849,-1.1121e-06,-5.4306e-08,0.52438,0.000993462,-1.27502e-06,5.19354e-08,0.525372,0.000991067,-1.11921e-06,-3.42262e-08,0.526362,0.000988726,-1.22189e-06,2.53646e-08,0.52735,0.000986359,-1.14579e-06,-7.62782e-09,0.528335,0.000984044,-1.16868e-06,5.14668e-09,0.529318,0.000981722,-1.15324e-06,-1.29589e-08,0.530298,0.000979377,-1.19211e-06,4.66888e-08,0.531276,0.000977133,-1.05205e-06,-5.45868e-08,0.532252,0.000974865,-1.21581e-06,5.24495e-08,0.533226,0.000972591,-1.05846e-06,-3.60019e-08,0.534198,0.000970366,-1.16647e-06,3.19537e-08,0.535167,0.000968129,-1.07061e-06,-3.2208e-08,0.536134,0.000965891,-1.16723e-06,3.72738e-08,0.537099,0.000963668,-1.05541e-06,2.32205e-09,0.538061,0.000961564,-1.04844e-06,-4.65618e-08,0.539022,0.000959328,-1.18813e-06,6.47159e-08,0.53998,0.000957146,-9.93979e-07,-3.3488e-08,0.540936,0.000955057,-1.09444e-06,9.63166e-09,0.54189,0.000952897,-1.06555e-06,-5.03871e-09,0.542842,0.000950751,-1.08066e-06,1.05232e-08,0.543792,0.000948621,-1.04909e-06,2.25503e-08,0.544739,0.000946591,-9.81444e-07,-4.11195e-08,0.545685,0.000944504,-1.1048e-06,2.27182e-08,0.546628,0.000942363,-1.03665e-06,9.85146e-09,0.54757,0.000940319,-1.00709e-06,-2.51938e-09,0.548509,0.000938297,-1.01465e-06,2.25858e-10,0.549446,0.000936269,-1.01397e-06,1.61598e-09,0.550381,0.000934246,-1.00913e-06,-6.68983e-09,0.551315,0.000932207,-1.0292e-06,2.51434e-08,0.552246,0.000930224,-9.53765e-07,-3.42793e-08,0.553175,0.000928214,-1.0566e-06,5.23688e-08,0.554102,0.000926258,-8.99497e-07,-5.59865e-08,0.555028,0.000924291,-1.06746e-06,5.23679e-08,0.555951,0.000922313,-9.10352e-07,-3.42763e-08,0.556872,0.00092039,-1.01318e-06,2.51326e-08,0.557792,0.000918439,-9.37783e-07,-6.64954e-09,0.558709,0.000916543,-9.57732e-07,1.46554e-09,0.559625,0.000914632,-9.53335e-07,7.87281e-10,0.560538,0.000912728,-9.50973e-07,-4.61466e-09,0.56145,0.000910812,-9.64817e-07,1.76713e-08,0.56236,0.000908935,-9.11804e-07,-6.46564e-09,0.563268,0.000907092,-9.312e-07,8.19121e-09,0.564174,0.000905255,-9.06627e-07,-2.62992e-08,0.565078,0.000903362,-9.85524e-07,3.74007e-08,0.565981,0.000901504,-8.73322e-07,-4.0942e-09,0.566882,0.000899745,-8.85605e-07,-2.1024e-08,0.56778,0.00089791,-9.48677e-07,2.85854e-08,0.568677,0.000896099,-8.62921e-07,-3.3713e-08,0.569573,0.000894272,-9.64059e-07,4.6662e-08,0.570466,0.000892484,-8.24073e-07,-3.37258e-08,0.571358,0.000890734,-9.25251e-07,2.86365e-08,0.572247,0.00088897,-8.39341e-07,-2.12155e-08,0.573135,0.000887227,-9.02988e-07,-3.37913e-09,0.574022,0.000885411,-9.13125e-07,3.47319e-08,0.574906,0.000883689,-8.08929e-07,-1.63394e-08,0.575789,0.000882022,-8.57947e-07,-2.8979e-08,0.57667,0.00088022,-9.44885e-07,7.26509e-08,0.57755,0.000878548,-7.26932e-07,-8.28106e-08,0.578427,0.000876845,-9.75364e-07,7.97774e-08,0.579303,0.000875134,-7.36032e-07,-5.74849e-08,0.580178,0.00087349,-9.08486e-07,3.09529e-08,0.58105,0.000871765,-8.15628e-07,-6.72206e-09,0.581921,0.000870114,-8.35794e-07,-4.06451e-09,0.582791,0.00086843,-8.47987e-07,2.29799e-08,0.583658,0.000866803,-7.79048e-07,-2.82503e-08,0.584524,0.00086516,-8.63799e-07,3.04167e-08,0.585388,0.000863524,-7.72548e-07,-3.38119e-08,0.586251,0.000861877,-8.73984e-07,4.52264e-08,0.587112,0.000860265,-7.38305e-07,-2.78842e-08,0.587972,0.000858705,-8.21958e-07,6.70567e-09,0.58883,0.000857081,-8.01841e-07,1.06161e-09,0.589686,0.000855481,-7.98656e-07,-1.09521e-08,0.590541,0.00085385,-8.31512e-07,4.27468e-08,0.591394,0.000852316,-7.03272e-07,-4.08257e-08,0.592245,0.000850787,-8.25749e-07,1.34677e-09,0.593095,0.000849139,-8.21709e-07,3.54387e-08,0.593944,0.000847602,-7.15393e-07,-2.38924e-08,0.59479,0.0008461,-7.8707e-07,5.26143e-10,0.595636,0.000844527,-7.85491e-07,2.17879e-08,0.596479,0.000843021,-7.20127e-07,-2.80733e-08,0.597322,0.000841497,-8.04347e-07,3.09005e-08,0.598162,0.000839981,-7.11646e-07,-3.5924e-08,0.599002,0.00083845,-8.19418e-07,5.3191e-08,0.599839,0.000836971,-6.59845e-07,-5.76307e-08,0.600676,0.000835478,-8.32737e-07,5.81227e-08,0.60151,0.000833987,-6.58369e-07,-5.56507e-08,0.602344,0.000832503,-8.25321e-07,4.52706e-08,0.603175,0.000830988,-6.89509e-07,-6.22236e-09,0.604006,0.000829591,-7.08176e-07,-2.03811e-08,0.604834,0.000828113,-7.6932e-07,2.8142e-08,0.605662,0.000826659,-6.84894e-07,-3.25822e-08,0.606488,0.000825191,-7.8264e-07,4.25823e-08,0.607312,0.000823754,-6.54893e-07,-1.85376e-08,0.608135,0.000822389,-7.10506e-07,-2.80365e-08,0.608957,0.000820883,-7.94616e-07,7.1079e-08,0.609777,0.000819507,-5.81379e-07,-7.74655e-08,0.610596,0.000818112,-8.13775e-07,5.9969e-08,0.611413,0.000816665,-6.33868e-07,-4.32013e-08,0.612229,0.000815267,-7.63472e-07,5.32313e-08,0.613044,0.0008139,-6.03778e-07,-5.05148e-08,0.613857,0.000812541,-7.55323e-07,2.96187e-08,0.614669,0.000811119,-6.66466e-07,-8.35545e-09,0.615479,0.000809761,-6.91533e-07,3.80301e-09,0.616288,0.00080839,-6.80124e-07,-6.85666e-09,0.617096,0.000807009,-7.00694e-07,2.36237e-08,0.617903,0.000805678,-6.29822e-07,-2.80336e-08,0.618708,0.000804334,-7.13923e-07,2.8906e-08,0.619511,0.000802993,-6.27205e-07,-2.79859e-08,0.620314,0.000801655,-7.11163e-07,2.34329e-08,0.621114,0.000800303,-6.40864e-07,-6.14108e-09,0.621914,0.000799003,-6.59287e-07,1.13151e-09,0.622712,0.000797688,-6.55893e-07,1.61507e-09,0.62351,0.000796381,-6.51048e-07,-7.59186e-09,0.624305,0.000795056,-6.73823e-07,2.87524e-08,0.6251,0.000793794,-5.87566e-07,-4.7813e-08,0.625893,0.000792476,-7.31005e-07,4.32901e-08,0.626685,0.000791144,-6.01135e-07,-6.13814e-09,0.627475,0.000789923,-6.19549e-07,-1.87376e-08,0.628264,0.000788628,-6.75762e-07,2.14837e-08,0.629052,0.000787341,-6.11311e-07,-7.59265e-09,0.629839,0.000786095,-6.34089e-07,8.88692e-09,0.630625,0.000784854,-6.07428e-07,-2.7955e-08,0.631409,0.000783555,-6.91293e-07,4.33285e-08,0.632192,0.000782302,-5.61307e-07,-2.61497e-08,0.632973,0.000781101,-6.39757e-07,1.6658e-09,0.633754,0.000779827,-6.34759e-07,1.94866e-08,0.634533,0.000778616,-5.76299e-07,-2.00076e-08,0.635311,0.000777403,-6.36322e-07,9.39091e-10,0.636088,0.000776133,-6.33505e-07,1.62512e-08,0.636863,0.000774915,-5.84751e-07,-6.33937e-09,0.637638,0.000773726,-6.03769e-07,9.10609e-09,0.638411,0.000772546,-5.76451e-07,-3.00849e-08,0.639183,0.000771303,-6.66706e-07,5.1629e-08,0.639953,0.000770125,-5.11819e-07,-5.7222e-08,0.640723,0.000768929,-6.83485e-07,5.80497e-08,0.641491,0.000767736,-5.09336e-07,-5.57674e-08,0.642259,0.000766551,-6.76638e-07,4.58105e-08,0.643024,0.000765335,-5.39206e-07,-8.26541e-09,0.643789,0.000764231,-5.64002e-07,-1.27488e-08,0.644553,0.000763065,-6.02249e-07,-3.44168e-10,0.645315,0.00076186,-6.03281e-07,1.41254e-08,0.646077,0.000760695,-5.60905e-07,3.44727e-09,0.646837,0.000759584,-5.50563e-07,-2.79144e-08,0.647596,0.000758399,-6.34307e-07,4.86057e-08,0.648354,0.000757276,-4.88489e-07,-4.72989e-08,0.64911,0.000756158,-6.30386e-07,2.13807e-08,0.649866,0.000754961,-5.66244e-07,2.13808e-08,0.65062,0.000753893,-5.02102e-07,-4.7299e-08,0.651374,0.000752746,-6.43999e-07,4.86059e-08,0.652126,0.000751604,-4.98181e-07,-2.79154e-08,0.652877,0.000750524,-5.81927e-07,3.45089e-09,0.653627,0.000749371,-5.71575e-07,1.41119e-08,0.654376,0.00074827,-5.29239e-07,-2.93748e-10,0.655123,0.00074721,-5.3012e-07,-1.29368e-08,0.65587,0.000746111,-5.68931e-07,-7.56355e-09,0.656616,0.000744951,-5.91621e-07,4.3191e-08,0.65736,0.000743897,-4.62048e-07,-4.59911e-08,0.658103,0.000742835,-6.00022e-07,2.15642e-08,0.658846,0.0007417,-5.35329e-07,1.93389e-08,0.659587,0.000740687,-4.77312e-07,-3.93152e-08,0.660327,0.000739615,-5.95258e-07,1.87126e-08,0.661066,0.00073848,-5.3912e-07,2.40695e-08,0.661804,0.000737474,-4.66912e-07,-5.53859e-08,0.662541,0.000736374,-6.33069e-07,7.82648e-08,0.663277,0.000735343,-3.98275e-07,-7.88593e-08,0.664012,0.00073431,-6.34853e-07,5.83585e-08,0.664745,0.000733215,-4.59777e-07,-3.53656e-08,0.665478,0.000732189,-5.65874e-07,2.34994e-08,0.66621,0.000731128,-4.95376e-07,9.72743e-10,0.66694,0.00073014,-4.92458e-07,-2.73903e-08,0.66767,0.000729073,-5.74629e-07,4.89839e-08,0.668398,0.000728071,-4.27677e-07,-4.93359e-08,0.669126,0.000727068,-5.75685e-07,2.91504e-08,0.669853,0.000726004,-4.88234e-07,-7.66109e-09,0.670578,0.000725004,-5.11217e-07,1.49392e-09,0.671303,0.000723986,-5.06735e-07,1.68533e-09,0.672026,0.000722978,-5.01679e-07,-8.23525e-09,0.672749,0.00072195,-5.26385e-07,3.12556e-08,0.67347,0.000720991,-4.32618e-07,-5.71825e-08,0.674191,0.000719954,-6.04166e-07,7.8265e-08,0.67491,0.00071898,-3.69371e-07,-7.70634e-08,0.675628,0.00071801,-6.00561e-07,5.11747e-08,0.676346,0.000716963,-4.47037e-07,-8.42615e-09,0.677062,0.000716044,-4.72315e-07,-1.747e-08,0.677778,0.000715046,-5.24725e-07,1.87015e-08,0.678493,0.000714053,-4.68621e-07,2.26856e-09,0.679206,0.000713123,-4.61815e-07,-2.77758e-08,0.679919,0.000712116,-5.45142e-07,4.92298e-08,0.68063,0.000711173,-3.97453e-07,-4.99339e-08,0.681341,0.000710228,-5.47255e-07,3.12967e-08,0.682051,0.000709228,-4.53365e-07,-1.56481e-08,0.68276,0.000708274,-5.00309e-07,3.12958e-08,0.683467,0.000707367,-4.06422e-07,-4.99303e-08,0.684174,0.000706405,-5.56213e-07,4.9216e-08,0.68488,0.00070544,-4.08565e-07,-2.77245e-08,0.685585,0.00070454,-4.91738e-07,2.07748e-09,0.686289,0.000703562,-4.85506e-07,1.94146e-08,0.686992,0.00070265,-4.27262e-07,-2.01314e-08,0.687695,0.000701735,-4.87656e-07,1.50616e-09,0.688396,0.000700764,-4.83137e-07,1.41067e-08,0.689096,0.00069984,-4.40817e-07,1.67168e-09,0.689795,0.000698963,-4.35802e-07,-2.07934e-08,0.690494,0.000698029,-4.98182e-07,2.18972e-08,0.691192,0.000697099,-4.32491e-07,-7.19092e-09,0.691888,0.000696212,-4.54064e-07,6.86642e-09,0.692584,0.000695325,-4.33464e-07,-2.02747e-08,0.693279,0.000694397,-4.94288e-07,1.46279e-08,0.693973,0.000693452,-4.50405e-07,2.13678e-08,0.694666,0.000692616,-3.86301e-07,-4.04945e-08,0.695358,0.000691721,-5.07785e-07,2.14009e-08,0.696049,0.00069077,-4.43582e-07,1.44955e-08,0.69674,0.000689926,-4.00096e-07,-1.97783e-08,0.697429,0.000689067,-4.5943e-07,5.01296e-09,0.698118,0.000688163,-4.44392e-07,-2.73521e-10,0.698805,0.000687273,-4.45212e-07,-3.91893e-09,0.699492,0.000686371,-4.56969e-07,1.59493e-08,0.700178,0.000685505,-4.09121e-07,-2.73351e-10,0.700863,0.000684686,-4.09941e-07,-1.4856e-08,0.701548,0.000683822,-4.54509e-07,9.25979e-11,0.702231,0.000682913,-4.54231e-07,1.44855e-08,0.702913,0.000682048,-4.10775e-07,1.56992e-09,0.703595,0.000681231,-4.06065e-07,-2.07652e-08,0.704276,0.000680357,-4.68361e-07,2.18864e-08,0.704956,0.000679486,-4.02701e-07,-7.17595e-09,0.705635,0.000678659,-4.24229e-07,6.81748e-09,0.706313,0.000677831,-4.03777e-07,-2.0094e-08,0.70699,0.000676963,-4.64059e-07,1.39538e-08,0.707667,0.000676077,-4.22197e-07,2.38835e-08,0.708343,0.000675304,-3.50547e-07,-4.98831e-08,0.709018,0.000674453,-5.00196e-07,5.64395e-08,0.709692,0.000673622,-3.30878e-07,-5.66657e-08,0.710365,0.00067279,-5.00875e-07,5.1014e-08,0.711037,0.000671942,-3.47833e-07,-2.81809e-08,0.711709,0.000671161,-4.32376e-07,2.10513e-09,0.712379,0.000670303,-4.2606e-07,1.97604e-08,0.713049,0.00066951,-3.66779e-07,-2.15422e-08,0.713718,0.000668712,-4.31406e-07,6.8038e-09,0.714387,0.000667869,-4.10994e-07,-5.67295e-09,0.715054,0.00066703,-4.28013e-07,1.5888e-08,0.715721,0.000666222,-3.80349e-07,1.72576e-09,0.716387,0.000665467,-3.75172e-07,-2.27911e-08,0.717052,0.000664648,-4.43545e-07,2.9834e-08,0.717716,0.00066385,-3.54043e-07,-3.69401e-08,0.718379,0.000663031,-4.64864e-07,5.83219e-08,0.719042,0.000662277,-2.89898e-07,-7.71382e-08,0.719704,0.000661465,-5.21313e-07,7.14171e-08,0.720365,0.000660637,-3.07061e-07,-2.97161e-08,0.721025,0.000659934,-3.96209e-07,-1.21575e-08,0.721685,0.000659105,-4.32682e-07,1.87412e-08,0.722343,0.000658296,-3.76458e-07,-3.2029e-09,0.723001,0.000657533,-3.86067e-07,-5.9296e-09,0.723659,0.000656743,-4.03856e-07,2.69213e-08,0.724315,0.000656016,-3.23092e-07,-4.21511e-08,0.724971,0.000655244,-4.49545e-07,2.24737e-08,0.725625,0.000654412,-3.82124e-07,1.18611e-08,0.726279,0.000653683,-3.46541e-07,-1.03132e-08,0.726933,0.000652959,-3.7748e-07,-3.02128e-08,0.727585,0.000652114,-4.68119e-07,7.15597e-08,0.728237,0.000651392,-2.5344e-07,-7.72119e-08,0.728888,0.000650654,-4.85075e-07,5.8474e-08,0.729538,0.000649859,-3.09654e-07,-3.74746e-08,0.730188,0.000649127,-4.22077e-07,3.18197e-08,0.730837,0.000648379,-3.26618e-07,-3.01997e-08,0.731485,0.000647635,-4.17217e-07,2.93747e-08,0.732132,0.000646888,-3.29093e-07,-2.76943e-08,0.732778,0.000646147,-4.12176e-07,2.17979e-08,0.733424,0.000645388,-3.46783e-07,1.07292e-10,0.734069,0.000644695,-3.46461e-07,-2.22271e-08,0.734713,0.000643935,-4.13142e-07,2.91963e-08,0.735357,0.000643197,-3.25553e-07,-3.49536e-08,0.736,0.000642441,-4.30414e-07,5.10133e-08,0.736642,0.000641733,-2.77374e-07,-4.98904e-08,0.737283,0.000641028,-4.27045e-07,2.93392e-08,0.737924,0.000640262,-3.39028e-07,-7.86156e-09,0.738564,0.000639561,-3.62612e-07,2.10703e-09,0.739203,0.000638842,-3.56291e-07,-5.6653e-10,0.739842,0.000638128,-3.57991e-07,1.59086e-10,0.740479,0.000637412,-3.57513e-07,-6.98321e-11,0.741116,0.000636697,-3.57723e-07,1.20214e-10,0.741753,0.000635982,-3.57362e-07,-4.10987e-10,0.742388,0.000635266,-3.58595e-07,1.5237e-09,0.743023,0.000634553,-3.54024e-07,-5.68376e-09,0.743657,0.000633828,-3.71075e-07,2.12113e-08,0.744291,0.00063315,-3.07441e-07,-1.95569e-08,0.744924,0.000632476,-3.66112e-07,-2.58816e-09,0.745556,0.000631736,-3.73877e-07,2.99096e-08,0.746187,0.000631078,-2.84148e-07,-5.74454e-08,0.746818,0.000630337,-4.56484e-07,8.06629e-08,0.747448,0.000629666,-2.14496e-07,-8.63922e-08,0.748077,0.000628978,-4.73672e-07,8.60918e-08,0.748706,0.000628289,-2.15397e-07,-7.91613e-08,0.749334,0.000627621,-4.5288e-07,5.17393e-08,0.749961,0.00062687,-2.97663e-07,-8.58662e-09,0.750588,0.000626249,-3.23422e-07,-1.73928e-08,0.751214,0.00062555,-3.75601e-07,1.85532e-08,0.751839,0.000624855,-3.19941e-07,2.78479e-09,0.752463,0.000624223,-3.11587e-07,-2.96923e-08,0.753087,0.000623511,-4.00664e-07,5.63799e-08,0.75371,0.000622879,-2.31524e-07,-7.66179e-08,0.754333,0.000622186,-4.61378e-07,7.12778e-08,0.754955,0.000621477,-2.47545e-07,-2.96794e-08,0.755576,0.000620893,-3.36583e-07,-1.21648e-08,0.756196,0.000620183,-3.73077e-07,1.87339e-08,0.756816,0.000619493,-3.16875e-07,-3.16622e-09,0.757435,0.00061885,-3.26374e-07,-6.0691e-09,0.758054,0.000618179,-3.44581e-07,2.74426e-08,0.758672,0.000617572,-2.62254e-07,-4.40968e-08,0.759289,0.000616915,-3.94544e-07,2.97352e-08,0.759906,0.000616215,-3.05338e-07,-1.52393e-08,0.760522,0.000615559,-3.51056e-07,3.12221e-08,0.761137,0.000614951,-2.5739e-07,-5.00443e-08,0.761751,0.000614286,-4.07523e-07,4.9746e-08,0.762365,0.00061362,-2.58285e-07,-2.97303e-08,0.762979,0.000613014,-3.47476e-07,9.57079e-09,0.763591,0.000612348,-3.18764e-07,-8.55287e-09,0.764203,0.000611685,-3.44422e-07,2.46407e-08,0.764815,0.00061107,-2.705e-07,-3.04053e-08,0.765426,0.000610437,-3.61716e-07,3.73759e-08,0.766036,0.000609826,-2.49589e-07,-5.94935e-08,0.766645,0.000609149,-4.28069e-07,8.13889e-08,0.767254,0.000608537,-1.83902e-07,-8.72483e-08,0.767862,0.000607907,-4.45647e-07,8.87901e-08,0.76847,0.000607282,-1.79277e-07,-8.90983e-08,0.769077,0.000606656,-4.46572e-07,8.87892e-08,0.769683,0.000606029,-1.80204e-07,-8.72446e-08,0.770289,0.000605407,-4.41938e-07,8.13752e-08,0.770894,0.000604768,-1.97812e-07,-5.94423e-08,0.771498,0.000604194,-3.76139e-07,3.71848e-08,0.772102,0.000603553,-2.64585e-07,-2.96922e-08,0.772705,0.000602935,-3.53661e-07,2.19793e-08,0.773308,0.000602293,-2.87723e-07,1.37955e-09,0.77391,0.000601722,-2.83585e-07,-2.74976e-08,0.774512,0.000601072,-3.66077e-07,4.9006e-08,0.775112,0.000600487,-2.19059e-07,-4.93171e-08,0.775712,0.000599901,-3.67011e-07,2.90531e-08,0.776312,0.000599254,-2.79851e-07,-7.29081e-09,0.776911,0.000598673,-3.01724e-07,1.10077e-10,0.777509,0.00059807,-3.01393e-07,6.85053e-09,0.778107,0.000597487,-2.80842e-07,-2.75123e-08,0.778704,0.000596843,-3.63379e-07,4.35939e-08,0.779301,0.000596247,-2.32597e-07,-2.7654e-08,0.779897,0.000595699,-3.15559e-07,7.41741e-09,0.780492,0.00059509,-2.93307e-07,-2.01562e-09,0.781087,0.000594497,-2.99354e-07,6.45059e-10,0.781681,0.000593901,-2.97418e-07,-5.64635e-10,0.782275,0.000593304,-2.99112e-07,1.61347e-09,0.782868,0.000592711,-2.94272e-07,-5.88926e-09,0.78346,0.000592105,-3.1194e-07,2.19436e-08,0.784052,0.000591546,-2.46109e-07,-2.22805e-08,0.784643,0.000590987,-3.1295e-07,7.57368e-09,0.785234,0.000590384,-2.90229e-07,-8.01428e-09,0.785824,0.00058978,-3.14272e-07,2.44834e-08,0.786414,0.000589225,-2.40822e-07,-3.03148e-08,0.787003,0.000588652,-3.31766e-07,3.7171e-08,0.787591,0.0005881,-2.20253e-07,-5.87646e-08,0.788179,0.000587483,-3.96547e-07,7.86782e-08,0.788766,0.000586926,-1.60512e-07,-7.71342e-08,0.789353,0.000586374,-3.91915e-07,5.10444e-08,0.789939,0.000585743,-2.38782e-07,-7.83422e-09,0.790524,0.000585242,-2.62284e-07,-1.97076e-08,0.791109,0.000584658,-3.21407e-07,2.70598e-08,0.791693,0.000584097,-2.40228e-07,-2.89269e-08,0.792277,0.000583529,-3.27008e-07,2.90431e-08,0.792861,0.000582963,-2.39879e-07,-2.76409e-08,0.793443,0.0005824,-3.22802e-07,2.1916e-08,0.794025,0.00058182,-2.57054e-07,-4.18368e-10,0.794607,0.000581305,-2.58309e-07,-2.02425e-08,0.795188,0.000580727,-3.19036e-07,2.17838e-08,0.795768,0.000580155,-2.53685e-07,-7.28814e-09,0.796348,0.000579625,-2.75549e-07,7.36871e-09,0.796928,0.000579096,-2.53443e-07,-2.21867e-08,0.797506,0.000578523,-3.20003e-07,2.17736e-08,0.798085,0.000577948,-2.54683e-07,-5.30296e-09,0.798662,0.000577423,-2.70592e-07,-5.61698e-10,0.799239,0.00057688,-2.72277e-07,7.54977e-09,0.799816,0.000576358,-2.49627e-07,-2.96374e-08,0.800392,0.00057577,-3.38539e-07,5.1395e-08,0.800968,0.000575247,-1.84354e-07,-5.67335e-08,0.801543,0.000574708,-3.54555e-07,5.63297e-08,0.802117,0.000574168,-1.85566e-07,-4.93759e-08,0.802691,0.000573649,-3.33693e-07,2.19646e-08,0.803264,0.000573047,-2.678e-07,2.1122e-08,0.803837,0.000572575,-2.04433e-07,-4.68482e-08,0.804409,0.000572026,-3.44978e-07,4.70613e-08,0.804981,0.000571477,-2.03794e-07,-2.21877e-08,0.805552,0.000571003,-2.70357e-07,-1.79153e-08,0.806123,0.000570408,-3.24103e-07,3.42443e-08,0.806693,0.000569863,-2.2137e-07,1.47556e-10,0.807263,0.000569421,-2.20928e-07,-3.48345e-08,0.807832,0.000568874,-3.25431e-07,1.99812e-08,0.808401,0.000568283,-2.65487e-07,1.45143e-08,0.808969,0.000567796,-2.21945e-07,-1.84338e-08,0.809536,0.000567297,-2.77246e-07,-3.83608e-10,0.810103,0.000566741,-2.78397e-07,1.99683e-08,0.81067,0.000566244,-2.18492e-07,-1.98848e-08,0.811236,0.000565747,-2.78146e-07,-3.38976e-11,0.811801,0.000565191,-2.78248e-07,2.00204e-08,0.812366,0.000564695,-2.18187e-07,-2.04429e-08,0.812931,0.000564197,-2.79516e-07,2.1467e-09,0.813495,0.000563644,-2.73076e-07,1.18561e-08,0.814058,0.000563134,-2.37507e-07,1.00334e-08,0.814621,0.000562689,-2.07407e-07,-5.19898e-08,0.815183,0.000562118,-3.63376e-07,7.87163e-08,0.815745,0.000561627,-1.27227e-07,-8.40616e-08,0.816306,0.000561121,-3.79412e-07,7.87163e-08,0.816867,0.000560598,-1.43263e-07,-5.19898e-08,0.817428,0.000560156,-2.99233e-07,1.00335e-08,0.817988,0.000559587,-2.69132e-07,1.18559e-08,0.818547,0.000559085,-2.33564e-07,2.14764e-09,0.819106,0.000558624,-2.27122e-07,-2.04464e-08,0.819664,0.000558108,-2.88461e-07,2.00334e-08,0.820222,0.000557591,-2.28361e-07,-8.24277e-11,0.820779,0.000557135,-2.28608e-07,-1.97037e-08,0.821336,0.000556618,-2.87719e-07,1.92925e-08,0.821893,0.000556101,-2.29841e-07,2.13831e-09,0.822448,0.000555647,-2.23427e-07,-2.78458e-08,0.823004,0.000555117,-3.06964e-07,4.96402e-08,0.823559,0.000554652,-1.58043e-07,-5.15058e-08,0.824113,0.000554181,-3.12561e-07,3.71737e-08,0.824667,0.000553668,-2.0104e-07,-3.75844e-08,0.82522,0.000553153,-3.13793e-07,5.35592e-08,0.825773,0.000552686,-1.53115e-07,-5.74431e-08,0.826326,0.000552207,-3.25444e-07,5.7004e-08,0.826878,0.000551728,-1.54433e-07,-5.13635e-08,0.827429,0.000551265,-3.08523e-07,2.92406e-08,0.82798,0.000550735,-2.20801e-07,-5.99424e-09,0.828531,0.000550276,-2.38784e-07,-5.26363e-09,0.829081,0.000549782,-2.54575e-07,2.70488e-08,0.82963,0.000549354,-1.73429e-07,-4.33268e-08,0.83018,0.000548878,-3.03409e-07,2.7049e-08,0.830728,0.000548352,-2.22262e-07,-5.26461e-09,0.831276,0.000547892,-2.38056e-07,-5.99057e-09,0.831824,0.000547397,-2.56027e-07,2.92269e-08,0.832371,0.000546973,-1.68347e-07,-5.13125e-08,0.832918,0.000546482,-3.22284e-07,5.68139e-08,0.833464,0.000546008,-1.51843e-07,-5.67336e-08,0.83401,0.000545534,-3.22043e-07,5.09113e-08,0.834555,0.000545043,-1.6931e-07,-2.77022e-08,0.8351,0.000544621,-2.52416e-07,2.92924e-10,0.835644,0.000544117,-2.51537e-07,2.65305e-08,0.836188,0.000543694,-1.71946e-07,-4.68105e-08,0.836732,0.00054321,-3.12377e-07,4.15021e-08,0.837275,0.000542709,-1.87871e-07,1.13355e-11,0.837817,0.000542334,-1.87837e-07,-4.15474e-08,0.838359,0.000541833,-3.12479e-07,4.69691e-08,0.838901,0.000541349,-1.71572e-07,-2.71196e-08,0.839442,0.000540925,-2.52931e-07,1.90462e-09,0.839983,0.000540425,-2.47217e-07,1.95011e-08,0.840523,0.000539989,-1.88713e-07,-2.03045e-08,0.841063,0.00053955,-2.49627e-07,2.11216e-09,0.841602,0.000539057,-2.4329e-07,1.18558e-08,0.842141,0.000538606,-2.07723e-07,1.00691e-08,0.842679,0.000538221,-1.77516e-07,-5.21324e-08,0.843217,0.00053771,-3.33913e-07,7.92513e-08,0.843755,0.00053728,-9.6159e-08,-8.60587e-08,0.844292,0.000536829,-3.54335e-07,8.61696e-08,0.844828,0.000536379,-9.58263e-08,-7.98057e-08,0.845364,0.000535948,-3.35243e-07,5.42394e-08,0.8459,0.00053544,-1.72525e-07,-1.79426e-08,0.846435,0.000535041,-2.26353e-07,1.75308e-08,0.84697,0.000534641,-1.73761e-07,-5.21806e-08,0.847505,0.000534137,-3.30302e-07,7.19824e-08,0.848038,0.000533692,-1.14355e-07,-5.69349e-08,0.848572,0.000533293,-2.8516e-07,3.65479e-08,0.849105,0.000532832,-1.75516e-07,-2.96519e-08,0.849638,0.000532392,-2.64472e-07,2.2455e-08,0.85017,0.000531931,-1.97107e-07,-5.63451e-10,0.850702,0.000531535,-1.98797e-07,-2.02011e-08,0.851233,0.000531077,-2.59401e-07,2.17634e-08,0.851764,0.000530623,-1.94111e-07,-7.24794e-09,0.852294,0.000530213,-2.15854e-07,7.22832e-09,0.852824,0.000529803,-1.94169e-07,-2.16653e-08,0.853354,0.00052935,-2.59165e-07,1.98283e-08,0.853883,0.000528891,-1.9968e-07,1.95678e-09,0.854412,0.000528497,-1.9381e-07,-2.76554e-08,0.85494,0.000528027,-2.76776e-07,4.90603e-08,0.855468,0.00052762,-1.29596e-07,-4.93764e-08,0.855995,0.000527213,-2.77725e-07,2.92361e-08,0.856522,0.000526745,-1.90016e-07,-7.96341e-09,0.857049,0.000526341,-2.13907e-07,2.61752e-09,0.857575,0.000525922,-2.06054e-07,-2.50665e-09,0.8581,0.000525502,-2.13574e-07,7.40906e-09,0.858626,0.000525097,-1.91347e-07,-2.71296e-08,0.859151,0.000524633,-2.72736e-07,4.15048e-08,0.859675,0.000524212,-1.48221e-07,-1.96802e-08,0.860199,0.000523856,-2.07262e-07,-2.23886e-08,0.860723,0.000523375,-2.74428e-07,4.96299e-08,0.861246,0.000522975,-1.25538e-07,-5.69216e-08,0.861769,0.000522553,-2.96303e-07,5.88473e-08,0.862291,0.000522137,-1.19761e-07,-5.92584e-08,0.862813,0.00052172,-2.97536e-07,5.8977e-08,0.863334,0.000521301,-1.20605e-07,-5.74403e-08,0.863855,0.000520888,-2.92926e-07,5.15751e-08,0.864376,0.000520457,-1.38201e-07,-2.96506e-08,0.864896,0.000520091,-2.27153e-07,7.42277e-09,0.865416,0.000519659,-2.04885e-07,-4.05057e-11,0.865936,0.00051925,-2.05006e-07,-7.26074e-09,0.866455,0.000518818,-2.26788e-07,2.90835e-08,0.866973,0.000518451,-1.39538e-07,-4.94686e-08,0.867492,0.000518024,-2.87944e-07,4.95814e-08,0.868009,0.000517597,-1.39199e-07,-2.96479e-08,0.868527,0.000517229,-2.28143e-07,9.40539e-09,0.869044,0.000516801,-1.99927e-07,-7.9737e-09,0.86956,0.000516378,-2.23848e-07,2.24894e-08,0.870077,0.000515997,-1.5638e-07,-2.23793e-08,0.870592,0.000515617,-2.23517e-07,7.42302e-09,0.871108,0.000515193,-2.01248e-07,-7.31283e-09,0.871623,0.000514768,-2.23187e-07,2.18283e-08,0.872137,0.000514387,-1.57702e-07,-2.03959e-08,0.872652,0.000514011,-2.1889e-07,1.50711e-10,0.873165,0.000513573,-2.18437e-07,1.97931e-08,0.873679,0.000513196,-1.59058e-07,-1.97183e-08,0.874192,0.000512819,-2.18213e-07,-5.24324e-10,0.874704,0.000512381,-2.19786e-07,2.18156e-08,0.875217,0.000512007,-1.54339e-07,-2.71336e-08,0.875728,0.000511616,-2.3574e-07,2.71141e-08,0.87624,0.000511226,-1.54398e-07,-2.17182e-08,0.876751,0.000510852,-2.19552e-07,1.54131e-10,0.877262,0.000510414,-2.1909e-07,2.11017e-08,0.877772,0.000510039,-1.55785e-07,-2.49562e-08,0.878282,0.000509652,-2.30654e-07,1.91183e-08,0.878791,0.000509248,-1.73299e-07,8.08751e-09,0.8793,0.000508926,-1.49036e-07,-5.14684e-08,0.879809,0.000508474,-3.03441e-07,7.85766e-08,0.880317,0.000508103,-6.77112e-08,-8.40242e-08,0.880825,0.000507715,-3.19784e-07,7.87063e-08,0.881333,0.000507312,-8.36649e-08,-5.19871e-08,0.88184,0.000506988,-2.39626e-07,1.00327e-08,0.882346,0.000506539,-2.09528e-07,1.18562e-08,0.882853,0.000506156,-1.73959e-07,2.14703e-09,0.883359,0.000505814,-1.67518e-07,-2.04444e-08,0.883864,0.000505418,-2.28851e-07,2.00258e-08,0.88437,0.00050502,-1.68774e-07,-5.42855e-11,0.884874,0.000504682,-1.68937e-07,-1.98087e-08,0.885379,0.000504285,-2.28363e-07,1.96842e-08,0.885883,0.000503887,-1.6931e-07,6.76342e-10,0.886387,0.000503551,-1.67281e-07,-2.23896e-08,0.88689,0.000503149,-2.3445e-07,2.92774e-08,0.887393,0.000502768,-1.46618e-07,-3.51152e-08,0.887896,0.00050237,-2.51963e-07,5.15787e-08,0.888398,0.00050202,-9.72271e-08,-5.19903e-08,0.8889,0.00050167,-2.53198e-07,3.71732e-08,0.889401,0.000501275,-1.41678e-07,-3.70978e-08,0.889902,0.00050088,-2.52972e-07,5.16132e-08,0.890403,0.000500529,-9.81321e-08,-5.01459e-08,0.890903,0.000500183,-2.4857e-07,2.9761e-08,0.891403,0.000499775,-1.59287e-07,-9.29351e-09,0.891903,0.000499428,-1.87167e-07,7.41301e-09,0.892402,0.000499076,-1.64928e-07,-2.03585e-08,0.892901,0.000498685,-2.26004e-07,1.44165e-08,0.893399,0.000498276,-1.82754e-07,2.22974e-08,0.893898,0.000497978,-1.15862e-07,-4.40013e-08,0.894395,0.000497614,-2.47866e-07,3.44985e-08,0.894893,0.000497222,-1.44371e-07,-3.43882e-08,0.89539,0.00049683,-2.47535e-07,4.34497e-08,0.895886,0.000496465,-1.17186e-07,-2.02012e-08,0.896383,0.00049617,-1.7779e-07,-2.22497e-08,0.896879,0.000495748,-2.44539e-07,4.95952e-08,0.897374,0.000495408,-9.57532e-08,-5.69217e-08,0.89787,0.000495045,-2.66518e-07,5.88823e-08,0.898364,0.000494689,-8.98713e-08,-5.93983e-08,0.898859,0.000494331,-2.68066e-07,5.95017e-08,0.899353,0.000493973,-8.95613e-08,-5.9399e-08,0.899847,0.000493616,-2.67758e-07,5.8885e-08,0.90034,0.000493257,-9.11033e-08,-5.69317e-08,0.900833,0.000492904,-2.61898e-07,4.96326e-08,0.901326,0.000492529,-1.13001e-07,-2.23893e-08,0.901819,0.000492236,-1.80169e-07,-1.968e-08,0.902311,0.000491817,-2.39209e-07,4.15047e-08,0.902802,0.000491463,-1.14694e-07,-2.71296e-08,0.903293,0.000491152,-1.96083e-07,7.409e-09,0.903784,0.000490782,-1.73856e-07,-2.50645e-09,0.904275,0.000490427,-1.81376e-07,2.61679e-09,0.904765,0.000490072,-1.73525e-07,-7.96072e-09,0.905255,0.000489701,-1.97407e-07,2.92261e-08,0.905745,0.000489394,-1.09729e-07,-4.93389e-08,0.906234,0.000489027,-2.57746e-07,4.89204e-08,0.906723,0.000488658,-1.10985e-07,-2.71333e-08,0.907211,0.000488354,-1.92385e-07,8.30861e-12,0.907699,0.00048797,-1.9236e-07,2.71001e-08,0.908187,0.000487666,-1.1106e-07,-4.88041e-08,0.908675,0.000487298,-2.57472e-07,4.89069e-08,0.909162,0.000486929,-1.10751e-07,-2.76143e-08,0.909649,0.000486625,-1.93594e-07,1.9457e-09,0.910135,0.000486244,-1.87757e-07,1.98315e-08,0.910621,0.000485928,-1.28262e-07,-2.16671e-08,0.911107,0.000485606,-1.93264e-07,7.23216e-09,0.911592,0.000485241,-1.71567e-07,-7.26152e-09,0.912077,0.000484877,-1.93352e-07,2.18139e-08,0.912562,0.000484555,-1.2791e-07,-2.03895e-08,0.913047,0.000484238,-1.89078e-07,1.39494e-10,0.913531,0.000483861,-1.8866e-07,1.98315e-08,0.914014,0.000483543,-1.29165e-07,-1.98609e-08,0.914498,0.000483225,-1.88748e-07,7.39912e-12,0.914981,0.000482847,-1.88726e-07,1.98313e-08,0.915463,0.000482529,-1.29232e-07,-1.9728e-08,0.915946,0.000482212,-1.88416e-07,-5.24035e-10,0.916428,0.000481833,-1.89988e-07,2.18241e-08,0.916909,0.000481519,-1.24516e-07,-2.71679e-08,0.917391,0.000481188,-2.06019e-07,2.72427e-08,0.917872,0.000480858,-1.24291e-07,-2.21985e-08,0.918353,0.000480543,-1.90886e-07,1.94644e-09,0.918833,0.000480167,-1.85047e-07,1.44127e-08,0.919313,0.00047984,-1.41809e-07,7.39438e-12,0.919793,0.000479556,-1.41787e-07,-1.44423e-08,0.920272,0.000479229,-1.85114e-07,-1.84291e-09,0.920751,0.000478854,-1.90642e-07,2.18139e-08,0.92123,0.000478538,-1.25201e-07,-2.58081e-08,0.921708,0.00047821,-2.02625e-07,2.18139e-08,0.922186,0.00047787,-1.37183e-07,-1.84291e-09,0.922664,0.00047759,-1.42712e-07,-1.44423e-08,0.923141,0.000477262,-1.86039e-07,7.34701e-12,0.923618,0.00047689,-1.86017e-07,1.44129e-08,0.924095,0.000476561,-1.42778e-07,1.94572e-09,0.924572,0.000476281,-1.36941e-07,-2.21958e-08,0.925048,0.000475941,-2.03528e-07,2.72327e-08,0.925523,0.000475615,-1.2183e-07,-2.71304e-08,0.925999,0.00047529,-2.03221e-07,2.16843e-08,0.926474,0.000474949,-1.38168e-07,-2.16005e-12,0.926949,0.000474672,-1.38175e-07,-2.16756e-08,0.927423,0.000474331,-2.03202e-07,2.71001e-08,0.927897,0.000474006,-1.21902e-07,-2.71201e-08,0.928371,0.000473681,-2.03262e-07,2.17757e-08,0.928845,0.00047334,-1.37935e-07,-3.78028e-10,0.929318,0.000473063,-1.39069e-07,-2.02636e-08,0.929791,0.000472724,-1.9986e-07,2.18276e-08,0.930263,0.000472389,-1.34377e-07,-7.44231e-09,0.930736,0.000472098,-1.56704e-07,7.94165e-09,0.931208,0.000471809,-1.32879e-07,-2.43243e-08,0.931679,0.00047147,-2.05851e-07,2.97508e-08,0.932151,0.000471148,-1.16599e-07,-3.50742e-08,0.932622,0.000470809,-2.21822e-07,5.09414e-08,0.933092,0.000470518,-6.89976e-08,-4.94821e-08,0.933563,0.000470232,-2.17444e-07,2.77775e-08,0.934033,0.00046988,-1.34111e-07,-2.02351e-09,0.934502,0.000469606,-1.40182e-07,-1.96835e-08,0.934972,0.000469267,-1.99232e-07,2.11529e-08,0.935441,0.000468932,-1.35774e-07,-5.32332e-09,0.93591,0.000468644,-1.51743e-07,1.40413e-10,0.936378,0.000468341,-1.51322e-07,4.76166e-09,0.936846,0.000468053,-1.37037e-07,-1.9187e-08,0.937314,0.000467721,-1.94598e-07,1.23819e-08,0.937782,0.000467369,-1.57453e-07,2.92642e-08,0.938249,0.000467142,-6.96601e-08,-6.98342e-08,0.938716,0.000466793,-2.79163e-07,7.12586e-08,0.939183,0.000466449,-6.53869e-08,-3.63863e-08,0.939649,0.000466209,-1.74546e-07,1.46818e-08,0.940115,0.000465904,-1.305e-07,-2.2341e-08,0.940581,0.000465576,-1.97523e-07,1.50774e-08,0.941046,0.000465226,-1.52291e-07,2.16359e-08,0.941511,0.000464986,-8.73832e-08,-4.20162e-08,0.941976,0.000464685,-2.13432e-07,2.72198e-08,0.942441,0.00046434,-1.31773e-07,-7.2581e-09,0.942905,0.000464055,-1.53547e-07,1.81263e-09,0.943369,0.000463753,-1.48109e-07,7.58386e-12,0.943832,0.000463457,-1.48086e-07,-1.84298e-09,0.944296,0.000463155,-1.53615e-07,7.36433e-09,0.944759,0.00046287,-1.31522e-07,-2.76143e-08,0.945221,0.000462524,-2.14365e-07,4.34883e-08,0.945684,0.000462226,-8.39003e-08,-2.71297e-08,0.946146,0.000461977,-1.65289e-07,5.42595e-09,0.946608,0.000461662,-1.49012e-07,5.42593e-09,0.947069,0.000461381,-1.32734e-07,-2.71297e-08,0.94753,0.000461034,-2.14123e-07,4.34881e-08,0.947991,0.000460736,-8.36585e-08,-2.76134e-08,0.948452,0.000460486,-1.66499e-07,7.36083e-09,0.948912,0.000460175,-1.44416e-07,-1.82993e-09,0.949372,0.000459881,-1.49906e-07,-4.11073e-11,0.949832,0.000459581,-1.50029e-07,1.99434e-09,0.950291,0.000459287,-1.44046e-07,-7.93627e-09,0.950751,0.000458975,-1.67855e-07,2.97507e-08,0.951209,0.000458728,-7.86029e-08,-5.1462e-08,0.951668,0.000458417,-2.32989e-07,5.6888e-08,0.952126,0.000458121,-6.2325e-08,-5.68806e-08,0.952584,0.000457826,-2.32967e-07,5.14251e-08,0.953042,0.000457514,-7.86914e-08,-2.96107e-08,0.953499,0.000457268,-1.67523e-07,7.41296e-09,0.953956,0.000456955,-1.45285e-07,-4.11262e-11,0.954413,0.000456665,-1.45408e-07,-7.24847e-09,0.95487,0.000456352,-1.67153e-07,2.9035e-08,0.955326,0.000456105,-8.00484e-08,-4.92869e-08,0.955782,0.000455797,-2.27909e-07,4.89032e-08,0.956238,0.000455488,-8.11994e-08,-2.71166e-08,0.956693,0.000455244,-1.62549e-07,-4.13678e-11,0.957148,0.000454919,-1.62673e-07,2.72821e-08,0.957603,0.000454675,-8.0827e-08,-4.94824e-08,0.958057,0.000454365,-2.29274e-07,5.14382e-08,0.958512,0.000454061,-7.49597e-08,-3.7061e-08,0.958965,0.0004538,-1.86143e-07,3.72013e-08,0.959419,0.000453539,-7.45389e-08,-5.21396e-08,0.959873,0.000453234,-2.30958e-07,5.21476e-08,0.960326,0.000452928,-7.45146e-08,-3.72416e-08,0.960778,0.000452667,-1.8624e-07,3.72143e-08,0.961231,0.000452407,-7.45967e-08,-5.20109e-08,0.961683,0.000452101,-2.30629e-07,5.16199e-08,0.962135,0.000451795,-7.57696e-08,-3.52595e-08,0.962587,0.000451538,-1.81548e-07,2.98133e-08,0.963038,0.000451264,-9.2108e-08,-2.43892e-08,0.963489,0.000451007,-1.65276e-07,8.13892e-09,0.96394,0.000450701,-1.40859e-07,-8.16647e-09,0.964391,0.000450394,-1.65358e-07,2.45269e-08,0.964841,0.000450137,-9.17775e-08,-3.03367e-08,0.965291,0.000449863,-1.82787e-07,3.7215e-08,0.965741,0.000449609,-7.11424e-08,-5.89188e-08,0.96619,0.00044929,-2.47899e-07,7.92509e-08,0.966639,0.000449032,-1.01462e-08,-7.92707e-08,0.967088,0.000448773,-2.47958e-07,5.90181e-08,0.967537,0.000448455,-7.0904e-08,-3.75925e-08,0.967985,0.0004482,-1.83681e-07,3.17471e-08,0.968433,0.000447928,-8.84401e-08,-2.97913e-08,0.968881,0.000447662,-1.77814e-07,2.78133e-08,0.969329,0.000447389,-9.4374e-08,-2.18572e-08,0.969776,0.000447135,-1.59946e-07,1.10134e-11,0.970223,0.000446815,-1.59913e-07,2.18132e-08,0.97067,0.000446561,-9.44732e-08,-2.76591e-08,0.971116,0.000446289,-1.7745e-07,2.92185e-08,0.971562,0.000446022,-8.97948e-08,-2.96104e-08,0.972008,0.000445753,-1.78626e-07,2.96185e-08,0.972454,0.000445485,-8.97706e-08,-2.92588e-08,0.972899,0.000445218,-1.77547e-07,2.78123e-08,0.973344,0.000444946,-9.41103e-08,-2.23856e-08,0.973789,0.000444691,-1.61267e-07,2.12559e-09,0.974233,0.000444374,-1.5489e-07,1.38833e-08,0.974678,0.000444106,-1.13241e-07,1.94591e-09,0.975122,0.000443886,-1.07403e-07,-2.16669e-08,0.975565,0.000443606,-1.72404e-07,2.5117e-08,0.976009,0.000443336,-9.70526e-08,-1.91963e-08,0.976452,0.000443085,-1.54642e-07,-7.93627e-09,0.976895,0.000442752,-1.7845e-07,5.09414e-08,0.977338,0.000442548,-2.56262e-08,-7.66201e-08,0.97778,0.000442266,-2.55486e-07,7.67249e-08,0.978222,0.000441986,-2.53118e-08,-5.14655e-08,0.978664,0.000441781,-1.79708e-07,9.92773e-09,0.979106,0.000441451,-1.49925e-07,1.17546e-08,0.979547,0.000441186,-1.14661e-07,2.65868e-09,0.979988,0.000440965,-1.06685e-07,-2.23893e-08,0.980429,0.000440684,-1.73853e-07,2.72939e-08,0.980869,0.000440419,-9.19716e-08,-2.71816e-08,0.98131,0.000440153,-1.73516e-07,2.18278e-08,0.98175,0.000439872,-1.08033e-07,-5.24833e-10,0.982189,0.000439654,-1.09607e-07,-1.97284e-08,0.982629,0.000439376,-1.68793e-07,1.98339e-08,0.983068,0.000439097,-1.09291e-07,-2.62901e-12,0.983507,0.000438879,-1.09299e-07,-1.98234e-08,0.983946,0.000438601,-1.68769e-07,1.96916e-08,0.984384,0.000438322,-1.09694e-07,6.6157e-10,0.984823,0.000438105,-1.0771e-07,-2.23379e-08,0.985261,0.000437823,-1.74723e-07,2.90855e-08,0.985698,0.00043756,-8.74669e-08,-3.43992e-08,0.986136,0.000437282,-1.90665e-07,4.89068e-08,0.986573,0.000437048,-4.39442e-08,-4.20188e-08,0.98701,0.000436834,-1.7e-07,-4.11073e-11,0.987446,0.000436494,-1.70124e-07,4.21832e-08,0.987883,0.00043628,-4.35742e-08,-4.94824e-08,0.988319,0.000436044,-1.92021e-07,3.6537e-08,0.988755,0.00043577,-8.24102e-08,-3.70611e-08,0.989191,0.000435494,-1.93593e-07,5.21026e-08,0.989626,0.000435263,-3.72855e-08,-5.21402e-08,0.990061,0.000435032,-1.93706e-07,3.7249e-08,0.990496,0.000434756,-8.19592e-08,-3.72512e-08,0.990931,0.000434481,-1.93713e-07,5.21511e-08,0.991365,0.00043425,-3.72595e-08,-5.21439e-08,0.991799,0.000434019,-1.93691e-07,3.72152e-08,0.992233,0.000433743,-8.20456e-08,-3.71123e-08,0.992667,0.000433468,-1.93382e-07,5.16292e-08,0.9931,0.000433236,-3.84947e-08,-5.01953e-08,0.993533,0.000433008,-1.89081e-07,2.99427e-08,0.993966,0.00043272,-9.92525e-08,-9.9708e-09,0.994399,0.000432491,-1.29165e-07,9.94051e-09,0.994831,0.000432263,-9.93434e-08,-2.97912e-08,0.995263,0.000431975,-1.88717e-07,4.96198e-08,0.995695,0.000431746,-3.98578e-08,-4.94785e-08,0.996127,0.000431518,-1.88293e-07,2.9085e-08,0.996558,0.000431229,-1.01038e-07,-7.25675e-09,0.996989,0.000431005,-1.22809e-07,-5.79945e-11,0.99742,0.000430759,-1.22983e-07,7.48873e-09,0.997851,0.000430536,-1.00516e-07,-2.98969e-08,0.998281,0.000430245,-1.90207e-07,5.24942e-08,0.998711,0.000430022,-3.27246e-08,-6.08706e-08,0.999141,0.000429774,-2.15336e-07,7.17788e-08,0.999571,0.000429392,0.,0.}; - - template - __device__ __forceinline__ void Lab2RGBConvert_f(const T& src, D& dst) - { - const float lThresh = 0.008856f * 903.3f; - const float fThresh = 7.787f * 0.008856f + 16.0f / 116.0f; - - float Y, fy; - - if (src.x <= lThresh) - { - Y = src.x / 903.3f; - fy = 7.787f * Y + 16.0f / 116.0f; - } - else - { - fy = (src.x + 16.0f) / 116.0f; - Y = fy * fy * fy; - } - - float X = src.y / 500.0f + fy; - float Z = fy - src.z / 200.0f; - - if (X <= fThresh) - X = (X - 16.0f / 116.0f) / 7.787f; - else - X = X * X * X; - - if (Z <= fThresh) - Z = (Z - 16.0f / 116.0f) / 7.787f; - else - Z = Z * Z * Z; - - float B = 0.052891f * X - 0.204043f * Y + 1.151152f * Z; - float G = -0.921235f * X + 1.875991f * Y + 0.045244f * Z; - float R = 3.079933f * X - 1.537150f * Y - 0.542782f * Z; - - if (srgb) - { - B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); - G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); - R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); - } - - dst.x = blueIdx == 0 ? B : R; - dst.y = G; - dst.z = blueIdx == 0 ? R : B; - setAlpha(dst, ColorChannel::max()); - } - - template - __device__ __forceinline__ void Lab2RGBConvert_b(const T& src, D& dst) - { - float3 srcf, dstf; - - srcf.x = src.x * (100.f / 255.f); - srcf.y = src.y - 128; - srcf.z = src.z - 128; - - Lab2RGBConvert_f(srcf, dstf); - - dst.x = saturate_cast(dstf.x * 255.f); - dst.y = saturate_cast(dstf.y * 255.f); - dst.z = saturate_cast(dstf.z * 255.f); - setAlpha(dst, ColorChannel::max()); - } - - template struct Lab2RGB; - template - struct Lab2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - Lab2RGBConvert_b(src, dst); - - return dst; - } - __host__ __device__ __forceinline__ Lab2RGB() {} - __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {} - }; - template - struct Lab2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - Lab2RGBConvert_f(src, dst); - - return dst; - } - __host__ __device__ __forceinline__ Lab2RGB() {} - __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::Lab2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -///////////////////////////////////// RGB <-> Luv ///////////////////////////////////// - - namespace color_detail - { - __constant__ float c_LabCbrtTab[] = {0.137931,0.0114066,0.,1.18859e-07,0.149338,0.011407,3.56578e-07,-5.79396e-07,0.160745,0.0114059,-1.38161e-06,2.16892e-06,0.172151,0.0114097,5.12516e-06,-8.0814e-06,0.183558,0.0113957,-1.9119e-05,3.01567e-05,0.194965,0.0114479,7.13509e-05,-0.000112545,0.206371,0.011253,-0.000266285,-0.000106493,0.217252,0.0104009,-0.000585765,7.32149e-05,0.22714,0.00944906,-0.00036612,1.21917e-05,0.236235,0.0087534,-0.000329545,2.01753e-05,0.244679,0.00815483,-0.000269019,1.24435e-05,0.252577,0.00765412,-0.000231689,1.05618e-05,0.26001,0.00722243,-0.000200003,8.26662e-06,0.267041,0.00684723,-0.000175203,6.76746e-06,0.27372,0.00651712,-0.000154901,5.61192e-06,0.280088,0.00622416,-0.000138065,4.67009e-06,0.286179,0.00596204,-0.000124055,3.99012e-06,0.292021,0.0057259,-0.000112085,3.36032e-06,0.297638,0.00551181,-0.000102004,2.95338e-06,0.30305,0.00531666,-9.31435e-05,2.52875e-06,0.308277,0.00513796,-8.55572e-05,2.22022e-06,0.313331,0.00497351,-7.88966e-05,1.97163e-06,0.318228,0.00482163,-7.29817e-05,1.7248e-06,0.322978,0.00468084,-6.78073e-05,1.55998e-06,0.327593,0.0045499,-6.31274e-05,1.36343e-06,0.332081,0.00442774,-5.90371e-05,1.27136e-06,0.336451,0.00431348,-5.5223e-05,1.09111e-06,0.34071,0.00420631,-5.19496e-05,1.0399e-06,0.344866,0.00410553,-4.88299e-05,9.18347e-07,0.348923,0.00401062,-4.60749e-05,8.29942e-07,0.352889,0.00392096,-4.35851e-05,7.98478e-07,0.356767,0.00383619,-4.11896e-05,6.84917e-07,0.360562,0.00375586,-3.91349e-05,6.63976e-07,0.36428,0.00367959,-3.7143e-05,5.93086e-07,0.367923,0.00360708,-3.53637e-05,5.6976e-07,0.371495,0.00353806,-3.36544e-05,4.95533e-07,0.375,0.00347224,-3.21678e-05,4.87951e-07,0.378441,0.00340937,-3.0704e-05,4.4349e-07,0.38182,0.00334929,-2.93735e-05,4.20297e-07,0.38514,0.0032918,-2.81126e-05,3.7872e-07,0.388404,0.00323671,-2.69764e-05,3.596e-07,0.391614,0.00318384,-2.58976e-05,3.5845e-07,0.394772,0.00313312,-2.48223e-05,2.92765e-07,0.397881,0.00308435,-2.3944e-05,3.18232e-07,0.400942,0.00303742,-2.29893e-05,2.82046e-07,0.403957,0.00299229,-2.21432e-05,2.52315e-07,0.406927,0.00294876,-2.13862e-05,2.58416e-07,0.409855,0.00290676,-2.0611e-05,2.33939e-07,0.412741,0.00286624,-1.99092e-05,2.36342e-07,0.415587,0.00282713,-1.92001e-05,1.916e-07,0.418396,0.00278931,-1.86253e-05,2.1915e-07,0.421167,0.00275271,-1.79679e-05,1.83498e-07,0.423901,0.00271733,-1.74174e-05,1.79343e-07,0.426602,0.00268303,-1.68794e-05,1.72013e-07,0.429268,0.00264979,-1.63633e-05,1.75686e-07,0.431901,0.00261759,-1.58363e-05,1.3852e-07,0.434503,0.00258633,-1.54207e-05,1.64304e-07,0.437074,0.00255598,-1.49278e-05,1.28136e-07,0.439616,0.00252651,-1.45434e-05,1.57618e-07,0.442128,0.0024979,-1.40705e-05,1.0566e-07,0.444612,0.00247007,-1.37535e-05,1.34998e-07,0.447068,0.00244297,-1.33485e-05,1.29207e-07,0.449498,0.00241666,-1.29609e-05,9.32347e-08,0.451902,0.00239102,-1.26812e-05,1.23703e-07,0.45428,0.00236603,-1.23101e-05,9.74072e-08,0.456634,0.0023417,-1.20179e-05,1.12518e-07,0.458964,0.002318,-1.16803e-05,7.83681e-08,0.46127,0.00229488,-1.14452e-05,1.10452e-07,0.463554,0.00227232,-1.11139e-05,7.58719e-08,0.465815,0.00225032,-1.08863e-05,9.2699e-08,0.468055,0.00222882,-1.06082e-05,8.97738e-08,0.470273,0.00220788,-1.03388e-05,5.4845e-08,0.47247,0.00218736,-1.01743e-05,1.0808e-07,0.474648,0.00216734,-9.85007e-06,4.9277e-08,0.476805,0.00214779,-9.70224e-06,8.22408e-08,0.478943,0.00212863,-9.45551e-06,6.87942e-08,0.481063,0.00210993,-9.24913e-06,5.98144e-08,0.483163,0.00209161,-9.06969e-06,7.93789e-08,0.485246,0.00207371,-8.83155e-06,3.99032e-08,0.487311,0.00205616,-8.71184e-06,8.88325e-08,0.489358,0.002039,-8.44534e-06,2.20004e-08,0.491389,0.00202218,-8.37934e-06,9.13872e-08,0.493403,0.0020057,-8.10518e-06,2.96829e-08,0.495401,0.00198957,-8.01613e-06,5.81028e-08,0.497382,0.00197372,-7.84183e-06,6.5731e-08,0.499348,0.00195823,-7.64463e-06,3.66019e-08,0.501299,0.00194305,-7.53483e-06,2.62811e-08,0.503234,0.00192806,-7.45598e-06,9.66907e-08,0.505155,0.00191344,-7.16591e-06,4.18928e-09,0.507061,0.00189912,-7.15334e-06,6.53665e-08,0.508953,0.00188501,-6.95724e-06,3.23686e-08,0.510831,0.00187119,-6.86014e-06,4.35774e-08,0.512696,0.0018576,-6.72941e-06,3.17406e-08,0.514547,0.00184424,-6.63418e-06,6.78785e-08,0.516384,0.00183117,-6.43055e-06,-5.23126e-09,0.518209,0.0018183,-6.44624e-06,7.22562e-08,0.520021,0.00180562,-6.22947e-06,1.42292e-08,0.52182,0.0017932,-6.18679e-06,4.9641e-08,0.523607,0.00178098,-6.03786e-06,2.56259e-08,0.525382,0.00176898,-5.96099e-06,2.66696e-08,0.527145,0.00175714,-5.88098e-06,4.65094e-08,0.528897,0.00174552,-5.74145e-06,2.57114e-08,0.530637,0.00173411,-5.66431e-06,2.94588e-08,0.532365,0.00172287,-5.57594e-06,3.52667e-08,0.534082,0.00171182,-5.47014e-06,8.28868e-09,0.535789,0.00170091,-5.44527e-06,5.07871e-08,0.537484,0.00169017,-5.29291e-06,2.69817e-08,0.539169,0.00167967,-5.21197e-06,2.01009e-08,0.540844,0.0016693,-5.15166e-06,1.18237e-08,0.542508,0.00165903,-5.11619e-06,5.18135e-08,0.544162,0.00164896,-4.96075e-06,1.9341e-08,0.545806,0.00163909,-4.90273e-06,-9.96867e-09,0.54744,0.00162926,-4.93263e-06,8.01382e-08,0.549064,0.00161963,-4.69222e-06,-1.25601e-08,0.550679,0.00161021,-4.7299e-06,2.97067e-08,0.552285,0.00160084,-4.64078e-06,1.29426e-08,0.553881,0.0015916,-4.60195e-06,3.77327e-08,0.555468,0.00158251,-4.48875e-06,1.49412e-08,0.557046,0.00157357,-4.44393e-06,2.17118e-08,0.558615,0.00156475,-4.3788e-06,1.74206e-08,0.560176,0.00155605,-4.32653e-06,2.78152e-08,0.561727,0.00154748,-4.24309e-06,-9.47239e-09,0.563271,0.00153896,-4.27151e-06,6.9679e-08,0.564805,0.00153063,-4.06247e-06,-3.08246e-08,0.566332,0.00152241,-4.15494e-06,5.36188e-08,0.56785,0.00151426,-3.99409e-06,-4.83594e-09,0.56936,0.00150626,-4.00859e-06,2.53293e-08,0.570863,0.00149832,-3.93261e-06,2.27286e-08,0.572357,0.00149052,-3.86442e-06,2.96541e-09,0.573844,0.0014828,-3.85552e-06,2.50147e-08,0.575323,0.00147516,-3.78048e-06,1.61842e-08,0.576794,0.00146765,-3.73193e-06,2.94582e-08,0.578258,0.00146028,-3.64355e-06,-1.48076e-08,0.579715,0.00145295,-3.68798e-06,2.97724e-08,0.581164,0.00144566,-3.59866e-06,1.49272e-08,0.582606,0.00143851,-3.55388e-06,2.97285e-08,0.584041,0.00143149,-3.46469e-06,-1.46323e-08,0.585469,0.00142451,-3.50859e-06,2.88004e-08,0.58689,0.00141758,-3.42219e-06,1.864e-08,0.588304,0.00141079,-3.36627e-06,1.58482e-08,0.589712,0.00140411,-3.31872e-06,-2.24279e-08,0.591112,0.00139741,-3.38601e-06,7.38639e-08,0.592507,0.00139085,-3.16441e-06,-3.46088e-08,0.593894,0.00138442,-3.26824e-06,4.96675e-09,0.595275,0.0013779,-3.25334e-06,7.4346e-08,0.59665,0.00137162,-3.0303e-06,-6.39319e-08,0.598019,0.00136536,-3.2221e-06,6.21725e-08,0.599381,0.00135911,-3.03558e-06,-5.94423e-09,0.600737,0.00135302,-3.05341e-06,2.12091e-08,0.602087,0.00134697,-2.98979e-06,-1.92876e-08,0.603431,0.00134094,-3.04765e-06,5.5941e-08,0.604769,0.00133501,-2.87983e-06,-2.56622e-08,0.606101,0.00132917,-2.95681e-06,4.67078e-08,0.607427,0.0013234,-2.81669e-06,-4.19592e-08,0.608748,0.00131764,-2.94257e-06,6.15243e-08,0.610062,0.00131194,-2.75799e-06,-2.53244e-08,0.611372,0.00130635,-2.83397e-06,3.97739e-08,0.612675,0.0013008,-2.71465e-06,-1.45618e-08,0.613973,0.00129533,-2.75833e-06,1.84733e-08,0.615266,0.00128986,-2.70291e-06,2.73606e-10,0.616553,0.00128446,-2.70209e-06,4.00367e-08,0.617835,0.00127918,-2.58198e-06,-4.12113e-08,0.619111,0.00127389,-2.70561e-06,6.52039e-08,0.620383,0.00126867,-2.51e-06,-4.07901e-08,0.621649,0.00126353,-2.63237e-06,3.83516e-08,0.62291,0.00125838,-2.51732e-06,6.59315e-09,0.624166,0.00125337,-2.49754e-06,-5.11939e-09,0.625416,0.00124836,-2.5129e-06,1.38846e-08,0.626662,0.00124337,-2.47124e-06,9.18514e-09,0.627903,0.00123846,-2.44369e-06,8.97952e-09,0.629139,0.0012336,-2.41675e-06,1.45012e-08,0.63037,0.00122881,-2.37325e-06,-7.37949e-09,0.631597,0.00122404,-2.39538e-06,1.50169e-08,0.632818,0.00121929,-2.35033e-06,6.91648e-09,0.634035,0.00121461,-2.32958e-06,1.69219e-08,0.635248,0.00121,-2.27882e-06,-1.49997e-08,0.636455,0.0012054,-2.32382e-06,4.30769e-08,0.637659,0.00120088,-2.19459e-06,-3.80986e-08,0.638857,0.00119638,-2.30888e-06,4.97134e-08,0.640051,0.00119191,-2.15974e-06,-4.15463e-08,0.641241,0.00118747,-2.28438e-06,5.68667e-08,0.642426,0.00118307,-2.11378e-06,-7.10641e-09,0.643607,0.00117882,-2.1351e-06,-2.8441e-08,0.644784,0.00117446,-2.22042e-06,6.12658e-08,0.645956,0.00117021,-2.03663e-06,-3.78083e-08,0.647124,0.00116602,-2.15005e-06,3.03627e-08,0.648288,0.00116181,-2.05896e-06,-2.40379e-08,0.649448,0.00115762,-2.13108e-06,6.57887e-08,0.650603,0.00115356,-1.93371e-06,-6.03028e-08,0.651755,0.00114951,-2.11462e-06,5.62134e-08,0.652902,0.00114545,-1.94598e-06,-4.53417e-08,0.654046,0.00114142,-2.082e-06,6.55489e-08,0.655185,0.00113745,-1.88536e-06,-3.80396e-08,0.656321,0.00113357,-1.99948e-06,2.70049e-08,0.657452,0.00112965,-1.91846e-06,-1.03755e-08,0.65858,0.00112578,-1.94959e-06,1.44973e-08,0.659704,0.00112192,-1.9061e-06,1.1991e-08,0.660824,0.00111815,-1.87012e-06,-2.85634e-09,0.66194,0.0011144,-1.87869e-06,-5.65782e-10,0.663053,0.00111064,-1.88039e-06,5.11947e-09,0.664162,0.0011069,-1.86503e-06,3.96924e-08,0.665267,0.00110328,-1.74595e-06,-4.46795e-08,0.666368,0.00109966,-1.87999e-06,1.98161e-08,0.667466,0.00109596,-1.82054e-06,2.502e-08,0.66856,0.00109239,-1.74548e-06,-6.86593e-10,0.669651,0.0010889,-1.74754e-06,-2.22739e-08,0.670738,0.00108534,-1.81437e-06,3.01776e-08,0.671821,0.0010818,-1.72383e-06,2.07732e-08,0.672902,0.00107841,-1.66151e-06,-5.36658e-08,0.673978,0.00107493,-1.82251e-06,7.46802e-08,0.675051,0.00107151,-1.59847e-06,-6.62411e-08,0.676121,0.00106811,-1.79719e-06,7.10748e-08,0.677188,0.00106473,-1.58397e-06,-3.92441e-08,0.678251,0.00106145,-1.7017e-06,2.62973e-08,0.679311,0.00105812,-1.62281e-06,-6.34035e-09,0.680367,0.00105486,-1.64183e-06,-9.36249e-10,0.68142,0.00105157,-1.64464e-06,1.00854e-08,0.68247,0.00104831,-1.61438e-06,2.01995e-08,0.683517,0.00104514,-1.55378e-06,-3.1279e-08,0.68456,0.00104194,-1.64762e-06,4.53114e-08,0.685601,0.00103878,-1.51169e-06,-3.07573e-08,0.686638,0.00103567,-1.60396e-06,1.81133e-08,0.687672,0.00103251,-1.54962e-06,1.79085e-08,0.688703,0.00102947,-1.49589e-06,-3.01428e-08,0.689731,0.00102639,-1.58632e-06,4.30583e-08,0.690756,0.00102334,-1.45715e-06,-2.28814e-08,0.691778,0.00102036,-1.52579e-06,-1.11373e-08,0.692797,0.00101727,-1.5592e-06,6.74305e-08,0.693812,0.00101436,-1.35691e-06,-7.97709e-08,0.694825,0.0010114,-1.59622e-06,7.28391e-08,0.695835,0.00100843,-1.37771e-06,-3.27715e-08,0.696842,0.00100558,-1.47602e-06,-1.35807e-09,0.697846,0.00100262,-1.48009e-06,3.82037e-08,0.698847,0.000999775,-1.36548e-06,-3.22474e-08,0.699846,0.000996948,-1.46223e-06,3.11809e-08,0.700841,0.000994117,-1.36868e-06,-3.28714e-08,0.701834,0.000991281,-1.4673e-06,4.07001e-08,0.702824,0.000988468,-1.3452e-06,-1.07197e-08,0.703811,0.000985746,-1.37736e-06,2.17866e-09,0.704795,0.000982998,-1.37082e-06,2.00521e-09,0.705777,0.000980262,-1.3648e-06,-1.01996e-08,0.706756,0.000977502,-1.3954e-06,3.87931e-08,0.707732,0.000974827,-1.27902e-06,-2.57632e-08,0.708706,0.000972192,-1.35631e-06,4.65513e-09,0.709676,0.000969493,-1.34235e-06,7.14257e-09,0.710645,0.00096683,-1.32092e-06,2.63791e-08,0.71161,0.000964267,-1.24178e-06,-5.30543e-08,0.712573,0.000961625,-1.40095e-06,6.66289e-08,0.713533,0.000959023,-1.20106e-06,-3.46474e-08,0.714491,0.000956517,-1.305e-06,1.23559e-08,0.715446,0.000953944,-1.26793e-06,-1.47763e-08,0.716399,0.000951364,-1.31226e-06,4.67494e-08,0.717349,0.000948879,-1.17201e-06,-5.3012e-08,0.718297,0.000946376,-1.33105e-06,4.60894e-08,0.719242,0.000943852,-1.19278e-06,-1.21366e-08,0.720185,0.00094143,-1.22919e-06,2.45673e-09,0.721125,0.000938979,-1.22182e-06,2.30966e-09,0.722063,0.000936543,-1.21489e-06,-1.16954e-08,0.722998,0.000934078,-1.24998e-06,4.44718e-08,0.723931,0.000931711,-1.11656e-06,-4.69823e-08,0.724861,0.000929337,-1.25751e-06,2.4248e-08,0.725789,0.000926895,-1.18477e-06,9.5949e-09,0.726715,0.000924554,-1.15598e-06,-3.02286e-09,0.727638,0.000922233,-1.16505e-06,2.49649e-09,0.72856,0.00091991,-1.15756e-06,-6.96321e-09,0.729478,0.000917575,-1.17845e-06,2.53564e-08,0.730395,0.000915294,-1.10238e-06,-3.48578e-08,0.731309,0.000912984,-1.20695e-06,5.44704e-08,0.732221,0.000910734,-1.04354e-06,-6.38144e-08,0.73313,0.000908455,-1.23499e-06,8.15781e-08,0.734038,0.00090623,-9.90253e-07,-8.3684e-08,0.734943,0.000903999,-1.2413e-06,7.43441e-08,0.735846,0.000901739,-1.01827e-06,-3.48787e-08,0.736746,0.000899598,-1.12291e-06,5.56596e-09,0.737645,0.000897369,-1.10621e-06,1.26148e-08,0.738541,0.000895194,-1.06837e-06,3.57935e-09,0.739435,0.000893068,-1.05763e-06,-2.69322e-08,0.740327,0.000890872,-1.13842e-06,4.45448e-08,0.741217,0.000888729,-1.00479e-06,-3.20376e-08,0.742105,0.000886623,-1.1009e-06,2.40011e-08,0.74299,0.000884493,-1.0289e-06,-4.36209e-09,0.743874,0.000882422,-1.04199e-06,-6.55268e-09,0.744755,0.000880319,-1.06164e-06,3.05728e-08,0.745634,0.000878287,-9.69926e-07,-5.61338e-08,0.746512,0.000876179,-1.13833e-06,7.4753e-08,0.747387,0.000874127,-9.14068e-07,-6.40644e-08,0.74826,0.000872106,-1.10626e-06,6.22955e-08,0.749131,0.000870081,-9.19375e-07,-6.59083e-08,0.75,0.000868044,-1.1171e-06,8.21284e-08,0.750867,0.000866056,-8.70714e-07,-8.37915e-08,0.751732,0.000864064,-1.12209e-06,7.42237e-08,0.752595,0.000862042,-8.99418e-07,-3.42894e-08,0.753456,0.00086014,-1.00229e-06,3.32955e-09,0.754315,0.000858146,-9.92297e-07,2.09712e-08,0.755173,0.000856224,-9.29384e-07,-2.76096e-08,0.756028,0.000854282,-1.01221e-06,2.98627e-08,0.756881,0.000852348,-9.22625e-07,-3.22365e-08,0.757733,0.000850406,-1.01933e-06,3.94786e-08,0.758582,0.000848485,-9.00898e-07,-6.46833e-09,0.75943,0.000846664,-9.20303e-07,-1.36052e-08,0.760275,0.000844783,-9.61119e-07,1.28447e-09,0.761119,0.000842864,-9.57266e-07,8.4674e-09,0.761961,0.000840975,-9.31864e-07,2.44506e-08,0.762801,0.000839185,-8.58512e-07,-4.6665e-08,0.763639,0.000837328,-9.98507e-07,4.30001e-08,0.764476,0.00083546,-8.69507e-07,-6.12609e-09,0.76531,0.000833703,-8.87885e-07,-1.84959e-08,0.766143,0.000831871,-9.43372e-07,2.05052e-08,0.766974,0.000830046,-8.81857e-07,-3.92026e-09,0.767803,0.000828271,-8.93618e-07,-4.82426e-09,0.768631,0.000826469,-9.0809e-07,2.32172e-08,0.769456,0.000824722,-8.38439e-07,-2.84401e-08,0.77028,0.00082296,-9.23759e-07,3.09386e-08,0.771102,0.000821205,-8.30943e-07,-3.57099e-08,0.771922,0.000819436,-9.38073e-07,5.22963e-08,0.772741,0.000817717,-7.81184e-07,-5.42658e-08,0.773558,0.000815992,-9.43981e-07,4.55579e-08,0.774373,0.000814241,-8.07308e-07,-8.75656e-09,0.775186,0.0008126,-8.33578e-07,-1.05315e-08,0.775998,0.000810901,-8.65172e-07,-8.72188e-09,0.776808,0.000809145,-8.91338e-07,4.54191e-08,0.777616,0.000807498,-7.5508e-07,-5.37454e-08,0.778423,0.000805827,-9.16317e-07,5.03532e-08,0.779228,0.000804145,-7.65257e-07,-2.84584e-08,0.780031,0.000802529,-8.50632e-07,3.87579e-09,0.780833,0.00080084,-8.39005e-07,1.29552e-08,0.781633,0.0007992,-8.00139e-07,3.90804e-09,0.782432,0.000797612,-7.88415e-07,-2.85874e-08,0.783228,0.000795949,-8.74177e-07,5.0837e-08,0.784023,0.000794353,-7.21666e-07,-5.55513e-08,0.784817,0.000792743,-8.8832e-07,5.21587e-08,0.785609,0.000791123,-7.31844e-07,-3.38744e-08,0.786399,0.000789558,-8.33467e-07,2.37342e-08,0.787188,0.000787962,-7.62264e-07,-1.45775e-09,0.787975,0.000786433,-7.66638e-07,-1.79034e-08,0.788761,0.000784846,-8.20348e-07,1.34665e-08,0.789545,0.000783246,-7.79948e-07,2.3642e-08,0.790327,0.000781757,-7.09022e-07,-4.84297e-08,0.791108,0.000780194,-8.54311e-07,5.08674e-08,0.791888,0.000778638,-7.01709e-07,-3.58303e-08,0.792666,0.000777127,-8.092e-07,3.28493e-08,0.793442,0.000775607,-7.10652e-07,-3.59624e-08,0.794217,0.000774078,-8.1854e-07,5.13959e-08,0.79499,0.000772595,-6.64352e-07,-5.04121e-08,0.795762,0.000771115,-8.15588e-07,3.10431e-08,0.796532,0.000769577,-7.22459e-07,-1.41557e-08,0.797301,0.00076809,-7.64926e-07,2.55795e-08,0.798069,0.000766636,-6.88187e-07,-2.85578e-08,0.798835,0.000765174,-7.73861e-07,2.90472e-08,0.799599,0.000763714,-6.86719e-07,-2.80262e-08,0.800362,0.000762256,-7.70798e-07,2.34531e-08,0.801123,0.000760785,-7.00438e-07,-6.18144e-09,0.801884,0.000759366,-7.18983e-07,1.27263e-09,0.802642,0.000757931,-7.15165e-07,1.09101e-09,0.803399,0.000756504,-7.11892e-07,-5.63675e-09,0.804155,0.000755064,-7.28802e-07,2.14559e-08,0.80491,0.00075367,-6.64434e-07,-2.05821e-08,0.805663,0.00075228,-7.26181e-07,1.26812e-09,0.806414,0.000750831,-7.22377e-07,1.55097e-08,0.807164,0.000749433,-6.75848e-07,-3.70216e-09,0.807913,0.00074807,-6.86954e-07,-7.0105e-10,0.80866,0.000746694,-6.89057e-07,6.5063e-09,0.809406,0.000745336,-6.69538e-07,-2.53242e-08,0.810151,0.000743921,-7.45511e-07,3.51858e-08,0.810894,0.000742535,-6.39953e-07,3.79034e-09,0.811636,0.000741267,-6.28582e-07,-5.03471e-08,0.812377,0.000739858,-7.79624e-07,7.83886e-08,0.813116,0.000738534,-5.44458e-07,-8.43935e-08,0.813854,0.000737192,-7.97638e-07,8.03714e-08,0.81459,0.000735838,-5.56524e-07,-5.82784e-08,0.815325,0.00073455,-7.31359e-07,3.35329e-08,0.816059,0.000733188,-6.3076e-07,-1.62486e-08,0.816792,0.000731878,-6.79506e-07,3.14614e-08,0.817523,0.000730613,-5.85122e-07,-4.99925e-08,0.818253,0.000729293,-7.35099e-07,4.92994e-08,0.818982,0.000727971,-5.87201e-07,-2.79959e-08,0.819709,0.000726712,-6.71189e-07,3.07959e-09,0.820435,0.000725379,-6.6195e-07,1.56777e-08,0.82116,0.000724102,-6.14917e-07,-6.18564e-09,0.821883,0.000722854,-6.33474e-07,9.06488e-09,0.822606,0.000721614,-6.06279e-07,-3.00739e-08,0.823327,0.000720311,-6.96501e-07,5.16262e-08,0.824046,0.000719073,-5.41623e-07,-5.72214e-08,0.824765,0.000717818,-7.13287e-07,5.80503e-08,0.825482,0.000716566,-5.39136e-07,-5.57703e-08,0.826198,0.00071532,-7.06447e-07,4.58215e-08,0.826912,0.000714045,-5.68983e-07,-8.30636e-09,0.827626,0.000712882,-5.93902e-07,-1.25961e-08,0.828338,0.000711656,-6.3169e-07,-9.13985e-10,0.829049,0.00071039,-6.34432e-07,1.62519e-08,0.829759,0.00070917,-5.85676e-07,-4.48904e-09,0.830468,0.000707985,-5.99143e-07,1.70418e-09,0.831175,0.000706792,-5.9403e-07,-2.32768e-09,0.831881,0.000705597,-6.01014e-07,7.60648e-09,0.832586,0.000704418,-5.78194e-07,-2.80982e-08,0.83329,0.000703177,-6.62489e-07,4.51817e-08,0.833993,0.000701988,-5.26944e-07,-3.34192e-08,0.834694,0.000700834,-6.27201e-07,2.88904e-08,0.835394,0.000699666,-5.4053e-07,-2.25378e-08,0.836093,0.000698517,-6.08143e-07,1.65589e-09,0.836791,0.000697306,-6.03176e-07,1.59142e-08,0.837488,0.000696147,-5.55433e-07,-5.70801e-09,0.838184,0.000695019,-5.72557e-07,6.91792e-09,0.838878,0.000693895,-5.51803e-07,-2.19637e-08,0.839571,0.000692725,-6.17694e-07,2.13321e-08,0.840263,0.000691554,-5.53698e-07,-3.75996e-09,0.840954,0.000690435,-5.64978e-07,-6.29219e-09,0.841644,0.000689287,-5.83855e-07,2.89287e-08,0.842333,0.000688206,-4.97068e-07,-4.98181e-08,0.843021,0.000687062,-6.46523e-07,5.11344e-08,0.843707,0.000685922,-4.9312e-07,-3.55102e-08,0.844393,0.00068483,-5.9965e-07,3.13019e-08,0.845077,0.000683724,-5.05745e-07,-3.00925e-08,0.84576,0.000682622,-5.96022e-07,2.94636e-08,0.846442,0.000681519,-5.07631e-07,-2.81572e-08,0.847123,0.000680419,-5.92103e-07,2.35606e-08,0.847803,0.000679306,-5.21421e-07,-6.48045e-09,0.848482,0.000678243,-5.40863e-07,2.36124e-09,0.849159,0.000677169,-5.33779e-07,-2.96461e-09,0.849836,0.000676092,-5.42673e-07,9.49728e-09,0.850512,0.000675035,-5.14181e-07,-3.50245e-08,0.851186,0.000673902,-6.19254e-07,7.09959e-08,0.851859,0.000672876,-4.06267e-07,-7.01453e-08,0.852532,0.000671853,-6.16703e-07,3.07714e-08,0.853203,0.000670712,-5.24388e-07,6.66423e-09,0.853873,0.000669684,-5.04396e-07,2.17629e-09,0.854542,0.000668681,-4.97867e-07,-1.53693e-08,0.855211,0.000667639,-5.43975e-07,-3.03752e-10,0.855878,0.000666551,-5.44886e-07,1.65844e-08,0.856544,0.000665511,-4.95133e-07,-6.42907e-09,0.857209,0.000664501,-5.1442e-07,9.13195e-09,0.857873,0.0006635,-4.87024e-07,-3.00987e-08,0.858536,0.000662435,-5.7732e-07,5.16584e-08,0.859198,0.000661436,-4.22345e-07,-5.73255e-08,0.859859,0.000660419,-5.94322e-07,5.84343e-08,0.860518,0.000659406,-4.19019e-07,-5.72022e-08,0.861177,0.000658396,-5.90626e-07,5.11653e-08,0.861835,0.000657368,-4.3713e-07,-2.82495e-08,0.862492,0.000656409,-5.21878e-07,2.22788e-09,0.863148,0.000655372,-5.15195e-07,1.9338e-08,0.863803,0.0006544,-4.5718e-07,-1.99754e-08,0.864457,0.000653425,-5.17107e-07,9.59024e-10,0.86511,0.000652394,-5.1423e-07,1.61393e-08,0.865762,0.000651414,-4.65812e-07,-5.91149e-09,0.866413,0.000650465,-4.83546e-07,7.50665e-09,0.867063,0.00064952,-4.61026e-07,-2.4115e-08,0.867712,0.000648526,-5.33371e-07,2.93486e-08,0.86836,0.000647547,-4.45325e-07,-3.36748e-08,0.869007,0.000646555,-5.4635e-07,4.57461e-08,0.869653,0.0006456,-4.09112e-07,-3.01002e-08,0.870298,0.000644691,-4.99412e-07,1.50501e-08,0.870942,0.000643738,-4.54262e-07,-3.01002e-08,0.871585,0.000642739,-5.44563e-07,4.57461e-08,0.872228,0.000641787,-4.07324e-07,-3.36748e-08,0.872869,0.000640871,-5.08349e-07,2.93486e-08,0.873509,0.000639943,-4.20303e-07,-2.4115e-08,0.874149,0.00063903,-4.92648e-07,7.50655e-09,0.874787,0.000638067,-4.70128e-07,-5.91126e-09,0.875425,0.000637109,-4.87862e-07,1.61385e-08,0.876062,0.000636182,-4.39447e-07,9.61961e-10,0.876697,0.000635306,-4.36561e-07,-1.99863e-08,0.877332,0.000634373,-4.9652e-07,1.93785e-08,0.877966,0.000633438,-4.38384e-07,2.07697e-09,0.878599,0.000632567,-4.32153e-07,-2.76864e-08,0.879231,0.00063162,-5.15212e-07,4.90641e-08,0.879862,0.000630737,-3.6802e-07,-4.93606e-08,0.880493,0.000629852,-5.16102e-07,2.9169e-08,0.881122,0.000628908,-4.28595e-07,-7.71083e-09,0.881751,0.000628027,-4.51727e-07,1.6744e-09,0.882378,0.000627129,-4.46704e-07,1.01317e-09,0.883005,0.000626239,-4.43665e-07,-5.72703e-09,0.883631,0.000625334,-4.60846e-07,2.1895e-08,0.884255,0.000624478,-3.95161e-07,-2.22481e-08,0.88488,0.000623621,-4.61905e-07,7.4928e-09,0.885503,0.00062272,-4.39427e-07,-7.72306e-09,0.886125,0.000621818,-4.62596e-07,2.33995e-08,0.886746,0.000620963,-3.92398e-07,-2.62704e-08,0.887367,0.000620099,-4.71209e-07,2.20775e-08,0.887987,0.000619223,-4.04976e-07,-2.43496e-09,0.888605,0.000618406,-4.12281e-07,-1.23377e-08,0.889223,0.000617544,-4.49294e-07,-7.81876e-09,0.88984,0.000616622,-4.72751e-07,4.36128e-08,0.890457,0.000615807,-3.41912e-07,-4.7423e-08,0.891072,0.000614981,-4.84181e-07,2.68698e-08,0.891687,0.000614093,-4.03572e-07,-4.51384e-10,0.8923,0.000613285,-4.04926e-07,-2.50643e-08,0.892913,0.0006124,-4.80119e-07,4.11038e-08,0.893525,0.000611563,-3.56808e-07,-2.01414e-08,0.894136,0.000610789,-4.17232e-07,-2.01426e-08,0.894747,0.000609894,-4.7766e-07,4.11073e-08,0.895356,0.000609062,-3.54338e-07,-2.50773e-08,0.895965,0.000608278,-4.2957e-07,-4.02954e-10,0.896573,0.000607418,-4.30779e-07,2.66891e-08,0.89718,0.000606636,-3.50711e-07,-4.67489e-08,0.897786,0.000605795,-4.90958e-07,4.10972e-08,0.898391,0.000604936,-3.67666e-07,1.56948e-09,0.898996,0.000604205,-3.62958e-07,-4.73751e-08,0.8996,0.000603337,-5.05083e-07,6.87214e-08,0.900202,0.000602533,-2.98919e-07,-4.86966e-08,0.900805,0.000601789,-4.45009e-07,6.85589e-09,0.901406,0.00060092,-4.24441e-07,2.1273e-08,0.902007,0.000600135,-3.60622e-07,-3.23434e-08,0.902606,0.000599317,-4.57652e-07,4.84959e-08,0.903205,0.000598547,-3.12164e-07,-4.24309e-08,0.903803,0.000597795,-4.39457e-07,2.01844e-09,0.904401,0.000596922,-4.33402e-07,3.43571e-08,0.904997,0.000596159,-3.30331e-07,-2.02374e-08,0.905593,0.000595437,-3.91043e-07,-1.30123e-08,0.906188,0.000594616,-4.3008e-07,1.26819e-08,0.906782,0.000593794,-3.92034e-07,2.18894e-08,0.907376,0.000593076,-3.26366e-07,-4.06349e-08,0.907968,0.000592301,-4.4827e-07,2.1441e-08,0.90856,0.000591469,-3.83947e-07,1.44754e-08,0.909151,0.000590744,-3.40521e-07,-1.97379e-08,0.909742,0.000590004,-3.99735e-07,4.87161e-09,0.910331,0.000589219,-3.8512e-07,2.51532e-10,0.91092,0.00058845,-3.84366e-07,-5.87776e-09,0.911508,0.000587663,-4.01999e-07,2.32595e-08,0.912096,0.000586929,-3.3222e-07,-2.75554e-08,0.912682,0.000586182,-4.14887e-07,2.73573e-08,0.913268,0.000585434,-3.32815e-07,-2.22692e-08,0.913853,0.000584702,-3.99622e-07,2.11486e-09,0.914437,0.000583909,-3.93278e-07,1.38098e-08,0.915021,0.000583164,-3.51848e-07,2.25042e-09,0.915604,0.000582467,-3.45097e-07,-2.28115e-08,0.916186,0.000581708,-4.13531e-07,2.93911e-08,0.916767,0.000580969,-3.25358e-07,-3.51481e-08,0.917348,0.000580213,-4.30803e-07,5.15967e-08,0.917928,0.000579506,-2.76012e-07,-5.20296e-08,0.918507,0.000578798,-4.32101e-07,3.73124e-08,0.919085,0.000578046,-3.20164e-07,-3.76154e-08,0.919663,0.000577293,-4.3301e-07,5.35447e-08,0.92024,0.000576587,-2.72376e-07,-5.7354e-08,0.920816,0.000575871,-4.44438e-07,5.66621e-08,0.921391,0.000575152,-2.74452e-07,-5.00851e-08,0.921966,0.000574453,-4.24707e-07,2.4469e-08,0.92254,0.000573677,-3.513e-07,1.18138e-08,0.923114,0.000573009,-3.15859e-07,-1.21195e-08,0.923686,0.000572341,-3.52217e-07,-2.29403e-08,0.924258,0.000571568,-4.21038e-07,4.4276e-08,0.924829,0.000570859,-2.8821e-07,-3.49546e-08,0.9254,0.000570178,-3.93074e-07,3.59377e-08,0.92597,0.000569499,-2.85261e-07,-4.91915e-08,0.926539,0.000568781,-4.32835e-07,4.16189e-08,0.927107,0.00056804,-3.07979e-07,1.92523e-09,0.927675,0.00056743,-3.02203e-07,-4.93198e-08,0.928242,0.000566678,-4.50162e-07,7.61447e-08,0.928809,0.000566006,-2.21728e-07,-7.6445e-08,0.929374,0.000565333,-4.51063e-07,5.08216e-08,0.929939,0.000564583,-2.98599e-07,-7.63212e-09,0.930503,0.000563963,-3.21495e-07,-2.02931e-08,0.931067,0.000563259,-3.82374e-07,2.92001e-08,0.93163,0.000562582,-2.94774e-07,-3.69025e-08,0.932192,0.000561882,-4.05482e-07,5.88053e-08,0.932754,0.000561247,-2.29066e-07,-7.91094e-08,0.933315,0.000560552,-4.66394e-07,7.88184e-08,0.933875,0.000559856,-2.29939e-07,-5.73501e-08,0.934434,0.000559224,-4.01989e-07,3.13727e-08,0.934993,0.000558514,-3.07871e-07,-8.53611e-09,0.935551,0.000557873,-3.33479e-07,2.77175e-09,0.936109,0.000557214,-3.25164e-07,-2.55091e-09,0.936666,0.000556556,-3.32817e-07,7.43188e-09,0.937222,0.000555913,-3.10521e-07,-2.71766e-08,0.937778,0.00055521,-3.92051e-07,4.167e-08,0.938333,0.000554551,-2.67041e-07,-2.02941e-08,0.938887,0.000553956,-3.27923e-07,-2.00984e-08,0.93944,0.00055324,-3.88218e-07,4.10828e-08,0.939993,0.000552587,-2.6497e-07,-2.50237e-08,0.940546,0.000551982,-3.40041e-07,-5.92583e-10,0.941097,0.0005513,-3.41819e-07,2.7394e-08,0.941648,0.000550698,-2.59637e-07,-4.93788e-08,0.942199,0.000550031,-4.07773e-07,5.09119e-08,0.942748,0.000549368,-2.55038e-07,-3.50595e-08,0.943297,0.000548753,-3.60216e-07,2.97214e-08,0.943846,0.000548122,-2.71052e-07,-2.42215e-08,0.944394,0.000547507,-3.43716e-07,7.55985e-09,0.944941,0.000546842,-3.21037e-07,-6.01796e-09,0.945487,0.000546182,-3.3909e-07,1.65119e-08,0.946033,0.000545553,-2.89555e-07,-4.2498e-10,0.946578,0.000544973,-2.9083e-07,-1.4812e-08,0.947123,0.000544347,-3.35266e-07,6.83068e-11,0.947667,0.000543676,-3.35061e-07,1.45388e-08,0.94821,0.00054305,-2.91444e-07,1.38123e-09,0.948753,0.000542471,-2.87301e-07,-2.00637e-08,0.949295,0.000541836,-3.47492e-07,1.92688e-08,0.949837,0.000541199,-2.89685e-07,2.59298e-09,0.950378,0.000540628,-2.81906e-07,-2.96407e-08,0.950918,0.000539975,-3.70829e-07,5.63652e-08,0.951458,0.000539402,-2.01733e-07,-7.66107e-08,0.951997,0.000538769,-4.31565e-07,7.12638e-08,0.952535,0.00053812,-2.17774e-07,-2.96305e-08,0.953073,0.000537595,-3.06665e-07,-1.23464e-08,0.95361,0.000536945,-3.43704e-07,1.94114e-08,0.954147,0.000536316,-2.8547e-07,-5.69451e-09,0.954683,0.000535728,-3.02554e-07,3.36666e-09,0.955219,0.000535133,-2.92454e-07,-7.77208e-09,0.955753,0.000534525,-3.1577e-07,2.77216e-08,0.956288,0.000533976,-2.32605e-07,-4.35097e-08,0.956821,0.00053338,-3.63134e-07,2.7108e-08,0.957354,0.000532735,-2.8181e-07,-5.31772e-09,0.957887,0.000532156,-2.97764e-07,-5.83718e-09,0.958419,0.000531543,-3.15275e-07,2.86664e-08,0.95895,0.000530998,-2.29276e-07,-4.9224e-08,0.959481,0.000530392,-3.76948e-07,4.90201e-08,0.960011,0.000529785,-2.29887e-07,-2.76471e-08,0.96054,0.000529243,-3.12829e-07,1.96385e-09,0.961069,0.000528623,-3.06937e-07,1.97917e-08,0.961598,0.000528068,-2.47562e-07,-2.15261e-08,0.962125,0.000527508,-3.1214e-07,6.70795e-09,0.962653,0.000526904,-2.92016e-07,-5.30573e-09,0.963179,0.000526304,-3.07934e-07,1.4515e-08,0.963705,0.000525732,-2.64389e-07,6.85048e-09,0.964231,0.000525224,-2.43837e-07,-4.19169e-08,0.964756,0.00052461,-3.69588e-07,4.1608e-08,0.96528,0.000523996,-2.44764e-07,-5.30598e-09,0.965804,0.000523491,-2.60682e-07,-2.03841e-08,0.966327,0.000522908,-3.21834e-07,2.72378e-08,0.966849,0.000522346,-2.40121e-07,-2.89625e-08,0.967371,0.000521779,-3.27008e-07,2.90075e-08,0.967893,0.000521212,-2.39986e-07,-2.74629e-08,0.968414,0.00052065,-3.22374e-07,2.12396e-08,0.968934,0.000520069,-2.58656e-07,2.10922e-09,0.969454,0.000519558,-2.52328e-07,-2.96765e-08,0.969973,0.000518964,-3.41357e-07,5.6992e-08,0.970492,0.000518452,-1.70382e-07,-7.90821e-08,0.97101,0.000517874,-4.07628e-07,8.05224e-08,0.971528,0.000517301,-1.66061e-07,-6.41937e-08,0.972045,0.000516776,-3.58642e-07,5.70429e-08,0.972561,0.00051623,-1.87513e-07,-4.47686e-08,0.973077,0.00051572,-3.21819e-07,2.82237e-09,0.973593,0.000515085,-3.13352e-07,3.34792e-08,0.974108,0.000514559,-2.12914e-07,-1.75298e-08,0.974622,0.000514081,-2.65503e-07,-2.29648e-08,0.975136,0.000513481,-3.34398e-07,4.97843e-08,0.975649,0.000512961,-1.85045e-07,-5.6963e-08,0.976162,0.00051242,-3.55934e-07,5.88585e-08,0.976674,0.000511885,-1.79359e-07,-5.92616e-08,0.977185,0.000511348,-3.57143e-07,5.89785e-08,0.977696,0.000510811,-1.80208e-07,-5.74433e-08,0.978207,0.000510278,-3.52538e-07,5.15854e-08,0.978717,0.000509728,-1.97781e-07,-2.9689e-08,0.979226,0.000509243,-2.86848e-07,7.56591e-09,0.979735,0.000508692,-2.64151e-07,-5.74649e-10,0.980244,0.000508162,-2.65875e-07,-5.26732e-09,0.980752,0.000507615,-2.81677e-07,2.16439e-08,0.981259,0.000507116,-2.16745e-07,-2.17037e-08,0.981766,0.000506618,-2.81856e-07,5.56636e-09,0.982272,0.000506071,-2.65157e-07,-5.61689e-10,0.982778,0.000505539,-2.66842e-07,-3.31963e-09,0.983283,0.000504995,-2.76801e-07,1.38402e-08,0.983788,0.000504483,-2.3528e-07,7.56339e-09,0.984292,0.000504035,-2.1259e-07,-4.40938e-08,0.984796,0.000503478,-3.44871e-07,4.96026e-08,0.985299,0.000502937,-1.96064e-07,-3.51071e-08,0.985802,0.000502439,-3.01385e-07,3.12212e-08,0.986304,0.00050193,-2.07721e-07,-3.0173e-08,0.986806,0.000501424,-2.9824e-07,2.9866e-08,0.987307,0.000500917,-2.08642e-07,-2.96865e-08,0.987808,0.000500411,-2.97702e-07,2.92753e-08,0.988308,0.000499903,-2.09876e-07,-2.78101e-08,0.988807,0.0004994,-2.93306e-07,2.23604e-08,0.989307,0.000498881,-2.26225e-07,-2.02681e-09,0.989805,0.000498422,-2.32305e-07,-1.42531e-08,0.990303,0.000497915,-2.75065e-07,-5.65232e-10,0.990801,0.000497363,-2.76761e-07,1.65141e-08,0.991298,0.000496859,-2.27218e-07,-5.88639e-09,0.991795,0.000496387,-2.44878e-07,7.0315e-09,0.992291,0.000495918,-2.23783e-07,-2.22396e-08,0.992787,0.000495404,-2.90502e-07,2.23224e-08,0.993282,0.00049489,-2.23535e-07,-7.44543e-09,0.993776,0.000494421,-2.45871e-07,7.45924e-09,0.994271,0.000493951,-2.23493e-07,-2.23915e-08,0.994764,0.000493437,-2.90668e-07,2.25021e-08,0.995257,0.000492923,-2.23161e-07,-8.01218e-09,0.99575,0.000492453,-2.47198e-07,9.54669e-09,0.996242,0.000491987,-2.18558e-07,-3.01746e-08,0.996734,0.000491459,-3.09082e-07,5.1547e-08,0.997225,0.000490996,-1.54441e-07,-5.68039e-08,0.997716,0.000490517,-3.24853e-07,5.64594e-08,0.998206,0.000490036,-1.55474e-07,-4.98245e-08,0.998696,0.000489576,-3.04948e-07,2.36292e-08,0.999186,0.000489037,-2.3406e-07,1.49121e-08,0.999674,0.000488613,-1.89324e-07,-2.3673e-08,1.00016,0.000488164,-2.60343e-07,2.01754e-08,1.00065,0.000487704,-1.99816e-07,-5.70288e-08,1.00114,0.000487133,-3.70903e-07,8.87303e-08,1.00162,0.000486657,-1.04712e-07,-5.94737e-08,1.00211,0.000486269,-2.83133e-07,2.99553e-08,1.0026,0.000485793,-1.93267e-07,-6.03474e-08,1.00308,0.000485225,-3.74309e-07,9.2225e-08,1.00357,0.000484754,-9.76345e-08,-7.0134e-08,1.00405,0.000484348,-3.08036e-07,6.91016e-08,1.00454,0.000483939,-1.00731e-07,-8.70633e-08,1.00502,0.000483476,-3.61921e-07,4.07328e-08,1.0055,0.000482875,-2.39723e-07,4.33413e-08,1.00599,0.000482525,-1.09699e-07,-9.48886e-08,1.00647,0.000482021,-3.94365e-07,9.77947e-08,1.00695,0.000481526,-1.00981e-07,-5.78713e-08,1.00743,0.00048115,-2.74595e-07,1.44814e-08,1.00791,0.000480645,-2.31151e-07,-5.42665e-11,1.00839,0.000480182,-2.31314e-07,-1.42643e-08,1.00887,0.000479677,-2.74106e-07,5.71115e-08,1.00935,0.0004793,-1.02772e-07,-9.49724e-08,1.00983,0.000478809,-3.87689e-07,8.43596e-08,1.01031,0.000478287,-1.3461e-07,-4.04755e-09,1.01079,0.000478006,-1.46753e-07,-6.81694e-08,1.01127,0.000477508,-3.51261e-07,3.83067e-08,1.01174,0.00047692,-2.36341e-07,3.41521e-08,1.01222,0.00047655,-1.33885e-07,-5.57058e-08,1.0127,0.000476115,-3.01002e-07,6.94616e-08,1.01317,0.000475721,-9.26174e-08,-1.02931e-07,1.01365,0.000475227,-4.01412e-07,1.03846e-07,1.01412,0.000474736,-8.98751e-08,-7.40321e-08,1.0146,0.000474334,-3.11971e-07,7.30735e-08,1.01507,0.00047393,-9.27508e-08,-9.90527e-08,1.01554,0.000473447,-3.89909e-07,8.47188e-08,1.01602,0.000472921,-1.35753e-07,-1.40381e-09,1.01649,0.000472645,-1.39964e-07,-7.91035e-08,1.01696,0.000472128,-3.77275e-07,7.93993e-08,1.01744,0.000471612,-1.39077e-07,-7.52607e-11,1.01791,0.000471334,-1.39302e-07,-7.90983e-08,1.01838,0.000470818,-3.76597e-07,7.80499e-08,1.01885,0.000470299,-1.42448e-07,5.31733e-09,1.01932,0.00047003,-1.26496e-07,-9.93193e-08,1.01979,0.000469479,-4.24453e-07,1.53541e-07,1.02026,0.00046909,3.617e-08,-1.57217e-07,1.02073,0.000468691,-4.35482e-07,1.177e-07,1.02119,0.000468173,-8.23808e-08,-7.51659e-08,1.02166,0.000467783,-3.07878e-07,6.37538e-08,1.02213,0.000467358,-1.16617e-07,-6.064e-08,1.0226,0.000466943,-2.98537e-07,5.9597e-08,1.02306,0.000466525,-1.19746e-07,-5.85386e-08,1.02353,0.00046611,-2.95362e-07,5.53482e-08,1.024,0.000465685,-1.29317e-07,-4.36449e-08,1.02446,0.000465296,-2.60252e-07,2.20268e-11,1.02493,0.000464775,-2.60186e-07,4.35568e-08,1.02539,0.000464386,-1.29516e-07,-5.50398e-08,1.02586,0.000463961,-2.94635e-07,5.73932e-08,1.02632,0.000463544,-1.22456e-07,-5.53236e-08,1.02678,0.000463133,-2.88426e-07,4.46921e-08,1.02725,0.000462691,-1.5435e-07,-4.23534e-09,1.02771,0.000462369,-1.67056e-07,-2.77507e-08,1.02817,0.000461952,-2.50308e-07,-3.97101e-09,1.02863,0.000461439,-2.62221e-07,4.36348e-08,1.02909,0.000461046,-1.31317e-07,-5.13589e-08,1.02955,0.000460629,-2.85394e-07,4.25913e-08,1.03001,0.000460186,-1.5762e-07,2.0285e-10,1.03047,0.000459871,-1.57011e-07,-4.34027e-08,1.03093,0.000459427,-2.87219e-07,5.41987e-08,1.03139,0.000459015,-1.24623e-07,-5.4183e-08,1.03185,0.000458604,-2.87172e-07,4.33239e-08,1.03231,0.000458159,-1.572e-07,9.65817e-11,1.03277,0.000457845,-1.56911e-07,-4.37103e-08,1.03323,0.0004574,-2.88041e-07,5.55351e-08,1.03368,0.000456991,-1.21436e-07,-5.9221e-08,1.03414,0.00045657,-2.99099e-07,6.21394e-08,1.0346,0.000456158,-1.1268e-07,-7.01275e-08,1.03505,0.000455723,-3.23063e-07,9.91614e-08,1.03551,0.000455374,-2.55788e-08,-8.80996e-08,1.03596,0.000455058,-2.89878e-07,1.48184e-08,1.03642,0.000454523,-2.45422e-07,2.88258e-08,1.03687,0.000454119,-1.58945e-07,-1.09125e-08,1.03733,0.000453768,-1.91682e-07,1.48241e-08,1.03778,0.000453429,-1.4721e-07,-4.83838e-08,1.03823,0.00045299,-2.92361e-07,5.95019e-08,1.03869,0.000452584,-1.13856e-07,-7.04146e-08,1.03914,0.000452145,-3.25099e-07,1.02947e-07,1.03959,0.000451803,-1.62583e-08,-1.02955e-07,1.04004,0.000451462,-3.25123e-07,7.04544e-08,1.04049,0.000451023,-1.1376e-07,-5.96534e-08,1.04094,0.000450616,-2.9272e-07,4.89499e-08,1.04139,0.000450178,-1.45871e-07,-1.69369e-08,1.04184,0.000449835,-1.96681e-07,1.87977e-08,1.04229,0.000449498,-1.40288e-07,-5.82539e-08,1.04274,0.000449043,-3.1505e-07,9.50087e-08,1.04319,0.000448698,-3.00238e-08,-8.33623e-08,1.04364,0.000448388,-2.80111e-07,2.20363e-11,1.04409,0.000447828,-2.80045e-07,8.32742e-08,1.04454,0.000447517,-3.02221e-08,-9.47002e-08,1.04498,0.000447173,-3.14323e-07,5.7108e-08,1.04543,0.000446716,-1.42999e-07,-1.45225e-08,1.04588,0.000446386,-1.86566e-07,9.82022e-10,1.04632,0.000446016,-1.8362e-07,1.05944e-08,1.04677,0.00044568,-1.51837e-07,-4.33597e-08,1.04721,0.000445247,-2.81916e-07,4.36352e-08,1.04766,0.000444814,-1.51011e-07,-1.19717e-08,1.0481,0.000444476,-1.86926e-07,4.25158e-09,1.04855,0.000444115,-1.74171e-07,-5.03461e-09,1.04899,0.000443751,-1.89275e-07,1.58868e-08,1.04944,0.00044342,-1.41614e-07,-5.85127e-08,1.04988,0.000442961,-3.17152e-07,9.89548e-08,1.05032,0.000442624,-2.0288e-08,-9.88878e-08,1.05076,0.000442287,-3.16951e-07,5.81779e-08,1.05121,0.000441827,-1.42418e-07,-1.46144e-08,1.05165,0.000441499,-1.86261e-07,2.79892e-10,1.05209,0.000441127,-1.85421e-07,1.34949e-08,1.05253,0.000440797,-1.44937e-07,-5.42594e-08,1.05297,0.000440344,-3.07715e-07,8.43335e-08,1.05341,0.000439982,-5.47146e-08,-4.46558e-08,1.05385,0.000439738,-1.88682e-07,-2.49193e-08,1.05429,0.000439286,-2.6344e-07,2.5124e-08,1.05473,0.000438835,-1.88068e-07,4.36328e-08,1.05517,0.000438589,-5.71699e-08,-8.04459e-08,1.05561,0.000438234,-2.98508e-07,3.97324e-08,1.05605,0.000437756,-1.79311e-07,4.07258e-08,1.05648,0.000437519,-5.71332e-08,-8.34263e-08,1.05692,0.000437155,-3.07412e-07,5.45608e-08,1.05736,0.000436704,-1.4373e-07,-1.56078e-08,1.05779,0.000436369,-1.90553e-07,7.87043e-09,1.05823,0.000436012,-1.66942e-07,-1.58739e-08,1.05867,0.00043563,-2.14563e-07,5.56251e-08,1.0591,0.000435368,-4.76881e-08,-8.74172e-08,1.05954,0.000435011,-3.0994e-07,5.56251e-08,1.05997,0.000434558,-1.43064e-07,-1.58739e-08,1.06041,0.000434224,-1.90686e-07,7.87042e-09,1.06084,0.000433866,-1.67075e-07,-1.56078e-08,1.06127,0.000433485,-2.13898e-07,5.45609e-08,1.06171,0.000433221,-5.02157e-08,-8.34263e-08,1.06214,0.00043287,-3.00495e-07,4.07258e-08,1.06257,0.000432391,-1.78317e-07,3.97325e-08,1.063,0.000432154,-5.91198e-08,-8.04464e-08,1.06344,0.000431794,-3.00459e-07,4.36347e-08,1.06387,0.000431324,-1.69555e-07,2.5117e-08,1.0643,0.000431061,-9.42041e-08,-2.48934e-08,1.06473,0.000430798,-1.68884e-07,-4.47527e-08,1.06516,0.000430326,-3.03142e-07,8.46951e-08,1.06559,0.000429973,-4.90573e-08,-5.56089e-08,1.06602,0.000429708,-2.15884e-07,1.85314e-08,1.06645,0.000429332,-1.6029e-07,-1.85166e-08,1.06688,0.000428956,-2.1584e-07,5.5535e-08,1.06731,0.000428691,-4.92347e-08,-8.44142e-08,1.06774,0.000428339,-3.02477e-07,4.37032e-08,1.06816,0.000427865,-1.71368e-07,2.88107e-08,1.06859,0.000427609,-8.49356e-08,-3.97367e-08,1.06902,0.00042732,-2.04146e-07,1.09267e-08,1.06945,0.000426945,-1.71365e-07,-3.97023e-09,1.06987,0.00042659,-1.83276e-07,4.9542e-09,1.0703,0.000426238,-1.68414e-07,-1.58466e-08,1.07073,0.000425854,-2.15953e-07,5.84321e-08,1.07115,0.000425597,-4.0657e-08,-9.86725e-08,1.07158,0.00042522,-3.36674e-07,9.78392e-08,1.072,0.00042484,-4.31568e-08,-5.42658e-08,1.07243,0.000424591,-2.05954e-07,1.45377e-11,1.07285,0.000424179,-2.0591e-07,5.42076e-08,1.07328,0.00042393,-4.32877e-08,-9.76357e-08,1.0737,0.00042355,-3.36195e-07,9.79165e-08,1.07412,0.000423172,-4.24451e-08,-5.56118e-08,1.07455,0.00042292,-2.09281e-07,5.32143e-09,1.07497,0.000422518,-1.93316e-07,3.43261e-08,1.07539,0.000422234,-9.0338e-08,-2.34165e-08,1.07581,0.000421983,-1.60588e-07,-5.98692e-08,1.07623,0.000421482,-3.40195e-07,1.43684e-07,1.07666,0.000421233,9.08574e-08,-1.5724e-07,1.07708,0.000420943,-3.80862e-07,1.27647e-07,1.0775,0.000420564,2.0791e-09,-1.1493e-07,1.07792,0.000420223,-3.4271e-07,9.36534e-08,1.07834,0.000419819,-6.17499e-08,-2.12653e-08,1.07876,0.000419632,-1.25546e-07,-8.59219e-09,1.07918,0.000419355,-1.51322e-07,-6.35752e-08,1.0796,0.000418861,-3.42048e-07,1.43684e-07,1.08002,0.000418608,8.90034e-08,-1.53532e-07,1.08043,0.000418326,-3.71593e-07,1.12817e-07,1.08085,0.000417921,-3.31414e-08,-5.93184e-08,1.08127,0.000417677,-2.11097e-07,5.24697e-09,1.08169,0.00041727,-1.95356e-07,3.83305e-08,1.0821,0.000416995,-8.03642e-08,-3.93597e-08,1.08252,0.000416716,-1.98443e-07,-1.0094e-10,1.08294,0.000416319,-1.98746e-07,3.97635e-08,1.08335,0.00041604,-7.94557e-08,-3.97437e-08,1.08377,0.000415762,-1.98687e-07,1.94215e-12,1.08419,0.000415365,-1.98681e-07,3.97359e-08,1.0846,0.000415087,-7.94732e-08,-3.97362e-08,1.08502,0.000414809,-1.98682e-07,-4.31063e-13,1.08543,0.000414411,-1.98683e-07,3.97379e-08,1.08584,0.000414133,-7.94694e-08,-3.97418e-08,1.08626,0.000413855,-1.98695e-07,2.00563e-11,1.08667,0.000413458,-1.98635e-07,3.96616e-08,1.08709,0.000413179,-7.965e-08,-3.9457e-08,1.0875,0.000412902,-1.98021e-07,-1.04281e-09,1.08791,0.000412502,-2.01149e-07,4.36282e-08,1.08832,0.000412231,-7.02648e-08,-5.42608e-08,1.08874,0.000411928,-2.33047e-07,5.42057e-08,1.08915,0.000411624,-7.04301e-08,-4.33527e-08,1.08956,0.000411353,-2.00488e-07,-4.07378e-12,1.08997,0.000410952,-2.005e-07,4.3369e-08,1.09038,0.000410681,-7.03934e-08,-5.42627e-08,1.09079,0.000410378,-2.33182e-07,5.44726e-08,1.0912,0.000410075,-6.97637e-08,-4.44186e-08,1.09161,0.000409802,-2.03019e-07,3.99235e-09,1.09202,0.000409408,-1.91042e-07,2.84491e-08,1.09243,0.000409111,-1.05695e-07,1.42043e-09,1.09284,0.000408904,-1.01434e-07,-3.41308e-08,1.09325,0.000408599,-2.03826e-07,1.58937e-08,1.09366,0.000408239,-1.56145e-07,-2.94438e-08,1.09406,0.000407838,-2.44476e-07,1.01881e-07,1.09447,0.000407655,6.11676e-08,-1.39663e-07,1.09488,0.000407358,-3.57822e-07,9.91432e-08,1.09529,0.00040694,-6.03921e-08,-1.84912e-08,1.09569,0.000406764,-1.15866e-07,-2.51785e-08,1.0961,0.000406457,-1.91401e-07,-4.03115e-12,1.09651,0.000406074,-1.91413e-07,2.51947e-08,1.09691,0.000405767,-1.15829e-07,1.84346e-08,1.09732,0.00040559,-6.05254e-08,-9.89332e-08,1.09772,0.000405172,-3.57325e-07,1.3888e-07,1.09813,0.000404874,5.93136e-08,-9.8957e-08,1.09853,0.000404696,-2.37557e-07,1.853e-08,1.09894,0.000404277,-1.81968e-07,2.48372e-08,1.09934,0.000403987,-1.07456e-07,1.33047e-09,1.09975,0.000403776,-1.03465e-07,-3.01591e-08,1.10015,0.000403479,-1.93942e-07,9.66054e-11,1.10055,0.000403091,-1.93652e-07,2.97727e-08,1.10096,0.000402793,-1.04334e-07,2.19273e-11,1.10136,0.000402585,-1.04268e-07,-2.98604e-08,1.10176,0.000402287,-1.93849e-07,2.10325e-10,1.10216,0.0004019,-1.93218e-07,2.90191e-08,1.10256,0.0004016,-1.06161e-07,2.92264e-09,1.10297,0.000401397,-9.73931e-08,-4.07096e-08,1.10337,0.00040108,-2.19522e-07,4.07067e-08,1.10377,0.000400763,-9.7402e-08,-2.90783e-09,1.10417,0.000400559,-1.06126e-07,-2.90754e-08,1.10457,0.00040026,-1.93352e-07,9.00021e-14,1.10497,0.000399873,-1.93351e-07,2.9075e-08,1.10537,0.000399574,-1.06126e-07,2.90902e-09,1.10577,0.00039937,-9.73992e-08,-4.07111e-08,1.10617,0.000399053,-2.19533e-07,4.07262e-08,1.10657,0.000398736,-9.73541e-08,-2.98424e-09,1.10697,0.000398533,-1.06307e-07,-2.87892e-08,1.10736,0.000398234,-1.92674e-07,-1.06824e-09,1.10776,0.000397845,-1.95879e-07,3.30622e-08,1.10816,0.000397552,-9.66926e-08,-1.19712e-08,1.10856,0.000397323,-1.32606e-07,1.48225e-08,1.10895,0.000397102,-8.81387e-08,-4.73187e-08,1.10935,0.000396784,-2.30095e-07,5.52429e-08,1.10975,0.00039649,-6.4366e-08,-5.44437e-08,1.11014,0.000396198,-2.27697e-07,4.33226e-08,1.11054,0.000395872,-9.77293e-08,3.62656e-10,1.11094,0.000395678,-9.66414e-08,-4.47732e-08,1.11133,0.00039535,-2.30961e-07,5.95208e-08,1.11173,0.000395067,-5.23985e-08,-7.41008e-08,1.11212,0.00039474,-2.74701e-07,1.17673e-07,1.11252,0.000394543,7.83181e-08,-1.58172e-07,1.11291,0.000394225,-3.96199e-07,1.57389e-07,1.1133,0.000393905,7.59679e-08,-1.13756e-07,1.1137,0.000393716,-2.653e-07,5.92165e-08,1.11409,0.000393363,-8.76507e-08,-3.90074e-09,1.11449,0.000393176,-9.93529e-08,-4.36136e-08,1.11488,0.000392846,-2.30194e-07,5.91457e-08,1.11527,0.000392563,-5.27564e-08,-7.376e-08,1.11566,0.000392237,-2.74037e-07,1.16685e-07,1.11606,0.000392039,7.60189e-08,-1.54562e-07,1.11645,0.000391727,-3.87667e-07,1.43935e-07,1.11684,0.000391384,4.4137e-08,-6.35487e-08,1.11723,0.000391281,-1.46509e-07,-8.94896e-09,1.11762,0.000390961,-1.73356e-07,-1.98647e-08,1.11801,0.000390555,-2.3295e-07,8.8408e-08,1.1184,0.000390354,3.22736e-08,-9.53486e-08,1.11879,0.000390133,-2.53772e-07,5.45677e-08,1.11918,0.000389789,-9.0069e-08,-3.71296e-09,1.11957,0.000389598,-1.01208e-07,-3.97159e-08,1.11996,0.000389276,-2.20355e-07,4.33671e-08,1.12035,0.000388966,-9.02542e-08,-1.45431e-08,1.12074,0.000388741,-1.33883e-07,1.48052e-08,1.12113,0.000388518,-8.94678e-08,-4.46778e-08,1.12152,0.000388205,-2.23501e-07,4.46966e-08,1.12191,0.000387892,-8.94114e-08,-1.48992e-08,1.12229,0.000387669,-1.34109e-07,1.49003e-08,1.12268,0.000387445,-8.94082e-08,-4.47019e-08,1.12307,0.000387132,-2.23514e-07,4.4698e-08,1.12345,0.000386819,-8.942e-08,-1.48806e-08,1.12384,0.000386596,-1.34062e-07,1.48245e-08,1.12423,0.000386372,-8.95885e-08,-4.44172e-08,1.12461,0.00038606,-2.2284e-07,4.36351e-08,1.125,0.000385745,-9.19348e-08,-1.09139e-08,1.12539,0.000385528,-1.24677e-07,2.05584e-11,1.12577,0.000385279,-1.24615e-07,1.08317e-08,1.12616,0.000385062,-9.21198e-08,-4.33473e-08,1.12654,0.000384748,-2.22162e-07,4.33481e-08,1.12693,0.000384434,-9.21174e-08,-1.08356e-08,1.12731,0.000384217,-1.24624e-07,-5.50907e-12,1.12769,0.000383968,-1.24641e-07,1.08577e-08,1.12808,0.000383751,-9.20679e-08,-4.34252e-08,1.12846,0.000383437,-2.22343e-07,4.36337e-08,1.12884,0.000383123,-9.14422e-08,-1.19005e-08,1.12923,0.000382904,-1.27144e-07,3.96813e-09,1.12961,0.000382662,-1.15239e-07,-3.97207e-09,1.12999,0.000382419,-1.27155e-07,1.19201e-08,1.13038,0.000382201,-9.1395e-08,-4.37085e-08,1.13076,0.000381887,-2.2252e-07,4.37046e-08,1.13114,0.000381573,-9.14068e-08,-1.19005e-08,1.13152,0.000381355,-1.27108e-07,3.89734e-09,1.1319,0.000381112,-1.15416e-07,-3.68887e-09,1.13228,0.00038087,-1.26483e-07,1.08582e-08,1.13266,0.00038065,-9.39083e-08,-3.97438e-08,1.13304,0.000380343,-2.1314e-07,2.89076e-08,1.13342,0.000380003,-1.26417e-07,4.33225e-08,1.1338,0.00037988,3.55072e-09,-8.29883e-08,1.13418,0.000379638,-2.45414e-07,5.0212e-08,1.13456,0.000379298,-9.47781e-08,1.34964e-09,1.13494,0.000379113,-9.07292e-08,-5.56105e-08,1.13532,0.000378764,-2.57561e-07,1.01883e-07,1.1357,0.000378555,4.80889e-08,-1.13504e-07,1.13608,0.000378311,-2.92423e-07,1.13713e-07,1.13646,0.000378067,4.87176e-08,-1.02931e-07,1.13683,0.000377856,-2.60076e-07,5.95923e-08,1.13721,0.000377514,-8.12988e-08,-1.62288e-08,1.13759,0.000377303,-1.29985e-07,5.32278e-09,1.13797,0.000377059,-1.14017e-07,-5.06237e-09,1.13834,0.000376816,-1.29204e-07,1.49267e-08,1.13872,0.000376602,-8.44237e-08,-5.46444e-08,1.1391,0.000376269,-2.48357e-07,8.44417e-08,1.13947,0.000376026,4.96815e-09,-4.47039e-08,1.13985,0.000375902,-1.29143e-07,-2.48355e-08,1.14023,0.000375569,-2.0365e-07,2.48368e-08,1.1406,0.000375236,-1.2914e-07,4.46977e-08,1.14098,0.000375112,4.95341e-09,-8.44184e-08,1.14135,0.000374869,-2.48302e-07,5.45572e-08,1.14173,0.000374536,-8.463e-08,-1.46013e-08,1.1421,0.000374323,-1.28434e-07,3.8478e-09,1.14247,0.000374077,-1.1689e-07,-7.89941e-10,1.14285,0.000373841,-1.1926e-07,-6.88042e-10,1.14322,0.0003736,-1.21324e-07,3.54213e-09,1.1436,0.000373368,-1.10698e-07,-1.34805e-08,1.14397,0.000373107,-1.51139e-07,5.03798e-08,1.14434,0.000372767,0.,0.}; - - template - __device__ __forceinline__ void RGB2LuvConvert_f(const T& src, D& dst) - { - const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3); - const float _un = 13 * (4 * 0.950456f * _d); - const float _vn = 13 * (9 * _d); - - float B = blueIdx == 0 ? src.x : src.z; - float G = src.y; - float R = blueIdx == 0 ? src.z : src.x; - - if (srgb) - { - B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); - G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); - R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); - } - - float X = R * 0.412453f + G * 0.357580f + B * 0.180423f; - float Y = R * 0.212671f + G * 0.715160f + B * 0.072169f; - float Z = R * 0.019334f + G * 0.119193f + B * 0.950227f; - - float L = splineInterpolate(Y * (LAB_CBRT_TAB_SIZE / 1.5f), c_LabCbrtTab, LAB_CBRT_TAB_SIZE); - L = 116.f * L - 16.f; - - const float d = (4 * 13) / ::fmaxf(X + 15 * Y + 3 * Z, numeric_limits::epsilon()); - float u = L * (X * d - _un); - float v = L * ((9 * 0.25f) * Y * d - _vn); - - dst.x = L; - dst.y = u; - dst.z = v; - } - - template - __device__ __forceinline__ void RGB2LuvConvert_b(const T& src, D& dst) - { - float3 srcf, dstf; - - srcf.x = src.x * (1.f / 255.f); - srcf.y = src.y * (1.f / 255.f); - srcf.z = src.z * (1.f / 255.f); - - RGB2LuvConvert_f(srcf, dstf); - - dst.x = saturate_cast(dstf.x * 2.55f); - dst.y = saturate_cast(dstf.y * 0.72033898305084743f + 96.525423728813564f); - dst.z = saturate_cast(dstf.z * 0.9732824427480916f + 136.259541984732824f); - } - - template struct RGB2Luv; - template - struct RGB2Luv - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - RGB2LuvConvert_b(src, dst); - - return dst; - } - __host__ __device__ __forceinline__ RGB2Luv() {} - __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {} - }; - template - struct RGB2Luv - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - RGB2LuvConvert_f(src, dst); - - return dst; - } - __host__ __device__ __forceinline__ RGB2Luv() {} - __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(name, scn, dcn, srgb, blueIdx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2Luv functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - template - __device__ __forceinline__ void Luv2RGBConvert_f(const T& src, D& dst) - { - const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3); - const float _un = 4 * 0.950456f * _d; - const float _vn = 9 * _d; - - float L = src.x; - float u = src.y; - float v = src.z; - - float Y = (L + 16.f) * (1.f / 116.f); - Y = Y * Y * Y; - - float d = (1.f / 13.f) / L; - u = u * d + _un; - v = v * d + _vn; - - float iv = 1.f / v; - float X = 2.25f * u * Y * iv; - float Z = (12 - 3 * u - 20 * v) * Y * 0.25f * iv; - - float B = 0.055648f * X - 0.204043f * Y + 1.057311f * Z; - float G = -0.969256f * X + 1.875991f * Y + 0.041556f * Z; - float R = 3.240479f * X - 1.537150f * Y - 0.498535f * Z; - - if (srgb) - { - B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); - G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); - R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); - } - - dst.x = blueIdx == 0 ? B : R; - dst.y = G; - dst.z = blueIdx == 0 ? R : B; - setAlpha(dst, ColorChannel::max()); - } - - template - __device__ __forceinline__ void Luv2RGBConvert_b(const T& src, D& dst) - { - float3 srcf, dstf; - - srcf.x = src.x * (100.f / 255.f); - srcf.y = src.y * 1.388235294117647f - 134.f; - srcf.z = src.z * 1.027450980392157f - 140.f; - - Luv2RGBConvert_f(srcf, dstf); - - dst.x = saturate_cast(dstf.x * 255.f); - dst.y = saturate_cast(dstf.y * 255.f); - dst.z = saturate_cast(dstf.z * 255.f); - setAlpha(dst, ColorChannel::max()); - } - - template struct Luv2RGB; - template - struct Luv2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - Luv2RGBConvert_b(src, dst); - - return dst; - } - __host__ __device__ __forceinline__ Luv2RGB() {} - __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {} - }; - template - struct Luv2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - Luv2RGBConvert_f(src, dst); - - return dst; - } - __host__ __device__ __forceinline__ Luv2RGB() {} - __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::Luv2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - #undef CV_DESCALE - -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // __OPENCV_CUDA_COLOR_DETAIL_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/detail/reduce.hpp b/IPL/include/opencv/opencv2/core/cuda/detail/reduce.hpp deleted file mode 100644 index 44400c8..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/detail/reduce.hpp +++ /dev/null @@ -1,365 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_REDUCE_DETAIL_HPP__ -#define __OPENCV_CUDA_REDUCE_DETAIL_HPP__ - -#include -#include "../warp.hpp" -#include "../warp_shuffle.hpp" - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - namespace reduce_detail - { - template struct GetType; - template struct GetType - { - typedef T type; - }; - template struct GetType - { - typedef T type; - }; - template struct GetType - { - typedef T type; - }; - - template - struct For - { - template - static __device__ void loadToSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid) - { - thrust::get(smem)[tid] = thrust::get(val); - - For::loadToSmem(smem, val, tid); - } - template - static __device__ void loadFromSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid) - { - thrust::get(val) = thrust::get(smem)[tid]; - - For::loadFromSmem(smem, val, tid); - } - - template - static __device__ void merge(const PointerTuple& smem, const ValTuple& val, unsigned int tid, unsigned int delta, const OpTuple& op) - { - typename GetType::type>::type reg = thrust::get(smem)[tid + delta]; - thrust::get(smem)[tid] = thrust::get(val) = thrust::get(op)(thrust::get(val), reg); - - For::merge(smem, val, tid, delta, op); - } - template - static __device__ void mergeShfl(const ValTuple& val, unsigned int delta, unsigned int width, const OpTuple& op) - { - typename GetType::type>::type reg = shfl_down(thrust::get(val), delta, width); - thrust::get(val) = thrust::get(op)(thrust::get(val), reg); - - For::mergeShfl(val, delta, width, op); - } - }; - template - struct For - { - template - static __device__ void loadToSmem(const PointerTuple&, const ValTuple&, unsigned int) - { - } - template - static __device__ void loadFromSmem(const PointerTuple&, const ValTuple&, unsigned int) - { - } - - template - static __device__ void merge(const PointerTuple&, const ValTuple&, unsigned int, unsigned int, const OpTuple&) - { - } - template - static __device__ void mergeShfl(const ValTuple&, unsigned int, unsigned int, const OpTuple&) - { - } - }; - - template - __device__ __forceinline__ void loadToSmem(volatile T* smem, T& val, unsigned int tid) - { - smem[tid] = val; - } - template - __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& val, unsigned int tid) - { - val = smem[tid]; - } - template - __device__ __forceinline__ void loadToSmem(const thrust::tuple& smem, - const thrust::tuple& val, - unsigned int tid) - { - For<0, thrust::tuple_size >::value>::loadToSmem(smem, val, tid); - } - template - __device__ __forceinline__ void loadFromSmem(const thrust::tuple& smem, - const thrust::tuple& val, - unsigned int tid) - { - For<0, thrust::tuple_size >::value>::loadFromSmem(smem, val, tid); - } - - template - __device__ __forceinline__ void merge(volatile T* smem, T& val, unsigned int tid, unsigned int delta, const Op& op) - { - T reg = smem[tid + delta]; - smem[tid] = val = op(val, reg); - } - template - __device__ __forceinline__ void mergeShfl(T& val, unsigned int delta, unsigned int width, const Op& op) - { - T reg = shfl_down(val, delta, width); - val = op(val, reg); - } - template - __device__ __forceinline__ void merge(const thrust::tuple& smem, - const thrust::tuple& val, - unsigned int tid, - unsigned int delta, - const thrust::tuple& op) - { - For<0, thrust::tuple_size >::value>::merge(smem, val, tid, delta, op); - } - template - __device__ __forceinline__ void mergeShfl(const thrust::tuple& val, - unsigned int delta, - unsigned int width, - const thrust::tuple& op) - { - For<0, thrust::tuple_size >::value>::mergeShfl(val, delta, width, op); - } - - template struct Generic - { - template - static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) - { - loadToSmem(smem, val, tid); - if (N >= 32) - __syncthreads(); - - if (N >= 2048) - { - if (tid < 1024) - merge(smem, val, tid, 1024, op); - - __syncthreads(); - } - if (N >= 1024) - { - if (tid < 512) - merge(smem, val, tid, 512, op); - - __syncthreads(); - } - if (N >= 512) - { - if (tid < 256) - merge(smem, val, tid, 256, op); - - __syncthreads(); - } - if (N >= 256) - { - if (tid < 128) - merge(smem, val, tid, 128, op); - - __syncthreads(); - } - if (N >= 128) - { - if (tid < 64) - merge(smem, val, tid, 64, op); - - __syncthreads(); - } - if (N >= 64) - { - if (tid < 32) - merge(smem, val, tid, 32, op); - } - - if (tid < 16) - { - merge(smem, val, tid, 16, op); - merge(smem, val, tid, 8, op); - merge(smem, val, tid, 4, op); - merge(smem, val, tid, 2, op); - merge(smem, val, tid, 1, op); - } - } - }; - - template - struct Unroll - { - static __device__ void loopShfl(Reference val, Op op, unsigned int N) - { - mergeShfl(val, I, N, op); - Unroll::loopShfl(val, op, N); - } - static __device__ void loop(Pointer smem, Reference val, unsigned int tid, Op op) - { - merge(smem, val, tid, I, op); - Unroll::loop(smem, val, tid, op); - } - }; - template - struct Unroll<0, Pointer, Reference, Op> - { - static __device__ void loopShfl(Reference, Op, unsigned int) - { - } - static __device__ void loop(Pointer, Reference, unsigned int, Op) - { - } - }; - - template struct WarpOptimized - { - template - static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - (void) smem; - (void) tid; - - Unroll::loopShfl(val, op, N); - #else - loadToSmem(smem, val, tid); - - if (tid < N / 2) - Unroll::loop(smem, val, tid, op); - #endif - } - }; - - template struct GenericOptimized32 - { - enum { M = N / 32 }; - - template - static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) - { - const unsigned int laneId = Warp::laneId(); - - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - Unroll<16, Pointer, Reference, Op>::loopShfl(val, op, warpSize); - - if (laneId == 0) - loadToSmem(smem, val, tid / 32); - #else - loadToSmem(smem, val, tid); - - if (laneId < 16) - Unroll<16, Pointer, Reference, Op>::loop(smem, val, tid, op); - - __syncthreads(); - - if (laneId == 0) - loadToSmem(smem, val, tid / 32); - #endif - - __syncthreads(); - - loadFromSmem(smem, val, tid); - - if (tid < 32) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - Unroll::loopShfl(val, op, M); - #else - Unroll::loop(smem, val, tid, op); - #endif - } - } - }; - - template struct StaticIf; - template struct StaticIf - { - typedef T1 type; - }; - template struct StaticIf - { - typedef T2 type; - }; - - template struct IsPowerOf2 - { - enum { value = ((N != 0) && !(N & (N - 1))) }; - }; - - template struct Dispatcher - { - typedef typename StaticIf< - (N <= 32) && IsPowerOf2::value, - WarpOptimized, - typename StaticIf< - (N <= 1024) && IsPowerOf2::value, - GenericOptimized32, - Generic - >::type - >::type reductor; - }; - } -}}} - -//! @endcond - -#endif // __OPENCV_CUDA_REDUCE_DETAIL_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/detail/reduce_key_val.hpp b/IPL/include/opencv/opencv2/core/cuda/detail/reduce_key_val.hpp deleted file mode 100644 index bab85d7..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/detail/reduce_key_val.hpp +++ /dev/null @@ -1,502 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP__ -#define __OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP__ - -#include -#include "../warp.hpp" -#include "../warp_shuffle.hpp" - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - namespace reduce_key_val_detail - { - template struct GetType; - template struct GetType - { - typedef T type; - }; - template struct GetType - { - typedef T type; - }; - template struct GetType - { - typedef T type; - }; - - template - struct For - { - template - static __device__ void loadToSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid) - { - thrust::get(smem)[tid] = thrust::get(data); - - For::loadToSmem(smem, data, tid); - } - template - static __device__ void loadFromSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid) - { - thrust::get(data) = thrust::get(smem)[tid]; - - For::loadFromSmem(smem, data, tid); - } - - template - static __device__ void copyShfl(const ReferenceTuple& val, unsigned int delta, int width) - { - thrust::get(val) = shfl_down(thrust::get(val), delta, width); - - For::copyShfl(val, delta, width); - } - template - static __device__ void copy(const PointerTuple& svals, const ReferenceTuple& val, unsigned int tid, unsigned int delta) - { - thrust::get(svals)[tid] = thrust::get(val) = thrust::get(svals)[tid + delta]; - - For::copy(svals, val, tid, delta); - } - - template - static __device__ void mergeShfl(const KeyReferenceTuple& key, const ValReferenceTuple& val, const CmpTuple& cmp, unsigned int delta, int width) - { - typename GetType::type>::type reg = shfl_down(thrust::get(key), delta, width); - - if (thrust::get(cmp)(reg, thrust::get(key))) - { - thrust::get(key) = reg; - thrust::get(val) = shfl_down(thrust::get(val), delta, width); - } - - For::mergeShfl(key, val, cmp, delta, width); - } - template - static __device__ void merge(const KeyPointerTuple& skeys, const KeyReferenceTuple& key, - const ValPointerTuple& svals, const ValReferenceTuple& val, - const CmpTuple& cmp, - unsigned int tid, unsigned int delta) - { - typename GetType::type>::type reg = thrust::get(skeys)[tid + delta]; - - if (thrust::get(cmp)(reg, thrust::get(key))) - { - thrust::get(skeys)[tid] = thrust::get(key) = reg; - thrust::get(svals)[tid] = thrust::get(val) = thrust::get(svals)[tid + delta]; - } - - For::merge(skeys, key, svals, val, cmp, tid, delta); - } - }; - template - struct For - { - template - static __device__ void loadToSmem(const PointerTuple&, const ReferenceTuple&, unsigned int) - { - } - template - static __device__ void loadFromSmem(const PointerTuple&, const ReferenceTuple&, unsigned int) - { - } - - template - static __device__ void copyShfl(const ReferenceTuple&, unsigned int, int) - { - } - template - static __device__ void copy(const PointerTuple&, const ReferenceTuple&, unsigned int, unsigned int) - { - } - - template - static __device__ void mergeShfl(const KeyReferenceTuple&, const ValReferenceTuple&, const CmpTuple&, unsigned int, int) - { - } - template - static __device__ void merge(const KeyPointerTuple&, const KeyReferenceTuple&, - const ValPointerTuple&, const ValReferenceTuple&, - const CmpTuple&, - unsigned int, unsigned int) - { - } - }; - - ////////////////////////////////////////////////////// - // loadToSmem - - template - __device__ __forceinline__ void loadToSmem(volatile T* smem, T& data, unsigned int tid) - { - smem[tid] = data; - } - template - __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& data, unsigned int tid) - { - data = smem[tid]; - } - template - __device__ __forceinline__ void loadToSmem(const thrust::tuple& smem, - const thrust::tuple& data, - unsigned int tid) - { - For<0, thrust::tuple_size >::value>::loadToSmem(smem, data, tid); - } - template - __device__ __forceinline__ void loadFromSmem(const thrust::tuple& smem, - const thrust::tuple& data, - unsigned int tid) - { - For<0, thrust::tuple_size >::value>::loadFromSmem(smem, data, tid); - } - - ////////////////////////////////////////////////////// - // copyVals - - template - __device__ __forceinline__ void copyValsShfl(V& val, unsigned int delta, int width) - { - val = shfl_down(val, delta, width); - } - template - __device__ __forceinline__ void copyVals(volatile V* svals, V& val, unsigned int tid, unsigned int delta) - { - svals[tid] = val = svals[tid + delta]; - } - template - __device__ __forceinline__ void copyValsShfl(const thrust::tuple& val, - unsigned int delta, - int width) - { - For<0, thrust::tuple_size >::value>::copyShfl(val, delta, width); - } - template - __device__ __forceinline__ void copyVals(const thrust::tuple& svals, - const thrust::tuple& val, - unsigned int tid, unsigned int delta) - { - For<0, thrust::tuple_size >::value>::copy(svals, val, tid, delta); - } - - ////////////////////////////////////////////////////// - // merge - - template - __device__ __forceinline__ void mergeShfl(K& key, V& val, const Cmp& cmp, unsigned int delta, int width) - { - K reg = shfl_down(key, delta, width); - - if (cmp(reg, key)) - { - key = reg; - copyValsShfl(val, delta, width); - } - } - template - __device__ __forceinline__ void merge(volatile K* skeys, K& key, volatile V* svals, V& val, const Cmp& cmp, unsigned int tid, unsigned int delta) - { - K reg = skeys[tid + delta]; - - if (cmp(reg, key)) - { - skeys[tid] = key = reg; - copyVals(svals, val, tid, delta); - } - } - template - __device__ __forceinline__ void mergeShfl(K& key, - const thrust::tuple& val, - const Cmp& cmp, - unsigned int delta, int width) - { - K reg = shfl_down(key, delta, width); - - if (cmp(reg, key)) - { - key = reg; - copyValsShfl(val, delta, width); - } - } - template - __device__ __forceinline__ void merge(volatile K* skeys, K& key, - const thrust::tuple& svals, - const thrust::tuple& val, - const Cmp& cmp, unsigned int tid, unsigned int delta) - { - K reg = skeys[tid + delta]; - - if (cmp(reg, key)) - { - skeys[tid] = key = reg; - copyVals(svals, val, tid, delta); - } - } - template - __device__ __forceinline__ void mergeShfl(const thrust::tuple& key, - const thrust::tuple& val, - const thrust::tuple& cmp, - unsigned int delta, int width) - { - For<0, thrust::tuple_size >::value>::mergeShfl(key, val, cmp, delta, width); - } - template - __device__ __forceinline__ void merge(const thrust::tuple& skeys, - const thrust::tuple& key, - const thrust::tuple& svals, - const thrust::tuple& val, - const thrust::tuple& cmp, - unsigned int tid, unsigned int delta) - { - For<0, thrust::tuple_size >::value>::merge(skeys, key, svals, val, cmp, tid, delta); - } - - ////////////////////////////////////////////////////// - // Generic - - template struct Generic - { - template - static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) - { - loadToSmem(skeys, key, tid); - loadValsToSmem(svals, val, tid); - if (N >= 32) - __syncthreads(); - - if (N >= 2048) - { - if (tid < 1024) - merge(skeys, key, svals, val, cmp, tid, 1024); - - __syncthreads(); - } - if (N >= 1024) - { - if (tid < 512) - merge(skeys, key, svals, val, cmp, tid, 512); - - __syncthreads(); - } - if (N >= 512) - { - if (tid < 256) - merge(skeys, key, svals, val, cmp, tid, 256); - - __syncthreads(); - } - if (N >= 256) - { - if (tid < 128) - merge(skeys, key, svals, val, cmp, tid, 128); - - __syncthreads(); - } - if (N >= 128) - { - if (tid < 64) - merge(skeys, key, svals, val, cmp, tid, 64); - - __syncthreads(); - } - if (N >= 64) - { - if (tid < 32) - merge(skeys, key, svals, val, cmp, tid, 32); - } - - if (tid < 16) - { - merge(skeys, key, svals, val, cmp, tid, 16); - merge(skeys, key, svals, val, cmp, tid, 8); - merge(skeys, key, svals, val, cmp, tid, 4); - merge(skeys, key, svals, val, cmp, tid, 2); - merge(skeys, key, svals, val, cmp, tid, 1); - } - } - }; - - template - struct Unroll - { - static __device__ void loopShfl(KR key, VR val, Cmp cmp, unsigned int N) - { - mergeShfl(key, val, cmp, I, N); - Unroll::loopShfl(key, val, cmp, N); - } - static __device__ void loop(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) - { - merge(skeys, key, svals, val, cmp, tid, I); - Unroll::loop(skeys, key, svals, val, tid, cmp); - } - }; - template - struct Unroll<0, KP, KR, VP, VR, Cmp> - { - static __device__ void loopShfl(KR, VR, Cmp, unsigned int) - { - } - static __device__ void loop(KP, KR, VP, VR, unsigned int, Cmp) - { - } - }; - - template struct WarpOptimized - { - template - static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) - { - #if 0 // __CUDA_ARCH__ >= 300 - (void) skeys; - (void) svals; - (void) tid; - - Unroll::loopShfl(key, val, cmp, N); - #else - loadToSmem(skeys, key, tid); - loadToSmem(svals, val, tid); - - if (tid < N / 2) - Unroll::loop(skeys, key, svals, val, tid, cmp); - #endif - } - }; - - template struct GenericOptimized32 - { - enum { M = N / 32 }; - - template - static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) - { - const unsigned int laneId = Warp::laneId(); - - #if 0 // __CUDA_ARCH__ >= 300 - Unroll<16, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, warpSize); - - if (laneId == 0) - { - loadToSmem(skeys, key, tid / 32); - loadToSmem(svals, val, tid / 32); - } - #else - loadToSmem(skeys, key, tid); - loadToSmem(svals, val, tid); - - if (laneId < 16) - Unroll<16, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp); - - __syncthreads(); - - if (laneId == 0) - { - loadToSmem(skeys, key, tid / 32); - loadToSmem(svals, val, tid / 32); - } - #endif - - __syncthreads(); - - loadFromSmem(skeys, key, tid); - - if (tid < 32) - { - #if 0 // __CUDA_ARCH__ >= 300 - loadFromSmem(svals, val, tid); - - Unroll::loopShfl(key, val, cmp, M); - #else - Unroll::loop(skeys, key, svals, val, tid, cmp); - #endif - } - } - }; - - template struct StaticIf; - template struct StaticIf - { - typedef T1 type; - }; - template struct StaticIf - { - typedef T2 type; - }; - - template struct IsPowerOf2 - { - enum { value = ((N != 0) && !(N & (N - 1))) }; - }; - - template struct Dispatcher - { - typedef typename StaticIf< - (N <= 32) && IsPowerOf2::value, - WarpOptimized, - typename StaticIf< - (N <= 1024) && IsPowerOf2::value, - GenericOptimized32, - Generic - >::type - >::type reductor; - }; - } -}}} - -//! @endcond - -#endif // __OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/detail/transform_detail.hpp b/IPL/include/opencv/opencv2/core/cuda/detail/transform_detail.hpp deleted file mode 100644 index 96031c8..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/detail/transform_detail.hpp +++ /dev/null @@ -1,399 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_TRANSFORM_DETAIL_HPP__ -#define __OPENCV_CUDA_TRANSFORM_DETAIL_HPP__ - -#include "../common.hpp" -#include "../vec_traits.hpp" -#include "../functional.hpp" - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - namespace transform_detail - { - //! Read Write Traits - - template struct UnaryReadWriteTraits - { - typedef typename TypeVec::vec_type read_type; - typedef typename TypeVec::vec_type write_type; - }; - - template struct BinaryReadWriteTraits - { - typedef typename TypeVec::vec_type read_type1; - typedef typename TypeVec::vec_type read_type2; - typedef typename TypeVec::vec_type write_type; - }; - - //! Transform kernels - - template struct OpUnroller; - template <> struct OpUnroller<1> - { - template - static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.x = op(src.x); - } - - template - static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.x = op(src1.x, src2.x); - } - }; - template <> struct OpUnroller<2> - { - template - static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.x = op(src.x); - if (mask(y, x_shifted + 1)) - dst.y = op(src.y); - } - - template - static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.x = op(src1.x, src2.x); - if (mask(y, x_shifted + 1)) - dst.y = op(src1.y, src2.y); - } - }; - template <> struct OpUnroller<3> - { - template - static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.x = op(src.x); - if (mask(y, x_shifted + 1)) - dst.y = op(src.y); - if (mask(y, x_shifted + 2)) - dst.z = op(src.z); - } - - template - static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.x = op(src1.x, src2.x); - if (mask(y, x_shifted + 1)) - dst.y = op(src1.y, src2.y); - if (mask(y, x_shifted + 2)) - dst.z = op(src1.z, src2.z); - } - }; - template <> struct OpUnroller<4> - { - template - static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.x = op(src.x); - if (mask(y, x_shifted + 1)) - dst.y = op(src.y); - if (mask(y, x_shifted + 2)) - dst.z = op(src.z); - if (mask(y, x_shifted + 3)) - dst.w = op(src.w); - } - - template - static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.x = op(src1.x, src2.x); - if (mask(y, x_shifted + 1)) - dst.y = op(src1.y, src2.y); - if (mask(y, x_shifted + 2)) - dst.z = op(src1.z, src2.z); - if (mask(y, x_shifted + 3)) - dst.w = op(src1.w, src2.w); - } - }; - template <> struct OpUnroller<8> - { - template - static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.a0 = op(src.a0); - if (mask(y, x_shifted + 1)) - dst.a1 = op(src.a1); - if (mask(y, x_shifted + 2)) - dst.a2 = op(src.a2); - if (mask(y, x_shifted + 3)) - dst.a3 = op(src.a3); - if (mask(y, x_shifted + 4)) - dst.a4 = op(src.a4); - if (mask(y, x_shifted + 5)) - dst.a5 = op(src.a5); - if (mask(y, x_shifted + 6)) - dst.a6 = op(src.a6); - if (mask(y, x_shifted + 7)) - dst.a7 = op(src.a7); - } - - template - static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.a0 = op(src1.a0, src2.a0); - if (mask(y, x_shifted + 1)) - dst.a1 = op(src1.a1, src2.a1); - if (mask(y, x_shifted + 2)) - dst.a2 = op(src1.a2, src2.a2); - if (mask(y, x_shifted + 3)) - dst.a3 = op(src1.a3, src2.a3); - if (mask(y, x_shifted + 4)) - dst.a4 = op(src1.a4, src2.a4); - if (mask(y, x_shifted + 5)) - dst.a5 = op(src1.a5, src2.a5); - if (mask(y, x_shifted + 6)) - dst.a6 = op(src1.a6, src2.a6); - if (mask(y, x_shifted + 7)) - dst.a7 = op(src1.a7, src2.a7); - } - }; - - template - static __global__ void transformSmart(const PtrStepSz src_, PtrStep dst_, const Mask mask, const UnOp op) - { - typedef TransformFunctorTraits ft; - typedef typename UnaryReadWriteTraits::read_type read_type; - typedef typename UnaryReadWriteTraits::write_type write_type; - - const int x = threadIdx.x + blockIdx.x * blockDim.x; - const int y = threadIdx.y + blockIdx.y * blockDim.y; - const int x_shifted = x * ft::smart_shift; - - if (y < src_.rows) - { - const T* src = src_.ptr(y); - D* dst = dst_.ptr(y); - - if (x_shifted + ft::smart_shift - 1 < src_.cols) - { - const read_type src_n_el = ((const read_type*)src)[x]; - write_type dst_n_el = ((const write_type*)dst)[x]; - - OpUnroller::unroll(src_n_el, dst_n_el, mask, op, x_shifted, y); - - ((write_type*)dst)[x] = dst_n_el; - } - else - { - for (int real_x = x_shifted; real_x < src_.cols; ++real_x) - { - if (mask(y, real_x)) - dst[real_x] = op(src[real_x]); - } - } - } - } - - template - __global__ static void transformSimple(const PtrStepSz src, PtrStep dst, const Mask mask, const UnOp op) - { - const int x = blockDim.x * blockIdx.x + threadIdx.x; - const int y = blockDim.y * blockIdx.y + threadIdx.y; - - if (x < src.cols && y < src.rows && mask(y, x)) - { - dst.ptr(y)[x] = op(src.ptr(y)[x]); - } - } - - template - static __global__ void transformSmart(const PtrStepSz src1_, const PtrStep src2_, PtrStep dst_, - const Mask mask, const BinOp op) - { - typedef TransformFunctorTraits ft; - typedef typename BinaryReadWriteTraits::read_type1 read_type1; - typedef typename BinaryReadWriteTraits::read_type2 read_type2; - typedef typename BinaryReadWriteTraits::write_type write_type; - - const int x = threadIdx.x + blockIdx.x * blockDim.x; - const int y = threadIdx.y + blockIdx.y * blockDim.y; - const int x_shifted = x * ft::smart_shift; - - if (y < src1_.rows) - { - const T1* src1 = src1_.ptr(y); - const T2* src2 = src2_.ptr(y); - D* dst = dst_.ptr(y); - - if (x_shifted + ft::smart_shift - 1 < src1_.cols) - { - const read_type1 src1_n_el = ((const read_type1*)src1)[x]; - const read_type2 src2_n_el = ((const read_type2*)src2)[x]; - write_type dst_n_el = ((const write_type*)dst)[x]; - - OpUnroller::unroll(src1_n_el, src2_n_el, dst_n_el, mask, op, x_shifted, y); - - ((write_type*)dst)[x] = dst_n_el; - } - else - { - for (int real_x = x_shifted; real_x < src1_.cols; ++real_x) - { - if (mask(y, real_x)) - dst[real_x] = op(src1[real_x], src2[real_x]); - } - } - } - } - - template - static __global__ void transformSimple(const PtrStepSz src1, const PtrStep src2, PtrStep dst, - const Mask mask, const BinOp op) - { - const int x = blockDim.x * blockIdx.x + threadIdx.x; - const int y = blockDim.y * blockIdx.y + threadIdx.y; - - if (x < src1.cols && y < src1.rows && mask(y, x)) - { - const T1 src1_data = src1.ptr(y)[x]; - const T2 src2_data = src2.ptr(y)[x]; - dst.ptr(y)[x] = op(src1_data, src2_data); - } - } - - template struct TransformDispatcher; - template<> struct TransformDispatcher - { - template - static void call(PtrStepSz src, PtrStepSz dst, UnOp op, Mask mask, cudaStream_t stream) - { - typedef TransformFunctorTraits ft; - - const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1); - const dim3 grid(divUp(src.cols, threads.x), divUp(src.rows, threads.y), 1); - - transformSimple<<>>(src, dst, mask, op); - cudaSafeCall( cudaGetLastError() ); - - if (stream == 0) - cudaSafeCall( cudaDeviceSynchronize() ); - } - - template - static void call(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, Mask mask, cudaStream_t stream) - { - typedef TransformFunctorTraits ft; - - const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1); - const dim3 grid(divUp(src1.cols, threads.x), divUp(src1.rows, threads.y), 1); - - transformSimple<<>>(src1, src2, dst, mask, op); - cudaSafeCall( cudaGetLastError() ); - - if (stream == 0) - cudaSafeCall( cudaDeviceSynchronize() ); - } - }; - template<> struct TransformDispatcher - { - template - static void call(PtrStepSz src, PtrStepSz dst, UnOp op, Mask mask, cudaStream_t stream) - { - typedef TransformFunctorTraits ft; - - CV_StaticAssert(ft::smart_shift != 1, ""); - - if (!isAligned(src.data, ft::smart_shift * sizeof(T)) || !isAligned(src.step, ft::smart_shift * sizeof(T)) || - !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D))) - { - TransformDispatcher::call(src, dst, op, mask, stream); - return; - } - - const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1); - const dim3 grid(divUp(src.cols, threads.x * ft::smart_shift), divUp(src.rows, threads.y), 1); - - transformSmart<<>>(src, dst, mask, op); - cudaSafeCall( cudaGetLastError() ); - - if (stream == 0) - cudaSafeCall( cudaDeviceSynchronize() ); - } - - template - static void call(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, Mask mask, cudaStream_t stream) - { - typedef TransformFunctorTraits ft; - - CV_StaticAssert(ft::smart_shift != 1, ""); - - if (!isAligned(src1.data, ft::smart_shift * sizeof(T1)) || !isAligned(src1.step, ft::smart_shift * sizeof(T1)) || - !isAligned(src2.data, ft::smart_shift * sizeof(T2)) || !isAligned(src2.step, ft::smart_shift * sizeof(T2)) || - !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D))) - { - TransformDispatcher::call(src1, src2, dst, op, mask, stream); - return; - } - - const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1); - const dim3 grid(divUp(src1.cols, threads.x * ft::smart_shift), divUp(src1.rows, threads.y), 1); - - transformSmart<<>>(src1, src2, dst, mask, op); - cudaSafeCall( cudaGetLastError() ); - - if (stream == 0) - cudaSafeCall( cudaDeviceSynchronize() ); - } - }; - } // namespace transform_detail -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // __OPENCV_CUDA_TRANSFORM_DETAIL_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/detail/type_traits_detail.hpp b/IPL/include/opencv/opencv2/core/cuda/detail/type_traits_detail.hpp deleted file mode 100644 index 3463c78..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/detail/type_traits_detail.hpp +++ /dev/null @@ -1,191 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP__ -#define __OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP__ - -#include "../common.hpp" -#include "../vec_traits.hpp" - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - namespace type_traits_detail - { - template struct Select { typedef T1 type; }; - template struct Select { typedef T2 type; }; - - template struct IsSignedIntergral { enum {value = 0}; }; - template <> struct IsSignedIntergral { enum {value = 1}; }; - template <> struct IsSignedIntergral { enum {value = 1}; }; - template <> struct IsSignedIntergral { enum {value = 1}; }; - template <> struct IsSignedIntergral { enum {value = 1}; }; - template <> struct IsSignedIntergral { enum {value = 1}; }; - template <> struct IsSignedIntergral { enum {value = 1}; }; - - template struct IsUnsignedIntegral { enum {value = 0}; }; - template <> struct IsUnsignedIntegral { enum {value = 1}; }; - template <> struct IsUnsignedIntegral { enum {value = 1}; }; - template <> struct IsUnsignedIntegral { enum {value = 1}; }; - template <> struct IsUnsignedIntegral { enum {value = 1}; }; - template <> struct IsUnsignedIntegral { enum {value = 1}; }; - template <> struct IsUnsignedIntegral { enum {value = 1}; }; - - template struct IsIntegral { enum {value = IsSignedIntergral::value || IsUnsignedIntegral::value}; }; - template <> struct IsIntegral { enum {value = 1}; }; - template <> struct IsIntegral { enum {value = 1}; }; - - template struct IsFloat { enum {value = 0}; }; - template <> struct IsFloat { enum {value = 1}; }; - template <> struct IsFloat { enum {value = 1}; }; - - template struct IsVec { enum {value = 0}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - - template struct AddParameterType { typedef const U& type; }; - template struct AddParameterType { typedef U& type; }; - template <> struct AddParameterType { typedef void type; }; - - template struct ReferenceTraits - { - enum { value = false }; - typedef U type; - }; - template struct ReferenceTraits - { - enum { value = true }; - typedef U type; - }; - - template struct PointerTraits - { - enum { value = false }; - typedef void type; - }; - template struct PointerTraits - { - enum { value = true }; - typedef U type; - }; - template struct PointerTraits - { - enum { value = true }; - typedef U type; - }; - - template struct UnConst - { - typedef U type; - enum { value = 0 }; - }; - template struct UnConst - { - typedef U type; - enum { value = 1 }; - }; - template struct UnConst - { - typedef U& type; - enum { value = 1 }; - }; - - template struct UnVolatile - { - typedef U type; - enum { value = 0 }; - }; - template struct UnVolatile - { - typedef U type; - enum { value = 1 }; - }; - template struct UnVolatile - { - typedef U& type; - enum { value = 1 }; - }; - } // namespace type_traits_detail -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // __OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/detail/vec_distance_detail.hpp b/IPL/include/opencv/opencv2/core/cuda/detail/vec_distance_detail.hpp deleted file mode 100644 index 9ca85a5..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/detail/vec_distance_detail.hpp +++ /dev/null @@ -1,121 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP__ -#define __OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP__ - -#include "../datamov_utils.hpp" - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - namespace vec_distance_detail - { - template struct UnrollVecDiffCached - { - template - static __device__ void calcCheck(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int ind) - { - if (ind < len) - { - T1 val1 = *vecCached++; - - T2 val2; - ForceGlob::Load(vecGlob, ind, val2); - - dist.reduceIter(val1, val2); - - UnrollVecDiffCached::calcCheck(vecCached, vecGlob, len, dist, ind + THREAD_DIM); - } - } - - template - static __device__ void calcWithoutCheck(const T1* vecCached, const T2* vecGlob, Dist& dist) - { - T1 val1 = *vecCached++; - - T2 val2; - ForceGlob::Load(vecGlob, 0, val2); - vecGlob += THREAD_DIM; - - dist.reduceIter(val1, val2); - - UnrollVecDiffCached::calcWithoutCheck(vecCached, vecGlob, dist); - } - }; - template struct UnrollVecDiffCached - { - template - static __device__ __forceinline__ void calcCheck(const T1*, const T2*, int, Dist&, int) - { - } - - template - static __device__ __forceinline__ void calcWithoutCheck(const T1*, const T2*, Dist&) - { - } - }; - - template struct VecDiffCachedCalculator; - template struct VecDiffCachedCalculator - { - template - static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid) - { - UnrollVecDiffCached::calcCheck(vecCached, vecGlob, len, dist, tid); - } - }; - template struct VecDiffCachedCalculator - { - template - static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid) - { - UnrollVecDiffCached::calcWithoutCheck(vecCached, vecGlob + tid, dist); - } - }; - } // namespace vec_distance_detail -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // __OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/dynamic_smem.hpp b/IPL/include/opencv/opencv2/core/cuda/dynamic_smem.hpp deleted file mode 100644 index 3488463..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/dynamic_smem.hpp +++ /dev/null @@ -1,88 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_DYNAMIC_SMEM_HPP__ -#define __OPENCV_CUDA_DYNAMIC_SMEM_HPP__ - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template struct DynamicSharedMem - { - __device__ __forceinline__ operator T*() - { - extern __shared__ int __smem[]; - return (T*)__smem; - } - - __device__ __forceinline__ operator const T*() const - { - extern __shared__ int __smem[]; - return (T*)__smem; - } - }; - - // specialize for double to avoid unaligned memory access compile errors - template<> struct DynamicSharedMem - { - __device__ __forceinline__ operator double*() - { - extern __shared__ double __smem_d[]; - return (double*)__smem_d; - } - - __device__ __forceinline__ operator const double*() const - { - extern __shared__ double __smem_d[]; - return (double*)__smem_d; - } - }; -}}} - -//! @endcond - -#endif // __OPENCV_CUDA_DYNAMIC_SMEM_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/emulation.hpp b/IPL/include/opencv/opencv2/core/cuda/emulation.hpp deleted file mode 100644 index d346865..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/emulation.hpp +++ /dev/null @@ -1,269 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_EMULATION_HPP_ -#define OPENCV_CUDA_EMULATION_HPP_ - -#include "common.hpp" -#include "warp_reduce.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - struct Emulation - { - - static __device__ __forceinline__ int syncthreadsOr(int pred) - { -#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 200) - // just campilation stab - return 0; -#else - return __syncthreads_or(pred); -#endif - } - - template - static __forceinline__ __device__ int Ballot(int predicate) - { -#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 200) - return __ballot(predicate); -#else - __shared__ volatile int cta_buffer[CTA_SIZE]; - - int tid = threadIdx.x; - cta_buffer[tid] = predicate ? (1 << (tid & 31)) : 0; - return warp_reduce(cta_buffer); -#endif - } - - struct smem - { - enum { TAG_MASK = (1U << ( (sizeof(unsigned int) << 3) - 5U)) - 1U }; - - template - static __device__ __forceinline__ T atomicInc(T* address, T val) - { -#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) - T count; - unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U); - do - { - count = *address & TAG_MASK; - count = tag | (count + 1); - *address = count; - } while (*address != count); - - return (count & TAG_MASK) - 1; -#else - return ::atomicInc(address, val); -#endif - } - - template - static __device__ __forceinline__ T atomicAdd(T* address, T val) - { -#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) - T count; - unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U); - do - { - count = *address & TAG_MASK; - count = tag | (count + val); - *address = count; - } while (*address != count); - - return (count & TAG_MASK) - val; -#else - return ::atomicAdd(address, val); -#endif - } - - template - static __device__ __forceinline__ T atomicMin(T* address, T val) - { -#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) - T count = ::min(*address, val); - do - { - *address = count; - } while (*address > count); - - return count; -#else - return ::atomicMin(address, val); -#endif - } - }; // struct cmem - - struct glob - { - static __device__ __forceinline__ int atomicAdd(int* address, int val) - { - return ::atomicAdd(address, val); - } - static __device__ __forceinline__ unsigned int atomicAdd(unsigned int* address, unsigned int val) - { - return ::atomicAdd(address, val); - } - static __device__ __forceinline__ float atomicAdd(float* address, float val) - { - #if __CUDA_ARCH__ >= 200 - return ::atomicAdd(address, val); - #else - int* address_as_i = (int*) address; - int old = *address_as_i, assumed; - do { - assumed = old; - old = ::atomicCAS(address_as_i, assumed, - __float_as_int(val + __int_as_float(assumed))); - } while (assumed != old); - return __int_as_float(old); - #endif - } - static __device__ __forceinline__ double atomicAdd(double* address, double val) - { - #if __CUDA_ARCH__ >= 130 - unsigned long long int* address_as_ull = (unsigned long long int*) address; - unsigned long long int old = *address_as_ull, assumed; - do { - assumed = old; - old = ::atomicCAS(address_as_ull, assumed, - __double_as_longlong(val + __longlong_as_double(assumed))); - } while (assumed != old); - return __longlong_as_double(old); - #else - (void) address; - (void) val; - return 0.0; - #endif - } - - static __device__ __forceinline__ int atomicMin(int* address, int val) - { - return ::atomicMin(address, val); - } - static __device__ __forceinline__ float atomicMin(float* address, float val) - { - #if __CUDA_ARCH__ >= 120 - int* address_as_i = (int*) address; - int old = *address_as_i, assumed; - do { - assumed = old; - old = ::atomicCAS(address_as_i, assumed, - __float_as_int(::fminf(val, __int_as_float(assumed)))); - } while (assumed != old); - return __int_as_float(old); - #else - (void) address; - (void) val; - return 0.0f; - #endif - } - static __device__ __forceinline__ double atomicMin(double* address, double val) - { - #if __CUDA_ARCH__ >= 130 - unsigned long long int* address_as_ull = (unsigned long long int*) address; - unsigned long long int old = *address_as_ull, assumed; - do { - assumed = old; - old = ::atomicCAS(address_as_ull, assumed, - __double_as_longlong(::fmin(val, __longlong_as_double(assumed)))); - } while (assumed != old); - return __longlong_as_double(old); - #else - (void) address; - (void) val; - return 0.0; - #endif - } - - static __device__ __forceinline__ int atomicMax(int* address, int val) - { - return ::atomicMax(address, val); - } - static __device__ __forceinline__ float atomicMax(float* address, float val) - { - #if __CUDA_ARCH__ >= 120 - int* address_as_i = (int*) address; - int old = *address_as_i, assumed; - do { - assumed = old; - old = ::atomicCAS(address_as_i, assumed, - __float_as_int(::fmaxf(val, __int_as_float(assumed)))); - } while (assumed != old); - return __int_as_float(old); - #else - (void) address; - (void) val; - return 0.0f; - #endif - } - static __device__ __forceinline__ double atomicMax(double* address, double val) - { - #if __CUDA_ARCH__ >= 130 - unsigned long long int* address_as_ull = (unsigned long long int*) address; - unsigned long long int old = *address_as_ull, assumed; - do { - assumed = old; - old = ::atomicCAS(address_as_ull, assumed, - __double_as_longlong(::fmax(val, __longlong_as_double(assumed)))); - } while (assumed != old); - return __longlong_as_double(old); - #else - (void) address; - (void) val; - return 0.0; - #endif - } - }; - }; //struct Emulation -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif /* OPENCV_CUDA_EMULATION_HPP_ */ diff --git a/IPL/include/opencv/opencv2/core/cuda/filters.hpp b/IPL/include/opencv/opencv2/core/cuda/filters.hpp deleted file mode 100644 index 9adc00c..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/filters.hpp +++ /dev/null @@ -1,286 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_FILTERS_HPP__ -#define __OPENCV_CUDA_FILTERS_HPP__ - -#include "saturate_cast.hpp" -#include "vec_traits.hpp" -#include "vec_math.hpp" -#include "type_traits.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template struct PointFilter - { - typedef typename Ptr2D::elem_type elem_type; - typedef float index_type; - - explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) - : src(src_) - { - (void)fx; - (void)fy; - } - - __device__ __forceinline__ elem_type operator ()(float y, float x) const - { - return src(__float2int_rz(y), __float2int_rz(x)); - } - - Ptr2D src; - }; - - template struct LinearFilter - { - typedef typename Ptr2D::elem_type elem_type; - typedef float index_type; - - explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) - : src(src_) - { - (void)fx; - (void)fy; - } - __device__ __forceinline__ elem_type operator ()(float y, float x) const - { - typedef typename TypeVec::cn>::vec_type work_type; - - work_type out = VecTraits::all(0); - - const int x1 = __float2int_rd(x); - const int y1 = __float2int_rd(y); - const int x2 = x1 + 1; - const int y2 = y1 + 1; - - elem_type src_reg = src(y1, x1); - out = out + src_reg * ((x2 - x) * (y2 - y)); - - src_reg = src(y1, x2); - out = out + src_reg * ((x - x1) * (y2 - y)); - - src_reg = src(y2, x1); - out = out + src_reg * ((x2 - x) * (y - y1)); - - src_reg = src(y2, x2); - out = out + src_reg * ((x - x1) * (y - y1)); - - return saturate_cast(out); - } - - Ptr2D src; - }; - - template struct CubicFilter - { - typedef typename Ptr2D::elem_type elem_type; - typedef float index_type; - typedef typename TypeVec::cn>::vec_type work_type; - - explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) - : src(src_) - { - (void)fx; - (void)fy; - } - - static __device__ __forceinline__ float bicubicCoeff(float x_) - { - float x = fabsf(x_); - if (x <= 1.0f) - { - return x * x * (1.5f * x - 2.5f) + 1.0f; - } - else if (x < 2.0f) - { - return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f; - } - else - { - return 0.0f; - } - } - - __device__ elem_type operator ()(float y, float x) const - { - const float xmin = ::ceilf(x - 2.0f); - const float xmax = ::floorf(x + 2.0f); - - const float ymin = ::ceilf(y - 2.0f); - const float ymax = ::floorf(y + 2.0f); - - work_type sum = VecTraits::all(0); - float wsum = 0.0f; - - for (float cy = ymin; cy <= ymax; cy += 1.0f) - { - for (float cx = xmin; cx <= xmax; cx += 1.0f) - { - const float w = bicubicCoeff(x - cx) * bicubicCoeff(y - cy); - sum = sum + w * src(__float2int_rd(cy), __float2int_rd(cx)); - wsum += w; - } - } - - work_type res = (!wsum)? VecTraits::all(0) : sum / wsum; - - return saturate_cast(res); - } - - Ptr2D src; - }; - // for integer scaling - template struct IntegerAreaFilter - { - typedef typename Ptr2D::elem_type elem_type; - typedef float index_type; - - explicit __host__ __device__ __forceinline__ IntegerAreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_) - : src(src_), scale_x(scale_x_), scale_y(scale_y_), scale(1.f / (scale_x * scale_y)) {} - - __device__ __forceinline__ elem_type operator ()(float y, float x) const - { - float fsx1 = x * scale_x; - float fsx2 = fsx1 + scale_x; - - int sx1 = __float2int_ru(fsx1); - int sx2 = __float2int_rd(fsx2); - - float fsy1 = y * scale_y; - float fsy2 = fsy1 + scale_y; - - int sy1 = __float2int_ru(fsy1); - int sy2 = __float2int_rd(fsy2); - - typedef typename TypeVec::cn>::vec_type work_type; - work_type out = VecTraits::all(0.f); - - for(int dy = sy1; dy < sy2; ++dy) - for(int dx = sx1; dx < sx2; ++dx) - { - out = out + src(dy, dx) * scale; - } - - return saturate_cast(out); - } - - Ptr2D src; - float scale_x, scale_y ,scale; - }; - - template struct AreaFilter - { - typedef typename Ptr2D::elem_type elem_type; - typedef float index_type; - - explicit __host__ __device__ __forceinline__ AreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_) - : src(src_), scale_x(scale_x_), scale_y(scale_y_){} - - __device__ __forceinline__ elem_type operator ()(float y, float x) const - { - float fsx1 = x * scale_x; - float fsx2 = fsx1 + scale_x; - - int sx1 = __float2int_ru(fsx1); - int sx2 = __float2int_rd(fsx2); - - float fsy1 = y * scale_y; - float fsy2 = fsy1 + scale_y; - - int sy1 = __float2int_ru(fsy1); - int sy2 = __float2int_rd(fsy2); - - float scale = 1.f / (fminf(scale_x, src.width - fsx1) * fminf(scale_y, src.height - fsy1)); - - typedef typename TypeVec::cn>::vec_type work_type; - work_type out = VecTraits::all(0.f); - - for (int dy = sy1; dy < sy2; ++dy) - { - for (int dx = sx1; dx < sx2; ++dx) - out = out + src(dy, dx) * scale; - - if (sx1 > fsx1) - out = out + src(dy, (sx1 -1) ) * ((sx1 - fsx1) * scale); - - if (sx2 < fsx2) - out = out + src(dy, sx2) * ((fsx2 -sx2) * scale); - } - - if (sy1 > fsy1) - for (int dx = sx1; dx < sx2; ++dx) - out = out + src( (sy1 - 1) , dx) * ((sy1 -fsy1) * scale); - - if (sy2 < fsy2) - for (int dx = sx1; dx < sx2; ++dx) - out = out + src(sy2, dx) * ((fsy2 -sy2) * scale); - - if ((sy1 > fsy1) && (sx1 > fsx1)) - out = out + src( (sy1 - 1) , (sx1 - 1)) * ((sy1 -fsy1) * (sx1 -fsx1) * scale); - - if ((sy1 > fsy1) && (sx2 < fsx2)) - out = out + src( (sy1 - 1) , sx2) * ((sy1 -fsy1) * (fsx2 -sx2) * scale); - - if ((sy2 < fsy2) && (sx2 < fsx2)) - out = out + src(sy2, sx2) * ((fsy2 -sy2) * (fsx2 -sx2) * scale); - - if ((sy2 < fsy2) && (sx1 > fsx1)) - out = out + src(sy2, (sx1 - 1)) * ((fsy2 -sy2) * (sx1 -fsx1) * scale); - - return saturate_cast(out); - } - - Ptr2D src; - float scale_x, scale_y; - int width, haight; - }; -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // __OPENCV_CUDA_FILTERS_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/funcattrib.hpp b/IPL/include/opencv/opencv2/core/cuda/funcattrib.hpp deleted file mode 100644 index fbb236b..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/funcattrib.hpp +++ /dev/null @@ -1,79 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP_ -#define __OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP_ - -#include - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template - void printFuncAttrib(Func& func) - { - - cudaFuncAttributes attrs; - cudaFuncGetAttributes(&attrs, func); - - printf("=== Function stats ===\n"); - printf("Name: \n"); - printf("sharedSizeBytes = %d\n", attrs.sharedSizeBytes); - printf("constSizeBytes = %d\n", attrs.constSizeBytes); - printf("localSizeBytes = %d\n", attrs.localSizeBytes); - printf("maxThreadsPerBlock = %d\n", attrs.maxThreadsPerBlock); - printf("numRegs = %d\n", attrs.numRegs); - printf("ptxVersion = %d\n", attrs.ptxVersion); - printf("binaryVersion = %d\n", attrs.binaryVersion); - printf("\n"); - fflush(stdout); - } -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif /* __OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP_ */ diff --git a/IPL/include/opencv/opencv2/core/cuda/functional.hpp b/IPL/include/opencv/opencv2/core/cuda/functional.hpp deleted file mode 100644 index ed3943d..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/functional.hpp +++ /dev/null @@ -1,797 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_FUNCTIONAL_HPP__ -#define __OPENCV_CUDA_FUNCTIONAL_HPP__ - -#include -#include "saturate_cast.hpp" -#include "vec_traits.hpp" -#include "type_traits.hpp" -#include "device_functions.h" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - // Function Objects - template struct unary_function : public std::unary_function {}; - template struct binary_function : public std::binary_function {}; - - // Arithmetic Operations - template struct plus : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a + b; - } - __host__ __device__ __forceinline__ plus() {} - __host__ __device__ __forceinline__ plus(const plus&) {} - }; - - template struct minus : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a - b; - } - __host__ __device__ __forceinline__ minus() {} - __host__ __device__ __forceinline__ minus(const minus&) {} - }; - - template struct multiplies : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a * b; - } - __host__ __device__ __forceinline__ multiplies() {} - __host__ __device__ __forceinline__ multiplies(const multiplies&) {} - }; - - template struct divides : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a / b; - } - __host__ __device__ __forceinline__ divides() {} - __host__ __device__ __forceinline__ divides(const divides&) {} - }; - - template struct modulus : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a % b; - } - __host__ __device__ __forceinline__ modulus() {} - __host__ __device__ __forceinline__ modulus(const modulus&) {} - }; - - template struct negate : unary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a) const - { - return -a; - } - __host__ __device__ __forceinline__ negate() {} - __host__ __device__ __forceinline__ negate(const negate&) {} - }; - - // Comparison Operations - template struct equal_to : binary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a == b; - } - __host__ __device__ __forceinline__ equal_to() {} - __host__ __device__ __forceinline__ equal_to(const equal_to&) {} - }; - - template struct not_equal_to : binary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a != b; - } - __host__ __device__ __forceinline__ not_equal_to() {} - __host__ __device__ __forceinline__ not_equal_to(const not_equal_to&) {} - }; - - template struct greater : binary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a > b; - } - __host__ __device__ __forceinline__ greater() {} - __host__ __device__ __forceinline__ greater(const greater&) {} - }; - - template struct less : binary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a < b; - } - __host__ __device__ __forceinline__ less() {} - __host__ __device__ __forceinline__ less(const less&) {} - }; - - template struct greater_equal : binary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a >= b; - } - __host__ __device__ __forceinline__ greater_equal() {} - __host__ __device__ __forceinline__ greater_equal(const greater_equal&) {} - }; - - template struct less_equal : binary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a <= b; - } - __host__ __device__ __forceinline__ less_equal() {} - __host__ __device__ __forceinline__ less_equal(const less_equal&) {} - }; - - // Logical Operations - template struct logical_and : binary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a && b; - } - __host__ __device__ __forceinline__ logical_and() {} - __host__ __device__ __forceinline__ logical_and(const logical_and&) {} - }; - - template struct logical_or : binary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a || b; - } - __host__ __device__ __forceinline__ logical_or() {} - __host__ __device__ __forceinline__ logical_or(const logical_or&) {} - }; - - template struct logical_not : unary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a) const - { - return !a; - } - __host__ __device__ __forceinline__ logical_not() {} - __host__ __device__ __forceinline__ logical_not(const logical_not&) {} - }; - - // Bitwise Operations - template struct bit_and : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a & b; - } - __host__ __device__ __forceinline__ bit_and() {} - __host__ __device__ __forceinline__ bit_and(const bit_and&) {} - }; - - template struct bit_or : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a | b; - } - __host__ __device__ __forceinline__ bit_or() {} - __host__ __device__ __forceinline__ bit_or(const bit_or&) {} - }; - - template struct bit_xor : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a ^ b; - } - __host__ __device__ __forceinline__ bit_xor() {} - __host__ __device__ __forceinline__ bit_xor(const bit_xor&) {} - }; - - template struct bit_not : unary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType v) const - { - return ~v; - } - __host__ __device__ __forceinline__ bit_not() {} - __host__ __device__ __forceinline__ bit_not(const bit_not&) {} - }; - - // Generalized Identity Operations - template struct identity : unary_function - { - __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType x) const - { - return x; - } - __host__ __device__ __forceinline__ identity() {} - __host__ __device__ __forceinline__ identity(const identity&) {} - }; - - template struct project1st : binary_function - { - __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const - { - return lhs; - } - __host__ __device__ __forceinline__ project1st() {} - __host__ __device__ __forceinline__ project1st(const project1st&) {} - }; - - template struct project2nd : binary_function - { - __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const - { - return rhs; - } - __host__ __device__ __forceinline__ project2nd() {} - __host__ __device__ __forceinline__ project2nd(const project2nd&) {} - }; - - // Min/Max Operations - -#define OPENCV_CUDA_IMPLEMENT_MINMAX(name, type, op) \ - template <> struct name : binary_function \ - { \ - __device__ __forceinline__ type operator()(type lhs, type rhs) const {return op(lhs, rhs);} \ - __host__ __device__ __forceinline__ name() {}\ - __host__ __device__ __forceinline__ name(const name&) {}\ - }; - - template struct maximum : binary_function - { - __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const - { - return max(lhs, rhs); - } - __host__ __device__ __forceinline__ maximum() {} - __host__ __device__ __forceinline__ maximum(const maximum&) {} - }; - - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uchar, ::max) - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, schar, ::max) - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, char, ::max) - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, ushort, ::max) - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, short, ::max) - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, int, ::max) - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uint, ::max) - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, float, ::fmax) - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, double, ::fmax) - - template struct minimum : binary_function - { - __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const - { - return min(lhs, rhs); - } - __host__ __device__ __forceinline__ minimum() {} - __host__ __device__ __forceinline__ minimum(const minimum&) {} - }; - - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uchar, ::min) - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, schar, ::min) - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, char, ::min) - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, ushort, ::min) - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, short, ::min) - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, int, ::min) - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uint, ::min) - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, float, ::fmin) - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, double, ::fmin) - -#undef OPENCV_CUDA_IMPLEMENT_MINMAX - - // Math functions - - template struct abs_func : unary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType x) const - { - return abs(x); - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ unsigned char operator ()(unsigned char x) const - { - return x; - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ signed char operator ()(signed char x) const - { - return ::abs((int)x); - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ char operator ()(char x) const - { - return ::abs((int)x); - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ unsigned short operator ()(unsigned short x) const - { - return x; - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ short operator ()(short x) const - { - return ::abs((int)x); - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ unsigned int operator ()(unsigned int x) const - { - return x; - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ int operator ()(int x) const - { - return ::abs(x); - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ float operator ()(float x) const - { - return ::fabsf(x); - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ double operator ()(double x) const - { - return ::fabs(x); - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - -#define OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(name, func) \ - template struct name ## _func : unary_function \ - { \ - __device__ __forceinline__ float operator ()(typename TypeTraits::ParameterType v) const \ - { \ - return func ## f(v); \ - } \ - __host__ __device__ __forceinline__ name ## _func() {} \ - __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ - }; \ - template <> struct name ## _func : unary_function \ - { \ - __device__ __forceinline__ double operator ()(double v) const \ - { \ - return func(v); \ - } \ - __host__ __device__ __forceinline__ name ## _func() {} \ - __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ - }; - -#define OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(name, func) \ - template struct name ## _func : binary_function \ - { \ - __device__ __forceinline__ float operator ()(typename TypeTraits::ParameterType v1, typename TypeTraits::ParameterType v2) const \ - { \ - return func ## f(v1, v2); \ - } \ - __host__ __device__ __forceinline__ name ## _func() {} \ - __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ - }; \ - template <> struct name ## _func : binary_function \ - { \ - __device__ __forceinline__ double operator ()(double v1, double v2) const \ - { \ - return func(v1, v2); \ - } \ - __host__ __device__ __forceinline__ name ## _func() {} \ - __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ - }; - - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sqrt, ::sqrt) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp, ::exp) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp2, ::exp2) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp10, ::exp10) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log, ::log) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log2, ::log2) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log10, ::log10) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sin, ::sin) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cos, ::cos) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tan, ::tan) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asin, ::asin) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acos, ::acos) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atan, ::atan) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sinh, ::sinh) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cosh, ::cosh) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tanh, ::tanh) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asinh, ::asinh) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acosh, ::acosh) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atanh, ::atanh) - - OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(hypot, ::hypot) - OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(atan2, ::atan2) - OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(pow, ::pow) - - #undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR - #undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR_NO_DOUBLE - #undef OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR - - template struct hypot_sqr_func : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType src1, typename TypeTraits::ParameterType src2) const - { - return src1 * src1 + src2 * src2; - } - __host__ __device__ __forceinline__ hypot_sqr_func() {} - __host__ __device__ __forceinline__ hypot_sqr_func(const hypot_sqr_func&) {} - }; - - // Saturate Cast Functor - template struct saturate_cast_func : unary_function - { - __device__ __forceinline__ D operator ()(typename TypeTraits::ParameterType v) const - { - return saturate_cast(v); - } - __host__ __device__ __forceinline__ saturate_cast_func() {} - __host__ __device__ __forceinline__ saturate_cast_func(const saturate_cast_func&) {} - }; - - // Threshold Functors - template struct thresh_binary_func : unary_function - { - __host__ __device__ __forceinline__ thresh_binary_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {} - - __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const - { - return (src > thresh) * maxVal; - } - - __host__ __device__ __forceinline__ thresh_binary_func() {} - __host__ __device__ __forceinline__ thresh_binary_func(const thresh_binary_func& other) - : thresh(other.thresh), maxVal(other.maxVal) {} - - T thresh; - T maxVal; - }; - - template struct thresh_binary_inv_func : unary_function - { - __host__ __device__ __forceinline__ thresh_binary_inv_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {} - - __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const - { - return (src <= thresh) * maxVal; - } - - __host__ __device__ __forceinline__ thresh_binary_inv_func() {} - __host__ __device__ __forceinline__ thresh_binary_inv_func(const thresh_binary_inv_func& other) - : thresh(other.thresh), maxVal(other.maxVal) {} - - T thresh; - T maxVal; - }; - - template struct thresh_trunc_func : unary_function - { - explicit __host__ __device__ __forceinline__ thresh_trunc_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;} - - __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const - { - return minimum()(src, thresh); - } - - __host__ __device__ __forceinline__ thresh_trunc_func() {} - __host__ __device__ __forceinline__ thresh_trunc_func(const thresh_trunc_func& other) - : thresh(other.thresh) {} - - T thresh; - }; - - template struct thresh_to_zero_func : unary_function - { - explicit __host__ __device__ __forceinline__ thresh_to_zero_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;} - - __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const - { - return (src > thresh) * src; - } - - __host__ __device__ __forceinline__ thresh_to_zero_func() {} - __host__ __device__ __forceinline__ thresh_to_zero_func(const thresh_to_zero_func& other) - : thresh(other.thresh) {} - - T thresh; - }; - - template struct thresh_to_zero_inv_func : unary_function - { - explicit __host__ __device__ __forceinline__ thresh_to_zero_inv_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;} - - __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const - { - return (src <= thresh) * src; - } - - __host__ __device__ __forceinline__ thresh_to_zero_inv_func() {} - __host__ __device__ __forceinline__ thresh_to_zero_inv_func(const thresh_to_zero_inv_func& other) - : thresh(other.thresh) {} - - T thresh; - }; - - // Function Object Adaptors - template struct unary_negate : unary_function - { - explicit __host__ __device__ __forceinline__ unary_negate(const Predicate& p) : pred(p) {} - - __device__ __forceinline__ bool operator()(typename TypeTraits::ParameterType x) const - { - return !pred(x); - } - - __host__ __device__ __forceinline__ unary_negate() {} - __host__ __device__ __forceinline__ unary_negate(const unary_negate& other) : pred(other.pred) {} - - Predicate pred; - }; - - template __host__ __device__ __forceinline__ unary_negate not1(const Predicate& pred) - { - return unary_negate(pred); - } - - template struct binary_negate : binary_function - { - explicit __host__ __device__ __forceinline__ binary_negate(const Predicate& p) : pred(p) {} - - __device__ __forceinline__ bool operator()(typename TypeTraits::ParameterType x, - typename TypeTraits::ParameterType y) const - { - return !pred(x,y); - } - - __host__ __device__ __forceinline__ binary_negate() {} - __host__ __device__ __forceinline__ binary_negate(const binary_negate& other) : pred(other.pred) {} - - Predicate pred; - }; - - template __host__ __device__ __forceinline__ binary_negate not2(const BinaryPredicate& pred) - { - return binary_negate(pred); - } - - template struct binder1st : unary_function - { - __host__ __device__ __forceinline__ binder1st(const Op& op_, const typename Op::first_argument_type& arg1_) : op(op_), arg1(arg1_) {} - - __device__ __forceinline__ typename Op::result_type operator ()(typename TypeTraits::ParameterType a) const - { - return op(arg1, a); - } - - __host__ __device__ __forceinline__ binder1st() {} - __host__ __device__ __forceinline__ binder1st(const binder1st& other) : op(other.op), arg1(other.arg1) {} - - Op op; - typename Op::first_argument_type arg1; - }; - - template __host__ __device__ __forceinline__ binder1st bind1st(const Op& op, const T& x) - { - return binder1st(op, typename Op::first_argument_type(x)); - } - - template struct binder2nd : unary_function - { - __host__ __device__ __forceinline__ binder2nd(const Op& op_, const typename Op::second_argument_type& arg2_) : op(op_), arg2(arg2_) {} - - __forceinline__ __device__ typename Op::result_type operator ()(typename TypeTraits::ParameterType a) const - { - return op(a, arg2); - } - - __host__ __device__ __forceinline__ binder2nd() {} - __host__ __device__ __forceinline__ binder2nd(const binder2nd& other) : op(other.op), arg2(other.arg2) {} - - Op op; - typename Op::second_argument_type arg2; - }; - - template __host__ __device__ __forceinline__ binder2nd bind2nd(const Op& op, const T& x) - { - return binder2nd(op, typename Op::second_argument_type(x)); - } - - // Functor Traits - template struct IsUnaryFunction - { - typedef char Yes; - struct No {Yes a[2];}; - - template static Yes check(unary_function); - static No check(...); - - static F makeF(); - - enum { value = (sizeof(check(makeF())) == sizeof(Yes)) }; - }; - - template struct IsBinaryFunction - { - typedef char Yes; - struct No {Yes a[2];}; - - template static Yes check(binary_function); - static No check(...); - - static F makeF(); - - enum { value = (sizeof(check(makeF())) == sizeof(Yes)) }; - }; - - namespace functional_detail - { - template struct UnOpShift { enum { shift = 1 }; }; - template struct UnOpShift { enum { shift = 4 }; }; - template struct UnOpShift { enum { shift = 2 }; }; - - template struct DefaultUnaryShift - { - enum { shift = UnOpShift::shift }; - }; - - template struct BinOpShift { enum { shift = 1 }; }; - template struct BinOpShift { enum { shift = 4 }; }; - template struct BinOpShift { enum { shift = 2 }; }; - - template struct DefaultBinaryShift - { - enum { shift = BinOpShift::shift }; - }; - - template ::value> struct ShiftDispatcher; - template struct ShiftDispatcher - { - enum { shift = DefaultUnaryShift::shift }; - }; - template struct ShiftDispatcher - { - enum { shift = DefaultBinaryShift::shift }; - }; - } - - template struct DefaultTransformShift - { - enum { shift = functional_detail::ShiftDispatcher::shift }; - }; - - template struct DefaultTransformFunctorTraits - { - enum { simple_block_dim_x = 16 }; - enum { simple_block_dim_y = 16 }; - - enum { smart_block_dim_x = 16 }; - enum { smart_block_dim_y = 16 }; - enum { smart_shift = DefaultTransformShift::shift }; - }; - - template struct TransformFunctorTraits : DefaultTransformFunctorTraits {}; - -#define OPENCV_CUDA_TRANSFORM_FUNCTOR_TRAITS(type) \ - template <> struct TransformFunctorTraits< type > : DefaultTransformFunctorTraits< type > -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // __OPENCV_CUDA_FUNCTIONAL_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/limits.hpp b/IPL/include/opencv/opencv2/core/cuda/limits.hpp deleted file mode 100644 index b98bdf2..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/limits.hpp +++ /dev/null @@ -1,128 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_LIMITS_HPP__ -#define __OPENCV_CUDA_LIMITS_HPP__ - -#include -#include -#include "common.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ -template struct numeric_limits; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static bool min() { return false; } - __device__ __forceinline__ static bool max() { return true; } - static const bool is_signed = false; -}; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static signed char min() { return SCHAR_MIN; } - __device__ __forceinline__ static signed char max() { return SCHAR_MAX; } - static const bool is_signed = true; -}; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static unsigned char min() { return 0; } - __device__ __forceinline__ static unsigned char max() { return UCHAR_MAX; } - static const bool is_signed = false; -}; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static short min() { return SHRT_MIN; } - __device__ __forceinline__ static short max() { return SHRT_MAX; } - static const bool is_signed = true; -}; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static unsigned short min() { return 0; } - __device__ __forceinline__ static unsigned short max() { return USHRT_MAX; } - static const bool is_signed = false; -}; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static int min() { return INT_MIN; } - __device__ __forceinline__ static int max() { return INT_MAX; } - static const bool is_signed = true; -}; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static unsigned int min() { return 0; } - __device__ __forceinline__ static unsigned int max() { return UINT_MAX; } - static const bool is_signed = false; -}; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static float min() { return FLT_MIN; } - __device__ __forceinline__ static float max() { return FLT_MAX; } - __device__ __forceinline__ static float epsilon() { return FLT_EPSILON; } - static const bool is_signed = true; -}; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static double min() { return DBL_MIN; } - __device__ __forceinline__ static double max() { return DBL_MAX; } - __device__ __forceinline__ static double epsilon() { return DBL_EPSILON; } - static const bool is_signed = true; -}; -}}} // namespace cv { namespace cuda { namespace cudev { - -//! @endcond - -#endif // __OPENCV_CUDA_LIMITS_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/reduce.hpp b/IPL/include/opencv/opencv2/core/cuda/reduce.hpp deleted file mode 100644 index 3133c9a..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/reduce.hpp +++ /dev/null @@ -1,205 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_REDUCE_HPP__ -#define __OPENCV_CUDA_REDUCE_HPP__ - -#include -#include "detail/reduce.hpp" -#include "detail/reduce_key_val.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template - __device__ __forceinline__ void reduce(volatile T* smem, T& val, unsigned int tid, const Op& op) - { - reduce_detail::Dispatcher::reductor::template reduce(smem, val, tid, op); - } - template - __device__ __forceinline__ void reduce(const thrust::tuple& smem, - const thrust::tuple& val, - unsigned int tid, - const thrust::tuple& op) - { - reduce_detail::Dispatcher::reductor::template reduce< - const thrust::tuple&, - const thrust::tuple&, - const thrust::tuple&>(smem, val, tid, op); - } - - template - __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, volatile V* svals, V& val, unsigned int tid, const Cmp& cmp) - { - reduce_key_val_detail::Dispatcher::reductor::template reduce(skeys, key, svals, val, tid, cmp); - } - template - __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, - const thrust::tuple& svals, - const thrust::tuple& val, - unsigned int tid, const Cmp& cmp) - { - reduce_key_val_detail::Dispatcher::reductor::template reduce&, - const thrust::tuple&, - const Cmp&>(skeys, key, svals, val, tid, cmp); - } - template - __device__ __forceinline__ void reduceKeyVal(const thrust::tuple& skeys, - const thrust::tuple& key, - const thrust::tuple& svals, - const thrust::tuple& val, - unsigned int tid, - const thrust::tuple& cmp) - { - reduce_key_val_detail::Dispatcher::reductor::template reduce< - const thrust::tuple&, - const thrust::tuple&, - const thrust::tuple&, - const thrust::tuple&, - const thrust::tuple& - >(skeys, key, svals, val, tid, cmp); - } - - // smem_tuple - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0) - { - return thrust::make_tuple((volatile T0*) t0); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1, T2* t2) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8, T9* t9) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8, (volatile T9*) t9); - } -}}} - -//! @endcond - -#endif // __OPENCV_CUDA_UTILITY_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/saturate_cast.hpp b/IPL/include/opencv/opencv2/core/cuda/saturate_cast.hpp deleted file mode 100644 index f55ae4f..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/saturate_cast.hpp +++ /dev/null @@ -1,292 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_SATURATE_CAST_HPP__ -#define __OPENCV_CUDA_SATURATE_CAST_HPP__ - -#include "common.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template __device__ __forceinline__ _Tp saturate_cast(uchar v) { return _Tp(v); } - template __device__ __forceinline__ _Tp saturate_cast(schar v) { return _Tp(v); } - template __device__ __forceinline__ _Tp saturate_cast(ushort v) { return _Tp(v); } - template __device__ __forceinline__ _Tp saturate_cast(short v) { return _Tp(v); } - template __device__ __forceinline__ _Tp saturate_cast(uint v) { return _Tp(v); } - template __device__ __forceinline__ _Tp saturate_cast(int v) { return _Tp(v); } - template __device__ __forceinline__ _Tp saturate_cast(float v) { return _Tp(v); } - template __device__ __forceinline__ _Tp saturate_cast(double v) { return _Tp(v); } - - template<> __device__ __forceinline__ uchar saturate_cast(schar v) - { - uint res = 0; - int vi = v; - asm("cvt.sat.u8.s8 %0, %1;" : "=r"(res) : "r"(vi)); - return res; - } - template<> __device__ __forceinline__ uchar saturate_cast(short v) - { - uint res = 0; - asm("cvt.sat.u8.s16 %0, %1;" : "=r"(res) : "h"(v)); - return res; - } - template<> __device__ __forceinline__ uchar saturate_cast(ushort v) - { - uint res = 0; - asm("cvt.sat.u8.u16 %0, %1;" : "=r"(res) : "h"(v)); - return res; - } - template<> __device__ __forceinline__ uchar saturate_cast(int v) - { - uint res = 0; - asm("cvt.sat.u8.s32 %0, %1;" : "=r"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ uchar saturate_cast(uint v) - { - uint res = 0; - asm("cvt.sat.u8.u32 %0, %1;" : "=r"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ uchar saturate_cast(float v) - { - uint res = 0; - asm("cvt.rni.sat.u8.f32 %0, %1;" : "=r"(res) : "f"(v)); - return res; - } - template<> __device__ __forceinline__ uchar saturate_cast(double v) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 - uint res = 0; - asm("cvt.rni.sat.u8.f64 %0, %1;" : "=r"(res) : "d"(v)); - return res; - #else - return saturate_cast((float)v); - #endif - } - - template<> __device__ __forceinline__ schar saturate_cast(uchar v) - { - uint res = 0; - uint vi = v; - asm("cvt.sat.s8.u8 %0, %1;" : "=r"(res) : "r"(vi)); - return res; - } - template<> __device__ __forceinline__ schar saturate_cast(short v) - { - uint res = 0; - asm("cvt.sat.s8.s16 %0, %1;" : "=r"(res) : "h"(v)); - return res; - } - template<> __device__ __forceinline__ schar saturate_cast(ushort v) - { - uint res = 0; - asm("cvt.sat.s8.u16 %0, %1;" : "=r"(res) : "h"(v)); - return res; - } - template<> __device__ __forceinline__ schar saturate_cast(int v) - { - uint res = 0; - asm("cvt.sat.s8.s32 %0, %1;" : "=r"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ schar saturate_cast(uint v) - { - uint res = 0; - asm("cvt.sat.s8.u32 %0, %1;" : "=r"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ schar saturate_cast(float v) - { - uint res = 0; - asm("cvt.rni.sat.s8.f32 %0, %1;" : "=r"(res) : "f"(v)); - return res; - } - template<> __device__ __forceinline__ schar saturate_cast(double v) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 - uint res = 0; - asm("cvt.rni.sat.s8.f64 %0, %1;" : "=r"(res) : "d"(v)); - return res; - #else - return saturate_cast((float)v); - #endif - } - - template<> __device__ __forceinline__ ushort saturate_cast(schar v) - { - ushort res = 0; - int vi = v; - asm("cvt.sat.u16.s8 %0, %1;" : "=h"(res) : "r"(vi)); - return res; - } - template<> __device__ __forceinline__ ushort saturate_cast(short v) - { - ushort res = 0; - asm("cvt.sat.u16.s16 %0, %1;" : "=h"(res) : "h"(v)); - return res; - } - template<> __device__ __forceinline__ ushort saturate_cast(int v) - { - ushort res = 0; - asm("cvt.sat.u16.s32 %0, %1;" : "=h"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ ushort saturate_cast(uint v) - { - ushort res = 0; - asm("cvt.sat.u16.u32 %0, %1;" : "=h"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ ushort saturate_cast(float v) - { - ushort res = 0; - asm("cvt.rni.sat.u16.f32 %0, %1;" : "=h"(res) : "f"(v)); - return res; - } - template<> __device__ __forceinline__ ushort saturate_cast(double v) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 - ushort res = 0; - asm("cvt.rni.sat.u16.f64 %0, %1;" : "=h"(res) : "d"(v)); - return res; - #else - return saturate_cast((float)v); - #endif - } - - template<> __device__ __forceinline__ short saturate_cast(ushort v) - { - short res = 0; - asm("cvt.sat.s16.u16 %0, %1;" : "=h"(res) : "h"(v)); - return res; - } - template<> __device__ __forceinline__ short saturate_cast(int v) - { - short res = 0; - asm("cvt.sat.s16.s32 %0, %1;" : "=h"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ short saturate_cast(uint v) - { - short res = 0; - asm("cvt.sat.s16.u32 %0, %1;" : "=h"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ short saturate_cast(float v) - { - short res = 0; - asm("cvt.rni.sat.s16.f32 %0, %1;" : "=h"(res) : "f"(v)); - return res; - } - template<> __device__ __forceinline__ short saturate_cast(double v) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 - short res = 0; - asm("cvt.rni.sat.s16.f64 %0, %1;" : "=h"(res) : "d"(v)); - return res; - #else - return saturate_cast((float)v); - #endif - } - - template<> __device__ __forceinline__ int saturate_cast(uint v) - { - int res = 0; - asm("cvt.sat.s32.u32 %0, %1;" : "=r"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ int saturate_cast(float v) - { - return __float2int_rn(v); - } - template<> __device__ __forceinline__ int saturate_cast(double v) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 - return __double2int_rn(v); - #else - return saturate_cast((float)v); - #endif - } - - template<> __device__ __forceinline__ uint saturate_cast(schar v) - { - uint res = 0; - int vi = v; - asm("cvt.sat.u32.s8 %0, %1;" : "=r"(res) : "r"(vi)); - return res; - } - template<> __device__ __forceinline__ uint saturate_cast(short v) - { - uint res = 0; - asm("cvt.sat.u32.s16 %0, %1;" : "=r"(res) : "h"(v)); - return res; - } - template<> __device__ __forceinline__ uint saturate_cast(int v) - { - uint res = 0; - asm("cvt.sat.u32.s32 %0, %1;" : "=r"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ uint saturate_cast(float v) - { - return __float2uint_rn(v); - } - template<> __device__ __forceinline__ uint saturate_cast(double v) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 - return __double2uint_rn(v); - #else - return saturate_cast((float)v); - #endif - } -}}} - -//! @endcond - -#endif /* __OPENCV_CUDA_SATURATE_CAST_HPP__ */ diff --git a/IPL/include/opencv/opencv2/core/cuda/scan.hpp b/IPL/include/opencv/opencv2/core/cuda/scan.hpp deleted file mode 100644 index 687abb5..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/scan.hpp +++ /dev/null @@ -1,258 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_SCAN_HPP__ -#define __OPENCV_CUDA_SCAN_HPP__ - -#include "opencv2/core/cuda/common.hpp" -#include "opencv2/core/cuda/utility.hpp" -#include "opencv2/core/cuda/warp.hpp" -#include "opencv2/core/cuda/warp_shuffle.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - enum ScanKind { EXCLUSIVE = 0, INCLUSIVE = 1 }; - - template struct WarpScan - { - __device__ __forceinline__ WarpScan() {} - __device__ __forceinline__ WarpScan(const WarpScan& other) { (void)other; } - - __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx) - { - const unsigned int lane = idx & 31; - F op; - - if ( lane >= 1) ptr [idx ] = op(ptr [idx - 1], ptr [idx]); - if ( lane >= 2) ptr [idx ] = op(ptr [idx - 2], ptr [idx]); - if ( lane >= 4) ptr [idx ] = op(ptr [idx - 4], ptr [idx]); - if ( lane >= 8) ptr [idx ] = op(ptr [idx - 8], ptr [idx]); - if ( lane >= 16) ptr [idx ] = op(ptr [idx - 16], ptr [idx]); - - if( Kind == INCLUSIVE ) - return ptr [idx]; - else - return (lane > 0) ? ptr [idx - 1] : 0; - } - - __device__ __forceinline__ unsigned int index(const unsigned int tid) - { - return tid; - } - - __device__ __forceinline__ void init(volatile T *ptr){} - - static const int warp_offset = 0; - - typedef WarpScan merge; - }; - - template struct WarpScanNoComp - { - __device__ __forceinline__ WarpScanNoComp() {} - __device__ __forceinline__ WarpScanNoComp(const WarpScanNoComp& other) { (void)other; } - - __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx) - { - const unsigned int lane = threadIdx.x & 31; - F op; - - ptr [idx ] = op(ptr [idx - 1], ptr [idx]); - ptr [idx ] = op(ptr [idx - 2], ptr [idx]); - ptr [idx ] = op(ptr [idx - 4], ptr [idx]); - ptr [idx ] = op(ptr [idx - 8], ptr [idx]); - ptr [idx ] = op(ptr [idx - 16], ptr [idx]); - - if( Kind == INCLUSIVE ) - return ptr [idx]; - else - return (lane > 0) ? ptr [idx - 1] : 0; - } - - __device__ __forceinline__ unsigned int index(const unsigned int tid) - { - return (tid >> warp_log) * warp_smem_stride + 16 + (tid & warp_mask); - } - - __device__ __forceinline__ void init(volatile T *ptr) - { - ptr[threadIdx.x] = 0; - } - - static const int warp_smem_stride = 32 + 16 + 1; - static const int warp_offset = 16; - static const int warp_log = 5; - static const int warp_mask = 31; - - typedef WarpScanNoComp merge; - }; - - template struct BlockScan - { - __device__ __forceinline__ BlockScan() {} - __device__ __forceinline__ BlockScan(const BlockScan& other) { (void)other; } - - __device__ __forceinline__ T operator()(volatile T *ptr) - { - const unsigned int tid = threadIdx.x; - const unsigned int lane = tid & warp_mask; - const unsigned int warp = tid >> warp_log; - - Sc scan; - typename Sc::merge merge_scan; - const unsigned int idx = scan.index(tid); - - T val = scan(ptr, idx); - __syncthreads (); - - if( warp == 0) - scan.init(ptr); - __syncthreads (); - - if( lane == 31 ) - ptr [scan.warp_offset + warp ] = (Kind == INCLUSIVE) ? val : ptr [idx]; - __syncthreads (); - - if( warp == 0 ) - merge_scan(ptr, idx); - __syncthreads(); - - if ( warp > 0) - val = ptr [scan.warp_offset + warp - 1] + val; - __syncthreads (); - - ptr[idx] = val; - __syncthreads (); - - return val ; - } - - static const int warp_log = 5; - static const int warp_mask = 31; - }; - - template - __device__ T warpScanInclusive(T idata, volatile T* s_Data, unsigned int tid) - { - #if __CUDA_ARCH__ >= 300 - const unsigned int laneId = cv::cuda::device::Warp::laneId(); - - // scan on shuffl functions - #pragma unroll - for (int i = 1; i <= (OPENCV_CUDA_WARP_SIZE / 2); i *= 2) - { - const T n = cv::cuda::device::shfl_up(idata, i); - if (laneId >= i) - idata += n; - } - - return idata; - #else - unsigned int pos = 2 * tid - (tid & (OPENCV_CUDA_WARP_SIZE - 1)); - s_Data[pos] = 0; - pos += OPENCV_CUDA_WARP_SIZE; - s_Data[pos] = idata; - - s_Data[pos] += s_Data[pos - 1]; - s_Data[pos] += s_Data[pos - 2]; - s_Data[pos] += s_Data[pos - 4]; - s_Data[pos] += s_Data[pos - 8]; - s_Data[pos] += s_Data[pos - 16]; - - return s_Data[pos]; - #endif - } - - template - __device__ __forceinline__ T warpScanExclusive(T idata, volatile T* s_Data, unsigned int tid) - { - return warpScanInclusive(idata, s_Data, tid) - idata; - } - - template - __device__ T blockScanInclusive(T idata, volatile T* s_Data, unsigned int tid) - { - if (tiNumScanThreads > OPENCV_CUDA_WARP_SIZE) - { - //Bottom-level inclusive warp scan - T warpResult = warpScanInclusive(idata, s_Data, tid); - - //Save top elements of each warp for exclusive warp scan - //sync to wait for warp scans to complete (because s_Data is being overwritten) - __syncthreads(); - if ((tid & (OPENCV_CUDA_WARP_SIZE - 1)) == (OPENCV_CUDA_WARP_SIZE - 1)) - { - s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE] = warpResult; - } - - //wait for warp scans to complete - __syncthreads(); - - if (tid < (tiNumScanThreads / OPENCV_CUDA_WARP_SIZE) ) - { - //grab top warp elements - T val = s_Data[tid]; - //calculate exclusive scan and write back to shared memory - s_Data[tid] = warpScanExclusive(val, s_Data, tid); - } - - //return updated warp scans with exclusive scan results - __syncthreads(); - - return warpResult + s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE]; - } - else - { - return warpScanInclusive(idata, s_Data, tid); - } - } -}}} - -//! @endcond - -#endif // __OPENCV_CUDA_SCAN_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/simd_functions.hpp b/IPL/include/opencv/opencv2/core/cuda/simd_functions.hpp deleted file mode 100644 index b9e0041..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/simd_functions.hpp +++ /dev/null @@ -1,869 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -/* - * Copyright (c) 2013 NVIDIA Corporation. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions are met: - * - * Redistributions of source code must retain the above copyright notice, - * this list of conditions and the following disclaimer. - * - * Redistributions in binary form must reproduce the above copyright notice, - * this list of conditions and the following disclaimer in the documentation - * and/or other materials provided with the distribution. - * - * Neither the name of NVIDIA Corporation nor the names of its contributors - * may be used to endorse or promote products derived from this software - * without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" - * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE - * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE - * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR - * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF - * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS - * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN - * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) - * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE - * POSSIBILITY OF SUCH DAMAGE. - */ - -#ifndef __OPENCV_CUDA_SIMD_FUNCTIONS_HPP__ -#define __OPENCV_CUDA_SIMD_FUNCTIONS_HPP__ - -#include "common.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - // 2 - - static __device__ __forceinline__ unsigned int vadd2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vadd2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vadd.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vadd.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s; - s = a ^ b; // sum bits - r = a + b; // actual sum - s = s ^ r; // determine carry-ins for each bit position - s = s & 0x00010000; // carry-in to high word (= carry-out from low word) - r = r - s; // subtract out carry-out from low word - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsub2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vsub2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vsub.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vsub.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s; - s = a ^ b; // sum bits - r = a - b; // actual sum - s = s ^ r; // determine carry-ins for each bit position - s = s & 0x00010000; // borrow to high word - r = r + s; // compensate for borrow from low word - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vabsdiff2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vabsdiff2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vabsdiff.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vabsdiff.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s, t, u, v; - s = a & 0x0000ffff; // extract low halfword - r = b & 0x0000ffff; // extract low halfword - u = ::max(r, s); // maximum of low halfwords - v = ::min(r, s); // minimum of low halfwords - s = a & 0xffff0000; // extract high halfword - r = b & 0xffff0000; // extract high halfword - t = ::max(r, s); // maximum of high halfwords - s = ::min(r, s); // minimum of high halfwords - r = u | t; // maximum of both halfwords - s = v | s; // minimum of both halfwords - r = r - s; // |a - b| = max(a,b) - min(a,b); - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vavg2(unsigned int a, unsigned int b) - { - unsigned int r, s; - - // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==> - // (a + b) / 2 = (a & b) + ((a ^ b) >> 1) - s = a ^ b; - r = a & b; - s = s & 0xfffefffe; // ensure shift doesn't cross halfword boundaries - s = s >> 1; - s = r + s; - - return s; - } - - static __device__ __forceinline__ unsigned int vavrg2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vavrg2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==> - // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1) - unsigned int s; - s = a ^ b; - r = a | b; - s = s & 0xfffefffe; // ensure shift doesn't cross half-word boundaries - s = s >> 1; - r = r - s; - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vseteq2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset2.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - // inspired by Alan Mycroft's null-byte detection algorithm: - // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) - unsigned int c; - r = a ^ b; // 0x0000 if a == b - c = r | 0x80008000; // set msbs, to catch carry out - r = r ^ c; // extract msbs, msb = 1 if r < 0x8000 - c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 - c = r & ~c; // msb = 1, if r was 0x0000 - r = c >> 15; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmpeq2(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vseteq2(a, b); - c = r << 16; // convert bool - r = c - r; // into mask - #else - // inspired by Alan Mycroft's null-byte detection algorithm: - // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) - r = a ^ b; // 0x0000 if a == b - c = r | 0x80008000; // set msbs, to catch carry out - r = r ^ c; // extract msbs, msb = 1 if r < 0x8000 - c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 - c = r & ~c; // msb = 1, if r was 0x0000 - r = c >> 15; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetge2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset2.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int c; - asm("not.b32 %0, %0;" : "+r"(b)); - c = vavrg2(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 - c = c & 0x80008000; // msb = carry-outs - r = c >> 15; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmpge2(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetge2(a, b); - c = r << 16; // convert bool - r = c - r; // into mask - #else - asm("not.b32 %0, %0;" : "+r"(b)); - c = vavrg2(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 - c = c & 0x80008000; // msb = carry-outs - r = c >> 15; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetgt2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset2.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int c; - asm("not.b32 %0, %0;" : "+r"(b)); - c = vavg2(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] - c = c & 0x80008000; // msbs = carry-outs - r = c >> 15; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmpgt2(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetgt2(a, b); - c = r << 16; // convert bool - r = c - r; // into mask - #else - asm("not.b32 %0, %0;" : "+r"(b)); - c = vavg2(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] - c = c & 0x80008000; // msbs = carry-outs - r = c >> 15; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetle2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset2.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int c; - asm("not.b32 %0, %0;" : "+r"(a)); - c = vavrg2(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 - c = c & 0x80008000; // msb = carry-outs - r = c >> 15; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmple2(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetle2(a, b); - c = r << 16; // convert bool - r = c - r; // into mask - #else - asm("not.b32 %0, %0;" : "+r"(a)); - c = vavrg2(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 - c = c & 0x80008000; // msb = carry-outs - r = c >> 15; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetlt2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset2.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int c; - asm("not.b32 %0, %0;" : "+r"(a)); - c = vavg2(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] - c = c & 0x80008000; // msb = carry-outs - r = c >> 15; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmplt2(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetlt2(a, b); - c = r << 16; // convert bool - r = c - r; // into mask - #else - asm("not.b32 %0, %0;" : "+r"(a)); - c = vavg2(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] - c = c & 0x80008000; // msb = carry-outs - r = c >> 15; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetne2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm ("vset2.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - // inspired by Alan Mycroft's null-byte detection algorithm: - // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) - unsigned int c; - r = a ^ b; // 0x0000 if a == b - c = r | 0x80008000; // set msbs, to catch carry out - c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 - c = r | c; // msb = 1, if r was not 0x0000 - c = c & 0x80008000; // extract msbs - r = c >> 15; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmpne2(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetne2(a, b); - c = r << 16; // convert bool - r = c - r; // into mask - #else - // inspired by Alan Mycroft's null-byte detection algorithm: - // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) - r = a ^ b; // 0x0000 if a == b - c = r | 0x80008000; // set msbs, to catch carry out - c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 - c = r | c; // msb = 1, if r was not 0x0000 - c = c & 0x80008000; // extract msbs - r = c >> 15; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vmax2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vmax2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vmax.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vmax.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s, t, u; - r = a & 0x0000ffff; // extract low halfword - s = b & 0x0000ffff; // extract low halfword - t = ::max(r, s); // maximum of low halfwords - r = a & 0xffff0000; // extract high halfword - s = b & 0xffff0000; // extract high halfword - u = ::max(r, s); // maximum of high halfwords - r = t | u; // combine halfword maximums - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vmin2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vmin2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vmin.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vmin.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s, t, u; - r = a & 0x0000ffff; // extract low halfword - s = b & 0x0000ffff; // extract low halfword - t = ::min(r, s); // minimum of low halfwords - r = a & 0xffff0000; // extract high halfword - s = b & 0xffff0000; // extract high halfword - u = ::min(r, s); // minimum of high halfwords - r = t | u; // combine halfword minimums - #endif - - return r; - } - - // 4 - - static __device__ __forceinline__ unsigned int vadd4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vadd4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vadd.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vadd.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vadd.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vadd.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s, t; - s = a ^ b; // sum bits - r = a & 0x7f7f7f7f; // clear msbs - t = b & 0x7f7f7f7f; // clear msbs - s = s & 0x80808080; // msb sum bits - r = r + t; // add without msbs, record carry-out in msbs - r = r ^ s; // sum of msb sum and carry-in bits, w/o carry-out - #endif /* __CUDA_ARCH__ >= 300 */ - - return r; - } - - static __device__ __forceinline__ unsigned int vsub4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vsub4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vsub.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vsub.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vsub.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vsub.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s, t; - s = a ^ ~b; // inverted sum bits - r = a | 0x80808080; // set msbs - t = b & 0x7f7f7f7f; // clear msbs - s = s & 0x80808080; // inverted msb sum bits - r = r - t; // subtract w/o msbs, record inverted borrows in msb - r = r ^ s; // combine inverted msb sum bits and borrows - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vavg4(unsigned int a, unsigned int b) - { - unsigned int r, s; - - // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==> - // (a + b) / 2 = (a & b) + ((a ^ b) >> 1) - s = a ^ b; - r = a & b; - s = s & 0xfefefefe; // ensure following shift doesn't cross byte boundaries - s = s >> 1; - s = r + s; - - return s; - } - - static __device__ __forceinline__ unsigned int vavrg4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vavrg4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==> - // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1) - unsigned int c; - c = a ^ b; - r = a | b; - c = c & 0xfefefefe; // ensure following shift doesn't cross byte boundaries - c = c >> 1; - r = r - c; - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vseteq4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset4.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - // inspired by Alan Mycroft's null-byte detection algorithm: - // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) - unsigned int c; - r = a ^ b; // 0x00 if a == b - c = r | 0x80808080; // set msbs, to catch carry out - r = r ^ c; // extract msbs, msb = 1 if r < 0x80 - c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 - c = r & ~c; // msb = 1, if r was 0x00 - r = c >> 7; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmpeq4(unsigned int a, unsigned int b) - { - unsigned int r, t; - - #if __CUDA_ARCH__ >= 300 - r = vseteq4(a, b); - t = r << 8; // convert bool - r = t - r; // to mask - #else - // inspired by Alan Mycroft's null-byte detection algorithm: - // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) - t = a ^ b; // 0x00 if a == b - r = t | 0x80808080; // set msbs, to catch carry out - t = t ^ r; // extract msbs, msb = 1 if t < 0x80 - r = r - 0x01010101; // msb = 0, if t was 0x00 or 0x80 - r = t & ~r; // msb = 1, if t was 0x00 - t = r >> 7; // build mask - t = r - t; // from - r = t | r; // msbs - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetle4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset4.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int c; - asm("not.b32 %0, %0;" : "+r"(a)); - c = vavrg4(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 - c = c & 0x80808080; // msb = carry-outs - r = c >> 7; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmple4(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetle4(a, b); - c = r << 8; // convert bool - r = c - r; // to mask - #else - asm("not.b32 %0, %0;" : "+r"(a)); - c = vavrg4(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 - c = c & 0x80808080; // msbs = carry-outs - r = c >> 7; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetlt4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset4.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int c; - asm("not.b32 %0, %0;" : "+r"(a)); - c = vavg4(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] - c = c & 0x80808080; // msb = carry-outs - r = c >> 7; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmplt4(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetlt4(a, b); - c = r << 8; // convert bool - r = c - r; // to mask - #else - asm("not.b32 %0, %0;" : "+r"(a)); - c = vavg4(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] - c = c & 0x80808080; // msbs = carry-outs - r = c >> 7; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetge4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset4.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int c; - asm("not.b32 %0, %0;" : "+r"(b)); - c = vavrg4(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 - c = c & 0x80808080; // msb = carry-outs - r = c >> 7; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmpge4(unsigned int a, unsigned int b) - { - unsigned int r, s; - - #if __CUDA_ARCH__ >= 300 - r = vsetge4(a, b); - s = r << 8; // convert bool - r = s - r; // to mask - #else - asm ("not.b32 %0,%0;" : "+r"(b)); - r = vavrg4 (a, b); // (a + ~b + 1) / 2 = (a - b) / 2 - r = r & 0x80808080; // msb = carry-outs - s = r >> 7; // build mask - s = r - s; // from - r = s | r; // msbs - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetgt4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset4.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int c; - asm("not.b32 %0, %0;" : "+r"(b)); - c = vavg4(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] - c = c & 0x80808080; // msb = carry-outs - r = c >> 7; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmpgt4(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetgt4(a, b); - c = r << 8; // convert bool - r = c - r; // to mask - #else - asm("not.b32 %0, %0;" : "+r"(b)); - c = vavg4(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] - c = c & 0x80808080; // msb = carry-outs - r = c >> 7; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetne4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset4.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - // inspired by Alan Mycroft's null-byte detection algorithm: - // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) - unsigned int c; - r = a ^ b; // 0x00 if a == b - c = r | 0x80808080; // set msbs, to catch carry out - c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 - c = r | c; // msb = 1, if r was not 0x00 - c = c & 0x80808080; // extract msbs - r = c >> 7; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmpne4(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetne4(a, b); - c = r << 8; // convert bool - r = c - r; // to mask - #else - // inspired by Alan Mycroft's null-byte detection algorithm: - // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) - r = a ^ b; // 0x00 if a == b - c = r | 0x80808080; // set msbs, to catch carry out - c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 - c = r | c; // msb = 1, if r was not 0x00 - c = c & 0x80808080; // extract msbs - r = c >> 7; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vabsdiff4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vabsdiff4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vabsdiff.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vabsdiff.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vabsdiff.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vabsdiff.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s; - s = vcmpge4(a, b); // mask = 0xff if a >= b - r = a ^ b; // - s = (r & s) ^ b; // select a when a >= b, else select b => max(a,b) - r = s ^ r; // select a when b >= a, else select b => min(a,b) - r = s - r; // |a - b| = max(a,b) - min(a,b); - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vmax4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vmax4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vmax.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vmax.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vmax.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vmax.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s; - s = vcmpge4(a, b); // mask = 0xff if a >= b - r = a & s; // select a when b >= a - s = b & ~s; // select b when b < a - r = r | s; // combine byte selections - #endif - - return r; // byte-wise unsigned maximum - } - - static __device__ __forceinline__ unsigned int vmin4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vmin4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vmin.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vmin.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vmin.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vmin.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s; - s = vcmpge4(b, a); // mask = 0xff if a >= b - r = a & s; // select a when b >= a - s = b & ~s; // select b when b < a - r = r | s; // combine byte selections - #endif - - return r; - } -}}} - -//! @endcond - -#endif // __OPENCV_CUDA_SIMD_FUNCTIONS_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/transform.hpp b/IPL/include/opencv/opencv2/core/cuda/transform.hpp deleted file mode 100644 index 08a313d..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/transform.hpp +++ /dev/null @@ -1,75 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_TRANSFORM_HPP__ -#define __OPENCV_CUDA_TRANSFORM_HPP__ - -#include "common.hpp" -#include "utility.hpp" -#include "detail/transform_detail.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template - static inline void transform(PtrStepSz src, PtrStepSz dst, UnOp op, const Mask& mask, cudaStream_t stream) - { - typedef TransformFunctorTraits ft; - transform_detail::TransformDispatcher::cn == 1 && VecTraits::cn == 1 && ft::smart_shift != 1>::call(src, dst, op, mask, stream); - } - - template - static inline void transform(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, const Mask& mask, cudaStream_t stream) - { - typedef TransformFunctorTraits ft; - transform_detail::TransformDispatcher::cn == 1 && VecTraits::cn == 1 && VecTraits::cn == 1 && ft::smart_shift != 1>::call(src1, src2, dst, op, mask, stream); - } -}}} - -//! @endcond - -#endif // __OPENCV_CUDA_TRANSFORM_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/type_traits.hpp b/IPL/include/opencv/opencv2/core/cuda/type_traits.hpp deleted file mode 100644 index f2471eb..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/type_traits.hpp +++ /dev/null @@ -1,90 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_TYPE_TRAITS_HPP__ -#define __OPENCV_CUDA_TYPE_TRAITS_HPP__ - -#include "detail/type_traits_detail.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template struct IsSimpleParameter - { - enum {value = type_traits_detail::IsIntegral::value || type_traits_detail::IsFloat::value || - type_traits_detail::PointerTraits::type>::value}; - }; - - template struct TypeTraits - { - typedef typename type_traits_detail::UnConst::type NonConstType; - typedef typename type_traits_detail::UnVolatile::type NonVolatileType; - typedef typename type_traits_detail::UnVolatile::type>::type UnqualifiedType; - typedef typename type_traits_detail::PointerTraits::type PointeeType; - typedef typename type_traits_detail::ReferenceTraits::type ReferredType; - - enum { isConst = type_traits_detail::UnConst::value }; - enum { isVolatile = type_traits_detail::UnVolatile::value }; - - enum { isReference = type_traits_detail::ReferenceTraits::value }; - enum { isPointer = type_traits_detail::PointerTraits::type>::value }; - - enum { isUnsignedInt = type_traits_detail::IsUnsignedIntegral::value }; - enum { isSignedInt = type_traits_detail::IsSignedIntergral::value }; - enum { isIntegral = type_traits_detail::IsIntegral::value }; - enum { isFloat = type_traits_detail::IsFloat::value }; - enum { isArith = isIntegral || isFloat }; - enum { isVec = type_traits_detail::IsVec::value }; - - typedef typename type_traits_detail::Select::value, - T, typename type_traits_detail::AddParameterType::type>::type ParameterType; - }; -}}} - -//! @endcond - -#endif // __OPENCV_CUDA_TYPE_TRAITS_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/utility.hpp b/IPL/include/opencv/opencv2/core/cuda/utility.hpp deleted file mode 100644 index ed60471..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/utility.hpp +++ /dev/null @@ -1,221 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_UTILITY_HPP__ -#define __OPENCV_CUDA_UTILITY_HPP__ - -#include "saturate_cast.hpp" -#include "datamov_utils.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - #define OPENCV_CUDA_LOG_WARP_SIZE (5) - #define OPENCV_CUDA_WARP_SIZE (1 << OPENCV_CUDA_LOG_WARP_SIZE) - #define OPENCV_CUDA_LOG_MEM_BANKS ((__CUDA_ARCH__ >= 200) ? 5 : 4) // 32 banks on fermi, 16 on tesla - #define OPENCV_CUDA_MEM_BANKS (1 << OPENCV_CUDA_LOG_MEM_BANKS) - - /////////////////////////////////////////////////////////////////////////////// - // swap - - template void __device__ __host__ __forceinline__ swap(T& a, T& b) - { - const T temp = a; - a = b; - b = temp; - } - - /////////////////////////////////////////////////////////////////////////////// - // Mask Reader - - struct SingleMask - { - explicit __host__ __device__ __forceinline__ SingleMask(PtrStepb mask_) : mask(mask_) {} - __host__ __device__ __forceinline__ SingleMask(const SingleMask& mask_): mask(mask_.mask){} - - __device__ __forceinline__ bool operator()(int y, int x) const - { - return mask.ptr(y)[x] != 0; - } - - PtrStepb mask; - }; - - struct SingleMaskChannels - { - __host__ __device__ __forceinline__ SingleMaskChannels(PtrStepb mask_, int channels_) - : mask(mask_), channels(channels_) {} - __host__ __device__ __forceinline__ SingleMaskChannels(const SingleMaskChannels& mask_) - :mask(mask_.mask), channels(mask_.channels){} - - __device__ __forceinline__ bool operator()(int y, int x) const - { - return mask.ptr(y)[x / channels] != 0; - } - - PtrStepb mask; - int channels; - }; - - struct MaskCollection - { - explicit __host__ __device__ __forceinline__ MaskCollection(PtrStepb* maskCollection_) - : maskCollection(maskCollection_) {} - - __device__ __forceinline__ MaskCollection(const MaskCollection& masks_) - : maskCollection(masks_.maskCollection), curMask(masks_.curMask){} - - __device__ __forceinline__ void next() - { - curMask = *maskCollection++; - } - __device__ __forceinline__ void setMask(int z) - { - curMask = maskCollection[z]; - } - - __device__ __forceinline__ bool operator()(int y, int x) const - { - uchar val; - return curMask.data == 0 || (ForceGlob::Load(curMask.ptr(y), x, val), (val != 0)); - } - - const PtrStepb* maskCollection; - PtrStepb curMask; - }; - - struct WithOutMask - { - __host__ __device__ __forceinline__ WithOutMask(){} - __host__ __device__ __forceinline__ WithOutMask(const WithOutMask&){} - - __device__ __forceinline__ void next() const - { - } - __device__ __forceinline__ void setMask(int) const - { - } - - __device__ __forceinline__ bool operator()(int, int) const - { - return true; - } - - __device__ __forceinline__ bool operator()(int, int, int) const - { - return true; - } - - static __device__ __forceinline__ bool check(int, int) - { - return true; - } - - static __device__ __forceinline__ bool check(int, int, int) - { - return true; - } - }; - - /////////////////////////////////////////////////////////////////////////////// - // Solve linear system - - // solve 2x2 linear system Ax=b - template __device__ __forceinline__ bool solve2x2(const T A[2][2], const T b[2], T x[2]) - { - T det = A[0][0] * A[1][1] - A[1][0] * A[0][1]; - - if (det != 0) - { - double invdet = 1.0 / det; - - x[0] = saturate_cast(invdet * (b[0] * A[1][1] - b[1] * A[0][1])); - - x[1] = saturate_cast(invdet * (A[0][0] * b[1] - A[1][0] * b[0])); - - return true; - } - - return false; - } - - // solve 3x3 linear system Ax=b - template __device__ __forceinline__ bool solve3x3(const T A[3][3], const T b[3], T x[3]) - { - T det = A[0][0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) - - A[0][1] * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) - + A[0][2] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]); - - if (det != 0) - { - double invdet = 1.0 / det; - - x[0] = saturate_cast(invdet * - (b[0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) - - A[0][1] * (b[1] * A[2][2] - A[1][2] * b[2] ) + - A[0][2] * (b[1] * A[2][1] - A[1][1] * b[2] ))); - - x[1] = saturate_cast(invdet * - (A[0][0] * (b[1] * A[2][2] - A[1][2] * b[2] ) - - b[0] * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) + - A[0][2] * (A[1][0] * b[2] - b[1] * A[2][0]))); - - x[2] = saturate_cast(invdet * - (A[0][0] * (A[1][1] * b[2] - b[1] * A[2][1]) - - A[0][1] * (A[1][0] * b[2] - b[1] * A[2][0]) + - b[0] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]))); - - return true; - } - - return false; - } -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // __OPENCV_CUDA_UTILITY_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/vec_distance.hpp b/IPL/include/opencv/opencv2/core/cuda/vec_distance.hpp deleted file mode 100644 index 013b747..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/vec_distance.hpp +++ /dev/null @@ -1,232 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_VEC_DISTANCE_HPP__ -#define __OPENCV_CUDA_VEC_DISTANCE_HPP__ - -#include "reduce.hpp" -#include "functional.hpp" -#include "detail/vec_distance_detail.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template struct L1Dist - { - typedef int value_type; - typedef int result_type; - - __device__ __forceinline__ L1Dist() : mySum(0) {} - - __device__ __forceinline__ void reduceIter(int val1, int val2) - { - mySum = __sad(val1, val2, mySum); - } - - template __device__ __forceinline__ void reduceAll(int* smem, int tid) - { - reduce(smem, mySum, tid, plus()); - } - - __device__ __forceinline__ operator int() const - { - return mySum; - } - - int mySum; - }; - template <> struct L1Dist - { - typedef float value_type; - typedef float result_type; - - __device__ __forceinline__ L1Dist() : mySum(0.0f) {} - - __device__ __forceinline__ void reduceIter(float val1, float val2) - { - mySum += ::fabs(val1 - val2); - } - - template __device__ __forceinline__ void reduceAll(float* smem, int tid) - { - reduce(smem, mySum, tid, plus()); - } - - __device__ __forceinline__ operator float() const - { - return mySum; - } - - float mySum; - }; - - struct L2Dist - { - typedef float value_type; - typedef float result_type; - - __device__ __forceinline__ L2Dist() : mySum(0.0f) {} - - __device__ __forceinline__ void reduceIter(float val1, float val2) - { - float reg = val1 - val2; - mySum += reg * reg; - } - - template __device__ __forceinline__ void reduceAll(float* smem, int tid) - { - reduce(smem, mySum, tid, plus()); - } - - __device__ __forceinline__ operator float() const - { - return sqrtf(mySum); - } - - float mySum; - }; - - struct HammingDist - { - typedef int value_type; - typedef int result_type; - - __device__ __forceinline__ HammingDist() : mySum(0) {} - - __device__ __forceinline__ void reduceIter(int val1, int val2) - { - mySum += __popc(val1 ^ val2); - } - - template __device__ __forceinline__ void reduceAll(int* smem, int tid) - { - reduce(smem, mySum, tid, plus()); - } - - __device__ __forceinline__ operator int() const - { - return mySum; - } - - int mySum; - }; - - // calc distance between two vectors in global memory - template - __device__ void calcVecDiffGlobal(const T1* vec1, const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) - { - for (int i = tid; i < len; i += THREAD_DIM) - { - T1 val1; - ForceGlob::Load(vec1, i, val1); - - T2 val2; - ForceGlob::Load(vec2, i, val2); - - dist.reduceIter(val1, val2); - } - - dist.reduceAll(smem, tid); - } - - // calc distance between two vectors, first vector is cached in register or shared memory, second vector is in global memory - template - __device__ __forceinline__ void calcVecDiffCached(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, typename Dist::result_type* smem, int tid) - { - vec_distance_detail::VecDiffCachedCalculator::calc(vecCached, vecGlob, len, dist, tid); - - dist.reduceAll(smem, tid); - } - - // calc distance between two vectors in global memory - template struct VecDiffGlobal - { - explicit __device__ __forceinline__ VecDiffGlobal(const T1* vec1_, int = 0, void* = 0, int = 0, int = 0) - { - vec1 = vec1_; - } - - template - __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const - { - calcVecDiffGlobal(vec1, vec2, len, dist, smem, tid); - } - - const T1* vec1; - }; - - // calc distance between two vectors, first vector is cached in register memory, second vector is in global memory - template struct VecDiffCachedRegister - { - template __device__ __forceinline__ VecDiffCachedRegister(const T1* vec1, int len, U* smem, int glob_tid, int tid) - { - if (glob_tid < len) - smem[glob_tid] = vec1[glob_tid]; - __syncthreads(); - - U* vec1ValsPtr = vec1Vals; - - #pragma unroll - for (int i = tid; i < MAX_LEN; i += THREAD_DIM) - *vec1ValsPtr++ = smem[i]; - - __syncthreads(); - } - - template - __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const - { - calcVecDiffCached(vec1Vals, vec2, len, dist, smem, tid); - } - - U vec1Vals[MAX_LEN / THREAD_DIM]; - }; -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // __OPENCV_CUDA_VEC_DISTANCE_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/vec_math.hpp b/IPL/include/opencv/opencv2/core/cuda/vec_math.hpp deleted file mode 100644 index 8595fb8..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/vec_math.hpp +++ /dev/null @@ -1,930 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_VECMATH_HPP__ -#define __OPENCV_CUDA_VECMATH_HPP__ - -#include "vec_traits.hpp" -#include "saturate_cast.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - -// saturate_cast - -namespace vec_math_detail -{ - template struct SatCastHelper; - template struct SatCastHelper<1, VecD> - { - template static __device__ __forceinline__ VecD cast(const VecS& v) - { - typedef typename VecTraits::elem_type D; - return VecTraits::make(saturate_cast(v.x)); - } - }; - template struct SatCastHelper<2, VecD> - { - template static __device__ __forceinline__ VecD cast(const VecS& v) - { - typedef typename VecTraits::elem_type D; - return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y)); - } - }; - template struct SatCastHelper<3, VecD> - { - template static __device__ __forceinline__ VecD cast(const VecS& v) - { - typedef typename VecTraits::elem_type D; - return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y), saturate_cast(v.z)); - } - }; - template struct SatCastHelper<4, VecD> - { - template static __device__ __forceinline__ VecD cast(const VecS& v) - { - typedef typename VecTraits::elem_type D; - return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y), saturate_cast(v.z), saturate_cast(v.w)); - } - }; - - template static __device__ __forceinline__ VecD saturate_cast_helper(const VecS& v) - { - return SatCastHelper::cn, VecD>::cast(v); - } -} - -template static __device__ __forceinline__ T saturate_cast(const uchar1& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const char1& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const ushort1& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const short1& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const uint1& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const int1& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const float1& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const double1& v) {return vec_math_detail::saturate_cast_helper(v);} - -template static __device__ __forceinline__ T saturate_cast(const uchar2& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const char2& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const ushort2& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const short2& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const uint2& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const int2& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const float2& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const double2& v) {return vec_math_detail::saturate_cast_helper(v);} - -template static __device__ __forceinline__ T saturate_cast(const uchar3& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const char3& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const ushort3& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const short3& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const uint3& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const int3& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const float3& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const double3& v) {return vec_math_detail::saturate_cast_helper(v);} - -template static __device__ __forceinline__ T saturate_cast(const uchar4& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const char4& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const ushort4& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const short4& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const uint4& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const int4& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const float4& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const double4& v) {return vec_math_detail::saturate_cast_helper(v);} - -// unary operators - -#define CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(op, input_type, output_type) \ - __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a) \ - { \ - return VecTraits::make(op (a.x)); \ - } \ - __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a) \ - { \ - return VecTraits::make(op (a.x), op (a.y)); \ - } \ - __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a) \ - { \ - return VecTraits::make(op (a.x), op (a.y), op (a.z)); \ - } \ - __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a) \ - { \ - return VecTraits::make(op (a.x), op (a.y), op (a.z), op (a.w)); \ - } - -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, char, char) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, short, short) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, int, int) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, char, char) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, ushort, ushort) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, short, short) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, int, int) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uint, uint) - -#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_OP - -// unary functions - -#define CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(func_name, func, input_type, output_type) \ - __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a) \ - { \ - return VecTraits::make(func (a.x)); \ - } \ - __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a) \ - { \ - return VecTraits::make(func (a.x), func (a.y)); \ - } \ - __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a) \ - { \ - return VecTraits::make(func (a.x), func (a.y), func (a.z)); \ - } \ - __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a) \ - { \ - return VecTraits::make(func (a.x), func (a.y), func (a.z), func (a.w)); \ - } - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, char, char) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, ushort, ushort) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, short, short) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, int, int) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uint, uint) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabsf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabs, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrt, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::exp, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::log, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sin, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cos, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tan, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asin, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acos, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atan, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinh, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::cosh, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanh, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinh, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acosh, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanh, double, double) - -#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC - -// binary operators (vec & vec) - -#define CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(op, input_type, output_type) \ - __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, const input_type ## 1 & b) \ - { \ - return VecTraits::make(a.x op b.x); \ - } \ - __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, const input_type ## 2 & b) \ - { \ - return VecTraits::make(a.x op b.x, a.y op b.y); \ - } \ - __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, const input_type ## 3 & b) \ - { \ - return VecTraits::make(a.x op b.x, a.y op b.y, a.z op b.z); \ - } \ - __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, const input_type ## 4 & b) \ - { \ - return VecTraits::make(a.x op b.x, a.y op b.y, a.z op b.z, a.w op b.w); \ - } - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uchar, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, char, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, ushort, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, short, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uint, uint) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, float, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, double, double) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uchar, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, char, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, ushort, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, short, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uint, uint) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, float, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, double, double) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uchar, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, char, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, ushort, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, short, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uint, uint) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, float, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, double, double) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uchar, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, char, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, ushort, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, short, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uint, uint) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, float, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, double, double) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, char, char) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, ushort, ushort) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, short, short) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uint, uint) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, char, char) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, ushort, ushort) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, short, short) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uint, uint) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, char, char) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, ushort, ushort) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, short, short) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uint, uint) - -#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_OP - -// binary operators (vec & scalar) - -#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(op, input_type, scalar_type, output_type) \ - __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, scalar_type s) \ - { \ - return VecTraits::make(a.x op s); \ - } \ - __device__ __forceinline__ output_type ## 1 operator op(scalar_type s, const input_type ## 1 & b) \ - { \ - return VecTraits::make(s op b.x); \ - } \ - __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, scalar_type s) \ - { \ - return VecTraits::make(a.x op s, a.y op s); \ - } \ - __device__ __forceinline__ output_type ## 2 operator op(scalar_type s, const input_type ## 2 & b) \ - { \ - return VecTraits::make(s op b.x, s op b.y); \ - } \ - __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, scalar_type s) \ - { \ - return VecTraits::make(a.x op s, a.y op s, a.z op s); \ - } \ - __device__ __forceinline__ output_type ## 3 operator op(scalar_type s, const input_type ## 3 & b) \ - { \ - return VecTraits::make(s op b.x, s op b.y, s op b.z); \ - } \ - __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, scalar_type s) \ - { \ - return VecTraits::make(a.x op s, a.y op s, a.z op s, a.w op s); \ - } \ - __device__ __forceinline__ output_type ## 4 operator op(scalar_type s, const input_type ## 4 & b) \ - { \ - return VecTraits::make(s op b.x, s op b.y, s op b.z, s op b.w); \ - } - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, uint, uint) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, double, double, double) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, uint, uint) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, double, double, double) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, uint, uint) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, double, double, double) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, uint, uint) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, double, double, double) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, char, char, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, ushort, ushort, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, short, short, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, int, int, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uint, uint, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, float, float, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, double, double, uchar) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, char, char, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, ushort, ushort, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, short, short, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, int, int, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uint, uint, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, float, float, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, double, double, uchar) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, char, char, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, ushort, ushort, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, short, short, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, int, int, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uint, uint, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, float, float, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, double, double, uchar) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, char, char, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, ushort, ushort, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, short, short, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, int, int, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uint, uint, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, float, float, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, double, double, uchar) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, char, char, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, ushort, ushort, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, short, short, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, int, int, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uint, uint, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, float, float, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, double, double, uchar) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, char, char, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, ushort, ushort, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, short, short, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, int, int, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uint, uint, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, float, float, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, double, double, uchar) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, char, char, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, ushort, ushort, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, short, short, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, int, int, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uint, uint, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, float, float, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, double, double, uchar) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, char, char, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, ushort, ushort, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, short, short, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, int, int, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uint, uint, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, float, float, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, double, double, uchar) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, char, char, char) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, ushort, ushort, ushort) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, short, short, short) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uint, uint, uint) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, char, char, char) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, ushort, ushort, ushort) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, short, short, short) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uint, uint, uint) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, char, char, char) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, ushort, ushort, ushort) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, short, short, short) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uint, uint, uint) - -#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP - -// binary function (vec & vec) - -#define CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(func_name, func, input_type, output_type) \ - __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, const input_type ## 1 & b) \ - { \ - return VecTraits::make(func (a.x, b.x)); \ - } \ - __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, const input_type ## 2 & b) \ - { \ - return VecTraits::make(func (a.x, b.x), func (a.y, b.y)); \ - } \ - __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, const input_type ## 3 & b) \ - { \ - return VecTraits::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z)); \ - } \ - __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, const input_type ## 4 & b) \ - { \ - return VecTraits::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z), func (a.w, b.w)); \ - } - -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, char, char) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, ushort, ushort) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, short, short) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uint, uint) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmaxf, float, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmax, double, double) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, char, char) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, ushort, ushort) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, short, short) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uint, uint) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fminf, float, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fmin, double, double) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, char, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, short, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, int, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, float, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypot, double, double) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, char, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, short, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uint, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, int, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, float, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2, double, double) - -#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC - -// binary function (vec & scalar) - -#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(func_name, func, input_type, scalar_type, output_type) \ - __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, scalar_type s) \ - { \ - return VecTraits::make(func ((output_type) a.x, (output_type) s)); \ - } \ - __device__ __forceinline__ output_type ## 1 func_name(scalar_type s, const input_type ## 1 & b) \ - { \ - return VecTraits::make(func ((output_type) s, (output_type) b.x)); \ - } \ - __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, scalar_type s) \ - { \ - return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s)); \ - } \ - __device__ __forceinline__ output_type ## 2 func_name(scalar_type s, const input_type ## 2 & b) \ - { \ - return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y)); \ - } \ - __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, scalar_type s) \ - { \ - return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s)); \ - } \ - __device__ __forceinline__ output_type ## 3 func_name(scalar_type s, const input_type ## 3 & b) \ - { \ - return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z)); \ - } \ - __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, scalar_type s) \ - { \ - return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s), func ((output_type) a.w, (output_type) s)); \ - } \ - __device__ __forceinline__ output_type ## 4 func_name(scalar_type s, const input_type ## 4 & b) \ - { \ - return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z), func ((output_type) s, (output_type) b.w)); \ - } - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uchar, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uchar, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, char, char, char) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, char, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, char, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, ushort, ushort, ushort) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, ushort, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, ushort, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, short, short, short) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, short, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, short, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uint, uint, uint) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uint, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uint, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, int, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, int, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, float, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, float, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, double, double, double) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uchar, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uchar, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, char, char, char) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, char, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, char, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, ushort, ushort, ushort) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, ushort, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, ushort, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, short, short, short) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, short, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, short, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uint, uint, uint) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uint, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uint, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, int, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, int, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, float, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, float, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, double, double, double) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uchar, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uchar, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, char, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, char, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, ushort, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, ushort, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, short, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, short, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uint, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uint, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, int, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, int, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, float, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, float, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, double, double, double) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uchar, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uchar, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, char, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, char, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, ushort, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, ushort, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, short, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, short, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uint, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uint, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, int, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, int, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, float, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, float, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, double, double, double) - -#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC - -}}} // namespace cv { namespace cuda { namespace device - -//! @endcond - -#endif // __OPENCV_CUDA_VECMATH_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/vec_traits.hpp b/IPL/include/opencv/opencv2/core/cuda/vec_traits.hpp deleted file mode 100644 index 905e37f..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/vec_traits.hpp +++ /dev/null @@ -1,288 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_VEC_TRAITS_HPP__ -#define __OPENCV_CUDA_VEC_TRAITS_HPP__ - -#include "common.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template struct TypeVec; - - struct __align__(8) uchar8 - { - uchar a0, a1, a2, a3, a4, a5, a6, a7; - }; - static __host__ __device__ __forceinline__ uchar8 make_uchar8(uchar a0, uchar a1, uchar a2, uchar a3, uchar a4, uchar a5, uchar a6, uchar a7) - { - uchar8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; - return val; - } - struct __align__(8) char8 - { - schar a0, a1, a2, a3, a4, a5, a6, a7; - }; - static __host__ __device__ __forceinline__ char8 make_char8(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7) - { - char8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; - return val; - } - struct __align__(16) ushort8 - { - ushort a0, a1, a2, a3, a4, a5, a6, a7; - }; - static __host__ __device__ __forceinline__ ushort8 make_ushort8(ushort a0, ushort a1, ushort a2, ushort a3, ushort a4, ushort a5, ushort a6, ushort a7) - { - ushort8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; - return val; - } - struct __align__(16) short8 - { - short a0, a1, a2, a3, a4, a5, a6, a7; - }; - static __host__ __device__ __forceinline__ short8 make_short8(short a0, short a1, short a2, short a3, short a4, short a5, short a6, short a7) - { - short8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; - return val; - } - struct __align__(32) uint8 - { - uint a0, a1, a2, a3, a4, a5, a6, a7; - }; - static __host__ __device__ __forceinline__ uint8 make_uint8(uint a0, uint a1, uint a2, uint a3, uint a4, uint a5, uint a6, uint a7) - { - uint8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; - return val; - } - struct __align__(32) int8 - { - int a0, a1, a2, a3, a4, a5, a6, a7; - }; - static __host__ __device__ __forceinline__ int8 make_int8(int a0, int a1, int a2, int a3, int a4, int a5, int a6, int a7) - { - int8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; - return val; - } - struct __align__(32) float8 - { - float a0, a1, a2, a3, a4, a5, a6, a7; - }; - static __host__ __device__ __forceinline__ float8 make_float8(float a0, float a1, float a2, float a3, float a4, float a5, float a6, float a7) - { - float8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; - return val; - } - struct double8 - { - double a0, a1, a2, a3, a4, a5, a6, a7; - }; - static __host__ __device__ __forceinline__ double8 make_double8(double a0, double a1, double a2, double a3, double a4, double a5, double a6, double a7) - { - double8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; - return val; - } - -#define OPENCV_CUDA_IMPLEMENT_TYPE_VEC(type) \ - template<> struct TypeVec { typedef type vec_type; }; \ - template<> struct TypeVec { typedef type ## 1 vec_type; }; \ - template<> struct TypeVec { typedef type ## 2 vec_type; }; \ - template<> struct TypeVec { typedef type ## 2 vec_type; }; \ - template<> struct TypeVec { typedef type ## 3 vec_type; }; \ - template<> struct TypeVec { typedef type ## 3 vec_type; }; \ - template<> struct TypeVec { typedef type ## 4 vec_type; }; \ - template<> struct TypeVec { typedef type ## 4 vec_type; }; \ - template<> struct TypeVec { typedef type ## 8 vec_type; }; \ - template<> struct TypeVec { typedef type ## 8 vec_type; }; - - OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uchar) - OPENCV_CUDA_IMPLEMENT_TYPE_VEC(char) - OPENCV_CUDA_IMPLEMENT_TYPE_VEC(ushort) - OPENCV_CUDA_IMPLEMENT_TYPE_VEC(short) - OPENCV_CUDA_IMPLEMENT_TYPE_VEC(int) - OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uint) - OPENCV_CUDA_IMPLEMENT_TYPE_VEC(float) - OPENCV_CUDA_IMPLEMENT_TYPE_VEC(double) - - #undef OPENCV_CUDA_IMPLEMENT_TYPE_VEC - - template<> struct TypeVec { typedef schar vec_type; }; - template<> struct TypeVec { typedef char2 vec_type; }; - template<> struct TypeVec { typedef char3 vec_type; }; - template<> struct TypeVec { typedef char4 vec_type; }; - template<> struct TypeVec { typedef char8 vec_type; }; - - template<> struct TypeVec { typedef uchar vec_type; }; - template<> struct TypeVec { typedef uchar2 vec_type; }; - template<> struct TypeVec { typedef uchar3 vec_type; }; - template<> struct TypeVec { typedef uchar4 vec_type; }; - template<> struct TypeVec { typedef uchar8 vec_type; }; - - template struct VecTraits; - -#define OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(type) \ - template<> struct VecTraits \ - { \ - typedef type elem_type; \ - enum {cn=1}; \ - static __device__ __host__ __forceinline__ type all(type v) {return v;} \ - static __device__ __host__ __forceinline__ type make(type x) {return x;} \ - static __device__ __host__ __forceinline__ type make(const type* v) {return *v;} \ - }; \ - template<> struct VecTraits \ - { \ - typedef type elem_type; \ - enum {cn=1}; \ - static __device__ __host__ __forceinline__ type ## 1 all(type v) {return make_ ## type ## 1(v);} \ - static __device__ __host__ __forceinline__ type ## 1 make(type x) {return make_ ## type ## 1(x);} \ - static __device__ __host__ __forceinline__ type ## 1 make(const type* v) {return make_ ## type ## 1(*v);} \ - }; \ - template<> struct VecTraits \ - { \ - typedef type elem_type; \ - enum {cn=2}; \ - static __device__ __host__ __forceinline__ type ## 2 all(type v) {return make_ ## type ## 2(v, v);} \ - static __device__ __host__ __forceinline__ type ## 2 make(type x, type y) {return make_ ## type ## 2(x, y);} \ - static __device__ __host__ __forceinline__ type ## 2 make(const type* v) {return make_ ## type ## 2(v[0], v[1]);} \ - }; \ - template<> struct VecTraits \ - { \ - typedef type elem_type; \ - enum {cn=3}; \ - static __device__ __host__ __forceinline__ type ## 3 all(type v) {return make_ ## type ## 3(v, v, v);} \ - static __device__ __host__ __forceinline__ type ## 3 make(type x, type y, type z) {return make_ ## type ## 3(x, y, z);} \ - static __device__ __host__ __forceinline__ type ## 3 make(const type* v) {return make_ ## type ## 3(v[0], v[1], v[2]);} \ - }; \ - template<> struct VecTraits \ - { \ - typedef type elem_type; \ - enum {cn=4}; \ - static __device__ __host__ __forceinline__ type ## 4 all(type v) {return make_ ## type ## 4(v, v, v, v);} \ - static __device__ __host__ __forceinline__ type ## 4 make(type x, type y, type z, type w) {return make_ ## type ## 4(x, y, z, w);} \ - static __device__ __host__ __forceinline__ type ## 4 make(const type* v) {return make_ ## type ## 4(v[0], v[1], v[2], v[3]);} \ - }; \ - template<> struct VecTraits \ - { \ - typedef type elem_type; \ - enum {cn=8}; \ - static __device__ __host__ __forceinline__ type ## 8 all(type v) {return make_ ## type ## 8(v, v, v, v, v, v, v, v);} \ - static __device__ __host__ __forceinline__ type ## 8 make(type a0, type a1, type a2, type a3, type a4, type a5, type a6, type a7) {return make_ ## type ## 8(a0, a1, a2, a3, a4, a5, a6, a7);} \ - static __device__ __host__ __forceinline__ type ## 8 make(const type* v) {return make_ ## type ## 8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);} \ - }; - - OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uchar) - OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(ushort) - OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(short) - OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(int) - OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uint) - OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(float) - OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(double) - - #undef OPENCV_CUDA_IMPLEMENT_VEC_TRAITS - - template<> struct VecTraits - { - typedef char elem_type; - enum {cn=1}; - static __device__ __host__ __forceinline__ char all(char v) {return v;} - static __device__ __host__ __forceinline__ char make(char x) {return x;} - static __device__ __host__ __forceinline__ char make(const char* x) {return *x;} - }; - template<> struct VecTraits - { - typedef schar elem_type; - enum {cn=1}; - static __device__ __host__ __forceinline__ schar all(schar v) {return v;} - static __device__ __host__ __forceinline__ schar make(schar x) {return x;} - static __device__ __host__ __forceinline__ schar make(const schar* x) {return *x;} - }; - template<> struct VecTraits - { - typedef schar elem_type; - enum {cn=1}; - static __device__ __host__ __forceinline__ char1 all(schar v) {return make_char1(v);} - static __device__ __host__ __forceinline__ char1 make(schar x) {return make_char1(x);} - static __device__ __host__ __forceinline__ char1 make(const schar* v) {return make_char1(v[0]);} - }; - template<> struct VecTraits - { - typedef schar elem_type; - enum {cn=2}; - static __device__ __host__ __forceinline__ char2 all(schar v) {return make_char2(v, v);} - static __device__ __host__ __forceinline__ char2 make(schar x, schar y) {return make_char2(x, y);} - static __device__ __host__ __forceinline__ char2 make(const schar* v) {return make_char2(v[0], v[1]);} - }; - template<> struct VecTraits - { - typedef schar elem_type; - enum {cn=3}; - static __device__ __host__ __forceinline__ char3 all(schar v) {return make_char3(v, v, v);} - static __device__ __host__ __forceinline__ char3 make(schar x, schar y, schar z) {return make_char3(x, y, z);} - static __device__ __host__ __forceinline__ char3 make(const schar* v) {return make_char3(v[0], v[1], v[2]);} - }; - template<> struct VecTraits - { - typedef schar elem_type; - enum {cn=4}; - static __device__ __host__ __forceinline__ char4 all(schar v) {return make_char4(v, v, v, v);} - static __device__ __host__ __forceinline__ char4 make(schar x, schar y, schar z, schar w) {return make_char4(x, y, z, w);} - static __device__ __host__ __forceinline__ char4 make(const schar* v) {return make_char4(v[0], v[1], v[2], v[3]);} - }; - template<> struct VecTraits - { - typedef schar elem_type; - enum {cn=8}; - static __device__ __host__ __forceinline__ char8 all(schar v) {return make_char8(v, v, v, v, v, v, v, v);} - static __device__ __host__ __forceinline__ char8 make(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7) {return make_char8(a0, a1, a2, a3, a4, a5, a6, a7);} - static __device__ __host__ __forceinline__ char8 make(const schar* v) {return make_char8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);} - }; -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // __OPENCV_CUDA_VEC_TRAITS_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda/warp.hpp b/IPL/include/opencv/opencv2/core/cuda/warp.hpp deleted file mode 100644 index d93afe7..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/warp.hpp +++ /dev/null @@ -1,139 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_DEVICE_WARP_HPP__ -#define __OPENCV_CUDA_DEVICE_WARP_HPP__ - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - struct Warp - { - enum - { - LOG_WARP_SIZE = 5, - WARP_SIZE = 1 << LOG_WARP_SIZE, - STRIDE = WARP_SIZE - }; - - /** \brief Returns the warp lane ID of the calling thread. */ - static __device__ __forceinline__ unsigned int laneId() - { - unsigned int ret; - asm("mov.u32 %0, %laneid;" : "=r"(ret) ); - return ret; - } - - template - static __device__ __forceinline__ void fill(It beg, It end, const T& value) - { - for(It t = beg + laneId(); t < end; t += STRIDE) - *t = value; - } - - template - static __device__ __forceinline__ OutIt copy(InIt beg, InIt end, OutIt out) - { - for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE) - *out = *t; - return out; - } - - template - static __device__ __forceinline__ OutIt transform(InIt beg, InIt end, OutIt out, UnOp op) - { - for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE) - *out = op(*t); - return out; - } - - template - static __device__ __forceinline__ OutIt transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op) - { - unsigned int lane = laneId(); - - InIt1 t1 = beg1 + lane; - InIt2 t2 = beg2 + lane; - for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, out += STRIDE) - *out = op(*t1, *t2); - return out; - } - - template - static __device__ __forceinline__ T reduce(volatile T *ptr, BinOp op) - { - const unsigned int lane = laneId(); - - if (lane < 16) - { - T partial = ptr[lane]; - - ptr[lane] = partial = op(partial, ptr[lane + 16]); - ptr[lane] = partial = op(partial, ptr[lane + 8]); - ptr[lane] = partial = op(partial, ptr[lane + 4]); - ptr[lane] = partial = op(partial, ptr[lane + 2]); - ptr[lane] = partial = op(partial, ptr[lane + 1]); - } - - return *ptr; - } - - template - static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value) - { - unsigned int lane = laneId(); - value += lane; - - for(OutIt t = beg + lane; t < end; t += STRIDE, value += STRIDE) - *t = value; - } - }; -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif /* __OPENCV_CUDA_DEVICE_WARP_HPP__ */ diff --git a/IPL/include/opencv/opencv2/core/cuda/warp_reduce.hpp b/IPL/include/opencv/opencv2/core/cuda/warp_reduce.hpp deleted file mode 100644 index 530303d..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/warp_reduce.hpp +++ /dev/null @@ -1,76 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_WARP_REDUCE_HPP__ -#define OPENCV_CUDA_WARP_REDUCE_HPP__ - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template - __device__ __forceinline__ T warp_reduce(volatile T *ptr , const unsigned int tid = threadIdx.x) - { - const unsigned int lane = tid & 31; // index of thread in warp (0..31) - - if (lane < 16) - { - T partial = ptr[tid]; - - ptr[tid] = partial = partial + ptr[tid + 16]; - ptr[tid] = partial = partial + ptr[tid + 8]; - ptr[tid] = partial = partial + ptr[tid + 4]; - ptr[tid] = partial = partial + ptr[tid + 2]; - ptr[tid] = partial = partial + ptr[tid + 1]; - } - - return ptr[tid - lane]; - } -}}} // namespace cv { namespace cuda { namespace cudev { - -//! @endcond - -#endif /* OPENCV_CUDA_WARP_REDUCE_HPP__ */ diff --git a/IPL/include/opencv/opencv2/core/cuda/warp_shuffle.hpp b/IPL/include/opencv/opencv2/core/cuda/warp_shuffle.hpp deleted file mode 100644 index 256fc2a..0000000 --- a/IPL/include/opencv/opencv2/core/cuda/warp_shuffle.hpp +++ /dev/null @@ -1,153 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CUDA_WARP_SHUFFLE_HPP__ -#define __OPENCV_CUDA_WARP_SHUFFLE_HPP__ - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template - __device__ __forceinline__ T shfl(T val, int srcLane, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - return __shfl(val, srcLane, width); - #else - return T(); - #endif - } - __device__ __forceinline__ unsigned int shfl(unsigned int val, int srcLane, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - return (unsigned int) __shfl((int) val, srcLane, width); - #else - return 0; - #endif - } - __device__ __forceinline__ double shfl(double val, int srcLane, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - int lo = __double2loint(val); - int hi = __double2hiint(val); - - lo = __shfl(lo, srcLane, width); - hi = __shfl(hi, srcLane, width); - - return __hiloint2double(hi, lo); - #else - return 0.0; - #endif - } - - template - __device__ __forceinline__ T shfl_down(T val, unsigned int delta, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - return __shfl_down(val, delta, width); - #else - return T(); - #endif - } - __device__ __forceinline__ unsigned int shfl_down(unsigned int val, unsigned int delta, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - return (unsigned int) __shfl_down((int) val, delta, width); - #else - return 0; - #endif - } - __device__ __forceinline__ double shfl_down(double val, unsigned int delta, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - int lo = __double2loint(val); - int hi = __double2hiint(val); - - lo = __shfl_down(lo, delta, width); - hi = __shfl_down(hi, delta, width); - - return __hiloint2double(hi, lo); - #else - return 0.0; - #endif - } - - template - __device__ __forceinline__ T shfl_up(T val, unsigned int delta, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - return __shfl_up(val, delta, width); - #else - return T(); - #endif - } - __device__ __forceinline__ unsigned int shfl_up(unsigned int val, unsigned int delta, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - return (unsigned int) __shfl_up((int) val, delta, width); - #else - return 0; - #endif - } - __device__ __forceinline__ double shfl_up(double val, unsigned int delta, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - int lo = __double2loint(val); - int hi = __double2hiint(val); - - lo = __shfl_up(lo, delta, width); - hi = __shfl_up(hi, delta, width); - - return __hiloint2double(hi, lo); - #else - return 0.0; - #endif - } -}}} - -//! @endcond - -#endif // __OPENCV_CUDA_WARP_SHUFFLE_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cuda_stream_accessor.hpp b/IPL/include/opencv/opencv2/core/cuda_stream_accessor.hpp deleted file mode 100644 index 0f8ee9b..0000000 --- a/IPL/include/opencv/opencv2/core/cuda_stream_accessor.hpp +++ /dev/null @@ -1,86 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP__ -#define __OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP__ - -#ifndef __cplusplus -# error cuda_stream_accessor.hpp header must be compiled as C++ -#endif - -/** @file cuda_stream_accessor.hpp - * This is only header file that depends on CUDA Runtime API. All other headers are independent. - */ - -#include -#include "opencv2/core/cuda.hpp" - -namespace cv -{ - namespace cuda - { - -//! @addtogroup cudacore_struct -//! @{ - - /** @brief Class that enables getting cudaStream_t from cuda::Stream - */ - struct StreamAccessor - { - CV_EXPORTS static cudaStream_t getStream(const Stream& stream); - CV_EXPORTS static Stream wrapStream(cudaStream_t stream); - }; - - /** @brief Class that enables getting cudaEvent_t from cuda::Event - */ - struct EventAccessor - { - CV_EXPORTS static cudaEvent_t getEvent(const Event& event); - CV_EXPORTS static Event wrapEvent(cudaEvent_t event); - }; - -//! @} - - } -} - -#endif /* __OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP__ */ diff --git a/IPL/include/opencv/opencv2/core/cuda_types.hpp b/IPL/include/opencv/opencv2/core/cuda_types.hpp deleted file mode 100644 index 8df816e..0000000 --- a/IPL/include/opencv/opencv2/core/cuda_types.hpp +++ /dev/null @@ -1,135 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_CUDA_TYPES_HPP__ -#define __OPENCV_CORE_CUDA_TYPES_HPP__ - -#ifndef __cplusplus -# error cuda_types.hpp header must be compiled as C++ -#endif - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -#ifdef __CUDACC__ - #define __CV_CUDA_HOST_DEVICE__ __host__ __device__ __forceinline__ -#else - #define __CV_CUDA_HOST_DEVICE__ -#endif - -namespace cv -{ - namespace cuda - { - - // Simple lightweight structures that encapsulates information about an image on device. - // It is intended to pass to nvcc-compiled code. GpuMat depends on headers that nvcc can't compile - - template struct DevPtr - { - typedef T elem_type; - typedef int index_type; - - enum { elem_size = sizeof(elem_type) }; - - T* data; - - __CV_CUDA_HOST_DEVICE__ DevPtr() : data(0) {} - __CV_CUDA_HOST_DEVICE__ DevPtr(T* data_) : data(data_) {} - - __CV_CUDA_HOST_DEVICE__ size_t elemSize() const { return elem_size; } - __CV_CUDA_HOST_DEVICE__ operator T*() { return data; } - __CV_CUDA_HOST_DEVICE__ operator const T*() const { return data; } - }; - - template struct PtrSz : public DevPtr - { - __CV_CUDA_HOST_DEVICE__ PtrSz() : size(0) {} - __CV_CUDA_HOST_DEVICE__ PtrSz(T* data_, size_t size_) : DevPtr(data_), size(size_) {} - - size_t size; - }; - - template struct PtrStep : public DevPtr - { - __CV_CUDA_HOST_DEVICE__ PtrStep() : step(0) {} - __CV_CUDA_HOST_DEVICE__ PtrStep(T* data_, size_t step_) : DevPtr(data_), step(step_) {} - - size_t step; - - __CV_CUDA_HOST_DEVICE__ T* ptr(int y = 0) { return ( T*)( ( char*)DevPtr::data + y * step); } - __CV_CUDA_HOST_DEVICE__ const T* ptr(int y = 0) const { return (const T*)( (const char*)DevPtr::data + y * step); } - - __CV_CUDA_HOST_DEVICE__ T& operator ()(int y, int x) { return ptr(y)[x]; } - __CV_CUDA_HOST_DEVICE__ const T& operator ()(int y, int x) const { return ptr(y)[x]; } - }; - - template struct PtrStepSz : public PtrStep - { - __CV_CUDA_HOST_DEVICE__ PtrStepSz() : cols(0), rows(0) {} - __CV_CUDA_HOST_DEVICE__ PtrStepSz(int rows_, int cols_, T* data_, size_t step_) - : PtrStep(data_, step_), cols(cols_), rows(rows_) {} - - template - explicit PtrStepSz(const PtrStepSz& d) : PtrStep((T*)d.data, d.step), cols(d.cols), rows(d.rows){} - - int cols; - int rows; - }; - - typedef PtrStepSz PtrStepSzb; - typedef PtrStepSz PtrStepSzf; - typedef PtrStepSz PtrStepSzi; - - typedef PtrStep PtrStepb; - typedef PtrStep PtrStepf; - typedef PtrStep PtrStepi; - - } -} - -//! @endcond - -#endif /* __OPENCV_CORE_CUDA_TYPES_HPP__ */ diff --git a/IPL/include/opencv/opencv2/core/cvdef.h b/IPL/include/opencv/opencv2/core/cvdef.h deleted file mode 100644 index af2abfb..0000000 --- a/IPL/include/opencv/opencv2/core/cvdef.h +++ /dev/null @@ -1,515 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_CVDEF_H__ -#define __OPENCV_CORE_CVDEF_H__ - -//! @addtogroup core_utils -//! @{ - -#if !defined _CRT_SECURE_NO_DEPRECATE && defined _MSC_VER && _MSC_VER > 1300 -# define _CRT_SECURE_NO_DEPRECATE /* to avoid multiple Visual Studio warnings */ -#endif - -// undef problematic defines sometimes defined by system headers (windows.h in particular) -#undef small -#undef min -#undef max -#undef abs -#undef Complex - -#if !defined _CRT_SECURE_NO_DEPRECATE && defined _MSC_VER && _MSC_VER > 1300 -# define _CRT_SECURE_NO_DEPRECATE /* to avoid multiple Visual Studio warnings */ -#endif - -#include -#include "opencv2/core/hal/interface.h" - -#if defined __ICL -# define CV_ICC __ICL -#elif defined __ICC -# define CV_ICC __ICC -#elif defined __ECL -# define CV_ICC __ECL -#elif defined __ECC -# define CV_ICC __ECC -#elif defined __INTEL_COMPILER -# define CV_ICC __INTEL_COMPILER -#endif - -#ifndef CV_INLINE -# if defined __cplusplus -# define CV_INLINE static inline -# elif defined _MSC_VER -# define CV_INLINE __inline -# else -# define CV_INLINE static -# endif -#endif - -#if defined CV_ICC && !defined CV_ENABLE_UNROLLED -# define CV_ENABLE_UNROLLED 0 -#else -# define CV_ENABLE_UNROLLED 1 -#endif - -#ifdef __GNUC__ -# define CV_DECL_ALIGNED(x) __attribute__ ((aligned (x))) -#elif defined _MSC_VER -# define CV_DECL_ALIGNED(x) __declspec(align(x)) -#else -# define CV_DECL_ALIGNED(x) -#endif - -/* CPU features and intrinsics support */ -#define CV_CPU_NONE 0 -#define CV_CPU_MMX 1 -#define CV_CPU_SSE 2 -#define CV_CPU_SSE2 3 -#define CV_CPU_SSE3 4 -#define CV_CPU_SSSE3 5 -#define CV_CPU_SSE4_1 6 -#define CV_CPU_SSE4_2 7 -#define CV_CPU_POPCNT 8 - -#define CV_CPU_AVX 10 -#define CV_CPU_AVX2 11 -#define CV_CPU_FMA3 12 - -#define CV_CPU_AVX_512F 13 -#define CV_CPU_AVX_512BW 14 -#define CV_CPU_AVX_512CD 15 -#define CV_CPU_AVX_512DQ 16 -#define CV_CPU_AVX_512ER 17 -#define CV_CPU_AVX_512IFMA512 18 -#define CV_CPU_AVX_512PF 19 -#define CV_CPU_AVX_512VBMI 20 -#define CV_CPU_AVX_512VL 21 - -#define CV_CPU_NEON 100 - -// when adding to this list remember to update the following enum -#define CV_HARDWARE_MAX_FEATURE 255 - -/** @brief Available CPU features. -*/ -enum CpuFeatures { - CPU_MMX = 1, - CPU_SSE = 2, - CPU_SSE2 = 3, - CPU_SSE3 = 4, - CPU_SSSE3 = 5, - CPU_SSE4_1 = 6, - CPU_SSE4_2 = 7, - CPU_POPCNT = 8, - - CPU_AVX = 10, - CPU_AVX2 = 11, - CPU_FMA3 = 12, - - CPU_AVX_512F = 13, - CPU_AVX_512BW = 14, - CPU_AVX_512CD = 15, - CPU_AVX_512DQ = 16, - CPU_AVX_512ER = 17, - CPU_AVX_512IFMA512 = 18, - CPU_AVX_512PF = 19, - CPU_AVX_512VBMI = 20, - CPU_AVX_512VL = 21, - - CPU_NEON = 100 -}; - -// do not include SSE/AVX/NEON headers for NVCC compiler -#ifndef __CUDACC__ - -#if defined __SSE2__ || defined _M_X64 || (defined _M_IX86_FP && _M_IX86_FP >= 2) -# include -# define CV_MMX 1 -# define CV_SSE 1 -# define CV_SSE2 1 -# if defined __SSE3__ || (defined _MSC_VER && _MSC_VER >= 1500) -# include -# define CV_SSE3 1 -# endif -# if defined __SSSE3__ || (defined _MSC_VER && _MSC_VER >= 1500) -# include -# define CV_SSSE3 1 -# endif -# if defined __SSE4_1__ || (defined _MSC_VER && _MSC_VER >= 1500) -# include -# define CV_SSE4_1 1 -# endif -# if defined __SSE4_2__ || (defined _MSC_VER && _MSC_VER >= 1500) -# include -# define CV_SSE4_2 1 -# endif -# if defined __POPCNT__ || (defined _MSC_VER && _MSC_VER >= 1500) -# ifdef _MSC_VER -# include -# else -# include -# endif -# define CV_POPCNT 1 -# endif -# if defined __AVX__ || (defined _MSC_VER && _MSC_VER >= 1600 && 0) -// MS Visual Studio 2010 (2012?) has no macro pre-defined to identify the use of /arch:AVX -// See: http://connect.microsoft.com/VisualStudio/feedback/details/605858/arch-avx-should-define-a-predefined-macro-in-x64-and-set-a-unique-value-for-m-ix86-fp-in-win32 -# include -# define CV_AVX 1 -# if defined(_XCR_XFEATURE_ENABLED_MASK) -# define __xgetbv() _xgetbv(_XCR_XFEATURE_ENABLED_MASK) -# else -# define __xgetbv() 0 -# endif -# endif -# if defined __AVX2__ || (defined _MSC_VER && _MSC_VER >= 1800 && 0) -# include -# define CV_AVX2 1 -# if defined __FMA__ -# define CV_FMA3 1 -# endif -# endif -#endif - -#if (defined WIN32 || defined _WIN32) && defined(_M_ARM) -# include -# include "arm_neon.h" -# define CV_NEON 1 -# define CPU_HAS_NEON_FEATURE (true) -#elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__)) -# include -# define CV_NEON 1 -#endif - -#if defined __GNUC__ && defined __arm__ && (defined __ARM_PCS_VFP || defined __ARM_VFPV3__ || defined __ARM_NEON__) && !defined __SOFTFP__ -# define CV_VFP 1 -#endif - -#endif // __CUDACC__ - -#ifndef CV_POPCNT -#define CV_POPCNT 0 -#endif -#ifndef CV_MMX -# define CV_MMX 0 -#endif -#ifndef CV_SSE -# define CV_SSE 0 -#endif -#ifndef CV_SSE2 -# define CV_SSE2 0 -#endif -#ifndef CV_SSE3 -# define CV_SSE3 0 -#endif -#ifndef CV_SSSE3 -# define CV_SSSE3 0 -#endif -#ifndef CV_SSE4_1 -# define CV_SSE4_1 0 -#endif -#ifndef CV_SSE4_2 -# define CV_SSE4_2 0 -#endif -#ifndef CV_AVX -# define CV_AVX 0 -#endif -#ifndef CV_AVX2 -# define CV_AVX2 0 -#endif -#ifndef CV_FMA3 -# define CV_FMA3 0 -#endif -#ifndef CV_AVX_512F -# define CV_AVX_512F 0 -#endif -#ifndef CV_AVX_512BW -# define CV_AVX_512BW 0 -#endif -#ifndef CV_AVX_512CD -# define CV_AVX_512CD 0 -#endif -#ifndef CV_AVX_512DQ -# define CV_AVX_512DQ 0 -#endif -#ifndef CV_AVX_512ER -# define CV_AVX_512ER 0 -#endif -#ifndef CV_AVX_512IFMA512 -# define CV_AVX_512IFMA512 0 -#endif -#ifndef CV_AVX_512PF -# define CV_AVX_512PF 0 -#endif -#ifndef CV_AVX_512VBMI -# define CV_AVX_512VBMI 0 -#endif -#ifndef CV_AVX_512VL -# define CV_AVX_512VL 0 -#endif - -#ifndef CV_NEON -# define CV_NEON 0 -#endif - -#ifndef CV_VFP -# define CV_VFP 0 -#endif - -/* fundamental constants */ -#define CV_PI 3.1415926535897932384626433832795 -#define CV_2PI 6.283185307179586476925286766559 -#define CV_LOG2 0.69314718055994530941723212145818 - -typedef union Cv32suf -{ - int i; - unsigned u; - float f; -} -Cv32suf; - -typedef union Cv64suf -{ - int64 i; - uint64 u; - double f; -} -Cv64suf; - -#define OPENCV_ABI_COMPATIBILITY 300 - -#ifdef __OPENCV_BUILD -# define DISABLE_OPENCV_24_COMPATIBILITY -#endif - -#if (defined WIN32 || defined _WIN32 || defined WINCE || defined __CYGWIN__) && defined CVAPI_EXPORTS -# define CV_EXPORTS __declspec(dllexport) -#elif defined __GNUC__ && __GNUC__ >= 4 -# define CV_EXPORTS __attribute__ ((visibility ("default"))) -#else -# define CV_EXPORTS -#endif - -#ifndef CV_EXTERN_C -# ifdef __cplusplus -# define CV_EXTERN_C extern "C" -# else -# define CV_EXTERN_C -# endif -#endif - -/* special informative macros for wrapper generators */ -#define CV_EXPORTS_W CV_EXPORTS -#define CV_EXPORTS_W_SIMPLE CV_EXPORTS -#define CV_EXPORTS_AS(synonym) CV_EXPORTS -#define CV_EXPORTS_W_MAP CV_EXPORTS -#define CV_IN_OUT -#define CV_OUT -#define CV_PROP -#define CV_PROP_RW -#define CV_WRAP -#define CV_WRAP_AS(synonym) - -/****************************************************************************************\ -* Matrix type (Mat) * -\****************************************************************************************/ - -#define CV_CN_MAX 512 -#define CV_CN_SHIFT 3 -#define CV_DEPTH_MAX (1 << CV_CN_SHIFT) - -#define CV_8U 0 -#define CV_8S 1 -#define CV_16U 2 -#define CV_16S 3 -#define CV_32S 4 -#define CV_32F 5 -#define CV_64F 6 -#define CV_USRTYPE1 7 - -#define CV_MAT_DEPTH_MASK (CV_DEPTH_MAX - 1) -#define CV_MAT_DEPTH(flags) ((flags) & CV_MAT_DEPTH_MASK) - -#define CV_MAKETYPE(depth,cn) (CV_MAT_DEPTH(depth) + (((cn)-1) << CV_CN_SHIFT)) -#define CV_MAKE_TYPE CV_MAKETYPE - -#define CV_8UC1 CV_MAKETYPE(CV_8U,1) -#define CV_8UC2 CV_MAKETYPE(CV_8U,2) -#define CV_8UC3 CV_MAKETYPE(CV_8U,3) -#define CV_8UC4 CV_MAKETYPE(CV_8U,4) -#define CV_8UC(n) CV_MAKETYPE(CV_8U,(n)) - -#define CV_8SC1 CV_MAKETYPE(CV_8S,1) -#define CV_8SC2 CV_MAKETYPE(CV_8S,2) -#define CV_8SC3 CV_MAKETYPE(CV_8S,3) -#define CV_8SC4 CV_MAKETYPE(CV_8S,4) -#define CV_8SC(n) CV_MAKETYPE(CV_8S,(n)) - -#define CV_16UC1 CV_MAKETYPE(CV_16U,1) -#define CV_16UC2 CV_MAKETYPE(CV_16U,2) -#define CV_16UC3 CV_MAKETYPE(CV_16U,3) -#define CV_16UC4 CV_MAKETYPE(CV_16U,4) -#define CV_16UC(n) CV_MAKETYPE(CV_16U,(n)) - -#define CV_16SC1 CV_MAKETYPE(CV_16S,1) -#define CV_16SC2 CV_MAKETYPE(CV_16S,2) -#define CV_16SC3 CV_MAKETYPE(CV_16S,3) -#define CV_16SC4 CV_MAKETYPE(CV_16S,4) -#define CV_16SC(n) CV_MAKETYPE(CV_16S,(n)) - -#define CV_32SC1 CV_MAKETYPE(CV_32S,1) -#define CV_32SC2 CV_MAKETYPE(CV_32S,2) -#define CV_32SC3 CV_MAKETYPE(CV_32S,3) -#define CV_32SC4 CV_MAKETYPE(CV_32S,4) -#define CV_32SC(n) CV_MAKETYPE(CV_32S,(n)) - -#define CV_32FC1 CV_MAKETYPE(CV_32F,1) -#define CV_32FC2 CV_MAKETYPE(CV_32F,2) -#define CV_32FC3 CV_MAKETYPE(CV_32F,3) -#define CV_32FC4 CV_MAKETYPE(CV_32F,4) -#define CV_32FC(n) CV_MAKETYPE(CV_32F,(n)) - -#define CV_64FC1 CV_MAKETYPE(CV_64F,1) -#define CV_64FC2 CV_MAKETYPE(CV_64F,2) -#define CV_64FC3 CV_MAKETYPE(CV_64F,3) -#define CV_64FC4 CV_MAKETYPE(CV_64F,4) -#define CV_64FC(n) CV_MAKETYPE(CV_64F,(n)) - -#define CV_MAT_CN_MASK ((CV_CN_MAX - 1) << CV_CN_SHIFT) -#define CV_MAT_CN(flags) ((((flags) & CV_MAT_CN_MASK) >> CV_CN_SHIFT) + 1) -#define CV_MAT_TYPE_MASK (CV_DEPTH_MAX*CV_CN_MAX - 1) -#define CV_MAT_TYPE(flags) ((flags) & CV_MAT_TYPE_MASK) -#define CV_MAT_CONT_FLAG_SHIFT 14 -#define CV_MAT_CONT_FLAG (1 << CV_MAT_CONT_FLAG_SHIFT) -#define CV_IS_MAT_CONT(flags) ((flags) & CV_MAT_CONT_FLAG) -#define CV_IS_CONT_MAT CV_IS_MAT_CONT -#define CV_SUBMAT_FLAG_SHIFT 15 -#define CV_SUBMAT_FLAG (1 << CV_SUBMAT_FLAG_SHIFT) -#define CV_IS_SUBMAT(flags) ((flags) & CV_MAT_SUBMAT_FLAG) - -/** Size of each channel item, - 0x124489 = 1000 0100 0100 0010 0010 0001 0001 ~ array of sizeof(arr_type_elem) */ -#define CV_ELEM_SIZE1(type) \ - ((((sizeof(size_t)<<28)|0x8442211) >> CV_MAT_DEPTH(type)*4) & 15) - -/** 0x3a50 = 11 10 10 01 01 00 00 ~ array of log2(sizeof(arr_type_elem)) */ -#define CV_ELEM_SIZE(type) \ - (CV_MAT_CN(type) << ((((sizeof(size_t)/4+1)*16384|0x3a50) >> CV_MAT_DEPTH(type)*2) & 3)) - -#ifndef MIN -# define MIN(a,b) ((a) > (b) ? (b) : (a)) -#endif - -#ifndef MAX -# define MAX(a,b) ((a) < (b) ? (b) : (a)) -#endif - -/****************************************************************************************\ -* exchange-add operation for atomic operations on reference counters * -\****************************************************************************************/ - -#if defined __INTEL_COMPILER && !(defined WIN32 || defined _WIN32) - // atomic increment on the linux version of the Intel(tm) compiler -# define CV_XADD(addr, delta) (int)_InterlockedExchangeAdd(const_cast(reinterpret_cast(addr)), delta) -#elif defined __GNUC__ -# if defined __clang__ && __clang_major__ >= 3 && !defined __ANDROID__ && !defined __EMSCRIPTEN__ && !defined(__CUDACC__) -# ifdef __ATOMIC_ACQ_REL -# define CV_XADD(addr, delta) __c11_atomic_fetch_add((_Atomic(int)*)(addr), delta, __ATOMIC_ACQ_REL) -# else -# define CV_XADD(addr, delta) __atomic_fetch_add((_Atomic(int)*)(addr), delta, 4) -# endif -# else -# if defined __ATOMIC_ACQ_REL && !defined __clang__ - // version for gcc >= 4.7 -# define CV_XADD(addr, delta) (int)__atomic_fetch_add((unsigned*)(addr), (unsigned)(delta), __ATOMIC_ACQ_REL) -# else -# define CV_XADD(addr, delta) (int)__sync_fetch_and_add((unsigned*)(addr), (unsigned)(delta)) -# endif -# endif -#elif defined _MSC_VER && !defined RC_INVOKED -# include -# define CV_XADD(addr, delta) (int)_InterlockedExchangeAdd((long volatile*)addr, delta) -#else - CV_INLINE CV_XADD(int* addr, int delta) { int tmp = *addr; *addr += delta; return tmp; } -#endif - - -/****************************************************************************************\ -* CV_NORETURN attribute * -\****************************************************************************************/ - -#ifndef CV_NORETURN -# if defined(__GNUC__) -# define CV_NORETURN __attribute__((__noreturn__)) -# elif defined(_MSC_VER) && (_MSC_VER >= 1300) -# define CV_NORETURN __declspec(noreturn) -# else -# define CV_NORETURN /* nothing by default */ -# endif -#endif - - -/****************************************************************************************\ -* C++ Move semantics * -\****************************************************************************************/ - -#ifndef CV_CXX_MOVE_SEMANTICS -# if __cplusplus >= 201103L || defined(__GXX_EXPERIMENTAL_CXX0X__) || defined(_MSC_VER) && _MSC_VER >= 1600 -# define CV_CXX_MOVE_SEMANTICS 1 -# elif defined(__clang) -# if __has_feature(cxx_rvalue_references) -# define CV_CXX_MOVE_SEMANTICS 1 -# endif -# endif -#else -# if CV_CXX_MOVE_SEMANTICS == 0 -# undef CV_CXX_MOVE_SEMANTICS -# endif -#endif - -//! @} - -#endif // __OPENCV_CORE_CVDEF_H__ diff --git a/IPL/include/opencv/opencv2/core/cvstd.hpp b/IPL/include/opencv/opencv2/core/cvstd.hpp deleted file mode 100644 index edae954..0000000 --- a/IPL/include/opencv/opencv2/core/cvstd.hpp +++ /dev/null @@ -1,1069 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_CVSTD_HPP__ -#define __OPENCV_CORE_CVSTD_HPP__ - -#ifndef __cplusplus -# error cvstd.hpp header must be compiled as C++ -#endif - -#include "opencv2/core/cvdef.h" - -#include -#include -#include - -#ifndef OPENCV_NOSTL -# include -#endif - -// import useful primitives from stl -#ifndef OPENCV_NOSTL_TRANSITIONAL -# include -# include -# include //for abs(int) -# include - -namespace cv -{ - using std::min; - using std::max; - using std::abs; - using std::swap; - using std::sqrt; - using std::exp; - using std::pow; - using std::log; -} - -namespace std -{ - static inline uchar abs(uchar a) { return a; } - static inline ushort abs(ushort a) { return a; } - static inline unsigned abs(unsigned a) { return a; } - static inline uint64 abs(uint64 a) { return a; } -} - -#else -namespace cv -{ - template static inline T min(T a, T b) { return a < b ? a : b; } - template static inline T max(T a, T b) { return a > b ? a : b; } - template static inline T abs(T a) { return a < 0 ? -a : a; } - template static inline void swap(T& a, T& b) { T tmp = a; a = b; b = tmp; } - - template<> inline uchar abs(uchar a) { return a; } - template<> inline ushort abs(ushort a) { return a; } - template<> inline unsigned abs(unsigned a) { return a; } - template<> inline uint64 abs(uint64 a) { return a; } -} -#endif - -namespace cv { - -//! @addtogroup core_utils -//! @{ - -//////////////////////////// memory management functions //////////////////////////// - -/** @brief Allocates an aligned memory buffer. - -The function allocates the buffer of the specified size and returns it. When the buffer size is 16 -bytes or more, the returned buffer is aligned to 16 bytes. -@param bufSize Allocated buffer size. - */ -CV_EXPORTS void* fastMalloc(size_t bufSize); - -/** @brief Deallocates a memory buffer. - -The function deallocates the buffer allocated with fastMalloc . If NULL pointer is passed, the -function does nothing. C version of the function clears the pointer *pptr* to avoid problems with -double memory deallocation. -@param ptr Pointer to the allocated buffer. - */ -CV_EXPORTS void fastFree(void* ptr); - -/*! - The STL-compilant memory Allocator based on cv::fastMalloc() and cv::fastFree() -*/ -template class Allocator -{ -public: - typedef _Tp value_type; - typedef value_type* pointer; - typedef const value_type* const_pointer; - typedef value_type& reference; - typedef const value_type& const_reference; - typedef size_t size_type; - typedef ptrdiff_t difference_type; - template class rebind { typedef Allocator other; }; - - explicit Allocator() {} - ~Allocator() {} - explicit Allocator(Allocator const&) {} - template - explicit Allocator(Allocator const&) {} - - // address - pointer address(reference r) { return &r; } - const_pointer address(const_reference r) { return &r; } - - pointer allocate(size_type count, const void* =0) { return reinterpret_cast(fastMalloc(count * sizeof (_Tp))); } - void deallocate(pointer p, size_type) { fastFree(p); } - - void construct(pointer p, const _Tp& v) { new(static_cast(p)) _Tp(v); } - void destroy(pointer p) { p->~_Tp(); } - - size_type max_size() const { return cv::max(static_cast<_Tp>(-1)/sizeof(_Tp), 1); } -}; - -//! @} core_utils - -//! @cond IGNORED - -namespace detail -{ - -// Metafunction to avoid taking a reference to void. -template -struct RefOrVoid { typedef T& type; }; - -template<> -struct RefOrVoid{ typedef void type; }; - -template<> -struct RefOrVoid{ typedef const void type; }; - -template<> -struct RefOrVoid{ typedef volatile void type; }; - -template<> -struct RefOrVoid{ typedef const volatile void type; }; - -// This class would be private to Ptr, if it didn't have to be a non-template. -struct PtrOwner; - -} - -template -struct DefaultDeleter -{ - void operator () (Y* p) const; -}; - -//! @endcond - -//! @addtogroup core_basic -//! @{ - -/** @brief Template class for smart pointers with shared ownership - -A Ptr\ pretends to be a pointer to an object of type T. Unlike an ordinary pointer, however, the -object will be automatically cleaned up once all Ptr instances pointing to it are destroyed. - -Ptr is similar to boost::shared_ptr that is part of the Boost library -() and std::shared_ptr from -the [C++11](http://en.wikipedia.org/wiki/C++11) standard. - -This class provides the following advantages: -- Default constructor, copy constructor, and assignment operator for an arbitrary C++ class or C - structure. For some objects, like files, windows, mutexes, sockets, and others, a copy - constructor or an assignment operator are difficult to define. For some other objects, like - complex classifiers in OpenCV, copy constructors are absent and not easy to implement. Finally, - some of complex OpenCV and your own data structures may be written in C. However, copy - constructors and default constructors can simplify programming a lot. Besides, they are often - required (for example, by STL containers). By using a Ptr to such an object instead of the - object itself, you automatically get all of the necessary constructors and the assignment - operator. -- *O(1)* complexity of the above-mentioned operations. While some structures, like std::vector, - provide a copy constructor and an assignment operator, the operations may take a considerable - amount of time if the data structures are large. But if the structures are put into a Ptr, the - overhead is small and independent of the data size. -- Automatic and customizable cleanup, even for C structures. See the example below with FILE\*. -- Heterogeneous collections of objects. The standard STL and most other C++ and OpenCV containers - can store only objects of the same type and the same size. The classical solution to store - objects of different types in the same container is to store pointers to the base class (Base\*) - instead but then you lose the automatic memory management. Again, by using Ptr\ instead - of raw pointers, you can solve the problem. - -A Ptr is said to *own* a pointer - that is, for each Ptr there is a pointer that will be deleted -once all Ptr instances that own it are destroyed. The owned pointer may be null, in which case -nothing is deleted. Each Ptr also *stores* a pointer. The stored pointer is the pointer the Ptr -pretends to be; that is, the one you get when you use Ptr::get or the conversion to T\*. It's -usually the same as the owned pointer, but if you use casts or the general shared-ownership -constructor, the two may diverge: the Ptr will still own the original pointer, but will itself point -to something else. - -The owned pointer is treated as a black box. The only thing Ptr needs to know about it is how to -delete it. This knowledge is encapsulated in the *deleter* - an auxiliary object that is associated -with the owned pointer and shared between all Ptr instances that own it. The default deleter is an -instance of DefaultDeleter, which uses the standard C++ delete operator; as such it will work with -any pointer allocated with the standard new operator. - -However, if the pointer must be deleted in a different way, you must specify a custom deleter upon -Ptr construction. A deleter is simply a callable object that accepts the pointer as its sole -argument. For example, if you want to wrap FILE, you may do so as follows: -@code - Ptr f(fopen("myfile.txt", "w"), fclose); - if(!f) throw ...; - fprintf(f, ....); - ... - // the file will be closed automatically by f's destructor. -@endcode -Alternatively, if you want all pointers of a particular type to be deleted the same way, you can -specialize DefaultDeleter::operator() for that type, like this: -@code - namespace cv { - template<> void DefaultDeleter::operator ()(FILE * obj) const - { - fclose(obj); - } - } -@endcode -For convenience, the following types from the OpenCV C API already have such a specialization that -calls the appropriate release function: -- CvCapture -- CvFileStorage -- CvHaarClassifierCascade -- CvMat -- CvMatND -- CvMemStorage -- CvSparseMat -- CvVideoWriter -- IplImage -@note The shared ownership mechanism is implemented with reference counting. As such, cyclic -ownership (e.g. when object a contains a Ptr to object b, which contains a Ptr to object a) will -lead to all involved objects never being cleaned up. Avoid such situations. -@note It is safe to concurrently read (but not write) a Ptr instance from multiple threads and -therefore it is normally safe to use it in multi-threaded applications. The same is true for Mat and -other C++ OpenCV classes that use internal reference counts. -*/ -template -struct Ptr -{ - /** Generic programming support. */ - typedef T element_type; - - /** The default constructor creates a null Ptr - one that owns and stores a null pointer. - */ - Ptr(); - - /** - If p is null, these are equivalent to the default constructor. - Otherwise, these constructors assume ownership of p - that is, the created Ptr owns and stores p - and assumes it is the sole owner of it. Don't use them if p is already owned by another Ptr, or - else p will get deleted twice. - With the first constructor, DefaultDeleter\() becomes the associated deleter (so p will - eventually be deleted with the standard delete operator). Y must be a complete type at the point - of invocation. - With the second constructor, d becomes the associated deleter. - Y\* must be convertible to T\*. - @param p Pointer to own. - @note It is often easier to use makePtr instead. - */ - template -#ifdef DISABLE_OPENCV_24_COMPATIBILITY - explicit -#endif - Ptr(Y* p); - - /** @overload - @param d Deleter to use for the owned pointer. - @param p Pointer to own. - */ - template - Ptr(Y* p, D d); - - /** - These constructors create a Ptr that shares ownership with another Ptr - that is, own the same - pointer as o. - With the first two, the same pointer is stored, as well; for the second, Y\* must be convertible - to T\*. - With the third, p is stored, and Y may be any type. This constructor allows to have completely - unrelated owned and stored pointers, and should be used with care to avoid confusion. A relatively - benign use is to create a non-owning Ptr, like this: - @code - ptr = Ptr(Ptr(), dont_delete_me); // owns nothing; will not delete the pointer. - @endcode - @param o Ptr to share ownership with. - */ - Ptr(const Ptr& o); - - /** @overload - @param o Ptr to share ownership with. - */ - template - Ptr(const Ptr& o); - - /** @overload - @param o Ptr to share ownership with. - @param p Pointer to store. - */ - template - Ptr(const Ptr& o, T* p); - - /** The destructor is equivalent to calling Ptr::release. */ - ~Ptr(); - - /** - Assignment replaces the current Ptr instance with one that owns and stores same pointers as o and - then destroys the old instance. - @param o Ptr to share ownership with. - */ - Ptr& operator = (const Ptr& o); - - /** @overload */ - template - Ptr& operator = (const Ptr& o); - - /** If no other Ptr instance owns the owned pointer, deletes it with the associated deleter. Then sets - both the owned and the stored pointers to NULL. - */ - void release(); - - /** - `ptr.reset(...)` is equivalent to `ptr = Ptr(...)`. - @param p Pointer to own. - */ - template - void reset(Y* p); - - /** @overload - @param d Deleter to use for the owned pointer. - @param p Pointer to own. - */ - template - void reset(Y* p, D d); - - /** - Swaps the owned and stored pointers (and deleters, if any) of this and o. - @param o Ptr to swap with. - */ - void swap(Ptr& o); - - /** Returns the stored pointer. */ - T* get() const; - - /** Ordinary pointer emulation. */ - typename detail::RefOrVoid::type operator * () const; - - /** Ordinary pointer emulation. */ - T* operator -> () const; - - /** Equivalent to get(). */ - operator T* () const; - - /** ptr.empty() is equivalent to `!ptr.get()`. */ - bool empty() const; - - /** Returns a Ptr that owns the same pointer as this, and stores the same - pointer as this, except converted via static_cast to Y*. - */ - template - Ptr staticCast() const; - - /** Ditto for const_cast. */ - template - Ptr constCast() const; - - /** Ditto for dynamic_cast. */ - template - Ptr dynamicCast() const; - -#ifdef CV_CXX_MOVE_SEMANTICS - Ptr(Ptr&& o); - Ptr& operator = (Ptr&& o); -#endif - -private: - detail::PtrOwner* owner; - T* stored; - - template - friend struct Ptr; // have to do this for the cross-type copy constructor -}; - -/** Equivalent to ptr1.swap(ptr2). Provided to help write generic algorithms. */ -template -void swap(Ptr& ptr1, Ptr& ptr2); - -/** Return whether ptr1.get() and ptr2.get() are equal and not equal, respectively. */ -template -bool operator == (const Ptr& ptr1, const Ptr& ptr2); -template -bool operator != (const Ptr& ptr1, const Ptr& ptr2); - -/** `makePtr(...)` is equivalent to `Ptr(new T(...))`. It is shorter than the latter, and it's -marginally safer than using a constructor or Ptr::reset, since it ensures that the owned pointer -is new and thus not owned by any other Ptr instance. -Unfortunately, perfect forwarding is impossible to implement in C++03, and so makePtr is limited -to constructors of T that have up to 10 arguments, none of which are non-const references. - */ -template -Ptr makePtr(); -/** @overload */ -template -Ptr makePtr(const A1& a1); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10); - -//////////////////////////////// string class //////////////////////////////// - -class CV_EXPORTS FileNode; //for string constructor from FileNode - -class CV_EXPORTS String -{ -public: - typedef char value_type; - typedef char& reference; - typedef const char& const_reference; - typedef char* pointer; - typedef const char* const_pointer; - typedef ptrdiff_t difference_type; - typedef size_t size_type; - typedef char* iterator; - typedef const char* const_iterator; - - static const size_t npos = size_t(-1); - - explicit String(); - String(const String& str); - String(const String& str, size_t pos, size_t len = npos); - String(const char* s); - String(const char* s, size_t n); - String(size_t n, char c); - String(const char* first, const char* last); - template String(Iterator first, Iterator last); - explicit String(const FileNode& fn); - ~String(); - - String& operator=(const String& str); - String& operator=(const char* s); - String& operator=(char c); - - String& operator+=(const String& str); - String& operator+=(const char* s); - String& operator+=(char c); - - size_t size() const; - size_t length() const; - - char operator[](size_t idx) const; - char operator[](int idx) const; - - const char* begin() const; - const char* end() const; - - const char* c_str() const; - - bool empty() const; - void clear(); - - int compare(const char* s) const; - int compare(const String& str) const; - - void swap(String& str); - String substr(size_t pos = 0, size_t len = npos) const; - - size_t find(const char* s, size_t pos, size_t n) const; - size_t find(char c, size_t pos = 0) const; - size_t find(const String& str, size_t pos = 0) const; - size_t find(const char* s, size_t pos = 0) const; - - size_t rfind(const char* s, size_t pos, size_t n) const; - size_t rfind(char c, size_t pos = npos) const; - size_t rfind(const String& str, size_t pos = npos) const; - size_t rfind(const char* s, size_t pos = npos) const; - - size_t find_first_of(const char* s, size_t pos, size_t n) const; - size_t find_first_of(char c, size_t pos = 0) const; - size_t find_first_of(const String& str, size_t pos = 0) const; - size_t find_first_of(const char* s, size_t pos = 0) const; - - size_t find_last_of(const char* s, size_t pos, size_t n) const; - size_t find_last_of(char c, size_t pos = npos) const; - size_t find_last_of(const String& str, size_t pos = npos) const; - size_t find_last_of(const char* s, size_t pos = npos) const; - - friend String operator+ (const String& lhs, const String& rhs); - friend String operator+ (const String& lhs, const char* rhs); - friend String operator+ (const char* lhs, const String& rhs); - friend String operator+ (const String& lhs, char rhs); - friend String operator+ (char lhs, const String& rhs); - - String toLowerCase() const; - -#ifndef OPENCV_NOSTL - String(const std::string& str); - String(const std::string& str, size_t pos, size_t len = npos); - String& operator=(const std::string& str); - String& operator+=(const std::string& str); - operator std::string() const; - - friend String operator+ (const String& lhs, const std::string& rhs); - friend String operator+ (const std::string& lhs, const String& rhs); -#endif - -private: - char* cstr_; - size_t len_; - - char* allocate(size_t len); // len without trailing 0 - void deallocate(); - - String(int); // disabled and invalid. Catch invalid usages like, commandLineParser.has(0) problem -}; - -//! @} core_basic - -////////////////////////// cv::String implementation ///////////////////////// - -//! @cond IGNORED - -inline -String::String() - : cstr_(0), len_(0) -{} - -inline -String::String(const String& str) - : cstr_(str.cstr_), len_(str.len_) -{ - if (cstr_) - CV_XADD(((int*)cstr_)-1, 1); -} - -inline -String::String(const String& str, size_t pos, size_t len) - : cstr_(0), len_(0) -{ - pos = min(pos, str.len_); - len = min(str.len_ - pos, len); - if (!len) return; - if (len == str.len_) - { - CV_XADD(((int*)str.cstr_)-1, 1); - cstr_ = str.cstr_; - len_ = str.len_; - return; - } - memcpy(allocate(len), str.cstr_ + pos, len); -} - -inline -String::String(const char* s) - : cstr_(0), len_(0) -{ - if (!s) return; - size_t len = strlen(s); - memcpy(allocate(len), s, len); -} - -inline -String::String(const char* s, size_t n) - : cstr_(0), len_(0) -{ - if (!n) return; - memcpy(allocate(n), s, n); -} - -inline -String::String(size_t n, char c) - : cstr_(0), len_(0) -{ - memset(allocate(n), c, n); -} - -inline -String::String(const char* first, const char* last) - : cstr_(0), len_(0) -{ - size_t len = (size_t)(last - first); - memcpy(allocate(len), first, len); -} - -template inline -String::String(Iterator first, Iterator last) - : cstr_(0), len_(0) -{ - size_t len = (size_t)(last - first); - char* str = allocate(len); - while (first != last) - { - *str++ = *first; - ++first; - } -} - -inline -String::~String() -{ - deallocate(); -} - -inline -String& String::operator=(const String& str) -{ - if (&str == this) return *this; - - deallocate(); - if (str.cstr_) CV_XADD(((int*)str.cstr_)-1, 1); - cstr_ = str.cstr_; - len_ = str.len_; - return *this; -} - -inline -String& String::operator=(const char* s) -{ - deallocate(); - if (!s) return *this; - size_t len = strlen(s); - memcpy(allocate(len), s, len); - return *this; -} - -inline -String& String::operator=(char c) -{ - deallocate(); - allocate(1)[0] = c; - return *this; -} - -inline -String& String::operator+=(const String& str) -{ - *this = *this + str; - return *this; -} - -inline -String& String::operator+=(const char* s) -{ - *this = *this + s; - return *this; -} - -inline -String& String::operator+=(char c) -{ - *this = *this + c; - return *this; -} - -inline -size_t String::size() const -{ - return len_; -} - -inline -size_t String::length() const -{ - return len_; -} - -inline -char String::operator[](size_t idx) const -{ - return cstr_[idx]; -} - -inline -char String::operator[](int idx) const -{ - return cstr_[idx]; -} - -inline -const char* String::begin() const -{ - return cstr_; -} - -inline -const char* String::end() const -{ - return len_ ? cstr_ + 1 : 0; -} - -inline -bool String::empty() const -{ - return len_ == 0; -} - -inline -const char* String::c_str() const -{ - return cstr_ ? cstr_ : ""; -} - -inline -void String::swap(String& str) -{ - cv::swap(cstr_, str.cstr_); - cv::swap(len_, str.len_); -} - -inline -void String::clear() -{ - deallocate(); -} - -inline -int String::compare(const char* s) const -{ - if (cstr_ == s) return 0; - return strcmp(c_str(), s); -} - -inline -int String::compare(const String& str) const -{ - if (cstr_ == str.cstr_) return 0; - return strcmp(c_str(), str.c_str()); -} - -inline -String String::substr(size_t pos, size_t len) const -{ - return String(*this, pos, len); -} - -inline -size_t String::find(const char* s, size_t pos, size_t n) const -{ - if (n == 0 || pos + n > len_) return npos; - const char* lmax = cstr_ + len_ - n; - for (const char* i = cstr_ + pos; i <= lmax; ++i) - { - size_t j = 0; - while (j < n && s[j] == i[j]) ++j; - if (j == n) return (size_t)(i - cstr_); - } - return npos; -} - -inline -size_t String::find(char c, size_t pos) const -{ - return find(&c, pos, 1); -} - -inline -size_t String::find(const String& str, size_t pos) const -{ - return find(str.c_str(), pos, str.len_); -} - -inline -size_t String::find(const char* s, size_t pos) const -{ - if (pos >= len_ || !s[0]) return npos; - const char* lmax = cstr_ + len_; - for (const char* i = cstr_ + pos; i < lmax; ++i) - { - size_t j = 0; - while (s[j] && s[j] == i[j]) - { if(i + j >= lmax) return npos; - ++j; - } - if (!s[j]) return (size_t)(i - cstr_); - } - return npos; -} - -inline -size_t String::rfind(const char* s, size_t pos, size_t n) const -{ - if (n > len_) return npos; - if (pos > len_ - n) pos = len_ - n; - for (const char* i = cstr_ + pos; i >= cstr_; --i) - { - size_t j = 0; - while (j < n && s[j] == i[j]) ++j; - if (j == n) return (size_t)(i - cstr_); - } - return npos; -} - -inline -size_t String::rfind(char c, size_t pos) const -{ - return rfind(&c, pos, 1); -} - -inline -size_t String::rfind(const String& str, size_t pos) const -{ - return rfind(str.c_str(), pos, str.len_); -} - -inline -size_t String::rfind(const char* s, size_t pos) const -{ - return rfind(s, pos, strlen(s)); -} - -inline -size_t String::find_first_of(const char* s, size_t pos, size_t n) const -{ - if (n == 0 || pos + n > len_) return npos; - const char* lmax = cstr_ + len_; - for (const char* i = cstr_ + pos; i < lmax; ++i) - { - for (size_t j = 0; j < n; ++j) - if (s[j] == *i) - return (size_t)(i - cstr_); - } - return npos; -} - -inline -size_t String::find_first_of(char c, size_t pos) const -{ - return find_first_of(&c, pos, 1); -} - -inline -size_t String::find_first_of(const String& str, size_t pos) const -{ - return find_first_of(str.c_str(), pos, str.len_); -} - -inline -size_t String::find_first_of(const char* s, size_t pos) const -{ - if (len_ == 0) return npos; - if (pos >= len_ || !s[0]) return npos; - const char* lmax = cstr_ + len_; - for (const char* i = cstr_ + pos; i < lmax; ++i) - { - for (size_t j = 0; s[j]; ++j) - if (s[j] == *i) - return (size_t)(i - cstr_); - } - return npos; -} - -inline -size_t String::find_last_of(const char* s, size_t pos, size_t n) const -{ - if (len_ == 0) return npos; - if (pos >= len_) pos = len_ - 1; - for (const char* i = cstr_ + pos; i >= cstr_; --i) - { - for (size_t j = 0; j < n; ++j) - if (s[j] == *i) - return (size_t)(i - cstr_); - } - return npos; -} - -inline -size_t String::find_last_of(char c, size_t pos) const -{ - return find_last_of(&c, pos, 1); -} - -inline -size_t String::find_last_of(const String& str, size_t pos) const -{ - return find_last_of(str.c_str(), pos, str.len_); -} - -inline -size_t String::find_last_of(const char* s, size_t pos) const -{ - if (len_ == 0) return npos; - if (pos >= len_) pos = len_ - 1; - for (const char* i = cstr_ + pos; i >= cstr_; --i) - { - for (size_t j = 0; s[j]; ++j) - if (s[j] == *i) - return (size_t)(i - cstr_); - } - return npos; -} - -inline -String String::toLowerCase() const -{ - String res(cstr_, len_); - - for (size_t i = 0; i < len_; ++i) - res.cstr_[i] = (char) ::tolower(cstr_[i]); - - return res; -} - -//! @endcond - -// ************************* cv::String non-member functions ************************* - -//! @relates cv::String -//! @{ - -inline -String operator + (const String& lhs, const String& rhs) -{ - String s; - s.allocate(lhs.len_ + rhs.len_); - memcpy(s.cstr_, lhs.cstr_, lhs.len_); - memcpy(s.cstr_ + lhs.len_, rhs.cstr_, rhs.len_); - return s; -} - -inline -String operator + (const String& lhs, const char* rhs) -{ - String s; - size_t rhslen = strlen(rhs); - s.allocate(lhs.len_ + rhslen); - memcpy(s.cstr_, lhs.cstr_, lhs.len_); - memcpy(s.cstr_ + lhs.len_, rhs, rhslen); - return s; -} - -inline -String operator + (const char* lhs, const String& rhs) -{ - String s; - size_t lhslen = strlen(lhs); - s.allocate(lhslen + rhs.len_); - memcpy(s.cstr_, lhs, lhslen); - memcpy(s.cstr_ + lhslen, rhs.cstr_, rhs.len_); - return s; -} - -inline -String operator + (const String& lhs, char rhs) -{ - String s; - s.allocate(lhs.len_ + 1); - memcpy(s.cstr_, lhs.cstr_, lhs.len_); - s.cstr_[lhs.len_] = rhs; - return s; -} - -inline -String operator + (char lhs, const String& rhs) -{ - String s; - s.allocate(rhs.len_ + 1); - s.cstr_[0] = lhs; - memcpy(s.cstr_ + 1, rhs.cstr_, rhs.len_); - return s; -} - -static inline bool operator== (const String& lhs, const String& rhs) { return 0 == lhs.compare(rhs); } -static inline bool operator== (const char* lhs, const String& rhs) { return 0 == rhs.compare(lhs); } -static inline bool operator== (const String& lhs, const char* rhs) { return 0 == lhs.compare(rhs); } -static inline bool operator!= (const String& lhs, const String& rhs) { return 0 != lhs.compare(rhs); } -static inline bool operator!= (const char* lhs, const String& rhs) { return 0 != rhs.compare(lhs); } -static inline bool operator!= (const String& lhs, const char* rhs) { return 0 != lhs.compare(rhs); } -static inline bool operator< (const String& lhs, const String& rhs) { return lhs.compare(rhs) < 0; } -static inline bool operator< (const char* lhs, const String& rhs) { return rhs.compare(lhs) > 0; } -static inline bool operator< (const String& lhs, const char* rhs) { return lhs.compare(rhs) < 0; } -static inline bool operator<= (const String& lhs, const String& rhs) { return lhs.compare(rhs) <= 0; } -static inline bool operator<= (const char* lhs, const String& rhs) { return rhs.compare(lhs) >= 0; } -static inline bool operator<= (const String& lhs, const char* rhs) { return lhs.compare(rhs) <= 0; } -static inline bool operator> (const String& lhs, const String& rhs) { return lhs.compare(rhs) > 0; } -static inline bool operator> (const char* lhs, const String& rhs) { return rhs.compare(lhs) < 0; } -static inline bool operator> (const String& lhs, const char* rhs) { return lhs.compare(rhs) > 0; } -static inline bool operator>= (const String& lhs, const String& rhs) { return lhs.compare(rhs) >= 0; } -static inline bool operator>= (const char* lhs, const String& rhs) { return rhs.compare(lhs) <= 0; } -static inline bool operator>= (const String& lhs, const char* rhs) { return lhs.compare(rhs) >= 0; } - -//! @} relates cv::String - -} // cv - -#ifndef OPENCV_NOSTL_TRANSITIONAL -namespace std -{ - static inline void swap(cv::String& a, cv::String& b) { a.swap(b); } -} -#else -namespace cv -{ - template<> inline - void swap(cv::String& a, cv::String& b) - { - a.swap(b); - } -} -#endif - -#include "opencv2/core/ptr.inl.hpp" - -#endif //__OPENCV_CORE_CVSTD_HPP__ diff --git a/IPL/include/opencv/opencv2/core/cvstd.inl.hpp b/IPL/include/opencv/opencv2/core/cvstd.inl.hpp deleted file mode 100644 index ad15406..0000000 --- a/IPL/include/opencv/opencv2/core/cvstd.inl.hpp +++ /dev/null @@ -1,267 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_CVSTDINL_HPP__ -#define __OPENCV_CORE_CVSTDINL_HPP__ - -#ifndef OPENCV_NOSTL -# include -# include -#endif - -//! @cond IGNORED - -namespace cv -{ -#ifndef OPENCV_NOSTL - -template class DataType< std::complex<_Tp> > -{ -public: - typedef std::complex<_Tp> value_type; - typedef value_type work_type; - typedef _Tp channel_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = 2, - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) }; - - typedef Vec vec_type; -}; - -inline -String::String(const std::string& str) - : cstr_(0), len_(0) -{ - if (!str.empty()) - { - size_t len = str.size(); - memcpy(allocate(len), str.c_str(), len); - } -} - -inline -String::String(const std::string& str, size_t pos, size_t len) - : cstr_(0), len_(0) -{ - size_t strlen = str.size(); - pos = min(pos, strlen); - len = min(strlen - pos, len); - if (!len) return; - memcpy(allocate(len), str.c_str() + pos, len); -} - -inline -String& String::operator = (const std::string& str) -{ - deallocate(); - if (!str.empty()) - { - size_t len = str.size(); - memcpy(allocate(len), str.c_str(), len); - } - return *this; -} - -inline -String& String::operator += (const std::string& str) -{ - *this = *this + str; - return *this; -} - -inline -String::operator std::string() const -{ - return std::string(cstr_, len_); -} - -inline -String operator + (const String& lhs, const std::string& rhs) -{ - String s; - size_t rhslen = rhs.size(); - s.allocate(lhs.len_ + rhslen); - memcpy(s.cstr_, lhs.cstr_, lhs.len_); - memcpy(s.cstr_ + lhs.len_, rhs.c_str(), rhslen); - return s; -} - -inline -String operator + (const std::string& lhs, const String& rhs) -{ - String s; - size_t lhslen = lhs.size(); - s.allocate(lhslen + rhs.len_); - memcpy(s.cstr_, lhs.c_str(), lhslen); - memcpy(s.cstr_ + lhslen, rhs.cstr_, rhs.len_); - return s; -} - -inline -FileNode::operator std::string() const -{ - String value; - read(*this, value, value); - return value; -} - -template<> inline -void operator >> (const FileNode& n, std::string& value) -{ - String val; - read(n, val, val); - value = val; -} - -template<> inline -FileStorage& operator << (FileStorage& fs, const std::string& value) -{ - return fs << cv::String(value); -} - -static inline -std::ostream& operator << (std::ostream& os, const String& str) -{ - return os << str.c_str(); -} - -static inline -std::ostream& operator << (std::ostream& out, Ptr fmtd) -{ - fmtd->reset(); - for(const char* str = fmtd->next(); str; str = fmtd->next()) - out << str; - return out; -} - -static inline -std::ostream& operator << (std::ostream& out, const Mat& mtx) -{ - return out << Formatter::get()->format(mtx); -} - -template static inline -std::ostream& operator << (std::ostream& out, const std::vector >& vec) -{ - return out << Formatter::get()->format(Mat(vec)); -} - - -template static inline -std::ostream& operator << (std::ostream& out, const std::vector >& vec) -{ - return out << Formatter::get()->format(Mat(vec)); -} - - -template static inline -std::ostream& operator << (std::ostream& out, const Matx<_Tp, m, n>& matx) -{ - return out << Formatter::get()->format(Mat(matx)); -} - -template static inline -std::ostream& operator << (std::ostream& out, const Point_<_Tp>& p) -{ - out << "[" << p.x << ", " << p.y << "]"; - return out; -} - -template static inline -std::ostream& operator << (std::ostream& out, const Point3_<_Tp>& p) -{ - out << "[" << p.x << ", " << p.y << ", " << p.z << "]"; - return out; -} - -template static inline -std::ostream& operator << (std::ostream& out, const Vec<_Tp, n>& vec) -{ - out << "["; -#ifdef _MSC_VER -#pragma warning( push ) -#pragma warning( disable: 4127 ) -#endif - if(Vec<_Tp, n>::depth < CV_32F) -#ifdef _MSC_VER -#pragma warning( pop ) -#endif - { - for (int i = 0; i < n - 1; ++i) { - out << (int)vec[i] << ", "; - } - out << (int)vec[n-1] << "]"; - } - else - { - for (int i = 0; i < n - 1; ++i) { - out << vec[i] << ", "; - } - out << vec[n-1] << "]"; - } - - return out; -} - -template static inline -std::ostream& operator << (std::ostream& out, const Size_<_Tp>& size) -{ - return out << "[" << size.width << " x " << size.height << "]"; -} - -template static inline -std::ostream& operator << (std::ostream& out, const Rect_<_Tp>& rect) -{ - return out << "[" << rect.width << " x " << rect.height << " from (" << rect.x << ", " << rect.y << ")]"; -} - - -#endif // OPENCV_NOSTL -} // cv - -//! @endcond - -#endif // __OPENCV_CORE_CVSTDINL_HPP__ diff --git a/IPL/include/opencv/opencv2/core/directx.hpp b/IPL/include/opencv/opencv2/core/directx.hpp deleted file mode 100644 index 764af74..0000000 --- a/IPL/include/opencv/opencv2/core/directx.hpp +++ /dev/null @@ -1,184 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors as is and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the copyright holders or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_DIRECTX_HPP__ -#define __OPENCV_CORE_DIRECTX_HPP__ - -#include "mat.hpp" -#include "ocl.hpp" - -#if !defined(__d3d11_h__) -struct ID3D11Device; -struct ID3D11Texture2D; -#endif - -#if !defined(__d3d10_h__) -struct ID3D10Device; -struct ID3D10Texture2D; -#endif - -#if !defined(_D3D9_H_) -struct IDirect3DDevice9; -struct IDirect3DDevice9Ex; -struct IDirect3DSurface9; -#endif - - -namespace cv { namespace directx { - -namespace ocl { -using namespace cv::ocl; - -//! @addtogroup core_directx -// This section describes OpenCL and DirectX interoperability. -// -// To enable DirectX support, configure OpenCV using CMake with WITH_DIRECTX=ON . Note, DirectX is -// supported only on Windows. -// -// To use OpenCL functionality you should first initialize OpenCL context from DirectX resource. -// -//! @{ - -// TODO static functions in the Context class -//! @brief Creates OpenCL context from D3D11 device -// -//! @param pD3D11Device - pointer to D3D11 device -//! @return Returns reference to OpenCL Context -CV_EXPORTS Context& initializeContextFromD3D11Device(ID3D11Device* pD3D11Device); - -//! @brief Creates OpenCL context from D3D10 device -// -//! @param pD3D10Device - pointer to D3D10 device -//! @return Returns reference to OpenCL Context -CV_EXPORTS Context& initializeContextFromD3D10Device(ID3D10Device* pD3D10Device); - -//! @brief Creates OpenCL context from Direct3DDevice9Ex device -// -//! @param pDirect3DDevice9Ex - pointer to Direct3DDevice9Ex device -//! @return Returns reference to OpenCL Context -CV_EXPORTS Context& initializeContextFromDirect3DDevice9Ex(IDirect3DDevice9Ex* pDirect3DDevice9Ex); - -//! @brief Creates OpenCL context from Direct3DDevice9 device -// -//! @param pDirect3DDevice9 - pointer to Direct3Device9 device -//! @return Returns reference to OpenCL Context -CV_EXPORTS Context& initializeContextFromDirect3DDevice9(IDirect3DDevice9* pDirect3DDevice9); - -//! @} - -} // namespace cv::directx::ocl - -//! @addtogroup core_directx -//! @{ - -//! @brief Converts InputArray to ID3D11Texture2D. If destination texture format is DXGI_FORMAT_NV12 then -//! input UMat expected to be in BGR format and data will be downsampled and color-converted to NV12. -// -//! @note Note: Destination texture must be allocated by application. Function does memory copy from src to -//! pD3D11Texture2D -// -//! @param src - source InputArray -//! @param pD3D11Texture2D - destination D3D11 texture -CV_EXPORTS void convertToD3D11Texture2D(InputArray src, ID3D11Texture2D* pD3D11Texture2D); - -//! @brief Converts ID3D11Texture2D to OutputArray. If input texture format is DXGI_FORMAT_NV12 then -//! data will be upsampled and color-converted to BGR format. -// -//! @note Note: Destination matrix will be re-allocated if it has not enough memory to match texture size. -//! function does memory copy from pD3D11Texture2D to dst -// -//! @param pD3D11Texture2D - source D3D11 texture -//! @param dst - destination OutputArray -CV_EXPORTS void convertFromD3D11Texture2D(ID3D11Texture2D* pD3D11Texture2D, OutputArray dst); - -//! @brief Converts InputArray to ID3D10Texture2D -// -//! @note Note: function does memory copy from src to -//! pD3D10Texture2D -// -//! @param src - source InputArray -//! @param pD3D10Texture2D - destination D3D10 texture -CV_EXPORTS void convertToD3D10Texture2D(InputArray src, ID3D10Texture2D* pD3D10Texture2D); - -//! @brief Converts ID3D10Texture2D to OutputArray -// -//! @note Note: function does memory copy from pD3D10Texture2D -//! to dst -// -//! @param pD3D10Texture2D - source D3D10 texture -//! @param dst - destination OutputArray -CV_EXPORTS void convertFromD3D10Texture2D(ID3D10Texture2D* pD3D10Texture2D, OutputArray dst); - -//! @brief Converts InputArray to IDirect3DSurface9 -// -//! @note Note: function does memory copy from src to -//! pDirect3DSurface9 -// -//! @param src - source InputArray -//! @param pDirect3DSurface9 - destination D3D10 texture -//! @param surfaceSharedHandle - shared handle -CV_EXPORTS void convertToDirect3DSurface9(InputArray src, IDirect3DSurface9* pDirect3DSurface9, void* surfaceSharedHandle = NULL); - -//! @brief Converts IDirect3DSurface9 to OutputArray -// -//! @note Note: function does memory copy from pDirect3DSurface9 -//! to dst -// -//! @param pDirect3DSurface9 - source D3D10 texture -//! @param dst - destination OutputArray -//! @param surfaceSharedHandle - shared handle -CV_EXPORTS void convertFromDirect3DSurface9(IDirect3DSurface9* pDirect3DSurface9, OutputArray dst, void* surfaceSharedHandle = NULL); - -//! @brief Get OpenCV type from DirectX type -//! @param iDXGI_FORMAT - enum DXGI_FORMAT for D3D10/D3D11 -//! @return OpenCV type or -1 if there is no equivalent -CV_EXPORTS int getTypeFromDXGI_FORMAT(const int iDXGI_FORMAT); // enum DXGI_FORMAT for D3D10/D3D11 - -//! @brief Get OpenCV type from DirectX type -//! @param iD3DFORMAT - enum D3DTYPE for D3D9 -//! @return OpenCV type or -1 if there is no equivalent -CV_EXPORTS int getTypeFromD3DFORMAT(const int iD3DFORMAT); // enum D3DTYPE for D3D9 - -//! @} - -} } // namespace cv::directx - -#endif // __OPENCV_CORE_DIRECTX_HPP__ diff --git a/IPL/include/opencv/opencv2/core/eigen.hpp b/IPL/include/opencv/opencv2/core/eigen.hpp deleted file mode 100644 index 44df04c..0000000 --- a/IPL/include/opencv/opencv2/core/eigen.hpp +++ /dev/null @@ -1,280 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - - -#ifndef __OPENCV_CORE_EIGEN_HPP__ -#define __OPENCV_CORE_EIGEN_HPP__ - -#include "opencv2/core.hpp" - -#if defined _MSC_VER && _MSC_VER >= 1200 -#pragma warning( disable: 4714 ) //__forceinline is not inlined -#pragma warning( disable: 4127 ) //conditional expression is constant -#pragma warning( disable: 4244 ) //conversion from '__int64' to 'int', possible loss of data -#endif - -namespace cv -{ - -//! @addtogroup core_eigen -//! @{ - -template static inline -void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, Mat& dst ) -{ - if( !(src.Flags & Eigen::RowMajorBit) ) - { - Mat _src(src.cols(), src.rows(), DataType<_Tp>::type, - (void*)src.data(), src.stride()*sizeof(_Tp)); - transpose(_src, dst); - } - else - { - Mat _src(src.rows(), src.cols(), DataType<_Tp>::type, - (void*)src.data(), src.stride()*sizeof(_Tp)); - _src.copyTo(dst); - } -} - -// Matx case -template static inline -void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, - Matx<_Tp, _rows, _cols>& dst ) -{ - if( !(src.Flags & Eigen::RowMajorBit) ) - { - dst = Matx<_Tp, _cols, _rows>(static_cast(src.data())).t(); - } - else - { - dst = Matx<_Tp, _rows, _cols>(static_cast(src.data())); - } -} - -template static inline -void cv2eigen( const Mat& src, - Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst ) -{ - CV_DbgAssert(src.rows == _rows && src.cols == _cols); - if( !(dst.Flags & Eigen::RowMajorBit) ) - { - const Mat _dst(src.cols, src.rows, DataType<_Tp>::type, - dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); - if( src.type() == _dst.type() ) - transpose(src, _dst); - else if( src.cols == src.rows ) - { - src.convertTo(_dst, _dst.type()); - transpose(_dst, _dst); - } - else - Mat(src.t()).convertTo(_dst, _dst.type()); - } - else - { - const Mat _dst(src.rows, src.cols, DataType<_Tp>::type, - dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); - src.convertTo(_dst, _dst.type()); - } -} - -// Matx case -template static inline -void cv2eigen( const Matx<_Tp, _rows, _cols>& src, - Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst ) -{ - if( !(dst.Flags & Eigen::RowMajorBit) ) - { - const Mat _dst(_cols, _rows, DataType<_Tp>::type, - dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); - transpose(src, _dst); - } - else - { - const Mat _dst(_rows, _cols, DataType<_Tp>::type, - dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); - Mat(src).copyTo(_dst); - } -} - -template static inline -void cv2eigen( const Mat& src, - Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst ) -{ - dst.resize(src.rows, src.cols); - if( !(dst.Flags & Eigen::RowMajorBit) ) - { - const Mat _dst(src.cols, src.rows, DataType<_Tp>::type, - dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); - if( src.type() == _dst.type() ) - transpose(src, _dst); - else if( src.cols == src.rows ) - { - src.convertTo(_dst, _dst.type()); - transpose(_dst, _dst); - } - else - Mat(src.t()).convertTo(_dst, _dst.type()); - } - else - { - const Mat _dst(src.rows, src.cols, DataType<_Tp>::type, - dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); - src.convertTo(_dst, _dst.type()); - } -} - -// Matx case -template static inline -void cv2eigen( const Matx<_Tp, _rows, _cols>& src, - Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst ) -{ - dst.resize(_rows, _cols); - if( !(dst.Flags & Eigen::RowMajorBit) ) - { - const Mat _dst(_cols, _rows, DataType<_Tp>::type, - dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); - transpose(src, _dst); - } - else - { - const Mat _dst(_rows, _cols, DataType<_Tp>::type, - dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); - Mat(src).copyTo(_dst); - } -} - -template static inline -void cv2eigen( const Mat& src, - Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst ) -{ - CV_Assert(src.cols == 1); - dst.resize(src.rows); - - if( !(dst.Flags & Eigen::RowMajorBit) ) - { - const Mat _dst(src.cols, src.rows, DataType<_Tp>::type, - dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); - if( src.type() == _dst.type() ) - transpose(src, _dst); - else - Mat(src.t()).convertTo(_dst, _dst.type()); - } - else - { - const Mat _dst(src.rows, src.cols, DataType<_Tp>::type, - dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); - src.convertTo(_dst, _dst.type()); - } -} - -// Matx case -template static inline -void cv2eigen( const Matx<_Tp, _rows, 1>& src, - Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst ) -{ - dst.resize(_rows); - - if( !(dst.Flags & Eigen::RowMajorBit) ) - { - const Mat _dst(1, _rows, DataType<_Tp>::type, - dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); - transpose(src, _dst); - } - else - { - const Mat _dst(_rows, 1, DataType<_Tp>::type, - dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); - src.copyTo(_dst); - } -} - - -template static inline -void cv2eigen( const Mat& src, - Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst ) -{ - CV_Assert(src.rows == 1); - dst.resize(src.cols); - if( !(dst.Flags & Eigen::RowMajorBit) ) - { - const Mat _dst(src.cols, src.rows, DataType<_Tp>::type, - dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); - if( src.type() == _dst.type() ) - transpose(src, _dst); - else - Mat(src.t()).convertTo(_dst, _dst.type()); - } - else - { - const Mat _dst(src.rows, src.cols, DataType<_Tp>::type, - dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); - src.convertTo(_dst, _dst.type()); - } -} - -//Matx -template static inline -void cv2eigen( const Matx<_Tp, 1, _cols>& src, - Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst ) -{ - dst.resize(_cols); - if( !(dst.Flags & Eigen::RowMajorBit) ) - { - const Mat _dst(_cols, 1, DataType<_Tp>::type, - dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); - transpose(src, _dst); - } - else - { - const Mat _dst(1, _cols, DataType<_Tp>::type, - dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); - Mat(src).copyTo(_dst); - } -} - -//! @} - -} // cv - -#endif diff --git a/IPL/include/opencv/opencv2/core/fast_math.hpp b/IPL/include/opencv/opencv2/core/fast_math.hpp deleted file mode 100644 index b8b241b..0000000 --- a/IPL/include/opencv/opencv2/core/fast_math.hpp +++ /dev/null @@ -1,302 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_FAST_MATH_HPP__ -#define __OPENCV_CORE_FAST_MATH_HPP__ - -#include "opencv2/core/cvdef.h" - -//! @addtogroup core_utils -//! @{ - -/****************************************************************************************\ -* fast math * -\****************************************************************************************/ - -#if defined __BORLANDC__ -# include -#elif defined __cplusplus -# include -#else -# include -#endif - -#ifdef HAVE_TEGRA_OPTIMIZATION -# include "tegra_round.hpp" -#endif - -#if CV_VFP - // 1. general scheme - #define ARM_ROUND(_value, _asm_string) \ - int res; \ - float temp; \ - asm(_asm_string : [res] "=r" (res), [temp] "=w" (temp) : [value] "w" (_value)); \ - return res - // 2. version for double - #ifdef __clang__ - #define ARM_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %[value] \n vmov %[res], %[temp]") - #else - #define ARM_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %P[value] \n vmov %[res], %[temp]") - #endif - // 3. version for float - #define ARM_ROUND_FLT(value) ARM_ROUND(value, "vcvtr.s32.f32 %[temp], %[value]\n vmov %[res], %[temp]") -#endif // CV_VFP - -/** @brief Rounds floating-point number to the nearest integer - - @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the - result is not defined. - */ -CV_INLINE int -cvRound( double value ) -{ -#if ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \ - && defined __SSE2__ && !defined __APPLE__)) && !defined(__CUDACC__) - __m128d t = _mm_set_sd( value ); - return _mm_cvtsd_si32(t); -#elif defined _MSC_VER && defined _M_IX86 - int t; - __asm - { - fld value; - fistp t; - } - return t; -#elif ((defined _MSC_VER && defined _M_ARM) || defined CV_ICC || \ - defined __GNUC__) && defined HAVE_TEGRA_OPTIMIZATION - TEGRA_ROUND_DBL(value); -#elif defined CV_ICC || defined __GNUC__ -# if CV_VFP - ARM_ROUND_DBL(value); -# else - return (int)lrint(value); -# endif -#else - /* it's ok if round does not comply with IEEE754 standard; - the tests should allow +/-1 difference when the tested functions use round */ - return (int)(value + (value >= 0 ? 0.5 : -0.5)); -#endif -} - - -/** @brief Rounds floating-point number to the nearest integer not larger than the original. - - The function computes an integer i such that: - \f[i \le \texttt{value} < i+1\f] - @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the - result is not defined. - */ -CV_INLINE int cvFloor( double value ) -{ -#if (defined _MSC_VER && defined _M_X64 || (defined __GNUC__ && defined __SSE2__ && !defined __APPLE__)) && !defined(__CUDACC__) - __m128d t = _mm_set_sd( value ); - int i = _mm_cvtsd_si32(t); - return i - _mm_movemask_pd(_mm_cmplt_sd(t, _mm_cvtsi32_sd(t,i))); -#elif defined __GNUC__ - int i = (int)value; - return i - (i > value); -#else - int i = cvRound(value); - float diff = (float)(value - i); - return i - (diff < 0); -#endif -} - -/** @brief Rounds floating-point number to the nearest integer not smaller than the original. - - The function computes an integer i such that: - \f[i \le \texttt{value} < i+1\f] - @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the - result is not defined. - */ -CV_INLINE int cvCeil( double value ) -{ -#if (defined _MSC_VER && defined _M_X64 || (defined __GNUC__ && defined __SSE2__&& !defined __APPLE__)) && !defined(__CUDACC__) - __m128d t = _mm_set_sd( value ); - int i = _mm_cvtsd_si32(t); - return i + _mm_movemask_pd(_mm_cmplt_sd(_mm_cvtsi32_sd(t,i), t)); -#elif defined __GNUC__ - int i = (int)value; - return i + (i < value); -#else - int i = cvRound(value); - float diff = (float)(i - value); - return i + (diff < 0); -#endif -} - -/** @brief Determines if the argument is Not A Number. - - @param value The input floating-point value - - The function returns 1 if the argument is Not A Number (as defined by IEEE754 standard), 0 - otherwise. */ -CV_INLINE int cvIsNaN( double value ) -{ - Cv64suf ieee754; - ieee754.f = value; - return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) + - ((unsigned)ieee754.u != 0) > 0x7ff00000; -} - -/** @brief Determines if the argument is Infinity. - - @param value The input floating-point value - - The function returns 1 if the argument is a plus or minus infinity (as defined by IEEE754 standard) - and 0 otherwise. */ -CV_INLINE int cvIsInf( double value ) -{ - Cv64suf ieee754; - ieee754.f = value; - return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) == 0x7ff00000 && - (unsigned)ieee754.u == 0; -} - -#ifdef __cplusplus - -/** @overload */ -CV_INLINE int cvRound(float value) -{ -#if ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ && \ - defined __SSE2__ && !defined __APPLE__)) && !defined(__CUDACC__) - __m128 t = _mm_set_ss( value ); - return _mm_cvtss_si32(t); -#elif defined _MSC_VER && defined _M_IX86 - int t; - __asm - { - fld value; - fistp t; - } - return t; -#elif ((defined _MSC_VER && defined _M_ARM) || defined CV_ICC || \ - defined __GNUC__) && defined HAVE_TEGRA_OPTIMIZATION - TEGRA_ROUND_FLT(value); -#elif defined CV_ICC || defined __GNUC__ -# if CV_VFP - ARM_ROUND_FLT(value); -# else - return (int)lrintf(value); -# endif -#else - /* it's ok if round does not comply with IEEE754 standard; - the tests should allow +/-1 difference when the tested functions use round */ - return (int)(value + (value >= 0 ? 0.5f : -0.5f)); -#endif -} - -/** @overload */ -CV_INLINE int cvRound( int value ) -{ - return value; -} - -/** @overload */ -CV_INLINE int cvFloor( float value ) -{ -#if (defined _MSC_VER && defined _M_X64 || (defined __GNUC__ && defined __SSE2__ && !defined __APPLE__)) && !defined(__CUDACC__) - __m128 t = _mm_set_ss( value ); - int i = _mm_cvtss_si32(t); - return i - _mm_movemask_ps(_mm_cmplt_ss(t, _mm_cvtsi32_ss(t,i))); -#elif defined __GNUC__ - int i = (int)value; - return i - (i > value); -#else - int i = cvRound(value); - float diff = (float)(value - i); - return i - (diff < 0); -#endif -} - -/** @overload */ -CV_INLINE int cvFloor( int value ) -{ - return value; -} - -/** @overload */ -CV_INLINE int cvCeil( float value ) -{ -#if (defined _MSC_VER && defined _M_X64 || (defined __GNUC__ && defined __SSE2__&& !defined __APPLE__)) && !defined(__CUDACC__) - __m128 t = _mm_set_ss( value ); - int i = _mm_cvtss_si32(t); - return i + _mm_movemask_ps(_mm_cmplt_ss(_mm_cvtsi32_ss(t,i), t)); -#elif defined __GNUC__ - int i = (int)value; - return i + (i < value); -#else - int i = cvRound(value); - float diff = (float)(i - value); - return i + (diff < 0); -#endif -} - -/** @overload */ -CV_INLINE int cvCeil( int value ) -{ - return value; -} - -/** @overload */ -CV_INLINE int cvIsNaN( float value ) -{ - Cv32suf ieee754; - ieee754.f = value; - return (ieee754.u & 0x7fffffff) > 0x7f800000; -} - -/** @overload */ -CV_INLINE int cvIsInf( float value ) -{ - Cv32suf ieee754; - ieee754.f = value; - return (ieee754.u & 0x7fffffff) == 0x7f800000; -} - -#endif // __cplusplus - -//! @} core_utils - -#endif diff --git a/IPL/include/opencv/opencv2/core/hal/hal.hpp b/IPL/include/opencv/opencv2/core/hal/hal.hpp deleted file mode 100644 index 118913e..0000000 --- a/IPL/include/opencv/opencv2/core/hal/hal.hpp +++ /dev/null @@ -1,218 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_HAL_HPP__ -#define __OPENCV_HAL_HPP__ - -#include "opencv2/core/cvdef.h" -#include "opencv2/core/hal/interface.h" - -//! @cond IGNORED -#define CALL_HAL(name, fun, ...) \ - int res = fun(__VA_ARGS__); \ - if (res == CV_HAL_ERROR_OK) \ - return; \ - else if (res != CV_HAL_ERROR_NOT_IMPLEMENTED) \ - CV_Error_(cv::Error::StsInternal, \ - ("HAL implementation " CVAUX_STR(name) " ==> " CVAUX_STR(fun) " returned %d (0x%08x)", res, res)); -//! @endcond - - -namespace cv { namespace hal { - -//! @addtogroup core_hal_functions -//! @{ - -CV_EXPORTS int normHamming(const uchar* a, int n); -CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n); - -CV_EXPORTS int normHamming(const uchar* a, int n, int cellSize); -CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n, int cellSize); - -CV_EXPORTS int LU32f(float* A, size_t astep, int m, float* b, size_t bstep, int n); -CV_EXPORTS int LU64f(double* A, size_t astep, int m, double* b, size_t bstep, int n); -CV_EXPORTS bool Cholesky32f(float* A, size_t astep, int m, float* b, size_t bstep, int n); -CV_EXPORTS bool Cholesky64f(double* A, size_t astep, int m, double* b, size_t bstep, int n); - -CV_EXPORTS int normL1_(const uchar* a, const uchar* b, int n); -CV_EXPORTS float normL1_(const float* a, const float* b, int n); -CV_EXPORTS float normL2Sqr_(const float* a, const float* b, int n); - -CV_EXPORTS void exp32f(const float* src, float* dst, int n); -CV_EXPORTS void exp64f(const double* src, double* dst, int n); -CV_EXPORTS void log32f(const float* src, float* dst, int n); -CV_EXPORTS void log64f(const double* src, double* dst, int n); - -CV_EXPORTS void fastAtan2(const float* y, const float* x, float* dst, int n, bool angleInDegrees); -CV_EXPORTS void magnitude32f(const float* x, const float* y, float* dst, int n); -CV_EXPORTS void magnitude64f(const double* x, const double* y, double* dst, int n); -CV_EXPORTS void sqrt32f(const float* src, float* dst, int len); -CV_EXPORTS void sqrt64f(const double* src, double* dst, int len); -CV_EXPORTS void invSqrt32f(const float* src, float* dst, int len); -CV_EXPORTS void invSqrt64f(const double* src, double* dst, int len); - -CV_EXPORTS void split8u(const uchar* src, uchar** dst, int len, int cn ); -CV_EXPORTS void split16u(const ushort* src, ushort** dst, int len, int cn ); -CV_EXPORTS void split32s(const int* src, int** dst, int len, int cn ); -CV_EXPORTS void split64s(const int64* src, int64** dst, int len, int cn ); - -CV_EXPORTS void merge8u(const uchar** src, uchar* dst, int len, int cn ); -CV_EXPORTS void merge16u(const ushort** src, ushort* dst, int len, int cn ); -CV_EXPORTS void merge32s(const int** src, int* dst, int len, int cn ); -CV_EXPORTS void merge64s(const int64** src, int64* dst, int len, int cn ); - -CV_EXPORTS void add8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void add8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void add16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void add16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void add32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void add32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void add64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); - -CV_EXPORTS void sub8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void sub8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void sub16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void sub16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void sub32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void sub32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void sub64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); - -CV_EXPORTS void max8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void max8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void max16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void max16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void max32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void max32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void max64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); - -CV_EXPORTS void min8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void min8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void min16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void min16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void min32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void min32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void min64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); - -CV_EXPORTS void absdiff8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void absdiff8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void absdiff16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void absdiff16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void absdiff32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void absdiff32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void absdiff64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); - -CV_EXPORTS void and8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void or8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void xor8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void not8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); - -CV_EXPORTS void cmp8u(const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); -CV_EXPORTS void cmp8s(const schar* src1, size_t step1, const schar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); -CV_EXPORTS void cmp16u(const ushort* src1, size_t step1, const ushort* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); -CV_EXPORTS void cmp16s(const short* src1, size_t step1, const short* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); -CV_EXPORTS void cmp32s(const int* src1, size_t step1, const int* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); -CV_EXPORTS void cmp32f(const float* src1, size_t step1, const float* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); -CV_EXPORTS void cmp64f(const double* src1, size_t step1, const double* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); - -CV_EXPORTS void mul8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void mul8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void mul16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void mul16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void mul32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void mul32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void mul64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); - -CV_EXPORTS void div8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void div8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void div16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void div16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void div32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void div32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void div64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); - -CV_EXPORTS void recip8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void recip8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void recip16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void recip16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void recip32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void recip32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void recip64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); - -CV_EXPORTS void addWeighted8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _scalars ); -CV_EXPORTS void addWeighted8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scalars ); -CV_EXPORTS void addWeighted16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scalars ); -CV_EXPORTS void addWeighted16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scalars ); -CV_EXPORTS void addWeighted32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scalars ); -CV_EXPORTS void addWeighted32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scalars ); -CV_EXPORTS void addWeighted64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scalars ); - -//! @} core_hal - -//============================================================================= -// for binary compatibility with 3.0 - -//! @cond IGNORED - -CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n); -CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n); -CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n); -CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n); - -CV_EXPORTS void exp(const float* src, float* dst, int n); -CV_EXPORTS void exp(const double* src, double* dst, int n); -CV_EXPORTS void log(const float* src, float* dst, int n); -CV_EXPORTS void log(const double* src, double* dst, int n); - -CV_EXPORTS void magnitude(const float* x, const float* y, float* dst, int n); -CV_EXPORTS void magnitude(const double* x, const double* y, double* dst, int n); -CV_EXPORTS void sqrt(const float* src, float* dst, int len); -CV_EXPORTS void sqrt(const double* src, double* dst, int len); -CV_EXPORTS void invSqrt(const float* src, float* dst, int len); -CV_EXPORTS void invSqrt(const double* src, double* dst, int len); - -//! @endcond - -}} //cv::hal - -#endif //__OPENCV_HAL_HPP__ diff --git a/IPL/include/opencv/opencv2/core/hal/interface.h b/IPL/include/opencv/opencv2/core/hal/interface.h deleted file mode 100644 index 51f7606..0000000 --- a/IPL/include/opencv/opencv2/core/hal/interface.h +++ /dev/null @@ -1,69 +0,0 @@ -#ifndef _HAL_INTERFACE_HPP_INCLUDED_ -#define _HAL_INTERFACE_HPP_INCLUDED_ - -//! @addtogroup core_hal_interface -//! @{ - -#define CV_HAL_ERROR_OK 0 -#define CV_HAL_ERROR_NOT_IMPLEMENTED 1 -#define CV_HAL_ERROR_UNKNOWN -1 - -#define CV_HAL_CMP_EQ 0 -#define CV_HAL_CMP_GT 1 -#define CV_HAL_CMP_GE 2 -#define CV_HAL_CMP_LT 3 -#define CV_HAL_CMP_LE 4 -#define CV_HAL_CMP_NE 5 - -#ifdef __cplusplus -#include -#else -#include -#endif - -/* primitive types */ -/* - schar - signed 1 byte integer - uchar - unsigned 1 byte integer - short - signed 2 byte integer - ushort - unsigned 2 byte integer - int - signed 4 byte integer - uint - unsigned 4 byte integer - int64 - signed 8 byte integer - uint64 - unsigned 8 byte integer -*/ - -#if !defined _MSC_VER && !defined __BORLANDC__ -# if defined __cplusplus && __cplusplus >= 201103L && !defined __APPLE__ -# include - typedef std::uint32_t uint; -# else -# include - typedef uint32_t uint; -# endif -#else - typedef unsigned uint; -#endif - -typedef signed char schar; - -#ifndef __IPL_H__ - typedef unsigned char uchar; - typedef unsigned short ushort; -#endif - -#if defined _MSC_VER || defined __BORLANDC__ - typedef __int64 int64; - typedef unsigned __int64 uint64; -# define CV_BIG_INT(n) n##I64 -# define CV_BIG_UINT(n) n##UI64 -#else - typedef int64_t int64; - typedef uint64_t uint64; -# define CV_BIG_INT(n) n##LL -# define CV_BIG_UINT(n) n##ULL -#endif - -//! @} - -#endif diff --git a/IPL/include/opencv/opencv2/core/hal/intrin.hpp b/IPL/include/opencv/opencv2/core/hal/intrin.hpp deleted file mode 100644 index 33e14b4..0000000 --- a/IPL/include/opencv/opencv2/core/hal/intrin.hpp +++ /dev/null @@ -1,320 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_HAL_INTRIN_HPP__ -#define __OPENCV_HAL_INTRIN_HPP__ - -#include -#include -#include -#include "opencv2/core/cvdef.h" - -#define OPENCV_HAL_ADD(a, b) ((a) + (b)) -#define OPENCV_HAL_AND(a, b) ((a) & (b)) -#define OPENCV_HAL_NOP(a) (a) -#define OPENCV_HAL_1ST(a, b) (a) - -// unlike HAL API, which is in cv::hal, -// we put intrinsics into cv namespace to make its -// access from within opencv code more accessible -namespace cv { - -//! @addtogroup core_hal_intrin -//! @{ - -//! @cond IGNORED -template struct V_TypeTraits -{ - typedef _Tp int_type; - typedef _Tp uint_type; - typedef _Tp abs_type; - typedef _Tp sum_type; - - enum { delta = 0, shift = 0 }; - - static int_type reinterpret_int(_Tp x) { return x; } - static uint_type reinterpet_uint(_Tp x) { return x; } - static _Tp reinterpret_from_int(int_type x) { return (_Tp)x; } -}; - -template<> struct V_TypeTraits -{ - typedef uchar value_type; - typedef schar int_type; - typedef uchar uint_type; - typedef uchar abs_type; - typedef int sum_type; - - typedef ushort w_type; - typedef unsigned q_type; - - enum { delta = 128, shift = 8 }; - - static int_type reinterpret_int(value_type x) { return (int_type)x; } - static uint_type reinterpret_uint(value_type x) { return (uint_type)x; } - static value_type reinterpret_from_int(int_type x) { return (value_type)x; } -}; - -template<> struct V_TypeTraits -{ - typedef schar value_type; - typedef schar int_type; - typedef uchar uint_type; - typedef uchar abs_type; - typedef int sum_type; - - typedef short w_type; - typedef int q_type; - - enum { delta = 128, shift = 8 }; - - static int_type reinterpret_int(value_type x) { return (int_type)x; } - static uint_type reinterpret_uint(value_type x) { return (uint_type)x; } - static value_type reinterpret_from_int(int_type x) { return (value_type)x; } -}; - -template<> struct V_TypeTraits -{ - typedef ushort value_type; - typedef short int_type; - typedef ushort uint_type; - typedef ushort abs_type; - typedef int sum_type; - - typedef unsigned w_type; - typedef uchar nu_type; - - enum { delta = 32768, shift = 16 }; - - static int_type reinterpret_int(value_type x) { return (int_type)x; } - static uint_type reinterpret_uint(value_type x) { return (uint_type)x; } - static value_type reinterpret_from_int(int_type x) { return (value_type)x; } -}; - -template<> struct V_TypeTraits -{ - typedef short value_type; - typedef short int_type; - typedef ushort uint_type; - typedef ushort abs_type; - typedef int sum_type; - - typedef int w_type; - typedef uchar nu_type; - typedef schar n_type; - - enum { delta = 128, shift = 8 }; - - static int_type reinterpret_int(value_type x) { return (int_type)x; } - static uint_type reinterpret_uint(value_type x) { return (uint_type)x; } - static value_type reinterpret_from_int(int_type x) { return (value_type)x; } -}; - -template<> struct V_TypeTraits -{ - typedef unsigned value_type; - typedef int int_type; - typedef unsigned uint_type; - typedef unsigned abs_type; - typedef unsigned sum_type; - - typedef uint64 w_type; - typedef ushort nu_type; - - static int_type reinterpret_int(value_type x) { return (int_type)x; } - static uint_type reinterpret_uint(value_type x) { return (uint_type)x; } - static value_type reinterpret_from_int(int_type x) { return (value_type)x; } -}; - -template<> struct V_TypeTraits -{ - typedef int value_type; - typedef int int_type; - typedef unsigned uint_type; - typedef unsigned abs_type; - typedef int sum_type; - - typedef int64 w_type; - typedef short n_type; - typedef ushort nu_type; - - static int_type reinterpret_int(value_type x) { return (int_type)x; } - static uint_type reinterpret_uint(value_type x) { return (uint_type)x; } - static value_type reinterpret_from_int(int_type x) { return (value_type)x; } -}; - -template<> struct V_TypeTraits -{ - typedef uint64 value_type; - typedef int64 int_type; - typedef uint64 uint_type; - typedef uint64 abs_type; - typedef uint64 sum_type; - - typedef unsigned nu_type; - - static int_type reinterpret_int(value_type x) { return (int_type)x; } - static uint_type reinterpret_uint(value_type x) { return (uint_type)x; } - static value_type reinterpret_from_int(int_type x) { return (value_type)x; } -}; - -template<> struct V_TypeTraits -{ - typedef int64 value_type; - typedef int64 int_type; - typedef uint64 uint_type; - typedef uint64 abs_type; - typedef int64 sum_type; - - typedef int nu_type; - - static int_type reinterpret_int(value_type x) { return (int_type)x; } - static uint_type reinterpret_uint(value_type x) { return (uint_type)x; } - static value_type reinterpret_from_int(int_type x) { return (value_type)x; } -}; - - -template<> struct V_TypeTraits -{ - typedef float value_type; - typedef int int_type; - typedef unsigned uint_type; - typedef float abs_type; - typedef float sum_type; - - typedef double w_type; - - static int_type reinterpret_int(value_type x) - { - Cv32suf u; - u.f = x; - return u.i; - } - static uint_type reinterpet_uint(value_type x) - { - Cv32suf u; - u.f = x; - return u.u; - } - static value_type reinterpret_from_int(int_type x) - { - Cv32suf u; - u.i = x; - return u.f; - } -}; - -template<> struct V_TypeTraits -{ - typedef double value_type; - typedef int64 int_type; - typedef uint64 uint_type; - typedef double abs_type; - typedef double sum_type; - static int_type reinterpret_int(value_type x) - { - Cv64suf u; - u.f = x; - return u.i; - } - static uint_type reinterpet_uint(value_type x) - { - Cv64suf u; - u.f = x; - return u.u; - } - static value_type reinterpret_from_int(int_type x) - { - Cv64suf u; - u.i = x; - return u.f; - } -}; - -template struct V_SIMD128Traits -{ - enum { nlanes = 16 / sizeof(T) }; -}; - -//! @endcond - -//! @} - -} - -#ifdef CV_DOXYGEN -# undef CV_SSE2 -# undef CV_NEON -#endif - -#if CV_SSE2 - -#include "opencv2/core/hal/intrin_sse.hpp" - -#elif CV_NEON - -#include "opencv2/core/hal/intrin_neon.hpp" - -#else - -#include "opencv2/core/hal/intrin_cpp.hpp" - -#endif - -//! @addtogroup core_hal_intrin -//! @{ - -#ifndef CV_SIMD128 -//! Set to 1 if current compiler supports vector extensions (NEON or SSE is enabled) -#define CV_SIMD128 0 -#endif - -#ifndef CV_SIMD128_64F -//! Set to 1 if current intrinsics implementation supports 64-bit float vectors -#define CV_SIMD128_64F 0 -#endif - -//! @} - -#endif diff --git a/IPL/include/opencv/opencv2/core/hal/intrin_cpp.hpp b/IPL/include/opencv/opencv2/core/hal/intrin_cpp.hpp deleted file mode 100644 index 3929e0d..0000000 --- a/IPL/include/opencv/opencv2/core/hal/intrin_cpp.hpp +++ /dev/null @@ -1,1738 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_HAL_INTRIN_CPP_HPP__ -#define __OPENCV_HAL_INTRIN_CPP_HPP__ - -#include -#include -#include -#include "opencv2/core/saturate.hpp" - -namespace cv -{ - -/** @addtogroup core_hal_intrin - -"Universal intrinsics" is a types and functions set intended to simplify vectorization of code on -different platforms. Currently there are two supported SIMD extensions: __SSE/SSE2__ on x86 -architectures and __NEON__ on ARM architectures, both allow working with 128 bit registers -containing packed values of different types. In case when there is no SIMD extension available -during compilation, fallback C++ implementation of intrinsics will be chosen and code will work as -expected although it could be slower. - -### Types - -There are several types representing 128-bit register as a vector of packed values, each type is -implemented as a structure based on a one SIMD register. - -- cv::v_uint8x16 and cv::v_int8x16: sixteen 8-bit integer values (unsigned/signed) - char -- cv::v_uint16x8 and cv::v_int16x8: eight 16-bit integer values (unsigned/signed) - short -- cv::v_uint32x4 and cv::v_int32x4: four 32-bit integer values (unsgined/signed) - int -- cv::v_uint64x2 and cv::v_int64x2: two 64-bit integer values (unsigned/signed) - int64 -- cv::v_float32x4: four 32-bit floating point values (signed) - float -- cv::v_float64x2: two 64-bit floating point valies (signed) - double - -@note -cv::v_float64x2 is not implemented in NEON variant, if you want to use this type, don't forget to -check the CV_SIMD128_64F preprocessor definition: -@code -#if CV_SIMD128_64F -//... -#endif -@endcode - -### Load and store operations - -These operations allow to set contents of the register explicitly or by loading it from some memory -block and to save contents of the register to memory block. - -- Constructors: -@ref v_reg::v_reg(const _Tp *ptr) "from memory", -@ref v_reg::v_reg(_Tp s0, _Tp s1) "from two values", ... -- Other create methods: -@ref v_setall_s8, @ref v_setall_u8, ..., -@ref v_setzero_u8, @ref v_setzero_s8, ... -- Memory operations: -@ref v_load, @ref v_load_aligned, @ref v_load_halves, -@ref v_store, @ref v_store_aligned, -@ref v_store_high, @ref v_store_low - -### Value reordering - -These operations allow to reorder or recombine elements in one or multiple vectors. - -- Interleave, deinterleave (3 and 4 channels): @ref v_load_deinterleave, @ref v_store_interleave -- Expand: @ref v_load_expand, @ref v_load_expand_q, @ref v_expand -- Pack: @ref v_pack, @ref v_pack_u, @ref v_rshr_pack, @ref v_rshr_pack_u, -@ref v_pack_store, @ref v_pack_u_store, @ref v_rshr_pack_store, @ref v_rshr_pack_u_store -- Recombine: @ref v_zip, @ref v_recombine, @ref v_combine_low, @ref v_combine_high -- Extract: @ref v_extract - - -### Arithmetic, bitwise and comparison operations - -Element-wise binary and unary operations. - -- Arithmetics: -@ref operator+(const v_reg &a, const v_reg &b) "+", -@ref operator-(const v_reg &a, const v_reg &b) "-", -@ref operator*(const v_reg &a, const v_reg &b) "*", -@ref operator/(const v_reg &a, const v_reg &b) "/", -@ref v_mul_expand - -- Non-saturating arithmetics: @ref v_add_wrap, @ref v_sub_wrap - -- Bitwise shifts: -@ref operator<<(const v_reg &a, int s) "<<", -@ref operator>>(const v_reg &a, int s) ">>", -@ref v_shl, @ref v_shr - -- Bitwise logic: -@ref operator&(const v_reg &a, const v_reg &b) "&", -@ref operator|(const v_reg &a, const v_reg &b) "|", -@ref operator^(const v_reg &a, const v_reg &b) "^", -@ref operator~(const v_reg &a) "~" - -- Comparison: -@ref operator>(const v_reg &a, const v_reg &b) ">", -@ref operator>=(const v_reg &a, const v_reg &b) ">=", -@ref operator<(const v_reg &a, const v_reg &b) "<", -@ref operator<=(const v_reg &a, const v_reg &b) "<=", -@ref operator==(const v_reg &a, const v_reg &b) "==", -@ref operator!=(const v_reg &a, const v_reg &b) "!=" - -- min/max: @ref v_min, @ref v_max - -### Reduce and mask - -Most of these operations return only one value. - -- Reduce: @ref v_reduce_min, @ref v_reduce_max, @ref v_reduce_sum -- Mask: @ref v_signmask, @ref v_check_all, @ref v_check_any, @ref v_select - -### Other math - -- Some frequent operations: @ref v_sqrt, @ref v_invsqrt, @ref v_magnitude, @ref v_sqr_magnitude -- Absolute values: @ref v_abs, @ref v_absdiff - -### Conversions - -Different type conversions and casts: - -- Rounding: @ref v_round, @ref v_floor, @ref v_ceil, @ref v_trunc, -- To float: @ref v_cvt_f32, @ref v_cvt_f64 -- Reinterpret: @ref v_reinterpret_as_u8, @ref v_reinterpret_as_s8, ... - -### Matrix operations - -In these operations vectors represent matrix rows/columns: @ref v_dotprod, @ref v_matmul, @ref v_transpose4x4 - -### Usability - -Most operations are implemented only for some subset of the available types, following matrices -shows the applicability of different operations to the types. - -Regular integers: - -| Operations\\Types | uint 8x16 | int 8x16 | uint 16x8 | int 16x8 | uint 32x4 | int 32x4 | -|-------------------|:-:|:-:|:-:|:-:|:-:|:-:| -|load, store | x | x | x | x | x | x | -|interleave | x | x | x | x | x | x | -|expand | x | x | x | x | x | x | -|expand_q | x | x | | | | | -|add, sub | x | x | x | x | x | x | -|add_wrap, sub_wrap | x | x | x | x | | | -|mul | | | x | x | x | x | -|mul_expand | | | x | x | x | | -|compare | x | x | x | x | x | x | -|shift | | | x | x | x | x | -|dotprod | | | | x | | | -|logical | x | x | x | x | x | x | -|min, max | x | x | x | x | x | x | -|absdiff | x | x | x | x | x | x | -|reduce | | | | | x | x | -|mask | x | x | x | x | x | x | -|pack | x | x | x | x | x | x | -|pack_u | x | | x | | | | -|unpack | x | x | x | x | x | x | -|extract | x | x | x | x | x | x | -|cvt_flt32 | | | | | | x | -|cvt_flt64 | | | | | | x | -|transpose4x4 | | | | | x | x | - -Big integers: - -| Operations\\Types | uint 64x2 | int 64x2 | -|-------------------|:-:|:-:| -|load, store | x | x | -|add, sub | x | x | -|shift | x | x | -|logical | x | x | -|extract | x | x | - -Floating point: - -| Operations\\Types | float 32x4 | float 64x2 | -|-------------------|:-:|:-:| -|load, store | x | x | -|interleave | x | | -|add, sub | x | x | -|mul | x | x | -|div | x | x | -|compare | x | x | -|min, max | x | x | -|absdiff | x | x | -|reduce | x | | -|mask | x | x | -|unpack | x | x | -|cvt_flt32 | | x | -|cvt_flt64 | x | | -|sqrt, abs | x | x | -|float math | x | x | -|transpose4x4 | x | | - - - @{ */ - -template struct v_reg -{ -//! @cond IGNORED - typedef _Tp lane_type; - typedef v_reg::int_type, n> int_vec; - typedef v_reg::abs_type, n> abs_vec; - enum { nlanes = n }; -// !@endcond - - /** @brief Constructor - - Initializes register with data from memory - @param ptr pointer to memory block with data for register */ - explicit v_reg(const _Tp* ptr) { for( int i = 0; i < n; i++ ) s[i] = ptr[i]; } - - /** @brief Constructor - - Initializes register with two 64-bit values */ - v_reg(_Tp s0, _Tp s1) { s[0] = s0; s[1] = s1; } - - /** @brief Constructor - - Initializes register with four 32-bit values */ - v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3) { s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; } - - /** @brief Constructor - - Initializes register with eight 16-bit values */ - v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3, - _Tp s4, _Tp s5, _Tp s6, _Tp s7) - { - s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; - s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7; - } - - /** @brief Constructor - - Initializes register with sixteen 8-bit values */ - v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3, - _Tp s4, _Tp s5, _Tp s6, _Tp s7, - _Tp s8, _Tp s9, _Tp s10, _Tp s11, - _Tp s12, _Tp s13, _Tp s14, _Tp s15) - { - s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; - s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7; - s[8] = s8; s[9] = s9; s[10] = s10; s[11] = s11; - s[12] = s12; s[13] = s13; s[14] = s14; s[15] = s15; - } - - /** @brief Default constructor - - Does not initialize anything*/ - v_reg() {} - - /** @brief Copy constructor */ - v_reg(const v_reg<_Tp, n> & r) - { - for( int i = 0; i < n; i++ ) - s[i] = r.s[i]; - } - /** @brief Access first value - - Returns value of the first lane according to register type, for example: - @code{.cpp} - v_int32x4 r(1, 2, 3, 4); - int v = r.get0(); // returns 1 - v_uint64x2 r(1, 2); - uint64_t v = r.get0(); // returns 1 - @endcode - */ - _Tp get0() const { return s[0]; } - -//! @cond IGNORED - _Tp get(const int i) const { return s[i]; } - v_reg<_Tp, n> high() const - { - v_reg<_Tp, n> c; - int i; - for( i = 0; i < n/2; i++ ) - { - c.s[i] = s[i+(n/2)]; - c.s[i+(n/2)] = 0; - } - return c; - } - - static v_reg<_Tp, n> zero() - { - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = (_Tp)0; - return c; - } - - static v_reg<_Tp, n> all(_Tp s) - { - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = s; - return c; - } - - template v_reg<_Tp2, n2> reinterpret_as() const - { - size_t bytes = std::min(sizeof(_Tp2)*n2, sizeof(_Tp)*n); - v_reg<_Tp2, n2> c; - std::memcpy(&c.s[0], &s[0], bytes); - return c; - } - - _Tp s[n]; -//! @endcond -}; - -/** @brief Sixteen 8-bit unsigned integer values */ -typedef v_reg v_uint8x16; -/** @brief Sixteen 8-bit signed integer values */ -typedef v_reg v_int8x16; -/** @brief Eight 16-bit unsigned integer values */ -typedef v_reg v_uint16x8; -/** @brief Eight 16-bit signed integer values */ -typedef v_reg v_int16x8; -/** @brief Four 32-bit unsigned integer values */ -typedef v_reg v_uint32x4; -/** @brief Four 32-bit signed integer values */ -typedef v_reg v_int32x4; -/** @brief Four 32-bit floating point values (single precision) */ -typedef v_reg v_float32x4; -/** @brief Two 64-bit floating point values (double precision) */ -typedef v_reg v_float64x2; -/** @brief Two 64-bit unsigned integer values */ -typedef v_reg v_uint64x2; -/** @brief Two 64-bit signed integer values */ -typedef v_reg v_int64x2; - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_BIN_OP(bin_op) \ -template inline v_reg<_Tp, n> \ - operator bin_op (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - v_reg<_Tp, n> c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \ - return c; \ -} \ -template inline v_reg<_Tp, n>& \ - operator bin_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - for( int i = 0; i < n; i++ ) \ - a.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \ - return a; \ -} - -/** @brief Add values - -For all types. */ -OPENCV_HAL_IMPL_BIN_OP(+) - -/** @brief Subtract values - -For all types. */ -OPENCV_HAL_IMPL_BIN_OP(-) - -/** @brief Multiply values - -For 16- and 32-bit integer types and floating types. */ -OPENCV_HAL_IMPL_BIN_OP(*) - -/** @brief Divide values - -For floating types only. */ -OPENCV_HAL_IMPL_BIN_OP(/) - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_BIT_OP(bit_op) \ -template inline v_reg<_Tp, n> operator bit_op \ - (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - v_reg<_Tp, n> c; \ - typedef typename V_TypeTraits<_Tp>::int_type itype; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \ - V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \ - return c; \ -} \ -template inline v_reg<_Tp, n>& operator \ - bit_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - typedef typename V_TypeTraits<_Tp>::int_type itype; \ - for( int i = 0; i < n; i++ ) \ - a.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \ - V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \ - return a; \ -} - -/** @brief Bitwise AND - -Only for integer types. */ -OPENCV_HAL_IMPL_BIT_OP(&) - -/** @brief Bitwise OR - -Only for integer types. */ -OPENCV_HAL_IMPL_BIT_OP(|) - -/** @brief Bitwise XOR - -Only for integer types.*/ -OPENCV_HAL_IMPL_BIT_OP(^) - -/** @brief Bitwise NOT - -Only for integer types.*/ -template inline v_reg<_Tp, n> operator ~ (const v_reg<_Tp, n>& a) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int(~V_TypeTraits<_Tp>::reinterpret_int(a.s[i])); - return c; -} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_MATH_FUNC(func, cfunc, _Tp2) \ -template inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a) \ -{ \ - v_reg<_Tp2, n> c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = cfunc(a.s[i]); \ - return c; \ -} - -/** @brief Square root of elements - -Only for floating point types.*/ -OPENCV_HAL_IMPL_MATH_FUNC(v_sqrt, std::sqrt, _Tp) - -//! @cond IGNORED -OPENCV_HAL_IMPL_MATH_FUNC(v_sin, std::sin, _Tp) -OPENCV_HAL_IMPL_MATH_FUNC(v_cos, std::cos, _Tp) -OPENCV_HAL_IMPL_MATH_FUNC(v_exp, std::exp, _Tp) -OPENCV_HAL_IMPL_MATH_FUNC(v_log, std::log, _Tp) -//! @endcond - -/** @brief Absolute value of elements - -Only for floating point types.*/ -OPENCV_HAL_IMPL_MATH_FUNC(v_abs, (typename V_TypeTraits<_Tp>::abs_type)std::abs, - typename V_TypeTraits<_Tp>::abs_type) - -/** @brief Round elements - -Only for floating point types.*/ -OPENCV_HAL_IMPL_MATH_FUNC(v_round, cvRound, int) - -/** @brief Floor elements - -Only for floating point types.*/ -OPENCV_HAL_IMPL_MATH_FUNC(v_floor, cvFloor, int) - -/** @brief Ceil elements - -Only for floating point types.*/ -OPENCV_HAL_IMPL_MATH_FUNC(v_ceil, cvCeil, int) - -/** @brief Truncate elements - -Only for floating point types.*/ -OPENCV_HAL_IMPL_MATH_FUNC(v_trunc, int, int) - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_MINMAX_FUNC(func, cfunc) \ -template inline v_reg<_Tp, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - v_reg<_Tp, n> c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = cfunc(a.s[i], b.s[i]); \ - return c; \ -} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(func, cfunc) \ -template inline _Tp func(const v_reg<_Tp, n>& a) \ -{ \ - _Tp c = a.s[0]; \ - for( int i = 1; i < n; i++ ) \ - c = cfunc(c, a.s[i]); \ - return c; \ -} - -/** @brief Choose min values for each pair - -Scheme: -@code -{A1 A2 ...} -{B1 B2 ...} --------------- -{min(A1,B1) min(A2,B2) ...} -@endcode -For all types except 64-bit integer. */ -OPENCV_HAL_IMPL_MINMAX_FUNC(v_min, std::min) - -/** @brief Choose max values for each pair - -Scheme: -@code -{A1 A2 ...} -{B1 B2 ...} --------------- -{max(A1,B1) max(A2,B2) ...} -@endcode -For all types except 64-bit integer. */ -OPENCV_HAL_IMPL_MINMAX_FUNC(v_max, std::max) - -/** @brief Find one min value - -Scheme: -@code -{A1 A2 A3 ...} => min(A1,A2,A3,...) -@endcode -For 32-bit integer and 32-bit floating point types. */ -OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_min, std::min) - -/** @brief Find one max value - -Scheme: -@code -{A1 A2 A3 ...} => max(A1,A2,A3,...) -@endcode -For 32-bit integer and 32-bit floating point types. */ -OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_max, std::max) - -//! @cond IGNORED -template -inline void v_minmax( const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - v_reg<_Tp, n>& minval, v_reg<_Tp, n>& maxval ) -{ - for( int i = 0; i < n; i++ ) - { - minval.s[i] = std::min(a.s[i], b.s[i]); - maxval.s[i] = std::max(a.s[i], b.s[i]); - } -} -//! @endcond - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_CMP_OP(cmp_op) \ -template \ -inline v_reg<_Tp, n> operator cmp_op(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - typedef typename V_TypeTraits<_Tp>::int_type itype; \ - v_reg<_Tp, n> c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)-(int)(a.s[i] cmp_op b.s[i])); \ - return c; \ -} - -/** @brief Less-than comparison - -For all types except 64-bit integer values. */ -OPENCV_HAL_IMPL_CMP_OP(<) - -/** @brief Greater-than comparison - -For all types except 64-bit integer values. */ -OPENCV_HAL_IMPL_CMP_OP(>) - -/** @brief Less-than or equal comparison - -For all types except 64-bit integer values. */ -OPENCV_HAL_IMPL_CMP_OP(<=) - -/** @brief Greater-than or equal comparison - -For all types except 64-bit integer values. */ -OPENCV_HAL_IMPL_CMP_OP(>=) - -/** @brief Equal comparison - -For all types except 64-bit integer values. */ -OPENCV_HAL_IMPL_CMP_OP(==) - -/** @brief Not equal comparison - -For all types except 64-bit integer values. */ -OPENCV_HAL_IMPL_CMP_OP(!=) - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_ADD_SUB_OP(func, bin_op, cast_op, _Tp2) \ -template \ -inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - typedef _Tp2 rtype; \ - v_reg c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = cast_op(a.s[i] bin_op b.s[i]); \ - return c; \ -} - -/** @brief Add values without saturation - -For 8- and 16-bit integer values. */ -OPENCV_HAL_IMPL_ADD_SUB_OP(v_add_wrap, +, (_Tp), _Tp) - -/** @brief Subtract values without saturation - -For 8- and 16-bit integer values. */ -OPENCV_HAL_IMPL_ADD_SUB_OP(v_sub_wrap, -, (_Tp), _Tp) - -//! @cond IGNORED -template inline T _absdiff(T a, T b) -{ - return a > b ? a - b : b - a; -} -//! @endcond - -/** @brief Absolute difference - -Returns \f$ |a - b| \f$ converted to corresponding unsigned type. -Example: -@code{.cpp} -v_int32x4 a, b; // {1, 2, 3, 4} and {4, 3, 2, 1} -v_uint32x4 c = v_absdiff(a, b); // result is {3, 1, 1, 3} -@endcode -For 8-, 16-, 32-bit integer source types. */ -template -inline v_reg::abs_type, n> v_absdiff(const v_reg<_Tp, n>& a, const v_reg<_Tp, n> & b) -{ - typedef typename V_TypeTraits<_Tp>::abs_type rtype; - v_reg c; - const rtype mask = std::numeric_limits<_Tp>::is_signed ? (1 << (sizeof(rtype)*8 - 1)) : 0; - for( int i = 0; i < n; i++ ) - { - rtype ua = a.s[i] ^ mask; - rtype ub = b.s[i] ^ mask; - c.s[i] = _absdiff(ua, ub); - } - return c; -} - -/** @overload - -For 32-bit floating point values */ -inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b) -{ - v_float32x4 c; - for( int i = 0; i < c.nlanes; i++ ) - c.s[i] = _absdiff(a.s[i], b.s[i]); - return c; -} - -/** @overload - -For 64-bit floating point values */ -inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b) -{ - v_float64x2 c; - for( int i = 0; i < c.nlanes; i++ ) - c.s[i] = _absdiff(a.s[i], b.s[i]); - return c; -} - -/** @brief Inversed square root - -Returns \f$ 1/sqrt(a) \f$ -For floating point types only. */ -template -inline v_reg<_Tp, n> v_invsqrt(const v_reg<_Tp, n>& a) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = 1.f/std::sqrt(a.s[i]); - return c; -} - -/** @brief Magnitude - -Returns \f$ sqrt(a^2 + b^2) \f$ -For floating point types only. */ -template -inline v_reg<_Tp, n> v_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = std::sqrt(a.s[i]*a.s[i] + b.s[i]*b.s[i]); - return c; -} - -/** @brief Square of the magnitude - -Returns \f$ a^2 + b^2 \f$ -For floating point types only. */ -template -inline v_reg<_Tp, n> v_sqr_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = a.s[i]*a.s[i] + b.s[i]*b.s[i]; - return c; -} - -/** @brief Multiply and add - -Returns \f$ a*b + c \f$ -For floating point types only. */ -template -inline v_reg<_Tp, n> v_muladd(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - const v_reg<_Tp, n>& c) -{ - v_reg<_Tp, n> d; - for( int i = 0; i < n; i++ ) - d.s[i] = a.s[i]*b.s[i] + c.s[i]; - return d; -} - -/** @brief Dot product of elements - -Multiply values in two registers and sum adjacent result pairs. -Scheme: -@code - {A1 A2 ...} // 16-bit -x {B1 B2 ...} // 16-bit -------------- -{A1B1+A2B2 ...} // 32-bit -@endcode -Implemented only for 16-bit signed source type (v_int16x8). -*/ -template inline v_reg::w_type, n/2> - v_dotprod(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - typedef typename V_TypeTraits<_Tp>::w_type w_type; - v_reg c; - for( int i = 0; i < (n/2); i++ ) - c.s[i] = (w_type)a.s[i*2]*b.s[i*2] + (w_type)a.s[i*2+1]*b.s[i*2+1]; - return c; -} - -/** @brief Multiply and expand - -Multiply values two registers and store results in two registers with wider pack type. -Scheme: -@code - {A B C D} // 32-bit -x {E F G H} // 32-bit ---------------- -{AE BF} // 64-bit - {CG DH} // 64-bit -@endcode -Example: -@code{.cpp} -v_uint32x4 a, b; // {1,2,3,4} and {2,2,2,2} -v_uint64x2 c, d; // results -v_mul_expand(a, b, c, d); // c, d = {2,4}, {6, 8} -@endcode -Implemented only for 16- and unsigned 32-bit source types (v_int16x8, v_uint16x8, v_uint32x4). -*/ -template inline void v_mul_expand(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - v_reg::w_type, n/2>& c, - v_reg::w_type, n/2>& d) -{ - typedef typename V_TypeTraits<_Tp>::w_type w_type; - for( int i = 0; i < (n/2); i++ ) - { - c.s[i] = (w_type)a.s[i]*b.s[i]; - d.s[i] = (w_type)a.s[i+(n/2)]*b.s[i+(n/2)]; - } -} - -//! @cond IGNORED -template inline void v_hsum(const v_reg<_Tp, n>& a, - v_reg::w_type, n/2>& c) -{ - typedef typename V_TypeTraits<_Tp>::w_type w_type; - for( int i = 0; i < (n/2); i++ ) - { - c.s[i] = (w_type)a.s[i*2] + a.s[i*2+1]; - } -} -//! @endcond - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_SHIFT_OP(shift_op) \ -template inline v_reg<_Tp, n> operator shift_op(const v_reg<_Tp, n>& a, int imm) \ -{ \ - v_reg<_Tp, n> c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = (_Tp)(a.s[i] shift_op imm); \ - return c; \ -} - -/** @brief Bitwise shift left - -For 16-, 32- and 64-bit integer values. */ -OPENCV_HAL_IMPL_SHIFT_OP(<<) - -/** @brief Bitwise shift right - -For 16-, 32- and 64-bit integer values. */ -OPENCV_HAL_IMPL_SHIFT_OP(>>) - -/** @brief Sum packed values - -Scheme: -@code -{A1 A2 A3 ...} => sum{A1,A2,A3,...} -@endcode -For 32-bit integer and 32-bit floating point types.*/ -template inline typename V_TypeTraits<_Tp>::sum_type v_reduce_sum(const v_reg<_Tp, n>& a) -{ - typename V_TypeTraits<_Tp>::sum_type c = a.s[0]; - for( int i = 1; i < n; i++ ) - c += a.s[i]; - return c; -} - -/** @brief Get negative values mask - -Returned value is a bit mask with bits set to 1 on places corresponding to negative packed values indexes. -Example: -@code{.cpp} -v_int32x4 r; // set to {-1, -1, 1, 1} -int mask = v_signmask(r); // mask = 3 <== 00000000 00000000 00000000 00000011 -@endcode -For all types except 64-bit. */ -template inline int v_signmask(const v_reg<_Tp, n>& a) -{ - int mask = 0; - for( int i = 0; i < n; i++ ) - mask |= (V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0) << i; - return mask; -} - -/** @brief Check if all packed values are less than zero - -Unsigned values will be casted to signed: `uchar 254 => char -2`. -For all types except 64-bit. */ -template inline bool v_check_all(const v_reg<_Tp, n>& a) -{ - for( int i = 0; i < n; i++ ) - if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) >= 0 ) - return false; - return true; -} - -/** @brief Check if any of packed values is less than zero - -Unsigned values will be casted to signed: `uchar 254 => char -2`. -For all types except 64-bit. */ -template inline bool v_check_any(const v_reg<_Tp, n>& a) -{ - for( int i = 0; i < n; i++ ) - if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0 ) - return true; - return false; -} - -/** @brief Bitwise select - -Return value will be built by combining values a and b using the following scheme: -If the i-th bit in _mask_ is 1 - select i-th bit from _a_ -else - select i-th bit from _b_ */ -template inline v_reg<_Tp, n> v_select(const v_reg<_Tp, n>& mask, - const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - typedef V_TypeTraits<_Tp> Traits; - typedef typename Traits::int_type int_type; - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - { - int_type m = Traits::reinterpret_int(mask.s[i]); - c.s[i] = Traits::reinterpret_from_int((Traits::reinterpret_int(a.s[i]) & m) - | (Traits::reinterpret_int(b.s[i]) & ~m)); - } - return c; -} - -/** @brief Expand values to the wider pack type - -Copy contents of register to two registers with 2x wider pack type. -Scheme: -@code - int32x4 int64x2 int64x2 -{A B C D} ==> {A B} , {C D} -@endcode */ -template inline void v_expand(const v_reg<_Tp, n>& a, - v_reg::w_type, n/2>& b0, - v_reg::w_type, n/2>& b1) -{ - for( int i = 0; i < (n/2); i++ ) - { - b0.s[i] = a.s[i]; - b1.s[i] = a.s[i+(n/2)]; - } -} - -//! @cond IGNORED -template inline v_reg::int_type, n> - v_reinterpret_as_int(const v_reg<_Tp, n>& a) -{ - v_reg::int_type, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = V_TypeTraits<_Tp>::reinterpret_int(a.s[i]); - return c; -} - -template inline v_reg::uint_type, n> - v_reinterpret_as_uint(const v_reg<_Tp, n>& a) -{ - v_reg::uint_type, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = V_TypeTraits<_Tp>::reinterpret_uint(a.s[i]); - return c; -} -//! @endcond - -/** @brief Interleave two vectors - -Scheme: -@code - {A1 A2 A3 A4} - {B1 B2 B3 B4} ---------------- - {A1 B1 A2 B2} and {A3 B3 A4 B4} -@endcode -For all types except 64-bit. -*/ -template inline void v_zip( const v_reg<_Tp, n>& a0, const v_reg<_Tp, n>& a1, - v_reg<_Tp, n>& b0, v_reg<_Tp, n>& b1 ) -{ - int i; - for( i = 0; i < n/2; i++ ) - { - b0.s[i*2] = a0.s[i]; - b0.s[i*2+1] = a1.s[i]; - } - for( ; i < n; i++ ) - { - b1.s[i*2-n] = a0.s[i]; - b1.s[i*2-n+1] = a1.s[i]; - } -} - -/** @brief Load register contents from memory - -@param ptr pointer to memory block with data -@return register object - -@note Returned type will be detected from passed pointer type, for example uchar ==> cv::v_uint8x16, int ==> cv::v_int32x4, etc. - */ -template -inline v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes> v_load(const _Tp* ptr) -{ - return v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes>(ptr); -} - -/** @brief Load register contents from memory (aligned) - -similar to cv::v_load, but source memory block should be aligned (to 16-byte boundary) - */ -template -inline v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes> v_load_aligned(const _Tp* ptr) -{ - return v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes>(ptr); -} - -/** @brief Load register contents from two memory blocks - -@param loptr memory block containing data for first half (0..n/2) -@param hiptr memory block containing data for second half (n/2..n) - -@code{.cpp} -int lo[2] = { 1, 2 }, hi[2] = { 3, 4 }; -v_int32x4 r = v_load_halves(lo, hi); -@endcode - */ -template -inline v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes> v_load_halves(const _Tp* loptr, const _Tp* hiptr) -{ - v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes> c; - for( int i = 0; i < c.nlanes/2; i++ ) - { - c.s[i] = loptr[i]; - c.s[i+c.nlanes/2] = hiptr[i]; - } - return c; -} - -/** @brief Load register contents from memory with double expand - -Same as cv::v_load, but result pack type will be 2x wider than memory type. - -@code{.cpp} -short buf[4] = {1, 2, 3, 4}; // type is int16 -v_int32x4 r = v_load_expand(buf); // r = {1, 2, 3, 4} - type is int32 -@endcode -For 8-, 16-, 32-bit integer source types. */ -template -inline v_reg::w_type, V_SIMD128Traits<_Tp>::nlanes / 2> -v_load_expand(const _Tp* ptr) -{ - typedef typename V_TypeTraits<_Tp>::w_type w_type; - v_reg::nlanes> c; - for( int i = 0; i < c.nlanes; i++ ) - { - c.s[i] = ptr[i]; - } - return c; -} - -/** @brief Load register contents from memory with quad expand - -Same as cv::v_load_expand, but result type is 4 times wider than source. -@code{.cpp} -char buf[4] = {1, 2, 3, 4}; // type is int8 -v_int32x4 r = v_load_q(buf); // r = {1, 2, 3, 4} - type is int32 -@endcode -For 8-bit integer source types. */ -template -inline v_reg::q_type, V_SIMD128Traits<_Tp>::nlanes / 4> -v_load_expand_q(const _Tp* ptr) -{ - typedef typename V_TypeTraits<_Tp>::q_type q_type; - v_reg::nlanes> c; - for( int i = 0; i < c.nlanes; i++ ) - { - c.s[i] = ptr[i]; - } - return c; -} - -/** @brief Load and deinterleave (4 channels) - -Load data from memory deinterleave and store to 4 registers. -Scheme: -@code -{A1 B1 C1 D1 A2 B2 C2 D2 ...} ==> {A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...} -@endcode -For all types except 64-bit. */ -template inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, - v_reg<_Tp, n>& b, v_reg<_Tp, n>& c) -{ - int i, i3; - for( i = i3 = 0; i < n; i++, i3 += 3 ) - { - a.s[i] = ptr[i3]; - b.s[i] = ptr[i3+1]; - c.s[i] = ptr[i3+2]; - } -} - -/** @brief Load and deinterleave (3 channels) - -Load data from memory deinterleave and store to 3 registers. -Scheme: -@code -{A1 B1 C1 A2 B2 C2 ...} ==> {A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...} -@endcode -For all types except 64-bit. */ -template -inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, - v_reg<_Tp, n>& b, v_reg<_Tp, n>& c, - v_reg<_Tp, n>& d) -{ - int i, i4; - for( i = i4 = 0; i < n; i++, i4 += 4 ) - { - a.s[i] = ptr[i4]; - b.s[i] = ptr[i4+1]; - c.s[i] = ptr[i4+2]; - d.s[i] = ptr[i4+3]; - } -} - -/** @brief Interleave and store (3 channels) - -Interleave and store data from 3 registers to memory. -Scheme: -@code -{A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...} ==> {A1 B1 C1 D1 A2 B2 C2 D2 ...} -@endcode -For all types except 64-bit. */ -template -inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, - const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c) -{ - int i, i3; - for( i = i3 = 0; i < n; i++, i3 += 3 ) - { - ptr[i3] = a.s[i]; - ptr[i3+1] = b.s[i]; - ptr[i3+2] = c.s[i]; - } -} - -/** @brief Interleave and store (4 channels) - -Interleave and store data from 4 registers to memory. -Scheme: -@code -{A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...} ==> {A1 B1 C1 D1 A2 B2 C2 D2 ...} -@endcode -For all types except 64-bit. */ -template inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, - const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c, - const v_reg<_Tp, n>& d) -{ - int i, i4; - for( i = i4 = 0; i < n; i++, i4 += 4 ) - { - ptr[i4] = a.s[i]; - ptr[i4+1] = b.s[i]; - ptr[i4+2] = c.s[i]; - ptr[i4+3] = d.s[i]; - } -} - -/** @brief Store data to memory - -Store register contents to memory. -Scheme: -@code - REG {A B C D} ==> MEM {A B C D} -@endcode -Pointer can be unaligned. */ -template -inline void v_store(_Tp* ptr, const v_reg<_Tp, n>& a) -{ - for( int i = 0; i < n; i++ ) - ptr[i] = a.s[i]; -} - -/** @brief Store data to memory (lower half) - -Store lower half of register contents to memory. -Scheme: -@code - REG {A B C D} ==> MEM {A B} -@endcode */ -template -inline void v_store_low(_Tp* ptr, const v_reg<_Tp, n>& a) -{ - for( int i = 0; i < (n/2); i++ ) - ptr[i] = a.s[i]; -} - -/** @brief Store data to memory (higher half) - -Store higher half of register contents to memory. -Scheme: -@code - REG {A B C D} ==> MEM {C D} -@endcode */ -template -inline void v_store_high(_Tp* ptr, const v_reg<_Tp, n>& a) -{ - for( int i = 0; i < (n/2); i++ ) - ptr[i] = a.s[i+(n/2)]; -} - -/** @brief Store data to memory (aligned) - -Store register contents to memory. -Scheme: -@code - REG {A B C D} ==> MEM {A B C D} -@endcode -Pointer __should__ be aligned by 16-byte boundary. */ -template -inline void v_store_aligned(_Tp* ptr, const v_reg<_Tp, n>& a) -{ - for( int i = 0; i < n; i++ ) - ptr[i] = a.s[i]; -} - -/** @brief Combine vector from first elements of two vectors - -Scheme: -@code - {A1 A2 A3 A4} - {B1 B2 B3 B4} ---------------- - {A1 A2 B1 B2} -@endcode -For all types except 64-bit. */ -template -inline v_reg<_Tp, n> v_combine_low(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < (n/2); i++ ) - { - c.s[i] = a.s[i]; - c.s[i+(n/2)] = b.s[i]; - } - return c; -} - -/** @brief Combine vector from last elements of two vectors - -Scheme: -@code - {A1 A2 A3 A4} - {B1 B2 B3 B4} ---------------- - {A3 A4 B3 B4} -@endcode -For all types except 64-bit. */ -template -inline v_reg<_Tp, n> v_combine_high(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < (n/2); i++ ) - { - c.s[i] = a.s[i+(n/2)]; - c.s[i+(n/2)] = b.s[i+(n/2)]; - } - return c; -} - -/** @brief Combine two vectors from lower and higher parts of two other vectors - -@code{.cpp} -low = cv::v_combine_low(a, b); -high = cv::v_combine_high(a, b); -@endcode */ -template -inline void v_recombine(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - v_reg<_Tp, n>& low, v_reg<_Tp, n>& high) -{ - for( int i = 0; i < (n/2); i++ ) - { - low.s[i] = a.s[i]; - low.s[i+(n/2)] = b.s[i]; - high.s[i] = a.s[i+(n/2)]; - high.s[i+(n/2)] = b.s[i+(n/2)]; - } -} - -/** @brief Vector extract - -Scheme: -@code - {A1 A2 A3 A4} - {B1 B2 B3 B4} -======================== -shift = 1 {A2 A3 A4 B1} -shift = 2 {A3 A4 B1 B2} -shift = 3 {A4 B1 B2 B3} -@endcode -Restriction: 0 <= shift < nlanes - -Usage: -@code -v_int32x4 a, b, c; -c = v_extract<2>(a, b); -@endcode -For integer types only. */ -template -inline v_reg<_Tp, n> v_extract(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> r; - const int shift = n - s; - int i = 0; - for (; i < shift; ++i) - r.s[i] = a.s[i+s]; - for (; i < n; ++i) - r.s[i] = b.s[i-shift]; - return r; -} - -/** @brief Round - -Rounds each value. Input type is float vector ==> output type is int vector.*/ -template inline v_reg v_round(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = cvRound(a.s[i]); - return c; -} - -/** @brief Floor - -Floor each value. Input type is float vector ==> output type is int vector.*/ -template inline v_reg v_floor(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = cvFloor(a.s[i]); - return c; -} - -/** @brief Ceil - -Ceil each value. Input type is float vector ==> output type is int vector.*/ -template inline v_reg v_ceil(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = cvCeil(a.s[i]); - return c; -} - -/** @brief Trunc - -Truncate each value. Input type is float vector ==> output type is int vector.*/ -template inline v_reg v_trunc(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = (int)(a.s[i]); - return c; -} - -/** @overload */ -template inline v_reg v_round(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = cvRound(a.s[i]); - c.s[i+n] = 0; - } - return c; -} - -/** @overload */ -template inline v_reg v_floor(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = cvFloor(a.s[i]); - c.s[i+n] = 0; - } - return c; -} - -/** @overload */ -template inline v_reg v_ceil(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = cvCeil(a.s[i]); - c.s[i+n] = 0; - } - return c; -} - -/** @overload */ -template inline v_reg v_trunc(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = cvCeil(a.s[i]); - c.s[i+n] = 0; - } - return c; -} - -/** @brief Convert to float - -Supported input type is cv::v_int32x4. */ -template inline v_reg v_cvt_f32(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = (float)a.s[i]; - return c; -} - -/** @brief Convert to double - -Supported input type is cv::v_int32x4. */ -template inline v_reg v_cvt_f64(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = (double)a.s[i]; - return c; -} - -/** @brief Convert to double - -Supported input type is cv::v_float32x4. */ -template inline v_reg v_cvt_f64(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = (double)a.s[i]; - return c; -} - -/** @brief Transpose 4x4 matrix - -Scheme: -@code -a0 {A1 A2 A3 A4} -a1 {B1 B2 B3 B4} -a2 {C1 C2 C3 C4} -a3 {D1 D2 D3 D4} -=============== -b0 {A1 B1 C1 D1} -b1 {A2 B2 C2 D2} -b2 {A3 B3 C3 D3} -b3 {A4 B4 C4 D4} -@endcode -*/ -template -inline void v_transpose4x4( v_reg<_Tp, 4>& a0, const v_reg<_Tp, 4>& a1, - const v_reg<_Tp, 4>& a2, const v_reg<_Tp, 4>& a3, - v_reg<_Tp, 4>& b0, v_reg<_Tp, 4>& b1, - v_reg<_Tp, 4>& b2, v_reg<_Tp, 4>& b3 ) -{ - b0 = v_reg<_Tp, 4>(a0.s[0], a1.s[0], a2.s[0], a3.s[0]); - b1 = v_reg<_Tp, 4>(a0.s[1], a1.s[1], a2.s[1], a3.s[1]); - b2 = v_reg<_Tp, 4>(a0.s[2], a1.s[2], a2.s[2], a3.s[2]); - b3 = v_reg<_Tp, 4>(a0.s[3], a1.s[3], a2.s[3], a3.s[3]); -} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_INIT_ZERO(_Tpvec, _Tp, suffix) \ -inline _Tpvec v_setzero_##suffix() { return _Tpvec::zero(); } - -//! @name Init with zero -//! @{ -//! @brief Create new vector with zero elements -OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint8x16, uchar, u8) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_int8x16, schar, s8) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_int16x8, short, s16) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_int32x4, int, s32) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_float32x4, float, f32) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_float64x2, double, f64) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint64x2, uint64, u64) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_int64x2, int64, s64) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_INIT_VAL(_Tpvec, _Tp, suffix) \ -inline _Tpvec v_setall_##suffix(_Tp val) { return _Tpvec::all(val); } - -//! @name Init with value -//! @{ -//! @brief Create new vector with elements set to a specific value -OPENCV_HAL_IMPL_C_INIT_VAL(v_uint8x16, uchar, u8) -OPENCV_HAL_IMPL_C_INIT_VAL(v_int8x16, schar, s8) -OPENCV_HAL_IMPL_C_INIT_VAL(v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_C_INIT_VAL(v_int16x8, short, s16) -OPENCV_HAL_IMPL_C_INIT_VAL(v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_C_INIT_VAL(v_int32x4, int, s32) -OPENCV_HAL_IMPL_C_INIT_VAL(v_float32x4, float, f32) -OPENCV_HAL_IMPL_C_INIT_VAL(v_float64x2, double, f64) -OPENCV_HAL_IMPL_C_INIT_VAL(v_uint64x2, uint64, u64) -OPENCV_HAL_IMPL_C_INIT_VAL(v_int64x2, int64, s64) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_REINTERPRET(_Tpvec, _Tp, suffix) \ -template inline _Tpvec \ - v_reinterpret_as_##suffix(const v_reg<_Tp0, n0>& a) \ -{ return a.template reinterpret_as<_Tp, _Tpvec::nlanes>(); } - -//! @name Reinterpret -//! @{ -//! @brief Convert vector to different type without modifying underlying data. -OPENCV_HAL_IMPL_C_REINTERPRET(v_uint8x16, uchar, u8) -OPENCV_HAL_IMPL_C_REINTERPRET(v_int8x16, schar, s8) -OPENCV_HAL_IMPL_C_REINTERPRET(v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_C_REINTERPRET(v_int16x8, short, s16) -OPENCV_HAL_IMPL_C_REINTERPRET(v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_C_REINTERPRET(v_int32x4, int, s32) -OPENCV_HAL_IMPL_C_REINTERPRET(v_float32x4, float, f32) -OPENCV_HAL_IMPL_C_REINTERPRET(v_float64x2, double, f64) -OPENCV_HAL_IMPL_C_REINTERPRET(v_uint64x2, uint64, u64) -OPENCV_HAL_IMPL_C_REINTERPRET(v_int64x2, int64, s64) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_SHIFTL(_Tpvec, _Tp) \ -template inline _Tpvec v_shl(const _Tpvec& a) \ -{ return a << n; } - -//! @name Left shift -//! @{ -//! @brief Shift left -OPENCV_HAL_IMPL_C_SHIFTL(v_uint16x8, ushort) -OPENCV_HAL_IMPL_C_SHIFTL(v_int16x8, short) -OPENCV_HAL_IMPL_C_SHIFTL(v_uint32x4, unsigned) -OPENCV_HAL_IMPL_C_SHIFTL(v_int32x4, int) -OPENCV_HAL_IMPL_C_SHIFTL(v_uint64x2, uint64) -OPENCV_HAL_IMPL_C_SHIFTL(v_int64x2, int64) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_SHIFTR(_Tpvec, _Tp) \ -template inline _Tpvec v_shr(const _Tpvec& a) \ -{ return a >> n; } - -//! @name Right shift -//! @{ -//! @brief Shift right -OPENCV_HAL_IMPL_C_SHIFTR(v_uint16x8, ushort) -OPENCV_HAL_IMPL_C_SHIFTR(v_int16x8, short) -OPENCV_HAL_IMPL_C_SHIFTR(v_uint32x4, unsigned) -OPENCV_HAL_IMPL_C_SHIFTR(v_int32x4, int) -OPENCV_HAL_IMPL_C_SHIFTR(v_uint64x2, uint64) -OPENCV_HAL_IMPL_C_SHIFTR(v_int64x2, int64) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_RSHIFTR(_Tpvec, _Tp) \ -template inline _Tpvec v_rshr(const _Tpvec& a) \ -{ \ - _Tpvec c; \ - for( int i = 0; i < _Tpvec::nlanes; i++ ) \ - c.s[i] = (_Tp)((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ - return c; \ -} - -//! @name Rounding shift -//! @{ -//! @brief Rounding shift right -OPENCV_HAL_IMPL_C_RSHIFTR(v_uint16x8, ushort) -OPENCV_HAL_IMPL_C_RSHIFTR(v_int16x8, short) -OPENCV_HAL_IMPL_C_RSHIFTR(v_uint32x4, unsigned) -OPENCV_HAL_IMPL_C_RSHIFTR(v_int32x4, int) -OPENCV_HAL_IMPL_C_RSHIFTR(v_uint64x2, uint64) -OPENCV_HAL_IMPL_C_RSHIFTR(v_int64x2, int64) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_PACK(_Tpvec, _Tpnvec, _Tpn, pack_suffix) \ -inline _Tpnvec v_##pack_suffix(const _Tpvec& a, const _Tpvec& b) \ -{ \ - _Tpnvec c; \ - for( int i = 0; i < _Tpvec::nlanes; i++ ) \ - { \ - c.s[i] = saturate_cast<_Tpn>(a.s[i]); \ - c.s[i+_Tpvec::nlanes] = saturate_cast<_Tpn>(b.s[i]); \ - } \ - return c; \ -} - -//! @name Pack -//! @{ -//! @brief Pack values from two vectors to one -//! -//! Return vector type have twice more elements than input vector types. Variant with _u_ suffix also -//! converts to corresponding unsigned type. -//! -//! - pack: for 16-, 32- and 64-bit integer input types -//! - pack_u: for 16- and 32-bit signed integer input types -OPENCV_HAL_IMPL_C_PACK(v_uint16x8, v_uint8x16, uchar, pack) -OPENCV_HAL_IMPL_C_PACK(v_int16x8, v_int8x16, schar, pack) -OPENCV_HAL_IMPL_C_PACK(v_uint32x4, v_uint16x8, ushort, pack) -OPENCV_HAL_IMPL_C_PACK(v_int32x4, v_int16x8, short, pack) -OPENCV_HAL_IMPL_C_PACK(v_uint64x2, v_uint32x4, unsigned, pack) -OPENCV_HAL_IMPL_C_PACK(v_int64x2, v_int32x4, int, pack) -OPENCV_HAL_IMPL_C_PACK(v_int16x8, v_uint8x16, uchar, pack_u) -OPENCV_HAL_IMPL_C_PACK(v_int32x4, v_uint16x8, ushort, pack_u) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_RSHR_PACK(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix) \ -template inline _Tpnvec v_rshr_##pack_suffix(const _Tpvec& a, const _Tpvec& b) \ -{ \ - _Tpnvec c; \ - for( int i = 0; i < _Tpvec::nlanes; i++ ) \ - { \ - c.s[i] = saturate_cast<_Tpn>((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ - c.s[i+_Tpvec::nlanes] = saturate_cast<_Tpn>((b.s[i] + ((_Tp)1 << (n - 1))) >> n); \ - } \ - return c; \ -} - -//! @name Pack with rounding shift -//! @{ -//! @brief Pack values from two vectors to one with rounding shift -//! -//! Values from the input vectors will be shifted right by _n_ bits with rounding, converted to narrower -//! type and returned in the result vector. Variant with _u_ suffix converts to unsigned type. -//! -//! - pack: for 16-, 32- and 64-bit integer input types -//! - pack_u: for 16- and 32-bit signed integer input types -OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint16x8, ushort, v_uint8x16, uchar, pack) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_int16x8, short, v_int8x16, schar, pack) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint32x4, unsigned, v_uint16x8, ushort, pack) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_int32x4, int, v_int16x8, short, pack) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint64x2, uint64, v_uint32x4, unsigned, pack) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_int64x2, int64, v_int32x4, int, pack) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_int16x8, short, v_uint8x16, uchar, pack_u) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_int32x4, int, v_uint16x8, ushort, pack_u) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_PACK_STORE(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix) \ -inline void v_##pack_suffix##_store(_Tpn* ptr, const _Tpvec& a) \ -{ \ - for( int i = 0; i < _Tpvec::nlanes; i++ ) \ - ptr[i] = saturate_cast<_Tpn>(a.s[i]); \ -} - -//! @name Pack and store -//! @{ -//! @brief Store values from the input vector into memory with pack -//! -//! Values will be stored into memory with saturating conversion to narrower type. -//! Variant with _u_ suffix converts to corresponding unsigned type. -//! -//! - pack: for 16-, 32- and 64-bit integer input types -//! - pack_u: for 16- and 32-bit signed integer input types -OPENCV_HAL_IMPL_C_PACK_STORE(v_uint16x8, ushort, v_uint8x16, uchar, pack) -OPENCV_HAL_IMPL_C_PACK_STORE(v_int16x8, short, v_int8x16, schar, pack) -OPENCV_HAL_IMPL_C_PACK_STORE(v_uint32x4, unsigned, v_uint16x8, ushort, pack) -OPENCV_HAL_IMPL_C_PACK_STORE(v_int32x4, int, v_int16x8, short, pack) -OPENCV_HAL_IMPL_C_PACK_STORE(v_uint64x2, uint64, v_uint32x4, unsigned, pack) -OPENCV_HAL_IMPL_C_PACK_STORE(v_int64x2, int64, v_int32x4, int, pack) -OPENCV_HAL_IMPL_C_PACK_STORE(v_int16x8, short, v_uint8x16, uchar, pack_u) -OPENCV_HAL_IMPL_C_PACK_STORE(v_int32x4, int, v_uint16x8, ushort, pack_u) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix) \ -template inline void v_rshr_##pack_suffix##_store(_Tpn* ptr, const _Tpvec& a) \ -{ \ - for( int i = 0; i < _Tpvec::nlanes; i++ ) \ - ptr[i] = saturate_cast<_Tpn>((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ -} - -//! @name Pack and store with rounding shift -//! @{ -//! @brief Store values from the input vector into memory with pack -//! -//! Values will be shifted _n_ bits right with rounding, converted to narrower type and stored into -//! memory. Variant with _u_ suffix converts to unsigned type. -//! -//! - pack: for 16-, 32- and 64-bit integer input types -//! - pack_u: for 16- and 32-bit signed integer input types -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint16x8, ushort, v_uint8x16, uchar, pack) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int16x8, short, v_int8x16, schar, pack) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint32x4, unsigned, v_uint16x8, ushort, pack) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int32x4, int, v_int16x8, short, pack) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint64x2, uint64, v_uint32x4, unsigned, pack) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int64x2, int64, v_int32x4, int, pack) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int16x8, short, v_uint8x16, uchar, pack_u) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int32x4, int, v_uint16x8, ushort, pack_u) -//! @} - -/** @brief Matrix multiplication - -Scheme: -@code -{A0 A1 A2 A3} |V0| -{B0 B1 B2 B3} |V1| -{C0 C1 C2 C3} |V2| -{D0 D1 D2 D3} x |V3| -==================== -{R0 R1 R2 R3}, where: -R0 = A0V0 + A1V1 + A2V2 + A3V3, -R1 = B0V0 + B1V1 + B2V2 + B3V3 -... -@endcode -*/ -inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& m3) -{ - return v_float32x4(v.s[0]*m0.s[0] + v.s[1]*m1.s[0] + v.s[2]*m2.s[0] + v.s[3]*m3.s[0], - v.s[0]*m0.s[1] + v.s[1]*m1.s[1] + v.s[2]*m2.s[1] + v.s[3]*m3.s[1], - v.s[0]*m0.s[2] + v.s[1]*m1.s[2] + v.s[2]*m2.s[2] + v.s[3]*m3.s[2], - v.s[0]*m0.s[3] + v.s[1]*m1.s[3] + v.s[2]*m2.s[3] + v.s[3]*m3.s[3]); -} - -//! @} - -} - -#endif diff --git a/IPL/include/opencv/opencv2/core/hal/intrin_neon.hpp b/IPL/include/opencv/opencv2/core/hal/intrin_neon.hpp deleted file mode 100644 index f3e47ca..0000000 --- a/IPL/include/opencv/opencv2/core/hal/intrin_neon.hpp +++ /dev/null @@ -1,864 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_HAL_INTRIN_NEON_HPP__ -#define __OPENCV_HAL_INTRIN_NEON_HPP__ - -#include - -namespace cv -{ - -//! @cond IGNORED - -#define CV_SIMD128 1 - -struct v_uint8x16 -{ - typedef uchar lane_type; - enum { nlanes = 16 }; - - v_uint8x16() {} - explicit v_uint8x16(uint8x16_t v) : val(v) {} - v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, - uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) - { - uchar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; - val = vld1q_u8(v); - } - uchar get0() const - { - return vgetq_lane_u8(val, 0); - } - - uint8x16_t val; -}; - -struct v_int8x16 -{ - typedef schar lane_type; - enum { nlanes = 16 }; - - v_int8x16() {} - explicit v_int8x16(int8x16_t v) : val(v) {} - v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, - schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) - { - schar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; - val = vld1q_s8(v); - } - schar get0() const - { - return vgetq_lane_s8(val, 0); - } - - int8x16_t val; -}; - -struct v_uint16x8 -{ - typedef ushort lane_type; - enum { nlanes = 8 }; - - v_uint16x8() {} - explicit v_uint16x8(uint16x8_t v) : val(v) {} - v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) - { - ushort v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; - val = vld1q_u16(v); - } - ushort get0() const - { - return vgetq_lane_u16(val, 0); - } - - uint16x8_t val; -}; - -struct v_int16x8 -{ - typedef short lane_type; - enum { nlanes = 8 }; - - v_int16x8() {} - explicit v_int16x8(int16x8_t v) : val(v) {} - v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) - { - short v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; - val = vld1q_s16(v); - } - short get0() const - { - return vgetq_lane_s16(val, 0); - } - - int16x8_t val; -}; - -struct v_uint32x4 -{ - typedef unsigned lane_type; - enum { nlanes = 4 }; - - v_uint32x4() {} - explicit v_uint32x4(uint32x4_t v) : val(v) {} - v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) - { - unsigned v[] = {v0, v1, v2, v3}; - val = vld1q_u32(v); - } - unsigned get0() const - { - return vgetq_lane_u32(val, 0); - } - - uint32x4_t val; -}; - -struct v_int32x4 -{ - typedef int lane_type; - enum { nlanes = 4 }; - - v_int32x4() {} - explicit v_int32x4(int32x4_t v) : val(v) {} - v_int32x4(int v0, int v1, int v2, int v3) - { - int v[] = {v0, v1, v2, v3}; - val = vld1q_s32(v); - } - int get0() const - { - return vgetq_lane_s32(val, 0); - } - int32x4_t val; -}; - -struct v_float32x4 -{ - typedef float lane_type; - enum { nlanes = 4 }; - - v_float32x4() {} - explicit v_float32x4(float32x4_t v) : val(v) {} - v_float32x4(float v0, float v1, float v2, float v3) - { - float v[] = {v0, v1, v2, v3}; - val = vld1q_f32(v); - } - float get0() const - { - return vgetq_lane_f32(val, 0); - } - float32x4_t val; -}; - -struct v_uint64x2 -{ - typedef uint64 lane_type; - enum { nlanes = 2 }; - - v_uint64x2() {} - explicit v_uint64x2(uint64x2_t v) : val(v) {} - v_uint64x2(unsigned v0, unsigned v1) - { - uint64 v[] = {v0, v1}; - val = vld1q_u64(v); - } - uint64 get0() const - { - return vgetq_lane_u64(val, 0); - } - uint64x2_t val; -}; - -struct v_int64x2 -{ - typedef int64 lane_type; - enum { nlanes = 2 }; - - v_int64x2() {} - explicit v_int64x2(int64x2_t v) : val(v) {} - v_int64x2(int v0, int v1) - { - int64 v[] = {v0, v1}; - val = vld1q_s64(v); - } - int64 get0() const - { - return vgetq_lane_s64(val, 0); - } - int64x2_t val; -}; - -#define OPENCV_HAL_IMPL_NEON_INIT(_Tpv, _Tp, suffix) \ -inline v_##_Tpv v_setzero_##suffix() { return v_##_Tpv(vdupq_n_##suffix((_Tp)0)); } \ -inline v_##_Tpv v_setall_##suffix(_Tp v) { return v_##_Tpv(vdupq_n_##suffix(v)); } \ -inline _Tpv##_t vreinterpretq_##suffix##_##suffix(_Tpv##_t v) { return v; } \ -inline v_uint8x16 v_reinterpret_as_u8(const v_##_Tpv& v) { return v_uint8x16(vreinterpretq_u8_##suffix(v.val)); } \ -inline v_int8x16 v_reinterpret_as_s8(const v_##_Tpv& v) { return v_int8x16(vreinterpretq_s8_##suffix(v.val)); } \ -inline v_uint16x8 v_reinterpret_as_u16(const v_##_Tpv& v) { return v_uint16x8(vreinterpretq_u16_##suffix(v.val)); } \ -inline v_int16x8 v_reinterpret_as_s16(const v_##_Tpv& v) { return v_int16x8(vreinterpretq_s16_##suffix(v.val)); } \ -inline v_uint32x4 v_reinterpret_as_u32(const v_##_Tpv& v) { return v_uint32x4(vreinterpretq_u32_##suffix(v.val)); } \ -inline v_int32x4 v_reinterpret_as_s32(const v_##_Tpv& v) { return v_int32x4(vreinterpretq_s32_##suffix(v.val)); } \ -inline v_uint64x2 v_reinterpret_as_u64(const v_##_Tpv& v) { return v_uint64x2(vreinterpretq_u64_##suffix(v.val)); } \ -inline v_int64x2 v_reinterpret_as_s64(const v_##_Tpv& v) { return v_int64x2(vreinterpretq_s64_##suffix(v.val)); } \ -inline v_float32x4 v_reinterpret_as_f32(const v_##_Tpv& v) { return v_float32x4(vreinterpretq_f32_##suffix(v.val)); } - -OPENCV_HAL_IMPL_NEON_INIT(uint8x16, uchar, u8) -OPENCV_HAL_IMPL_NEON_INIT(int8x16, schar, s8) -OPENCV_HAL_IMPL_NEON_INIT(uint16x8, ushort, u16) -OPENCV_HAL_IMPL_NEON_INIT(int16x8, short, s16) -OPENCV_HAL_IMPL_NEON_INIT(uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_NEON_INIT(int32x4, int, s32) -OPENCV_HAL_IMPL_NEON_INIT(uint64x2, uint64, u64) -OPENCV_HAL_IMPL_NEON_INIT(int64x2, int64, s64) -OPENCV_HAL_IMPL_NEON_INIT(float32x4, float, f32) - -#define OPENCV_HAL_IMPL_NEON_PACK(_Tpvec, _Tp, hreg, suffix, _Tpwvec, wsuffix, pack, op) \ -inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b) \ -{ \ - hreg a1 = vqmov##op##_##wsuffix(a.val), b1 = vqmov##op##_##wsuffix(b.val); \ - return _Tpvec(vcombine_##suffix(a1, b1)); \ -} \ -inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ -{ \ - hreg a1 = vqmov##op##_##wsuffix(a.val); \ - vst1_##suffix(ptr, a1); \ -} \ -template inline \ -_Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b) \ -{ \ - hreg a1 = vqrshr##op##_n_##wsuffix(a.val, n); \ - hreg b1 = vqrshr##op##_n_##wsuffix(b.val, n); \ - return _Tpvec(vcombine_##suffix(a1, b1)); \ -} \ -template inline \ -void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ -{ \ - hreg a1 = vqrshr##op##_n_##wsuffix(a.val, n); \ - vst1_##suffix(ptr, a1); \ -} - -OPENCV_HAL_IMPL_NEON_PACK(v_uint8x16, uchar, uint8x8_t, u8, v_uint16x8, u16, pack, n) -OPENCV_HAL_IMPL_NEON_PACK(v_int8x16, schar, int8x8_t, s8, v_int16x8, s16, pack, n) -OPENCV_HAL_IMPL_NEON_PACK(v_uint16x8, ushort, uint16x4_t, u16, v_uint32x4, u32, pack, n) -OPENCV_HAL_IMPL_NEON_PACK(v_int16x8, short, int16x4_t, s16, v_int32x4, s32, pack, n) -OPENCV_HAL_IMPL_NEON_PACK(v_uint32x4, unsigned, uint32x2_t, u32, v_uint64x2, u64, pack, n) -OPENCV_HAL_IMPL_NEON_PACK(v_int32x4, int, int32x2_t, s32, v_int64x2, s64, pack, n) - -OPENCV_HAL_IMPL_NEON_PACK(v_uint8x16, uchar, uint8x8_t, u8, v_int16x8, s16, pack_u, un) -OPENCV_HAL_IMPL_NEON_PACK(v_uint16x8, ushort, uint16x4_t, u16, v_int32x4, s32, pack_u, un) - -inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& m3) -{ - float32x2_t vl = vget_low_f32(v.val), vh = vget_high_f32(v.val); - float32x4_t res = vmulq_lane_f32(m0.val, vl, 0); - res = vmlaq_lane_f32(res, m1.val, vl, 1); - res = vmlaq_lane_f32(res, m2.val, vh, 0); - res = vmlaq_lane_f32(res, m3.val, vh, 1); - return v_float32x4(res); -} - -#define OPENCV_HAL_IMPL_NEON_BIN_OP(bin_op, _Tpvec, intrin) \ -inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(intrin(a.val, b.val)); \ -} \ -inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ -{ \ - a.val = intrin(a.val, b.val); \ - return a; \ -} - -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint8x16, vqaddq_u8) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint8x16, vqsubq_u8) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int8x16, vqaddq_s8) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int8x16, vqsubq_s8) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint16x8, vqaddq_u16) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint16x8, vqsubq_u16) -OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_uint16x8, vmulq_u16) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int16x8, vqaddq_s16) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int16x8, vqsubq_s16) -OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_int16x8, vmulq_s16) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int32x4, vaddq_s32) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int32x4, vsubq_s32) -OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_int32x4, vmulq_s32) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint32x4, vaddq_u32) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint32x4, vsubq_u32) -OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_uint32x4, vmulq_u32) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_float32x4, vaddq_f32) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_float32x4, vsubq_f32) -OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_float32x4, vmulq_f32) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int64x2, vaddq_s64) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int64x2, vsubq_s64) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint64x2, vaddq_u64) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint64x2, vsubq_u64) - -inline v_float32x4 operator / (const v_float32x4& a, const v_float32x4& b) -{ - float32x4_t reciprocal = vrecpeq_f32(b.val); - reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); - reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); - return v_float32x4(vmulq_f32(a.val, reciprocal)); -} -inline v_float32x4& operator /= (v_float32x4& a, const v_float32x4& b) -{ - float32x4_t reciprocal = vrecpeq_f32(b.val); - reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); - reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); - a.val = vmulq_f32(a.val, reciprocal); - return a; -} - -inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b, - v_int32x4& c, v_int32x4& d) -{ - c.val = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val)); - d.val = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)); -} - -inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b, - v_uint32x4& c, v_uint32x4& d) -{ - c.val = vmull_u16(vget_low_u16(a.val), vget_low_u16(b.val)); - d.val = vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val)); -} - -inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, - v_uint64x2& c, v_uint64x2& d) -{ - c.val = vmull_u32(vget_low_u32(a.val), vget_low_u32(b.val)); - d.val = vmull_u32(vget_high_u32(a.val), vget_high_u32(b.val)); -} - -inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) -{ - int32x4_t c = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val)); - int32x4_t d = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)); - int32x4x2_t cd = vuzpq_s32(c, d); - return v_int32x4(vaddq_s32(cd.val[0], cd.val[1])); -} - -#define OPENCV_HAL_IMPL_NEON_LOGIC_OP(_Tpvec, suffix) \ - OPENCV_HAL_IMPL_NEON_BIN_OP(&, _Tpvec, vandq_##suffix) \ - OPENCV_HAL_IMPL_NEON_BIN_OP(|, _Tpvec, vorrq_##suffix) \ - OPENCV_HAL_IMPL_NEON_BIN_OP(^, _Tpvec, veorq_##suffix) \ - inline _Tpvec operator ~ (const _Tpvec& a) \ - { \ - return _Tpvec(vreinterpretq_##suffix##_u8(vmvnq_u8(vreinterpretq_u8_##suffix(a.val)))); \ - } - -OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint8x16, u8) -OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int8x16, s8) -OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint16x8, u16) -OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int16x8, s16) -OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint32x4, u32) -OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int32x4, s32) -OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint64x2, u64) -OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int64x2, s64) - -#define OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(bin_op, intrin) \ -inline v_float32x4 operator bin_op (const v_float32x4& a, const v_float32x4& b) \ -{ \ - return v_float32x4(vreinterpretq_f32_s32(intrin(vreinterpretq_s32_f32(a.val), vreinterpretq_s32_f32(b.val)))); \ -} \ -inline v_float32x4& operator bin_op##= (v_float32x4& a, const v_float32x4& b) \ -{ \ - a.val = vreinterpretq_f32_s32(intrin(vreinterpretq_s32_f32(a.val), vreinterpretq_s32_f32(b.val))); \ - return a; \ -} - -OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(&, vandq_s32) -OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(|, vorrq_s32) -OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(^, veorq_s32) - -inline v_float32x4 operator ~ (const v_float32x4& a) -{ - return v_float32x4(vreinterpretq_f32_s32(vmvnq_s32(vreinterpretq_s32_f32(a.val)))); -} - -inline v_float32x4 v_sqrt(const v_float32x4& x) -{ - float32x4_t x1 = vmaxq_f32(x.val, vdupq_n_f32(FLT_MIN)); - float32x4_t e = vrsqrteq_f32(x1); - e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x1, e), e), e); - e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x1, e), e), e); - return v_float32x4(vmulq_f32(x.val, e)); -} - -inline v_float32x4 v_invsqrt(const v_float32x4& x) -{ - float32x4_t e = vrsqrteq_f32(x.val); - e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x.val, e), e), e); - e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x.val, e), e), e); - return v_float32x4(e); -} - -inline v_float32x4 v_abs(v_float32x4 x) -{ return v_float32x4(vabsq_f32(x.val)); } - -// TODO: exp, log, sin, cos - -#define OPENCV_HAL_IMPL_NEON_BIN_FUNC(_Tpvec, func, intrin) \ -inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(intrin(a.val, b.val)); \ -} - -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_min, vminq_u8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_max, vmaxq_u8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_min, vminq_s8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_max, vmaxq_s8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_min, vminq_u16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_max, vmaxq_u16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_min, vminq_s16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_max, vmaxq_s16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_min, vminq_u32) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_max, vmaxq_u32) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int32x4, v_min, vminq_s32) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int32x4, v_max, vmaxq_s32) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_min, vminq_f32) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_max, vmaxq_f32) - - -#define OPENCV_HAL_IMPL_NEON_INT_CMP_OP(_Tpvec, cast, suffix, not_suffix) \ -inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(cast(vceqq_##suffix(a.val, b.val))); } \ -inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(cast(vmvnq_##not_suffix(vceqq_##suffix(a.val, b.val)))); } \ -inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(cast(vcltq_##suffix(a.val, b.val))); } \ -inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(cast(vcgtq_##suffix(a.val, b.val))); } \ -inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(cast(vcleq_##suffix(a.val, b.val))); } \ -inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(cast(vcgeq_##suffix(a.val, b.val))); } - -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint8x16, OPENCV_HAL_NOP, u8, u8) -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int8x16, vreinterpretq_s8_u8, s8, u8) -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint16x8, OPENCV_HAL_NOP, u16, u16) -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int16x8, vreinterpretq_s16_u16, s16, u16) -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint32x4, OPENCV_HAL_NOP, u32, u32) -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int32x4, vreinterpretq_s32_u32, s32, u32) -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_float32x4, vreinterpretq_f32_u32, f32, u32) - -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_add_wrap, vaddq_u8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_add_wrap, vaddq_s8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_add_wrap, vaddq_u16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_add_wrap, vaddq_s16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_sub_wrap, vsubq_u8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_sub_wrap, vsubq_s8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_sub_wrap, vsubq_u16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_sub_wrap, vsubq_s16) - -// TODO: absdiff for signed integers -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_absdiff, vabdq_u8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_absdiff, vabdq_u16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_absdiff, vabdq_u32) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_absdiff, vabdq_f32) - -#define OPENCV_HAL_IMPL_NEON_BIN_FUNC2(_Tpvec, _Tpvec2, cast, func, intrin) \ -inline _Tpvec2 func(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec2(cast(intrin(a.val, b.val))); \ -} - -OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int8x16, v_uint8x16, vreinterpretq_u8_s8, v_absdiff, vabdq_s8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int16x8, v_uint16x8, vreinterpretq_u16_s16, v_absdiff, vabdq_s16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int32x4, v_uint32x4, vreinterpretq_u32_s32, v_absdiff, vabdq_s32) - -inline v_float32x4 v_magnitude(const v_float32x4& a, const v_float32x4& b) -{ - v_float32x4 x(vmlaq_f32(vmulq_f32(a.val, a.val), b.val, b.val)); - return v_sqrt(x); -} - -inline v_float32x4 v_sqr_magnitude(const v_float32x4& a, const v_float32x4& b) -{ - return v_float32x4(vmlaq_f32(vmulq_f32(a.val, a.val), b.val, b.val)); -} - -inline v_float32x4 v_muladd(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) -{ - return v_float32x4(vmlaq_f32(c.val, a.val, b.val)); -} - -// trade efficiency for convenience -#define OPENCV_HAL_IMPL_NEON_SHIFT_OP(_Tpvec, suffix, _Tps, ssuffix) \ -inline _Tpvec operator << (const _Tpvec& a, int n) \ -{ return _Tpvec(vshlq_##suffix(a.val, vdupq_n_##ssuffix((_Tps)n))); } \ -inline _Tpvec operator >> (const _Tpvec& a, int n) \ -{ return _Tpvec(vshlq_##suffix(a.val, vdupq_n_##ssuffix((_Tps)-n))); } \ -template inline _Tpvec v_shl(const _Tpvec& a) \ -{ return _Tpvec(vshlq_n_##suffix(a.val, n)); } \ -template inline _Tpvec v_shr(const _Tpvec& a) \ -{ return _Tpvec(vshrq_n_##suffix(a.val, n)); } \ -template inline _Tpvec v_rshr(const _Tpvec& a) \ -{ return _Tpvec(vrshrq_n_##suffix(a.val, n)); } - -OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint8x16, u8, schar, s8) -OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int8x16, s8, schar, s8) -OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint16x8, u16, short, s16) -OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int16x8, s16, short, s16) -OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint32x4, u32, int, s32) -OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int32x4, s32, int, s32) -OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint64x2, u64, int64, s64) -OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int64x2, s64, int64, s64) - -#define OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(_Tpvec, _Tp, suffix) \ -inline _Tpvec v_load(const _Tp* ptr) \ -{ return _Tpvec(vld1q_##suffix(ptr)); } \ -inline _Tpvec v_load_aligned(const _Tp* ptr) \ -{ return _Tpvec(vld1q_##suffix(ptr)); } \ -inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ -{ return _Tpvec(vcombine_##suffix(vld1_##suffix(ptr0), vld1_##suffix(ptr1))); } \ -inline void v_store(_Tp* ptr, const _Tpvec& a) \ -{ vst1q_##suffix(ptr, a.val); } \ -inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ -{ vst1q_##suffix(ptr, a.val); } \ -inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ -{ vst1_##suffix(ptr, vget_low_##suffix(a.val)); } \ -inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ -{ vst1_##suffix(ptr, vget_high_##suffix(a.val)); } - -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint8x16, uchar, u8) -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int8x16, schar, s8) -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int16x8, short, s16) -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int32x4, int, s32) -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint64x2, uint64, u64) -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int64x2, int64, s64) -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float32x4, float, f32) - -#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(_Tpvec, scalartype, func, scalar_func) \ -inline scalartype v_reduce_##func(const _Tpvec& a) \ -{ \ - scalartype CV_DECL_ALIGNED(16) buf[4]; \ - v_store_aligned(buf, a); \ - scalartype s0 = scalar_func(buf[0], buf[1]); \ - scalartype s1 = scalar_func(buf[2], buf[3]); \ - return scalar_func(s0, s1); \ -} - -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, unsigned, sum, OPENCV_HAL_ADD) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, unsigned, max, std::max) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, unsigned, min, std::min) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int, sum, OPENCV_HAL_ADD) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int, max, std::max) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int, min, std::min) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float, sum, OPENCV_HAL_ADD) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float, max, std::max) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float, min, std::min) - -inline int v_signmask(const v_uint8x16& a) -{ - int8x8_t m0 = vcreate_s8(CV_BIG_UINT(0x0706050403020100)); - uint8x16_t v0 = vshlq_u8(vshrq_n_u8(a.val, 7), vcombine_s8(m0, m0)); - uint64x2_t v1 = vpaddlq_u32(vpaddlq_u16(vpaddlq_u8(v0))); - return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 8); -} -inline int v_signmask(const v_int8x16& a) -{ return v_signmask(v_reinterpret_as_u8(a)); } - -inline int v_signmask(const v_uint16x8& a) -{ - int16x4_t m0 = vcreate_s16(CV_BIG_UINT(0x0003000200010000)); - uint16x8_t v0 = vshlq_u16(vshrq_n_u16(a.val, 15), vcombine_s16(m0, m0)); - uint64x2_t v1 = vpaddlq_u32(vpaddlq_u16(v0)); - return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 4); -} -inline int v_signmask(const v_int16x8& a) -{ return v_signmask(v_reinterpret_as_u16(a)); } - -inline int v_signmask(const v_uint32x4& a) -{ - int32x2_t m0 = vcreate_s32(CV_BIG_UINT(0x0000000100000000)); - uint32x4_t v0 = vshlq_u32(vshrq_n_u32(a.val, 31), vcombine_s32(m0, m0)); - uint64x2_t v1 = vpaddlq_u32(v0); - return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 2); -} -inline int v_signmask(const v_int32x4& a) -{ return v_signmask(v_reinterpret_as_u32(a)); } -inline int v_signmask(const v_float32x4& a) -{ return v_signmask(v_reinterpret_as_u32(a)); } - -#define OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(_Tpvec, suffix, shift) \ -inline bool v_check_all(const v_##_Tpvec& a) \ -{ \ - _Tpvec##_t v0 = vshrq_n_##suffix(vmvnq_##suffix(a.val), shift); \ - uint64x2_t v1 = vreinterpretq_u64_##suffix(v0); \ - return (vgetq_lane_u64(v1, 0) | vgetq_lane_u64(v1, 1)) == 0; \ -} \ -inline bool v_check_any(const v_##_Tpvec& a) \ -{ \ - _Tpvec##_t v0 = vshrq_n_##suffix(a.val, shift); \ - uint64x2_t v1 = vreinterpretq_u64_##suffix(v0); \ - return (vgetq_lane_u64(v1, 0) | vgetq_lane_u64(v1, 1)) != 0; \ -} - -OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint8x16, u8, 7) -OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint16x8, u16, 15) -OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint32x4, u32, 31) - -inline bool v_check_all(const v_int8x16& a) -{ return v_check_all(v_reinterpret_as_u8(a)); } -inline bool v_check_all(const v_int16x8& a) -{ return v_check_all(v_reinterpret_as_u16(a)); } -inline bool v_check_all(const v_int32x4& a) -{ return v_check_all(v_reinterpret_as_u32(a)); } -inline bool v_check_all(const v_float32x4& a) -{ return v_check_all(v_reinterpret_as_u32(a)); } - -inline bool v_check_any(const v_int8x16& a) -{ return v_check_any(v_reinterpret_as_u8(a)); } -inline bool v_check_any(const v_int16x8& a) -{ return v_check_any(v_reinterpret_as_u16(a)); } -inline bool v_check_any(const v_int32x4& a) -{ return v_check_any(v_reinterpret_as_u32(a)); } -inline bool v_check_any(const v_float32x4& a) -{ return v_check_any(v_reinterpret_as_u32(a)); } - -#define OPENCV_HAL_IMPL_NEON_SELECT(_Tpvec, suffix, usuffix) \ -inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(vbslq_##suffix(vreinterpretq_##usuffix##_##suffix(mask.val), a.val, b.val)); \ -} - -OPENCV_HAL_IMPL_NEON_SELECT(v_uint8x16, u8, u8) -OPENCV_HAL_IMPL_NEON_SELECT(v_int8x16, s8, u8) -OPENCV_HAL_IMPL_NEON_SELECT(v_uint16x8, u16, u16) -OPENCV_HAL_IMPL_NEON_SELECT(v_int16x8, s16, u16) -OPENCV_HAL_IMPL_NEON_SELECT(v_uint32x4, u32, u32) -OPENCV_HAL_IMPL_NEON_SELECT(v_int32x4, s32, u32) -OPENCV_HAL_IMPL_NEON_SELECT(v_float32x4, f32, u32) - -#define OPENCV_HAL_IMPL_NEON_EXPAND(_Tpvec, _Tpwvec, _Tp, suffix) \ -inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ -{ \ - b0.val = vmovl_##suffix(vget_low_##suffix(a.val)); \ - b1.val = vmovl_##suffix(vget_high_##suffix(a.val)); \ -} \ -inline _Tpwvec v_load_expand(const _Tp* ptr) \ -{ \ - return _Tpwvec(vmovl_##suffix(vld1_##suffix(ptr))); \ -} - -OPENCV_HAL_IMPL_NEON_EXPAND(v_uint8x16, v_uint16x8, uchar, u8) -OPENCV_HAL_IMPL_NEON_EXPAND(v_int8x16, v_int16x8, schar, s8) -OPENCV_HAL_IMPL_NEON_EXPAND(v_uint16x8, v_uint32x4, ushort, u16) -OPENCV_HAL_IMPL_NEON_EXPAND(v_int16x8, v_int32x4, short, s16) -OPENCV_HAL_IMPL_NEON_EXPAND(v_uint32x4, v_uint64x2, uint, u32) -OPENCV_HAL_IMPL_NEON_EXPAND(v_int32x4, v_int64x2, int, s32) - -inline v_uint32x4 v_load_expand_q(const uchar* ptr) -{ - uint8x8_t v0 = vcreate_u8(*(unsigned*)ptr); - uint16x4_t v1 = vget_low_u16(vmovl_u8(v0)); - return v_uint32x4(vmovl_u16(v1)); -} - -inline v_int32x4 v_load_expand_q(const schar* ptr) -{ - int8x8_t v0 = vcreate_s8(*(unsigned*)ptr); - int16x4_t v1 = vget_low_s16(vmovl_s8(v0)); - return v_int32x4(vmovl_s16(v1)); -} - -#define OPENCV_HAL_IMPL_NEON_UNPACKS(_Tpvec, suffix) \ -inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \ -{ \ - _Tpvec##x2_t p = vzipq_##suffix(a0.val, a1.val); \ - b0.val = p.val[0]; \ - b1.val = p.val[1]; \ -} \ -inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \ -{ \ - return v_##_Tpvec(vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val))); \ -} \ -inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \ -{ \ - return v_##_Tpvec(vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val))); \ -} \ -inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \ -{ \ - c.val = vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val)); \ - d.val = vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val)); \ -} - -OPENCV_HAL_IMPL_NEON_UNPACKS(uint8x16, u8) -OPENCV_HAL_IMPL_NEON_UNPACKS(int8x16, s8) -OPENCV_HAL_IMPL_NEON_UNPACKS(uint16x8, u16) -OPENCV_HAL_IMPL_NEON_UNPACKS(int16x8, s16) -OPENCV_HAL_IMPL_NEON_UNPACKS(uint32x4, u32) -OPENCV_HAL_IMPL_NEON_UNPACKS(int32x4, s32) -OPENCV_HAL_IMPL_NEON_UNPACKS(float32x4, f32) - -#define OPENCV_HAL_IMPL_NEON_EXTRACT(_Tpvec, suffix) \ -template \ -inline v_##_Tpvec v_extract(const v_##_Tpvec& a, const v_##_Tpvec& b) \ -{ \ - return v_##_Tpvec(vextq_##suffix(a.val, b.val, s)); \ -} - -OPENCV_HAL_IMPL_NEON_EXTRACT(uint8x16, u8) -OPENCV_HAL_IMPL_NEON_EXTRACT(int8x16, s8) -OPENCV_HAL_IMPL_NEON_EXTRACT(uint16x8, u16) -OPENCV_HAL_IMPL_NEON_EXTRACT(int16x8, s16) -OPENCV_HAL_IMPL_NEON_EXTRACT(uint32x4, u32) -OPENCV_HAL_IMPL_NEON_EXTRACT(int32x4, s32) -OPENCV_HAL_IMPL_NEON_EXTRACT(uint64x2, u64) -OPENCV_HAL_IMPL_NEON_EXTRACT(int64x2, s64) -OPENCV_HAL_IMPL_NEON_EXTRACT(float32x4, f32) - -inline v_int32x4 v_round(const v_float32x4& a) -{ - static const int32x4_t v_sign = vdupq_n_s32(1 << 31), - v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f)); - - int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(a.val))); - return v_int32x4(vcvtq_s32_f32(vaddq_f32(a.val, vreinterpretq_f32_s32(v_addition)))); -} - -inline v_int32x4 v_floor(const v_float32x4& a) -{ - int32x4_t a1 = vcvtq_s32_f32(a.val); - uint32x4_t mask = vcgtq_f32(vcvtq_f32_s32(a1), a.val); - return v_int32x4(vaddq_s32(a1, vreinterpretq_s32_u32(mask))); -} - -inline v_int32x4 v_ceil(const v_float32x4& a) -{ - int32x4_t a1 = vcvtq_s32_f32(a.val); - uint32x4_t mask = vcgtq_f32(a.val, vcvtq_f32_s32(a1)); - return v_int32x4(vsubq_s32(a1, vreinterpretq_s32_u32(mask))); -} - -inline v_int32x4 v_trunc(const v_float32x4& a) -{ return v_int32x4(vcvtq_s32_f32(a.val)); } - -#define OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(_Tpvec, suffix) \ -inline void v_transpose4x4(const v_##_Tpvec& a0, const v_##_Tpvec& a1, \ - const v_##_Tpvec& a2, const v_##_Tpvec& a3, \ - v_##_Tpvec& b0, v_##_Tpvec& b1, \ - v_##_Tpvec& b2, v_##_Tpvec& b3) \ -{ \ - /* m00 m01 m02 m03 */ \ - /* m10 m11 m12 m13 */ \ - /* m20 m21 m22 m23 */ \ - /* m30 m31 m32 m33 */ \ - _Tpvec##x2_t t0 = vtrnq_##suffix(a0.val, a1.val); \ - _Tpvec##x2_t t1 = vtrnq_##suffix(a2.val, a3.val); \ - /* m00 m10 m02 m12 */ \ - /* m01 m11 m03 m13 */ \ - /* m20 m30 m22 m32 */ \ - /* m21 m31 m23 m33 */ \ - b0.val = vcombine_##suffix(vget_low_##suffix(t0.val[0]), vget_low_##suffix(t1.val[0])); \ - b1.val = vcombine_##suffix(vget_low_##suffix(t0.val[1]), vget_low_##suffix(t1.val[1])); \ - b2.val = vcombine_##suffix(vget_high_##suffix(t0.val[0]), vget_high_##suffix(t1.val[0])); \ - b3.val = vcombine_##suffix(vget_high_##suffix(t0.val[1]), vget_high_##suffix(t1.val[1])); \ -} - -OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(uint32x4, u32) -OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(int32x4, s32) -OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(float32x4, f32) - -#define OPENCV_HAL_IMPL_NEON_INTERLEAVED(_Tpvec, _Tp, suffix) \ -inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, v_##_Tpvec& c) \ -{ \ - _Tpvec##x3_t v = vld3q_##suffix(ptr); \ - a.val = v.val[0]; \ - b.val = v.val[1]; \ - c.val = v.val[2]; \ -} \ -inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, \ - v_##_Tpvec& c, v_##_Tpvec& d) \ -{ \ - _Tpvec##x4_t v = vld4q_##suffix(ptr); \ - a.val = v.val[0]; \ - b.val = v.val[1]; \ - c.val = v.val[2]; \ - d.val = v.val[3]; \ -} \ -inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, const v_##_Tpvec& c) \ -{ \ - _Tpvec##x3_t v; \ - v.val[0] = a.val; \ - v.val[1] = b.val; \ - v.val[2] = c.val; \ - vst3q_##suffix(ptr, v); \ -} \ -inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ - const v_##_Tpvec& c, const v_##_Tpvec& d) \ -{ \ - _Tpvec##x4_t v; \ - v.val[0] = a.val; \ - v.val[1] = b.val; \ - v.val[2] = c.val; \ - v.val[3] = d.val; \ - vst4q_##suffix(ptr, v); \ -} - -OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint8x16, uchar, u8) -OPENCV_HAL_IMPL_NEON_INTERLEAVED(int8x16, schar, s8) -OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint16x8, ushort, u16) -OPENCV_HAL_IMPL_NEON_INTERLEAVED(int16x8, short, s16) -OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_NEON_INTERLEAVED(int32x4, int, s32) -OPENCV_HAL_IMPL_NEON_INTERLEAVED(float32x4, float, f32) - -inline v_float32x4 v_cvt_f32(const v_int32x4& a) -{ - return v_float32x4(vcvtq_f32_s32(a.val)); -} - -//! @endcond - -} - -#endif diff --git a/IPL/include/opencv/opencv2/core/hal/intrin_sse.hpp b/IPL/include/opencv/opencv2/core/hal/intrin_sse.hpp deleted file mode 100644 index 1840e03..0000000 --- a/IPL/include/opencv/opencv2/core/hal/intrin_sse.hpp +++ /dev/null @@ -1,1599 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_HAL_SSE_HPP__ -#define __OPENCV_HAL_SSE_HPP__ - -#include - -#define CV_SIMD128 1 -#define CV_SIMD128_64F 1 - -namespace cv -{ - -//! @cond IGNORED - -struct v_uint8x16 -{ - typedef uchar lane_type; - enum { nlanes = 16 }; - - v_uint8x16() {} - explicit v_uint8x16(__m128i v) : val(v) {} - v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, - uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) - { - val = _mm_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3, - (char)v4, (char)v5, (char)v6, (char)v7, - (char)v8, (char)v9, (char)v10, (char)v11, - (char)v12, (char)v13, (char)v14, (char)v15); - } - uchar get0() const - { - return (uchar)_mm_cvtsi128_si32(val); - } - - __m128i val; -}; - -struct v_int8x16 -{ - typedef schar lane_type; - enum { nlanes = 16 }; - - v_int8x16() {} - explicit v_int8x16(__m128i v) : val(v) {} - v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, - schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) - { - val = _mm_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3, - (char)v4, (char)v5, (char)v6, (char)v7, - (char)v8, (char)v9, (char)v10, (char)v11, - (char)v12, (char)v13, (char)v14, (char)v15); - } - schar get0() const - { - return (schar)_mm_cvtsi128_si32(val); - } - - __m128i val; -}; - -struct v_uint16x8 -{ - typedef ushort lane_type; - enum { nlanes = 8 }; - - v_uint16x8() {} - explicit v_uint16x8(__m128i v) : val(v) {} - v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) - { - val = _mm_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3, - (short)v4, (short)v5, (short)v6, (short)v7); - } - ushort get0() const - { - return (ushort)_mm_cvtsi128_si32(val); - } - - __m128i val; -}; - -struct v_int16x8 -{ - typedef short lane_type; - enum { nlanes = 8 }; - - v_int16x8() {} - explicit v_int16x8(__m128i v) : val(v) {} - v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) - { - val = _mm_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3, - (short)v4, (short)v5, (short)v6, (short)v7); - } - short get0() const - { - return (short)_mm_cvtsi128_si32(val); - } - __m128i val; -}; - -struct v_uint32x4 -{ - typedef unsigned lane_type; - enum { nlanes = 4 }; - - v_uint32x4() {} - explicit v_uint32x4(__m128i v) : val(v) {} - v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) - { - val = _mm_setr_epi32((int)v0, (int)v1, (int)v2, (int)v3); - } - unsigned get0() const - { - return (unsigned)_mm_cvtsi128_si32(val); - } - __m128i val; -}; - -struct v_int32x4 -{ - typedef int lane_type; - enum { nlanes = 4 }; - - v_int32x4() {} - explicit v_int32x4(__m128i v) : val(v) {} - v_int32x4(int v0, int v1, int v2, int v3) - { - val = _mm_setr_epi32(v0, v1, v2, v3); - } - int get0() const - { - return _mm_cvtsi128_si32(val); - } - __m128i val; -}; - -struct v_float32x4 -{ - typedef float lane_type; - enum { nlanes = 4 }; - - v_float32x4() {} - explicit v_float32x4(__m128 v) : val(v) {} - v_float32x4(float v0, float v1, float v2, float v3) - { - val = _mm_setr_ps(v0, v1, v2, v3); - } - float get0() const - { - return _mm_cvtss_f32(val); - } - __m128 val; -}; - -struct v_uint64x2 -{ - typedef uint64 lane_type; - enum { nlanes = 2 }; - - v_uint64x2() {} - explicit v_uint64x2(__m128i v) : val(v) {} - v_uint64x2(uint64 v0, uint64 v1) - { - val = _mm_setr_epi32((int)v0, (int)(v0 >> 32), (int)v1, (int)(v1 >> 32)); - } - uint64 get0() const - { - int a = _mm_cvtsi128_si32(val); - int b = _mm_cvtsi128_si32(_mm_srli_epi64(val, 32)); - return (unsigned)a | ((uint64)(unsigned)b << 32); - } - __m128i val; -}; - -struct v_int64x2 -{ - typedef int64 lane_type; - enum { nlanes = 2 }; - - v_int64x2() {} - explicit v_int64x2(__m128i v) : val(v) {} - v_int64x2(int64 v0, int64 v1) - { - val = _mm_setr_epi32((int)v0, (int)(v0 >> 32), (int)v1, (int)(v1 >> 32)); - } - int64 get0() const - { - int a = _mm_cvtsi128_si32(val); - int b = _mm_cvtsi128_si32(_mm_srli_epi64(val, 32)); - return (int64)((unsigned)a | ((uint64)(unsigned)b << 32)); - } - __m128i val; -}; - -struct v_float64x2 -{ - typedef double lane_type; - enum { nlanes = 2 }; - - v_float64x2() {} - explicit v_float64x2(__m128d v) : val(v) {} - v_float64x2(double v0, double v1) - { - val = _mm_setr_pd(v0, v1); - } - double get0() const - { - return _mm_cvtsd_f64(val); - } - __m128d val; -}; - -#define OPENCV_HAL_IMPL_SSE_INITVEC(_Tpvec, _Tp, suffix, zsuffix, ssuffix, _Tps, cast) \ -inline _Tpvec v_setzero_##suffix() { return _Tpvec(_mm_setzero_##zsuffix()); } \ -inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(_mm_set1_##ssuffix((_Tps)v)); } \ -template inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0& a) \ -{ return _Tpvec(cast(a.val)); } - -OPENCV_HAL_IMPL_SSE_INITVEC(v_uint8x16, uchar, u8, si128, epi8, char, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_INITVEC(v_int8x16, schar, s8, si128, epi8, char, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_INITVEC(v_uint16x8, ushort, u16, si128, epi16, short, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_INITVEC(v_int16x8, short, s16, si128, epi16, short, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_INITVEC(v_uint32x4, unsigned, u32, si128, epi32, int, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_INITVEC(v_int32x4, int, s32, si128, epi32, int, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_INITVEC(v_float32x4, float, f32, ps, ps, float, _mm_castsi128_ps) -OPENCV_HAL_IMPL_SSE_INITVEC(v_float64x2, double, f64, pd, pd, double, _mm_castsi128_pd) - -inline v_uint64x2 v_setzero_u64() { return v_uint64x2(_mm_setzero_si128()); } -inline v_int64x2 v_setzero_s64() { return v_int64x2(_mm_setzero_si128()); } -inline v_uint64x2 v_setall_u64(uint64 val) { return v_uint64x2(val, val); } -inline v_int64x2 v_setall_s64(int64 val) { return v_int64x2(val, val); } - -template inline -v_uint64x2 v_reinterpret_as_u64(const _Tpvec& a) { return v_uint64x2(a.val); } -template inline -v_int64x2 v_reinterpret_as_s64(const _Tpvec& a) { return v_int64x2(a.val); } -inline v_float32x4 v_reinterpret_as_f32(const v_uint64x2& a) -{ return v_float32x4(_mm_castsi128_ps(a.val)); } -inline v_float32x4 v_reinterpret_as_f32(const v_int64x2& a) -{ return v_float32x4(_mm_castsi128_ps(a.val)); } -inline v_float64x2 v_reinterpret_as_f64(const v_uint64x2& a) -{ return v_float64x2(_mm_castsi128_pd(a.val)); } -inline v_float64x2 v_reinterpret_as_f64(const v_int64x2& a) -{ return v_float64x2(_mm_castsi128_pd(a.val)); } - -#define OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(_Tpvec, suffix) \ -inline _Tpvec v_reinterpret_as_##suffix(const v_float32x4& a) \ -{ return _Tpvec(_mm_castps_si128(a.val)); } \ -inline _Tpvec v_reinterpret_as_##suffix(const v_float64x2& a) \ -{ return _Tpvec(_mm_castpd_si128(a.val)); } - -OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint8x16, u8) -OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int8x16, s8) -OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint16x8, u16) -OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int16x8, s16) -OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint32x4, u32) -OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int32x4, s32) -OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint64x2, u64) -OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int64x2, s64) - -inline v_float32x4 v_reinterpret_as_f32(const v_float32x4& a) {return a; } -inline v_float64x2 v_reinterpret_as_f64(const v_float64x2& a) {return a; } -inline v_float32x4 v_reinterpret_as_f32(const v_float64x2& a) {return v_float32x4(_mm_castpd_ps(a.val)); } -inline v_float64x2 v_reinterpret_as_f64(const v_float32x4& a) {return v_float64x2(_mm_castps_pd(a.val)); } - -//////////////// PACK /////////////// -inline v_uint8x16 v_pack(const v_uint16x8& a, const v_uint16x8& b) -{ - __m128i delta = _mm_set1_epi16(255); - return v_uint8x16(_mm_packus_epi16(_mm_subs_epu16(a.val, _mm_subs_epu16(a.val, delta)), - _mm_subs_epu16(b.val, _mm_subs_epu16(b.val, delta)))); -} - -inline void v_pack_store(uchar* ptr, const v_uint16x8& a) -{ - __m128i delta = _mm_set1_epi16(255); - __m128i a1 = _mm_subs_epu16(a.val, _mm_subs_epu16(a.val, delta)); - _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); -} - -inline v_uint8x16 v_pack_u(const v_int16x8& a, const v_int16x8& b) -{ return v_uint8x16(_mm_packus_epi16(a.val, b.val)); } - -inline void v_pack_u_store(uchar* ptr, const v_int16x8& a) -{ _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a.val, a.val)); } - -template inline -v_uint8x16 v_rshr_pack(const v_uint16x8& a, const v_uint16x8& b) -{ - // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. - __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); - return v_uint8x16(_mm_packus_epi16(_mm_srli_epi16(_mm_adds_epu16(a.val, delta), n), - _mm_srli_epi16(_mm_adds_epu16(b.val, delta), n))); -} - -template inline -void v_rshr_pack_store(uchar* ptr, const v_uint16x8& a) -{ - __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); - __m128i a1 = _mm_srli_epi16(_mm_adds_epu16(a.val, delta), n); - _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); -} - -template inline -v_uint8x16 v_rshr_pack_u(const v_int16x8& a, const v_int16x8& b) -{ - __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); - return v_uint8x16(_mm_packus_epi16(_mm_srai_epi16(_mm_adds_epi16(a.val, delta), n), - _mm_srai_epi16(_mm_adds_epi16(b.val, delta), n))); -} - -template inline -void v_rshr_pack_u_store(uchar* ptr, const v_int16x8& a) -{ - __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); - __m128i a1 = _mm_srai_epi16(_mm_adds_epi16(a.val, delta), n); - _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); -} - -inline v_int8x16 v_pack(const v_int16x8& a, const v_int16x8& b) -{ return v_int8x16(_mm_packs_epi16(a.val, b.val)); } - -inline void v_pack_store(schar* ptr, v_int16x8& a) -{ _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi16(a.val, a.val)); } - -template inline -v_int8x16 v_rshr_pack(const v_int16x8& a, const v_int16x8& b) -{ - // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. - __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); - return v_int8x16(_mm_packs_epi16(_mm_srai_epi16(_mm_adds_epi16(a.val, delta), n), - _mm_srai_epi16(_mm_adds_epi16(b.val, delta), n))); -} -template inline -void v_rshr_pack_store(schar* ptr, const v_int16x8& a) -{ - // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. - __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); - __m128i a1 = _mm_srai_epi16(_mm_adds_epi16(a.val, delta), n); - _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi16(a1, a1)); -} - - -// bit-wise "mask ? a : b" -inline __m128i v_select_si128(__m128i mask, __m128i a, __m128i b) -{ - return _mm_xor_si128(b, _mm_and_si128(_mm_xor_si128(a, b), mask)); -} - -inline v_uint16x8 v_pack(const v_uint32x4& a, const v_uint32x4& b) -{ - __m128i z = _mm_setzero_si128(), maxval32 = _mm_set1_epi32(65535), delta32 = _mm_set1_epi32(32768); - __m128i a1 = _mm_sub_epi32(v_select_si128(_mm_cmpgt_epi32(z, a.val), maxval32, a.val), delta32); - __m128i b1 = _mm_sub_epi32(v_select_si128(_mm_cmpgt_epi32(z, b.val), maxval32, b.val), delta32); - __m128i r = _mm_packs_epi32(a1, b1); - return v_uint16x8(_mm_sub_epi16(r, _mm_set1_epi16(-32768))); -} - -inline void v_pack_store(ushort* ptr, const v_uint32x4& a) -{ - __m128i z = _mm_setzero_si128(), maxval32 = _mm_set1_epi32(65535), delta32 = _mm_set1_epi32(32768); - __m128i a1 = _mm_sub_epi32(v_select_si128(_mm_cmpgt_epi32(z, a.val), maxval32, a.val), delta32); - __m128i r = _mm_packs_epi32(a1, a1); - _mm_storel_epi64((__m128i*)ptr, _mm_sub_epi16(r, _mm_set1_epi16(-32768))); -} - -template inline -v_uint16x8 v_rshr_pack(const v_uint32x4& a, const v_uint32x4& b) -{ - __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); - __m128i a1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(a.val, delta), n), delta32); - __m128i b1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(b.val, delta), n), delta32); - return v_uint16x8(_mm_sub_epi16(_mm_packs_epi32(a1, b1), _mm_set1_epi16(-32768))); -} - -template inline -void v_rshr_pack_store(ushort* ptr, const v_uint32x4& a) -{ - __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); - __m128i a1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(a.val, delta), n), delta32); - __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); - _mm_storel_epi64((__m128i*)ptr, a2); -} - -inline v_uint16x8 v_pack_u(const v_int32x4& a, const v_int32x4& b) -{ - __m128i delta32 = _mm_set1_epi32(32768); - __m128i r = _mm_packs_epi32(_mm_sub_epi32(a.val, delta32), _mm_sub_epi32(b.val, delta32)); - return v_uint16x8(_mm_sub_epi16(r, _mm_set1_epi16(-32768))); -} - -inline void v_pack_u_store(ushort* ptr, const v_int32x4& a) -{ - __m128i delta32 = _mm_set1_epi32(32768); - __m128i a1 = _mm_sub_epi32(a.val, delta32); - __m128i r = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); - _mm_storel_epi64((__m128i*)ptr, r); -} - -template inline -v_uint16x8 v_rshr_pack_u(const v_int32x4& a, const v_int32x4& b) -{ - __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); - __m128i a1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), delta32); - __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); - __m128i b1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(b.val, delta), n), delta32); - __m128i b2 = _mm_sub_epi16(_mm_packs_epi32(b1, b1), _mm_set1_epi16(-32768)); - return v_uint16x8(_mm_unpacklo_epi64(a2, b2)); -} - -template inline -void v_rshr_pack_u_store(ushort* ptr, const v_int32x4& a) -{ - __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); - __m128i a1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), delta32); - __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); - _mm_storel_epi64((__m128i*)ptr, a2); -} - -inline v_int16x8 v_pack(const v_int32x4& a, const v_int32x4& b) -{ return v_int16x8(_mm_packs_epi32(a.val, b.val)); } - -inline void v_pack_store(short* ptr, const v_int32x4& a) -{ - _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi32(a.val, a.val)); -} - -template inline -v_int16x8 v_rshr_pack(const v_int32x4& a, const v_int32x4& b) -{ - __m128i delta = _mm_set1_epi32(1 << (n-1)); - return v_int16x8(_mm_packs_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), - _mm_srai_epi32(_mm_add_epi32(b.val, delta), n))); -} - -template inline -void v_rshr_pack_store(short* ptr, const v_int32x4& a) -{ - __m128i delta = _mm_set1_epi32(1 << (n-1)); - __m128i a1 = _mm_srai_epi32(_mm_add_epi32(a.val, delta), n); - _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi32(a1, a1)); -} - - -// [a0 0 | b0 0] [a1 0 | b1 0] -inline v_uint32x4 v_pack(const v_uint64x2& a, const v_uint64x2& b) -{ - __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); // a0 a1 0 0 - __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); // b0 b1 0 0 - return v_uint32x4(_mm_unpacklo_epi32(v0, v1)); -} - -inline void v_pack_store(unsigned* ptr, const v_uint64x2& a) -{ - __m128i a1 = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 2, 2, 0)); - _mm_storel_epi64((__m128i*)ptr, a1); -} - -// [a0 0 | b0 0] [a1 0 | b1 0] -inline v_int32x4 v_pack(const v_int64x2& a, const v_int64x2& b) -{ - __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); // a0 a1 0 0 - __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); // b0 b1 0 0 - return v_int32x4(_mm_unpacklo_epi32(v0, v1)); -} - -inline void v_pack_store(int* ptr, const v_int64x2& a) -{ - __m128i a1 = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 2, 2, 0)); - _mm_storel_epi64((__m128i*)ptr, a1); -} - -template inline -v_uint32x4 v_rshr_pack(const v_uint64x2& a, const v_uint64x2& b) -{ - uint64 delta = (uint64)1 << (n-1); - v_uint64x2 delta2(delta, delta); - __m128i a1 = _mm_srli_epi64(_mm_add_epi64(a.val, delta2.val), n); - __m128i b1 = _mm_srli_epi64(_mm_add_epi64(b.val, delta2.val), n); - __m128i v0 = _mm_unpacklo_epi32(a1, b1); // a0 a1 0 0 - __m128i v1 = _mm_unpackhi_epi32(a1, b1); // b0 b1 0 0 - return v_uint32x4(_mm_unpacklo_epi32(v0, v1)); -} - -template inline -void v_rshr_pack_store(unsigned* ptr, const v_uint64x2& a) -{ - uint64 delta = (uint64)1 << (n-1); - v_uint64x2 delta2(delta, delta); - __m128i a1 = _mm_srli_epi64(_mm_add_epi64(a.val, delta2.val), n); - __m128i a2 = _mm_shuffle_epi32(a1, _MM_SHUFFLE(0, 2, 2, 0)); - _mm_storel_epi64((__m128i*)ptr, a2); -} - -inline __m128i v_sign_epi64(__m128i a) -{ - return _mm_shuffle_epi32(_mm_srai_epi32(a, 31), _MM_SHUFFLE(3, 3, 1, 1)); // x m0 | x m1 -} - -inline __m128i v_srai_epi64(__m128i a, int imm) -{ - __m128i smask = v_sign_epi64(a); - return _mm_xor_si128(_mm_srli_epi64(_mm_xor_si128(a, smask), imm), smask); -} - -template inline -v_int32x4 v_rshr_pack(const v_int64x2& a, const v_int64x2& b) -{ - int64 delta = (int64)1 << (n-1); - v_int64x2 delta2(delta, delta); - __m128i a1 = v_srai_epi64(_mm_add_epi64(a.val, delta2.val), n); - __m128i b1 = v_srai_epi64(_mm_add_epi64(b.val, delta2.val), n); - __m128i v0 = _mm_unpacklo_epi32(a1, b1); // a0 a1 0 0 - __m128i v1 = _mm_unpackhi_epi32(a1, b1); // b0 b1 0 0 - return v_int32x4(_mm_unpacklo_epi32(v0, v1)); -} - -template inline -void v_rshr_pack_store(int* ptr, const v_int64x2& a) -{ - int64 delta = (int64)1 << (n-1); - v_int64x2 delta2(delta, delta); - __m128i a1 = v_srai_epi64(_mm_add_epi64(a.val, delta2.val), n); - __m128i a2 = _mm_shuffle_epi32(a1, _MM_SHUFFLE(0, 2, 2, 0)); - _mm_storel_epi64((__m128i*)ptr, a2); -} - -inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& m3) -{ - __m128 v0 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(0, 0, 0, 0)), m0.val); - __m128 v1 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(1, 1, 1, 1)), m1.val); - __m128 v2 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(2, 2, 2, 2)), m2.val); - __m128 v3 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(3, 3, 3, 3)), m3.val); - - return v_float32x4(_mm_add_ps(_mm_add_ps(v0, v1), _mm_add_ps(v2, v3))); -} - - -#define OPENCV_HAL_IMPL_SSE_BIN_OP(bin_op, _Tpvec, intrin) \ - inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ - { \ - return _Tpvec(intrin(a.val, b.val)); \ - } \ - inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ - { \ - a.val = intrin(a.val, b.val); \ - return a; \ - } - -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint8x16, _mm_adds_epu8) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint8x16, _mm_subs_epu8) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int8x16, _mm_adds_epi8) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int8x16, _mm_subs_epi8) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint16x8, _mm_adds_epu16) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint16x8, _mm_subs_epu16) -OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_uint16x8, _mm_mullo_epi16) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int16x8, _mm_adds_epi16) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int16x8, _mm_subs_epi16) -OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_int16x8, _mm_mullo_epi16) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint32x4, _mm_add_epi32) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint32x4, _mm_sub_epi32) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int32x4, _mm_add_epi32) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int32x4, _mm_sub_epi32) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_float32x4, _mm_add_ps) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_float32x4, _mm_sub_ps) -OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_float32x4, _mm_mul_ps) -OPENCV_HAL_IMPL_SSE_BIN_OP(/, v_float32x4, _mm_div_ps) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_float64x2, _mm_add_pd) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_float64x2, _mm_sub_pd) -OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_float64x2, _mm_mul_pd) -OPENCV_HAL_IMPL_SSE_BIN_OP(/, v_float64x2, _mm_div_pd) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint64x2, _mm_add_epi64) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint64x2, _mm_sub_epi64) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int64x2, _mm_add_epi64) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int64x2, _mm_sub_epi64) - -inline v_uint32x4 operator * (const v_uint32x4& a, const v_uint32x4& b) -{ - __m128i c0 = _mm_mul_epu32(a.val, b.val); - __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32)); - __m128i d0 = _mm_unpacklo_epi32(c0, c1); - __m128i d1 = _mm_unpackhi_epi32(c0, c1); - return v_uint32x4(_mm_unpacklo_epi64(d0, d1)); -} -inline v_int32x4 operator * (const v_int32x4& a, const v_int32x4& b) -{ - __m128i c0 = _mm_mul_epu32(a.val, b.val); - __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32)); - __m128i d0 = _mm_unpacklo_epi32(c0, c1); - __m128i d1 = _mm_unpackhi_epi32(c0, c1); - return v_int32x4(_mm_unpacklo_epi64(d0, d1)); -} -inline v_uint32x4& operator *= (v_uint32x4& a, const v_uint32x4& b) -{ - a = a * b; - return a; -} -inline v_int32x4& operator *= (v_int32x4& a, const v_int32x4& b) -{ - a = a * b; - return a; -} - -inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b, - v_int32x4& c, v_int32x4& d) -{ - __m128i v0 = _mm_mullo_epi16(a.val, b.val); - __m128i v1 = _mm_mulhi_epi16(a.val, b.val); - c.val = _mm_unpacklo_epi16(v0, v1); - d.val = _mm_unpackhi_epi16(v0, v1); -} - -inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b, - v_uint32x4& c, v_uint32x4& d) -{ - __m128i v0 = _mm_mullo_epi16(a.val, b.val); - __m128i v1 = _mm_mulhi_epu16(a.val, b.val); - c.val = _mm_unpacklo_epi16(v0, v1); - d.val = _mm_unpackhi_epi16(v0, v1); -} - -inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, - v_uint64x2& c, v_uint64x2& d) -{ - __m128i c0 = _mm_mul_epu32(a.val, b.val); - __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32)); - c.val = _mm_unpacklo_epi64(c0, c1); - d.val = _mm_unpackhi_epi64(c0, c1); -} - -inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) -{ - return v_int32x4(_mm_madd_epi16(a.val, b.val)); -} - -#define OPENCV_HAL_IMPL_SSE_LOGIC_OP(_Tpvec, suffix, not_const) \ - OPENCV_HAL_IMPL_SSE_BIN_OP(&, _Tpvec, _mm_and_##suffix) \ - OPENCV_HAL_IMPL_SSE_BIN_OP(|, _Tpvec, _mm_or_##suffix) \ - OPENCV_HAL_IMPL_SSE_BIN_OP(^, _Tpvec, _mm_xor_##suffix) \ - inline _Tpvec operator ~ (const _Tpvec& a) \ - { \ - return _Tpvec(_mm_xor_##suffix(a.val, not_const)); \ - } - -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint8x16, si128, _mm_set1_epi32(-1)) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int8x16, si128, _mm_set1_epi32(-1)) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint16x8, si128, _mm_set1_epi32(-1)) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int16x8, si128, _mm_set1_epi32(-1)) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint32x4, si128, _mm_set1_epi32(-1)) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int32x4, si128, _mm_set1_epi32(-1)) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint64x2, si128, _mm_set1_epi32(-1)) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int64x2, si128, _mm_set1_epi32(-1)) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_float32x4, ps, _mm_castsi128_ps(_mm_set1_epi32(-1))) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_float64x2, pd, _mm_castsi128_pd(_mm_set1_epi32(-1))) - -inline v_float32x4 v_sqrt(const v_float32x4& x) -{ return v_float32x4(_mm_sqrt_ps(x.val)); } - -inline v_float32x4 v_invsqrt(const v_float32x4& x) -{ - static const __m128 _0_5 = _mm_set1_ps(0.5f), _1_5 = _mm_set1_ps(1.5f); - __m128 t = x.val; - __m128 h = _mm_mul_ps(t, _0_5); - t = _mm_rsqrt_ps(t); - t = _mm_mul_ps(t, _mm_sub_ps(_1_5, _mm_mul_ps(_mm_mul_ps(t, t), h))); - return v_float32x4(t); -} - -inline v_float64x2 v_sqrt(const v_float64x2& x) -{ return v_float64x2(_mm_sqrt_pd(x.val)); } - -inline v_float64x2 v_invsqrt(const v_float64x2& x) -{ - static const __m128d v_1 = _mm_set1_pd(1.); - return v_float64x2(_mm_div_pd(v_1, _mm_sqrt_pd(x.val))); -} - -inline v_float32x4 v_abs(const v_float32x4& x) -{ return v_float32x4(_mm_and_ps(x.val, _mm_castsi128_ps(_mm_set1_epi32(0x7fffffff)))); } -inline v_float64x2 v_abs(const v_float64x2& x) -{ - return v_float64x2(_mm_and_pd(x.val, - _mm_castsi128_pd(_mm_srli_epi64(_mm_set1_epi32(-1), 1)))); -} - -// TODO: exp, log, sin, cos - -#define OPENCV_HAL_IMPL_SSE_BIN_FUNC(_Tpvec, func, intrin) \ -inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(intrin(a.val, b.val)); \ -} - -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_min, _mm_min_epu8) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_max, _mm_max_epu8) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_min, _mm_min_epi16) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_max, _mm_max_epi16) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float32x4, v_min, _mm_min_ps) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float32x4, v_max, _mm_max_ps) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float64x2, v_min, _mm_min_pd) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float64x2, v_max, _mm_max_pd) - -inline v_int8x16 v_min(const v_int8x16& a, const v_int8x16& b) -{ - __m128i delta = _mm_set1_epi8((char)-128); - return v_int8x16(_mm_xor_si128(delta, _mm_min_epu8(_mm_xor_si128(a.val, delta), - _mm_xor_si128(b.val, delta)))); -} -inline v_int8x16 v_max(const v_int8x16& a, const v_int8x16& b) -{ - __m128i delta = _mm_set1_epi8((char)-128); - return v_int8x16(_mm_xor_si128(delta, _mm_max_epu8(_mm_xor_si128(a.val, delta), - _mm_xor_si128(b.val, delta)))); -} -inline v_uint16x8 v_min(const v_uint16x8& a, const v_uint16x8& b) -{ - return v_uint16x8(_mm_subs_epu16(a.val, _mm_subs_epu16(a.val, b.val))); -} -inline v_uint16x8 v_max(const v_uint16x8& a, const v_uint16x8& b) -{ - return v_uint16x8(_mm_adds_epu16(_mm_subs_epu16(a.val, b.val), b.val)); -} -inline v_uint32x4 v_min(const v_uint32x4& a, const v_uint32x4& b) -{ - __m128i delta = _mm_set1_epi32((int)0x80000000); - __m128i mask = _mm_cmpgt_epi32(_mm_xor_si128(a.val, delta), _mm_xor_si128(b.val, delta)); - return v_uint32x4(v_select_si128(mask, b.val, a.val)); -} -inline v_uint32x4 v_max(const v_uint32x4& a, const v_uint32x4& b) -{ - __m128i delta = _mm_set1_epi32((int)0x80000000); - __m128i mask = _mm_cmpgt_epi32(_mm_xor_si128(a.val, delta), _mm_xor_si128(b.val, delta)); - return v_uint32x4(v_select_si128(mask, a.val, b.val)); -} -inline v_int32x4 v_min(const v_int32x4& a, const v_int32x4& b) -{ - return v_int32x4(v_select_si128(_mm_cmpgt_epi32(a.val, b.val), b.val, a.val)); -} -inline v_int32x4 v_max(const v_int32x4& a, const v_int32x4& b) -{ - return v_int32x4(v_select_si128(_mm_cmpgt_epi32(a.val, b.val), a.val, b.val)); -} - -#define OPENCV_HAL_IMPL_SSE_INT_CMP_OP(_Tpuvec, _Tpsvec, suffix, sbit) \ -inline _Tpuvec operator == (const _Tpuvec& a, const _Tpuvec& b) \ -{ return _Tpuvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ -inline _Tpuvec operator != (const _Tpuvec& a, const _Tpuvec& b) \ -{ \ - __m128i not_mask = _mm_set1_epi32(-1); \ - return _Tpuvec(_mm_xor_si128(_mm_cmpeq_##suffix(a.val, b.val), not_mask)); \ -} \ -inline _Tpsvec operator == (const _Tpsvec& a, const _Tpsvec& b) \ -{ return _Tpsvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ -inline _Tpsvec operator != (const _Tpsvec& a, const _Tpsvec& b) \ -{ \ - __m128i not_mask = _mm_set1_epi32(-1); \ - return _Tpsvec(_mm_xor_si128(_mm_cmpeq_##suffix(a.val, b.val), not_mask)); \ -} \ -inline _Tpuvec operator < (const _Tpuvec& a, const _Tpuvec& b) \ -{ \ - __m128i smask = _mm_set1_##suffix(sbit); \ - return _Tpuvec(_mm_cmpgt_##suffix(_mm_xor_si128(b.val, smask), _mm_xor_si128(a.val, smask))); \ -} \ -inline _Tpuvec operator > (const _Tpuvec& a, const _Tpuvec& b) \ -{ \ - __m128i smask = _mm_set1_##suffix(sbit); \ - return _Tpuvec(_mm_cmpgt_##suffix(_mm_xor_si128(a.val, smask), _mm_xor_si128(b.val, smask))); \ -} \ -inline _Tpuvec operator <= (const _Tpuvec& a, const _Tpuvec& b) \ -{ \ - __m128i smask = _mm_set1_##suffix(sbit); \ - __m128i not_mask = _mm_set1_epi32(-1); \ - __m128i res = _mm_cmpgt_##suffix(_mm_xor_si128(a.val, smask), _mm_xor_si128(b.val, smask)); \ - return _Tpuvec(_mm_xor_si128(res, not_mask)); \ -} \ -inline _Tpuvec operator >= (const _Tpuvec& a, const _Tpuvec& b) \ -{ \ - __m128i smask = _mm_set1_##suffix(sbit); \ - __m128i not_mask = _mm_set1_epi32(-1); \ - __m128i res = _mm_cmpgt_##suffix(_mm_xor_si128(b.val, smask), _mm_xor_si128(a.val, smask)); \ - return _Tpuvec(_mm_xor_si128(res, not_mask)); \ -} \ -inline _Tpsvec operator < (const _Tpsvec& a, const _Tpsvec& b) \ -{ \ - return _Tpsvec(_mm_cmpgt_##suffix(b.val, a.val)); \ -} \ -inline _Tpsvec operator > (const _Tpsvec& a, const _Tpsvec& b) \ -{ \ - return _Tpsvec(_mm_cmpgt_##suffix(a.val, b.val)); \ -} \ -inline _Tpsvec operator <= (const _Tpsvec& a, const _Tpsvec& b) \ -{ \ - __m128i not_mask = _mm_set1_epi32(-1); \ - return _Tpsvec(_mm_xor_si128(_mm_cmpgt_##suffix(a.val, b.val), not_mask)); \ -} \ -inline _Tpsvec operator >= (const _Tpsvec& a, const _Tpsvec& b) \ -{ \ - __m128i not_mask = _mm_set1_epi32(-1); \ - return _Tpsvec(_mm_xor_si128(_mm_cmpgt_##suffix(b.val, a.val), not_mask)); \ -} - -OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint8x16, v_int8x16, epi8, (char)-128) -OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint16x8, v_int16x8, epi16, (short)-32768) -OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint32x4, v_int32x4, epi32, (int)0x80000000) - -#define OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(_Tpvec, suffix) \ -inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ -inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(_mm_cmpneq_##suffix(a.val, b.val)); } \ -inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(_mm_cmplt_##suffix(a.val, b.val)); } \ -inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(_mm_cmpgt_##suffix(a.val, b.val)); } \ -inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(_mm_cmple_##suffix(a.val, b.val)); } \ -inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(_mm_cmpge_##suffix(a.val, b.val)); } - -OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(v_float32x4, ps) -OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(v_float64x2, pd) - -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_add_wrap, _mm_add_epi8) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_add_wrap, _mm_add_epi8) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_add_wrap, _mm_add_epi16) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_add_wrap, _mm_add_epi16) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_sub_wrap, _mm_sub_epi8) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_sub_wrap, _mm_sub_epi8) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_sub_wrap, _mm_sub_epi16) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_sub_wrap, _mm_sub_epi16) - -#define OPENCV_HAL_IMPL_SSE_ABSDIFF_8_16(_Tpuvec, _Tpsvec, bits, smask32) \ -inline _Tpuvec v_absdiff(const _Tpuvec& a, const _Tpuvec& b) \ -{ \ - return _Tpuvec(_mm_add_epi##bits(_mm_subs_epu##bits(a.val, b.val), _mm_subs_epu##bits(b.val, a.val))); \ -} \ -inline _Tpuvec v_absdiff(const _Tpsvec& a, const _Tpsvec& b) \ -{ \ - __m128i smask = _mm_set1_epi32(smask32); \ - __m128i a1 = _mm_xor_si128(a.val, smask); \ - __m128i b1 = _mm_xor_si128(b.val, smask); \ - return _Tpuvec(_mm_add_epi##bits(_mm_subs_epu##bits(a1, b1), _mm_subs_epu##bits(b1, a1))); \ -} - -OPENCV_HAL_IMPL_SSE_ABSDIFF_8_16(v_uint8x16, v_int8x16, 8, (int)0x80808080) -OPENCV_HAL_IMPL_SSE_ABSDIFF_8_16(v_uint16x8, v_int16x8, 16, (int)0x80008000) - -inline v_uint32x4 v_absdiff(const v_uint32x4& a, const v_uint32x4& b) -{ - return v_max(a, b) - v_min(a, b); -} - -inline v_uint32x4 v_absdiff(const v_int32x4& a, const v_int32x4& b) -{ - __m128i d = _mm_sub_epi32(a.val, b.val); - __m128i m = _mm_cmpgt_epi32(b.val, a.val); - return v_uint32x4(_mm_sub_epi32(_mm_xor_si128(d, m), m)); -} - -#define OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(_Tpvec, _Tp, _Tpreg, suffix, absmask_vec) \ -inline _Tpvec v_absdiff(const _Tpvec& a, const _Tpvec& b) \ -{ \ - _Tpreg absmask = _mm_castsi128_##suffix(absmask_vec); \ - return _Tpvec(_mm_and_##suffix(_mm_sub_##suffix(a.val, b.val), absmask)); \ -} \ -inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \ -{ \ - _Tpreg res = _mm_add_##suffix(_mm_mul_##suffix(a.val, a.val), _mm_mul_##suffix(b.val, b.val)); \ - return _Tpvec(_mm_sqrt_##suffix(res)); \ -} \ -inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \ -{ \ - _Tpreg res = _mm_add_##suffix(_mm_mul_##suffix(a.val, a.val), _mm_mul_##suffix(b.val, b.val)); \ - return _Tpvec(res); \ -} \ -inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ -{ \ - return _Tpvec(_mm_add_##suffix(_mm_mul_##suffix(a.val, b.val), c.val)); \ -} - -OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(v_float32x4, float, __m128, ps, _mm_set1_epi32((int)0x7fffffff)) -OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(v_float64x2, double, __m128d, pd, _mm_srli_epi64(_mm_set1_epi32(-1), 1)) - -#define OPENCV_HAL_IMPL_SSE_SHIFT_OP(_Tpuvec, _Tpsvec, suffix, srai) \ -inline _Tpuvec operator << (const _Tpuvec& a, int imm) \ -{ \ - return _Tpuvec(_mm_slli_##suffix(a.val, imm)); \ -} \ -inline _Tpsvec operator << (const _Tpsvec& a, int imm) \ -{ \ - return _Tpsvec(_mm_slli_##suffix(a.val, imm)); \ -} \ -inline _Tpuvec operator >> (const _Tpuvec& a, int imm) \ -{ \ - return _Tpuvec(_mm_srli_##suffix(a.val, imm)); \ -} \ -inline _Tpsvec operator >> (const _Tpsvec& a, int imm) \ -{ \ - return _Tpsvec(srai(a.val, imm)); \ -} \ -template \ -inline _Tpuvec v_shl(const _Tpuvec& a) \ -{ \ - return _Tpuvec(_mm_slli_##suffix(a.val, imm)); \ -} \ -template \ -inline _Tpsvec v_shl(const _Tpsvec& a) \ -{ \ - return _Tpsvec(_mm_slli_##suffix(a.val, imm)); \ -} \ -template \ -inline _Tpuvec v_shr(const _Tpuvec& a) \ -{ \ - return _Tpuvec(_mm_srli_##suffix(a.val, imm)); \ -} \ -template \ -inline _Tpsvec v_shr(const _Tpsvec& a) \ -{ \ - return _Tpsvec(srai(a.val, imm)); \ -} - -OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint16x8, v_int16x8, epi16, _mm_srai_epi16) -OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint32x4, v_int32x4, epi32, _mm_srai_epi32) -OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint64x2, v_int64x2, epi64, v_srai_epi64) - -#define OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(_Tpvec, _Tp) \ -inline _Tpvec v_load(const _Tp* ptr) \ -{ return _Tpvec(_mm_loadu_si128((const __m128i*)ptr)); } \ -inline _Tpvec v_load_aligned(const _Tp* ptr) \ -{ return _Tpvec(_mm_load_si128((const __m128i*)ptr)); } \ -inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ -{ \ - return _Tpvec(_mm_unpacklo_epi64(_mm_loadl_epi64((const __m128i*)ptr0), \ - _mm_loadl_epi64((const __m128i*)ptr1))); \ -} \ -inline void v_store(_Tp* ptr, const _Tpvec& a) \ -{ _mm_storeu_si128((__m128i*)ptr, a.val); } \ -inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ -{ _mm_store_si128((__m128i*)ptr, a.val); } \ -inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ -{ _mm_storel_epi64((__m128i*)ptr, a.val); } \ -inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ -{ _mm_storel_epi64((__m128i*)ptr, _mm_unpackhi_epi64(a.val, a.val)); } - -OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint8x16, uchar) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int8x16, schar) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint16x8, ushort) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int16x8, short) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint32x4, unsigned) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int32x4, int) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint64x2, uint64) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int64x2, int64) - -#define OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(_Tpvec, _Tp, suffix) \ -inline _Tpvec v_load(const _Tp* ptr) \ -{ return _Tpvec(_mm_loadu_##suffix(ptr)); } \ -inline _Tpvec v_load_aligned(const _Tp* ptr) \ -{ return _Tpvec(_mm_load_##suffix(ptr)); } \ -inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ -{ \ - return _Tpvec(_mm_castsi128_##suffix( \ - _mm_unpacklo_epi64(_mm_loadl_epi64((const __m128i*)ptr0), \ - _mm_loadl_epi64((const __m128i*)ptr1)))); \ -} \ -inline void v_store(_Tp* ptr, const _Tpvec& a) \ -{ _mm_storeu_##suffix(ptr, a.val); } \ -inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ -{ _mm_store_##suffix(ptr, a.val); } \ -inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ -{ _mm_storel_epi64((__m128i*)ptr, _mm_cast##suffix##_si128(a.val)); } \ -inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ -{ \ - __m128i a1 = _mm_cast##suffix##_si128(a.val); \ - _mm_storel_epi64((__m128i*)ptr, _mm_unpackhi_epi64(a1, a1)); \ -} - -OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float32x4, float, ps) -OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float64x2, double, pd) - -#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(_Tpvec, scalartype, func, scalar_func) \ -inline scalartype v_reduce_##func(const _Tpvec& a) \ -{ \ - scalartype CV_DECL_ALIGNED(16) buf[4]; \ - v_store_aligned(buf, a); \ - scalartype s0 = scalar_func(buf[0], buf[1]); \ - scalartype s1 = scalar_func(buf[2], buf[3]); \ - return scalar_func(s0, s1); \ -} - -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, sum, OPENCV_HAL_ADD) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, max, std::max) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, min, std::min) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, sum, OPENCV_HAL_ADD) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, max, std::max) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, min, std::min) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, sum, OPENCV_HAL_ADD) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, max, std::max) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, min, std::min) - -#define OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(_Tpvec, suffix, pack_op, and_op, signmask, allmask) \ -inline int v_signmask(const _Tpvec& a) \ -{ \ - return and_op(_mm_movemask_##suffix(pack_op(a.val)), signmask); \ -} \ -inline bool v_check_all(const _Tpvec& a) \ -{ return and_op(_mm_movemask_##suffix(a.val), allmask) == allmask; } \ -inline bool v_check_any(const _Tpvec& a) \ -{ return and_op(_mm_movemask_##suffix(a.val), allmask) != 0; } - -#define OPENCV_HAL_PACKS(a) _mm_packs_epi16(a, a) -inline __m128i v_packq_epi32(__m128i a) -{ - __m128i b = _mm_packs_epi32(a, a); - return _mm_packs_epi16(b, b); -} - -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 65535, 65535) -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 65535, 65535) -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint16x8, epi8, OPENCV_HAL_PACKS, OPENCV_HAL_AND, 255, (int)0xaaaa) -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int16x8, epi8, OPENCV_HAL_PACKS, OPENCV_HAL_AND, 255, (int)0xaaaa) -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint32x4, epi8, v_packq_epi32, OPENCV_HAL_AND, 15, (int)0x8888) -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int32x4, epi8, v_packq_epi32, OPENCV_HAL_AND, 15, (int)0x8888) -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_float32x4, ps, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 15, 15) -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_float64x2, pd, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 3, 3) - -#define OPENCV_HAL_IMPL_SSE_SELECT(_Tpvec, suffix) \ -inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(_mm_xor_##suffix(b.val, _mm_and_##suffix(_mm_xor_##suffix(b.val, a.val), mask.val))); \ -} - -OPENCV_HAL_IMPL_SSE_SELECT(v_uint8x16, si128) -OPENCV_HAL_IMPL_SSE_SELECT(v_int8x16, si128) -OPENCV_HAL_IMPL_SSE_SELECT(v_uint16x8, si128) -OPENCV_HAL_IMPL_SSE_SELECT(v_int16x8, si128) -OPENCV_HAL_IMPL_SSE_SELECT(v_uint32x4, si128) -OPENCV_HAL_IMPL_SSE_SELECT(v_int32x4, si128) -// OPENCV_HAL_IMPL_SSE_SELECT(v_uint64x2, si128) -// OPENCV_HAL_IMPL_SSE_SELECT(v_int64x2, si128) -OPENCV_HAL_IMPL_SSE_SELECT(v_float32x4, ps) -OPENCV_HAL_IMPL_SSE_SELECT(v_float64x2, pd) - -#define OPENCV_HAL_IMPL_SSE_EXPAND(_Tpuvec, _Tpwuvec, _Tpu, _Tpsvec, _Tpwsvec, _Tps, suffix, wsuffix, shift) \ -inline void v_expand(const _Tpuvec& a, _Tpwuvec& b0, _Tpwuvec& b1) \ -{ \ - __m128i z = _mm_setzero_si128(); \ - b0.val = _mm_unpacklo_##suffix(a.val, z); \ - b1.val = _mm_unpackhi_##suffix(a.val, z); \ -} \ -inline _Tpwuvec v_load_expand(const _Tpu* ptr) \ -{ \ - __m128i z = _mm_setzero_si128(); \ - return _Tpwuvec(_mm_unpacklo_##suffix(_mm_loadl_epi64((const __m128i*)ptr), z)); \ -} \ -inline void v_expand(const _Tpsvec& a, _Tpwsvec& b0, _Tpwsvec& b1) \ -{ \ - b0.val = _mm_srai_##wsuffix(_mm_unpacklo_##suffix(a.val, a.val), shift); \ - b1.val = _mm_srai_##wsuffix(_mm_unpackhi_##suffix(a.val, a.val), shift); \ -} \ -inline _Tpwsvec v_load_expand(const _Tps* ptr) \ -{ \ - __m128i a = _mm_loadl_epi64((const __m128i*)ptr); \ - return _Tpwsvec(_mm_srai_##wsuffix(_mm_unpacklo_##suffix(a, a), shift)); \ -} - -OPENCV_HAL_IMPL_SSE_EXPAND(v_uint8x16, v_uint16x8, uchar, v_int8x16, v_int16x8, schar, epi8, epi16, 8) -OPENCV_HAL_IMPL_SSE_EXPAND(v_uint16x8, v_uint32x4, ushort, v_int16x8, v_int32x4, short, epi16, epi32, 16) - -inline void v_expand(const v_uint32x4& a, v_uint64x2& b0, v_uint64x2& b1) -{ - __m128i z = _mm_setzero_si128(); - b0.val = _mm_unpacklo_epi32(a.val, z); - b1.val = _mm_unpackhi_epi32(a.val, z); -} -inline v_uint64x2 v_load_expand(const unsigned* ptr) -{ - __m128i z = _mm_setzero_si128(); - return v_uint64x2(_mm_unpacklo_epi32(_mm_loadl_epi64((const __m128i*)ptr), z)); -} -inline void v_expand(const v_int32x4& a, v_int64x2& b0, v_int64x2& b1) -{ - __m128i s = _mm_srai_epi32(a.val, 31); - b0.val = _mm_unpacklo_epi32(a.val, s); - b1.val = _mm_unpackhi_epi32(a.val, s); -} -inline v_int64x2 v_load_expand(const int* ptr) -{ - __m128i a = _mm_loadl_epi64((const __m128i*)ptr); - __m128i s = _mm_srai_epi32(a, 31); - return v_int64x2(_mm_unpacklo_epi32(a, s)); -} - -inline v_uint32x4 v_load_expand_q(const uchar* ptr) -{ - __m128i z = _mm_setzero_si128(); - __m128i a = _mm_cvtsi32_si128(*(const int*)ptr); - return v_uint32x4(_mm_unpacklo_epi16(_mm_unpacklo_epi8(a, z), z)); -} - -inline v_int32x4 v_load_expand_q(const schar* ptr) -{ - __m128i a = _mm_cvtsi32_si128(*(const int*)ptr); - a = _mm_unpacklo_epi8(a, a); - a = _mm_unpacklo_epi8(a, a); - return v_int32x4(_mm_srai_epi32(a, 24)); -} - -#define OPENCV_HAL_IMPL_SSE_UNPACKS(_Tpvec, suffix, cast_from, cast_to) \ -inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1) \ -{ \ - b0.val = _mm_unpacklo_##suffix(a0.val, a1.val); \ - b1.val = _mm_unpackhi_##suffix(a0.val, a1.val); \ -} \ -inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ -{ \ - __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ - return _Tpvec(cast_to(_mm_unpacklo_epi64(a1, b1))); \ -} \ -inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ -{ \ - __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ - return _Tpvec(cast_to(_mm_unpackhi_epi64(a1, b1))); \ -} \ -inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d) \ -{ \ - __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ - c.val = cast_to(_mm_unpacklo_epi64(a1, b1)); \ - d.val = cast_to(_mm_unpackhi_epi64(a1, b1)); \ -} - -OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_UNPACKS(v_int8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint16x8, epi16, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_UNPACKS(v_int16x8, epi16, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_UNPACKS(v_int32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_UNPACKS(v_float32x4, ps, _mm_castps_si128, _mm_castsi128_ps) -OPENCV_HAL_IMPL_SSE_UNPACKS(v_float64x2, pd, _mm_castpd_si128, _mm_castsi128_pd) - -template -inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) -{ - const int w = sizeof(typename _Tpvec::lane_type); - const int n = _Tpvec::nlanes; - __m128i ra, rb; - ra = _mm_srli_si128(a.val, s*w); - rb = _mm_slli_si128(b.val, (n-s)*w); - return _Tpvec(_mm_or_si128(ra, rb)); -} - -inline v_int32x4 v_round(const v_float32x4& a) -{ return v_int32x4(_mm_cvtps_epi32(a.val)); } - -inline v_int32x4 v_floor(const v_float32x4& a) -{ - __m128i a1 = _mm_cvtps_epi32(a.val); - __m128i mask = _mm_castps_si128(_mm_cmpgt_ps(_mm_cvtepi32_ps(a1), a.val)); - return v_int32x4(_mm_add_epi32(a1, mask)); -} - -inline v_int32x4 v_ceil(const v_float32x4& a) -{ - __m128i a1 = _mm_cvtps_epi32(a.val); - __m128i mask = _mm_castps_si128(_mm_cmpgt_ps(a.val, _mm_cvtepi32_ps(a1))); - return v_int32x4(_mm_sub_epi32(a1, mask)); -} - -inline v_int32x4 v_trunc(const v_float32x4& a) -{ return v_int32x4(_mm_cvttps_epi32(a.val)); } - -inline v_int32x4 v_round(const v_float64x2& a) -{ return v_int32x4(_mm_cvtpd_epi32(a.val)); } - -inline v_int32x4 v_floor(const v_float64x2& a) -{ - __m128i a1 = _mm_cvtpd_epi32(a.val); - __m128i mask = _mm_castpd_si128(_mm_cmpgt_pd(_mm_cvtepi32_pd(a1), a.val)); - mask = _mm_srli_si128(_mm_slli_si128(mask, 4), 8); // m0 m0 m1 m1 => m0 m1 0 0 - return v_int32x4(_mm_add_epi32(a1, mask)); -} - -inline v_int32x4 v_ceil(const v_float64x2& a) -{ - __m128i a1 = _mm_cvtpd_epi32(a.val); - __m128i mask = _mm_castpd_si128(_mm_cmpgt_pd(a.val, _mm_cvtepi32_pd(a1))); - mask = _mm_srli_si128(_mm_slli_si128(mask, 4), 8); // m0 m0 m1 m1 => m0 m1 0 0 - return v_int32x4(_mm_sub_epi32(a1, mask)); -} - -inline v_int32x4 v_trunc(const v_float64x2& a) -{ return v_int32x4(_mm_cvttpd_epi32(a.val)); } - -#define OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(_Tpvec, suffix, cast_from, cast_to) \ -inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ - const _Tpvec& a2, const _Tpvec& a3, \ - _Tpvec& b0, _Tpvec& b1, \ - _Tpvec& b2, _Tpvec& b3) \ -{ \ - __m128i t0 = cast_from(_mm_unpacklo_##suffix(a0.val, a1.val)); \ - __m128i t1 = cast_from(_mm_unpacklo_##suffix(a2.val, a3.val)); \ - __m128i t2 = cast_from(_mm_unpackhi_##suffix(a0.val, a1.val)); \ - __m128i t3 = cast_from(_mm_unpackhi_##suffix(a2.val, a3.val)); \ -\ - b0.val = cast_to(_mm_unpacklo_epi64(t0, t1)); \ - b1.val = cast_to(_mm_unpackhi_epi64(t0, t1)); \ - b2.val = cast_to(_mm_unpacklo_epi64(t2, t3)); \ - b3.val = cast_to(_mm_unpackhi_epi64(t2, t3)); \ -} - -OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_uint32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_int32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_float32x4, ps, _mm_castps_si128, _mm_castsi128_ps) - -// adopted from sse_utils.hpp -inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c) -{ - __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); - __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 16)); - __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 32)); - - __m128i t10 = _mm_unpacklo_epi8(t00, _mm_unpackhi_epi64(t01, t01)); - __m128i t11 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t00, t00), t02); - __m128i t12 = _mm_unpacklo_epi8(t01, _mm_unpackhi_epi64(t02, t02)); - - __m128i t20 = _mm_unpacklo_epi8(t10, _mm_unpackhi_epi64(t11, t11)); - __m128i t21 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t10, t10), t12); - __m128i t22 = _mm_unpacklo_epi8(t11, _mm_unpackhi_epi64(t12, t12)); - - __m128i t30 = _mm_unpacklo_epi8(t20, _mm_unpackhi_epi64(t21, t21)); - __m128i t31 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t20, t20), t22); - __m128i t32 = _mm_unpacklo_epi8(t21, _mm_unpackhi_epi64(t22, t22)); - - a.val = _mm_unpacklo_epi8(t30, _mm_unpackhi_epi64(t31, t31)); - b.val = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t30, t30), t32); - c.val = _mm_unpacklo_epi8(t31, _mm_unpackhi_epi64(t32, t32)); -} - -inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c, v_uint8x16& d) -{ - __m128i u0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 c0 d0 a1 b1 c1 d1 ... - __m128i u1 = _mm_loadu_si128((const __m128i*)(ptr + 16)); // a4 b4 c4 d4 ... - __m128i u2 = _mm_loadu_si128((const __m128i*)(ptr + 32)); // a8 b8 c8 d8 ... - __m128i u3 = _mm_loadu_si128((const __m128i*)(ptr + 48)); // a12 b12 c12 d12 ... - - __m128i v0 = _mm_unpacklo_epi8(u0, u2); // a0 a8 b0 b8 ... - __m128i v1 = _mm_unpackhi_epi8(u0, u2); // a2 a10 b2 b10 ... - __m128i v2 = _mm_unpacklo_epi8(u1, u3); // a4 a12 b4 b12 ... - __m128i v3 = _mm_unpackhi_epi8(u1, u3); // a6 a14 b6 b14 ... - - u0 = _mm_unpacklo_epi8(v0, v2); // a0 a4 a8 a12 ... - u1 = _mm_unpacklo_epi8(v1, v3); // a2 a6 a10 a14 ... - u2 = _mm_unpackhi_epi8(v0, v2); // a1 a5 a9 a13 ... - u3 = _mm_unpackhi_epi8(v1, v3); // a3 a7 a11 a15 ... - - v0 = _mm_unpacklo_epi8(u0, u1); // a0 a2 a4 a6 ... - v1 = _mm_unpacklo_epi8(u2, u3); // a1 a3 a5 a7 ... - v2 = _mm_unpackhi_epi8(u0, u1); // c0 c2 c4 c6 ... - v3 = _mm_unpackhi_epi8(u2, u3); // c1 c3 c5 c7 ... - - a.val = _mm_unpacklo_epi8(v0, v1); - b.val = _mm_unpackhi_epi8(v0, v1); - c.val = _mm_unpacklo_epi8(v2, v3); - d.val = _mm_unpackhi_epi8(v2, v3); -} - -inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c) -{ - __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); - __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 8)); - __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 16)); - - __m128i t10 = _mm_unpacklo_epi16(t00, _mm_unpackhi_epi64(t01, t01)); - __m128i t11 = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t00, t00), t02); - __m128i t12 = _mm_unpacklo_epi16(t01, _mm_unpackhi_epi64(t02, t02)); - - __m128i t20 = _mm_unpacklo_epi16(t10, _mm_unpackhi_epi64(t11, t11)); - __m128i t21 = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t10, t10), t12); - __m128i t22 = _mm_unpacklo_epi16(t11, _mm_unpackhi_epi64(t12, t12)); - - a.val = _mm_unpacklo_epi16(t20, _mm_unpackhi_epi64(t21, t21)); - b.val = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t20, t20), t22); - c.val = _mm_unpacklo_epi16(t21, _mm_unpackhi_epi64(t22, t22)); -} - -inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c, v_uint16x8& d) -{ - __m128i u0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 c0 d0 a1 b1 c1 d1 - __m128i u1 = _mm_loadu_si128((const __m128i*)(ptr + 8)); // a2 b2 c2 d2 ... - __m128i u2 = _mm_loadu_si128((const __m128i*)(ptr + 16)); // a4 b4 c4 d4 ... - __m128i u3 = _mm_loadu_si128((const __m128i*)(ptr + 24)); // a6 b6 c6 d6 ... - - __m128i v0 = _mm_unpacklo_epi16(u0, u2); // a0 a4 b0 b4 ... - __m128i v1 = _mm_unpackhi_epi16(u0, u2); // a1 a5 b1 b5 ... - __m128i v2 = _mm_unpacklo_epi16(u1, u3); // a2 a6 b2 b6 ... - __m128i v3 = _mm_unpackhi_epi16(u1, u3); // a3 a7 b3 b7 ... - - u0 = _mm_unpacklo_epi16(v0, v2); // a0 a2 a4 a6 ... - u1 = _mm_unpacklo_epi16(v1, v3); // a1 a3 a5 a7 ... - u2 = _mm_unpackhi_epi16(v0, v2); // c0 c2 c4 c6 ... - u3 = _mm_unpackhi_epi16(v1, v3); // c1 c3 c5 c7 ... - - a.val = _mm_unpacklo_epi16(u0, u1); - b.val = _mm_unpackhi_epi16(u0, u1); - c.val = _mm_unpacklo_epi16(u2, u3); - d.val = _mm_unpackhi_epi16(u2, u3); -} - -inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c) -{ - __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); - __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 4)); - __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 8)); - - __m128i t10 = _mm_unpacklo_epi32(t00, _mm_unpackhi_epi64(t01, t01)); - __m128i t11 = _mm_unpacklo_epi32(_mm_unpackhi_epi64(t00, t00), t02); - __m128i t12 = _mm_unpacklo_epi32(t01, _mm_unpackhi_epi64(t02, t02)); - - a.val = _mm_unpacklo_epi32(t10, _mm_unpackhi_epi64(t11, t11)); - b.val = _mm_unpacklo_epi32(_mm_unpackhi_epi64(t10, t10), t12); - c.val = _mm_unpacklo_epi32(t11, _mm_unpackhi_epi64(t12, t12)); -} - -inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c, v_uint32x4& d) -{ - v_uint32x4 u0(_mm_loadu_si128((const __m128i*)ptr)); // a0 b0 c0 d0 - v_uint32x4 u1(_mm_loadu_si128((const __m128i*)(ptr + 4))); // a1 b1 c1 d1 - v_uint32x4 u2(_mm_loadu_si128((const __m128i*)(ptr + 8))); // a2 b2 c2 d2 - v_uint32x4 u3(_mm_loadu_si128((const __m128i*)(ptr + 12))); // a3 b3 c3 d3 - - v_transpose4x4(u0, u1, u2, u3, a, b, c, d); -} - -inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, - const v_uint8x16& c ) -{ - __m128i z = _mm_setzero_si128(); - __m128i ab0 = _mm_unpacklo_epi8(a.val, b.val); - __m128i ab1 = _mm_unpackhi_epi8(a.val, b.val); - __m128i c0 = _mm_unpacklo_epi8(c.val, z); - __m128i c1 = _mm_unpackhi_epi8(c.val, z); - - __m128i p00 = _mm_unpacklo_epi16(ab0, c0); - __m128i p01 = _mm_unpackhi_epi16(ab0, c0); - __m128i p02 = _mm_unpacklo_epi16(ab1, c1); - __m128i p03 = _mm_unpackhi_epi16(ab1, c1); - - __m128i p10 = _mm_unpacklo_epi32(p00, p01); - __m128i p11 = _mm_unpackhi_epi32(p00, p01); - __m128i p12 = _mm_unpacklo_epi32(p02, p03); - __m128i p13 = _mm_unpackhi_epi32(p02, p03); - - __m128i p20 = _mm_unpacklo_epi64(p10, p11); - __m128i p21 = _mm_unpackhi_epi64(p10, p11); - __m128i p22 = _mm_unpacklo_epi64(p12, p13); - __m128i p23 = _mm_unpackhi_epi64(p12, p13); - - p20 = _mm_slli_si128(p20, 1); - p22 = _mm_slli_si128(p22, 1); - - __m128i p30 = _mm_slli_epi64(_mm_unpacklo_epi32(p20, p21), 8); - __m128i p31 = _mm_srli_epi64(_mm_unpackhi_epi32(p20, p21), 8); - __m128i p32 = _mm_slli_epi64(_mm_unpacklo_epi32(p22, p23), 8); - __m128i p33 = _mm_srli_epi64(_mm_unpackhi_epi32(p22, p23), 8); - - __m128i p40 = _mm_unpacklo_epi64(p30, p31); - __m128i p41 = _mm_unpackhi_epi64(p30, p31); - __m128i p42 = _mm_unpacklo_epi64(p32, p33); - __m128i p43 = _mm_unpackhi_epi64(p32, p33); - - __m128i v0 = _mm_or_si128(_mm_srli_si128(p40, 2), _mm_slli_si128(p41, 10)); - __m128i v1 = _mm_or_si128(_mm_srli_si128(p41, 6), _mm_slli_si128(p42, 6)); - __m128i v2 = _mm_or_si128(_mm_srli_si128(p42, 10), _mm_slli_si128(p43, 2)); - - _mm_storeu_si128((__m128i*)(ptr), v0); - _mm_storeu_si128((__m128i*)(ptr + 16), v1); - _mm_storeu_si128((__m128i*)(ptr + 32), v2); -} - -inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, - const v_uint8x16& c, const v_uint8x16& d) -{ - // a0 a1 a2 a3 .... - // b0 b1 b2 b3 .... - // c0 c1 c2 c3 .... - // d0 d1 d2 d3 .... - __m128i u0 = _mm_unpacklo_epi8(a.val, c.val); // a0 c0 a1 c1 ... - __m128i u1 = _mm_unpackhi_epi8(a.val, c.val); // a8 c8 a9 c9 ... - __m128i u2 = _mm_unpacklo_epi8(b.val, d.val); // b0 d0 b1 d1 ... - __m128i u3 = _mm_unpackhi_epi8(b.val, d.val); // b8 d8 b9 d9 ... - - __m128i v0 = _mm_unpacklo_epi8(u0, u2); // a0 b0 c0 d0 ... - __m128i v1 = _mm_unpacklo_epi8(u1, u3); // a8 b8 c8 d8 ... - __m128i v2 = _mm_unpackhi_epi8(u0, u2); // a4 b4 c4 d4 ... - __m128i v3 = _mm_unpackhi_epi8(u1, u3); // a12 b12 c12 d12 ... - - _mm_storeu_si128((__m128i*)ptr, v0); - _mm_storeu_si128((__m128i*)(ptr + 16), v2); - _mm_storeu_si128((__m128i*)(ptr + 32), v1); - _mm_storeu_si128((__m128i*)(ptr + 48), v3); -} - -inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, - const v_uint16x8& b, - const v_uint16x8& c ) -{ - __m128i z = _mm_setzero_si128(); - __m128i ab0 = _mm_unpacklo_epi16(a.val, b.val); - __m128i ab1 = _mm_unpackhi_epi16(a.val, b.val); - __m128i c0 = _mm_unpacklo_epi16(c.val, z); - __m128i c1 = _mm_unpackhi_epi16(c.val, z); - - __m128i p10 = _mm_unpacklo_epi32(ab0, c0); - __m128i p11 = _mm_unpackhi_epi32(ab0, c0); - __m128i p12 = _mm_unpacklo_epi32(ab1, c1); - __m128i p13 = _mm_unpackhi_epi32(ab1, c1); - - __m128i p20 = _mm_unpacklo_epi64(p10, p11); - __m128i p21 = _mm_unpackhi_epi64(p10, p11); - __m128i p22 = _mm_unpacklo_epi64(p12, p13); - __m128i p23 = _mm_unpackhi_epi64(p12, p13); - - p20 = _mm_slli_si128(p20, 2); - p22 = _mm_slli_si128(p22, 2); - - __m128i p30 = _mm_unpacklo_epi64(p20, p21); - __m128i p31 = _mm_unpackhi_epi64(p20, p21); - __m128i p32 = _mm_unpacklo_epi64(p22, p23); - __m128i p33 = _mm_unpackhi_epi64(p22, p23); - - __m128i v0 = _mm_or_si128(_mm_srli_si128(p30, 2), _mm_slli_si128(p31, 10)); - __m128i v1 = _mm_or_si128(_mm_srli_si128(p31, 6), _mm_slli_si128(p32, 6)); - __m128i v2 = _mm_or_si128(_mm_srli_si128(p32, 10), _mm_slli_si128(p33, 2)); - - _mm_storeu_si128((__m128i*)(ptr), v0); - _mm_storeu_si128((__m128i*)(ptr + 8), v1); - _mm_storeu_si128((__m128i*)(ptr + 16), v2); -} - -inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b, - const v_uint16x8& c, const v_uint16x8& d) -{ - // a0 a1 a2 a3 .... - // b0 b1 b2 b3 .... - // c0 c1 c2 c3 .... - // d0 d1 d2 d3 .... - __m128i u0 = _mm_unpacklo_epi16(a.val, c.val); // a0 c0 a1 c1 ... - __m128i u1 = _mm_unpackhi_epi16(a.val, c.val); // a4 c4 a5 c5 ... - __m128i u2 = _mm_unpacklo_epi16(b.val, d.val); // b0 d0 b1 d1 ... - __m128i u3 = _mm_unpackhi_epi16(b.val, d.val); // b4 d4 b5 d5 ... - - __m128i v0 = _mm_unpacklo_epi16(u0, u2); // a0 b0 c0 d0 ... - __m128i v1 = _mm_unpacklo_epi16(u1, u3); // a4 b4 c4 d4 ... - __m128i v2 = _mm_unpackhi_epi16(u0, u2); // a2 b2 c2 d2 ... - __m128i v3 = _mm_unpackhi_epi16(u1, u3); // a6 b6 c6 d6 ... - - _mm_storeu_si128((__m128i*)ptr, v0); - _mm_storeu_si128((__m128i*)(ptr + 8), v2); - _mm_storeu_si128((__m128i*)(ptr + 16), v1); - _mm_storeu_si128((__m128i*)(ptr + 24), v3); -} - -inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, - const v_uint32x4& c ) -{ - v_uint32x4 z = v_setzero_u32(), u0, u1, u2, u3; - v_transpose4x4(a, b, c, z, u0, u1, u2, u3); - - __m128i v0 = _mm_or_si128(u0.val, _mm_slli_si128(u1.val, 12)); - __m128i v1 = _mm_or_si128(_mm_srli_si128(u1.val, 4), _mm_slli_si128(u2.val, 8)); - __m128i v2 = _mm_or_si128(_mm_srli_si128(u2.val, 8), _mm_slli_si128(u3.val, 4)); - - _mm_storeu_si128((__m128i*)ptr, v0); - _mm_storeu_si128((__m128i*)(ptr + 4), v1); - _mm_storeu_si128((__m128i*)(ptr + 8), v2); -} - -inline void v_store_interleave(unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, - const v_uint32x4& c, const v_uint32x4& d) -{ - v_uint32x4 t0, t1, t2, t3; - v_transpose4x4(a, b, c, d, t0, t1, t2, t3); - v_store(ptr, t0); - v_store(ptr + 4, t1); - v_store(ptr + 8, t2); - v_store(ptr + 12, t3); -} - -#define OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(_Tpvec, _Tp, suffix, _Tpuvec, _Tpu, usuffix) \ -inline void v_load_deinterleave( const _Tp* ptr, _Tpvec& a0, \ - _Tpvec& b0, _Tpvec& c0 ) \ -{ \ - _Tpuvec a1, b1, c1; \ - v_load_deinterleave((const _Tpu*)ptr, a1, b1, c1); \ - a0 = v_reinterpret_as_##suffix(a1); \ - b0 = v_reinterpret_as_##suffix(b1); \ - c0 = v_reinterpret_as_##suffix(c1); \ -} \ -inline void v_load_deinterleave( const _Tp* ptr, _Tpvec& a0, \ - _Tpvec& b0, _Tpvec& c0, _Tpvec& d0 ) \ -{ \ - _Tpuvec a1, b1, c1, d1; \ - v_load_deinterleave((const _Tpu*)ptr, a1, b1, c1, d1); \ - a0 = v_reinterpret_as_##suffix(a1); \ - b0 = v_reinterpret_as_##suffix(b1); \ - c0 = v_reinterpret_as_##suffix(c1); \ - d0 = v_reinterpret_as_##suffix(d1); \ -} \ -inline void v_store_interleave( _Tp* ptr, const _Tpvec& a0, \ - const _Tpvec& b0, const _Tpvec& c0 ) \ -{ \ - _Tpuvec a1 = v_reinterpret_as_##usuffix(a0); \ - _Tpuvec b1 = v_reinterpret_as_##usuffix(b0); \ - _Tpuvec c1 = v_reinterpret_as_##usuffix(c0); \ - v_store_interleave((_Tpu*)ptr, a1, b1, c1); \ -} \ -inline void v_store_interleave( _Tp* ptr, const _Tpvec& a0, const _Tpvec& b0, \ - const _Tpvec& c0, const _Tpvec& d0 ) \ -{ \ - _Tpuvec a1 = v_reinterpret_as_##usuffix(a0); \ - _Tpuvec b1 = v_reinterpret_as_##usuffix(b0); \ - _Tpuvec c1 = v_reinterpret_as_##usuffix(c0); \ - _Tpuvec d1 = v_reinterpret_as_##usuffix(d0); \ - v_store_interleave((_Tpu*)ptr, a1, b1, c1, d1); \ -} - -OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int8x16, schar, s8, v_uint8x16, uchar, u8) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int16x8, short, s16, v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int32x4, int, s32, v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_float32x4, float, f32, v_uint32x4, unsigned, u32) - -inline v_float32x4 v_cvt_f32(const v_int32x4& a) -{ - return v_float32x4(_mm_cvtepi32_ps(a.val)); -} - -inline v_float32x4 v_cvt_f32(const v_float64x2& a) -{ - return v_float32x4(_mm_cvtpd_ps(a.val)); -} - -inline v_float64x2 v_cvt_f64(const v_int32x4& a) -{ - return v_float64x2(_mm_cvtepi32_pd(a.val)); -} - -inline v_float64x2 v_cvt_f64(const v_float32x4& a) -{ - return v_float64x2(_mm_cvtps_pd(a.val)); -} - -//! @endcond - -} - -#endif diff --git a/IPL/include/opencv/opencv2/core/ippasync.hpp b/IPL/include/opencv/opencv2/core/ippasync.hpp deleted file mode 100644 index 4de8611..0000000 --- a/IPL/include/opencv/opencv2/core/ippasync.hpp +++ /dev/null @@ -1,195 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2015, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_IPPASYNC_HPP__ -#define __OPENCV_CORE_IPPASYNC_HPP__ - -#ifdef HAVE_IPP_A - -#include "opencv2/core.hpp" -#include -#include - -namespace cv -{ - -namespace hpp -{ - -/** @addtogroup core_ipp -This section describes conversion between OpenCV and [Intel® IPP Asynchronous -C/C++](http://software.intel.com/en-us/intel-ipp-preview) library. [Getting Started -Guide](http://registrationcenter.intel.com/irc_nas/3727/ipp_async_get_started.htm) help you to -install the library, configure header and library build paths. - */ -//! @{ - - //! convert OpenCV data type to hppDataType - inline int toHppType(const int cvType) - { - int depth = CV_MAT_DEPTH(cvType); - int hppType = depth == CV_8U ? HPP_DATA_TYPE_8U : - depth == CV_16U ? HPP_DATA_TYPE_16U : - depth == CV_16S ? HPP_DATA_TYPE_16S : - depth == CV_32S ? HPP_DATA_TYPE_32S : - depth == CV_32F ? HPP_DATA_TYPE_32F : - depth == CV_64F ? HPP_DATA_TYPE_64F : -1; - CV_Assert( hppType >= 0 ); - return hppType; - } - - //! convert hppDataType to OpenCV data type - inline int toCvType(const int hppType) - { - int cvType = hppType == HPP_DATA_TYPE_8U ? CV_8U : - hppType == HPP_DATA_TYPE_16U ? CV_16U : - hppType == HPP_DATA_TYPE_16S ? CV_16S : - hppType == HPP_DATA_TYPE_32S ? CV_32S : - hppType == HPP_DATA_TYPE_32F ? CV_32F : - hppType == HPP_DATA_TYPE_64F ? CV_64F : -1; - CV_Assert( cvType >= 0 ); - return cvType; - } - - /** @brief Convert hppiMatrix to Mat. - - This function allocates and initializes new matrix (if needed) that has the same size and type as - input matrix. Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F. - @param src input hppiMatrix. - @param dst output matrix. - @param accel accelerator instance (see hpp::getHpp for the list of acceleration framework types). - @param cn number of channels. - */ - inline void copyHppToMat(hppiMatrix* src, Mat& dst, hppAccel accel, int cn) - { - hppDataType type; - hpp32u width, height; - hppStatus sts; - - if (src == NULL) - return dst.release(); - - sts = hppiInquireMatrix(src, &type, &width, &height); - - CV_Assert( sts == HPP_STATUS_NO_ERROR); - - int matType = CV_MAKETYPE(toCvType(type), cn); - - CV_Assert(width%cn == 0); - - width /= cn; - - dst.create((int)height, (int)width, (int)matType); - - size_t newSize = (size_t)(height*(hpp32u)(dst.step)); - - sts = hppiGetMatrixData(accel,src,(hpp32u)(dst.step),dst.data,&newSize); - - CV_Assert( sts == HPP_STATUS_NO_ERROR); - } - - /** @brief Create Mat from hppiMatrix. - - This function allocates and initializes the Mat that has the same size and type as input matrix. - Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F. - @param src input hppiMatrix. - @param accel accelerator instance (see hpp::getHpp for the list of acceleration framework types). - @param cn number of channels. - @sa howToUseIPPAconversion, hpp::copyHppToMat, hpp::getHpp. - */ - inline Mat getMat(hppiMatrix* src, hppAccel accel, int cn) - { - Mat dst; - copyHppToMat(src, dst, accel, cn); - return dst; - } - - /** @brief Create hppiMatrix from Mat. - - This function allocates and initializes the hppiMatrix that has the same size and type as input - matrix, returns the hppiMatrix*. - - If you want to use zero-copy for GPU you should to have 4KB aligned matrix data. See details - [hppiCreateSharedMatrix](http://software.intel.com/ru-ru/node/501697). - - Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F. - - @note The hppiMatrix pointer to the image buffer in system memory refers to the src.data. Control - the lifetime of the matrix and don't change its data, if there is no special need. - @param src input matrix. - @param accel accelerator instance. Supports type: - - **HPP_ACCEL_TYPE_CPU** - accelerated by optimized CPU instructions. - - **HPP_ACCEL_TYPE_GPU** - accelerated by GPU programmable units or fixed-function - accelerators. - - **HPP_ACCEL_TYPE_ANY** - any acceleration or no acceleration available. - @sa howToUseIPPAconversion, hpp::getMat - */ - inline hppiMatrix* getHpp(const Mat& src, hppAccel accel) - { - int htype = toHppType(src.type()); - int cn = src.channels(); - - CV_Assert(src.data); - hppAccelType accelType = hppQueryAccelType(accel); - - if (accelType!=HPP_ACCEL_TYPE_CPU) - { - hpp32u pitch, size; - hppQueryMatrixAllocParams(accel, src.cols*cn, src.rows, htype, &pitch, &size); - if (pitch!=0 && size!=0) - if ((int)(src.data)%4096==0 && pitch==(hpp32u)(src.step)) - { - return hppiCreateSharedMatrix(htype, src.cols*cn, src.rows, src.data, pitch, size); - } - } - - return hppiCreateMatrix(htype, src.cols*cn, src.rows, src.data, (hpp32s)(src.step));; - } - -//! @} -}} - -#endif - -#endif diff --git a/IPL/include/opencv/opencv2/core/mat.hpp b/IPL/include/opencv/opencv2/core/mat.hpp deleted file mode 100644 index d554663..0000000 --- a/IPL/include/opencv/opencv2/core/mat.hpp +++ /dev/null @@ -1,3442 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_MAT_HPP__ -#define __OPENCV_CORE_MAT_HPP__ - -#ifndef __cplusplus -# error mat.hpp header must be compiled as C++ -#endif - -#include "opencv2/core/matx.hpp" -#include "opencv2/core/types.hpp" - -#include "opencv2/core/bufferpool.hpp" - -namespace cv -{ - -//! @addtogroup core_basic -//! @{ - -enum { ACCESS_READ=1<<24, ACCESS_WRITE=1<<25, - ACCESS_RW=3<<24, ACCESS_MASK=ACCESS_RW, ACCESS_FAST=1<<26 }; - -class CV_EXPORTS _OutputArray; - -//////////////////////// Input/Output Array Arguments ///////////////////////////////// - -/** @brief This is the proxy class for passing read-only input arrays into OpenCV functions. - -It is defined as: -@code - typedef const _InputArray& InputArray; -@endcode -where _InputArray is a class that can be constructed from `Mat`, `Mat_`, `Matx`, -`std::vector`, `std::vector >` or `std::vector`. It can also be constructed -from a matrix expression. - -Since this is mostly implementation-level class, and its interface may change in future versions, we -do not describe it in details. There are a few key things, though, that should be kept in mind: - -- When you see in the reference manual or in OpenCV source code a function that takes - InputArray, it means that you can actually pass `Mat`, `Matx`, `vector` etc. (see above the - complete list). -- Optional input arguments: If some of the input arrays may be empty, pass cv::noArray() (or - simply cv::Mat() as you probably did before). -- The class is designed solely for passing parameters. That is, normally you *should not* - declare class members, local and global variables of this type. -- If you want to design your own function or a class method that can operate of arrays of - multiple types, you can use InputArray (or OutputArray) for the respective parameters. Inside - a function you should use _InputArray::getMat() method to construct a matrix header for the - array (without copying data). _InputArray::kind() can be used to distinguish Mat from - `vector<>` etc., but normally it is not needed. - -Here is how you can use a function that takes InputArray : -@code - std::vector vec; - // points or a circle - for( int i = 0; i < 30; i++ ) - vec.push_back(Point2f((float)(100 + 30*cos(i*CV_PI*2/5)), - (float)(100 - 30*sin(i*CV_PI*2/5)))); - cv::transform(vec, vec, cv::Matx23f(0.707, -0.707, 10, 0.707, 0.707, 20)); -@endcode -That is, we form an STL vector containing points, and apply in-place affine transformation to the -vector using the 2x3 matrix created inline as `Matx` instance. - -Here is how such a function can be implemented (for simplicity, we implement a very specific case of -it, according to the assertion statement inside) : -@code - void myAffineTransform(InputArray _src, OutputArray _dst, InputArray _m) - { - // get Mat headers for input arrays. This is O(1) operation, - // unless _src and/or _m are matrix expressions. - Mat src = _src.getMat(), m = _m.getMat(); - CV_Assert( src.type() == CV_32FC2 && m.type() == CV_32F && m.size() == Size(3, 2) ); - - // [re]create the output array so that it has the proper size and type. - // In case of Mat it calls Mat::create, in case of STL vector it calls vector::resize. - _dst.create(src.size(), src.type()); - Mat dst = _dst.getMat(); - - for( int i = 0; i < src.rows; i++ ) - for( int j = 0; j < src.cols; j++ ) - { - Point2f pt = src.at(i, j); - dst.at(i, j) = Point2f(m.at(0, 0)*pt.x + - m.at(0, 1)*pt.y + - m.at(0, 2), - m.at(1, 0)*pt.x + - m.at(1, 1)*pt.y + - m.at(1, 2)); - } - } -@endcode -There is another related type, InputArrayOfArrays, which is currently defined as a synonym for -InputArray: -@code - typedef InputArray InputArrayOfArrays; -@endcode -It denotes function arguments that are either vectors of vectors or vectors of matrices. A separate -synonym is needed to generate Python/Java etc. wrappers properly. At the function implementation -level their use is similar, but _InputArray::getMat(idx) should be used to get header for the -idx-th component of the outer vector and _InputArray::size().area() should be used to find the -number of components (vectors/matrices) of the outer vector. - */ -class CV_EXPORTS _InputArray -{ -public: - enum { - KIND_SHIFT = 16, - FIXED_TYPE = 0x8000 << KIND_SHIFT, - FIXED_SIZE = 0x4000 << KIND_SHIFT, - KIND_MASK = 31 << KIND_SHIFT, - - NONE = 0 << KIND_SHIFT, - MAT = 1 << KIND_SHIFT, - MATX = 2 << KIND_SHIFT, - STD_VECTOR = 3 << KIND_SHIFT, - STD_VECTOR_VECTOR = 4 << KIND_SHIFT, - STD_VECTOR_MAT = 5 << KIND_SHIFT, - EXPR = 6 << KIND_SHIFT, - OPENGL_BUFFER = 7 << KIND_SHIFT, - CUDA_HOST_MEM = 8 << KIND_SHIFT, - CUDA_GPU_MAT = 9 << KIND_SHIFT, - UMAT =10 << KIND_SHIFT, - STD_VECTOR_UMAT =11 << KIND_SHIFT, - STD_BOOL_VECTOR =12 << KIND_SHIFT, - STD_VECTOR_CUDA_GPU_MAT = 13 << KIND_SHIFT - }; - - _InputArray(); - _InputArray(int _flags, void* _obj); - _InputArray(const Mat& m); - _InputArray(const MatExpr& expr); - _InputArray(const std::vector& vec); - template _InputArray(const Mat_<_Tp>& m); - template _InputArray(const std::vector<_Tp>& vec); - _InputArray(const std::vector& vec); - template _InputArray(const std::vector >& vec); - template _InputArray(const std::vector >& vec); - template _InputArray(const _Tp* vec, int n); - template _InputArray(const Matx<_Tp, m, n>& matx); - _InputArray(const double& val); - _InputArray(const cuda::GpuMat& d_mat); - _InputArray(const std::vector& d_mat_array); - _InputArray(const ogl::Buffer& buf); - _InputArray(const cuda::HostMem& cuda_mem); - template _InputArray(const cudev::GpuMat_<_Tp>& m); - _InputArray(const UMat& um); - _InputArray(const std::vector& umv); - - Mat getMat(int idx=-1) const; - Mat getMat_(int idx=-1) const; - UMat getUMat(int idx=-1) const; - void getMatVector(std::vector& mv) const; - void getUMatVector(std::vector& umv) const; - void getGpuMatVector(std::vector& gpumv) const; - cuda::GpuMat getGpuMat() const; - ogl::Buffer getOGlBuffer() const; - - int getFlags() const; - void* getObj() const; - Size getSz() const; - - int kind() const; - int dims(int i=-1) const; - int cols(int i=-1) const; - int rows(int i=-1) const; - Size size(int i=-1) const; - int sizend(int* sz, int i=-1) const; - bool sameSize(const _InputArray& arr) const; - size_t total(int i=-1) const; - int type(int i=-1) const; - int depth(int i=-1) const; - int channels(int i=-1) const; - bool isContinuous(int i=-1) const; - bool isSubmatrix(int i=-1) const; - bool empty() const; - void copyTo(const _OutputArray& arr) const; - void copyTo(const _OutputArray& arr, const _InputArray & mask) const; - size_t offset(int i=-1) const; - size_t step(int i=-1) const; - bool isMat() const; - bool isUMat() const; - bool isMatVector() const; - bool isUMatVector() const; - bool isMatx() const; - bool isVector() const; - bool isGpuMatVector() const; - ~_InputArray(); - -protected: - int flags; - void* obj; - Size sz; - - void init(int _flags, const void* _obj); - void init(int _flags, const void* _obj, Size _sz); -}; - - -/** @brief This type is very similar to InputArray except that it is used for input/output and output function -parameters. - -Just like with InputArray, OpenCV users should not care about OutputArray, they just pass `Mat`, -`vector` etc. to the functions. The same limitation as for `InputArray`: *Do not explicitly -create OutputArray instances* applies here too. - -If you want to make your function polymorphic (i.e. accept different arrays as output parameters), -it is also not very difficult. Take the sample above as the reference. Note that -_OutputArray::create() needs to be called before _OutputArray::getMat(). This way you guarantee -that the output array is properly allocated. - -Optional output parameters. If you do not need certain output array to be computed and returned to -you, pass cv::noArray(), just like you would in the case of optional input array. At the -implementation level, use _OutputArray::needed() to check if certain output array needs to be -computed or not. - -There are several synonyms for OutputArray that are used to assist automatic Python/Java/... wrapper -generators: -@code - typedef OutputArray OutputArrayOfArrays; - typedef OutputArray InputOutputArray; - typedef OutputArray InputOutputArrayOfArrays; -@endcode - */ -class CV_EXPORTS _OutputArray : public _InputArray -{ -public: - enum - { - DEPTH_MASK_8U = 1 << CV_8U, - DEPTH_MASK_8S = 1 << CV_8S, - DEPTH_MASK_16U = 1 << CV_16U, - DEPTH_MASK_16S = 1 << CV_16S, - DEPTH_MASK_32S = 1 << CV_32S, - DEPTH_MASK_32F = 1 << CV_32F, - DEPTH_MASK_64F = 1 << CV_64F, - DEPTH_MASK_ALL = (DEPTH_MASK_64F<<1)-1, - DEPTH_MASK_ALL_BUT_8S = DEPTH_MASK_ALL & ~DEPTH_MASK_8S, - DEPTH_MASK_FLT = DEPTH_MASK_32F + DEPTH_MASK_64F - }; - - _OutputArray(); - _OutputArray(int _flags, void* _obj); - _OutputArray(Mat& m); - _OutputArray(std::vector& vec); - _OutputArray(cuda::GpuMat& d_mat); - _OutputArray(std::vector& d_mat); - _OutputArray(ogl::Buffer& buf); - _OutputArray(cuda::HostMem& cuda_mem); - template _OutputArray(cudev::GpuMat_<_Tp>& m); - template _OutputArray(std::vector<_Tp>& vec); - _OutputArray(std::vector& vec); - template _OutputArray(std::vector >& vec); - template _OutputArray(std::vector >& vec); - template _OutputArray(Mat_<_Tp>& m); - template _OutputArray(_Tp* vec, int n); - template _OutputArray(Matx<_Tp, m, n>& matx); - _OutputArray(UMat& m); - _OutputArray(std::vector& vec); - - _OutputArray(const Mat& m); - _OutputArray(const std::vector& vec); - _OutputArray(const cuda::GpuMat& d_mat); - _OutputArray(const std::vector& d_mat); - _OutputArray(const ogl::Buffer& buf); - _OutputArray(const cuda::HostMem& cuda_mem); - template _OutputArray(const cudev::GpuMat_<_Tp>& m); - template _OutputArray(const std::vector<_Tp>& vec); - template _OutputArray(const std::vector >& vec); - template _OutputArray(const std::vector >& vec); - template _OutputArray(const Mat_<_Tp>& m); - template _OutputArray(const _Tp* vec, int n); - template _OutputArray(const Matx<_Tp, m, n>& matx); - _OutputArray(const UMat& m); - _OutputArray(const std::vector& vec); - - bool fixedSize() const; - bool fixedType() const; - bool needed() const; - Mat& getMatRef(int i=-1) const; - UMat& getUMatRef(int i=-1) const; - cuda::GpuMat& getGpuMatRef() const; - std::vector& getGpuMatVecRef() const; - ogl::Buffer& getOGlBufferRef() const; - cuda::HostMem& getHostMemRef() const; - void create(Size sz, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; - void create(int rows, int cols, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; - void create(int dims, const int* size, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; - void createSameSize(const _InputArray& arr, int mtype) const; - void release() const; - void clear() const; - void setTo(const _InputArray& value, const _InputArray & mask = _InputArray()) const; - - void assign(const UMat& u) const; - void assign(const Mat& m) const; -}; - - -class CV_EXPORTS _InputOutputArray : public _OutputArray -{ -public: - _InputOutputArray(); - _InputOutputArray(int _flags, void* _obj); - _InputOutputArray(Mat& m); - _InputOutputArray(std::vector& vec); - _InputOutputArray(cuda::GpuMat& d_mat); - _InputOutputArray(ogl::Buffer& buf); - _InputOutputArray(cuda::HostMem& cuda_mem); - template _InputOutputArray(cudev::GpuMat_<_Tp>& m); - template _InputOutputArray(std::vector<_Tp>& vec); - _InputOutputArray(std::vector& vec); - template _InputOutputArray(std::vector >& vec); - template _InputOutputArray(std::vector >& vec); - template _InputOutputArray(Mat_<_Tp>& m); - template _InputOutputArray(_Tp* vec, int n); - template _InputOutputArray(Matx<_Tp, m, n>& matx); - _InputOutputArray(UMat& m); - _InputOutputArray(std::vector& vec); - - _InputOutputArray(const Mat& m); - _InputOutputArray(const std::vector& vec); - _InputOutputArray(const cuda::GpuMat& d_mat); - _InputOutputArray(const std::vector& d_mat); - _InputOutputArray(const ogl::Buffer& buf); - _InputOutputArray(const cuda::HostMem& cuda_mem); - template _InputOutputArray(const cudev::GpuMat_<_Tp>& m); - template _InputOutputArray(const std::vector<_Tp>& vec); - template _InputOutputArray(const std::vector >& vec); - template _InputOutputArray(const std::vector >& vec); - template _InputOutputArray(const Mat_<_Tp>& m); - template _InputOutputArray(const _Tp* vec, int n); - template _InputOutputArray(const Matx<_Tp, m, n>& matx); - _InputOutputArray(const UMat& m); - _InputOutputArray(const std::vector& vec); -}; - -typedef const _InputArray& InputArray; -typedef InputArray InputArrayOfArrays; -typedef const _OutputArray& OutputArray; -typedef OutputArray OutputArrayOfArrays; -typedef const _InputOutputArray& InputOutputArray; -typedef InputOutputArray InputOutputArrayOfArrays; - -CV_EXPORTS InputOutputArray noArray(); - -/////////////////////////////////// MatAllocator ////////////////////////////////////// - -//! Usage flags for allocator -enum UMatUsageFlags -{ - USAGE_DEFAULT = 0, - - // buffer allocation policy is platform and usage specific - USAGE_ALLOCATE_HOST_MEMORY = 1 << 0, - USAGE_ALLOCATE_DEVICE_MEMORY = 1 << 1, - USAGE_ALLOCATE_SHARED_MEMORY = 1 << 2, // It is not equal to: USAGE_ALLOCATE_HOST_MEMORY | USAGE_ALLOCATE_DEVICE_MEMORY - - __UMAT_USAGE_FLAGS_32BIT = 0x7fffffff // Binary compatibility hint -}; - -struct CV_EXPORTS UMatData; - -/** @brief Custom array allocator -*/ -class CV_EXPORTS MatAllocator -{ -public: - MatAllocator() {} - virtual ~MatAllocator() {} - - // let's comment it off for now to detect and fix all the uses of allocator - //virtual void allocate(int dims, const int* sizes, int type, int*& refcount, - // uchar*& datastart, uchar*& data, size_t* step) = 0; - //virtual void deallocate(int* refcount, uchar* datastart, uchar* data) = 0; - virtual UMatData* allocate(int dims, const int* sizes, int type, - void* data, size_t* step, int flags, UMatUsageFlags usageFlags) const = 0; - virtual bool allocate(UMatData* data, int accessflags, UMatUsageFlags usageFlags) const = 0; - virtual void deallocate(UMatData* data) const = 0; - virtual void map(UMatData* data, int accessflags) const; - virtual void unmap(UMatData* data) const; - virtual void download(UMatData* data, void* dst, int dims, const size_t sz[], - const size_t srcofs[], const size_t srcstep[], - const size_t dststep[]) const; - virtual void upload(UMatData* data, const void* src, int dims, const size_t sz[], - const size_t dstofs[], const size_t dststep[], - const size_t srcstep[]) const; - virtual void copy(UMatData* srcdata, UMatData* dstdata, int dims, const size_t sz[], - const size_t srcofs[], const size_t srcstep[], - const size_t dstofs[], const size_t dststep[], bool sync) const; - - // default implementation returns DummyBufferPoolController - virtual BufferPoolController* getBufferPoolController(const char* id = NULL) const; -}; - - -//////////////////////////////// MatCommaInitializer ////////////////////////////////// - -/** @brief Comma-separated Matrix Initializer - - The class instances are usually not created explicitly. - Instead, they are created on "matrix << firstValue" operator. - - The sample below initializes 2x2 rotation matrix: - - \code - double angle = 30, a = cos(angle*CV_PI/180), b = sin(angle*CV_PI/180); - Mat R = (Mat_(2,2) << a, -b, b, a); - \endcode -*/ -template class MatCommaInitializer_ -{ -public: - //! the constructor, created by "matrix << firstValue" operator, where matrix is cv::Mat - MatCommaInitializer_(Mat_<_Tp>* _m); - //! the operator that takes the next value and put it to the matrix - template MatCommaInitializer_<_Tp>& operator , (T2 v); - //! another form of conversion operator - operator Mat_<_Tp>() const; -protected: - MatIterator_<_Tp> it; -}; - - -/////////////////////////////////////// Mat /////////////////////////////////////////// - -// note that umatdata might be allocated together -// with the matrix data, not as a separate object. -// therefore, it does not have constructor or destructor; -// it should be explicitly initialized using init(). -struct CV_EXPORTS UMatData -{ - enum { COPY_ON_MAP=1, HOST_COPY_OBSOLETE=2, - DEVICE_COPY_OBSOLETE=4, TEMP_UMAT=8, TEMP_COPIED_UMAT=24, - USER_ALLOCATED=32, DEVICE_MEM_MAPPED=64}; - UMatData(const MatAllocator* allocator); - ~UMatData(); - - // provide atomic access to the structure - void lock(); - void unlock(); - - bool hostCopyObsolete() const; - bool deviceCopyObsolete() const; - bool deviceMemMapped() const; - bool copyOnMap() const; - bool tempUMat() const; - bool tempCopiedUMat() const; - void markHostCopyObsolete(bool flag); - void markDeviceCopyObsolete(bool flag); - void markDeviceMemMapped(bool flag); - - const MatAllocator* prevAllocator; - const MatAllocator* currAllocator; - int urefcount; - int refcount; - uchar* data; - uchar* origdata; - size_t size; - - int flags; - void* handle; - void* userdata; - int allocatorFlags_; - int mapcount; - UMatData* originalUMatData; -}; - - -struct CV_EXPORTS UMatDataAutoLock -{ - explicit UMatDataAutoLock(UMatData* u); - ~UMatDataAutoLock(); - UMatData* u; -}; - - -struct CV_EXPORTS MatSize -{ - explicit MatSize(int* _p); - Size operator()() const; - const int& operator[](int i) const; - int& operator[](int i); - operator const int*() const; - bool operator == (const MatSize& sz) const; - bool operator != (const MatSize& sz) const; - - int* p; -}; - -struct CV_EXPORTS MatStep -{ - MatStep(); - explicit MatStep(size_t s); - const size_t& operator[](int i) const; - size_t& operator[](int i); - operator size_t() const; - MatStep& operator = (size_t s); - - size_t* p; - size_t buf[2]; -protected: - MatStep& operator = (const MatStep&); -}; - -/** @example cout_mat.cpp -An example demonstrating the serial out capabilities of cv::Mat -*/ - - /** @brief n-dimensional dense array class - -The class Mat represents an n-dimensional dense numerical single-channel or multi-channel array. It -can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel -volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms -may be better stored in a SparseMat ). The data layout of the array `M` is defined by the array -`M.step[]`, so that the address of element \f$(i_0,...,i_{M.dims-1})\f$, where \f$0\leq i_k= M.step[i+1]` (in fact, `M.step[i] >= M.step[i+1]*M.size[i+1]` ). This means -that 2-dimensional matrices are stored row-by-row, 3-dimensional matrices are stored plane-by-plane, -and so on. M.step[M.dims-1] is minimal and always equal to the element size M.elemSize() . - -So, the data layout in Mat is fully compatible with CvMat, IplImage, and CvMatND types from OpenCV -1.x. It is also compatible with the majority of dense array types from the standard toolkits and -SDKs, such as Numpy (ndarray), Win32 (independent device bitmaps), and others, that is, with any -array that uses *steps* (or *strides*) to compute the position of a pixel. Due to this -compatibility, it is possible to make a Mat header for user-allocated data and process it in-place -using OpenCV functions. - -There are many different ways to create a Mat object. The most popular options are listed below: - -- Use the create(nrows, ncols, type) method or the similar Mat(nrows, ncols, type[, fillValue]) -constructor. A new array of the specified size and type is allocated. type has the same meaning as -in the cvCreateMat method. For example, CV_8UC1 means a 8-bit single-channel array, CV_32FC2 -means a 2-channel (complex) floating-point array, and so on. -@code - // make a 7x7 complex matrix filled with 1+3j. - Mat M(7,7,CV_32FC2,Scalar(1,3)); - // and now turn M to a 100x60 15-channel 8-bit matrix. - // The old content will be deallocated - M.create(100,60,CV_8UC(15)); -@endcode -As noted in the introduction to this chapter, create() allocates only a new array when the shape -or type of the current array are different from the specified ones. - -- Create a multi-dimensional array: -@code - // create a 100x100x100 8-bit array - int sz[] = {100, 100, 100}; - Mat bigCube(3, sz, CV_8U, Scalar::all(0)); -@endcode -It passes the number of dimensions =1 to the Mat constructor but the created array will be -2-dimensional with the number of columns set to 1. So, Mat::dims is always \>= 2 (can also be 0 -when the array is empty). - -- Use a copy constructor or assignment operator where there can be an array or expression on the -right side (see below). As noted in the introduction, the array assignment is an O(1) operation -because it only copies the header and increases the reference counter. The Mat::clone() method can -be used to get a full (deep) copy of the array when you need it. - -- Construct a header for a part of another array. It can be a single row, single column, several -rows, several columns, rectangular region in the array (called a *minor* in algebra) or a -diagonal. Such operations are also O(1) because the new header references the same data. You can -actually modify a part of the array using this feature, for example: -@code - // add the 5-th row, multiplied by 3 to the 3rd row - M.row(3) = M.row(3) + M.row(5)*3; - // now copy the 7-th column to the 1-st column - // M.col(1) = M.col(7); // this will not work - Mat M1 = M.col(1); - M.col(7).copyTo(M1); - // create a new 320x240 image - Mat img(Size(320,240),CV_8UC3); - // select a ROI - Mat roi(img, Rect(10,10,100,100)); - // fill the ROI with (0,255,0) (which is green in RGB space); - // the original 320x240 image will be modified - roi = Scalar(0,255,0); -@endcode -Due to the additional datastart and dataend members, it is possible to compute a relative -sub-array position in the main *container* array using locateROI(): -@code - Mat A = Mat::eye(10, 10, CV_32S); - // extracts A columns, 1 (inclusive) to 3 (exclusive). - Mat B = A(Range::all(), Range(1, 3)); - // extracts B rows, 5 (inclusive) to 9 (exclusive). - // that is, C \~ A(Range(5, 9), Range(1, 3)) - Mat C = B(Range(5, 9), Range::all()); - Size size; Point ofs; - C.locateROI(size, ofs); - // size will be (width=10,height=10) and the ofs will be (x=1, y=5) -@endcode -As in case of whole matrices, if you need a deep copy, use the `clone()` method of the extracted -sub-matrices. - -- Make a header for user-allocated data. It can be useful to do the following: - -# Process "foreign" data using OpenCV (for example, when you implement a DirectShow\* filter or - a processing module for gstreamer, and so on). For example: - @code - void process_video_frame(const unsigned char* pixels, - int width, int height, int step) - { - Mat img(height, width, CV_8UC3, pixels, step); - GaussianBlur(img, img, Size(7,7), 1.5, 1.5); - } - @endcode - -# Quickly initialize small matrices and/or get a super-fast element access. - @code - double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}}; - Mat M = Mat(3, 3, CV_64F, m).inv(); - @endcode - . - Partial yet very common cases of this *user-allocated data* case are conversions from CvMat and - IplImage to Mat. For this purpose, there is function cv::cvarrToMat taking pointers to CvMat or - IplImage and the optional flag indicating whether to copy the data or not. - @snippet samples/cpp/image.cpp iplimage - -- Use MATLAB-style array initializers, zeros(), ones(), eye(), for example: -@code - // create a double-precision identity martix and add it to M. - M += Mat::eye(M.rows, M.cols, CV_64F); -@endcode - -- Use a comma-separated initializer: -@code - // create a 3x3 double-precision identity matrix - Mat M = (Mat_(3,3) << 1, 0, 0, 0, 1, 0, 0, 0, 1); -@endcode -With this approach, you first call a constructor of the Mat class with the proper parameters, and -then you just put `<< operator` followed by comma-separated values that can be constants, -variables, expressions, and so on. Also, note the extra parentheses required to avoid compilation -errors. - -Once the array is created, it is automatically managed via a reference-counting mechanism. If the -array header is built on top of user-allocated data, you should handle the data by yourself. The -array data is deallocated when no one points to it. If you want to release the data pointed by a -array header before the array destructor is called, use Mat::release(). - -The next important thing to learn about the array class is element access. This manual already -described how to compute an address of each array element. Normally, you are not required to use the -formula directly in the code. If you know the array element type (which can be retrieved using the -method Mat::type() ), you can access the element \f$M_{ij}\f$ of a 2-dimensional array as: -@code - M.at(i,j) += 1.f; -@endcode -assuming that `M` is a double-precision floating-point array. There are several variants of the method -at for a different number of dimensions. - -If you need to process a whole row of a 2D array, the most efficient way is to get the pointer to -the row first, and then just use the plain C operator [] : -@code - // compute sum of positive matrix elements - // (assuming that M isa double-precision matrix) - double sum=0; - for(int i = 0; i < M.rows; i++) - { - const double* Mi = M.ptr(i); - for(int j = 0; j < M.cols; j++) - sum += std::max(Mi[j], 0.); - } -@endcode -Some operations, like the one above, do not actually depend on the array shape. They just process -elements of an array one by one (or elements from multiple arrays that have the same coordinates, -for example, array addition). Such operations are called *element-wise*. It makes sense to check -whether all the input/output arrays are continuous, namely, have no gaps at the end of each row. If -yes, process them as a long single row: -@code - // compute the sum of positive matrix elements, optimized variant - double sum=0; - int cols = M.cols, rows = M.rows; - if(M.isContinuous()) - { - cols *= rows; - rows = 1; - } - for(int i = 0; i < rows; i++) - { - const double* Mi = M.ptr(i); - for(int j = 0; j < cols; j++) - sum += std::max(Mi[j], 0.); - } -@endcode -In case of the continuous matrix, the outer loop body is executed just once. So, the overhead is -smaller, which is especially noticeable in case of small matrices. - -Finally, there are STL-style iterators that are smart enough to skip gaps between successive rows: -@code - // compute sum of positive matrix elements, iterator-based variant - double sum=0; - MatConstIterator_ it = M.begin(), it_end = M.end(); - for(; it != it_end; ++it) - sum += std::max(*it, 0.); -@endcode -The matrix iterators are random-access iterators, so they can be passed to any STL algorithm, -including std::sort(). -*/ -class CV_EXPORTS Mat -{ -public: - /** - These are various constructors that form a matrix. As noted in the AutomaticAllocation, often - the default constructor is enough, and the proper matrix will be allocated by an OpenCV function. - The constructed matrix can further be assigned to another matrix or matrix expression or can be - allocated with Mat::create . In the former case, the old content is de-referenced. - */ - Mat(); - - /** @overload - @param rows Number of rows in a 2D array. - @param cols Number of columns in a 2D array. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - */ - Mat(int rows, int cols, int type); - - /** @overload - @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the - number of columns go in the reverse order. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - */ - Mat(Size size, int type); - - /** @overload - @param rows Number of rows in a 2D array. - @param cols Number of columns in a 2D array. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - @param s An optional value to initialize each matrix element with. To set all the matrix elements to - the particular value after the construction, use the assignment operator - Mat::operator=(const Scalar& value) . - */ - Mat(int rows, int cols, int type, const Scalar& s); - - /** @overload - @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the - number of columns go in the reverse order. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - @param s An optional value to initialize each matrix element with. To set all the matrix elements to - the particular value after the construction, use the assignment operator - Mat::operator=(const Scalar& value) . - */ - Mat(Size size, int type, const Scalar& s); - - /** @overload - @param ndims Array dimensionality. - @param sizes Array of integers specifying an n-dimensional array shape. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - */ - Mat(int ndims, const int* sizes, int type); - - /** @overload - @param ndims Array dimensionality. - @param sizes Array of integers specifying an n-dimensional array shape. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - @param s An optional value to initialize each matrix element with. To set all the matrix elements to - the particular value after the construction, use the assignment operator - Mat::operator=(const Scalar& value) . - */ - Mat(int ndims, const int* sizes, int type, const Scalar& s); - - /** @overload - @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied - by these constructors. Instead, the header pointing to m data or its sub-array is constructed and - associated with it. The reference counter, if any, is incremented. So, when you modify the matrix - formed using such a constructor, you also modify the corresponding elements of m . If you want to - have an independent copy of the sub-array, use Mat::clone() . - */ - Mat(const Mat& m); - - /** @overload - @param rows Number of rows in a 2D array. - @param cols Number of columns in a 2D array. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - @param data Pointer to the user data. Matrix constructors that take data and step parameters do not - allocate matrix data. Instead, they just initialize the matrix header that points to the specified - data, which means that no data is copied. This operation is very efficient and can be used to - process external data using OpenCV functions. The external data is not automatically deallocated, so - you should take care of it. - @param step Number of bytes each matrix row occupies. The value should include the padding bytes at - the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed - and the actual step is calculated as cols*elemSize(). See Mat::elemSize. - */ - Mat(int rows, int cols, int type, void* data, size_t step=AUTO_STEP); - - /** @overload - @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the - number of columns go in the reverse order. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - @param data Pointer to the user data. Matrix constructors that take data and step parameters do not - allocate matrix data. Instead, they just initialize the matrix header that points to the specified - data, which means that no data is copied. This operation is very efficient and can be used to - process external data using OpenCV functions. The external data is not automatically deallocated, so - you should take care of it. - @param step Number of bytes each matrix row occupies. The value should include the padding bytes at - the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed - and the actual step is calculated as cols*elemSize(). See Mat::elemSize. - */ - Mat(Size size, int type, void* data, size_t step=AUTO_STEP); - - /** @overload - @param ndims Array dimensionality. - @param sizes Array of integers specifying an n-dimensional array shape. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - @param data Pointer to the user data. Matrix constructors that take data and step parameters do not - allocate matrix data. Instead, they just initialize the matrix header that points to the specified - data, which means that no data is copied. This operation is very efficient and can be used to - process external data using OpenCV functions. The external data is not automatically deallocated, so - you should take care of it. - @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always - set to the element size). If not specified, the matrix is assumed to be continuous. - */ - Mat(int ndims, const int* sizes, int type, void* data, const size_t* steps=0); - - /** @overload - @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied - by these constructors. Instead, the header pointing to m data or its sub-array is constructed and - associated with it. The reference counter, if any, is incremented. So, when you modify the matrix - formed using such a constructor, you also modify the corresponding elements of m . If you want to - have an independent copy of the sub-array, use Mat::clone() . - @param rowRange Range of the m rows to take. As usual, the range start is inclusive and the range - end is exclusive. Use Range::all() to take all the rows. - @param colRange Range of the m columns to take. Use Range::all() to take all the columns. - */ - Mat(const Mat& m, const Range& rowRange, const Range& colRange=Range::all()); - - /** @overload - @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied - by these constructors. Instead, the header pointing to m data or its sub-array is constructed and - associated with it. The reference counter, if any, is incremented. So, when you modify the matrix - formed using such a constructor, you also modify the corresponding elements of m . If you want to - have an independent copy of the sub-array, use Mat::clone() . - @param roi Region of interest. - */ - Mat(const Mat& m, const Rect& roi); - - /** @overload - @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied - by these constructors. Instead, the header pointing to m data or its sub-array is constructed and - associated with it. The reference counter, if any, is incremented. So, when you modify the matrix - formed using such a constructor, you also modify the corresponding elements of m . If you want to - have an independent copy of the sub-array, use Mat::clone() . - @param ranges Array of selected ranges of m along each dimensionality. - */ - Mat(const Mat& m, const Range* ranges); - - /** @overload - @param vec STL vector whose elements form the matrix. The matrix has a single column and the number - of rows equal to the number of vector elements. Type of the matrix matches the type of vector - elements. The constructor can handle arbitrary types, for which there is a properly declared - DataType . This means that the vector elements must be primitive numbers or uni-type numerical - tuples of numbers. Mixed-type structures are not supported. The corresponding constructor is - explicit. Since STL vectors are not automatically converted to Mat instances, you should write - Mat(vec) explicitly. Unless you copy the data into the matrix ( copyData=true ), no new elements - will be added to the vector because it can potentially yield vector data reallocation, and, thus, - the matrix data pointer will be invalid. - @param copyData Flag to specify whether the underlying data of the STL vector should be copied - to (true) or shared with (false) the newly constructed matrix. When the data is copied, the - allocated buffer is managed using Mat reference counting mechanism. While the data is shared, - the reference counter is NULL, and you should not deallocate the data until the matrix is not - destructed. - */ - template explicit Mat(const std::vector<_Tp>& vec, bool copyData=false); - - /** @overload - */ - template explicit Mat(const Vec<_Tp, n>& vec, bool copyData=true); - - /** @overload - */ - template explicit Mat(const Matx<_Tp, m, n>& mtx, bool copyData=true); - - /** @overload - */ - template explicit Mat(const Point_<_Tp>& pt, bool copyData=true); - - /** @overload - */ - template explicit Mat(const Point3_<_Tp>& pt, bool copyData=true); - - /** @overload - */ - template explicit Mat(const MatCommaInitializer_<_Tp>& commaInitializer); - - //! download data from GpuMat - explicit Mat(const cuda::GpuMat& m); - - //! destructor - calls release() - ~Mat(); - - /** @brief assignment operators - - These are available assignment operators. Since they all are very different, make sure to read the - operator parameters description. - @param m Assigned, right-hand-side matrix. Matrix assignment is an O(1) operation. This means that - no data is copied but the data is shared and the reference counter, if any, is incremented. Before - assigning new data, the old data is de-referenced via Mat::release . - */ - Mat& operator = (const Mat& m); - - /** @overload - @param expr Assigned matrix expression object. As opposite to the first form of the assignment - operation, the second form can reuse already allocated matrix if it has the right size and type to - fit the matrix expression result. It is automatically handled by the real function that the matrix - expressions is expanded to. For example, C=A+B is expanded to add(A, B, C), and add takes care of - automatic C reallocation. - */ - Mat& operator = (const MatExpr& expr); - - //! retrieve UMat from Mat - UMat getUMat(int accessFlags, UMatUsageFlags usageFlags = USAGE_DEFAULT) const; - - /** @brief Creates a matrix header for the specified matrix row. - - The method makes a new header for the specified matrix row and returns it. This is an O(1) - operation, regardless of the matrix size. The underlying data of the new matrix is shared with the - original matrix. Here is the example of one of the classical basic matrix processing operations, - axpy, used by LU and many other algorithms: - @code - inline void matrix_axpy(Mat& A, int i, int j, double alpha) - { - A.row(i) += A.row(j)*alpha; - } - @endcode - @note In the current implementation, the following code does not work as expected: - @code - Mat A; - ... - A.row(i) = A.row(j); // will not work - @endcode - This happens because A.row(i) forms a temporary header that is further assigned to another header. - Remember that each of these operations is O(1), that is, no data is copied. Thus, the above - assignment is not true if you may have expected the j-th row to be copied to the i-th row. To - achieve that, you should either turn this simple assignment into an expression or use the - Mat::copyTo method: - @code - Mat A; - ... - // works, but looks a bit obscure. - A.row(i) = A.row(j) + 0; - // this is a bit longer, but the recommended method. - A.row(j).copyTo(A.row(i)); - @endcode - @param y A 0-based row index. - */ - Mat row(int y) const; - - /** @brief Creates a matrix header for the specified matrix column. - - The method makes a new header for the specified matrix column and returns it. This is an O(1) - operation, regardless of the matrix size. The underlying data of the new matrix is shared with the - original matrix. See also the Mat::row description. - @param x A 0-based column index. - */ - Mat col(int x) const; - - /** @brief Creates a matrix header for the specified row span. - - The method makes a new header for the specified row span of the matrix. Similarly to Mat::row and - Mat::col , this is an O(1) operation. - @param startrow An inclusive 0-based start index of the row span. - @param endrow An exclusive 0-based ending index of the row span. - */ - Mat rowRange(int startrow, int endrow) const; - - /** @overload - @param r Range structure containing both the start and the end indices. - */ - Mat rowRange(const Range& r) const; - - /** @brief Creates a matrix header for the specified column span. - - The method makes a new header for the specified column span of the matrix. Similarly to Mat::row and - Mat::col , this is an O(1) operation. - @param startcol An inclusive 0-based start index of the column span. - @param endcol An exclusive 0-based ending index of the column span. - */ - Mat colRange(int startcol, int endcol) const; - - /** @overload - @param r Range structure containing both the start and the end indices. - */ - Mat colRange(const Range& r) const; - - /** @brief Extracts a diagonal from a matrix - - The method makes a new header for the specified matrix diagonal. The new matrix is represented as a - single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation. - @param d index of the diagonal, with the following values: - - `d=0` is the main diagonal. - - `d>0` is a diagonal from the lower half. For example, d=1 means the diagonal is set - immediately below the main one. - - `d<0` is a diagonal from the upper half. For example, d=-1 means the diagonal is set - immediately above the main one. - */ - Mat diag(int d=0) const; - - /** @brief creates a diagonal matrix - - The method makes a new header for the specified matrix diagonal. The new matrix is represented as a - single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation. - @param d Single-column matrix that forms a diagonal matrix - */ - static Mat diag(const Mat& d); - - /** @brief Creates a full copy of the array and the underlying data. - - The method creates a full copy of the array. The original step[] is not taken into account. So, the - array copy is a continuous array occupying total()*elemSize() bytes. - */ - Mat clone() const; - - /** @brief Copies the matrix to another one. - - The method copies the matrix data to another matrix. Before copying the data, the method invokes : - @code - m.create(this->size(), this->type()); - @endcode - so that the destination matrix is reallocated if needed. While m.copyTo(m); works flawlessly, the - function does not handle the case of a partial overlap between the source and the destination - matrices. - - When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, - the newly allocated matrix is initialized with all zeros before copying the data. - @param m Destination matrix. If it does not have a proper size or type before the operation, it is - reallocated. - */ - void copyTo( OutputArray m ) const; - - /** @overload - @param m Destination matrix. If it does not have a proper size or type before the operation, it is - reallocated. - @param mask Operation mask. Its non-zero elements indicate which matrix elements need to be copied. - The mask has to be of type CV_8U and can have 1 or multiple channels. - */ - void copyTo( OutputArray m, InputArray mask ) const; - - /** @brief Converts an array to another data type with optional scaling. - - The method converts source pixel values to the target data type. saturate_cast\<\> is applied at - the end to avoid possible overflows: - - \f[m(x,y) = saturate \_ cast( \alpha (*this)(x,y) + \beta )\f] - @param m output matrix; if it does not have a proper size or type before the operation, it is - reallocated. - @param rtype desired output matrix type or, rather, the depth since the number of channels are the - same as the input has; if rtype is negative, the output matrix will have the same type as the input. - @param alpha optional scale factor. - @param beta optional delta added to the scaled values. - */ - void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const; - - /** @brief Provides a functional form of convertTo. - - This is an internally used method called by the @ref MatrixExpressions engine. - @param m Destination array. - @param type Desired destination array depth (or -1 if it should be the same as the source type). - */ - void assignTo( Mat& m, int type=-1 ) const; - - /** @brief Sets all or some of the array elements to the specified value. - @param s Assigned scalar converted to the actual array type. - */ - Mat& operator = (const Scalar& s); - - /** @brief Sets all or some of the array elements to the specified value. - - This is an advanced variant of the Mat::operator=(const Scalar& s) operator. - @param value Assigned scalar converted to the actual array type. - @param mask Operation mask of the same size as \*this. - */ - Mat& setTo(InputArray value, InputArray mask=noArray()); - - /** @brief Changes the shape and/or the number of channels of a 2D matrix without copying the data. - - The method makes a new matrix header for \*this elements. The new matrix may have a different size - and/or different number of channels. Any combination is possible if: - - No extra elements are included into the new matrix and no elements are excluded. Consequently, - the product rows\*cols\*channels() must stay the same after the transformation. - - No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of - rows, or the operation changes the indices of elements row in some other way, the matrix must be - continuous. See Mat::isContinuous . - - For example, if there is a set of 3D points stored as an STL vector, and you want to represent the - points as a 3xN matrix, do the following: - @code - std::vector vec; - ... - Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation - reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel. - // Also, an O(1) operation - t(); // finally, transpose the Nx3 matrix. - // This involves copying all the elements - @endcode - @param cn New number of channels. If the parameter is 0, the number of channels remains the same. - @param rows New number of rows. If the parameter is 0, the number of rows remains the same. - */ - Mat reshape(int cn, int rows=0) const; - - /** @overload */ - Mat reshape(int cn, int newndims, const int* newsz) const; - - /** @brief Transposes a matrix. - - The method performs matrix transposition by means of matrix expressions. It does not perform the - actual transposition but returns a temporary matrix transposition object that can be further used as - a part of more complex matrix expressions or can be assigned to a matrix: - @code - Mat A1 = A + Mat::eye(A.size(), A.type())*lambda; - Mat C = A1.t()*A1; // compute (A + lambda*I)^t * (A + lamda*I) - @endcode - */ - MatExpr t() const; - - /** @brief Inverses a matrix. - - The method performs a matrix inversion by means of matrix expressions. This means that a temporary - matrix inversion object is returned by the method and can be used further as a part of more complex - matrix expressions or can be assigned to a matrix. - @param method Matrix inversion method. One of cv::DecompTypes - */ - MatExpr inv(int method=DECOMP_LU) const; - - /** @brief Performs an element-wise multiplication or division of the two matrices. - - The method returns a temporary object encoding per-element array multiplication, with optional - scale. Note that this is not a matrix multiplication that corresponds to a simpler "\*" operator. - - Example: - @code - Mat C = A.mul(5/B); // equivalent to divide(A, B, C, 5) - @endcode - @param m Another array of the same type and the same size as \*this, or a matrix expression. - @param scale Optional scale factor. - */ - MatExpr mul(InputArray m, double scale=1) const; - - /** @brief Computes a cross-product of two 3-element vectors. - - The method computes a cross-product of two 3-element vectors. The vectors must be 3-element - floating-point vectors of the same shape and size. The result is another 3-element vector of the - same shape and type as operands. - @param m Another cross-product operand. - */ - Mat cross(InputArray m) const; - - /** @brief Computes a dot-product of two vectors. - - The method computes a dot-product of two matrices. If the matrices are not single-column or - single-row vectors, the top-to-bottom left-to-right scan ordering is used to treat them as 1D - vectors. The vectors must have the same size and type. If the matrices have more than one channel, - the dot products from all the channels are summed together. - @param m another dot-product operand. - */ - double dot(InputArray m) const; - - /** @brief Returns a zero array of the specified size and type. - - The method returns a Matlab-style zero array initializer. It can be used to quickly form a constant - array as a function parameter, part of a matrix expression, or as a matrix initializer. : - @code - Mat A; - A = Mat::zeros(3, 3, CV_32F); - @endcode - In the example above, a new matrix is allocated only if A is not a 3x3 floating-point matrix. - Otherwise, the existing matrix A is filled with zeros. - @param rows Number of rows. - @param cols Number of columns. - @param type Created matrix type. - */ - static MatExpr zeros(int rows, int cols, int type); - - /** @overload - @param size Alternative to the matrix size specification Size(cols, rows) . - @param type Created matrix type. - */ - static MatExpr zeros(Size size, int type); - - /** @overload - @param ndims Array dimensionality. - @param sz Array of integers specifying the array shape. - @param type Created matrix type. - */ - static MatExpr zeros(int ndims, const int* sz, int type); - - /** @brief Returns an array of all 1's of the specified size and type. - - The method returns a Matlab-style 1's array initializer, similarly to Mat::zeros. Note that using - this method you can initialize an array with an arbitrary value, using the following Matlab idiom: - @code - Mat A = Mat::ones(100, 100, CV_8U)*3; // make 100x100 matrix filled with 3. - @endcode - The above operation does not form a 100x100 matrix of 1's and then multiply it by 3. Instead, it - just remembers the scale factor (3 in this case) and use it when actually invoking the matrix - initializer. - @param rows Number of rows. - @param cols Number of columns. - @param type Created matrix type. - */ - static MatExpr ones(int rows, int cols, int type); - - /** @overload - @param size Alternative to the matrix size specification Size(cols, rows) . - @param type Created matrix type. - */ - static MatExpr ones(Size size, int type); - - /** @overload - @param ndims Array dimensionality. - @param sz Array of integers specifying the array shape. - @param type Created matrix type. - */ - static MatExpr ones(int ndims, const int* sz, int type); - - /** @brief Returns an identity matrix of the specified size and type. - - The method returns a Matlab-style identity matrix initializer, similarly to Mat::zeros. Similarly to - Mat::ones, you can use a scale operation to create a scaled identity matrix efficiently: - @code - // make a 4x4 diagonal matrix with 0.1's on the diagonal. - Mat A = Mat::eye(4, 4, CV_32F)*0.1; - @endcode - @param rows Number of rows. - @param cols Number of columns. - @param type Created matrix type. - */ - static MatExpr eye(int rows, int cols, int type); - - /** @overload - @param size Alternative matrix size specification as Size(cols, rows) . - @param type Created matrix type. - */ - static MatExpr eye(Size size, int type); - - /** @brief Allocates new array data if needed. - - This is one of the key Mat methods. Most new-style OpenCV functions and methods that produce arrays - call this method for each output array. The method uses the following algorithm: - - -# If the current array shape and the type match the new ones, return immediately. Otherwise, - de-reference the previous data by calling Mat::release. - -# Initialize the new header. - -# Allocate the new data of total()\*elemSize() bytes. - -# Allocate the new, associated with the data, reference counter and set it to 1. - - Such a scheme makes the memory management robust and efficient at the same time and helps avoid - extra typing for you. This means that usually there is no need to explicitly allocate output arrays. - That is, instead of writing: - @code - Mat color; - ... - Mat gray(color.rows, color.cols, color.depth()); - cvtColor(color, gray, COLOR_BGR2GRAY); - @endcode - you can simply write: - @code - Mat color; - ... - Mat gray; - cvtColor(color, gray, COLOR_BGR2GRAY); - @endcode - because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array - internally. - @param rows New number of rows. - @param cols New number of columns. - @param type New matrix type. - */ - void create(int rows, int cols, int type); - - /** @overload - @param size Alternative new matrix size specification: Size(cols, rows) - @param type New matrix type. - */ - void create(Size size, int type); - - /** @overload - @param ndims New array dimensionality. - @param sizes Array of integers specifying a new array shape. - @param type New matrix type. - */ - void create(int ndims, const int* sizes, int type); - - /** @brief Increments the reference counter. - - The method increments the reference counter associated with the matrix data. If the matrix header - points to an external data set (see Mat::Mat ), the reference counter is NULL, and the method has no - effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It - is called implicitly by the matrix assignment operator. The reference counter increment is an atomic - operation on the platforms that support it. Thus, it is safe to operate on the same matrices - asynchronously in different threads. - */ - void addref(); - - /** @brief Decrements the reference counter and deallocates the matrix if needed. - - The method decrements the reference counter associated with the matrix data. When the reference - counter reaches 0, the matrix data is deallocated and the data and the reference counter pointers - are set to NULL's. If the matrix header points to an external data set (see Mat::Mat ), the - reference counter is NULL, and the method has no effect in this case. - - This method can be called manually to force the matrix data deallocation. But since this method is - automatically called in the destructor, or by any other method that changes the data pointer, it is - usually not needed. The reference counter decrement and check for 0 is an atomic operation on the - platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in - different threads. - */ - void release(); - - //! deallocates the matrix data - void deallocate(); - //! internal use function; properly re-allocates _size, _step arrays - void copySize(const Mat& m); - - /** @brief Reserves space for the certain number of rows. - - The method reserves space for sz rows. If the matrix already has enough space to store sz rows, - nothing happens. If the matrix is reallocated, the first Mat::rows rows are preserved. The method - emulates the corresponding method of the STL vector class. - @param sz Number of rows. - */ - void reserve(size_t sz); - - /** @brief Changes the number of matrix rows. - - The methods change the number of matrix rows. If the matrix is reallocated, the first - min(Mat::rows, sz) rows are preserved. The methods emulate the corresponding methods of the STL - vector class. - @param sz New number of rows. - */ - void resize(size_t sz); - - /** @overload - @param sz New number of rows. - @param s Value assigned to the newly added elements. - */ - void resize(size_t sz, const Scalar& s); - - //! internal function - void push_back_(const void* elem); - - /** @brief Adds elements to the bottom of the matrix. - - The methods add one or more elements to the bottom of the matrix. They emulate the corresponding - method of the STL vector class. When elem is Mat , its type and the number of columns must be the - same as in the container matrix. - @param elem Added element(s). - */ - template void push_back(const _Tp& elem); - - /** @overload - @param elem Added element(s). - */ - template void push_back(const Mat_<_Tp>& elem); - - /** @overload - @param m Added line(s). - */ - void push_back(const Mat& m); - - /** @brief Removes elements from the bottom of the matrix. - - The method removes one or more rows from the bottom of the matrix. - @param nelems Number of removed rows. If it is greater than the total number of rows, an exception - is thrown. - */ - void pop_back(size_t nelems=1); - - /** @brief Locates the matrix header within a parent matrix. - - After you extracted a submatrix from a matrix using Mat::row, Mat::col, Mat::rowRange, - Mat::colRange, and others, the resultant submatrix points just to the part of the original big - matrix. However, each submatrix contains information (represented by datastart and dataend - fields) that helps reconstruct the original matrix size and the position of the extracted - submatrix within the original matrix. The method locateROI does exactly that. - @param wholeSize Output parameter that contains the size of the whole matrix containing *this* - as a part. - @param ofs Output parameter that contains an offset of *this* inside the whole matrix. - */ - void locateROI( Size& wholeSize, Point& ofs ) const; - - /** @brief Adjusts a submatrix size and position within the parent matrix. - - The method is complimentary to Mat::locateROI . The typical use of these functions is to determine - the submatrix position within the parent matrix and then shift the position somehow. Typically, it - can be required for filtering operations when pixels outside of the ROI should be taken into - account. When all the method parameters are positive, the ROI needs to grow in all directions by the - specified amount, for example: - @code - A.adjustROI(2, 2, 2, 2); - @endcode - In this example, the matrix size is increased by 4 elements in each direction. The matrix is shifted - by 2 elements to the left and 2 elements up, which brings in all the necessary pixels for the - filtering with the 5x5 kernel. - - adjustROI forces the adjusted ROI to be inside of the parent matrix that is boundaries of the - adjusted ROI are constrained by boundaries of the parent matrix. For example, if the submatrix A is - located in the first row of a parent matrix and you called A.adjustROI(2, 2, 2, 2) then A will not - be increased in the upward direction. - - The function is used internally by the OpenCV filtering functions, like filter2D , morphological - operations, and so on. - @param dtop Shift of the top submatrix boundary upwards. - @param dbottom Shift of the bottom submatrix boundary downwards. - @param dleft Shift of the left submatrix boundary to the left. - @param dright Shift of the right submatrix boundary to the right. - @sa copyMakeBorder - */ - Mat& adjustROI( int dtop, int dbottom, int dleft, int dright ); - - /** @brief Extracts a rectangular submatrix. - - The operators make a new header for the specified sub-array of \*this . They are the most - generalized forms of Mat::row, Mat::col, Mat::rowRange, and Mat::colRange . For example, - `A(Range(0, 10), Range::all())` is equivalent to `A.rowRange(0, 10)`. Similarly to all of the above, - the operators are O(1) operations, that is, no matrix data is copied. - @param rowRange Start and end row of the extracted submatrix. The upper boundary is not included. To - select all the rows, use Range::all(). - @param colRange Start and end column of the extracted submatrix. The upper boundary is not included. - To select all the columns, use Range::all(). - */ - Mat operator()( Range rowRange, Range colRange ) const; - - /** @overload - @param roi Extracted submatrix specified as a rectangle. - */ - Mat operator()( const Rect& roi ) const; - - /** @overload - @param ranges Array of selected ranges along each array dimension. - */ - Mat operator()( const Range* ranges ) const; - - // //! converts header to CvMat; no data is copied - // operator CvMat() const; - // //! converts header to CvMatND; no data is copied - // operator CvMatND() const; - // //! converts header to IplImage; no data is copied - // operator IplImage() const; - - template operator std::vector<_Tp>() const; - template operator Vec<_Tp, n>() const; - template operator Matx<_Tp, m, n>() const; - - /** @brief Reports whether the matrix is continuous or not. - - The method returns true if the matrix elements are stored continuously without gaps at the end of - each row. Otherwise, it returns false. Obviously, 1x1 or 1xN matrices are always continuous. - Matrices created with Mat::create are always continuous. But if you extract a part of the matrix - using Mat::col, Mat::diag, and so on, or constructed a matrix header for externally allocated data, - such matrices may no longer have this property. - - The continuity flag is stored as a bit in the Mat::flags field and is computed automatically when - you construct a matrix header. Thus, the continuity check is a very fast operation, though - theoretically it could be done as follows: - @code - // alternative implementation of Mat::isContinuous() - bool myCheckMatContinuity(const Mat& m) - { - //return (m.flags & Mat::CONTINUOUS_FLAG) != 0; - return m.rows == 1 || m.step == m.cols*m.elemSize(); - } - @endcode - The method is used in quite a few of OpenCV functions. The point is that element-wise operations - (such as arithmetic and logical operations, math functions, alpha blending, color space - transformations, and others) do not depend on the image geometry. Thus, if all the input and output - arrays are continuous, the functions can process them as very long single-row vectors. The example - below illustrates how an alpha-blending function can be implemented: - @code - template - void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst) - { - const float alpha_scale = (float)std::numeric_limits::max(), - inv_scale = 1.f/alpha_scale; - - CV_Assert( src1.type() == src2.type() && - src1.type() == CV_MAKETYPE(DataType::depth, 4) && - src1.size() == src2.size()); - Size size = src1.size(); - dst.create(size, src1.type()); - - // here is the idiom: check the arrays for continuity and, - // if this is the case, - // treat the arrays as 1D vectors - if( src1.isContinuous() && src2.isContinuous() && dst.isContinuous() ) - { - size.width *= size.height; - size.height = 1; - } - size.width *= 4; - - for( int i = 0; i < size.height; i++ ) - { - // when the arrays are continuous, - // the outer loop is executed only once - const T* ptr1 = src1.ptr(i); - const T* ptr2 = src2.ptr(i); - T* dptr = dst.ptr(i); - - for( int j = 0; j < size.width; j += 4 ) - { - float alpha = ptr1[j+3]*inv_scale, beta = ptr2[j+3]*inv_scale; - dptr[j] = saturate_cast(ptr1[j]*alpha + ptr2[j]*beta); - dptr[j+1] = saturate_cast(ptr1[j+1]*alpha + ptr2[j+1]*beta); - dptr[j+2] = saturate_cast(ptr1[j+2]*alpha + ptr2[j+2]*beta); - dptr[j+3] = saturate_cast((1 - (1-alpha)*(1-beta))*alpha_scale); - } - } - } - @endcode - This approach, while being very simple, can boost the performance of a simple element-operation by - 10-20 percents, especially if the image is rather small and the operation is quite simple. - - Another OpenCV idiom in this function, a call of Mat::create for the destination array, that - allocates the destination array unless it already has the proper size and type. And while the newly - allocated arrays are always continuous, you still need to check the destination array because - Mat::create does not always allocate a new matrix. - */ - bool isContinuous() const; - - //! returns true if the matrix is a submatrix of another matrix - bool isSubmatrix() const; - - /** @brief Returns the matrix element size in bytes. - - The method returns the matrix element size in bytes. For example, if the matrix type is CV_16SC3 , - the method returns 3\*sizeof(short) or 6. - */ - size_t elemSize() const; - - /** @brief Returns the size of each matrix element channel in bytes. - - The method returns the matrix element channel size in bytes, that is, it ignores the number of - channels. For example, if the matrix type is CV_16SC3 , the method returns sizeof(short) or 2. - */ - size_t elemSize1() const; - - /** @brief Returns the type of a matrix element. - - The method returns a matrix element type. This is an identifier compatible with the CvMat type - system, like CV_16SC3 or 16-bit signed 3-channel array, and so on. - */ - int type() const; - - /** @brief Returns the depth of a matrix element. - - The method returns the identifier of the matrix element depth (the type of each individual channel). - For example, for a 16-bit signed element array, the method returns CV_16S . A complete list of - matrix types contains the following values: - - CV_8U - 8-bit unsigned integers ( 0..255 ) - - CV_8S - 8-bit signed integers ( -128..127 ) - - CV_16U - 16-bit unsigned integers ( 0..65535 ) - - CV_16S - 16-bit signed integers ( -32768..32767 ) - - CV_32S - 32-bit signed integers ( -2147483648..2147483647 ) - - CV_32F - 32-bit floating-point numbers ( -FLT_MAX..FLT_MAX, INF, NAN ) - - CV_64F - 64-bit floating-point numbers ( -DBL_MAX..DBL_MAX, INF, NAN ) - */ - int depth() const; - - /** @brief Returns the number of matrix channels. - - The method returns the number of matrix channels. - */ - int channels() const; - - /** @brief Returns a normalized step. - - The method returns a matrix step divided by Mat::elemSize1() . It can be useful to quickly access an - arbitrary matrix element. - */ - size_t step1(int i=0) const; - - /** @brief Returns true if the array has no elements. - - The method returns true if Mat::total() is 0 or if Mat::data is NULL. Because of pop_back() and - resize() methods `M.total() == 0` does not imply that `M.data == NULL`. - */ - bool empty() const; - - /** @brief Returns the total number of array elements. - - The method returns the number of array elements (a number of pixels if the array represents an - image). - */ - size_t total() const; - - //! returns N if the matrix is 1-channel (N x ptdim) or ptdim-channel (1 x N) or (N x 1); negative number otherwise - int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const; - - /** @brief Returns a pointer to the specified matrix row. - - The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in - Mat::isContinuous to know how to use these methods. - @param i0 A 0-based row index. - */ - uchar* ptr(int i0=0); - /** @overload */ - const uchar* ptr(int i0=0) const; - - /** @overload */ - uchar* ptr(int i0, int i1); - /** @overload */ - const uchar* ptr(int i0, int i1) const; - - /** @overload */ - uchar* ptr(int i0, int i1, int i2); - /** @overload */ - const uchar* ptr(int i0, int i1, int i2) const; - - /** @overload */ - uchar* ptr(const int* idx); - /** @overload */ - const uchar* ptr(const int* idx) const; - /** @overload */ - template uchar* ptr(const Vec& idx); - /** @overload */ - template const uchar* ptr(const Vec& idx) const; - - /** @overload */ - template _Tp* ptr(int i0=0); - /** @overload */ - template const _Tp* ptr(int i0=0) const; - /** @overload */ - template _Tp* ptr(int i0, int i1); - /** @overload */ - template const _Tp* ptr(int i0, int i1) const; - /** @overload */ - template _Tp* ptr(int i0, int i1, int i2); - /** @overload */ - template const _Tp* ptr(int i0, int i1, int i2) const; - /** @overload */ - template _Tp* ptr(const int* idx); - /** @overload */ - template const _Tp* ptr(const int* idx) const; - /** @overload */ - template _Tp* ptr(const Vec& idx); - /** @overload */ - template const _Tp* ptr(const Vec& idx) const; - - /** @brief Returns a reference to the specified array element. - - The template methods return a reference to the specified array element. For the sake of higher - performance, the index range checks are only performed in the Debug configuration. - - Note that the variants with a single index (i) can be used to access elements of single-row or - single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and - B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` - instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. - - The example below initializes a Hilbert matrix: - @code - Mat H(100, 100, CV_64F); - for(int i = 0; i < H.rows; i++) - for(int j = 0; j < H.cols; j++) - H.at(i,j)=1./(i+j+1); - @endcode - - Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends - on the image from which you are trying to retrieve the data. The table below gives a better insight in this: - - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. - - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. - - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. - - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. - - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. - - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. - - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. - - @param i0 Index along the dimension 0 - */ - template _Tp& at(int i0=0); - /** @overload - @param i0 Index along the dimension 0 - */ - template const _Tp& at(int i0=0) const; - /** @overload - @param i0 Index along the dimension 0 - @param i1 Index along the dimension 1 - */ - template _Tp& at(int i0, int i1); - /** @overload - @param i0 Index along the dimension 0 - @param i1 Index along the dimension 1 - */ - template const _Tp& at(int i0, int i1) const; - - /** @overload - @param i0 Index along the dimension 0 - @param i1 Index along the dimension 1 - @param i2 Index along the dimension 2 - */ - template _Tp& at(int i0, int i1, int i2); - /** @overload - @param i0 Index along the dimension 0 - @param i1 Index along the dimension 1 - @param i2 Index along the dimension 2 - */ - template const _Tp& at(int i0, int i1, int i2) const; - - /** @overload - @param idx Array of Mat::dims indices. - */ - template _Tp& at(const int* idx); - /** @overload - @param idx Array of Mat::dims indices. - */ - template const _Tp& at(const int* idx) const; - - /** @overload */ - template _Tp& at(const Vec& idx); - /** @overload */ - template const _Tp& at(const Vec& idx) const; - - /** @overload - special versions for 2D arrays (especially convenient for referencing image pixels) - @param pt Element position specified as Point(j,i) . - */ - template _Tp& at(Point pt); - /** @overload - special versions for 2D arrays (especially convenient for referencing image pixels) - @param pt Element position specified as Point(j,i) . - */ - template const _Tp& at(Point pt) const; - - /** @brief Returns the matrix iterator and sets it to the first matrix element. - - The methods return the matrix read-only or read-write iterators. The use of matrix iterators is very - similar to the use of bi-directional STL iterators. In the example below, the alpha blending - function is rewritten using the matrix iterators: - @code - template - void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst) - { - typedef Vec VT; - - const float alpha_scale = (float)std::numeric_limits::max(), - inv_scale = 1.f/alpha_scale; - - CV_Assert( src1.type() == src2.type() && - src1.type() == DataType::type && - src1.size() == src2.size()); - Size size = src1.size(); - dst.create(size, src1.type()); - - MatConstIterator_ it1 = src1.begin(), it1_end = src1.end(); - MatConstIterator_ it2 = src2.begin(); - MatIterator_ dst_it = dst.begin(); - - for( ; it1 != it1_end; ++it1, ++it2, ++dst_it ) - { - VT pix1 = *it1, pix2 = *it2; - float alpha = pix1[3]*inv_scale, beta = pix2[3]*inv_scale; - *dst_it = VT(saturate_cast(pix1[0]*alpha + pix2[0]*beta), - saturate_cast(pix1[1]*alpha + pix2[1]*beta), - saturate_cast(pix1[2]*alpha + pix2[2]*beta), - saturate_cast((1 - (1-alpha)*(1-beta))*alpha_scale)); - } - } - @endcode - */ - template MatIterator_<_Tp> begin(); - template MatConstIterator_<_Tp> begin() const; - - /** @brief Returns the matrix iterator and sets it to the after-last matrix element. - - The methods return the matrix read-only or read-write iterators, set to the point following the last - matrix element. - */ - template MatIterator_<_Tp> end(); - template MatConstIterator_<_Tp> end() const; - - /** @brief Invoke with arguments functor, and runs the functor over all matrix element. - - The methods runs operation in parallel. Operation is passed by arguments. Operation have to be a - function pointer, a function object or a lambda(C++11). - - All of below operation is equal. Put 0xFF to first channel of all matrix elements: - @code - Mat image(1920, 1080, CV_8UC3); - typedef cv::Point3_ Pixel; - - // first. raw pointer access. - for (int r = 0; r < image.rows; ++r) { - Pixel* ptr = image.ptr(0, r); - const Pixel* ptr_end = ptr + image.cols; - for (; ptr != ptr_end; ++ptr) { - ptr->x = 255; - } - } - - // Using MatIterator. (Simple but there are a Iterator's overhead) - for (Pixel &p : cv::Mat_(image)) { - p.x = 255; - } - - // Parallel execution with function object. - struct Operator { - void operator ()(Pixel &pixel, const int * position) { - pixel.x = 255; - } - }; - image.forEach(Operator()); - - // Parallel execution using C++11 lambda. - image.forEach([](Pixel &p, const int * position) -> void { - p.x = 255; - }); - @endcode - position parameter is index of current pixel: - @code - // Creating 3D matrix (255 x 255 x 255) typed uint8_t, - // and initialize all elements by the value which equals elements position. - // i.e. pixels (x,y,z) = (1,2,3) is (b,g,r) = (1,2,3). - - int sizes[] = { 255, 255, 255 }; - typedef cv::Point3_ Pixel; - - Mat_ image = Mat::zeros(3, sizes, CV_8UC3); - - image.forEachWithPosition([&](Pixel& pixel, const int position[]) -> void{ - pixel.x = position[0]; - pixel.y = position[1]; - pixel.z = position[2]; - }); - @endcode - */ - template void forEach(const Functor& operation); - /** @overload */ - template void forEach(const Functor& operation) const; - -#ifdef CV_CXX_MOVE_SEMANTICS - Mat(Mat&& m); - Mat& operator = (Mat&& m); -#endif - - enum { MAGIC_VAL = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG }; - enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 }; - - /*! includes several bit-fields: - - the magic signature - - continuity flag - - depth - - number of channels - */ - int flags; - //! the matrix dimensionality, >= 2 - int dims; - //! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions - int rows, cols; - //! pointer to the data - uchar* data; - - //! helper fields used in locateROI and adjustROI - const uchar* datastart; - const uchar* dataend; - const uchar* datalimit; - - //! custom allocator - MatAllocator* allocator; - //! and the standard allocator - static MatAllocator* getStdAllocator(); - static MatAllocator* getDefaultAllocator(); - static void setDefaultAllocator(MatAllocator* allocator); - - //! interaction with UMat - UMatData* u; - - MatSize size; - MatStep step; - -protected: - template void forEach_impl(const Functor& operation); -}; - - -///////////////////////////////// Mat_<_Tp> //////////////////////////////////// - -/** @brief Template matrix class derived from Mat - -@code - template class Mat_ : public Mat - { - public: - // ... some specific methods - // and - // no new extra fields - }; -@endcode -The class `Mat_<_Tp>` is a *thin* template wrapper on top of the Mat class. It does not have any -extra data fields. Nor this class nor Mat has any virtual methods. Thus, references or pointers to -these two classes can be freely but carefully converted one to another. For example: -@code - // create a 100x100 8-bit matrix - Mat M(100,100,CV_8U); - // this will be compiled fine. no any data conversion will be done. - Mat_& M1 = (Mat_&)M; - // the program is likely to crash at the statement below - M1(99,99) = 1.f; -@endcode -While Mat is sufficient in most cases, Mat_ can be more convenient if you use a lot of element -access operations and if you know matrix type at the compilation time. Note that -`Mat::at(int y,int x)` and `Mat_::operator()(int y,int x)` do absolutely the same -and run at the same speed, but the latter is certainly shorter: -@code - Mat_ M(20,20); - for(int i = 0; i < M.rows; i++) - for(int j = 0; j < M.cols; j++) - M(i,j) = 1./(i+j+1); - Mat E, V; - eigen(M,E,V); - cout << E.at(0,0)/E.at(M.rows-1,0); -@endcode -To use Mat_ for multi-channel images/matrices, pass Vec as a Mat_ parameter: -@code - // allocate a 320x240 color image and fill it with green (in RGB space) - Mat_ img(240, 320, Vec3b(0,255,0)); - // now draw a diagonal white line - for(int i = 0; i < 100; i++) - img(i,i)=Vec3b(255,255,255); - // and now scramble the 2nd (red) channel of each pixel - for(int i = 0; i < img.rows; i++) - for(int j = 0; j < img.cols; j++) - img(i,j)[2] ^= (uchar)(i ^ j); -@endcode - */ -template class Mat_ : public Mat -{ -public: - typedef _Tp value_type; - typedef typename DataType<_Tp>::channel_type channel_type; - typedef MatIterator_<_Tp> iterator; - typedef MatConstIterator_<_Tp> const_iterator; - - //! default constructor - Mat_(); - //! equivalent to Mat(_rows, _cols, DataType<_Tp>::type) - Mat_(int _rows, int _cols); - //! constructor that sets each matrix element to specified value - Mat_(int _rows, int _cols, const _Tp& value); - //! equivalent to Mat(_size, DataType<_Tp>::type) - explicit Mat_(Size _size); - //! constructor that sets each matrix element to specified value - Mat_(Size _size, const _Tp& value); - //! n-dim array constructor - Mat_(int _ndims, const int* _sizes); - //! n-dim array constructor that sets each matrix element to specified value - Mat_(int _ndims, const int* _sizes, const _Tp& value); - //! copy/conversion contructor. If m is of different type, it's converted - Mat_(const Mat& m); - //! copy constructor - Mat_(const Mat_& m); - //! constructs a matrix on top of user-allocated data. step is in bytes(!!!), regardless of the type - Mat_(int _rows, int _cols, _Tp* _data, size_t _step=AUTO_STEP); - //! constructs n-dim matrix on top of user-allocated data. steps are in bytes(!!!), regardless of the type - Mat_(int _ndims, const int* _sizes, _Tp* _data, const size_t* _steps=0); - //! selects a submatrix - Mat_(const Mat_& m, const Range& rowRange, const Range& colRange=Range::all()); - //! selects a submatrix - Mat_(const Mat_& m, const Rect& roi); - //! selects a submatrix, n-dim version - Mat_(const Mat_& m, const Range* ranges); - //! from a matrix expression - explicit Mat_(const MatExpr& e); - //! makes a matrix out of Vec, std::vector, Point_ or Point3_. The matrix will have a single column - explicit Mat_(const std::vector<_Tp>& vec, bool copyData=false); - template explicit Mat_(const Vec::channel_type, n>& vec, bool copyData=true); - template explicit Mat_(const Matx::channel_type, m, n>& mtx, bool copyData=true); - explicit Mat_(const Point_::channel_type>& pt, bool copyData=true); - explicit Mat_(const Point3_::channel_type>& pt, bool copyData=true); - explicit Mat_(const MatCommaInitializer_<_Tp>& commaInitializer); - - Mat_& operator = (const Mat& m); - Mat_& operator = (const Mat_& m); - //! set all the elements to s. - Mat_& operator = (const _Tp& s); - //! assign a matrix expression - Mat_& operator = (const MatExpr& e); - - //! iterators; they are smart enough to skip gaps in the end of rows - iterator begin(); - iterator end(); - const_iterator begin() const; - const_iterator end() const; - - //! template methods for for operation over all matrix elements. - // the operations take care of skipping gaps in the end of rows (if any) - template void forEach(const Functor& operation); - template void forEach(const Functor& operation) const; - - //! equivalent to Mat::create(_rows, _cols, DataType<_Tp>::type) - void create(int _rows, int _cols); - //! equivalent to Mat::create(_size, DataType<_Tp>::type) - void create(Size _size); - //! equivalent to Mat::create(_ndims, _sizes, DatType<_Tp>::type) - void create(int _ndims, const int* _sizes); - //! cross-product - Mat_ cross(const Mat_& m) const; - //! data type conversion - template operator Mat_() const; - //! overridden forms of Mat::row() etc. - Mat_ row(int y) const; - Mat_ col(int x) const; - Mat_ diag(int d=0) const; - Mat_ clone() const; - - //! overridden forms of Mat::elemSize() etc. - size_t elemSize() const; - size_t elemSize1() const; - int type() const; - int depth() const; - int channels() const; - size_t step1(int i=0) const; - //! returns step()/sizeof(_Tp) - size_t stepT(int i=0) const; - - //! overridden forms of Mat::zeros() etc. Data type is omitted, of course - static MatExpr zeros(int rows, int cols); - static MatExpr zeros(Size size); - static MatExpr zeros(int _ndims, const int* _sizes); - static MatExpr ones(int rows, int cols); - static MatExpr ones(Size size); - static MatExpr ones(int _ndims, const int* _sizes); - static MatExpr eye(int rows, int cols); - static MatExpr eye(Size size); - - //! some more overriden methods - Mat_& adjustROI( int dtop, int dbottom, int dleft, int dright ); - Mat_ operator()( const Range& rowRange, const Range& colRange ) const; - Mat_ operator()( const Rect& roi ) const; - Mat_ operator()( const Range* ranges ) const; - - //! more convenient forms of row and element access operators - _Tp* operator [](int y); - const _Tp* operator [](int y) const; - - //! returns reference to the specified element - _Tp& operator ()(const int* idx); - //! returns read-only reference to the specified element - const _Tp& operator ()(const int* idx) const; - - //! returns reference to the specified element - template _Tp& operator ()(const Vec& idx); - //! returns read-only reference to the specified element - template const _Tp& operator ()(const Vec& idx) const; - - //! returns reference to the specified element (1D case) - _Tp& operator ()(int idx0); - //! returns read-only reference to the specified element (1D case) - const _Tp& operator ()(int idx0) const; - //! returns reference to the specified element (2D case) - _Tp& operator ()(int idx0, int idx1); - //! returns read-only reference to the specified element (2D case) - const _Tp& operator ()(int idx0, int idx1) const; - //! returns reference to the specified element (3D case) - _Tp& operator ()(int idx0, int idx1, int idx2); - //! returns read-only reference to the specified element (3D case) - const _Tp& operator ()(int idx0, int idx1, int idx2) const; - - _Tp& operator ()(Point pt); - const _Tp& operator ()(Point pt) const; - - //! conversion to vector. - operator std::vector<_Tp>() const; - //! conversion to Vec - template operator Vec::channel_type, n>() const; - //! conversion to Matx - template operator Matx::channel_type, m, n>() const; - -#ifdef CV_CXX_MOVE_SEMANTICS - Mat_(Mat_&& m); - Mat_& operator = (Mat_&& m); - - Mat_(Mat&& m); - Mat_& operator = (Mat&& m); - - Mat_(MatExpr&& e); -#endif -}; - -typedef Mat_ Mat1b; -typedef Mat_ Mat2b; -typedef Mat_ Mat3b; -typedef Mat_ Mat4b; - -typedef Mat_ Mat1s; -typedef Mat_ Mat2s; -typedef Mat_ Mat3s; -typedef Mat_ Mat4s; - -typedef Mat_ Mat1w; -typedef Mat_ Mat2w; -typedef Mat_ Mat3w; -typedef Mat_ Mat4w; - -typedef Mat_ Mat1i; -typedef Mat_ Mat2i; -typedef Mat_ Mat3i; -typedef Mat_ Mat4i; - -typedef Mat_ Mat1f; -typedef Mat_ Mat2f; -typedef Mat_ Mat3f; -typedef Mat_ Mat4f; - -typedef Mat_ Mat1d; -typedef Mat_ Mat2d; -typedef Mat_ Mat3d; -typedef Mat_ Mat4d; - -/** @todo document */ -class CV_EXPORTS UMat -{ -public: - //! default constructor - UMat(UMatUsageFlags usageFlags = USAGE_DEFAULT); - //! constructs 2D matrix of the specified size and type - // (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.) - UMat(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); - UMat(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); - //! constucts 2D matrix and fills it with the specified value _s. - UMat(int rows, int cols, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); - UMat(Size size, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); - - //! constructs n-dimensional matrix - UMat(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); - UMat(int ndims, const int* sizes, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); - - //! copy constructor - UMat(const UMat& m); - - //! creates a matrix header for a part of the bigger matrix - UMat(const UMat& m, const Range& rowRange, const Range& colRange=Range::all()); - UMat(const UMat& m, const Rect& roi); - UMat(const UMat& m, const Range* ranges); - //! builds matrix from std::vector with or without copying the data - template explicit UMat(const std::vector<_Tp>& vec, bool copyData=false); - //! builds matrix from cv::Vec; the data is copied by default - template explicit UMat(const Vec<_Tp, n>& vec, bool copyData=true); - //! builds matrix from cv::Matx; the data is copied by default - template explicit UMat(const Matx<_Tp, m, n>& mtx, bool copyData=true); - //! builds matrix from a 2D point - template explicit UMat(const Point_<_Tp>& pt, bool copyData=true); - //! builds matrix from a 3D point - template explicit UMat(const Point3_<_Tp>& pt, bool copyData=true); - //! builds matrix from comma initializer - template explicit UMat(const MatCommaInitializer_<_Tp>& commaInitializer); - - //! destructor - calls release() - ~UMat(); - //! assignment operators - UMat& operator = (const UMat& m); - - Mat getMat(int flags) const; - - //! returns a new matrix header for the specified row - UMat row(int y) const; - //! returns a new matrix header for the specified column - UMat col(int x) const; - //! ... for the specified row span - UMat rowRange(int startrow, int endrow) const; - UMat rowRange(const Range& r) const; - //! ... for the specified column span - UMat colRange(int startcol, int endcol) const; - UMat colRange(const Range& r) const; - //! ... for the specified diagonal - // (d=0 - the main diagonal, - // >0 - a diagonal from the lower half, - // <0 - a diagonal from the upper half) - UMat diag(int d=0) const; - //! constructs a square diagonal matrix which main diagonal is vector "d" - static UMat diag(const UMat& d); - - //! returns deep copy of the matrix, i.e. the data is copied - UMat clone() const; - //! copies the matrix content to "m". - // It calls m.create(this->size(), this->type()). - void copyTo( OutputArray m ) const; - //! copies those matrix elements to "m" that are marked with non-zero mask elements. - void copyTo( OutputArray m, InputArray mask ) const; - //! converts matrix to another datatype with optional scalng. See cvConvertScale. - void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const; - - void assignTo( UMat& m, int type=-1 ) const; - - //! sets every matrix element to s - UMat& operator = (const Scalar& s); - //! sets some of the matrix elements to s, according to the mask - UMat& setTo(InputArray value, InputArray mask=noArray()); - //! creates alternative matrix header for the same data, with different - // number of channels and/or different number of rows. see cvReshape. - UMat reshape(int cn, int rows=0) const; - UMat reshape(int cn, int newndims, const int* newsz) const; - - //! matrix transposition by means of matrix expressions - UMat t() const; - //! matrix inversion by means of matrix expressions - UMat inv(int method=DECOMP_LU) const; - //! per-element matrix multiplication by means of matrix expressions - UMat mul(InputArray m, double scale=1) const; - - //! computes dot-product - double dot(InputArray m) const; - - //! Matlab-style matrix initialization - static UMat zeros(int rows, int cols, int type); - static UMat zeros(Size size, int type); - static UMat zeros(int ndims, const int* sz, int type); - static UMat ones(int rows, int cols, int type); - static UMat ones(Size size, int type); - static UMat ones(int ndims, const int* sz, int type); - static UMat eye(int rows, int cols, int type); - static UMat eye(Size size, int type); - - //! allocates new matrix data unless the matrix already has specified size and type. - // previous data is unreferenced if needed. - void create(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); - void create(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); - void create(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); - - //! increases the reference counter; use with care to avoid memleaks - void addref(); - //! decreases reference counter; - // deallocates the data when reference counter reaches 0. - void release(); - - //! deallocates the matrix data - void deallocate(); - //! internal use function; properly re-allocates _size, _step arrays - void copySize(const UMat& m); - - //! locates matrix header within a parent matrix. See below - void locateROI( Size& wholeSize, Point& ofs ) const; - //! moves/resizes the current matrix ROI inside the parent matrix. - UMat& adjustROI( int dtop, int dbottom, int dleft, int dright ); - //! extracts a rectangular sub-matrix - // (this is a generalized form of row, rowRange etc.) - UMat operator()( Range rowRange, Range colRange ) const; - UMat operator()( const Rect& roi ) const; - UMat operator()( const Range* ranges ) const; - - //! returns true iff the matrix data is continuous - // (i.e. when there are no gaps between successive rows). - // similar to CV_IS_MAT_CONT(cvmat->type) - bool isContinuous() const; - - //! returns true if the matrix is a submatrix of another matrix - bool isSubmatrix() const; - - //! returns element size in bytes, - // similar to CV_ELEM_SIZE(cvmat->type) - size_t elemSize() const; - //! returns the size of element channel in bytes. - size_t elemSize1() const; - //! returns element type, similar to CV_MAT_TYPE(cvmat->type) - int type() const; - //! returns element type, similar to CV_MAT_DEPTH(cvmat->type) - int depth() const; - //! returns element type, similar to CV_MAT_CN(cvmat->type) - int channels() const; - //! returns step/elemSize1() - size_t step1(int i=0) const; - //! returns true if matrix data is NULL - bool empty() const; - //! returns the total number of matrix elements - size_t total() const; - - //! returns N if the matrix is 1-channel (N x ptdim) or ptdim-channel (1 x N) or (N x 1); negative number otherwise - int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const; - -#ifdef CV_CXX_MOVE_SEMANTICS - UMat(UMat&& m); - UMat& operator = (UMat&& m); -#endif - - void* handle(int accessFlags) const; - void ndoffset(size_t* ofs) const; - - enum { MAGIC_VAL = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG }; - enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 }; - - /*! includes several bit-fields: - - the magic signature - - continuity flag - - depth - - number of channels - */ - int flags; - //! the matrix dimensionality, >= 2 - int dims; - //! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions - int rows, cols; - - //! custom allocator - MatAllocator* allocator; - UMatUsageFlags usageFlags; // usage flags for allocator - //! and the standard allocator - static MatAllocator* getStdAllocator(); - - // black-box container of UMat data - UMatData* u; - - // offset of the submatrix (or 0) - size_t offset; - - MatSize size; - MatStep step; - -protected: -}; - - -/////////////////////////// multi-dimensional sparse matrix ////////////////////////// - -/** @brief The class SparseMat represents multi-dimensional sparse numerical arrays. - -Such a sparse array can store elements of any type that Mat can store. *Sparse* means that only -non-zero elements are stored (though, as a result of operations on a sparse matrix, some of its -stored elements can actually become 0. It is up to you to detect such elements and delete them -using SparseMat::erase ). The non-zero elements are stored in a hash table that grows when it is -filled so that the search time is O(1) in average (regardless of whether element is there or not). -Elements can be accessed using the following methods: -- Query operations (SparseMat::ptr and the higher-level SparseMat::ref, SparseMat::value and - SparseMat::find), for example: - @code - const int dims = 5; - int size[] = {10, 10, 10, 10, 10}; - SparseMat sparse_mat(dims, size, CV_32F); - for(int i = 0; i < 1000; i++) - { - int idx[dims]; - for(int k = 0; k < dims; k++) - idx[k] = rand() - sparse_mat.ref(idx) += 1.f; - } - @endcode -- Sparse matrix iterators. They are similar to MatIterator but different from NAryMatIterator. - That is, the iteration loop is familiar to STL users: - @code - // prints elements of a sparse floating-point matrix - // and the sum of elements. - SparseMatConstIterator_ - it = sparse_mat.begin(), - it_end = sparse_mat.end(); - double s = 0; - int dims = sparse_mat.dims(); - for(; it != it_end; ++it) - { - // print element indices and the element value - const SparseMat::Node* n = it.node(); - printf("("); - for(int i = 0; i < dims; i++) - printf("%d%s", n->idx[i], i < dims-1 ? ", " : ")"); - printf(": %g\n", it.value()); - s += *it; - } - printf("Element sum is %g\n", s); - @endcode - If you run this loop, you will notice that elements are not enumerated in a logical order - (lexicographical, and so on). They come in the same order as they are stored in the hash table - (semi-randomly). You may collect pointers to the nodes and sort them to get the proper ordering. - Note, however, that pointers to the nodes may become invalid when you add more elements to the - matrix. This may happen due to possible buffer reallocation. -- Combination of the above 2 methods when you need to process 2 or more sparse matrices - simultaneously. For example, this is how you can compute unnormalized cross-correlation of the 2 - floating-point sparse matrices: - @code - double cross_corr(const SparseMat& a, const SparseMat& b) - { - const SparseMat *_a = &a, *_b = &b; - // if b contains less elements than a, - // it is faster to iterate through b - if(_a->nzcount() > _b->nzcount()) - std::swap(_a, _b); - SparseMatConstIterator_ it = _a->begin(), - it_end = _a->end(); - double ccorr = 0; - for(; it != it_end; ++it) - { - // take the next element from the first matrix - float avalue = *it; - const Node* anode = it.node(); - // and try to find an element with the same index in the second matrix. - // since the hash value depends only on the element index, - // reuse the hash value stored in the node - float bvalue = _b->value(anode->idx,&anode->hashval); - ccorr += avalue*bvalue; - } - return ccorr; - } - @endcode - */ -class CV_EXPORTS SparseMat -{ -public: - typedef SparseMatIterator iterator; - typedef SparseMatConstIterator const_iterator; - - enum { MAGIC_VAL=0x42FD0000, MAX_DIM=32, HASH_SCALE=0x5bd1e995, HASH_BIT=0x80000000 }; - - //! the sparse matrix header - struct CV_EXPORTS Hdr - { - Hdr(int _dims, const int* _sizes, int _type); - void clear(); - int refcount; - int dims; - int valueOffset; - size_t nodeSize; - size_t nodeCount; - size_t freeList; - std::vector pool; - std::vector hashtab; - int size[MAX_DIM]; - }; - - //! sparse matrix node - element of a hash table - struct CV_EXPORTS Node - { - //! hash value - size_t hashval; - //! index of the next node in the same hash table entry - size_t next; - //! index of the matrix element - int idx[MAX_DIM]; - }; - - /** @brief Various SparseMat constructors. - */ - SparseMat(); - - /** @overload - @param dims Array dimensionality. - @param _sizes Sparce matrix size on all dementions. - @param _type Sparse matrix data type. - */ - SparseMat(int dims, const int* _sizes, int _type); - - /** @overload - @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted - to sparse representation. - */ - SparseMat(const SparseMat& m); - - /** @overload - @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted - to sparse representation. - */ - explicit SparseMat(const Mat& m); - - //! the destructor - ~SparseMat(); - - //! assignment operator. This is O(1) operation, i.e. no data is copied - SparseMat& operator = (const SparseMat& m); - //! equivalent to the corresponding constructor - SparseMat& operator = (const Mat& m); - - //! creates full copy of the matrix - SparseMat clone() const; - - //! copies all the data to the destination matrix. All the previous content of m is erased - void copyTo( SparseMat& m ) const; - //! converts sparse matrix to dense matrix. - void copyTo( Mat& m ) const; - //! multiplies all the matrix elements by the specified scale factor alpha and converts the results to the specified data type - void convertTo( SparseMat& m, int rtype, double alpha=1 ) const; - //! converts sparse matrix to dense n-dim matrix with optional type conversion and scaling. - /*! - @param [out] m - output matrix; if it does not have a proper size or type before the operation, - it is reallocated - @param [in] rtype – desired output matrix type or, rather, the depth since the number of channels - are the same as the input has; if rtype is negative, the output matrix will have the - same type as the input. - @param [in] alpha – optional scale factor - @param [in] beta – optional delta added to the scaled values - */ - void convertTo( Mat& m, int rtype, double alpha=1, double beta=0 ) const; - - // not used now - void assignTo( SparseMat& m, int type=-1 ) const; - - //! reallocates sparse matrix. - /*! - If the matrix already had the proper size and type, - it is simply cleared with clear(), otherwise, - the old matrix is released (using release()) and the new one is allocated. - */ - void create(int dims, const int* _sizes, int _type); - //! sets all the sparse matrix elements to 0, which means clearing the hash table. - void clear(); - //! manually increments the reference counter to the header. - void addref(); - // decrements the header reference counter. When the counter reaches 0, the header and all the underlying data are deallocated. - void release(); - - //! converts sparse matrix to the old-style representation; all the elements are copied. - //operator CvSparseMat*() const; - //! returns the size of each element in bytes (not including the overhead - the space occupied by SparseMat::Node elements) - size_t elemSize() const; - //! returns elemSize()/channels() - size_t elemSize1() const; - - //! returns type of sparse matrix elements - int type() const; - //! returns the depth of sparse matrix elements - int depth() const; - //! returns the number of channels - int channels() const; - - //! returns the array of sizes, or NULL if the matrix is not allocated - const int* size() const; - //! returns the size of i-th matrix dimension (or 0) - int size(int i) const; - //! returns the matrix dimensionality - int dims() const; - //! returns the number of non-zero elements (=the number of hash table nodes) - size_t nzcount() const; - - //! computes the element hash value (1D case) - size_t hash(int i0) const; - //! computes the element hash value (2D case) - size_t hash(int i0, int i1) const; - //! computes the element hash value (3D case) - size_t hash(int i0, int i1, int i2) const; - //! computes the element hash value (nD case) - size_t hash(const int* idx) const; - - //!@{ - /*! - specialized variants for 1D, 2D, 3D cases and the generic_type one for n-D case. - return pointer to the matrix element. - - if the element is there (it's non-zero), the pointer to it is returned - - if it's not there and createMissing=false, NULL pointer is returned - - if it's not there and createMissing=true, then the new element - is created and initialized with 0. Pointer to it is returned - - if the optional hashval pointer is not NULL, the element hash value is - not computed, but *hashval is taken instead. - */ - //! returns pointer to the specified element (1D case) - uchar* ptr(int i0, bool createMissing, size_t* hashval=0); - //! returns pointer to the specified element (2D case) - uchar* ptr(int i0, int i1, bool createMissing, size_t* hashval=0); - //! returns pointer to the specified element (3D case) - uchar* ptr(int i0, int i1, int i2, bool createMissing, size_t* hashval=0); - //! returns pointer to the specified element (nD case) - uchar* ptr(const int* idx, bool createMissing, size_t* hashval=0); - //!@} - - //!@{ - /*! - return read-write reference to the specified sparse matrix element. - - `ref<_Tp>(i0,...[,hashval])` is equivalent to `*(_Tp*)ptr(i0,...,true[,hashval])`. - The methods always return a valid reference. - If the element did not exist, it is created and initialiazed with 0. - */ - //! returns reference to the specified element (1D case) - template _Tp& ref(int i0, size_t* hashval=0); - //! returns reference to the specified element (2D case) - template _Tp& ref(int i0, int i1, size_t* hashval=0); - //! returns reference to the specified element (3D case) - template _Tp& ref(int i0, int i1, int i2, size_t* hashval=0); - //! returns reference to the specified element (nD case) - template _Tp& ref(const int* idx, size_t* hashval=0); - //!@} - - //!@{ - /*! - return value of the specified sparse matrix element. - - `value<_Tp>(i0,...[,hashval])` is equivalent to - @code - { const _Tp* p = find<_Tp>(i0,...[,hashval]); return p ? *p : _Tp(); } - @endcode - - That is, if the element did not exist, the methods return 0. - */ - //! returns value of the specified element (1D case) - template _Tp value(int i0, size_t* hashval=0) const; - //! returns value of the specified element (2D case) - template _Tp value(int i0, int i1, size_t* hashval=0) const; - //! returns value of the specified element (3D case) - template _Tp value(int i0, int i1, int i2, size_t* hashval=0) const; - //! returns value of the specified element (nD case) - template _Tp value(const int* idx, size_t* hashval=0) const; - //!@} - - //!@{ - /*! - Return pointer to the specified sparse matrix element if it exists - - `find<_Tp>(i0,...[,hashval])` is equivalent to `(_const Tp*)ptr(i0,...false[,hashval])`. - - If the specified element does not exist, the methods return NULL. - */ - //! returns pointer to the specified element (1D case) - template const _Tp* find(int i0, size_t* hashval=0) const; - //! returns pointer to the specified element (2D case) - template const _Tp* find(int i0, int i1, size_t* hashval=0) const; - //! returns pointer to the specified element (3D case) - template const _Tp* find(int i0, int i1, int i2, size_t* hashval=0) const; - //! returns pointer to the specified element (nD case) - template const _Tp* find(const int* idx, size_t* hashval=0) const; - //!@} - - //! erases the specified element (2D case) - void erase(int i0, int i1, size_t* hashval=0); - //! erases the specified element (3D case) - void erase(int i0, int i1, int i2, size_t* hashval=0); - //! erases the specified element (nD case) - void erase(const int* idx, size_t* hashval=0); - - //!@{ - /*! - return the sparse matrix iterator pointing to the first sparse matrix element - */ - //! returns the sparse matrix iterator at the matrix beginning - SparseMatIterator begin(); - //! returns the sparse matrix iterator at the matrix beginning - template SparseMatIterator_<_Tp> begin(); - //! returns the read-only sparse matrix iterator at the matrix beginning - SparseMatConstIterator begin() const; - //! returns the read-only sparse matrix iterator at the matrix beginning - template SparseMatConstIterator_<_Tp> begin() const; - //!@} - /*! - return the sparse matrix iterator pointing to the element following the last sparse matrix element - */ - //! returns the sparse matrix iterator at the matrix end - SparseMatIterator end(); - //! returns the read-only sparse matrix iterator at the matrix end - SparseMatConstIterator end() const; - //! returns the typed sparse matrix iterator at the matrix end - template SparseMatIterator_<_Tp> end(); - //! returns the typed read-only sparse matrix iterator at the matrix end - template SparseMatConstIterator_<_Tp> end() const; - - //! returns the value stored in the sparse martix node - template _Tp& value(Node* n); - //! returns the value stored in the sparse martix node - template const _Tp& value(const Node* n) const; - - ////////////// some internal-use methods /////////////// - Node* node(size_t nidx); - const Node* node(size_t nidx) const; - - uchar* newNode(const int* idx, size_t hashval); - void removeNode(size_t hidx, size_t nidx, size_t previdx); - void resizeHashTab(size_t newsize); - - int flags; - Hdr* hdr; -}; - - - -///////////////////////////////// SparseMat_<_Tp> //////////////////////////////////// - -/** @brief Template sparse n-dimensional array class derived from SparseMat - -SparseMat_ is a thin wrapper on top of SparseMat created in the same way as Mat_ . It simplifies -notation of some operations: -@code - int sz[] = {10, 20, 30}; - SparseMat_ M(3, sz); - ... - M.ref(1, 2, 3) = M(4, 5, 6) + M(7, 8, 9); -@endcode - */ -template class SparseMat_ : public SparseMat -{ -public: - typedef SparseMatIterator_<_Tp> iterator; - typedef SparseMatConstIterator_<_Tp> const_iterator; - - //! the default constructor - SparseMat_(); - //! the full constructor equivelent to SparseMat(dims, _sizes, DataType<_Tp>::type) - SparseMat_(int dims, const int* _sizes); - //! the copy constructor. If DataType<_Tp>.type != m.type(), the m elements are converted - SparseMat_(const SparseMat& m); - //! the copy constructor. This is O(1) operation - no data is copied - SparseMat_(const SparseMat_& m); - //! converts dense matrix to the sparse form - SparseMat_(const Mat& m); - //! converts the old-style sparse matrix to the C++ class. All the elements are copied - //SparseMat_(const CvSparseMat* m); - //! the assignment operator. If DataType<_Tp>.type != m.type(), the m elements are converted - SparseMat_& operator = (const SparseMat& m); - //! the assignment operator. This is O(1) operation - no data is copied - SparseMat_& operator = (const SparseMat_& m); - //! converts dense matrix to the sparse form - SparseMat_& operator = (const Mat& m); - - //! makes full copy of the matrix. All the elements are duplicated - SparseMat_ clone() const; - //! equivalent to cv::SparseMat::create(dims, _sizes, DataType<_Tp>::type) - void create(int dims, const int* _sizes); - //! converts sparse matrix to the old-style CvSparseMat. All the elements are copied - //operator CvSparseMat*() const; - - //! returns type of the matrix elements - int type() const; - //! returns depth of the matrix elements - int depth() const; - //! returns the number of channels in each matrix element - int channels() const; - - //! equivalent to SparseMat::ref<_Tp>(i0, hashval) - _Tp& ref(int i0, size_t* hashval=0); - //! equivalent to SparseMat::ref<_Tp>(i0, i1, hashval) - _Tp& ref(int i0, int i1, size_t* hashval=0); - //! equivalent to SparseMat::ref<_Tp>(i0, i1, i2, hashval) - _Tp& ref(int i0, int i1, int i2, size_t* hashval=0); - //! equivalent to SparseMat::ref<_Tp>(idx, hashval) - _Tp& ref(const int* idx, size_t* hashval=0); - - //! equivalent to SparseMat::value<_Tp>(i0, hashval) - _Tp operator()(int i0, size_t* hashval=0) const; - //! equivalent to SparseMat::value<_Tp>(i0, i1, hashval) - _Tp operator()(int i0, int i1, size_t* hashval=0) const; - //! equivalent to SparseMat::value<_Tp>(i0, i1, i2, hashval) - _Tp operator()(int i0, int i1, int i2, size_t* hashval=0) const; - //! equivalent to SparseMat::value<_Tp>(idx, hashval) - _Tp operator()(const int* idx, size_t* hashval=0) const; - - //! returns sparse matrix iterator pointing to the first sparse matrix element - SparseMatIterator_<_Tp> begin(); - //! returns read-only sparse matrix iterator pointing to the first sparse matrix element - SparseMatConstIterator_<_Tp> begin() const; - //! returns sparse matrix iterator pointing to the element following the last sparse matrix element - SparseMatIterator_<_Tp> end(); - //! returns read-only sparse matrix iterator pointing to the element following the last sparse matrix element - SparseMatConstIterator_<_Tp> end() const; -}; - - - -////////////////////////////////// MatConstIterator ////////////////////////////////// - -class CV_EXPORTS MatConstIterator -{ -public: - typedef uchar* value_type; - typedef ptrdiff_t difference_type; - typedef const uchar** pointer; - typedef uchar* reference; - -#ifndef OPENCV_NOSTL - typedef std::random_access_iterator_tag iterator_category; -#endif - - //! default constructor - MatConstIterator(); - //! constructor that sets the iterator to the beginning of the matrix - MatConstIterator(const Mat* _m); - //! constructor that sets the iterator to the specified element of the matrix - MatConstIterator(const Mat* _m, int _row, int _col=0); - //! constructor that sets the iterator to the specified element of the matrix - MatConstIterator(const Mat* _m, Point _pt); - //! constructor that sets the iterator to the specified element of the matrix - MatConstIterator(const Mat* _m, const int* _idx); - //! copy constructor - MatConstIterator(const MatConstIterator& it); - - //! copy operator - MatConstIterator& operator = (const MatConstIterator& it); - //! returns the current matrix element - const uchar* operator *() const; - //! returns the i-th matrix element, relative to the current - const uchar* operator [](ptrdiff_t i) const; - - //! shifts the iterator forward by the specified number of elements - MatConstIterator& operator += (ptrdiff_t ofs); - //! shifts the iterator backward by the specified number of elements - MatConstIterator& operator -= (ptrdiff_t ofs); - //! decrements the iterator - MatConstIterator& operator --(); - //! decrements the iterator - MatConstIterator operator --(int); - //! increments the iterator - MatConstIterator& operator ++(); - //! increments the iterator - MatConstIterator operator ++(int); - //! returns the current iterator position - Point pos() const; - //! returns the current iterator position - void pos(int* _idx) const; - - ptrdiff_t lpos() const; - void seek(ptrdiff_t ofs, bool relative = false); - void seek(const int* _idx, bool relative = false); - - const Mat* m; - size_t elemSize; - const uchar* ptr; - const uchar* sliceStart; - const uchar* sliceEnd; -}; - - - -////////////////////////////////// MatConstIterator_ ///////////////////////////////// - -/** @brief Matrix read-only iterator - */ -template -class MatConstIterator_ : public MatConstIterator -{ -public: - typedef _Tp value_type; - typedef ptrdiff_t difference_type; - typedef const _Tp* pointer; - typedef const _Tp& reference; - -#ifndef OPENCV_NOSTL - typedef std::random_access_iterator_tag iterator_category; -#endif - - //! default constructor - MatConstIterator_(); - //! constructor that sets the iterator to the beginning of the matrix - MatConstIterator_(const Mat_<_Tp>* _m); - //! constructor that sets the iterator to the specified element of the matrix - MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col=0); - //! constructor that sets the iterator to the specified element of the matrix - MatConstIterator_(const Mat_<_Tp>* _m, Point _pt); - //! constructor that sets the iterator to the specified element of the matrix - MatConstIterator_(const Mat_<_Tp>* _m, const int* _idx); - //! copy constructor - MatConstIterator_(const MatConstIterator_& it); - - //! copy operator - MatConstIterator_& operator = (const MatConstIterator_& it); - //! returns the current matrix element - _Tp operator *() const; - //! returns the i-th matrix element, relative to the current - _Tp operator [](ptrdiff_t i) const; - - //! shifts the iterator forward by the specified number of elements - MatConstIterator_& operator += (ptrdiff_t ofs); - //! shifts the iterator backward by the specified number of elements - MatConstIterator_& operator -= (ptrdiff_t ofs); - //! decrements the iterator - MatConstIterator_& operator --(); - //! decrements the iterator - MatConstIterator_ operator --(int); - //! increments the iterator - MatConstIterator_& operator ++(); - //! increments the iterator - MatConstIterator_ operator ++(int); - //! returns the current iterator position - Point pos() const; -}; - - - -//////////////////////////////////// MatIterator_ //////////////////////////////////// - -/** @brief Matrix read-write iterator -*/ -template -class MatIterator_ : public MatConstIterator_<_Tp> -{ -public: - typedef _Tp* pointer; - typedef _Tp& reference; - -#ifndef OPENCV_NOSTL - typedef std::random_access_iterator_tag iterator_category; -#endif - - //! the default constructor - MatIterator_(); - //! constructor that sets the iterator to the beginning of the matrix - MatIterator_(Mat_<_Tp>* _m); - //! constructor that sets the iterator to the specified element of the matrix - MatIterator_(Mat_<_Tp>* _m, int _row, int _col=0); - //! constructor that sets the iterator to the specified element of the matrix - MatIterator_(Mat_<_Tp>* _m, Point _pt); - //! constructor that sets the iterator to the specified element of the matrix - MatIterator_(Mat_<_Tp>* _m, const int* _idx); - //! copy constructor - MatIterator_(const MatIterator_& it); - //! copy operator - MatIterator_& operator = (const MatIterator_<_Tp>& it ); - - //! returns the current matrix element - _Tp& operator *() const; - //! returns the i-th matrix element, relative to the current - _Tp& operator [](ptrdiff_t i) const; - - //! shifts the iterator forward by the specified number of elements - MatIterator_& operator += (ptrdiff_t ofs); - //! shifts the iterator backward by the specified number of elements - MatIterator_& operator -= (ptrdiff_t ofs); - //! decrements the iterator - MatIterator_& operator --(); - //! decrements the iterator - MatIterator_ operator --(int); - //! increments the iterator - MatIterator_& operator ++(); - //! increments the iterator - MatIterator_ operator ++(int); -}; - - - -/////////////////////////////// SparseMatConstIterator /////////////////////////////// - -/** @brief Read-Only Sparse Matrix Iterator. - - Here is how to use the iterator to compute the sum of floating-point sparse matrix elements: - - \code - SparseMatConstIterator it = m.begin(), it_end = m.end(); - double s = 0; - CV_Assert( m.type() == CV_32F ); - for( ; it != it_end; ++it ) - s += it.value(); - \endcode -*/ -class CV_EXPORTS SparseMatConstIterator -{ -public: - //! the default constructor - SparseMatConstIterator(); - //! the full constructor setting the iterator to the first sparse matrix element - SparseMatConstIterator(const SparseMat* _m); - //! the copy constructor - SparseMatConstIterator(const SparseMatConstIterator& it); - - //! the assignment operator - SparseMatConstIterator& operator = (const SparseMatConstIterator& it); - - //! template method returning the current matrix element - template const _Tp& value() const; - //! returns the current node of the sparse matrix. it.node->idx is the current element index - const SparseMat::Node* node() const; - - //! moves iterator to the previous element - SparseMatConstIterator& operator --(); - //! moves iterator to the previous element - SparseMatConstIterator operator --(int); - //! moves iterator to the next element - SparseMatConstIterator& operator ++(); - //! moves iterator to the next element - SparseMatConstIterator operator ++(int); - - //! moves iterator to the element after the last element - void seekEnd(); - - const SparseMat* m; - size_t hashidx; - uchar* ptr; -}; - - - -////////////////////////////////// SparseMatIterator ///////////////////////////////// - -/** @brief Read-write Sparse Matrix Iterator - - The class is similar to cv::SparseMatConstIterator, - but can be used for in-place modification of the matrix elements. -*/ -class CV_EXPORTS SparseMatIterator : public SparseMatConstIterator -{ -public: - //! the default constructor - SparseMatIterator(); - //! the full constructor setting the iterator to the first sparse matrix element - SparseMatIterator(SparseMat* _m); - //! the full constructor setting the iterator to the specified sparse matrix element - SparseMatIterator(SparseMat* _m, const int* idx); - //! the copy constructor - SparseMatIterator(const SparseMatIterator& it); - - //! the assignment operator - SparseMatIterator& operator = (const SparseMatIterator& it); - //! returns read-write reference to the current sparse matrix element - template _Tp& value() const; - //! returns pointer to the current sparse matrix node. it.node->idx is the index of the current element (do not modify it!) - SparseMat::Node* node() const; - - //! moves iterator to the next element - SparseMatIterator& operator ++(); - //! moves iterator to the next element - SparseMatIterator operator ++(int); -}; - - - -/////////////////////////////// SparseMatConstIterator_ ////////////////////////////// - -/** @brief Template Read-Only Sparse Matrix Iterator Class. - - This is the derived from SparseMatConstIterator class that - introduces more convenient operator *() for accessing the current element. -*/ -template class SparseMatConstIterator_ : public SparseMatConstIterator -{ -public: - -#ifndef OPENCV_NOSTL - typedef std::forward_iterator_tag iterator_category; -#endif - - //! the default constructor - SparseMatConstIterator_(); - //! the full constructor setting the iterator to the first sparse matrix element - SparseMatConstIterator_(const SparseMat_<_Tp>* _m); - SparseMatConstIterator_(const SparseMat* _m); - //! the copy constructor - SparseMatConstIterator_(const SparseMatConstIterator_& it); - - //! the assignment operator - SparseMatConstIterator_& operator = (const SparseMatConstIterator_& it); - //! the element access operator - const _Tp& operator *() const; - - //! moves iterator to the next element - SparseMatConstIterator_& operator ++(); - //! moves iterator to the next element - SparseMatConstIterator_ operator ++(int); -}; - - - -///////////////////////////////// SparseMatIterator_ ///////////////////////////////// - -/** @brief Template Read-Write Sparse Matrix Iterator Class. - - This is the derived from cv::SparseMatConstIterator_ class that - introduces more convenient operator *() for accessing the current element. -*/ -template class SparseMatIterator_ : public SparseMatConstIterator_<_Tp> -{ -public: - -#ifndef OPENCV_NOSTL - typedef std::forward_iterator_tag iterator_category; -#endif - - //! the default constructor - SparseMatIterator_(); - //! the full constructor setting the iterator to the first sparse matrix element - SparseMatIterator_(SparseMat_<_Tp>* _m); - SparseMatIterator_(SparseMat* _m); - //! the copy constructor - SparseMatIterator_(const SparseMatIterator_& it); - - //! the assignment operator - SparseMatIterator_& operator = (const SparseMatIterator_& it); - //! returns the reference to the current element - _Tp& operator *() const; - - //! moves the iterator to the next element - SparseMatIterator_& operator ++(); - //! moves the iterator to the next element - SparseMatIterator_ operator ++(int); -}; - - - -/////////////////////////////////// NAryMatIterator ////////////////////////////////// - -/** @brief n-ary multi-dimensional array iterator. - -Use the class to implement unary, binary, and, generally, n-ary element-wise operations on -multi-dimensional arrays. Some of the arguments of an n-ary function may be continuous arrays, some -may be not. It is possible to use conventional MatIterator 's for each array but incrementing all of -the iterators after each small operations may be a big overhead. In this case consider using -NAryMatIterator to iterate through several matrices simultaneously as long as they have the same -geometry (dimensionality and all the dimension sizes are the same). On each iteration `it.planes[0]`, -`it.planes[1]`,... will be the slices of the corresponding matrices. - -The example below illustrates how you can compute a normalized and threshold 3D color histogram: -@code - void computeNormalizedColorHist(const Mat& image, Mat& hist, int N, double minProb) - { - const int histSize[] = {N, N, N}; - - // make sure that the histogram has a proper size and type - hist.create(3, histSize, CV_32F); - - // and clear it - hist = Scalar(0); - - // the loop below assumes that the image - // is a 8-bit 3-channel. check it. - CV_Assert(image.type() == CV_8UC3); - MatConstIterator_ it = image.begin(), - it_end = image.end(); - for( ; it != it_end; ++it ) - { - const Vec3b& pix = *it; - hist.at(pix[0]*N/256, pix[1]*N/256, pix[2]*N/256) += 1.f; - } - - minProb *= image.rows*image.cols; - Mat plane; - NAryMatIterator it(&hist, &plane, 1); - double s = 0; - // iterate through the matrix. on each iteration - // it.planes[*] (of type Mat) will be set to the current plane. - for(int p = 0; p < it.nplanes; p++, ++it) - { - threshold(it.planes[0], it.planes[0], minProb, 0, THRESH_TOZERO); - s += sum(it.planes[0])[0]; - } - - s = 1./s; - it = NAryMatIterator(&hist, &plane, 1); - for(int p = 0; p < it.nplanes; p++, ++it) - it.planes[0] *= s; - } -@endcode - */ -class CV_EXPORTS NAryMatIterator -{ -public: - //! the default constructor - NAryMatIterator(); - //! the full constructor taking arbitrary number of n-dim matrices - NAryMatIterator(const Mat** arrays, uchar** ptrs, int narrays=-1); - //! the full constructor taking arbitrary number of n-dim matrices - NAryMatIterator(const Mat** arrays, Mat* planes, int narrays=-1); - //! the separate iterator initialization method - void init(const Mat** arrays, Mat* planes, uchar** ptrs, int narrays=-1); - - //! proceeds to the next plane of every iterated matrix - NAryMatIterator& operator ++(); - //! proceeds to the next plane of every iterated matrix (postfix increment operator) - NAryMatIterator operator ++(int); - - //! the iterated arrays - const Mat** arrays; - //! the current planes - Mat* planes; - //! data pointers - uchar** ptrs; - //! the number of arrays - int narrays; - //! the number of hyper-planes that the iterator steps through - size_t nplanes; - //! the size of each segment (in elements) - size_t size; -protected: - int iterdepth; - size_t idx; -}; - - - -///////////////////////////////// Matrix Expressions ///////////////////////////////// - -class CV_EXPORTS MatOp -{ -public: - MatOp(); - virtual ~MatOp(); - - virtual bool elementWise(const MatExpr& expr) const; - virtual void assign(const MatExpr& expr, Mat& m, int type=-1) const = 0; - virtual void roi(const MatExpr& expr, const Range& rowRange, - const Range& colRange, MatExpr& res) const; - virtual void diag(const MatExpr& expr, int d, MatExpr& res) const; - virtual void augAssignAdd(const MatExpr& expr, Mat& m) const; - virtual void augAssignSubtract(const MatExpr& expr, Mat& m) const; - virtual void augAssignMultiply(const MatExpr& expr, Mat& m) const; - virtual void augAssignDivide(const MatExpr& expr, Mat& m) const; - virtual void augAssignAnd(const MatExpr& expr, Mat& m) const; - virtual void augAssignOr(const MatExpr& expr, Mat& m) const; - virtual void augAssignXor(const MatExpr& expr, Mat& m) const; - - virtual void add(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; - virtual void add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const; - - virtual void subtract(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; - virtual void subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const; - - virtual void multiply(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const; - virtual void multiply(const MatExpr& expr1, double s, MatExpr& res) const; - - virtual void divide(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const; - virtual void divide(double s, const MatExpr& expr, MatExpr& res) const; - - virtual void abs(const MatExpr& expr, MatExpr& res) const; - - virtual void transpose(const MatExpr& expr, MatExpr& res) const; - virtual void matmul(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; - virtual void invert(const MatExpr& expr, int method, MatExpr& res) const; - - virtual Size size(const MatExpr& expr) const; - virtual int type(const MatExpr& expr) const; -}; - -/** @brief Matrix expression representation -@anchor MatrixExpressions -This is a list of implemented matrix operations that can be combined in arbitrary complex -expressions (here A, B stand for matrices ( Mat ), s for a scalar ( Scalar ), alpha for a -real-valued scalar ( double )): -- Addition, subtraction, negation: `A+B`, `A-B`, `A+s`, `A-s`, `s+A`, `s-A`, `-A` -- Scaling: `A*alpha` -- Per-element multiplication and division: `A.mul(B)`, `A/B`, `alpha/A` -- Matrix multiplication: `A*B` -- Transposition: `A.t()` (means AT) -- Matrix inversion and pseudo-inversion, solving linear systems and least-squares problems: - `A.inv([method]) (~ A-1)`, `A.inv([method])*B (~ X: AX=B)` -- Comparison: `A cmpop B`, `A cmpop alpha`, `alpha cmpop A`, where *cmpop* is one of - `>`, `>=`, `==`, `!=`, `<=`, `<`. The result of comparison is an 8-bit single channel mask whose - elements are set to 255 (if the particular element or pair of elements satisfy the condition) or - 0. -- Bitwise logical operations: `A logicop B`, `A logicop s`, `s logicop A`, `~A`, where *logicop* is one of - `&`, `|`, `^`. -- Element-wise minimum and maximum: `min(A, B)`, `min(A, alpha)`, `max(A, B)`, `max(A, alpha)` -- Element-wise absolute value: `abs(A)` -- Cross-product, dot-product: `A.cross(B)`, `A.dot(B)` -- Any function of matrix or matrices and scalars that returns a matrix or a scalar, such as norm, - mean, sum, countNonZero, trace, determinant, repeat, and others. -- Matrix initializers ( Mat::eye(), Mat::zeros(), Mat::ones() ), matrix comma-separated - initializers, matrix constructors and operators that extract sub-matrices (see Mat description). -- Mat_() constructors to cast the result to the proper type. -@note Comma-separated initializers and probably some other operations may require additional -explicit Mat() or Mat_() constructor calls to resolve a possible ambiguity. - -Here are examples of matrix expressions: -@code - // compute pseudo-inverse of A, equivalent to A.inv(DECOMP_SVD) - SVD svd(A); - Mat pinvA = svd.vt.t()*Mat::diag(1./svd.w)*svd.u.t(); - - // compute the new vector of parameters in the Levenberg-Marquardt algorithm - x -= (A.t()*A + lambda*Mat::eye(A.cols,A.cols,A.type())).inv(DECOMP_CHOLESKY)*(A.t()*err); - - // sharpen image using "unsharp mask" algorithm - Mat blurred; double sigma = 1, threshold = 5, amount = 1; - GaussianBlur(img, blurred, Size(), sigma, sigma); - Mat lowContrastMask = abs(img - blurred) < threshold; - Mat sharpened = img*(1+amount) + blurred*(-amount); - img.copyTo(sharpened, lowContrastMask); -@endcode -*/ -class CV_EXPORTS MatExpr -{ -public: - MatExpr(); - explicit MatExpr(const Mat& m); - - MatExpr(const MatOp* _op, int _flags, const Mat& _a = Mat(), const Mat& _b = Mat(), - const Mat& _c = Mat(), double _alpha = 1, double _beta = 1, const Scalar& _s = Scalar()); - - operator Mat() const; - template operator Mat_<_Tp>() const; - - Size size() const; - int type() const; - - MatExpr row(int y) const; - MatExpr col(int x) const; - MatExpr diag(int d = 0) const; - MatExpr operator()( const Range& rowRange, const Range& colRange ) const; - MatExpr operator()( const Rect& roi ) const; - - MatExpr t() const; - MatExpr inv(int method = DECOMP_LU) const; - MatExpr mul(const MatExpr& e, double scale=1) const; - MatExpr mul(const Mat& m, double scale=1) const; - - Mat cross(const Mat& m) const; - double dot(const Mat& m) const; - - const MatOp* op; - int flags; - - Mat a, b, c; - double alpha, beta; - Scalar s; -}; - -//! @} core_basic - -//! @relates cv::MatExpr -//! @{ -CV_EXPORTS MatExpr operator + (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator + (const Mat& a, const Scalar& s); -CV_EXPORTS MatExpr operator + (const Scalar& s, const Mat& a); -CV_EXPORTS MatExpr operator + (const MatExpr& e, const Mat& m); -CV_EXPORTS MatExpr operator + (const Mat& m, const MatExpr& e); -CV_EXPORTS MatExpr operator + (const MatExpr& e, const Scalar& s); -CV_EXPORTS MatExpr operator + (const Scalar& s, const MatExpr& e); -CV_EXPORTS MatExpr operator + (const MatExpr& e1, const MatExpr& e2); - -CV_EXPORTS MatExpr operator - (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator - (const Mat& a, const Scalar& s); -CV_EXPORTS MatExpr operator - (const Scalar& s, const Mat& a); -CV_EXPORTS MatExpr operator - (const MatExpr& e, const Mat& m); -CV_EXPORTS MatExpr operator - (const Mat& m, const MatExpr& e); -CV_EXPORTS MatExpr operator - (const MatExpr& e, const Scalar& s); -CV_EXPORTS MatExpr operator - (const Scalar& s, const MatExpr& e); -CV_EXPORTS MatExpr operator - (const MatExpr& e1, const MatExpr& e2); - -CV_EXPORTS MatExpr operator - (const Mat& m); -CV_EXPORTS MatExpr operator - (const MatExpr& e); - -CV_EXPORTS MatExpr operator * (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator * (const Mat& a, double s); -CV_EXPORTS MatExpr operator * (double s, const Mat& a); -CV_EXPORTS MatExpr operator * (const MatExpr& e, const Mat& m); -CV_EXPORTS MatExpr operator * (const Mat& m, const MatExpr& e); -CV_EXPORTS MatExpr operator * (const MatExpr& e, double s); -CV_EXPORTS MatExpr operator * (double s, const MatExpr& e); -CV_EXPORTS MatExpr operator * (const MatExpr& e1, const MatExpr& e2); - -CV_EXPORTS MatExpr operator / (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator / (const Mat& a, double s); -CV_EXPORTS MatExpr operator / (double s, const Mat& a); -CV_EXPORTS MatExpr operator / (const MatExpr& e, const Mat& m); -CV_EXPORTS MatExpr operator / (const Mat& m, const MatExpr& e); -CV_EXPORTS MatExpr operator / (const MatExpr& e, double s); -CV_EXPORTS MatExpr operator / (double s, const MatExpr& e); -CV_EXPORTS MatExpr operator / (const MatExpr& e1, const MatExpr& e2); - -CV_EXPORTS MatExpr operator < (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator < (const Mat& a, double s); -CV_EXPORTS MatExpr operator < (double s, const Mat& a); - -CV_EXPORTS MatExpr operator <= (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator <= (const Mat& a, double s); -CV_EXPORTS MatExpr operator <= (double s, const Mat& a); - -CV_EXPORTS MatExpr operator == (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator == (const Mat& a, double s); -CV_EXPORTS MatExpr operator == (double s, const Mat& a); - -CV_EXPORTS MatExpr operator != (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator != (const Mat& a, double s); -CV_EXPORTS MatExpr operator != (double s, const Mat& a); - -CV_EXPORTS MatExpr operator >= (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator >= (const Mat& a, double s); -CV_EXPORTS MatExpr operator >= (double s, const Mat& a); - -CV_EXPORTS MatExpr operator > (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator > (const Mat& a, double s); -CV_EXPORTS MatExpr operator > (double s, const Mat& a); - -CV_EXPORTS MatExpr operator & (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator & (const Mat& a, const Scalar& s); -CV_EXPORTS MatExpr operator & (const Scalar& s, const Mat& a); - -CV_EXPORTS MatExpr operator | (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator | (const Mat& a, const Scalar& s); -CV_EXPORTS MatExpr operator | (const Scalar& s, const Mat& a); - -CV_EXPORTS MatExpr operator ^ (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator ^ (const Mat& a, const Scalar& s); -CV_EXPORTS MatExpr operator ^ (const Scalar& s, const Mat& a); - -CV_EXPORTS MatExpr operator ~(const Mat& m); - -CV_EXPORTS MatExpr min(const Mat& a, const Mat& b); -CV_EXPORTS MatExpr min(const Mat& a, double s); -CV_EXPORTS MatExpr min(double s, const Mat& a); - -CV_EXPORTS MatExpr max(const Mat& a, const Mat& b); -CV_EXPORTS MatExpr max(const Mat& a, double s); -CV_EXPORTS MatExpr max(double s, const Mat& a); - -/** @brief Calculates an absolute value of each matrix element. - -abs is a meta-function that is expanded to one of absdiff or convertScaleAbs forms: -- C = abs(A-B) is equivalent to `absdiff(A, B, C)` -- C = abs(A) is equivalent to `absdiff(A, Scalar::all(0), C)` -- C = `Mat_ >(abs(A*alpha + beta))` is equivalent to `convertScaleAbs(A, C, alpha, -beta)` - -The output matrix has the same size and the same type as the input one except for the last case, -where C is depth=CV_8U . -@param m matrix. -@sa @ref MatrixExpressions, absdiff, convertScaleAbs - */ -CV_EXPORTS MatExpr abs(const Mat& m); -/** @overload -@param e matrix expression. -*/ -CV_EXPORTS MatExpr abs(const MatExpr& e); -//! @} relates cv::MatExpr - -} // cv - -#include "opencv2/core/mat.inl.hpp" - -#endif // __OPENCV_CORE_MAT_HPP__ diff --git a/IPL/include/opencv/opencv2/core/mat.inl.hpp b/IPL/include/opencv/opencv2/core/mat.inl.hpp deleted file mode 100644 index b4b1418..0000000 --- a/IPL/include/opencv/opencv2/core/mat.inl.hpp +++ /dev/null @@ -1,3673 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_MATRIX_OPERATIONS_HPP__ -#define __OPENCV_CORE_MATRIX_OPERATIONS_HPP__ - -#ifndef __cplusplus -# error mat.inl.hpp header must be compiled as C++ -#endif - -namespace cv -{ - -//! @cond IGNORED - -//////////////////////// Input/Output Arrays //////////////////////// - -inline void _InputArray::init(int _flags, const void* _obj) -{ flags = _flags; obj = (void*)_obj; } - -inline void _InputArray::init(int _flags, const void* _obj, Size _sz) -{ flags = _flags; obj = (void*)_obj; sz = _sz; } - -inline void* _InputArray::getObj() const { return obj; } -inline int _InputArray::getFlags() const { return flags; } -inline Size _InputArray::getSz() const { return sz; } - -inline _InputArray::_InputArray() { init(NONE, 0); } -inline _InputArray::_InputArray(int _flags, void* _obj) { init(_flags, _obj); } -inline _InputArray::_InputArray(const Mat& m) { init(MAT+ACCESS_READ, &m); } -inline _InputArray::_InputArray(const std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_READ, &vec); } -inline _InputArray::_InputArray(const UMat& m) { init(UMAT+ACCESS_READ, &m); } -inline _InputArray::_InputArray(const std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_READ, &vec); } - -template inline -_InputArray::_InputArray(const std::vector<_Tp>& vec) -{ init(FIXED_TYPE + STD_VECTOR + DataType<_Tp>::type + ACCESS_READ, &vec); } - -inline -_InputArray::_InputArray(const std::vector& vec) -{ init(FIXED_TYPE + STD_BOOL_VECTOR + DataType::type + ACCESS_READ, &vec); } - -template inline -_InputArray::_InputArray(const std::vector >& vec) -{ init(FIXED_TYPE + STD_VECTOR_VECTOR + DataType<_Tp>::type + ACCESS_READ, &vec); } - -template inline -_InputArray::_InputArray(const std::vector >& vec) -{ init(FIXED_TYPE + STD_VECTOR_MAT + DataType<_Tp>::type + ACCESS_READ, &vec); } - -template inline -_InputArray::_InputArray(const Matx<_Tp, m, n>& mtx) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_READ, &mtx, Size(n, m)); } - -template inline -_InputArray::_InputArray(const _Tp* vec, int n) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_READ, vec, Size(n, 1)); } - -template inline -_InputArray::_InputArray(const Mat_<_Tp>& m) -{ init(FIXED_TYPE + MAT + DataType<_Tp>::type + ACCESS_READ, &m); } - -inline _InputArray::_InputArray(const double& val) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + CV_64F + ACCESS_READ, &val, Size(1,1)); } - -inline _InputArray::_InputArray(const MatExpr& expr) -{ init(FIXED_TYPE + FIXED_SIZE + EXPR + ACCESS_READ, &expr); } - -inline _InputArray::_InputArray(const cuda::GpuMat& d_mat) -{ init(CUDA_GPU_MAT + ACCESS_READ, &d_mat); } - -inline _InputArray::_InputArray(const std::vector& d_mat) -{ init(STD_VECTOR_CUDA_GPU_MAT + ACCESS_READ, &d_mat);} - -inline _InputArray::_InputArray(const ogl::Buffer& buf) -{ init(OPENGL_BUFFER + ACCESS_READ, &buf); } - -inline _InputArray::_InputArray(const cuda::HostMem& cuda_mem) -{ init(CUDA_HOST_MEM + ACCESS_READ, &cuda_mem); } - -inline _InputArray::~_InputArray() {} - -inline Mat _InputArray::getMat(int i) const -{ - if( kind() == MAT && i < 0 ) - return *(const Mat*)obj; - return getMat_(i); -} - -inline bool _InputArray::isMat() const { return kind() == _InputArray::MAT; } -inline bool _InputArray::isUMat() const { return kind() == _InputArray::UMAT; } -inline bool _InputArray::isMatVector() const { return kind() == _InputArray::STD_VECTOR_MAT; } -inline bool _InputArray::isUMatVector() const { return kind() == _InputArray::STD_VECTOR_UMAT; } -inline bool _InputArray::isMatx() const { return kind() == _InputArray::MATX; } -inline bool _InputArray::isVector() const { return kind() == _InputArray::STD_VECTOR || kind() == _InputArray::STD_BOOL_VECTOR; } -inline bool _InputArray::isGpuMatVector() const { return kind() == _InputArray::STD_VECTOR_CUDA_GPU_MAT; } - -//////////////////////////////////////////////////////////////////////////////////////// - -inline _OutputArray::_OutputArray() { init(ACCESS_WRITE, 0); } -inline _OutputArray::_OutputArray(int _flags, void* _obj) { init(_flags|ACCESS_WRITE, _obj); } -inline _OutputArray::_OutputArray(Mat& m) { init(MAT+ACCESS_WRITE, &m); } -inline _OutputArray::_OutputArray(std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_WRITE, &vec); } -inline _OutputArray::_OutputArray(UMat& m) { init(UMAT+ACCESS_WRITE, &m); } -inline _OutputArray::_OutputArray(std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_WRITE, &vec); } - -template inline -_OutputArray::_OutputArray(std::vector<_Tp>& vec) -{ init(FIXED_TYPE + STD_VECTOR + DataType<_Tp>::type + ACCESS_WRITE, &vec); } - -inline -_OutputArray::_OutputArray(std::vector&) -{ CV_Error(Error::StsUnsupportedFormat, "std::vector cannot be an output array\n"); } - -template inline -_OutputArray::_OutputArray(std::vector >& vec) -{ init(FIXED_TYPE + STD_VECTOR_VECTOR + DataType<_Tp>::type + ACCESS_WRITE, &vec); } - -template inline -_OutputArray::_OutputArray(std::vector >& vec) -{ init(FIXED_TYPE + STD_VECTOR_MAT + DataType<_Tp>::type + ACCESS_WRITE, &vec); } - -template inline -_OutputArray::_OutputArray(Mat_<_Tp>& m) -{ init(FIXED_TYPE + MAT + DataType<_Tp>::type + ACCESS_WRITE, &m); } - -template inline -_OutputArray::_OutputArray(Matx<_Tp, m, n>& mtx) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_WRITE, &mtx, Size(n, m)); } - -template inline -_OutputArray::_OutputArray(_Tp* vec, int n) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_WRITE, vec, Size(n, 1)); } - -template inline -_OutputArray::_OutputArray(const std::vector<_Tp>& vec) -{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR + DataType<_Tp>::type + ACCESS_WRITE, &vec); } - -template inline -_OutputArray::_OutputArray(const std::vector >& vec) -{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_VECTOR + DataType<_Tp>::type + ACCESS_WRITE, &vec); } - -template inline -_OutputArray::_OutputArray(const std::vector >& vec) -{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_MAT + DataType<_Tp>::type + ACCESS_WRITE, &vec); } - -template inline -_OutputArray::_OutputArray(const Mat_<_Tp>& m) -{ init(FIXED_TYPE + FIXED_SIZE + MAT + DataType<_Tp>::type + ACCESS_WRITE, &m); } - -template inline -_OutputArray::_OutputArray(const Matx<_Tp, m, n>& mtx) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_WRITE, &mtx, Size(n, m)); } - -template inline -_OutputArray::_OutputArray(const _Tp* vec, int n) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_WRITE, vec, Size(n, 1)); } - -inline _OutputArray::_OutputArray(cuda::GpuMat& d_mat) -{ init(CUDA_GPU_MAT + ACCESS_WRITE, &d_mat); } - -inline _OutputArray::_OutputArray(std::vector& d_mat) -{ init(STD_VECTOR_CUDA_GPU_MAT + ACCESS_WRITE, &d_mat);} - -inline _OutputArray::_OutputArray(ogl::Buffer& buf) -{ init(OPENGL_BUFFER + ACCESS_WRITE, &buf); } - -inline _OutputArray::_OutputArray(cuda::HostMem& cuda_mem) -{ init(CUDA_HOST_MEM + ACCESS_WRITE, &cuda_mem); } - -inline _OutputArray::_OutputArray(const Mat& m) -{ init(FIXED_TYPE + FIXED_SIZE + MAT + ACCESS_WRITE, &m); } - -inline _OutputArray::_OutputArray(const std::vector& vec) -{ init(FIXED_SIZE + STD_VECTOR_MAT + ACCESS_WRITE, &vec); } - -inline _OutputArray::_OutputArray(const UMat& m) -{ init(FIXED_TYPE + FIXED_SIZE + UMAT + ACCESS_WRITE, &m); } - -inline _OutputArray::_OutputArray(const std::vector& vec) -{ init(FIXED_SIZE + STD_VECTOR_UMAT + ACCESS_WRITE, &vec); } - -inline _OutputArray::_OutputArray(const cuda::GpuMat& d_mat) -{ init(FIXED_TYPE + FIXED_SIZE + CUDA_GPU_MAT + ACCESS_WRITE, &d_mat); } - - -inline _OutputArray::_OutputArray(const ogl::Buffer& buf) -{ init(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER + ACCESS_WRITE, &buf); } - -inline _OutputArray::_OutputArray(const cuda::HostMem& cuda_mem) -{ init(FIXED_TYPE + FIXED_SIZE + CUDA_HOST_MEM + ACCESS_WRITE, &cuda_mem); } - -/////////////////////////////////////////////////////////////////////////////////////////// - -inline _InputOutputArray::_InputOutputArray() { init(ACCESS_RW, 0); } -inline _InputOutputArray::_InputOutputArray(int _flags, void* _obj) { init(_flags|ACCESS_RW, _obj); } -inline _InputOutputArray::_InputOutputArray(Mat& m) { init(MAT+ACCESS_RW, &m); } -inline _InputOutputArray::_InputOutputArray(std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_RW, &vec); } -inline _InputOutputArray::_InputOutputArray(UMat& m) { init(UMAT+ACCESS_RW, &m); } -inline _InputOutputArray::_InputOutputArray(std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_RW, &vec); } - -template inline -_InputOutputArray::_InputOutputArray(std::vector<_Tp>& vec) -{ init(FIXED_TYPE + STD_VECTOR + DataType<_Tp>::type + ACCESS_RW, &vec); } - -inline _InputOutputArray::_InputOutputArray(std::vector&) -{ CV_Error(Error::StsUnsupportedFormat, "std::vector cannot be an input/output array\n"); } - -template inline -_InputOutputArray::_InputOutputArray(std::vector >& vec) -{ init(FIXED_TYPE + STD_VECTOR_VECTOR + DataType<_Tp>::type + ACCESS_RW, &vec); } - -template inline -_InputOutputArray::_InputOutputArray(std::vector >& vec) -{ init(FIXED_TYPE + STD_VECTOR_MAT + DataType<_Tp>::type + ACCESS_RW, &vec); } - -template inline -_InputOutputArray::_InputOutputArray(Mat_<_Tp>& m) -{ init(FIXED_TYPE + MAT + DataType<_Tp>::type + ACCESS_RW, &m); } - -template inline -_InputOutputArray::_InputOutputArray(Matx<_Tp, m, n>& mtx) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_RW, &mtx, Size(n, m)); } - -template inline -_InputOutputArray::_InputOutputArray(_Tp* vec, int n) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_RW, vec, Size(n, 1)); } - -template inline -_InputOutputArray::_InputOutputArray(const std::vector<_Tp>& vec) -{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR + DataType<_Tp>::type + ACCESS_RW, &vec); } - -template inline -_InputOutputArray::_InputOutputArray(const std::vector >& vec) -{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_VECTOR + DataType<_Tp>::type + ACCESS_RW, &vec); } - -template inline -_InputOutputArray::_InputOutputArray(const std::vector >& vec) -{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_MAT + DataType<_Tp>::type + ACCESS_RW, &vec); } - -template inline -_InputOutputArray::_InputOutputArray(const Mat_<_Tp>& m) -{ init(FIXED_TYPE + FIXED_SIZE + MAT + DataType<_Tp>::type + ACCESS_RW, &m); } - -template inline -_InputOutputArray::_InputOutputArray(const Matx<_Tp, m, n>& mtx) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_RW, &mtx, Size(n, m)); } - -template inline -_InputOutputArray::_InputOutputArray(const _Tp* vec, int n) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_RW, vec, Size(n, 1)); } - -inline _InputOutputArray::_InputOutputArray(cuda::GpuMat& d_mat) -{ init(CUDA_GPU_MAT + ACCESS_RW, &d_mat); } - -inline _InputOutputArray::_InputOutputArray(ogl::Buffer& buf) -{ init(OPENGL_BUFFER + ACCESS_RW, &buf); } - -inline _InputOutputArray::_InputOutputArray(cuda::HostMem& cuda_mem) -{ init(CUDA_HOST_MEM + ACCESS_RW, &cuda_mem); } - -inline _InputOutputArray::_InputOutputArray(const Mat& m) -{ init(FIXED_TYPE + FIXED_SIZE + MAT + ACCESS_RW, &m); } - -inline _InputOutputArray::_InputOutputArray(const std::vector& vec) -{ init(FIXED_SIZE + STD_VECTOR_MAT + ACCESS_RW, &vec); } - -inline _InputOutputArray::_InputOutputArray(const UMat& m) -{ init(FIXED_TYPE + FIXED_SIZE + UMAT + ACCESS_RW, &m); } - -inline _InputOutputArray::_InputOutputArray(const std::vector& vec) -{ init(FIXED_SIZE + STD_VECTOR_UMAT + ACCESS_RW, &vec); } - -inline _InputOutputArray::_InputOutputArray(const cuda::GpuMat& d_mat) -{ init(FIXED_TYPE + FIXED_SIZE + CUDA_GPU_MAT + ACCESS_RW, &d_mat); } -inline _InputOutputArray::_InputOutputArray(const std::vector& d_mat) -{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_CUDA_GPU_MAT + ACCESS_RW, &d_mat);} - -inline _InputOutputArray::_InputOutputArray(const ogl::Buffer& buf) -{ init(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER + ACCESS_RW, &buf); } - -inline _InputOutputArray::_InputOutputArray(const cuda::HostMem& cuda_mem) -{ init(FIXED_TYPE + FIXED_SIZE + CUDA_HOST_MEM + ACCESS_RW, &cuda_mem); } - -//////////////////////////////////////////// Mat ////////////////////////////////////////// - -inline -Mat::Mat() - : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), - datalimit(0), allocator(0), u(0), size(&rows) -{} - -inline -Mat::Mat(int _rows, int _cols, int _type) - : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), - datalimit(0), allocator(0), u(0), size(&rows) -{ - create(_rows, _cols, _type); -} - -inline -Mat::Mat(int _rows, int _cols, int _type, const Scalar& _s) - : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), - datalimit(0), allocator(0), u(0), size(&rows) -{ - create(_rows, _cols, _type); - *this = _s; -} - -inline -Mat::Mat(Size _sz, int _type) - : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), - datalimit(0), allocator(0), u(0), size(&rows) -{ - create( _sz.height, _sz.width, _type ); -} - -inline -Mat::Mat(Size _sz, int _type, const Scalar& _s) - : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), - datalimit(0), allocator(0), u(0), size(&rows) -{ - create(_sz.height, _sz.width, _type); - *this = _s; -} - -inline -Mat::Mat(int _dims, const int* _sz, int _type) - : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), - datalimit(0), allocator(0), u(0), size(&rows) -{ - create(_dims, _sz, _type); -} - -inline -Mat::Mat(int _dims, const int* _sz, int _type, const Scalar& _s) - : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), - datalimit(0), allocator(0), u(0), size(&rows) -{ - create(_dims, _sz, _type); - *this = _s; -} - -inline -Mat::Mat(const Mat& m) - : flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), data(m.data), - datastart(m.datastart), dataend(m.dataend), datalimit(m.datalimit), allocator(m.allocator), - u(m.u), size(&rows) -{ - if( u ) - CV_XADD(&u->refcount, 1); - if( m.dims <= 2 ) - { - step[0] = m.step[0]; step[1] = m.step[1]; - } - else - { - dims = 0; - copySize(m); - } -} - -inline -Mat::Mat(int _rows, int _cols, int _type, void* _data, size_t _step) - : flags(MAGIC_VAL + (_type & TYPE_MASK)), dims(2), rows(_rows), cols(_cols), - data((uchar*)_data), datastart((uchar*)_data), dataend(0), datalimit(0), - allocator(0), u(0), size(&rows) -{ - CV_Assert(total() == 0 || data != NULL); - - size_t esz = CV_ELEM_SIZE(_type), esz1 = CV_ELEM_SIZE1(_type); - size_t minstep = cols * esz; - if( _step == AUTO_STEP ) - { - _step = minstep; - flags |= CONTINUOUS_FLAG; - } - else - { - if( rows == 1 ) _step = minstep; - CV_DbgAssert( _step >= minstep ); - - if (_step % esz1 != 0) - { - CV_Error(Error::BadStep, "Step must be a multiple of esz1"); - } - - flags |= _step == minstep ? CONTINUOUS_FLAG : 0; - } - step[0] = _step; - step[1] = esz; - datalimit = datastart + _step * rows; - dataend = datalimit - _step + minstep; -} - -inline -Mat::Mat(Size _sz, int _type, void* _data, size_t _step) - : flags(MAGIC_VAL + (_type & TYPE_MASK)), dims(2), rows(_sz.height), cols(_sz.width), - data((uchar*)_data), datastart((uchar*)_data), dataend(0), datalimit(0), - allocator(0), u(0), size(&rows) -{ - CV_Assert(total() == 0 || data != NULL); - - size_t esz = CV_ELEM_SIZE(_type), esz1 = CV_ELEM_SIZE1(_type); - size_t minstep = cols*esz; - if( _step == AUTO_STEP ) - { - _step = minstep; - flags |= CONTINUOUS_FLAG; - } - else - { - if( rows == 1 ) _step = minstep; - CV_DbgAssert( _step >= minstep ); - - if (_step % esz1 != 0) - { - CV_Error(Error::BadStep, "Step must be a multiple of esz1"); - } - - flags |= _step == minstep ? CONTINUOUS_FLAG : 0; - } - step[0] = _step; - step[1] = esz; - datalimit = datastart + _step*rows; - dataend = datalimit - _step + minstep; -} - -template inline -Mat::Mat(const std::vector<_Tp>& vec, bool copyData) - : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()), - cols(1), data(0), datastart(0), dataend(0), allocator(0), u(0), size(&rows) -{ - if(vec.empty()) - return; - if( !copyData ) - { - step[0] = step[1] = sizeof(_Tp); - datastart = data = (uchar*)&vec[0]; - datalimit = dataend = datastart + rows * step[0]; - } - else - Mat((int)vec.size(), 1, DataType<_Tp>::type, (uchar*)&vec[0]).copyTo(*this); -} - -template inline -Mat::Mat(const Vec<_Tp, n>& vec, bool copyData) - : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows(n), cols(1), data(0), - datastart(0), dataend(0), allocator(0), u(0), size(&rows) -{ - if( !copyData ) - { - step[0] = step[1] = sizeof(_Tp); - datastart = data = (uchar*)vec.val; - datalimit = dataend = datastart + rows * step[0]; - } - else - Mat(n, 1, DataType<_Tp>::type, (void*)vec.val).copyTo(*this); -} - - -template inline -Mat::Mat(const Matx<_Tp,m,n>& M, bool copyData) - : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows(m), cols(n), data(0), - datastart(0), dataend(0), allocator(0), u(0), size(&rows) -{ - if( !copyData ) - { - step[0] = cols * sizeof(_Tp); - step[1] = sizeof(_Tp); - datastart = data = (uchar*)M.val; - datalimit = dataend = datastart + rows * step[0]; - } - else - Mat(m, n, DataType<_Tp>::type, (uchar*)M.val).copyTo(*this); -} - -template inline -Mat::Mat(const Point_<_Tp>& pt, bool copyData) - : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows(2), cols(1), data(0), - datastart(0), dataend(0), allocator(0), u(0), size(&rows) -{ - if( !copyData ) - { - step[0] = step[1] = sizeof(_Tp); - datastart = data = (uchar*)&pt.x; - datalimit = dataend = datastart + rows * step[0]; - } - else - { - create(2, 1, DataType<_Tp>::type); - ((_Tp*)data)[0] = pt.x; - ((_Tp*)data)[1] = pt.y; - } -} - -template inline -Mat::Mat(const Point3_<_Tp>& pt, bool copyData) - : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows(3), cols(1), data(0), - datastart(0), dataend(0), allocator(0), u(0), size(&rows) -{ - if( !copyData ) - { - step[0] = step[1] = sizeof(_Tp); - datastart = data = (uchar*)&pt.x; - datalimit = dataend = datastart + rows * step[0]; - } - else - { - create(3, 1, DataType<_Tp>::type); - ((_Tp*)data)[0] = pt.x; - ((_Tp*)data)[1] = pt.y; - ((_Tp*)data)[2] = pt.z; - } -} - -template inline -Mat::Mat(const MatCommaInitializer_<_Tp>& commaInitializer) - : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(0), rows(0), cols(0), data(0), - datastart(0), dataend(0), allocator(0), u(0), size(&rows) -{ - *this = commaInitializer.operator Mat_<_Tp>(); -} - -inline -Mat::~Mat() -{ - release(); - if( step.p != step.buf ) - fastFree(step.p); -} - -inline -Mat& Mat::operator = (const Mat& m) -{ - if( this != &m ) - { - if( m.u ) - CV_XADD(&m.u->refcount, 1); - release(); - flags = m.flags; - if( dims <= 2 && m.dims <= 2 ) - { - dims = m.dims; - rows = m.rows; - cols = m.cols; - step[0] = m.step[0]; - step[1] = m.step[1]; - } - else - copySize(m); - data = m.data; - datastart = m.datastart; - dataend = m.dataend; - datalimit = m.datalimit; - allocator = m.allocator; - u = m.u; - } - return *this; -} - -inline -Mat Mat::row(int y) const -{ - return Mat(*this, Range(y, y + 1), Range::all()); -} - -inline -Mat Mat::col(int x) const -{ - return Mat(*this, Range::all(), Range(x, x + 1)); -} - -inline -Mat Mat::rowRange(int startrow, int endrow) const -{ - return Mat(*this, Range(startrow, endrow), Range::all()); -} - -inline -Mat Mat::rowRange(const Range& r) const -{ - return Mat(*this, r, Range::all()); -} - -inline -Mat Mat::colRange(int startcol, int endcol) const -{ - return Mat(*this, Range::all(), Range(startcol, endcol)); -} - -inline -Mat Mat::colRange(const Range& r) const -{ - return Mat(*this, Range::all(), r); -} - -inline -Mat Mat::clone() const -{ - Mat m; - copyTo(m); - return m; -} - -inline -void Mat::assignTo( Mat& m, int _type ) const -{ - if( _type < 0 ) - m = *this; - else - convertTo(m, _type); -} - -inline -void Mat::create(int _rows, int _cols, int _type) -{ - _type &= TYPE_MASK; - if( dims <= 2 && rows == _rows && cols == _cols && type() == _type && data ) - return; - int sz[] = {_rows, _cols}; - create(2, sz, _type); -} - -inline -void Mat::create(Size _sz, int _type) -{ - create(_sz.height, _sz.width, _type); -} - -inline -void Mat::addref() -{ - if( u ) - CV_XADD(&u->refcount, 1); -} - -inline void Mat::release() -{ - if( u && CV_XADD(&u->refcount, -1) == 1 ) - deallocate(); - u = NULL; - datastart = dataend = datalimit = data = 0; - for(int i = 0; i < dims; i++) - size.p[i] = 0; -} - -inline -Mat Mat::operator()( Range _rowRange, Range _colRange ) const -{ - return Mat(*this, _rowRange, _colRange); -} - -inline -Mat Mat::operator()( const Rect& roi ) const -{ - return Mat(*this, roi); -} - -inline -Mat Mat::operator()(const Range* ranges) const -{ - return Mat(*this, ranges); -} - -inline -bool Mat::isContinuous() const -{ - return (flags & CONTINUOUS_FLAG) != 0; -} - -inline -bool Mat::isSubmatrix() const -{ - return (flags & SUBMATRIX_FLAG) != 0; -} - -inline -size_t Mat::elemSize() const -{ - return dims > 0 ? step.p[dims - 1] : 0; -} - -inline -size_t Mat::elemSize1() const -{ - return CV_ELEM_SIZE1(flags); -} - -inline -int Mat::type() const -{ - return CV_MAT_TYPE(flags); -} - -inline -int Mat::depth() const -{ - return CV_MAT_DEPTH(flags); -} - -inline -int Mat::channels() const -{ - return CV_MAT_CN(flags); -} - -inline -size_t Mat::step1(int i) const -{ - return step.p[i] / elemSize1(); -} - -inline -bool Mat::empty() const -{ - return data == 0 || total() == 0; -} - -inline -size_t Mat::total() const -{ - if( dims <= 2 ) - return (size_t)rows * cols; - size_t p = 1; - for( int i = 0; i < dims; i++ ) - p *= size[i]; - return p; -} - -inline -uchar* Mat::ptr(int y) -{ - CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); - return data + step.p[0] * y; -} - -inline -const uchar* Mat::ptr(int y) const -{ - CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); - return data + step.p[0] * y; -} - -template inline -_Tp* Mat::ptr(int y) -{ - CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); - return (_Tp*)(data + step.p[0] * y); -} - -template inline -const _Tp* Mat::ptr(int y) const -{ - CV_DbgAssert( y == 0 || (data && dims >= 1 && data && (unsigned)y < (unsigned)size.p[0]) ); - return (const _Tp*)(data + step.p[0] * y); -} - -inline -uchar* Mat::ptr(int i0, int i1) -{ - CV_DbgAssert(dims >= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - return data + i0 * step.p[0] + i1 * step.p[1]; -} - -inline -const uchar* Mat::ptr(int i0, int i1) const -{ - CV_DbgAssert(dims >= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - return data + i0 * step.p[0] + i1 * step.p[1]; -} - -template inline -_Tp* Mat::ptr(int i0, int i1) -{ - CV_DbgAssert(dims >= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - return (_Tp*)(data + i0 * step.p[0] + i1 * step.p[1]); -} - -template inline -const _Tp* Mat::ptr(int i0, int i1) const -{ - CV_DbgAssert(dims >= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - return (const _Tp*)(data + i0 * step.p[0] + i1 * step.p[1]); -} - -inline -uchar* Mat::ptr(int i0, int i1, int i2) -{ - CV_DbgAssert(dims >= 3); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); - return data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]; -} - -inline -const uchar* Mat::ptr(int i0, int i1, int i2) const -{ - CV_DbgAssert(dims >= 3); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); - return data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]; -} - -template inline -_Tp* Mat::ptr(int i0, int i1, int i2) -{ - CV_DbgAssert(dims >= 3); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); - return (_Tp*)(data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]); -} - -template inline -const _Tp* Mat::ptr(int i0, int i1, int i2) const -{ - CV_DbgAssert(dims >= 3); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); - return (const _Tp*)(data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]); -} - -inline -uchar* Mat::ptr(const int* idx) -{ - int i, d = dims; - uchar* p = data; - CV_DbgAssert( d >= 1 && p ); - for( i = 0; i < d; i++ ) - { - CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); - p += idx[i] * step.p[i]; - } - return p; -} - -inline -const uchar* Mat::ptr(const int* idx) const -{ - int i, d = dims; - uchar* p = data; - CV_DbgAssert( d >= 1 && p ); - for( i = 0; i < d; i++ ) - { - CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); - p += idx[i] * step.p[i]; - } - return p; -} - -template inline -_Tp& Mat::at(int i0, int i1) -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); - CV_DbgAssert(CV_ELEM_SIZE1(DataType<_Tp>::depth) == elemSize1()); - return ((_Tp*)(data + step.p[0] * i0))[i1]; -} - -template inline -const _Tp& Mat::at(int i0, int i1) const -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); - CV_DbgAssert(CV_ELEM_SIZE1(DataType<_Tp>::depth) == elemSize1()); - return ((const _Tp*)(data + step.p[0] * i0))[i1]; -} - -template inline -_Tp& Mat::at(Point pt) -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)(pt.x * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); - CV_DbgAssert(CV_ELEM_SIZE1(DataType<_Tp>::depth) == elemSize1()); - return ((_Tp*)(data + step.p[0] * pt.y))[pt.x]; -} - -template inline -const _Tp& Mat::at(Point pt) const -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)(pt.x * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); - CV_DbgAssert(CV_ELEM_SIZE1(DataType<_Tp>::depth) == elemSize1()); - return ((const _Tp*)(data + step.p[0] * pt.y))[pt.x]; -} - -template inline -_Tp& Mat::at(int i0) -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)(size.p[0] * size.p[1])); - CV_DbgAssert(elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type)); - if( isContinuous() || size.p[0] == 1 ) - return ((_Tp*)data)[i0]; - if( size.p[1] == 1 ) - return *(_Tp*)(data + step.p[0] * i0); - int i = i0 / cols, j = i0 - i * cols; - return ((_Tp*)(data + step.p[0] * i))[j]; -} - -template inline -const _Tp& Mat::at(int i0) const -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)(size.p[0] * size.p[1])); - CV_DbgAssert(elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type)); - if( isContinuous() || size.p[0] == 1 ) - return ((const _Tp*)data)[i0]; - if( size.p[1] == 1 ) - return *(const _Tp*)(data + step.p[0] * i0); - int i = i0 / cols, j = i0 - i * cols; - return ((const _Tp*)(data + step.p[0] * i))[j]; -} - -template inline -_Tp& Mat::at(int i0, int i1, int i2) -{ - CV_DbgAssert( elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type) ); - return *(_Tp*)ptr(i0, i1, i2); -} - -template inline -const _Tp& Mat::at(int i0, int i1, int i2) const -{ - CV_DbgAssert( elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type) ); - return *(const _Tp*)ptr(i0, i1, i2); -} - -template inline -_Tp& Mat::at(const int* idx) -{ - CV_DbgAssert( elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type) ); - return *(_Tp*)ptr(idx); -} - -template inline -const _Tp& Mat::at(const int* idx) const -{ - CV_DbgAssert( elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type) ); - return *(const _Tp*)ptr(idx); -} - -template inline -_Tp& Mat::at(const Vec& idx) -{ - CV_DbgAssert( elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type) ); - return *(_Tp*)ptr(idx.val); -} - -template inline -const _Tp& Mat::at(const Vec& idx) const -{ - CV_DbgAssert( elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type) ); - return *(const _Tp*)ptr(idx.val); -} - -template inline -MatConstIterator_<_Tp> Mat::begin() const -{ - CV_DbgAssert( elemSize() == sizeof(_Tp) ); - return MatConstIterator_<_Tp>((const Mat_<_Tp>*)this); -} - -template inline -MatConstIterator_<_Tp> Mat::end() const -{ - CV_DbgAssert( elemSize() == sizeof(_Tp) ); - MatConstIterator_<_Tp> it((const Mat_<_Tp>*)this); - it += total(); - return it; -} - -template inline -MatIterator_<_Tp> Mat::begin() -{ - CV_DbgAssert( elemSize() == sizeof(_Tp) ); - return MatIterator_<_Tp>((Mat_<_Tp>*)this); -} - -template inline -MatIterator_<_Tp> Mat::end() -{ - CV_DbgAssert( elemSize() == sizeof(_Tp) ); - MatIterator_<_Tp> it((Mat_<_Tp>*)this); - it += total(); - return it; -} - -template inline -void Mat::forEach(const Functor& operation) { - this->forEach_impl<_Tp>(operation); -} - -template inline -void Mat::forEach(const Functor& operation) const { - // call as not const - (const_cast(this))->forEach(operation); -} - -template inline -Mat::operator std::vector<_Tp>() const -{ - std::vector<_Tp> v; - copyTo(v); - return v; -} - -template inline -Mat::operator Vec<_Tp, n>() const -{ - CV_Assert( data && dims <= 2 && (rows == 1 || cols == 1) && - rows + cols - 1 == n && channels() == 1 ); - - if( isContinuous() && type() == DataType<_Tp>::type ) - return Vec<_Tp, n>((_Tp*)data); - Vec<_Tp, n> v; - Mat tmp(rows, cols, DataType<_Tp>::type, v.val); - convertTo(tmp, tmp.type()); - return v; -} - -template inline -Mat::operator Matx<_Tp, m, n>() const -{ - CV_Assert( data && dims <= 2 && rows == m && cols == n && channels() == 1 ); - - if( isContinuous() && type() == DataType<_Tp>::type ) - return Matx<_Tp, m, n>((_Tp*)data); - Matx<_Tp, m, n> mtx; - Mat tmp(rows, cols, DataType<_Tp>::type, mtx.val); - convertTo(tmp, tmp.type()); - return mtx; -} - -template inline -void Mat::push_back(const _Tp& elem) -{ - if( !data ) - { - *this = Mat(1, 1, DataType<_Tp>::type, (void*)&elem).clone(); - return; - } - CV_Assert(DataType<_Tp>::type == type() && cols == 1 - /* && dims == 2 (cols == 1 implies dims == 2) */); - const uchar* tmp = dataend + step[0]; - if( !isSubmatrix() && isContinuous() && tmp <= datalimit ) - { - *(_Tp*)(data + (size.p[0]++) * step.p[0]) = elem; - dataend = tmp; - } - else - push_back_(&elem); -} - -template inline -void Mat::push_back(const Mat_<_Tp>& m) -{ - push_back((const Mat&)m); -} - -template<> inline -void Mat::push_back(const MatExpr& expr) -{ - push_back(static_cast(expr)); -} - -#ifdef CV_CXX_MOVE_SEMANTICS - -inline -Mat::Mat(Mat&& m) - : flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), data(m.data), - datastart(m.datastart), dataend(m.dataend), datalimit(m.datalimit), allocator(m.allocator), - u(m.u), size(&rows) -{ - if (m.dims <= 2) // move new step/size info - { - step[0] = m.step[0]; - step[1] = m.step[1]; - } - else - { - CV_DbgAssert(m.step.p != m.step.buf); - step.p = m.step.p; - size.p = m.size.p; - m.step.p = m.step.buf; - m.size.p = &m.rows; - } - m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; - m.data = NULL; m.datastart = NULL; m.dataend = NULL; m.datalimit = NULL; - m.allocator = NULL; - m.u = NULL; -} - -inline -Mat& Mat::operator = (Mat&& m) -{ - release(); - flags = m.flags; dims = m.dims; rows = m.rows; cols = m.cols; data = m.data; - datastart = m.datastart; dataend = m.dataend; datalimit = m.datalimit; allocator = m.allocator; - u = m.u; - if (step.p != step.buf) // release self step/size - { - fastFree(step.p); - step.p = step.buf; - size.p = &rows; - } - if (m.dims <= 2) // move new step/size info - { - step[0] = m.step[0]; - step[1] = m.step[1]; - } - else - { - CV_DbgAssert(m.step.p != m.step.buf); - step.p = m.step.p; - size.p = m.size.p; - m.step.p = m.step.buf; - m.size.p = &m.rows; - } - m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; - m.data = NULL; m.datastart = NULL; m.dataend = NULL; m.datalimit = NULL; - m.allocator = NULL; - m.u = NULL; - return *this; -} - -#endif - - -///////////////////////////// MatSize //////////////////////////// - -inline -MatSize::MatSize(int* _p) - : p(_p) {} - -inline -Size MatSize::operator()() const -{ - CV_DbgAssert(p[-1] <= 2); - return Size(p[1], p[0]); -} - -inline -const int& MatSize::operator[](int i) const -{ - return p[i]; -} - -inline -int& MatSize::operator[](int i) -{ - return p[i]; -} - -inline -MatSize::operator const int*() const -{ - return p; -} - -inline -bool MatSize::operator == (const MatSize& sz) const -{ - int d = p[-1]; - int dsz = sz.p[-1]; - if( d != dsz ) - return false; - if( d == 2 ) - return p[0] == sz.p[0] && p[1] == sz.p[1]; - - for( int i = 0; i < d; i++ ) - if( p[i] != sz.p[i] ) - return false; - return true; -} - -inline -bool MatSize::operator != (const MatSize& sz) const -{ - return !(*this == sz); -} - - - -///////////////////////////// MatStep //////////////////////////// - -inline -MatStep::MatStep() -{ - p = buf; p[0] = p[1] = 0; -} - -inline -MatStep::MatStep(size_t s) -{ - p = buf; p[0] = s; p[1] = 0; -} - -inline -const size_t& MatStep::operator[](int i) const -{ - return p[i]; -} - -inline -size_t& MatStep::operator[](int i) -{ - return p[i]; -} - -inline MatStep::operator size_t() const -{ - CV_DbgAssert( p == buf ); - return buf[0]; -} - -inline MatStep& MatStep::operator = (size_t s) -{ - CV_DbgAssert( p == buf ); - buf[0] = s; - return *this; -} - - - -////////////////////////////// Mat_<_Tp> //////////////////////////// - -template inline -Mat_<_Tp>::Mat_() - : Mat() -{ - flags = (flags & ~CV_MAT_TYPE_MASK) | DataType<_Tp>::type; -} - -template inline -Mat_<_Tp>::Mat_(int _rows, int _cols) - : Mat(_rows, _cols, DataType<_Tp>::type) -{ -} - -template inline -Mat_<_Tp>::Mat_(int _rows, int _cols, const _Tp& value) - : Mat(_rows, _cols, DataType<_Tp>::type) -{ - *this = value; -} - -template inline -Mat_<_Tp>::Mat_(Size _sz) - : Mat(_sz.height, _sz.width, DataType<_Tp>::type) -{} - -template inline -Mat_<_Tp>::Mat_(Size _sz, const _Tp& value) - : Mat(_sz.height, _sz.width, DataType<_Tp>::type) -{ - *this = value; -} - -template inline -Mat_<_Tp>::Mat_(int _dims, const int* _sz) - : Mat(_dims, _sz, DataType<_Tp>::type) -{} - -template inline -Mat_<_Tp>::Mat_(int _dims, const int* _sz, const _Tp& _s) - : Mat(_dims, _sz, DataType<_Tp>::type, Scalar(_s)) -{} - -template inline -Mat_<_Tp>::Mat_(int _dims, const int* _sz, _Tp* _data, const size_t* _steps) - : Mat(_dims, _sz, DataType<_Tp>::type, _data, _steps) -{} - -template inline -Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const Range* ranges) - : Mat(m, ranges) -{} - -template inline -Mat_<_Tp>::Mat_(const Mat& m) - : Mat() -{ - flags = (flags & ~CV_MAT_TYPE_MASK) | DataType<_Tp>::type; - *this = m; -} - -template inline -Mat_<_Tp>::Mat_(const Mat_& m) - : Mat(m) -{} - -template inline -Mat_<_Tp>::Mat_(int _rows, int _cols, _Tp* _data, size_t steps) - : Mat(_rows, _cols, DataType<_Tp>::type, _data, steps) -{} - -template inline -Mat_<_Tp>::Mat_(const Mat_& m, const Range& _rowRange, const Range& _colRange) - : Mat(m, _rowRange, _colRange) -{} - -template inline -Mat_<_Tp>::Mat_(const Mat_& m, const Rect& roi) - : Mat(m, roi) -{} - -template template inline -Mat_<_Tp>::Mat_(const Vec::channel_type, n>& vec, bool copyData) - : Mat(n / DataType<_Tp>::channels, 1, DataType<_Tp>::type, (void*)&vec) -{ - CV_Assert(n%DataType<_Tp>::channels == 0); - if( copyData ) - *this = clone(); -} - -template template inline -Mat_<_Tp>::Mat_(const Matx::channel_type, m, n>& M, bool copyData) - : Mat(m, n / DataType<_Tp>::channels, DataType<_Tp>::type, (void*)&M) -{ - CV_Assert(n % DataType<_Tp>::channels == 0); - if( copyData ) - *this = clone(); -} - -template inline -Mat_<_Tp>::Mat_(const Point_::channel_type>& pt, bool copyData) - : Mat(2 / DataType<_Tp>::channels, 1, DataType<_Tp>::type, (void*)&pt) -{ - CV_Assert(2 % DataType<_Tp>::channels == 0); - if( copyData ) - *this = clone(); -} - -template inline -Mat_<_Tp>::Mat_(const Point3_::channel_type>& pt, bool copyData) - : Mat(3 / DataType<_Tp>::channels, 1, DataType<_Tp>::type, (void*)&pt) -{ - CV_Assert(3 % DataType<_Tp>::channels == 0); - if( copyData ) - *this = clone(); -} - -template inline -Mat_<_Tp>::Mat_(const MatCommaInitializer_<_Tp>& commaInitializer) - : Mat(commaInitializer) -{} - -template inline -Mat_<_Tp>::Mat_(const std::vector<_Tp>& vec, bool copyData) - : Mat(vec, copyData) -{} - -template inline -Mat_<_Tp>& Mat_<_Tp>::operator = (const Mat& m) -{ - if( DataType<_Tp>::type == m.type() ) - { - Mat::operator = (m); - return *this; - } - if( DataType<_Tp>::depth == m.depth() ) - { - return (*this = m.reshape(DataType<_Tp>::channels, m.dims, 0)); - } - CV_DbgAssert(DataType<_Tp>::channels == m.channels()); - m.convertTo(*this, type()); - return *this; -} - -template inline -Mat_<_Tp>& Mat_<_Tp>::operator = (const Mat_& m) -{ - Mat::operator=(m); - return *this; -} - -template inline -Mat_<_Tp>& Mat_<_Tp>::operator = (const _Tp& s) -{ - typedef typename DataType<_Tp>::vec_type VT; - Mat::operator=(Scalar((const VT&)s)); - return *this; -} - -template inline -void Mat_<_Tp>::create(int _rows, int _cols) -{ - Mat::create(_rows, _cols, DataType<_Tp>::type); -} - -template inline -void Mat_<_Tp>::create(Size _sz) -{ - Mat::create(_sz, DataType<_Tp>::type); -} - -template inline -void Mat_<_Tp>::create(int _dims, const int* _sz) -{ - Mat::create(_dims, _sz, DataType<_Tp>::type); -} - -template inline -Mat_<_Tp> Mat_<_Tp>::cross(const Mat_& m) const -{ - return Mat_<_Tp>(Mat::cross(m)); -} - -template template inline -Mat_<_Tp>::operator Mat_() const -{ - return Mat_(*this); -} - -template inline -Mat_<_Tp> Mat_<_Tp>::row(int y) const -{ - return Mat_(*this, Range(y, y+1), Range::all()); -} - -template inline -Mat_<_Tp> Mat_<_Tp>::col(int x) const -{ - return Mat_(*this, Range::all(), Range(x, x+1)); -} - -template inline -Mat_<_Tp> Mat_<_Tp>::diag(int d) const -{ - return Mat_(Mat::diag(d)); -} - -template inline -Mat_<_Tp> Mat_<_Tp>::clone() const -{ - return Mat_(Mat::clone()); -} - -template inline -size_t Mat_<_Tp>::elemSize() const -{ - CV_DbgAssert( Mat::elemSize() == sizeof(_Tp) ); - return sizeof(_Tp); -} - -template inline -size_t Mat_<_Tp>::elemSize1() const -{ - CV_DbgAssert( Mat::elemSize1() == sizeof(_Tp) / DataType<_Tp>::channels ); - return sizeof(_Tp) / DataType<_Tp>::channels; -} - -template inline -int Mat_<_Tp>::type() const -{ - CV_DbgAssert( Mat::type() == DataType<_Tp>::type ); - return DataType<_Tp>::type; -} - -template inline -int Mat_<_Tp>::depth() const -{ - CV_DbgAssert( Mat::depth() == DataType<_Tp>::depth ); - return DataType<_Tp>::depth; -} - -template inline -int Mat_<_Tp>::channels() const -{ - CV_DbgAssert( Mat::channels() == DataType<_Tp>::channels ); - return DataType<_Tp>::channels; -} - -template inline -size_t Mat_<_Tp>::stepT(int i) const -{ - return step.p[i] / elemSize(); -} - -template inline -size_t Mat_<_Tp>::step1(int i) const -{ - return step.p[i] / elemSize1(); -} - -template inline -Mat_<_Tp>& Mat_<_Tp>::adjustROI( int dtop, int dbottom, int dleft, int dright ) -{ - return (Mat_<_Tp>&)(Mat::adjustROI(dtop, dbottom, dleft, dright)); -} - -template inline -Mat_<_Tp> Mat_<_Tp>::operator()( const Range& _rowRange, const Range& _colRange ) const -{ - return Mat_<_Tp>(*this, _rowRange, _colRange); -} - -template inline -Mat_<_Tp> Mat_<_Tp>::operator()( const Rect& roi ) const -{ - return Mat_<_Tp>(*this, roi); -} - -template inline -Mat_<_Tp> Mat_<_Tp>::operator()( const Range* ranges ) const -{ - return Mat_<_Tp>(*this, ranges); -} - -template inline -_Tp* Mat_<_Tp>::operator [](int y) -{ - CV_DbgAssert( 0 <= y && y < rows ); - return (_Tp*)(data + y*step.p[0]); -} - -template inline -const _Tp* Mat_<_Tp>::operator [](int y) const -{ - CV_DbgAssert( 0 <= y && y < rows ); - return (const _Tp*)(data + y*step.p[0]); -} - -template inline -_Tp& Mat_<_Tp>::operator ()(int i0, int i1) -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - CV_DbgAssert(type() == DataType<_Tp>::type); - return ((_Tp*)(data + step.p[0] * i0))[i1]; -} - -template inline -const _Tp& Mat_<_Tp>::operator ()(int i0, int i1) const -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - CV_DbgAssert(type() == DataType<_Tp>::type); - return ((const _Tp*)(data + step.p[0] * i0))[i1]; -} - -template inline -_Tp& Mat_<_Tp>::operator ()(Point pt) -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)pt.x < (unsigned)size.p[1]); - CV_DbgAssert(type() == DataType<_Tp>::type); - return ((_Tp*)(data + step.p[0] * pt.y))[pt.x]; -} - -template inline -const _Tp& Mat_<_Tp>::operator ()(Point pt) const -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)pt.x < (unsigned)size.p[1]); - CV_DbgAssert(type() == DataType<_Tp>::type); - return ((const _Tp*)(data + step.p[0] * pt.y))[pt.x]; -} - -template inline -_Tp& Mat_<_Tp>::operator ()(const int* idx) -{ - return Mat::at<_Tp>(idx); -} - -template inline -const _Tp& Mat_<_Tp>::operator ()(const int* idx) const -{ - return Mat::at<_Tp>(idx); -} - -template template inline -_Tp& Mat_<_Tp>::operator ()(const Vec& idx) -{ - return Mat::at<_Tp>(idx); -} - -template template inline -const _Tp& Mat_<_Tp>::operator ()(const Vec& idx) const -{ - return Mat::at<_Tp>(idx); -} - -template inline -_Tp& Mat_<_Tp>::operator ()(int i0) -{ - return this->at<_Tp>(i0); -} - -template inline -const _Tp& Mat_<_Tp>::operator ()(int i0) const -{ - return this->at<_Tp>(i0); -} - -template inline -_Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) -{ - return this->at<_Tp>(i0, i1, i2); -} - -template inline -const _Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) const -{ - return this->at<_Tp>(i0, i1, i2); -} - -template inline -Mat_<_Tp>::operator std::vector<_Tp>() const -{ - std::vector<_Tp> v; - copyTo(v); - return v; -} - -template template inline -Mat_<_Tp>::operator Vec::channel_type, n>() const -{ - CV_Assert(n % DataType<_Tp>::channels == 0); - -#if defined _MSC_VER - const Mat* pMat = (const Mat*)this; // workaround for MSVS <= 2012 compiler bugs (but GCC 4.6 dislikes this workaround) - return pMat->operator Vec::channel_type, n>(); -#else - return this->Mat::operator Vec::channel_type, n>(); -#endif -} - -template template inline -Mat_<_Tp>::operator Matx::channel_type, m, n>() const -{ - CV_Assert(n % DataType<_Tp>::channels == 0); - -#if defined _MSC_VER - const Mat* pMat = (const Mat*)this; // workaround for MSVS <= 2012 compiler bugs (but GCC 4.6 dislikes this workaround) - Matx::channel_type, m, n> res = pMat->operator Matx::channel_type, m, n>(); - return res; -#else - Matx::channel_type, m, n> res = this->Mat::operator Matx::channel_type, m, n>(); - return res; -#endif -} - -template inline -MatConstIterator_<_Tp> Mat_<_Tp>::begin() const -{ - return Mat::begin<_Tp>(); -} - -template inline -MatConstIterator_<_Tp> Mat_<_Tp>::end() const -{ - return Mat::end<_Tp>(); -} - -template inline -MatIterator_<_Tp> Mat_<_Tp>::begin() -{ - return Mat::begin<_Tp>(); -} - -template inline -MatIterator_<_Tp> Mat_<_Tp>::end() -{ - return Mat::end<_Tp>(); -} - -template template inline -void Mat_<_Tp>::forEach(const Functor& operation) { - Mat::forEach<_Tp, Functor>(operation); -} - -template template inline -void Mat_<_Tp>::forEach(const Functor& operation) const { - Mat::forEach<_Tp, Functor>(operation); -} - -#ifdef CV_CXX_MOVE_SEMANTICS - -template inline -Mat_<_Tp>::Mat_(Mat_&& m) - : Mat(m) -{ -} - -template inline -Mat_<_Tp>& Mat_<_Tp>::operator = (Mat_&& m) -{ - Mat::operator = (m); - return *this; -} - -template inline -Mat_<_Tp>::Mat_(Mat&& m) - : Mat() -{ - flags = (flags & ~CV_MAT_TYPE_MASK) | DataType<_Tp>::type; - *this = m; -} - -template inline -Mat_<_Tp>& Mat_<_Tp>::operator = (Mat&& m) -{ - if( DataType<_Tp>::type == m.type() ) - { - Mat::operator = ((Mat&&)m); - return *this; - } - if( DataType<_Tp>::depth == m.depth() ) - { - Mat::operator = ((Mat&&)m.reshape(DataType<_Tp>::channels, m.dims, 0)); - return *this; - } - CV_DbgAssert(DataType<_Tp>::channels == m.channels()); - m.convertTo(*this, type()); - return *this; -} - -template inline -Mat_<_Tp>::Mat_(MatExpr&& e) - : Mat() -{ - flags = (flags & ~CV_MAT_TYPE_MASK) | DataType<_Tp>::type; - *this = Mat(e); -} - -#endif - -///////////////////////////// SparseMat ///////////////////////////// - -inline -SparseMat::SparseMat() - : flags(MAGIC_VAL), hdr(0) -{} - -inline -SparseMat::SparseMat(int _dims, const int* _sizes, int _type) - : flags(MAGIC_VAL), hdr(0) -{ - create(_dims, _sizes, _type); -} - -inline -SparseMat::SparseMat(const SparseMat& m) - : flags(m.flags), hdr(m.hdr) -{ - addref(); -} - -inline -SparseMat::~SparseMat() -{ - release(); -} - -inline -SparseMat& SparseMat::operator = (const SparseMat& m) -{ - if( this != &m ) - { - if( m.hdr ) - CV_XADD(&m.hdr->refcount, 1); - release(); - flags = m.flags; - hdr = m.hdr; - } - return *this; -} - -inline -SparseMat& SparseMat::operator = (const Mat& m) -{ - return (*this = SparseMat(m)); -} - -inline -SparseMat SparseMat::clone() const -{ - SparseMat temp; - this->copyTo(temp); - return temp; -} - -inline -void SparseMat::assignTo( SparseMat& m, int _type ) const -{ - if( _type < 0 ) - m = *this; - else - convertTo(m, _type); -} - -inline -void SparseMat::addref() -{ - if( hdr ) - CV_XADD(&hdr->refcount, 1); -} - -inline -void SparseMat::release() -{ - if( hdr && CV_XADD(&hdr->refcount, -1) == 1 ) - delete hdr; - hdr = 0; -} - -inline -size_t SparseMat::elemSize() const -{ - return CV_ELEM_SIZE(flags); -} - -inline -size_t SparseMat::elemSize1() const -{ - return CV_ELEM_SIZE1(flags); -} - -inline -int SparseMat::type() const -{ - return CV_MAT_TYPE(flags); -} - -inline -int SparseMat::depth() const -{ - return CV_MAT_DEPTH(flags); -} - -inline -int SparseMat::channels() const -{ - return CV_MAT_CN(flags); -} - -inline -const int* SparseMat::size() const -{ - return hdr ? hdr->size : 0; -} - -inline -int SparseMat::size(int i) const -{ - if( hdr ) - { - CV_DbgAssert((unsigned)i < (unsigned)hdr->dims); - return hdr->size[i]; - } - return 0; -} - -inline -int SparseMat::dims() const -{ - return hdr ? hdr->dims : 0; -} - -inline -size_t SparseMat::nzcount() const -{ - return hdr ? hdr->nodeCount : 0; -} - -inline -size_t SparseMat::hash(int i0) const -{ - return (size_t)i0; -} - -inline -size_t SparseMat::hash(int i0, int i1) const -{ - return (size_t)(unsigned)i0 * HASH_SCALE + (unsigned)i1; -} - -inline -size_t SparseMat::hash(int i0, int i1, int i2) const -{ - return ((size_t)(unsigned)i0 * HASH_SCALE + (unsigned)i1) * HASH_SCALE + (unsigned)i2; -} - -inline -size_t SparseMat::hash(const int* idx) const -{ - size_t h = (unsigned)idx[0]; - if( !hdr ) - return 0; - int d = hdr->dims; - for(int i = 1; i < d; i++ ) - h = h * HASH_SCALE + (unsigned)idx[i]; - return h; -} - -template inline -_Tp& SparseMat::ref(int i0, size_t* hashval) -{ - return *(_Tp*)((SparseMat*)this)->ptr(i0, true, hashval); -} - -template inline -_Tp& SparseMat::ref(int i0, int i1, size_t* hashval) -{ - return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, true, hashval); -} - -template inline -_Tp& SparseMat::ref(int i0, int i1, int i2, size_t* hashval) -{ - return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, i2, true, hashval); -} - -template inline -_Tp& SparseMat::ref(const int* idx, size_t* hashval) -{ - return *(_Tp*)((SparseMat*)this)->ptr(idx, true, hashval); -} - -template inline -_Tp SparseMat::value(int i0, size_t* hashval) const -{ - const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval); - return p ? *p : _Tp(); -} - -template inline -_Tp SparseMat::value(int i0, int i1, size_t* hashval) const -{ - const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval); - return p ? *p : _Tp(); -} - -template inline -_Tp SparseMat::value(int i0, int i1, int i2, size_t* hashval) const -{ - const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, i2, false, hashval); - return p ? *p : _Tp(); -} - -template inline -_Tp SparseMat::value(const int* idx, size_t* hashval) const -{ - const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(idx, false, hashval); - return p ? *p : _Tp(); -} - -template inline -const _Tp* SparseMat::find(int i0, size_t* hashval) const -{ - return (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval); -} - -template inline -const _Tp* SparseMat::find(int i0, int i1, size_t* hashval) const -{ - return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval); -} - -template inline -const _Tp* SparseMat::find(int i0, int i1, int i2, size_t* hashval) const -{ - return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, i2, false, hashval); -} - -template inline -const _Tp* SparseMat::find(const int* idx, size_t* hashval) const -{ - return (const _Tp*)((SparseMat*)this)->ptr(idx, false, hashval); -} - -template inline -_Tp& SparseMat::value(Node* n) -{ - return *(_Tp*)((uchar*)n + hdr->valueOffset); -} - -template inline -const _Tp& SparseMat::value(const Node* n) const -{ - return *(const _Tp*)((const uchar*)n + hdr->valueOffset); -} - -inline -SparseMat::Node* SparseMat::node(size_t nidx) -{ - return (Node*)(void*)&hdr->pool[nidx]; -} - -inline -const SparseMat::Node* SparseMat::node(size_t nidx) const -{ - return (const Node*)(const void*)&hdr->pool[nidx]; -} - -inline -SparseMatIterator SparseMat::begin() -{ - return SparseMatIterator(this); -} - -inline -SparseMatConstIterator SparseMat::begin() const -{ - return SparseMatConstIterator(this); -} - -inline -SparseMatIterator SparseMat::end() -{ - SparseMatIterator it(this); - it.seekEnd(); - return it; -} - -inline -SparseMatConstIterator SparseMat::end() const -{ - SparseMatConstIterator it(this); - it.seekEnd(); - return it; -} - -template inline -SparseMatIterator_<_Tp> SparseMat::begin() -{ - return SparseMatIterator_<_Tp>(this); -} - -template inline -SparseMatConstIterator_<_Tp> SparseMat::begin() const -{ - return SparseMatConstIterator_<_Tp>(this); -} - -template inline -SparseMatIterator_<_Tp> SparseMat::end() -{ - SparseMatIterator_<_Tp> it(this); - it.seekEnd(); - return it; -} - -template inline -SparseMatConstIterator_<_Tp> SparseMat::end() const -{ - SparseMatConstIterator_<_Tp> it(this); - it.seekEnd(); - return it; -} - - - -///////////////////////////// SparseMat_ //////////////////////////// - -template inline -SparseMat_<_Tp>::SparseMat_() -{ - flags = MAGIC_VAL | DataType<_Tp>::type; -} - -template inline -SparseMat_<_Tp>::SparseMat_(int _dims, const int* _sizes) - : SparseMat(_dims, _sizes, DataType<_Tp>::type) -{} - -template inline -SparseMat_<_Tp>::SparseMat_(const SparseMat& m) -{ - if( m.type() == DataType<_Tp>::type ) - *this = (const SparseMat_<_Tp>&)m; - else - m.convertTo(*this, DataType<_Tp>::type); -} - -template inline -SparseMat_<_Tp>::SparseMat_(const SparseMat_<_Tp>& m) -{ - this->flags = m.flags; - this->hdr = m.hdr; - if( this->hdr ) - CV_XADD(&this->hdr->refcount, 1); -} - -template inline -SparseMat_<_Tp>::SparseMat_(const Mat& m) -{ - SparseMat sm(m); - *this = sm; -} - -template inline -SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const SparseMat_<_Tp>& m) -{ - if( this != &m ) - { - if( m.hdr ) CV_XADD(&m.hdr->refcount, 1); - release(); - flags = m.flags; - hdr = m.hdr; - } - return *this; -} - -template inline -SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const SparseMat& m) -{ - if( m.type() == DataType<_Tp>::type ) - return (*this = (const SparseMat_<_Tp>&)m); - m.convertTo(*this, DataType<_Tp>::type); - return *this; -} - -template inline -SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const Mat& m) -{ - return (*this = SparseMat(m)); -} - -template inline -SparseMat_<_Tp> SparseMat_<_Tp>::clone() const -{ - SparseMat_<_Tp> m; - this->copyTo(m); - return m; -} - -template inline -void SparseMat_<_Tp>::create(int _dims, const int* _sizes) -{ - SparseMat::create(_dims, _sizes, DataType<_Tp>::type); -} - -template inline -int SparseMat_<_Tp>::type() const -{ - return DataType<_Tp>::type; -} - -template inline -int SparseMat_<_Tp>::depth() const -{ - return DataType<_Tp>::depth; -} - -template inline -int SparseMat_<_Tp>::channels() const -{ - return DataType<_Tp>::channels; -} - -template inline -_Tp& SparseMat_<_Tp>::ref(int i0, size_t* hashval) -{ - return SparseMat::ref<_Tp>(i0, hashval); -} - -template inline -_Tp SparseMat_<_Tp>::operator()(int i0, size_t* hashval) const -{ - return SparseMat::value<_Tp>(i0, hashval); -} - -template inline -_Tp& SparseMat_<_Tp>::ref(int i0, int i1, size_t* hashval) -{ - return SparseMat::ref<_Tp>(i0, i1, hashval); -} - -template inline -_Tp SparseMat_<_Tp>::operator()(int i0, int i1, size_t* hashval) const -{ - return SparseMat::value<_Tp>(i0, i1, hashval); -} - -template inline -_Tp& SparseMat_<_Tp>::ref(int i0, int i1, int i2, size_t* hashval) -{ - return SparseMat::ref<_Tp>(i0, i1, i2, hashval); -} - -template inline -_Tp SparseMat_<_Tp>::operator()(int i0, int i1, int i2, size_t* hashval) const -{ - return SparseMat::value<_Tp>(i0, i1, i2, hashval); -} - -template inline -_Tp& SparseMat_<_Tp>::ref(const int* idx, size_t* hashval) -{ - return SparseMat::ref<_Tp>(idx, hashval); -} - -template inline -_Tp SparseMat_<_Tp>::operator()(const int* idx, size_t* hashval) const -{ - return SparseMat::value<_Tp>(idx, hashval); -} - -template inline -SparseMatIterator_<_Tp> SparseMat_<_Tp>::begin() -{ - return SparseMatIterator_<_Tp>(this); -} - -template inline -SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::begin() const -{ - return SparseMatConstIterator_<_Tp>(this); -} - -template inline -SparseMatIterator_<_Tp> SparseMat_<_Tp>::end() -{ - SparseMatIterator_<_Tp> it(this); - it.seekEnd(); - return it; -} - -template inline -SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::end() const -{ - SparseMatConstIterator_<_Tp> it(this); - it.seekEnd(); - return it; -} - - - -////////////////////////// MatConstIterator ///////////////////////// - -inline -MatConstIterator::MatConstIterator() - : m(0), elemSize(0), ptr(0), sliceStart(0), sliceEnd(0) -{} - -inline -MatConstIterator::MatConstIterator(const Mat* _m) - : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) -{ - if( m && m->isContinuous() ) - { - sliceStart = m->ptr(); - sliceEnd = sliceStart + m->total()*elemSize; - } - seek((const int*)0); -} - -inline -MatConstIterator::MatConstIterator(const Mat* _m, int _row, int _col) - : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) -{ - CV_Assert(m && m->dims <= 2); - if( m->isContinuous() ) - { - sliceStart = m->ptr(); - sliceEnd = sliceStart + m->total()*elemSize; - } - int idx[] = {_row, _col}; - seek(idx); -} - -inline -MatConstIterator::MatConstIterator(const Mat* _m, Point _pt) - : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) -{ - CV_Assert(m && m->dims <= 2); - if( m->isContinuous() ) - { - sliceStart = m->ptr(); - sliceEnd = sliceStart + m->total()*elemSize; - } - int idx[] = {_pt.y, _pt.x}; - seek(idx); -} - -inline -MatConstIterator::MatConstIterator(const MatConstIterator& it) - : m(it.m), elemSize(it.elemSize), ptr(it.ptr), sliceStart(it.sliceStart), sliceEnd(it.sliceEnd) -{} - -inline -MatConstIterator& MatConstIterator::operator = (const MatConstIterator& it ) -{ - m = it.m; elemSize = it.elemSize; ptr = it.ptr; - sliceStart = it.sliceStart; sliceEnd = it.sliceEnd; - return *this; -} - -inline -const uchar* MatConstIterator::operator *() const -{ - return ptr; -} - -inline MatConstIterator& MatConstIterator::operator += (ptrdiff_t ofs) -{ - if( !m || ofs == 0 ) - return *this; - ptrdiff_t ofsb = ofs*elemSize; - ptr += ofsb; - if( ptr < sliceStart || sliceEnd <= ptr ) - { - ptr -= ofsb; - seek(ofs, true); - } - return *this; -} - -inline -MatConstIterator& MatConstIterator::operator -= (ptrdiff_t ofs) -{ - return (*this += -ofs); -} - -inline -MatConstIterator& MatConstIterator::operator --() -{ - if( m && (ptr -= elemSize) < sliceStart ) - { - ptr += elemSize; - seek(-1, true); - } - return *this; -} - -inline -MatConstIterator MatConstIterator::operator --(int) -{ - MatConstIterator b = *this; - *this += -1; - return b; -} - -inline -MatConstIterator& MatConstIterator::operator ++() -{ - if( m && (ptr += elemSize) >= sliceEnd ) - { - ptr -= elemSize; - seek(1, true); - } - return *this; -} - -inline MatConstIterator MatConstIterator::operator ++(int) -{ - MatConstIterator b = *this; - *this += 1; - return b; -} - - -static inline -bool operator == (const MatConstIterator& a, const MatConstIterator& b) -{ - return a.m == b.m && a.ptr == b.ptr; -} - -static inline -bool operator != (const MatConstIterator& a, const MatConstIterator& b) -{ - return !(a == b); -} - -static inline -bool operator < (const MatConstIterator& a, const MatConstIterator& b) -{ - return a.ptr < b.ptr; -} - -static inline -bool operator > (const MatConstIterator& a, const MatConstIterator& b) -{ - return a.ptr > b.ptr; -} - -static inline -bool operator <= (const MatConstIterator& a, const MatConstIterator& b) -{ - return a.ptr <= b.ptr; -} - -static inline -bool operator >= (const MatConstIterator& a, const MatConstIterator& b) -{ - return a.ptr >= b.ptr; -} - -static inline -ptrdiff_t operator - (const MatConstIterator& b, const MatConstIterator& a) -{ - if( a.m != b.m ) - return ((size_t)(-1) >> 1); - if( a.sliceEnd == b.sliceEnd ) - return (b.ptr - a.ptr)/b.elemSize; - - return b.lpos() - a.lpos(); -} - -static inline -MatConstIterator operator + (const MatConstIterator& a, ptrdiff_t ofs) -{ - MatConstIterator b = a; - return b += ofs; -} - -static inline -MatConstIterator operator + (ptrdiff_t ofs, const MatConstIterator& a) -{ - MatConstIterator b = a; - return b += ofs; -} - -static inline -MatConstIterator operator - (const MatConstIterator& a, ptrdiff_t ofs) -{ - MatConstIterator b = a; - return b += -ofs; -} - - -inline -const uchar* MatConstIterator::operator [](ptrdiff_t i) const -{ - return *(*this + i); -} - - - -///////////////////////// MatConstIterator_ ///////////////////////// - -template inline -MatConstIterator_<_Tp>::MatConstIterator_() -{} - -template inline -MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m) - : MatConstIterator(_m) -{} - -template inline -MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col) - : MatConstIterator(_m, _row, _col) -{} - -template inline -MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m, Point _pt) - : MatConstIterator(_m, _pt) -{} - -template inline -MatConstIterator_<_Tp>::MatConstIterator_(const MatConstIterator_& it) - : MatConstIterator(it) -{} - -template inline -MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator = (const MatConstIterator_& it ) -{ - MatConstIterator::operator = (it); - return *this; -} - -template inline -_Tp MatConstIterator_<_Tp>::operator *() const -{ - return *(_Tp*)(this->ptr); -} - -template inline -MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator += (ptrdiff_t ofs) -{ - MatConstIterator::operator += (ofs); - return *this; -} - -template inline -MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator -= (ptrdiff_t ofs) -{ - return (*this += -ofs); -} - -template inline -MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator --() -{ - MatConstIterator::operator --(); - return *this; -} - -template inline -MatConstIterator_<_Tp> MatConstIterator_<_Tp>::operator --(int) -{ - MatConstIterator_ b = *this; - MatConstIterator::operator --(); - return b; -} - -template inline -MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator ++() -{ - MatConstIterator::operator ++(); - return *this; -} - -template inline -MatConstIterator_<_Tp> MatConstIterator_<_Tp>::operator ++(int) -{ - MatConstIterator_ b = *this; - MatConstIterator::operator ++(); - return b; -} - - -template inline -Point MatConstIterator_<_Tp>::pos() const -{ - if( !m ) - return Point(); - CV_DbgAssert( m->dims <= 2 ); - if( m->isContinuous() ) - { - ptrdiff_t ofs = (const _Tp*)ptr - (const _Tp*)m->data; - int y = (int)(ofs / m->cols); - int x = (int)(ofs - (ptrdiff_t)y * m->cols); - return Point(x, y); - } - else - { - ptrdiff_t ofs = (uchar*)ptr - m->data; - int y = (int)(ofs / m->step); - int x = (int)((ofs - y * m->step)/sizeof(_Tp)); - return Point(x, y); - } -} - - -template static inline -bool operator == (const MatConstIterator_<_Tp>& a, const MatConstIterator_<_Tp>& b) -{ - return a.m == b.m && a.ptr == b.ptr; -} - -template static inline -bool operator != (const MatConstIterator_<_Tp>& a, const MatConstIterator_<_Tp>& b) -{ - return a.m != b.m || a.ptr != b.ptr; -} - -template static inline -MatConstIterator_<_Tp> operator + (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) -{ - MatConstIterator t = (const MatConstIterator&)a + ofs; - return (MatConstIterator_<_Tp>&)t; -} - -template static inline -MatConstIterator_<_Tp> operator + (ptrdiff_t ofs, const MatConstIterator_<_Tp>& a) -{ - MatConstIterator t = (const MatConstIterator&)a + ofs; - return (MatConstIterator_<_Tp>&)t; -} - -template static inline -MatConstIterator_<_Tp> operator - (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) -{ - MatConstIterator t = (const MatConstIterator&)a - ofs; - return (MatConstIterator_<_Tp>&)t; -} - -template inline -_Tp MatConstIterator_<_Tp>::operator [](ptrdiff_t i) const -{ - return *(_Tp*)MatConstIterator::operator [](i); -} - - - -//////////////////////////// MatIterator_ /////////////////////////// - -template inline -MatIterator_<_Tp>::MatIterator_() - : MatConstIterator_<_Tp>() -{} - -template inline -MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m) - : MatConstIterator_<_Tp>(_m) -{} - -template inline -MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, int _row, int _col) - : MatConstIterator_<_Tp>(_m, _row, _col) -{} - -template inline -MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, Point _pt) - : MatConstIterator_<_Tp>(_m, _pt) -{} - -template inline -MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, const int* _idx) - : MatConstIterator_<_Tp>(_m, _idx) -{} - -template inline -MatIterator_<_Tp>::MatIterator_(const MatIterator_& it) - : MatConstIterator_<_Tp>(it) -{} - -template inline -MatIterator_<_Tp>& MatIterator_<_Tp>::operator = (const MatIterator_<_Tp>& it ) -{ - MatConstIterator::operator = (it); - return *this; -} - -template inline -_Tp& MatIterator_<_Tp>::operator *() const -{ - return *(_Tp*)(this->ptr); -} - -template inline -MatIterator_<_Tp>& MatIterator_<_Tp>::operator += (ptrdiff_t ofs) -{ - MatConstIterator::operator += (ofs); - return *this; -} - -template inline -MatIterator_<_Tp>& MatIterator_<_Tp>::operator -= (ptrdiff_t ofs) -{ - MatConstIterator::operator += (-ofs); - return *this; -} - -template inline -MatIterator_<_Tp>& MatIterator_<_Tp>::operator --() -{ - MatConstIterator::operator --(); - return *this; -} - -template inline -MatIterator_<_Tp> MatIterator_<_Tp>::operator --(int) -{ - MatIterator_ b = *this; - MatConstIterator::operator --(); - return b; -} - -template inline -MatIterator_<_Tp>& MatIterator_<_Tp>::operator ++() -{ - MatConstIterator::operator ++(); - return *this; -} - -template inline -MatIterator_<_Tp> MatIterator_<_Tp>::operator ++(int) -{ - MatIterator_ b = *this; - MatConstIterator::operator ++(); - return b; -} - -template inline -_Tp& MatIterator_<_Tp>::operator [](ptrdiff_t i) const -{ - return *(*this + i); -} - - -template static inline -bool operator == (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b) -{ - return a.m == b.m && a.ptr == b.ptr; -} - -template static inline -bool operator != (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b) -{ - return a.m != b.m || a.ptr != b.ptr; -} - -template static inline -MatIterator_<_Tp> operator + (const MatIterator_<_Tp>& a, ptrdiff_t ofs) -{ - MatConstIterator t = (const MatConstIterator&)a + ofs; - return (MatIterator_<_Tp>&)t; -} - -template static inline -MatIterator_<_Tp> operator + (ptrdiff_t ofs, const MatIterator_<_Tp>& a) -{ - MatConstIterator t = (const MatConstIterator&)a + ofs; - return (MatIterator_<_Tp>&)t; -} - -template static inline -MatIterator_<_Tp> operator - (const MatIterator_<_Tp>& a, ptrdiff_t ofs) -{ - MatConstIterator t = (const MatConstIterator&)a - ofs; - return (MatIterator_<_Tp>&)t; -} - - - -/////////////////////// SparseMatConstIterator ////////////////////// - -inline -SparseMatConstIterator::SparseMatConstIterator() - : m(0), hashidx(0), ptr(0) -{} - -inline -SparseMatConstIterator::SparseMatConstIterator(const SparseMatConstIterator& it) - : m(it.m), hashidx(it.hashidx), ptr(it.ptr) -{} - -inline SparseMatConstIterator& SparseMatConstIterator::operator = (const SparseMatConstIterator& it) -{ - if( this != &it ) - { - m = it.m; - hashidx = it.hashidx; - ptr = it.ptr; - } - return *this; -} - -template inline -const _Tp& SparseMatConstIterator::value() const -{ - return *(const _Tp*)ptr; -} - -inline -const SparseMat::Node* SparseMatConstIterator::node() const -{ - return (ptr && m && m->hdr) ? (const SparseMat::Node*)(const void*)(ptr - m->hdr->valueOffset) : 0; -} - -inline -SparseMatConstIterator SparseMatConstIterator::operator ++(int) -{ - SparseMatConstIterator it = *this; - ++*this; - return it; -} - -inline -void SparseMatConstIterator::seekEnd() -{ - if( m && m->hdr ) - { - hashidx = m->hdr->hashtab.size(); - ptr = 0; - } -} - - -static inline -bool operator == (const SparseMatConstIterator& it1, const SparseMatConstIterator& it2) -{ - return it1.m == it2.m && it1.ptr == it2.ptr; -} - -static inline -bool operator != (const SparseMatConstIterator& it1, const SparseMatConstIterator& it2) -{ - return !(it1 == it2); -} - - - -///////////////////////// SparseMatIterator ///////////////////////// - -inline -SparseMatIterator::SparseMatIterator() -{} - -inline -SparseMatIterator::SparseMatIterator(SparseMat* _m) - : SparseMatConstIterator(_m) -{} - -inline -SparseMatIterator::SparseMatIterator(const SparseMatIterator& it) - : SparseMatConstIterator(it) -{} - -inline -SparseMatIterator& SparseMatIterator::operator = (const SparseMatIterator& it) -{ - (SparseMatConstIterator&)*this = it; - return *this; -} - -template inline -_Tp& SparseMatIterator::value() const -{ - return *(_Tp*)ptr; -} - -inline -SparseMat::Node* SparseMatIterator::node() const -{ - return (SparseMat::Node*)SparseMatConstIterator::node(); -} - -inline -SparseMatIterator& SparseMatIterator::operator ++() -{ - SparseMatConstIterator::operator ++(); - return *this; -} - -inline -SparseMatIterator SparseMatIterator::operator ++(int) -{ - SparseMatIterator it = *this; - ++*this; - return it; -} - - - -////////////////////// SparseMatConstIterator_ ////////////////////// - -template inline -SparseMatConstIterator_<_Tp>::SparseMatConstIterator_() -{} - -template inline -SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMat_<_Tp>* _m) - : SparseMatConstIterator(_m) -{} - -template inline -SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMat* _m) - : SparseMatConstIterator(_m) -{ - CV_Assert( _m->type() == DataType<_Tp>::type ); -} - -template inline -SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMatConstIterator_<_Tp>& it) - : SparseMatConstIterator(it) -{} - -template inline -SparseMatConstIterator_<_Tp>& SparseMatConstIterator_<_Tp>::operator = (const SparseMatConstIterator_<_Tp>& it) -{ - return reinterpret_cast&> - (*reinterpret_cast(this) = - reinterpret_cast(it)); -} - -template inline -const _Tp& SparseMatConstIterator_<_Tp>::operator *() const -{ - return *(const _Tp*)this->ptr; -} - -template inline -SparseMatConstIterator_<_Tp>& SparseMatConstIterator_<_Tp>::operator ++() -{ - SparseMatConstIterator::operator ++(); - return *this; -} - -template inline -SparseMatConstIterator_<_Tp> SparseMatConstIterator_<_Tp>::operator ++(int) -{ - SparseMatConstIterator_<_Tp> it = *this; - SparseMatConstIterator::operator ++(); - return it; -} - - - -///////////////////////// SparseMatIterator_ //////////////////////// - -template inline -SparseMatIterator_<_Tp>::SparseMatIterator_() -{} - -template inline -SparseMatIterator_<_Tp>::SparseMatIterator_(SparseMat_<_Tp>* _m) - : SparseMatConstIterator_<_Tp>(_m) -{} - -template inline -SparseMatIterator_<_Tp>::SparseMatIterator_(SparseMat* _m) - : SparseMatConstIterator_<_Tp>(_m) -{} - -template inline -SparseMatIterator_<_Tp>::SparseMatIterator_(const SparseMatIterator_<_Tp>& it) - : SparseMatConstIterator_<_Tp>(it) -{} - -template inline -SparseMatIterator_<_Tp>& SparseMatIterator_<_Tp>::operator = (const SparseMatIterator_<_Tp>& it) -{ - return reinterpret_cast&> - (*reinterpret_cast(this) = - reinterpret_cast(it)); -} - -template inline -_Tp& SparseMatIterator_<_Tp>::operator *() const -{ - return *(_Tp*)this->ptr; -} - -template inline -SparseMatIterator_<_Tp>& SparseMatIterator_<_Tp>::operator ++() -{ - SparseMatConstIterator::operator ++(); - return *this; -} - -template inline -SparseMatIterator_<_Tp> SparseMatIterator_<_Tp>::operator ++(int) -{ - SparseMatIterator_<_Tp> it = *this; - SparseMatConstIterator::operator ++(); - return it; -} - - - -//////////////////////// MatCommaInitializer_ /////////////////////// - -template inline -MatCommaInitializer_<_Tp>::MatCommaInitializer_(Mat_<_Tp>* _m) - : it(_m) -{} - -template template inline -MatCommaInitializer_<_Tp>& MatCommaInitializer_<_Tp>::operator , (T2 v) -{ - CV_DbgAssert( this->it < ((const Mat_<_Tp>*)this->it.m)->end() ); - *this->it = _Tp(v); - ++this->it; - return *this; -} - -template inline -MatCommaInitializer_<_Tp>::operator Mat_<_Tp>() const -{ - CV_DbgAssert( this->it == ((const Mat_<_Tp>*)this->it.m)->end() ); - return Mat_<_Tp>(*this->it.m); -} - - -template static inline -MatCommaInitializer_<_Tp> operator << (const Mat_<_Tp>& m, T2 val) -{ - MatCommaInitializer_<_Tp> commaInitializer((Mat_<_Tp>*)&m); - return (commaInitializer, val); -} - - - -///////////////////////// Matrix Expressions //////////////////////// - -inline -Mat& Mat::operator = (const MatExpr& e) -{ - e.op->assign(e, *this); - return *this; -} - -template inline -Mat_<_Tp>::Mat_(const MatExpr& e) -{ - e.op->assign(e, *this, DataType<_Tp>::type); -} - -template inline -Mat_<_Tp>& Mat_<_Tp>::operator = (const MatExpr& e) -{ - e.op->assign(e, *this, DataType<_Tp>::type); - return *this; -} - -template inline -MatExpr Mat_<_Tp>::zeros(int rows, int cols) -{ - return Mat::zeros(rows, cols, DataType<_Tp>::type); -} - -template inline -MatExpr Mat_<_Tp>::zeros(Size sz) -{ - return Mat::zeros(sz, DataType<_Tp>::type); -} - -template inline -MatExpr Mat_<_Tp>::ones(int rows, int cols) -{ - return Mat::ones(rows, cols, DataType<_Tp>::type); -} - -template inline -MatExpr Mat_<_Tp>::ones(Size sz) -{ - return Mat::ones(sz, DataType<_Tp>::type); -} - -template inline -MatExpr Mat_<_Tp>::eye(int rows, int cols) -{ - return Mat::eye(rows, cols, DataType<_Tp>::type); -} - -template inline -MatExpr Mat_<_Tp>::eye(Size sz) -{ - return Mat::eye(sz, DataType<_Tp>::type); -} - -inline -MatExpr::MatExpr() - : op(0), flags(0), a(Mat()), b(Mat()), c(Mat()), alpha(0), beta(0), s() -{} - -inline -MatExpr::MatExpr(const MatOp* _op, int _flags, const Mat& _a, const Mat& _b, - const Mat& _c, double _alpha, double _beta, const Scalar& _s) - : op(_op), flags(_flags), a(_a), b(_b), c(_c), alpha(_alpha), beta(_beta), s(_s) -{} - -inline -MatExpr::operator Mat() const -{ - Mat m; - op->assign(*this, m); - return m; -} - -template inline -MatExpr::operator Mat_<_Tp>() const -{ - Mat_<_Tp> m; - op->assign(*this, m, DataType<_Tp>::type); - return m; -} - - -template static inline -MatExpr min(const Mat_<_Tp>& a, const Mat_<_Tp>& b) -{ - return cv::min((const Mat&)a, (const Mat&)b); -} - -template static inline -MatExpr min(const Mat_<_Tp>& a, double s) -{ - return cv::min((const Mat&)a, s); -} - -template static inline -MatExpr min(double s, const Mat_<_Tp>& a) -{ - return cv::min((const Mat&)a, s); -} - -template static inline -MatExpr max(const Mat_<_Tp>& a, const Mat_<_Tp>& b) -{ - return cv::max((const Mat&)a, (const Mat&)b); -} - -template static inline -MatExpr max(const Mat_<_Tp>& a, double s) -{ - return cv::max((const Mat&)a, s); -} - -template static inline -MatExpr max(double s, const Mat_<_Tp>& a) -{ - return cv::max((const Mat&)a, s); -} - -template static inline -MatExpr abs(const Mat_<_Tp>& m) -{ - return cv::abs((const Mat&)m); -} - - -static inline -Mat& operator += (Mat& a, const MatExpr& b) -{ - b.op->augAssignAdd(b, a); - return a; -} - -static inline -const Mat& operator += (const Mat& a, const MatExpr& b) -{ - b.op->augAssignAdd(b, (Mat&)a); - return a; -} - -template static inline -Mat_<_Tp>& operator += (Mat_<_Tp>& a, const MatExpr& b) -{ - b.op->augAssignAdd(b, a); - return a; -} - -template static inline -const Mat_<_Tp>& operator += (const Mat_<_Tp>& a, const MatExpr& b) -{ - b.op->augAssignAdd(b, (Mat&)a); - return a; -} - -static inline -Mat& operator -= (Mat& a, const MatExpr& b) -{ - b.op->augAssignSubtract(b, a); - return a; -} - -static inline -const Mat& operator -= (const Mat& a, const MatExpr& b) -{ - b.op->augAssignSubtract(b, (Mat&)a); - return a; -} - -template static inline -Mat_<_Tp>& operator -= (Mat_<_Tp>& a, const MatExpr& b) -{ - b.op->augAssignSubtract(b, a); - return a; -} - -template static inline -const Mat_<_Tp>& operator -= (const Mat_<_Tp>& a, const MatExpr& b) -{ - b.op->augAssignSubtract(b, (Mat&)a); - return a; -} - -static inline -Mat& operator *= (Mat& a, const MatExpr& b) -{ - b.op->augAssignMultiply(b, a); - return a; -} - -static inline -const Mat& operator *= (const Mat& a, const MatExpr& b) -{ - b.op->augAssignMultiply(b, (Mat&)a); - return a; -} - -template static inline -Mat_<_Tp>& operator *= (Mat_<_Tp>& a, const MatExpr& b) -{ - b.op->augAssignMultiply(b, a); - return a; -} - -template static inline -const Mat_<_Tp>& operator *= (const Mat_<_Tp>& a, const MatExpr& b) -{ - b.op->augAssignMultiply(b, (Mat&)a); - return a; -} - -static inline -Mat& operator /= (Mat& a, const MatExpr& b) -{ - b.op->augAssignDivide(b, a); - return a; -} - -static inline -const Mat& operator /= (const Mat& a, const MatExpr& b) -{ - b.op->augAssignDivide(b, (Mat&)a); - return a; -} - -template static inline -Mat_<_Tp>& operator /= (Mat_<_Tp>& a, const MatExpr& b) -{ - b.op->augAssignDivide(b, a); - return a; -} - -template static inline -const Mat_<_Tp>& operator /= (const Mat_<_Tp>& a, const MatExpr& b) -{ - b.op->augAssignDivide(b, (Mat&)a); - return a; -} - - -//////////////////////////////// UMat //////////////////////////////// - -inline -UMat::UMat(UMatUsageFlags _usageFlags) -: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) -{} - -inline -UMat::UMat(int _rows, int _cols, int _type, UMatUsageFlags _usageFlags) -: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) -{ - create(_rows, _cols, _type); -} - -inline -UMat::UMat(int _rows, int _cols, int _type, const Scalar& _s, UMatUsageFlags _usageFlags) -: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) -{ - create(_rows, _cols, _type); - *this = _s; -} - -inline -UMat::UMat(Size _sz, int _type, UMatUsageFlags _usageFlags) -: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) -{ - create( _sz.height, _sz.width, _type ); -} - -inline -UMat::UMat(Size _sz, int _type, const Scalar& _s, UMatUsageFlags _usageFlags) -: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) -{ - create(_sz.height, _sz.width, _type); - *this = _s; -} - -inline -UMat::UMat(int _dims, const int* _sz, int _type, UMatUsageFlags _usageFlags) -: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) -{ - create(_dims, _sz, _type); -} - -inline -UMat::UMat(int _dims, const int* _sz, int _type, const Scalar& _s, UMatUsageFlags _usageFlags) -: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) -{ - create(_dims, _sz, _type); - *this = _s; -} - -inline -UMat::UMat(const UMat& m) -: flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), allocator(m.allocator), - usageFlags(m.usageFlags), u(m.u), offset(m.offset), size(&rows) -{ - addref(); - if( m.dims <= 2 ) - { - step[0] = m.step[0]; step[1] = m.step[1]; - } - else - { - dims = 0; - copySize(m); - } -} - - -template inline -UMat::UMat(const std::vector<_Tp>& vec, bool copyData) -: flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()), -cols(1), allocator(0), usageFlags(USAGE_DEFAULT), u(0), offset(0), size(&rows) -{ - if(vec.empty()) - return; - if( !copyData ) - { - // !!!TODO!!! - CV_Error(Error::StsNotImplemented, ""); - } - else - Mat((int)vec.size(), 1, DataType<_Tp>::type, (uchar*)&vec[0]).copyTo(*this); -} - - -inline -UMat& UMat::operator = (const UMat& m) -{ - if( this != &m ) - { - const_cast(m).addref(); - release(); - flags = m.flags; - if( dims <= 2 && m.dims <= 2 ) - { - dims = m.dims; - rows = m.rows; - cols = m.cols; - step[0] = m.step[0]; - step[1] = m.step[1]; - } - else - copySize(m); - allocator = m.allocator; - if (usageFlags == USAGE_DEFAULT) - usageFlags = m.usageFlags; - u = m.u; - offset = m.offset; - } - return *this; -} - -inline -UMat UMat::row(int y) const -{ - return UMat(*this, Range(y, y + 1), Range::all()); -} - -inline -UMat UMat::col(int x) const -{ - return UMat(*this, Range::all(), Range(x, x + 1)); -} - -inline -UMat UMat::rowRange(int startrow, int endrow) const -{ - return UMat(*this, Range(startrow, endrow), Range::all()); -} - -inline -UMat UMat::rowRange(const Range& r) const -{ - return UMat(*this, r, Range::all()); -} - -inline -UMat UMat::colRange(int startcol, int endcol) const -{ - return UMat(*this, Range::all(), Range(startcol, endcol)); -} - -inline -UMat UMat::colRange(const Range& r) const -{ - return UMat(*this, Range::all(), r); -} - -inline -UMat UMat::clone() const -{ - UMat m; - copyTo(m); - return m; -} - -inline -void UMat::assignTo( UMat& m, int _type ) const -{ - if( _type < 0 ) - m = *this; - else - convertTo(m, _type); -} - -inline -void UMat::create(int _rows, int _cols, int _type, UMatUsageFlags _usageFlags) -{ - _type &= TYPE_MASK; - if( dims <= 2 && rows == _rows && cols == _cols && type() == _type && u ) - return; - int sz[] = {_rows, _cols}; - create(2, sz, _type, _usageFlags); -} - -inline -void UMat::create(Size _sz, int _type, UMatUsageFlags _usageFlags) -{ - create(_sz.height, _sz.width, _type, _usageFlags); -} - -inline -void UMat::addref() -{ - if( u ) - CV_XADD(&(u->urefcount), 1); -} - -inline void UMat::release() -{ - if( u && CV_XADD(&(u->urefcount), -1) == 1 ) - deallocate(); - for(int i = 0; i < dims; i++) - size.p[i] = 0; - u = 0; -} - -inline -UMat UMat::operator()( Range _rowRange, Range _colRange ) const -{ - return UMat(*this, _rowRange, _colRange); -} - -inline -UMat UMat::operator()( const Rect& roi ) const -{ - return UMat(*this, roi); -} - -inline -UMat UMat::operator()(const Range* ranges) const -{ - return UMat(*this, ranges); -} - -inline -bool UMat::isContinuous() const -{ - return (flags & CONTINUOUS_FLAG) != 0; -} - -inline -bool UMat::isSubmatrix() const -{ - return (flags & SUBMATRIX_FLAG) != 0; -} - -inline -size_t UMat::elemSize() const -{ - return dims > 0 ? step.p[dims - 1] : 0; -} - -inline -size_t UMat::elemSize1() const -{ - return CV_ELEM_SIZE1(flags); -} - -inline -int UMat::type() const -{ - return CV_MAT_TYPE(flags); -} - -inline -int UMat::depth() const -{ - return CV_MAT_DEPTH(flags); -} - -inline -int UMat::channels() const -{ - return CV_MAT_CN(flags); -} - -inline -size_t UMat::step1(int i) const -{ - return step.p[i] / elemSize1(); -} - -inline -bool UMat::empty() const -{ - return u == 0 || total() == 0; -} - -inline -size_t UMat::total() const -{ - if( dims <= 2 ) - return (size_t)rows * cols; - size_t p = 1; - for( int i = 0; i < dims; i++ ) - p *= size[i]; - return p; -} - -#ifdef CV_CXX_MOVE_SEMANTICS - -inline -UMat::UMat(UMat&& m) -: flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), allocator(m.allocator), - usageFlags(m.usageFlags), u(m.u), offset(m.offset), size(&rows) -{ - if (m.dims <= 2) // move new step/size info - { - step[0] = m.step[0]; - step[1] = m.step[1]; - } - else - { - CV_DbgAssert(m.step.p != m.step.buf); - step.p = m.step.p; - size.p = m.size.p; - m.step.p = m.step.buf; - m.size.p = &m.rows; - } - m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; - m.allocator = NULL; - m.u = NULL; - m.offset = 0; -} - -inline -UMat& UMat::operator = (UMat&& m) -{ - release(); - flags = m.flags; dims = m.dims; rows = m.rows; cols = m.cols; - allocator = m.allocator; usageFlags = m.usageFlags; - u = m.u; - offset = m.offset; - if (step.p != step.buf) // release self step/size - { - fastFree(step.p); - step.p = step.buf; - size.p = &rows; - } - if (m.dims <= 2) // move new step/size info - { - step[0] = m.step[0]; - step[1] = m.step[1]; - } - else - { - CV_DbgAssert(m.step.p != m.step.buf); - step.p = m.step.p; - size.p = m.size.p; - m.step.p = m.step.buf; - m.size.p = &m.rows; - } - m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; - m.allocator = NULL; - m.u = NULL; - m.offset = 0; - return *this; -} - -#endif - - -inline bool UMatData::hostCopyObsolete() const { return (flags & HOST_COPY_OBSOLETE) != 0; } -inline bool UMatData::deviceCopyObsolete() const { return (flags & DEVICE_COPY_OBSOLETE) != 0; } -inline bool UMatData::deviceMemMapped() const { return (flags & DEVICE_MEM_MAPPED) != 0; } -inline bool UMatData::copyOnMap() const { return (flags & COPY_ON_MAP) != 0; } -inline bool UMatData::tempUMat() const { return (flags & TEMP_UMAT) != 0; } -inline bool UMatData::tempCopiedUMat() const { return (flags & TEMP_COPIED_UMAT) == TEMP_COPIED_UMAT; } - -inline void UMatData::markDeviceMemMapped(bool flag) -{ - if(flag) - flags |= DEVICE_MEM_MAPPED; - else - flags &= ~DEVICE_MEM_MAPPED; -} - -inline void UMatData::markHostCopyObsolete(bool flag) -{ - if(flag) - flags |= HOST_COPY_OBSOLETE; - else - flags &= ~HOST_COPY_OBSOLETE; -} -inline void UMatData::markDeviceCopyObsolete(bool flag) -{ - if(flag) - flags |= DEVICE_COPY_OBSOLETE; - else - flags &= ~DEVICE_COPY_OBSOLETE; -} - -inline UMatDataAutoLock::UMatDataAutoLock(UMatData* _u) : u(_u) { u->lock(); } -inline UMatDataAutoLock::~UMatDataAutoLock() { u->unlock(); } - -//! @endcond - -} //cv - -#endif diff --git a/IPL/include/opencv/opencv2/core/matx.hpp b/IPL/include/opencv/opencv2/core/matx.hpp deleted file mode 100644 index e4d72f7..0000000 --- a/IPL/include/opencv/opencv2/core/matx.hpp +++ /dev/null @@ -1,1407 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_MATX_HPP__ -#define __OPENCV_CORE_MATX_HPP__ - -#ifndef __cplusplus -# error matx.hpp header must be compiled as C++ -#endif - -#include "opencv2/core/cvdef.h" -#include "opencv2/core/base.hpp" -#include "opencv2/core/traits.hpp" -#include "opencv2/core/saturate.hpp" - -namespace cv -{ - -//! @addtogroup core_basic -//! @{ - -////////////////////////////// Small Matrix /////////////////////////// - -//! @cond IGNORED -struct CV_EXPORTS Matx_AddOp {}; -struct CV_EXPORTS Matx_SubOp {}; -struct CV_EXPORTS Matx_ScaleOp {}; -struct CV_EXPORTS Matx_MulOp {}; -struct CV_EXPORTS Matx_DivOp {}; -struct CV_EXPORTS Matx_MatMulOp {}; -struct CV_EXPORTS Matx_TOp {}; -//! @endcond - -/** @brief Template class for small matrices whose type and size are known at compilation time - -If you need a more flexible type, use Mat . The elements of the matrix M are accessible using the -M(i,j) notation. Most of the common matrix operations (see also @ref MatrixExpressions ) are -available. To do an operation on Matx that is not implemented, you can easily convert the matrix to -Mat and backwards: -@code - Matx33f m(1, 2, 3, - 4, 5, 6, - 7, 8, 9); - cout << sum(Mat(m*m.t())) << endl; - @endcode - */ -template class Matx -{ -public: - enum { depth = DataType<_Tp>::depth, - rows = m, - cols = n, - channels = rows*cols, - type = CV_MAKETYPE(depth, channels), - shortdim = (m < n ? m : n) - }; - - typedef _Tp value_type; - typedef Matx<_Tp, m, n> mat_type; - typedef Matx<_Tp, shortdim, 1> diag_type; - - //! default constructor - Matx(); - - Matx(_Tp v0); //!< 1x1 matrix - Matx(_Tp v0, _Tp v1); //!< 1x2 or 2x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2); //!< 1x3 or 3x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 1x4, 2x2 or 4x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 1x5 or 5x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 1x6, 2x3, 3x2 or 6x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 1x7 or 7x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 1x8, 2x4, 4x2 or 8x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 1x9, 3x3 or 9x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 1x10, 2x5 or 5x2 or 10x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, - _Tp v4, _Tp v5, _Tp v6, _Tp v7, - _Tp v8, _Tp v9, _Tp v10, _Tp v11); //!< 1x12, 2x6, 3x4, 4x3, 6x2 or 12x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, - _Tp v4, _Tp v5, _Tp v6, _Tp v7, - _Tp v8, _Tp v9, _Tp v10, _Tp v11, - _Tp v12, _Tp v13); //!< 1x14, 2x7, 7x2 or 14x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, - _Tp v4, _Tp v5, _Tp v6, _Tp v7, - _Tp v8, _Tp v9, _Tp v10, _Tp v11, - _Tp v12, _Tp v13, _Tp v14, _Tp v15); //!< 1x16, 4x4 or 16x1 matrix - explicit Matx(const _Tp* vals); //!< initialize from a plain array - - static Matx all(_Tp alpha); - static Matx zeros(); - static Matx ones(); - static Matx eye(); - static Matx diag(const diag_type& d); - static Matx randu(_Tp a, _Tp b); - static Matx randn(_Tp a, _Tp b); - - //! dot product computed with the default precision - _Tp dot(const Matx<_Tp, m, n>& v) const; - - //! dot product computed in double-precision arithmetics - double ddot(const Matx<_Tp, m, n>& v) const; - - //! conversion to another data type - template operator Matx() const; - - //! change the matrix shape - template Matx<_Tp, m1, n1> reshape() const; - - //! extract part of the matrix - template Matx<_Tp, m1, n1> get_minor(int i, int j) const; - - //! extract the matrix row - Matx<_Tp, 1, n> row(int i) const; - - //! extract the matrix column - Matx<_Tp, m, 1> col(int i) const; - - //! extract the matrix diagonal - diag_type diag() const; - - //! transpose the matrix - Matx<_Tp, n, m> t() const; - - //! invert the matrix - Matx<_Tp, n, m> inv(int method=DECOMP_LU, bool *p_is_ok = NULL) const; - - //! solve linear system - template Matx<_Tp, n, l> solve(const Matx<_Tp, m, l>& rhs, int flags=DECOMP_LU) const; - Vec<_Tp, n> solve(const Vec<_Tp, m>& rhs, int method) const; - - //! multiply two matrices element-wise - Matx<_Tp, m, n> mul(const Matx<_Tp, m, n>& a) const; - - //! divide two matrices element-wise - Matx<_Tp, m, n> div(const Matx<_Tp, m, n>& a) const; - - //! element access - const _Tp& operator ()(int i, int j) const; - _Tp& operator ()(int i, int j); - - //! 1D element access - const _Tp& operator ()(int i) const; - _Tp& operator ()(int i); - - Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp); - Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp); - template Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp); - Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp); - Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp); - template Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp); - Matx(const Matx<_Tp, n, m>& a, Matx_TOp); - - _Tp val[m*n]; //< matrix elements -}; - -typedef Matx Matx12f; -typedef Matx Matx12d; -typedef Matx Matx13f; -typedef Matx Matx13d; -typedef Matx Matx14f; -typedef Matx Matx14d; -typedef Matx Matx16f; -typedef Matx Matx16d; - -typedef Matx Matx21f; -typedef Matx Matx21d; -typedef Matx Matx31f; -typedef Matx Matx31d; -typedef Matx Matx41f; -typedef Matx Matx41d; -typedef Matx Matx61f; -typedef Matx Matx61d; - -typedef Matx Matx22f; -typedef Matx Matx22d; -typedef Matx Matx23f; -typedef Matx Matx23d; -typedef Matx Matx32f; -typedef Matx Matx32d; - -typedef Matx Matx33f; -typedef Matx Matx33d; - -typedef Matx Matx34f; -typedef Matx Matx34d; -typedef Matx Matx43f; -typedef Matx Matx43d; - -typedef Matx Matx44f; -typedef Matx Matx44d; -typedef Matx Matx66f; -typedef Matx Matx66d; - -/*! - traits -*/ -template class DataType< Matx<_Tp, m, n> > -{ -public: - typedef Matx<_Tp, m, n> value_type; - typedef Matx::work_type, m, n> work_type; - typedef _Tp channel_type; - typedef value_type vec_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = m * n, - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) - }; -}; - -/** @brief Comma-separated Matrix Initializer -*/ -template class MatxCommaInitializer -{ -public: - MatxCommaInitializer(Matx<_Tp, m, n>* _mtx); - template MatxCommaInitializer<_Tp, m, n>& operator , (T2 val); - Matx<_Tp, m, n> operator *() const; - - Matx<_Tp, m, n>* dst; - int idx; -}; - -/* - Utility methods -*/ -template static double determinant(const Matx<_Tp, m, m>& a); -template static double trace(const Matx<_Tp, m, n>& a); -template static double norm(const Matx<_Tp, m, n>& M); -template static double norm(const Matx<_Tp, m, n>& M, int normType); - - - -/////////////////////// Vec (used as element of multi-channel images ///////////////////// - -/** @brief Template class for short numerical vectors, a partial case of Matx - -This template class represents short numerical vectors (of 1, 2, 3, 4 ... elements) on which you -can perform basic arithmetical operations, access individual elements using [] operator etc. The -vectors are allocated on stack, as opposite to std::valarray, std::vector, cv::Mat etc., which -elements are dynamically allocated in the heap. - -The template takes 2 parameters: -@tparam _Tp element type -@tparam cn the number of elements - -In addition to the universal notation like Vec, you can use shorter aliases -for the most popular specialized variants of Vec, e.g. Vec3f ~ Vec. - -It is possible to convert Vec\ to/from Point_, Vec\ to/from Point3_ , and Vec\ -to CvScalar or Scalar_. Use operator[] to access the elements of Vec. - -All the expected vector operations are also implemented: -- v1 = v2 + v3 -- v1 = v2 - v3 -- v1 = v2 \* scale -- v1 = scale \* v2 -- v1 = -v2 -- v1 += v2 and other augmenting operations -- v1 == v2, v1 != v2 -- norm(v1) (euclidean norm) -The Vec class is commonly used to describe pixel types of multi-channel arrays. See Mat for details. -*/ -template class Vec : public Matx<_Tp, cn, 1> -{ -public: - typedef _Tp value_type; - enum { depth = Matx<_Tp, cn, 1>::depth, - channels = cn, - type = CV_MAKETYPE(depth, channels) - }; - - //! default constructor - Vec(); - - Vec(_Tp v0); //!< 1-element vector constructor - Vec(_Tp v0, _Tp v1); //!< 2-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2); //!< 3-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 4-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 5-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 6-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 7-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 8-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 9-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 10-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13); //!< 14-element vector constructor - explicit Vec(const _Tp* values); - - Vec(const Vec<_Tp, cn>& v); - - static Vec all(_Tp alpha); - - //! per-element multiplication - Vec mul(const Vec<_Tp, cn>& v) const; - - //! conjugation (makes sense for complex numbers and quaternions) - Vec conj() const; - - /*! - cross product of the two 3D vectors. - - For other dimensionalities the exception is raised - */ - Vec cross(const Vec& v) const; - //! conversion to another data type - template operator Vec() const; - - /*! element access */ - const _Tp& operator [](int i) const; - _Tp& operator[](int i); - const _Tp& operator ()(int i) const; - _Tp& operator ()(int i); - - Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp); - Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp); - template Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp); -}; - -/** @name Shorter aliases for the most popular specializations of Vec - @{ -*/ -typedef Vec Vec2b; -typedef Vec Vec3b; -typedef Vec Vec4b; - -typedef Vec Vec2s; -typedef Vec Vec3s; -typedef Vec Vec4s; - -typedef Vec Vec2w; -typedef Vec Vec3w; -typedef Vec Vec4w; - -typedef Vec Vec2i; -typedef Vec Vec3i; -typedef Vec Vec4i; -typedef Vec Vec6i; -typedef Vec Vec8i; - -typedef Vec Vec2f; -typedef Vec Vec3f; -typedef Vec Vec4f; -typedef Vec Vec6f; - -typedef Vec Vec2d; -typedef Vec Vec3d; -typedef Vec Vec4d; -typedef Vec Vec6d; -/** @} */ - -/*! - traits -*/ -template class DataType< Vec<_Tp, cn> > -{ -public: - typedef Vec<_Tp, cn> value_type; - typedef Vec::work_type, cn> work_type; - typedef _Tp channel_type; - typedef value_type vec_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = cn, - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) - }; -}; - -/** @brief Comma-separated Vec Initializer -*/ -template class VecCommaInitializer : public MatxCommaInitializer<_Tp, m, 1> -{ -public: - VecCommaInitializer(Vec<_Tp, m>* _vec); - template VecCommaInitializer<_Tp, m>& operator , (T2 val); - Vec<_Tp, m> operator *() const; -}; - -template static Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v); - -//! @} core_basic - -//! @cond IGNORED - -///////////////////////////////////// helper classes ///////////////////////////////////// -namespace internal -{ - -template struct Matx_DetOp -{ - double operator ()(const Matx<_Tp, m, m>& a) const - { - Matx<_Tp, m, m> temp = a; - double p = LU(temp.val, m*sizeof(_Tp), m, 0, 0, 0); - if( p == 0 ) - return p; - for( int i = 0; i < m; i++ ) - p *= temp(i, i); - return 1./p; - } -}; - -template struct Matx_DetOp<_Tp, 1> -{ - double operator ()(const Matx<_Tp, 1, 1>& a) const - { - return a(0,0); - } -}; - -template struct Matx_DetOp<_Tp, 2> -{ - double operator ()(const Matx<_Tp, 2, 2>& a) const - { - return a(0,0)*a(1,1) - a(0,1)*a(1,0); - } -}; - -template struct Matx_DetOp<_Tp, 3> -{ - double operator ()(const Matx<_Tp, 3, 3>& a) const - { - return a(0,0)*(a(1,1)*a(2,2) - a(2,1)*a(1,2)) - - a(0,1)*(a(1,0)*a(2,2) - a(2,0)*a(1,2)) + - a(0,2)*(a(1,0)*a(2,1) - a(2,0)*a(1,1)); - } -}; - -template Vec<_Tp, 2> inline conjugate(const Vec<_Tp, 2>& v) -{ - return Vec<_Tp, 2>(v[0], -v[1]); -} - -template Vec<_Tp, 4> inline conjugate(const Vec<_Tp, 4>& v) -{ - return Vec<_Tp, 4>(v[0], -v[1], -v[2], -v[3]); -} - -} // internal - - - -////////////////////////////////// Matx Implementation /////////////////////////////////// - -template inline -Matx<_Tp, m, n>::Matx() -{ - for(int i = 0; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0) -{ - val[0] = v0; - for(int i = 1; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1) -{ - CV_StaticAssert(channels >= 2, "Matx should have at least 2 elements."); - val[0] = v0; val[1] = v1; - for(int i = 2; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2) -{ - CV_StaticAssert(channels >= 3, "Matx should have at least 3 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; - for(int i = 3; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3) -{ - CV_StaticAssert(channels >= 4, "Matx should have at least 4 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - for(int i = 4; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4) -{ - CV_StaticAssert(channels >= 5, "Matx should have at least 5 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; val[4] = v4; - for(int i = 5; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5) -{ - CV_StaticAssert(channels >= 6, "Matx should have at least 6 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - val[4] = v4; val[5] = v5; - for(int i = 6; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6) -{ - CV_StaticAssert(channels >= 7, "Matx should have at least 7 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - val[4] = v4; val[5] = v5; val[6] = v6; - for(int i = 7; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7) -{ - CV_StaticAssert(channels >= 8, "Matx should have at least 8 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; - for(int i = 8; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8) -{ - CV_StaticAssert(channels >= 9, "Matx should have at least 9 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; - val[8] = v8; - for(int i = 9; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9) -{ - CV_StaticAssert(channels >= 10, "Matx should have at least 10 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; - val[8] = v8; val[9] = v9; - for(int i = 10; i < channels; i++) val[i] = _Tp(0); -} - - -template inline -Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11) -{ - CV_StaticAssert(channels >= 12, "Matx should have at least 12 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; - val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; - for(int i = 12; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13) -{ - CV_StaticAssert(channels == 14, "Matx should have at least 14 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; - val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; - val[12] = v12; val[13] = v13; -} - - -template inline -Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13, _Tp v14, _Tp v15) -{ - CV_StaticAssert(channels >= 16, "Matx should have at least 16 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; - val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; - val[12] = v12; val[13] = v13; val[14] = v14; val[15] = v15; - for(int i = 16; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(const _Tp* values) -{ - for( int i = 0; i < channels; i++ ) val[i] = values[i]; -} - -template inline -Matx<_Tp, m, n> Matx<_Tp, m, n>::all(_Tp alpha) -{ - Matx<_Tp, m, n> M; - for( int i = 0; i < m*n; i++ ) M.val[i] = alpha; - return M; -} - -template inline -Matx<_Tp,m,n> Matx<_Tp,m,n>::zeros() -{ - return all(0); -} - -template inline -Matx<_Tp,m,n> Matx<_Tp,m,n>::ones() -{ - return all(1); -} - -template inline -Matx<_Tp,m,n> Matx<_Tp,m,n>::eye() -{ - Matx<_Tp,m,n> M; - for(int i = 0; i < shortdim; i++) - M(i,i) = 1; - return M; -} - -template inline -_Tp Matx<_Tp, m, n>::dot(const Matx<_Tp, m, n>& M) const -{ - _Tp s = 0; - for( int i = 0; i < channels; i++ ) s += val[i]*M.val[i]; - return s; -} - -template inline -double Matx<_Tp, m, n>::ddot(const Matx<_Tp, m, n>& M) const -{ - double s = 0; - for( int i = 0; i < channels; i++ ) s += (double)val[i]*M.val[i]; - return s; -} - -template inline -Matx<_Tp,m,n> Matx<_Tp,m,n>::diag(const typename Matx<_Tp,m,n>::diag_type& d) -{ - Matx<_Tp,m,n> M; - for(int i = 0; i < shortdim; i++) - M(i,i) = d(i, 0); - return M; -} - -template template -inline Matx<_Tp, m, n>::operator Matx() const -{ - Matx M; - for( int i = 0; i < m*n; i++ ) M.val[i] = saturate_cast(val[i]); - return M; -} - -template template inline -Matx<_Tp, m1, n1> Matx<_Tp, m, n>::reshape() const -{ - CV_StaticAssert(m1*n1 == m*n, "Input and destnarion matrices must have the same number of elements"); - return (const Matx<_Tp, m1, n1>&)*this; -} - -template -template inline -Matx<_Tp, m1, n1> Matx<_Tp, m, n>::get_minor(int i, int j) const -{ - CV_DbgAssert(0 <= i && i+m1 <= m && 0 <= j && j+n1 <= n); - Matx<_Tp, m1, n1> s; - for( int di = 0; di < m1; di++ ) - for( int dj = 0; dj < n1; dj++ ) - s(di, dj) = (*this)(i+di, j+dj); - return s; -} - -template inline -Matx<_Tp, 1, n> Matx<_Tp, m, n>::row(int i) const -{ - CV_DbgAssert((unsigned)i < (unsigned)m); - return Matx<_Tp, 1, n>(&val[i*n]); -} - -template inline -Matx<_Tp, m, 1> Matx<_Tp, m, n>::col(int j) const -{ - CV_DbgAssert((unsigned)j < (unsigned)n); - Matx<_Tp, m, 1> v; - for( int i = 0; i < m; i++ ) - v.val[i] = val[i*n + j]; - return v; -} - -template inline -typename Matx<_Tp, m, n>::diag_type Matx<_Tp, m, n>::diag() const -{ - diag_type d; - for( int i = 0; i < shortdim; i++ ) - d.val[i] = val[i*n + i]; - return d; -} - -template inline -const _Tp& Matx<_Tp, m, n>::operator()(int i, int j) const -{ - CV_DbgAssert( (unsigned)i < (unsigned)m && (unsigned)j < (unsigned)n ); - return this->val[i*n + j]; -} - -template inline -_Tp& Matx<_Tp, m, n>::operator ()(int i, int j) -{ - CV_DbgAssert( (unsigned)i < (unsigned)m && (unsigned)j < (unsigned)n ); - return val[i*n + j]; -} - -template inline -const _Tp& Matx<_Tp, m, n>::operator ()(int i) const -{ - CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row"); - CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) ); - return val[i]; -} - -template inline -_Tp& Matx<_Tp, m, n>::operator ()(int i) -{ - CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row"); - CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) ); - return val[i]; -} - -template inline -Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp) -{ - for( int i = 0; i < channels; i++ ) - val[i] = saturate_cast<_Tp>(a.val[i] + b.val[i]); -} - -template inline -Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp) -{ - for( int i = 0; i < channels; i++ ) - val[i] = saturate_cast<_Tp>(a.val[i] - b.val[i]); -} - -template template inline -Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp) -{ - for( int i = 0; i < channels; i++ ) - val[i] = saturate_cast<_Tp>(a.val[i] * alpha); -} - -template inline -Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp) -{ - for( int i = 0; i < channels; i++ ) - val[i] = saturate_cast<_Tp>(a.val[i] * b.val[i]); -} - -template inline -Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp) -{ - for( int i = 0; i < channels; i++ ) - val[i] = saturate_cast<_Tp>(a.val[i] / b.val[i]); -} - -template template inline -Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp) -{ - for( int i = 0; i < m; i++ ) - for( int j = 0; j < n; j++ ) - { - _Tp s = 0; - for( int k = 0; k < l; k++ ) - s += a(i, k) * b(k, j); - val[i*n + j] = s; - } -} - -template inline -Matx<_Tp,m,n>::Matx(const Matx<_Tp, n, m>& a, Matx_TOp) -{ - for( int i = 0; i < m; i++ ) - for( int j = 0; j < n; j++ ) - val[i*n + j] = a(j, i); -} - -template inline -Matx<_Tp, m, n> Matx<_Tp, m, n>::mul(const Matx<_Tp, m, n>& a) const -{ - return Matx<_Tp, m, n>(*this, a, Matx_MulOp()); -} - -template inline -Matx<_Tp, m, n> Matx<_Tp, m, n>::div(const Matx<_Tp, m, n>& a) const -{ - return Matx<_Tp, m, n>(*this, a, Matx_DivOp()); -} - -template inline -Matx<_Tp, n, m> Matx<_Tp, m, n>::t() const -{ - return Matx<_Tp, n, m>(*this, Matx_TOp()); -} - -template inline -Vec<_Tp, n> Matx<_Tp, m, n>::solve(const Vec<_Tp, m>& rhs, int method) const -{ - Matx<_Tp, n, 1> x = solve((const Matx<_Tp, m, 1>&)(rhs), method); - return (Vec<_Tp, n>&)(x); -} - -template static inline -double determinant(const Matx<_Tp, m, m>& a) -{ - return cv::internal::Matx_DetOp<_Tp, m>()(a); -} - -template static inline -double trace(const Matx<_Tp, m, n>& a) -{ - _Tp s = 0; - for( int i = 0; i < std::min(m, n); i++ ) - s += a(i,i); - return s; -} - -template static inline -double norm(const Matx<_Tp, m, n>& M) -{ - return std::sqrt(normL2Sqr<_Tp, double>(M.val, m*n)); -} - -template static inline -double norm(const Matx<_Tp, m, n>& M, int normType) -{ - switch(normType) { - case NORM_INF: - return (double)normInf<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); - case NORM_L1: - return (double)normL1<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); - case NORM_L2SQR: - return (double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); - default: - case NORM_L2: - return std::sqrt((double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n)); - } -} - - - -//////////////////////////////// matx comma initializer ////////////////////////////////// - -template static inline -MatxCommaInitializer<_Tp, m, n> operator << (const Matx<_Tp, m, n>& mtx, _T2 val) -{ - MatxCommaInitializer<_Tp, m, n> commaInitializer((Matx<_Tp, m, n>*)&mtx); - return (commaInitializer, val); -} - -template inline -MatxCommaInitializer<_Tp, m, n>::MatxCommaInitializer(Matx<_Tp, m, n>* _mtx) - : dst(_mtx), idx(0) -{} - -template template inline -MatxCommaInitializer<_Tp, m, n>& MatxCommaInitializer<_Tp, m, n>::operator , (_T2 value) -{ - CV_DbgAssert( idx < m*n ); - dst->val[idx++] = saturate_cast<_Tp>(value); - return *this; -} - -template inline -Matx<_Tp, m, n> MatxCommaInitializer<_Tp, m, n>::operator *() const -{ - CV_DbgAssert( idx == n*m ); - return *dst; -} - - - -/////////////////////////////////// Vec Implementation /////////////////////////////////// - -template inline -Vec<_Tp, cn>::Vec() {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0) - : Matx<_Tp, cn, 1>(v0) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1) - : Matx<_Tp, cn, 1>(v0, v1) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2) - : Matx<_Tp, cn, 1>(v0, v1, v2) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3) - : Matx<_Tp, cn, 1>(v0, v1, v2, v3) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4) - : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5) - : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6) - : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7) - : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8) - : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9) - : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13) - : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13) {} - -template inline -Vec<_Tp, cn>::Vec(const _Tp* values) - : Matx<_Tp, cn, 1>(values) {} - -template inline -Vec<_Tp, cn>::Vec(const Vec<_Tp, cn>& m) - : Matx<_Tp, cn, 1>(m.val) {} - -template inline -Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp op) - : Matx<_Tp, cn, 1>(a, b, op) {} - -template inline -Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp op) - : Matx<_Tp, cn, 1>(a, b, op) {} - -template template inline -Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp op) - : Matx<_Tp, cn, 1>(a, alpha, op) {} - -template inline -Vec<_Tp, cn> Vec<_Tp, cn>::all(_Tp alpha) -{ - Vec v; - for( int i = 0; i < cn; i++ ) v.val[i] = alpha; - return v; -} - -template inline -Vec<_Tp, cn> Vec<_Tp, cn>::mul(const Vec<_Tp, cn>& v) const -{ - Vec<_Tp, cn> w; - for( int i = 0; i < cn; i++ ) w.val[i] = saturate_cast<_Tp>(this->val[i]*v.val[i]); - return w; -} - -template<> inline -Vec Vec::conj() const -{ - return cv::internal::conjugate(*this); -} - -template<> inline -Vec Vec::conj() const -{ - return cv::internal::conjugate(*this); -} - -template<> inline -Vec Vec::conj() const -{ - return cv::internal::conjugate(*this); -} - -template<> inline -Vec Vec::conj() const -{ - return cv::internal::conjugate(*this); -} - -template inline -Vec<_Tp, cn> Vec<_Tp, cn>::cross(const Vec<_Tp, cn>&) const -{ - CV_StaticAssert(cn == 3, "for arbitrary-size vector there is no cross-product defined"); - return Vec<_Tp, cn>(); -} - -template<> inline -Vec Vec::cross(const Vec& v) const -{ - return Vec(this->val[1]*v.val[2] - this->val[2]*v.val[1], - this->val[2]*v.val[0] - this->val[0]*v.val[2], - this->val[0]*v.val[1] - this->val[1]*v.val[0]); -} - -template<> inline -Vec Vec::cross(const Vec& v) const -{ - return Vec(this->val[1]*v.val[2] - this->val[2]*v.val[1], - this->val[2]*v.val[0] - this->val[0]*v.val[2], - this->val[0]*v.val[1] - this->val[1]*v.val[0]); -} - -template template inline -Vec<_Tp, cn>::operator Vec() const -{ - Vec v; - for( int i = 0; i < cn; i++ ) v.val[i] = saturate_cast(this->val[i]); - return v; -} - -template inline -const _Tp& Vec<_Tp, cn>::operator [](int i) const -{ - CV_DbgAssert( (unsigned)i < (unsigned)cn ); - return this->val[i]; -} - -template inline -_Tp& Vec<_Tp, cn>::operator [](int i) -{ - CV_DbgAssert( (unsigned)i < (unsigned)cn ); - return this->val[i]; -} - -template inline -const _Tp& Vec<_Tp, cn>::operator ()(int i) const -{ - CV_DbgAssert( (unsigned)i < (unsigned)cn ); - return this->val[i]; -} - -template inline -_Tp& Vec<_Tp, cn>::operator ()(int i) -{ - CV_DbgAssert( (unsigned)i < (unsigned)cn ); - return this->val[i]; -} - -template inline -Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v) -{ - double nv = norm(v); - return v * (nv ? 1./nv : 0.); -} - - - -//////////////////////////////// matx comma initializer ////////////////////////////////// - - -template static inline -VecCommaInitializer<_Tp, cn> operator << (const Vec<_Tp, cn>& vec, _T2 val) -{ - VecCommaInitializer<_Tp, cn> commaInitializer((Vec<_Tp, cn>*)&vec); - return (commaInitializer, val); -} - -template inline -VecCommaInitializer<_Tp, cn>::VecCommaInitializer(Vec<_Tp, cn>* _vec) - : MatxCommaInitializer<_Tp, cn, 1>(_vec) -{} - -template template inline -VecCommaInitializer<_Tp, cn>& VecCommaInitializer<_Tp, cn>::operator , (_T2 value) -{ - CV_DbgAssert( this->idx < cn ); - this->dst->val[this->idx++] = saturate_cast<_Tp>(value); - return *this; -} - -template inline -Vec<_Tp, cn> VecCommaInitializer<_Tp, cn>::operator *() const -{ - CV_DbgAssert( this->idx == cn ); - return *this->dst; -} - -//! @endcond - -///////////////////////////// Matx out-of-class operators //////////////////////////////// - -//! @relates cv::Matx -//! @{ - -template static inline -Matx<_Tp1, m, n>& operator += (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b) -{ - for( int i = 0; i < m*n; i++ ) - a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]); - return a; -} - -template static inline -Matx<_Tp1, m, n>& operator -= (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b) -{ - for( int i = 0; i < m*n; i++ ) - a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]); - return a; -} - -template static inline -Matx<_Tp, m, n> operator + (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) -{ - return Matx<_Tp, m, n>(a, b, Matx_AddOp()); -} - -template static inline -Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) -{ - return Matx<_Tp, m, n>(a, b, Matx_SubOp()); -} - -template static inline -Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, int alpha) -{ - for( int i = 0; i < m*n; i++ ) - a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); - return a; -} - -template static inline -Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, float alpha) -{ - for( int i = 0; i < m*n; i++ ) - a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); - return a; -} - -template static inline -Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, double alpha) -{ - for( int i = 0; i < m*n; i++ ) - a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); - return a; -} - -template static inline -Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, int alpha) -{ - return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, float alpha) -{ - return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, double alpha) -{ - return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Matx<_Tp, m, n> operator * (int alpha, const Matx<_Tp, m, n>& a) -{ - return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Matx<_Tp, m, n> operator * (float alpha, const Matx<_Tp, m, n>& a) -{ - return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Matx<_Tp, m, n> operator * (double alpha, const Matx<_Tp, m, n>& a) -{ - return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a) -{ - return Matx<_Tp, m, n>(a, -1, Matx_ScaleOp()); -} - -template static inline -Matx<_Tp, m, n> operator * (const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b) -{ - return Matx<_Tp, m, n>(a, b, Matx_MatMulOp()); -} - -template static inline -Vec<_Tp, m> operator * (const Matx<_Tp, m, n>& a, const Vec<_Tp, n>& b) -{ - Matx<_Tp, m, 1> c(a, b, Matx_MatMulOp()); - return (const Vec<_Tp, m>&)(c); -} - -template static inline -bool operator == (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) -{ - for( int i = 0; i < m*n; i++ ) - if( a.val[i] != b.val[i] ) return false; - return true; -} - -template static inline -bool operator != (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) -{ - return !(a == b); -} - -//! @} - -////////////////////////////// Vec out-of-class operators //////////////////////////////// - -//! @relates cv::Vec -//! @{ - -template static inline -Vec<_Tp1, cn>& operator += (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b) -{ - for( int i = 0; i < cn; i++ ) - a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]); - return a; -} - -template static inline -Vec<_Tp1, cn>& operator -= (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b) -{ - for( int i = 0; i < cn; i++ ) - a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]); - return a; -} - -template static inline -Vec<_Tp, cn> operator + (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b) -{ - return Vec<_Tp, cn>(a, b, Matx_AddOp()); -} - -template static inline -Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b) -{ - return Vec<_Tp, cn>(a, b, Matx_SubOp()); -} - -template static inline -Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, int alpha) -{ - for( int i = 0; i < cn; i++ ) - a[i] = saturate_cast<_Tp>(a[i]*alpha); - return a; -} - -template static inline -Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, float alpha) -{ - for( int i = 0; i < cn; i++ ) - a[i] = saturate_cast<_Tp>(a[i]*alpha); - return a; -} - -template static inline -Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, double alpha) -{ - for( int i = 0; i < cn; i++ ) - a[i] = saturate_cast<_Tp>(a[i]*alpha); - return a; -} - -template static inline -Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, int alpha) -{ - double ialpha = 1./alpha; - for( int i = 0; i < cn; i++ ) - a[i] = saturate_cast<_Tp>(a[i]*ialpha); - return a; -} - -template static inline -Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, float alpha) -{ - float ialpha = 1.f/alpha; - for( int i = 0; i < cn; i++ ) - a[i] = saturate_cast<_Tp>(a[i]*ialpha); - return a; -} - -template static inline -Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, double alpha) -{ - double ialpha = 1./alpha; - for( int i = 0; i < cn; i++ ) - a[i] = saturate_cast<_Tp>(a[i]*ialpha); - return a; -} - -template static inline -Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, int alpha) -{ - return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator * (int alpha, const Vec<_Tp, cn>& a) -{ - return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, float alpha) -{ - return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator * (float alpha, const Vec<_Tp, cn>& a) -{ - return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, double alpha) -{ - return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator * (double alpha, const Vec<_Tp, cn>& a) -{ - return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, int alpha) -{ - return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, float alpha) -{ - return Vec<_Tp, cn>(a, 1.f/alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, double alpha) -{ - return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a) -{ - Vec<_Tp,cn> t; - for( int i = 0; i < cn; i++ ) t.val[i] = saturate_cast<_Tp>(-a.val[i]); - return t; -} - -template inline Vec<_Tp, 4> operator * (const Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2) -{ - return Vec<_Tp, 4>(saturate_cast<_Tp>(v1[0]*v2[0] - v1[1]*v2[1] - v1[2]*v2[2] - v1[3]*v2[3]), - saturate_cast<_Tp>(v1[0]*v2[1] + v1[1]*v2[0] + v1[2]*v2[3] - v1[3]*v2[2]), - saturate_cast<_Tp>(v1[0]*v2[2] - v1[1]*v2[3] + v1[2]*v2[0] + v1[3]*v2[1]), - saturate_cast<_Tp>(v1[0]*v2[3] + v1[1]*v2[2] - v1[2]*v2[1] + v1[3]*v2[0])); -} - -template inline Vec<_Tp, 4>& operator *= (Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2) -{ - v1 = v1 * v2; - return v1; -} - -//! @} - -} // cv - -#endif // __OPENCV_CORE_MATX_HPP__ diff --git a/IPL/include/opencv/opencv2/core/neon_utils.hpp b/IPL/include/opencv/opencv2/core/neon_utils.hpp deleted file mode 100644 index adb750f..0000000 --- a/IPL/include/opencv/opencv2/core/neon_utils.hpp +++ /dev/null @@ -1,128 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_HAL_NEON_UTILS_HPP__ -#define __OPENCV_HAL_NEON_UTILS_HPP__ - -#include "opencv2/core/cvdef.h" - -//! @addtogroup core_utils_neon -//! @{ - -#if CV_NEON - -inline int32x2_t cv_vrnd_s32_f32(float32x2_t v) -{ - static int32x2_t v_sign = vdup_n_s32(1 << 31), - v_05 = vreinterpret_s32_f32(vdup_n_f32(0.5f)); - - int32x2_t v_addition = vorr_s32(v_05, vand_s32(v_sign, vreinterpret_s32_f32(v))); - return vcvt_s32_f32(vadd_f32(v, vreinterpret_f32_s32(v_addition))); -} - -inline int32x4_t cv_vrndq_s32_f32(float32x4_t v) -{ - static int32x4_t v_sign = vdupq_n_s32(1 << 31), - v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f)); - - int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(v))); - return vcvtq_s32_f32(vaddq_f32(v, vreinterpretq_f32_s32(v_addition))); -} - -inline uint32x2_t cv_vrnd_u32_f32(float32x2_t v) -{ - static float32x2_t v_05 = vdup_n_f32(0.5f); - return vcvt_u32_f32(vadd_f32(v, v_05)); -} - -inline uint32x4_t cv_vrndq_u32_f32(float32x4_t v) -{ - static float32x4_t v_05 = vdupq_n_f32(0.5f); - return vcvtq_u32_f32(vaddq_f32(v, v_05)); -} - -inline float32x4_t cv_vrecpq_f32(float32x4_t val) -{ - float32x4_t reciprocal = vrecpeq_f32(val); - reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal); - reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal); - return reciprocal; -} - -inline float32x2_t cv_vrecp_f32(float32x2_t val) -{ - float32x2_t reciprocal = vrecpe_f32(val); - reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal); - reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal); - return reciprocal; -} - -inline float32x4_t cv_vrsqrtq_f32(float32x4_t val) -{ - float32x4_t e = vrsqrteq_f32(val); - e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e); - e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e); - return e; -} - -inline float32x2_t cv_vrsqrt_f32(float32x2_t val) -{ - float32x2_t e = vrsqrte_f32(val); - e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e); - e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e); - return e; -} - -inline float32x4_t cv_vsqrtq_f32(float32x4_t val) -{ - return cv_vrecpq_f32(cv_vrsqrtq_f32(val)); -} - -inline float32x2_t cv_vsqrt_f32(float32x2_t val) -{ - return cv_vrecp_f32(cv_vrsqrt_f32(val)); -} - -#endif - -//! @} - -#endif // __OPENCV_HAL_NEON_UTILS_HPP__ diff --git a/IPL/include/opencv/opencv2/core/ocl.hpp b/IPL/include/opencv/opencv2/core/ocl.hpp deleted file mode 100644 index bc989a3..0000000 --- a/IPL/include/opencv/opencv2/core/ocl.hpp +++ /dev/null @@ -1,743 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the OpenCV Foundation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OPENCL_HPP__ -#define __OPENCV_OPENCL_HPP__ - -#include "opencv2/core.hpp" - -namespace cv { namespace ocl { - -//! @addtogroup core_opencl -//! @{ - -CV_EXPORTS_W bool haveOpenCL(); -CV_EXPORTS_W bool useOpenCL(); -CV_EXPORTS_W bool haveAmdBlas(); -CV_EXPORTS_W bool haveAmdFft(); -CV_EXPORTS_W void setUseOpenCL(bool flag); -CV_EXPORTS_W void finish(); - -CV_EXPORTS bool haveSVM(); - -class CV_EXPORTS Context; -class CV_EXPORTS Device; -class CV_EXPORTS Kernel; -class CV_EXPORTS Program; -class CV_EXPORTS ProgramSource; -class CV_EXPORTS Queue; -class CV_EXPORTS PlatformInfo; -class CV_EXPORTS Image2D; - -class CV_EXPORTS Device -{ -public: - Device(); - explicit Device(void* d); - Device(const Device& d); - Device& operator = (const Device& d); - ~Device(); - - void set(void* d); - - enum - { - TYPE_DEFAULT = (1 << 0), - TYPE_CPU = (1 << 1), - TYPE_GPU = (1 << 2), - TYPE_ACCELERATOR = (1 << 3), - TYPE_DGPU = TYPE_GPU + (1 << 16), - TYPE_IGPU = TYPE_GPU + (1 << 17), - TYPE_ALL = 0xFFFFFFFF - }; - - String name() const; - String extensions() const; - String version() const; - String vendorName() const; - String OpenCL_C_Version() const; - String OpenCLVersion() const; - int deviceVersionMajor() const; - int deviceVersionMinor() const; - String driverVersion() const; - void* ptr() const; - - int type() const; - - int addressBits() const; - bool available() const; - bool compilerAvailable() const; - bool linkerAvailable() const; - - enum - { - FP_DENORM=(1 << 0), - FP_INF_NAN=(1 << 1), - FP_ROUND_TO_NEAREST=(1 << 2), - FP_ROUND_TO_ZERO=(1 << 3), - FP_ROUND_TO_INF=(1 << 4), - FP_FMA=(1 << 5), - FP_SOFT_FLOAT=(1 << 6), - FP_CORRECTLY_ROUNDED_DIVIDE_SQRT=(1 << 7) - }; - int doubleFPConfig() const; - int singleFPConfig() const; - int halfFPConfig() const; - - bool endianLittle() const; - bool errorCorrectionSupport() const; - - enum - { - EXEC_KERNEL=(1 << 0), - EXEC_NATIVE_KERNEL=(1 << 1) - }; - int executionCapabilities() const; - - size_t globalMemCacheSize() const; - - enum - { - NO_CACHE=0, - READ_ONLY_CACHE=1, - READ_WRITE_CACHE=2 - }; - int globalMemCacheType() const; - int globalMemCacheLineSize() const; - size_t globalMemSize() const; - - size_t localMemSize() const; - enum - { - NO_LOCAL_MEM=0, - LOCAL_IS_LOCAL=1, - LOCAL_IS_GLOBAL=2 - }; - int localMemType() const; - bool hostUnifiedMemory() const; - - bool imageSupport() const; - - bool imageFromBufferSupport() const; - uint imagePitchAlignment() const; - uint imageBaseAddressAlignment() const; - - size_t image2DMaxWidth() const; - size_t image2DMaxHeight() const; - - size_t image3DMaxWidth() const; - size_t image3DMaxHeight() const; - size_t image3DMaxDepth() const; - - size_t imageMaxBufferSize() const; - size_t imageMaxArraySize() const; - - enum - { - UNKNOWN_VENDOR=0, - VENDOR_AMD=1, - VENDOR_INTEL=2, - VENDOR_NVIDIA=3 - }; - int vendorID() const; - // FIXIT - // dev.isAMD() doesn't work for OpenCL CPU devices from AMD OpenCL platform. - // This method should use platform name instead of vendor name. - // After fix restore code in arithm.cpp: ocl_compare() - inline bool isAMD() const { return vendorID() == VENDOR_AMD; } - inline bool isIntel() const { return vendorID() == VENDOR_INTEL; } - inline bool isNVidia() const { return vendorID() == VENDOR_NVIDIA; } - - int maxClockFrequency() const; - int maxComputeUnits() const; - int maxConstantArgs() const; - size_t maxConstantBufferSize() const; - - size_t maxMemAllocSize() const; - size_t maxParameterSize() const; - - int maxReadImageArgs() const; - int maxWriteImageArgs() const; - int maxSamplers() const; - - size_t maxWorkGroupSize() const; - int maxWorkItemDims() const; - void maxWorkItemSizes(size_t*) const; - - int memBaseAddrAlign() const; - - int nativeVectorWidthChar() const; - int nativeVectorWidthShort() const; - int nativeVectorWidthInt() const; - int nativeVectorWidthLong() const; - int nativeVectorWidthFloat() const; - int nativeVectorWidthDouble() const; - int nativeVectorWidthHalf() const; - - int preferredVectorWidthChar() const; - int preferredVectorWidthShort() const; - int preferredVectorWidthInt() const; - int preferredVectorWidthLong() const; - int preferredVectorWidthFloat() const; - int preferredVectorWidthDouble() const; - int preferredVectorWidthHalf() const; - - size_t printfBufferSize() const; - size_t profilingTimerResolution() const; - - static const Device& getDefault(); - -protected: - struct Impl; - Impl* p; -}; - - -class CV_EXPORTS Context -{ -public: - Context(); - explicit Context(int dtype); - ~Context(); - Context(const Context& c); - Context& operator = (const Context& c); - - bool create(); - bool create(int dtype); - size_t ndevices() const; - const Device& device(size_t idx) const; - Program getProg(const ProgramSource& prog, - const String& buildopt, String& errmsg); - - static Context& getDefault(bool initialize = true); - void* ptr() const; - - friend void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); - - bool useSVM() const; - void setUseSVM(bool enabled); - - struct Impl; - Impl* p; -}; - -class CV_EXPORTS Platform -{ -public: - Platform(); - ~Platform(); - Platform(const Platform& p); - Platform& operator = (const Platform& p); - - void* ptr() const; - static Platform& getDefault(); - - friend void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); -protected: - struct Impl; - Impl* p; -}; - -/* -//! @brief Attaches OpenCL context to OpenCV -// -//! @note Note: -// OpenCV will check if available OpenCL platform has platformName name, -// then assign context to OpenCV and call clRetainContext function. -// The deviceID device will be used as target device and new command queue -// will be created. -// -// Params: -//! @param platformName - name of OpenCL platform to attach, -//! this string is used to check if platform is available -//! to OpenCV at runtime -//! @param platfromID - ID of platform attached context was created for -//! @param context - OpenCL context to be attached to OpenCV -//! @param deviceID - ID of device, must be created from attached context -*/ -CV_EXPORTS void attachContext(const String& platformName, void* platformID, void* context, void* deviceID); - -/* -//! @brief Convert OpenCL buffer to UMat -// -//! @note Note: -// OpenCL buffer (cl_mem_buffer) should contain 2D image data, compatible with OpenCV. -// Memory content is not copied from clBuffer to UMat. Instead, buffer handle assigned -// to UMat and clRetainMemObject is called. -// -// Params: -//! @param cl_mem_buffer - source clBuffer handle -//! @param step - num of bytes in single row -//! @param rows - number of rows -//! @param cols - number of cols -//! @param type - OpenCV type of image -//! @param dst - destination UMat -*/ -CV_EXPORTS void convertFromBuffer(void* cl_mem_buffer, size_t step, int rows, int cols, int type, UMat& dst); - -/* -//! @brief Convert OpenCL image2d_t to UMat -// -//! @note Note: -// OpenCL image2d_t (cl_mem_image), should be compatible with OpenCV -// UMat formats. -// Memory content is copied from image to UMat with -// clEnqueueCopyImageToBuffer function. -// -// Params: -//! @param cl_mem_image - source image2d_t handle -//! @param dst - destination UMat -*/ -CV_EXPORTS void convertFromImage(void* cl_mem_image, UMat& dst); - -// TODO Move to internal header -void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); - -class CV_EXPORTS Queue -{ -public: - Queue(); - explicit Queue(const Context& c, const Device& d=Device()); - ~Queue(); - Queue(const Queue& q); - Queue& operator = (const Queue& q); - - bool create(const Context& c=Context(), const Device& d=Device()); - void finish(); - void* ptr() const; - static Queue& getDefault(); - -protected: - struct Impl; - Impl* p; -}; - - -class CV_EXPORTS KernelArg -{ -public: - enum { LOCAL=1, READ_ONLY=2, WRITE_ONLY=4, READ_WRITE=6, CONSTANT=8, PTR_ONLY = 16, NO_SIZE=256 }; - KernelArg(int _flags, UMat* _m, int wscale=1, int iwscale=1, const void* _obj=0, size_t _sz=0); - KernelArg(); - - static KernelArg Local() { return KernelArg(LOCAL, 0); } - static KernelArg PtrWriteOnly(const UMat& m) - { return KernelArg(PTR_ONLY+WRITE_ONLY, (UMat*)&m); } - static KernelArg PtrReadOnly(const UMat& m) - { return KernelArg(PTR_ONLY+READ_ONLY, (UMat*)&m); } - static KernelArg PtrReadWrite(const UMat& m) - { return KernelArg(PTR_ONLY+READ_WRITE, (UMat*)&m); } - static KernelArg ReadWrite(const UMat& m, int wscale=1, int iwscale=1) - { return KernelArg(READ_WRITE, (UMat*)&m, wscale, iwscale); } - static KernelArg ReadWriteNoSize(const UMat& m, int wscale=1, int iwscale=1) - { return KernelArg(READ_WRITE+NO_SIZE, (UMat*)&m, wscale, iwscale); } - static KernelArg ReadOnly(const UMat& m, int wscale=1, int iwscale=1) - { return KernelArg(READ_ONLY, (UMat*)&m, wscale, iwscale); } - static KernelArg WriteOnly(const UMat& m, int wscale=1, int iwscale=1) - { return KernelArg(WRITE_ONLY, (UMat*)&m, wscale, iwscale); } - static KernelArg ReadOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1) - { return KernelArg(READ_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); } - static KernelArg WriteOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1) - { return KernelArg(WRITE_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); } - static KernelArg Constant(const Mat& m); - template static KernelArg Constant(const _Tp* arr, size_t n) - { return KernelArg(CONSTANT, 0, 1, 1, (void*)arr, n); } - - int flags; - UMat* m; - const void* obj; - size_t sz; - int wscale, iwscale; -}; - - -class CV_EXPORTS Kernel -{ -public: - Kernel(); - Kernel(const char* kname, const Program& prog); - Kernel(const char* kname, const ProgramSource& prog, - const String& buildopts = String(), String* errmsg=0); - ~Kernel(); - Kernel(const Kernel& k); - Kernel& operator = (const Kernel& k); - - bool empty() const; - bool create(const char* kname, const Program& prog); - bool create(const char* kname, const ProgramSource& prog, - const String& buildopts, String* errmsg=0); - - int set(int i, const void* value, size_t sz); - int set(int i, const Image2D& image2D); - int set(int i, const UMat& m); - int set(int i, const KernelArg& arg); - template int set(int i, const _Tp& value) - { return set(i, &value, sizeof(value)); } - - template - Kernel& args(const _Tp0& a0) - { - set(0, a0); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1) - { - int i = set(0, a0); set(i, a1); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2) - { - int i = set(0, a0); i = set(i, a1); set(i, a2); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, - const _Tp3& a3, const _Tp4& a4) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); - i = set(i, a3); set(i, a4); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, - const _Tp3& a3, const _Tp4& a4, const _Tp5& a5) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); - i = set(i, a3); i = set(i, a4); set(i, a5); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); - i = set(i, a4); i = set(i, a5); set(i, a6); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); - i = set(i, a4); i = set(i, a5); i = set(i, a6); set(i, a7); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, - const _Tp8& a8) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); - i = set(i, a5); i = set(i, a6); i = set(i, a7); set(i, a8); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, - const _Tp8& a8, const _Tp9& a9) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); - i = set(i, a6); i = set(i, a7); i = set(i, a8); set(i, a9); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, - const _Tp8& a8, const _Tp9& a9, const _Tp10& a10) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); - i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); set(i, a10); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, - const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); - i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); set(i, a11); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, - const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, - const _Tp12& a12) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); - i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); - set(i, a12); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, - const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, - const _Tp12& a12, const _Tp13& a13) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); - i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); - i = set(i, a12); set(i, a13); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, - const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, - const _Tp12& a12, const _Tp13& a13, const _Tp14& a14) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); - i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); - i = set(i, a12); i = set(i, a13); set(i, a14); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, - const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, - const _Tp12& a12, const _Tp13& a13, const _Tp14& a14, const _Tp15& a15) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); - i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); - i = set(i, a12); i = set(i, a13); i = set(i, a14); set(i, a15); return *this; - } - - bool run(int dims, size_t globalsize[], - size_t localsize[], bool sync, const Queue& q=Queue()); - bool runTask(bool sync, const Queue& q=Queue()); - - size_t workGroupSize() const; - size_t preferedWorkGroupSizeMultiple() const; - bool compileWorkGroupSize(size_t wsz[]) const; - size_t localMemSize() const; - - void* ptr() const; - struct Impl; - -protected: - Impl* p; -}; - -class CV_EXPORTS Program -{ -public: - Program(); - Program(const ProgramSource& src, - const String& buildflags, String& errmsg); - explicit Program(const String& buf); - Program(const Program& prog); - - Program& operator = (const Program& prog); - ~Program(); - - bool create(const ProgramSource& src, - const String& buildflags, String& errmsg); - bool read(const String& buf, const String& buildflags); - bool write(String& buf) const; - - const ProgramSource& source() const; - void* ptr() const; - - String getPrefix() const; - static String getPrefix(const String& buildflags); - -protected: - struct Impl; - Impl* p; -}; - - -class CV_EXPORTS ProgramSource -{ -public: - typedef uint64 hash_t; - - ProgramSource(); - explicit ProgramSource(const String& prog); - explicit ProgramSource(const char* prog); - ~ProgramSource(); - ProgramSource(const ProgramSource& prog); - ProgramSource& operator = (const ProgramSource& prog); - - const String& source() const; - hash_t hash() const; - -protected: - struct Impl; - Impl* p; -}; - -class CV_EXPORTS PlatformInfo -{ -public: - PlatformInfo(); - explicit PlatformInfo(void* id); - ~PlatformInfo(); - - PlatformInfo(const PlatformInfo& i); - PlatformInfo& operator =(const PlatformInfo& i); - - String name() const; - String vendor() const; - String version() const; - int deviceNumber() const; - void getDevice(Device& device, int d) const; - -protected: - struct Impl; - Impl* p; -}; - -CV_EXPORTS const char* convertTypeStr(int sdepth, int ddepth, int cn, char* buf); -CV_EXPORTS const char* typeToStr(int t); -CV_EXPORTS const char* memopTypeToStr(int t); -CV_EXPORTS const char* vecopTypeToStr(int t); -CV_EXPORTS String kernelToStr(InputArray _kernel, int ddepth = -1, const char * name = NULL); -CV_EXPORTS void getPlatfomsInfo(std::vector& platform_info); - - -enum OclVectorStrategy -{ - // all matrices have its own vector width - OCL_VECTOR_OWN = 0, - // all matrices have maximal vector width among all matrices - // (useful for cases when matrices have different data types) - OCL_VECTOR_MAX = 1, - - // default strategy - OCL_VECTOR_DEFAULT = OCL_VECTOR_OWN -}; - -CV_EXPORTS int predictOptimalVectorWidth(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), - InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), - InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(), - OclVectorStrategy strat = OCL_VECTOR_DEFAULT); - -CV_EXPORTS int checkOptimalVectorWidth(const int *vectorWidths, - InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), - InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), - InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(), - OclVectorStrategy strat = OCL_VECTOR_DEFAULT); - -// with OCL_VECTOR_MAX strategy -CV_EXPORTS int predictOptimalVectorWidthMax(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), - InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), - InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray()); - -CV_EXPORTS void buildOptionsAddMatrixDescription(String& buildOptions, const String& name, InputArray _m); - -class CV_EXPORTS Image2D -{ -public: - Image2D(); - - // src: The UMat from which to get image properties and data - // norm: Flag to enable the use of normalized channel data types - // alias: Flag indicating that the image should alias the src UMat. - // If true, changes to the image or src will be reflected in - // both objects. - explicit Image2D(const UMat &src, bool norm = false, bool alias = false); - Image2D(const Image2D & i); - ~Image2D(); - - Image2D & operator = (const Image2D & i); - - // Indicates if creating an aliased image should succeed. Depends on the - // underlying platform and the dimensions of the UMat. - static bool canCreateAlias(const UMat &u); - - // Indicates if the image format is supported. - static bool isFormatSupported(int depth, int cn, bool norm); - - void* ptr() const; -protected: - struct Impl; - Impl* p; -}; - - -CV_EXPORTS MatAllocator* getOpenCLAllocator(); - - -#ifdef __OPENCV_BUILD -namespace internal { - -CV_EXPORTS bool isPerformanceCheckBypassed(); -#define OCL_PERFORMANCE_CHECK(condition) (cv::ocl::internal::isPerformanceCheckBypassed() || (condition)) - -CV_EXPORTS bool isCLBuffer(UMat& u); - -} // namespace internal -#endif - -//! @} - -}} - -#endif diff --git a/IPL/include/opencv/opencv2/core/ocl_genbase.hpp b/IPL/include/opencv/opencv2/core/ocl_genbase.hpp deleted file mode 100644 index d53bc1a..0000000 --- a/IPL/include/opencv/opencv2/core/ocl_genbase.hpp +++ /dev/null @@ -1,64 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the OpenCV Foundation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OPENCL_GENBASE_HPP__ -#define __OPENCV_OPENCL_GENBASE_HPP__ - -namespace cv -{ -namespace ocl -{ - -//! @cond IGNORED - -struct ProgramEntry -{ - const char* name; - const char* programStr; - const char* programHash; -}; - -//! @endcond - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/core/opengl.hpp b/IPL/include/opencv/opencv2/core/opengl.hpp deleted file mode 100644 index fd47c52..0000000 --- a/IPL/include/opencv/opencv2/core/opengl.hpp +++ /dev/null @@ -1,729 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_OPENGL_HPP__ -#define __OPENCV_CORE_OPENGL_HPP__ - -#ifndef __cplusplus -# error opengl.hpp header must be compiled as C++ -#endif - -#include "opencv2/core.hpp" -#include "ocl.hpp" - -namespace cv { namespace ogl { - -/** @addtogroup core_opengl -This section describes OpenGL interoperability. - -To enable OpenGL support, configure OpenCV using CMake with WITH_OPENGL=ON . Currently OpenGL is -supported only with WIN32, GTK and Qt backends on Windows and Linux (MacOS and Android are not -supported). For GTK backend gtkglext-1.0 library is required. - -To use OpenGL functionality you should first create OpenGL context (window or frame buffer). You can -do this with namedWindow function or with other OpenGL toolkit (GLUT, for example). -*/ -//! @{ - -/////////////////// OpenGL Objects /////////////////// - -/** @brief Smart pointer for OpenGL buffer object with reference counting. - -Buffer Objects are OpenGL objects that store an array of unformatted memory allocated by the OpenGL -context. These can be used to store vertex data, pixel data retrieved from images or the -framebuffer, and a variety of other things. - -ogl::Buffer has interface similar with Mat interface and represents 2D array memory. - -ogl::Buffer supports memory transfers between host and device and also can be mapped to CUDA memory. - */ -class CV_EXPORTS Buffer -{ -public: - /** @brief The target defines how you intend to use the buffer object. - */ - enum Target - { - ARRAY_BUFFER = 0x8892, //!< The buffer will be used as a source for vertex data - ELEMENT_ARRAY_BUFFER = 0x8893, //!< The buffer will be used for indices (in glDrawElements, for example) - PIXEL_PACK_BUFFER = 0x88EB, //!< The buffer will be used for reading from OpenGL textures - PIXEL_UNPACK_BUFFER = 0x88EC //!< The buffer will be used for writing to OpenGL textures - }; - - enum Access - { - READ_ONLY = 0x88B8, - WRITE_ONLY = 0x88B9, - READ_WRITE = 0x88BA - }; - - /** @brief The constructors. - - Creates empty ogl::Buffer object, creates ogl::Buffer object from existed buffer ( abufId - parameter), allocates memory for ogl::Buffer object or copies from host/device memory. - */ - Buffer(); - - /** @overload - @param arows Number of rows in a 2D array. - @param acols Number of columns in a 2D array. - @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. - @param abufId Buffer object name. - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - Buffer(int arows, int acols, int atype, unsigned int abufId, bool autoRelease = false); - - /** @overload - @param asize 2D array size. - @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. - @param abufId Buffer object name. - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - Buffer(Size asize, int atype, unsigned int abufId, bool autoRelease = false); - - /** @overload - @param arows Number of rows in a 2D array. - @param acols Number of columns in a 2D array. - @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. - @param target Buffer usage. See cv::ogl::Buffer::Target . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - Buffer(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); - - /** @overload - @param asize 2D array size. - @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. - @param target Buffer usage. See cv::ogl::Buffer::Target . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - Buffer(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); - - /** @overload - @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ). - @param target Buffer usage. See cv::ogl::Buffer::Target . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - explicit Buffer(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false); - - /** @brief Allocates memory for ogl::Buffer object. - - @param arows Number of rows in a 2D array. - @param acols Number of columns in a 2D array. - @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. - @param target Buffer usage. See cv::ogl::Buffer::Target . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - void create(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); - - /** @overload - @param asize 2D array size. - @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. - @param target Buffer usage. See cv::ogl::Buffer::Target . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - void create(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); - - /** @brief Decrements the reference counter and destroys the buffer object if needed. - - The function will call setAutoRelease(true) . - */ - void release(); - - /** @brief Sets auto release mode. - - The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was - bound to a window it could be released at any time (user can close a window). If object's destructor - is called after destruction of the context it will cause an error. Thus ogl::Buffer doesn't destroy - OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL context). - This function can force ogl::Buffer destructor to destroy OpenGL object. - @param flag Auto release mode (if true, release will be called in object's destructor). - */ - void setAutoRelease(bool flag); - - /** @brief Copies from host/device memory to OpenGL buffer. - @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ). - @param target Buffer usage. See cv::ogl::Buffer::Target . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - void copyFrom(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false); - - /** @overload */ - void copyFrom(InputArray arr, cuda::Stream& stream, Target target = ARRAY_BUFFER, bool autoRelease = false); - - /** @brief Copies from OpenGL buffer to host/device memory or another OpenGL buffer object. - - @param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , std::vector or - ogl::Buffer ). - */ - void copyTo(OutputArray arr) const; - - /** @overload */ - void copyTo(OutputArray arr, cuda::Stream& stream) const; - - /** @brief Creates a full copy of the buffer object and the underlying data. - - @param target Buffer usage for destination buffer. - @param autoRelease Auto release mode for destination buffer. - */ - Buffer clone(Target target = ARRAY_BUFFER, bool autoRelease = false) const; - - /** @brief Binds OpenGL buffer to the specified buffer binding point. - - @param target Binding point. See cv::ogl::Buffer::Target . - */ - void bind(Target target) const; - - /** @brief Unbind any buffers from the specified binding point. - - @param target Binding point. See cv::ogl::Buffer::Target . - */ - static void unbind(Target target); - - /** @brief Maps OpenGL buffer to host memory. - - mapHost maps to the client's address space the entire data store of the buffer object. The data can - then be directly read and/or written relative to the returned pointer, depending on the specified - access policy. - - A mapped data store must be unmapped with ogl::Buffer::unmapHost before its buffer object is used. - - This operation can lead to memory transfers between host and device. - - Only one buffer object can be mapped at a time. - @param access Access policy, indicating whether it will be possible to read from, write to, or both - read from and write to the buffer object's mapped data store. The symbolic constant must be - ogl::Buffer::READ_ONLY , ogl::Buffer::WRITE_ONLY or ogl::Buffer::READ_WRITE . - */ - Mat mapHost(Access access); - - /** @brief Unmaps OpenGL buffer. - */ - void unmapHost(); - - //! map to device memory (blocking) - cuda::GpuMat mapDevice(); - void unmapDevice(); - - /** @brief Maps OpenGL buffer to CUDA device memory. - - This operatation doesn't copy data. Several buffer objects can be mapped to CUDA memory at a time. - - A mapped data store must be unmapped with ogl::Buffer::unmapDevice before its buffer object is used. - */ - cuda::GpuMat mapDevice(cuda::Stream& stream); - - /** @brief Unmaps OpenGL buffer. - */ - void unmapDevice(cuda::Stream& stream); - - int rows() const; - int cols() const; - Size size() const; - bool empty() const; - - int type() const; - int depth() const; - int channels() const; - int elemSize() const; - int elemSize1() const; - - //! get OpenGL opject id - unsigned int bufId() const; - - class Impl; - -private: - Ptr impl_; - int rows_; - int cols_; - int type_; -}; - -/** @brief Smart pointer for OpenGL 2D texture memory with reference counting. - */ -class CV_EXPORTS Texture2D -{ -public: - /** @brief An Image Format describes the way that the images in Textures store their data. - */ - enum Format - { - NONE = 0, - DEPTH_COMPONENT = 0x1902, //!< Depth - RGB = 0x1907, //!< Red, Green, Blue - RGBA = 0x1908 //!< Red, Green, Blue, Alpha - }; - - /** @brief The constructors. - - Creates empty ogl::Texture2D object, allocates memory for ogl::Texture2D object or copies from - host/device memory. - */ - Texture2D(); - - /** @overload */ - Texture2D(int arows, int acols, Format aformat, unsigned int atexId, bool autoRelease = false); - - /** @overload */ - Texture2D(Size asize, Format aformat, unsigned int atexId, bool autoRelease = false); - - /** @overload - @param arows Number of rows. - @param acols Number of columns. - @param aformat Image format. See cv::ogl::Texture2D::Format . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - Texture2D(int arows, int acols, Format aformat, bool autoRelease = false); - - /** @overload - @param asize 2D array size. - @param aformat Image format. See cv::ogl::Texture2D::Format . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - Texture2D(Size asize, Format aformat, bool autoRelease = false); - - /** @overload - @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ). - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - explicit Texture2D(InputArray arr, bool autoRelease = false); - - /** @brief Allocates memory for ogl::Texture2D object. - - @param arows Number of rows. - @param acols Number of columns. - @param aformat Image format. See cv::ogl::Texture2D::Format . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - void create(int arows, int acols, Format aformat, bool autoRelease = false); - /** @overload - @param asize 2D array size. - @param aformat Image format. See cv::ogl::Texture2D::Format . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - void create(Size asize, Format aformat, bool autoRelease = false); - - /** @brief Decrements the reference counter and destroys the texture object if needed. - - The function will call setAutoRelease(true) . - */ - void release(); - - /** @brief Sets auto release mode. - - @param flag Auto release mode (if true, release will be called in object's destructor). - - The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was - bound to a window it could be released at any time (user can close a window). If object's destructor - is called after destruction of the context it will cause an error. Thus ogl::Texture2D doesn't - destroy OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL - context). This function can force ogl::Texture2D destructor to destroy OpenGL object. - */ - void setAutoRelease(bool flag); - - /** @brief Copies from host/device memory to OpenGL texture. - - @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ). - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - void copyFrom(InputArray arr, bool autoRelease = false); - - /** @brief Copies from OpenGL texture to host/device memory or another OpenGL texture object. - - @param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , ogl::Buffer or - ogl::Texture2D ). - @param ddepth Destination depth. - @param autoRelease Auto release mode for destination buffer (if arr is OpenGL buffer or texture). - */ - void copyTo(OutputArray arr, int ddepth = CV_32F, bool autoRelease = false) const; - - /** @brief Binds texture to current active texture unit for GL_TEXTURE_2D target. - */ - void bind() const; - - int rows() const; - int cols() const; - Size size() const; - bool empty() const; - - Format format() const; - - //! get OpenGL opject id - unsigned int texId() const; - - class Impl; - -private: - Ptr impl_; - int rows_; - int cols_; - Format format_; -}; - -/** @brief Wrapper for OpenGL Client-Side Vertex arrays. - -ogl::Arrays stores vertex data in ogl::Buffer objects. - */ -class CV_EXPORTS Arrays -{ -public: - /** @brief Default constructor - */ - Arrays(); - - /** @brief Sets an array of vertex coordinates. - @param vertex array with vertex coordinates, can be both host and device memory. - */ - void setVertexArray(InputArray vertex); - - /** @brief Resets vertex coordinates. - */ - void resetVertexArray(); - - /** @brief Sets an array of vertex colors. - @param color array with vertex colors, can be both host and device memory. - */ - void setColorArray(InputArray color); - - /** @brief Resets vertex colors. - */ - void resetColorArray(); - - /** @brief Sets an array of vertex normals. - @param normal array with vertex normals, can be both host and device memory. - */ - void setNormalArray(InputArray normal); - - /** @brief Resets vertex normals. - */ - void resetNormalArray(); - - /** @brief Sets an array of vertex texture coordinates. - @param texCoord array with vertex texture coordinates, can be both host and device memory. - */ - void setTexCoordArray(InputArray texCoord); - - /** @brief Resets vertex texture coordinates. - */ - void resetTexCoordArray(); - - /** @brief Releases all inner buffers. - */ - void release(); - - /** @brief Sets auto release mode all inner buffers. - @param flag Auto release mode. - */ - void setAutoRelease(bool flag); - - /** @brief Binds all vertex arrays. - */ - void bind() const; - - /** @brief Returns the vertex count. - */ - int size() const; - bool empty() const; - -private: - int size_; - Buffer vertex_; - Buffer color_; - Buffer normal_; - Buffer texCoord_; -}; - -/////////////////// Render Functions /////////////////// - -//! render mode -enum RenderModes { - POINTS = 0x0000, - LINES = 0x0001, - LINE_LOOP = 0x0002, - LINE_STRIP = 0x0003, - TRIANGLES = 0x0004, - TRIANGLE_STRIP = 0x0005, - TRIANGLE_FAN = 0x0006, - QUADS = 0x0007, - QUAD_STRIP = 0x0008, - POLYGON = 0x0009 -}; - -/** @brief Render OpenGL texture or primitives. -@param tex Texture to draw. -@param wndRect Region of window, where to draw a texture (normalized coordinates). -@param texRect Region of texture to draw (normalized coordinates). - */ -CV_EXPORTS void render(const Texture2D& tex, - Rect_ wndRect = Rect_(0.0, 0.0, 1.0, 1.0), - Rect_ texRect = Rect_(0.0, 0.0, 1.0, 1.0)); - -/** @overload -@param arr Array of privitives vertices. -@param mode Render mode. One of cv::ogl::RenderModes -@param color Color for all vertices. Will be used if arr doesn't contain color array. -*/ -CV_EXPORTS void render(const Arrays& arr, int mode = POINTS, Scalar color = Scalar::all(255)); - -/** @overload -@param arr Array of privitives vertices. -@param indices Array of vertices indices (host or device memory). -@param mode Render mode. One of cv::ogl::RenderModes -@param color Color for all vertices. Will be used if arr doesn't contain color array. -*/ -CV_EXPORTS void render(const Arrays& arr, InputArray indices, int mode = POINTS, Scalar color = Scalar::all(255)); - -/////////////////// CL-GL Interoperability Functions /////////////////// - -namespace ocl { -using namespace cv::ocl; - -// TODO static functions in the Context class -/** @brief Creates OpenCL context from GL. -@return Returns reference to OpenCL Context - */ -CV_EXPORTS Context& initializeContextFromGL(); - -} // namespace cv::ogl::ocl - -/** @brief Converts InputArray to Texture2D object. -@param src - source InputArray. -@param texture - destination Texture2D object. - */ -CV_EXPORTS void convertToGLTexture2D(InputArray src, Texture2D& texture); - -/** @brief Converts Texture2D object to OutputArray. -@param texture - source Texture2D object. -@param dst - destination OutputArray. - */ -CV_EXPORTS void convertFromGLTexture2D(const Texture2D& texture, OutputArray dst); - -/** @brief Maps Buffer object to process on CL side (convert to UMat). - -Function creates CL buffer from GL one, and then constructs UMat that can be used -to process buffer data with OpenCV functions. Note that in current implementation -UMat constructed this way doesn't own corresponding GL buffer object, so it is -the user responsibility to close down CL/GL buffers relationships by explicitly -calling unmapGLBuffer() function. -@param buffer - source Buffer object. -@param accessFlags - data access flags (ACCESS_READ|ACCESS_WRITE). -@return Returns UMat object - */ -CV_EXPORTS UMat mapGLBuffer(const Buffer& buffer, int accessFlags = ACCESS_READ|ACCESS_WRITE); - -/** @brief Unmaps Buffer object (releases UMat, previously mapped from Buffer). - -Function must be called explicitly by the user for each UMat previously constructed -by the call to mapGLBuffer() function. -@param u - source UMat, created by mapGLBuffer(). - */ -CV_EXPORTS void unmapGLBuffer(UMat& u); - -}} // namespace cv::ogl - -namespace cv { namespace cuda { - -//! @addtogroup cuda -//! @{ - -/** @brief Sets a CUDA device and initializes it for the current thread with OpenGL interoperability. - -This function should be explicitly called after OpenGL context creation and before any CUDA calls. -@param device System index of a CUDA device starting with 0. -@ingroup core_opengl - */ -CV_EXPORTS void setGlDevice(int device = 0); - -//! @} - -}} - -//! @cond IGNORED - -//////////////////////////////////////////////////////////////////////// -//////////////////////////////////////////////////////////////////////// -//////////////////////////////////////////////////////////////////////// - -inline -cv::ogl::Buffer::Buffer(int arows, int acols, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0) -{ - create(arows, acols, atype, target, autoRelease); -} - -inline -cv::ogl::Buffer::Buffer(Size asize, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0) -{ - create(asize, atype, target, autoRelease); -} - -inline -void cv::ogl::Buffer::create(Size asize, int atype, Target target, bool autoRelease) -{ - create(asize.height, asize.width, atype, target, autoRelease); -} - -inline -int cv::ogl::Buffer::rows() const -{ - return rows_; -} - -inline -int cv::ogl::Buffer::cols() const -{ - return cols_; -} - -inline -cv::Size cv::ogl::Buffer::size() const -{ - return Size(cols_, rows_); -} - -inline -bool cv::ogl::Buffer::empty() const -{ - return rows_ == 0 || cols_ == 0; -} - -inline -int cv::ogl::Buffer::type() const -{ - return type_; -} - -inline -int cv::ogl::Buffer::depth() const -{ - return CV_MAT_DEPTH(type_); -} - -inline -int cv::ogl::Buffer::channels() const -{ - return CV_MAT_CN(type_); -} - -inline -int cv::ogl::Buffer::elemSize() const -{ - return CV_ELEM_SIZE(type_); -} - -inline -int cv::ogl::Buffer::elemSize1() const -{ - return CV_ELEM_SIZE1(type_); -} - -/////// - -inline -cv::ogl::Texture2D::Texture2D(int arows, int acols, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE) -{ - create(arows, acols, aformat, autoRelease); -} - -inline -cv::ogl::Texture2D::Texture2D(Size asize, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE) -{ - create(asize, aformat, autoRelease); -} - -inline -void cv::ogl::Texture2D::create(Size asize, Format aformat, bool autoRelease) -{ - create(asize.height, asize.width, aformat, autoRelease); -} - -inline -int cv::ogl::Texture2D::rows() const -{ - return rows_; -} - -inline -int cv::ogl::Texture2D::cols() const -{ - return cols_; -} - -inline -cv::Size cv::ogl::Texture2D::size() const -{ - return Size(cols_, rows_); -} - -inline -bool cv::ogl::Texture2D::empty() const -{ - return rows_ == 0 || cols_ == 0; -} - -inline -cv::ogl::Texture2D::Format cv::ogl::Texture2D::format() const -{ - return format_; -} - -/////// - -inline -cv::ogl::Arrays::Arrays() : size_(0) -{ -} - -inline -int cv::ogl::Arrays::size() const -{ - return size_; -} - -inline -bool cv::ogl::Arrays::empty() const -{ - return size_ == 0; -} - -//! @endcond - -#endif /* __OPENCV_CORE_OPENGL_HPP__ */ diff --git a/IPL/include/opencv/opencv2/core/operations.hpp b/IPL/include/opencv/opencv2/core/operations.hpp deleted file mode 100644 index bced1a7..0000000 --- a/IPL/include/opencv/opencv2/core/operations.hpp +++ /dev/null @@ -1,530 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_OPERATIONS_HPP__ -#define __OPENCV_CORE_OPERATIONS_HPP__ - -#ifndef __cplusplus -# error operations.hpp header must be compiled as C++ -#endif - -#include - -//! @cond IGNORED - -namespace cv -{ - -////////////////////////////// Matx methods depending on core API ///////////////////////////// - -namespace internal -{ - -template struct Matx_FastInvOp -{ - bool operator()(const Matx<_Tp, m, m>& a, Matx<_Tp, m, m>& b, int method) const - { - Matx<_Tp, m, m> temp = a; - - // assume that b is all 0's on input => make it a unity matrix - for( int i = 0; i < m; i++ ) - b(i, i) = (_Tp)1; - - if( method == DECOMP_CHOLESKY ) - return Cholesky(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m); - - return LU(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m) != 0; - } -}; - -template struct Matx_FastInvOp<_Tp, 2> -{ - bool operator()(const Matx<_Tp, 2, 2>& a, Matx<_Tp, 2, 2>& b, int) const - { - _Tp d = determinant(a); - if( d == 0 ) - return false; - d = 1/d; - b(1,1) = a(0,0)*d; - b(0,0) = a(1,1)*d; - b(0,1) = -a(0,1)*d; - b(1,0) = -a(1,0)*d; - return true; - } -}; - -template struct Matx_FastInvOp<_Tp, 3> -{ - bool operator()(const Matx<_Tp, 3, 3>& a, Matx<_Tp, 3, 3>& b, int) const - { - _Tp d = (_Tp)determinant(a); - if( d == 0 ) - return false; - d = 1/d; - b(0,0) = (a(1,1) * a(2,2) - a(1,2) * a(2,1)) * d; - b(0,1) = (a(0,2) * a(2,1) - a(0,1) * a(2,2)) * d; - b(0,2) = (a(0,1) * a(1,2) - a(0,2) * a(1,1)) * d; - - b(1,0) = (a(1,2) * a(2,0) - a(1,0) * a(2,2)) * d; - b(1,1) = (a(0,0) * a(2,2) - a(0,2) * a(2,0)) * d; - b(1,2) = (a(0,2) * a(1,0) - a(0,0) * a(1,2)) * d; - - b(2,0) = (a(1,0) * a(2,1) - a(1,1) * a(2,0)) * d; - b(2,1) = (a(0,1) * a(2,0) - a(0,0) * a(2,1)) * d; - b(2,2) = (a(0,0) * a(1,1) - a(0,1) * a(1,0)) * d; - return true; - } -}; - - -template struct Matx_FastSolveOp -{ - bool operator()(const Matx<_Tp, m, m>& a, const Matx<_Tp, m, n>& b, - Matx<_Tp, m, n>& x, int method) const - { - Matx<_Tp, m, m> temp = a; - x = b; - if( method == DECOMP_CHOLESKY ) - return Cholesky(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n); - - return LU(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n) != 0; - } -}; - -template struct Matx_FastSolveOp<_Tp, 2, 1> -{ - bool operator()(const Matx<_Tp, 2, 2>& a, const Matx<_Tp, 2, 1>& b, - Matx<_Tp, 2, 1>& x, int) const - { - _Tp d = determinant(a); - if( d == 0 ) - return false; - d = 1/d; - x(0) = (b(0)*a(1,1) - b(1)*a(0,1))*d; - x(1) = (b(1)*a(0,0) - b(0)*a(1,0))*d; - return true; - } -}; - -template struct Matx_FastSolveOp<_Tp, 3, 1> -{ - bool operator()(const Matx<_Tp, 3, 3>& a, const Matx<_Tp, 3, 1>& b, - Matx<_Tp, 3, 1>& x, int) const - { - _Tp d = (_Tp)determinant(a); - if( d == 0 ) - return false; - d = 1/d; - x(0) = d*(b(0)*(a(1,1)*a(2,2) - a(1,2)*a(2,1)) - - a(0,1)*(b(1)*a(2,2) - a(1,2)*b(2)) + - a(0,2)*(b(1)*a(2,1) - a(1,1)*b(2))); - - x(1) = d*(a(0,0)*(b(1)*a(2,2) - a(1,2)*b(2)) - - b(0)*(a(1,0)*a(2,2) - a(1,2)*a(2,0)) + - a(0,2)*(a(1,0)*b(2) - b(1)*a(2,0))); - - x(2) = d*(a(0,0)*(a(1,1)*b(2) - b(1)*a(2,1)) - - a(0,1)*(a(1,0)*b(2) - b(1)*a(2,0)) + - b(0)*(a(1,0)*a(2,1) - a(1,1)*a(2,0))); - return true; - } -}; - -} // internal - -template inline -Matx<_Tp,m,n> Matx<_Tp,m,n>::randu(_Tp a, _Tp b) -{ - Matx<_Tp,m,n> M; - cv::randu(M, Scalar(a), Scalar(b)); - return M; -} - -template inline -Matx<_Tp,m,n> Matx<_Tp,m,n>::randn(_Tp a, _Tp b) -{ - Matx<_Tp,m,n> M; - cv::randn(M, Scalar(a), Scalar(b)); - return M; -} - -template inline -Matx<_Tp, n, m> Matx<_Tp, m, n>::inv(int method, bool *p_is_ok /*= NULL*/) const -{ - Matx<_Tp, n, m> b; - bool ok; - if( method == DECOMP_LU || method == DECOMP_CHOLESKY ) - ok = cv::internal::Matx_FastInvOp<_Tp, m>()(*this, b, method); - else - { - Mat A(*this, false), B(b, false); - ok = (invert(A, B, method) != 0); - } - if( NULL != p_is_ok ) { *p_is_ok = ok; } - return ok ? b : Matx<_Tp, n, m>::zeros(); -} - -template template inline -Matx<_Tp, n, l> Matx<_Tp, m, n>::solve(const Matx<_Tp, m, l>& rhs, int method) const -{ - Matx<_Tp, n, l> x; - bool ok; - if( method == DECOMP_LU || method == DECOMP_CHOLESKY ) - ok = cv::internal::Matx_FastSolveOp<_Tp, m, l>()(*this, rhs, x, method); - else - { - Mat A(*this, false), B(rhs, false), X(x, false); - ok = cv::solve(A, B, X, method); - } - - return ok ? x : Matx<_Tp, n, l>::zeros(); -} - - - -////////////////////////// Augmenting algebraic & logical operations ////////////////////////// - -#define CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ - static inline A& operator op (A& a, const B& b) { cvop; return a; } - -#define CV_MAT_AUG_OPERATOR(op, cvop, A, B) \ - CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ - CV_MAT_AUG_OPERATOR1(op, cvop, const A, B) - -#define CV_MAT_AUG_OPERATOR_T(op, cvop, A, B) \ - template CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ - template CV_MAT_AUG_OPERATOR1(op, cvop, const A, B) - -CV_MAT_AUG_OPERATOR (+=, cv::add(a,b,a), Mat, Mat) -CV_MAT_AUG_OPERATOR (+=, cv::add(a,b,a), Mat, Scalar) -CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Mat) -CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Scalar) -CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Mat_<_Tp>) - -CV_MAT_AUG_OPERATOR (-=, cv::subtract(a,b,a), Mat, Mat) -CV_MAT_AUG_OPERATOR (-=, cv::subtract(a,b,a), Mat, Scalar) -CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Mat) -CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Scalar) -CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Mat_<_Tp>) - -CV_MAT_AUG_OPERATOR (*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat, Mat) -CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat) -CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat_<_Tp>) -CV_MAT_AUG_OPERATOR (*=, a.convertTo(a, -1, b), Mat, double) -CV_MAT_AUG_OPERATOR_T(*=, a.convertTo(a, -1, b), Mat_<_Tp>, double) - -CV_MAT_AUG_OPERATOR (/=, cv::divide(a,b,a), Mat, Mat) -CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a,b,a), Mat_<_Tp>, Mat) -CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a,b,a), Mat_<_Tp>, Mat_<_Tp>) -CV_MAT_AUG_OPERATOR (/=, a.convertTo((Mat&)a, -1, 1./b), Mat, double) -CV_MAT_AUG_OPERATOR_T(/=, a.convertTo((Mat&)a, -1, 1./b), Mat_<_Tp>, double) - -CV_MAT_AUG_OPERATOR (&=, cv::bitwise_and(a,b,a), Mat, Mat) -CV_MAT_AUG_OPERATOR (&=, cv::bitwise_and(a,b,a), Mat, Scalar) -CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Mat) -CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Scalar) -CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Mat_<_Tp>) - -CV_MAT_AUG_OPERATOR (|=, cv::bitwise_or(a,b,a), Mat, Mat) -CV_MAT_AUG_OPERATOR (|=, cv::bitwise_or(a,b,a), Mat, Scalar) -CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Mat) -CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Scalar) -CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Mat_<_Tp>) - -CV_MAT_AUG_OPERATOR (^=, cv::bitwise_xor(a,b,a), Mat, Mat) -CV_MAT_AUG_OPERATOR (^=, cv::bitwise_xor(a,b,a), Mat, Scalar) -CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Mat) -CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Scalar) -CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Mat_<_Tp>) - -#undef CV_MAT_AUG_OPERATOR_T -#undef CV_MAT_AUG_OPERATOR -#undef CV_MAT_AUG_OPERATOR1 - - - -///////////////////////////////////////////// SVD ///////////////////////////////////////////// - -inline SVD::SVD() {} -inline SVD::SVD( InputArray m, int flags ) { operator ()(m, flags); } -inline void SVD::solveZ( InputArray m, OutputArray _dst ) -{ - Mat mtx = m.getMat(); - SVD svd(mtx, (mtx.rows >= mtx.cols ? 0 : SVD::FULL_UV)); - _dst.create(svd.vt.cols, 1, svd.vt.type()); - Mat dst = _dst.getMat(); - svd.vt.row(svd.vt.rows-1).reshape(1,svd.vt.cols).copyTo(dst); -} - -template inline void - SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt ) -{ - CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); - Mat _a(a, false), _u(u, false), _w(w, false), _vt(vt, false); - SVD::compute(_a, _w, _u, _vt); - CV_Assert(_w.data == (uchar*)&w.val[0] && _u.data == (uchar*)&u.val[0] && _vt.data == (uchar*)&vt.val[0]); -} - -template inline void -SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w ) -{ - CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); - Mat _a(a, false), _w(w, false); - SVD::compute(_a, _w); - CV_Assert(_w.data == (uchar*)&w.val[0]); -} - -template inline void -SVD::backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, - const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, - Matx<_Tp, n, nb>& dst ) -{ - CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); - Mat _u(u, false), _w(w, false), _vt(vt, false), _rhs(rhs, false), _dst(dst, false); - SVD::backSubst(_w, _u, _vt, _rhs, _dst); - CV_Assert(_dst.data == (uchar*)&dst.val[0]); -} - - - -/////////////////////////////////// Multiply-with-Carry RNG /////////////////////////////////// - -inline RNG::RNG() { state = 0xffffffff; } -inline RNG::RNG(uint64 _state) { state = _state ? _state : 0xffffffff; } - -inline RNG::operator uchar() { return (uchar)next(); } -inline RNG::operator schar() { return (schar)next(); } -inline RNG::operator ushort() { return (ushort)next(); } -inline RNG::operator short() { return (short)next(); } -inline RNG::operator int() { return (int)next(); } -inline RNG::operator unsigned() { return next(); } -inline RNG::operator float() { return next()*2.3283064365386962890625e-10f; } -inline RNG::operator double() { unsigned t = next(); return (((uint64)t << 32) | next()) * 5.4210108624275221700372640043497e-20; } - -inline unsigned RNG::operator ()(unsigned N) { return (unsigned)uniform(0,N); } -inline unsigned RNG::operator ()() { return next(); } - -inline int RNG::uniform(int a, int b) { return a == b ? a : (int)(next() % (b - a) + a); } -inline float RNG::uniform(float a, float b) { return ((float)*this)*(b - a) + a; } -inline double RNG::uniform(double a, double b) { return ((double)*this)*(b - a) + a; } - -inline unsigned RNG::next() -{ - state = (uint64)(unsigned)state* /*CV_RNG_COEFF*/ 4164903690U + (unsigned)(state >> 32); - return (unsigned)state; -} - -//! returns the next unifomly-distributed random number of the specified type -template static inline _Tp randu() -{ - return (_Tp)theRNG(); -} - -///////////////////////////////// Formatted string generation ///////////////////////////////// - -CV_EXPORTS String format( const char* fmt, ... ); - -///////////////////////////////// Formatted output of cv::Mat ///////////////////////////////// - -static inline -Ptr format(InputArray mtx, int fmt) -{ - return Formatter::get(fmt)->format(mtx.getMat()); -} - -static inline -int print(Ptr fmtd, FILE* stream = stdout) -{ - int written = 0; - fmtd->reset(); - for(const char* str = fmtd->next(); str; str = fmtd->next()) - written += fputs(str, stream); - - return written; -} - -static inline -int print(const Mat& mtx, FILE* stream = stdout) -{ - return print(Formatter::get()->format(mtx), stream); -} - -static inline -int print(const UMat& mtx, FILE* stream = stdout) -{ - return print(Formatter::get()->format(mtx.getMat(ACCESS_READ)), stream); -} - -template static inline -int print(const std::vector >& vec, FILE* stream = stdout) -{ - return print(Formatter::get()->format(Mat(vec)), stream); -} - -template static inline -int print(const std::vector >& vec, FILE* stream = stdout) -{ - return print(Formatter::get()->format(Mat(vec)), stream); -} - -template static inline -int print(const Matx<_Tp, m, n>& matx, FILE* stream = stdout) -{ - return print(Formatter::get()->format(cv::Mat(matx)), stream); -} - -//! @endcond - -/****************************************************************************************\ -* Auxiliary algorithms * -\****************************************************************************************/ - -/** @brief Splits an element set into equivalency classes. - -The generic function partition implements an \f$O(N^2)\f$ algorithm for splitting a set of \f$N\f$ elements -into one or more equivalency classes, as described in - . The function returns the number of -equivalency classes. -@param _vec Set of elements stored as a vector. -@param labels Output vector of labels. It contains as many elements as vec. Each label labels[i] is -a 0-based cluster index of `vec[i]`. -@param predicate Equivalence predicate (pointer to a boolean function of two arguments or an -instance of the class that has the method bool operator()(const _Tp& a, const _Tp& b) ). The -predicate returns true when the elements are certainly in the same class, and returns false if they -may or may not be in the same class. -@ingroup core_cluster -*/ -template int -partition( const std::vector<_Tp>& _vec, std::vector& labels, - _EqPredicate predicate=_EqPredicate()) -{ - int i, j, N = (int)_vec.size(); - const _Tp* vec = &_vec[0]; - - const int PARENT=0; - const int RANK=1; - - std::vector _nodes(N*2); - int (*nodes)[2] = (int(*)[2])&_nodes[0]; - - // The first O(N) pass: create N single-vertex trees - for(i = 0; i < N; i++) - { - nodes[i][PARENT]=-1; - nodes[i][RANK] = 0; - } - - // The main O(N^2) pass: merge connected components - for( i = 0; i < N; i++ ) - { - int root = i; - - // find root - while( nodes[root][PARENT] >= 0 ) - root = nodes[root][PARENT]; - - for( j = 0; j < N; j++ ) - { - if( i == j || !predicate(vec[i], vec[j])) - continue; - int root2 = j; - - while( nodes[root2][PARENT] >= 0 ) - root2 = nodes[root2][PARENT]; - - if( root2 != root ) - { - // unite both trees - int rank = nodes[root][RANK], rank2 = nodes[root2][RANK]; - if( rank > rank2 ) - nodes[root2][PARENT] = root; - else - { - nodes[root][PARENT] = root2; - nodes[root2][RANK] += rank == rank2; - root = root2; - } - CV_Assert( nodes[root][PARENT] < 0 ); - - int k = j, parent; - - // compress the path from node2 to root - while( (parent = nodes[k][PARENT]) >= 0 ) - { - nodes[k][PARENT] = root; - k = parent; - } - - // compress the path from node to root - k = i; - while( (parent = nodes[k][PARENT]) >= 0 ) - { - nodes[k][PARENT] = root; - k = parent; - } - } - } - } - - // Final O(N) pass: enumerate classes - labels.resize(N); - int nclasses = 0; - - for( i = 0; i < N; i++ ) - { - int root = i; - while( nodes[root][PARENT] >= 0 ) - root = nodes[root][PARENT]; - // re-use the rank as the class label - if( nodes[root][RANK] >= 0 ) - nodes[root][RANK] = ~nclasses++; - labels[i] = ~nodes[root][RANK]; - } - - return nclasses; -} - -} // cv - -#endif diff --git a/IPL/include/opencv/opencv2/core/optim.hpp b/IPL/include/opencv/opencv2/core/optim.hpp deleted file mode 100644 index 23e2155..0000000 --- a/IPL/include/opencv/opencv2/core/optim.hpp +++ /dev/null @@ -1,302 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the OpenCV Foundation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OPTIM_HPP__ -#define __OPENCV_OPTIM_HPP__ - -#include "opencv2/core.hpp" - -namespace cv -{ - -/** @addtogroup core_optim -The algorithms in this section minimize or maximize function value within specified constraints or -without any constraints. -@{ -*/ - -/** @brief Basic interface for all solvers - */ -class CV_EXPORTS MinProblemSolver : public Algorithm -{ -public: - /** @brief Represents function being optimized - */ - class CV_EXPORTS Function - { - public: - virtual ~Function() {} - virtual int getDims() const = 0; - virtual double getGradientEps() const; - virtual double calc(const double* x) const = 0; - virtual void getGradient(const double* x,double* grad); - }; - - /** @brief Getter for the optimized function. - - The optimized function is represented by Function interface, which requires derivatives to - implement the sole method calc(double*) to evaluate the function. - - @return Smart-pointer to an object that implements Function interface - it represents the - function that is being optimized. It can be empty, if no function was given so far. - */ - virtual Ptr getFunction() const = 0; - - /** @brief Setter for the optimized function. - - *It should be called at least once before the call to* minimize(), as default value is not usable. - - @param f The new function to optimize. - */ - virtual void setFunction(const Ptr& f) = 0; - - /** @brief Getter for the previously set terminal criteria for this algorithm. - - @return Deep copy of the terminal criteria used at the moment. - */ - virtual TermCriteria getTermCriteria() const = 0; - - /** @brief Set terminal criteria for solver. - - This method *is not necessary* to be called before the first call to minimize(), as the default - value is sensible. - - Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when - the function values at the vertices of simplex are within termcrit.epsilon range or simplex - becomes so small that it can enclosed in a box with termcrit.epsilon sides, whatever comes - first. - @param termcrit Terminal criteria to be used, represented as cv::TermCriteria structure. - */ - virtual void setTermCriteria(const TermCriteria& termcrit) = 0; - - /** @brief actually runs the algorithm and performs the minimization. - - The sole input parameter determines the centroid of the starting simplex (roughly, it tells - where to start), all the others (terminal criteria, initial step, function to be minimized) are - supposed to be set via the setters before the call to this method or the default values (not - always sensible) will be used. - - @param x The initial point, that will become a centroid of an initial simplex. After the algorithm - will terminate, it will be setted to the point where the algorithm stops, the point of possible - minimum. - @return The value of a function at the point found. - */ - virtual double minimize(InputOutputArray x) = 0; -}; - -/** @brief This class is used to perform the non-linear non-constrained minimization of a function, - -defined on an `n`-dimensional Euclidean space, using the **Nelder-Mead method**, also known as -**downhill simplex method**. The basic idea about the method can be obtained from -. - -It should be noted, that this method, although deterministic, is rather a heuristic and therefore -may converge to a local minima, not necessary a global one. It is iterative optimization technique, -which at each step uses an information about the values of a function evaluated only at `n+1` -points, arranged as a *simplex* in `n`-dimensional space (hence the second name of the method). At -each step new point is chosen to evaluate function at, obtained value is compared with previous -ones and based on this information simplex changes it's shape , slowly moving to the local minimum. -Thus this method is using *only* function values to make decision, on contrary to, say, Nonlinear -Conjugate Gradient method (which is also implemented in optim). - -Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when the -function values at the vertices of simplex are within termcrit.epsilon range or simplex becomes so -small that it can enclosed in a box with termcrit.epsilon sides, whatever comes first, for some -defined by user positive integer termcrit.maxCount and positive non-integer termcrit.epsilon. - -@note DownhillSolver is a derivative of the abstract interface -cv::MinProblemSolver, which in turn is derived from the Algorithm interface and is used to -encapsulate the functionality, common to all non-linear optimization algorithms in the optim -module. - -@note term criteria should meet following condition: -@code - termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0 -@endcode - */ -class CV_EXPORTS DownhillSolver : public MinProblemSolver -{ -public: - /** @brief Returns the initial step that will be used in downhill simplex algorithm. - - @param step Initial step that will be used in algorithm. Note, that although corresponding setter - accepts column-vectors as well as row-vectors, this method will return a row-vector. - @see DownhillSolver::setInitStep - */ - virtual void getInitStep(OutputArray step) const=0; - - /** @brief Sets the initial step that will be used in downhill simplex algorithm. - - Step, together with initial point (givin in DownhillSolver::minimize) are two `n`-dimensional - vectors that are used to determine the shape of initial simplex. Roughly said, initial point - determines the position of a simplex (it will become simplex's centroid), while step determines the - spread (size in each dimension) of a simplex. To be more precise, if \f$s,x_0\in\mathbb{R}^n\f$ are - the initial step and initial point respectively, the vertices of a simplex will be: - \f$v_0:=x_0-\frac{1}{2} s\f$ and \f$v_i:=x_0+s_i\f$ for \f$i=1,2,\dots,n\f$ where \f$s_i\f$ denotes - projections of the initial step of *n*-th coordinate (the result of projection is treated to be - vector given by \f$s_i:=e_i\cdot\left\f$, where \f$e_i\f$ form canonical basis) - - @param step Initial step that will be used in algorithm. Roughly said, it determines the spread - (size in each dimension) of an initial simplex. - */ - virtual void setInitStep(InputArray step)=0; - - /** @brief This function returns the reference to the ready-to-use DownhillSolver object. - - All the parameters are optional, so this procedure can be called even without parameters at - all. In this case, the default values will be used. As default value for terminal criteria are - the only sensible ones, MinProblemSolver::setFunction() and DownhillSolver::setInitStep() - should be called upon the obtained object, if the respective parameters were not given to - create(). Otherwise, the two ways (give parameters to createDownhillSolver() or miss them out - and call the MinProblemSolver::setFunction() and DownhillSolver::setInitStep()) are absolutely - equivalent (and will drop the same errors in the same way, should invalid input be detected). - @param f Pointer to the function that will be minimized, similarly to the one you submit via - MinProblemSolver::setFunction. - @param initStep Initial step, that will be used to construct the initial simplex, similarly to the one - you submit via MinProblemSolver::setInitStep. - @param termcrit Terminal criteria to the algorithm, similarly to the one you submit via - MinProblemSolver::setTermCriteria. - */ - static Ptr create(const Ptr& f=Ptr(), - InputArray initStep=Mat_(1,1,0.0), - TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)); -}; - -/** @brief This class is used to perform the non-linear non-constrained minimization of a function -with known gradient, - -defined on an *n*-dimensional Euclidean space, using the **Nonlinear Conjugate Gradient method**. -The implementation was done based on the beautifully clear explanatory article [An Introduction to -the Conjugate Gradient Method Without the Agonizing -Pain](http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf) by Jonathan Richard -Shewchuk. The method can be seen as an adaptation of a standard Conjugate Gradient method (see, for -example ) for numerically solving the -systems of linear equations. - -It should be noted, that this method, although deterministic, is rather a heuristic method and -therefore may converge to a local minima, not necessary a global one. What is even more disastrous, -most of its behaviour is ruled by gradient, therefore it essentially cannot distinguish between -local minima and maxima. Therefore, if it starts sufficiently near to the local maximum, it may -converge to it. Another obvious restriction is that it should be possible to compute the gradient of -a function at any point, thus it is preferable to have analytic expression for gradient and -computational burden should be born by the user. - -The latter responsibility is accompilished via the getGradient method of a -MinProblemSolver::Function interface (which represents function being optimized). This method takes -point a point in *n*-dimensional space (first argument represents the array of coordinates of that -point) and comput its gradient (it should be stored in the second argument as an array). - -@note class ConjGradSolver thus does not add any new methods to the basic MinProblemSolver interface. - -@note term criteria should meet following condition: -@code - termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0 - // or - termcrit.type == TermCriteria::MAX_ITER) && termcrit.maxCount > 0 -@endcode - */ -class CV_EXPORTS ConjGradSolver : public MinProblemSolver -{ -public: - /** @brief This function returns the reference to the ready-to-use ConjGradSolver object. - - All the parameters are optional, so this procedure can be called even without parameters at - all. In this case, the default values will be used. As default value for terminal criteria are - the only sensible ones, MinProblemSolver::setFunction() should be called upon the obtained - object, if the function was not given to create(). Otherwise, the two ways (submit it to - create() or miss it out and call the MinProblemSolver::setFunction()) are absolutely equivalent - (and will drop the same errors in the same way, should invalid input be detected). - @param f Pointer to the function that will be minimized, similarly to the one you submit via - MinProblemSolver::setFunction. - @param termcrit Terminal criteria to the algorithm, similarly to the one you submit via - MinProblemSolver::setTermCriteria. - */ - static Ptr create(const Ptr& f=Ptr(), - TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)); -}; - -//! return codes for cv::solveLP() function -enum SolveLPResult -{ - SOLVELP_UNBOUNDED = -2, //!< problem is unbounded (target function can achieve arbitrary high values) - SOLVELP_UNFEASIBLE = -1, //!< problem is unfeasible (there are no points that satisfy all the constraints imposed) - SOLVELP_SINGLE = 0, //!< there is only one maximum for target function - SOLVELP_MULTI = 1 //!< there are multiple maxima for target function - the arbitrary one is returned -}; - -/** @brief Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method). - -What we mean here by "linear programming problem" (or LP problem, for short) can be formulated as: - -\f[\mbox{Maximize } c\cdot x\\ - \mbox{Subject to:}\\ - Ax\leq b\\ - x\geq 0\f] - -Where \f$c\f$ is fixed `1`-by-`n` row-vector, \f$A\f$ is fixed `m`-by-`n` matrix, \f$b\f$ is fixed `m`-by-`1` -column vector and \f$x\f$ is an arbitrary `n`-by-`1` column vector, which satisfies the constraints. - -Simplex algorithm is one of many algorithms that are designed to handle this sort of problems -efficiently. Although it is not optimal in theoretical sense (there exist algorithms that can solve -any problem written as above in polynomial time, while simplex method degenerates to exponential -time for some special cases), it is well-studied, easy to implement and is shown to work well for -real-life purposes. - -The particular implementation is taken almost verbatim from **Introduction to Algorithms, third -edition** by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the -Bland's rule is used to prevent cycling. - -@param Func This row-vector corresponds to \f$c\f$ in the LP problem formulation (see above). It should -contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted, -in the latter case it is understood to correspond to \f$c^T\f$. -@param Constr `m`-by-`n+1` matrix, whose rightmost column corresponds to \f$b\f$ in formulation above -and the remaining to \f$A\f$. It should containt 32- or 64-bit floating point numbers. -@param z The solution will be returned here as a column-vector - it corresponds to \f$c\f$ in the -formulation above. It will contain 64-bit floating point numbers. -@return One of cv::SolveLPResult - */ -CV_EXPORTS_W int solveLP(const Mat& Func, const Mat& Constr, Mat& z); - -//! @} - -}// cv - -#endif diff --git a/IPL/include/opencv/opencv2/core/persistence.hpp b/IPL/include/opencv/opencv2/core/persistence.hpp deleted file mode 100644 index 17686dd..0000000 --- a/IPL/include/opencv/opencv2/core/persistence.hpp +++ /dev/null @@ -1,1195 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_PERSISTENCE_HPP__ -#define __OPENCV_CORE_PERSISTENCE_HPP__ - -#ifndef __cplusplus -# error persistence.hpp header must be compiled as C++ -#endif - -//! @addtogroup core_c -//! @{ - -/** @brief "black box" representation of the file storage associated with a file on disk. - -Several functions that are described below take CvFileStorage\* as inputs and allow the user to -save or to load hierarchical collections that consist of scalar values, standard CXCore objects -(such as matrices, sequences, graphs), and user-defined objects. - -OpenCV can read and write data in XML () or YAML () -formats. Below is an example of 3x3 floating-point identity matrix A, stored in XML and YAML files -using CXCore functions: -XML: -@code{.xml} - - - - 3 - 3 -
f
- 1. 0. 0. 0. 1. 0. 0. 0. 1. - - -@endcode -YAML: -@code{.yaml} - %YAML:1.0 - A: !!opencv-matrix - rows: 3 - cols: 3 - dt: f - data: [ 1., 0., 0., 0., 1., 0., 0., 0., 1.] -@endcode -As it can be seen from the examples, XML uses nested tags to represent hierarchy, while YAML uses -indentation for that purpose (similar to the Python programming language). - -The same functions can read and write data in both formats; the particular format is determined by -the extension of the opened file, ".xml" for XML files and ".yml" or ".yaml" for YAML. - */ -typedef struct CvFileStorage CvFileStorage; -typedef struct CvFileNode CvFileNode; - -//! @} core_c - -#include "opencv2/core/types.hpp" -#include "opencv2/core/mat.hpp" - -namespace cv { - -/** @addtogroup core_xml - -XML/YAML file storages. {#xml_storage} -======================= -Writing to a file storage. --------------------------- -You can store and then restore various OpenCV data structures to/from XML () -or YAML () formats. Also, it is possible store and load arbitrarily complex -data structures, which include OpenCV data structures, as well as primitive data types (integer and -floating-point numbers and text strings) as their elements. - -Use the following procedure to write something to XML or YAML: --# Create new FileStorage and open it for writing. It can be done with a single call to -FileStorage::FileStorage constructor that takes a filename, or you can use the default constructor -and then call FileStorage::open. Format of the file (XML or YAML) is determined from the filename -extension (".xml" and ".yml"/".yaml", respectively) --# Write all the data you want using the streaming operator `<<`, just like in the case of STL -streams. --# Close the file using FileStorage::release. FileStorage destructor also closes the file. - -Here is an example: -@code - #include "opencv2/opencv.hpp" - #include - - using namespace cv; - - int main(int, char** argv) - { - FileStorage fs("test.yml", FileStorage::WRITE); - - fs << "frameCount" << 5; - time_t rawtime; time(&rawtime); - fs << "calibrationDate" << asctime(localtime(&rawtime)); - Mat cameraMatrix = (Mat_(3,3) << 1000, 0, 320, 0, 1000, 240, 0, 0, 1); - Mat distCoeffs = (Mat_(5,1) << 0.1, 0.01, -0.001, 0, 0); - fs << "cameraMatrix" << cameraMatrix << "distCoeffs" << distCoeffs; - fs << "features" << "["; - for( int i = 0; i < 3; i++ ) - { - int x = rand() % 640; - int y = rand() % 480; - uchar lbp = rand() % 256; - - fs << "{:" << "x" << x << "y" << y << "lbp" << "[:"; - for( int j = 0; j < 8; j++ ) - fs << ((lbp >> j) & 1); - fs << "]" << "}"; - } - fs << "]"; - fs.release(); - return 0; - } -@endcode -The sample above stores to XML and integer, text string (calibration date), 2 matrices, and a custom -structure "feature", which includes feature coordinates and LBP (local binary pattern) value. Here -is output of the sample: -@code{.yaml} -%YAML:1.0 -frameCount: 5 -calibrationDate: "Fri Jun 17 14:09:29 2011\n" -cameraMatrix: !!opencv-matrix - rows: 3 - cols: 3 - dt: d - data: [ 1000., 0., 320., 0., 1000., 240., 0., 0., 1. ] -distCoeffs: !!opencv-matrix - rows: 5 - cols: 1 - dt: d - data: [ 1.0000000000000001e-01, 1.0000000000000000e-02, - -1.0000000000000000e-03, 0., 0. ] -features: - - { x:167, y:49, lbp:[ 1, 0, 0, 1, 1, 0, 1, 1 ] } - - { x:298, y:130, lbp:[ 0, 0, 0, 1, 0, 0, 1, 1 ] } - - { x:344, y:158, lbp:[ 1, 1, 0, 0, 0, 0, 1, 0 ] } -@endcode - -As an exercise, you can replace ".yml" with ".xml" in the sample above and see, how the -corresponding XML file will look like. - -Several things can be noted by looking at the sample code and the output: - -- The produced YAML (and XML) consists of heterogeneous collections that can be nested. There are 2 - types of collections: named collections (mappings) and unnamed collections (sequences). In mappings - each element has a name and is accessed by name. This is similar to structures and std::map in - C/C++ and dictionaries in Python. In sequences elements do not have names, they are accessed by - indices. This is similar to arrays and std::vector in C/C++ and lists, tuples in Python. - "Heterogeneous" means that elements of each single collection can have different types. - - Top-level collection in YAML/XML is a mapping. Each matrix is stored as a mapping, and the matrix - elements are stored as a sequence. Then, there is a sequence of features, where each feature is - represented a mapping, and lbp value in a nested sequence. - -- When you write to a mapping (a structure), you write element name followed by its value. When you - write to a sequence, you simply write the elements one by one. OpenCV data structures (such as - cv::Mat) are written in absolutely the same way as simple C data structures - using `<<` - operator. - -- To write a mapping, you first write the special string `{` to the storage, then write the - elements as pairs (`fs << << `) and then write the closing - `}`. - -- To write a sequence, you first write the special string `[`, then write the elements, then - write the closing `]`. - -- In YAML (but not XML), mappings and sequences can be written in a compact Python-like inline - form. In the sample above matrix elements, as well as each feature, including its lbp value, is - stored in such inline form. To store a mapping/sequence in a compact form, put `:` after the - opening character, e.g. use `{:` instead of `{` and `[:` instead of `[`. When the - data is written to XML, those extra `:` are ignored. - -Reading data from a file storage. ---------------------------------- -To read the previously written XML or YAML file, do the following: --# Open the file storage using FileStorage::FileStorage constructor or FileStorage::open method. - In the current implementation the whole file is parsed and the whole representation of file - storage is built in memory as a hierarchy of file nodes (see FileNode) - --# Read the data you are interested in. Use FileStorage::operator [], FileNode::operator [] - and/or FileNodeIterator. - --# Close the storage using FileStorage::release. - -Here is how to read the file created by the code sample above: -@code - FileStorage fs2("test.yml", FileStorage::READ); - - // first method: use (type) operator on FileNode. - int frameCount = (int)fs2["frameCount"]; - - String date; - // second method: use FileNode::operator >> - fs2["calibrationDate"] >> date; - - Mat cameraMatrix2, distCoeffs2; - fs2["cameraMatrix"] >> cameraMatrix2; - fs2["distCoeffs"] >> distCoeffs2; - - cout << "frameCount: " << frameCount << endl - << "calibration date: " << date << endl - << "camera matrix: " << cameraMatrix2 << endl - << "distortion coeffs: " << distCoeffs2 << endl; - - FileNode features = fs2["features"]; - FileNodeIterator it = features.begin(), it_end = features.end(); - int idx = 0; - std::vector lbpval; - - // iterate through a sequence using FileNodeIterator - for( ; it != it_end; ++it, idx++ ) - { - cout << "feature #" << idx << ": "; - cout << "x=" << (int)(*it)["x"] << ", y=" << (int)(*it)["y"] << ", lbp: ("; - // you can also easily read numerical arrays using FileNode >> std::vector operator. - (*it)["lbp"] >> lbpval; - for( int i = 0; i < (int)lbpval.size(); i++ ) - cout << " " << (int)lbpval[i]; - cout << ")" << endl; - } - fs2.release(); -@endcode - -Format specification {#format_spec} --------------------- -`([count]{u|c|w|s|i|f|d})`... where the characters correspond to fundamental C++ types: -- `u` 8-bit unsigned number -- `c` 8-bit signed number -- `w` 16-bit unsigned number -- `s` 16-bit signed number -- `i` 32-bit signed number -- `f` single precision floating-point number -- `d` double precision floating-point number -- `r` pointer, 32 lower bits of which are written as a signed integer. The type can be used to - store structures with links between the elements. - -`count` is the optional counter of values of a given type. For example, `2if` means that each array -element is a structure of 2 integers, followed by a single-precision floating-point number. The -equivalent notations of the above specification are `iif`, `2i1f` and so forth. Other examples: `u` -means that the array consists of bytes, and `2d` means the array consists of pairs of doubles. - -@see @ref filestorage.cpp -*/ - -//! @{ - -/** @example filestorage.cpp -A complete example using the FileStorage interface -*/ - -////////////////////////// XML & YAML I/O ////////////////////////// - -class CV_EXPORTS FileNode; -class CV_EXPORTS FileNodeIterator; - -/** @brief XML/YAML file storage class that encapsulates all the information necessary for writing or reading -data to/from a file. - */ -class CV_EXPORTS_W FileStorage -{ -public: - //! file storage mode - enum Mode - { - READ = 0, //!< value, open the file for reading - WRITE = 1, //!< value, open the file for writing - APPEND = 2, //!< value, open the file for appending - MEMORY = 4, //!< flag, read data from source or write data to the internal buffer (which is - //!< returned by FileStorage::release) - FORMAT_MASK = (7<<3), //!< mask for format flags - FORMAT_AUTO = 0, //!< flag, auto format - FORMAT_XML = (1<<3), //!< flag, XML format - FORMAT_YAML = (2<<3) //!< flag, YAML format - }; - enum - { - UNDEFINED = 0, - VALUE_EXPECTED = 1, - NAME_EXPECTED = 2, - INSIDE_MAP = 4 - }; - - /** @brief The constructors. - - The full constructor opens the file. Alternatively you can use the default constructor and then - call FileStorage::open. - */ - CV_WRAP FileStorage(); - - /** @overload - @param source Name of the file to open or the text string to read the data from. Extension of the - file (.xml or .yml/.yaml) determines its format (XML or YAML respectively). Also you can append .gz - to work with compressed files, for example myHugeMatrix.xml.gz. If both FileStorage::WRITE and - FileStorage::MEMORY flags are specified, source is used just to specify the output file format (e.g. - mydata.xml, .yml etc.). - @param flags Mode of operation. See FileStorage::Mode - @param encoding Encoding of the file. Note that UTF-16 XML encoding is not supported currently and - you should use 8-bit encoding instead of it. - */ - CV_WRAP FileStorage(const String& source, int flags, const String& encoding=String()); - - /** @overload */ - FileStorage(CvFileStorage* fs, bool owning=true); - - //! the destructor. calls release() - virtual ~FileStorage(); - - /** @brief Opens a file. - - See description of parameters in FileStorage::FileStorage. The method calls FileStorage::release - before opening the file. - @param filename Name of the file to open or the text string to read the data from. - Extension of the file (.xml or .yml/.yaml) determines its format (XML or YAML respectively). - Also you can append .gz to work with compressed files, for example myHugeMatrix.xml.gz. If both - FileStorage::WRITE and FileStorage::MEMORY flags are specified, source is used just to specify - the output file format (e.g. mydata.xml, .yml etc.). - @param flags Mode of operation. One of FileStorage::Mode - @param encoding Encoding of the file. Note that UTF-16 XML encoding is not supported currently and - you should use 8-bit encoding instead of it. - */ - CV_WRAP virtual bool open(const String& filename, int flags, const String& encoding=String()); - - /** @brief Checks whether the file is opened. - - @returns true if the object is associated with the current file and false otherwise. It is a - good practice to call this method after you tried to open a file. - */ - CV_WRAP virtual bool isOpened() const; - - /** @brief Closes the file and releases all the memory buffers. - - Call this method after all I/O operations with the storage are finished. - */ - CV_WRAP virtual void release(); - - /** @brief Closes the file and releases all the memory buffers. - - Call this method after all I/O operations with the storage are finished. If the storage was - opened for writing data and FileStorage::WRITE was specified - */ - CV_WRAP virtual String releaseAndGetString(); - - /** @brief Returns the first element of the top-level mapping. - @returns The first element of the top-level mapping. - */ - CV_WRAP FileNode getFirstTopLevelNode() const; - - /** @brief Returns the top-level mapping - @param streamidx Zero-based index of the stream. In most cases there is only one stream in the file. - However, YAML supports multiple streams and so there can be several. - @returns The top-level mapping. - */ - CV_WRAP FileNode root(int streamidx=0) const; - - /** @brief Returns the specified element of the top-level mapping. - @param nodename Name of the file node. - @returns Node with the given name. - */ - FileNode operator[](const String& nodename) const; - - /** @overload */ - CV_WRAP FileNode operator[](const char* nodename) const; - - /** @brief Returns the obsolete C FileStorage structure. - @returns Pointer to the underlying C FileStorage structure - */ - CvFileStorage* operator *() { return fs.get(); } - - /** @overload */ - const CvFileStorage* operator *() const { return fs.get(); } - - /** @brief Writes multiple numbers. - - Writes one or more numbers of the specified format to the currently written structure. Usually it is - more convenient to use operator `<<` instead of this method. - @param fmt Specification of each array element, see @ref format_spec "format specification" - @param vec Pointer to the written array. - @param len Number of the uchar elements to write. - */ - void writeRaw( const String& fmt, const uchar* vec, size_t len ); - - /** @brief Writes the registered C structure (CvMat, CvMatND, CvSeq). - @param name Name of the written object. - @param obj Pointer to the object. - @see ocvWrite for details. - */ - void writeObj( const String& name, const void* obj ); - - /** @brief Returns the normalized object name for the specified name of a file. - @param filename Name of a file - @returns The normalized object name. - */ - static String getDefaultObjectName(const String& filename); - - Ptr fs; //!< the underlying C FileStorage structure - String elname; //!< the currently written element - std::vector structs; //!< the stack of written structures - int state; //!< the writer state -}; - -template<> CV_EXPORTS void DefaultDeleter::operator ()(CvFileStorage* obj) const; - -/** @brief File Storage Node class. - -The node is used to store each and every element of the file storage opened for reading. When -XML/YAML file is read, it is first parsed and stored in the memory as a hierarchical collection of -nodes. Each node can be a “leaf” that is contain a single number or a string, or be a collection of -other nodes. There can be named collections (mappings) where each element has a name and it is -accessed by a name, and ordered collections (sequences) where elements do not have names but rather -accessed by index. Type of the file node can be determined using FileNode::type method. - -Note that file nodes are only used for navigating file storages opened for reading. When a file -storage is opened for writing, no data is stored in memory after it is written. - */ -class CV_EXPORTS_W_SIMPLE FileNode -{ -public: - //! type of the file storage node - enum Type - { - NONE = 0, //!< empty node - INT = 1, //!< an integer - REAL = 2, //!< floating-point number - FLOAT = REAL, //!< synonym or REAL - STR = 3, //!< text string in UTF-8 encoding - STRING = STR, //!< synonym for STR - REF = 4, //!< integer of size size_t. Typically used for storing complex dynamic structures where some elements reference the others - SEQ = 5, //!< sequence - MAP = 6, //!< mapping - TYPE_MASK = 7, - FLOW = 8, //!< compact representation of a sequence or mapping. Used only by YAML writer - USER = 16, //!< a registered object (e.g. a matrix) - EMPTY = 32, //!< empty structure (sequence or mapping) - NAMED = 64 //!< the node has a name (i.e. it is element of a mapping) - }; - /** @brief The constructors. - - These constructors are used to create a default file node, construct it from obsolete structures or - from the another file node. - */ - CV_WRAP FileNode(); - - /** @overload - @param fs Pointer to the obsolete file storage structure. - @param node File node to be used as initialization for the created file node. - */ - FileNode(const CvFileStorage* fs, const CvFileNode* node); - - /** @overload - @param node File node to be used as initialization for the created file node. - */ - FileNode(const FileNode& node); - - /** @brief Returns element of a mapping node or a sequence node. - @param nodename Name of an element in the mapping node. - @returns Returns the element with the given identifier. - */ - FileNode operator[](const String& nodename) const; - - /** @overload - @param nodename Name of an element in the mapping node. - */ - CV_WRAP FileNode operator[](const char* nodename) const; - - /** @overload - @param i Index of an element in the sequence node. - */ - CV_WRAP FileNode operator[](int i) const; - - /** @brief Returns type of the node. - @returns Type of the node. See FileNode::Type - */ - CV_WRAP int type() const; - - //! returns true if the node is empty - CV_WRAP bool empty() const; - //! returns true if the node is a "none" object - CV_WRAP bool isNone() const; - //! returns true if the node is a sequence - CV_WRAP bool isSeq() const; - //! returns true if the node is a mapping - CV_WRAP bool isMap() const; - //! returns true if the node is an integer - CV_WRAP bool isInt() const; - //! returns true if the node is a floating-point number - CV_WRAP bool isReal() const; - //! returns true if the node is a text string - CV_WRAP bool isString() const; - //! returns true if the node has a name - CV_WRAP bool isNamed() const; - //! returns the node name or an empty string if the node is nameless - CV_WRAP String name() const; - //! returns the number of elements in the node, if it is a sequence or mapping, or 1 otherwise. - CV_WRAP size_t size() const; - //! returns the node content as an integer. If the node stores floating-point number, it is rounded. - operator int() const; - //! returns the node content as float - operator float() const; - //! returns the node content as double - operator double() const; - //! returns the node content as text string - operator String() const; -#ifndef OPENCV_NOSTL - operator std::string() const; -#endif - - //! returns pointer to the underlying file node - CvFileNode* operator *(); - //! returns pointer to the underlying file node - const CvFileNode* operator* () const; - - //! returns iterator pointing to the first node element - FileNodeIterator begin() const; - //! returns iterator pointing to the element following the last node element - FileNodeIterator end() const; - - /** @brief Reads node elements to the buffer with the specified format. - - Usually it is more convenient to use operator `>>` instead of this method. - @param fmt Specification of each array element. See @ref format_spec "format specification" - @param vec Pointer to the destination array. - @param len Number of elements to read. If it is greater than number of remaining elements then all - of them will be read. - */ - void readRaw( const String& fmt, uchar* vec, size_t len ) const; - - //! reads the registered object and returns pointer to it - void* readObj() const; - - // do not use wrapper pointer classes for better efficiency - const CvFileStorage* fs; - const CvFileNode* node; -}; - - -/** @brief used to iterate through sequences and mappings. - -A standard STL notation, with node.begin(), node.end() denoting the beginning and the end of a -sequence, stored in node. See the data reading sample in the beginning of the section. - */ -class CV_EXPORTS FileNodeIterator -{ -public: - /** @brief The constructors. - - These constructors are used to create a default iterator, set it to specific element in a file node - or construct it from another iterator. - */ - FileNodeIterator(); - - /** @overload - @param fs File storage for the iterator. - @param node File node for the iterator. - @param ofs Index of the element in the node. The created iterator will point to this element. - */ - FileNodeIterator(const CvFileStorage* fs, const CvFileNode* node, size_t ofs=0); - - /** @overload - @param it Iterator to be used as initialization for the created iterator. - */ - FileNodeIterator(const FileNodeIterator& it); - - //! returns the currently observed element - FileNode operator *() const; - //! accesses the currently observed element methods - FileNode operator ->() const; - - //! moves iterator to the next node - FileNodeIterator& operator ++ (); - //! moves iterator to the next node - FileNodeIterator operator ++ (int); - //! moves iterator to the previous node - FileNodeIterator& operator -- (); - //! moves iterator to the previous node - FileNodeIterator operator -- (int); - //! moves iterator forward by the specified offset (possibly negative) - FileNodeIterator& operator += (int ofs); - //! moves iterator backward by the specified offset (possibly negative) - FileNodeIterator& operator -= (int ofs); - - /** @brief Reads node elements to the buffer with the specified format. - - Usually it is more convenient to use operator `>>` instead of this method. - @param fmt Specification of each array element. See @ref format_spec "format specification" - @param vec Pointer to the destination array. - @param maxCount Number of elements to read. If it is greater than number of remaining elements then - all of them will be read. - */ - FileNodeIterator& readRaw( const String& fmt, uchar* vec, - size_t maxCount=(size_t)INT_MAX ); - - struct SeqReader - { - int header_size; - void* seq; /* sequence, beign read; CvSeq */ - void* block; /* current block; CvSeqBlock */ - schar* ptr; /* pointer to element be read next */ - schar* block_min; /* pointer to the beginning of block */ - schar* block_max; /* pointer to the end of block */ - int delta_index;/* = seq->first->start_index */ - schar* prev_elem; /* pointer to previous element */ - }; - - const CvFileStorage* fs; - const CvFileNode* container; - SeqReader reader; - size_t remaining; -}; - -//! @} core_xml - -/////////////////// XML & YAML I/O implementation ////////////////// - -//! @relates cv::FileStorage -//! @{ - -CV_EXPORTS void write( FileStorage& fs, const String& name, int value ); -CV_EXPORTS void write( FileStorage& fs, const String& name, float value ); -CV_EXPORTS void write( FileStorage& fs, const String& name, double value ); -CV_EXPORTS void write( FileStorage& fs, const String& name, const String& value ); -CV_EXPORTS void write( FileStorage& fs, const String& name, const Mat& value ); -CV_EXPORTS void write( FileStorage& fs, const String& name, const SparseMat& value ); -CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector& value); -CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector& value); - -CV_EXPORTS void writeScalar( FileStorage& fs, int value ); -CV_EXPORTS void writeScalar( FileStorage& fs, float value ); -CV_EXPORTS void writeScalar( FileStorage& fs, double value ); -CV_EXPORTS void writeScalar( FileStorage& fs, const String& value ); - -//! @} - -//! @relates cv::FileNode -//! @{ - -CV_EXPORTS void read(const FileNode& node, int& value, int default_value); -CV_EXPORTS void read(const FileNode& node, float& value, float default_value); -CV_EXPORTS void read(const FileNode& node, double& value, double default_value); -CV_EXPORTS void read(const FileNode& node, String& value, const String& default_value); -CV_EXPORTS void read(const FileNode& node, Mat& mat, const Mat& default_mat = Mat() ); -CV_EXPORTS void read(const FileNode& node, SparseMat& mat, const SparseMat& default_mat = SparseMat() ); -CV_EXPORTS void read(const FileNode& node, std::vector& keypoints); -CV_EXPORTS void read(const FileNode& node, std::vector& matches); - -template static inline void read(const FileNode& node, Point_<_Tp>& value, const Point_<_Tp>& default_value) -{ - std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; - value = temp.size() != 2 ? default_value : Point_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); -} - -template static inline void read(const FileNode& node, Point3_<_Tp>& value, const Point3_<_Tp>& default_value) -{ - std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; - value = temp.size() != 3 ? default_value : Point3_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), - saturate_cast<_Tp>(temp[2])); -} - -template static inline void read(const FileNode& node, Size_<_Tp>& value, const Size_<_Tp>& default_value) -{ - std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; - value = temp.size() != 2 ? default_value : Size_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); -} - -template static inline void read(const FileNode& node, Complex<_Tp>& value, const Complex<_Tp>& default_value) -{ - std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; - value = temp.size() != 2 ? default_value : Complex<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); -} - -template static inline void read(const FileNode& node, Rect_<_Tp>& value, const Rect_<_Tp>& default_value) -{ - std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; - value = temp.size() != 4 ? default_value : Rect_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), - saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3])); -} - -template static inline void read(const FileNode& node, Vec<_Tp, cn>& value, const Vec<_Tp, cn>& default_value) -{ - std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; - value = temp.size() != cn ? default_value : Vec<_Tp, cn>(&temp[0]); -} - -template static inline void read(const FileNode& node, Scalar_<_Tp>& value, const Scalar_<_Tp>& default_value) -{ - std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; - value = temp.size() != 4 ? default_value : Scalar_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), - saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3])); -} - -static inline void read(const FileNode& node, Range& value, const Range& default_value) -{ - Point2i temp(value.start, value.end); const Point2i default_temp = Point2i(default_value.start, default_value.end); - read(node, temp, default_temp); - value.start = temp.x; value.end = temp.y; -} - -//! @} - -/** @brief Writes string to a file storage. -@relates cv::FileStorage - */ -CV_EXPORTS FileStorage& operator << (FileStorage& fs, const String& str); - -//! @cond IGNORED - -namespace internal -{ - class CV_EXPORTS WriteStructContext - { - public: - WriteStructContext(FileStorage& _fs, const String& name, int flags, const String& typeName = String()); - ~WriteStructContext(); - private: - FileStorage* fs; - }; - - template class VecWriterProxy - { - public: - VecWriterProxy( FileStorage* _fs ) : fs(_fs) {} - void operator()(const std::vector<_Tp>& vec) const - { - size_t count = vec.size(); - for (size_t i = 0; i < count; i++) - write(*fs, vec[i]); - } - private: - FileStorage* fs; - }; - - template class VecWriterProxy<_Tp, 1> - { - public: - VecWriterProxy( FileStorage* _fs ) : fs(_fs) {} - void operator()(const std::vector<_Tp>& vec) const - { - int _fmt = DataType<_Tp>::fmt; - char fmt[] = { (char)((_fmt >> 8) + '1'), (char)_fmt, '\0' }; - fs->writeRaw(fmt, !vec.empty() ? (uchar*)&vec[0] : 0, vec.size() * sizeof(_Tp)); - } - private: - FileStorage* fs; - }; - - template class VecReaderProxy - { - public: - VecReaderProxy( FileNodeIterator* _it ) : it(_it) {} - void operator()(std::vector<_Tp>& vec, size_t count) const - { - count = std::min(count, it->remaining); - vec.resize(count); - for (size_t i = 0; i < count; i++, ++(*it)) - read(**it, vec[i], _Tp()); - } - private: - FileNodeIterator* it; - }; - - template class VecReaderProxy<_Tp, 1> - { - public: - VecReaderProxy( FileNodeIterator* _it ) : it(_it) {} - void operator()(std::vector<_Tp>& vec, size_t count) const - { - size_t remaining = it->remaining; - size_t cn = DataType<_Tp>::channels; - int _fmt = DataType<_Tp>::fmt; - char fmt[] = { (char)((_fmt >> 8)+'1'), (char)_fmt, '\0' }; - size_t remaining1 = remaining / cn; - count = count < remaining1 ? count : remaining1; - vec.resize(count); - it->readRaw(fmt, !vec.empty() ? (uchar*)&vec[0] : 0, count*sizeof(_Tp)); - } - private: - FileNodeIterator* it; - }; - -} // internal - -//! @endcond - -//! @relates cv::FileStorage -//! @{ - -template static inline -void write(FileStorage& fs, const _Tp& value) -{ - write(fs, String(), value); -} - -template<> inline -void write( FileStorage& fs, const int& value ) -{ - writeScalar(fs, value); -} - -template<> inline -void write( FileStorage& fs, const float& value ) -{ - writeScalar(fs, value); -} - -template<> inline -void write( FileStorage& fs, const double& value ) -{ - writeScalar(fs, value); -} - -template<> inline -void write( FileStorage& fs, const String& value ) -{ - writeScalar(fs, value); -} - -template static inline -void write(FileStorage& fs, const Point_<_Tp>& pt ) -{ - write(fs, pt.x); - write(fs, pt.y); -} - -template static inline -void write(FileStorage& fs, const Point3_<_Tp>& pt ) -{ - write(fs, pt.x); - write(fs, pt.y); - write(fs, pt.z); -} - -template static inline -void write(FileStorage& fs, const Size_<_Tp>& sz ) -{ - write(fs, sz.width); - write(fs, sz.height); -} - -template static inline -void write(FileStorage& fs, const Complex<_Tp>& c ) -{ - write(fs, c.re); - write(fs, c.im); -} - -template static inline -void write(FileStorage& fs, const Rect_<_Tp>& r ) -{ - write(fs, r.x); - write(fs, r.y); - write(fs, r.width); - write(fs, r.height); -} - -template static inline -void write(FileStorage& fs, const Vec<_Tp, cn>& v ) -{ - for(int i = 0; i < cn; i++) - write(fs, v.val[i]); -} - -template static inline -void write(FileStorage& fs, const Scalar_<_Tp>& s ) -{ - write(fs, s.val[0]); - write(fs, s.val[1]); - write(fs, s.val[2]); - write(fs, s.val[3]); -} - -static inline -void write(FileStorage& fs, const Range& r ) -{ - write(fs, r.start); - write(fs, r.end); -} - -template static inline -void write( FileStorage& fs, const std::vector<_Tp>& vec ) -{ - cv::internal::VecWriterProxy<_Tp, DataType<_Tp>::fmt != 0> w(&fs); - w(vec); -} - - -template static inline -void write(FileStorage& fs, const String& name, const Point_<_Tp>& pt ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, pt); -} - -template static inline -void write(FileStorage& fs, const String& name, const Point3_<_Tp>& pt ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, pt); -} - -template static inline -void write(FileStorage& fs, const String& name, const Size_<_Tp>& sz ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, sz); -} - -template static inline -void write(FileStorage& fs, const String& name, const Complex<_Tp>& c ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, c); -} - -template static inline -void write(FileStorage& fs, const String& name, const Rect_<_Tp>& r ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, r); -} - -template static inline -void write(FileStorage& fs, const String& name, const Vec<_Tp, cn>& v ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, v); -} - -template static inline -void write(FileStorage& fs, const String& name, const Scalar_<_Tp>& s ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, s); -} - -static inline -void write(FileStorage& fs, const String& name, const Range& r ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, r); -} - -template static inline -void write( FileStorage& fs, const String& name, const std::vector<_Tp>& vec ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+(DataType<_Tp>::fmt != 0 ? FileNode::FLOW : 0)); - write(fs, vec); -} - -//! @} FileStorage - -//! @relates cv::FileNode -//! @{ - -static inline -void read(const FileNode& node, bool& value, bool default_value) -{ - int temp; - read(node, temp, (int)default_value); - value = temp != 0; -} - -static inline -void read(const FileNode& node, uchar& value, uchar default_value) -{ - int temp; - read(node, temp, (int)default_value); - value = saturate_cast(temp); -} - -static inline -void read(const FileNode& node, schar& value, schar default_value) -{ - int temp; - read(node, temp, (int)default_value); - value = saturate_cast(temp); -} - -static inline -void read(const FileNode& node, ushort& value, ushort default_value) -{ - int temp; - read(node, temp, (int)default_value); - value = saturate_cast(temp); -} - -static inline -void read(const FileNode& node, short& value, short default_value) -{ - int temp; - read(node, temp, (int)default_value); - value = saturate_cast(temp); -} - -template static inline -void read( FileNodeIterator& it, std::vector<_Tp>& vec, size_t maxCount = (size_t)INT_MAX ) -{ - cv::internal::VecReaderProxy<_Tp, DataType<_Tp>::fmt != 0> r(&it); - r(vec, maxCount); -} - -template static inline -void read( const FileNode& node, std::vector<_Tp>& vec, const std::vector<_Tp>& default_value = std::vector<_Tp>() ) -{ - if(!node.node) - vec = default_value; - else - { - FileNodeIterator it = node.begin(); - read( it, vec ); - } -} - -//! @} FileNode - -//! @relates cv::FileStorage -//! @{ - -/** @brief Writes data to a file storage. - */ -template static inline -FileStorage& operator << (FileStorage& fs, const _Tp& value) -{ - if( !fs.isOpened() ) - return fs; - if( fs.state == FileStorage::NAME_EXPECTED + FileStorage::INSIDE_MAP ) - CV_Error( Error::StsError, "No element name has been given" ); - write( fs, fs.elname, value ); - if( fs.state & FileStorage::INSIDE_MAP ) - fs.state = FileStorage::NAME_EXPECTED + FileStorage::INSIDE_MAP; - return fs; -} - -/** @brief Writes data to a file storage. - */ -static inline -FileStorage& operator << (FileStorage& fs, const char* str) -{ - return (fs << String(str)); -} - -/** @brief Writes data to a file storage. - */ -static inline -FileStorage& operator << (FileStorage& fs, char* value) -{ - return (fs << String(value)); -} - -//! @} FileStorage - -//! @relates cv::FileNodeIterator -//! @{ - -/** @brief Reads data from a file storage. - */ -template static inline -FileNodeIterator& operator >> (FileNodeIterator& it, _Tp& value) -{ - read( *it, value, _Tp()); - return ++it; -} - -/** @brief Reads data from a file storage. - */ -template static inline -FileNodeIterator& operator >> (FileNodeIterator& it, std::vector<_Tp>& vec) -{ - cv::internal::VecReaderProxy<_Tp, DataType<_Tp>::fmt != 0> r(&it); - r(vec, (size_t)INT_MAX); - return it; -} - -//! @} FileNodeIterator - -//! @relates cv::FileNode -//! @{ - -/** @brief Reads data from a file storage. - */ -template static inline -void operator >> (const FileNode& n, _Tp& value) -{ - read( n, value, _Tp()); -} - -/** @brief Reads data from a file storage. - */ -template static inline -void operator >> (const FileNode& n, std::vector<_Tp>& vec) -{ - FileNodeIterator it = n.begin(); - it >> vec; -} - -//! @} FileNode - -//! @relates cv::FileNodeIterator -//! @{ - -static inline -bool operator == (const FileNodeIterator& it1, const FileNodeIterator& it2) -{ - return it1.fs == it2.fs && it1.container == it2.container && - it1.reader.ptr == it2.reader.ptr && it1.remaining == it2.remaining; -} - -static inline -bool operator != (const FileNodeIterator& it1, const FileNodeIterator& it2) -{ - return !(it1 == it2); -} - -static inline -ptrdiff_t operator - (const FileNodeIterator& it1, const FileNodeIterator& it2) -{ - return it2.remaining - it1.remaining; -} - -static inline -bool operator < (const FileNodeIterator& it1, const FileNodeIterator& it2) -{ - return it1.remaining > it2.remaining; -} - -//! @} FileNodeIterator - -//! @cond IGNORED - -inline FileNode FileStorage::getFirstTopLevelNode() const { FileNode r = root(); FileNodeIterator it = r.begin(); return it != r.end() ? *it : FileNode(); } -inline FileNode::FileNode() : fs(0), node(0) {} -inline FileNode::FileNode(const CvFileStorage* _fs, const CvFileNode* _node) : fs(_fs), node(_node) {} -inline FileNode::FileNode(const FileNode& _node) : fs(_node.fs), node(_node.node) {} -inline bool FileNode::empty() const { return node == 0; } -inline bool FileNode::isNone() const { return type() == NONE; } -inline bool FileNode::isSeq() const { return type() == SEQ; } -inline bool FileNode::isMap() const { return type() == MAP; } -inline bool FileNode::isInt() const { return type() == INT; } -inline bool FileNode::isReal() const { return type() == REAL; } -inline bool FileNode::isString() const { return type() == STR; } -inline CvFileNode* FileNode::operator *() { return (CvFileNode*)node; } -inline const CvFileNode* FileNode::operator* () const { return node; } -inline FileNode::operator int() const { int value; read(*this, value, 0); return value; } -inline FileNode::operator float() const { float value; read(*this, value, 0.f); return value; } -inline FileNode::operator double() const { double value; read(*this, value, 0.); return value; } -inline FileNode::operator String() const { String value; read(*this, value, value); return value; } -inline FileNodeIterator FileNode::begin() const { return FileNodeIterator(fs, node); } -inline FileNodeIterator FileNode::end() const { return FileNodeIterator(fs, node, size()); } -inline void FileNode::readRaw( const String& fmt, uchar* vec, size_t len ) const { begin().readRaw( fmt, vec, len ); } -inline FileNode FileNodeIterator::operator *() const { return FileNode(fs, (const CvFileNode*)(const void*)reader.ptr); } -inline FileNode FileNodeIterator::operator ->() const { return FileNode(fs, (const CvFileNode*)(const void*)reader.ptr); } -inline String::String(const FileNode& fn): cstr_(0), len_(0) { read(fn, *this, *this); } - -//! @endcond - -} // cv - -#endif // __OPENCV_CORE_PERSISTENCE_HPP__ diff --git a/IPL/include/opencv/opencv2/core/private.cuda.hpp b/IPL/include/opencv/opencv2/core/private.cuda.hpp deleted file mode 100644 index d676ce8..0000000 --- a/IPL/include/opencv/opencv2/core/private.cuda.hpp +++ /dev/null @@ -1,172 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_PRIVATE_CUDA_HPP__ -#define __OPENCV_CORE_PRIVATE_CUDA_HPP__ - -#ifndef __OPENCV_BUILD -# error this is a private header which should not be used from outside of the OpenCV library -#endif - -#include "cvconfig.h" - -#include "opencv2/core/cvdef.h" -#include "opencv2/core/base.hpp" - -#include "opencv2/core/cuda.hpp" - -#ifdef HAVE_CUDA -# include -# include -# include -# include "opencv2/core/cuda_stream_accessor.hpp" -# include "opencv2/core/cuda/common.hpp" - -# define NPP_VERSION (NPP_VERSION_MAJOR * 1000 + NPP_VERSION_MINOR * 100 + NPP_VERSION_BUILD) - -# define CUDART_MINIMUM_REQUIRED_VERSION 4020 - -# if (CUDART_VERSION < CUDART_MINIMUM_REQUIRED_VERSION) -# error "Insufficient Cuda Runtime library version, please update it." -# endif - -# if defined(CUDA_ARCH_BIN_OR_PTX_10) -# error "OpenCV CUDA module doesn't support NVIDIA compute capability 1.0" -# endif -#endif - -//! @cond IGNORED - -namespace cv { namespace cuda { - CV_EXPORTS cv::String getNppErrorMessage(int code); - CV_EXPORTS cv::String getCudaDriverApiErrorMessage(int code); - - CV_EXPORTS GpuMat getInputMat(InputArray _src, Stream& stream); - - CV_EXPORTS GpuMat getOutputMat(OutputArray _dst, int rows, int cols, int type, Stream& stream); - static inline GpuMat getOutputMat(OutputArray _dst, Size size, int type, Stream& stream) - { - return getOutputMat(_dst, size.height, size.width, type, stream); - } - - CV_EXPORTS void syncOutput(const GpuMat& dst, OutputArray _dst, Stream& stream); -}} - -#ifndef HAVE_CUDA - -static inline void throw_no_cuda() { CV_Error(cv::Error::GpuNotSupported, "The library is compiled without CUDA support"); } - -#else // HAVE_CUDA - -static inline void throw_no_cuda() { CV_Error(cv::Error::StsNotImplemented, "The called functionality is disabled for current build or platform"); } - -namespace cv { namespace cuda -{ - class CV_EXPORTS BufferPool - { - public: - explicit BufferPool(Stream& stream); - - GpuMat getBuffer(int rows, int cols, int type); - GpuMat getBuffer(Size size, int type) { return getBuffer(size.height, size.width, type); } - - GpuMat::Allocator* getAllocator() const { return allocator_; } - - private: - GpuMat::Allocator* allocator_; - }; - - static inline void checkNppError(int code, const char* file, const int line, const char* func) - { - if (code < 0) - cv::error(cv::Error::GpuApiCallError, getNppErrorMessage(code), func, file, line); - } - - static inline void checkCudaDriverApiError(int code, const char* file, const int line, const char* func) - { - if (code != CUDA_SUCCESS) - cv::error(cv::Error::GpuApiCallError, getCudaDriverApiErrorMessage(code), func, file, line); - } - - template struct NPPTypeTraits; - template<> struct NPPTypeTraits { typedef Npp8u npp_type; }; - template<> struct NPPTypeTraits { typedef Npp8s npp_type; }; - template<> struct NPPTypeTraits { typedef Npp16u npp_type; }; - template<> struct NPPTypeTraits { typedef Npp16s npp_type; }; - template<> struct NPPTypeTraits { typedef Npp32s npp_type; }; - template<> struct NPPTypeTraits { typedef Npp32f npp_type; }; - template<> struct NPPTypeTraits { typedef Npp64f npp_type; }; - - class NppStreamHandler - { - public: - inline explicit NppStreamHandler(Stream& newStream) - { - oldStream = nppGetStream(); - nppSetStream(StreamAccessor::getStream(newStream)); - } - - inline explicit NppStreamHandler(cudaStream_t newStream) - { - oldStream = nppGetStream(); - nppSetStream(newStream); - } - - inline ~NppStreamHandler() - { - nppSetStream(oldStream); - } - - private: - cudaStream_t oldStream; - }; -}} - -#define nppSafeCall(expr) cv::cuda::checkNppError(expr, __FILE__, __LINE__, CV_Func) -#define cuSafeCall(expr) cv::cuda::checkCudaDriverApiError(expr, __FILE__, __LINE__, CV_Func) - -#endif // HAVE_CUDA - -//! @endcond - -#endif // __OPENCV_CORE_CUDA_PRIVATE_HPP__ diff --git a/IPL/include/opencv/opencv2/core/private.hpp b/IPL/include/opencv/opencv2/core/private.hpp deleted file mode 100644 index c71ec62..0000000 --- a/IPL/include/opencv/opencv2/core/private.hpp +++ /dev/null @@ -1,425 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_PRIVATE_HPP__ -#define __OPENCV_CORE_PRIVATE_HPP__ - -#ifndef __OPENCV_BUILD -# error this is a private header which should not be used from outside of the OpenCV library -#endif - -#include "opencv2/core.hpp" -#include "cvconfig.h" - -#ifdef HAVE_EIGEN -# if defined __GNUC__ && defined __APPLE__ -# pragma GCC diagnostic ignored "-Wshadow" -# endif -# include -# include "opencv2/core/eigen.hpp" -#endif - -#ifdef HAVE_TBB -# include "tbb/tbb_stddef.h" -# if TBB_VERSION_MAJOR*100 + TBB_VERSION_MINOR >= 202 -# include "tbb/tbb.h" -# include "tbb/task.h" -# undef min -# undef max -# else -# undef HAVE_TBB -# endif -#endif - -//! @cond IGNORED - -namespace cv -{ -#ifdef HAVE_TBB - - typedef tbb::blocked_range BlockedRange; - - template static inline - void parallel_for( const BlockedRange& range, const Body& body ) - { - tbb::parallel_for(range, body); - } - - typedef tbb::split Split; - - template static inline - void parallel_reduce( const BlockedRange& range, Body& body ) - { - tbb::parallel_reduce(range, body); - } - - typedef tbb::concurrent_vector ConcurrentRectVector; -#else - class BlockedRange - { - public: - BlockedRange() : _begin(0), _end(0), _grainsize(0) {} - BlockedRange(int b, int e, int g=1) : _begin(b), _end(e), _grainsize(g) {} - int begin() const { return _begin; } - int end() const { return _end; } - int grainsize() const { return _grainsize; } - - protected: - int _begin, _end, _grainsize; - }; - - template static inline - void parallel_for( const BlockedRange& range, const Body& body ) - { - body(range); - } - typedef std::vector ConcurrentRectVector; - - class Split {}; - - template static inline - void parallel_reduce( const BlockedRange& range, Body& body ) - { - body(range); - } -#endif - - // Returns a static string if there is a parallel framework, - // NULL otherwise. - CV_EXPORTS const char* currentParallelFramework(); -} //namespace cv - -/****************************************************************************************\ -* Common declarations * -\****************************************************************************************/ - -/* the alignment of all the allocated buffers */ -#define CV_MALLOC_ALIGN 16 - -/* IEEE754 constants and macros */ -#define CV_TOGGLE_FLT(x) ((x)^((int)(x) < 0 ? 0x7fffffff : 0)) -#define CV_TOGGLE_DBL(x) ((x)^((int64)(x) < 0 ? CV_BIG_INT(0x7fffffffffffffff) : 0)) - -static inline void* cvAlignPtr( const void* ptr, int align = 32 ) -{ - CV_DbgAssert ( (align & (align-1)) == 0 ); - return (void*)( ((size_t)ptr + align - 1) & ~(size_t)(align-1) ); -} - -static inline int cvAlign( int size, int align ) -{ - CV_DbgAssert( (align & (align-1)) == 0 && size < INT_MAX ); - return (size + align - 1) & -align; -} - -#ifdef IPL_DEPTH_8U -static inline cv::Size cvGetMatSize( const CvMat* mat ) -{ - return cv::Size(mat->cols, mat->rows); -} -#endif - -namespace cv -{ -CV_EXPORTS void scalarToRawData(const cv::Scalar& s, void* buf, int type, int unroll_to = 0); -} - -// property implementation macros - -#define CV_IMPL_PROPERTY_RO(type, name, member) \ - inline type get##name() const { return member; } - -#define CV_HELP_IMPL_PROPERTY(r_type, w_type, name, member) \ - CV_IMPL_PROPERTY_RO(r_type, name, member) \ - inline void set##name(w_type val) { member = val; } - -#define CV_HELP_WRAP_PROPERTY(r_type, w_type, name, internal_name, internal_obj) \ - r_type get##name() const { return internal_obj.get##internal_name(); } \ - void set##name(w_type val) { internal_obj.set##internal_name(val); } - -#define CV_IMPL_PROPERTY(type, name, member) CV_HELP_IMPL_PROPERTY(type, type, name, member) -#define CV_IMPL_PROPERTY_S(type, name, member) CV_HELP_IMPL_PROPERTY(type, const type &, name, member) - -#define CV_WRAP_PROPERTY(type, name, internal_name, internal_obj) CV_HELP_WRAP_PROPERTY(type, type, name, internal_name, internal_obj) -#define CV_WRAP_PROPERTY_S(type, name, internal_name, internal_obj) CV_HELP_WRAP_PROPERTY(type, const type &, name, internal_name, internal_obj) - -#define CV_WRAP_SAME_PROPERTY(type, name, internal_obj) CV_WRAP_PROPERTY(type, name, name, internal_obj) -#define CV_WRAP_SAME_PROPERTY_S(type, name, internal_obj) CV_WRAP_PROPERTY_S(type, name, name, internal_obj) - -/****************************************************************************************\ -* Structures and macros for integration with IPP * -\****************************************************************************************/ - -#ifdef HAVE_IPP -#include "ipp.h" - -#ifndef IPP_VERSION_UPDATE // prior to 7.1 -#define IPP_VERSION_UPDATE 0 -#endif - -#define IPP_VERSION_X100 (IPP_VERSION_MAJOR * 100 + IPP_VERSION_MINOR*10 + IPP_VERSION_UPDATE) - -// General define for ipp function disabling -#define IPP_DISABLE_BLOCK 0 - -#ifdef CV_MALLOC_ALIGN -#undef CV_MALLOC_ALIGN -#endif -#define CV_MALLOC_ALIGN 32 // required for AVX optimization - -#define setIppErrorStatus() cv::ipp::setIppStatus(-1, CV_Func, __FILE__, __LINE__) - -static inline IppiSize ippiSize(int width, int height) -{ - IppiSize size = { width, height }; - return size; -} - -static inline IppiSize ippiSize(const cv::Size & _size) -{ - IppiSize size = { _size.width, _size.height }; - return size; -} - -static inline IppiBorderType ippiGetBorderType(int borderTypeNI) -{ - return borderTypeNI == cv::BORDER_CONSTANT ? ippBorderConst : - borderTypeNI == cv::BORDER_WRAP ? ippBorderWrap : - borderTypeNI == cv::BORDER_REPLICATE ? ippBorderRepl : - borderTypeNI == cv::BORDER_REFLECT_101 ? ippBorderMirror : - borderTypeNI == cv::BORDER_REFLECT ? ippBorderMirrorR : (IppiBorderType)-1; -} - -static inline IppDataType ippiGetDataType(int depth) -{ - return depth == CV_8U ? ipp8u : - depth == CV_8S ? ipp8s : - depth == CV_16U ? ipp16u : - depth == CV_16S ? ipp16s : - depth == CV_32S ? ipp32s : - depth == CV_32F ? ipp32f : - depth == CV_64F ? ipp64f : (IppDataType)-1; -} - -// IPP temporary buffer hepler -template -class IppAutoBuffer -{ -public: - IppAutoBuffer() { m_pBuffer = NULL; } - IppAutoBuffer(int size) { Alloc(size); } - ~IppAutoBuffer() { Release(); } - T* Alloc(int size) { m_pBuffer = (T*)ippMalloc(size); return m_pBuffer; } - void Release() { if(m_pBuffer) ippFree(m_pBuffer); } - inline operator T* () { return (T*)m_pBuffer;} - inline operator const T* () const { return (const T*)m_pBuffer;} -private: - // Disable copy operations - IppAutoBuffer(IppAutoBuffer &) {}; - IppAutoBuffer& operator =(const IppAutoBuffer &) {return *this;}; - - T* m_pBuffer; -}; - -#else -#define IPP_VERSION_X100 0 -#endif - -// There shoud be no API difference in OpenCV between ICV and IPP since 9.0 -#if (defined HAVE_IPP_ICV_ONLY) && IPP_VERSION_X100 >= 900 -#undef HAVE_IPP_ICV_ONLY -#endif - -#ifdef HAVE_IPP_ICV_ONLY -#define HAVE_ICV 1 -#else -#define HAVE_ICV 0 -#endif - -#if defined HAVE_IPP -#if IPP_VERSION_X100 >= 900 -#define IPP_INITIALIZER(FEAT) \ -{ \ - if(FEAT) \ - ippSetCpuFeatures(FEAT); \ - else \ - ippInit(); \ -} -#elif IPP_VERSION_X100 >= 800 -#define IPP_INITIALIZER(FEAT) \ -{ \ - ippInit(); \ -} -#else -#define IPP_INITIALIZER(FEAT) \ -{ \ - ippStaticInit(); \ -} -#endif - -#ifdef CVAPI_EXPORTS -#define IPP_INITIALIZER_AUTO \ -struct __IppInitializer__ \ -{ \ - __IppInitializer__() \ - {IPP_INITIALIZER(cv::ipp::getIppFeatures())} \ -}; \ -static struct __IppInitializer__ __ipp_initializer__; -#else -#define IPP_INITIALIZER_AUTO -#endif -#else -#define IPP_INITIALIZER -#define IPP_INITIALIZER_AUTO -#endif - -#define CV_IPP_CHECK_COND (cv::ipp::useIPP()) -#define CV_IPP_CHECK() if(CV_IPP_CHECK_COND) - -#ifdef HAVE_IPP - -#ifdef CV_IPP_RUN_VERBOSE -#define CV_IPP_RUN_(condition, func, ...) \ - { \ - if (cv::ipp::useIPP() && (condition) && func) \ - { \ - printf("%s: IPP implementation is running\n", CV_Func); \ - fflush(stdout); \ - CV_IMPL_ADD(CV_IMPL_IPP); \ - return __VA_ARGS__; \ - } \ - else \ - { \ - printf("%s: Plain implementation is running\n", CV_Func); \ - fflush(stdout); \ - } \ - } -#elif defined CV_IPP_RUN_ASSERT -#define CV_IPP_RUN_(condition, func, ...) \ - { \ - if (cv::ipp::useIPP() && (condition)) \ - { \ - if(func) \ - { \ - CV_IMPL_ADD(CV_IMPL_IPP); \ - } \ - else \ - { \ - setIppErrorStatus(); \ - CV_Error(cv::Error::StsAssert, #func); \ - } \ - return __VA_ARGS__; \ - } \ - } -#else -#define CV_IPP_RUN_(condition, func, ...) \ - if (cv::ipp::useIPP() && (condition) && func) \ - { \ - CV_IMPL_ADD(CV_IMPL_IPP); \ - return __VA_ARGS__; \ - } -#endif - -#else -#define CV_IPP_RUN_(condition, func, ...) -#endif - -#define CV_IPP_RUN(condition, func, ...) CV_IPP_RUN_(condition, func, __VA_ARGS__) - - -#ifndef IPPI_CALL -# define IPPI_CALL(func) CV_Assert((func) >= 0) -#endif - -/* IPP-compatible return codes */ -typedef enum CvStatus -{ - CV_BADMEMBLOCK_ERR = -113, - CV_INPLACE_NOT_SUPPORTED_ERR= -112, - CV_UNMATCHED_ROI_ERR = -111, - CV_NOTFOUND_ERR = -110, - CV_BADCONVERGENCE_ERR = -109, - - CV_BADDEPTH_ERR = -107, - CV_BADROI_ERR = -106, - CV_BADHEADER_ERR = -105, - CV_UNMATCHED_FORMATS_ERR = -104, - CV_UNSUPPORTED_COI_ERR = -103, - CV_UNSUPPORTED_CHANNELS_ERR = -102, - CV_UNSUPPORTED_DEPTH_ERR = -101, - CV_UNSUPPORTED_FORMAT_ERR = -100, - - CV_BADARG_ERR = -49, //ipp comp - CV_NOTDEFINED_ERR = -48, //ipp comp - - CV_BADCHANNELS_ERR = -47, //ipp comp - CV_BADRANGE_ERR = -44, //ipp comp - CV_BADSTEP_ERR = -29, //ipp comp - - CV_BADFLAG_ERR = -12, - CV_DIV_BY_ZERO_ERR = -11, //ipp comp - CV_BADCOEF_ERR = -10, - - CV_BADFACTOR_ERR = -7, - CV_BADPOINT_ERR = -6, - CV_BADSCALE_ERR = -4, - CV_OUTOFMEM_ERR = -3, - CV_NULLPTR_ERR = -2, - CV_BADSIZE_ERR = -1, - CV_NO_ERR = 0, - CV_OK = CV_NO_ERR -} -CvStatus; - -#ifdef HAVE_TEGRA_OPTIMIZATION -namespace tegra { - -CV_EXPORTS bool useTegra(); -CV_EXPORTS void setUseTegra(bool flag); - -} -#endif - -//! @endcond - -#endif // __OPENCV_CORE_PRIVATE_HPP__ diff --git a/IPL/include/opencv/opencv2/core/ptr.inl.hpp b/IPL/include/opencv/opencv2/core/ptr.inl.hpp deleted file mode 100644 index 3f6f214..0000000 --- a/IPL/include/opencv/opencv2/core/ptr.inl.hpp +++ /dev/null @@ -1,365 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2013, NVIDIA Corporation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the copyright holders or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_PTR_INL_HPP__ -#define __OPENCV_CORE_PTR_INL_HPP__ - -#include - -//! @cond IGNORED - -namespace cv { - -template -void DefaultDeleter::operator () (Y* p) const -{ - delete p; -} - -namespace detail -{ - -struct PtrOwner -{ - PtrOwner() : refCount(1) - {} - - void incRef() - { - CV_XADD(&refCount, 1); - } - - void decRef() - { - if (CV_XADD(&refCount, -1) == 1) deleteSelf(); - } - -protected: - /* This doesn't really need to be virtual, since PtrOwner is never deleted - directly, but it doesn't hurt and it helps avoid warnings. */ - virtual ~PtrOwner() - {} - - virtual void deleteSelf() = 0; - -private: - unsigned int refCount; - - // noncopyable - PtrOwner(const PtrOwner&); - PtrOwner& operator = (const PtrOwner&); -}; - -template -struct PtrOwnerImpl : PtrOwner -{ - PtrOwnerImpl(Y* p, D d) : owned(p), deleter(d) - {} - - void deleteSelf() - { - deleter(owned); - delete this; - } - -private: - Y* owned; - D deleter; -}; - - -} - -template -Ptr::Ptr() : owner(NULL), stored(NULL) -{} - -template -template -Ptr::Ptr(Y* p) - : owner(p - ? new detail::PtrOwnerImpl >(p, DefaultDeleter()) - : NULL), - stored(p) -{} - -template -template -Ptr::Ptr(Y* p, D d) - : owner(p - ? new detail::PtrOwnerImpl(p, d) - : NULL), - stored(p) -{} - -template -Ptr::Ptr(const Ptr& o) : owner(o.owner), stored(o.stored) -{ - if (owner) owner->incRef(); -} - -template -template -Ptr::Ptr(const Ptr& o) : owner(o.owner), stored(o.stored) -{ - if (owner) owner->incRef(); -} - -template -template -Ptr::Ptr(const Ptr& o, T* p) : owner(o.owner), stored(p) -{ - if (owner) owner->incRef(); -} - -template -Ptr::~Ptr() -{ - release(); -} - -template -Ptr& Ptr::operator = (const Ptr& o) -{ - Ptr(o).swap(*this); - return *this; -} - -template -template -Ptr& Ptr::operator = (const Ptr& o) -{ - Ptr(o).swap(*this); - return *this; -} - -template -void Ptr::release() -{ - if (owner) owner->decRef(); - owner = NULL; - stored = NULL; -} - -template -template -void Ptr::reset(Y* p) -{ - Ptr(p).swap(*this); -} - -template -template -void Ptr::reset(Y* p, D d) -{ - Ptr(p, d).swap(*this); -} - -template -void Ptr::swap(Ptr& o) -{ - std::swap(owner, o.owner); - std::swap(stored, o.stored); -} - -template -T* Ptr::get() const -{ - return stored; -} - -template -typename detail::RefOrVoid::type Ptr::operator * () const -{ - return *stored; -} - -template -T* Ptr::operator -> () const -{ - return stored; -} - -template -Ptr::operator T* () const -{ - return stored; -} - - -template -bool Ptr::empty() const -{ - return !stored; -} - -template -template -Ptr Ptr::staticCast() const -{ - return Ptr(*this, static_cast(stored)); -} - -template -template -Ptr Ptr::constCast() const -{ - return Ptr(*this, const_cast(stored)); -} - -template -template -Ptr Ptr::dynamicCast() const -{ - return Ptr(*this, dynamic_cast(stored)); -} - -#ifdef CV_CXX_MOVE_SEMANTICS - -template -Ptr::Ptr(Ptr&& o) : owner(o.owner), stored(o.stored) -{ - o.owner = NULL; - o.stored = NULL; -} - -template -Ptr& Ptr::operator = (Ptr&& o) -{ - release(); - owner = o.owner; - stored = o.stored; - o.owner = NULL; - o.stored = NULL; - return *this; -} - -#endif - - -template -void swap(Ptr& ptr1, Ptr& ptr2){ - ptr1.swap(ptr2); -} - -template -bool operator == (const Ptr& ptr1, const Ptr& ptr2) -{ - return ptr1.get() == ptr2.get(); -} - -template -bool operator != (const Ptr& ptr1, const Ptr& ptr2) -{ - return ptr1.get() != ptr2.get(); -} - -template -Ptr makePtr() -{ - return Ptr(new T()); -} - -template -Ptr makePtr(const A1& a1) -{ - return Ptr(new T(a1)); -} - -template -Ptr makePtr(const A1& a1, const A2& a2) -{ - return Ptr(new T(a1, a2)); -} - -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3) -{ - return Ptr(new T(a1, a2, a3)); -} - -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4) -{ - return Ptr(new T(a1, a2, a3, a4)); -} - -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5) -{ - return Ptr(new T(a1, a2, a3, a4, a5)); -} - -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6) -{ - return Ptr(new T(a1, a2, a3, a4, a5, a6)); -} - -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7) -{ - return Ptr(new T(a1, a2, a3, a4, a5, a6, a7)); -} - -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8) -{ - return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8)); -} - -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9) -{ - return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9)); -} - -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10) -{ - return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10)); -} - -} // namespace cv - -//! @endcond - -#endif // __OPENCV_CORE_PTR_INL_HPP__ diff --git a/IPL/include/opencv/opencv2/core/saturate.hpp b/IPL/include/opencv/opencv2/core/saturate.hpp deleted file mode 100644 index 1442eab..0000000 --- a/IPL/include/opencv/opencv2/core/saturate.hpp +++ /dev/null @@ -1,150 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2014, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_SATURATE_HPP__ -#define __OPENCV_CORE_SATURATE_HPP__ - -#include "opencv2/core/cvdef.h" -#include "opencv2/core/fast_math.hpp" - -namespace cv -{ - -//! @addtogroup core_utils -//! @{ - -/////////////// saturate_cast (used in image & signal processing) /////////////////// - -/** @brief Template function for accurate conversion from one primitive type to another. - - The functions saturate_cast resemble the standard C++ cast operations, such as static_cast\() - and others. They perform an efficient and accurate conversion from one primitive type to another - (see the introduction chapter). saturate in the name means that when the input value v is out of the - range of the target type, the result is not formed just by taking low bits of the input, but instead - the value is clipped. For example: - @code - uchar a = saturate_cast(-100); // a = 0 (UCHAR_MIN) - short b = saturate_cast(33333.33333); // b = 32767 (SHRT_MAX) - @endcode - Such clipping is done when the target type is unsigned char , signed char , unsigned short or - signed short . For 32-bit integers, no clipping is done. - - When the parameter is a floating-point value and the target type is an integer (8-, 16- or 32-bit), - the floating-point value is first rounded to the nearest integer and then clipped if needed (when - the target type is 8- or 16-bit). - - This operation is used in the simplest or most complex image processing functions in OpenCV. - - @param v Function parameter. - @sa add, subtract, multiply, divide, Mat::convertTo - */ -template static inline _Tp saturate_cast(uchar v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(schar v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(ushort v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(short v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(unsigned v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(int v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(float v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(double v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(int64 v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(uint64 v) { return _Tp(v); } - -template<> inline uchar saturate_cast(schar v) { return (uchar)std::max((int)v, 0); } -template<> inline uchar saturate_cast(ushort v) { return (uchar)std::min((unsigned)v, (unsigned)UCHAR_MAX); } -template<> inline uchar saturate_cast(int v) { return (uchar)((unsigned)v <= UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); } -template<> inline uchar saturate_cast(short v) { return saturate_cast((int)v); } -template<> inline uchar saturate_cast(unsigned v) { return (uchar)std::min(v, (unsigned)UCHAR_MAX); } -template<> inline uchar saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } -template<> inline uchar saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } -template<> inline uchar saturate_cast(int64 v) { return (uchar)((uint64)v <= (uint64)UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); } -template<> inline uchar saturate_cast(uint64 v) { return (uchar)std::min(v, (uint64)UCHAR_MAX); } - -template<> inline schar saturate_cast(uchar v) { return (schar)std::min((int)v, SCHAR_MAX); } -template<> inline schar saturate_cast(ushort v) { return (schar)std::min((unsigned)v, (unsigned)SCHAR_MAX); } -template<> inline schar saturate_cast(int v) { return (schar)((unsigned)(v-SCHAR_MIN) <= (unsigned)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); } -template<> inline schar saturate_cast(short v) { return saturate_cast((int)v); } -template<> inline schar saturate_cast(unsigned v) { return (schar)std::min(v, (unsigned)SCHAR_MAX); } -template<> inline schar saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } -template<> inline schar saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } -template<> inline schar saturate_cast(int64 v) { return (schar)((uint64)((int64)v-SCHAR_MIN) <= (uint64)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); } -template<> inline schar saturate_cast(uint64 v) { return (schar)std::min(v, (uint64)SCHAR_MAX); } - -template<> inline ushort saturate_cast(schar v) { return (ushort)std::max((int)v, 0); } -template<> inline ushort saturate_cast(short v) { return (ushort)std::max((int)v, 0); } -template<> inline ushort saturate_cast(int v) { return (ushort)((unsigned)v <= (unsigned)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); } -template<> inline ushort saturate_cast(unsigned v) { return (ushort)std::min(v, (unsigned)USHRT_MAX); } -template<> inline ushort saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } -template<> inline ushort saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } -template<> inline ushort saturate_cast(int64 v) { return (ushort)((uint64)v <= (uint64)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); } -template<> inline ushort saturate_cast(uint64 v) { return (ushort)std::min(v, (uint64)USHRT_MAX); } - -template<> inline short saturate_cast(ushort v) { return (short)std::min((int)v, SHRT_MAX); } -template<> inline short saturate_cast(int v) { return (short)((unsigned)(v - SHRT_MIN) <= (unsigned)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); } -template<> inline short saturate_cast(unsigned v) { return (short)std::min(v, (unsigned)SHRT_MAX); } -template<> inline short saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } -template<> inline short saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } -template<> inline short saturate_cast(int64 v) { return (short)((uint64)((int64)v - SHRT_MIN) <= (uint64)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); } -template<> inline short saturate_cast(uint64 v) { return (short)std::min(v, (uint64)SHRT_MAX); } - -template<> inline int saturate_cast(float v) { return cvRound(v); } -template<> inline int saturate_cast(double v) { return cvRound(v); } - -// we intentionally do not clip negative numbers, to make -1 become 0xffffffff etc. -template<> inline unsigned saturate_cast(float v) { return cvRound(v); } -template<> inline unsigned saturate_cast(double v) { return cvRound(v); } - -//! @} - -} // cv - -#endif // __OPENCV_CORE_SATURATE_HPP__ diff --git a/IPL/include/opencv/opencv2/core/sse_utils.hpp b/IPL/include/opencv/opencv2/core/sse_utils.hpp deleted file mode 100644 index c87b029..0000000 --- a/IPL/include/opencv/opencv2/core/sse_utils.hpp +++ /dev/null @@ -1,652 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_SSE_UTILS_HPP__ -#define __OPENCV_CORE_SSE_UTILS_HPP__ - -#ifndef __cplusplus -# error sse_utils.hpp header must be compiled as C++ -#endif - -#include "opencv2/core/cvdef.h" - -//! @addtogroup core_utils_sse -//! @{ - -#if CV_SSE2 - -inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) -{ - __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_g0); - __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_g0); - __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_g1); - __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_g1); - - __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk2); - __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk2); - __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk3); - __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk3); - - __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk2); - __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk2); - __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk3); - __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk3); - - __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk2); - __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk2); - __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk3); - __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk3); - - v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk2); - v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk2); - v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk3); - v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk3); -} - -inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, - __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) -{ - __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_g1); - __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_g1); - __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_b0); - __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_b0); - __m128i layer1_chunk4 = _mm_unpacklo_epi8(v_g0, v_b1); - __m128i layer1_chunk5 = _mm_unpackhi_epi8(v_g0, v_b1); - - __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk3); - __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk3); - __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk4); - __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk4); - __m128i layer2_chunk4 = _mm_unpacklo_epi8(layer1_chunk2, layer1_chunk5); - __m128i layer2_chunk5 = _mm_unpackhi_epi8(layer1_chunk2, layer1_chunk5); - - __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk3); - __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk3); - __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk4); - __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk4); - __m128i layer3_chunk4 = _mm_unpacklo_epi8(layer2_chunk2, layer2_chunk5); - __m128i layer3_chunk5 = _mm_unpackhi_epi8(layer2_chunk2, layer2_chunk5); - - __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk3); - __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk3); - __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk4); - __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk4); - __m128i layer4_chunk4 = _mm_unpacklo_epi8(layer3_chunk2, layer3_chunk5); - __m128i layer4_chunk5 = _mm_unpackhi_epi8(layer3_chunk2, layer3_chunk5); - - v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk3); - v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk3); - v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk4); - v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk4); - v_b0 = _mm_unpacklo_epi8(layer4_chunk2, layer4_chunk5); - v_b1 = _mm_unpackhi_epi8(layer4_chunk2, layer4_chunk5); -} - -inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, - __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) -{ - __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_b0); - __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_b0); - __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_b1); - __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_b1); - __m128i layer1_chunk4 = _mm_unpacklo_epi8(v_g0, v_a0); - __m128i layer1_chunk5 = _mm_unpackhi_epi8(v_g0, v_a0); - __m128i layer1_chunk6 = _mm_unpacklo_epi8(v_g1, v_a1); - __m128i layer1_chunk7 = _mm_unpackhi_epi8(v_g1, v_a1); - - __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk4); - __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk4); - __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk5); - __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk5); - __m128i layer2_chunk4 = _mm_unpacklo_epi8(layer1_chunk2, layer1_chunk6); - __m128i layer2_chunk5 = _mm_unpackhi_epi8(layer1_chunk2, layer1_chunk6); - __m128i layer2_chunk6 = _mm_unpacklo_epi8(layer1_chunk3, layer1_chunk7); - __m128i layer2_chunk7 = _mm_unpackhi_epi8(layer1_chunk3, layer1_chunk7); - - __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk4); - __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk4); - __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk5); - __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk5); - __m128i layer3_chunk4 = _mm_unpacklo_epi8(layer2_chunk2, layer2_chunk6); - __m128i layer3_chunk5 = _mm_unpackhi_epi8(layer2_chunk2, layer2_chunk6); - __m128i layer3_chunk6 = _mm_unpacklo_epi8(layer2_chunk3, layer2_chunk7); - __m128i layer3_chunk7 = _mm_unpackhi_epi8(layer2_chunk3, layer2_chunk7); - - __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk4); - __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk4); - __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk5); - __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk5); - __m128i layer4_chunk4 = _mm_unpacklo_epi8(layer3_chunk2, layer3_chunk6); - __m128i layer4_chunk5 = _mm_unpackhi_epi8(layer3_chunk2, layer3_chunk6); - __m128i layer4_chunk6 = _mm_unpacklo_epi8(layer3_chunk3, layer3_chunk7); - __m128i layer4_chunk7 = _mm_unpackhi_epi8(layer3_chunk3, layer3_chunk7); - - v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk4); - v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk4); - v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk5); - v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk5); - v_b0 = _mm_unpacklo_epi8(layer4_chunk2, layer4_chunk6); - v_b1 = _mm_unpackhi_epi8(layer4_chunk2, layer4_chunk6); - v_a0 = _mm_unpacklo_epi8(layer4_chunk3, layer4_chunk7); - v_a1 = _mm_unpackhi_epi8(layer4_chunk3, layer4_chunk7); -} - -inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) -{ - __m128i v_mask = _mm_set1_epi16(0x00ff); - - __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); - __m128i layer4_chunk2 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); - __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); - __m128i layer4_chunk3 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); - - __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); - __m128i layer3_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); - __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); - __m128i layer3_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); - - __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); - __m128i layer2_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); - __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); - __m128i layer2_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); - - __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); - __m128i layer1_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); - __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); - __m128i layer1_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); - - v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); - v_g0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); - v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); - v_g1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); -} - -inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, - __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) -{ - __m128i v_mask = _mm_set1_epi16(0x00ff); - - __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); - __m128i layer4_chunk3 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); - __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); - __m128i layer4_chunk4 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); - __m128i layer4_chunk2 = _mm_packus_epi16(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); - __m128i layer4_chunk5 = _mm_packus_epi16(_mm_srli_epi16(v_b0, 8), _mm_srli_epi16(v_b1, 8)); - - __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); - __m128i layer3_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); - __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); - __m128i layer3_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); - __m128i layer3_chunk2 = _mm_packus_epi16(_mm_and_si128(layer4_chunk4, v_mask), _mm_and_si128(layer4_chunk5, v_mask)); - __m128i layer3_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk4, 8), _mm_srli_epi16(layer4_chunk5, 8)); - - __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); - __m128i layer2_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); - __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); - __m128i layer2_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); - __m128i layer2_chunk2 = _mm_packus_epi16(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); - __m128i layer2_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk4, 8), _mm_srli_epi16(layer3_chunk5, 8)); - - __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); - __m128i layer1_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); - __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); - __m128i layer1_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); - __m128i layer1_chunk2 = _mm_packus_epi16(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); - __m128i layer1_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk4, 8), _mm_srli_epi16(layer2_chunk5, 8)); - - v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); - v_g1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); - v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); - v_b0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); - v_g0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); - v_b1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk4, 8), _mm_srli_epi16(layer1_chunk5, 8)); -} - -inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, - __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) -{ - __m128i v_mask = _mm_set1_epi16(0x00ff); - - __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); - __m128i layer4_chunk4 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); - __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); - __m128i layer4_chunk5 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); - __m128i layer4_chunk2 = _mm_packus_epi16(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); - __m128i layer4_chunk6 = _mm_packus_epi16(_mm_srli_epi16(v_b0, 8), _mm_srli_epi16(v_b1, 8)); - __m128i layer4_chunk3 = _mm_packus_epi16(_mm_and_si128(v_a0, v_mask), _mm_and_si128(v_a1, v_mask)); - __m128i layer4_chunk7 = _mm_packus_epi16(_mm_srli_epi16(v_a0, 8), _mm_srli_epi16(v_a1, 8)); - - __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); - __m128i layer3_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); - __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); - __m128i layer3_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); - __m128i layer3_chunk2 = _mm_packus_epi16(_mm_and_si128(layer4_chunk4, v_mask), _mm_and_si128(layer4_chunk5, v_mask)); - __m128i layer3_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk4, 8), _mm_srli_epi16(layer4_chunk5, 8)); - __m128i layer3_chunk3 = _mm_packus_epi16(_mm_and_si128(layer4_chunk6, v_mask), _mm_and_si128(layer4_chunk7, v_mask)); - __m128i layer3_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk6, 8), _mm_srli_epi16(layer4_chunk7, 8)); - - __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); - __m128i layer2_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); - __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); - __m128i layer2_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); - __m128i layer2_chunk2 = _mm_packus_epi16(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); - __m128i layer2_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk4, 8), _mm_srli_epi16(layer3_chunk5, 8)); - __m128i layer2_chunk3 = _mm_packus_epi16(_mm_and_si128(layer3_chunk6, v_mask), _mm_and_si128(layer3_chunk7, v_mask)); - __m128i layer2_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk6, 8), _mm_srli_epi16(layer3_chunk7, 8)); - - __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); - __m128i layer1_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); - __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); - __m128i layer1_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); - __m128i layer1_chunk2 = _mm_packus_epi16(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); - __m128i layer1_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk4, 8), _mm_srli_epi16(layer2_chunk5, 8)); - __m128i layer1_chunk3 = _mm_packus_epi16(_mm_and_si128(layer2_chunk6, v_mask), _mm_and_si128(layer2_chunk7, v_mask)); - __m128i layer1_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk6, 8), _mm_srli_epi16(layer2_chunk7, 8)); - - v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); - v_b0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); - v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); - v_b1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); - v_g0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); - v_a0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk4, 8), _mm_srli_epi16(layer1_chunk5, 8)); - v_g1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk6, v_mask), _mm_and_si128(layer1_chunk7, v_mask)); - v_a1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk6, 8), _mm_srli_epi16(layer1_chunk7, 8)); -} - -inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) -{ - __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_g0); - __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_g0); - __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_g1); - __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_g1); - - __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk2); - __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk2); - __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk3); - __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk3); - - __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk2); - __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk2); - __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk3); - __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk3); - - v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk2); - v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk2); - v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk3); - v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk3); -} - -inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, - __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) -{ - __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_g1); - __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_g1); - __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_b0); - __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_b0); - __m128i layer1_chunk4 = _mm_unpacklo_epi16(v_g0, v_b1); - __m128i layer1_chunk5 = _mm_unpackhi_epi16(v_g0, v_b1); - - __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk3); - __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk3); - __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk4); - __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk4); - __m128i layer2_chunk4 = _mm_unpacklo_epi16(layer1_chunk2, layer1_chunk5); - __m128i layer2_chunk5 = _mm_unpackhi_epi16(layer1_chunk2, layer1_chunk5); - - __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk3); - __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk3); - __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk4); - __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk4); - __m128i layer3_chunk4 = _mm_unpacklo_epi16(layer2_chunk2, layer2_chunk5); - __m128i layer3_chunk5 = _mm_unpackhi_epi16(layer2_chunk2, layer2_chunk5); - - v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk3); - v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk3); - v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk4); - v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk4); - v_b0 = _mm_unpacklo_epi16(layer3_chunk2, layer3_chunk5); - v_b1 = _mm_unpackhi_epi16(layer3_chunk2, layer3_chunk5); -} - -inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, - __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) -{ - __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_b0); - __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_b0); - __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_b1); - __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_b1); - __m128i layer1_chunk4 = _mm_unpacklo_epi16(v_g0, v_a0); - __m128i layer1_chunk5 = _mm_unpackhi_epi16(v_g0, v_a0); - __m128i layer1_chunk6 = _mm_unpacklo_epi16(v_g1, v_a1); - __m128i layer1_chunk7 = _mm_unpackhi_epi16(v_g1, v_a1); - - __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk4); - __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk4); - __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk5); - __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk5); - __m128i layer2_chunk4 = _mm_unpacklo_epi16(layer1_chunk2, layer1_chunk6); - __m128i layer2_chunk5 = _mm_unpackhi_epi16(layer1_chunk2, layer1_chunk6); - __m128i layer2_chunk6 = _mm_unpacklo_epi16(layer1_chunk3, layer1_chunk7); - __m128i layer2_chunk7 = _mm_unpackhi_epi16(layer1_chunk3, layer1_chunk7); - - __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk4); - __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk4); - __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk5); - __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk5); - __m128i layer3_chunk4 = _mm_unpacklo_epi16(layer2_chunk2, layer2_chunk6); - __m128i layer3_chunk5 = _mm_unpackhi_epi16(layer2_chunk2, layer2_chunk6); - __m128i layer3_chunk6 = _mm_unpacklo_epi16(layer2_chunk3, layer2_chunk7); - __m128i layer3_chunk7 = _mm_unpackhi_epi16(layer2_chunk3, layer2_chunk7); - - v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk4); - v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk4); - v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk5); - v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk5); - v_b0 = _mm_unpacklo_epi16(layer3_chunk2, layer3_chunk6); - v_b1 = _mm_unpackhi_epi16(layer3_chunk2, layer3_chunk6); - v_a0 = _mm_unpacklo_epi16(layer3_chunk3, layer3_chunk7); - v_a1 = _mm_unpackhi_epi16(layer3_chunk3, layer3_chunk7); -} - -#if CV_SSE4_1 - -inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) -{ - __m128i v_mask = _mm_set1_epi32(0x0000ffff); - - __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); - __m128i layer3_chunk2 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); - __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); - __m128i layer3_chunk3 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); - - __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); - __m128i layer2_chunk2 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); - __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); - __m128i layer2_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); - - __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); - __m128i layer1_chunk2 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); - __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); - __m128i layer1_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); - - v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); - v_g0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); - v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); - v_g1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); -} - -inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, - __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) -{ - __m128i v_mask = _mm_set1_epi32(0x0000ffff); - - __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); - __m128i layer3_chunk3 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); - __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); - __m128i layer3_chunk4 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); - __m128i layer3_chunk2 = _mm_packus_epi32(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); - __m128i layer3_chunk5 = _mm_packus_epi32(_mm_srli_epi32(v_b0, 16), _mm_srli_epi32(v_b1, 16)); - - __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); - __m128i layer2_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); - __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); - __m128i layer2_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); - __m128i layer2_chunk2 = _mm_packus_epi32(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); - __m128i layer2_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk4, 16), _mm_srli_epi32(layer3_chunk5, 16)); - - __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); - __m128i layer1_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); - __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); - __m128i layer1_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); - __m128i layer1_chunk2 = _mm_packus_epi32(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); - __m128i layer1_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk4, 16), _mm_srli_epi32(layer2_chunk5, 16)); - - v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); - v_g1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); - v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); - v_b0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); - v_g0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); - v_b1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk4, 16), _mm_srli_epi32(layer1_chunk5, 16)); -} - -inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, - __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) -{ - __m128i v_mask = _mm_set1_epi32(0x0000ffff); - - __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); - __m128i layer3_chunk4 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); - __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); - __m128i layer3_chunk5 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); - __m128i layer3_chunk2 = _mm_packus_epi32(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); - __m128i layer3_chunk6 = _mm_packus_epi32(_mm_srli_epi32(v_b0, 16), _mm_srli_epi32(v_b1, 16)); - __m128i layer3_chunk3 = _mm_packus_epi32(_mm_and_si128(v_a0, v_mask), _mm_and_si128(v_a1, v_mask)); - __m128i layer3_chunk7 = _mm_packus_epi32(_mm_srli_epi32(v_a0, 16), _mm_srli_epi32(v_a1, 16)); - - __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); - __m128i layer2_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); - __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); - __m128i layer2_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); - __m128i layer2_chunk2 = _mm_packus_epi32(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); - __m128i layer2_chunk6 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk4, 16), _mm_srli_epi32(layer3_chunk5, 16)); - __m128i layer2_chunk3 = _mm_packus_epi32(_mm_and_si128(layer3_chunk6, v_mask), _mm_and_si128(layer3_chunk7, v_mask)); - __m128i layer2_chunk7 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk6, 16), _mm_srli_epi32(layer3_chunk7, 16)); - - __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); - __m128i layer1_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); - __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); - __m128i layer1_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); - __m128i layer1_chunk2 = _mm_packus_epi32(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); - __m128i layer1_chunk6 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk4, 16), _mm_srli_epi32(layer2_chunk5, 16)); - __m128i layer1_chunk3 = _mm_packus_epi32(_mm_and_si128(layer2_chunk6, v_mask), _mm_and_si128(layer2_chunk7, v_mask)); - __m128i layer1_chunk7 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk6, 16), _mm_srli_epi32(layer2_chunk7, 16)); - - v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); - v_b0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); - v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); - v_b1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); - v_g0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); - v_a0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk4, 16), _mm_srli_epi32(layer1_chunk5, 16)); - v_g1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk6, v_mask), _mm_and_si128(layer1_chunk7, v_mask)); - v_a1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk6, 16), _mm_srli_epi32(layer1_chunk7, 16)); -} - -#endif // CV_SSE4_1 - -inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1) -{ - __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_g0); - __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_g0); - __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_g1); - __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_g1); - - __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk2); - __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk2); - __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk3); - __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk3); - - v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk2); - v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk2); - v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk3); - v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk3); -} - -inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, - __m128 & v_g1, __m128 & v_b0, __m128 & v_b1) -{ - __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_g1); - __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_g1); - __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_b0); - __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_b0); - __m128 layer1_chunk4 = _mm_unpacklo_ps(v_g0, v_b1); - __m128 layer1_chunk5 = _mm_unpackhi_ps(v_g0, v_b1); - - __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk3); - __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk3); - __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk4); - __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk4); - __m128 layer2_chunk4 = _mm_unpacklo_ps(layer1_chunk2, layer1_chunk5); - __m128 layer2_chunk5 = _mm_unpackhi_ps(layer1_chunk2, layer1_chunk5); - - v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk3); - v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk3); - v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk4); - v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk4); - v_b0 = _mm_unpacklo_ps(layer2_chunk2, layer2_chunk5); - v_b1 = _mm_unpackhi_ps(layer2_chunk2, layer2_chunk5); -} - -inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1, - __m128 & v_b0, __m128 & v_b1, __m128 & v_a0, __m128 & v_a1) -{ - __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_b0); - __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_b0); - __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_b1); - __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_b1); - __m128 layer1_chunk4 = _mm_unpacklo_ps(v_g0, v_a0); - __m128 layer1_chunk5 = _mm_unpackhi_ps(v_g0, v_a0); - __m128 layer1_chunk6 = _mm_unpacklo_ps(v_g1, v_a1); - __m128 layer1_chunk7 = _mm_unpackhi_ps(v_g1, v_a1); - - __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk4); - __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk4); - __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk5); - __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk5); - __m128 layer2_chunk4 = _mm_unpacklo_ps(layer1_chunk2, layer1_chunk6); - __m128 layer2_chunk5 = _mm_unpackhi_ps(layer1_chunk2, layer1_chunk6); - __m128 layer2_chunk6 = _mm_unpacklo_ps(layer1_chunk3, layer1_chunk7); - __m128 layer2_chunk7 = _mm_unpackhi_ps(layer1_chunk3, layer1_chunk7); - - v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk4); - v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk4); - v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk5); - v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk5); - v_b0 = _mm_unpacklo_ps(layer2_chunk2, layer2_chunk6); - v_b1 = _mm_unpackhi_ps(layer2_chunk2, layer2_chunk6); - v_a0 = _mm_unpacklo_ps(layer2_chunk3, layer2_chunk7); - v_a1 = _mm_unpackhi_ps(layer2_chunk3, layer2_chunk7); -} - -inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1) -{ - const int mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1); - - __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); - __m128 layer2_chunk2 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); - __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); - __m128 layer2_chunk3 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); - - __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); - __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); - __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); - __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); - - v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); - v_g0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); - v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); - v_g1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); -} - -inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, - __m128 & v_g1, __m128 & v_b0, __m128 & v_b1) -{ - const int mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1); - - __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); - __m128 layer2_chunk3 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); - __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); - __m128 layer2_chunk4 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); - __m128 layer2_chunk2 = _mm_shuffle_ps(v_b0, v_b1, mask_lo); - __m128 layer2_chunk5 = _mm_shuffle_ps(v_b0, v_b1, mask_hi); - - __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); - __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); - __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); - __m128 layer1_chunk4 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); - __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_lo); - __m128 layer1_chunk5 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_hi); - - v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); - v_g1 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); - v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); - v_b0 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); - v_g0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_lo); - v_b1 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_hi); -} - -inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1, - __m128 & v_b0, __m128 & v_b1, __m128 & v_a0, __m128 & v_a1) -{ - const int mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1); - - __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); - __m128 layer2_chunk4 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); - __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); - __m128 layer2_chunk5 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); - __m128 layer2_chunk2 = _mm_shuffle_ps(v_b0, v_b1, mask_lo); - __m128 layer2_chunk6 = _mm_shuffle_ps(v_b0, v_b1, mask_hi); - __m128 layer2_chunk3 = _mm_shuffle_ps(v_a0, v_a1, mask_lo); - __m128 layer2_chunk7 = _mm_shuffle_ps(v_a0, v_a1, mask_hi); - - __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); - __m128 layer1_chunk4 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); - __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); - __m128 layer1_chunk5 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); - __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_lo); - __m128 layer1_chunk6 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_hi); - __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk6, layer2_chunk7, mask_lo); - __m128 layer1_chunk7 = _mm_shuffle_ps(layer2_chunk6, layer2_chunk7, mask_hi); - - v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); - v_b0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); - v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); - v_b1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); - v_g0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_lo); - v_a0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_hi); - v_g1 = _mm_shuffle_ps(layer1_chunk6, layer1_chunk7, mask_lo); - v_a1 = _mm_shuffle_ps(layer1_chunk6, layer1_chunk7, mask_hi); -} - -#endif // CV_SSE2 - -//! @} - -#endif //__OPENCV_CORE_SSE_UTILS_HPP__ diff --git a/IPL/include/opencv/opencv2/core/traits.hpp b/IPL/include/opencv/opencv2/core/traits.hpp deleted file mode 100644 index 49bc844..0000000 --- a/IPL/include/opencv/opencv2/core/traits.hpp +++ /dev/null @@ -1,326 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_TRAITS_HPP__ -#define __OPENCV_CORE_TRAITS_HPP__ - -#include "opencv2/core/cvdef.h" - -namespace cv -{ - -//! @addtogroup core_basic -//! @{ - -/** @brief Template "trait" class for OpenCV primitive data types. - -A primitive OpenCV data type is one of unsigned char, bool, signed char, unsigned short, signed -short, int, float, double, or a tuple of values of one of these types, where all the values in the -tuple have the same type. Any primitive type from the list can be defined by an identifier in the -form CV_\{U|S|F}C(\), for example: uchar \~ CV_8UC1, 3-element -floating-point tuple \~ CV_32FC3, and so on. A universal OpenCV structure that is able to store a -single instance of such a primitive data type is Vec. Multiple instances of such a type can be -stored in a std::vector, Mat, Mat_, SparseMat, SparseMat_, or any other container that is able to -store Vec instances. - -The DataType class is basically used to provide a description of such primitive data types without -adding any fields or methods to the corresponding classes (and it is actually impossible to add -anything to primitive C/C++ data types). This technique is known in C++ as class traits. It is not -DataType itself that is used but its specialized versions, such as: -@code - template<> class DataType - { - typedef uchar value_type; - typedef int work_type; - typedef uchar channel_type; - enum { channel_type = CV_8U, channels = 1, fmt='u', type = CV_8U }; - }; - ... - template DataType > - { - typedef std::complex<_Tp> value_type; - typedef std::complex<_Tp> work_type; - typedef _Tp channel_type; - // DataDepth is another helper trait class - enum { depth = DataDepth<_Tp>::value, channels=2, - fmt=(channels-1)*256+DataDepth<_Tp>::fmt, - type=CV_MAKETYPE(depth, channels) }; - }; - ... -@endcode -The main purpose of this class is to convert compilation-time type information to an -OpenCV-compatible data type identifier, for example: -@code - // allocates a 30x40 floating-point matrix - Mat A(30, 40, DataType::type); - - Mat B = Mat_ >(3, 3); - // the statement below will print 6, 2 , that is depth == CV_64F, channels == 2 - cout << B.depth() << ", " << B.channels() << endl; -@endcode -So, such traits are used to tell OpenCV which data type you are working with, even if such a type is -not native to OpenCV. For example, the matrix B initialization above is compiled because OpenCV -defines the proper specialized template class DataType\ \> . This mechanism is also -useful (and used in OpenCV this way) for generic algorithms implementations. -*/ -template class DataType -{ -public: - typedef _Tp value_type; - typedef value_type work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 1, - depth = -1, - channels = 1, - fmt = 0, - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef bool value_type; - typedef int work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_8U, - channels = 1, - fmt = (int)'u', - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef uchar value_type; - typedef int work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_8U, - channels = 1, - fmt = (int)'u', - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef schar value_type; - typedef int work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_8S, - channels = 1, - fmt = (int)'c', - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef schar value_type; - typedef int work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_8S, - channels = 1, - fmt = (int)'c', - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef ushort value_type; - typedef int work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_16U, - channels = 1, - fmt = (int)'w', - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef short value_type; - typedef int work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_16S, - channels = 1, - fmt = (int)'s', - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef int value_type; - typedef value_type work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_32S, - channels = 1, - fmt = (int)'i', - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef float value_type; - typedef value_type work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_32F, - channels = 1, - fmt = (int)'f', - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef double value_type; - typedef value_type work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_64F, - channels = 1, - fmt = (int)'d', - type = CV_MAKETYPE(depth, channels) - }; -}; - - -/** @brief A helper class for cv::DataType - -The class is specialized for each fundamental numerical data type supported by OpenCV. It provides -DataDepth::value constant. -*/ -template class DataDepth -{ -public: - enum - { - value = DataType<_Tp>::depth, - fmt = DataType<_Tp>::fmt - }; -}; - - - -template class TypeDepth -{ - enum { depth = CV_USRTYPE1 }; - typedef void value_type; -}; - -template<> class TypeDepth -{ - enum { depth = CV_8U }; - typedef uchar value_type; -}; - -template<> class TypeDepth -{ - enum { depth = CV_8S }; - typedef schar value_type; -}; - -template<> class TypeDepth -{ - enum { depth = CV_16U }; - typedef ushort value_type; -}; - -template<> class TypeDepth -{ - enum { depth = CV_16S }; - typedef short value_type; -}; - -template<> class TypeDepth -{ - enum { depth = CV_32S }; - typedef int value_type; -}; - -template<> class TypeDepth -{ - enum { depth = CV_32F }; - typedef float value_type; -}; - -template<> class TypeDepth -{ - enum { depth = CV_64F }; - typedef double value_type; -}; - -//! @} - -} // cv - -#endif // __OPENCV_CORE_TRAITS_HPP__ diff --git a/IPL/include/opencv/opencv2/core/types.hpp b/IPL/include/opencv/opencv2/core/types.hpp deleted file mode 100644 index e166556..0000000 --- a/IPL/include/opencv/opencv2/core/types.hpp +++ /dev/null @@ -1,2228 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_TYPES_HPP__ -#define __OPENCV_CORE_TYPES_HPP__ - -#ifndef __cplusplus -# error types.hpp header must be compiled as C++ -#endif - -#include -#include -#include - -#include "opencv2/core/cvdef.h" -#include "opencv2/core/cvstd.hpp" -#include "opencv2/core/matx.hpp" - -namespace cv -{ - -//! @addtogroup core_basic -//! @{ - -//////////////////////////////// Complex ////////////////////////////// - -/** @brief A complex number class. - - The template class is similar and compatible with std::complex, however it provides slightly - more convenient access to the real and imaginary parts using through the simple field access, as opposite - to std::complex::real() and std::complex::imag(). -*/ -template class Complex -{ -public: - - //! constructors - Complex(); - Complex( _Tp _re, _Tp _im = 0 ); - - //! conversion to another data type - template operator Complex() const; - //! conjugation - Complex conj() const; - - _Tp re, im; //< the real and the imaginary parts -}; - -typedef Complex Complexf; -typedef Complex Complexd; - -template class DataType< Complex<_Tp> > -{ -public: - typedef Complex<_Tp> value_type; - typedef value_type work_type; - typedef _Tp channel_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = 2, - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) }; - - typedef Vec vec_type; -}; - - - -//////////////////////////////// Point_ //////////////////////////////// - -/** @brief Template class for 2D points specified by its coordinates `x` and `y`. - -An instance of the class is interchangeable with C structures, CvPoint and CvPoint2D32f . There is -also a cast operator to convert point coordinates to the specified type. The conversion from -floating-point coordinates to integer coordinates is done by rounding. Commonly, the conversion -uses this operation for each of the coordinates. Besides the class members listed in the -declaration above, the following operations on points are implemented: -@code - pt1 = pt2 + pt3; - pt1 = pt2 - pt3; - pt1 = pt2 * a; - pt1 = a * pt2; - pt1 = pt2 / a; - pt1 += pt2; - pt1 -= pt2; - pt1 *= a; - pt1 /= a; - double value = norm(pt); // L2 norm - pt1 == pt2; - pt1 != pt2; -@endcode -For your convenience, the following type aliases are defined: -@code - typedef Point_ Point2i; - typedef Point2i Point; - typedef Point_ Point2f; - typedef Point_ Point2d; -@endcode -Example: -@code - Point2f a(0.3f, 0.f), b(0.f, 0.4f); - Point pt = (a + b)*10.f; - cout << pt.x << ", " << pt.y << endl; -@endcode -*/ -template class Point_ -{ -public: - typedef _Tp value_type; - - // various constructors - Point_(); - Point_(_Tp _x, _Tp _y); - Point_(const Point_& pt); - Point_(const Size_<_Tp>& sz); - Point_(const Vec<_Tp, 2>& v); - - Point_& operator = (const Point_& pt); - //! conversion to another data type - template operator Point_<_Tp2>() const; - - //! conversion to the old-style C structures - operator Vec<_Tp, 2>() const; - - //! dot product - _Tp dot(const Point_& pt) const; - //! dot product computed in double-precision arithmetics - double ddot(const Point_& pt) const; - //! cross-product - double cross(const Point_& pt) const; - //! checks whether the point is inside the specified rectangle - bool inside(const Rect_<_Tp>& r) const; - - _Tp x, y; //< the point coordinates -}; - -typedef Point_ Point2i; -typedef Point_ Point2f; -typedef Point_ Point2d; -typedef Point2i Point; - -template class DataType< Point_<_Tp> > -{ -public: - typedef Point_<_Tp> value_type; - typedef Point_::work_type> work_type; - typedef _Tp channel_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = 2, - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) - }; - - typedef Vec vec_type; -}; - - - -//////////////////////////////// Point3_ //////////////////////////////// - -/** @brief Template class for 3D points specified by its coordinates `x`, `y` and `z`. - -An instance of the class is interchangeable with the C structure CvPoint2D32f . Similarly to -Point_ , the coordinates of 3D points can be converted to another type. The vector arithmetic and -comparison operations are also supported. - -The following Point3_\<\> aliases are available: -@code - typedef Point3_ Point3i; - typedef Point3_ Point3f; - typedef Point3_ Point3d; -@endcode -@see cv::Point3i, cv::Point3f and cv::Point3d -*/ -template class Point3_ -{ -public: - typedef _Tp value_type; - - // various constructors - Point3_(); - Point3_(_Tp _x, _Tp _y, _Tp _z); - Point3_(const Point3_& pt); - explicit Point3_(const Point_<_Tp>& pt); - Point3_(const Vec<_Tp, 3>& v); - - Point3_& operator = (const Point3_& pt); - //! conversion to another data type - template operator Point3_<_Tp2>() const; - //! conversion to cv::Vec<> - operator Vec<_Tp, 3>() const; - - //! dot product - _Tp dot(const Point3_& pt) const; - //! dot product computed in double-precision arithmetics - double ddot(const Point3_& pt) const; - //! cross product of the 2 3D points - Point3_ cross(const Point3_& pt) const; - - _Tp x, y, z; //< the point coordinates -}; - -typedef Point3_ Point3i; -typedef Point3_ Point3f; -typedef Point3_ Point3d; - -template class DataType< Point3_<_Tp> > -{ -public: - typedef Point3_<_Tp> value_type; - typedef Point3_::work_type> work_type; - typedef _Tp channel_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = 3, - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) - }; - - typedef Vec vec_type; -}; - - - -//////////////////////////////// Size_ //////////////////////////////// - -/** @brief Template class for specifying the size of an image or rectangle. - -The class includes two members called width and height. The structure can be converted to and from -the old OpenCV structures CvSize and CvSize2D32f . The same set of arithmetic and comparison -operations as for Point_ is available. - -OpenCV defines the following Size_\<\> aliases: -@code - typedef Size_ Size2i; - typedef Size2i Size; - typedef Size_ Size2f; -@endcode -*/ -template class Size_ -{ -public: - typedef _Tp value_type; - - //! various constructors - Size_(); - Size_(_Tp _width, _Tp _height); - Size_(const Size_& sz); - Size_(const Point_<_Tp>& pt); - - Size_& operator = (const Size_& sz); - //! the area (width*height) - _Tp area() const; - - //! conversion of another data type. - template operator Size_<_Tp2>() const; - - _Tp width, height; // the width and the height -}; - -typedef Size_ Size2i; -typedef Size_ Size2f; -typedef Size_ Size2d; -typedef Size2i Size; - -template class DataType< Size_<_Tp> > -{ -public: - typedef Size_<_Tp> value_type; - typedef Size_::work_type> work_type; - typedef _Tp channel_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = 2, - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) - }; - - typedef Vec vec_type; -}; - - - -//////////////////////////////// Rect_ //////////////////////////////// - -/** @brief Template class for 2D rectangles - -described by the following parameters: -- Coordinates of the top-left corner. This is a default interpretation of Rect_::x and Rect_::y - in OpenCV. Though, in your algorithms you may count x and y from the bottom-left corner. -- Rectangle width and height. - -OpenCV typically assumes that the top and left boundary of the rectangle are inclusive, while the -right and bottom boundaries are not. For example, the method Rect_::contains returns true if - -\f[x \leq pt.x < x+width, - y \leq pt.y < y+height\f] - -Virtually every loop over an image ROI in OpenCV (where ROI is specified by Rect_\ ) is -implemented as: -@code - for(int y = roi.y; y < roi.y + roi.height; y++) - for(int x = roi.x; x < roi.x + roi.width; x++) - { - // ... - } -@endcode -In addition to the class members, the following operations on rectangles are implemented: -- \f$\texttt{rect} = \texttt{rect} \pm \texttt{point}\f$ (shifting a rectangle by a certain offset) -- \f$\texttt{rect} = \texttt{rect} \pm \texttt{size}\f$ (expanding or shrinking a rectangle by a - certain amount) -- rect += point, rect -= point, rect += size, rect -= size (augmenting operations) -- rect = rect1 & rect2 (rectangle intersection) -- rect = rect1 | rect2 (minimum area rectangle containing rect1 and rect2 ) -- rect &= rect1, rect |= rect1 (and the corresponding augmenting operations) -- rect == rect1, rect != rect1 (rectangle comparison) - -This is an example how the partial ordering on rectangles can be established (rect1 \f$\subseteq\f$ -rect2): -@code - template inline bool - operator <= (const Rect_<_Tp>& r1, const Rect_<_Tp>& r2) - { - return (r1 & r2) == r1; - } -@endcode -For your convenience, the Rect_\<\> alias is available: cv::Rect -*/ -template class Rect_ -{ -public: - typedef _Tp value_type; - - //! various constructors - Rect_(); - Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height); - Rect_(const Rect_& r); - Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz); - Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2); - - Rect_& operator = ( const Rect_& r ); - //! the top-left corner - Point_<_Tp> tl() const; - //! the bottom-right corner - Point_<_Tp> br() const; - - //! size (width, height) of the rectangle - Size_<_Tp> size() const; - //! area (width*height) of the rectangle - _Tp area() const; - - //! conversion to another data type - template operator Rect_<_Tp2>() const; - - //! checks whether the rectangle contains the point - bool contains(const Point_<_Tp>& pt) const; - - _Tp x, y, width, height; //< the top-left corner, as well as width and height of the rectangle -}; - -typedef Rect_ Rect2i; -typedef Rect_ Rect2f; -typedef Rect_ Rect2d; -typedef Rect2i Rect; - -template class DataType< Rect_<_Tp> > -{ -public: - typedef Rect_<_Tp> value_type; - typedef Rect_::work_type> work_type; - typedef _Tp channel_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = 4, - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) - }; - - typedef Vec vec_type; -}; - - - -///////////////////////////// RotatedRect ///////////////////////////// - -/** @brief The class represents rotated (i.e. not up-right) rectangles on a plane. - -Each rectangle is specified by the center point (mass center), length of each side (represented by -cv::Size2f structure) and the rotation angle in degrees. - -The sample below demonstrates how to use RotatedRect: -@code - Mat image(200, 200, CV_8UC3, Scalar(0)); - RotatedRect rRect = RotatedRect(Point2f(100,100), Size2f(100,50), 30); - - Point2f vertices[4]; - rRect.points(vertices); - for (int i = 0; i < 4; i++) - line(image, vertices[i], vertices[(i+1)%4], Scalar(0,255,0)); - - Rect brect = rRect.boundingRect(); - rectangle(image, brect, Scalar(255,0,0)); - - imshow("rectangles", image); - waitKey(0); -@endcode -![image](pics/rotatedrect.png) - -@sa CamShift, fitEllipse, minAreaRect, CvBox2D -*/ -class CV_EXPORTS RotatedRect -{ -public: - //! various constructors - RotatedRect(); - /** - @param center The rectangle mass center. - @param size Width and height of the rectangle. - @param angle The rotation angle in a clockwise direction. When the angle is 0, 90, 180, 270 etc., - the rectangle becomes an up-right rectangle. - */ - RotatedRect(const Point2f& center, const Size2f& size, float angle); - /** - Any 3 end points of the RotatedRect. They must be given in order (either clockwise or - anticlockwise). - */ - RotatedRect(const Point2f& point1, const Point2f& point2, const Point2f& point3); - - /** returns 4 vertices of the rectangle - @param pts The points array for storing rectangle vertices. - */ - void points(Point2f pts[]) const; - //! returns the minimal up-right rectangle containing the rotated rectangle - Rect boundingRect() const; - - Point2f center; //< the rectangle mass center - Size2f size; //< width and height of the rectangle - float angle; //< the rotation angle. When the angle is 0, 90, 180, 270 etc., the rectangle becomes an up-right rectangle. -}; - -template<> class DataType< RotatedRect > -{ -public: - typedef RotatedRect value_type; - typedef value_type work_type; - typedef float channel_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = (int)sizeof(value_type)/sizeof(channel_type), // 5 - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) - }; - - typedef Vec vec_type; -}; - - - -//////////////////////////////// Range ///////////////////////////////// - -/** @brief Template class specifying a continuous subsequence (slice) of a sequence. - -The class is used to specify a row or a column span in a matrix ( Mat ) and for many other purposes. -Range(a,b) is basically the same as a:b in Matlab or a..b in Python. As in Python, start is an -inclusive left boundary of the range and end is an exclusive right boundary of the range. Such a -half-opened interval is usually denoted as \f$[start,end)\f$ . - -The static method Range::all() returns a special variable that means "the whole sequence" or "the -whole range", just like " : " in Matlab or " ... " in Python. All the methods and functions in -OpenCV that take Range support this special Range::all() value. But, of course, in case of your own -custom processing, you will probably have to check and handle it explicitly: -@code - void my_function(..., const Range& r, ....) - { - if(r == Range::all()) { - // process all the data - } - else { - // process [r.start, r.end) - } - } -@endcode -*/ -class CV_EXPORTS Range -{ -public: - Range(); - Range(int _start, int _end); - int size() const; - bool empty() const; - static Range all(); - - int start, end; -}; - -template<> class DataType -{ -public: - typedef Range value_type; - typedef value_type work_type; - typedef int channel_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = 2, - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) - }; - - typedef Vec vec_type; -}; - - - -//////////////////////////////// Scalar_ /////////////////////////////// - -/** @brief Template class for a 4-element vector derived from Vec. - -Being derived from Vec\<_Tp, 4\> , Scalar_ and Scalar can be used just as typical 4-element -vectors. In addition, they can be converted to/from CvScalar . The type Scalar is widely used in -OpenCV to pass pixel values. -*/ -template class Scalar_ : public Vec<_Tp, 4> -{ -public: - //! various constructors - Scalar_(); - Scalar_(_Tp v0, _Tp v1, _Tp v2=0, _Tp v3=0); - Scalar_(_Tp v0); - - template - Scalar_(const Vec<_Tp2, cn>& v); - - //! returns a scalar with all elements set to v0 - static Scalar_<_Tp> all(_Tp v0); - - //! conversion to another data type - template operator Scalar_() const; - - //! per-element product - Scalar_<_Tp> mul(const Scalar_<_Tp>& a, double scale=1 ) const; - - // returns (v0, -v1, -v2, -v3) - Scalar_<_Tp> conj() const; - - // returns true iff v1 == v2 == v3 == 0 - bool isReal() const; -}; - -typedef Scalar_ Scalar; - -template class DataType< Scalar_<_Tp> > -{ -public: - typedef Scalar_<_Tp> value_type; - typedef Scalar_::work_type> work_type; - typedef _Tp channel_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = 4, - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) - }; - - typedef Vec vec_type; -}; - - - -/////////////////////////////// KeyPoint //////////////////////////////// - -/** @brief Data structure for salient point detectors. - -The class instance stores a keypoint, i.e. a point feature found by one of many available keypoint -detectors, such as Harris corner detector, cv::FAST, cv::StarDetector, cv::SURF, cv::SIFT, -cv::LDetector etc. - -The keypoint is characterized by the 2D position, scale (proportional to the diameter of the -neighborhood that needs to be taken into account), orientation and some other parameters. The -keypoint neighborhood is then analyzed by another algorithm that builds a descriptor (usually -represented as a feature vector). The keypoints representing the same object in different images -can then be matched using cv::KDTree or another method. -*/ -class CV_EXPORTS_W_SIMPLE KeyPoint -{ -public: - //! the default constructor - CV_WRAP KeyPoint(); - /** - @param _pt x & y coordinates of the keypoint - @param _size keypoint diameter - @param _angle keypoint orientation - @param _response keypoint detector response on the keypoint (that is, strength of the keypoint) - @param _octave pyramid octave in which the keypoint has been detected - @param _class_id object id - */ - KeyPoint(Point2f _pt, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1); - /** - @param x x-coordinate of the keypoint - @param y y-coordinate of the keypoint - @param _size keypoint diameter - @param _angle keypoint orientation - @param _response keypoint detector response on the keypoint (that is, strength of the keypoint) - @param _octave pyramid octave in which the keypoint has been detected - @param _class_id object id - */ - CV_WRAP KeyPoint(float x, float y, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1); - - size_t hash() const; - - /** - This method converts vector of keypoints to vector of points or the reverse, where each keypoint is - assigned the same size and the same orientation. - - @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB - @param points2f Array of (x,y) coordinates of each keypoint - @param keypointIndexes Array of indexes of keypoints to be converted to points. (Acts like a mask to - convert only specified keypoints) - */ - CV_WRAP static void convert(const std::vector& keypoints, - CV_OUT std::vector& points2f, - const std::vector& keypointIndexes=std::vector()); - /** @overload - @param points2f Array of (x,y) coordinates of each keypoint - @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB - @param size keypoint diameter - @param response keypoint detector response on the keypoint (that is, strength of the keypoint) - @param octave pyramid octave in which the keypoint has been detected - @param class_id object id - */ - CV_WRAP static void convert(const std::vector& points2f, - CV_OUT std::vector& keypoints, - float size=1, float response=1, int octave=0, int class_id=-1); - - /** - This method computes overlap for pair of keypoints. Overlap is the ratio between area of keypoint - regions' intersection and area of keypoint regions' union (considering keypoint region as circle). - If they don't overlap, we get zero. If they coincide at same location with same size, we get 1. - @param kp1 First keypoint - @param kp2 Second keypoint - */ - CV_WRAP static float overlap(const KeyPoint& kp1, const KeyPoint& kp2); - - CV_PROP_RW Point2f pt; //!< coordinates of the keypoints - CV_PROP_RW float size; //!< diameter of the meaningful keypoint neighborhood - CV_PROP_RW float angle; //!< computed orientation of the keypoint (-1 if not applicable); - //!< it's in [0,360) degrees and measured relative to - //!< image coordinate system, ie in clockwise. - CV_PROP_RW float response; //!< the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling - CV_PROP_RW int octave; //!< octave (pyramid layer) from which the keypoint has been extracted - CV_PROP_RW int class_id; //!< object class (if the keypoints need to be clustered by an object they belong to) -}; - -template<> class DataType -{ -public: - typedef KeyPoint value_type; - typedef float work_type; - typedef float channel_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 7 - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) - }; - - typedef Vec vec_type; -}; - - - -//////////////////////////////// DMatch ///////////////////////////////// - -/** @brief Class for matching keypoint descriptors - -query descriptor index, train descriptor index, train image index, and distance between -descriptors. -*/ -class CV_EXPORTS_W_SIMPLE DMatch -{ -public: - CV_WRAP DMatch(); - CV_WRAP DMatch(int _queryIdx, int _trainIdx, float _distance); - CV_WRAP DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance); - - CV_PROP_RW int queryIdx; // query descriptor index - CV_PROP_RW int trainIdx; // train descriptor index - CV_PROP_RW int imgIdx; // train image index - - CV_PROP_RW float distance; - - // less is better - bool operator<(const DMatch &m) const; -}; - -template<> class DataType -{ -public: - typedef DMatch value_type; - typedef int work_type; - typedef int channel_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 4 - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) - }; - - typedef Vec vec_type; -}; - - - -///////////////////////////// TermCriteria ////////////////////////////// - -/** @brief The class defining termination criteria for iterative algorithms. - -You can initialize it by default constructor and then override any parameters, or the structure may -be fully initialized using the advanced variant of the constructor. -*/ -class CV_EXPORTS TermCriteria -{ -public: - /** - Criteria type, can be one of: COUNT, EPS or COUNT + EPS - */ - enum Type - { - COUNT=1, //!< the maximum number of iterations or elements to compute - MAX_ITER=COUNT, //!< ditto - EPS=2 //!< the desired accuracy or change in parameters at which the iterative algorithm stops - }; - - //! default constructor - TermCriteria(); - /** - @param type The type of termination criteria, one of TermCriteria::Type - @param maxCount The maximum number of iterations or elements to compute. - @param epsilon The desired accuracy or change in parameters at which the iterative algorithm stops. - */ - TermCriteria(int type, int maxCount, double epsilon); - - int type; //!< the type of termination criteria: COUNT, EPS or COUNT + EPS - int maxCount; // the maximum number of iterations/elements - double epsilon; // the desired accuracy -}; - - -//! @} core_basic - -///////////////////////// raster image moments ////////////////////////// - -//! @addtogroup imgproc_shape -//! @{ - -/** @brief struct returned by cv::moments - -The spatial moments \f$\texttt{Moments::m}_{ji}\f$ are computed as: - -\f[\texttt{m} _{ji}= \sum _{x,y} \left ( \texttt{array} (x,y) \cdot x^j \cdot y^i \right )\f] - -The central moments \f$\texttt{Moments::mu}_{ji}\f$ are computed as: - -\f[\texttt{mu} _{ji}= \sum _{x,y} \left ( \texttt{array} (x,y) \cdot (x - \bar{x} )^j \cdot (y - \bar{y} )^i \right )\f] - -where \f$(\bar{x}, \bar{y})\f$ is the mass center: - -\f[\bar{x} = \frac{\texttt{m}_{10}}{\texttt{m}_{00}} , \; \bar{y} = \frac{\texttt{m}_{01}}{\texttt{m}_{00}}\f] - -The normalized central moments \f$\texttt{Moments::nu}_{ij}\f$ are computed as: - -\f[\texttt{nu} _{ji}= \frac{\texttt{mu}_{ji}}{\texttt{m}_{00}^{(i+j)/2+1}} .\f] - -@note -\f$\texttt{mu}_{00}=\texttt{m}_{00}\f$, \f$\texttt{nu}_{00}=1\f$ -\f$\texttt{nu}_{10}=\texttt{mu}_{10}=\texttt{mu}_{01}=\texttt{mu}_{10}=0\f$ , hence the values are not -stored. - -The moments of a contour are defined in the same way but computed using the Green's formula (see -). So, due to a limited raster resolution, the moments -computed for a contour are slightly different from the moments computed for the same rasterized -contour. - -@note -Since the contour moments are computed using Green formula, you may get seemingly odd results for -contours with self-intersections, e.g. a zero area (m00) for butterfly-shaped contours. - */ -class CV_EXPORTS_W_MAP Moments -{ -public: - //! the default constructor - Moments(); - //! the full constructor - Moments(double m00, double m10, double m01, double m20, double m11, - double m02, double m30, double m21, double m12, double m03 ); - ////! the conversion from CvMoments - //Moments( const CvMoments& moments ); - ////! the conversion to CvMoments - //operator CvMoments() const; - - //! @name spatial moments - //! @{ - CV_PROP_RW double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03; - //! @} - - //! @name central moments - //! @{ - CV_PROP_RW double mu20, mu11, mu02, mu30, mu21, mu12, mu03; - //! @} - - //! @name central normalized moments - //! @{ - CV_PROP_RW double nu20, nu11, nu02, nu30, nu21, nu12, nu03; - //! @} -}; - -template<> class DataType -{ -public: - typedef Moments value_type; - typedef double work_type; - typedef double channel_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 24 - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) - }; - - typedef Vec vec_type; -}; - -//! @} imgproc_shape - -//! @cond IGNORED - -///////////////////////////////////////////////////////////////////////// -///////////////////////////// Implementation //////////////////////////// -///////////////////////////////////////////////////////////////////////// - -//////////////////////////////// Complex //////////////////////////////// - -template inline -Complex<_Tp>::Complex() - : re(0), im(0) {} - -template inline -Complex<_Tp>::Complex( _Tp _re, _Tp _im ) - : re(_re), im(_im) {} - -template template inline -Complex<_Tp>::operator Complex() const -{ - return Complex(saturate_cast(re), saturate_cast(im)); -} - -template inline -Complex<_Tp> Complex<_Tp>::conj() const -{ - return Complex<_Tp>(re, -im); -} - - -template static inline -bool operator == (const Complex<_Tp>& a, const Complex<_Tp>& b) -{ - return a.re == b.re && a.im == b.im; -} - -template static inline -bool operator != (const Complex<_Tp>& a, const Complex<_Tp>& b) -{ - return a.re != b.re || a.im != b.im; -} - -template static inline -Complex<_Tp> operator + (const Complex<_Tp>& a, const Complex<_Tp>& b) -{ - return Complex<_Tp>( a.re + b.re, a.im + b.im ); -} - -template static inline -Complex<_Tp>& operator += (Complex<_Tp>& a, const Complex<_Tp>& b) -{ - a.re += b.re; a.im += b.im; - return a; -} - -template static inline -Complex<_Tp> operator - (const Complex<_Tp>& a, const Complex<_Tp>& b) -{ - return Complex<_Tp>( a.re - b.re, a.im - b.im ); -} - -template static inline -Complex<_Tp>& operator -= (Complex<_Tp>& a, const Complex<_Tp>& b) -{ - a.re -= b.re; a.im -= b.im; - return a; -} - -template static inline -Complex<_Tp> operator - (const Complex<_Tp>& a) -{ - return Complex<_Tp>(-a.re, -a.im); -} - -template static inline -Complex<_Tp> operator * (const Complex<_Tp>& a, const Complex<_Tp>& b) -{ - return Complex<_Tp>( a.re*b.re - a.im*b.im, a.re*b.im + a.im*b.re ); -} - -template static inline -Complex<_Tp> operator * (const Complex<_Tp>& a, _Tp b) -{ - return Complex<_Tp>( a.re*b, a.im*b ); -} - -template static inline -Complex<_Tp> operator * (_Tp b, const Complex<_Tp>& a) -{ - return Complex<_Tp>( a.re*b, a.im*b ); -} - -template static inline -Complex<_Tp> operator + (const Complex<_Tp>& a, _Tp b) -{ - return Complex<_Tp>( a.re + b, a.im ); -} - -template static inline -Complex<_Tp> operator - (const Complex<_Tp>& a, _Tp b) -{ return Complex<_Tp>( a.re - b, a.im ); } - -template static inline -Complex<_Tp> operator + (_Tp b, const Complex<_Tp>& a) -{ - return Complex<_Tp>( a.re + b, a.im ); -} - -template static inline -Complex<_Tp> operator - (_Tp b, const Complex<_Tp>& a) -{ - return Complex<_Tp>( b - a.re, -a.im ); -} - -template static inline -Complex<_Tp>& operator += (Complex<_Tp>& a, _Tp b) -{ - a.re += b; return a; -} - -template static inline -Complex<_Tp>& operator -= (Complex<_Tp>& a, _Tp b) -{ - a.re -= b; return a; -} - -template static inline -Complex<_Tp>& operator *= (Complex<_Tp>& a, _Tp b) -{ - a.re *= b; a.im *= b; return a; -} - -template static inline -double abs(const Complex<_Tp>& a) -{ - return std::sqrt( (double)a.re*a.re + (double)a.im*a.im); -} - -template static inline -Complex<_Tp> operator / (const Complex<_Tp>& a, const Complex<_Tp>& b) -{ - double t = 1./((double)b.re*b.re + (double)b.im*b.im); - return Complex<_Tp>( (_Tp)((a.re*b.re + a.im*b.im)*t), - (_Tp)((-a.re*b.im + a.im*b.re)*t) ); -} - -template static inline -Complex<_Tp>& operator /= (Complex<_Tp>& a, const Complex<_Tp>& b) -{ - return (a = a / b); -} - -template static inline -Complex<_Tp> operator / (const Complex<_Tp>& a, _Tp b) -{ - _Tp t = (_Tp)1/b; - return Complex<_Tp>( a.re*t, a.im*t ); -} - -template static inline -Complex<_Tp> operator / (_Tp b, const Complex<_Tp>& a) -{ - return Complex<_Tp>(b)/a; -} - -template static inline -Complex<_Tp> operator /= (const Complex<_Tp>& a, _Tp b) -{ - _Tp t = (_Tp)1/b; - a.re *= t; a.im *= t; return a; -} - - - -//////////////////////////////// 2D Point /////////////////////////////// - -template inline -Point_<_Tp>::Point_() - : x(0), y(0) {} - -template inline -Point_<_Tp>::Point_(_Tp _x, _Tp _y) - : x(_x), y(_y) {} - -template inline -Point_<_Tp>::Point_(const Point_& pt) - : x(pt.x), y(pt.y) {} - -template inline -Point_<_Tp>::Point_(const Size_<_Tp>& sz) - : x(sz.width), y(sz.height) {} - -template inline -Point_<_Tp>::Point_(const Vec<_Tp,2>& v) - : x(v[0]), y(v[1]) {} - -template inline -Point_<_Tp>& Point_<_Tp>::operator = (const Point_& pt) -{ - x = pt.x; y = pt.y; - return *this; -} - -template template inline -Point_<_Tp>::operator Point_<_Tp2>() const -{ - return Point_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y)); -} - -template inline -Point_<_Tp>::operator Vec<_Tp, 2>() const -{ - return Vec<_Tp, 2>(x, y); -} - -template inline -_Tp Point_<_Tp>::dot(const Point_& pt) const -{ - return saturate_cast<_Tp>(x*pt.x + y*pt.y); -} - -template inline -double Point_<_Tp>::ddot(const Point_& pt) const -{ - return (double)x*pt.x + (double)y*pt.y; -} - -template inline -double Point_<_Tp>::cross(const Point_& pt) const -{ - return (double)x*pt.y - (double)y*pt.x; -} - -template inline bool -Point_<_Tp>::inside( const Rect_<_Tp>& r ) const -{ - return r.contains(*this); -} - - -template static inline -Point_<_Tp>& operator += (Point_<_Tp>& a, const Point_<_Tp>& b) -{ - a.x += b.x; - a.y += b.y; - return a; -} - -template static inline -Point_<_Tp>& operator -= (Point_<_Tp>& a, const Point_<_Tp>& b) -{ - a.x -= b.x; - a.y -= b.y; - return a; -} - -template static inline -Point_<_Tp>& operator *= (Point_<_Tp>& a, int b) -{ - a.x = saturate_cast<_Tp>(a.x * b); - a.y = saturate_cast<_Tp>(a.y * b); - return a; -} - -template static inline -Point_<_Tp>& operator *= (Point_<_Tp>& a, float b) -{ - a.x = saturate_cast<_Tp>(a.x * b); - a.y = saturate_cast<_Tp>(a.y * b); - return a; -} - -template static inline -Point_<_Tp>& operator *= (Point_<_Tp>& a, double b) -{ - a.x = saturate_cast<_Tp>(a.x * b); - a.y = saturate_cast<_Tp>(a.y * b); - return a; -} - -template static inline -Point_<_Tp>& operator /= (Point_<_Tp>& a, int b) -{ - a.x = saturate_cast<_Tp>(a.x / b); - a.y = saturate_cast<_Tp>(a.y / b); - return a; -} - -template static inline -Point_<_Tp>& operator /= (Point_<_Tp>& a, float b) -{ - a.x = saturate_cast<_Tp>(a.x / b); - a.y = saturate_cast<_Tp>(a.y / b); - return a; -} - -template static inline -Point_<_Tp>& operator /= (Point_<_Tp>& a, double b) -{ - a.x = saturate_cast<_Tp>(a.x / b); - a.y = saturate_cast<_Tp>(a.y / b); - return a; -} - -template static inline -double norm(const Point_<_Tp>& pt) -{ - return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y); -} - -template static inline -bool operator == (const Point_<_Tp>& a, const Point_<_Tp>& b) -{ - return a.x == b.x && a.y == b.y; -} - -template static inline -bool operator != (const Point_<_Tp>& a, const Point_<_Tp>& b) -{ - return a.x != b.x || a.y != b.y; -} - -template static inline -Point_<_Tp> operator + (const Point_<_Tp>& a, const Point_<_Tp>& b) -{ - return Point_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y) ); -} - -template static inline -Point_<_Tp> operator - (const Point_<_Tp>& a, const Point_<_Tp>& b) -{ - return Point_<_Tp>( saturate_cast<_Tp>(a.x - b.x), saturate_cast<_Tp>(a.y - b.y) ); -} - -template static inline -Point_<_Tp> operator - (const Point_<_Tp>& a) -{ - return Point_<_Tp>( saturate_cast<_Tp>(-a.x), saturate_cast<_Tp>(-a.y) ); -} - -template static inline -Point_<_Tp> operator * (const Point_<_Tp>& a, int b) -{ - return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); -} - -template static inline -Point_<_Tp> operator * (int a, const Point_<_Tp>& b) -{ - return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); -} - -template static inline -Point_<_Tp> operator * (const Point_<_Tp>& a, float b) -{ - return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); -} - -template static inline -Point_<_Tp> operator * (float a, const Point_<_Tp>& b) -{ - return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); -} - -template static inline -Point_<_Tp> operator * (const Point_<_Tp>& a, double b) -{ - return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); -} - -template static inline -Point_<_Tp> operator * (double a, const Point_<_Tp>& b) -{ - return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); -} - -template static inline -Point_<_Tp> operator * (const Matx<_Tp, 2, 2>& a, const Point_<_Tp>& b) -{ - Matx<_Tp, 2, 1> tmp = a * Vec<_Tp,2>(b.x, b.y); - return Point_<_Tp>(tmp.val[0], tmp.val[1]); -} - -template static inline -Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point_<_Tp>& b) -{ - Matx<_Tp, 3, 1> tmp = a * Vec<_Tp,3>(b.x, b.y, 1); - return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]); -} - -template static inline -Point_<_Tp> operator / (const Point_<_Tp>& a, int b) -{ - Point_<_Tp> tmp(a); - tmp /= b; - return tmp; -} - -template static inline -Point_<_Tp> operator / (const Point_<_Tp>& a, float b) -{ - Point_<_Tp> tmp(a); - tmp /= b; - return tmp; -} - -template static inline -Point_<_Tp> operator / (const Point_<_Tp>& a, double b) -{ - Point_<_Tp> tmp(a); - tmp /= b; - return tmp; -} - - - -//////////////////////////////// 3D Point /////////////////////////////// - -template inline -Point3_<_Tp>::Point3_() - : x(0), y(0), z(0) {} - -template inline -Point3_<_Tp>::Point3_(_Tp _x, _Tp _y, _Tp _z) - : x(_x), y(_y), z(_z) {} - -template inline -Point3_<_Tp>::Point3_(const Point3_& pt) - : x(pt.x), y(pt.y), z(pt.z) {} - -template inline -Point3_<_Tp>::Point3_(const Point_<_Tp>& pt) - : x(pt.x), y(pt.y), z(_Tp()) {} - -template inline -Point3_<_Tp>::Point3_(const Vec<_Tp, 3>& v) - : x(v[0]), y(v[1]), z(v[2]) {} - -template template inline -Point3_<_Tp>::operator Point3_<_Tp2>() const -{ - return Point3_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y), saturate_cast<_Tp2>(z)); -} - -template inline -Point3_<_Tp>::operator Vec<_Tp, 3>() const -{ - return Vec<_Tp, 3>(x, y, z); -} - -template inline -Point3_<_Tp>& Point3_<_Tp>::operator = (const Point3_& pt) -{ - x = pt.x; y = pt.y; z = pt.z; - return *this; -} - -template inline -_Tp Point3_<_Tp>::dot(const Point3_& pt) const -{ - return saturate_cast<_Tp>(x*pt.x + y*pt.y + z*pt.z); -} - -template inline -double Point3_<_Tp>::ddot(const Point3_& pt) const -{ - return (double)x*pt.x + (double)y*pt.y + (double)z*pt.z; -} - -template inline -Point3_<_Tp> Point3_<_Tp>::cross(const Point3_<_Tp>& pt) const -{ - return Point3_<_Tp>(y*pt.z - z*pt.y, z*pt.x - x*pt.z, x*pt.y - y*pt.x); -} - - -template static inline -Point3_<_Tp>& operator += (Point3_<_Tp>& a, const Point3_<_Tp>& b) -{ - a.x += b.x; - a.y += b.y; - a.z += b.z; - return a; -} - -template static inline -Point3_<_Tp>& operator -= (Point3_<_Tp>& a, const Point3_<_Tp>& b) -{ - a.x -= b.x; - a.y -= b.y; - a.z -= b.z; - return a; -} - -template static inline -Point3_<_Tp>& operator *= (Point3_<_Tp>& a, int b) -{ - a.x = saturate_cast<_Tp>(a.x * b); - a.y = saturate_cast<_Tp>(a.y * b); - a.z = saturate_cast<_Tp>(a.z * b); - return a; -} - -template static inline -Point3_<_Tp>& operator *= (Point3_<_Tp>& a, float b) -{ - a.x = saturate_cast<_Tp>(a.x * b); - a.y = saturate_cast<_Tp>(a.y * b); - a.z = saturate_cast<_Tp>(a.z * b); - return a; -} - -template static inline -Point3_<_Tp>& operator *= (Point3_<_Tp>& a, double b) -{ - a.x = saturate_cast<_Tp>(a.x * b); - a.y = saturate_cast<_Tp>(a.y * b); - a.z = saturate_cast<_Tp>(a.z * b); - return a; -} - -template static inline -Point3_<_Tp>& operator /= (Point3_<_Tp>& a, int b) -{ - a.x = saturate_cast<_Tp>(a.x / b); - a.y = saturate_cast<_Tp>(a.y / b); - a.z = saturate_cast<_Tp>(a.z / b); - return a; -} - -template static inline -Point3_<_Tp>& operator /= (Point3_<_Tp>& a, float b) -{ - a.x = saturate_cast<_Tp>(a.x / b); - a.y = saturate_cast<_Tp>(a.y / b); - a.z = saturate_cast<_Tp>(a.z / b); - return a; -} - -template static inline -Point3_<_Tp>& operator /= (Point3_<_Tp>& a, double b) -{ - a.x = saturate_cast<_Tp>(a.x / b); - a.y = saturate_cast<_Tp>(a.y / b); - a.z = saturate_cast<_Tp>(a.z / b); - return a; -} - -template static inline -double norm(const Point3_<_Tp>& pt) -{ - return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y + (double)pt.z*pt.z); -} - -template static inline -bool operator == (const Point3_<_Tp>& a, const Point3_<_Tp>& b) -{ - return a.x == b.x && a.y == b.y && a.z == b.z; -} - -template static inline -bool operator != (const Point3_<_Tp>& a, const Point3_<_Tp>& b) -{ - return a.x != b.x || a.y != b.y || a.z != b.z; -} - -template static inline -Point3_<_Tp> operator + (const Point3_<_Tp>& a, const Point3_<_Tp>& b) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y), saturate_cast<_Tp>(a.z + b.z)); -} - -template static inline -Point3_<_Tp> operator - (const Point3_<_Tp>& a, const Point3_<_Tp>& b) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(a.x - b.x), saturate_cast<_Tp>(a.y - b.y), saturate_cast<_Tp>(a.z - b.z)); -} - -template static inline -Point3_<_Tp> operator - (const Point3_<_Tp>& a) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(-a.x), saturate_cast<_Tp>(-a.y), saturate_cast<_Tp>(-a.z) ); -} - -template static inline -Point3_<_Tp> operator * (const Point3_<_Tp>& a, int b) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b), saturate_cast<_Tp>(a.z*b) ); -} - -template static inline -Point3_<_Tp> operator * (int a, const Point3_<_Tp>& b) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); -} - -template static inline -Point3_<_Tp> operator * (const Point3_<_Tp>& a, float b) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(a.x * b), saturate_cast<_Tp>(a.y * b), saturate_cast<_Tp>(a.z * b) ); -} - -template static inline -Point3_<_Tp> operator * (float a, const Point3_<_Tp>& b) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); -} - -template static inline -Point3_<_Tp> operator * (const Point3_<_Tp>& a, double b) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(a.x * b), saturate_cast<_Tp>(a.y * b), saturate_cast<_Tp>(a.z * b) ); -} - -template static inline -Point3_<_Tp> operator * (double a, const Point3_<_Tp>& b) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); -} - -template static inline -Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point3_<_Tp>& b) -{ - Matx<_Tp, 3, 1> tmp = a * Vec<_Tp,3>(b.x, b.y, b.z); - return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]); -} - -template static inline -Matx<_Tp, 4, 1> operator * (const Matx<_Tp, 4, 4>& a, const Point3_<_Tp>& b) -{ - return a * Matx<_Tp, 4, 1>(b.x, b.y, b.z, 1); -} - -template static inline -Point3_<_Tp> operator / (const Point3_<_Tp>& a, int b) -{ - Point3_<_Tp> tmp(a); - tmp /= b; - return tmp; -} - -template static inline -Point3_<_Tp> operator / (const Point3_<_Tp>& a, float b) -{ - Point3_<_Tp> tmp(a); - tmp /= b; - return tmp; -} - -template static inline -Point3_<_Tp> operator / (const Point3_<_Tp>& a, double b) -{ - Point3_<_Tp> tmp(a); - tmp /= b; - return tmp; -} - - - -////////////////////////////////// Size ///////////////////////////////// - -template inline -Size_<_Tp>::Size_() - : width(0), height(0) {} - -template inline -Size_<_Tp>::Size_(_Tp _width, _Tp _height) - : width(_width), height(_height) {} - -template inline -Size_<_Tp>::Size_(const Size_& sz) - : width(sz.width), height(sz.height) {} - -template inline -Size_<_Tp>::Size_(const Point_<_Tp>& pt) - : width(pt.x), height(pt.y) {} - -template template inline -Size_<_Tp>::operator Size_<_Tp2>() const -{ - return Size_<_Tp2>(saturate_cast<_Tp2>(width), saturate_cast<_Tp2>(height)); -} - -template inline -Size_<_Tp>& Size_<_Tp>::operator = (const Size_<_Tp>& sz) -{ - width = sz.width; height = sz.height; - return *this; -} - -template inline -_Tp Size_<_Tp>::area() const -{ - return width * height; -} - -template static inline -Size_<_Tp>& operator *= (Size_<_Tp>& a, _Tp b) -{ - a.width *= b; - a.height *= b; - return a; -} - -template static inline -Size_<_Tp> operator * (const Size_<_Tp>& a, _Tp b) -{ - Size_<_Tp> tmp(a); - tmp *= b; - return tmp; -} - -template static inline -Size_<_Tp>& operator /= (Size_<_Tp>& a, _Tp b) -{ - a.width /= b; - a.height /= b; - return a; -} - -template static inline -Size_<_Tp> operator / (const Size_<_Tp>& a, _Tp b) -{ - Size_<_Tp> tmp(a); - tmp /= b; - return tmp; -} - -template static inline -Size_<_Tp>& operator += (Size_<_Tp>& a, const Size_<_Tp>& b) -{ - a.width += b.width; - a.height += b.height; - return a; -} - -template static inline -Size_<_Tp> operator + (const Size_<_Tp>& a, const Size_<_Tp>& b) -{ - Size_<_Tp> tmp(a); - tmp += b; - return tmp; -} - -template static inline -Size_<_Tp>& operator -= (Size_<_Tp>& a, const Size_<_Tp>& b) -{ - a.width -= b.width; - a.height -= b.height; - return a; -} - -template static inline -Size_<_Tp> operator - (const Size_<_Tp>& a, const Size_<_Tp>& b) -{ - Size_<_Tp> tmp(a); - tmp -= b; - return tmp; -} - -template static inline -bool operator == (const Size_<_Tp>& a, const Size_<_Tp>& b) -{ - return a.width == b.width && a.height == b.height; -} - -template static inline -bool operator != (const Size_<_Tp>& a, const Size_<_Tp>& b) -{ - return !(a == b); -} - - - -////////////////////////////////// Rect ///////////////////////////////// - -template inline -Rect_<_Tp>::Rect_() - : x(0), y(0), width(0), height(0) {} - -template inline -Rect_<_Tp>::Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height) - : x(_x), y(_y), width(_width), height(_height) {} - -template inline -Rect_<_Tp>::Rect_(const Rect_<_Tp>& r) - : x(r.x), y(r.y), width(r.width), height(r.height) {} - -template inline -Rect_<_Tp>::Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz) - : x(org.x), y(org.y), width(sz.width), height(sz.height) {} - -template inline -Rect_<_Tp>::Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2) -{ - x = std::min(pt1.x, pt2.x); - y = std::min(pt1.y, pt2.y); - width = std::max(pt1.x, pt2.x) - x; - height = std::max(pt1.y, pt2.y) - y; -} - -template inline -Rect_<_Tp>& Rect_<_Tp>::operator = ( const Rect_<_Tp>& r ) -{ - x = r.x; - y = r.y; - width = r.width; - height = r.height; - return *this; -} - -template inline -Point_<_Tp> Rect_<_Tp>::tl() const -{ - return Point_<_Tp>(x,y); -} - -template inline -Point_<_Tp> Rect_<_Tp>::br() const -{ - return Point_<_Tp>(x + width, y + height); -} - -template inline -Size_<_Tp> Rect_<_Tp>::size() const -{ - return Size_<_Tp>(width, height); -} - -template inline -_Tp Rect_<_Tp>::area() const -{ - return width * height; -} - -template template inline -Rect_<_Tp>::operator Rect_<_Tp2>() const -{ - return Rect_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y), saturate_cast<_Tp2>(width), saturate_cast<_Tp2>(height)); -} - -template inline -bool Rect_<_Tp>::contains(const Point_<_Tp>& pt) const -{ - return x <= pt.x && pt.x < x + width && y <= pt.y && pt.y < y + height; -} - - -template static inline -Rect_<_Tp>& operator += ( Rect_<_Tp>& a, const Point_<_Tp>& b ) -{ - a.x += b.x; - a.y += b.y; - return a; -} - -template static inline -Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Point_<_Tp>& b ) -{ - a.x -= b.x; - a.y -= b.y; - return a; -} - -template static inline -Rect_<_Tp>& operator += ( Rect_<_Tp>& a, const Size_<_Tp>& b ) -{ - a.width += b.width; - a.height += b.height; - return a; -} - -template static inline -Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Size_<_Tp>& b ) -{ - a.width -= b.width; - a.height -= b.height; - return a; -} - -template static inline -Rect_<_Tp>& operator &= ( Rect_<_Tp>& a, const Rect_<_Tp>& b ) -{ - _Tp x1 = std::max(a.x, b.x); - _Tp y1 = std::max(a.y, b.y); - a.width = std::min(a.x + a.width, b.x + b.width) - x1; - a.height = std::min(a.y + a.height, b.y + b.height) - y1; - a.x = x1; - a.y = y1; - if( a.width <= 0 || a.height <= 0 ) - a = Rect(); - return a; -} - -template static inline -Rect_<_Tp>& operator |= ( Rect_<_Tp>& a, const Rect_<_Tp>& b ) -{ - _Tp x1 = std::min(a.x, b.x); - _Tp y1 = std::min(a.y, b.y); - a.width = std::max(a.x + a.width, b.x + b.width) - x1; - a.height = std::max(a.y + a.height, b.y + b.height) - y1; - a.x = x1; - a.y = y1; - return a; -} - -template static inline -bool operator == (const Rect_<_Tp>& a, const Rect_<_Tp>& b) -{ - return a.x == b.x && a.y == b.y && a.width == b.width && a.height == b.height; -} - -template static inline -bool operator != (const Rect_<_Tp>& a, const Rect_<_Tp>& b) -{ - return a.x != b.x || a.y != b.y || a.width != b.width || a.height != b.height; -} - -template static inline -Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Point_<_Tp>& b) -{ - return Rect_<_Tp>( a.x + b.x, a.y + b.y, a.width, a.height ); -} - -template static inline -Rect_<_Tp> operator - (const Rect_<_Tp>& a, const Point_<_Tp>& b) -{ - return Rect_<_Tp>( a.x - b.x, a.y - b.y, a.width, a.height ); -} - -template static inline -Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Size_<_Tp>& b) -{ - return Rect_<_Tp>( a.x, a.y, a.width + b.width, a.height + b.height ); -} - -template static inline -Rect_<_Tp> operator & (const Rect_<_Tp>& a, const Rect_<_Tp>& b) -{ - Rect_<_Tp> c = a; - return c &= b; -} - -template static inline -Rect_<_Tp> operator | (const Rect_<_Tp>& a, const Rect_<_Tp>& b) -{ - Rect_<_Tp> c = a; - return c |= b; -} - - - -////////////////////////////// RotatedRect ////////////////////////////// - -inline -RotatedRect::RotatedRect() - : center(), size(), angle(0) {} - -inline -RotatedRect::RotatedRect(const Point2f& _center, const Size2f& _size, float _angle) - : center(_center), size(_size), angle(_angle) {} - - - -///////////////////////////////// Range ///////////////////////////////// - -inline -Range::Range() - : start(0), end(0) {} - -inline -Range::Range(int _start, int _end) - : start(_start), end(_end) {} - -inline -int Range::size() const -{ - return end - start; -} - -inline -bool Range::empty() const -{ - return start == end; -} - -inline -Range Range::all() -{ - return Range(INT_MIN, INT_MAX); -} - - -static inline -bool operator == (const Range& r1, const Range& r2) -{ - return r1.start == r2.start && r1.end == r2.end; -} - -static inline -bool operator != (const Range& r1, const Range& r2) -{ - return !(r1 == r2); -} - -static inline -bool operator !(const Range& r) -{ - return r.start == r.end; -} - -static inline -Range operator & (const Range& r1, const Range& r2) -{ - Range r(std::max(r1.start, r2.start), std::min(r1.end, r2.end)); - r.end = std::max(r.end, r.start); - return r; -} - -static inline -Range& operator &= (Range& r1, const Range& r2) -{ - r1 = r1 & r2; - return r1; -} - -static inline -Range operator + (const Range& r1, int delta) -{ - return Range(r1.start + delta, r1.end + delta); -} - -static inline -Range operator + (int delta, const Range& r1) -{ - return Range(r1.start + delta, r1.end + delta); -} - -static inline -Range operator - (const Range& r1, int delta) -{ - return r1 + (-delta); -} - - - -///////////////////////////////// Scalar //////////////////////////////// - -template inline -Scalar_<_Tp>::Scalar_() -{ - this->val[0] = this->val[1] = this->val[2] = this->val[3] = 0; -} - -template inline -Scalar_<_Tp>::Scalar_(_Tp v0, _Tp v1, _Tp v2, _Tp v3) -{ - this->val[0] = v0; - this->val[1] = v1; - this->val[2] = v2; - this->val[3] = v3; -} - -template template inline -Scalar_<_Tp>::Scalar_(const Vec<_Tp2, cn>& v) -{ - int i; - for( i = 0; i < (cn < 4 ? cn : 4); i++ ) - this->val[i] = cv::saturate_cast<_Tp>(v.val[i]); - for( ; i < 4; i++ ) - this->val[i] = 0; -} - -template inline -Scalar_<_Tp>::Scalar_(_Tp v0) -{ - this->val[0] = v0; - this->val[1] = this->val[2] = this->val[3] = 0; -} - -template inline -Scalar_<_Tp> Scalar_<_Tp>::all(_Tp v0) -{ - return Scalar_<_Tp>(v0, v0, v0, v0); -} - - -template inline -Scalar_<_Tp> Scalar_<_Tp>::mul(const Scalar_<_Tp>& a, double scale ) const -{ - return Scalar_<_Tp>(saturate_cast<_Tp>(this->val[0] * a.val[0] * scale), - saturate_cast<_Tp>(this->val[1] * a.val[1] * scale), - saturate_cast<_Tp>(this->val[2] * a.val[2] * scale), - saturate_cast<_Tp>(this->val[3] * a.val[3] * scale)); -} - -template inline -Scalar_<_Tp> Scalar_<_Tp>::conj() const -{ - return Scalar_<_Tp>(saturate_cast<_Tp>( this->val[0]), - saturate_cast<_Tp>(-this->val[1]), - saturate_cast<_Tp>(-this->val[2]), - saturate_cast<_Tp>(-this->val[3])); -} - -template inline -bool Scalar_<_Tp>::isReal() const -{ - return this->val[1] == 0 && this->val[2] == 0 && this->val[3] == 0; -} - - -template template inline -Scalar_<_Tp>::operator Scalar_() const -{ - return Scalar_(saturate_cast(this->val[0]), - saturate_cast(this->val[1]), - saturate_cast(this->val[2]), - saturate_cast(this->val[3])); -} - - -template static inline -Scalar_<_Tp>& operator += (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) -{ - a.val[0] += b.val[0]; - a.val[1] += b.val[1]; - a.val[2] += b.val[2]; - a.val[3] += b.val[3]; - return a; -} - -template static inline -Scalar_<_Tp>& operator -= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) -{ - a.val[0] -= b.val[0]; - a.val[1] -= b.val[1]; - a.val[2] -= b.val[2]; - a.val[3] -= b.val[3]; - return a; -} - -template static inline -Scalar_<_Tp>& operator *= ( Scalar_<_Tp>& a, _Tp v ) -{ - a.val[0] *= v; - a.val[1] *= v; - a.val[2] *= v; - a.val[3] *= v; - return a; -} - -template static inline -bool operator == ( const Scalar_<_Tp>& a, const Scalar_<_Tp>& b ) -{ - return a.val[0] == b.val[0] && a.val[1] == b.val[1] && - a.val[2] == b.val[2] && a.val[3] == b.val[3]; -} - -template static inline -bool operator != ( const Scalar_<_Tp>& a, const Scalar_<_Tp>& b ) -{ - return a.val[0] != b.val[0] || a.val[1] != b.val[1] || - a.val[2] != b.val[2] || a.val[3] != b.val[3]; -} - -template static inline -Scalar_<_Tp> operator + (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) -{ - return Scalar_<_Tp>(a.val[0] + b.val[0], - a.val[1] + b.val[1], - a.val[2] + b.val[2], - a.val[3] + b.val[3]); -} - -template static inline -Scalar_<_Tp> operator - (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) -{ - return Scalar_<_Tp>(saturate_cast<_Tp>(a.val[0] - b.val[0]), - saturate_cast<_Tp>(a.val[1] - b.val[1]), - saturate_cast<_Tp>(a.val[2] - b.val[2]), - saturate_cast<_Tp>(a.val[3] - b.val[3])); -} - -template static inline -Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, _Tp alpha) -{ - return Scalar_<_Tp>(a.val[0] * alpha, - a.val[1] * alpha, - a.val[2] * alpha, - a.val[3] * alpha); -} - -template static inline -Scalar_<_Tp> operator * (_Tp alpha, const Scalar_<_Tp>& a) -{ - return a*alpha; -} - -template static inline -Scalar_<_Tp> operator - (const Scalar_<_Tp>& a) -{ - return Scalar_<_Tp>(saturate_cast<_Tp>(-a.val[0]), - saturate_cast<_Tp>(-a.val[1]), - saturate_cast<_Tp>(-a.val[2]), - saturate_cast<_Tp>(-a.val[3])); -} - - -template static inline -Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) -{ - return Scalar_<_Tp>(saturate_cast<_Tp>(a[0]*b[0] - a[1]*b[1] - a[2]*b[2] - a[3]*b[3]), - saturate_cast<_Tp>(a[0]*b[1] + a[1]*b[0] + a[2]*b[3] - a[3]*b[2]), - saturate_cast<_Tp>(a[0]*b[2] - a[1]*b[3] + a[2]*b[0] + a[3]*b[1]), - saturate_cast<_Tp>(a[0]*b[3] + a[1]*b[2] - a[2]*b[1] + a[3]*b[0])); -} - -template static inline -Scalar_<_Tp>& operator *= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) -{ - a = a * b; - return a; -} - -template static inline -Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, _Tp alpha) -{ - return Scalar_<_Tp>(a.val[0] / alpha, - a.val[1] / alpha, - a.val[2] / alpha, - a.val[3] / alpha); -} - -template static inline -Scalar_ operator / (const Scalar_& a, float alpha) -{ - float s = 1 / alpha; - return Scalar_(a.val[0] * s, a.val[1] * s, a.val[2] * s, a.val[3] * s); -} - -template static inline -Scalar_ operator / (const Scalar_& a, double alpha) -{ - double s = 1 / alpha; - return Scalar_(a.val[0] * s, a.val[1] * s, a.val[2] * s, a.val[3] * s); -} - -template static inline -Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, _Tp alpha) -{ - a = a / alpha; - return a; -} - -template static inline -Scalar_<_Tp> operator / (_Tp a, const Scalar_<_Tp>& b) -{ - _Tp s = a / (b[0]*b[0] + b[1]*b[1] + b[2]*b[2] + b[3]*b[3]); - return b.conj() * s; -} - -template static inline -Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) -{ - return a * ((_Tp)1 / b); -} - -template static inline -Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) -{ - a = a / b; - return a; -} - -template static inline -Scalar operator * (const Matx<_Tp, 4, 4>& a, const Scalar& b) -{ - Matx c((Matx)a, b, Matx_MatMulOp()); - return reinterpret_cast(c); -} - -template<> inline -Scalar operator * (const Matx& a, const Scalar& b) -{ - Matx c(a, b, Matx_MatMulOp()); - return reinterpret_cast(c); -} - - - -//////////////////////////////// KeyPoint /////////////////////////////// - -inline -KeyPoint::KeyPoint() - : pt(0,0), size(0), angle(-1), response(0), octave(0), class_id(-1) {} - -inline -KeyPoint::KeyPoint(Point2f _pt, float _size, float _angle, float _response, int _octave, int _class_id) - : pt(_pt), size(_size), angle(_angle), response(_response), octave(_octave), class_id(_class_id) {} - -inline -KeyPoint::KeyPoint(float x, float y, float _size, float _angle, float _response, int _octave, int _class_id) - : pt(x, y), size(_size), angle(_angle), response(_response), octave(_octave), class_id(_class_id) {} - - - -///////////////////////////////// DMatch //////////////////////////////// - -inline -DMatch::DMatch() - : queryIdx(-1), trainIdx(-1), imgIdx(-1), distance(FLT_MAX) {} - -inline -DMatch::DMatch(int _queryIdx, int _trainIdx, float _distance) - : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(-1), distance(_distance) {} - -inline -DMatch::DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance) - : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(_imgIdx), distance(_distance) {} - -inline -bool DMatch::operator < (const DMatch &m) const -{ - return distance < m.distance; -} - - - -////////////////////////////// TermCriteria ///////////////////////////// - -inline -TermCriteria::TermCriteria() - : type(0), maxCount(0), epsilon(0) {} - -inline -TermCriteria::TermCriteria(int _type, int _maxCount, double _epsilon) - : type(_type), maxCount(_maxCount), epsilon(_epsilon) {} - -//! @endcond - -} // cv - -#endif //__OPENCV_CORE_TYPES_HPP__ diff --git a/IPL/include/opencv/opencv2/core/types_c.h b/IPL/include/opencv/opencv2/core/types_c.h deleted file mode 100644 index cb39587..0000000 --- a/IPL/include/opencv/opencv2/core/types_c.h +++ /dev/null @@ -1,1834 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_TYPES_H__ -#define __OPENCV_CORE_TYPES_H__ - -#ifdef HAVE_IPL -# ifndef __IPL_H__ -# if defined WIN32 || defined _WIN32 -# include -# else -# include -# endif -# endif -#elif defined __IPL_H__ -# define HAVE_IPL -#endif - -#include "opencv2/core/cvdef.h" - -#ifndef SKIP_INCLUDES -#include -#include -#include -#include -#endif // SKIP_INCLUDES - -#if defined WIN32 || defined _WIN32 -# define CV_CDECL __cdecl -# define CV_STDCALL __stdcall -#else -# define CV_CDECL -# define CV_STDCALL -#endif - -#ifndef CV_DEFAULT -# ifdef __cplusplus -# define CV_DEFAULT(val) = val -# else -# define CV_DEFAULT(val) -# endif -#endif - -#ifndef CV_EXTERN_C_FUNCPTR -# ifdef __cplusplus -# define CV_EXTERN_C_FUNCPTR(x) extern "C" { typedef x; } -# else -# define CV_EXTERN_C_FUNCPTR(x) typedef x -# endif -#endif - -#ifndef CVAPI -# define CVAPI(rettype) CV_EXTERN_C CV_EXPORTS rettype CV_CDECL -#endif - -#ifndef CV_IMPL -# define CV_IMPL CV_EXTERN_C -#endif - -#ifdef __cplusplus -# include "opencv2/core.hpp" -#endif - -/** @addtogroup core_c - @{ -*/ - -/** @brief This is the "metatype" used *only* as a function parameter. - -It denotes that the function accepts arrays of multiple types, such as IplImage*, CvMat* or even -CvSeq* sometimes. The particular array type is determined at runtime by analyzing the first 4 -bytes of the header. In C++ interface the role of CvArr is played by InputArray and OutputArray. - */ -typedef void CvArr; - -typedef int CVStatus; - -/** @see cv::Error::Code */ -enum { - CV_StsOk= 0, /**< everything is ok */ - CV_StsBackTrace= -1, /**< pseudo error for back trace */ - CV_StsError= -2, /**< unknown /unspecified error */ - CV_StsInternal= -3, /**< internal error (bad state) */ - CV_StsNoMem= -4, /**< insufficient memory */ - CV_StsBadArg= -5, /**< function arg/param is bad */ - CV_StsBadFunc= -6, /**< unsupported function */ - CV_StsNoConv= -7, /**< iter. didn't converge */ - CV_StsAutoTrace= -8, /**< tracing */ - CV_HeaderIsNull= -9, /**< image header is NULL */ - CV_BadImageSize= -10, /**< image size is invalid */ - CV_BadOffset= -11, /**< offset is invalid */ - CV_BadDataPtr= -12, /**/ - CV_BadStep= -13, /**/ - CV_BadModelOrChSeq= -14, /**/ - CV_BadNumChannels= -15, /**/ - CV_BadNumChannel1U= -16, /**/ - CV_BadDepth= -17, /**/ - CV_BadAlphaChannel= -18, /**/ - CV_BadOrder= -19, /**/ - CV_BadOrigin= -20, /**/ - CV_BadAlign= -21, /**/ - CV_BadCallBack= -22, /**/ - CV_BadTileSize= -23, /**/ - CV_BadCOI= -24, /**/ - CV_BadROISize= -25, /**/ - CV_MaskIsTiled= -26, /**/ - CV_StsNullPtr= -27, /**< null pointer */ - CV_StsVecLengthErr= -28, /**< incorrect vector length */ - CV_StsFilterStructContentErr= -29, /**< incorr. filter structure content */ - CV_StsKernelStructContentErr= -30, /**< incorr. transform kernel content */ - CV_StsFilterOffsetErr= -31, /**< incorrect filter offset value */ - CV_StsBadSize= -201, /**< the input/output structure size is incorrect */ - CV_StsDivByZero= -202, /**< division by zero */ - CV_StsInplaceNotSupported= -203, /**< in-place operation is not supported */ - CV_StsObjectNotFound= -204, /**< request can't be completed */ - CV_StsUnmatchedFormats= -205, /**< formats of input/output arrays differ */ - CV_StsBadFlag= -206, /**< flag is wrong or not supported */ - CV_StsBadPoint= -207, /**< bad CvPoint */ - CV_StsBadMask= -208, /**< bad format of mask (neither 8uC1 nor 8sC1)*/ - CV_StsUnmatchedSizes= -209, /**< sizes of input/output structures do not match */ - CV_StsUnsupportedFormat= -210, /**< the data format/type is not supported by the function*/ - CV_StsOutOfRange= -211, /**< some of parameters are out of range */ - CV_StsParseError= -212, /**< invalid syntax/structure of the parsed file */ - CV_StsNotImplemented= -213, /**< the requested function/feature is not implemented */ - CV_StsBadMemBlock= -214, /**< an allocated block has been corrupted */ - CV_StsAssert= -215, /**< assertion failed */ - CV_GpuNotSupported= -216, - CV_GpuApiCallError= -217, - CV_OpenGlNotSupported= -218, - CV_OpenGlApiCallError= -219, - CV_OpenCLApiCallError= -220, - CV_OpenCLDoubleNotSupported= -221, - CV_OpenCLInitError= -222, - CV_OpenCLNoAMDBlasFft= -223 -}; - -/****************************************************************************************\ -* Common macros and inline functions * -\****************************************************************************************/ - -#define CV_SWAP(a,b,t) ((t) = (a), (a) = (b), (b) = (t)) - -/** min & max without jumps */ -#define CV_IMIN(a, b) ((a) ^ (((a)^(b)) & (((a) < (b)) - 1))) - -#define CV_IMAX(a, b) ((a) ^ (((a)^(b)) & (((a) > (b)) - 1))) - -/** absolute value without jumps */ -#ifndef __cplusplus -# define CV_IABS(a) (((a) ^ ((a) < 0 ? -1 : 0)) - ((a) < 0 ? -1 : 0)) -#else -# define CV_IABS(a) abs(a) -#endif -#define CV_CMP(a,b) (((a) > (b)) - ((a) < (b))) -#define CV_SIGN(a) CV_CMP((a),0) - -#define cvInvSqrt(value) ((float)(1./sqrt(value))) -#define cvSqrt(value) ((float)sqrt(value)) - - -/*************** Random number generation *******************/ - -typedef uint64 CvRNG; - -#define CV_RNG_COEFF 4164903690U - -/** @brief Initializes a random number generator state. - -The function initializes a random number generator and returns the state. The pointer to the state -can be then passed to the cvRandInt, cvRandReal and cvRandArr functions. In the current -implementation a multiply-with-carry generator is used. -@param seed 64-bit value used to initiate a random sequence -@sa the C++ class RNG replaced CvRNG. - */ -CV_INLINE CvRNG cvRNG( int64 seed CV_DEFAULT(-1)) -{ - CvRNG rng = seed ? (uint64)seed : (uint64)(int64)-1; - return rng; -} - -/** @brief Returns a 32-bit unsigned integer and updates RNG. - -The function returns a uniformly-distributed random 32-bit unsigned integer and updates the RNG -state. It is similar to the rand() function from the C runtime library, except that OpenCV functions -always generates a 32-bit random number, regardless of the platform. -@param rng CvRNG state initialized by cvRNG. - */ -CV_INLINE unsigned cvRandInt( CvRNG* rng ) -{ - uint64 temp = *rng; - temp = (uint64)(unsigned)temp*CV_RNG_COEFF + (temp >> 32); - *rng = temp; - return (unsigned)temp; -} - -/** @brief Returns a floating-point random number and updates RNG. - -The function returns a uniformly-distributed random floating-point number between 0 and 1 (1 is not -included). -@param rng RNG state initialized by cvRNG - */ -CV_INLINE double cvRandReal( CvRNG* rng ) -{ - return cvRandInt(rng)*2.3283064365386962890625e-10 /* 2^-32 */; -} - -/****************************************************************************************\ -* Image type (IplImage) * -\****************************************************************************************/ - -#ifndef HAVE_IPL - -/* - * The following definitions (until #endif) - * is an extract from IPL headers. - * Copyright (c) 1995 Intel Corporation. - */ -#define IPL_DEPTH_SIGN 0x80000000 - -#define IPL_DEPTH_1U 1 -#define IPL_DEPTH_8U 8 -#define IPL_DEPTH_16U 16 -#define IPL_DEPTH_32F 32 - -#define IPL_DEPTH_8S (IPL_DEPTH_SIGN| 8) -#define IPL_DEPTH_16S (IPL_DEPTH_SIGN|16) -#define IPL_DEPTH_32S (IPL_DEPTH_SIGN|32) - -#define IPL_DATA_ORDER_PIXEL 0 -#define IPL_DATA_ORDER_PLANE 1 - -#define IPL_ORIGIN_TL 0 -#define IPL_ORIGIN_BL 1 - -#define IPL_ALIGN_4BYTES 4 -#define IPL_ALIGN_8BYTES 8 -#define IPL_ALIGN_16BYTES 16 -#define IPL_ALIGN_32BYTES 32 - -#define IPL_ALIGN_DWORD IPL_ALIGN_4BYTES -#define IPL_ALIGN_QWORD IPL_ALIGN_8BYTES - -#define IPL_BORDER_CONSTANT 0 -#define IPL_BORDER_REPLICATE 1 -#define IPL_BORDER_REFLECT 2 -#define IPL_BORDER_WRAP 3 - -/** The IplImage is taken from the Intel Image Processing Library, in which the format is native. OpenCV -only supports a subset of possible IplImage formats, as outlined in the parameter list above. - -In addition to the above restrictions, OpenCV handles ROIs differently. OpenCV functions require -that the image size or ROI size of all source and destination images match exactly. On the other -hand, the Intel Image Processing Library processes the area of intersection between the source and -destination images (or ROIs), allowing them to vary independently. -*/ -typedef struct -#ifdef __cplusplus - CV_EXPORTS -#endif -_IplImage -{ - int nSize; /**< sizeof(IplImage) */ - int ID; /**< version (=0)*/ - int nChannels; /**< Most of OpenCV functions support 1,2,3 or 4 channels */ - int alphaChannel; /**< Ignored by OpenCV */ - int depth; /**< Pixel depth in bits: IPL_DEPTH_8U, IPL_DEPTH_8S, IPL_DEPTH_16S, - IPL_DEPTH_32S, IPL_DEPTH_32F and IPL_DEPTH_64F are supported. */ - char colorModel[4]; /**< Ignored by OpenCV */ - char channelSeq[4]; /**< ditto */ - int dataOrder; /**< 0 - interleaved color channels, 1 - separate color channels. - cvCreateImage can only create interleaved images */ - int origin; /**< 0 - top-left origin, - 1 - bottom-left origin (Windows bitmaps style). */ - int align; /**< Alignment of image rows (4 or 8). - OpenCV ignores it and uses widthStep instead. */ - int width; /**< Image width in pixels. */ - int height; /**< Image height in pixels. */ - struct _IplROI *roi; /**< Image ROI. If NULL, the whole image is selected. */ - struct _IplImage *maskROI; /**< Must be NULL. */ - void *imageId; /**< " " */ - struct _IplTileInfo *tileInfo; /**< " " */ - int imageSize; /**< Image data size in bytes - (==image->height*image->widthStep - in case of interleaved data)*/ - char *imageData; /**< Pointer to aligned image data. */ - int widthStep; /**< Size of aligned image row in bytes. */ - int BorderMode[4]; /**< Ignored by OpenCV. */ - int BorderConst[4]; /**< Ditto. */ - char *imageDataOrigin; /**< Pointer to very origin of image data - (not necessarily aligned) - - needed for correct deallocation */ - -#ifdef __cplusplus - _IplImage() {} - _IplImage(const cv::Mat& m); -#endif -} -IplImage; - -typedef struct _IplTileInfo IplTileInfo; - -typedef struct _IplROI -{ - int coi; /**< 0 - no COI (all channels are selected), 1 - 0th channel is selected ...*/ - int xOffset; - int yOffset; - int width; - int height; -} -IplROI; - -typedef struct _IplConvKernel -{ - int nCols; - int nRows; - int anchorX; - int anchorY; - int *values; - int nShiftR; -} -IplConvKernel; - -typedef struct _IplConvKernelFP -{ - int nCols; - int nRows; - int anchorX; - int anchorY; - float *values; -} -IplConvKernelFP; - -#define IPL_IMAGE_HEADER 1 -#define IPL_IMAGE_DATA 2 -#define IPL_IMAGE_ROI 4 - -#endif/*HAVE_IPL*/ - -/** extra border mode */ -#define IPL_BORDER_REFLECT_101 4 -#define IPL_BORDER_TRANSPARENT 5 - -#define IPL_IMAGE_MAGIC_VAL ((int)sizeof(IplImage)) -#define CV_TYPE_NAME_IMAGE "opencv-image" - -#define CV_IS_IMAGE_HDR(img) \ - ((img) != NULL && ((const IplImage*)(img))->nSize == sizeof(IplImage)) - -#define CV_IS_IMAGE(img) \ - (CV_IS_IMAGE_HDR(img) && ((IplImage*)img)->imageData != NULL) - -/** for storing double-precision - floating point data in IplImage's */ -#define IPL_DEPTH_64F 64 - -/** get reference to pixel at (col,row), - for multi-channel images (col) should be multiplied by number of channels */ -#define CV_IMAGE_ELEM( image, elemtype, row, col ) \ - (((elemtype*)((image)->imageData + (image)->widthStep*(row)))[(col)]) - -/****************************************************************************************\ -* Matrix type (CvMat) * -\****************************************************************************************/ - -#define CV_AUTO_STEP 0x7fffffff -#define CV_WHOLE_ARR cvSlice( 0, 0x3fffffff ) - -#define CV_MAGIC_MASK 0xFFFF0000 -#define CV_MAT_MAGIC_VAL 0x42420000 -#define CV_TYPE_NAME_MAT "opencv-matrix" - -/** Matrix elements are stored row by row. Element (i, j) (i - 0-based row index, j - 0-based column -index) of a matrix can be retrieved or modified using CV_MAT_ELEM macro: - - uchar pixval = CV_MAT_ELEM(grayimg, uchar, i, j) - CV_MAT_ELEM(cameraMatrix, float, 0, 2) = image.width*0.5f; - -To access multiple-channel matrices, you can use -CV_MAT_ELEM(matrix, type, i, j\*nchannels + channel_idx). - -@deprecated CvMat is now obsolete; consider using Mat instead. - */ -typedef struct CvMat -{ - int type; - int step; - - /* for internal use only */ - int* refcount; - int hdr_refcount; - - union - { - uchar* ptr; - short* s; - int* i; - float* fl; - double* db; - } data; - -#ifdef __cplusplus - union - { - int rows; - int height; - }; - - union - { - int cols; - int width; - }; -#else - int rows; - int cols; -#endif - - -#ifdef __cplusplus - CvMat() {} - CvMat(const CvMat& m) { memcpy(this, &m, sizeof(CvMat));} - CvMat(const cv::Mat& m); -#endif - -} -CvMat; - - -#define CV_IS_MAT_HDR(mat) \ - ((mat) != NULL && \ - (((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \ - ((const CvMat*)(mat))->cols > 0 && ((const CvMat*)(mat))->rows > 0) - -#define CV_IS_MAT_HDR_Z(mat) \ - ((mat) != NULL && \ - (((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \ - ((const CvMat*)(mat))->cols >= 0 && ((const CvMat*)(mat))->rows >= 0) - -#define CV_IS_MAT(mat) \ - (CV_IS_MAT_HDR(mat) && ((const CvMat*)(mat))->data.ptr != NULL) - -#define CV_IS_MASK_ARR(mat) \ - (((mat)->type & (CV_MAT_TYPE_MASK & ~CV_8SC1)) == 0) - -#define CV_ARE_TYPES_EQ(mat1, mat2) \ - ((((mat1)->type ^ (mat2)->type) & CV_MAT_TYPE_MASK) == 0) - -#define CV_ARE_CNS_EQ(mat1, mat2) \ - ((((mat1)->type ^ (mat2)->type) & CV_MAT_CN_MASK) == 0) - -#define CV_ARE_DEPTHS_EQ(mat1, mat2) \ - ((((mat1)->type ^ (mat2)->type) & CV_MAT_DEPTH_MASK) == 0) - -#define CV_ARE_SIZES_EQ(mat1, mat2) \ - ((mat1)->rows == (mat2)->rows && (mat1)->cols == (mat2)->cols) - -#define CV_IS_MAT_CONST(mat) \ - (((mat)->rows|(mat)->cols) == 1) - -#define IPL2CV_DEPTH(depth) \ - ((((CV_8U)+(CV_16U<<4)+(CV_32F<<8)+(CV_64F<<16)+(CV_8S<<20)+ \ - (CV_16S<<24)+(CV_32S<<28)) >> ((((depth) & 0xF0) >> 2) + \ - (((depth) & IPL_DEPTH_SIGN) ? 20 : 0))) & 15) - -/** Inline constructor. No data is allocated internally!!! - * (Use together with cvCreateData, or use cvCreateMat instead to - * get a matrix with allocated data): - */ -CV_INLINE CvMat cvMat( int rows, int cols, int type, void* data CV_DEFAULT(NULL)) -{ - CvMat m; - - assert( (unsigned)CV_MAT_DEPTH(type) <= CV_64F ); - type = CV_MAT_TYPE(type); - m.type = CV_MAT_MAGIC_VAL | CV_MAT_CONT_FLAG | type; - m.cols = cols; - m.rows = rows; - m.step = m.cols*CV_ELEM_SIZE(type); - m.data.ptr = (uchar*)data; - m.refcount = NULL; - m.hdr_refcount = 0; - - return m; -} - -#ifdef __cplusplus -inline CvMat::CvMat(const cv::Mat& m) -{ - CV_DbgAssert(m.dims <= 2); - *this = cvMat(m.rows, m.dims == 1 ? 1 : m.cols, m.type(), m.data); - step = (int)m.step[0]; - type = (type & ~cv::Mat::CONTINUOUS_FLAG) | (m.flags & cv::Mat::CONTINUOUS_FLAG); -} -#endif - - -#define CV_MAT_ELEM_PTR_FAST( mat, row, col, pix_size ) \ - (assert( (unsigned)(row) < (unsigned)(mat).rows && \ - (unsigned)(col) < (unsigned)(mat).cols ), \ - (mat).data.ptr + (size_t)(mat).step*(row) + (pix_size)*(col)) - -#define CV_MAT_ELEM_PTR( mat, row, col ) \ - CV_MAT_ELEM_PTR_FAST( mat, row, col, CV_ELEM_SIZE((mat).type) ) - -#define CV_MAT_ELEM( mat, elemtype, row, col ) \ - (*(elemtype*)CV_MAT_ELEM_PTR_FAST( mat, row, col, sizeof(elemtype))) - -/** @brief Returns the particular element of single-channel floating-point matrix. - -The function is a fast replacement for cvGetReal2D in the case of single-channel floating-point -matrices. It is faster because it is inline, it does fewer checks for array type and array element -type, and it checks for the row and column ranges only in debug mode. -@param mat Input matrix -@param row The zero-based index of row -@param col The zero-based index of column - */ -CV_INLINE double cvmGet( const CvMat* mat, int row, int col ) -{ - int type; - - type = CV_MAT_TYPE(mat->type); - assert( (unsigned)row < (unsigned)mat->rows && - (unsigned)col < (unsigned)mat->cols ); - - if( type == CV_32FC1 ) - return ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col]; - else - { - assert( type == CV_64FC1 ); - return ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col]; - } -} - -/** @brief Sets a specific element of a single-channel floating-point matrix. - -The function is a fast replacement for cvSetReal2D in the case of single-channel floating-point -matrices. It is faster because it is inline, it does fewer checks for array type and array element -type, and it checks for the row and column ranges only in debug mode. -@param mat The matrix -@param row The zero-based index of row -@param col The zero-based index of column -@param value The new value of the matrix element - */ -CV_INLINE void cvmSet( CvMat* mat, int row, int col, double value ) -{ - int type; - type = CV_MAT_TYPE(mat->type); - assert( (unsigned)row < (unsigned)mat->rows && - (unsigned)col < (unsigned)mat->cols ); - - if( type == CV_32FC1 ) - ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = (float)value; - else - { - assert( type == CV_64FC1 ); - ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = value; - } -} - - -CV_INLINE int cvIplDepth( int type ) -{ - int depth = CV_MAT_DEPTH(type); - return CV_ELEM_SIZE1(depth)*8 | (depth == CV_8S || depth == CV_16S || - depth == CV_32S ? IPL_DEPTH_SIGN : 0); -} - - -/****************************************************************************************\ -* Multi-dimensional dense array (CvMatND) * -\****************************************************************************************/ - -#define CV_MATND_MAGIC_VAL 0x42430000 -#define CV_TYPE_NAME_MATND "opencv-nd-matrix" - -#define CV_MAX_DIM 32 -#define CV_MAX_DIM_HEAP 1024 - -/** - @deprecated consider using cv::Mat instead - */ -typedef struct -#ifdef __cplusplus - CV_EXPORTS -#endif -CvMatND -{ - int type; - int dims; - - int* refcount; - int hdr_refcount; - - union - { - uchar* ptr; - float* fl; - double* db; - int* i; - short* s; - } data; - - struct - { - int size; - int step; - } - dim[CV_MAX_DIM]; - -#ifdef __cplusplus - CvMatND() {} - CvMatND(const cv::Mat& m); -#endif -} -CvMatND; - -#define CV_IS_MATND_HDR(mat) \ - ((mat) != NULL && (((const CvMatND*)(mat))->type & CV_MAGIC_MASK) == CV_MATND_MAGIC_VAL) - -#define CV_IS_MATND(mat) \ - (CV_IS_MATND_HDR(mat) && ((const CvMatND*)(mat))->data.ptr != NULL) - - -/****************************************************************************************\ -* Multi-dimensional sparse array (CvSparseMat) * -\****************************************************************************************/ - -#define CV_SPARSE_MAT_MAGIC_VAL 0x42440000 -#define CV_TYPE_NAME_SPARSE_MAT "opencv-sparse-matrix" - -struct CvSet; - -typedef struct -#ifdef __cplusplus - CV_EXPORTS -#endif -CvSparseMat -{ - int type; - int dims; - int* refcount; - int hdr_refcount; - - struct CvSet* heap; - void** hashtable; - int hashsize; - int valoffset; - int idxoffset; - int size[CV_MAX_DIM]; - -#ifdef __cplusplus - void copyToSparseMat(cv::SparseMat& m) const; -#endif -} -CvSparseMat; - -#ifdef __cplusplus - CV_EXPORTS CvSparseMat* cvCreateSparseMat(const cv::SparseMat& m); -#endif - -#define CV_IS_SPARSE_MAT_HDR(mat) \ - ((mat) != NULL && \ - (((const CvSparseMat*)(mat))->type & CV_MAGIC_MASK) == CV_SPARSE_MAT_MAGIC_VAL) - -#define CV_IS_SPARSE_MAT(mat) \ - CV_IS_SPARSE_MAT_HDR(mat) - -/**************** iteration through a sparse array *****************/ - -typedef struct CvSparseNode -{ - unsigned hashval; - struct CvSparseNode* next; -} -CvSparseNode; - -typedef struct CvSparseMatIterator -{ - CvSparseMat* mat; - CvSparseNode* node; - int curidx; -} -CvSparseMatIterator; - -#define CV_NODE_VAL(mat,node) ((void*)((uchar*)(node) + (mat)->valoffset)) -#define CV_NODE_IDX(mat,node) ((int*)((uchar*)(node) + (mat)->idxoffset)) - -/****************************************************************************************\ -* Histogram * -\****************************************************************************************/ - -typedef int CvHistType; - -#define CV_HIST_MAGIC_VAL 0x42450000 -#define CV_HIST_UNIFORM_FLAG (1 << 10) - -/** indicates whether bin ranges are set already or not */ -#define CV_HIST_RANGES_FLAG (1 << 11) - -#define CV_HIST_ARRAY 0 -#define CV_HIST_SPARSE 1 -#define CV_HIST_TREE CV_HIST_SPARSE - -/** should be used as a parameter only, - it turns to CV_HIST_UNIFORM_FLAG of hist->type */ -#define CV_HIST_UNIFORM 1 - -typedef struct CvHistogram -{ - int type; - CvArr* bins; - float thresh[CV_MAX_DIM][2]; /**< For uniform histograms. */ - float** thresh2; /**< For non-uniform histograms. */ - CvMatND mat; /**< Embedded matrix header for array histograms. */ -} -CvHistogram; - -#define CV_IS_HIST( hist ) \ - ((hist) != NULL && \ - (((CvHistogram*)(hist))->type & CV_MAGIC_MASK) == CV_HIST_MAGIC_VAL && \ - (hist)->bins != NULL) - -#define CV_IS_UNIFORM_HIST( hist ) \ - (((hist)->type & CV_HIST_UNIFORM_FLAG) != 0) - -#define CV_IS_SPARSE_HIST( hist ) \ - CV_IS_SPARSE_MAT((hist)->bins) - -#define CV_HIST_HAS_RANGES( hist ) \ - (((hist)->type & CV_HIST_RANGES_FLAG) != 0) - -/****************************************************************************************\ -* Other supplementary data type definitions * -\****************************************************************************************/ - -/*************************************** CvRect *****************************************/ -/** @sa Rect_ */ -typedef struct CvRect -{ - int x; - int y; - int width; - int height; - -#ifdef __cplusplus - CvRect(int _x = 0, int _y = 0, int w = 0, int h = 0): x(_x), y(_y), width(w), height(h) {} - template - CvRect(const cv::Rect_<_Tp>& r): x(cv::saturate_cast(r.x)), y(cv::saturate_cast(r.y)), width(cv::saturate_cast(r.width)), height(cv::saturate_cast(r.height)) {} - template - operator cv::Rect_<_Tp>() const { return cv::Rect_<_Tp>((_Tp)x, (_Tp)y, (_Tp)width, (_Tp)height); } -#endif -} -CvRect; - -/** constructs CvRect structure. */ -CV_INLINE CvRect cvRect( int x, int y, int width, int height ) -{ - CvRect r; - - r.x = x; - r.y = y; - r.width = width; - r.height = height; - - return r; -} - - -CV_INLINE IplROI cvRectToROI( CvRect rect, int coi ) -{ - IplROI roi; - roi.xOffset = rect.x; - roi.yOffset = rect.y; - roi.width = rect.width; - roi.height = rect.height; - roi.coi = coi; - - return roi; -} - - -CV_INLINE CvRect cvROIToRect( IplROI roi ) -{ - return cvRect( roi.xOffset, roi.yOffset, roi.width, roi.height ); -} - -/*********************************** CvTermCriteria *************************************/ - -#define CV_TERMCRIT_ITER 1 -#define CV_TERMCRIT_NUMBER CV_TERMCRIT_ITER -#define CV_TERMCRIT_EPS 2 - -/** @sa TermCriteria - */ -typedef struct CvTermCriteria -{ - int type; /**< may be combination of - CV_TERMCRIT_ITER - CV_TERMCRIT_EPS */ - int max_iter; - double epsilon; - -#ifdef __cplusplus - CvTermCriteria(int _type = 0, int _iter = 0, double _eps = 0) : type(_type), max_iter(_iter), epsilon(_eps) {} - CvTermCriteria(const cv::TermCriteria& t) : type(t.type), max_iter(t.maxCount), epsilon(t.epsilon) {} - operator cv::TermCriteria() const { return cv::TermCriteria(type, max_iter, epsilon); } -#endif - -} -CvTermCriteria; - -CV_INLINE CvTermCriteria cvTermCriteria( int type, int max_iter, double epsilon ) -{ - CvTermCriteria t; - - t.type = type; - t.max_iter = max_iter; - t.epsilon = (float)epsilon; - - return t; -} - - -/******************************* CvPoint and variants ***********************************/ - -typedef struct CvPoint -{ - int x; - int y; - -#ifdef __cplusplus - CvPoint(int _x = 0, int _y = 0): x(_x), y(_y) {} - template - CvPoint(const cv::Point_<_Tp>& pt): x((int)pt.x), y((int)pt.y) {} - template - operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); } -#endif -} -CvPoint; - -/** constructs CvPoint structure. */ -CV_INLINE CvPoint cvPoint( int x, int y ) -{ - CvPoint p; - - p.x = x; - p.y = y; - - return p; -} - - -typedef struct CvPoint2D32f -{ - float x; - float y; - -#ifdef __cplusplus - CvPoint2D32f(float _x = 0, float _y = 0): x(_x), y(_y) {} - template - CvPoint2D32f(const cv::Point_<_Tp>& pt): x((float)pt.x), y((float)pt.y) {} - template - operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); } -#endif -} -CvPoint2D32f; - -/** constructs CvPoint2D32f structure. */ -CV_INLINE CvPoint2D32f cvPoint2D32f( double x, double y ) -{ - CvPoint2D32f p; - - p.x = (float)x; - p.y = (float)y; - - return p; -} - -/** converts CvPoint to CvPoint2D32f. */ -CV_INLINE CvPoint2D32f cvPointTo32f( CvPoint point ) -{ - return cvPoint2D32f( (float)point.x, (float)point.y ); -} - -/** converts CvPoint2D32f to CvPoint. */ -CV_INLINE CvPoint cvPointFrom32f( CvPoint2D32f point ) -{ - CvPoint ipt; - ipt.x = cvRound(point.x); - ipt.y = cvRound(point.y); - - return ipt; -} - - -typedef struct CvPoint3D32f -{ - float x; - float y; - float z; - -#ifdef __cplusplus - CvPoint3D32f(float _x = 0, float _y = 0, float _z = 0): x(_x), y(_y), z(_z) {} - template - CvPoint3D32f(const cv::Point3_<_Tp>& pt): x((float)pt.x), y((float)pt.y), z((float)pt.z) {} - template - operator cv::Point3_<_Tp>() const { return cv::Point3_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y), cv::saturate_cast<_Tp>(z)); } -#endif -} -CvPoint3D32f; - -/** constructs CvPoint3D32f structure. */ -CV_INLINE CvPoint3D32f cvPoint3D32f( double x, double y, double z ) -{ - CvPoint3D32f p; - - p.x = (float)x; - p.y = (float)y; - p.z = (float)z; - - return p; -} - - -typedef struct CvPoint2D64f -{ - double x; - double y; -} -CvPoint2D64f; - -/** constructs CvPoint2D64f structure.*/ -CV_INLINE CvPoint2D64f cvPoint2D64f( double x, double y ) -{ - CvPoint2D64f p; - - p.x = x; - p.y = y; - - return p; -} - - -typedef struct CvPoint3D64f -{ - double x; - double y; - double z; -} -CvPoint3D64f; - -/** constructs CvPoint3D64f structure. */ -CV_INLINE CvPoint3D64f cvPoint3D64f( double x, double y, double z ) -{ - CvPoint3D64f p; - - p.x = x; - p.y = y; - p.z = z; - - return p; -} - - -/******************************** CvSize's & CvBox **************************************/ - -typedef struct CvSize -{ - int width; - int height; - -#ifdef __cplusplus - CvSize(int w = 0, int h = 0): width(w), height(h) {} - template - CvSize(const cv::Size_<_Tp>& sz): width(cv::saturate_cast(sz.width)), height(cv::saturate_cast(sz.height)) {} - template - operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); } -#endif -} -CvSize; - -/** constructs CvSize structure. */ -CV_INLINE CvSize cvSize( int width, int height ) -{ - CvSize s; - - s.width = width; - s.height = height; - - return s; -} - -typedef struct CvSize2D32f -{ - float width; - float height; - -#ifdef __cplusplus - CvSize2D32f(float w = 0, float h = 0): width(w), height(h) {} - template - CvSize2D32f(const cv::Size_<_Tp>& sz): width(cv::saturate_cast(sz.width)), height(cv::saturate_cast(sz.height)) {} - template - operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); } -#endif -} -CvSize2D32f; - -/** constructs CvSize2D32f structure. */ -CV_INLINE CvSize2D32f cvSize2D32f( double width, double height ) -{ - CvSize2D32f s; - - s.width = (float)width; - s.height = (float)height; - - return s; -} - -/** @sa RotatedRect - */ -typedef struct CvBox2D -{ - CvPoint2D32f center; /**< Center of the box. */ - CvSize2D32f size; /**< Box width and length. */ - float angle; /**< Angle between the horizontal axis */ - /**< and the first side (i.e. length) in degrees */ - -#ifdef __cplusplus - CvBox2D(CvPoint2D32f c = CvPoint2D32f(), CvSize2D32f s = CvSize2D32f(), float a = 0) : center(c), size(s), angle(a) {} - CvBox2D(const cv::RotatedRect& rr) : center(rr.center), size(rr.size), angle(rr.angle) {} - operator cv::RotatedRect() const { return cv::RotatedRect(center, size, angle); } -#endif -} -CvBox2D; - - -/** Line iterator state: */ -typedef struct CvLineIterator -{ - /** Pointer to the current point: */ - uchar* ptr; - - /* Bresenham algorithm state: */ - int err; - int plus_delta; - int minus_delta; - int plus_step; - int minus_step; -} -CvLineIterator; - - - -/************************************* CvSlice ******************************************/ -#define CV_WHOLE_SEQ_END_INDEX 0x3fffffff -#define CV_WHOLE_SEQ cvSlice(0, CV_WHOLE_SEQ_END_INDEX) - -typedef struct CvSlice -{ - int start_index, end_index; - -#if defined(__cplusplus) && !defined(__CUDACC__) - CvSlice(int start = 0, int end = 0) : start_index(start), end_index(end) {} - CvSlice(const cv::Range& r) { *this = (r.start != INT_MIN && r.end != INT_MAX) ? CvSlice(r.start, r.end) : CvSlice(0, CV_WHOLE_SEQ_END_INDEX); } - operator cv::Range() const { return (start_index == 0 && end_index == CV_WHOLE_SEQ_END_INDEX ) ? cv::Range::all() : cv::Range(start_index, end_index); } -#endif -} -CvSlice; - -CV_INLINE CvSlice cvSlice( int start, int end ) -{ - CvSlice slice; - slice.start_index = start; - slice.end_index = end; - - return slice; -} - - - -/************************************* CvScalar *****************************************/ -/** @sa Scalar_ - */ -typedef struct CvScalar -{ - double val[4]; - -#ifdef __cplusplus - CvScalar() {} - CvScalar(double d0, double d1 = 0, double d2 = 0, double d3 = 0) { val[0] = d0; val[1] = d1; val[2] = d2; val[3] = d3; } - template - CvScalar(const cv::Scalar_<_Tp>& s) { val[0] = s.val[0]; val[1] = s.val[1]; val[2] = s.val[2]; val[3] = s.val[3]; } - template - operator cv::Scalar_<_Tp>() const { return cv::Scalar_<_Tp>(cv::saturate_cast<_Tp>(val[0]), cv::saturate_cast<_Tp>(val[1]), cv::saturate_cast<_Tp>(val[2]), cv::saturate_cast<_Tp>(val[3])); } - template - CvScalar(const cv::Vec<_Tp, cn>& v) - { - int i; - for( i = 0; i < (cn < 4 ? cn : 4); i++ ) val[i] = v.val[i]; - for( ; i < 4; i++ ) val[i] = 0; - } -#endif -} -CvScalar; - -CV_INLINE CvScalar cvScalar( double val0, double val1 CV_DEFAULT(0), - double val2 CV_DEFAULT(0), double val3 CV_DEFAULT(0)) -{ - CvScalar scalar; - scalar.val[0] = val0; scalar.val[1] = val1; - scalar.val[2] = val2; scalar.val[3] = val3; - return scalar; -} - - -CV_INLINE CvScalar cvRealScalar( double val0 ) -{ - CvScalar scalar; - scalar.val[0] = val0; - scalar.val[1] = scalar.val[2] = scalar.val[3] = 0; - return scalar; -} - -CV_INLINE CvScalar cvScalarAll( double val0123 ) -{ - CvScalar scalar; - scalar.val[0] = val0123; - scalar.val[1] = val0123; - scalar.val[2] = val0123; - scalar.val[3] = val0123; - return scalar; -} - -/****************************************************************************************\ -* Dynamic Data structures * -\****************************************************************************************/ - -/******************************** Memory storage ****************************************/ - -typedef struct CvMemBlock -{ - struct CvMemBlock* prev; - struct CvMemBlock* next; -} -CvMemBlock; - -#define CV_STORAGE_MAGIC_VAL 0x42890000 - -typedef struct CvMemStorage -{ - int signature; - CvMemBlock* bottom; /**< First allocated block. */ - CvMemBlock* top; /**< Current memory block - top of the stack. */ - struct CvMemStorage* parent; /**< We get new blocks from parent as needed. */ - int block_size; /**< Block size. */ - int free_space; /**< Remaining free space in current block. */ -} -CvMemStorage; - -#define CV_IS_STORAGE(storage) \ - ((storage) != NULL && \ - (((CvMemStorage*)(storage))->signature & CV_MAGIC_MASK) == CV_STORAGE_MAGIC_VAL) - - -typedef struct CvMemStoragePos -{ - CvMemBlock* top; - int free_space; -} -CvMemStoragePos; - - -/*********************************** Sequence *******************************************/ - -typedef struct CvSeqBlock -{ - struct CvSeqBlock* prev; /**< Previous sequence block. */ - struct CvSeqBlock* next; /**< Next sequence block. */ - int start_index; /**< Index of the first element in the block + */ - /**< sequence->first->start_index. */ - int count; /**< Number of elements in the block. */ - schar* data; /**< Pointer to the first element of the block. */ -} -CvSeqBlock; - - -#define CV_TREE_NODE_FIELDS(node_type) \ - int flags; /**< Miscellaneous flags. */ \ - int header_size; /**< Size of sequence header. */ \ - struct node_type* h_prev; /**< Previous sequence. */ \ - struct node_type* h_next; /**< Next sequence. */ \ - struct node_type* v_prev; /**< 2nd previous sequence. */ \ - struct node_type* v_next /**< 2nd next sequence. */ - -/** - Read/Write sequence. - Elements can be dynamically inserted to or deleted from the sequence. -*/ -#define CV_SEQUENCE_FIELDS() \ - CV_TREE_NODE_FIELDS(CvSeq); \ - int total; /**< Total number of elements. */ \ - int elem_size; /**< Size of sequence element in bytes. */ \ - schar* block_max; /**< Maximal bound of the last block. */ \ - schar* ptr; /**< Current write pointer. */ \ - int delta_elems; /**< Grow seq this many at a time. */ \ - CvMemStorage* storage; /**< Where the seq is stored. */ \ - CvSeqBlock* free_blocks; /**< Free blocks list. */ \ - CvSeqBlock* first; /**< Pointer to the first sequence block. */ - -typedef struct CvSeq -{ - CV_SEQUENCE_FIELDS() -} -CvSeq; - -#define CV_TYPE_NAME_SEQ "opencv-sequence" -#define CV_TYPE_NAME_SEQ_TREE "opencv-sequence-tree" - -/*************************************** Set ********************************************/ -/** @brief Set - Order is not preserved. There can be gaps between sequence elements. - After the element has been inserted it stays in the same place all the time. - The MSB(most-significant or sign bit) of the first field (flags) is 0 iff the element exists. -*/ -#define CV_SET_ELEM_FIELDS(elem_type) \ - int flags; \ - struct elem_type* next_free; - -typedef struct CvSetElem -{ - CV_SET_ELEM_FIELDS(CvSetElem) -} -CvSetElem; - -#define CV_SET_FIELDS() \ - CV_SEQUENCE_FIELDS() \ - CvSetElem* free_elems; \ - int active_count; - -typedef struct CvSet -{ - CV_SET_FIELDS() -} -CvSet; - - -#define CV_SET_ELEM_IDX_MASK ((1 << 26) - 1) -#define CV_SET_ELEM_FREE_FLAG (1 << (sizeof(int)*8-1)) - -/** Checks whether the element pointed by ptr belongs to a set or not */ -#define CV_IS_SET_ELEM( ptr ) (((CvSetElem*)(ptr))->flags >= 0) - -/************************************* Graph ********************************************/ - -/** @name Graph - -We represent a graph as a set of vertices. Vertices contain their adjacency lists (more exactly, -pointers to first incoming or outcoming edge (or 0 if isolated vertex)). Edges are stored in -another set. There is a singly-linked list of incoming/outcoming edges for each vertex. - -Each edge consists of: - -- Two pointers to the starting and ending vertices (vtx[0] and vtx[1] respectively). - - A graph may be oriented or not. In the latter case, edges between vertex i to vertex j are not -distinguished during search operations. - -- Two pointers to next edges for the starting and ending vertices, where next[0] points to the -next edge in the vtx[0] adjacency list and next[1] points to the next edge in the vtx[1] -adjacency list. - -@see CvGraphEdge, CvGraphVtx, CvGraphVtx2D, CvGraph -@{ -*/ -#define CV_GRAPH_EDGE_FIELDS() \ - int flags; \ - float weight; \ - struct CvGraphEdge* next[2]; \ - struct CvGraphVtx* vtx[2]; - - -#define CV_GRAPH_VERTEX_FIELDS() \ - int flags; \ - struct CvGraphEdge* first; - - -typedef struct CvGraphEdge -{ - CV_GRAPH_EDGE_FIELDS() -} -CvGraphEdge; - -typedef struct CvGraphVtx -{ - CV_GRAPH_VERTEX_FIELDS() -} -CvGraphVtx; - -typedef struct CvGraphVtx2D -{ - CV_GRAPH_VERTEX_FIELDS() - CvPoint2D32f* ptr; -} -CvGraphVtx2D; - -/** - Graph is "derived" from the set (this is set a of vertices) - and includes another set (edges) -*/ -#define CV_GRAPH_FIELDS() \ - CV_SET_FIELDS() \ - CvSet* edges; - -typedef struct CvGraph -{ - CV_GRAPH_FIELDS() -} -CvGraph; - -#define CV_TYPE_NAME_GRAPH "opencv-graph" - -/** @} */ - -/*********************************** Chain/Countour *************************************/ - -typedef struct CvChain -{ - CV_SEQUENCE_FIELDS() - CvPoint origin; -} -CvChain; - -#define CV_CONTOUR_FIELDS() \ - CV_SEQUENCE_FIELDS() \ - CvRect rect; \ - int color; \ - int reserved[3]; - -typedef struct CvContour -{ - CV_CONTOUR_FIELDS() -} -CvContour; - -typedef CvContour CvPoint2DSeq; - -/****************************************************************************************\ -* Sequence types * -\****************************************************************************************/ - -#define CV_SEQ_MAGIC_VAL 0x42990000 - -#define CV_IS_SEQ(seq) \ - ((seq) != NULL && (((CvSeq*)(seq))->flags & CV_MAGIC_MASK) == CV_SEQ_MAGIC_VAL) - -#define CV_SET_MAGIC_VAL 0x42980000 -#define CV_IS_SET(set) \ - ((set) != NULL && (((CvSeq*)(set))->flags & CV_MAGIC_MASK) == CV_SET_MAGIC_VAL) - -#define CV_SEQ_ELTYPE_BITS 12 -#define CV_SEQ_ELTYPE_MASK ((1 << CV_SEQ_ELTYPE_BITS) - 1) - -#define CV_SEQ_ELTYPE_POINT CV_32SC2 /**< (x,y) */ -#define CV_SEQ_ELTYPE_CODE CV_8UC1 /**< freeman code: 0..7 */ -#define CV_SEQ_ELTYPE_GENERIC 0 -#define CV_SEQ_ELTYPE_PTR CV_USRTYPE1 -#define CV_SEQ_ELTYPE_PPOINT CV_SEQ_ELTYPE_PTR /**< &(x,y) */ -#define CV_SEQ_ELTYPE_INDEX CV_32SC1 /**< #(x,y) */ -#define CV_SEQ_ELTYPE_GRAPH_EDGE 0 /**< &next_o, &next_d, &vtx_o, &vtx_d */ -#define CV_SEQ_ELTYPE_GRAPH_VERTEX 0 /**< first_edge, &(x,y) */ -#define CV_SEQ_ELTYPE_TRIAN_ATR 0 /**< vertex of the binary tree */ -#define CV_SEQ_ELTYPE_CONNECTED_COMP 0 /**< connected component */ -#define CV_SEQ_ELTYPE_POINT3D CV_32FC3 /**< (x,y,z) */ - -#define CV_SEQ_KIND_BITS 2 -#define CV_SEQ_KIND_MASK (((1 << CV_SEQ_KIND_BITS) - 1)<flags & CV_SEQ_ELTYPE_MASK) -#define CV_SEQ_KIND( seq ) ((seq)->flags & CV_SEQ_KIND_MASK ) - -/** flag checking */ -#define CV_IS_SEQ_INDEX( seq ) ((CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_INDEX) && \ - (CV_SEQ_KIND(seq) == CV_SEQ_KIND_GENERIC)) - -#define CV_IS_SEQ_CURVE( seq ) (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE) -#define CV_IS_SEQ_CLOSED( seq ) (((seq)->flags & CV_SEQ_FLAG_CLOSED) != 0) -#define CV_IS_SEQ_CONVEX( seq ) 0 -#define CV_IS_SEQ_HOLE( seq ) (((seq)->flags & CV_SEQ_FLAG_HOLE) != 0) -#define CV_IS_SEQ_SIMPLE( seq ) 1 - -/** type checking macros */ -#define CV_IS_SEQ_POINT_SET( seq ) \ - ((CV_SEQ_ELTYPE(seq) == CV_32SC2 || CV_SEQ_ELTYPE(seq) == CV_32FC2)) - -#define CV_IS_SEQ_POINT_SUBSET( seq ) \ - (CV_IS_SEQ_INDEX( seq ) || CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_PPOINT) - -#define CV_IS_SEQ_POLYLINE( seq ) \ - (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && CV_IS_SEQ_POINT_SET(seq)) - -#define CV_IS_SEQ_POLYGON( seq ) \ - (CV_IS_SEQ_POLYLINE(seq) && CV_IS_SEQ_CLOSED(seq)) - -#define CV_IS_SEQ_CHAIN( seq ) \ - (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && (seq)->elem_size == 1) - -#define CV_IS_SEQ_CONTOUR( seq ) \ - (CV_IS_SEQ_CLOSED(seq) && (CV_IS_SEQ_POLYLINE(seq) || CV_IS_SEQ_CHAIN(seq))) - -#define CV_IS_SEQ_CHAIN_CONTOUR( seq ) \ - (CV_IS_SEQ_CHAIN( seq ) && CV_IS_SEQ_CLOSED( seq )) - -#define CV_IS_SEQ_POLYGON_TREE( seq ) \ - (CV_SEQ_ELTYPE (seq) == CV_SEQ_ELTYPE_TRIAN_ATR && \ - CV_SEQ_KIND( seq ) == CV_SEQ_KIND_BIN_TREE ) - -#define CV_IS_GRAPH( seq ) \ - (CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_GRAPH) - -#define CV_IS_GRAPH_ORIENTED( seq ) \ - (((seq)->flags & CV_GRAPH_FLAG_ORIENTED) != 0) - -#define CV_IS_SUBDIV2D( seq ) \ - (CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_SUBDIV2D) - -/****************************************************************************************/ -/* Sequence writer & reader */ -/****************************************************************************************/ - -#define CV_SEQ_WRITER_FIELDS() \ - int header_size; \ - CvSeq* seq; /**< the sequence written */ \ - CvSeqBlock* block; /**< current block */ \ - schar* ptr; /**< pointer to free space */ \ - schar* block_min; /**< pointer to the beginning of block*/\ - schar* block_max; /**< pointer to the end of block */ - -typedef struct CvSeqWriter -{ - CV_SEQ_WRITER_FIELDS() -} -CvSeqWriter; - - -#define CV_SEQ_READER_FIELDS() \ - int header_size; \ - CvSeq* seq; /**< sequence, beign read */ \ - CvSeqBlock* block; /**< current block */ \ - schar* ptr; /**< pointer to element be read next */ \ - schar* block_min; /**< pointer to the beginning of block */\ - schar* block_max; /**< pointer to the end of block */ \ - int delta_index;/**< = seq->first->start_index */ \ - schar* prev_elem; /**< pointer to previous element */ - -typedef struct CvSeqReader -{ - CV_SEQ_READER_FIELDS() -} -CvSeqReader; - -/****************************************************************************************/ -/* Operations on sequences */ -/****************************************************************************************/ - -#define CV_SEQ_ELEM( seq, elem_type, index ) \ -/** assert gives some guarantee that parameter is valid */ \ -( assert(sizeof((seq)->first[0]) == sizeof(CvSeqBlock) && \ - (seq)->elem_size == sizeof(elem_type)), \ - (elem_type*)((seq)->first && (unsigned)index < \ - (unsigned)((seq)->first->count) ? \ - (seq)->first->data + (index) * sizeof(elem_type) : \ - cvGetSeqElem( (CvSeq*)(seq), (index) ))) -#define CV_GET_SEQ_ELEM( elem_type, seq, index ) CV_SEQ_ELEM( (seq), elem_type, (index) ) - -/** Add element to sequence: */ -#define CV_WRITE_SEQ_ELEM_VAR( elem_ptr, writer ) \ -{ \ - if( (writer).ptr >= (writer).block_max ) \ - { \ - cvCreateSeqBlock( &writer); \ - } \ - memcpy((writer).ptr, elem_ptr, (writer).seq->elem_size);\ - (writer).ptr += (writer).seq->elem_size; \ -} - -#define CV_WRITE_SEQ_ELEM( elem, writer ) \ -{ \ - assert( (writer).seq->elem_size == sizeof(elem)); \ - if( (writer).ptr >= (writer).block_max ) \ - { \ - cvCreateSeqBlock( &writer); \ - } \ - assert( (writer).ptr <= (writer).block_max - sizeof(elem));\ - memcpy((writer).ptr, &(elem), sizeof(elem)); \ - (writer).ptr += sizeof(elem); \ -} - - -/** Move reader position forward: */ -#define CV_NEXT_SEQ_ELEM( elem_size, reader ) \ -{ \ - if( ((reader).ptr += (elem_size)) >= (reader).block_max ) \ - { \ - cvChangeSeqBlock( &(reader), 1 ); \ - } \ -} - - -/** Move reader position backward: */ -#define CV_PREV_SEQ_ELEM( elem_size, reader ) \ -{ \ - if( ((reader).ptr -= (elem_size)) < (reader).block_min ) \ - { \ - cvChangeSeqBlock( &(reader), -1 ); \ - } \ -} - -/** Read element and move read position forward: */ -#define CV_READ_SEQ_ELEM( elem, reader ) \ -{ \ - assert( (reader).seq->elem_size == sizeof(elem)); \ - memcpy( &(elem), (reader).ptr, sizeof((elem))); \ - CV_NEXT_SEQ_ELEM( sizeof(elem), reader ) \ -} - -/** Read element and move read position backward: */ -#define CV_REV_READ_SEQ_ELEM( elem, reader ) \ -{ \ - assert( (reader).seq->elem_size == sizeof(elem)); \ - memcpy(&(elem), (reader).ptr, sizeof((elem))); \ - CV_PREV_SEQ_ELEM( sizeof(elem), reader ) \ -} - - -#define CV_READ_CHAIN_POINT( _pt, reader ) \ -{ \ - (_pt) = (reader).pt; \ - if( (reader).ptr ) \ - { \ - CV_READ_SEQ_ELEM( (reader).code, (reader)); \ - assert( ((reader).code & ~7) == 0 ); \ - (reader).pt.x += (reader).deltas[(int)(reader).code][0]; \ - (reader).pt.y += (reader).deltas[(int)(reader).code][1]; \ - } \ -} - -#define CV_CURRENT_POINT( reader ) (*((CvPoint*)((reader).ptr))) -#define CV_PREV_POINT( reader ) (*((CvPoint*)((reader).prev_elem))) - -#define CV_READ_EDGE( pt1, pt2, reader ) \ -{ \ - assert( sizeof(pt1) == sizeof(CvPoint) && \ - sizeof(pt2) == sizeof(CvPoint) && \ - reader.seq->elem_size == sizeof(CvPoint)); \ - (pt1) = CV_PREV_POINT( reader ); \ - (pt2) = CV_CURRENT_POINT( reader ); \ - (reader).prev_elem = (reader).ptr; \ - CV_NEXT_SEQ_ELEM( sizeof(CvPoint), (reader)); \ -} - -/************ Graph macros ************/ - -/** Return next graph edge for given vertex: */ -#define CV_NEXT_GRAPH_EDGE( edge, vertex ) \ - (assert((edge)->vtx[0] == (vertex) || (edge)->vtx[1] == (vertex)), \ - (edge)->next[(edge)->vtx[1] == (vertex)]) - - - -/****************************************************************************************\ -* Data structures for persistence (a.k.a serialization) functionality * -\****************************************************************************************/ - -/** "black box" file storage */ -typedef struct CvFileStorage CvFileStorage; - -/** Storage flags: */ -#define CV_STORAGE_READ 0 -#define CV_STORAGE_WRITE 1 -#define CV_STORAGE_WRITE_TEXT CV_STORAGE_WRITE -#define CV_STORAGE_WRITE_BINARY CV_STORAGE_WRITE -#define CV_STORAGE_APPEND 2 -#define CV_STORAGE_MEMORY 4 -#define CV_STORAGE_FORMAT_MASK (7<<3) -#define CV_STORAGE_FORMAT_AUTO 0 -#define CV_STORAGE_FORMAT_XML 8 -#define CV_STORAGE_FORMAT_YAML 16 - -/** @brief List of attributes. : - -In the current implementation, attributes are used to pass extra parameters when writing user -objects (see cvWrite). XML attributes inside tags are not supported, aside from the object type -specification (type_id attribute). -@see cvAttrList, cvAttrValue - */ -typedef struct CvAttrList -{ - const char** attr; /**< NULL-terminated array of (attribute_name,attribute_value) pairs. */ - struct CvAttrList* next; /**< Pointer to next chunk of the attributes list. */ -} -CvAttrList; - -/** initializes CvAttrList structure */ -CV_INLINE CvAttrList cvAttrList( const char** attr CV_DEFAULT(NULL), - CvAttrList* next CV_DEFAULT(NULL) ) -{ - CvAttrList l; - l.attr = attr; - l.next = next; - - return l; -} - -struct CvTypeInfo; - -#define CV_NODE_NONE 0 -#define CV_NODE_INT 1 -#define CV_NODE_INTEGER CV_NODE_INT -#define CV_NODE_REAL 2 -#define CV_NODE_FLOAT CV_NODE_REAL -#define CV_NODE_STR 3 -#define CV_NODE_STRING CV_NODE_STR -#define CV_NODE_REF 4 /**< not used */ -#define CV_NODE_SEQ 5 -#define CV_NODE_MAP 6 -#define CV_NODE_TYPE_MASK 7 - -#define CV_NODE_TYPE(flags) ((flags) & CV_NODE_TYPE_MASK) - -/** file node flags */ -#define CV_NODE_FLOW 8 /**= CV_NODE_SEQ) -#define CV_NODE_IS_FLOW(flags) (((flags) & CV_NODE_FLOW) != 0) -#define CV_NODE_IS_EMPTY(flags) (((flags) & CV_NODE_EMPTY) != 0) -#define CV_NODE_IS_USER(flags) (((flags) & CV_NODE_USER) != 0) -#define CV_NODE_HAS_NAME(flags) (((flags) & CV_NODE_NAMED) != 0) - -#define CV_NODE_SEQ_SIMPLE 256 -#define CV_NODE_SEQ_IS_SIMPLE(seq) (((seq)->flags & CV_NODE_SEQ_SIMPLE) != 0) - -typedef struct CvString -{ - int len; - char* ptr; -} -CvString; - -/** All the keys (names) of elements in the readed file storage - are stored in the hash to speed up the lookup operations: */ -typedef struct CvStringHashNode -{ - unsigned hashval; - CvString str; - struct CvStringHashNode* next; -} -CvStringHashNode; - -typedef struct CvGenericHash CvFileNodeHash; - -/** Basic element of the file storage - scalar or collection: */ -typedef struct CvFileNode -{ - int tag; - struct CvTypeInfo* info; /**< type information - (only for user-defined object, for others it is 0) */ - union - { - double f; /**< scalar floating-point number */ - int i; /**< scalar integer number */ - CvString str; /**< text string */ - CvSeq* seq; /**< sequence (ordered collection of file nodes) */ - CvFileNodeHash* map; /**< map (collection of named file nodes) */ - } data; -} -CvFileNode; - -#ifdef __cplusplus -extern "C" { -#endif -typedef int (CV_CDECL *CvIsInstanceFunc)( const void* struct_ptr ); -typedef void (CV_CDECL *CvReleaseFunc)( void** struct_dblptr ); -typedef void* (CV_CDECL *CvReadFunc)( CvFileStorage* storage, CvFileNode* node ); -typedef void (CV_CDECL *CvWriteFunc)( CvFileStorage* storage, const char* name, - const void* struct_ptr, CvAttrList attributes ); -typedef void* (CV_CDECL *CvCloneFunc)( const void* struct_ptr ); -#ifdef __cplusplus -} -#endif - -/** @brief Type information - -The structure contains information about one of the standard or user-defined types. Instances of the -type may or may not contain a pointer to the corresponding CvTypeInfo structure. In any case, there -is a way to find the type info structure for a given object using the cvTypeOf function. -Alternatively, type info can be found by type name using cvFindType, which is used when an object -is read from file storage. The user can register a new type with cvRegisterType that adds the type -information structure into the beginning of the type list. Thus, it is possible to create -specialized types from generic standard types and override the basic methods. - */ -typedef struct CvTypeInfo -{ - int flags; /**< not used */ - int header_size; /**< sizeof(CvTypeInfo) */ - struct CvTypeInfo* prev; /**< previous registered type in the list */ - struct CvTypeInfo* next; /**< next registered type in the list */ - const char* type_name; /**< type name, written to file storage */ - CvIsInstanceFunc is_instance; /**< checks if the passed object belongs to the type */ - CvReleaseFunc release; /**< releases object (memory etc.) */ - CvReadFunc read; /**< reads object from file storage */ - CvWriteFunc write; /**< writes object to file storage */ - CvCloneFunc clone; /**< creates a copy of the object */ -} -CvTypeInfo; - - -/**** System data types ******/ - -typedef struct CvPluginFuncInfo -{ - void** func_addr; - void* default_func_addr; - const char* func_names; - int search_modules; - int loaded_from; -} -CvPluginFuncInfo; - -typedef struct CvModuleInfo -{ - struct CvModuleInfo* next; - const char* name; - const char* version; - CvPluginFuncInfo* func_tab; -} -CvModuleInfo; - -/** @} */ - -#endif /*__OPENCV_CORE_TYPES_H__*/ - -/* End of file. */ diff --git a/IPL/include/opencv/opencv2/core/utility.hpp b/IPL/include/opencv/opencv2/core/utility.hpp deleted file mode 100644 index 1e6249d..0000000 --- a/IPL/include/opencv/opencv2/core/utility.hpp +++ /dev/null @@ -1,893 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_CORE_UTILITY_H__ -#define __OPENCV_CORE_UTILITY_H__ - -#ifndef __cplusplus -# error utility.hpp header must be compiled as C++ -#endif - -#if defined(check) -# warning Detected Apple 'check' macro definition, it can cause build conflicts. Please, include this header before any Apple headers. -#endif - -#include "opencv2/core.hpp" - -namespace cv -{ - -#ifdef CV_COLLECT_IMPL_DATA -CV_EXPORTS void setImpl(int flags); // set implementation flags and reset storage arrays -CV_EXPORTS void addImpl(int flag, const char* func = 0); // add implementation and function name to storage arrays -// Get stored implementation flags and fucntions names arrays -// Each implementation entry correspond to function name entry, so you can find which implementation was executed in which fucntion -CV_EXPORTS int getImpl(std::vector &impl, std::vector &funName); - -CV_EXPORTS bool useCollection(); // return implementation collection state -CV_EXPORTS void setUseCollection(bool flag); // set implementation collection state - -#define CV_IMPL_PLAIN 0x01 // native CPU OpenCV implementation -#define CV_IMPL_OCL 0x02 // OpenCL implementation -#define CV_IMPL_IPP 0x04 // IPP implementation -#define CV_IMPL_MT 0x10 // multithreaded implementation - -#define CV_IMPL_ADD(impl) \ - if(cv::useCollection()) \ - { \ - cv::addImpl(impl, CV_Func); \ - } -#else -#define CV_IMPL_ADD(impl) -#endif - -//! @addtogroup core_utils -//! @{ - -/** @brief Automatically Allocated Buffer Class - - The class is used for temporary buffers in functions and methods. - If a temporary buffer is usually small (a few K's of memory), - but its size depends on the parameters, it makes sense to create a small - fixed-size array on stack and use it if it's large enough. If the required buffer size - is larger than the fixed size, another buffer of sufficient size is allocated dynamically - and released after the processing. Therefore, in typical cases, when the buffer size is small, - there is no overhead associated with malloc()/free(). - At the same time, there is no limit on the size of processed data. - - This is what AutoBuffer does. The template takes 2 parameters - type of the buffer elements and - the number of stack-allocated elements. Here is how the class is used: - - \code - void my_func(const cv::Mat& m) - { - cv::AutoBuffer buf; // create automatic buffer containing 1000 floats - - buf.allocate(m.rows); // if m.rows <= 1000, the pre-allocated buffer is used, - // otherwise the buffer of "m.rows" floats will be allocated - // dynamically and deallocated in cv::AutoBuffer destructor - ... - } - \endcode -*/ -template class AutoBuffer -{ -public: - typedef _Tp value_type; - - //! the default constructor - AutoBuffer(); - //! constructor taking the real buffer size - AutoBuffer(size_t _size); - - //! the copy constructor - AutoBuffer(const AutoBuffer<_Tp, fixed_size>& buf); - //! the assignment operator - AutoBuffer<_Tp, fixed_size>& operator = (const AutoBuffer<_Tp, fixed_size>& buf); - - //! destructor. calls deallocate() - ~AutoBuffer(); - - //! allocates the new buffer of size _size. if the _size is small enough, stack-allocated buffer is used - void allocate(size_t _size); - //! deallocates the buffer if it was dynamically allocated - void deallocate(); - //! resizes the buffer and preserves the content - void resize(size_t _size); - //! returns the current buffer size - size_t size() const; - //! returns pointer to the real buffer, stack-allocated or head-allocated - operator _Tp* (); - //! returns read-only pointer to the real buffer, stack-allocated or head-allocated - operator const _Tp* () const; - -protected: - //! pointer to the real buffer, can point to buf if the buffer is small enough - _Tp* ptr; - //! size of the real buffer - size_t sz; - //! pre-allocated buffer. At least 1 element to confirm C++ standard reqirements - _Tp buf[(fixed_size > 0) ? fixed_size : 1]; -}; - -/** @brief Sets/resets the break-on-error mode. - -When the break-on-error mode is set, the default error handler issues a hardware exception, which -can make debugging more convenient. - -\return the previous state - */ -CV_EXPORTS bool setBreakOnError(bool flag); - -extern "C" typedef int (*ErrorCallback)( int status, const char* func_name, - const char* err_msg, const char* file_name, - int line, void* userdata ); - - -/** @brief Sets the new error handler and the optional user data. - - The function sets the new error handler, called from cv::error(). - - \param errCallback the new error handler. If NULL, the default error handler is used. - \param userdata the optional user data pointer, passed to the callback. - \param prevUserdata the optional output parameter where the previous user data pointer is stored - - \return the previous error handler -*/ -CV_EXPORTS ErrorCallback redirectError( ErrorCallback errCallback, void* userdata=0, void** prevUserdata=0); - -/** @brief Returns a text string formatted using the printf-like expression. - -The function acts like sprintf but forms and returns an STL string. It can be used to form an error -message in the Exception constructor. -@param fmt printf-compatible formatting specifiers. - */ -CV_EXPORTS String format( const char* fmt, ... ); -CV_EXPORTS String tempfile( const char* suffix = 0); -CV_EXPORTS void glob(String pattern, std::vector& result, bool recursive = false); - -/** @brief OpenCV will try to set the number of threads for the next parallel region. - -If threads == 0, OpenCV will disable threading optimizations and run all it's functions -sequentially. Passing threads \< 0 will reset threads number to system default. This function must -be called outside of parallel region. - -OpenCV will try to run it's functions with specified threads number, but some behaviour differs from -framework: -- `TBB` – User-defined parallel constructions will run with the same threads number, if - another does not specified. If late on user creates own scheduler, OpenCV will be use it. -- `OpenMP` – No special defined behaviour. -- `Concurrency` – If threads == 1, OpenCV will disable threading optimizations and run it's - functions sequentially. -- `GCD` – Supports only values \<= 0. -- `C=` – No special defined behaviour. -@param nthreads Number of threads used by OpenCV. -@sa getNumThreads, getThreadNum - */ -CV_EXPORTS_W void setNumThreads(int nthreads); - -/** @brief Returns the number of threads used by OpenCV for parallel regions. - -Always returns 1 if OpenCV is built without threading support. - -The exact meaning of return value depends on the threading framework used by OpenCV library: -- `TBB` – The number of threads, that OpenCV will try to use for parallel regions. If there is - any tbb::thread_scheduler_init in user code conflicting with OpenCV, then function returns - default number of threads used by TBB library. -- `OpenMP` – An upper bound on the number of threads that could be used to form a new team. -- `Concurrency` – The number of threads, that OpenCV will try to use for parallel regions. -- `GCD` – Unsupported; returns the GCD thread pool limit (512) for compatibility. -- `C=` – The number of threads, that OpenCV will try to use for parallel regions, if before - called setNumThreads with threads \> 0, otherwise returns the number of logical CPUs, - available for the process. -@sa setNumThreads, getThreadNum - */ -CV_EXPORTS_W int getNumThreads(); - -/** @brief Returns the index of the currently executed thread within the current parallel region. Always -returns 0 if called outside of parallel region. - -The exact meaning of return value depends on the threading framework used by OpenCV library: -- `TBB` – Unsupported with current 4.1 TBB release. May be will be supported in future. -- `OpenMP` – The thread number, within the current team, of the calling thread. -- `Concurrency` – An ID for the virtual processor that the current context is executing on (0 - for master thread and unique number for others, but not necessary 1,2,3,...). -- `GCD` – System calling thread's ID. Never returns 0 inside parallel region. -- `C=` – The index of the current parallel task. -@sa setNumThreads, getNumThreads - */ -CV_EXPORTS_W int getThreadNum(); - -/** @brief Returns full configuration time cmake output. - -Returned value is raw cmake output including version control system revision, compiler version, -compiler flags, enabled modules and third party libraries, etc. Output format depends on target -architecture. - */ -CV_EXPORTS_W const String& getBuildInformation(); - -/** @brief Returns the number of ticks. - -The function returns the number of ticks after the certain event (for example, when the machine was -turned on). It can be used to initialize RNG or to measure a function execution time by reading the -tick count before and after the function call. See also the tick frequency. - */ -CV_EXPORTS_W int64 getTickCount(); - -/** @brief Returns the number of ticks per second. - -The function returns the number of ticks per second. That is, the following code computes the -execution time in seconds: -@code - double t = (double)getTickCount(); - // do something ... - t = ((double)getTickCount() - t)/getTickFrequency(); -@endcode - */ -CV_EXPORTS_W double getTickFrequency(); - -/** @brief Returns the number of CPU ticks. - -The function returns the current number of CPU ticks on some architectures (such as x86, x64, -PowerPC). On other platforms the function is equivalent to getTickCount. It can also be used for -very accurate time measurements, as well as for RNG initialization. Note that in case of multi-CPU -systems a thread, from which getCPUTickCount is called, can be suspended and resumed at another CPU -with its own counter. So, theoretically (and practically) the subsequent calls to the function do -not necessary return the monotonously increasing values. Also, since a modern CPU varies the CPU -frequency depending on the load, the number of CPU clocks spent in some code cannot be directly -converted to time units. Therefore, getTickCount is generally a preferable solution for measuring -execution time. - */ -CV_EXPORTS_W int64 getCPUTickCount(); - -/** @brief Returns true if the specified feature is supported by the host hardware. - -The function returns true if the host hardware supports the specified feature. When user calls -setUseOptimized(false), the subsequent calls to checkHardwareSupport() will return false until -setUseOptimized(true) is called. This way user can dynamically switch on and off the optimized code -in OpenCV. -@param feature The feature of interest, one of cv::CpuFeatures - */ -CV_EXPORTS_W bool checkHardwareSupport(int feature); - -/** @brief Returns the number of logical CPUs available for the process. - */ -CV_EXPORTS_W int getNumberOfCPUs(); - - -/** @brief Aligns a pointer to the specified number of bytes. - -The function returns the aligned pointer of the same type as the input pointer: -\f[\texttt{(_Tp*)(((size_t)ptr + n-1) & -n)}\f] -@param ptr Aligned pointer. -@param n Alignment size that must be a power of two. - */ -template static inline _Tp* alignPtr(_Tp* ptr, int n=(int)sizeof(_Tp)) -{ - return (_Tp*)(((size_t)ptr + n-1) & -n); -} - -/** @brief Aligns a buffer size to the specified number of bytes. - -The function returns the minimum number that is greater or equal to sz and is divisible by n : -\f[\texttt{(sz + n-1) & -n}\f] -@param sz Buffer size to align. -@param n Alignment size that must be a power of two. - */ -static inline size_t alignSize(size_t sz, int n) -{ - CV_DbgAssert((n & (n - 1)) == 0); // n is a power of 2 - return (sz + n-1) & -n; -} - -/** @brief Enables or disables the optimized code. - -The function can be used to dynamically turn on and off optimized code (code that uses SSE2, AVX, -and other instructions on the platforms that support it). It sets a global flag that is further -checked by OpenCV functions. Since the flag is not checked in the inner OpenCV loops, it is only -safe to call the function on the very top level in your application where you can be sure that no -other OpenCV function is currently executed. - -By default, the optimized code is enabled unless you disable it in CMake. The current status can be -retrieved using useOptimized. -@param onoff The boolean flag specifying whether the optimized code should be used (onoff=true) -or not (onoff=false). - */ -CV_EXPORTS_W void setUseOptimized(bool onoff); - -/** @brief Returns the status of optimized code usage. - -The function returns true if the optimized code is enabled. Otherwise, it returns false. - */ -CV_EXPORTS_W bool useOptimized(); - -static inline size_t getElemSize(int type) { return CV_ELEM_SIZE(type); } - -/////////////////////////////// Parallel Primitives ////////////////////////////////// - -/** @brief Base class for parallel data processors -*/ -class CV_EXPORTS ParallelLoopBody -{ -public: - virtual ~ParallelLoopBody(); - virtual void operator() (const Range& range) const = 0; -}; - -/** @brief Parallel data processor -*/ -CV_EXPORTS void parallel_for_(const Range& range, const ParallelLoopBody& body, double nstripes=-1.); - -/////////////////////////////// forEach method of cv::Mat //////////////////////////// -template inline -void Mat::forEach_impl(const Functor& operation) { - if (false) { - operation(*reinterpret_cast<_Tp*>(0), reinterpret_cast(NULL)); - // If your compiler fail in this line. - // Please check that your functor signature is - // (_Tp&, const int*) <- multidimential - // or (_Tp&, void*) <- in case of you don't need current idx. - } - - CV_Assert(this->total() / this->size[this->dims - 1] <= INT_MAX); - const int LINES = static_cast(this->total() / this->size[this->dims - 1]); - - class PixelOperationWrapper :public ParallelLoopBody - { - public: - PixelOperationWrapper(Mat_<_Tp>* const frame, const Functor& _operation) - : mat(frame), op(_operation) {}; - virtual ~PixelOperationWrapper(){}; - // ! Overloaded virtual operator - // convert range call to row call. - virtual void operator()(const Range &range) const { - const int DIMS = mat->dims; - const int COLS = mat->size[DIMS - 1]; - if (DIMS <= 2) { - for (int row = range.start; row < range.end; ++row) { - this->rowCall2(row, COLS); - } - } else { - std::vector idx(COLS); /// idx is modified in this->rowCall - idx[DIMS - 2] = range.start - 1; - - for (int line_num = range.start; line_num < range.end; ++line_num) { - idx[DIMS - 2]++; - for (int i = DIMS - 2; i >= 0; --i) { - if (idx[i] >= mat->size[i]) { - idx[i - 1] += idx[i] / mat->size[i]; - idx[i] %= mat->size[i]; - continue; // carry-over; - } - else { - break; - } - } - this->rowCall(&idx[0], COLS, DIMS); - } - } - }; - private: - Mat_<_Tp>* const mat; - const Functor op; - // ! Call operator for each elements in this row. - inline void rowCall(int* const idx, const int COLS, const int DIMS) const { - int &col = idx[DIMS - 1]; - col = 0; - _Tp* pixel = &(mat->template at<_Tp>(idx)); - - while (col < COLS) { - op(*pixel, const_cast(idx)); - pixel++; col++; - } - col = 0; - } - // ! Call operator for each elements in this row. 2d mat special version. - inline void rowCall2(const int row, const int COLS) const { - union Index{ - int body[2]; - operator const int*() const { - return reinterpret_cast(this); - } - int& operator[](const int i) { - return body[i]; - } - } idx = {{row, 0}}; - // Special union is needed to avoid - // "error: array subscript is above array bounds [-Werror=array-bounds]" - // when call the functor `op` such that access idx[3]. - - _Tp* pixel = &(mat->template at<_Tp>(idx)); - const _Tp* const pixel_end = pixel + COLS; - while(pixel < pixel_end) { - op(*pixel++, static_cast(idx)); - idx[1]++; - } - }; - PixelOperationWrapper& operator=(const PixelOperationWrapper &) { - CV_Assert(false); - // We can not remove this implementation because Visual Studio warning C4822. - return *this; - }; - }; - - parallel_for_(cv::Range(0, LINES), PixelOperationWrapper(reinterpret_cast*>(this), operation)); -} - -/////////////////////////// Synchronization Primitives /////////////////////////////// - -class CV_EXPORTS Mutex -{ -public: - Mutex(); - ~Mutex(); - Mutex(const Mutex& m); - Mutex& operator = (const Mutex& m); - - void lock(); - bool trylock(); - void unlock(); - - struct Impl; -protected: - Impl* impl; -}; - -class CV_EXPORTS AutoLock -{ -public: - AutoLock(Mutex& m) : mutex(&m) { mutex->lock(); } - ~AutoLock() { mutex->unlock(); } -protected: - Mutex* mutex; -private: - AutoLock(const AutoLock&); - AutoLock& operator = (const AutoLock&); -}; - -// TLS interface -class CV_EXPORTS TLSDataContainer -{ -protected: - TLSDataContainer(); - virtual ~TLSDataContainer(); - - void gatherData(std::vector &data) const; -#if OPENCV_ABI_COMPATIBILITY > 300 - void* getData() const; - void release(); - -private: -#else - void release(); - -public: - void* getData() const; -#endif - virtual void* createDataInstance() const = 0; - virtual void deleteDataInstance(void* pData) const = 0; - - int key_; -}; - -// Main TLS data class -template -class TLSData : protected TLSDataContainer -{ -public: - inline TLSData() {} - inline ~TLSData() { release(); } // Release key and delete associated data - inline T* get() const { return (T*)getData(); } // Get data assosiated with key - - // Get data from all threads - inline void gather(std::vector &data) const - { - std::vector &dataVoid = reinterpret_cast&>(data); - gatherData(dataVoid); - } - -private: - virtual void* createDataInstance() const {return new T;} // Wrapper to allocate data by template - virtual void deleteDataInstance(void* pData) const {delete (T*)pData;} // Wrapper to release data by template - - // Disable TLS copy operations - TLSData(TLSData &) {}; - TLSData& operator =(const TLSData &) {return *this;}; -}; - -/** @brief Designed for command line parsing - -The sample below demonstrates how to use CommandLineParser: -@code - CommandLineParser parser(argc, argv, keys); - parser.about("Application name v1.0.0"); - - if (parser.has("help")) - { - parser.printMessage(); - return 0; - } - - int N = parser.get("N"); - double fps = parser.get("fps"); - String path = parser.get("path"); - - use_time_stamp = parser.has("timestamp"); - - String img1 = parser.get(0); - String img2 = parser.get(1); - - int repeat = parser.get(2); - - if (!parser.check()) - { - parser.printErrors(); - return 0; - } -@endcode - -### Keys syntax - -The keys parameter is a string containing several blocks, each one is enclosed in curley braces and -describes one argument. Each argument contains three parts separated by the `|` symbol: - --# argument names is a space-separated list of option synonyms (to mark argument as positional, prefix it with the `@` symbol) --# default value will be used if the argument was not provided (can be empty) --# help message (can be empty) - -For example: - -@code{.cpp} - const String keys = - "{help h usage ? | | print this message }" - "{@image1 | | image1 for compare }" - "{@image2 || image2 for compare }" - "{@repeat |1 | number }" - "{path |. | path to file }" - "{fps | -1.0 | fps for output video }" - "{N count |100 | count of objects }" - "{ts timestamp | | use time stamp }" - ; -} -@endcode - -Note that there are no default values for `help` and `timestamp` so we can check their presence using the `has()` method. -Arguments with default values are considered to be always present. Use the `get()` method in these cases to check their -actual value instead. - -String keys like `get("@image1")` return the empty string `""` by default - even with an empty default value. -Use the special `` default value to enforce that the returned string must not be empty. (like in `get("@image2")`) - -### Usage - -For the described keys: - -@code{.sh} - # Good call (3 positional parameters: image1, image2 and repeat; N is 200, ts is true) - $ ./app -N=200 1.png 2.jpg 19 -ts - - # Bad call - $ ./app -fps=aaa - ERRORS: - Parameter 'fps': can not convert: [aaa] to [double] -@endcode - */ -class CV_EXPORTS CommandLineParser -{ -public: - - /** @brief Constructor - - Initializes command line parser object - - @param argc number of command line arguments (from main()) - @param argv array of command line arguments (from main()) - @param keys string describing acceptable command line parameters (see class description for syntax) - */ - CommandLineParser(int argc, const char* const argv[], const String& keys); - - /** @brief Copy constructor */ - CommandLineParser(const CommandLineParser& parser); - - /** @brief Assignment operator */ - CommandLineParser& operator = (const CommandLineParser& parser); - - /** @brief Destructor */ - ~CommandLineParser(); - - /** @brief Returns application path - - This method returns the path to the executable from the command line (`argv[0]`). - - For example, if the application has been started with such command: - @code{.sh} - $ ./bin/my-executable - @endcode - this method will return `./bin`. - */ - String getPathToApplication() const; - - /** @brief Access arguments by name - - Returns argument converted to selected type. If the argument is not known or can not be - converted to selected type, the error flag is set (can be checked with @ref check). - - For example, define: - @code{.cpp} - String keys = "{N count||}"; - @endcode - - Call: - @code{.sh} - $ ./my-app -N=20 - # or - $ ./my-app --count=20 - @endcode - - Access: - @code{.cpp} - int N = parser.get("N"); - @endcode - - @param name name of the argument - @param space_delete remove spaces from the left and right of the string - @tparam T the argument will be converted to this type if possible - - @note You can access positional arguments by their `@`-prefixed name: - @code{.cpp} - parser.get("@image"); - @endcode - */ - template - T get(const String& name, bool space_delete = true) const - { - T val = T(); - getByName(name, space_delete, ParamType::type, (void*)&val); - return val; - } - - /** @brief Access positional arguments by index - - Returns argument converted to selected type. Indexes are counted from zero. - - For example, define: - @code{.cpp} - String keys = "{@arg1||}{@arg2||}" - @endcode - - Call: - @code{.sh} - ./my-app abc qwe - @endcode - - Access arguments: - @code{.cpp} - String val_1 = parser.get(0); // returns "abc", arg1 - String val_2 = parser.get(1); // returns "qwe", arg2 - @endcode - - @param index index of the argument - @param space_delete remove spaces from the left and right of the string - @tparam T the argument will be converted to this type if possible - */ - template - T get(int index, bool space_delete = true) const - { - T val = T(); - getByIndex(index, space_delete, ParamType::type, (void*)&val); - return val; - } - - /** @brief Check if field was provided in the command line - - @param name argument name to check - */ - bool has(const String& name) const; - - /** @brief Check for parsing errors - - Returns true if error occured while accessing the parameters (bad conversion, missing arguments, - etc.). Call @ref printErrors to print error messages list. - */ - bool check() const; - - /** @brief Set the about message - - The about message will be shown when @ref printMessage is called, right before arguments table. - */ - void about(const String& message); - - /** @brief Print help message - - This method will print standard help message containing the about message and arguments description. - - @sa about - */ - void printMessage() const; - - /** @brief Print list of errors occured - - @sa check - */ - void printErrors() const; - -protected: - void getByName(const String& name, bool space_delete, int type, void* dst) const; - void getByIndex(int index, bool space_delete, int type, void* dst) const; - - struct Impl; - Impl* impl; -}; - -//! @} core_utils - -//! @cond IGNORED - -/////////////////////////////// AutoBuffer implementation //////////////////////////////////////// - -template inline -AutoBuffer<_Tp, fixed_size>::AutoBuffer() -{ - ptr = buf; - sz = fixed_size; -} - -template inline -AutoBuffer<_Tp, fixed_size>::AutoBuffer(size_t _size) -{ - ptr = buf; - sz = fixed_size; - allocate(_size); -} - -template inline -AutoBuffer<_Tp, fixed_size>::AutoBuffer(const AutoBuffer<_Tp, fixed_size>& abuf ) -{ - ptr = buf; - sz = fixed_size; - allocate(abuf.size()); - for( size_t i = 0; i < sz; i++ ) - ptr[i] = abuf.ptr[i]; -} - -template inline AutoBuffer<_Tp, fixed_size>& -AutoBuffer<_Tp, fixed_size>::operator = (const AutoBuffer<_Tp, fixed_size>& abuf) -{ - if( this != &abuf ) - { - deallocate(); - allocate(abuf.size()); - for( size_t i = 0; i < sz; i++ ) - ptr[i] = abuf.ptr[i]; - } - return *this; -} - -template inline -AutoBuffer<_Tp, fixed_size>::~AutoBuffer() -{ deallocate(); } - -template inline void -AutoBuffer<_Tp, fixed_size>::allocate(size_t _size) -{ - if(_size <= sz) - { - sz = _size; - return; - } - deallocate(); - if(_size > fixed_size) - { - ptr = new _Tp[_size]; - sz = _size; - } -} - -template inline void -AutoBuffer<_Tp, fixed_size>::deallocate() -{ - if( ptr != buf ) - { - delete[] ptr; - ptr = buf; - sz = fixed_size; - } -} - -template inline void -AutoBuffer<_Tp, fixed_size>::resize(size_t _size) -{ - if(_size <= sz) - { - sz = _size; - return; - } - size_t i, prevsize = sz, minsize = MIN(prevsize, _size); - _Tp* prevptr = ptr; - - ptr = _size > fixed_size ? new _Tp[_size] : buf; - sz = _size; - - if( ptr != prevptr ) - for( i = 0; i < minsize; i++ ) - ptr[i] = prevptr[i]; - for( i = prevsize; i < _size; i++ ) - ptr[i] = _Tp(); - - if( prevptr != buf ) - delete[] prevptr; -} - -template inline size_t -AutoBuffer<_Tp, fixed_size>::size() const -{ return sz; } - -template inline -AutoBuffer<_Tp, fixed_size>::operator _Tp* () -{ return ptr; } - -template inline -AutoBuffer<_Tp, fixed_size>::operator const _Tp* () const -{ return ptr; } - -#ifndef OPENCV_NOSTL -template<> inline std::string CommandLineParser::get(int index, bool space_delete) const -{ - return get(index, space_delete); -} -template<> inline std::string CommandLineParser::get(const String& name, bool space_delete) const -{ - return get(name, space_delete); -} -#endif // OPENCV_NOSTL - -//! @endcond - -} //namespace cv - -#ifndef DISABLE_OPENCV_24_COMPATIBILITY -#include "opencv2/core/core_c.h" -#endif - -#endif //__OPENCV_CORE_UTILITY_H__ diff --git a/IPL/include/opencv/opencv2/core/va_intel.hpp b/IPL/include/opencv/opencv2/core/va_intel.hpp deleted file mode 100644 index f4bb8a6..0000000 --- a/IPL/include/opencv/opencv2/core/va_intel.hpp +++ /dev/null @@ -1,77 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -// Copyright (C) 2015, Itseez, Inc., all rights reserved. -// Third party copyrights are property of their respective owners. - -#ifndef __OPENCV_CORE_VA_INTEL_HPP__ -#define __OPENCV_CORE_VA_INTEL_HPP__ - -#ifndef __cplusplus -# error va_intel.hpp header must be compiled as C++ -#endif - -#include "opencv2/core.hpp" -#include "ocl.hpp" - -#if defined(HAVE_VA) -# include "va/va.h" -#else // HAVE_VA -# if !defined(_VA_H_) - typedef void* VADisplay; - typedef unsigned int VASurfaceID; -# endif // !_VA_H_ -#endif // HAVE_VA - -namespace cv { namespace va_intel { - -/** @addtogroup core_va_intel -This section describes Intel VA-API/OpenCL (CL-VA) interoperability. - -To enable CL-VA interoperability support, configure OpenCV using CMake with WITH_VA_INTEL=ON . Currently VA-API is -supported on Linux only. You should also install Intel Media Server Studio (MSS) to use this feature. You may -have to specify the path(s) to MSS components for cmake in environment variables: VA_INTEL_MSDK_ROOT for Media SDK -(default is "/opt/intel/mediasdk"), and VA_INTEL_IOCL_ROOT for Intel OpenCL (default is "/opt/intel/opencl"). - -To use CL-VA interoperability you should first create VADisplay (libva), and then call initializeContextFromVA() -function to create OpenCL context and set up interoperability. -*/ -//! @{ - -/////////////////// CL-VA Interoperability Functions /////////////////// - -namespace ocl { -using namespace cv::ocl; - -// TODO static functions in the Context class -/** @brief Creates OpenCL context from VA. -@param display - VADisplay for which CL interop should be established. -@param tryInterop - try to set up for interoperability, if true; set up for use slow copy if false. -@return Returns reference to OpenCL Context - */ -CV_EXPORTS Context& initializeContextFromVA(VADisplay display, bool tryInterop = true); - -} // namespace cv::va_intel::ocl - -/** @brief Converts InputArray to VASurfaceID object. -@param display - VADisplay object. -@param src - source InputArray. -@param surface - destination VASurfaceID object. -@param size - size of image represented by VASurfaceID object. - */ -CV_EXPORTS void convertToVASurface(VADisplay display, InputArray src, VASurfaceID surface, Size size); - -/** @brief Converts VASurfaceID object to OutputArray. -@param display - VADisplay object. -@param surface - source VASurfaceID object. -@param size - size of image represented by VASurfaceID object. -@param dst - destination OutputArray. - */ -CV_EXPORTS void convertFromVASurface(VADisplay display, VASurfaceID surface, Size size, OutputArray dst); - -//! @} - -}} // namespace cv::va_intel - -#endif /* __OPENCV_CORE_VA_INTEL_HPP__ */ diff --git a/IPL/include/opencv/opencv2/core/version.hpp b/IPL/include/opencv/opencv2/core/version.hpp deleted file mode 100644 index a69d42f..0000000 --- a/IPL/include/opencv/opencv2/core/version.hpp +++ /dev/null @@ -1,71 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// Intel License Agreement -// For Open Source Computer Vision Library -// -// Copyright( C) 2000-2015, Intel Corporation, all rights reserved. -// Copyright (C) 2011-2013, NVIDIA Corporation, all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of Intel Corporation may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -//(including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort(including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -/* - definition of the current version of OpenCV - Usefull to test in user programs -*/ - -#ifndef __OPENCV_VERSION_HPP__ -#define __OPENCV_VERSION_HPP__ - -#define CV_VERSION_MAJOR 3 -#define CV_VERSION_MINOR 1 -#define CV_VERSION_REVISION 0 -#define CV_VERSION_STATUS "-dev" - -#define CVAUX_STR_EXP(__A) #__A -#define CVAUX_STR(__A) CVAUX_STR_EXP(__A) - -#define CVAUX_STRW_EXP(__A) L#__A -#define CVAUX_STRW(__A) CVAUX_STRW_EXP(__A) - -#define CV_VERSION CVAUX_STR(CV_VERSION_MAJOR) "." CVAUX_STR(CV_VERSION_MINOR) "." CVAUX_STR(CV_VERSION_REVISION) CV_VERSION_STATUS - -/* old style version constants*/ -#define CV_MAJOR_VERSION CV_VERSION_MAJOR -#define CV_MINOR_VERSION CV_VERSION_MINOR -#define CV_SUBMINOR_VERSION CV_VERSION_REVISION - -#endif diff --git a/IPL/include/opencv/opencv2/core/wimage.hpp b/IPL/include/opencv/opencv2/core/wimage.hpp deleted file mode 100644 index ef9d398..0000000 --- a/IPL/include/opencv/opencv2/core/wimage.hpp +++ /dev/null @@ -1,603 +0,0 @@ -/*M////////////////////////////////////////////////////////////////////////////// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to -// this license. If you do not agree to this license, do not download, -// install, copy or use the software. -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2008, Google, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of Intel Corporation or contributors may not be used to endorse -// or promote products derived from this software without specific -// prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" -// and any express or implied warranties, including, but not limited to, the -// implied warranties of merchantability and fitness for a particular purpose -// are disclaimed. In no event shall the Intel Corporation or contributors be -// liable for any direct, indirect, incidental, special, exemplary, or -// consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -///////////////////////////////////////////////////////////////////////////////// -//M*/ - -#ifndef __OPENCV_CORE_WIMAGE_HPP__ -#define __OPENCV_CORE_WIMAGE_HPP__ - -#include "opencv2/core/core_c.h" - -#ifdef __cplusplus - -namespace cv { - -//! @addtogroup core -//! @{ - -template class WImage; -template class WImageBuffer; -template class WImageView; - -template class WImageC; -template class WImageBufferC; -template class WImageViewC; - -// Commonly used typedefs. -typedef WImage WImage_b; -typedef WImageView WImageView_b; -typedef WImageBuffer WImageBuffer_b; - -typedef WImageC WImage1_b; -typedef WImageViewC WImageView1_b; -typedef WImageBufferC WImageBuffer1_b; - -typedef WImageC WImage3_b; -typedef WImageViewC WImageView3_b; -typedef WImageBufferC WImageBuffer3_b; - -typedef WImage WImage_f; -typedef WImageView WImageView_f; -typedef WImageBuffer WImageBuffer_f; - -typedef WImageC WImage1_f; -typedef WImageViewC WImageView1_f; -typedef WImageBufferC WImageBuffer1_f; - -typedef WImageC WImage3_f; -typedef WImageViewC WImageView3_f; -typedef WImageBufferC WImageBuffer3_f; - -// There isn't a standard for signed and unsigned short so be more -// explicit in the typename for these cases. -typedef WImage WImage_16s; -typedef WImageView WImageView_16s; -typedef WImageBuffer WImageBuffer_16s; - -typedef WImageC WImage1_16s; -typedef WImageViewC WImageView1_16s; -typedef WImageBufferC WImageBuffer1_16s; - -typedef WImageC WImage3_16s; -typedef WImageViewC WImageView3_16s; -typedef WImageBufferC WImageBuffer3_16s; - -typedef WImage WImage_16u; -typedef WImageView WImageView_16u; -typedef WImageBuffer WImageBuffer_16u; - -typedef WImageC WImage1_16u; -typedef WImageViewC WImageView1_16u; -typedef WImageBufferC WImageBuffer1_16u; - -typedef WImageC WImage3_16u; -typedef WImageViewC WImageView3_16u; -typedef WImageBufferC WImageBuffer3_16u; - -/** @brief Image class which provides a thin layer around an IplImage. - -The goals of the class design are: - - -# All the data has explicit ownership to avoid memory leaks - -# No hidden allocations or copies for performance. - -# Easy access to OpenCV methods (which will access IPP if available) - -# Can easily treat external data as an image - -# Easy to create images which are subsets of other images - -# Fast pixel access which can take advantage of number of channels if known at compile time. - -The WImage class is the image class which provides the data accessors. The 'W' comes from the fact -that it is also a wrapper around the popular but inconvenient IplImage class. A WImage can be -constructed either using a WImageBuffer class which allocates and frees the data, or using a -WImageView class which constructs a subimage or a view into external data. The view class does no -memory management. Each class actually has two versions, one when the number of channels is known -at compile time and one when it isn't. Using the one with the number of channels specified can -provide some compile time optimizations by using the fact that the number of channels is a -constant. - -We use the convention (c,r) to refer to column c and row r with (0,0) being the upper left corner. -This is similar to standard Euclidean coordinates with the first coordinate varying in the -horizontal direction and the second coordinate varying in the vertical direction. Thus (c,r) is -usually in the domain [0, width) X [0, height) - -Example usage: -@code -WImageBuffer3_b im(5,7); // Make a 5X7 3 channel image of type uchar -WImageView3_b sub_im(im, 2,2, 3,3); // 3X3 submatrix -vector vec(10, 3.0f); -WImageView1_f user_im(&vec[0], 2, 5); // 2X5 image w/ supplied data - -im.SetZero(); // same as cvSetZero(im.Ipl()) -*im(2, 3) = 15; // Modify the element at column 2, row 3 -MySetRand(&sub_im); - -// Copy the second row into the first. This can be done with no memory -// allocation and will use SSE if IPP is available. -int w = im.Width(); -im.View(0,0, w,1).CopyFrom(im.View(0,1, w,1)); - -// Doesn't care about source of data since using WImage -void MySetRand(WImage_b* im) { // Works with any number of channels -for (int r = 0; r < im->Height(); ++r) { - float* row = im->Row(r); - for (int c = 0; c < im->Width(); ++c) { - for (int ch = 0; ch < im->Channels(); ++ch, ++row) { - *row = uchar(rand() & 255); - } - } -} -} -@endcode - -Functions that are not part of the basic image allocation, viewing, and access should come from -OpenCV, except some useful functions that are not part of OpenCV can be found in wimage_util.h -*/ -template -class WImage -{ -public: - typedef T BaseType; - - // WImage is an abstract class with no other virtual methods so make the - // destructor virtual. - virtual ~WImage() = 0; - - // Accessors - IplImage* Ipl() {return image_; } - const IplImage* Ipl() const {return image_; } - T* ImageData() { return reinterpret_cast(image_->imageData); } - const T* ImageData() const { - return reinterpret_cast(image_->imageData); - } - - int Width() const {return image_->width; } - int Height() const {return image_->height; } - - // WidthStep is the number of bytes to go to the pixel with the next y coord - int WidthStep() const {return image_->widthStep; } - - int Channels() const {return image_->nChannels; } - int ChannelSize() const {return sizeof(T); } // number of bytes per channel - - // Number of bytes per pixel - int PixelSize() const {return Channels() * ChannelSize(); } - - // Return depth type (e.g. IPL_DEPTH_8U, IPL_DEPTH_32F) which is the number - // of bits per channel and with the signed bit set. - // This is known at compile time using specializations. - int Depth() const; - - inline const T* Row(int r) const { - return reinterpret_cast(image_->imageData + r*image_->widthStep); - } - - inline T* Row(int r) { - return reinterpret_cast(image_->imageData + r*image_->widthStep); - } - - // Pixel accessors which returns a pointer to the start of the channel - inline T* operator() (int c, int r) { - return reinterpret_cast(image_->imageData + r*image_->widthStep) + - c*Channels(); - } - - inline const T* operator() (int c, int r) const { - return reinterpret_cast(image_->imageData + r*image_->widthStep) + - c*Channels(); - } - - // Copy the contents from another image which is just a convenience to cvCopy - void CopyFrom(const WImage& src) { cvCopy(src.Ipl(), image_); } - - // Set contents to zero which is just a convenient to cvSetZero - void SetZero() { cvSetZero(image_); } - - // Construct a view into a region of this image - WImageView View(int c, int r, int width, int height); - -protected: - // Disallow copy and assignment - WImage(const WImage&); - void operator=(const WImage&); - - explicit WImage(IplImage* img) : image_(img) { - assert(!img || img->depth == Depth()); - } - - void SetIpl(IplImage* image) { - assert(!image || image->depth == Depth()); - image_ = image; - } - - IplImage* image_; -}; - - -/** Image class when both the pixel type and number of channels -are known at compile time. This wrapper will speed up some of the operations -like accessing individual pixels using the () operator. -*/ -template -class WImageC : public WImage -{ -public: - typedef typename WImage::BaseType BaseType; - enum { kChannels = C }; - - explicit WImageC(IplImage* img) : WImage(img) { - assert(!img || img->nChannels == Channels()); - } - - // Construct a view into a region of this image - WImageViewC View(int c, int r, int width, int height); - - // Copy the contents from another image which is just a convenience to cvCopy - void CopyFrom(const WImageC& src) { - cvCopy(src.Ipl(), WImage::image_); - } - - // WImageC is an abstract class with no other virtual methods so make the - // destructor virtual. - virtual ~WImageC() = 0; - - int Channels() const {return C; } - -protected: - // Disallow copy and assignment - WImageC(const WImageC&); - void operator=(const WImageC&); - - void SetIpl(IplImage* image) { - assert(!image || image->depth == WImage::Depth()); - WImage::SetIpl(image); - } -}; - -/** Image class which owns the data, so it can be allocated and is always -freed. It cannot be copied but can be explicity cloned. -*/ -template -class WImageBuffer : public WImage -{ -public: - typedef typename WImage::BaseType BaseType; - - // Default constructor which creates an object that can be - WImageBuffer() : WImage(0) {} - - WImageBuffer(int width, int height, int nchannels) : WImage(0) { - Allocate(width, height, nchannels); - } - - // Constructor which takes ownership of a given IplImage so releases - // the image on destruction. - explicit WImageBuffer(IplImage* img) : WImage(img) {} - - // Allocate an image. Does nothing if current size is the same as - // the new size. - void Allocate(int width, int height, int nchannels); - - // Set the data to point to an image, releasing the old data - void SetIpl(IplImage* img) { - ReleaseImage(); - WImage::SetIpl(img); - } - - // Clone an image which reallocates the image if of a different dimension. - void CloneFrom(const WImage& src) { - Allocate(src.Width(), src.Height(), src.Channels()); - CopyFrom(src); - } - - ~WImageBuffer() { - ReleaseImage(); - } - - // Release the image if it isn't null. - void ReleaseImage() { - if (WImage::image_) { - IplImage* image = WImage::image_; - cvReleaseImage(&image); - WImage::SetIpl(0); - } - } - - bool IsNull() const {return WImage::image_ == NULL; } - -private: - // Disallow copy and assignment - WImageBuffer(const WImageBuffer&); - void operator=(const WImageBuffer&); -}; - -/** Like a WImageBuffer class but when the number of channels is known at compile time. -*/ -template -class WImageBufferC : public WImageC -{ -public: - typedef typename WImage::BaseType BaseType; - enum { kChannels = C }; - - // Default constructor which creates an object that can be - WImageBufferC() : WImageC(0) {} - - WImageBufferC(int width, int height) : WImageC(0) { - Allocate(width, height); - } - - // Constructor which takes ownership of a given IplImage so releases - // the image on destruction. - explicit WImageBufferC(IplImage* img) : WImageC(img) {} - - // Allocate an image. Does nothing if current size is the same as - // the new size. - void Allocate(int width, int height); - - // Set the data to point to an image, releasing the old data - void SetIpl(IplImage* img) { - ReleaseImage(); - WImageC::SetIpl(img); - } - - // Clone an image which reallocates the image if of a different dimension. - void CloneFrom(const WImageC& src) { - Allocate(src.Width(), src.Height()); - CopyFrom(src); - } - - ~WImageBufferC() { - ReleaseImage(); - } - - // Release the image if it isn't null. - void ReleaseImage() { - if (WImage::image_) { - IplImage* image = WImage::image_; - cvReleaseImage(&image); - WImageC::SetIpl(0); - } - } - - bool IsNull() const {return WImage::image_ == NULL; } - -private: - // Disallow copy and assignment - WImageBufferC(const WImageBufferC&); - void operator=(const WImageBufferC&); -}; - -/** View into an image class which allows treating a subimage as an image or treating external data -as an image -*/ -template class WImageView : public WImage -{ -public: - typedef typename WImage::BaseType BaseType; - - // Construct a subimage. No checks are done that the subimage lies - // completely inside the original image. - WImageView(WImage* img, int c, int r, int width, int height); - - // Refer to external data. - // If not given width_step assumed to be same as width. - WImageView(T* data, int width, int height, int channels, int width_step = -1); - - // Refer to external data. This does NOT take ownership - // of the supplied IplImage. - WImageView(IplImage* img) : WImage(img) {} - - // Copy constructor - WImageView(const WImage& img) : WImage(0) { - header_ = *(img.Ipl()); - WImage::SetIpl(&header_); - } - - WImageView& operator=(const WImage& img) { - header_ = *(img.Ipl()); - WImage::SetIpl(&header_); - return *this; - } - -protected: - IplImage header_; -}; - - -template -class WImageViewC : public WImageC -{ -public: - typedef typename WImage::BaseType BaseType; - enum { kChannels = C }; - - // Default constructor needed for vectors of views. - WImageViewC(); - - virtual ~WImageViewC() {} - - // Construct a subimage. No checks are done that the subimage lies - // completely inside the original image. - WImageViewC(WImageC* img, - int c, int r, int width, int height); - - // Refer to external data - WImageViewC(T* data, int width, int height, int width_step = -1); - - // Refer to external data. This does NOT take ownership - // of the supplied IplImage. - WImageViewC(IplImage* img) : WImageC(img) {} - - // Copy constructor which does a shallow copy to allow multiple views - // of same data. gcc-4.1.1 gets confused if both versions of - // the constructor and assignment operator are not provided. - WImageViewC(const WImageC& img) : WImageC(0) { - header_ = *(img.Ipl()); - WImageC::SetIpl(&header_); - } - WImageViewC(const WImageViewC& img) : WImageC(0) { - header_ = *(img.Ipl()); - WImageC::SetIpl(&header_); - } - - WImageViewC& operator=(const WImageC& img) { - header_ = *(img.Ipl()); - WImageC::SetIpl(&header_); - return *this; - } - WImageViewC& operator=(const WImageViewC& img) { - header_ = *(img.Ipl()); - WImageC::SetIpl(&header_); - return *this; - } - -protected: - IplImage header_; -}; - - -// Specializations for depth -template<> -inline int WImage::Depth() const {return IPL_DEPTH_8U; } -template<> -inline int WImage::Depth() const {return IPL_DEPTH_8S; } -template<> -inline int WImage::Depth() const {return IPL_DEPTH_16S; } -template<> -inline int WImage::Depth() const {return IPL_DEPTH_16U; } -template<> -inline int WImage::Depth() const {return IPL_DEPTH_32S; } -template<> -inline int WImage::Depth() const {return IPL_DEPTH_32F; } -template<> -inline int WImage::Depth() const {return IPL_DEPTH_64F; } - -template inline WImage::~WImage() {} -template inline WImageC::~WImageC() {} - -template -inline void WImageBuffer::Allocate(int width, int height, int nchannels) -{ - if (IsNull() || WImage::Width() != width || - WImage::Height() != height || WImage::Channels() != nchannels) { - ReleaseImage(); - WImage::image_ = cvCreateImage(cvSize(width, height), - WImage::Depth(), nchannels); - } -} - -template -inline void WImageBufferC::Allocate(int width, int height) -{ - if (IsNull() || WImage::Width() != width || WImage::Height() != height) { - ReleaseImage(); - WImageC::SetIpl(cvCreateImage(cvSize(width, height),WImage::Depth(), C)); - } -} - -template -WImageView::WImageView(WImage* img, int c, int r, int width, int height) - : WImage(0) -{ - header_ = *(img->Ipl()); - header_.imageData = reinterpret_cast((*img)(c, r)); - header_.width = width; - header_.height = height; - WImage::SetIpl(&header_); -} - -template -WImageView::WImageView(T* data, int width, int height, int nchannels, int width_step) - : WImage(0) -{ - cvInitImageHeader(&header_, cvSize(width, height), WImage::Depth(), nchannels); - header_.imageData = reinterpret_cast(data); - if (width_step > 0) { - header_.widthStep = width_step; - } - WImage::SetIpl(&header_); -} - -template -WImageViewC::WImageViewC(WImageC* img, int c, int r, int width, int height) - : WImageC(0) -{ - header_ = *(img->Ipl()); - header_.imageData = reinterpret_cast((*img)(c, r)); - header_.width = width; - header_.height = height; - WImageC::SetIpl(&header_); -} - -template -WImageViewC::WImageViewC() : WImageC(0) { - cvInitImageHeader(&header_, cvSize(0, 0), WImage::Depth(), C); - header_.imageData = reinterpret_cast(0); - WImageC::SetIpl(&header_); -} - -template -WImageViewC::WImageViewC(T* data, int width, int height, int width_step) - : WImageC(0) -{ - cvInitImageHeader(&header_, cvSize(width, height), WImage::Depth(), C); - header_.imageData = reinterpret_cast(data); - if (width_step > 0) { - header_.widthStep = width_step; - } - WImageC::SetIpl(&header_); -} - -// Construct a view into a region of an image -template -WImageView WImage::View(int c, int r, int width, int height) { - return WImageView(this, c, r, width, height); -} - -template -WImageViewC WImageC::View(int c, int r, int width, int height) { - return WImageViewC(this, c, r, width, height); -} - -//! @} core - -} // end of namespace - -#endif // __cplusplus - -#endif diff --git a/IPL/include/opencv/opencv2/cvconfig.h b/IPL/include/opencv/opencv2/cvconfig.h deleted file mode 100644 index 1a6b85e..0000000 --- a/IPL/include/opencv/opencv2/cvconfig.h +++ /dev/null @@ -1,199 +0,0 @@ -/* OpenCV compiled as static or dynamic libs */ -#define BUILD_SHARED_LIBS - -/* Compile for 'real' NVIDIA GPU architectures */ -#define CUDA_ARCH_BIN "" - -/* Create PTX or BIN for 1.0 compute capability */ -/* #undef CUDA_ARCH_BIN_OR_PTX_10 */ - -/* NVIDIA GPU features are used */ -#define CUDA_ARCH_FEATURES "" - -/* Compile for 'virtual' NVIDIA PTX architectures */ -#define CUDA_ARCH_PTX "" - -/* AVFoundation video libraries */ -/* #undef HAVE_AVFOUNDATION */ - -/* V4L capturing support */ -/* #undef HAVE_CAMV4L */ - -/* V4L2 capturing support */ -/* #undef HAVE_CAMV4L2 */ - -/* Carbon windowing environment */ -/* #undef HAVE_CARBON */ - -/* AMD's Basic Linear Algebra Subprograms Library*/ -/* #undef HAVE_CLAMDBLAS */ - -/* AMD's OpenCL Fast Fourier Transform Library*/ -/* #undef HAVE_CLAMDFFT */ - -/* Clp support */ -/* #undef HAVE_CLP */ - -/* Cocoa API */ -/* #undef HAVE_COCOA */ - -/* C= */ -/* #undef HAVE_CSTRIPES */ - -/* NVidia Cuda Basic Linear Algebra Subprograms (BLAS) API*/ -/* #undef HAVE_CUBLAS */ - -/* NVidia Cuda Runtime API*/ -/* #undef HAVE_CUDA */ - -/* NVidia Cuda Fast Fourier Transform (FFT) API*/ -/* #undef HAVE_CUFFT */ - -/* IEEE1394 capturing support */ -/* #undef HAVE_DC1394 */ - -/* IEEE1394 capturing support - libdc1394 v2.x */ -/* #undef HAVE_DC1394_2 */ - -/* DirectX */ -#define HAVE_DIRECTX -#define HAVE_DIRECTX_NV12 -#define HAVE_D3D11 -#define HAVE_D3D10 -#define HAVE_D3D9 - -/* DirectShow Video Capture library */ -#define HAVE_DSHOW - -/* Eigen Matrix & Linear Algebra Library */ -/* #undef HAVE_EIGEN */ - -/* FFMpeg video library */ -#define HAVE_FFMPEG - -/* ffmpeg's libswscale */ -#define HAVE_FFMPEG_SWSCALE - -/* ffmpeg in Gentoo */ -#define HAVE_GENTOO_FFMPEG - -/* Geospatial Data Abstraction Library */ -/* #undef HAVE_GDAL */ - -/* GStreamer multimedia framework */ -/* #undef HAVE_GSTREAMER */ - -/* GTK+ 2.0 Thread support */ -/* #undef HAVE_GTHREAD */ - -/* GTK+ 2.x toolkit */ -/* #undef HAVE_GTK */ - -/* Define to 1 if you have the header file. */ -/* #undef HAVE_INTTYPES_H */ - -/* Intel Perceptual Computing SDK library */ -/* #undef HAVE_INTELPERC */ - -/* Intel Integrated Performance Primitives */ -#define HAVE_IPP -#define HAVE_IPP_ICV_ONLY - -/* Intel IPP Async */ -/* #undef HAVE_IPP_A */ - -/* JPEG-2000 codec */ -#define HAVE_JASPER - -/* IJG JPEG codec */ -#define HAVE_JPEG - -/* libpng/png.h needs to be included */ -/* #undef HAVE_LIBPNG_PNG_H */ - -/* V4L/V4L2 capturing support via libv4l */ -/* #undef HAVE_LIBV4L */ - -/* Microsoft Media Foundation Capture library */ -/* #undef HAVE_MSMF */ - -/* NVidia Video Decoding API*/ -/* #undef HAVE_NVCUVID */ - -/* NVidia Video Encoding API*/ -/* #undef HAVE_NVCUVENC */ - -/* OpenCL Support */ -#define HAVE_OPENCL -/* #undef HAVE_OPENCL_STATIC */ -/* #undef HAVE_OPENCL_SVM */ - -/* OpenEXR codec */ -#define HAVE_OPENEXR - -/* OpenGL support*/ -/* #undef HAVE_OPENGL */ - -/* OpenNI library */ -/* #undef HAVE_OPENNI */ - -/* OpenNI library */ -/* #undef HAVE_OPENNI2 */ - -/* PNG codec */ -#define HAVE_PNG - -/* Posix threads (pthreads) */ -/* #undef HAVE_PTHREADS */ - -/* parallel_for with pthreads */ -/* #undef HAVE_PTHREADS_PF */ - -/* Qt support */ -/* #undef HAVE_QT */ - -/* Qt OpenGL support */ -/* #undef HAVE_QT_OPENGL */ - -/* QuickTime video libraries */ -/* #undef HAVE_QUICKTIME */ - -/* QTKit video libraries */ -/* #undef HAVE_QTKIT */ - -/* Intel Threading Building Blocks */ -/* #undef HAVE_TBB */ - -/* TIFF codec */ -#define HAVE_TIFF - -/* Unicap video capture library */ -/* #undef HAVE_UNICAP */ - -/* Video for Windows support */ -#define HAVE_VFW - -/* V4L2 capturing support in videoio.h */ -/* #undef HAVE_VIDEOIO */ - -/* Win32 UI */ -#define HAVE_WIN32UI - -/* XIMEA camera support */ -/* #undef HAVE_XIMEA */ - -/* Xine video library */ -/* #undef HAVE_XINE */ - -/* Define if your processor stores words with the most significant byte - first (like Motorola and SPARC, unlike Intel and VAX). */ -/* #undef WORDS_BIGENDIAN */ - -/* gPhoto2 library */ -/* #undef HAVE_GPHOTO2 */ - -/* VA library (libva) */ -/* #undef HAVE_VA */ - -/* Intel VA-API/OpenCL */ -/* #undef HAVE_VA_INTEL */ diff --git a/IPL/include/opencv/opencv2/datasets/ar_hmdb.hpp b/IPL/include/opencv/opencv2/datasets/ar_hmdb.hpp deleted file mode 100644 index 8941583..0000000 --- a/IPL/include/opencv/opencv2/datasets/ar_hmdb.hpp +++ /dev/null @@ -1,80 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_AR_HMDB_HPP -#define OPENCV_DATASETS_AR_HMDB_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_ar -//! @{ - -struct AR_hmdbObj : public Object -{ - int id; - std::string name; - std::string videoName; -}; - -class CV_EXPORTS AR_hmdb : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/ar_sports.hpp b/IPL/include/opencv/opencv2/datasets/ar_sports.hpp deleted file mode 100644 index 7f51405..0000000 --- a/IPL/include/opencv/opencv2/datasets/ar_sports.hpp +++ /dev/null @@ -1,79 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_AR_SPORTS_HPP -#define OPENCV_DATASETS_AR_SPORTS_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_ar -//! @{ - -struct AR_sportsObj : public Object -{ - std::string videoUrl; - std::vector labels; -}; - -class CV_EXPORTS AR_sports : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/dataset.hpp b/IPL/include/opencv/opencv2/datasets/dataset.hpp deleted file mode 100644 index ccf2b66..0000000 --- a/IPL/include/opencv/opencv2/datasets/dataset.hpp +++ /dev/null @@ -1,545 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_DATASET_HPP -#define OPENCV_DATASETS_DATASET_HPP - -#include -#include - -#include - -/** @defgroup datasets Framework for working with different datasets - -The datasets module includes classes for working with different datasets: load data, evaluate -different algorithms on them, contains benchmarks, etc. - -It is planned to have: - -- basic: loading code for all datasets to help start work with them. -- next stage: quick benchmarks for all datasets to show how to solve them using OpenCV and -implement evaluation code. -- finally: implement on OpenCV state-of-the-art algorithms, which solve these tasks. - -@{ -@defgroup datasets_ar Action Recognition - -### HMDB: A Large Human Motion Database - -Implements loading dataset: - -"HMDB: A Large Human Motion Database": - -Usage: --# From link above download dataset files: `hmdb51_org.rar` & `test_train_splits.rar`. --# Unpack them. Unpack all archives from directory: `hmdb51_org/` and remove them. --# To load data run: -~~~ -./opencv/build/bin/example_datasets_ar_hmdb -p=/home/user/path_to_unpacked_folders/ -~~~ - -#### Benchmark - -For this dataset was implemented benchmark with accuracy: 0.107407 (using precomputed HOG/HOF -"STIP" features from site, averaging for 3 splits) - -To run this benchmark execute: -~~~ -./opencv/build/bin/example_datasets_ar_hmdb_benchmark -p=/home/user/path_to_unpacked_folders/ -~~~ - -@note -Precomputed features should be unpacked in the same folder: `/home/user/path_to_unpacked_folders/hmdb51_org_stips/`. -Also unpack all archives from directory: `hmdb51_org_stips/` and remove them. - -### Sports-1M %Dataset - -Implements loading dataset: - -"Sports-1M Dataset": - -Usage: --# From link above download dataset files (`git clone https://code.google.com/p/sports-1m-dataset/`). --# To load data run: -~~~ -./opencv/build/bin/example_datasets_ar_sports -p=/home/user/path_to_downloaded_folders/ -~~~ - -@defgroup datasets_fr Face Recognition - -### Adience - -Implements loading dataset: - -"Adience": - -Usage: --# From link above download any dataset file: `faces.tar.gz\aligned.tar.gz` and files with splits: -`fold_0_data.txt-fold_4_data.txt`, `fold_frontal_0_data.txt-fold_frontal_4_data.txt`. (For -face recognition task another splits should be created) --# Unpack dataset file to some folder and place split files into the same folder. --# To load data run: -~~~ -./opencv/build/bin/example_datasets_fr_adience -p=/home/user/path_to_created_folder/ -~~~ - -### Labeled Faces in the Wild - -Implements loading dataset: - -"Labeled Faces in the Wild": - -Usage: --# From link above download any dataset file: -`lfw.tgz\lfwa.tar.gz\lfw-deepfunneled.tgz\lfw-funneled.tgz` and files with pairs: 10 test -splits: `pairs.txt` and developer train split: `pairsDevTrain.txt`. --# Unpack dataset file and place `pairs.txt` and `pairsDevTrain.txt` in created folder. --# To load data run: -~~~ -./opencv/build/bin/example_datasets_fr_lfw -p=/home/user/path_to_unpacked_folder/lfw2/ -~~~ - -#### Benchmark - -For this dataset was implemented benchmark with accuracy: 0.623833 +- 0.005223 (train split: -`pairsDevTrain.txt`, dataset: lfwa) - -To run this benchmark execute: -~~~ -./opencv/build/bin/example_datasets_fr_lfw_benchmark -p=/home/user/path_to_unpacked_folder/lfw2/ -~~~ - -@defgroup datasets_gr Gesture Recognition - -### ChaLearn Looking at People - -Implements loading dataset: - -"ChaLearn Looking at People": - -Usage --# Follow instruction from site above, download files for dataset "Track 3: Gesture Recognition": -`Train1.zip`-`Train5.zip`, `Validation1.zip`-`Validation3.zip` (Register on site: www.codalab.org and -accept the terms and conditions of competition: - There are three mirrors for -downloading dataset files. When I downloaded data only mirror: "Universitat Oberta de Catalunya" -works). --# Unpack train archives `Train1.zip`-`Train5.zip` to folder `Train/`, validation archives -`Validation1.zip`-`Validation3.zip` to folder `Validation/` --# Unpack all archives in `Train/` & `Validation/` in the folders with the same names, for example: -`Sample0001.zip` to `Sample0001/` --# To load data run: -~~~ -./opencv/build/bin/example_datasets_gr_chalearn -p=/home/user/path_to_unpacked_folders/ -~~~ - -### Sheffield Kinect Gesture Dataset - -Implements loading dataset: - -"Sheffield Kinect Gesture Dataset": - -Usage: --# From link above download dataset files: `subject1_dep.7z`-`subject6_dep.7z`, `subject1_rgb.7z`-`subject6_rgb.7z`. --# Unpack them. --# To load data run: -~~~ -./opencv/build/bin/example_datasets_gr_skig -p=/home/user/path_to_unpacked_folders/ -~~~ - -@defgroup datasets_hpe Human Pose Estimation - -### HumanEva Dataset - -Implements loading dataset: - -"HumanEva Dataset": - -Usage: --# From link above download dataset files for `HumanEva-I` (tar) & `HumanEva-II`. --# Unpack them to `HumanEva_1` & `HumanEva_2` accordingly. --# To load data run: -~~~ -./opencv/build/bin/example_datasets_hpe_humaneva -p=/home/user/path_to_unpacked_folders/ -~~~ - -### PARSE Dataset - -Implements loading dataset: - -"PARSE Dataset": - -Usage: --# From link above download dataset file: `people.zip`. --# Unpack it. --# To load data run: -~~~ -./opencv/build/bin/example_datasets_hpe_parse -p=/home/user/path_to_unpacked_folder/people_all/ -~~~ - -@defgroup datasets_ir Image Registration - -### Affine Covariant Regions Datasets - -Implements loading dataset: - -"Affine Covariant Regions Datasets": - -Usage: --# From link above download dataset files: -`bark\bikes\boat\graf\leuven\trees\ubc\wall.tar.gz`. --# Unpack them. --# To load data, for example, for "bark", run: -``` -./opencv/build/bin/example_datasets_ir_affine -p=/home/user/path_to_unpacked_folder/bark/ -``` - -### Robot Data Set - -Implements loading dataset: - -"Robot Data Set, Point Feature Data Set – 2010": - -Usage: --# From link above download dataset files: `SET001_6.tar.gz`-`SET055_60.tar.gz` --# Unpack them to one folder. --# To load data run: -~~~ -./opencv/build/bin/example_datasets_ir_robot -p=/home/user/path_to_unpacked_folder/ -~~~ - -@defgroup datasets_is Image Segmentation - -### The Berkeley Segmentation Dataset and Benchmark - -Implements loading dataset: - -"The Berkeley Segmentation Dataset and Benchmark": - -Usage: --# From link above download dataset files: `BSDS300-human.tgz` & `BSDS300-images.tgz`. --# Unpack them. --# To load data run: -~~~ -./opencv/build/bin/example_datasets_is_bsds -p=/home/user/path_to_unpacked_folder/BSDS300/ -~~~ - -### Weizmann Segmentation Evaluation Database - -Implements loading dataset: - -"Weizmann Segmentation Evaluation Database": - -Usage: --# From link above download dataset files: `Weizmann_Seg_DB_1obj.ZIP` & `Weizmann_Seg_DB_2obj.ZIP`. --# Unpack them. --# To load data, for example, for `1 object` dataset, run: -~~~ -./opencv/build/bin/example_datasets_is_weizmann -p=/home/user/path_to_unpacked_folder/1obj/ -~~~ - -@defgroup datasets_msm Multiview Stereo Matching - -### EPFL Multi-View Stereo - -Implements loading dataset: - -"EPFL Multi-View Stereo": - -Usage: --# From link above download dataset files: -`castle_dense\castle_dense_large\castle_entry\fountain\herzjesu_dense\herzjesu_dense_large_bounding\cameras\images\p.tar.gz`. --# Unpack them in separate folder for each object. For example, for "fountain", in folder `fountain/` : -`fountain_dense_bounding.tar.gz -> bounding/`, -`fountain_dense_cameras.tar.gz -> camera/`, -`fountain_dense_images.tar.gz -> png/`, -`fountain_dense_p.tar.gz -> P/` --# To load data, for example, for "fountain", run: -~~~ -./opencv/build/bin/example_datasets_msm_epfl -p=/home/user/path_to_unpacked_folder/fountain/ -~~~ - -### Stereo – Middlebury Computer Vision - -Implements loading dataset: - -"Stereo – Middlebury Computer Vision": - -Usage: --# From link above download dataset files: -`dino\dinoRing\dinoSparseRing\temple\templeRing\templeSparseRing.zip` --# Unpack them. --# To load data, for example "temple" dataset, run: -~~~ -./opencv/build/bin/example_datasets_msm_middlebury -p=/home/user/path_to_unpacked_folder/temple/ -~~~ - -@defgroup datasets_or Object Recognition - -### ImageNet - -Implements loading dataset: "ImageNet": - -Usage: --# From link above download dataset files: -`ILSVRC2010_images_train.tar\ILSVRC2010_images_test.tar\ILSVRC2010_images_val.tar` & devkit: -`ILSVRC2010_devkit-1.0.tar.gz` (Implemented loading of 2010 dataset as only this dataset has ground -truth for test data, but structure for ILSVRC2014 is similar) --# Unpack them to: `some_folder/train/`, `some_folder/test/`, `some_folder/val` & -`some_folder/ILSVRC2010_validation_ground_truth.txt`, -`some_folder/ILSVRC2010_test_ground_truth.txt`. --# Create file with labels: `some_folder/labels.txt`, for example, using python script below (each -file's row format: `synset,labelID,description`. For example: "n07751451,18,plum"). --# Unpack all tar files in train. --# To load data run: -~~~ -./opencv/build/bin/example_datasets_or_imagenet -p=/home/user/some_folder/ -~~~ - -Python script to parse `meta.mat`: -~~~{py} - import scipy.io - meta_mat = scipy.io.loadmat("devkit-1.0/data/meta.mat") - - labels_dic = dict((m[0][1][0], m[0][0][0][0]-1) for m in meta_mat['synsets'] - label_names_dic = dict((m[0][1][0], m[0][2][0]) for m in meta_mat['synsets'] - - for label in labels_dic.keys(): - print "{0},{1},{2}".format(label, labels_dic[label], label_names_dic[label]) -~~~ - -### MNIST - -Implements loading dataset: - -"MNIST": - -Usage: --# From link above download dataset files: -`t10k-images-idx3-ubyte.gz`, `t10k-labels-idx1-ubyte.gz`, `train-images-idx3-ubyte.gz`, `train-labels-idx1-ubyte.gz`. --# Unpack them. --# To load data run: -~~~ -./opencv/build/bin/example_datasets_or_mnist -p=/home/user/path_to_unpacked_files/ -~~~ - -### SUN Database - -Implements loading dataset: - -"SUN Database, Scene Recognition Benchmark. SUN397": - -Usage: --# From link above download dataset file: `SUN397.tar` & file with splits: `Partitions.zip` --# Unpack `SUN397.tar` into folder: `SUN397/` & `Partitions.zip` into folder: `SUN397/Partitions/` --# To load data run: -~~~ -./opencv/build/bin/example_datasets_or_sun -p=/home/user/path_to_unpacked_files/SUN397/ -~~~ - -@defgroup datasets_pd Pedestrian Detection - -### Caltech Pedestrian Detection Benchmark - -Implements loading dataset: - -"Caltech Pedestrian Detection Benchmark": - -@note First version of Caltech Pedestrian dataset loading. Code to unpack all frames from seq files -commented as their number is huge! So currently load only meta information without data. Also -ground truth isn't processed, as need to convert it from mat files first. - -Usage: --# From link above download dataset files: `set00.tar`-`set10.tar`. --# Unpack them to separate folder. --# To load data run: -~~~ -./opencv/build/bin/example_datasets_pd_caltech -p=/home/user/path_to_unpacked_folders/ -~~~ - -@defgroup datasets_slam SLAM - -### KITTI Vision Benchmark - -Implements loading dataset: - -"KITTI Vision Benchmark": - -Usage: --# From link above download "Odometry" dataset files: -`data_odometry_gray\data_odometry_color\data_odometry_velodyne\data_odometry_poses\data_odometry_calib.zip`. --# Unpack `data_odometry_poses.zip`, it creates folder `dataset/poses/`. After that unpack -`data_odometry_gray.zip`, `data_odometry_color.zip`, `data_odometry_velodyne.zip`. Folder -`dataset/sequences/` will be created with folders `00/..21/`. Each of these folders will contain: -`image_0/`, `image_1/`, `image_2/`, `image_3/`, `velodyne/` and files `calib.txt` & `times.txt`. -These two last files will be replaced after unpacking `data_odometry_calib.zip` at the end. --# To load data run: -~~~ -./opencv/build/bin/example_datasets_slam_kitti -p=/home/user/path_to_unpacked_folder/dataset/ -~~~ - -### TUMindoor Dataset - -Implements loading dataset: - -"TUMindoor Dataset": - -Usage: --# From link above download dataset files: `dslr\info\ladybug\pointcloud.tar.bz2` for each dataset: -`11-11-28 (1st floor)\11-12-13 (1st floor N1)\11-12-17a (4th floor)\11-12-17b (3rd floor)\11-12-17c (Ground I)\11-12-18a (Ground II)\11-12-18b (2nd floor)` --# Unpack them in separate folder for each dataset. -`dslr.tar.bz2 -> dslr/`, -`info.tar.bz2 -> info/`, -`ladybug.tar.bz2 -> ladybug/`, -`pointcloud.tar.bz2 -> pointcloud/`. --# To load each dataset run: -~~~ -./opencv/build/bin/example_datasets_slam_tumindoor -p=/home/user/path_to_unpacked_folders/ -~~~ - -@defgroup datasets_tr Text Recognition - -### The Chars74K Dataset - -Implements loading dataset: - -"The Chars74K Dataset": - -Usage: --# From link above download dataset files: -`EnglishFnt\EnglishHnd\EnglishImg\KannadaHnd\KannadaImg.tgz`, `ListsTXT.tgz`. --# Unpack them. --# Move `.m` files from folder `ListsTXT/` to appropriate folder. For example, -`English/list_English_Img.m` for `EnglishImg.tgz`. --# To load data, for example "EnglishImg", run: -~~~ -./opencv/build/bin/example_datasets_tr_chars -p=/home/user/path_to_unpacked_folder/English/ -~~~ - -### The Street View Text Dataset - -Implements loading dataset: - -"The Street View Text Dataset": - -Usage: --# From link above download dataset file: `svt.zip`. --# Unpack it. --# To load data run: -~~~ -./opencv/build/bin/example_datasets_tr_svt -p=/home/user/path_to_unpacked_folder/svt/svt1/ -~~~ - -#### Benchmark - -For this dataset was implemented benchmark with accuracy (mean f1): 0.217 - -To run benchmark execute: -~~~ -./opencv/build/bin/example_datasets_tr_svt_benchmark -p=/home/user/path_to_unpacked_folders/svt/svt1/ -~~~ - -@defgroup datasets_track Tracking - -### VOT 2015 Database - -Implements loading dataset: - -"VOT 2015 dataset comprises 60 short sequences showing various objects in challenging backgrounds. -The sequences were chosen from a large pool of sequences including the ALOV dataset, OTB2 dataset, -non-tracking datasets, Computer Vision Online, Professor Bob Fisher’s Image Database, Videezy, -Center for Research in Computer Vision, University of Central Florida, USA, NYU Center for Genomics -and Systems Biology, Data Wrangling, Open Access Directory and Learning and Recognition in Vision -Group, INRIA, France. The VOT sequence selection protocol was applied to obtain a representative -set of challenging sequences.": - -Usage: --# From link above download dataset file: `vot2015.zip` --# Unpack `vot2015.zip` into folder: `VOT2015/` --# To load data run: -~~~ -./opencv/build/bin/example_datasets_track_vot -p=/home/user/path_to_unpacked_files/VOT2015/ -~~~ -@} - -*/ - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets -//! @{ - -struct Object -{ -}; - -class CV_EXPORTS Dataset -{ -public: - Dataset() {} - virtual ~Dataset() {} - - virtual void load(const std::string &path) = 0; - - std::vector< Ptr >& getTrain(int splitNum = 0); - std::vector< Ptr >& getTest(int splitNum = 0); - std::vector< Ptr >& getValidation(int splitNum = 0); - - int getNumSplits() const; - -protected: - std::vector< std::vector< Ptr > > train; - std::vector< std::vector< Ptr > > test; - std::vector< std::vector< Ptr > > validation; - -private: - std::vector< Ptr > empty; -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/fr_adience.hpp b/IPL/include/opencv/opencv2/datasets/fr_adience.hpp deleted file mode 100644 index c84bce1..0000000 --- a/IPL/include/opencv/opencv2/datasets/fr_adience.hpp +++ /dev/null @@ -1,98 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_FR_ADIENCE_HPP -#define OPENCV_DATASETS_FR_ADIENCE_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_fr -//! @{ - -enum genderType -{ - male = 0, - female, - none -}; - -struct FR_adienceObj : public Object -{ - std::string user_id; - std::string original_image; - int face_id; - std::string age; - genderType gender; - int x; - int y; - int dx; - int dy; - int tilt_ang; - int fiducial_yaw_angle; - int fiducial_score; -}; - -class CV_EXPORTS FR_adience : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); - - std::vector paths; -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/fr_lfw.hpp b/IPL/include/opencv/opencv2/datasets/fr_lfw.hpp deleted file mode 100644 index 7065da7..0000000 --- a/IPL/include/opencv/opencv2/datasets/fr_lfw.hpp +++ /dev/null @@ -1,79 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_FR_LFW_HPP -#define OPENCV_DATASETS_FR_LFW_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_fr -//! @{ - -struct FR_lfwObj : public Object -{ - std::string image1, image2; - bool same; -}; - -class CV_EXPORTS FR_lfw : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/gr_chalearn.hpp b/IPL/include/opencv/opencv2/datasets/gr_chalearn.hpp deleted file mode 100644 index a8eaa6c..0000000 --- a/IPL/include/opencv/opencv2/datasets/gr_chalearn.hpp +++ /dev/null @@ -1,96 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_GR_CHALEARN_HPP -#define OPENCV_DATASETS_GR_CHALEARN_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_gr -//! @{ - -struct groundTruth -{ - int gestureID, initialFrame, lastFrame; -}; - -struct join -{ - double Wx, Wy, Wz, Rx, Ry, Rz, Rw, Px, Py; -}; - -struct skeleton -{ - join s[20]; -}; - -struct GR_chalearnObj : public Object -{ - std::string name, nameColor, nameDepth, nameUser; - int numFrames, fps, depth; - std::vector groundTruths; - std::vector skeletons; -}; - -class CV_EXPORTS GR_chalearn : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/gr_skig.hpp b/IPL/include/opencv/opencv2/datasets/gr_skig.hpp deleted file mode 100644 index 9c86224..0000000 --- a/IPL/include/opencv/opencv2/datasets/gr_skig.hpp +++ /dev/null @@ -1,118 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_GR_SKIG_HPP -#define OPENCV_DATASETS_GR_SKIG_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_gr -//! @{ - -enum actionType -{ - circle = 1, - triangle, - updown, - rightleft, - wave, - z, - cross, - comehere, - turnaround, - pat -}; - -enum poseType -{ - fist = 1, - index, - flat -}; - -enum illuminationType -{ - light = 1, - dark -}; - -enum backgroundType -{ - woodenBoard = 1, - whitePaper, - paperWithCharacters -}; - -struct GR_skigObj : public Object -{ - std::string rgb; - std::string dep; - char person; // 1..6 - backgroundType background; - illuminationType illumination; - poseType pose; - actionType type; -}; - -class CV_EXPORTS GR_skig : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/hpe_humaneva.hpp b/IPL/include/opencv/opencv2/datasets/hpe_humaneva.hpp deleted file mode 100644 index 5366e0d..0000000 --- a/IPL/include/opencv/opencv2/datasets/hpe_humaneva.hpp +++ /dev/null @@ -1,90 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_HPE_HUMANEVA_HPP -#define OPENCV_DATASETS_HPE_HUMANEVA_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_hpe -//! @{ - -struct HPE_humanevaObj : public Object -{ - char person; // 1..4 - std::string action; - int type1; - std::string type2; - Matx13d ofs; - std::string fileName; - std::vector imageNames; // for HumanEva_II -}; - -enum datasetType -{ - humaneva_1 = 1, - humaneva_2 -}; - -class CV_EXPORTS HPE_humaneva : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(int num=humaneva_1); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/hpe_parse.hpp b/IPL/include/opencv/opencv2/datasets/hpe_parse.hpp deleted file mode 100644 index 7629e2c..0000000 --- a/IPL/include/opencv/opencv2/datasets/hpe_parse.hpp +++ /dev/null @@ -1,78 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_HPE_PARSE_HPP -#define OPENCV_DATASETS_HPE_PARSE_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_hpe -//! @{ - -struct HPE_parseObj : public Object -{ - std::string name; -}; - -class CV_EXPORTS HPE_parse : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/ir_affine.hpp b/IPL/include/opencv/opencv2/datasets/ir_affine.hpp deleted file mode 100644 index 3b04a4b..0000000 --- a/IPL/include/opencv/opencv2/datasets/ir_affine.hpp +++ /dev/null @@ -1,80 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_IR_AFFINE_HPP -#define OPENCV_DATASETS_IR_AFFINE_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_ir -//! @{ - -struct IR_affineObj : public Object -{ - std::string imageName; - Matx33d mat; -}; - -class CV_EXPORTS IR_affine : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/ir_robot.hpp b/IPL/include/opencv/opencv2/datasets/ir_robot.hpp deleted file mode 100644 index 0acfe0a..0000000 --- a/IPL/include/opencv/opencv2/datasets/ir_robot.hpp +++ /dev/null @@ -1,89 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_IR_ROBOT_HPP -#define OPENCV_DATASETS_IR_ROBOT_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_ir -//! @{ - -// calibration matrix from calibrationFile.mat -// 2.8290e+03 0.0000e+00 8.0279e+02 -// 0.0000e+00 2.8285e+03 6.1618e+02 -// 0.0000e+00 0.0000e+00 1.0000e+00 - -struct cameraPos -{ - std::vector images; -}; - -struct IR_robotObj : public Object -{ - std::string name; - std::vector pos; -}; - -class CV_EXPORTS IR_robot : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/is_bsds.hpp b/IPL/include/opencv/opencv2/datasets/is_bsds.hpp deleted file mode 100644 index 7357a67..0000000 --- a/IPL/include/opencv/opencv2/datasets/is_bsds.hpp +++ /dev/null @@ -1,78 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_IS_BSDS_HPP -#define OPENCV_DATASETS_IS_BSDS_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_is -//! @{ - -struct IS_bsdsObj : public Object -{ - std::string name; -}; - -class CV_EXPORTS IS_bsds : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/is_weizmann.hpp b/IPL/include/opencv/opencv2/datasets/is_weizmann.hpp deleted file mode 100644 index 5daa420..0000000 --- a/IPL/include/opencv/opencv2/datasets/is_weizmann.hpp +++ /dev/null @@ -1,81 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_IS_WEIZMANN_HPP -#define OPENCV_DATASETS_IS_WEIZMANN_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_is -//! @{ - -struct IS_weizmannObj : public Object -{ - std::string imageName; - std::string srcBw; - std::string srcColor; - std::string humanSeg; // TODO: read human segmented -}; - -class CV_EXPORTS IS_weizmann : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/msm_epfl.hpp b/IPL/include/opencv/opencv2/datasets/msm_epfl.hpp deleted file mode 100644 index a08fc4b..0000000 --- a/IPL/include/opencv/opencv2/datasets/msm_epfl.hpp +++ /dev/null @@ -1,90 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_MSM_EPFL_HPP -#define OPENCV_DATASETS_MSM_EPFL_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_msm -//! @{ - -struct cameraParam -{ - Matx33d mat1; - double mat2[3]; - Matx33d mat3; - double mat4[3]; - int imageWidth, imageHeight; -}; - -struct MSM_epflObj : public Object -{ - std::string imageName; - Matx23d bounding; - Matx34d p; - cameraParam camera; -}; - -class CV_EXPORTS MSM_epfl : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/msm_middlebury.hpp b/IPL/include/opencv/opencv2/datasets/msm_middlebury.hpp deleted file mode 100644 index 2fd67bf..0000000 --- a/IPL/include/opencv/opencv2/datasets/msm_middlebury.hpp +++ /dev/null @@ -1,81 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_MSM_MIDDLEBURY_HPP -#define OPENCV_DATASETS_MSM_MIDDLEBURY_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_msm -//! @{ - -struct MSM_middleburyObj : public Object -{ - std::string imageName; - Matx33d k; - Matx33d r; - double t[3]; -}; - -class CV_EXPORTS MSM_middlebury : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/or_imagenet.hpp b/IPL/include/opencv/opencv2/datasets/or_imagenet.hpp deleted file mode 100644 index 26a8f63..0000000 --- a/IPL/include/opencv/opencv2/datasets/or_imagenet.hpp +++ /dev/null @@ -1,79 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_OR_IMAGENET_HPP -#define OPENCV_DATASETS_OR_IMAGENET_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_or -//! @{ - -struct OR_imagenetObj : public Object -{ - int id; - std::string image; -}; - -class CV_EXPORTS OR_imagenet : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/or_mnist.hpp b/IPL/include/opencv/opencv2/datasets/or_mnist.hpp deleted file mode 100644 index ff6bd60..0000000 --- a/IPL/include/opencv/opencv2/datasets/or_mnist.hpp +++ /dev/null @@ -1,79 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_OR_MNIST_HPP -#define OPENCV_DATASETS_OR_MNIST_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_or -//! @{ - -struct OR_mnistObj : public Object -{ - char label; // 0..9 - Mat image; // [28][28] -}; - -class CV_EXPORTS OR_mnist : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/or_pascal.hpp b/IPL/include/opencv/opencv2/datasets/or_pascal.hpp deleted file mode 100644 index bca8e62..0000000 --- a/IPL/include/opencv/opencv2/datasets/or_pascal.hpp +++ /dev/null @@ -1,102 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2015, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_VOC_PASCAL_HPP -#define OPENCV_DATASETS_VOC_PASCAL_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_or -//! @{ -struct PascalPart: public Object -{ - std::string name; - int xmin; - int ymin; - int xmax; - int ymax; -}; - -struct PascalObj: public PascalPart -{ - std::string pose; - bool truncated; - bool difficult; - bool occluded; - - std::vector parts; -}; - -struct OR_pascalObj : public Object -{ - std::string filename; - - int width; - int height; - int depth; - - std::vector objects; -}; - -class CV_EXPORTS OR_pascal : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -}// namespace dataset -}// namespace cv - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/or_sun.hpp b/IPL/include/opencv/opencv2/datasets/or_sun.hpp deleted file mode 100644 index 059c0d4..0000000 --- a/IPL/include/opencv/opencv2/datasets/or_sun.hpp +++ /dev/null @@ -1,81 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_OR_SUN_HPP -#define OPENCV_DATASETS_OR_SUN_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_or -//! @{ - -struct OR_sunObj : public Object -{ - int label; - std::string name; -}; - -class CV_EXPORTS OR_sun : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); - - std::vector paths; -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/pd_caltech.hpp b/IPL/include/opencv/opencv2/datasets/pd_caltech.hpp deleted file mode 100644 index 9ff7278..0000000 --- a/IPL/include/opencv/opencv2/datasets/pd_caltech.hpp +++ /dev/null @@ -1,89 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_PD_CALTECH_HPP -#define OPENCV_DATASETS_PD_CALTECH_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_pd -//! @{ - -struct PD_caltechObj : public Object -{ - //double groundTrue[][]; - //Mat image; - std::string name; - std::vector< std::string > imageNames; -}; - -// -// first version of Caltech Pedestrian dataset loading -// code to unpack all frames from seq files commented as their number is huge -// so currently load only meta information without data -// -// also ground truth isn't processed, as need to convert it from mat files first -// - -class CV_EXPORTS PD_caltech : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/pd_inria.hpp b/IPL/include/opencv/opencv2/datasets/pd_inria.hpp deleted file mode 100644 index 7586578..0000000 --- a/IPL/include/opencv/opencv2/datasets/pd_inria.hpp +++ /dev/null @@ -1,96 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2015, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_PD_INRIA_HPP -#define OPENCV_DATASETS_PD_INRIA_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_pd -//! @{ - -enum sampleType -{ - POS = 0, - NEG = 1 -}; - -struct PD_inriaObj : public Object -{ - // image file name - std::string filename; - - // positive or negative - sampleType sType; - - // image size - int width; - int height; - int depth; - - // bounding boxes - std::vector< Rect > bndboxes; -}; - -class CV_EXPORTS PD_inria : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/slam_kitti.hpp b/IPL/include/opencv/opencv2/datasets/slam_kitti.hpp deleted file mode 100644 index 1b7c408..0000000 --- a/IPL/include/opencv/opencv2/datasets/slam_kitti.hpp +++ /dev/null @@ -1,87 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_SLAM_KITTI_HPP -#define OPENCV_DATASETS_SLAM_KITTI_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_slam -//! @{ - -struct pose -{ - double elem[12]; -}; - -struct SLAM_kittiObj : public Object -{ - std::string name; - std::vector images[4]; - std::vector velodyne; - std::vector times, p[4]; - std::vector posesArray; -}; - -class CV_EXPORTS SLAM_kitti : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/slam_tumindoor.hpp b/IPL/include/opencv/opencv2/datasets/slam_tumindoor.hpp deleted file mode 100644 index 758dd13..0000000 --- a/IPL/include/opencv/opencv2/datasets/slam_tumindoor.hpp +++ /dev/null @@ -1,87 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_SLAM_TUMINDOOR_HPP -#define OPENCV_DATASETS_SLAM_TUMINDOOR_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_slam -//! @{ - -enum imageType -{ - LEFT = 0, - RIGHT, - LADYBUG -}; - -struct SLAM_tumindoorObj : public Object -{ - std::string name; - Matx44d transformMat; - imageType type; -}; - -class CV_EXPORTS SLAM_tumindoor : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/tr_chars.hpp b/IPL/include/opencv/opencv2/datasets/tr_chars.hpp deleted file mode 100644 index c213bff..0000000 --- a/IPL/include/opencv/opencv2/datasets/tr_chars.hpp +++ /dev/null @@ -1,79 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_TR_CHARS_HPP -#define OPENCV_DATASETS_TR_CHARS_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_tr -//! @{ - -struct TR_charsObj : public Object -{ - std::string imgName; - int label; -}; - -class CV_EXPORTS TR_chars : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/tr_icdar.hpp b/IPL/include/opencv/opencv2/datasets/tr_icdar.hpp deleted file mode 100644 index abfd7db..0000000 --- a/IPL/include/opencv/opencv2/datasets/tr_icdar.hpp +++ /dev/null @@ -1,87 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_TR_ICDAR_HPP -#define OPENCV_DATASETS_TR_ICDAR_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_tr -//! @{ - -struct word -{ - std::string value; - int height, width, x, y; -}; - -struct TR_icdarObj : public Object -{ - std::string fileName; - std::vector lex100; - std::vector lexFull; - std::vector words; -}; - -class CV_EXPORTS TR_icdar : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/tr_svt.hpp b/IPL/include/opencv/opencv2/datasets/tr_svt.hpp deleted file mode 100644 index 6c2d533..0000000 --- a/IPL/include/opencv/opencv2/datasets/tr_svt.hpp +++ /dev/null @@ -1,86 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_TR_SVT_HPP -#define OPENCV_DATASETS_TR_SVT_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_tr -//! @{ - -struct tag -{ - std::string value; - int height, width, x, y; -}; - -struct TR_svtObj : public Object -{ - std::string fileName; - std::vector lex; - std::vector tags; -}; - -class CV_EXPORTS TR_svt : public Dataset -{ -public: - virtual void load(const std::string &path) = 0; - - static Ptr create(); -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/track_vot.hpp b/IPL/include/opencv/opencv2/datasets/track_vot.hpp deleted file mode 100644 index 6249f02..0000000 --- a/IPL/include/opencv/opencv2/datasets/track_vot.hpp +++ /dev/null @@ -1,96 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_TRACK_VOT_HPP -#define OPENCV_DATASETS_TRACK_VOT_HPP - -#include -#include - -#include "opencv2/datasets/dataset.hpp" -#include "opencv2/datasets/util.hpp" - -using namespace std; - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets_track -//! @{ - -struct TRACK_votObj : public Object -{ - int id; - std::string imagePath; - vector gtbb; -}; - -class CV_EXPORTS TRACK_vot : public Dataset -{ -public: - static Ptr create(); - - virtual void load(const std::string &path) = 0; - - virtual int getDatasetsNum() = 0; - - virtual int getDatasetLength(int id) = 0; - - virtual bool initDataset(int id) = 0; - - virtual bool getNextFrame(Mat &frame) = 0; - - virtual vector getGT() = 0; - -protected: - vector > > data; - int activeDatasetID; - int frameCounter; -}; - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/datasets/util.hpp b/IPL/include/opencv/opencv2/datasets/util.hpp deleted file mode 100644 index 316de3a..0000000 --- a/IPL/include/opencv/opencv2/datasets/util.hpp +++ /dev/null @@ -1,74 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_DATASETS_UTIL_HPP -#define OPENCV_DATASETS_UTIL_HPP - -#include -#include - -#include -#include // atoi, atof - -#include - -#include - -namespace cv -{ -namespace datasets -{ - -//! @addtogroup datasets -//! @{ - -void CV_EXPORTS split(const std::string &s, std::vector &elems, char delim); - -void CV_EXPORTS createDirectory(const std::string &path); - -void CV_EXPORTS getDirList(const std::string &dirName, std::vector &fileNames); - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/dnn.hpp b/IPL/include/opencv/opencv2/dnn.hpp deleted file mode 100644 index 37be989..0000000 --- a/IPL/include/opencv/opencv2/dnn.hpp +++ /dev/null @@ -1,64 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_DNN_HPP__ -#define __OPENCV_DNN_HPP__ - -// This is an umbrealla header to include into you project. -// We are free to change headers layout in dnn subfolder, so please include -// this header for future compartibility - - -/** @defgroup dnn Deep Neural Network module - @{ - This module contains: - - API for new layers creation, layers are building bricks of neural networks; - - set of built-in most-useful Layers; - - API to constuct and modify comprehensive neural networks from layers; - - functionality for loading serialized networks models from differnet frameworks. - - Functionality of this module is designed only for forward pass computations (i. e. network testing). - A network training is in principle not supported. - @} -*/ -#include - -#endif /* __OPENCV_DNN_HPP__ */ diff --git a/IPL/include/opencv/opencv2/dnn/blob.hpp b/IPL/include/opencv/opencv2/dnn/blob.hpp deleted file mode 100644 index bc582c8..0000000 --- a/IPL/include/opencv/opencv2/dnn/blob.hpp +++ /dev/null @@ -1,238 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_DNN_DNN_BLOB_HPP__ -#define __OPENCV_DNN_DNN_BLOB_HPP__ -#include -#include -#include - -namespace cv -{ -namespace dnn -{ -//! @addtogroup dnn -//! @{ - - /** @brief Lightweight class for storing and processing a shape of blob (or anything else). */ - struct BlobShape - { - explicit BlobShape(int ndims = 4, int fill = 1); //!< Creates n-dim shape and fill its by @p fill - BlobShape(int num, int cn, int rows, int cols); //!< Creates 4-dim shape [@p num, @p cn, @p rows, @p cols] - BlobShape(int ndims, const int *sizes); //!< Creates n-dim shape from the @p sizes array - BlobShape(const std::vector &sizes); //!< Creates n-dim shape from the @p sizes vector - template - BlobShape(const Vec &shape); //!< Creates n-dim shape from @ref cv::Vec - - /** @brief Returns number of dimensions. */ - int dims() const; - - /** @brief Returns reference to the size of the specified @p axis. - * - * Negative @p axis is supported, in this case a counting starts from the last axis, - * i. e. -1 corresponds to last axis. - * If non-existing axis was passed then an error will be generated. - */ - int &size(int axis); - - /** @brief Returns the size of the specified @p axis. - * @see size() - */ - int size(int axis) const; - - int operator[](int axis) const; //!< Does the same thing as size(axis). - int &operator[](int axis); //!< Does the same thing as size(int) const. - - /** @brief Returns the size of the specified @p axis. - * - * Does the same thing as size(int) const, but if non-existing axis will be passed then 1 will be returned, - * therefore this function always finishes successfully. - */ - int xsize(int axis) const; - - /** @brief Returns the product of all sizes of axes. */ - ptrdiff_t total(); - - /** @brief Returns pointer to the first element of continuous size array. */ - const int *ptr() const; - - /** @brief Checks equality of two shapes. */ - bool equal(const BlobShape &other) const; - - bool operator== (const BlobShape &r) const; - - private: - cv::AutoBuffer sz; - }; - - - /** @brief This class provides methods for continuous n-dimensional CPU and GPU array processing. - * - * The class is realized as a wrapper over @ref cv::Mat and @ref cv::UMat. - * It will support methods for switching and logical synchronization between CPU and GPU. - */ - class CV_EXPORTS Blob - { - public: - explicit Blob(); - - /** @brief Constructs blob with specified @p shape and @p type. */ - explicit Blob(const BlobShape &shape, int type = CV_32F); - - /** @brief Constucts 4-dimensional blob (so-called batch) from image or array of images. - * @param image 2-dimensional multi-channel or 3-dimensional single-channel image (or array of images) - * @param dstCn specify size of second axis of ouptut blob - */ - explicit Blob(InputArray image, int dstCn = -1); - - /** @brief Creates blob with specified @p shape and @p type. */ - void create(const BlobShape &shape, int type = CV_32F); - - /** @brief Creates blob from cv::Mat or cv::UMat without copying the data */ - void fill(InputArray in); - /** @brief Creates blob from user data. - * @details If @p deepCopy is false then CPU data will not be allocated. - */ - void fill(const BlobShape &shape, int type, void *data, bool deepCopy = true); - - Mat& matRef(); //!< Returns reference to cv::Mat, containing blob data. - const Mat& matRefConst() const; //!< Returns reference to cv::Mat, containing blob data, for read-only purposes. - UMat &umatRef(); //!< Returns reference to cv::UMat, containing blob data (not implemented yet). - const UMat &umatRefConst() const; //!< Returns reference to cv::UMat, containing blob data, for read-only purposes (not implemented yet). - - /** @brief Returns number of blob dimensions. */ - int dims() const; - - /** @brief Returns the size of the specified @p axis. - * - * Negative @p axis is supported, in this case a counting starts from the last axis, - * i. e. -1 corresponds to last axis. - * If non-existing axis was passed then an error will be generated. - */ - int size(int axis) const; - - /** @brief Returns the size of the specified @p axis. - * - * Does the same thing as size(int) const, but if non-existing axis will be passed then 1 will be returned, - * therefore this function always finishes successfully. - */ - int xsize(int axis) const; - - /** @brief Computes the product of sizes of axes among the specified axes range [@p startAxis; @p endAxis). - * @param startAxis the first axis to include in the range. - * @param endAxis the first axis to exclude from the range. - * @details Negative axis indexing can be used. - */ - size_t total(int startAxis = 0, int endAxis = INT_MAX) const; - - /** @brief Converts @p axis index to canonical format (where 0 <= axis < dims()). */ - int canonicalAxis(int axis) const; - - /** @brief Returns shape of the blob. */ - BlobShape shape() const; - - /** @brief Checks equality of two blobs shapes. */ - bool equalShape(const Blob &other) const; - - /** @brief Returns slice of first two dimensions. - * @details The behaviour is similar to the following numpy code: blob[n, cn, ...] - */ - Mat getPlane(int n, int cn); - - /* Shape getters of 4-dimensional blobs. */ - int cols() const; //!< Returns size of the fourth axis blob. - int rows() const; //!< Returns size of the thrid axis blob. - int channels() const; //!< Returns size of the second axis blob. - int num() const; //!< Returns size of the first axis blob. - Size size2() const; //!< Returns cv::Size(cols(), rows()) - Vec4i shape4() const; //!< Returns shape of first four blob axes. - - /** @brief Returns linear index of the element with specified coordinates in the blob. - * - * If @p n < dims() then unspecified coordinates will be filled by zeros. - * If @p n > dims() then extra coordinates will be ignored. - */ - template - size_t offset(const Vec &pos) const; - /** @overload */ - size_t offset(int n = 0, int cn = 0, int row = 0, int col = 0) const; - - /* CPU pointer getters */ - /** @brief Returns pointer to the blob element with the specified position, stored in CPU memory. - * - * @p n correspond to the first axis, @p cn - to the second, etc. - * If dims() > 4 then unspecified coordinates will be filled by zeros. - * If dims() < 4 then extra coordinates will be ignored. - */ - uchar *ptr(int n = 0, int cn = 0, int row = 0, int col = 0); - /** @overload */ - template - TFloat *ptr(int n = 0, int cn = 0, int row = 0, int col = 0); - /** @overload ptr() */ - float *ptrf(int n = 0, int cn = 0, int row = 0, int col = 0); - //TODO: add const ptr methods - - /** @brief Shares data from other @p blob. - * @returns *this - */ - Blob &shareFrom(const Blob &blob); - - /** @brief Changes shape of the blob without copying the data. - * @returns *this - */ - Blob &reshape(const BlobShape &shape); - - /** @brief Returns type of the blob. */ - int type() const; - - private: - const int *sizes() const; - - Mat m; - }; - -//! @} -} -} - -#include "blob.inl.hpp" - -#endif diff --git a/IPL/include/opencv/opencv2/dnn/blob.inl.hpp b/IPL/include/opencv/opencv2/dnn/blob.inl.hpp deleted file mode 100644 index 4a6de48..0000000 --- a/IPL/include/opencv/opencv2/dnn/blob.inl.hpp +++ /dev/null @@ -1,342 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_DNN_DNN_BLOB_INL_HPP__ -#define __OPENCV_DNN_DNN_BLOB_INL_HPP__ -#include "blob.hpp" - -namespace cv -{ -namespace dnn -{ - -inline BlobShape::BlobShape(int ndims, int fill) : sz( (size_t)std::max(ndims, 0) ) -{ - CV_Assert(ndims >= 0); - for (int i = 0; i < ndims; i++) - sz[i] = fill; -} - -inline BlobShape::BlobShape(int ndims, const int *sizes) : sz( (size_t)std::max(ndims, 0) ) -{ - CV_Assert(ndims >= 0); - for (int i = 0; i < ndims; i++) - sz[i] = sizes[i]; -} - -inline BlobShape::BlobShape(int num, int cn, int rows, int cols) : sz(4) -{ - sz[0] = num; - sz[1] = cn; - sz[2] = rows; - sz[3] = cols; -} - -inline BlobShape::BlobShape(const std::vector &sizes) : sz( sizes.size() ) -{ - for (int i = 0; i < (int)sizes.size(); i++) - sz[i] = sizes[i]; -} - -template -inline BlobShape::BlobShape(const Vec &shape) : sz(n) -{ - for (int i = 0; i < n; i++) - sz[i] = shape[i]; -} - -inline int BlobShape::dims() const -{ - return (int)sz.size(); -} - -inline int BlobShape::xsize(int axis) const -{ - if (axis < -dims() || axis >= dims()) - return 1; - - return sz[(axis < 0) ? axis + dims() : axis]; -} - -inline int BlobShape::size(int axis) const -{ - CV_Assert(-dims() <= axis && axis < dims()); - return sz[(axis < 0) ? axis + dims() : axis]; -} - -inline int &BlobShape::size(int axis) -{ - CV_Assert(-dims() <= axis && axis < dims()); - return sz[(axis < 0) ? axis + dims() : axis]; -} - -inline int BlobShape::operator[] (int axis) const -{ - CV_Assert(-dims() <= axis && axis < dims()); - return sz[(axis < 0) ? axis + dims() : axis]; -} - -inline int &BlobShape::operator[] (int axis) -{ - CV_Assert(-dims() <= axis && axis < dims()); - return sz[(axis < 0) ? axis + dims() : axis]; -} - -inline ptrdiff_t BlobShape::total() -{ - if (dims() == 0) - return 0; - - ptrdiff_t res = 1; - for (int i = 0; i < dims(); i++) - res *= sz[i]; - return res; -} - -inline const int *BlobShape::ptr() const -{ - return sz; -} - -inline bool BlobShape::equal(const BlobShape &other) const -{ - if (this->dims() != other.dims()) - return false; - - for (int i = 0; i < other.dims(); i++) - { - if (sz[i] != other.sz[i]) - return false; - } - - return true; -} - -inline bool BlobShape::operator==(const BlobShape &r) const -{ - return this->equal(r); -} - -CV_EXPORTS std::ostream &operator<< (std::ostream &stream, const BlobShape &shape); - -///////////////////////////////////////////////////////////////////// - -inline int Blob::canonicalAxis(int axis) const -{ - CV_Assert(-dims() <= axis && axis < dims()); - return (axis < 0) ? axis + dims() : axis; -} - -inline int Blob::dims() const -{ - return m.dims; -} - -inline int Blob::xsize(int axis) const -{ - if (axis < -dims() || axis >= dims()) - return 1; - - return sizes()[(axis < 0) ? axis + dims() : axis]; -} - -inline int Blob::size(int axis) const -{ - CV_Assert(-dims() <= axis && axis < dims()); - return sizes()[(axis < 0) ? axis + dims() : axis]; -} - -inline size_t Blob::total(int startAxis, int endAxis) const -{ - if (startAxis < 0) - startAxis += dims(); - - if (endAxis == INT_MAX) - endAxis = dims(); - else if (endAxis < 0) - endAxis += dims(); - - CV_Assert(0 <= startAxis && startAxis <= endAxis && endAxis <= dims()); - - size_t size = 1; //fix: assume that slice isn't empty - for (int i = startAxis; i < endAxis; i++) - size *= (size_t)sizes()[i]; - - return size; -} - - -template -inline size_t Blob::offset(const Vec &pos) const -{ - size_t ofs = 0; - int i; - for (i = 0; i < std::min(n, dims()); i++) - { - CV_DbgAssert(pos[i] >= 0 && pos[i] < size(i)); - ofs = ofs * (size_t)size(i) + pos[i]; - } - for (; i < dims(); i++) - ofs *= (size_t)size(i); - return ofs; -} - -inline size_t Blob::offset(int n, int cn, int row, int col) const -{ - return offset(Vec4i(n, cn, row, col)); -} - -inline float *Blob::ptrf(int n, int cn, int row, int col) -{ - CV_Assert(type() == CV_32F); - return (float*)m.data + offset(n, cn, row, col); -} - -inline uchar *Blob::ptr(int n, int cn, int row, int col) -{ - return m.data + m.elemSize() * offset(n, cn, row, col); -} - -template -inline TFloat* Blob::ptr(int n, int cn, int row, int col) -{ - CV_Assert(type() == cv::DataDepth::value); - return (TFloat*) ptr(n, cn, row, col); -} - -inline BlobShape Blob::shape() const -{ - return BlobShape(dims(), sizes()); -} - -inline bool Blob::equalShape(const Blob &other) const -{ - if (this->dims() != other.dims()) - return false; - - for (int i = 0; i < dims(); i++) - { - if (this->sizes()[i] != other.sizes()[i]) - return false; - } - return true; -} - -inline Mat& Blob::matRef() -{ - return m; -} - -inline const Mat& Blob::matRefConst() const -{ - return m; -} - -inline UMat &Blob::umatRef() -{ - CV_Error(Error::StsNotImplemented, ""); - return *(new UMat()); -} - -inline const UMat &Blob::umatRefConst() const -{ - CV_Error(Error::StsNotImplemented, ""); - return *(new UMat()); -} - -inline Mat Blob::getPlane(int n, int cn) -{ - CV_Assert(dims() > 2); - return Mat(dims() - 2, sizes() + 2, type(), ptr(n, cn)); -} - -inline int Blob::cols() const -{ - return xsize(3); -} - -inline int Blob::rows() const -{ - return xsize(2); -} - -inline int Blob::channels() const -{ - return xsize(1); -} - -inline int Blob::num() const -{ - return xsize(0); -} - -inline Size Blob::size2() const -{ - return Size(cols(), rows()); -} - -inline int Blob::type() const -{ - return m.depth(); -} - -inline const int * Blob::sizes() const -{ - return &m.size[0]; -} - - -inline Blob &Blob::shareFrom(const Blob &blob) -{ - this->m = blob.m; - return *this; -} - -inline Blob &Blob::reshape(const BlobShape &shape) -{ - m = m.reshape(1, shape.dims(), shape.ptr()); - return *this; -} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/dnn/dict.hpp b/IPL/include/opencv/opencv2/dnn/dict.hpp deleted file mode 100644 index 61db133..0000000 --- a/IPL/include/opencv/opencv2/dnn/dict.hpp +++ /dev/null @@ -1,141 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_DNN_DNN_DICT_HPP__ -#define __OPENCV_DNN_DNN_DICT_HPP__ - -#include -#include -#include - -namespace cv -{ -namespace dnn -{ -//! @addtogroup dnn -//! @{ - -/** @brief This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64. - * @todo Maybe int64 is useless because double type exactly stores at least 2^52 integers. - */ -struct DictValue -{ - DictValue(const DictValue &r); - DictValue(int p = 0) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = p; } //!< Constructs integer scalar - DictValue(unsigned p) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = p; } //!< Constructs integer scalar - DictValue(double p) : type(Param::REAL), pd(new AutoBuffer) { (*pd)[0] = p; } //!< Constructs floating point scalar - DictValue(const String &p) : type(Param::STRING), ps(new AutoBuffer) { (*ps)[0] = p; } //!< Constructs string scalar - - template - static DictValue arrayInt(TypeIter begin, int size); //!< Constructs integer array - template - static DictValue arrayReal(TypeIter begin, int size); //!< Constructs floating point array - template - static DictValue arrayString(TypeIter begin, int size); //!< Constructs array of strings - - template - T get(int idx = -1) const; //!< Tries to convert array element with specified index to requested type and returns its. - - int size() const; - - bool isInt() const; - bool isString() const; - bool isReal() const; - - DictValue &operator=(const DictValue &r); - - friend std::ostream &operator<<(std::ostream &stream, const DictValue &dictv); - - ~DictValue(); - -private: - - int type; - - union - { - AutoBuffer *pi; - AutoBuffer *pd; - AutoBuffer *ps; - void *p; - }; - - DictValue(int _type, void *_p) : type(_type), p(_p) {} - void release(); -}; - -/** @brief This class implements name-value dictionary, values are instances of DictValue. */ -class CV_EXPORTS Dict -{ - typedef std::map _Dict; - _Dict dict; - -public: - - //! Checks a presence of the @p key in the dictionary. - bool has(const String &key); - - //! If the @p key in the dictionary then returns pointer to its value, else returns NULL. - DictValue *ptr(const String &key); - - //! If the @p key in the dictionary then returns its value, else an error will be generated. - const DictValue &get(const String &key) const; - - /** @overload */ - template - T get(const String &key) const; - - //! If the @p key in the dictionary then returns its value, else returns @p defaultValue. - template - T get(const String &key, const T &defaultValue) const; - - //! Sets new @p value for the @p key, or adds new key-value pair into the dictionary. - template - const T &set(const String &key, const T &value); - - friend std::ostream &operator<<(std::ostream &stream, const Dict &dict); -}; - -//! @} -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/dnn/dnn.hpp b/IPL/include/opencv/opencv2/dnn/dnn.hpp deleted file mode 100644 index 1d1244d..0000000 --- a/IPL/include/opencv/opencv2/dnn/dnn.hpp +++ /dev/null @@ -1,304 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_DNN_DNN_HPP__ -#define __OPENCV_DNN_DNN_HPP__ - -#include -#include -#include -#include - -namespace cv -{ -namespace dnn //! This namespace is used for dnn module functionlaity. -{ -//! @addtogroup dnn -//! @{ - - /** @brief Initialize dnn module and built-in layers. - * - * This function automatically called on most of OpenCV builds, - * but you need to call it manually on some specific configurations (iOS for example). - */ - CV_EXPORTS void initModule(); - - /** @brief This class provides all data needed to initialize layer. - * - * It includes dictionary with scalar params (which can be readed by using Dict interface), - * blob params #blobs and optional meta information: #name and #type of layer instance. - */ - struct CV_EXPORTS LayerParams : public Dict - { - std::vector blobs; //!< List of learned parameters stored as blobs. - - String name; //!< Name of the layer instance (optional, can be used internal purposes). - String type; //!< Type name which was used for creating layer by layer factory (optional). - }; - - /** @brief This interface class allows to build new Layers - are building blocks of networks. - * - * Each class, derived from Layer, must implement allocate() methods to declare own outputs and forward() to compute outputs. - * Also before using the new layer into networks you must register your layer by using one of @ref LayerFactoryModule "LayerFactory" macros. - */ - struct CV_EXPORTS Layer - { - //! List of learned parameters must be stored here to allow read them by using Net::getParam(). - std::vector blobs; - - /** @brief Allocates internal buffers and output blobs with respect to the shape of inputs. - * @param[in] input vector of already allocated input blobs - * @param[out] output vector of output blobs, which must be allocated - * - * This method must create each produced blob according to shape of @p input blobs and internal layer params. - * If this method is called first time then @p output vector consists from empty blobs and its size determined by number of output connections. - * This method can be called multiple times if size of any @p input blob was changed. - */ - virtual void allocate(const std::vector &input, std::vector &output) = 0; - - /** @brief Given the @p input blobs, computes the output @p blobs. - * @param[in] input the input blobs. - * @param[out] output allocated output blobs, which will store results of the computation. - */ - virtual void forward(std::vector &input, std::vector &output) = 0; - - /** @brief Returns index of input blob into the input array. - * @param inputName label of input blob - * - * Each layer input and output can be labeled to easily identify them using "%[.output_name]" notation. - * This method maps label of input blob to its index into input vector. - */ - virtual int inputNameToIndex(String inputName); - /** @brief Returns index of output blob in output array. - * @see inputNameToIndex() - */ - virtual int outputNameToIndex(String outputName); - - String name; //!< Name of the layer instance, can be used for logging or other internal purposes. - String type; //!< Type name which was used for creating layer by layer factory. - - Layer(); - explicit Layer(const LayerParams ¶ms); //!< Initialize only #name, #type and #blobs fields. - virtual ~Layer(); - }; - - /** @brief This class allows to create and manipulate comprehensive artificial neural networks. - * - * Neural network is presented as directed acyclic graph (DAG), where vertices are Layer instances, - * and edges specify relationships between layers inputs and outputs. - * - * Each network layer has unique integer id and unique string name inside its network. - * LayerId can store either layer name or layer id. - * - * This class supports reference counting of its instances, i. e. copies point to the same instance. - */ - class CV_EXPORTS Net - { - public: - - Net(); //!< Default constructor. - ~Net(); //!< Destructor frees the net only if there aren't references to the net anymore. - - /** @brief Adds new layer to the net. - * @param name unique name of the adding layer. - * @param type typename of the adding layer (type must be registered in LayerRegister). - * @param params parameters which will be used to initialize the creating layer. - * @returns unique identifier of created layer, or -1 if a failure will happen. - */ - int addLayer(const String &name, const String &type, LayerParams ¶ms); - /** @brief Adds new layer and connects its first input to the first output of previously added layer. - * @see addLayer() - */ - int addLayerToPrev(const String &name, const String &type, LayerParams ¶ms); - - /** @brief Converts string name of the layer to the integer identifier. - * @returns id of the layer, or -1 if the layer wasn't found. - */ - int getLayerId(const String &layer); - - /** @brief Container for strings and integers. */ - typedef DictValue LayerId; - - /** @brief Delete layer for the network (not implemented yet) */ - void deleteLayer(LayerId layer); - - /** @brief Connects output of the first layer to input of the second layer. - * @param outPin descriptor of the first layer output. - * @param inpPin descriptor of the second layer input. - * - * Descriptors have the following template <layer_name>[.input_number]: - * - the first part of the template layer_name is sting name of the added layer. - * If this part is empty then the network input pseudo layer will be used; - * - the second optional part of the template input_number - * is either number of the layer input, either label one. - * If this part is omitted then the first layer input will be used. - * - * @see setNetInputs(), Layer::inputNameToIndex(), Layer::outputNameToIndex() - */ - void connect(String outPin, String inpPin); - /** @brief Connects #@p outNum output of the first layer to #@p inNum input of the second layer. - * @param outLayerId identifier of the first layer - * @param inpLayerId identifier of the second layer - * @param outNum number of the first layer output - * @param inpNum number of the second layer input - */ - void connect(int outLayerId, int outNum, int inpLayerId, int inpNum); - /** @brief Sets ouputs names of the network input pseudo layer. - * - * Each net always has special own the network input pseudo layer with id=0. - * This layer stores the user blobs only and don't make any computations. - * In fact, this layer provides the only way to pass user data into the network. - * As any other layer, this layer can label its outputs and this function provides an easy way to do this. - */ - void setNetInputs(const std::vector &inputBlobNames); - - /** @brief Runs forward pass for the whole network */ - void forward(); - /** @brief Runs forward pass to compute output of layer @p toLayer */ - void forward(LayerId toLayer); - /** @brief Runs forward pass to compute output of layer @p toLayer, but computations start from @p startLayer */ - void forward(LayerId startLayer, LayerId toLayer); - /** @overload */ - void forward(const std::vector &startLayers, const std::vector &toLayers); - - //TODO: - /** @brief Optimized forward. - * @warning Not implemented yet. - * @details Makes forward only those layers which weren't changed after previous forward(). - */ - void forwardOpt(LayerId toLayer); - /** @overload */ - void forwardOpt(const std::vector &toLayers); - - /** @brief Sets the new value for the layer output blob - * @param outputName descriptor of the updating layer output blob. - * @param blob new blob. - * @see connect(String, String) to know format of the descriptor. - * @note If updating blob is not empty then @p blob must have the same shape, - * because network reshaping is not implemented yet. - */ - void setBlob(String outputName, const Blob &blob); - /** @brief Returns the layer output blob. - * @param outputName the descriptor of the returning layer output blob. - * @see connect(String, String) - */ - Blob getBlob(String outputName); - - /** @brief Sets the new value for the learned param of the layer. - * @param layer name or id of the layer. - * @param numParam index of the layer parameter in the Layer::blobs array. - * @param blob the new value. - * @see Layer::blobs - * @note If shape of the new blob differs from the previous shape, - * then the following forward pass may fail. - */ - void setParam(LayerId layer, int numParam, const Blob &blob); - /** @brief Returns parameter blob of the layer. - * @param layer name or id of the layer. - * @param numParam index of the layer parameter in the Layer::blobs array. - * @see Layer::blobs - */ - Blob getParam(LayerId layer, int numParam = 0); - - private: - - struct Impl; - Ptr impl; - }; - - /** @brief Small interface class for loading trained serialized models of different dnn-frameworks. */ - class Importer - { - public: - - /** @brief Adds loaded layers into the @p net and sets connetions between them. */ - virtual void populateNet(Net net) = 0; - - virtual ~Importer(); - }; - - /** @brief Creates the importer of Caffe framework network. - * @param prototxt path to the .prototxt file with text description of the network architecture. - * @param caffeModel path to the .caffemodel file with learned network. - * @returns Pointer to the created importer, NULL in failure cases. - */ - CV_EXPORTS Ptr createCaffeImporter(const String &prototxt, const String &caffeModel = String()); - - /** @brief Creates the importer of Torch7 framework network. - * @param filename path to the file, dumped from Torch by using torch.save() function. - * @param isBinary specifies whether the network was serialized in ascii mode or binary. - * @returns Pointer to the created importer, NULL in failure cases. - * - * @warning Torch7 importer is experimental now, you need explicitly set CMake opencv_dnn_BUILD_TORCH_IMPORTER flag to compile its. - * - * @note Ascii mode of Torch serializer is more preferable, because binary mode extensively use long type of C language, - * which has different bit-length on different systems. - * - * The loading file must contain serialized nn.Module object - * with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors. - * - * List of supported layers (i.e. object instances derived from Torch nn.Module class): - * - nn.Sequential - * - nn.Parallel - * - nn.Concat - * - nn.Linear - * - nn.SpatialConvolution - * - nn.SpatialMaxPooling, nn.SpatialAveragePooling - * - nn.ReLU, nn.TanH, nn.Sigmoid - * - nn.Reshape - * - * Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported. - */ - CV_EXPORTS Ptr createTorchImporter(const String &filename, bool isBinary = true); - - /** @brief Loads blob which was serialized as torch.Tensor object of Torch7 framework. - * @warning This function has the same limitations as createTorchImporter(). - */ - CV_EXPORTS Blob readTorchBlob(const String &filename, bool isBinary = true); - -//! @} -} -} - -#include -#include - -#endif /* __OPENCV_DNN_DNN_HPP__ */ diff --git a/IPL/include/opencv/opencv2/dnn/dnn.inl.hpp b/IPL/include/opencv/opencv2/dnn/dnn.inl.hpp deleted file mode 100644 index 300ae58..0000000 --- a/IPL/include/opencv/opencv2/dnn/dnn.inl.hpp +++ /dev/null @@ -1,351 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_DNN_DNN_INL_HPP__ -#define __OPENCV_DNN_DNN_INL_HPP__ - -#include - -namespace cv -{ -namespace dnn -{ - -template -DictValue DictValue::arrayInt(TypeIter begin, int size) -{ - DictValue res(Param::INT, new AutoBuffer(size)); - for (int j = 0; j < size; begin++, j++) - (*res.pi)[j] = *begin; - return res; -} - -template -DictValue DictValue::arrayReal(TypeIter begin, int size) -{ - DictValue res(Param::REAL, new AutoBuffer(size)); - for (int j = 0; j < size; begin++, j++) - (*res.pd)[j] = *begin; - return res; -} - -template -DictValue DictValue::arrayString(TypeIter begin, int size) -{ - DictValue res(Param::STRING, new AutoBuffer(size)); - for (int j = 0; j < size; begin++, j++) - (*res.ps)[j] = *begin; - return res; -} - -template<> -inline DictValue DictValue::get(int idx) const -{ - CV_Assert(idx == -1); - return *this; -} - -template<> -inline int64 DictValue::get(int idx) const -{ - CV_Assert(idx == -1 && size() == 1 || idx >= 0 && idx < size()); - idx = (idx == -1) ? 0 : idx; - - if (type == Param::INT) - { - return (*pi)[idx]; - } - else if (type == Param::REAL) - { - double doubleValue = (*pd)[idx]; - - double fracpart, intpart; - fracpart = std::modf(doubleValue, &intpart); - CV_Assert(fracpart == 0.0); - - return (int64)doubleValue; - } - else - { - CV_Assert(isInt() || isReal()); - return 0; - } -} - -template<> -inline int DictValue::get(int idx) const -{ - return (int)get(idx); -} - -template<> -inline unsigned DictValue::get(int idx) const -{ - return (unsigned)get(idx); -} - -template<> -inline bool DictValue::get(int idx) const -{ - return (get(idx) != 0); -} - -template<> -inline double DictValue::get(int idx) const -{ - CV_Assert(idx == -1 && size() == 1 || idx >= 0 && idx < size()); - idx = (idx == -1) ? 0 : idx; - - if (type == Param::REAL) - { - return (*pd)[idx]; - } - else if (type == Param::INT) - { - return (double)(*pi)[idx]; - } - else - { - CV_Assert(isReal() || isInt()); - return 0; - } -} - -template<> -inline float DictValue::get(int idx) const -{ - return (float)get(idx); -} - -template<> -inline String DictValue::get(int idx) const -{ - CV_Assert(isString()); - CV_Assert(idx == -1 && ps->size() == 1 || idx >= 0 && idx < (int)ps->size()); - return (*ps)[(idx == -1) ? 0 : idx]; -} - -inline void DictValue::release() -{ - switch (type) - { - case Param::INT: - delete pi; - break; - case Param::STRING: - delete ps; - break; - case Param::REAL: - delete pd; - break; - } -} - -inline DictValue::~DictValue() -{ - release(); -} - -inline DictValue & DictValue::operator=(const DictValue &r) -{ - if (&r == this) - return *this; - - if (r.type == Param::INT) - { - AutoBuffer *tmp = new AutoBuffer(*r.pi); - release(); - pi = tmp; - } - else if (r.type == Param::STRING) - { - AutoBuffer *tmp = new AutoBuffer(*r.ps); - release(); - ps = tmp; - } - else if (r.type == Param::REAL) - { - AutoBuffer *tmp = new AutoBuffer(*r.pd); - release(); - pd = tmp; - } - - type = r.type; - - return *this; -} - -inline DictValue::DictValue(const DictValue &r) -{ - type = r.type; - - if (r.type == Param::INT) - pi = new AutoBuffer(*r.pi); - else if (r.type == Param::STRING) - ps = new AutoBuffer(*r.ps); - else if (r.type == Param::REAL) - pd = new AutoBuffer(*r.pd); -} - -inline bool DictValue::isString() const -{ - return (type == Param::STRING); -} - -inline bool DictValue::isInt() const -{ - return (type == Param::INT); -} - -inline bool DictValue::isReal() const -{ - return (type == Param::REAL || type == Param::INT); -} - -inline int DictValue::size() const -{ - switch (type) - { - case Param::INT: - return (int)pi->size(); - break; - case Param::STRING: - return (int)ps->size(); - break; - case Param::REAL: - return (int)pd->size(); - break; - default: - CV_Error(Error::StsInternal, ""); - return -1; - } -} - -inline std::ostream &operator<<(std::ostream &stream, const DictValue &dictv) -{ - int i; - - if (dictv.isInt()) - { - for (i = 0; i < dictv.size() - 1; i++) - stream << dictv.get(i) << ", "; - stream << dictv.get(i); - } - else if (dictv.isReal()) - { - for (i = 0; i < dictv.size() - 1; i++) - stream << dictv.get(i) << ", "; - stream << dictv.get(i); - } - else if (dictv.isString()) - { - for (i = 0; i < dictv.size() - 1; i++) - stream << "\"" << dictv.get(i) << "\", "; - stream << dictv.get(i); - } - - return stream; -} - -///////////////////////////////////////////////////////////////// - -inline bool Dict::has(const String &key) -{ - return dict.count(key) != 0; -} - -inline DictValue *Dict::ptr(const String &key) -{ - _Dict::iterator i = dict.find(key); - return (i == dict.end()) ? NULL : &i->second; -} - -inline const DictValue &Dict::get(const String &key) const -{ - _Dict::const_iterator i = dict.find(key); - if (i == dict.end()) - CV_Error(Error::StsObjectNotFound, "Required argument \"" + key + "\" not found into dictionary"); - return i->second; -} - -template -inline T Dict::get(const String &key) const -{ - return this->get(key).get(); -} - -template -inline T Dict::get(const String &key, const T &defaultValue) const -{ - _Dict::const_iterator i = dict.find(key); - - if (i != dict.end()) - return i->second.get(); - else - return defaultValue; -} - -template -inline const T &Dict::set(const String &key, const T &value) -{ - _Dict::iterator i = dict.find(key); - - if (i != dict.end()) - i->second = DictValue(value); - else - dict.insert(std::make_pair(key, DictValue(value))); - - return value; -} - -inline std::ostream &operator<<(std::ostream &stream, const Dict &dict) -{ - Dict::_Dict::const_iterator it; - for (it = dict.dict.begin(); it != dict.dict.end(); it++) - stream << it->first << " : " << it->second << "\n"; - - return stream; -} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/dnn/layer.hpp b/IPL/include/opencv/opencv2/dnn/layer.hpp deleted file mode 100644 index b28b6ac..0000000 --- a/IPL/include/opencv/opencv2/dnn/layer.hpp +++ /dev/null @@ -1,147 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_DNN_LAYER_HPP__ -#define __OPENCV_DNN_LAYER_HPP__ -#include - -namespace cv -{ -namespace dnn -{ -//! @addtogroup dnn -//! @{ -//! -//! @defgroup LayerFactoryModule Utilities for new layers registration -//! @{ - -/** @brief %Layer factory allows to create instances of registered layers. */ -class CV_EXPORTS LayerFactory -{ -public: - - //! Each Layer class must provide this function to the factory - typedef Ptr(*Constuctor)(LayerParams ¶ms); - - //! Registers the layer class with typename @p type and specified @p constructor. - static void registerLayer(const String &type, Constuctor constructor); - - //! Unregisters registered layer with specified type name. - static void unregisterLayer(const String &type); - - /** @brief Creates instance of registered layer. - * @param type type name of creating layer. - * @param params parameters which will be used for layer initialization. - */ - static Ptr createLayerInstance(const String &type, LayerParams& params); - -private: - LayerFactory(); - - struct Impl; - static Ptr impl(); -}; - -/** @brief Registers layer constructor in runtime. -* @param type string, containing type name of the layer. -* @param constuctorFunc pointer to the function of type LayerRegister::Constuctor, which creates the layer. -* @details This macros must be placed inside the function code. -*/ -#define REG_RUNTIME_LAYER_FUNC(type, constuctorFunc) \ - LayerFactory::registerLayer(#type, constuctorFunc); - -/** @brief Registers layer class in runtime. - * @param type string, containing type name of the layer. - * @param class C++ class, derived from Layer. - * @details This macros must be placed inside the function code. - */ -#define REG_RUNTIME_LAYER_CLASS(type, class) \ - LayerFactory::registerLayer(#type, _layerDynamicRegisterer); - -/** @brief Registers layer constructor on module load time. -* @param type string, containing type name of the layer. -* @param constuctorFunc pointer to the function of type LayerRegister::Constuctor, which creates the layer. -* @details This macros must be placed outside the function code. -*/ -#define REG_STATIC_LAYER_FUNC(type, constuctorFunc) \ -static _LayerStaticRegisterer __LayerStaticRegisterer_##type(#type, constuctorFunc); - -/** @brief Registers layer class on module load time. - * @param type string, containing type name of the layer. - * @param class C++ class, derived from Layer. - * @details This macros must be placed outside the function code. - */ -#define REG_STATIC_LAYER_CLASS(type, class) \ -Ptr __LayerStaticRegisterer_func_##type(LayerParams ¶ms) \ - { return Ptr(new class(params)); } \ -static _LayerStaticRegisterer __LayerStaticRegisterer_##type(#type, __LayerStaticRegisterer_func_##type); - - -//! @} -//! @} - - -template -Ptr _layerDynamicRegisterer(LayerParams ¶ms) -{ - return Ptr(new LayerClass(params)); -} - -//allows automatically register created layer on module load time -struct _LayerStaticRegisterer -{ - String type; - - _LayerStaticRegisterer(const String &type, LayerFactory::Constuctor constuctor) - { - this->type = type; - LayerFactory::registerLayer(type, constuctor); - } - - ~_LayerStaticRegisterer() - { - LayerFactory::unregisterLayer(type); - } -}; - -} -} -#endif diff --git a/IPL/include/opencv/opencv2/dpm.hpp b/IPL/include/opencv/opencv2/dpm.hpp deleted file mode 100644 index 387a311..0000000 --- a/IPL/include/opencv/opencv2/dpm.hpp +++ /dev/null @@ -1,148 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2015, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Itseez Inc or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -// Implementation authors: -// Jiaolong Xu - jiaolongxu@gmail.com -// Evgeniy Kozinov - evgeniy.kozinov@gmail.com -// Valentina Kustikova - valentina.kustikova@gmail.com -// Nikolai Zolotykh - Nikolai.Zolotykh@gmail.com -// Iosif Meyerov - meerov@vmk.unn.ru -// Alexey Polovinkin - polovinkin.alexey@gmail.com -// -//M*/ - -#ifndef __OPENCV_LATENTSVM_HPP__ -#define __OPENCV_LATENTSVM_HPP__ - -#include "opencv2/core.hpp" - -#include -#include -#include - -/** @defgroup dpm Deformable Part-based Models - -Discriminatively Trained Part Based Models for Object Detection ---------------------------------------------------------------- - -The object detector described below has been initially proposed by P.F. Felzenszwalb in -@cite Felzenszwalb2010a . It is based on a Dalal-Triggs detector that uses a single filter on histogram -of oriented gradients (HOG) features to represent an object category. This detector uses a sliding -window approach, where a filter is applied at all positions and scales of an image. The first -innovation is enriching the Dalal-Triggs model using a star-structured part-based model defined by a -"root" filter (analogous to the Dalal-Triggs filter) plus a set of parts filters and associated -deformation models. The score of one of star models at a particular position and scale within an -image is the score of the root filter at the given location plus the sum over parts of the maximum, -over placements of that part, of the part filter score on its location minus a deformation cost -easuring the deviation of the part from its ideal location relative to the root. Both root and part -filter scores are defined by the dot product between a filter (a set of weights) and a subwindow of -a feature pyramid computed from the input image. Another improvement is a representation of the -class of models by a mixture of star models. The score of a mixture model at a particular position -and scale is the maximum over components, of the score of that component model at the given -location. - -The detector was dramatically speeded-up with cascade algorithm proposed by P.F. Felzenszwalb in -@cite Felzenszwalb2010b . The algorithm prunes partial hypotheses using thresholds on their scores.The -basic idea of the algorithm is to use a hierarchy of models defined by an ordering of the original -model's parts. For a model with (n+1) parts, including the root, a sequence of (n+1) models is -obtained. The i-th model in this sequence is defined by the first i parts from the original model. -Using this hierarchy, low scoring hypotheses can be pruned after looking at the best configuration -of a subset of the parts. Hypotheses that score high under a weak model are evaluated further using -a richer model. - -In OpenCV there is an C++ implementation of DPM cascade detector. - -*/ - -namespace cv -{ - -namespace dpm -{ - -/** @brief This is a C++ abstract class, it provides external user API to work with DPM. - */ -class CV_EXPORTS_W DPMDetector -{ -public: - - struct CV_EXPORTS_W ObjectDetection - { - ObjectDetection(); - ObjectDetection( const Rect& rect, float score, int classID=-1 ); - Rect rect; - float score; - int classID; - }; - - virtual bool isEmpty() const = 0; - - /** @brief Find rectangular regions in the given image that are likely to contain objects of loaded classes - (models) and corresponding confidence levels. - @param image An image. - @param objects The detections: rectangulars, scores and class IDs. - */ - virtual void detect(cv::Mat &image, CV_OUT std::vector &objects) = 0; - - /** @brief Return the class (model) names that were passed in constructor or method load or extracted from - models filenames in those methods. - */ - virtual std::vector const& getClassNames() const = 0; - - /** @brief Return a count of loaded models (classes). - */ - virtual size_t getClassCount() const = 0; - - /** @brief Load the trained models from given .xml files and return cv::Ptr\. - @param filenames A set of filenames storing the trained detectors (models). Each file contains one - model. See examples of such files here `/opencv_extra/testdata/cv/dpm/VOC2007_Cascade/`. - @param classNames A set of trained models names. If it's empty then the name of each model will be - constructed from the name of file containing the model. E.g. the model stored in - "/home/user/cat.xml" will get the name "cat". - */ - static cv::Ptr create(std::vector const &filenames, - std::vector const &classNames = std::vector()); - - virtual ~DPMDetector(){} -}; - -} // namespace dpm -} // namespace cv - -#endif diff --git a/IPL/include/opencv/opencv2/face.hpp b/IPL/include/opencv/opencv2/face.hpp deleted file mode 100644 index d7237bc..0000000 --- a/IPL/include/opencv/opencv2/face.hpp +++ /dev/null @@ -1,374 +0,0 @@ -/* -By downloading, copying, installing or using the software you agree to this -license. If you do not agree to this license, do not download, install, -copy or use the software. - - License Agreement - For Open Source Computer Vision Library - (3-clause BSD License) - -Copyright (C) 2013, OpenCV Foundation, all rights reserved. -Third party copyrights are property of their respective owners. - -Redistribution and use in source and binary forms, with or without modification, -are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. - - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - - * Neither the names of the copyright holders nor the names of the contributors - may be used to endorse or promote products derived from this software - without specific prior written permission. - -This software is provided by the copyright holders and contributors "as is" and -any express or implied warranties, including, but not limited to, the implied -warranties of merchantability and fitness for a particular purpose are -disclaimed. In no event shall copyright holders or contributors be liable for -any direct, indirect, incidental, special, exemplary, or consequential damages -(including, but not limited to, procurement of substitute goods or services; -loss of use, data, or profits; or business interruption) however caused -and on any theory of liability, whether in contract, strict liability, -or tort (including negligence or otherwise) arising in any way out of -the use of this software, even if advised of the possibility of such damage. -*/ - -#ifndef __OPENCV_FACE_HPP__ -#define __OPENCV_FACE_HPP__ - -/** -@defgroup face Face Recognition - -- @ref face_changelog -- @ref tutorial_face_main - -*/ - -#include "opencv2/core.hpp" -#include "face/predict_collector.hpp" -#include - -namespace cv { namespace face { - -//! @addtogroup face -//! @{ - -/** @brief Abstract base class for all face recognition models - -All face recognition models in OpenCV are derived from the abstract base class FaceRecognizer, which -provides a unified access to all face recongition algorithms in OpenCV. - -### Description - -I'll go a bit more into detail explaining FaceRecognizer, because it doesn't look like a powerful -interface at first sight. But: Every FaceRecognizer is an Algorithm, so you can easily get/set all -model internals (if allowed by the implementation). Algorithm is a relatively new OpenCV concept, -which is available since the 2.4 release. I suggest you take a look at its description. - -Algorithm provides the following features for all derived classes: - -- So called “virtual constructor”. That is, each Algorithm derivative is registered at program - start and you can get the list of registered algorithms and create instance of a particular - algorithm by its name (see Algorithm::create). If you plan to add your own algorithms, it is - good practice to add a unique prefix to your algorithms to distinguish them from other - algorithms. -- Setting/Retrieving algorithm parameters by name. If you used video capturing functionality from - OpenCV highgui module, you are probably familar with cv::cvSetCaptureProperty, -ocvcvGetCaptureProperty, VideoCapture::set and VideoCapture::get. Algorithm provides similar - method where instead of integer id's you specify the parameter names as text Strings. See - Algorithm::set and Algorithm::get for details. -- Reading and writing parameters from/to XML or YAML files. Every Algorithm derivative can store - all its parameters and then read them back. There is no need to re-implement it each time. - -Moreover every FaceRecognizer supports the: - -- **Training** of a FaceRecognizer with FaceRecognizer::train on a given set of images (your face - database!). -- **Prediction** of a given sample image, that means a face. The image is given as a Mat. -- **Loading/Saving** the model state from/to a given XML or YAML. -- **Setting/Getting labels info**, that is stored as a string. String labels info is useful for - keeping names of the recognized people. - -@note When using the FaceRecognizer interface in combination with Python, please stick to Python 2. -Some underlying scripts like create_csv will not work in other versions, like Python 3. Setting the -Thresholds +++++++++++++++++++++++ - -Sometimes you run into the situation, when you want to apply a threshold on the prediction. A common -scenario in face recognition is to tell, whether a face belongs to the training dataset or if it is -unknown. You might wonder, why there's no public API in FaceRecognizer to set the threshold for the -prediction, but rest assured: It's supported. It just means there's no generic way in an abstract -class to provide an interface for setting/getting the thresholds of *every possible* FaceRecognizer -algorithm. The appropriate place to set the thresholds is in the constructor of the specific -FaceRecognizer and since every FaceRecognizer is a Algorithm (see above), you can get/set the -thresholds at runtime! - -Here is an example of setting a threshold for the Eigenfaces method, when creating the model: - -@code -// Let's say we want to keep 10 Eigenfaces and have a threshold value of 10.0 -int num_components = 10; -double threshold = 10.0; -// Then if you want to have a cv::FaceRecognizer with a confidence threshold, -// create the concrete implementation with the appropiate parameters: -Ptr model = createEigenFaceRecognizer(num_components, threshold); -@endcode - -Sometimes it's impossible to train the model, just to experiment with threshold values. Thanks to -Algorithm it's possible to set internal model thresholds during runtime. Let's see how we would -set/get the prediction for the Eigenface model, we've created above: - -@code -// The following line reads the threshold from the Eigenfaces model: -double current_threshold = model->getDouble("threshold"); -// And this line sets the threshold to 0.0: -model->set("threshold", 0.0); -@endcode - -If you've set the threshold to 0.0 as we did above, then: - -@code -// -Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE); -// Get a prediction from the model. Note: We've set a threshold of 0.0 above, -// since the distance is almost always larger than 0.0, you'll get -1 as -// label, which indicates, this face is unknown -int predicted_label = model->predict(img); -// ... -@endcode - -is going to yield -1 as predicted label, which states this face is unknown. - -### Getting the name of a FaceRecognizer - -Since every FaceRecognizer is a Algorithm, you can use Algorithm::name to get the name of a -FaceRecognizer: - -@code -// Create a FaceRecognizer: -Ptr model = createEigenFaceRecognizer(); -// And here's how to get its name: -String name = model->name(); -@endcode - - */ -class CV_EXPORTS_W FaceRecognizer : public Algorithm -{ -public: - /** @brief Trains a FaceRecognizer with given data and associated labels. - - @param src The training images, that means the faces you want to learn. The data has to be - given as a vector\. - @param labels The labels corresponding to the images have to be given either as a vector\ - or a - - The following source code snippet shows you how to learn a Fisherfaces model on a given set of - images. The images are read with imread and pushed into a std::vector\. The labels of each - image are stored within a std::vector\ (you could also use a Mat of type CV_32SC1). Think of - the label as the subject (the person) this image belongs to, so same subjects (persons) should have - the same label. For the available FaceRecognizer you don't have to pay any attention to the order of - the labels, just make sure same persons have the same label: - - @code - // holds images and labels - vector images; - vector labels; - // images for first person - images.push_back(imread("person0/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0); - images.push_back(imread("person0/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0); - images.push_back(imread("person0/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0); - // images for second person - images.push_back(imread("person1/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1); - images.push_back(imread("person1/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1); - images.push_back(imread("person1/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1); - @endcode - - Now that you have read some images, we can create a new FaceRecognizer. In this example I'll create - a Fisherfaces model and decide to keep all of the possible Fisherfaces: - - @code - // Create a new Fisherfaces model and retain all available Fisherfaces, - // this is the most common usage of this specific FaceRecognizer: - // - Ptr model = createFisherFaceRecognizer(); - @endcode - - And finally train it on the given dataset (the face images and labels): - - @code - // This is the common interface to train all of the available cv::FaceRecognizer - // implementations: - // - model->train(images, labels); - @endcode - */ - CV_WRAP virtual void train(InputArrayOfArrays src, InputArray labels) = 0; - - /** @brief Updates a FaceRecognizer with given data and associated labels. - - @param src The training images, that means the faces you want to learn. The data has to be given - as a vector\. - @param labels The labels corresponding to the images have to be given either as a vector\ or - a - - This method updates a (probably trained) FaceRecognizer, but only if the algorithm supports it. The - Local Binary Patterns Histograms (LBPH) recognizer (see createLBPHFaceRecognizer) can be updated. - For the Eigenfaces and Fisherfaces method, this is algorithmically not possible and you have to - re-estimate the model with FaceRecognizer::train. In any case, a call to train empties the existing - model and learns a new model, while update does not delete any model data. - - @code - // Create a new LBPH model (it can be updated) and use the default parameters, - // this is the most common usage of this specific FaceRecognizer: - // - Ptr model = createLBPHFaceRecognizer(); - // This is the common interface to train all of the available cv::FaceRecognizer - // implementations: - // - model->train(images, labels); - // Some containers to hold new image: - vector newImages; - vector newLabels; - // You should add some images to the containers: - // - // ... - // - // Now updating the model is as easy as calling: - model->update(newImages,newLabels); - // This will preserve the old model data and extend the existing model - // with the new features extracted from newImages! - @endcode - - Calling update on an Eigenfaces model (see createEigenFaceRecognizer), which doesn't support - updating, will throw an error similar to: - - @code - OpenCV Error: The function/feature is not implemented (This FaceRecognizer (FaceRecognizer.Eigenfaces) does not support updating, you have to use FaceRecognizer::train to update it.) in update, file /home/philipp/git/opencv/modules/contrib/src/facerec.cpp, line 305 - terminate called after throwing an instance of 'cv::Exception' - @endcode - - @note The FaceRecognizer does not store your training images, because this would be very - memory intense and it's not the responsibility of te FaceRecognizer to do so. The caller is - responsible for maintaining the dataset, he want to work with. - */ - CV_WRAP virtual void update(InputArrayOfArrays src, InputArray labels); - - /** @overload */ - CV_WRAP int predict(InputArray src) const; - - - /** @brief Predicts a label and associated confidence (e.g. distance) for a given input image. - - @param src Sample image to get a prediction from. - @param label The predicted label for the given image. - @param confidence Associated confidence (e.g. distance) for the predicted label. - - The suffix const means that prediction does not affect the internal model state, so the method can - be safely called from within different threads. - - The following example shows how to get a prediction from a trained model: - - @code - using namespace cv; - // Do your initialization here (create the cv::FaceRecognizer model) ... - // ... - // Read in a sample image: - Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE); - // And get a prediction from the cv::FaceRecognizer: - int predicted = model->predict(img); - @endcode - - Or to get a prediction and the associated confidence (e.g. distance): - - @code - using namespace cv; - // Do your initialization here (create the cv::FaceRecognizer model) ... - // ... - Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE); - // Some variables for the predicted label and associated confidence (e.g. distance): - int predicted_label = -1; - double predicted_confidence = 0.0; - // Get the prediction and associated confidence from the model - model->predict(img, predicted_label, predicted_confidence); - @endcode - */ - CV_WRAP void predict(InputArray src, CV_OUT int &label, CV_OUT double &confidence) const; - - - /** @brief - if implemented - send all result of prediction to collector that can be used for somehow custom result handling - @param src Sample image to get a prediction from. - @param collector User-defined collector object that accepts all results - @param state - optional user-defined state token that should be passed back from FaceRecognizer implementation - - To implement this method u just have to do same internal cycle as in predict(InputArray src, CV_OUT int &label, CV_OUT double &confidence) but - not try to get "best@ result, just resend it to caller side with given collector - */ - CV_WRAP virtual void predict(InputArray src, Ptr collector, const int state = 0) const = 0; - - /** @brief Saves a FaceRecognizer and its model state. - - Saves this model to a given filename, either as XML or YAML. - @param filename The filename to store this FaceRecognizer to (either XML/YAML). - - Every FaceRecognizer overwrites FaceRecognizer::save(FileStorage& fs) to save the internal model - state. FaceRecognizer::save(const String& filename) saves the state of a model to the given - filename. - - The suffix const means that prediction does not affect the internal model state, so the method can - be safely called from within different threads. - */ - CV_WRAP virtual void save(const String& filename) const; - - /** @brief Loads a FaceRecognizer and its model state. - - Loads a persisted model and state from a given XML or YAML file . Every FaceRecognizer has to - overwrite FaceRecognizer::load(FileStorage& fs) to enable loading the model state. - FaceRecognizer::load(FileStorage& fs) in turn gets called by - FaceRecognizer::load(const String& filename), to ease saving a model. - */ - CV_WRAP virtual void load(const String& filename); - - /** @overload - Saves this model to a given FileStorage. - @param fs The FileStorage to store this FaceRecognizer to. - */ - virtual void save(FileStorage& fs) const = 0; - - /** @overload */ - virtual void load(const FileStorage& fs) = 0; - - /** @brief Sets string info for the specified model's label. - - The string info is replaced by the provided value if it was set before for the specified label. - */ - CV_WRAP virtual void setLabelInfo(int label, const String& strInfo); - - /** @brief Gets string information by label. - - If an unknown label id is provided or there is no label information associated with the specified - label id the method returns an empty string. - */ - CV_WRAP virtual String getLabelInfo(int label) const; - - /** @brief Gets vector of labels by string. - - The function searches for the labels containing the specified sub-string in the associated string - info. - */ - CV_WRAP virtual std::vector getLabelsByString(const String& str) const; - /** @brief threshhold parameter accessor - required for default BestMinDist collector */ - virtual double getThreshold() const = 0; -protected: - // Stored pairs "label id - string info" - std::map _labelsInfo; -}; - -//! @} - -}} - -#include "opencv2/face/facerec.hpp" - -#endif diff --git a/IPL/include/opencv/opencv2/face/facerec.hpp b/IPL/include/opencv/opencv2/face/facerec.hpp deleted file mode 100644 index 40f62f1..0000000 --- a/IPL/include/opencv/opencv2/face/facerec.hpp +++ /dev/null @@ -1,166 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -// Copyright (c) 2011,2012. Philipp Wagner . -// Third party copyrights are property of their respective owners. - -#ifndef __OPENCV_FACEREC_HPP__ -#define __OPENCV_FACEREC_HPP__ - -#include "opencv2/face.hpp" -#include "opencv2/core.hpp" - -namespace cv { namespace face { - -//! @addtogroup face -//! @{ - -// base for two classes -class CV_EXPORTS_W BasicFaceRecognizer : public FaceRecognizer -{ -public: - /** @see setNumComponents */ - CV_WRAP virtual int getNumComponents() const = 0; - /** @copybrief getNumComponents @see getNumComponents */ - CV_WRAP virtual void setNumComponents(int val) = 0; - /** @see setThreshold */ - CV_WRAP virtual double getThreshold() const = 0; - /** @copybrief getThreshold @see getThreshold */ - CV_WRAP virtual void setThreshold(double val) = 0; - CV_WRAP virtual std::vector getProjections() const = 0; - CV_WRAP virtual cv::Mat getLabels() const = 0; - CV_WRAP virtual cv::Mat getEigenValues() const = 0; - CV_WRAP virtual cv::Mat getEigenVectors() const = 0; - CV_WRAP virtual cv::Mat getMean() const = 0; -}; - -/** -@param num_components The number of components (read: Eigenfaces) kept for this Principal -Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be -kept for good reconstruction capabilities. It is based on your input data, so experiment with the -number. Keeping 80 components should almost always be sufficient. -@param threshold The threshold applied in the prediction. - -### Notes: - -- Training and prediction must be done on grayscale images, use cvtColor to convert between the - color spaces. -- **THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL - SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your - input data has the correct shape, else a meaningful exception is thrown. Use resize to resize - the images. -- This model does not support updating. - -### Model internal data: - -- num_components see createEigenFaceRecognizer. -- threshold see createEigenFaceRecognizer. -- eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending). -- eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their - eigenvalue). -- mean The sample mean calculated from the training data. -- projections The projections of the training data. -- labels The threshold applied in the prediction. If the distance to the nearest neighbor is - larger than the threshold, this method returns -1. - */ -CV_EXPORTS_W Ptr createEigenFaceRecognizer(int num_components = 0, double threshold = DBL_MAX); - -/** -@param num_components The number of components (read: Fisherfaces) kept for this Linear -Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that -means the number of your classes c (read: subjects, persons you want to recognize). If you leave -this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the -correct number (c-1) automatically. -@param threshold The threshold applied in the prediction. If the distance to the nearest neighbor -is larger than the threshold, this method returns -1. - -### Notes: - -- Training and prediction must be done on grayscale images, use cvtColor to convert between the - color spaces. -- **THE FISHERFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL - SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your - input data has the correct shape, else a meaningful exception is thrown. Use resize to resize - the images. -- This model does not support updating. - -### Model internal data: - -- num_components see createFisherFaceRecognizer. -- threshold see createFisherFaceRecognizer. -- eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending). -- eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their - eigenvalue). -- mean The sample mean calculated from the training data. -- projections The projections of the training data. -- labels The labels corresponding to the projections. - */ -CV_EXPORTS_W Ptr createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX); - -class CV_EXPORTS_W LBPHFaceRecognizer : public FaceRecognizer -{ -public: - /** @see setGridX */ - CV_WRAP virtual int getGridX() const = 0; - /** @copybrief getGridX @see getGridX */ - CV_WRAP virtual void setGridX(int val) = 0; - /** @see setGridY */ - CV_WRAP virtual int getGridY() const = 0; - /** @copybrief getGridY @see getGridY */ - CV_WRAP virtual void setGridY(int val) = 0; - /** @see setRadius */ - CV_WRAP virtual int getRadius() const = 0; - /** @copybrief getRadius @see getRadius */ - CV_WRAP virtual void setRadius(int val) = 0; - /** @see setNeighbors */ - CV_WRAP virtual int getNeighbors() const = 0; - /** @copybrief getNeighbors @see getNeighbors */ - CV_WRAP virtual void setNeighbors(int val) = 0; - /** @see setThreshold */ - CV_WRAP virtual double getThreshold() const = 0; - /** @copybrief getThreshold @see getThreshold */ - CV_WRAP virtual void setThreshold(double val) = 0; - CV_WRAP virtual std::vector getHistograms() const = 0; - CV_WRAP virtual cv::Mat getLabels() const = 0; -}; - -/** -@param radius The radius used for building the Circular Local Binary Pattern. The greater the -radius, the -@param neighbors The number of sample points to build a Circular Local Binary Pattern from. An -appropriate value is to use `8` sample points. Keep in mind: the more sample points you include, -the higher the computational cost. -@param grid_x The number of cells in the horizontal direction, 8 is a common value used in -publications. The more cells, the finer the grid, the higher the dimensionality of the resulting -feature vector. -@param grid_y The number of cells in the vertical direction, 8 is a common value used in -publications. The more cells, the finer the grid, the higher the dimensionality of the resulting -feature vector. -@param threshold The threshold applied in the prediction. If the distance to the nearest neighbor -is larger than the threshold, this method returns -1. - -### Notes: - -- The Circular Local Binary Patterns (used in training and prediction) expect the data given as - grayscale images, use cvtColor to convert between the color spaces. -- This model supports updating. - -### Model internal data: - -- radius see createLBPHFaceRecognizer. -- neighbors see createLBPHFaceRecognizer. -- grid_x see createLBPHFaceRecognizer. -- grid_y see createLBPHFaceRecognizer. -- threshold see createLBPHFaceRecognizer. -- histograms Local Binary Patterns Histograms calculated from the given training data (empty if - none was given). -- labels Labels corresponding to the calculated Local Binary Patterns Histograms. - */ -CV_EXPORTS_W Ptr createLBPHFaceRecognizer(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold = DBL_MAX); - -//! @} - -}} //namespace cv::face - -#endif //__OPENCV_FACEREC_HPP__ diff --git a/IPL/include/opencv/opencv2/face/predict_collector.hpp b/IPL/include/opencv/opencv2/face/predict_collector.hpp deleted file mode 100644 index 92de6c1..0000000 --- a/IPL/include/opencv/opencv2/face/predict_collector.hpp +++ /dev/null @@ -1,305 +0,0 @@ -/* -By downloading, copying, installing or using the software you agree to this license. -If you do not agree to this license, do not download, install, -copy or use the software. - - - License Agreement - For Open Source Computer Vision Library - (3-clause BSD License) - -Copyright (C) 2000-2015, Intel Corporation, all rights reserved. -Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. -Copyright (C) 2009-2015, NVIDIA Corporation, all rights reserved. -Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. -Copyright (C) 2015, OpenCV Foundation, all rights reserved. -Copyright (C) 2015, Itseez Inc., all rights reserved. -Third party copyrights are property of their respective owners. - -Redistribution and use in source and binary forms, with or without modification, -are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. - - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - - * Neither the names of the copyright holders nor the names of the contributors - may be used to endorse or promote products derived from this software - without specific prior written permission. - -This software is provided by the copyright holders and contributors "as is" and -any express or implied warranties, including, but not limited to, the implied -warranties of merchantability and fitness for a particular purpose are disclaimed. -In no event shall copyright holders or contributors be liable for any direct, -indirect, incidental, special, exemplary, or consequential damages -(including, but not limited to, procurement of substitute goods or services; -loss of use, data, or profits; or business interruption) however caused -and on any theory of liability, whether in contract, strict liability, -or tort (including negligence or otherwise) arising in any way out of -the use of this software, even if advised of the possibility of such damage. -*/ - -#ifndef __OPENCV_PREDICT_COLLECTOR_HPP__ -#define __OPENCV_PREDICT_COLLECTOR_HPP__ -#include -#include -#include -#include -#include "opencv2/core/cvdef.h" -#include "opencv2/core/cvstd.hpp" -#undef emit //fix for qt -namespace cv { -namespace face { -//! @addtogroup face -//! @{ -/** @brief Abstract base class for all strategies of prediction result handling -*/ -class CV_EXPORTS_W PredictCollector { -protected: - double _threshold; - int _size; - int _state; - int _excludeLabel; - double _distanceKoef; - double _minthreshold; -public: - /** @brief creates new predict collector with given threshold */ - PredictCollector(double threshold = DBL_MAX) { - _threshold = threshold; - _excludeLabel = 0; - _distanceKoef = 1; - _minthreshold = -1; - } - CV_WRAP virtual ~PredictCollector() {} - - /** @brief called once at start of recognition - @param size total size of prediction evaluation that recognizer could perform - @param state user defined send-to-back optional value to allow multi-thread, multi-session or aggregation scenarios - */ - CV_WRAP virtual void init(const int size, const int state = 0); - - /** @brief called by recognizer prior to emit to decide if prediction require emiting - @param label current predicted label - @param dist current predicted distance - @param state back send state parameter of prediction session - @return true if prediction is valid and required for emiting - @note can override given label and distance to another values - */ - CV_WRAP virtual bool defaultFilter(int* label, double* dist, const int state); - - /** @brief extension point for filter - called if base filter executed */ - CV_WRAP virtual bool filter(int* label, double* dist, const int state); - - /** @brief called with every recognition result - @param label current prediction label - @param dist current prediction distance (confidence) - @param state user defined send-to-back optional value to allow multi-thread, multi-session or aggregation scenarios - @return true if recognizer should proceed prediction , false - if recognizer should terminate prediction - */ - CV_WRAP virtual bool emit(const int label, const double dist, const int state = 0); //not abstract while Python generation require non-abstract class - - /** @brief outer interface method to be called from recognizer - @param label current prediction label - @param dist current prediction distance (confidence) - @param state user defined send-to-back optional value to allow multi-thread, multi-session or aggregation scenarios - @note wraps filter and emit calls, not tended to be overriden - */ - CV_WRAP virtual bool collect(int label, double dist, const int state = 0); - - /** - @brief get size of prediction - ### Description - Is set by recognizer and is amount of all available predicts - So we can use it to perform statistic collectors before prediction of whole set - */ - CV_WRAP virtual int getSize(); - - /** @brief set size of prediction */ - CV_WRAP virtual void setSize(int size); - - /** - @brief get state of prediction - ### Description - State is a custom value assigned for prediction session, 0 if it's no-state session - */ - CV_WRAP virtual int getState(); - - /** @brief set state of prediction */ - CV_WRAP virtual void setState(int state); - - /** - @brief returns currently excluded label, 0 if no set - ### Description - We require to exclude label if we want to test card in train set against others - */ - CV_WRAP virtual int getExcludeLabel(); - - /** @brief set exclude label of prediction */ - CV_WRAP virtual void setExcludeLabel(int excludeLabel); - - /** - @brief returns current distance koeficient (applyed to distance in filter stage) - ### Description - It's required if we want to predict with distinct algorithms in one session - so LBPH, Eigen and Fisher distance are different, but we can provide koef for them to translate to - each other (while their distribuition for same train set is close and started from 0) - Default 1 koef means that distance is not corrected - */ - CV_WRAP virtual double getDistanceKoef(); - - /** @brief set exclude label of prediction */ - CV_WRAP virtual void setDistanceKoef(double distanceKoef); - /** - @brief returns current minimal threshold - ### Description - It's required when we must exclude most closed predictions (for example we - search for close but not same faces - usable for mixed set where doubles exists - in train collection) - */ - CV_WRAP virtual double getMinThreshold(); - - /** @brief set minimal threshold for prediction */ - CV_WRAP virtual void setMinThreshold(double minthreshold); - -}; - -/** @brief default predict collector that trace minimal distance with treshhold checking (that is default behavior for most predict logic) -*/ -class CV_EXPORTS_W MinDistancePredictCollector : public PredictCollector { -private: - int _label; - double _dist; -public: - /** @brief creates new MinDistancePredictCollector with given threshold */ - CV_WRAP MinDistancePredictCollector(double threshold = DBL_MAX) : PredictCollector(threshold) { - _label = -1; - _dist = DBL_MAX; - }; - CV_WRAP bool emit(const int label, const double dist, const int state = 0); - CV_WRAP bool filter(int* label, double* dist, const int state); - /** @brief result label, -1 if not found */ - CV_WRAP int getLabel() const; - /** @brief result distance (confidence) DBL_MAX if not found */ - CV_WRAP double getDist() const; - /** @brief factory method to create cv-pointers to MinDistancePredictCollector */ - CV_WRAP static Ptr create(double threshold = DBL_MAX); -}; - -/** -@brief Collects top N most close predictions -@note Prevent doubling of same label - if one label is occured twice - most closed distance value will be set -*/ -class CV_EXPORTS_W TopNPredictCollector : public PredictCollector { -private: - size_t _size; - Ptr > > _idx; -public: - CV_WRAP TopNPredictCollector(size_t size = 5, double threshold = DBL_MAX) : PredictCollector(threshold) { - _size = size; - _idx = Ptr > >(new std::list >); - }; - CV_WRAP bool emit(const int label, const double dist, const int state = 0); - CV_WRAP bool filter(int* label, double* dist, const int state); - Ptr > > getResult(); - CV_WRAP std::vector > getResultVector(); // pythonable version - CV_WRAP static Ptr create(size_t size = 5, double threshold = DBL_MAX); -}; - - -/** -@brief Collects all predict results to single vector -@note this collector not analyze double labels in emit, it's raw copy of source prediction result, -remember that filter is still applyed so you can use min/max threshold , distanceKoef and excludeLabel -*/ -class CV_EXPORTS_W VectorPredictCollector : public PredictCollector { -private: - Ptr > > _idx; -public: - CV_WRAP static const int DEFAULT_SIZE = 5; // top 5 by default - CV_WRAP VectorPredictCollector(double threshold = DBL_MAX) : PredictCollector(threshold) { - _idx = Ptr > >(new std::vector >); - } - CV_WRAP bool emit(const int label, const double dist, const int state = 0); - Ptr > > getResult(); - CV_WRAP std::vector > getResultVector(); // pythonable version - CV_WRAP static Ptr create(double threshold = DBL_MAX); -}; - - -/** -@brief Collects all predict results to single vector -@note this collector not analyze double labels in emit, it's raw copy of source prediction result, -remember that filter is still applyed so you can use min/max threshold , distanceKoef and excludeLabel -*/ -class CV_EXPORTS_W MapPredictCollector : public PredictCollector { -private: - Ptr > _idx; -public: - CV_WRAP static const int DEFAULT_SIZE = 5; // top 5 by default - CV_WRAP MapPredictCollector(double threshold = DBL_MAX) : PredictCollector(threshold) { - _idx = Ptr >(new std::map); - } - CV_WRAP bool emit(const int label, const double dist, const int state = 0); - Ptr > getResult(); - CV_WRAP std::vector > getResultVector(); // pythonable version - CV_WRAP static Ptr create(double threshold = DBL_MAX); -}; - -/** -@brief Collects basic statistic information about prediction -@note stat predict collector is usefull for determining valid thresholds -on given trained set, additionally it's required to -evaluate unified koefs between algorithms -*/ -class CV_EXPORTS_W StatPredictCollector : public PredictCollector { -private: - double _min; - double _max; - int _count; - double _sum; -public: - CV_WRAP StatPredictCollector(double threshold = DBL_MAX) : PredictCollector(threshold) { - _min = DBL_MAX; - _max = DBL_MIN; - _count = 0; - _sum = 0; - } - CV_WRAP bool emit(const int label, const double dist, const int state = 0); - CV_WRAP double getMin(); - CV_WRAP double getMax(); - CV_WRAP double getSum(); - CV_WRAP int getCount(); - CV_WRAP static Ptr create(double threshold = DBL_MAX); -}; - -/** -@brief evaluates standard deviation of given prediction session over trained set -@note in combine with StatPredictCollector can provide statistically based metrices -for thresholds -*/ -class CV_EXPORTS_W StdPredictCollector : public PredictCollector { -private: - double _avg; - double _n; - double _s; -public: - CV_WRAP StdPredictCollector(double threshold = DBL_MAX, double avg = 0) : PredictCollector(threshold) { - _avg = avg; - _n = 0; - _s = 0; - } - CV_WRAP bool emit(const int label, const double dist, const int state = 0); - CV_WRAP double getResult(); - CV_WRAP static Ptr create(double threshold = DBL_MAX, double avg = 0); -}; - - - -//! @} -} -} - -#endif \ No newline at end of file diff --git a/IPL/include/opencv/opencv2/features2d.hpp b/IPL/include/opencv/opencv2/features2d.hpp deleted file mode 100644 index 692d3d9..0000000 --- a/IPL/include/opencv/opencv2/features2d.hpp +++ /dev/null @@ -1,1325 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_FEATURES_2D_HPP__ -#define __OPENCV_FEATURES_2D_HPP__ - -#include "opencv2/core.hpp" -#include "opencv2/flann/miniflann.hpp" - -/** - @defgroup features2d 2D Features Framework - @{ - @defgroup features2d_main Feature Detection and Description - @defgroup features2d_match Descriptor Matchers - -Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to -easily switch between different algorithms solving the same problem. This section is devoted to -matching descriptors that are represented as vectors in a multidimensional space. All objects that -implement vector descriptor matchers inherit the DescriptorMatcher interface. - -@note - - An example explaining keypoint matching can be found at - opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp - - An example on descriptor matching evaluation can be found at - opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp - - An example on one to many image matching can be found at - opencv_source_code/samples/cpp/matching_to_many_images.cpp - - @defgroup features2d_draw Drawing Function of Keypoints and Matches - @defgroup features2d_category Object Categorization - -This section describes approaches based on local 2D features and used to categorize objects. - -@note - - A complete Bag-Of-Words sample can be found at - opencv_source_code/samples/cpp/bagofwords_classification.cpp - - (Python) An example using the features2D framework to perform object categorization can be - found at opencv_source_code/samples/python/find_obj.py - - @} - */ - -namespace cv -{ - -//! @addtogroup features2d -//! @{ - -// //! writes vector of keypoints to the file storage -// CV_EXPORTS void write(FileStorage& fs, const String& name, const std::vector& keypoints); -// //! reads vector of keypoints from the specified file storage node -// CV_EXPORTS void read(const FileNode& node, CV_OUT std::vector& keypoints); - -/** @brief A class filters a vector of keypoints. - - Because now it is difficult to provide a convenient interface for all usage scenarios of the - keypoints filter class, it has only several needed by now static methods. - */ -class CV_EXPORTS KeyPointsFilter -{ -public: - KeyPointsFilter(){} - - /* - * Remove keypoints within borderPixels of an image edge. - */ - static void runByImageBorder( std::vector& keypoints, Size imageSize, int borderSize ); - /* - * Remove keypoints of sizes out of range. - */ - static void runByKeypointSize( std::vector& keypoints, float minSize, - float maxSize=FLT_MAX ); - /* - * Remove keypoints from some image by mask for pixels of this image. - */ - static void runByPixelsMask( std::vector& keypoints, const Mat& mask ); - /* - * Remove duplicated keypoints. - */ - static void removeDuplicated( std::vector& keypoints ); - - /* - * Retain the specified number of the best keypoints (according to the response) - */ - static void retainBest( std::vector& keypoints, int npoints ); -}; - - -/************************************ Base Classes ************************************/ - -/** @brief Abstract base class for 2D image feature detectors and descriptor extractors -*/ -class CV_EXPORTS_W Feature2D : public virtual Algorithm -{ -public: - virtual ~Feature2D(); - - /** @brief Detects keypoints in an image (first variant) or image set (second variant). - - @param image Image. - @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set - of keypoints detected in images[i] . - @param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer - matrix with non-zero values in the region of interest. - */ - CV_WRAP virtual void detect( InputArray image, - CV_OUT std::vector& keypoints, - InputArray mask=noArray() ); - - /** @overload - @param images Image set. - @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set - of keypoints detected in images[i] . - @param masks Masks for each input image specifying where to look for keypoints (optional). - masks[i] is a mask for images[i]. - */ - virtual void detect( InputArrayOfArrays images, - std::vector >& keypoints, - InputArrayOfArrays masks=noArray() ); - - /** @brief Computes the descriptors for a set of keypoints detected in an image (first variant) or image set - (second variant). - - @param image Image. - @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be - computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint - with several dominant orientations (for each orientation). - @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are - descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the - descriptor for keypoint j-th keypoint. - */ - CV_WRAP virtual void compute( InputArray image, - CV_OUT CV_IN_OUT std::vector& keypoints, - OutputArray descriptors ); - - /** @overload - - @param images Image set. - @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be - computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint - with several dominant orientations (for each orientation). - @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are - descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the - descriptor for keypoint j-th keypoint. - */ - virtual void compute( InputArrayOfArrays images, - std::vector >& keypoints, - OutputArrayOfArrays descriptors ); - - /** Detects keypoints and computes the descriptors */ - CV_WRAP virtual void detectAndCompute( InputArray image, InputArray mask, - CV_OUT std::vector& keypoints, - OutputArray descriptors, - bool useProvidedKeypoints=false ); - - CV_WRAP virtual int descriptorSize() const; - CV_WRAP virtual int descriptorType() const; - CV_WRAP virtual int defaultNorm() const; - - //! Return true if detector object is empty - CV_WRAP virtual bool empty() const; -}; - -/** Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch -between different algorithms solving the same problem. All objects that implement keypoint detectors -inherit the FeatureDetector interface. */ -typedef Feature2D FeatureDetector; - -/** Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you -to easily switch between different algorithms solving the same problem. This section is devoted to -computing descriptors represented as vectors in a multidimensional space. All objects that implement -the vector descriptor extractors inherit the DescriptorExtractor interface. - */ -typedef Feature2D DescriptorExtractor; - -//! @addtogroup features2d_main -//! @{ - -/** @brief Class implementing the BRISK keypoint detector and descriptor extractor, described in @cite LCS11 . - */ -class CV_EXPORTS_W BRISK : public Feature2D -{ -public: - /** @brief The BRISK constructor - - @param thresh AGAST detection threshold score. - @param octaves detection octaves. Use 0 to do single scale. - @param patternScale apply this scale to the pattern used for sampling the neighbourhood of a - keypoint. - */ - CV_WRAP static Ptr create(int thresh=30, int octaves=3, float patternScale=1.0f); - - /** @brief The BRISK constructor for a custom pattern - - @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for - keypoint scale 1). - @param numberList defines the number of sampling points on the sampling circle. Must be the same - size as radiusList.. - @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint - scale 1). - @param dMin threshold for the long pairings used for orientation determination (in pixels for - keypoint scale 1). - @param indexChange index remapping of the bits. */ - CV_WRAP static Ptr create(const std::vector &radiusList, const std::vector &numberList, - float dMax=5.85f, float dMin=8.2f, const std::vector& indexChange=std::vector()); -}; - -/** @brief Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor - -described in @cite RRKB11 . The algorithm uses FAST in pyramids to detect stable keypoints, selects -the strongest features using FAST or Harris response, finds their orientation using first-order -moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or -k-tuples) are rotated according to the measured orientation). - */ -class CV_EXPORTS_W ORB : public Feature2D -{ -public: - enum { kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1 }; - - /** @brief The ORB constructor - - @param nfeatures The maximum number of features to retain. - @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical - pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor - will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor - will mean that to cover certain scale range you will need more pyramid levels and so the speed - will suffer. - @param nlevels The number of pyramid levels. The smallest level will have linear size equal to - input_image_linear_size/pow(scaleFactor, nlevels). - @param edgeThreshold This is size of the border where the features are not detected. It should - roughly match the patchSize parameter. - @param firstLevel It should be 0 in the current implementation. - @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The - default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, - so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 - random points (of course, those point coordinates are random, but they are generated from the - pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel - rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such - output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, - denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each - bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3). - @param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features - (the score is written to KeyPoint::score and is used to retain best nfeatures features); - FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, - but it is a little faster to compute. - @param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller - pyramid layers the perceived image area covered by a feature will be larger. - @param fastThreshold - */ - CV_WRAP static Ptr create(int nfeatures=500, float scaleFactor=1.2f, int nlevels=8, int edgeThreshold=31, - int firstLevel=0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31, int fastThreshold=20); - - CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0; - CV_WRAP virtual int getMaxFeatures() const = 0; - - CV_WRAP virtual void setScaleFactor(double scaleFactor) = 0; - CV_WRAP virtual double getScaleFactor() const = 0; - - CV_WRAP virtual void setNLevels(int nlevels) = 0; - CV_WRAP virtual int getNLevels() const = 0; - - CV_WRAP virtual void setEdgeThreshold(int edgeThreshold) = 0; - CV_WRAP virtual int getEdgeThreshold() const = 0; - - CV_WRAP virtual void setFirstLevel(int firstLevel) = 0; - CV_WRAP virtual int getFirstLevel() const = 0; - - CV_WRAP virtual void setWTA_K(int wta_k) = 0; - CV_WRAP virtual int getWTA_K() const = 0; - - CV_WRAP virtual void setScoreType(int scoreType) = 0; - CV_WRAP virtual int getScoreType() const = 0; - - CV_WRAP virtual void setPatchSize(int patchSize) = 0; - CV_WRAP virtual int getPatchSize() const = 0; - - CV_WRAP virtual void setFastThreshold(int fastThreshold) = 0; - CV_WRAP virtual int getFastThreshold() const = 0; -}; - -/** @brief Maximally stable extremal region extractor - -The class encapsulates all the parameters of the %MSER extraction algorithm (see [wiki -article](http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions)). - -- there are two different implementation of %MSER: one for grey image, one for color image - -- the grey image algorithm is taken from: @cite nister2008linear ; the paper claims to be faster -than union-find method; it actually get 1.5~2m/s on my centrino L7200 1.2GHz laptop. - -- the color image algorithm is taken from: @cite forssen2007maximally ; it should be much slower -than grey image method ( 3~4 times ); the chi_table.h file is taken directly from paper's source -code which is distributed under GPL. - -- (Python) A complete example showing the use of the %MSER detector can be found at samples/python/mser.py -*/ -class CV_EXPORTS_W MSER : public Feature2D -{ -public: - /** @brief Full consturctor for %MSER detector - - @param _delta it compares \f$(size_{i}-size_{i-delta})/size_{i-delta}\f$ - @param _min_area prune the area which smaller than minArea - @param _max_area prune the area which bigger than maxArea - @param _max_variation prune the area have simliar size to its children - @param _min_diversity for color image, trace back to cut off mser with diversity less than min_diversity - @param _max_evolution for color image, the evolution steps - @param _area_threshold for color image, the area threshold to cause re-initialize - @param _min_margin for color image, ignore too small margin - @param _edge_blur_size for color image, the aperture size for edge blur - */ - CV_WRAP static Ptr create( int _delta=5, int _min_area=60, int _max_area=14400, - double _max_variation=0.25, double _min_diversity=.2, - int _max_evolution=200, double _area_threshold=1.01, - double _min_margin=0.003, int _edge_blur_size=5 ); - - /** @brief Detect %MSER regions - - @param image input image (8UC1, 8UC3 or 8UC4) - @param msers resulting list of point sets - @param bboxes resulting bounding boxes - */ - CV_WRAP virtual void detectRegions( InputArray image, - CV_OUT std::vector >& msers, - std::vector& bboxes ) = 0; - - CV_WRAP virtual void setDelta(int delta) = 0; - CV_WRAP virtual int getDelta() const = 0; - - CV_WRAP virtual void setMinArea(int minArea) = 0; - CV_WRAP virtual int getMinArea() const = 0; - - CV_WRAP virtual void setMaxArea(int maxArea) = 0; - CV_WRAP virtual int getMaxArea() const = 0; - - CV_WRAP virtual void setPass2Only(bool f) = 0; - CV_WRAP virtual bool getPass2Only() const = 0; -}; - -/** @overload */ -CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector& keypoints, - int threshold, bool nonmaxSuppression=true ); - -/** @brief Detects corners using the FAST algorithm - -@param image grayscale image where keypoints (corners) are detected. -@param keypoints keypoints detected on the image. -@param threshold threshold on difference between intensity of the central pixel and pixels of a -circle around this pixel. -@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners -(keypoints). -@param type one of the three neighborhoods as defined in the paper: -FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12, -FastFeatureDetector::TYPE_5_8 - -Detects corners using the FAST algorithm by @cite Rosten06 . - -@note In Python API, types are given as cv2.FAST_FEATURE_DETECTOR_TYPE_5_8, -cv2.FAST_FEATURE_DETECTOR_TYPE_7_12 and cv2.FAST_FEATURE_DETECTOR_TYPE_9_16. For corner -detection, use cv2.FAST.detect() method. - */ -CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector& keypoints, - int threshold, bool nonmaxSuppression, int type ); - -//! @} features2d_main - -//! @addtogroup features2d_main -//! @{ - -/** @brief Wrapping class for feature detection using the FAST method. : - */ -class CV_EXPORTS_W FastFeatureDetector : public Feature2D -{ -public: - enum - { - TYPE_5_8 = 0, TYPE_7_12 = 1, TYPE_9_16 = 2, - THRESHOLD = 10000, NONMAX_SUPPRESSION=10001, FAST_N=10002, - }; - - CV_WRAP static Ptr create( int threshold=10, - bool nonmaxSuppression=true, - int type=FastFeatureDetector::TYPE_9_16 ); - - CV_WRAP virtual void setThreshold(int threshold) = 0; - CV_WRAP virtual int getThreshold() const = 0; - - CV_WRAP virtual void setNonmaxSuppression(bool f) = 0; - CV_WRAP virtual bool getNonmaxSuppression() const = 0; - - CV_WRAP virtual void setType(int type) = 0; - CV_WRAP virtual int getType() const = 0; -}; - -/** @overload */ -CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector& keypoints, - int threshold, bool nonmaxSuppression=true ); - -/** @brief Detects corners using the AGAST algorithm - -@param image grayscale image where keypoints (corners) are detected. -@param keypoints keypoints detected on the image. -@param threshold threshold on difference between intensity of the central pixel and pixels of a -circle around this pixel. -@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners -(keypoints). -@param type one of the four neighborhoods as defined in the paper: -AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d, -AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16 - -For non-Intel platforms, there is a tree optimised variant of AGAST with same numerical results. -The 32-bit binary tree tables were generated automatically from original code using perl script. -The perl script and examples of tree generation are placed in features2d/doc folder. -Detects corners using the AGAST algorithm by @cite mair2010_agast . - - */ -CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector& keypoints, - int threshold, bool nonmaxSuppression, int type ); -//! @} features2d_main - -//! @addtogroup features2d_main -//! @{ - -/** @brief Wrapping class for feature detection using the AGAST method. : - */ -class CV_EXPORTS_W AgastFeatureDetector : public Feature2D -{ -public: - enum - { - AGAST_5_8 = 0, AGAST_7_12d = 1, AGAST_7_12s = 2, OAST_9_16 = 3, - THRESHOLD = 10000, NONMAX_SUPPRESSION = 10001, - }; - - CV_WRAP static Ptr create( int threshold=10, - bool nonmaxSuppression=true, - int type=AgastFeatureDetector::OAST_9_16 ); - - CV_WRAP virtual void setThreshold(int threshold) = 0; - CV_WRAP virtual int getThreshold() const = 0; - - CV_WRAP virtual void setNonmaxSuppression(bool f) = 0; - CV_WRAP virtual bool getNonmaxSuppression() const = 0; - - CV_WRAP virtual void setType(int type) = 0; - CV_WRAP virtual int getType() const = 0; -}; - -/** @brief Wrapping class for feature detection using the goodFeaturesToTrack function. : - */ -class CV_EXPORTS_W GFTTDetector : public Feature2D -{ -public: - CV_WRAP static Ptr create( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1, - int blockSize=3, bool useHarrisDetector=false, double k=0.04 ); - CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0; - CV_WRAP virtual int getMaxFeatures() const = 0; - - CV_WRAP virtual void setQualityLevel(double qlevel) = 0; - CV_WRAP virtual double getQualityLevel() const = 0; - - CV_WRAP virtual void setMinDistance(double minDistance) = 0; - CV_WRAP virtual double getMinDistance() const = 0; - - CV_WRAP virtual void setBlockSize(int blockSize) = 0; - CV_WRAP virtual int getBlockSize() const = 0; - - CV_WRAP virtual void setHarrisDetector(bool val) = 0; - CV_WRAP virtual bool getHarrisDetector() const = 0; - - CV_WRAP virtual void setK(double k) = 0; - CV_WRAP virtual double getK() const = 0; -}; - -/** @brief Class for extracting blobs from an image. : - -The class implements a simple algorithm for extracting blobs from an image: - -1. Convert the source image to binary images by applying thresholding with several thresholds from - minThreshold (inclusive) to maxThreshold (exclusive) with distance thresholdStep between - neighboring thresholds. -2. Extract connected components from every binary image by findContours and calculate their - centers. -3. Group centers from several binary images by their coordinates. Close centers form one group that - corresponds to one blob, which is controlled by the minDistBetweenBlobs parameter. -4. From the groups, estimate final centers of blobs and their radiuses and return as locations and - sizes of keypoints. - -This class performs several filtrations of returned blobs. You should set filterBy\* to true/false -to turn on/off corresponding filtration. Available filtrations: - -- **By color**. This filter compares the intensity of a binary image at the center of a blob to -blobColor. If they differ, the blob is filtered out. Use blobColor = 0 to extract dark blobs -and blobColor = 255 to extract light blobs. -- **By area**. Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive). -- **By circularity**. Extracted blobs have circularity -(\f$\frac{4*\pi*Area}{perimeter * perimeter}\f$) between minCircularity (inclusive) and -maxCircularity (exclusive). -- **By ratio of the minimum inertia to maximum inertia**. Extracted blobs have this ratio -between minInertiaRatio (inclusive) and maxInertiaRatio (exclusive). -- **By convexity**. Extracted blobs have convexity (area / area of blob convex hull) between -minConvexity (inclusive) and maxConvexity (exclusive). - -Default values of parameters are tuned to extract dark circular blobs. - */ -class CV_EXPORTS_W SimpleBlobDetector : public Feature2D -{ -public: - struct CV_EXPORTS_W_SIMPLE Params - { - CV_WRAP Params(); - CV_PROP_RW float thresholdStep; - CV_PROP_RW float minThreshold; - CV_PROP_RW float maxThreshold; - CV_PROP_RW size_t minRepeatability; - CV_PROP_RW float minDistBetweenBlobs; - - CV_PROP_RW bool filterByColor; - CV_PROP_RW uchar blobColor; - - CV_PROP_RW bool filterByArea; - CV_PROP_RW float minArea, maxArea; - - CV_PROP_RW bool filterByCircularity; - CV_PROP_RW float minCircularity, maxCircularity; - - CV_PROP_RW bool filterByInertia; - CV_PROP_RW float minInertiaRatio, maxInertiaRatio; - - CV_PROP_RW bool filterByConvexity; - CV_PROP_RW float minConvexity, maxConvexity; - - void read( const FileNode& fn ); - void write( FileStorage& fs ) const; - }; - - CV_WRAP static Ptr - create(const SimpleBlobDetector::Params ¶meters = SimpleBlobDetector::Params()); -}; - -//! @} features2d_main - -//! @addtogroup features2d_main -//! @{ - -/** @brief Class implementing the KAZE keypoint detector and descriptor extractor, described in @cite ABD12 . - -@note AKAZE descriptor can only be used with KAZE or AKAZE keypoints .. [ABD12] KAZE Features. Pablo -F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision -(ECCV), Fiorenze, Italy, October 2012. -*/ -class CV_EXPORTS_W KAZE : public Feature2D -{ -public: - enum - { - DIFF_PM_G1 = 0, - DIFF_PM_G2 = 1, - DIFF_WEICKERT = 2, - DIFF_CHARBONNIER = 3 - }; - - /** @brief The KAZE constructor - - @param extended Set to enable extraction of extended (128-byte) descriptor. - @param upright Set to enable use of upright descriptors (non rotation-invariant). - @param threshold Detector response threshold to accept point - @param nOctaves Maximum octave evolution of the image - @param nOctaveLayers Default number of sublevels per scale level - @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or - DIFF_CHARBONNIER - */ - CV_WRAP static Ptr create(bool extended=false, bool upright=false, - float threshold = 0.001f, - int nOctaves = 4, int nOctaveLayers = 4, - int diffusivity = KAZE::DIFF_PM_G2); - - CV_WRAP virtual void setExtended(bool extended) = 0; - CV_WRAP virtual bool getExtended() const = 0; - - CV_WRAP virtual void setUpright(bool upright) = 0; - CV_WRAP virtual bool getUpright() const = 0; - - CV_WRAP virtual void setThreshold(double threshold) = 0; - CV_WRAP virtual double getThreshold() const = 0; - - CV_WRAP virtual void setNOctaves(int octaves) = 0; - CV_WRAP virtual int getNOctaves() const = 0; - - CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0; - CV_WRAP virtual int getNOctaveLayers() const = 0; - - CV_WRAP virtual void setDiffusivity(int diff) = 0; - CV_WRAP virtual int getDiffusivity() const = 0; -}; - -/** @brief Class implementing the AKAZE keypoint detector and descriptor extractor, described in @cite ANB13 . : - -@note AKAZE descriptors can only be used with KAZE or AKAZE keypoints. Try to avoid using *extract* -and *detect* instead of *operator()* due to performance reasons. .. [ANB13] Fast Explicit Diffusion -for Accelerated Features in Nonlinear Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien -Bartoli. In British Machine Vision Conference (BMVC), Bristol, UK, September 2013. - */ -class CV_EXPORTS_W AKAZE : public Feature2D -{ -public: - // AKAZE descriptor type - enum - { - DESCRIPTOR_KAZE_UPRIGHT = 2, ///< Upright descriptors, not invariant to rotation - DESCRIPTOR_KAZE = 3, - DESCRIPTOR_MLDB_UPRIGHT = 4, ///< Upright descriptors, not invariant to rotation - DESCRIPTOR_MLDB = 5 - }; - - /** @brief The AKAZE constructor - - @param descriptor_type Type of the extracted descriptor: DESCRIPTOR_KAZE, - DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT. - @param descriptor_size Size of the descriptor in bits. 0 -\> Full size - @param descriptor_channels Number of channels in the descriptor (1, 2, 3) - @param threshold Detector response threshold to accept point - @param nOctaves Maximum octave evolution of the image - @param nOctaveLayers Default number of sublevels per scale level - @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or - DIFF_CHARBONNIER - */ - CV_WRAP static Ptr create(int descriptor_type=AKAZE::DESCRIPTOR_MLDB, - int descriptor_size = 0, int descriptor_channels = 3, - float threshold = 0.001f, int nOctaves = 4, - int nOctaveLayers = 4, int diffusivity = KAZE::DIFF_PM_G2); - - CV_WRAP virtual void setDescriptorType(int dtype) = 0; - CV_WRAP virtual int getDescriptorType() const = 0; - - CV_WRAP virtual void setDescriptorSize(int dsize) = 0; - CV_WRAP virtual int getDescriptorSize() const = 0; - - CV_WRAP virtual void setDescriptorChannels(int dch) = 0; - CV_WRAP virtual int getDescriptorChannels() const = 0; - - CV_WRAP virtual void setThreshold(double threshold) = 0; - CV_WRAP virtual double getThreshold() const = 0; - - CV_WRAP virtual void setNOctaves(int octaves) = 0; - CV_WRAP virtual int getNOctaves() const = 0; - - CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0; - CV_WRAP virtual int getNOctaveLayers() const = 0; - - CV_WRAP virtual void setDiffusivity(int diff) = 0; - CV_WRAP virtual int getDiffusivity() const = 0; -}; - -//! @} features2d_main - -/****************************************************************************************\ -* Distance * -\****************************************************************************************/ - -template -struct CV_EXPORTS Accumulator -{ - typedef T Type; -}; - -template<> struct Accumulator { typedef float Type; }; -template<> struct Accumulator { typedef float Type; }; -template<> struct Accumulator { typedef float Type; }; -template<> struct Accumulator { typedef float Type; }; - -/* - * Squared Euclidean distance functor - */ -template -struct CV_EXPORTS SL2 -{ - enum { normType = NORM_L2SQR }; - typedef T ValueType; - typedef typename Accumulator::Type ResultType; - - ResultType operator()( const T* a, const T* b, int size ) const - { - return normL2Sqr(a, b, size); - } -}; - -/* - * Euclidean distance functor - */ -template -struct CV_EXPORTS L2 -{ - enum { normType = NORM_L2 }; - typedef T ValueType; - typedef typename Accumulator::Type ResultType; - - ResultType operator()( const T* a, const T* b, int size ) const - { - return (ResultType)std::sqrt((double)normL2Sqr(a, b, size)); - } -}; - -/* - * Manhattan distance (city block distance) functor - */ -template -struct CV_EXPORTS L1 -{ - enum { normType = NORM_L1 }; - typedef T ValueType; - typedef typename Accumulator::Type ResultType; - - ResultType operator()( const T* a, const T* b, int size ) const - { - return normL1(a, b, size); - } -}; - -/****************************************************************************************\ -* DescriptorMatcher * -\****************************************************************************************/ - -//! @addtogroup features2d_match -//! @{ - -/** @brief Abstract base class for matching keypoint descriptors. - -It has two groups of match methods: for matching descriptors of an image with another image or with -an image set. - */ -class CV_EXPORTS_W DescriptorMatcher : public Algorithm -{ -public: - virtual ~DescriptorMatcher(); - - /** @brief Adds descriptors to train a CPU(trainDescCollectionis) or GPU(utrainDescCollectionis) descriptor - collection. - - If the collection is not empty, the new descriptors are added to existing train descriptors. - - @param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same - train image. - */ - CV_WRAP virtual void add( InputArrayOfArrays descriptors ); - - /** @brief Returns a constant link to the train descriptor collection trainDescCollection . - */ - CV_WRAP const std::vector& getTrainDescriptors() const; - - /** @brief Clears the train descriptor collections. - */ - CV_WRAP virtual void clear(); - - /** @brief Returns true if there are no train descriptors in the both collections. - */ - CV_WRAP virtual bool empty() const; - - /** @brief Returns true if the descriptor matcher supports masking permissible matches. - */ - CV_WRAP virtual bool isMaskSupported() const = 0; - - /** @brief Trains a descriptor matcher - - Trains a descriptor matcher (for example, the flann index). In all methods to match, the method - train() is run every time before matching. Some descriptor matchers (for example, BruteForceMatcher) - have an empty implementation of this method. Other matchers really train their inner structures (for - example, FlannBasedMatcher trains flann::Index ). - */ - CV_WRAP virtual void train(); - - /** @brief Finds the best match for each descriptor from a query set. - - @param queryDescriptors Query set of descriptors. - @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors - collection stored in the class object. - @param matches Matches. If a query descriptor is masked out in mask , no match is added for this - descriptor. So, matches size may be smaller than the query descriptors count. - @param mask Mask specifying permissible matches between an input query and train matrices of - descriptors. - - In the first variant of this method, the train descriptors are passed as an input argument. In the - second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is - used. Optional mask (or masks) can be passed to specify which query and training descriptors can be - matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if - mask.at\(i,j) is non-zero. - */ - CV_WRAP void match( InputArray queryDescriptors, InputArray trainDescriptors, - CV_OUT std::vector& matches, InputArray mask=noArray() ) const; - - /** @brief Finds the k best matches for each descriptor from a query set. - - @param queryDescriptors Query set of descriptors. - @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors - collection stored in the class object. - @param mask Mask specifying permissible matches between an input query and train matrices of - descriptors. - @param matches Matches. Each matches[i] is k or less matches for the same query descriptor. - @param k Count of best matches found per each query descriptor or less if a query descriptor has - less than k possible matches in total. - @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is - false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, - the matches vector does not contain matches for fully masked-out query descriptors. - - These extended variants of DescriptorMatcher::match methods find several best matches for each query - descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match - for the details about query and train descriptors. - */ - CV_WRAP void knnMatch( InputArray queryDescriptors, InputArray trainDescriptors, - CV_OUT std::vector >& matches, int k, - InputArray mask=noArray(), bool compactResult=false ) const; - - /** @brief For each query descriptor, finds the training descriptors not farther than the specified distance. - - @param queryDescriptors Query set of descriptors. - @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors - collection stored in the class object. - @param matches Found matches. - @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is - false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, - the matches vector does not contain matches for fully masked-out query descriptors. - @param maxDistance Threshold for the distance between matched descriptors. Distance means here - metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured - in Pixels)! - @param mask Mask specifying permissible matches between an input query and train matrices of - descriptors. - - For each query descriptor, the methods find such training descriptors that the distance between the - query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are - returned in the distance increasing order. - */ - void radiusMatch( InputArray queryDescriptors, InputArray trainDescriptors, - std::vector >& matches, float maxDistance, - InputArray mask=noArray(), bool compactResult=false ) const; - - /** @overload - @param queryDescriptors Query set of descriptors. - @param matches Matches. If a query descriptor is masked out in mask , no match is added for this - descriptor. So, matches size may be smaller than the query descriptors count. - @param masks Set of masks. Each masks[i] specifies permissible matches between the input query - descriptors and stored train descriptors from the i-th image trainDescCollection[i]. - */ - CV_WRAP void match( InputArray queryDescriptors, CV_OUT std::vector& matches, - InputArrayOfArrays masks=noArray() ); - /** @overload - @param queryDescriptors Query set of descriptors. - @param matches Matches. Each matches[i] is k or less matches for the same query descriptor. - @param k Count of best matches found per each query descriptor or less if a query descriptor has - less than k possible matches in total. - @param masks Set of masks. Each masks[i] specifies permissible matches between the input query - descriptors and stored train descriptors from the i-th image trainDescCollection[i]. - @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is - false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, - the matches vector does not contain matches for fully masked-out query descriptors. - */ - CV_WRAP void knnMatch( InputArray queryDescriptors, CV_OUT std::vector >& matches, int k, - InputArrayOfArrays masks=noArray(), bool compactResult=false ); - /** @overload - @param queryDescriptors Query set of descriptors. - @param matches Found matches. - @param maxDistance Threshold for the distance between matched descriptors. Distance means here - metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured - in Pixels)! - @param masks Set of masks. Each masks[i] specifies permissible matches between the input query - descriptors and stored train descriptors from the i-th image trainDescCollection[i]. - @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is - false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, - the matches vector does not contain matches for fully masked-out query descriptors. - */ - void radiusMatch( InputArray queryDescriptors, std::vector >& matches, float maxDistance, - InputArrayOfArrays masks=noArray(), bool compactResult=false ); - - // Reads matcher object from a file node - virtual void read( const FileNode& ); - // Writes matcher object to a file storage - virtual void write( FileStorage& ) const; - - /** @brief Clones the matcher. - - @param emptyTrainData If emptyTrainData is false, the method creates a deep copy of the object, - that is, copies both parameters and train data. If emptyTrainData is true, the method creates an - object copy with the current parameters but with empty train data. - */ - virtual Ptr clone( bool emptyTrainData=false ) const = 0; - - /** @brief Creates a descriptor matcher of a given type with the default parameters (using default - constructor). - - @param descriptorMatcherType Descriptor matcher type. Now the following matcher types are - supported: - - `BruteForce` (it uses L2 ) - - `BruteForce-L1` - - `BruteForce-Hamming` - - `BruteForce-Hamming(2)` - - `FlannBased` - */ - CV_WRAP static Ptr create( const String& descriptorMatcherType ); -protected: - /** - * Class to work with descriptors from several images as with one merged matrix. - * It is used e.g. in FlannBasedMatcher. - */ - class CV_EXPORTS DescriptorCollection - { - public: - DescriptorCollection(); - DescriptorCollection( const DescriptorCollection& collection ); - virtual ~DescriptorCollection(); - - // Vector of matrices "descriptors" will be merged to one matrix "mergedDescriptors" here. - void set( const std::vector& descriptors ); - virtual void clear(); - - const Mat& getDescriptors() const; - const Mat getDescriptor( int imgIdx, int localDescIdx ) const; - const Mat getDescriptor( int globalDescIdx ) const; - void getLocalIdx( int globalDescIdx, int& imgIdx, int& localDescIdx ) const; - - int size() const; - - protected: - Mat mergedDescriptors; - std::vector startIdxs; - }; - - //! In fact the matching is implemented only by the following two methods. These methods suppose - //! that the class object has been trained already. Public match methods call these methods - //! after calling train(). - virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, - InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0; - virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, - InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0; - - static bool isPossibleMatch( InputArray mask, int queryIdx, int trainIdx ); - static bool isMaskedOut( InputArrayOfArrays masks, int queryIdx ); - - static Mat clone_op( Mat m ) { return m.clone(); } - void checkMasks( InputArrayOfArrays masks, int queryDescriptorsCount ) const; - - //! Collection of descriptors from train images. - std::vector trainDescCollection; - std::vector utrainDescCollection; -}; - -/** @brief Brute-force descriptor matcher. - -For each descriptor in the first set, this matcher finds the closest descriptor in the second set -by trying each one. This descriptor matcher supports masking permissible matches of descriptor -sets. - */ -class CV_EXPORTS_W BFMatcher : public DescriptorMatcher -{ -public: - /** @brief Brute-force matcher constructor. - - @param normType One of NORM_L1, NORM_L2, NORM_HAMMING, NORM_HAMMING2. L1 and L2 norms are - preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and - BRIEF, NORM_HAMMING2 should be used with ORB when WTA_K==3 or 4 (see ORB::ORB constructor - description). - @param crossCheck If it is false, this is will be default BFMatcher behaviour when it finds the k - nearest neighbors for each query descriptor. If crossCheck==true, then the knnMatch() method with - k=1 will only return pairs (i,j) such that for i-th query descriptor the j-th descriptor in the - matcher's collection is the nearest and vice versa, i.e. the BFMatcher will only return consistent - pairs. Such technique usually produces best results with minimal number of outliers when there are - enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper. - */ - CV_WRAP BFMatcher( int normType=NORM_L2, bool crossCheck=false ); - virtual ~BFMatcher() {} - - virtual bool isMaskSupported() const { return true; } - - virtual Ptr clone( bool emptyTrainData=false ) const; -protected: - virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, - InputArrayOfArrays masks=noArray(), bool compactResult=false ); - virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, - InputArrayOfArrays masks=noArray(), bool compactResult=false ); - - int normType; - bool crossCheck; -}; - - -/** @brief Flann-based descriptor matcher. - -This matcher trains flann::Index_ on a train descriptor collection and calls its nearest search -methods to find the best matches. So, this matcher may be faster when matching a large train -collection than the brute force matcher. FlannBasedMatcher does not support masking permissible -matches of descriptor sets because flann::Index does not support this. : - */ -class CV_EXPORTS_W FlannBasedMatcher : public DescriptorMatcher -{ -public: - CV_WRAP FlannBasedMatcher( const Ptr& indexParams=makePtr(), - const Ptr& searchParams=makePtr() ); - - virtual void add( InputArrayOfArrays descriptors ); - virtual void clear(); - - // Reads matcher object from a file node - virtual void read( const FileNode& ); - // Writes matcher object to a file storage - virtual void write( FileStorage& ) const; - - virtual void train(); - virtual bool isMaskSupported() const; - - virtual Ptr clone( bool emptyTrainData=false ) const; -protected: - static void convertToDMatches( const DescriptorCollection& descriptors, - const Mat& indices, const Mat& distances, - std::vector >& matches ); - - virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, - InputArrayOfArrays masks=noArray(), bool compactResult=false ); - virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, - InputArrayOfArrays masks=noArray(), bool compactResult=false ); - - Ptr indexParams; - Ptr searchParams; - Ptr flannIndex; - - DescriptorCollection mergedDescriptors; - int addedDescCount; -}; - -//! @} features2d_match - -/****************************************************************************************\ -* Drawing functions * -\****************************************************************************************/ - -//! @addtogroup features2d_draw -//! @{ - -struct CV_EXPORTS DrawMatchesFlags -{ - enum{ DEFAULT = 0, //!< Output image matrix will be created (Mat::create), - //!< i.e. existing memory of output image may be reused. - //!< Two source image, matches and single keypoints will be drawn. - //!< For each keypoint only the center point will be drawn (without - //!< the circle around keypoint with keypoint size and orientation). - DRAW_OVER_OUTIMG = 1, //!< Output image matrix will not be created (Mat::create). - //!< Matches will be drawn on existing content of output image. - NOT_DRAW_SINGLE_POINTS = 2, //!< Single keypoints will not be drawn. - DRAW_RICH_KEYPOINTS = 4 //!< For each keypoint the circle around keypoint with keypoint size and - //!< orientation will be drawn. - }; -}; - -/** @brief Draws keypoints. - -@param image Source image. -@param keypoints Keypoints from the source image. -@param outImage Output image. Its content depends on the flags value defining what is drawn in the -output image. See possible flags bit values below. -@param color Color of keypoints. -@param flags Flags setting drawing features. Possible flags bit values are defined by -DrawMatchesFlags. See details above in drawMatches . - -@note -For Python API, flags are modified as cv2.DRAW_MATCHES_FLAGS_DEFAULT, -cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG, -cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS - */ -CV_EXPORTS_W void drawKeypoints( InputArray image, const std::vector& keypoints, InputOutputArray outImage, - const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT ); - -/** @brief Draws the found matches of keypoints from two images. - -@param img1 First source image. -@param keypoints1 Keypoints from the first source image. -@param img2 Second source image. -@param keypoints2 Keypoints from the second source image. -@param matches1to2 Matches from the first image to the second one, which means that keypoints1[i] -has a corresponding point in keypoints2[matches[i]] . -@param outImg Output image. Its content depends on the flags value defining what is drawn in the -output image. See possible flags bit values below. -@param matchColor Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1) -, the color is generated randomly. -@param singlePointColor Color of single keypoints (circles), which means that keypoints do not -have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly. -@param matchesMask Mask determining which matches are drawn. If the mask is empty, all matches are -drawn. -@param flags Flags setting drawing features. Possible flags bit values are defined by -DrawMatchesFlags. - -This function draws matches of keypoints from two images in the output image. Match is a line -connecting two keypoints (circles). See cv::DrawMatchesFlags. - */ -CV_EXPORTS_W void drawMatches( InputArray img1, const std::vector& keypoints1, - InputArray img2, const std::vector& keypoints2, - const std::vector& matches1to2, InputOutputArray outImg, - const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), - const std::vector& matchesMask=std::vector(), int flags=DrawMatchesFlags::DEFAULT ); - -/** @overload */ -CV_EXPORTS_AS(drawMatchesKnn) void drawMatches( InputArray img1, const std::vector& keypoints1, - InputArray img2, const std::vector& keypoints2, - const std::vector >& matches1to2, InputOutputArray outImg, - const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), - const std::vector >& matchesMask=std::vector >(), int flags=DrawMatchesFlags::DEFAULT ); - -//! @} features2d_draw - -/****************************************************************************************\ -* Functions to evaluate the feature detectors and [generic] descriptor extractors * -\****************************************************************************************/ - -CV_EXPORTS void evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2, - std::vector* keypoints1, std::vector* keypoints2, - float& repeatability, int& correspCount, - const Ptr& fdetector=Ptr() ); - -CV_EXPORTS void computeRecallPrecisionCurve( const std::vector >& matches1to2, - const std::vector >& correctMatches1to2Mask, - std::vector& recallPrecisionCurve ); - -CV_EXPORTS float getRecall( const std::vector& recallPrecisionCurve, float l_precision ); -CV_EXPORTS int getNearestPoint( const std::vector& recallPrecisionCurve, float l_precision ); - -/****************************************************************************************\ -* Bag of visual words * -\****************************************************************************************/ - -//! @addtogroup features2d_category -//! @{ - -/** @brief Abstract base class for training the *bag of visual words* vocabulary from a set of descriptors. - -For details, see, for example, *Visual Categorization with Bags of Keypoints* by Gabriella Csurka, -Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. : - */ -class CV_EXPORTS_W BOWTrainer -{ -public: - BOWTrainer(); - virtual ~BOWTrainer(); - - /** @brief Adds descriptors to a training set. - - @param descriptors Descriptors to add to a training set. Each row of the descriptors matrix is a - descriptor. - - The training set is clustered using clustermethod to construct the vocabulary. - */ - CV_WRAP void add( const Mat& descriptors ); - - /** @brief Returns a training set of descriptors. - */ - CV_WRAP const std::vector& getDescriptors() const; - - /** @brief Returns the count of all descriptors stored in the training set. - */ - CV_WRAP int descriptorsCount() const; - - CV_WRAP virtual void clear(); - - /** @overload */ - CV_WRAP virtual Mat cluster() const = 0; - - /** @brief Clusters train descriptors. - - @param descriptors Descriptors to cluster. Each row of the descriptors matrix is a descriptor. - Descriptors are not added to the inner train descriptor set. - - The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first - variant of the method, train descriptors stored in the object are clustered. In the second variant, - input descriptors are clustered. - */ - CV_WRAP virtual Mat cluster( const Mat& descriptors ) const = 0; - -protected: - std::vector descriptors; - int size; -}; - -/** @brief kmeans -based class to train visual vocabulary using the *bag of visual words* approach. : - */ -class CV_EXPORTS_W BOWKMeansTrainer : public BOWTrainer -{ -public: - /** @brief The constructor. - - @see cv::kmeans - */ - CV_WRAP BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(), - int attempts=3, int flags=KMEANS_PP_CENTERS ); - virtual ~BOWKMeansTrainer(); - - // Returns trained vocabulary (i.e. cluster centers). - CV_WRAP virtual Mat cluster() const; - CV_WRAP virtual Mat cluster( const Mat& descriptors ) const; - -protected: - - int clusterCount; - TermCriteria termcrit; - int attempts; - int flags; -}; - -/** @brief Class to compute an image descriptor using the *bag of visual words*. - -Such a computation consists of the following steps: - -1. Compute descriptors for a given image and its keypoints set. -2. Find the nearest visual words from the vocabulary for each keypoint descriptor. -3. Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words -encountered in the image. The i-th bin of the histogram is a frequency of i-th word of the -vocabulary in the given image. - */ -class CV_EXPORTS_W BOWImgDescriptorExtractor -{ -public: - /** @brief The constructor. - - @param dextractor Descriptor extractor that is used to compute descriptors for an input image and - its keypoints. - @param dmatcher Descriptor matcher that is used to find the nearest word of the trained vocabulary - for each keypoint descriptor of the image. - */ - CV_WRAP BOWImgDescriptorExtractor( const Ptr& dextractor, - const Ptr& dmatcher ); - /** @overload */ - BOWImgDescriptorExtractor( const Ptr& dmatcher ); - virtual ~BOWImgDescriptorExtractor(); - - /** @brief Sets a visual vocabulary. - - @param vocabulary Vocabulary (can be trained using the inheritor of BOWTrainer ). Each row of the - vocabulary is a visual word (cluster center). - */ - CV_WRAP void setVocabulary( const Mat& vocabulary ); - - /** @brief Returns the set vocabulary. - */ - CV_WRAP const Mat& getVocabulary() const; - - /** @brief Computes an image descriptor using the set visual vocabulary. - - @param image Image, for which the descriptor is computed. - @param keypoints Keypoints detected in the input image. - @param imgDescriptor Computed output image descriptor. - @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that - pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary) - returned if it is non-zero. - @param descriptors Descriptors of the image keypoints that are returned if they are non-zero. - */ - void compute( InputArray image, std::vector& keypoints, OutputArray imgDescriptor, - std::vector >* pointIdxsOfClusters=0, Mat* descriptors=0 ); - /** @overload - @param keypointDescriptors Computed descriptors to match with vocabulary. - @param imgDescriptor Computed output image descriptor. - @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that - pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary) - returned if it is non-zero. - */ - void compute( InputArray keypointDescriptors, OutputArray imgDescriptor, - std::vector >* pointIdxsOfClusters=0 ); - // compute() is not constant because DescriptorMatcher::match is not constant - - CV_WRAP_AS(compute) void compute2( const Mat& image, std::vector& keypoints, CV_OUT Mat& imgDescriptor ) - { compute(image,keypoints,imgDescriptor); } - - /** @brief Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0. - */ - CV_WRAP int descriptorSize() const; - - /** @brief Returns an image descriptor type. - */ - CV_WRAP int descriptorType() const; - -protected: - Mat vocabulary; - Ptr dextractor; - Ptr dmatcher; -}; - -//! @} features2d_category - -//! @} features2d - -} /* namespace cv */ - -#endif diff --git a/IPL/include/opencv/opencv2/features2d/features2d.hpp b/IPL/include/opencv/opencv2/features2d/features2d.hpp deleted file mode 100644 index e81df0a..0000000 --- a/IPL/include/opencv/opencv2/features2d/features2d.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/features2d.hpp" diff --git a/IPL/include/opencv/opencv2/flann.hpp b/IPL/include/opencv/opencv2/flann.hpp deleted file mode 100644 index 4f92d57..0000000 --- a/IPL/include/opencv/opencv2/flann.hpp +++ /dev/null @@ -1,561 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef _OPENCV_FLANN_HPP_ -#define _OPENCV_FLANN_HPP_ - -#include "opencv2/core.hpp" -#include "opencv2/flann/miniflann.hpp" -#include "opencv2/flann/flann_base.hpp" - -/** -@defgroup flann Clustering and Search in Multi-Dimensional Spaces - -This section documents OpenCV's interface to the FLANN library. FLANN (Fast Library for Approximate -Nearest Neighbors) is a library that contains a collection of algorithms optimized for fast nearest -neighbor search in large datasets and for high dimensional features. More information about FLANN -can be found in @cite Muja2009 . -*/ - -namespace cvflann -{ - CV_EXPORTS flann_distance_t flann_distance_type(); - FLANN_DEPRECATED CV_EXPORTS void set_distance_type(flann_distance_t distance_type, int order); -} - - -namespace cv -{ -namespace flann -{ - - -//! @addtogroup flann -//! @{ - -template struct CvType {}; -template <> struct CvType { static int type() { return CV_8U; } }; -template <> struct CvType { static int type() { return CV_8S; } }; -template <> struct CvType { static int type() { return CV_16U; } }; -template <> struct CvType { static int type() { return CV_16S; } }; -template <> struct CvType { static int type() { return CV_32S; } }; -template <> struct CvType { static int type() { return CV_32F; } }; -template <> struct CvType { static int type() { return CV_64F; } }; - - -// bring the flann parameters into this namespace -using ::cvflann::get_param; -using ::cvflann::print_params; - -// bring the flann distances into this namespace -using ::cvflann::L2_Simple; -using ::cvflann::L2; -using ::cvflann::L1; -using ::cvflann::MinkowskiDistance; -using ::cvflann::MaxDistance; -using ::cvflann::HammingLUT; -using ::cvflann::Hamming; -using ::cvflann::Hamming2; -using ::cvflann::HistIntersectionDistance; -using ::cvflann::HellingerDistance; -using ::cvflann::ChiSquareDistance; -using ::cvflann::KL_Divergence; - - -/** @brief The FLANN nearest neighbor index class. This class is templated with the type of elements for which -the index is built. - */ -template -class GenericIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - /** @brief Constructs a nearest neighbor search index for a given dataset. - - @param features Matrix of containing the features(points) to index. The size of the matrix is - num_features x feature_dimensionality and the data type of the elements in the matrix must - coincide with the type of the index. - @param params Structure containing the index parameters. The type of index that will be - constructed depends on the type of this parameter. See the description. - @param distance - - The method constructs a fast search structure from a set of features using the specified algorithm - with specified parameters, as defined by params. params is a reference to one of the following class - IndexParams descendants: - - - **LinearIndexParams** When passing an object of this type, the index will perform a linear, - brute-force search. : - @code - struct LinearIndexParams : public IndexParams - { - }; - @endcode - - **KDTreeIndexParams** When passing an object of this type the index constructed will consist of - a set of randomized kd-trees which will be searched in parallel. : - @code - struct KDTreeIndexParams : public IndexParams - { - KDTreeIndexParams( int trees = 4 ); - }; - @endcode - - **KMeansIndexParams** When passing an object of this type the index constructed will be a - hierarchical k-means tree. : - @code - struct KMeansIndexParams : public IndexParams - { - KMeansIndexParams( - int branching = 32, - int iterations = 11, - flann_centers_init_t centers_init = CENTERS_RANDOM, - float cb_index = 0.2 ); - }; - @endcode - - **CompositeIndexParams** When using a parameters object of this type the index created - combines the randomized kd-trees and the hierarchical k-means tree. : - @code - struct CompositeIndexParams : public IndexParams - { - CompositeIndexParams( - int trees = 4, - int branching = 32, - int iterations = 11, - flann_centers_init_t centers_init = CENTERS_RANDOM, - float cb_index = 0.2 ); - }; - @endcode - - **LshIndexParams** When using a parameters object of this type the index created uses - multi-probe LSH (by Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search - by Qin Lv, William Josephson, Zhe Wang, Moses Charikar, Kai Li., Proceedings of the 33rd - International Conference on Very Large Data Bases (VLDB). Vienna, Austria. September 2007) : - @code - struct LshIndexParams : public IndexParams - { - LshIndexParams( - unsigned int table_number, - unsigned int key_size, - unsigned int multi_probe_level ); - }; - @endcode - - **AutotunedIndexParams** When passing an object of this type the index created is - automatically tuned to offer the best performance, by choosing the optimal index type - (randomized kd-trees, hierarchical kmeans, linear) and parameters for the dataset provided. : - @code - struct AutotunedIndexParams : public IndexParams - { - AutotunedIndexParams( - float target_precision = 0.9, - float build_weight = 0.01, - float memory_weight = 0, - float sample_fraction = 0.1 ); - }; - @endcode - - **SavedIndexParams** This object type is used for loading a previously saved index from the - disk. : - @code - struct SavedIndexParams : public IndexParams - { - SavedIndexParams( String filename ); - }; - @endcode - */ - GenericIndex(const Mat& features, const ::cvflann::IndexParams& params, Distance distance = Distance()); - - ~GenericIndex(); - - /** @brief Performs a K-nearest neighbor search for a given query point using the index. - - @param query The query point - @param indices Vector that will contain the indices of the K-nearest neighbors found. It must have - at least knn size. - @param dists Vector that will contain the distances to the K-nearest neighbors found. It must have - at least knn size. - @param knn Number of nearest neighbors to search for. - @param params SearchParams - */ - void knnSearch(const std::vector& query, std::vector& indices, - std::vector& dists, int knn, const ::cvflann::SearchParams& params); - void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& params); - - int radiusSearch(const std::vector& query, std::vector& indices, - std::vector& dists, DistanceType radius, const ::cvflann::SearchParams& params); - int radiusSearch(const Mat& query, Mat& indices, Mat& dists, - DistanceType radius, const ::cvflann::SearchParams& params); - - void save(String filename) { nnIndex->save(filename); } - - int veclen() const { return nnIndex->veclen(); } - - int size() const { return nnIndex->size(); } - - ::cvflann::IndexParams getParameters() { return nnIndex->getParameters(); } - - FLANN_DEPRECATED const ::cvflann::IndexParams* getIndexParameters() { return nnIndex->getIndexParameters(); } - -private: - ::cvflann::Index* nnIndex; -}; - -//! @cond IGNORED - -#define FLANN_DISTANCE_CHECK \ - if ( ::cvflann::flann_distance_type() != cvflann::FLANN_DIST_L2) { \ - printf("[WARNING] You are using cv::flann::Index (or cv::flann::GenericIndex) and have also changed "\ - "the distance using cvflann::set_distance_type. This is no longer working as expected "\ - "(cv::flann::Index always uses L2). You should create the index templated on the distance, "\ - "for example for L1 distance use: GenericIndex< L1 > \n"); \ - } - - -template -GenericIndex::GenericIndex(const Mat& dataset, const ::cvflann::IndexParams& params, Distance distance) -{ - CV_Assert(dataset.type() == CvType::type()); - CV_Assert(dataset.isContinuous()); - ::cvflann::Matrix m_dataset((ElementType*)dataset.ptr(0), dataset.rows, dataset.cols); - - nnIndex = new ::cvflann::Index(m_dataset, params, distance); - - FLANN_DISTANCE_CHECK - - nnIndex->buildIndex(); -} - -template -GenericIndex::~GenericIndex() -{ - delete nnIndex; -} - -template -void GenericIndex::knnSearch(const std::vector& query, std::vector& indices, std::vector& dists, int knn, const ::cvflann::SearchParams& searchParams) -{ - ::cvflann::Matrix m_query((ElementType*)&query[0], 1, query.size()); - ::cvflann::Matrix m_indices(&indices[0], 1, indices.size()); - ::cvflann::Matrix m_dists(&dists[0], 1, dists.size()); - - FLANN_DISTANCE_CHECK - - nnIndex->knnSearch(m_query,m_indices,m_dists,knn,searchParams); -} - - -template -void GenericIndex::knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams) -{ - CV_Assert(queries.type() == CvType::type()); - CV_Assert(queries.isContinuous()); - ::cvflann::Matrix m_queries((ElementType*)queries.ptr(0), queries.rows, queries.cols); - - CV_Assert(indices.type() == CV_32S); - CV_Assert(indices.isContinuous()); - ::cvflann::Matrix m_indices((int*)indices.ptr(0), indices.rows, indices.cols); - - CV_Assert(dists.type() == CvType::type()); - CV_Assert(dists.isContinuous()); - ::cvflann::Matrix m_dists((DistanceType*)dists.ptr(0), dists.rows, dists.cols); - - FLANN_DISTANCE_CHECK - - nnIndex->knnSearch(m_queries,m_indices,m_dists,knn, searchParams); -} - -template -int GenericIndex::radiusSearch(const std::vector& query, std::vector& indices, std::vector& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams) -{ - ::cvflann::Matrix m_query((ElementType*)&query[0], 1, query.size()); - ::cvflann::Matrix m_indices(&indices[0], 1, indices.size()); - ::cvflann::Matrix m_dists(&dists[0], 1, dists.size()); - - FLANN_DISTANCE_CHECK - - return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); -} - -template -int GenericIndex::radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams) -{ - CV_Assert(query.type() == CvType::type()); - CV_Assert(query.isContinuous()); - ::cvflann::Matrix m_query((ElementType*)query.ptr(0), query.rows, query.cols); - - CV_Assert(indices.type() == CV_32S); - CV_Assert(indices.isContinuous()); - ::cvflann::Matrix m_indices((int*)indices.ptr(0), indices.rows, indices.cols); - - CV_Assert(dists.type() == CvType::type()); - CV_Assert(dists.isContinuous()); - ::cvflann::Matrix m_dists((DistanceType*)dists.ptr(0), dists.rows, dists.cols); - - FLANN_DISTANCE_CHECK - - return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); -} - -//! @endcond - -/** - * @deprecated Use GenericIndex class instead - */ -template -class -#ifndef _MSC_VER - FLANN_DEPRECATED -#endif - Index_ { -public: - typedef typename L2::ElementType ElementType; - typedef typename L2::ResultType DistanceType; - - Index_(const Mat& features, const ::cvflann::IndexParams& params); - - ~Index_(); - - void knnSearch(const std::vector& query, std::vector& indices, std::vector& dists, int knn, const ::cvflann::SearchParams& params); - void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& params); - - int radiusSearch(const std::vector& query, std::vector& indices, std::vector& dists, DistanceType radius, const ::cvflann::SearchParams& params); - int radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& params); - - void save(String filename) - { - if (nnIndex_L1) nnIndex_L1->save(filename); - if (nnIndex_L2) nnIndex_L2->save(filename); - } - - int veclen() const - { - if (nnIndex_L1) return nnIndex_L1->veclen(); - if (nnIndex_L2) return nnIndex_L2->veclen(); - } - - int size() const - { - if (nnIndex_L1) return nnIndex_L1->size(); - if (nnIndex_L2) return nnIndex_L2->size(); - } - - ::cvflann::IndexParams getParameters() - { - if (nnIndex_L1) return nnIndex_L1->getParameters(); - if (nnIndex_L2) return nnIndex_L2->getParameters(); - - } - - FLANN_DEPRECATED const ::cvflann::IndexParams* getIndexParameters() - { - if (nnIndex_L1) return nnIndex_L1->getIndexParameters(); - if (nnIndex_L2) return nnIndex_L2->getIndexParameters(); - } - -private: - // providing backwards compatibility for L2 and L1 distances (most common) - ::cvflann::Index< L2 >* nnIndex_L2; - ::cvflann::Index< L1 >* nnIndex_L1; -}; - -#ifdef _MSC_VER -template -class FLANN_DEPRECATED Index_; -#endif - -//! @cond IGNORED - -template -Index_::Index_(const Mat& dataset, const ::cvflann::IndexParams& params) -{ - printf("[WARNING] The cv::flann::Index_ class is deperecated, use cv::flann::GenericIndex instead\n"); - - CV_Assert(dataset.type() == CvType::type()); - CV_Assert(dataset.isContinuous()); - ::cvflann::Matrix m_dataset((ElementType*)dataset.ptr(0), dataset.rows, dataset.cols); - - if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) { - nnIndex_L1 = NULL; - nnIndex_L2 = new ::cvflann::Index< L2 >(m_dataset, params); - } - else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) { - nnIndex_L1 = new ::cvflann::Index< L1 >(m_dataset, params); - nnIndex_L2 = NULL; - } - else { - printf("[ERROR] cv::flann::Index_ only provides backwards compatibility for the L1 and L2 distances. " - "For other distance types you must use cv::flann::GenericIndex\n"); - CV_Assert(0); - } - if (nnIndex_L1) nnIndex_L1->buildIndex(); - if (nnIndex_L2) nnIndex_L2->buildIndex(); -} - -template -Index_::~Index_() -{ - if (nnIndex_L1) delete nnIndex_L1; - if (nnIndex_L2) delete nnIndex_L2; -} - -template -void Index_::knnSearch(const std::vector& query, std::vector& indices, std::vector& dists, int knn, const ::cvflann::SearchParams& searchParams) -{ - ::cvflann::Matrix m_query((ElementType*)&query[0], 1, query.size()); - ::cvflann::Matrix m_indices(&indices[0], 1, indices.size()); - ::cvflann::Matrix m_dists(&dists[0], 1, dists.size()); - - if (nnIndex_L1) nnIndex_L1->knnSearch(m_query,m_indices,m_dists,knn,searchParams); - if (nnIndex_L2) nnIndex_L2->knnSearch(m_query,m_indices,m_dists,knn,searchParams); -} - - -template -void Index_::knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams) -{ - CV_Assert(queries.type() == CvType::type()); - CV_Assert(queries.isContinuous()); - ::cvflann::Matrix m_queries((ElementType*)queries.ptr(0), queries.rows, queries.cols); - - CV_Assert(indices.type() == CV_32S); - CV_Assert(indices.isContinuous()); - ::cvflann::Matrix m_indices((int*)indices.ptr(0), indices.rows, indices.cols); - - CV_Assert(dists.type() == CvType::type()); - CV_Assert(dists.isContinuous()); - ::cvflann::Matrix m_dists((DistanceType*)dists.ptr(0), dists.rows, dists.cols); - - if (nnIndex_L1) nnIndex_L1->knnSearch(m_queries,m_indices,m_dists,knn, searchParams); - if (nnIndex_L2) nnIndex_L2->knnSearch(m_queries,m_indices,m_dists,knn, searchParams); -} - -template -int Index_::radiusSearch(const std::vector& query, std::vector& indices, std::vector& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams) -{ - ::cvflann::Matrix m_query((ElementType*)&query[0], 1, query.size()); - ::cvflann::Matrix m_indices(&indices[0], 1, indices.size()); - ::cvflann::Matrix m_dists(&dists[0], 1, dists.size()); - - if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); - if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); -} - -template -int Index_::radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams) -{ - CV_Assert(query.type() == CvType::type()); - CV_Assert(query.isContinuous()); - ::cvflann::Matrix m_query((ElementType*)query.ptr(0), query.rows, query.cols); - - CV_Assert(indices.type() == CV_32S); - CV_Assert(indices.isContinuous()); - ::cvflann::Matrix m_indices((int*)indices.ptr(0), indices.rows, indices.cols); - - CV_Assert(dists.type() == CvType::type()); - CV_Assert(dists.isContinuous()); - ::cvflann::Matrix m_dists((DistanceType*)dists.ptr(0), dists.rows, dists.cols); - - if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); - if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); -} - -//! @endcond - -/** @brief Clusters features using hierarchical k-means algorithm. - -@param features The points to be clustered. The matrix must have elements of type -Distance::ElementType. -@param centers The centers of the clusters obtained. The matrix must have type -Distance::ResultType. The number of rows in this matrix represents the number of clusters desired, -however, because of the way the cut in the hierarchical tree is chosen, the number of clusters -computed will be the highest number of the form (branching-1)\*k+1 that's lower than the number of -clusters desired, where branching is the tree's branching factor (see description of the -KMeansIndexParams). -@param params Parameters used in the construction of the hierarchical k-means tree. -@param d Distance to be used for clustering. - -The method clusters the given feature vectors by constructing a hierarchical k-means tree and -choosing a cut in the tree that minimizes the cluster's variance. It returns the number of clusters -found. - */ -template -int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params, - Distance d = Distance()) -{ - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - CV_Assert(features.type() == CvType::type()); - CV_Assert(features.isContinuous()); - ::cvflann::Matrix m_features((ElementType*)features.ptr(0), features.rows, features.cols); - - CV_Assert(centers.type() == CvType::type()); - CV_Assert(centers.isContinuous()); - ::cvflann::Matrix m_centers((DistanceType*)centers.ptr(0), centers.rows, centers.cols); - - return ::cvflann::hierarchicalClustering(m_features, m_centers, params, d); -} - -/** @deprecated -*/ -template -FLANN_DEPRECATED int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params) -{ - printf("[WARNING] cv::flann::hierarchicalClustering is deprecated, use " - "cv::flann::hierarchicalClustering instead\n"); - - if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) { - return hierarchicalClustering< L2 >(features, centers, params); - } - else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) { - return hierarchicalClustering< L1 >(features, centers, params); - } - else { - printf("[ERROR] cv::flann::hierarchicalClustering only provides backwards " - "compatibility for the L1 and L2 distances. " - "For other distance types you must use cv::flann::hierarchicalClustering\n"); - CV_Assert(0); - } -} - -//! @} flann - -} } // namespace cv::flann - -#endif diff --git a/IPL/include/opencv/opencv2/flann/all_indices.h b/IPL/include/opencv/opencv2/flann/all_indices.h deleted file mode 100644 index ff53fd8..0000000 --- a/IPL/include/opencv/opencv2/flann/all_indices.h +++ /dev/null @@ -1,155 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - - -#ifndef OPENCV_FLANN_ALL_INDICES_H_ -#define OPENCV_FLANN_ALL_INDICES_H_ - -#include "general.h" - -#include "nn_index.h" -#include "kdtree_index.h" -#include "kdtree_single_index.h" -#include "kmeans_index.h" -#include "composite_index.h" -#include "linear_index.h" -#include "hierarchical_clustering_index.h" -#include "lsh_index.h" -#include "autotuned_index.h" - - -namespace cvflann -{ - -template -struct index_creator -{ - static NNIndex* create(const Matrix& dataset, const IndexParams& params, const Distance& distance) - { - flann_algorithm_t index_type = get_param(params, "algorithm"); - - NNIndex* nnIndex; - switch (index_type) { - case FLANN_INDEX_LINEAR: - nnIndex = new LinearIndex(dataset, params, distance); - break; - case FLANN_INDEX_KDTREE_SINGLE: - nnIndex = new KDTreeSingleIndex(dataset, params, distance); - break; - case FLANN_INDEX_KDTREE: - nnIndex = new KDTreeIndex(dataset, params, distance); - break; - case FLANN_INDEX_KMEANS: - nnIndex = new KMeansIndex(dataset, params, distance); - break; - case FLANN_INDEX_COMPOSITE: - nnIndex = new CompositeIndex(dataset, params, distance); - break; - case FLANN_INDEX_AUTOTUNED: - nnIndex = new AutotunedIndex(dataset, params, distance); - break; - case FLANN_INDEX_HIERARCHICAL: - nnIndex = new HierarchicalClusteringIndex(dataset, params, distance); - break; - case FLANN_INDEX_LSH: - nnIndex = new LshIndex(dataset, params, distance); - break; - default: - throw FLANNException("Unknown index type"); - } - - return nnIndex; - } -}; - -template -struct index_creator -{ - static NNIndex* create(const Matrix& dataset, const IndexParams& params, const Distance& distance) - { - flann_algorithm_t index_type = get_param(params, "algorithm"); - - NNIndex* nnIndex; - switch (index_type) { - case FLANN_INDEX_LINEAR: - nnIndex = new LinearIndex(dataset, params, distance); - break; - case FLANN_INDEX_KMEANS: - nnIndex = new KMeansIndex(dataset, params, distance); - break; - case FLANN_INDEX_HIERARCHICAL: - nnIndex = new HierarchicalClusteringIndex(dataset, params, distance); - break; - case FLANN_INDEX_LSH: - nnIndex = new LshIndex(dataset, params, distance); - break; - default: - throw FLANNException("Unknown index type"); - } - - return nnIndex; - } -}; - -template -struct index_creator -{ - static NNIndex* create(const Matrix& dataset, const IndexParams& params, const Distance& distance) - { - flann_algorithm_t index_type = get_param(params, "algorithm"); - - NNIndex* nnIndex; - switch (index_type) { - case FLANN_INDEX_LINEAR: - nnIndex = new LinearIndex(dataset, params, distance); - break; - case FLANN_INDEX_HIERARCHICAL: - nnIndex = new HierarchicalClusteringIndex(dataset, params, distance); - break; - case FLANN_INDEX_LSH: - nnIndex = new LshIndex(dataset, params, distance); - break; - default: - throw FLANNException("Unknown index type"); - } - - return nnIndex; - } -}; - -template -NNIndex* create_index_by_type(const Matrix& dataset, const IndexParams& params, const Distance& distance) -{ - return index_creator::create(dataset, params,distance); -} - -} - -#endif /* OPENCV_FLANN_ALL_INDICES_H_ */ diff --git a/IPL/include/opencv/opencv2/flann/allocator.h b/IPL/include/opencv/opencv2/flann/allocator.h deleted file mode 100644 index 26091d0..0000000 --- a/IPL/include/opencv/opencv2/flann/allocator.h +++ /dev/null @@ -1,188 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_ALLOCATOR_H_ -#define OPENCV_FLANN_ALLOCATOR_H_ - -#include -#include - - -namespace cvflann -{ - -/** - * Allocates (using C's malloc) a generic type T. - * - * Params: - * count = number of instances to allocate. - * Returns: pointer (of type T*) to memory buffer - */ -template -T* allocate(size_t count = 1) -{ - T* mem = (T*) ::malloc(sizeof(T)*count); - return mem; -} - - -/** - * Pooled storage allocator - * - * The following routines allow for the efficient allocation of storage in - * small chunks from a specified pool. Rather than allowing each structure - * to be freed individually, an entire pool of storage is freed at once. - * This method has two advantages over just using malloc() and free(). First, - * it is far more efficient for allocating small objects, as there is - * no overhead for remembering all the information needed to free each - * object or consolidating fragmented memory. Second, the decision about - * how long to keep an object is made at the time of allocation, and there - * is no need to track down all the objects to free them. - * - */ - -const size_t WORDSIZE=16; -const size_t BLOCKSIZE=8192; - -class PooledAllocator -{ - /* We maintain memory alignment to word boundaries by requiring that all - allocations be in multiples of the machine wordsize. */ - /* Size of machine word in bytes. Must be power of 2. */ - /* Minimum number of bytes requested at a time from the system. Must be multiple of WORDSIZE. */ - - - int remaining; /* Number of bytes left in current block of storage. */ - void* base; /* Pointer to base of current block of storage. */ - void* loc; /* Current location in block to next allocate memory. */ - int blocksize; - - -public: - int usedMemory; - int wastedMemory; - - /** - Default constructor. Initializes a new pool. - */ - PooledAllocator(int blockSize = BLOCKSIZE) - { - blocksize = blockSize; - remaining = 0; - base = NULL; - - usedMemory = 0; - wastedMemory = 0; - } - - /** - * Destructor. Frees all the memory allocated in this pool. - */ - ~PooledAllocator() - { - void* prev; - - while (base != NULL) { - prev = *((void**) base); /* Get pointer to prev block. */ - ::free(base); - base = prev; - } - } - - /** - * Returns a pointer to a piece of new memory of the given size in bytes - * allocated from the pool. - */ - void* allocateMemory(int size) - { - int blockSize; - - /* Round size up to a multiple of wordsize. The following expression - only works for WORDSIZE that is a power of 2, by masking last bits of - incremented size to zero. - */ - size = (size + (WORDSIZE - 1)) & ~(WORDSIZE - 1); - - /* Check whether a new block must be allocated. Note that the first word - of a block is reserved for a pointer to the previous block. - */ - if (size > remaining) { - - wastedMemory += remaining; - - /* Allocate new storage. */ - blockSize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ? - size + sizeof(void*) + (WORDSIZE-1) : BLOCKSIZE; - - // use the standard C malloc to allocate memory - void* m = ::malloc(blockSize); - if (!m) { - fprintf(stderr,"Failed to allocate memory.\n"); - return NULL; - } - - /* Fill first word of new block with pointer to previous block. */ - ((void**) m)[0] = base; - base = m; - - int shift = 0; - //int shift = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & (WORDSIZE-1))) & (WORDSIZE-1); - - remaining = blockSize - sizeof(void*) - shift; - loc = ((char*)m + sizeof(void*) + shift); - } - void* rloc = loc; - loc = (char*)loc + size; - remaining -= size; - - usedMemory += size; - - return rloc; - } - - /** - * Allocates (using this pool) a generic type T. - * - * Params: - * count = number of instances to allocate. - * Returns: pointer (of type T*) to memory buffer - */ - template - T* allocate(size_t count = 1) - { - T* mem = (T*) this->allocateMemory((int)(sizeof(T)*count)); - return mem; - } - -}; - -} - -#endif //OPENCV_FLANN_ALLOCATOR_H_ diff --git a/IPL/include/opencv/opencv2/flann/any.h b/IPL/include/opencv/opencv2/flann/any.h deleted file mode 100644 index bfe06c8..0000000 --- a/IPL/include/opencv/opencv2/flann/any.h +++ /dev/null @@ -1,324 +0,0 @@ -#ifndef OPENCV_FLANN_ANY_H_ -#define OPENCV_FLANN_ANY_H_ -/* - * (C) Copyright Christopher Diggins 2005-2011 - * (C) Copyright Pablo Aguilar 2005 - * (C) Copyright Kevlin Henney 2001 - * - * Distributed under the Boost Software License, Version 1.0. (See - * accompanying file LICENSE_1_0.txt or copy at - * http://www.boost.org/LICENSE_1_0.txt - * - * Adapted for FLANN by Marius Muja - */ - -#include "defines.h" -#include -#include -#include - -namespace cvflann -{ - -namespace anyimpl -{ - -struct bad_any_cast -{ -}; - -struct empty_any -{ -}; - -inline std::ostream& operator <<(std::ostream& out, const empty_any&) -{ - out << "[empty_any]"; - return out; -} - -struct base_any_policy -{ - virtual void static_delete(void** x) = 0; - virtual void copy_from_value(void const* src, void** dest) = 0; - virtual void clone(void* const* src, void** dest) = 0; - virtual void move(void* const* src, void** dest) = 0; - virtual void* get_value(void** src) = 0; - virtual const void* get_value(void* const * src) = 0; - virtual ::size_t get_size() = 0; - virtual const std::type_info& type() = 0; - virtual void print(std::ostream& out, void* const* src) = 0; - virtual ~base_any_policy() {} -}; - -template -struct typed_base_any_policy : base_any_policy -{ - virtual ::size_t get_size() { return sizeof(T); } - virtual const std::type_info& type() { return typeid(T); } - -}; - -template -struct small_any_policy : typed_base_any_policy -{ - virtual void static_delete(void**) { } - virtual void copy_from_value(void const* src, void** dest) - { - new (dest) T(* reinterpret_cast(src)); - } - virtual void clone(void* const* src, void** dest) { *dest = *src; } - virtual void move(void* const* src, void** dest) { *dest = *src; } - virtual void* get_value(void** src) { return reinterpret_cast(src); } - virtual const void* get_value(void* const * src) { return reinterpret_cast(src); } - virtual void print(std::ostream& out, void* const* src) { out << *reinterpret_cast(src); } -}; - -template -struct big_any_policy : typed_base_any_policy -{ - virtual void static_delete(void** x) - { - if (* x) delete (* reinterpret_cast(x)); - *x = NULL; - } - virtual void copy_from_value(void const* src, void** dest) - { - *dest = new T(*reinterpret_cast(src)); - } - virtual void clone(void* const* src, void** dest) - { - *dest = new T(**reinterpret_cast(src)); - } - virtual void move(void* const* src, void** dest) - { - (*reinterpret_cast(dest))->~T(); - **reinterpret_cast(dest) = **reinterpret_cast(src); - } - virtual void* get_value(void** src) { return *src; } - virtual const void* get_value(void* const * src) { return *src; } - virtual void print(std::ostream& out, void* const* src) { out << *reinterpret_cast(*src); } -}; - -template<> inline void big_any_policy::print(std::ostream& out, void* const* src) -{ - out << int(*reinterpret_cast(*src)); -} - -template<> inline void big_any_policy::print(std::ostream& out, void* const* src) -{ - out << int(*reinterpret_cast(*src)); -} - -template<> inline void big_any_policy::print(std::ostream& out, void* const* src) -{ - out << (*reinterpret_cast(*src)).c_str(); -} - -template -struct choose_policy -{ - typedef big_any_policy type; -}; - -template -struct choose_policy -{ - typedef small_any_policy type; -}; - -struct any; - -/// Choosing the policy for an any type is illegal, but should never happen. -/// This is designed to throw a compiler error. -template<> -struct choose_policy -{ - typedef void type; -}; - -/// Specializations for small types. -#define SMALL_POLICY(TYPE) \ - template<> \ - struct choose_policy { typedef small_any_policy type; \ - } - -SMALL_POLICY(signed char); -SMALL_POLICY(unsigned char); -SMALL_POLICY(signed short); -SMALL_POLICY(unsigned short); -SMALL_POLICY(signed int); -SMALL_POLICY(unsigned int); -SMALL_POLICY(signed long); -SMALL_POLICY(unsigned long); -SMALL_POLICY(float); -SMALL_POLICY(bool); - -#undef SMALL_POLICY - -template -class SinglePolicy -{ - SinglePolicy(); - SinglePolicy(const SinglePolicy& other); - SinglePolicy& operator=(const SinglePolicy& other); - -public: - static base_any_policy* get_policy(); - -private: - static typename choose_policy::type policy; -}; - -template -typename choose_policy::type SinglePolicy::policy; - -/// This function will return a different policy for each type. -template -inline base_any_policy* SinglePolicy::get_policy() { return &policy; } - -} // namespace anyimpl - -struct any -{ -private: - // fields - anyimpl::base_any_policy* policy; - void* object; - -public: - /// Initializing constructor. - template - any(const T& x) - : policy(anyimpl::SinglePolicy::get_policy()), object(NULL) - { - assign(x); - } - - /// Empty constructor. - any() - : policy(anyimpl::SinglePolicy::get_policy()), object(NULL) - { } - - /// Special initializing constructor for string literals. - any(const char* x) - : policy(anyimpl::SinglePolicy::get_policy()), object(NULL) - { - assign(x); - } - - /// Copy constructor. - any(const any& x) - : policy(anyimpl::SinglePolicy::get_policy()), object(NULL) - { - assign(x); - } - - /// Destructor. - ~any() - { - policy->static_delete(&object); - } - - /// Assignment function from another any. - any& assign(const any& x) - { - reset(); - policy = x.policy; - policy->clone(&x.object, &object); - return *this; - } - - /// Assignment function. - template - any& assign(const T& x) - { - reset(); - policy = anyimpl::SinglePolicy::get_policy(); - policy->copy_from_value(&x, &object); - return *this; - } - - /// Assignment operator. - template - any& operator=(const T& x) - { - return assign(x); - } - - /// Assignment operator, specialed for literal strings. - /// They have types like const char [6] which don't work as expected. - any& operator=(const char* x) - { - return assign(x); - } - - /// Utility functions - any& swap(any& x) - { - std::swap(policy, x.policy); - std::swap(object, x.object); - return *this; - } - - /// Cast operator. You can only cast to the original type. - template - T& cast() - { - if (policy->type() != typeid(T)) throw anyimpl::bad_any_cast(); - T* r = reinterpret_cast(policy->get_value(&object)); - return *r; - } - - /// Cast operator. You can only cast to the original type. - template - const T& cast() const - { - if (policy->type() != typeid(T)) throw anyimpl::bad_any_cast(); - const T* r = reinterpret_cast(policy->get_value(&object)); - return *r; - } - - /// Returns true if the any contains no value. - bool empty() const - { - return policy->type() == typeid(anyimpl::empty_any); - } - - /// Frees any allocated memory, and sets the value to NULL. - void reset() - { - policy->static_delete(&object); - policy = anyimpl::SinglePolicy::get_policy(); - } - - /// Returns true if the two types are the same. - bool compatible(const any& x) const - { - return policy->type() == x.policy->type(); - } - - /// Returns if the type is compatible with the policy - template - bool has_type() - { - return policy->type() == typeid(T); - } - - const std::type_info& type() const - { - return policy->type(); - } - - friend std::ostream& operator <<(std::ostream& out, const any& any_val); -}; - -inline std::ostream& operator <<(std::ostream& out, const any& any_val) -{ - any_val.policy->print(out,&any_val.object); - return out; -} - -} - -#endif // OPENCV_FLANN_ANY_H_ diff --git a/IPL/include/opencv/opencv2/flann/autotuned_index.h b/IPL/include/opencv/opencv2/flann/autotuned_index.h deleted file mode 100644 index 6ffb929..0000000 --- a/IPL/include/opencv/opencv2/flann/autotuned_index.h +++ /dev/null @@ -1,588 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ -#ifndef OPENCV_FLANN_AUTOTUNED_INDEX_H_ -#define OPENCV_FLANN_AUTOTUNED_INDEX_H_ - -#include "general.h" -#include "nn_index.h" -#include "ground_truth.h" -#include "index_testing.h" -#include "sampling.h" -#include "kdtree_index.h" -#include "kdtree_single_index.h" -#include "kmeans_index.h" -#include "composite_index.h" -#include "linear_index.h" -#include "logger.h" - -namespace cvflann -{ - -template -NNIndex* create_index_by_type(const Matrix& dataset, const IndexParams& params, const Distance& distance); - - -struct AutotunedIndexParams : public IndexParams -{ - AutotunedIndexParams(float target_precision = 0.8, float build_weight = 0.01, float memory_weight = 0, float sample_fraction = 0.1) - { - (*this)["algorithm"] = FLANN_INDEX_AUTOTUNED; - // precision desired (used for autotuning, -1 otherwise) - (*this)["target_precision"] = target_precision; - // build tree time weighting factor - (*this)["build_weight"] = build_weight; - // index memory weighting factor - (*this)["memory_weight"] = memory_weight; - // what fraction of the dataset to use for autotuning - (*this)["sample_fraction"] = sample_fraction; - } -}; - - -template -class AutotunedIndex : public NNIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - AutotunedIndex(const Matrix& inputData, const IndexParams& params = AutotunedIndexParams(), Distance d = Distance()) : - dataset_(inputData), distance_(d) - { - target_precision_ = get_param(params, "target_precision",0.8f); - build_weight_ = get_param(params,"build_weight", 0.01f); - memory_weight_ = get_param(params, "memory_weight", 0.0f); - sample_fraction_ = get_param(params,"sample_fraction", 0.1f); - bestIndex_ = NULL; - } - - AutotunedIndex(const AutotunedIndex&); - AutotunedIndex& operator=(const AutotunedIndex&); - - virtual ~AutotunedIndex() - { - if (bestIndex_ != NULL) { - delete bestIndex_; - bestIndex_ = NULL; - } - } - - /** - * Method responsible with building the index. - */ - virtual void buildIndex() - { - std::ostringstream stream; - bestParams_ = estimateBuildParams(); - print_params(bestParams_, stream); - Logger::info("----------------------------------------------------\n"); - Logger::info("Autotuned parameters:\n"); - Logger::info("%s", stream.str().c_str()); - Logger::info("----------------------------------------------------\n"); - - bestIndex_ = create_index_by_type(dataset_, bestParams_, distance_); - bestIndex_->buildIndex(); - speedup_ = estimateSearchParams(bestSearchParams_); - stream.str(std::string()); - print_params(bestSearchParams_, stream); - Logger::info("----------------------------------------------------\n"); - Logger::info("Search parameters:\n"); - Logger::info("%s", stream.str().c_str()); - Logger::info("----------------------------------------------------\n"); - } - - /** - * Saves the index to a stream - */ - virtual void saveIndex(FILE* stream) - { - save_value(stream, (int)bestIndex_->getType()); - bestIndex_->saveIndex(stream); - save_value(stream, get_param(bestSearchParams_, "checks")); - } - - /** - * Loads the index from a stream - */ - virtual void loadIndex(FILE* stream) - { - int index_type; - - load_value(stream, index_type); - IndexParams params; - params["algorithm"] = (flann_algorithm_t)index_type; - bestIndex_ = create_index_by_type(dataset_, params, distance_); - bestIndex_->loadIndex(stream); - int checks; - load_value(stream, checks); - bestSearchParams_["checks"] = checks; - } - - /** - * Method that searches for nearest-neighbors - */ - virtual void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) - { - int checks = get_param(searchParams,"checks",FLANN_CHECKS_AUTOTUNED); - if (checks == FLANN_CHECKS_AUTOTUNED) { - bestIndex_->findNeighbors(result, vec, bestSearchParams_); - } - else { - bestIndex_->findNeighbors(result, vec, searchParams); - } - } - - - IndexParams getParameters() const - { - return bestIndex_->getParameters(); - } - - SearchParams getSearchParameters() const - { - return bestSearchParams_; - } - - float getSpeedup() const - { - return speedup_; - } - - - /** - * Number of features in this index. - */ - virtual size_t size() const - { - return bestIndex_->size(); - } - - /** - * The length of each vector in this index. - */ - virtual size_t veclen() const - { - return bestIndex_->veclen(); - } - - /** - * The amount of memory (in bytes) this index uses. - */ - virtual int usedMemory() const - { - return bestIndex_->usedMemory(); - } - - /** - * Algorithm name - */ - virtual flann_algorithm_t getType() const - { - return FLANN_INDEX_AUTOTUNED; - } - -private: - - struct CostData - { - float searchTimeCost; - float buildTimeCost; - float memoryCost; - float totalCost; - IndexParams params; - }; - - void evaluate_kmeans(CostData& cost) - { - StartStopTimer t; - int checks; - const int nn = 1; - - Logger::info("KMeansTree using params: max_iterations=%d, branching=%d\n", - get_param(cost.params,"iterations"), - get_param(cost.params,"branching")); - KMeansIndex kmeans(sampledDataset_, cost.params, distance_); - // measure index build time - t.start(); - kmeans.buildIndex(); - t.stop(); - float buildTime = (float)t.value; - - // measure search time - float searchTime = test_index_precision(kmeans, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn); - - float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float)); - cost.memoryCost = (kmeans.usedMemory() + datasetMemory) / datasetMemory; - cost.searchTimeCost = searchTime; - cost.buildTimeCost = buildTime; - Logger::info("KMeansTree buildTime=%g, searchTime=%g, build_weight=%g\n", buildTime, searchTime, build_weight_); - } - - - void evaluate_kdtree(CostData& cost) - { - StartStopTimer t; - int checks; - const int nn = 1; - - Logger::info("KDTree using params: trees=%d\n", get_param(cost.params,"trees")); - KDTreeIndex kdtree(sampledDataset_, cost.params, distance_); - - t.start(); - kdtree.buildIndex(); - t.stop(); - float buildTime = (float)t.value; - - //measure search time - float searchTime = test_index_precision(kdtree, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn); - - float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float)); - cost.memoryCost = (kdtree.usedMemory() + datasetMemory) / datasetMemory; - cost.searchTimeCost = searchTime; - cost.buildTimeCost = buildTime; - Logger::info("KDTree buildTime=%g, searchTime=%g\n", buildTime, searchTime); - } - - - // struct KMeansSimpleDownhillFunctor { - // - // Autotune& autotuner; - // KMeansSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {} - // - // float operator()(int* params) { - // - // float maxFloat = numeric_limits::max(); - // - // if (params[0]<2) return maxFloat; - // if (params[1]<0) return maxFloat; - // - // CostData c; - // c.params["algorithm"] = KMEANS; - // c.params["centers-init"] = CENTERS_RANDOM; - // c.params["branching"] = params[0]; - // c.params["max-iterations"] = params[1]; - // - // autotuner.evaluate_kmeans(c); - // - // return c.timeCost; - // - // } - // }; - // - // struct KDTreeSimpleDownhillFunctor { - // - // Autotune& autotuner; - // KDTreeSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {} - // - // float operator()(int* params) { - // float maxFloat = numeric_limits::max(); - // - // if (params[0]<1) return maxFloat; - // - // CostData c; - // c.params["algorithm"] = KDTREE; - // c.params["trees"] = params[0]; - // - // autotuner.evaluate_kdtree(c); - // - // return c.timeCost; - // - // } - // }; - - - - void optimizeKMeans(std::vector& costs) - { - Logger::info("KMEANS, Step 1: Exploring parameter space\n"); - - // explore kmeans parameters space using combinations of the parameters below - int maxIterations[] = { 1, 5, 10, 15 }; - int branchingFactors[] = { 16, 32, 64, 128, 256 }; - - int kmeansParamSpaceSize = FLANN_ARRAY_LEN(maxIterations) * FLANN_ARRAY_LEN(branchingFactors); - costs.reserve(costs.size() + kmeansParamSpaceSize); - - // evaluate kmeans for all parameter combinations - for (size_t i = 0; i < FLANN_ARRAY_LEN(maxIterations); ++i) { - for (size_t j = 0; j < FLANN_ARRAY_LEN(branchingFactors); ++j) { - CostData cost; - cost.params["algorithm"] = FLANN_INDEX_KMEANS; - cost.params["centers_init"] = FLANN_CENTERS_RANDOM; - cost.params["iterations"] = maxIterations[i]; - cost.params["branching"] = branchingFactors[j]; - - evaluate_kmeans(cost); - costs.push_back(cost); - } - } - - // Logger::info("KMEANS, Step 2: simplex-downhill optimization\n"); - // - // const int n = 2; - // // choose initial simplex points as the best parameters so far - // int kmeansNMPoints[n*(n+1)]; - // float kmeansVals[n+1]; - // for (int i=0;i& costs) - { - Logger::info("KD-TREE, Step 1: Exploring parameter space\n"); - - // explore kd-tree parameters space using the parameters below - int testTrees[] = { 1, 4, 8, 16, 32 }; - - // evaluate kdtree for all parameter combinations - for (size_t i = 0; i < FLANN_ARRAY_LEN(testTrees); ++i) { - CostData cost; - cost.params["algorithm"] = FLANN_INDEX_KDTREE; - cost.params["trees"] = testTrees[i]; - - evaluate_kdtree(cost); - costs.push_back(cost); - } - - // Logger::info("KD-TREE, Step 2: simplex-downhill optimization\n"); - // - // const int n = 1; - // // choose initial simplex points as the best parameters so far - // int kdtreeNMPoints[n*(n+1)]; - // float kdtreeVals[n+1]; - // for (int i=0;i costs; - - int sampleSize = int(sample_fraction_ * dataset_.rows); - int testSampleSize = std::min(sampleSize / 10, 1000); - - Logger::info("Entering autotuning, dataset size: %d, sampleSize: %d, testSampleSize: %d, target precision: %g\n", dataset_.rows, sampleSize, testSampleSize, target_precision_); - - // For a very small dataset, it makes no sense to build any fancy index, just - // use linear search - if (testSampleSize < 10) { - Logger::info("Choosing linear, dataset too small\n"); - return LinearIndexParams(); - } - - // We use a fraction of the original dataset to speedup the autotune algorithm - sampledDataset_ = random_sample(dataset_, sampleSize); - // We use a cross-validation approach, first we sample a testset from the dataset - testDataset_ = random_sample(sampledDataset_, testSampleSize, true); - - // We compute the ground truth using linear search - Logger::info("Computing ground truth... \n"); - gt_matches_ = Matrix(new int[testDataset_.rows], testDataset_.rows, 1); - StartStopTimer t; - t.start(); - compute_ground_truth(sampledDataset_, testDataset_, gt_matches_, 0, distance_); - t.stop(); - - CostData linear_cost; - linear_cost.searchTimeCost = (float)t.value; - linear_cost.buildTimeCost = 0; - linear_cost.memoryCost = 0; - linear_cost.params["algorithm"] = FLANN_INDEX_LINEAR; - - costs.push_back(linear_cost); - - // Start parameter autotune process - Logger::info("Autotuning parameters...\n"); - - optimizeKMeans(costs); - optimizeKDTree(costs); - - float bestTimeCost = costs[0].searchTimeCost; - for (size_t i = 0; i < costs.size(); ++i) { - float timeCost = costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost; - if (timeCost < bestTimeCost) { - bestTimeCost = timeCost; - } - } - - float bestCost = costs[0].searchTimeCost / bestTimeCost; - IndexParams bestParams = costs[0].params; - if (bestTimeCost > 0) { - for (size_t i = 0; i < costs.size(); ++i) { - float crtCost = (costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost) / bestTimeCost + - memory_weight_ * costs[i].memoryCost; - if (crtCost < bestCost) { - bestCost = crtCost; - bestParams = costs[i].params; - } - } - } - - delete[] gt_matches_.data; - delete[] testDataset_.data; - delete[] sampledDataset_.data; - - return bestParams; - } - - - - /** - * Estimates the search time parameters needed to get the desired precision. - * Precondition: the index is built - * Postcondition: the searchParams will have the optimum params set, also the speedup obtained over linear search. - */ - float estimateSearchParams(SearchParams& searchParams) - { - const int nn = 1; - const size_t SAMPLE_COUNT = 1000; - - assert(bestIndex_ != NULL); // must have a valid index - - float speedup = 0; - - int samples = (int)std::min(dataset_.rows / 10, SAMPLE_COUNT); - if (samples > 0) { - Matrix testDataset = random_sample(dataset_, samples); - - Logger::info("Computing ground truth\n"); - - // we need to compute the ground truth first - Matrix gt_matches(new int[testDataset.rows], testDataset.rows, 1); - StartStopTimer t; - t.start(); - compute_ground_truth(dataset_, testDataset, gt_matches, 1, distance_); - t.stop(); - float linear = (float)t.value; - - int checks; - Logger::info("Estimating number of checks\n"); - - float searchTime; - float cb_index; - if (bestIndex_->getType() == FLANN_INDEX_KMEANS) { - Logger::info("KMeans algorithm, estimating cluster border factor\n"); - KMeansIndex* kmeans = (KMeansIndex*)bestIndex_; - float bestSearchTime = -1; - float best_cb_index = -1; - int best_checks = -1; - for (cb_index = 0; cb_index < 1.1f; cb_index += 0.2f) { - kmeans->set_cb_index(cb_index); - searchTime = test_index_precision(*kmeans, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1); - if ((searchTime < bestSearchTime) || (bestSearchTime == -1)) { - bestSearchTime = searchTime; - best_cb_index = cb_index; - best_checks = checks; - } - } - searchTime = bestSearchTime; - cb_index = best_cb_index; - checks = best_checks; - - kmeans->set_cb_index(best_cb_index); - Logger::info("Optimum cb_index: %g\n", cb_index); - bestParams_["cb_index"] = cb_index; - } - else { - searchTime = test_index_precision(*bestIndex_, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1); - } - - Logger::info("Required number of checks: %d \n", checks); - searchParams["checks"] = checks; - - speedup = linear / searchTime; - - delete[] gt_matches.data; - delete[] testDataset.data; - } - - return speedup; - } - -private: - NNIndex* bestIndex_; - - IndexParams bestParams_; - SearchParams bestSearchParams_; - - Matrix sampledDataset_; - Matrix testDataset_; - Matrix gt_matches_; - - float speedup_; - - /** - * The dataset used by this index - */ - const Matrix dataset_; - - /** - * Index parameters - */ - float target_precision_; - float build_weight_; - float memory_weight_; - float sample_fraction_; - - Distance distance_; - - -}; -} - -#endif /* OPENCV_FLANN_AUTOTUNED_INDEX_H_ */ diff --git a/IPL/include/opencv/opencv2/flann/composite_index.h b/IPL/include/opencv/opencv2/flann/composite_index.h deleted file mode 100644 index 527ca1a..0000000 --- a/IPL/include/opencv/opencv2/flann/composite_index.h +++ /dev/null @@ -1,194 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_COMPOSITE_INDEX_H_ -#define OPENCV_FLANN_COMPOSITE_INDEX_H_ - -#include "general.h" -#include "nn_index.h" -#include "kdtree_index.h" -#include "kmeans_index.h" - -namespace cvflann -{ - -/** - * Index parameters for the CompositeIndex. - */ -struct CompositeIndexParams : public IndexParams -{ - CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11, - flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 ) - { - (*this)["algorithm"] = FLANN_INDEX_KMEANS; - // number of randomized trees to use (for kdtree) - (*this)["trees"] = trees; - // branching factor - (*this)["branching"] = branching; - // max iterations to perform in one kmeans clustering (kmeans tree) - (*this)["iterations"] = iterations; - // algorithm used for picking the initial cluster centers for kmeans tree - (*this)["centers_init"] = centers_init; - // cluster boundary index. Used when searching the kmeans tree - (*this)["cb_index"] = cb_index; - } -}; - - -/** - * This index builds a kd-tree index and a k-means index and performs nearest - * neighbour search both indexes. This gives a slight boost in search performance - * as some of the neighbours that are missed by one index are found by the other. - */ -template -class CompositeIndex : public NNIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - /** - * Index constructor - * @param inputData dataset containing the points to index - * @param params Index parameters - * @param d Distance functor - * @return - */ - CompositeIndex(const Matrix& inputData, const IndexParams& params = CompositeIndexParams(), - Distance d = Distance()) : index_params_(params) - { - kdtree_index_ = new KDTreeIndex(inputData, params, d); - kmeans_index_ = new KMeansIndex(inputData, params, d); - - } - - CompositeIndex(const CompositeIndex&); - CompositeIndex& operator=(const CompositeIndex&); - - virtual ~CompositeIndex() - { - delete kdtree_index_; - delete kmeans_index_; - } - - /** - * @return The index type - */ - flann_algorithm_t getType() const - { - return FLANN_INDEX_COMPOSITE; - } - - /** - * @return Size of the index - */ - size_t size() const - { - return kdtree_index_->size(); - } - - /** - * \returns The dimensionality of the features in this index. - */ - size_t veclen() const - { - return kdtree_index_->veclen(); - } - - /** - * \returns The amount of memory (in bytes) used by the index. - */ - int usedMemory() const - { - return kmeans_index_->usedMemory() + kdtree_index_->usedMemory(); - } - - /** - * \brief Builds the index - */ - void buildIndex() - { - Logger::info("Building kmeans tree...\n"); - kmeans_index_->buildIndex(); - Logger::info("Building kdtree tree...\n"); - kdtree_index_->buildIndex(); - } - - /** - * \brief Saves the index to a stream - * \param stream The stream to save the index to - */ - void saveIndex(FILE* stream) - { - kmeans_index_->saveIndex(stream); - kdtree_index_->saveIndex(stream); - } - - /** - * \brief Loads the index from a stream - * \param stream The stream from which the index is loaded - */ - void loadIndex(FILE* stream) - { - kmeans_index_->loadIndex(stream); - kdtree_index_->loadIndex(stream); - } - - /** - * \returns The index parameters - */ - IndexParams getParameters() const - { - return index_params_; - } - - /** - * \brief Method that searches for nearest-neighbours - */ - void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) - { - kmeans_index_->findNeighbors(result, vec, searchParams); - kdtree_index_->findNeighbors(result, vec, searchParams); - } - -private: - /** The k-means index */ - KMeansIndex* kmeans_index_; - - /** The kd-tree index */ - KDTreeIndex* kdtree_index_; - - /** The index parameters */ - const IndexParams index_params_; -}; - -} - -#endif //OPENCV_FLANN_COMPOSITE_INDEX_H_ diff --git a/IPL/include/opencv/opencv2/flann/config.h b/IPL/include/opencv/opencv2/flann/config.h deleted file mode 100644 index 56832fd..0000000 --- a/IPL/include/opencv/opencv2/flann/config.h +++ /dev/null @@ -1,38 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - - -#ifndef OPENCV_FLANN_CONFIG_H_ -#define OPENCV_FLANN_CONFIG_H_ - -#ifdef FLANN_VERSION_ -#undef FLANN_VERSION_ -#endif -#define FLANN_VERSION_ "1.6.10" - -#endif /* OPENCV_FLANN_CONFIG_H_ */ diff --git a/IPL/include/opencv/opencv2/flann/defines.h b/IPL/include/opencv/opencv2/flann/defines.h deleted file mode 100644 index f0264f7..0000000 --- a/IPL/include/opencv/opencv2/flann/defines.h +++ /dev/null @@ -1,177 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - - -#ifndef OPENCV_FLANN_DEFINES_H_ -#define OPENCV_FLANN_DEFINES_H_ - -#include "config.h" - -#ifdef FLANN_EXPORT -#undef FLANN_EXPORT -#endif -#ifdef WIN32 -/* win32 dll export/import directives */ - #ifdef FLANN_EXPORTS - #define FLANN_EXPORT __declspec(dllexport) - #elif defined(FLANN_STATIC) - #define FLANN_EXPORT - #else - #define FLANN_EXPORT __declspec(dllimport) - #endif -#else -/* unix needs nothing */ - #define FLANN_EXPORT -#endif - - -#ifdef FLANN_DEPRECATED -#undef FLANN_DEPRECATED -#endif -#ifdef __GNUC__ -#define FLANN_DEPRECATED __attribute__ ((deprecated)) -#elif defined(_MSC_VER) -#define FLANN_DEPRECATED __declspec(deprecated) -#else -#pragma message("WARNING: You need to implement FLANN_DEPRECATED for this compiler") -#define FLANN_DEPRECATED -#endif - - -#undef FLANN_PLATFORM_32_BIT -#undef FLANN_PLATFORM_64_BIT -#if defined __amd64__ || defined __x86_64__ || defined _WIN64 || defined _M_X64 -#define FLANN_PLATFORM_64_BIT -#else -#define FLANN_PLATFORM_32_BIT -#endif - - -#undef FLANN_ARRAY_LEN -#define FLANN_ARRAY_LEN(a) (sizeof(a)/sizeof(a[0])) - -namespace cvflann { - -/* Nearest neighbour index algorithms */ -enum flann_algorithm_t -{ - FLANN_INDEX_LINEAR = 0, - FLANN_INDEX_KDTREE = 1, - FLANN_INDEX_KMEANS = 2, - FLANN_INDEX_COMPOSITE = 3, - FLANN_INDEX_KDTREE_SINGLE = 4, - FLANN_INDEX_HIERARCHICAL = 5, - FLANN_INDEX_LSH = 6, - FLANN_INDEX_SAVED = 254, - FLANN_INDEX_AUTOTUNED = 255, - - // deprecated constants, should use the FLANN_INDEX_* ones instead - LINEAR = 0, - KDTREE = 1, - KMEANS = 2, - COMPOSITE = 3, - KDTREE_SINGLE = 4, - SAVED = 254, - AUTOTUNED = 255 -}; - - - -enum flann_centers_init_t -{ - FLANN_CENTERS_RANDOM = 0, - FLANN_CENTERS_GONZALES = 1, - FLANN_CENTERS_KMEANSPP = 2, - FLANN_CENTERS_GROUPWISE = 3, - - // deprecated constants, should use the FLANN_CENTERS_* ones instead - CENTERS_RANDOM = 0, - CENTERS_GONZALES = 1, - CENTERS_KMEANSPP = 2 -}; - -enum flann_log_level_t -{ - FLANN_LOG_NONE = 0, - FLANN_LOG_FATAL = 1, - FLANN_LOG_ERROR = 2, - FLANN_LOG_WARN = 3, - FLANN_LOG_INFO = 4 -}; - -enum flann_distance_t -{ - FLANN_DIST_EUCLIDEAN = 1, - FLANN_DIST_L2 = 1, - FLANN_DIST_MANHATTAN = 2, - FLANN_DIST_L1 = 2, - FLANN_DIST_MINKOWSKI = 3, - FLANN_DIST_MAX = 4, - FLANN_DIST_HIST_INTERSECT = 5, - FLANN_DIST_HELLINGER = 6, - FLANN_DIST_CHI_SQUARE = 7, - FLANN_DIST_CS = 7, - FLANN_DIST_KULLBACK_LEIBLER = 8, - FLANN_DIST_KL = 8, - FLANN_DIST_HAMMING = 9, - - // deprecated constants, should use the FLANN_DIST_* ones instead - EUCLIDEAN = 1, - MANHATTAN = 2, - MINKOWSKI = 3, - MAX_DIST = 4, - HIST_INTERSECT = 5, - HELLINGER = 6, - CS = 7, - KL = 8, - KULLBACK_LEIBLER = 8 -}; - -enum flann_datatype_t -{ - FLANN_INT8 = 0, - FLANN_INT16 = 1, - FLANN_INT32 = 2, - FLANN_INT64 = 3, - FLANN_UINT8 = 4, - FLANN_UINT16 = 5, - FLANN_UINT32 = 6, - FLANN_UINT64 = 7, - FLANN_FLOAT32 = 8, - FLANN_FLOAT64 = 9 -}; - -enum -{ - FLANN_CHECKS_UNLIMITED = -1, - FLANN_CHECKS_AUTOTUNED = -2 -}; - -} - -#endif /* OPENCV_FLANN_DEFINES_H_ */ diff --git a/IPL/include/opencv/opencv2/flann/dist.h b/IPL/include/opencv/opencv2/flann/dist.h deleted file mode 100644 index 9dbe527..0000000 --- a/IPL/include/opencv/opencv2/flann/dist.h +++ /dev/null @@ -1,905 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_DIST_H_ -#define OPENCV_FLANN_DIST_H_ - -#include -#include -#include -#ifdef _MSC_VER -typedef unsigned __int32 uint32_t; -typedef unsigned __int64 uint64_t; -#else -#include -#endif - -#include "defines.h" - -#if (defined WIN32 || defined _WIN32) && defined(_M_ARM) -# include -#endif - -#ifdef __ARM_NEON__ -# include "arm_neon.h" -#endif - -namespace cvflann -{ - -template -inline T abs(T x) { return (x<0) ? -x : x; } - -template<> -inline int abs(int x) { return ::abs(x); } - -template<> -inline float abs(float x) { return fabsf(x); } - -template<> -inline double abs(double x) { return fabs(x); } - -template -struct Accumulator { typedef T Type; }; -template<> -struct Accumulator { typedef float Type; }; -template<> -struct Accumulator { typedef float Type; }; -template<> -struct Accumulator { typedef float Type; }; -template<> -struct Accumulator { typedef float Type; }; -template<> -struct Accumulator { typedef float Type; }; -template<> -struct Accumulator { typedef float Type; }; - -#undef True -#undef False - -class True -{ -}; - -class False -{ -}; - - -/** - * Squared Euclidean distance functor. - * - * This is the simpler, unrolled version. This is preferable for - * very low dimensionality data (eg 3D points) - */ -template -struct L2_Simple -{ - typedef True is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const - { - ResultType result = ResultType(); - ResultType diff; - for(size_t i = 0; i < size; ++i ) { - diff = *a++ - *b++; - result += diff*diff; - } - return result; - } - - template - inline ResultType accum_dist(const U& a, const V& b, int) const - { - return (a-b)*(a-b); - } -}; - - - -/** - * Squared Euclidean distance functor, optimized version - */ -template -struct L2 -{ - typedef True is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - - /** - * Compute the squared Euclidean distance between two vectors. - * - * This is highly optimised, with loop unrolling, as it is one - * of the most expensive inner loops. - * - * The computation of squared root at the end is omitted for - * efficiency. - */ - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const - { - ResultType result = ResultType(); - ResultType diff0, diff1, diff2, diff3; - Iterator1 last = a + size; - Iterator1 lastgroup = last - 3; - - /* Process 4 items with each loop for efficiency. */ - while (a < lastgroup) { - diff0 = (ResultType)(a[0] - b[0]); - diff1 = (ResultType)(a[1] - b[1]); - diff2 = (ResultType)(a[2] - b[2]); - diff3 = (ResultType)(a[3] - b[3]); - result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3; - a += 4; - b += 4; - - if ((worst_dist>0)&&(result>worst_dist)) { - return result; - } - } - /* Process last 0-3 pixels. Not needed for standard vector lengths. */ - while (a < last) { - diff0 = (ResultType)(*a++ - *b++); - result += diff0 * diff0; - } - return result; - } - - /** - * Partial euclidean distance, using just one dimension. This is used by the - * kd-tree when computing partial distances while traversing the tree. - * - * Squared root is omitted for efficiency. - */ - template - inline ResultType accum_dist(const U& a, const V& b, int) const - { - return (a-b)*(a-b); - } -}; - - -/* - * Manhattan distance functor, optimized version - */ -template -struct L1 -{ - typedef True is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - - /** - * Compute the Manhattan (L_1) distance between two vectors. - * - * This is highly optimised, with loop unrolling, as it is one - * of the most expensive inner loops. - */ - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const - { - ResultType result = ResultType(); - ResultType diff0, diff1, diff2, diff3; - Iterator1 last = a + size; - Iterator1 lastgroup = last - 3; - - /* Process 4 items with each loop for efficiency. */ - while (a < lastgroup) { - diff0 = (ResultType)abs(a[0] - b[0]); - diff1 = (ResultType)abs(a[1] - b[1]); - diff2 = (ResultType)abs(a[2] - b[2]); - diff3 = (ResultType)abs(a[3] - b[3]); - result += diff0 + diff1 + diff2 + diff3; - a += 4; - b += 4; - - if ((worst_dist>0)&&(result>worst_dist)) { - return result; - } - } - /* Process last 0-3 pixels. Not needed for standard vector lengths. */ - while (a < last) { - diff0 = (ResultType)abs(*a++ - *b++); - result += diff0; - } - return result; - } - - /** - * Partial distance, used by the kd-tree. - */ - template - inline ResultType accum_dist(const U& a, const V& b, int) const - { - return abs(a-b); - } -}; - - - -template -struct MinkowskiDistance -{ - typedef True is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - - int order; - - MinkowskiDistance(int order_) : order(order_) {} - - /** - * Compute the Minkowsky (L_p) distance between two vectors. - * - * This is highly optimised, with loop unrolling, as it is one - * of the most expensive inner loops. - * - * The computation of squared root at the end is omitted for - * efficiency. - */ - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const - { - ResultType result = ResultType(); - ResultType diff0, diff1, diff2, diff3; - Iterator1 last = a + size; - Iterator1 lastgroup = last - 3; - - /* Process 4 items with each loop for efficiency. */ - while (a < lastgroup) { - diff0 = (ResultType)abs(a[0] - b[0]); - diff1 = (ResultType)abs(a[1] - b[1]); - diff2 = (ResultType)abs(a[2] - b[2]); - diff3 = (ResultType)abs(a[3] - b[3]); - result += pow(diff0,order) + pow(diff1,order) + pow(diff2,order) + pow(diff3,order); - a += 4; - b += 4; - - if ((worst_dist>0)&&(result>worst_dist)) { - return result; - } - } - /* Process last 0-3 pixels. Not needed for standard vector lengths. */ - while (a < last) { - diff0 = (ResultType)abs(*a++ - *b++); - result += pow(diff0,order); - } - return result; - } - - /** - * Partial distance, used by the kd-tree. - */ - template - inline ResultType accum_dist(const U& a, const V& b, int) const - { - return pow(static_cast(abs(a-b)),order); - } -}; - - - -template -struct MaxDistance -{ - typedef False is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - - /** - * Compute the max distance (L_infinity) between two vectors. - * - * This distance is not a valid kdtree distance, it's not dimensionwise additive. - */ - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const - { - ResultType result = ResultType(); - ResultType diff0, diff1, diff2, diff3; - Iterator1 last = a + size; - Iterator1 lastgroup = last - 3; - - /* Process 4 items with each loop for efficiency. */ - while (a < lastgroup) { - diff0 = abs(a[0] - b[0]); - diff1 = abs(a[1] - b[1]); - diff2 = abs(a[2] - b[2]); - diff3 = abs(a[3] - b[3]); - if (diff0>result) {result = diff0; } - if (diff1>result) {result = diff1; } - if (diff2>result) {result = diff2; } - if (diff3>result) {result = diff3; } - a += 4; - b += 4; - - if ((worst_dist>0)&&(result>worst_dist)) { - return result; - } - } - /* Process last 0-3 pixels. Not needed for standard vector lengths. */ - while (a < last) { - diff0 = abs(*a++ - *b++); - result = (diff0>result) ? diff0 : result; - } - return result; - } - - /* This distance functor is not dimension-wise additive, which - * makes it an invalid kd-tree distance, not implementing the accum_dist method */ - -}; - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -/** - * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor - * bit count of A exclusive XOR'ed with B - */ -struct HammingLUT -{ - typedef False is_kdtree_distance; - typedef False is_vector_space_distance; - - typedef unsigned char ElementType; - typedef int ResultType; - - /** this will count the bits in a ^ b - */ - ResultType operator()(const unsigned char* a, const unsigned char* b, size_t size) const - { - static const uchar popCountTable[] = - { - 0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8 - }; - ResultType result = 0; - for (size_t i = 0; i < size; i++) { - result += popCountTable[a[i] ^ b[i]]; - } - return result; - } -}; - -/** - * Hamming distance functor (pop count between two binary vectors, i.e. xor them and count the number of bits set) - * That code was taken from brief.cpp in OpenCV - */ -template -struct Hamming -{ - typedef False is_kdtree_distance; - typedef False is_vector_space_distance; - - - typedef T ElementType; - typedef int ResultType; - - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const - { - ResultType result = 0; -#ifdef __ARM_NEON__ - { - uint32x4_t bits = vmovq_n_u32(0); - for (size_t i = 0; i < size; i += 16) { - uint8x16_t A_vec = vld1q_u8 (a + i); - uint8x16_t B_vec = vld1q_u8 (b + i); - uint8x16_t AxorB = veorq_u8 (A_vec, B_vec); - uint8x16_t bitsSet = vcntq_u8 (AxorB); - uint16x8_t bitSet8 = vpaddlq_u8 (bitsSet); - uint32x4_t bitSet4 = vpaddlq_u16 (bitSet8); - bits = vaddq_u32(bits, bitSet4); - } - uint64x2_t bitSet2 = vpaddlq_u32 (bits); - result = vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),0); - result += vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),2); - } -#elif __GNUC__ - { - //for portability just use unsigned long -- and use the __builtin_popcountll (see docs for __builtin_popcountll) - typedef unsigned long long pop_t; - const size_t modulo = size % sizeof(pop_t); - const pop_t* a2 = reinterpret_cast (a); - const pop_t* b2 = reinterpret_cast (b); - const pop_t* a2_end = a2 + (size / sizeof(pop_t)); - - for (; a2 != a2_end; ++a2, ++b2) result += __builtin_popcountll((*a2) ^ (*b2)); - - if (modulo) { - //in the case where size is not dividable by sizeof(size_t) - //need to mask off the bits at the end - pop_t a_final = 0, b_final = 0; - memcpy(&a_final, a2, modulo); - memcpy(&b_final, b2, modulo); - result += __builtin_popcountll(a_final ^ b_final); - } - } -#else // NO NEON and NOT GNUC - typedef unsigned long long pop_t; - HammingLUT lut; - result = lut(reinterpret_cast (a), - reinterpret_cast (b), size * sizeof(pop_t)); -#endif - return result; - } -}; - -template -struct Hamming2 -{ - typedef False is_kdtree_distance; - typedef False is_vector_space_distance; - - typedef T ElementType; - typedef int ResultType; - - /** This is popcount_3() from: - * http://en.wikipedia.org/wiki/Hamming_weight */ - unsigned int popcnt32(uint32_t n) const - { - n -= ((n >> 1) & 0x55555555); - n = (n & 0x33333333) + ((n >> 2) & 0x33333333); - return (((n + (n >> 4))& 0xF0F0F0F)* 0x1010101) >> 24; - } - -#ifdef FLANN_PLATFORM_64_BIT - unsigned int popcnt64(uint64_t n) const - { - n -= ((n >> 1) & 0x5555555555555555); - n = (n & 0x3333333333333333) + ((n >> 2) & 0x3333333333333333); - return (((n + (n >> 4))& 0x0f0f0f0f0f0f0f0f)* 0x0101010101010101) >> 56; - } -#endif - - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const - { -#ifdef FLANN_PLATFORM_64_BIT - const uint64_t* pa = reinterpret_cast(a); - const uint64_t* pb = reinterpret_cast(b); - ResultType result = 0; - size /= (sizeof(uint64_t)/sizeof(unsigned char)); - for(size_t i = 0; i < size; ++i ) { - result += popcnt64(*pa ^ *pb); - ++pa; - ++pb; - } -#else - const uint32_t* pa = reinterpret_cast(a); - const uint32_t* pb = reinterpret_cast(b); - ResultType result = 0; - size /= (sizeof(uint32_t)/sizeof(unsigned char)); - for(size_t i = 0; i < size; ++i ) { - result += popcnt32(*pa ^ *pb); - ++pa; - ++pb; - } -#endif - return result; - } -}; - - - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -template -struct HistIntersectionDistance -{ - typedef True is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - - /** - * Compute the histogram intersection distance - */ - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const - { - ResultType result = ResultType(); - ResultType min0, min1, min2, min3; - Iterator1 last = a + size; - Iterator1 lastgroup = last - 3; - - /* Process 4 items with each loop for efficiency. */ - while (a < lastgroup) { - min0 = (ResultType)(a[0] < b[0] ? a[0] : b[0]); - min1 = (ResultType)(a[1] < b[1] ? a[1] : b[1]); - min2 = (ResultType)(a[2] < b[2] ? a[2] : b[2]); - min3 = (ResultType)(a[3] < b[3] ? a[3] : b[3]); - result += min0 + min1 + min2 + min3; - a += 4; - b += 4; - if ((worst_dist>0)&&(result>worst_dist)) { - return result; - } - } - /* Process last 0-3 pixels. Not needed for standard vector lengths. */ - while (a < last) { - min0 = (ResultType)(*a < *b ? *a : *b); - result += min0; - ++a; - ++b; - } - return result; - } - - /** - * Partial distance, used by the kd-tree. - */ - template - inline ResultType accum_dist(const U& a, const V& b, int) const - { - return a -struct HellingerDistance -{ - typedef True is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - - /** - * Compute the Hellinger distance - */ - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const - { - ResultType result = ResultType(); - ResultType diff0, diff1, diff2, diff3; - Iterator1 last = a + size; - Iterator1 lastgroup = last - 3; - - /* Process 4 items with each loop for efficiency. */ - while (a < lastgroup) { - diff0 = sqrt(static_cast(a[0])) - sqrt(static_cast(b[0])); - diff1 = sqrt(static_cast(a[1])) - sqrt(static_cast(b[1])); - diff2 = sqrt(static_cast(a[2])) - sqrt(static_cast(b[2])); - diff3 = sqrt(static_cast(a[3])) - sqrt(static_cast(b[3])); - result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3; - a += 4; - b += 4; - } - while (a < last) { - diff0 = sqrt(static_cast(*a++)) - sqrt(static_cast(*b++)); - result += diff0 * diff0; - } - return result; - } - - /** - * Partial distance, used by the kd-tree. - */ - template - inline ResultType accum_dist(const U& a, const V& b, int) const - { - ResultType diff = sqrt(static_cast(a)) - sqrt(static_cast(b)); - return diff * diff; - } -}; - - -template -struct ChiSquareDistance -{ - typedef True is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - - /** - * Compute the chi-square distance - */ - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const - { - ResultType result = ResultType(); - ResultType sum, diff; - Iterator1 last = a + size; - - while (a < last) { - sum = (ResultType)(*a + *b); - if (sum>0) { - diff = (ResultType)(*a - *b); - result += diff*diff/sum; - } - ++a; - ++b; - - if ((worst_dist>0)&&(result>worst_dist)) { - return result; - } - } - return result; - } - - /** - * Partial distance, used by the kd-tree. - */ - template - inline ResultType accum_dist(const U& a, const V& b, int) const - { - ResultType result = ResultType(); - ResultType sum, diff; - - sum = (ResultType)(a+b); - if (sum>0) { - diff = (ResultType)(a-b); - result = diff*diff/sum; - } - return result; - } -}; - - -template -struct KL_Divergence -{ - typedef True is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - - /** - * Compute the Kullback–Leibler divergence - */ - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const - { - ResultType result = ResultType(); - Iterator1 last = a + size; - - while (a < last) { - if (* b != 0) { - ResultType ratio = (ResultType)(*a / *b); - if (ratio>0) { - result += *a * log(ratio); - } - } - ++a; - ++b; - - if ((worst_dist>0)&&(result>worst_dist)) { - return result; - } - } - return result; - } - - /** - * Partial distance, used by the kd-tree. - */ - template - inline ResultType accum_dist(const U& a, const V& b, int) const - { - ResultType result = ResultType(); - if( *b != 0 ) { - ResultType ratio = (ResultType)(a / b); - if (ratio>0) { - result = a * log(ratio); - } - } - return result; - } -}; - - - -/* - * This is a "zero iterator". It basically behaves like a zero filled - * array to all algorithms that use arrays as iterators (STL style). - * It's useful when there's a need to compute the distance between feature - * and origin it and allows for better compiler optimisation than using a - * zero-filled array. - */ -template -struct ZeroIterator -{ - - T operator*() - { - return 0; - } - - T operator[](int) - { - return 0; - } - - const ZeroIterator& operator ++() - { - return *this; - } - - ZeroIterator operator ++(int) - { - return *this; - } - - ZeroIterator& operator+=(int) - { - return *this; - } - -}; - - -/* - * Depending on processed distances, some of them are already squared (e.g. L2) - * and some are not (e.g.Hamming). In KMeans++ for instance we want to be sure - * we are working on ^2 distances, thus following templates to ensure that. - */ -template -struct squareDistance -{ - typedef typename Distance::ResultType ResultType; - ResultType operator()( ResultType dist ) { return dist*dist; } -}; - - -template -struct squareDistance, ElementType> -{ - typedef typename L2_Simple::ResultType ResultType; - ResultType operator()( ResultType dist ) { return dist; } -}; - -template -struct squareDistance, ElementType> -{ - typedef typename L2::ResultType ResultType; - ResultType operator()( ResultType dist ) { return dist; } -}; - - -template -struct squareDistance, ElementType> -{ - typedef typename MinkowskiDistance::ResultType ResultType; - ResultType operator()( ResultType dist ) { return dist; } -}; - -template -struct squareDistance, ElementType> -{ - typedef typename HellingerDistance::ResultType ResultType; - ResultType operator()( ResultType dist ) { return dist; } -}; - -template -struct squareDistance, ElementType> -{ - typedef typename ChiSquareDistance::ResultType ResultType; - ResultType operator()( ResultType dist ) { return dist; } -}; - - -template -typename Distance::ResultType ensureSquareDistance( typename Distance::ResultType dist ) -{ - typedef typename Distance::ElementType ElementType; - - squareDistance dummy; - return dummy( dist ); -} - - -/* - * ...and a template to ensure the user that he will process the normal distance, - * and not squared distance, without loosing processing time calling sqrt(ensureSquareDistance) - * that will result in doing actually sqrt(dist*dist) for L1 distance for instance. - */ -template -struct simpleDistance -{ - typedef typename Distance::ResultType ResultType; - ResultType operator()( ResultType dist ) { return dist; } -}; - - -template -struct simpleDistance, ElementType> -{ - typedef typename L2_Simple::ResultType ResultType; - ResultType operator()( ResultType dist ) { return sqrt(dist); } -}; - -template -struct simpleDistance, ElementType> -{ - typedef typename L2::ResultType ResultType; - ResultType operator()( ResultType dist ) { return sqrt(dist); } -}; - - -template -struct simpleDistance, ElementType> -{ - typedef typename MinkowskiDistance::ResultType ResultType; - ResultType operator()( ResultType dist ) { return sqrt(dist); } -}; - -template -struct simpleDistance, ElementType> -{ - typedef typename HellingerDistance::ResultType ResultType; - ResultType operator()( ResultType dist ) { return sqrt(dist); } -}; - -template -struct simpleDistance, ElementType> -{ - typedef typename ChiSquareDistance::ResultType ResultType; - ResultType operator()( ResultType dist ) { return sqrt(dist); } -}; - - -template -typename Distance::ResultType ensureSimpleDistance( typename Distance::ResultType dist ) -{ - typedef typename Distance::ElementType ElementType; - - simpleDistance dummy; - return dummy( dist ); -} - -} - -#endif //OPENCV_FLANN_DIST_H_ diff --git a/IPL/include/opencv/opencv2/flann/dummy.h b/IPL/include/opencv/opencv2/flann/dummy.h deleted file mode 100644 index 26bd3fa..0000000 --- a/IPL/include/opencv/opencv2/flann/dummy.h +++ /dev/null @@ -1,16 +0,0 @@ - -#ifndef OPENCV_FLANN_DUMMY_H_ -#define OPENCV_FLANN_DUMMY_H_ - -namespace cvflann -{ - -#if (defined WIN32 || defined _WIN32 || defined WINCE) && defined CVAPI_EXPORTS -__declspec(dllexport) -#endif -void dummyfunc(); - -} - - -#endif /* OPENCV_FLANN_DUMMY_H_ */ diff --git a/IPL/include/opencv/opencv2/flann/dynamic_bitset.h b/IPL/include/opencv/opencv2/flann/dynamic_bitset.h deleted file mode 100644 index d795b5d..0000000 --- a/IPL/include/opencv/opencv2/flann/dynamic_bitset.h +++ /dev/null @@ -1,159 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -/*********************************************************************** - * Author: Vincent Rabaud - *************************************************************************/ - -#ifndef OPENCV_FLANN_DYNAMIC_BITSET_H_ -#define OPENCV_FLANN_DYNAMIC_BITSET_H_ - -#ifndef FLANN_USE_BOOST -# define FLANN_USE_BOOST 0 -#endif -//#define FLANN_USE_BOOST 1 -#if FLANN_USE_BOOST -#include -typedef boost::dynamic_bitset<> DynamicBitset; -#else - -#include - -#include "dist.h" - -namespace cvflann { - -/** Class re-implementing the boost version of it - * This helps not depending on boost, it also does not do the bound checks - * and has a way to reset a block for speed - */ -class DynamicBitset -{ -public: - /** default constructor - */ - DynamicBitset() - { - } - - /** only constructor we use in our code - * @param sz the size of the bitset (in bits) - */ - DynamicBitset(size_t sz) - { - resize(sz); - reset(); - } - - /** Sets all the bits to 0 - */ - void clear() - { - std::fill(bitset_.begin(), bitset_.end(), 0); - } - - /** @brief checks if the bitset is empty - * @return true if the bitset is empty - */ - bool empty() const - { - return bitset_.empty(); - } - - /** set all the bits to 0 - */ - void reset() - { - std::fill(bitset_.begin(), bitset_.end(), 0); - } - - /** @brief set one bit to 0 - * @param index - */ - void reset(size_t index) - { - bitset_[index / cell_bit_size_] &= ~(size_t(1) << (index % cell_bit_size_)); - } - - /** @brief sets a specific bit to 0, and more bits too - * This function is useful when resetting a given set of bits so that the - * whole bitset ends up being 0: if that's the case, we don't care about setting - * other bits to 0 - * @param index - */ - void reset_block(size_t index) - { - bitset_[index / cell_bit_size_] = 0; - } - - /** resize the bitset so that it contains at least sz bits - * @param sz - */ - void resize(size_t sz) - { - size_ = sz; - bitset_.resize(sz / cell_bit_size_ + 1); - } - - /** set a bit to true - * @param index the index of the bit to set to 1 - */ - void set(size_t index) - { - bitset_[index / cell_bit_size_] |= size_t(1) << (index % cell_bit_size_); - } - - /** gives the number of contained bits - */ - size_t size() const - { - return size_; - } - - /** check if a bit is set - * @param index the index of the bit to check - * @return true if the bit is set - */ - bool test(size_t index) const - { - return (bitset_[index / cell_bit_size_] & (size_t(1) << (index % cell_bit_size_))) != 0; - } - -private: - std::vector bitset_; - size_t size_; - static const unsigned int cell_bit_size_ = CHAR_BIT * sizeof(size_t); -}; - -} // namespace cvflann - -#endif - -#endif // OPENCV_FLANN_DYNAMIC_BITSET_H_ diff --git a/IPL/include/opencv/opencv2/flann/flann.hpp b/IPL/include/opencv/opencv2/flann/flann.hpp deleted file mode 100644 index 227683f..0000000 --- a/IPL/include/opencv/opencv2/flann/flann.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/flann.hpp" diff --git a/IPL/include/opencv/opencv2/flann/flann_base.hpp b/IPL/include/opencv/opencv2/flann/flann_base.hpp deleted file mode 100644 index 98c33cf..0000000 --- a/IPL/include/opencv/opencv2/flann/flann_base.hpp +++ /dev/null @@ -1,290 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_BASE_HPP_ -#define OPENCV_FLANN_BASE_HPP_ - -#include -#include -#include - -#include "general.h" -#include "matrix.h" -#include "params.h" -#include "saving.h" - -#include "all_indices.h" - -namespace cvflann -{ - -/** - * Sets the log level used for all flann functions - * @param level Verbosity level - */ -inline void log_verbosity(int level) -{ - if (level >= 0) { - Logger::setLevel(level); - } -} - -/** - * (Deprecated) Index parameters for creating a saved index. - */ -struct SavedIndexParams : public IndexParams -{ - SavedIndexParams(cv::String filename) - { - (* this)["algorithm"] = FLANN_INDEX_SAVED; - (*this)["filename"] = filename; - } -}; - - -template -NNIndex* load_saved_index(const Matrix& dataset, const cv::String& filename, Distance distance) -{ - typedef typename Distance::ElementType ElementType; - - FILE* fin = fopen(filename.c_str(), "rb"); - if (fin == NULL) { - return NULL; - } - IndexHeader header = load_header(fin); - if (header.data_type != Datatype::type()) { - throw FLANNException("Datatype of saved index is different than of the one to be created."); - } - if ((size_t(header.rows) != dataset.rows)||(size_t(header.cols) != dataset.cols)) { - throw FLANNException("The index saved belongs to a different dataset"); - } - - IndexParams params; - params["algorithm"] = header.index_type; - NNIndex* nnIndex = create_index_by_type(dataset, params, distance); - nnIndex->loadIndex(fin); - fclose(fin); - - return nnIndex; -} - - -template -class Index : public NNIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - Index(const Matrix& features, const IndexParams& params, Distance distance = Distance() ) - : index_params_(params) - { - flann_algorithm_t index_type = get_param(params,"algorithm"); - loaded_ = false; - - if (index_type == FLANN_INDEX_SAVED) { - nnIndex_ = load_saved_index(features, get_param(params,"filename"), distance); - loaded_ = true; - } - else { - nnIndex_ = create_index_by_type(features, params, distance); - } - } - - ~Index() - { - delete nnIndex_; - } - - /** - * Builds the index. - */ - void buildIndex() - { - if (!loaded_) { - nnIndex_->buildIndex(); - } - } - - void save(cv::String filename) - { - FILE* fout = fopen(filename.c_str(), "wb"); - if (fout == NULL) { - throw FLANNException("Cannot open file"); - } - save_header(fout, *nnIndex_); - saveIndex(fout); - fclose(fout); - } - - /** - * \brief Saves the index to a stream - * \param stream The stream to save the index to - */ - virtual void saveIndex(FILE* stream) - { - nnIndex_->saveIndex(stream); - } - - /** - * \brief Loads the index from a stream - * \param stream The stream from which the index is loaded - */ - virtual void loadIndex(FILE* stream) - { - nnIndex_->loadIndex(stream); - } - - /** - * \returns number of features in this index. - */ - size_t veclen() const - { - return nnIndex_->veclen(); - } - - /** - * \returns The dimensionality of the features in this index. - */ - size_t size() const - { - return nnIndex_->size(); - } - - /** - * \returns The index type (kdtree, kmeans,...) - */ - flann_algorithm_t getType() const - { - return nnIndex_->getType(); - } - - /** - * \returns The amount of memory (in bytes) used by the index. - */ - virtual int usedMemory() const - { - return nnIndex_->usedMemory(); - } - - - /** - * \returns The index parameters - */ - IndexParams getParameters() const - { - return nnIndex_->getParameters(); - } - - /** - * \brief Perform k-nearest neighbor search - * \param[in] queries The query points for which to find the nearest neighbors - * \param[out] indices The indices of the nearest neighbors found - * \param[out] dists Distances to the nearest neighbors found - * \param[in] knn Number of nearest neighbors to return - * \param[in] params Search parameters - */ - void knnSearch(const Matrix& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params) - { - nnIndex_->knnSearch(queries, indices, dists, knn, params); - } - - /** - * \brief Perform radius search - * \param[in] query The query point - * \param[out] indices The indinces of the neighbors found within the given radius - * \param[out] dists The distances to the nearest neighbors found - * \param[in] radius The radius used for search - * \param[in] params Search parameters - * \returns Number of neighbors found - */ - int radiusSearch(const Matrix& query, Matrix& indices, Matrix& dists, float radius, const SearchParams& params) - { - return nnIndex_->radiusSearch(query, indices, dists, radius, params); - } - - /** - * \brief Method that searches for nearest-neighbours - */ - void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) - { - nnIndex_->findNeighbors(result, vec, searchParams); - } - - /** - * \brief Returns actual index - */ - FLANN_DEPRECATED NNIndex* getIndex() - { - return nnIndex_; - } - - /** - * \brief Returns index parameters. - * \deprecated use getParameters() instead. - */ - FLANN_DEPRECATED const IndexParams* getIndexParameters() - { - return &index_params_; - } - -private: - /** Pointer to actual index class */ - NNIndex* nnIndex_; - /** Indices if the index was loaded from a file */ - bool loaded_; - /** Parameters passed to the index */ - IndexParams index_params_; -}; - -/** - * Performs a hierarchical clustering of the points passed as argument and then takes a cut in the - * the clustering tree to return a flat clustering. - * @param[in] points Points to be clustered - * @param centers The computed cluster centres. Matrix should be preallocated and centers.rows is the - * number of clusters requested. - * @param params Clustering parameters (The same as for cvflann::KMeansIndex) - * @param d Distance to be used for clustering (eg: cvflann::L2) - * @return number of clusters computed (can be different than clusters.rows and is the highest number - * of the form (branching-1)*K+1 smaller than clusters.rows). - */ -template -int hierarchicalClustering(const Matrix& points, Matrix& centers, - const KMeansIndexParams& params, Distance d = Distance()) -{ - KMeansIndex kmeans(points, params, d); - kmeans.buildIndex(); - - int clusterNum = kmeans.getClusterCenters(centers); - return clusterNum; -} - -} -#endif /* OPENCV_FLANN_BASE_HPP_ */ diff --git a/IPL/include/opencv/opencv2/flann/general.h b/IPL/include/opencv/opencv2/flann/general.h deleted file mode 100644 index 9d5402a..0000000 --- a/IPL/include/opencv/opencv2/flann/general.h +++ /dev/null @@ -1,50 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_GENERAL_H_ -#define OPENCV_FLANN_GENERAL_H_ - -#include "opencv2/core.hpp" - -namespace cvflann -{ - -class FLANNException : public cv::Exception -{ -public: - FLANNException(const char* message) : cv::Exception(0, message, "", __FILE__, __LINE__) { } - - FLANNException(const cv::String& message) : cv::Exception(0, message, "", __FILE__, __LINE__) { } -}; - -} - - -#endif /* OPENCV_FLANN_GENERAL_H_ */ diff --git a/IPL/include/opencv/opencv2/flann/ground_truth.h b/IPL/include/opencv/opencv2/flann/ground_truth.h deleted file mode 100644 index fd8f3ae..0000000 --- a/IPL/include/opencv/opencv2/flann/ground_truth.h +++ /dev/null @@ -1,94 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_GROUND_TRUTH_H_ -#define OPENCV_FLANN_GROUND_TRUTH_H_ - -#include "dist.h" -#include "matrix.h" - - -namespace cvflann -{ - -template -void find_nearest(const Matrix& dataset, typename Distance::ElementType* query, int* matches, int nn, - int skip = 0, Distance distance = Distance()) -{ - typedef typename Distance::ResultType DistanceType; - int n = nn + skip; - - std::vector match(n); - std::vector dists(n); - - dists[0] = distance(dataset[0], query, dataset.cols); - match[0] = 0; - int dcnt = 1; - - for (size_t i=1; i=1 && dists[j] -void compute_ground_truth(const Matrix& dataset, const Matrix& testset, Matrix& matches, - int skip=0, Distance d = Distance()) -{ - for (size_t i=0; i(dataset, testset[i], matches[i], (int)matches.cols, skip, d); - } -} - - -} - -#endif //OPENCV_FLANN_GROUND_TRUTH_H_ diff --git a/IPL/include/opencv/opencv2/flann/hdf5.h b/IPL/include/opencv/opencv2/flann/hdf5.h deleted file mode 100644 index 80d23b9..0000000 --- a/IPL/include/opencv/opencv2/flann/hdf5.h +++ /dev/null @@ -1,231 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - - -#ifndef OPENCV_FLANN_HDF5_H_ -#define OPENCV_FLANN_HDF5_H_ - -#include - -#include "matrix.h" - - -namespace cvflann -{ - -namespace -{ - -template -hid_t get_hdf5_type() -{ - throw FLANNException("Unsupported type for IO operations"); -} - -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_CHAR; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_UCHAR; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_SHORT; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_USHORT; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_INT; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_UINT; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_LONG; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_ULONG; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_FLOAT; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_DOUBLE; } -} - - -#define CHECK_ERROR(x,y) if ((x)<0) throw FLANNException((y)); - -template -void save_to_file(const cvflann::Matrix& dataset, const String& filename, const String& name) -{ - -#if H5Eset_auto_vers == 2 - H5Eset_auto( H5E_DEFAULT, NULL, NULL ); -#else - H5Eset_auto( NULL, NULL ); -#endif - - herr_t status; - hid_t file_id; - file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, H5P_DEFAULT); - if (file_id < 0) { - file_id = H5Fcreate(filename.c_str(), H5F_ACC_EXCL, H5P_DEFAULT, H5P_DEFAULT); - } - CHECK_ERROR(file_id,"Error creating hdf5 file."); - - hsize_t dimsf[2]; // dataset dimensions - dimsf[0] = dataset.rows; - dimsf[1] = dataset.cols; - - hid_t space_id = H5Screate_simple(2, dimsf, NULL); - hid_t memspace_id = H5Screate_simple(2, dimsf, NULL); - - hid_t dataset_id; -#if H5Dcreate_vers == 2 - dataset_id = H5Dcreate2(file_id, name.c_str(), get_hdf5_type(), space_id, H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT); -#else - dataset_id = H5Dcreate(file_id, name.c_str(), get_hdf5_type(), space_id, H5P_DEFAULT); -#endif - - if (dataset_id<0) { -#if H5Dopen_vers == 2 - dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT); -#else - dataset_id = H5Dopen(file_id, name.c_str()); -#endif - } - CHECK_ERROR(dataset_id,"Error creating or opening dataset in file."); - - status = H5Dwrite(dataset_id, get_hdf5_type(), memspace_id, space_id, H5P_DEFAULT, dataset.data ); - CHECK_ERROR(status, "Error writing to dataset"); - - H5Sclose(memspace_id); - H5Sclose(space_id); - H5Dclose(dataset_id); - H5Fclose(file_id); - -} - - -template -void load_from_file(cvflann::Matrix& dataset, const String& filename, const String& name) -{ - herr_t status; - hid_t file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, H5P_DEFAULT); - CHECK_ERROR(file_id,"Error opening hdf5 file."); - - hid_t dataset_id; -#if H5Dopen_vers == 2 - dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT); -#else - dataset_id = H5Dopen(file_id, name.c_str()); -#endif - CHECK_ERROR(dataset_id,"Error opening dataset in file."); - - hid_t space_id = H5Dget_space(dataset_id); - - hsize_t dims_out[2]; - H5Sget_simple_extent_dims(space_id, dims_out, NULL); - - dataset = cvflann::Matrix(new T[dims_out[0]*dims_out[1]], dims_out[0], dims_out[1]); - - status = H5Dread(dataset_id, get_hdf5_type(), H5S_ALL, H5S_ALL, H5P_DEFAULT, dataset[0]); - CHECK_ERROR(status, "Error reading dataset"); - - H5Sclose(space_id); - H5Dclose(dataset_id); - H5Fclose(file_id); -} - - -#ifdef HAVE_MPI - -namespace mpi -{ -/** - * Loads a the hyperslice corresponding to this processor from a hdf5 file. - * @param flann_dataset Dataset where the data is loaded - * @param filename HDF5 file name - * @param name Name of dataset inside file - */ -template -void load_from_file(cvflann::Matrix& dataset, const String& filename, const String& name) -{ - MPI_Comm comm = MPI_COMM_WORLD; - MPI_Info info = MPI_INFO_NULL; - - int mpi_size, mpi_rank; - MPI_Comm_size(comm, &mpi_size); - MPI_Comm_rank(comm, &mpi_rank); - - herr_t status; - - hid_t plist_id = H5Pcreate(H5P_FILE_ACCESS); - H5Pset_fapl_mpio(plist_id, comm, info); - hid_t file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, plist_id); - CHECK_ERROR(file_id,"Error opening hdf5 file."); - H5Pclose(plist_id); - hid_t dataset_id; -#if H5Dopen_vers == 2 - dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT); -#else - dataset_id = H5Dopen(file_id, name.c_str()); -#endif - CHECK_ERROR(dataset_id,"Error opening dataset in file."); - - hid_t space_id = H5Dget_space(dataset_id); - hsize_t dims[2]; - H5Sget_simple_extent_dims(space_id, dims, NULL); - - hsize_t count[2]; - hsize_t offset[2]; - - hsize_t item_cnt = dims[0]/mpi_size+(dims[0]%mpi_size==0 ? 0 : 1); - hsize_t cnt = (mpi_rank(), memspace_id, space_id, plist_id, dataset.data); - CHECK_ERROR(status, "Error reading dataset"); - - H5Pclose(plist_id); - H5Sclose(space_id); - H5Sclose(memspace_id); - H5Dclose(dataset_id); - H5Fclose(file_id); -} -} -#endif // HAVE_MPI -} // namespace cvflann::mpi - -#endif /* OPENCV_FLANN_HDF5_H_ */ diff --git a/IPL/include/opencv/opencv2/flann/heap.h b/IPL/include/opencv/opencv2/flann/heap.h deleted file mode 100644 index 92a6ea6..0000000 --- a/IPL/include/opencv/opencv2/flann/heap.h +++ /dev/null @@ -1,165 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_HEAP_H_ -#define OPENCV_FLANN_HEAP_H_ - -#include -#include - -namespace cvflann -{ - -/** - * Priority Queue Implementation - * - * The priority queue is implemented with a heap. A heap is a complete - * (full) binary tree in which each parent is less than both of its - * children, but the order of the children is unspecified. - */ -template -class Heap -{ - - /** - * Storage array for the heap. - * Type T must be comparable. - */ - std::vector heap; - int length; - - /** - * Number of element in the heap - */ - int count; - - - -public: - /** - * Constructor. - * - * Params: - * sz = heap size - */ - - Heap(int sz) - { - length = sz; - heap.reserve(length); - count = 0; - } - - /** - * - * Returns: heap size - */ - int size() - { - return count; - } - - /** - * Tests if the heap is empty - * - * Returns: true is heap empty, false otherwise - */ - bool empty() - { - return size()==0; - } - - /** - * Clears the heap. - */ - void clear() - { - heap.clear(); - count = 0; - } - - struct CompareT - { - bool operator()(const T& t_1, const T& t_2) const - { - return t_2 < t_1; - } - }; - - /** - * Insert a new element in the heap. - * - * We select the next empty leaf node, and then keep moving any larger - * parents down until the right location is found to store this element. - * - * Params: - * value = the new element to be inserted in the heap - */ - void insert(T value) - { - /* If heap is full, then return without adding this element. */ - if (count == length) { - return; - } - - heap.push_back(value); - static CompareT compareT; - std::push_heap(heap.begin(), heap.end(), compareT); - ++count; - } - - - - /** - * Returns the node of minimum value from the heap (top of the heap). - * - * Params: - * value = out parameter used to return the min element - * Returns: false if heap empty - */ - bool popMin(T& value) - { - if (count == 0) { - return false; - } - - value = heap[0]; - static CompareT compareT; - std::pop_heap(heap.begin(), heap.end(), compareT); - heap.pop_back(); - --count; - - return true; /* Return old last node. */ - } -}; - -} - -#endif //OPENCV_FLANN_HEAP_H_ diff --git a/IPL/include/opencv/opencv2/flann/hierarchical_clustering_index.h b/IPL/include/opencv/opencv2/flann/hierarchical_clustering_index.h deleted file mode 100644 index 9d890d4..0000000 --- a/IPL/include/opencv/opencv2/flann/hierarchical_clustering_index.h +++ /dev/null @@ -1,848 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_ -#define OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_ - -#include -#include -#include -#include -#include - -#include "general.h" -#include "nn_index.h" -#include "dist.h" -#include "matrix.h" -#include "result_set.h" -#include "heap.h" -#include "allocator.h" -#include "random.h" -#include "saving.h" - - -namespace cvflann -{ - -struct HierarchicalClusteringIndexParams : public IndexParams -{ - HierarchicalClusteringIndexParams(int branching = 32, - flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, - int trees = 4, int leaf_size = 100) - { - (*this)["algorithm"] = FLANN_INDEX_HIERARCHICAL; - // The branching factor used in the hierarchical clustering - (*this)["branching"] = branching; - // Algorithm used for picking the initial cluster centers - (*this)["centers_init"] = centers_init; - // number of parallel trees to build - (*this)["trees"] = trees; - // maximum leaf size - (*this)["leaf_size"] = leaf_size; - } -}; - - -/** - * Hierarchical index - * - * Contains a tree constructed through a hierarchical clustering - * and other information for indexing a set of points for nearest-neighbour matching. - */ -template -class HierarchicalClusteringIndex : public NNIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - -private: - - - typedef void (HierarchicalClusteringIndex::* centersAlgFunction)(int, int*, int, int*, int&); - - /** - * The function used for choosing the cluster centers. - */ - centersAlgFunction chooseCenters; - - - - /** - * Chooses the initial centers in the k-means clustering in a random manner. - * - * Params: - * k = number of centers - * vecs = the dataset of points - * indices = indices in the dataset - * indices_length = length of indices vector - * - */ - void chooseCentersRandom(int k, int* dsindices, int indices_length, int* centers, int& centers_length) - { - UniqueRandom r(indices_length); - - int index; - for (index=0; index=0 && rnd < n); - - centers[0] = dsindices[rnd]; - - int index; - for (index=1; indexbest_val) { - best_val = dist; - best_index = j; - } - } - if (best_index!=-1) { - centers[index] = dsindices[best_index]; - } - else { - break; - } - } - centers_length = index; - } - - - /** - * Chooses the initial centers in the k-means using the algorithm - * proposed in the KMeans++ paper: - * Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding - * - * Implementation of this function was converted from the one provided in Arthur's code. - * - * Params: - * k = number of centers - * vecs = the dataset of points - * indices = indices in the dataset - * Returns: - */ - void chooseCentersKMeanspp(int k, int* dsindices, int indices_length, int* centers, int& centers_length) - { - int n = indices_length; - - double currentPot = 0; - DistanceType* closestDistSq = new DistanceType[n]; - - // Choose one random center and set the closestDistSq values - int index = rand_int(n); - assert(index >=0 && index < n); - centers[0] = dsindices[index]; - - // Computing distance^2 will have the advantage of even higher probability further to pick new centers - // far from previous centers (and this complies to "k-means++: the advantages of careful seeding" article) - for (int i = 0; i < n; i++) { - closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); - closestDistSq[i] = ensureSquareDistance( closestDistSq[i] ); - currentPot += closestDistSq[i]; - } - - - const int numLocalTries = 1; - - // Choose each center - int centerCount; - for (centerCount = 1; centerCount < k; centerCount++) { - - // Repeat several trials - double bestNewPot = -1; - int bestNewIndex = 0; - for (int localTrial = 0; localTrial < numLocalTries; localTrial++) { - - // Choose our center - have to be slightly careful to return a valid answer even accounting - // for possible rounding errors - double randVal = rand_double(currentPot); - for (index = 0; index < n-1; index++) { - if (randVal <= closestDistSq[index]) break; - else randVal -= closestDistSq[index]; - } - - // Compute the new potential - double newPot = 0; - for (int i = 0; i < n; i++) { - DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); - newPot += std::min( ensureSquareDistance(dist), closestDistSq[i] ); - } - - // Store the best result - if ((bestNewPot < 0)||(newPot < bestNewPot)) { - bestNewPot = newPot; - bestNewIndex = index; - } - } - - // Add the appropriate center - centers[centerCount] = dsindices[bestNewIndex]; - currentPot = bestNewPot; - for (int i = 0; i < n; i++) { - DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols); - closestDistSq[i] = std::min( ensureSquareDistance(dist), closestDistSq[i] ); - } - } - - centers_length = centerCount; - - delete[] closestDistSq; - } - - - /** - * Chooses the initial centers in a way inspired by Gonzales (by Pierre-Emmanuel Viel): - * select the first point of the list as a candidate, then parse the points list. If another - * point is further than current candidate from the other centers, test if it is a good center - * of a local aggregation. If it is, replace current candidate by this point. And so on... - * - * Used with KMeansIndex that computes centers coordinates by averaging positions of clusters points, - * this doesn't make a real difference with previous methods. But used with HierarchicalClusteringIndex - * class that pick centers among existing points instead of computing the barycenters, there is a real - * improvement. - * - * Params: - * k = number of centers - * vecs = the dataset of points - * indices = indices in the dataset - * Returns: - */ - void GroupWiseCenterChooser(int k, int* dsindices, int indices_length, int* centers, int& centers_length) - { - const float kSpeedUpFactor = 1.3f; - - int n = indices_length; - - DistanceType* closestDistSq = new DistanceType[n]; - - // Choose one random center and set the closestDistSq values - int index = rand_int(n); - assert(index >=0 && index < n); - centers[0] = dsindices[index]; - - for (int i = 0; i < n; i++) { - closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); - } - - - // Choose each center - int centerCount; - for (centerCount = 1; centerCount < k; centerCount++) { - - // Repeat several trials - double bestNewPot = -1; - int bestNewIndex = 0; - DistanceType furthest = 0; - for (index = 0; index < n; index++) { - - // We will test only the potential of the points further than current candidate - if( closestDistSq[index] > kSpeedUpFactor * (float)furthest ) { - - // Compute the new potential - double newPot = 0; - for (int i = 0; i < n; i++) { - newPot += std::min( distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols) - , closestDistSq[i] ); - } - - // Store the best result - if ((bestNewPot < 0)||(newPot <= bestNewPot)) { - bestNewPot = newPot; - bestNewIndex = index; - furthest = closestDistSq[index]; - } - } - } - - // Add the appropriate center - centers[centerCount] = dsindices[bestNewIndex]; - for (int i = 0; i < n; i++) { - closestDistSq[i] = std::min( distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols) - , closestDistSq[i] ); - } - } - - centers_length = centerCount; - - delete[] closestDistSq; - } - - -public: - - - /** - * Index constructor - * - * Params: - * inputData = dataset with the input features - * params = parameters passed to the hierarchical k-means algorithm - */ - HierarchicalClusteringIndex(const Matrix& inputData, const IndexParams& index_params = HierarchicalClusteringIndexParams(), - Distance d = Distance()) - : dataset(inputData), params(index_params), root(NULL), indices(NULL), distance(d) - { - memoryCounter = 0; - - size_ = dataset.rows; - veclen_ = dataset.cols; - - branching_ = get_param(params,"branching",32); - centers_init_ = get_param(params,"centers_init", FLANN_CENTERS_RANDOM); - trees_ = get_param(params,"trees",4); - leaf_size_ = get_param(params,"leaf_size",100); - - if (centers_init_==FLANN_CENTERS_RANDOM) { - chooseCenters = &HierarchicalClusteringIndex::chooseCentersRandom; - } - else if (centers_init_==FLANN_CENTERS_GONZALES) { - chooseCenters = &HierarchicalClusteringIndex::chooseCentersGonzales; - } - else if (centers_init_==FLANN_CENTERS_KMEANSPP) { - chooseCenters = &HierarchicalClusteringIndex::chooseCentersKMeanspp; - } - else if (centers_init_==FLANN_CENTERS_GROUPWISE) { - chooseCenters = &HierarchicalClusteringIndex::GroupWiseCenterChooser; - } - else { - throw FLANNException("Unknown algorithm for choosing initial centers."); - } - - trees_ = get_param(params,"trees",4); - root = new NodePtr[trees_]; - indices = new int*[trees_]; - - for (int i=0; i(); - computeClustering(root[i], indices[i], (int)size_, branching_,0); - } - } - - - flann_algorithm_t getType() const - { - return FLANN_INDEX_HIERARCHICAL; - } - - - void saveIndex(FILE* stream) - { - save_value(stream, branching_); - save_value(stream, trees_); - save_value(stream, centers_init_); - save_value(stream, leaf_size_); - save_value(stream, memoryCounter); - for (int i=0; i& result, const ElementType* vec, const SearchParams& searchParams) - { - - int maxChecks = get_param(searchParams,"checks",32); - - // Priority queue storing intermediate branches in the best-bin-first search - Heap* heap = new Heap((int)size_); - - std::vector checked(size_,false); - int checks = 0; - for (int i=0; ipopMin(branch) && (checks BranchSt; - - - - void save_tree(FILE* stream, NodePtr node, int num) - { - save_value(stream, *node); - if (node->childs==NULL) { - int indices_offset = (int)(node->indices - indices[num]); - save_value(stream, indices_offset); - } - else { - for(int i=0; ichilds[i], num); - } - } - } - - - void load_tree(FILE* stream, NodePtr& node, int num) - { - node = pool.allocate(); - load_value(stream, *node); - if (node->childs==NULL) { - int indices_offset; - load_value(stream, indices_offset); - node->indices = indices[num] + indices_offset; - } - else { - node->childs = pool.allocate(branching_); - for(int i=0; ichilds[i], num); - } - } - } - - - - - void computeLabels(int* dsindices, int indices_length, int* centers, int centers_length, int* labels, DistanceType& cost) - { - cost = 0; - for (int i=0; inew_dist) { - labels[i] = j; - dist = new_dist; - } - } - cost += dist; - } - } - - /** - * The method responsible with actually doing the recursive hierarchical - * clustering - * - * Params: - * node = the node to cluster - * indices = indices of the points belonging to the current node - * branching = the branching factor to use in the clustering - * - * TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point) - */ - void computeClustering(NodePtr node, int* dsindices, int indices_length, int branching, int level) - { - node->size = indices_length; - node->level = level; - - if (indices_length < leaf_size_) { // leaf node - node->indices = dsindices; - std::sort(node->indices,node->indices+indices_length); - node->childs = NULL; - return; - } - - std::vector centers(branching); - std::vector labels(indices_length); - - int centers_length; - (this->*chooseCenters)(branching, dsindices, indices_length, ¢ers[0], centers_length); - - if (centers_lengthindices = dsindices; - std::sort(node->indices,node->indices+indices_length); - node->childs = NULL; - return; - } - - - // assign points to clusters - DistanceType cost; - computeLabels(dsindices, indices_length, ¢ers[0], centers_length, &labels[0], cost); - - node->childs = pool.allocate(branching); - int start = 0; - int end = start; - for (int i=0; ichilds[i] = pool.allocate(); - node->childs[i]->pivot = centers[i]; - node->childs[i]->indices = NULL; - computeClustering(node->childs[i],dsindices+start, end-start, branching, level+1); - start=end; - } - } - - - - /** - * Performs one descent in the hierarchical k-means tree. The branches not - * visited are stored in a priority queue. - * - * Params: - * node = node to explore - * result = container for the k-nearest neighbors found - * vec = query points - * checks = how many points in the dataset have been checked so far - * maxChecks = maximum dataset points to checks - */ - - - void findNN(NodePtr node, ResultSet& result, const ElementType* vec, int& checks, int maxChecks, - Heap* heap, std::vector& checked) - { - if (node->childs==NULL) { - if (checks>=maxChecks) { - if (result.full()) return; - } - for (int i=0; isize; ++i) { - int index = node->indices[i]; - if (!checked[index]) { - DistanceType dist = distance(dataset[index], vec, veclen_); - result.addPoint(dist, index); - checked[index] = true; - ++checks; - } - } - } - else { - DistanceType* domain_distances = new DistanceType[branching_]; - int best_index = 0; - domain_distances[best_index] = distance(vec, dataset[node->childs[best_index]->pivot], veclen_); - for (int i=1; ichilds[i]->pivot], veclen_); - if (domain_distances[i]insert(BranchSt(node->childs[i],domain_distances[i])); - } - } - delete[] domain_distances; - findNN(node->childs[best_index],result,vec, checks, maxChecks, heap, checked); - } - } - -private: - - - /** - * The dataset used by this index - */ - const Matrix dataset; - - /** - * Parameters used by this index - */ - IndexParams params; - - - /** - * Number of features in the dataset. - */ - size_t size_; - - /** - * Length of each feature. - */ - size_t veclen_; - - /** - * The root node in the tree. - */ - NodePtr* root; - - /** - * Array of indices to vectors in the dataset. - */ - int** indices; - - - /** - * The distance - */ - Distance distance; - - /** - * Pooled memory allocator. - * - * Using a pooled memory allocator is more efficient - * than allocating memory directly when there is a large - * number small of memory allocations. - */ - PooledAllocator pool; - - /** - * Memory occupied by the index. - */ - int memoryCounter; - - /** index parameters */ - int branching_; - int trees_; - flann_centers_init_t centers_init_; - int leaf_size_; - - -}; - -} - -#endif /* OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_ */ diff --git a/IPL/include/opencv/opencv2/flann/index_testing.h b/IPL/include/opencv/opencv2/flann/index_testing.h deleted file mode 100644 index d764004..0000000 --- a/IPL/include/opencv/opencv2/flann/index_testing.h +++ /dev/null @@ -1,318 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_INDEX_TESTING_H_ -#define OPENCV_FLANN_INDEX_TESTING_H_ - -#include -#include -#include - -#include "matrix.h" -#include "nn_index.h" -#include "result_set.h" -#include "logger.h" -#include "timer.h" - - -namespace cvflann -{ - -inline int countCorrectMatches(int* neighbors, int* groundTruth, int n) -{ - int count = 0; - for (int i=0; i -typename Distance::ResultType computeDistanceRaport(const Matrix& inputData, typename Distance::ElementType* target, - int* neighbors, int* groundTruth, int veclen, int n, const Distance& distance) -{ - typedef typename Distance::ResultType DistanceType; - - DistanceType ret = 0; - for (int i=0; i -float search_with_ground_truth(NNIndex& index, const Matrix& inputData, - const Matrix& testData, const Matrix& matches, int nn, int checks, - float& time, typename Distance::ResultType& dist, const Distance& distance, int skipMatches) -{ - typedef typename Distance::ResultType DistanceType; - - if (matches.cols resultSet(nn+skipMatches); - SearchParams searchParams(checks); - - std::vector indices(nn+skipMatches); - std::vector dists(nn+skipMatches); - int* neighbors = &indices[skipMatches]; - - int correct = 0; - DistanceType distR = 0; - StartStopTimer t; - int repeats = 0; - while (t.value<0.2) { - repeats++; - t.start(); - correct = 0; - distR = 0; - for (size_t i = 0; i < testData.rows; i++) { - resultSet.init(&indices[0], &dists[0]); - index.findNeighbors(resultSet, testData[i], searchParams); - - correct += countCorrectMatches(neighbors,matches[i], nn); - distR += computeDistanceRaport(inputData, testData[i], neighbors, matches[i], (int)testData.cols, nn, distance); - } - t.stop(); - } - time = float(t.value/repeats); - - float precicion = (float)correct/(nn*testData.rows); - - dist = distR/(testData.rows*nn); - - Logger::info("%8d %10.4g %10.5g %10.5g %10.5g\n", - checks, precicion, time, 1000.0 * time / testData.rows, dist); - - return precicion; -} - - -template -float test_index_checks(NNIndex& index, const Matrix& inputData, - const Matrix& testData, const Matrix& matches, - int checks, float& precision, const Distance& distance, int nn = 1, int skipMatches = 0) -{ - typedef typename Distance::ResultType DistanceType; - - Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n"); - Logger::info("---------------------------------------------------------\n"); - - float time = 0; - DistanceType dist = 0; - precision = search_with_ground_truth(index, inputData, testData, matches, nn, checks, time, dist, distance, skipMatches); - - return time; -} - -template -float test_index_precision(NNIndex& index, const Matrix& inputData, - const Matrix& testData, const Matrix& matches, - float precision, int& checks, const Distance& distance, int nn = 1, int skipMatches = 0) -{ - typedef typename Distance::ResultType DistanceType; - const float SEARCH_EPS = 0.001f; - - Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n"); - Logger::info("---------------------------------------------------------\n"); - - int c2 = 1; - float p2; - int c1 = 1; - //float p1; - float time; - DistanceType dist; - - p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches); - - if (p2>precision) { - Logger::info("Got as close as I can\n"); - checks = c2; - return time; - } - - while (p2SEARCH_EPS) { - Logger::info("Start linear estimation\n"); - // after we got to values in the vecinity of the desired precision - // use linear approximation get a better estimation - - cx = (c1+c2)/2; - realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches); - while (fabs(realPrecision-precision)>SEARCH_EPS) { - - if (realPrecision -void test_index_precisions(NNIndex& index, const Matrix& inputData, - const Matrix& testData, const Matrix& matches, - float* precisions, int precisions_length, const Distance& distance, int nn = 1, int skipMatches = 0, float maxTime = 0) -{ - typedef typename Distance::ResultType DistanceType; - - const float SEARCH_EPS = 0.001; - - // make sure precisions array is sorted - std::sort(precisions, precisions+precisions_length); - - int pindex = 0; - float precision = precisions[pindex]; - - Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n"); - Logger::info("---------------------------------------------------------\n"); - - int c2 = 1; - float p2; - - int c1 = 1; - float p1; - - float time; - DistanceType dist; - - p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches); - - // if precision for 1 run down the tree is already - // better then some of the requested precisions, then - // skip those - while (precisions[pindex] 0)&&(time > maxTime)&&(p2SEARCH_EPS) { - Logger::info("Start linear estimation\n"); - // after we got to values in the vecinity of the desired precision - // use linear approximation get a better estimation - - cx = (c1+c2)/2; - realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches); - while (fabs(realPrecision-precision)>SEARCH_EPS) { - - if (realPrecision -#include -#include -#include - -#include "general.h" -#include "nn_index.h" -#include "dynamic_bitset.h" -#include "matrix.h" -#include "result_set.h" -#include "heap.h" -#include "allocator.h" -#include "random.h" -#include "saving.h" - - -namespace cvflann -{ - -struct KDTreeIndexParams : public IndexParams -{ - KDTreeIndexParams(int trees = 4) - { - (*this)["algorithm"] = FLANN_INDEX_KDTREE; - (*this)["trees"] = trees; - } -}; - - -/** - * Randomized kd-tree index - * - * Contains the k-d trees and other information for indexing a set of points - * for nearest-neighbor matching. - */ -template -class KDTreeIndex : public NNIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - - /** - * KDTree constructor - * - * Params: - * inputData = dataset with the input features - * params = parameters passed to the kdtree algorithm - */ - KDTreeIndex(const Matrix& inputData, const IndexParams& params = KDTreeIndexParams(), - Distance d = Distance() ) : - dataset_(inputData), index_params_(params), distance_(d) - { - size_ = dataset_.rows; - veclen_ = dataset_.cols; - - trees_ = get_param(index_params_,"trees",4); - tree_roots_ = new NodePtr[trees_]; - - // Create a permutable array of indices to the input vectors. - vind_.resize(size_); - for (size_t i = 0; i < size_; ++i) { - vind_[i] = int(i); - } - - mean_ = new DistanceType[veclen_]; - var_ = new DistanceType[veclen_]; - } - - - KDTreeIndex(const KDTreeIndex&); - KDTreeIndex& operator=(const KDTreeIndex&); - - /** - * Standard destructor - */ - ~KDTreeIndex() - { - if (tree_roots_!=NULL) { - delete[] tree_roots_; - } - delete[] mean_; - delete[] var_; - } - - /** - * Builds the index - */ - void buildIndex() - { - /* Construct the randomized trees. */ - for (int i = 0; i < trees_; i++) { - /* Randomize the order of vectors to allow for unbiased sampling. */ - std::random_shuffle(vind_.begin(), vind_.end()); - tree_roots_[i] = divideTree(&vind_[0], int(size_) ); - } - } - - - flann_algorithm_t getType() const - { - return FLANN_INDEX_KDTREE; - } - - - void saveIndex(FILE* stream) - { - save_value(stream, trees_); - for (int i=0; i& result, const ElementType* vec, const SearchParams& searchParams) - { - int maxChecks = get_param(searchParams,"checks", 32); - float epsError = 1+get_param(searchParams,"eps",0.0f); - - if (maxChecks==FLANN_CHECKS_UNLIMITED) { - getExactNeighbors(result, vec, epsError); - } - else { - getNeighbors(result, vec, maxChecks, epsError); - } - } - - IndexParams getParameters() const - { - return index_params_; - } - -private: - - - /*--------------------- Internal Data Structures --------------------------*/ - struct Node - { - /** - * Dimension used for subdivision. - */ - int divfeat; - /** - * The values used for subdivision. - */ - DistanceType divval; - /** - * The child nodes. - */ - Node* child1, * child2; - }; - typedef Node* NodePtr; - typedef BranchStruct BranchSt; - typedef BranchSt* Branch; - - - - void save_tree(FILE* stream, NodePtr tree) - { - save_value(stream, *tree); - if (tree->child1!=NULL) { - save_tree(stream, tree->child1); - } - if (tree->child2!=NULL) { - save_tree(stream, tree->child2); - } - } - - - void load_tree(FILE* stream, NodePtr& tree) - { - tree = pool_.allocate(); - load_value(stream, *tree); - if (tree->child1!=NULL) { - load_tree(stream, tree->child1); - } - if (tree->child2!=NULL) { - load_tree(stream, tree->child2); - } - } - - - /** - * Create a tree node that subdivides the list of vecs from vind[first] - * to vind[last]. The routine is called recursively on each sublist. - * Place a pointer to this new tree node in the location pTree. - * - * Params: pTree = the new node to create - * first = index of the first vector - * last = index of the last vector - */ - NodePtr divideTree(int* ind, int count) - { - NodePtr node = pool_.allocate(); // allocate memory - - /* If too few exemplars remain, then make this a leaf node. */ - if ( count == 1) { - node->child1 = node->child2 = NULL; /* Mark as leaf node. */ - node->divfeat = *ind; /* Store index of this vec. */ - } - else { - int idx; - int cutfeat; - DistanceType cutval; - meanSplit(ind, count, idx, cutfeat, cutval); - - node->divfeat = cutfeat; - node->divval = cutval; - node->child1 = divideTree(ind, idx); - node->child2 = divideTree(ind+idx, count-idx); - } - - return node; - } - - - /** - * Choose which feature to use in order to subdivide this set of vectors. - * Make a random choice among those with the highest variance, and use - * its variance as the threshold value. - */ - void meanSplit(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval) - { - memset(mean_,0,veclen_*sizeof(DistanceType)); - memset(var_,0,veclen_*sizeof(DistanceType)); - - /* Compute mean values. Only the first SAMPLE_MEAN values need to be - sampled to get a good estimate. - */ - int cnt = std::min((int)SAMPLE_MEAN+1, count); - for (int j = 0; j < cnt; ++j) { - ElementType* v = dataset_[ind[j]]; - for (size_t k=0; kcount/2) index = lim1; - else if (lim2 v[topind[num-1]])) { - /* Put this element at end of topind. */ - if (num < RAND_DIM) { - topind[num++] = i; /* Add to list. */ - } - else { - topind[num-1] = i; /* Replace last element. */ - } - /* Bubble end value down to right location by repeated swapping. */ - int j = num - 1; - while (j > 0 && v[topind[j]] > v[topind[j-1]]) { - std::swap(topind[j], topind[j-1]); - --j; - } - } - } - /* Select a random integer in range [0,num-1], and return that index. */ - int rnd = rand_int(num); - return (int)topind[rnd]; - } - - - /** - * Subdivide the list of points by a plane perpendicular on axe corresponding - * to the 'cutfeat' dimension at 'cutval' position. - * - * On return: - * dataset[ind[0..lim1-1]][cutfeat]cutval - */ - void planeSplit(int* ind, int count, int cutfeat, DistanceType cutval, int& lim1, int& lim2) - { - /* Move vector indices for left subtree to front of list. */ - int left = 0; - int right = count-1; - for (;; ) { - while (left<=right && dataset_[ind[left]][cutfeat]=cutval) --right; - if (left>right) break; - std::swap(ind[left], ind[right]); ++left; --right; - } - lim1 = left; - right = count-1; - for (;; ) { - while (left<=right && dataset_[ind[left]][cutfeat]<=cutval) ++left; - while (left<=right && dataset_[ind[right]][cutfeat]>cutval) --right; - if (left>right) break; - std::swap(ind[left], ind[right]); ++left; --right; - } - lim2 = left; - } - - /** - * Performs an exact nearest neighbor search. The exact search performs a full - * traversal of the tree. - */ - void getExactNeighbors(ResultSet& result, const ElementType* vec, float epsError) - { - // checkID -= 1; /* Set a different unique ID for each search. */ - - if (trees_ > 1) { - fprintf(stderr,"It doesn't make any sense to use more than one tree for exact search"); - } - if (trees_>0) { - searchLevelExact(result, vec, tree_roots_[0], 0.0, epsError); - } - assert(result.full()); - } - - /** - * Performs the approximate nearest-neighbor search. The search is approximate - * because the tree traversal is abandoned after a given number of descends in - * the tree. - */ - void getNeighbors(ResultSet& result, const ElementType* vec, int maxCheck, float epsError) - { - int i; - BranchSt branch; - - int checkCount = 0; - Heap* heap = new Heap((int)size_); - DynamicBitset checked(size_); - - /* Search once through each tree down to root. */ - for (i = 0; i < trees_; ++i) { - searchLevel(result, vec, tree_roots_[i], 0, checkCount, maxCheck, epsError, heap, checked); - } - - /* Keep searching other branches from heap until finished. */ - while ( heap->popMin(branch) && (checkCount < maxCheck || !result.full() )) { - searchLevel(result, vec, branch.node, branch.mindist, checkCount, maxCheck, epsError, heap, checked); - } - - delete heap; - - assert(result.full()); - } - - - /** - * Search starting from a given node of the tree. Based on any mismatches at - * higher levels, all exemplars below this level must have a distance of - * at least "mindistsq". - */ - void searchLevel(ResultSet& result_set, const ElementType* vec, NodePtr node, DistanceType mindist, int& checkCount, int maxCheck, - float epsError, Heap* heap, DynamicBitset& checked) - { - if (result_set.worstDist()child1 == NULL)&&(node->child2 == NULL)) { - /* Do not check same node more than once when searching multiple trees. - Once a vector is checked, we set its location in vind to the - current checkID. - */ - int index = node->divfeat; - if ( checked.test(index) || ((checkCount>=maxCheck)&& result_set.full()) ) return; - checked.set(index); - checkCount++; - - DistanceType dist = distance_(dataset_[index], vec, veclen_); - result_set.addPoint(dist,index); - - return; - } - - /* Which child branch should be taken first? */ - ElementType val = vec[node->divfeat]; - DistanceType diff = val - node->divval; - NodePtr bestChild = (diff < 0) ? node->child1 : node->child2; - NodePtr otherChild = (diff < 0) ? node->child2 : node->child1; - - /* Create a branch record for the branch not taken. Add distance - of this feature boundary (we don't attempt to correct for any - use of this feature in a parent node, which is unlikely to - happen and would have only a small effect). Don't bother - adding more branches to heap after halfway point, as cost of - adding exceeds their value. - */ - - DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat); - // if (2 * checkCount < maxCheck || !result.full()) { - if ((new_distsq*epsError < result_set.worstDist())|| !result_set.full()) { - heap->insert( BranchSt(otherChild, new_distsq) ); - } - - /* Call recursively to search next level down. */ - searchLevel(result_set, vec, bestChild, mindist, checkCount, maxCheck, epsError, heap, checked); - } - - /** - * Performs an exact search in the tree starting from a node. - */ - void searchLevelExact(ResultSet& result_set, const ElementType* vec, const NodePtr node, DistanceType mindist, const float epsError) - { - /* If this is a leaf node, then do check and return. */ - if ((node->child1 == NULL)&&(node->child2 == NULL)) { - int index = node->divfeat; - DistanceType dist = distance_(dataset_[index], vec, veclen_); - result_set.addPoint(dist,index); - return; - } - - /* Which child branch should be taken first? */ - ElementType val = vec[node->divfeat]; - DistanceType diff = val - node->divval; - NodePtr bestChild = (diff < 0) ? node->child1 : node->child2; - NodePtr otherChild = (diff < 0) ? node->child2 : node->child1; - - /* Create a branch record for the branch not taken. Add distance - of this feature boundary (we don't attempt to correct for any - use of this feature in a parent node, which is unlikely to - happen and would have only a small effect). Don't bother - adding more branches to heap after halfway point, as cost of - adding exceeds their value. - */ - - DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat); - - /* Call recursively to search next level down. */ - searchLevelExact(result_set, vec, bestChild, mindist, epsError); - - if (new_distsq*epsError<=result_set.worstDist()) { - searchLevelExact(result_set, vec, otherChild, new_distsq, epsError); - } - } - - -private: - - enum - { - /** - * To improve efficiency, only SAMPLE_MEAN random values are used to - * compute the mean and variance at each level when building a tree. - * A value of 100 seems to perform as well as using all values. - */ - SAMPLE_MEAN = 100, - /** - * Top random dimensions to consider - * - * When creating random trees, the dimension on which to subdivide is - * selected at random from among the top RAND_DIM dimensions with the - * highest variance. A value of 5 works well. - */ - RAND_DIM=5 - }; - - - /** - * Number of randomized trees that are used - */ - int trees_; - - /** - * Array of indices to vectors in the dataset. - */ - std::vector vind_; - - /** - * The dataset used by this index - */ - const Matrix dataset_; - - IndexParams index_params_; - - size_t size_; - size_t veclen_; - - - DistanceType* mean_; - DistanceType* var_; - - - /** - * Array of k-d trees used to find neighbours. - */ - NodePtr* tree_roots_; - - /** - * Pooled memory allocator. - * - * Using a pooled memory allocator is more efficient - * than allocating memory directly when there is a large - * number small of memory allocations. - */ - PooledAllocator pool_; - - Distance distance_; - - -}; // class KDTreeForest - -} - -#endif //OPENCV_FLANN_KDTREE_INDEX_H_ diff --git a/IPL/include/opencv/opencv2/flann/kdtree_single_index.h b/IPL/include/opencv/opencv2/flann/kdtree_single_index.h deleted file mode 100644 index 30488ad..0000000 --- a/IPL/include/opencv/opencv2/flann/kdtree_single_index.h +++ /dev/null @@ -1,634 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_ -#define OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_ - -#include -#include -#include -#include - -#include "general.h" -#include "nn_index.h" -#include "matrix.h" -#include "result_set.h" -#include "heap.h" -#include "allocator.h" -#include "random.h" -#include "saving.h" - -namespace cvflann -{ - -struct KDTreeSingleIndexParams : public IndexParams -{ - KDTreeSingleIndexParams(int leaf_max_size = 10, bool reorder = true, int dim = -1) - { - (*this)["algorithm"] = FLANN_INDEX_KDTREE_SINGLE; - (*this)["leaf_max_size"] = leaf_max_size; - (*this)["reorder"] = reorder; - (*this)["dim"] = dim; - } -}; - - -/** - * Randomized kd-tree index - * - * Contains the k-d trees and other information for indexing a set of points - * for nearest-neighbor matching. - */ -template -class KDTreeSingleIndex : public NNIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - - /** - * KDTree constructor - * - * Params: - * inputData = dataset with the input features - * params = parameters passed to the kdtree algorithm - */ - KDTreeSingleIndex(const Matrix& inputData, const IndexParams& params = KDTreeSingleIndexParams(), - Distance d = Distance() ) : - dataset_(inputData), index_params_(params), distance_(d) - { - size_ = dataset_.rows; - dim_ = dataset_.cols; - int dim_param = get_param(params,"dim",-1); - if (dim_param>0) dim_ = dim_param; - leaf_max_size_ = get_param(params,"leaf_max_size",10); - reorder_ = get_param(params,"reorder",true); - - // Create a permutable array of indices to the input vectors. - vind_.resize(size_); - for (size_t i = 0; i < size_; i++) { - vind_[i] = (int)i; - } - } - - KDTreeSingleIndex(const KDTreeSingleIndex&); - KDTreeSingleIndex& operator=(const KDTreeSingleIndex&); - - /** - * Standard destructor - */ - ~KDTreeSingleIndex() - { - if (reorder_) delete[] data_.data; - } - - /** - * Builds the index - */ - void buildIndex() - { - computeBoundingBox(root_bbox_); - root_node_ = divideTree(0, (int)size_, root_bbox_ ); // construct the tree - - if (reorder_) { - delete[] data_.data; - data_ = cvflann::Matrix(new ElementType[size_*dim_], size_, dim_); - for (size_t i=0; i& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params) - { - assert(queries.cols == veclen()); - assert(indices.rows >= queries.rows); - assert(dists.rows >= queries.rows); - assert(int(indices.cols) >= knn); - assert(int(dists.cols) >= knn); - - KNNSimpleResultSet resultSet(knn); - for (size_t i = 0; i < queries.rows; i++) { - resultSet.init(indices[i], dists[i]); - findNeighbors(resultSet, queries[i], params); - } - } - - IndexParams getParameters() const - { - return index_params_; - } - - /** - * Find set of nearest neighbors to vec. Their indices are stored inside - * the result object. - * - * Params: - * result = the result object in which the indices of the nearest-neighbors are stored - * vec = the vector for which to search the nearest neighbors - * maxCheck = the maximum number of restarts (in a best-bin-first manner) - */ - void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) - { - float epsError = 1+get_param(searchParams,"eps",0.0f); - - std::vector dists(dim_,0); - DistanceType distsq = computeInitialDistances(vec, dists); - searchLevel(result, vec, root_node_, distsq, dists, epsError); - } - -private: - - - /*--------------------- Internal Data Structures --------------------------*/ - struct Node - { - /** - * Indices of points in leaf node - */ - int left, right; - /** - * Dimension used for subdivision. - */ - int divfeat; - /** - * The values used for subdivision. - */ - DistanceType divlow, divhigh; - /** - * The child nodes. - */ - Node* child1, * child2; - }; - typedef Node* NodePtr; - - - struct Interval - { - DistanceType low, high; - }; - - typedef std::vector BoundingBox; - - typedef BranchStruct BranchSt; - typedef BranchSt* Branch; - - - - - void save_tree(FILE* stream, NodePtr tree) - { - save_value(stream, *tree); - if (tree->child1!=NULL) { - save_tree(stream, tree->child1); - } - if (tree->child2!=NULL) { - save_tree(stream, tree->child2); - } - } - - - void load_tree(FILE* stream, NodePtr& tree) - { - tree = pool_.allocate(); - load_value(stream, *tree); - if (tree->child1!=NULL) { - load_tree(stream, tree->child1); - } - if (tree->child2!=NULL) { - load_tree(stream, tree->child2); - } - } - - - void computeBoundingBox(BoundingBox& bbox) - { - bbox.resize(dim_); - for (size_t i=0; ibbox[i].high) bbox[i].high = (DistanceType)dataset_[k][i]; - } - } - } - - - /** - * Create a tree node that subdivides the list of vecs from vind[first] - * to vind[last]. The routine is called recursively on each sublist. - * Place a pointer to this new tree node in the location pTree. - * - * Params: pTree = the new node to create - * first = index of the first vector - * last = index of the last vector - */ - NodePtr divideTree(int left, int right, BoundingBox& bbox) - { - NodePtr node = pool_.allocate(); // allocate memory - - /* If too few exemplars remain, then make this a leaf node. */ - if ( (right-left) <= leaf_max_size_) { - node->child1 = node->child2 = NULL; /* Mark as leaf node. */ - node->left = left; - node->right = right; - - // compute bounding-box of leaf points - for (size_t i=0; idataset_[vind_[k]][i]) bbox[i].low=(DistanceType)dataset_[vind_[k]][i]; - if (bbox[i].highdivfeat = cutfeat; - - BoundingBox left_bbox(bbox); - left_bbox[cutfeat].high = cutval; - node->child1 = divideTree(left, left+idx, left_bbox); - - BoundingBox right_bbox(bbox); - right_bbox[cutfeat].low = cutval; - node->child2 = divideTree(left+idx, right, right_bbox); - - node->divlow = left_bbox[cutfeat].high; - node->divhigh = right_bbox[cutfeat].low; - - for (size_t i=0; imax_elem) max_elem = val; - } - } - - void middleSplit(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval, const BoundingBox& bbox) - { - // find the largest span from the approximate bounding box - ElementType max_span = bbox[0].high-bbox[0].low; - cutfeat = 0; - cutval = (bbox[0].high+bbox[0].low)/2; - for (size_t i=1; imax_span) { - max_span = span; - cutfeat = i; - cutval = (bbox[i].high+bbox[i].low)/2; - } - } - - // compute exact span on the found dimension - ElementType min_elem, max_elem; - computeMinMax(ind, count, cutfeat, min_elem, max_elem); - cutval = (min_elem+max_elem)/2; - max_span = max_elem - min_elem; - - // check if a dimension of a largest span exists - size_t k = cutfeat; - for (size_t i=0; imax_span) { - computeMinMax(ind, count, i, min_elem, max_elem); - span = max_elem - min_elem; - if (span>max_span) { - max_span = span; - cutfeat = i; - cutval = (min_elem+max_elem)/2; - } - } - } - int lim1, lim2; - planeSplit(ind, count, cutfeat, cutval, lim1, lim2); - - if (lim1>count/2) index = lim1; - else if (lim2max_span) { - max_span = span; - } - } - DistanceType max_spread = -1; - cutfeat = 0; - for (size_t i=0; i(DistanceType)((1-EPS)*max_span)) { - ElementType min_elem, max_elem; - computeMinMax(ind, count, cutfeat, min_elem, max_elem); - DistanceType spread = (DistanceType)(max_elem-min_elem); - if (spread>max_spread) { - cutfeat = (int)i; - max_spread = spread; - } - } - } - // split in the middle - DistanceType split_val = (bbox[cutfeat].low+bbox[cutfeat].high)/2; - ElementType min_elem, max_elem; - computeMinMax(ind, count, cutfeat, min_elem, max_elem); - - if (split_valmax_elem) cutval = (DistanceType)max_elem; - else cutval = split_val; - - int lim1, lim2; - planeSplit(ind, count, cutfeat, cutval, lim1, lim2); - - if (lim1>count/2) index = lim1; - else if (lim2cutval - */ - void planeSplit(int* ind, int count, int cutfeat, DistanceType cutval, int& lim1, int& lim2) - { - /* Move vector indices for left subtree to front of list. */ - int left = 0; - int right = count-1; - for (;; ) { - while (left<=right && dataset_[ind[left]][cutfeat]=cutval) --right; - if (left>right) break; - std::swap(ind[left], ind[right]); ++left; --right; - } - /* If either list is empty, it means that all remaining features - * are identical. Split in the middle to maintain a balanced tree. - */ - lim1 = left; - right = count-1; - for (;; ) { - while (left<=right && dataset_[ind[left]][cutfeat]<=cutval) ++left; - while (left<=right && dataset_[ind[right]][cutfeat]>cutval) --right; - if (left>right) break; - std::swap(ind[left], ind[right]); ++left; --right; - } - lim2 = left; - } - - DistanceType computeInitialDistances(const ElementType* vec, std::vector& dists) - { - DistanceType distsq = 0.0; - - for (size_t i = 0; i < dim_; ++i) { - if (vec[i] < root_bbox_[i].low) { - dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].low, (int)i); - distsq += dists[i]; - } - if (vec[i] > root_bbox_[i].high) { - dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].high, (int)i); - distsq += dists[i]; - } - } - - return distsq; - } - - /** - * Performs an exact search in the tree starting from a node. - */ - void searchLevel(ResultSet& result_set, const ElementType* vec, const NodePtr node, DistanceType mindistsq, - std::vector& dists, const float epsError) - { - /* If this is a leaf node, then do check and return. */ - if ((node->child1 == NULL)&&(node->child2 == NULL)) { - DistanceType worst_dist = result_set.worstDist(); - for (int i=node->left; iright; ++i) { - int index = reorder_ ? i : vind_[i]; - DistanceType dist = distance_(vec, data_[index], dim_, worst_dist); - if (distdivfeat; - ElementType val = vec[idx]; - DistanceType diff1 = val - node->divlow; - DistanceType diff2 = val - node->divhigh; - - NodePtr bestChild; - NodePtr otherChild; - DistanceType cut_dist; - if ((diff1+diff2)<0) { - bestChild = node->child1; - otherChild = node->child2; - cut_dist = distance_.accum_dist(val, node->divhigh, idx); - } - else { - bestChild = node->child2; - otherChild = node->child1; - cut_dist = distance_.accum_dist( val, node->divlow, idx); - } - - /* Call recursively to search next level down. */ - searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError); - - DistanceType dst = dists[idx]; - mindistsq = mindistsq + cut_dist - dst; - dists[idx] = cut_dist; - if (mindistsq*epsError<=result_set.worstDist()) { - searchLevel(result_set, vec, otherChild, mindistsq, dists, epsError); - } - dists[idx] = dst; - } - -private: - - /** - * The dataset used by this index - */ - const Matrix dataset_; - - IndexParams index_params_; - - int leaf_max_size_; - bool reorder_; - - - /** - * Array of indices to vectors in the dataset. - */ - std::vector vind_; - - Matrix data_; - - size_t size_; - size_t dim_; - - /** - * Array of k-d trees used to find neighbours. - */ - NodePtr root_node_; - - BoundingBox root_bbox_; - - /** - * Pooled memory allocator. - * - * Using a pooled memory allocator is more efficient - * than allocating memory directly when there is a large - * number small of memory allocations. - */ - PooledAllocator pool_; - - Distance distance_; -}; // class KDTree - -} - -#endif //OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_ diff --git a/IPL/include/opencv/opencv2/flann/kmeans_index.h b/IPL/include/opencv/opencv2/flann/kmeans_index.h deleted file mode 100644 index 226fc71..0000000 --- a/IPL/include/opencv/opencv2/flann/kmeans_index.h +++ /dev/null @@ -1,1169 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_KMEANS_INDEX_H_ -#define OPENCV_FLANN_KMEANS_INDEX_H_ - -#include -#include -#include -#include -#include - -#include "general.h" -#include "nn_index.h" -#include "dist.h" -#include "matrix.h" -#include "result_set.h" -#include "heap.h" -#include "allocator.h" -#include "random.h" -#include "saving.h" -#include "logger.h" - - -namespace cvflann -{ - -struct KMeansIndexParams : public IndexParams -{ - KMeansIndexParams(int branching = 32, int iterations = 11, - flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 ) - { - (*this)["algorithm"] = FLANN_INDEX_KMEANS; - // branching factor - (*this)["branching"] = branching; - // max iterations to perform in one kmeans clustering (kmeans tree) - (*this)["iterations"] = iterations; - // algorithm used for picking the initial cluster centers for kmeans tree - (*this)["centers_init"] = centers_init; - // cluster boundary index. Used when searching the kmeans tree - (*this)["cb_index"] = cb_index; - } -}; - - -/** - * Hierarchical kmeans index - * - * Contains a tree constructed through a hierarchical kmeans clustering - * and other information for indexing a set of points for nearest-neighbour matching. - */ -template -class KMeansIndex : public NNIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - - - typedef void (KMeansIndex::* centersAlgFunction)(int, int*, int, int*, int&); - - /** - * The function used for choosing the cluster centers. - */ - centersAlgFunction chooseCenters; - - - - /** - * Chooses the initial centers in the k-means clustering in a random manner. - * - * Params: - * k = number of centers - * vecs = the dataset of points - * indices = indices in the dataset - * indices_length = length of indices vector - * - */ - void chooseCentersRandom(int k, int* indices, int indices_length, int* centers, int& centers_length) - { - UniqueRandom r(indices_length); - - int index; - for (index=0; index=0 && rnd < n); - - centers[0] = indices[rnd]; - - int index; - for (index=1; indexbest_val) { - best_val = dist; - best_index = j; - } - } - if (best_index!=-1) { - centers[index] = indices[best_index]; - } - else { - break; - } - } - centers_length = index; - } - - - /** - * Chooses the initial centers in the k-means using the algorithm - * proposed in the KMeans++ paper: - * Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding - * - * Implementation of this function was converted from the one provided in Arthur's code. - * - * Params: - * k = number of centers - * vecs = the dataset of points - * indices = indices in the dataset - * Returns: - */ - void chooseCentersKMeanspp(int k, int* indices, int indices_length, int* centers, int& centers_length) - { - int n = indices_length; - - double currentPot = 0; - DistanceType* closestDistSq = new DistanceType[n]; - - // Choose one random center and set the closestDistSq values - int index = rand_int(n); - assert(index >=0 && index < n); - centers[0] = indices[index]; - - for (int i = 0; i < n; i++) { - closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols); - closestDistSq[i] = ensureSquareDistance( closestDistSq[i] ); - currentPot += closestDistSq[i]; - } - - - const int numLocalTries = 1; - - // Choose each center - int centerCount; - for (centerCount = 1; centerCount < k; centerCount++) { - - // Repeat several trials - double bestNewPot = -1; - int bestNewIndex = -1; - for (int localTrial = 0; localTrial < numLocalTries; localTrial++) { - - // Choose our center - have to be slightly careful to return a valid answer even accounting - // for possible rounding errors - double randVal = rand_double(currentPot); - for (index = 0; index < n-1; index++) { - if (randVal <= closestDistSq[index]) break; - else randVal -= closestDistSq[index]; - } - - // Compute the new potential - double newPot = 0; - for (int i = 0; i < n; i++) { - DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols); - newPot += std::min( ensureSquareDistance(dist), closestDistSq[i] ); - } - - // Store the best result - if ((bestNewPot < 0)||(newPot < bestNewPot)) { - bestNewPot = newPot; - bestNewIndex = index; - } - } - - // Add the appropriate center - centers[centerCount] = indices[bestNewIndex]; - currentPot = bestNewPot; - for (int i = 0; i < n; i++) { - DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols); - closestDistSq[i] = std::min( ensureSquareDistance(dist), closestDistSq[i] ); - } - } - - centers_length = centerCount; - - delete[] closestDistSq; - } - - - -public: - - flann_algorithm_t getType() const - { - return FLANN_INDEX_KMEANS; - } - - class KMeansDistanceComputer : public cv::ParallelLoopBody - { - public: - KMeansDistanceComputer(Distance _distance, const Matrix& _dataset, - const int _branching, const int* _indices, const Matrix& _dcenters, const size_t _veclen, - int* _count, int* _belongs_to, std::vector& _radiuses, bool& _converged, cv::Mutex& _mtx) - : distance(_distance) - , dataset(_dataset) - , branching(_branching) - , indices(_indices) - , dcenters(_dcenters) - , veclen(_veclen) - , count(_count) - , belongs_to(_belongs_to) - , radiuses(_radiuses) - , converged(_converged) - , mtx(_mtx) - { - } - - void operator()(const cv::Range& range) const - { - const int begin = range.start; - const int end = range.end; - - for( int i = begin; inew_sq_dist) { - new_centroid = j; - sq_dist = new_sq_dist; - } - } - if (sq_dist > radiuses[new_centroid]) { - radiuses[new_centroid] = sq_dist; - } - if (new_centroid != belongs_to[i]) { - count[belongs_to[i]]--; - count[new_centroid]++; - belongs_to[i] = new_centroid; - mtx.lock(); - converged = false; - mtx.unlock(); - } - } - } - - private: - Distance distance; - const Matrix& dataset; - const int branching; - const int* indices; - const Matrix& dcenters; - const size_t veclen; - int* count; - int* belongs_to; - std::vector& radiuses; - bool& converged; - cv::Mutex& mtx; - KMeansDistanceComputer& operator=( const KMeansDistanceComputer & ) { return *this; } - }; - - /** - * Index constructor - * - * Params: - * inputData = dataset with the input features - * params = parameters passed to the hierarchical k-means algorithm - */ - KMeansIndex(const Matrix& inputData, const IndexParams& params = KMeansIndexParams(), - Distance d = Distance()) - : dataset_(inputData), index_params_(params), root_(NULL), indices_(NULL), distance_(d) - { - memoryCounter_ = 0; - - size_ = dataset_.rows; - veclen_ = dataset_.cols; - - branching_ = get_param(params,"branching",32); - iterations_ = get_param(params,"iterations",11); - if (iterations_<0) { - iterations_ = (std::numeric_limits::max)(); - } - centers_init_ = get_param(params,"centers_init",FLANN_CENTERS_RANDOM); - - if (centers_init_==FLANN_CENTERS_RANDOM) { - chooseCenters = &KMeansIndex::chooseCentersRandom; - } - else if (centers_init_==FLANN_CENTERS_GONZALES) { - chooseCenters = &KMeansIndex::chooseCentersGonzales; - } - else if (centers_init_==FLANN_CENTERS_KMEANSPP) { - chooseCenters = &KMeansIndex::chooseCentersKMeanspp; - } - else { - throw FLANNException("Unknown algorithm for choosing initial centers."); - } - cb_index_ = 0.4f; - - } - - - KMeansIndex(const KMeansIndex&); - KMeansIndex& operator=(const KMeansIndex&); - - - /** - * Index destructor. - * - * Release the memory used by the index. - */ - virtual ~KMeansIndex() - { - if (root_ != NULL) { - free_centers(root_); - } - if (indices_!=NULL) { - delete[] indices_; - } - } - - /** - * Returns size of index. - */ - size_t size() const - { - return size_; - } - - /** - * Returns the length of an index feature. - */ - size_t veclen() const - { - return veclen_; - } - - - void set_cb_index( float index) - { - cb_index_ = index; - } - - /** - * Computes the inde memory usage - * Returns: memory used by the index - */ - int usedMemory() const - { - return pool_.usedMemory+pool_.wastedMemory+memoryCounter_; - } - - /** - * Builds the index - */ - void buildIndex() - { - if (branching_<2) { - throw FLANNException("Branching factor must be at least 2"); - } - - indices_ = new int[size_]; - for (size_t i=0; i(); - std::memset(root_, 0, sizeof(KMeansNode)); - - computeNodeStatistics(root_, indices_, (int)size_); - computeClustering(root_, indices_, (int)size_, branching_,0); - } - - - void saveIndex(FILE* stream) - { - save_value(stream, branching_); - save_value(stream, iterations_); - save_value(stream, memoryCounter_); - save_value(stream, cb_index_); - save_value(stream, *indices_, (int)size_); - - save_tree(stream, root_); - } - - - void loadIndex(FILE* stream) - { - load_value(stream, branching_); - load_value(stream, iterations_); - load_value(stream, memoryCounter_); - load_value(stream, cb_index_); - if (indices_!=NULL) { - delete[] indices_; - } - indices_ = new int[size_]; - load_value(stream, *indices_, size_); - - if (root_!=NULL) { - free_centers(root_); - } - load_tree(stream, root_); - - index_params_["algorithm"] = getType(); - index_params_["branching"] = branching_; - index_params_["iterations"] = iterations_; - index_params_["centers_init"] = centers_init_; - index_params_["cb_index"] = cb_index_; - - } - - - /** - * Find set of nearest neighbors to vec. Their indices are stored inside - * the result object. - * - * Params: - * result = the result object in which the indices of the nearest-neighbors are stored - * vec = the vector for which to search the nearest neighbors - * searchParams = parameters that influence the search algorithm (checks, cb_index) - */ - void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) - { - - int maxChecks = get_param(searchParams,"checks",32); - - if (maxChecks==FLANN_CHECKS_UNLIMITED) { - findExactNN(root_, result, vec); - } - else { - // Priority queue storing intermediate branches in the best-bin-first search - Heap* heap = new Heap((int)size_); - - int checks = 0; - findNN(root_, result, vec, checks, maxChecks, heap); - - BranchSt branch; - while (heap->popMin(branch) && (checks& centers) - { - int numClusters = centers.rows; - if (numClusters<1) { - throw FLANNException("Number of clusters must be at least 1"); - } - - DistanceType variance; - KMeansNodePtr* clusters = new KMeansNodePtr[numClusters]; - - int clusterCount = getMinVarianceClusters(root_, clusters, numClusters, variance); - - Logger::info("Clusters requested: %d, returning %d\n",numClusters, clusterCount); - - for (int i=0; ipivot; - for (size_t j=0; j BranchSt; - - - - - void save_tree(FILE* stream, KMeansNodePtr node) - { - save_value(stream, *node); - save_value(stream, *(node->pivot), (int)veclen_); - if (node->childs==NULL) { - int indices_offset = (int)(node->indices - indices_); - save_value(stream, indices_offset); - } - else { - for(int i=0; ichilds[i]); - } - } - } - - - void load_tree(FILE* stream, KMeansNodePtr& node) - { - node = pool_.allocate(); - load_value(stream, *node); - node->pivot = new DistanceType[veclen_]; - load_value(stream, *(node->pivot), (int)veclen_); - if (node->childs==NULL) { - int indices_offset; - load_value(stream, indices_offset); - node->indices = indices_ + indices_offset; - } - else { - node->childs = pool_.allocate(branching_); - for(int i=0; ichilds[i]); - } - } - } - - - /** - * Helper function - */ - void free_centers(KMeansNodePtr node) - { - delete[] node->pivot; - if (node->childs!=NULL) { - for (int k=0; kchilds[k]); - } - } - } - - /** - * Computes the statistics of a node (mean, radius, variance). - * - * Params: - * node = the node to use - * indices = the indices of the points belonging to the node - */ - void computeNodeStatistics(KMeansNodePtr node, int* indices, int indices_length) - { - - DistanceType radius = 0; - DistanceType variance = 0; - DistanceType* mean = new DistanceType[veclen_]; - memoryCounter_ += int(veclen_*sizeof(DistanceType)); - - memset(mean,0,veclen_*sizeof(DistanceType)); - - for (size_t i=0; i(), veclen_); - } - for (size_t j=0; j(), veclen_); - - DistanceType tmp = 0; - for (int i=0; iradius) { - radius = tmp; - } - } - - node->variance = variance; - node->radius = radius; - node->pivot = mean; - } - - - /** - * The method responsible with actually doing the recursive hierarchical - * clustering - * - * Params: - * node = the node to cluster - * indices = indices of the points belonging to the current node - * branching = the branching factor to use in the clustering - * - * TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point) - */ - void computeClustering(KMeansNodePtr node, int* indices, int indices_length, int branching, int level) - { - node->size = indices_length; - node->level = level; - - if (indices_length < branching) { - node->indices = indices; - std::sort(node->indices,node->indices+indices_length); - node->childs = NULL; - return; - } - - cv::AutoBuffer centers_idx_buf(branching); - int* centers_idx = (int*)centers_idx_buf; - int centers_length; - (this->*chooseCenters)(branching, indices, indices_length, centers_idx, centers_length); - - if (centers_lengthindices = indices; - std::sort(node->indices,node->indices+indices_length); - node->childs = NULL; - return; - } - - - cv::AutoBuffer dcenters_buf(branching*veclen_); - Matrix dcenters((double*)dcenters_buf,branching,veclen_); - for (int i=0; i radiuses(branching); - cv::AutoBuffer count_buf(branching); - int* count = (int*)count_buf; - for (int i=0; i belongs_to_buf(indices_length); - int* belongs_to = (int*)belongs_to_buf; - for (int i=0; inew_sq_dist) { - belongs_to[i] = j; - sq_dist = new_sq_dist; - } - } - if (sq_dist>radiuses[belongs_to[i]]) { - radiuses[belongs_to[i]] = sq_dist; - } - count[belongs_to[i]]++; - } - - bool converged = false; - int iteration = 0; - while (!converged && iterationchilds = pool_.allocate(branching); - int start = 0; - int end = start; - for (int c=0; c(), veclen_); - variance += d; - mean_radius += sqrt(d); - std::swap(indices[i],indices[end]); - std::swap(belongs_to[i],belongs_to[end]); - end++; - } - } - variance /= s; - mean_radius /= s; - variance -= distance_(centers[c], ZeroIterator(), veclen_); - - node->childs[c] = pool_.allocate(); - std::memset(node->childs[c], 0, sizeof(KMeansNode)); - node->childs[c]->radius = radiuses[c]; - node->childs[c]->pivot = centers[c]; - node->childs[c]->variance = variance; - node->childs[c]->mean_radius = mean_radius; - computeClustering(node->childs[c],indices+start, end-start, branching, level+1); - start=end; - } - } - - - - /** - * Performs one descent in the hierarchical k-means tree. The branches not - * visited are stored in a priority queue. - * - * Params: - * node = node to explore - * result = container for the k-nearest neighbors found - * vec = query points - * checks = how many points in the dataset have been checked so far - * maxChecks = maximum dataset points to checks - */ - - - void findNN(KMeansNodePtr node, ResultSet& result, const ElementType* vec, int& checks, int maxChecks, - Heap* heap) - { - // Ignore those clusters that are too far away - { - DistanceType bsq = distance_(vec, node->pivot, veclen_); - DistanceType rsq = node->radius; - DistanceType wsq = result.worstDist(); - - DistanceType val = bsq-rsq-wsq; - DistanceType val2 = val*val-4*rsq*wsq; - - //if (val>0) { - if ((val>0)&&(val2>0)) { - return; - } - } - - if (node->childs==NULL) { - if (checks>=maxChecks) { - if (result.full()) return; - } - checks += node->size; - for (int i=0; isize; ++i) { - int index = node->indices[i]; - DistanceType dist = distance_(dataset_[index], vec, veclen_); - result.addPoint(dist, index); - } - } - else { - DistanceType* domain_distances = new DistanceType[branching_]; - int closest_center = exploreNodeBranches(node, vec, domain_distances, heap); - delete[] domain_distances; - findNN(node->childs[closest_center],result,vec, checks, maxChecks, heap); - } - } - - /** - * Helper function that computes the nearest childs of a node to a given query point. - * Params: - * node = the node - * q = the query point - * distances = array with the distances to each child node. - * Returns: - */ - int exploreNodeBranches(KMeansNodePtr node, const ElementType* q, DistanceType* domain_distances, Heap* heap) - { - - int best_index = 0; - domain_distances[best_index] = distance_(q, node->childs[best_index]->pivot, veclen_); - for (int i=1; ichilds[i]->pivot, veclen_); - if (domain_distances[i]childs[best_index]->pivot; - for (int i=0; ichilds[i]->variance; - - // float dist_to_border = getDistanceToBorder(node.childs[i].pivot,best_center,q); - // if (domain_distances[i]insert(BranchSt(node->childs[i],domain_distances[i])); - } - } - - return best_index; - } - - - /** - * Function the performs exact nearest neighbor search by traversing the entire tree. - */ - void findExactNN(KMeansNodePtr node, ResultSet& result, const ElementType* vec) - { - // Ignore those clusters that are too far away - { - DistanceType bsq = distance_(vec, node->pivot, veclen_); - DistanceType rsq = node->radius; - DistanceType wsq = result.worstDist(); - - DistanceType val = bsq-rsq-wsq; - DistanceType val2 = val*val-4*rsq*wsq; - - // if (val>0) { - if ((val>0)&&(val2>0)) { - return; - } - } - - - if (node->childs==NULL) { - for (int i=0; isize; ++i) { - int index = node->indices[i]; - DistanceType dist = distance_(dataset_[index], vec, veclen_); - result.addPoint(dist, index); - } - } - else { - int* sort_indices = new int[branching_]; - - getCenterOrdering(node, vec, sort_indices); - - for (int i=0; ichilds[sort_indices[i]],result,vec); - } - - delete[] sort_indices; - } - } - - - /** - * Helper function. - * - * I computes the order in which to traverse the child nodes of a particular node. - */ - void getCenterOrdering(KMeansNodePtr node, const ElementType* q, int* sort_indices) - { - DistanceType* domain_distances = new DistanceType[branching_]; - for (int i=0; ichilds[i]->pivot, veclen_); - - int j=0; - while (domain_distances[j]j; --k) { - domain_distances[k] = domain_distances[k-1]; - sort_indices[k] = sort_indices[k-1]; - } - domain_distances[j] = dist; - sort_indices[j] = i; - } - delete[] domain_distances; - } - - /** - * Method that computes the squared distance from the query point q - * from inside region with center c to the border between this - * region and the region with center p - */ - DistanceType getDistanceToBorder(DistanceType* p, DistanceType* c, DistanceType* q) - { - DistanceType sum = 0; - DistanceType sum2 = 0; - - for (int i=0; ivariance*root->size; - - while (clusterCount::max)(); - int splitIndex = -1; - - for (int i=0; ichilds != NULL) { - - DistanceType variance = meanVariance - clusters[i]->variance*clusters[i]->size; - - for (int j=0; jchilds[j]->variance*clusters[i]->childs[j]->size; - } - if (variance clusters_length) break; - - meanVariance = minVariance; - - // split node - KMeansNodePtr toSplit = clusters[splitIndex]; - clusters[splitIndex] = toSplit->childs[0]; - for (int i=1; ichilds[i]; - } - } - - varianceValue = meanVariance/root->size; - return clusterCount; - } - -private: - /** The branching factor used in the hierarchical k-means clustering */ - int branching_; - - /** Maximum number of iterations to use when performing k-means clustering */ - int iterations_; - - /** Algorithm for choosing the cluster centers */ - flann_centers_init_t centers_init_; - - /** - * Cluster border index. This is used in the tree search phase when determining - * the closest cluster to explore next. A zero value takes into account only - * the cluster centres, a value greater then zero also take into account the size - * of the cluster. - */ - float cb_index_; - - /** - * The dataset used by this index - */ - const Matrix dataset_; - - /** Index parameters */ - IndexParams index_params_; - - /** - * Number of features in the dataset. - */ - size_t size_; - - /** - * Length of each feature. - */ - size_t veclen_; - - /** - * The root node in the tree. - */ - KMeansNodePtr root_; - - /** - * Array of indices to vectors in the dataset. - */ - int* indices_; - - /** - * The distance - */ - Distance distance_; - - /** - * Pooled memory allocator. - */ - PooledAllocator pool_; - - /** - * Memory occupied by the index. - */ - int memoryCounter_; -}; - -} - -#endif //OPENCV_FLANN_KMEANS_INDEX_H_ diff --git a/IPL/include/opencv/opencv2/flann/linear_index.h b/IPL/include/opencv/opencv2/flann/linear_index.h deleted file mode 100644 index 5aa7a5c..0000000 --- a/IPL/include/opencv/opencv2/flann/linear_index.h +++ /dev/null @@ -1,132 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_LINEAR_INDEX_H_ -#define OPENCV_FLANN_LINEAR_INDEX_H_ - -#include "general.h" -#include "nn_index.h" - -namespace cvflann -{ - -struct LinearIndexParams : public IndexParams -{ - LinearIndexParams() - { - (* this)["algorithm"] = FLANN_INDEX_LINEAR; - } -}; - -template -class LinearIndex : public NNIndex -{ -public: - - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - - LinearIndex(const Matrix& inputData, const IndexParams& params = LinearIndexParams(), - Distance d = Distance()) : - dataset_(inputData), index_params_(params), distance_(d) - { - } - - LinearIndex(const LinearIndex&); - LinearIndex& operator=(const LinearIndex&); - - flann_algorithm_t getType() const - { - return FLANN_INDEX_LINEAR; - } - - - size_t size() const - { - return dataset_.rows; - } - - size_t veclen() const - { - return dataset_.cols; - } - - - int usedMemory() const - { - return 0; - } - - void buildIndex() - { - /* nothing to do here for linear search */ - } - - void saveIndex(FILE*) - { - /* nothing to do here for linear search */ - } - - - void loadIndex(FILE*) - { - /* nothing to do here for linear search */ - - index_params_["algorithm"] = getType(); - } - - void findNeighbors(ResultSet& resultSet, const ElementType* vec, const SearchParams& /*searchParams*/) - { - ElementType* data = dataset_.data; - for (size_t i = 0; i < dataset_.rows; ++i, data += dataset_.cols) { - DistanceType dist = distance_(data, vec, dataset_.cols); - resultSet.addPoint(dist, (int)i); - } - } - - IndexParams getParameters() const - { - return index_params_; - } - -private: - /** The dataset */ - const Matrix dataset_; - /** Index parameters */ - IndexParams index_params_; - /** Index distance */ - Distance distance_; - -}; - -} - -#endif // OPENCV_FLANN_LINEAR_INDEX_H_ diff --git a/IPL/include/opencv/opencv2/flann/logger.h b/IPL/include/opencv/opencv2/flann/logger.h deleted file mode 100644 index 24f3fb6..0000000 --- a/IPL/include/opencv/opencv2/flann/logger.h +++ /dev/null @@ -1,130 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_LOGGER_H -#define OPENCV_FLANN_LOGGER_H - -#include -#include - -#include "defines.h" - - -namespace cvflann -{ - -class Logger -{ - Logger() : stream(stdout), logLevel(FLANN_LOG_WARN) {} - - ~Logger() - { - if ((stream!=NULL)&&(stream!=stdout)) { - fclose(stream); - } - } - - static Logger& instance() - { - static Logger logger; - return logger; - } - - void _setDestination(const char* name) - { - if (name==NULL) { - stream = stdout; - } - else { - stream = fopen(name,"w"); - if (stream == NULL) { - stream = stdout; - } - } - } - - int _log(int level, const char* fmt, va_list arglist) - { - if (level > logLevel ) return -1; - int ret = vfprintf(stream, fmt, arglist); - return ret; - } - -public: - /** - * Sets the logging level. All messages with lower priority will be ignored. - * @param level Logging level - */ - static void setLevel(int level) { instance().logLevel = level; } - - /** - * Sets the logging destination - * @param name Filename or NULL for console - */ - static void setDestination(const char* name) { instance()._setDestination(name); } - - /** - * Print log message - * @param level Log level - * @param fmt Message format - * @return - */ - static int log(int level, const char* fmt, ...) - { - va_list arglist; - va_start(arglist, fmt); - int ret = instance()._log(level,fmt,arglist); - va_end(arglist); - return ret; - } - -#define LOG_METHOD(NAME,LEVEL) \ - static int NAME(const char* fmt, ...) \ - { \ - va_list ap; \ - va_start(ap, fmt); \ - int ret = instance()._log(LEVEL, fmt, ap); \ - va_end(ap); \ - return ret; \ - } - - LOG_METHOD(fatal, FLANN_LOG_FATAL) - LOG_METHOD(error, FLANN_LOG_ERROR) - LOG_METHOD(warn, FLANN_LOG_WARN) - LOG_METHOD(info, FLANN_LOG_INFO) - -private: - FILE* stream; - int logLevel; -}; - -} - -#endif //OPENCV_FLANN_LOGGER_H diff --git a/IPL/include/opencv/opencv2/flann/lsh_index.h b/IPL/include/opencv/opencv2/flann/lsh_index.h deleted file mode 100644 index 4d4670e..0000000 --- a/IPL/include/opencv/opencv2/flann/lsh_index.h +++ /dev/null @@ -1,392 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -/*********************************************************************** - * Author: Vincent Rabaud - *************************************************************************/ - -#ifndef OPENCV_FLANN_LSH_INDEX_H_ -#define OPENCV_FLANN_LSH_INDEX_H_ - -#include -#include -#include -#include -#include - -#include "general.h" -#include "nn_index.h" -#include "matrix.h" -#include "result_set.h" -#include "heap.h" -#include "lsh_table.h" -#include "allocator.h" -#include "random.h" -#include "saving.h" - -namespace cvflann -{ - -struct LshIndexParams : public IndexParams -{ - LshIndexParams(unsigned int table_number = 12, unsigned int key_size = 20, unsigned int multi_probe_level = 2) - { - (* this)["algorithm"] = FLANN_INDEX_LSH; - // The number of hash tables to use - (*this)["table_number"] = table_number; - // The length of the key in the hash tables - (*this)["key_size"] = key_size; - // Number of levels to use in multi-probe (0 for standard LSH) - (*this)["multi_probe_level"] = multi_probe_level; - } -}; - -/** - * Randomized kd-tree index - * - * Contains the k-d trees and other information for indexing a set of points - * for nearest-neighbor matching. - */ -template -class LshIndex : public NNIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - /** Constructor - * @param input_data dataset with the input features - * @param params parameters passed to the LSH algorithm - * @param d the distance used - */ - LshIndex(const Matrix& input_data, const IndexParams& params = LshIndexParams(), - Distance d = Distance()) : - dataset_(input_data), index_params_(params), distance_(d) - { - // cv::flann::IndexParams sets integer params as 'int', so it is used with get_param - // in place of 'unsigned int' - table_number_ = (unsigned int)get_param(index_params_,"table_number",12); - key_size_ = (unsigned int)get_param(index_params_,"key_size",20); - multi_probe_level_ = (unsigned int)get_param(index_params_,"multi_probe_level",2); - - feature_size_ = (unsigned)dataset_.cols; - fill_xor_mask(0, key_size_, multi_probe_level_, xor_masks_); - } - - - LshIndex(const LshIndex&); - LshIndex& operator=(const LshIndex&); - - /** - * Builds the index - */ - void buildIndex() - { - tables_.resize(table_number_); - for (unsigned int i = 0; i < table_number_; ++i) { - lsh::LshTable& table = tables_[i]; - table = lsh::LshTable(feature_size_, key_size_); - - // Add the features to the table - table.add(dataset_); - } - } - - flann_algorithm_t getType() const - { - return FLANN_INDEX_LSH; - } - - - void saveIndex(FILE* stream) - { - save_value(stream,table_number_); - save_value(stream,key_size_); - save_value(stream,multi_probe_level_); - save_value(stream, dataset_); - } - - void loadIndex(FILE* stream) - { - load_value(stream, table_number_); - load_value(stream, key_size_); - load_value(stream, multi_probe_level_); - load_value(stream, dataset_); - // Building the index is so fast we can afford not storing it - buildIndex(); - - index_params_["algorithm"] = getType(); - index_params_["table_number"] = table_number_; - index_params_["key_size"] = key_size_; - index_params_["multi_probe_level"] = multi_probe_level_; - } - - /** - * Returns size of index. - */ - size_t size() const - { - return dataset_.rows; - } - - /** - * Returns the length of an index feature. - */ - size_t veclen() const - { - return feature_size_; - } - - /** - * Computes the index memory usage - * Returns: memory used by the index - */ - int usedMemory() const - { - return (int)(dataset_.rows * sizeof(int)); - } - - - IndexParams getParameters() const - { - return index_params_; - } - - /** - * \brief Perform k-nearest neighbor search - * \param[in] queries The query points for which to find the nearest neighbors - * \param[out] indices The indices of the nearest neighbors found - * \param[out] dists Distances to the nearest neighbors found - * \param[in] knn Number of nearest neighbors to return - * \param[in] params Search parameters - */ - virtual void knnSearch(const Matrix& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params) - { - assert(queries.cols == veclen()); - assert(indices.rows >= queries.rows); - assert(dists.rows >= queries.rows); - assert(int(indices.cols) >= knn); - assert(int(dists.cols) >= knn); - - - KNNUniqueResultSet resultSet(knn); - for (size_t i = 0; i < queries.rows; i++) { - resultSet.clear(); - std::fill_n(indices[i], knn, -1); - std::fill_n(dists[i], knn, std::numeric_limits::max()); - findNeighbors(resultSet, queries[i], params); - if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices[i], dists[i], knn); - else resultSet.copy(indices[i], dists[i], knn); - } - } - - - /** - * Find set of nearest neighbors to vec. Their indices are stored inside - * the result object. - * - * Params: - * result = the result object in which the indices of the nearest-neighbors are stored - * vec = the vector for which to search the nearest neighbors - * maxCheck = the maximum number of restarts (in a best-bin-first manner) - */ - void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& /*searchParams*/) - { - getNeighbors(vec, result); - } - -private: - /** Defines the comparator on score and index - */ - typedef std::pair ScoreIndexPair; - struct SortScoreIndexPairOnSecond - { - bool operator()(const ScoreIndexPair& left, const ScoreIndexPair& right) const - { - return left.second < right.second; - } - }; - - /** Fills the different xor masks to use when getting the neighbors in multi-probe LSH - * @param key the key we build neighbors from - * @param lowest_index the lowest index of the bit set - * @param level the multi-probe level we are at - * @param xor_masks all the xor mask - */ - void fill_xor_mask(lsh::BucketKey key, int lowest_index, unsigned int level, - std::vector& xor_masks) - { - xor_masks.push_back(key); - if (level == 0) return; - for (int index = lowest_index - 1; index >= 0; --index) { - // Create a new key - lsh::BucketKey new_key = key | (1 << index); - fill_xor_mask(new_key, index, level - 1, xor_masks); - } - } - - /** Performs the approximate nearest-neighbor search. - * @param vec the feature to analyze - * @param do_radius flag indicating if we check the radius too - * @param radius the radius if it is a radius search - * @param do_k flag indicating if we limit the number of nn - * @param k_nn the number of nearest neighbors - * @param checked_average used for debugging - */ - void getNeighbors(const ElementType* vec, bool /*do_radius*/, float radius, bool do_k, unsigned int k_nn, - float& /*checked_average*/) - { - static std::vector score_index_heap; - - if (do_k) { - unsigned int worst_score = std::numeric_limits::max(); - typename std::vector >::const_iterator table = tables_.begin(); - typename std::vector >::const_iterator table_end = tables_.end(); - for (; table != table_end; ++table) { - size_t key = table->getKey(vec); - std::vector::const_iterator xor_mask = xor_masks_.begin(); - std::vector::const_iterator xor_mask_end = xor_masks_.end(); - for (; xor_mask != xor_mask_end; ++xor_mask) { - size_t sub_key = key ^ (*xor_mask); - const lsh::Bucket* bucket = table->getBucketFromKey(sub_key); - if (bucket == 0) continue; - - // Go over each descriptor index - std::vector::const_iterator training_index = bucket->begin(); - std::vector::const_iterator last_training_index = bucket->end(); - DistanceType hamming_distance; - - // Process the rest of the candidates - for (; training_index < last_training_index; ++training_index) { - hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols); - - if (hamming_distance < worst_score) { - // Insert the new element - score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index)); - std::push_heap(score_index_heap.begin(), score_index_heap.end()); - - if (score_index_heap.size() > (unsigned int)k_nn) { - // Remove the highest distance value as we have too many elements - std::pop_heap(score_index_heap.begin(), score_index_heap.end()); - score_index_heap.pop_back(); - // Keep track of the worst score - worst_score = score_index_heap.front().first; - } - } - } - } - } - } - else { - typename std::vector >::const_iterator table = tables_.begin(); - typename std::vector >::const_iterator table_end = tables_.end(); - for (; table != table_end; ++table) { - size_t key = table->getKey(vec); - std::vector::const_iterator xor_mask = xor_masks_.begin(); - std::vector::const_iterator xor_mask_end = xor_masks_.end(); - for (; xor_mask != xor_mask_end; ++xor_mask) { - size_t sub_key = key ^ (*xor_mask); - const lsh::Bucket* bucket = table->getBucketFromKey(sub_key); - if (bucket == 0) continue; - - // Go over each descriptor index - std::vector::const_iterator training_index = bucket->begin(); - std::vector::const_iterator last_training_index = bucket->end(); - DistanceType hamming_distance; - - // Process the rest of the candidates - for (; training_index < last_training_index; ++training_index) { - // Compute the Hamming distance - hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols); - if (hamming_distance < radius) score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index)); - } - } - } - } - } - - /** Performs the approximate nearest-neighbor search. - * This is a slower version than the above as it uses the ResultSet - * @param vec the feature to analyze - */ - void getNeighbors(const ElementType* vec, ResultSet& result) - { - typename std::vector >::const_iterator table = tables_.begin(); - typename std::vector >::const_iterator table_end = tables_.end(); - for (; table != table_end; ++table) { - size_t key = table->getKey(vec); - std::vector::const_iterator xor_mask = xor_masks_.begin(); - std::vector::const_iterator xor_mask_end = xor_masks_.end(); - for (; xor_mask != xor_mask_end; ++xor_mask) { - size_t sub_key = key ^ (*xor_mask); - const lsh::Bucket* bucket = table->getBucketFromKey((lsh::BucketKey)sub_key); - if (bucket == 0) continue; - - // Go over each descriptor index - std::vector::const_iterator training_index = bucket->begin(); - std::vector::const_iterator last_training_index = bucket->end(); - DistanceType hamming_distance; - - // Process the rest of the candidates - for (; training_index < last_training_index; ++training_index) { - // Compute the Hamming distance - hamming_distance = distance_(vec, dataset_[*training_index], (int)dataset_.cols); - result.addPoint(hamming_distance, *training_index); - } - } - } - } - - /** The different hash tables */ - std::vector > tables_; - - /** The data the LSH tables where built from */ - Matrix dataset_; - - /** The size of the features (as ElementType[]) */ - unsigned int feature_size_; - - IndexParams index_params_; - - /** table number */ - unsigned int table_number_; - /** key size */ - unsigned int key_size_; - /** How far should we look for neighbors in multi-probe LSH */ - unsigned int multi_probe_level_; - - /** The XOR masks to apply to a key to get the neighboring buckets */ - std::vector xor_masks_; - - Distance distance_; -}; -} - -#endif //OPENCV_FLANN_LSH_INDEX_H_ diff --git a/IPL/include/opencv/opencv2/flann/lsh_table.h b/IPL/include/opencv/opencv2/flann/lsh_table.h deleted file mode 100644 index 582dcdb..0000000 --- a/IPL/include/opencv/opencv2/flann/lsh_table.h +++ /dev/null @@ -1,492 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -/*********************************************************************** - * Author: Vincent Rabaud - *************************************************************************/ - -#ifndef OPENCV_FLANN_LSH_TABLE_H_ -#define OPENCV_FLANN_LSH_TABLE_H_ - -#include -#include -#include -#include -// TODO as soon as we use C++0x, use the code in USE_UNORDERED_MAP -#ifdef __GXX_EXPERIMENTAL_CXX0X__ -# define USE_UNORDERED_MAP 1 -#else -# define USE_UNORDERED_MAP 0 -#endif -#if USE_UNORDERED_MAP -#include -#else -#include -#endif -#include -#include - -#include "dynamic_bitset.h" -#include "matrix.h" - -namespace cvflann -{ - -namespace lsh -{ - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -/** What is stored in an LSH bucket - */ -typedef uint32_t FeatureIndex; -/** The id from which we can get a bucket back in an LSH table - */ -typedef unsigned int BucketKey; - -/** A bucket in an LSH table - */ -typedef std::vector Bucket; - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -/** POD for stats about an LSH table - */ -struct LshStats -{ - std::vector bucket_sizes_; - size_t n_buckets_; - size_t bucket_size_mean_; - size_t bucket_size_median_; - size_t bucket_size_min_; - size_t bucket_size_max_; - size_t bucket_size_std_dev; - /** Each contained vector contains three value: beginning/end for interval, number of elements in the bin - */ - std::vector > size_histogram_; -}; - -/** Overload the << operator for LshStats - * @param out the streams - * @param stats the stats to display - * @return the streams - */ -inline std::ostream& operator <<(std::ostream& out, const LshStats& stats) -{ - int w = 20; - out << "Lsh Table Stats:\n" << std::setw(w) << std::setiosflags(std::ios::right) << "N buckets : " - << stats.n_buckets_ << "\n" << std::setw(w) << std::setiosflags(std::ios::right) << "mean size : " - << std::setiosflags(std::ios::left) << stats.bucket_size_mean_ << "\n" << std::setw(w) - << std::setiosflags(std::ios::right) << "median size : " << stats.bucket_size_median_ << "\n" << std::setw(w) - << std::setiosflags(std::ios::right) << "min size : " << std::setiosflags(std::ios::left) - << stats.bucket_size_min_ << "\n" << std::setw(w) << std::setiosflags(std::ios::right) << "max size : " - << std::setiosflags(std::ios::left) << stats.bucket_size_max_; - - // Display the histogram - out << std::endl << std::setw(w) << std::setiosflags(std::ios::right) << "histogram : " - << std::setiosflags(std::ios::left); - for (std::vector >::const_iterator iterator = stats.size_histogram_.begin(), end = - stats.size_histogram_.end(); iterator != end; ++iterator) out << (*iterator)[0] << "-" << (*iterator)[1] << ": " << (*iterator)[2] << ", "; - - return out; -} - - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -/** Lsh hash table. As its key is a sub-feature, and as usually - * the size of it is pretty small, we keep it as a continuous memory array. - * The value is an index in the corpus of features (we keep it as an unsigned - * int for pure memory reasons, it could be a size_t) - */ -template -class LshTable -{ -public: - /** A container of all the feature indices. Optimized for space - */ -#if USE_UNORDERED_MAP - typedef std::unordered_map BucketsSpace; -#else - typedef std::map BucketsSpace; -#endif - - /** A container of all the feature indices. Optimized for speed - */ - typedef std::vector BucketsSpeed; - - /** Default constructor - */ - LshTable() - { - } - - /** Default constructor - * Create the mask and allocate the memory - * @param feature_size is the size of the feature (considered as a ElementType[]) - * @param key_size is the number of bits that are turned on in the feature - */ - LshTable(unsigned int feature_size, unsigned int key_size) - { - (void)feature_size; - (void)key_size; - std::cerr << "LSH is not implemented for that type" << std::endl; - assert(0); - } - - /** Add a feature to the table - * @param value the value to store for that feature - * @param feature the feature itself - */ - void add(unsigned int value, const ElementType* feature) - { - // Add the value to the corresponding bucket - BucketKey key = (lsh::BucketKey)getKey(feature); - - switch (speed_level_) { - case kArray: - // That means we get the buckets from an array - buckets_speed_[key].push_back(value); - break; - case kBitsetHash: - // That means we can check the bitset for the presence of a key - key_bitset_.set(key); - buckets_space_[key].push_back(value); - break; - case kHash: - { - // That means we have to check for the hash table for the presence of a key - buckets_space_[key].push_back(value); - break; - } - } - } - - /** Add a set of features to the table - * @param dataset the values to store - */ - void add(Matrix dataset) - { -#if USE_UNORDERED_MAP - buckets_space_.rehash((buckets_space_.size() + dataset.rows) * 1.2); -#endif - // Add the features to the table - for (unsigned int i = 0; i < dataset.rows; ++i) add(i, dataset[i]); - // Now that the table is full, optimize it for speed/space - optimize(); - } - - /** Get a bucket given the key - * @param key - * @return - */ - inline const Bucket* getBucketFromKey(BucketKey key) const - { - // Generate other buckets - switch (speed_level_) { - case kArray: - // That means we get the buckets from an array - return &buckets_speed_[key]; - break; - case kBitsetHash: - // That means we can check the bitset for the presence of a key - if (key_bitset_.test(key)) return &buckets_space_.find(key)->second; - else return 0; - break; - case kHash: - { - // That means we have to check for the hash table for the presence of a key - BucketsSpace::const_iterator bucket_it, bucket_end = buckets_space_.end(); - bucket_it = buckets_space_.find(key); - // Stop here if that bucket does not exist - if (bucket_it == bucket_end) return 0; - else return &bucket_it->second; - break; - } - } - return 0; - } - - /** Compute the sub-signature of a feature - */ - size_t getKey(const ElementType* /*feature*/) const - { - std::cerr << "LSH is not implemented for that type" << std::endl; - assert(0); - return 1; - } - - /** Get statistics about the table - * @return - */ - LshStats getStats() const; - -private: - /** defines the speed fo the implementation - * kArray uses a vector for storing data - * kBitsetHash uses a hash map but checks for the validity of a key with a bitset - * kHash uses a hash map only - */ - enum SpeedLevel - { - kArray, kBitsetHash, kHash - }; - - /** Initialize some variables - */ - void initialize(size_t key_size) - { - const size_t key_size_lower_bound = 1; - //a value (size_t(1) << key_size) must fit the size_t type so key_size has to be strictly less than size of size_t - const size_t key_size_upper_bound = std::min(sizeof(BucketKey) * CHAR_BIT + 1, sizeof(size_t) * CHAR_BIT); - if (key_size < key_size_lower_bound || key_size >= key_size_upper_bound) - { - CV_Error(cv::Error::StsBadArg, cv::format("Invalid key_size (=%d). Valid values for your system are %d <= key_size < %d.", (int)key_size, (int)key_size_lower_bound, (int)key_size_upper_bound)); - } - - speed_level_ = kHash; - key_size_ = (unsigned)key_size; - } - - /** Optimize the table for speed/space - */ - void optimize() - { - // If we are already using the fast storage, no need to do anything - if (speed_level_ == kArray) return; - - // Use an array if it will be more than half full - if (buckets_space_.size() > ((size_t(1) << key_size_) / 2)) { - speed_level_ = kArray; - // Fill the array version of it - buckets_speed_.resize(size_t(1) << key_size_); - for (BucketsSpace::const_iterator key_bucket = buckets_space_.begin(); key_bucket != buckets_space_.end(); ++key_bucket) buckets_speed_[key_bucket->first] = key_bucket->second; - - // Empty the hash table - buckets_space_.clear(); - return; - } - - // If the bitset is going to use less than 10% of the RAM of the hash map (at least 1 size_t for the key and two - // for the vector) or less than 512MB (key_size_ <= 30) - if (((std::max(buckets_space_.size(), buckets_speed_.size()) * CHAR_BIT * 3 * sizeof(BucketKey)) / 10 - >= (size_t(1) << key_size_)) || (key_size_ <= 32)) { - speed_level_ = kBitsetHash; - key_bitset_.resize(size_t(1) << key_size_); - key_bitset_.reset(); - // Try with the BucketsSpace - for (BucketsSpace::const_iterator key_bucket = buckets_space_.begin(); key_bucket != buckets_space_.end(); ++key_bucket) key_bitset_.set(key_bucket->first); - } - else { - speed_level_ = kHash; - key_bitset_.clear(); - } - } - - /** The vector of all the buckets if they are held for speed - */ - BucketsSpeed buckets_speed_; - - /** The hash table of all the buckets in case we cannot use the speed version - */ - BucketsSpace buckets_space_; - - /** What is used to store the data */ - SpeedLevel speed_level_; - - /** If the subkey is small enough, it will keep track of which subkeys are set through that bitset - * That is just a speedup so that we don't look in the hash table (which can be mush slower that checking a bitset) - */ - DynamicBitset key_bitset_; - - /** The size of the sub-signature in bits - */ - unsigned int key_size_; - - // Members only used for the unsigned char specialization - /** The mask to apply to a feature to get the hash key - * Only used in the unsigned char case - */ - std::vector mask_; -}; - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// -// Specialization for unsigned char - -template<> -inline LshTable::LshTable(unsigned int feature_size, unsigned int subsignature_size) -{ - initialize(subsignature_size); - // Allocate the mask - mask_ = std::vector((size_t)ceil((float)(feature_size * sizeof(char)) / (float)sizeof(size_t)), 0); - - // A bit brutal but fast to code - std::vector indices(feature_size * CHAR_BIT); - for (size_t i = 0; i < feature_size * CHAR_BIT; ++i) indices[i] = i; - std::random_shuffle(indices.begin(), indices.end()); - - // Generate a random set of order of subsignature_size_ bits - for (unsigned int i = 0; i < key_size_; ++i) { - size_t index = indices[i]; - - // Set that bit in the mask - size_t divisor = CHAR_BIT * sizeof(size_t); - size_t idx = index / divisor; //pick the right size_t index - mask_[idx] |= size_t(1) << (index % divisor); //use modulo to find the bit offset - } - - // Set to 1 if you want to display the mask for debug -#if 0 - { - size_t bcount = 0; - BOOST_FOREACH(size_t mask_block, mask_){ - out << std::setw(sizeof(size_t) * CHAR_BIT / 4) << std::setfill('0') << std::hex << mask_block - << std::endl; - bcount += __builtin_popcountll(mask_block); - } - out << "bit count : " << std::dec << bcount << std::endl; - out << "mask size : " << mask_.size() << std::endl; - return out; - } -#endif -} - -/** Return the Subsignature of a feature - * @param feature the feature to analyze - */ -template<> -inline size_t LshTable::getKey(const unsigned char* feature) const -{ - // no need to check if T is dividable by sizeof(size_t) like in the Hamming - // distance computation as we have a mask - const size_t* feature_block_ptr = reinterpret_cast ((const void*)feature); - - // Figure out the subsignature of the feature - // Given the feature ABCDEF, and the mask 001011, the output will be - // 000CEF - size_t subsignature = 0; - size_t bit_index = 1; - - for (std::vector::const_iterator pmask_block = mask_.begin(); pmask_block != mask_.end(); ++pmask_block) { - // get the mask and signature blocks - size_t feature_block = *feature_block_ptr; - size_t mask_block = *pmask_block; - while (mask_block) { - // Get the lowest set bit in the mask block - size_t lowest_bit = mask_block & (-(ptrdiff_t)mask_block); - // Add it to the current subsignature if necessary - subsignature += (feature_block & lowest_bit) ? bit_index : 0; - // Reset the bit in the mask block - mask_block ^= lowest_bit; - // increment the bit index for the subsignature - bit_index <<= 1; - } - // Check the next feature block - ++feature_block_ptr; - } - return subsignature; -} - -template<> -inline LshStats LshTable::getStats() const -{ - LshStats stats; - stats.bucket_size_mean_ = 0; - if ((buckets_speed_.empty()) && (buckets_space_.empty())) { - stats.n_buckets_ = 0; - stats.bucket_size_median_ = 0; - stats.bucket_size_min_ = 0; - stats.bucket_size_max_ = 0; - return stats; - } - - if (!buckets_speed_.empty()) { - for (BucketsSpeed::const_iterator pbucket = buckets_speed_.begin(); pbucket != buckets_speed_.end(); ++pbucket) { - stats.bucket_sizes_.push_back((lsh::FeatureIndex)pbucket->size()); - stats.bucket_size_mean_ += pbucket->size(); - } - stats.bucket_size_mean_ /= buckets_speed_.size(); - stats.n_buckets_ = buckets_speed_.size(); - } - else { - for (BucketsSpace::const_iterator x = buckets_space_.begin(); x != buckets_space_.end(); ++x) { - stats.bucket_sizes_.push_back((lsh::FeatureIndex)x->second.size()); - stats.bucket_size_mean_ += x->second.size(); - } - stats.bucket_size_mean_ /= buckets_space_.size(); - stats.n_buckets_ = buckets_space_.size(); - } - - std::sort(stats.bucket_sizes_.begin(), stats.bucket_sizes_.end()); - - // BOOST_FOREACH(int size, stats.bucket_sizes_) - // std::cout << size << " "; - // std::cout << std::endl; - stats.bucket_size_median_ = stats.bucket_sizes_[stats.bucket_sizes_.size() / 2]; - stats.bucket_size_min_ = stats.bucket_sizes_.front(); - stats.bucket_size_max_ = stats.bucket_sizes_.back(); - - // TODO compute mean and std - /*float mean, stddev; - stats.bucket_size_mean_ = mean; - stats.bucket_size_std_dev = stddev;*/ - - // Include a histogram of the buckets - unsigned int bin_start = 0; - unsigned int bin_end = 20; - bool is_new_bin = true; - for (std::vector::iterator iterator = stats.bucket_sizes_.begin(), end = stats.bucket_sizes_.end(); iterator - != end; ) - if (*iterator < bin_end) { - if (is_new_bin) { - stats.size_histogram_.push_back(std::vector(3, 0)); - stats.size_histogram_.back()[0] = bin_start; - stats.size_histogram_.back()[1] = bin_end - 1; - is_new_bin = false; - } - ++stats.size_histogram_.back()[2]; - ++iterator; - } - else { - bin_start += 20; - bin_end += 20; - is_new_bin = true; - } - - return stats; -} - -// End the two namespaces -} -} - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -#endif /* OPENCV_FLANN_LSH_TABLE_H_ */ diff --git a/IPL/include/opencv/opencv2/flann/matrix.h b/IPL/include/opencv/opencv2/flann/matrix.h deleted file mode 100644 index 51b6c63..0000000 --- a/IPL/include/opencv/opencv2/flann/matrix.h +++ /dev/null @@ -1,116 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_DATASET_H_ -#define OPENCV_FLANN_DATASET_H_ - -#include - -#include "general.h" - -namespace cvflann -{ - -/** - * Class that implements a simple rectangular matrix stored in a memory buffer and - * provides convenient matrix-like access using the [] operators. - */ -template -class Matrix -{ -public: - typedef T type; - - size_t rows; - size_t cols; - size_t stride; - T* data; - - Matrix() : rows(0), cols(0), stride(0), data(NULL) - { - } - - Matrix(T* data_, size_t rows_, size_t cols_, size_t stride_ = 0) : - rows(rows_), cols(cols_), stride(stride_), data(data_) - { - if (stride==0) stride = cols; - } - - /** - * Convenience function for deallocating the storage data. - */ - FLANN_DEPRECATED void free() - { - fprintf(stderr, "The cvflann::Matrix::free() method is deprecated " - "and it does not do any memory deallocation any more. You are" - "responsible for deallocating the matrix memory (by doing" - "'delete[] matrix.data' for example)"); - } - - /** - * Operator that return a (pointer to a) row of the data. - */ - T* operator[](size_t index) const - { - return data+index*stride; - } -}; - - -class UntypedMatrix -{ -public: - size_t rows; - size_t cols; - void* data; - flann_datatype_t type; - - UntypedMatrix(void* data_, long rows_, long cols_) : - rows(rows_), cols(cols_), data(data_) - { - } - - ~UntypedMatrix() - { - } - - - template - Matrix as() - { - return Matrix((T*)data, rows, cols); - } -}; - - - -} - -#endif //OPENCV_FLANN_DATASET_H_ diff --git a/IPL/include/opencv/opencv2/flann/miniflann.hpp b/IPL/include/opencv/opencv2/flann/miniflann.hpp deleted file mode 100644 index 02fa236..0000000 --- a/IPL/include/opencv/opencv2/flann/miniflann.hpp +++ /dev/null @@ -1,158 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef _OPENCV_MINIFLANN_HPP_ -#define _OPENCV_MINIFLANN_HPP_ - -#include "opencv2/core.hpp" -#include "opencv2/flann/defines.h" - -namespace cv -{ - -namespace flann -{ - -struct CV_EXPORTS IndexParams -{ - IndexParams(); - ~IndexParams(); - - String getString(const String& key, const String& defaultVal=String()) const; - int getInt(const String& key, int defaultVal=-1) const; - double getDouble(const String& key, double defaultVal=-1) const; - - void setString(const String& key, const String& value); - void setInt(const String& key, int value); - void setDouble(const String& key, double value); - void setFloat(const String& key, float value); - void setBool(const String& key, bool value); - void setAlgorithm(int value); - - void getAll(std::vector& names, - std::vector& types, - std::vector& strValues, - std::vector& numValues) const; - - void* params; -}; - -struct CV_EXPORTS KDTreeIndexParams : public IndexParams -{ - KDTreeIndexParams(int trees=4); -}; - -struct CV_EXPORTS LinearIndexParams : public IndexParams -{ - LinearIndexParams(); -}; - -struct CV_EXPORTS CompositeIndexParams : public IndexParams -{ - CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11, - cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, float cb_index = 0.2f ); -}; - -struct CV_EXPORTS AutotunedIndexParams : public IndexParams -{ - AutotunedIndexParams(float target_precision = 0.8f, float build_weight = 0.01f, - float memory_weight = 0, float sample_fraction = 0.1f); -}; - -struct CV_EXPORTS HierarchicalClusteringIndexParams : public IndexParams -{ - HierarchicalClusteringIndexParams(int branching = 32, - cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, int trees = 4, int leaf_size = 100 ); -}; - -struct CV_EXPORTS KMeansIndexParams : public IndexParams -{ - KMeansIndexParams(int branching = 32, int iterations = 11, - cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, float cb_index = 0.2f ); -}; - -struct CV_EXPORTS LshIndexParams : public IndexParams -{ - LshIndexParams(int table_number, int key_size, int multi_probe_level); -}; - -struct CV_EXPORTS SavedIndexParams : public IndexParams -{ - SavedIndexParams(const String& filename); -}; - -struct CV_EXPORTS SearchParams : public IndexParams -{ - SearchParams( int checks = 32, float eps = 0, bool sorted = true ); -}; - -class CV_EXPORTS_W Index -{ -public: - CV_WRAP Index(); - CV_WRAP Index(InputArray features, const IndexParams& params, cvflann::flann_distance_t distType=cvflann::FLANN_DIST_L2); - virtual ~Index(); - - CV_WRAP virtual void build(InputArray features, const IndexParams& params, cvflann::flann_distance_t distType=cvflann::FLANN_DIST_L2); - CV_WRAP virtual void knnSearch(InputArray query, OutputArray indices, - OutputArray dists, int knn, const SearchParams& params=SearchParams()); - - CV_WRAP virtual int radiusSearch(InputArray query, OutputArray indices, - OutputArray dists, double radius, int maxResults, - const SearchParams& params=SearchParams()); - - CV_WRAP virtual void save(const String& filename) const; - CV_WRAP virtual bool load(InputArray features, const String& filename); - CV_WRAP virtual void release(); - CV_WRAP cvflann::flann_distance_t getDistance() const; - CV_WRAP cvflann::flann_algorithm_t getAlgorithm() const; - -protected: - cvflann::flann_distance_t distType; - cvflann::flann_algorithm_t algo; - int featureType; - void* index; -}; - -} } // namespace cv::flann - -#endif diff --git a/IPL/include/opencv/opencv2/flann/nn_index.h b/IPL/include/opencv/opencv2/flann/nn_index.h deleted file mode 100644 index 381d4bc..0000000 --- a/IPL/include/opencv/opencv2/flann/nn_index.h +++ /dev/null @@ -1,177 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_NNINDEX_H -#define OPENCV_FLANN_NNINDEX_H - -#include "general.h" -#include "matrix.h" -#include "result_set.h" -#include "params.h" - -namespace cvflann -{ - -/** - * Nearest-neighbour index base class - */ -template -class NNIndex -{ - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - -public: - - virtual ~NNIndex() {} - - /** - * \brief Builds the index - */ - virtual void buildIndex() = 0; - - /** - * \brief Perform k-nearest neighbor search - * \param[in] queries The query points for which to find the nearest neighbors - * \param[out] indices The indices of the nearest neighbors found - * \param[out] dists Distances to the nearest neighbors found - * \param[in] knn Number of nearest neighbors to return - * \param[in] params Search parameters - */ - virtual void knnSearch(const Matrix& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params) - { - assert(queries.cols == veclen()); - assert(indices.rows >= queries.rows); - assert(dists.rows >= queries.rows); - assert(int(indices.cols) >= knn); - assert(int(dists.cols) >= knn); - -#if 0 - KNNResultSet resultSet(knn); - for (size_t i = 0; i < queries.rows; i++) { - resultSet.init(indices[i], dists[i]); - findNeighbors(resultSet, queries[i], params); - } -#else - KNNUniqueResultSet resultSet(knn); - for (size_t i = 0; i < queries.rows; i++) { - resultSet.clear(); - findNeighbors(resultSet, queries[i], params); - if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices[i], dists[i], knn); - else resultSet.copy(indices[i], dists[i], knn); - } -#endif - } - - /** - * \brief Perform radius search - * \param[in] query The query point - * \param[out] indices The indinces of the neighbors found within the given radius - * \param[out] dists The distances to the nearest neighbors found - * \param[in] radius The radius used for search - * \param[in] params Search parameters - * \returns Number of neighbors found - */ - virtual int radiusSearch(const Matrix& query, Matrix& indices, Matrix& dists, float radius, const SearchParams& params) - { - if (query.rows != 1) { - fprintf(stderr, "I can only search one feature at a time for range search\n"); - return -1; - } - assert(query.cols == veclen()); - assert(indices.cols == dists.cols); - - int n = 0; - int* indices_ptr = NULL; - DistanceType* dists_ptr = NULL; - if (indices.cols > 0) { - n = (int)indices.cols; - indices_ptr = indices[0]; - dists_ptr = dists[0]; - } - - RadiusUniqueResultSet resultSet((DistanceType)radius); - resultSet.clear(); - findNeighbors(resultSet, query[0], params); - if (n>0) { - if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices_ptr, dists_ptr, n); - else resultSet.copy(indices_ptr, dists_ptr, n); - } - - return (int)resultSet.size(); - } - - /** - * \brief Saves the index to a stream - * \param stream The stream to save the index to - */ - virtual void saveIndex(FILE* stream) = 0; - - /** - * \brief Loads the index from a stream - * \param stream The stream from which the index is loaded - */ - virtual void loadIndex(FILE* stream) = 0; - - /** - * \returns number of features in this index. - */ - virtual size_t size() const = 0; - - /** - * \returns The dimensionality of the features in this index. - */ - virtual size_t veclen() const = 0; - - /** - * \returns The amount of memory (in bytes) used by the index. - */ - virtual int usedMemory() const = 0; - - /** - * \returns The index type (kdtree, kmeans,...) - */ - virtual flann_algorithm_t getType() const = 0; - - /** - * \returns The index parameters - */ - virtual IndexParams getParameters() const = 0; - - - /** - * \brief Method that searches for nearest-neighbours - */ - virtual void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) = 0; -}; - -} - -#endif //OPENCV_FLANN_NNINDEX_H diff --git a/IPL/include/opencv/opencv2/flann/object_factory.h b/IPL/include/opencv/opencv2/flann/object_factory.h deleted file mode 100644 index 7f971c5..0000000 --- a/IPL/include/opencv/opencv2/flann/object_factory.h +++ /dev/null @@ -1,91 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_OBJECT_FACTORY_H_ -#define OPENCV_FLANN_OBJECT_FACTORY_H_ - -#include - -namespace cvflann -{ - -class CreatorNotFound -{ -}; - -template -class ObjectFactory -{ - typedef ObjectFactory ThisClass; - typedef std::map ObjectRegistry; - - // singleton class, private constructor - ObjectFactory() {} - -public: - - bool subscribe(UniqueIdType id, ObjectCreator creator) - { - if (object_registry.find(id) != object_registry.end()) return false; - - object_registry[id] = creator; - return true; - } - - bool unregister(UniqueIdType id) - { - return object_registry.erase(id) == 1; - } - - ObjectCreator create(UniqueIdType id) - { - typename ObjectRegistry::const_iterator iter = object_registry.find(id); - - if (iter == object_registry.end()) { - throw CreatorNotFound(); - } - - return iter->second; - } - - static ThisClass& instance() - { - static ThisClass the_factory; - return the_factory; - } -private: - ObjectRegistry object_registry; -}; - -} - -#endif /* OPENCV_FLANN_OBJECT_FACTORY_H_ */ diff --git a/IPL/include/opencv/opencv2/flann/params.h b/IPL/include/opencv/opencv2/flann/params.h deleted file mode 100644 index 95ef4cd..0000000 --- a/IPL/include/opencv/opencv2/flann/params.h +++ /dev/null @@ -1,99 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - - -#ifndef OPENCV_FLANN_PARAMS_H_ -#define OPENCV_FLANN_PARAMS_H_ - -#include "any.h" -#include "general.h" -#include -#include - - -namespace cvflann -{ - -typedef std::map IndexParams; - -struct SearchParams : public IndexParams -{ - SearchParams(int checks = 32, float eps = 0, bool sorted = true ) - { - // how many leafs to visit when searching for neighbours (-1 for unlimited) - (*this)["checks"] = checks; - // search for eps-approximate neighbours (default: 0) - (*this)["eps"] = eps; - // only for radius search, require neighbours sorted by distance (default: true) - (*this)["sorted"] = sorted; - } -}; - - -template -T get_param(const IndexParams& params, cv::String name, const T& default_value) -{ - IndexParams::const_iterator it = params.find(name); - if (it != params.end()) { - return it->second.cast(); - } - else { - return default_value; - } -} - -template -T get_param(const IndexParams& params, cv::String name) -{ - IndexParams::const_iterator it = params.find(name); - if (it != params.end()) { - return it->second.cast(); - } - else { - throw FLANNException(cv::String("Missing parameter '")+name+cv::String("' in the parameters given")); - } -} - -inline void print_params(const IndexParams& params, std::ostream& stream) -{ - IndexParams::const_iterator it; - - for(it=params.begin(); it!=params.end(); ++it) { - stream << it->first << " : " << it->second << std::endl; - } -} - -inline void print_params(const IndexParams& params) -{ - print_params(params, std::cout); -} - -} - - -#endif /* OPENCV_FLANN_PARAMS_H_ */ diff --git a/IPL/include/opencv/opencv2/flann/random.h b/IPL/include/opencv/opencv2/flann/random.h deleted file mode 100644 index a3cf5ec..0000000 --- a/IPL/include/opencv/opencv2/flann/random.h +++ /dev/null @@ -1,133 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_RANDOM_H -#define OPENCV_FLANN_RANDOM_H - -#include -#include -#include - -#include "general.h" - -namespace cvflann -{ - -/** - * Seeds the random number generator - * @param seed Random seed - */ -inline void seed_random(unsigned int seed) -{ - srand(seed); -} - -/* - * Generates a random double value. - */ -/** - * Generates a random double value. - * @param high Upper limit - * @param low Lower limit - * @return Random double value - */ -inline double rand_double(double high = 1.0, double low = 0) -{ - return low + ((high-low) * (std::rand() / (RAND_MAX + 1.0))); -} - -/** - * Generates a random integer value. - * @param high Upper limit - * @param low Lower limit - * @return Random integer value - */ -inline int rand_int(int high = RAND_MAX, int low = 0) -{ - return low + (int) ( double(high-low) * (std::rand() / (RAND_MAX + 1.0))); -} - -/** - * Random number generator that returns a distinct number from - * the [0,n) interval each time. - */ -class UniqueRandom -{ - std::vector vals_; - int size_; - int counter_; - -public: - /** - * Constructor. - * @param n Size of the interval from which to generate - * @return - */ - UniqueRandom(int n) - { - init(n); - } - - /** - * Initializes the number generator. - * @param n the size of the interval from which to generate random numbers. - */ - void init(int n) - { - // create and initialize an array of size n - vals_.resize(n); - size_ = n; - for (int i = 0; i < size_; ++i) vals_[i] = i; - - // shuffle the elements in the array - std::random_shuffle(vals_.begin(), vals_.end()); - - counter_ = 0; - } - - /** - * Return a distinct random integer in greater or equal to 0 and less - * than 'n' on each call. It should be called maximum 'n' times. - * Returns: a random integer - */ - int next() - { - if (counter_ == size_) { - return -1; - } - else { - return vals_[counter_++]; - } - } -}; - -} - -#endif //OPENCV_FLANN_RANDOM_H diff --git a/IPL/include/opencv/opencv2/flann/result_set.h b/IPL/include/opencv/opencv2/flann/result_set.h deleted file mode 100644 index 9750019..0000000 --- a/IPL/include/opencv/opencv2/flann/result_set.h +++ /dev/null @@ -1,543 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_RESULTSET_H -#define OPENCV_FLANN_RESULTSET_H - -#include -#include -#include -#include -#include -#include - -namespace cvflann -{ - -/* This record represents a branch point when finding neighbors in - the tree. It contains a record of the minimum distance to the query - point, as well as the node at which the search resumes. - */ - -template -struct BranchStruct -{ - T node; /* Tree node at which search resumes */ - DistanceType mindist; /* Minimum distance to query for all nodes below. */ - - BranchStruct() {} - BranchStruct(const T& aNode, DistanceType dist) : node(aNode), mindist(dist) {} - - bool operator<(const BranchStruct& rhs) const - { - return mindist -class ResultSet -{ -public: - virtual ~ResultSet() {} - - virtual bool full() const = 0; - - virtual void addPoint(DistanceType dist, int index) = 0; - - virtual DistanceType worstDist() const = 0; - -}; - -/** - * KNNSimpleResultSet does not ensure that the element it holds are unique. - * Is used in those cases where the nearest neighbour algorithm used does not - * attempt to insert the same element multiple times. - */ -template -class KNNSimpleResultSet : public ResultSet -{ - int* indices; - DistanceType* dists; - int capacity; - int count; - DistanceType worst_distance_; - -public: - KNNSimpleResultSet(int capacity_) : capacity(capacity_), count(0) - { - } - - void init(int* indices_, DistanceType* dists_) - { - indices = indices_; - dists = dists_; - count = 0; - worst_distance_ = (std::numeric_limits::max)(); - dists[capacity-1] = worst_distance_; - } - - size_t size() const - { - return count; - } - - bool full() const - { - return count == capacity; - } - - - void addPoint(DistanceType dist, int index) - { - if (dist >= worst_distance_) return; - int i; - for (i=count; i>0; --i) { -#ifdef FLANN_FIRST_MATCH - if ( (dists[i-1]>dist) || ((dist==dists[i-1])&&(indices[i-1]>index)) ) -#else - if (dists[i-1]>dist) -#endif - { - if (i -class KNNResultSet : public ResultSet -{ - int* indices; - DistanceType* dists; - int capacity; - int count; - DistanceType worst_distance_; - -public: - KNNResultSet(int capacity_) : capacity(capacity_), count(0) - { - } - - void init(int* indices_, DistanceType* dists_) - { - indices = indices_; - dists = dists_; - count = 0; - worst_distance_ = (std::numeric_limits::max)(); - dists[capacity-1] = worst_distance_; - } - - size_t size() const - { - return count; - } - - bool full() const - { - return count == capacity; - } - - - void addPoint(DistanceType dist, int index) - { - if (dist >= worst_distance_) return; - int i; - for (i = count; i > 0; --i) { -#ifdef FLANN_FIRST_MATCH - if ( (dists[i-1]<=dist) && ((dist!=dists[i-1])||(indices[i-1]<=index)) ) -#else - if (dists[i-1]<=dist) -#endif - { - // Check for duplicate indices - int j = i - 1; - while ((j >= 0) && (dists[j] == dist)) { - if (indices[j] == index) { - return; - } - --j; - } - break; - } - } - - if (count < capacity) ++count; - for (int j = count-1; j > i; --j) { - dists[j] = dists[j-1]; - indices[j] = indices[j-1]; - } - dists[i] = dist; - indices[i] = index; - worst_distance_ = dists[capacity-1]; - } - - DistanceType worstDist() const - { - return worst_distance_; - } -}; - - -/** - * A result-set class used when performing a radius based search. - */ -template -class RadiusResultSet : public ResultSet -{ - DistanceType radius; - int* indices; - DistanceType* dists; - size_t capacity; - size_t count; - -public: - RadiusResultSet(DistanceType radius_, int* indices_, DistanceType* dists_, int capacity_) : - radius(radius_), indices(indices_), dists(dists_), capacity(capacity_) - { - init(); - } - - ~RadiusResultSet() - { - } - - void init() - { - count = 0; - } - - size_t size() const - { - return count; - } - - bool full() const - { - return true; - } - - void addPoint(DistanceType dist, int index) - { - if (dist0)&&(count < capacity)) { - dists[count] = dist; - indices[count] = index; - } - count++; - } - } - - DistanceType worstDist() const - { - return radius; - } - -}; - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -/** Class that holds the k NN neighbors - * Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays - */ -template -class UniqueResultSet : public ResultSet -{ -public: - struct DistIndex - { - DistIndex(DistanceType dist, unsigned int index) : - dist_(dist), index_(index) - { - } - bool operator<(const DistIndex dist_index) const - { - return (dist_ < dist_index.dist_) || ((dist_ == dist_index.dist_) && index_ < dist_index.index_); - } - DistanceType dist_; - unsigned int index_; - }; - - /** Default cosntructor */ - UniqueResultSet() : - worst_distance_(std::numeric_limits::max()) - { - } - - /** Check the status of the set - * @return true if we have k NN - */ - inline bool full() const - { - return is_full_; - } - - /** Remove all elements in the set - */ - virtual void clear() = 0; - - /** Copy the set to two C arrays - * @param indices pointer to a C array of indices - * @param dist pointer to a C array of distances - * @param n_neighbors the number of neighbors to copy - */ - virtual void copy(int* indices, DistanceType* dist, int n_neighbors = -1) const - { - if (n_neighbors < 0) { - for (typename std::set::const_iterator dist_index = dist_indices_.begin(), dist_index_end = - dist_indices_.end(); dist_index != dist_index_end; ++dist_index, ++indices, ++dist) { - *indices = dist_index->index_; - *dist = dist_index->dist_; - } - } - else { - int i = 0; - for (typename std::set::const_iterator dist_index = dist_indices_.begin(), dist_index_end = - dist_indices_.end(); (dist_index != dist_index_end) && (i < n_neighbors); ++dist_index, ++indices, ++dist, ++i) { - *indices = dist_index->index_; - *dist = dist_index->dist_; - } - } - } - - /** Copy the set to two C arrays but sort it according to the distance first - * @param indices pointer to a C array of indices - * @param dist pointer to a C array of distances - * @param n_neighbors the number of neighbors to copy - */ - virtual void sortAndCopy(int* indices, DistanceType* dist, int n_neighbors = -1) const - { - copy(indices, dist, n_neighbors); - } - - /** The number of neighbors in the set - * @return - */ - size_t size() const - { - return dist_indices_.size(); - } - - /** The distance of the furthest neighbor - * If we don't have enough neighbors, it returns the max possible value - * @return - */ - inline DistanceType worstDist() const - { - return worst_distance_; - } -protected: - /** Flag to say if the set is full */ - bool is_full_; - - /** The worst distance found so far */ - DistanceType worst_distance_; - - /** The best candidates so far */ - std::set dist_indices_; -}; - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -/** Class that holds the k NN neighbors - * Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays - */ -template -class KNNUniqueResultSet : public UniqueResultSet -{ -public: - /** Constructor - * @param capacity the number of neighbors to store at max - */ - KNNUniqueResultSet(unsigned int capacity) : capacity_(capacity) - { - this->is_full_ = false; - this->clear(); - } - - /** Add a possible candidate to the best neighbors - * @param dist distance for that neighbor - * @param index index of that neighbor - */ - inline void addPoint(DistanceType dist, int index) - { - // Don't do anything if we are worse than the worst - if (dist >= worst_distance_) return; - dist_indices_.insert(DistIndex(dist, index)); - - if (is_full_) { - if (dist_indices_.size() > capacity_) { - dist_indices_.erase(*dist_indices_.rbegin()); - worst_distance_ = dist_indices_.rbegin()->dist_; - } - } - else if (dist_indices_.size() == capacity_) { - is_full_ = true; - worst_distance_ = dist_indices_.rbegin()->dist_; - } - } - - /** Remove all elements in the set - */ - void clear() - { - dist_indices_.clear(); - worst_distance_ = std::numeric_limits::max(); - is_full_ = false; - } - -protected: - typedef typename UniqueResultSet::DistIndex DistIndex; - using UniqueResultSet::is_full_; - using UniqueResultSet::worst_distance_; - using UniqueResultSet::dist_indices_; - - /** The number of neighbors to keep */ - unsigned int capacity_; -}; - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -/** Class that holds the radius nearest neighbors - * It is more accurate than RadiusResult as it is not limited in the number of neighbors - */ -template -class RadiusUniqueResultSet : public UniqueResultSet -{ -public: - /** Constructor - * @param radius the maximum distance of a neighbor - */ - RadiusUniqueResultSet(DistanceType radius) : - radius_(radius) - { - is_full_ = true; - } - - /** Add a possible candidate to the best neighbors - * @param dist distance for that neighbor - * @param index index of that neighbor - */ - void addPoint(DistanceType dist, int index) - { - if (dist <= radius_) dist_indices_.insert(DistIndex(dist, index)); - } - - /** Remove all elements in the set - */ - inline void clear() - { - dist_indices_.clear(); - } - - - /** Check the status of the set - * @return alwys false - */ - inline bool full() const - { - return true; - } - - /** The distance of the furthest neighbor - * If we don't have enough neighbors, it returns the max possible value - * @return - */ - inline DistanceType worstDist() const - { - return radius_; - } -private: - typedef typename UniqueResultSet::DistIndex DistIndex; - using UniqueResultSet::dist_indices_; - using UniqueResultSet::is_full_; - - /** The furthest distance a neighbor can be */ - DistanceType radius_; -}; - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -/** Class that holds the k NN neighbors within a radius distance - */ -template -class KNNRadiusUniqueResultSet : public KNNUniqueResultSet -{ -public: - /** Constructor - * @param capacity the number of neighbors to store at max - * @param radius the maximum distance of a neighbor - */ - KNNRadiusUniqueResultSet(unsigned int capacity, DistanceType radius) - { - this->capacity_ = capacity; - this->radius_ = radius; - this->dist_indices_.reserve(capacity_); - this->clear(); - } - - /** Remove all elements in the set - */ - void clear() - { - dist_indices_.clear(); - worst_distance_ = radius_; - is_full_ = false; - } -private: - using KNNUniqueResultSet::dist_indices_; - using KNNUniqueResultSet::is_full_; - using KNNUniqueResultSet::worst_distance_; - - /** The maximum number of neighbors to consider */ - unsigned int capacity_; - - /** The maximum distance of a neighbor */ - DistanceType radius_; -}; -} - -#endif //OPENCV_FLANN_RESULTSET_H diff --git a/IPL/include/opencv/opencv2/flann/sampling.h b/IPL/include/opencv/opencv2/flann/sampling.h deleted file mode 100644 index 396f177..0000000 --- a/IPL/include/opencv/opencv2/flann/sampling.h +++ /dev/null @@ -1,81 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - - -#ifndef OPENCV_FLANN_SAMPLING_H_ -#define OPENCV_FLANN_SAMPLING_H_ - -#include "matrix.h" -#include "random.h" - -namespace cvflann -{ - -template -Matrix random_sample(Matrix& srcMatrix, long size, bool remove = false) -{ - Matrix newSet(new T[size * srcMatrix.cols], size,srcMatrix.cols); - - T* src,* dest; - for (long i=0; i -Matrix random_sample(const Matrix& srcMatrix, size_t size) -{ - UniqueRandom rand((int)srcMatrix.rows); - Matrix newSet(new T[size * srcMatrix.cols], size,srcMatrix.cols); - - T* src,* dest; - for (size_t i=0; i -#include - -#include "general.h" -#include "nn_index.h" - -#ifdef FLANN_SIGNATURE_ -#undef FLANN_SIGNATURE_ -#endif -#define FLANN_SIGNATURE_ "FLANN_INDEX" - -namespace cvflann -{ - -template -struct Datatype {}; -template<> -struct Datatype { static flann_datatype_t type() { return FLANN_INT8; } }; -template<> -struct Datatype { static flann_datatype_t type() { return FLANN_INT16; } }; -template<> -struct Datatype { static flann_datatype_t type() { return FLANN_INT32; } }; -template<> -struct Datatype { static flann_datatype_t type() { return FLANN_UINT8; } }; -template<> -struct Datatype { static flann_datatype_t type() { return FLANN_UINT16; } }; -template<> -struct Datatype { static flann_datatype_t type() { return FLANN_UINT32; } }; -template<> -struct Datatype { static flann_datatype_t type() { return FLANN_FLOAT32; } }; -template<> -struct Datatype { static flann_datatype_t type() { return FLANN_FLOAT64; } }; - - -/** - * Structure representing the index header. - */ -struct IndexHeader -{ - char signature[16]; - char version[16]; - flann_datatype_t data_type; - flann_algorithm_t index_type; - size_t rows; - size_t cols; -}; - -/** - * Saves index header to stream - * - * @param stream - Stream to save to - * @param index - The index to save - */ -template -void save_header(FILE* stream, const NNIndex& index) -{ - IndexHeader header; - memset(header.signature, 0, sizeof(header.signature)); - strcpy(header.signature, FLANN_SIGNATURE_); - memset(header.version, 0, sizeof(header.version)); - strcpy(header.version, FLANN_VERSION_); - header.data_type = Datatype::type(); - header.index_type = index.getType(); - header.rows = index.size(); - header.cols = index.veclen(); - - std::fwrite(&header, sizeof(header),1,stream); -} - - -/** - * - * @param stream - Stream to load from - * @return Index header - */ -inline IndexHeader load_header(FILE* stream) -{ - IndexHeader header; - size_t read_size = fread(&header,sizeof(header),1,stream); - - if (read_size!=(size_t)1) { - throw FLANNException("Invalid index file, cannot read"); - } - - if (strcmp(header.signature,FLANN_SIGNATURE_)!=0) { - throw FLANNException("Invalid index file, wrong signature"); - } - - return header; - -} - - -template -void save_value(FILE* stream, const T& value, size_t count = 1) -{ - fwrite(&value, sizeof(value),count, stream); -} - -template -void save_value(FILE* stream, const cvflann::Matrix& value) -{ - fwrite(&value, sizeof(value),1, stream); - fwrite(value.data, sizeof(T),value.rows*value.cols, stream); -} - -template -void save_value(FILE* stream, const std::vector& value) -{ - size_t size = value.size(); - fwrite(&size, sizeof(size_t), 1, stream); - fwrite(&value[0], sizeof(T), size, stream); -} - -template -void load_value(FILE* stream, T& value, size_t count = 1) -{ - size_t read_cnt = fread(&value, sizeof(value), count, stream); - if (read_cnt != count) { - throw FLANNException("Cannot read from file"); - } -} - -template -void load_value(FILE* stream, cvflann::Matrix& value) -{ - size_t read_cnt = fread(&value, sizeof(value), 1, stream); - if (read_cnt != 1) { - throw FLANNException("Cannot read from file"); - } - value.data = new T[value.rows*value.cols]; - read_cnt = fread(value.data, sizeof(T), value.rows*value.cols, stream); - if (read_cnt != (size_t)(value.rows*value.cols)) { - throw FLANNException("Cannot read from file"); - } -} - - -template -void load_value(FILE* stream, std::vector& value) -{ - size_t size; - size_t read_cnt = fread(&size, sizeof(size_t), 1, stream); - if (read_cnt!=1) { - throw FLANNException("Cannot read from file"); - } - value.resize(size); - read_cnt = fread(&value[0], sizeof(T), size, stream); - if (read_cnt != size) { - throw FLANNException("Cannot read from file"); - } -} - -} - -#endif /* OPENCV_FLANN_SAVING_H_ */ diff --git a/IPL/include/opencv/opencv2/flann/simplex_downhill.h b/IPL/include/opencv/opencv2/flann/simplex_downhill.h deleted file mode 100644 index 145901a..0000000 --- a/IPL/include/opencv/opencv2/flann/simplex_downhill.h +++ /dev/null @@ -1,186 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_SIMPLEX_DOWNHILL_H_ -#define OPENCV_FLANN_SIMPLEX_DOWNHILL_H_ - -namespace cvflann -{ - -/** - Adds val to array vals (and point to array points) and keeping the arrays sorted by vals. - */ -template -void addValue(int pos, float val, float* vals, T* point, T* points, int n) -{ - vals[pos] = val; - for (int i=0; i0 && vals[j] -float optimizeSimplexDownhill(T* points, int n, F func, float* vals = NULL ) -{ - const int MAX_ITERATIONS = 10; - - assert(n>0); - - T* p_o = new T[n]; - T* p_r = new T[n]; - T* p_e = new T[n]; - - int alpha = 1; - - int iterations = 0; - - bool ownVals = false; - if (vals == NULL) { - ownVals = true; - vals = new float[n+1]; - for (int i=0; i MAX_ITERATIONS) break; - - // compute average of simplex points (except the highest point) - for (int j=0; j=vals[0])&&(val_r=vals[n]) { - for (int i=0; i -#include "opencv2/core.hpp" -#include "opencv2/core/utility.hpp" - -namespace cvflann -{ - -/** - * A start-stop timer class. - * - * Can be used to time portions of code. - */ -class StartStopTimer -{ - int64 startTime; - -public: - /** - * Value of the timer. - */ - double value; - - - /** - * Constructor. - */ - StartStopTimer() - { - reset(); - } - - /** - * Starts the timer. - */ - void start() - { - startTime = cv::getTickCount(); - } - - /** - * Stops the timer and updates timer value. - */ - void stop() - { - int64 stopTime = cv::getTickCount(); - value += ( (double)stopTime - startTime) / cv::getTickFrequency(); - } - - /** - * Resets the timer value to 0. - */ - void reset() - { - value = 0; - } - -}; - -} - -#endif // FLANN_TIMER_H diff --git a/IPL/include/opencv/opencv2/fuzzy.hpp b/IPL/include/opencv/opencv2/fuzzy.hpp deleted file mode 100644 index 8a532c0..0000000 --- a/IPL/include/opencv/opencv2/fuzzy.hpp +++ /dev/null @@ -1,66 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2015, University of Ostrava, Institute for Research and Applications of Fuzzy Modeling, -// Pavel Vlasanek, all rights reserved. Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_FUZZY_H__ -#define __OPENCV_FUZZY_H__ - -#include "opencv2/fuzzy/types.hpp" -#include "opencv2/fuzzy/fuzzy_F0_math.hpp" -#include "opencv2/fuzzy/fuzzy_image.hpp" - -/** -@defgroup fuzzy Image processing based on fuzzy mathematics - -Namespace for all functions is **ft**. The module brings implementation of the last image processing algorithms based on fuzzy mathematics. - - @{ - @defgroup f0_math Math with F0-transfrom support - -Fuzzy transform (F-transform) of the 0th degree transform whole image to a vector of its components. These components are used in latter computation. - - @defgroup f_image Fuzzy image processing - -Image proceesing based on F-transform is fast to process and easy to understand. - @} - -*/ - -#endif // __OPENCV_FUZZY_H__ diff --git a/IPL/include/opencv/opencv2/fuzzy/fuzzy_F0_math.hpp b/IPL/include/opencv/opencv2/fuzzy/fuzzy_F0_math.hpp deleted file mode 100644 index e0a2c48..0000000 --- a/IPL/include/opencv/opencv2/fuzzy/fuzzy_F0_math.hpp +++ /dev/null @@ -1,119 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2015, University of Ostrava, Institute for Research and Applications of Fuzzy Modeling, -// Pavel Vlasanek, all rights reserved. Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_FUZZY_F0_MATH_H__ -#define __OPENCV_FUZZY_F0_MATH_H__ - -#include "opencv2/fuzzy/types.hpp" -#include "opencv2/core.hpp" - -namespace cv -{ - -namespace ft -{ - //! @addtogroup f0_math - //! @{ - - /** @brief Computes components of the array using direct F0-transform. - @param matrix Input 1-channel array. - @param kernel Kernel used for processing. Function **createKernel** can be used. - @param components Output 32-bit array for the components. - @param mask Mask can be used for unwanted area marking. - - The function computes components using predefined kernel and mask. - - @note - F-transform technique is described in paper @cite Perf:FT. - */ - CV_EXPORTS void FT02D_components(InputArray matrix, InputArray kernel, OutputArray components, InputArray mask); - - /** @brief Computes components of the array using direct F0-transform. - @param matrix Input 1-channel array. - @param kernel Kernel used for processing. Function **createKernel** can be used. - @param components Output 32-bit array for the components. - - The function computes components using predefined kernel. - - @note - F-transform technique is described in paper @cite Perf:FT. - */ - CV_EXPORTS void FT02D_components(InputArray matrix, InputArray kernel, OutputArray components); - - /** @brief Computes inverse F0-transfrom. - @param components Input 32-bit array for the components. - @param kernel Kernel used for processing. Function **createKernel** can be used. - @param output Output 32-bit array. - @param width Width of the output array. - @param height Height of the output array. - - @note - F-transform technique is described in paper @cite Perf:FT. - */ - CV_EXPORTS void FT02D_inverseFT(InputArray components, InputArray kernel, OutputArray output, int width, int height); - - /** @brief Computes F0-transfrom and inverse F0-transfrom at once. - @param image Input image. - @param kernel Kernel used for processing. Function **createKernel** can be used. - @param output Output 32-bit array. - @param mask Mask used for unwanted area marking. - - This function computes F-transfrom and inverse F-transfotm in one step. It is fully sufficient and optimized for **Mat**. - */ - CV_EXPORTS void FT02D_process(const Mat &image, const Mat &kernel, Mat &output, const Mat &mask); - - /** @brief Computes F0-transfrom and inverse F0-transfrom at once and return state. - @param image Input image. - @param kernel Kernel used for processing. Function **createKernel** can be used. - @param imageOutput Output 32-bit array. - @param mask Mask used for unwanted area marking. - @param maskOutput Mask after one iteration. - @param firstStop If **true** function returns -1 when first problem appears. In case of **false**, the process is completed and summation of all problems returned. - - This function computes iteration of F-transfrom and inverse F-transfotm and handle image and mask change. The function is used in *inpaint* function. - */ - CV_EXPORTS int FT02D_iteration(const Mat &image, const Mat &kernel, Mat &imageOutput, const Mat &mask, Mat &maskOutput, bool firstStop = true); - - //! @} -} -} - -#endif // __OPENCV_FUZZY_F0_MATH_H__ diff --git a/IPL/include/opencv/opencv2/fuzzy/fuzzy_image.hpp b/IPL/include/opencv/opencv2/fuzzy/fuzzy_image.hpp deleted file mode 100644 index 00a8efa..0000000 --- a/IPL/include/opencv/opencv2/fuzzy/fuzzy_image.hpp +++ /dev/null @@ -1,109 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2015, University of Ostrava, Institute for Research and Applications of Fuzzy Modeling, -// Pavel Vlasanek, all rights reserved. Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_FUZZY_IMAGE_H__ -#define __OPENCV_FUZZY_IMAGE_H__ - -#include "types.hpp" -#include "opencv2/core.hpp" - -namespace cv -{ - -namespace ft -{ - //! @addtogroup f_image - //! @{ - - /** @brief Creates kernel from basic functions. - @param A Basic function used in axis **x**. - @param B Basic function used in axis **y**. - @param kernel Final 32-b kernel derived from **A** and **B**. - @param chn Number of kernel channels. - - The function creates kernel usable for latter fuzzy image processing. - */ - CV_EXPORTS void createKernel(cv::InputArray A, cv::InputArray B, cv::OutputArray kernel, const int chn = 1); - - /** @brief Creates kernel from general functions. - @param function Function type could be one of the following: - - **LINEAR** Linear basic function. - @param radius Radius of the basic function. - @param kernel Final 32-b kernel. - @param chn Number of kernel channels. - - The function creates kernel from predefined functions. - */ - CV_EXPORTS void createKernel(int function, int radius, cv::OutputArray kernel, const int chn = 1); - - /** @brief Image inpainting - @param image Input image. - @param mask Mask used for unwanted area marking. - @param output Output 32-bit image. - @param radius Radius of the basic function. - @param function Function type could be one of the following: - - **LINEAR** Linear basic function. - @param algorithm Algorithm could be one of the following: - - **ONE_STEP** One step algorithm. - - **MULTI_STEP** Algorithm automaticaly increasing radius of the basic function. - - **ITERATIVE** Iterative algorithm running in more steps using partial computations. - - This function provides inpainting technique based on the fuzzy mathematic. - - @note - The algorithms are described in paper @cite Perf:rec. - */ - CV_EXPORTS void inpaint(const cv::Mat &image, const cv::Mat &mask, cv::Mat &output, int radius = 2, int function = ft::LINEAR, int algorithm = ft::ONE_STEP); - - /** @brief Image filtering - @param image Input image. - @param kernel Final 32-b kernel. - @param output Output 32-bit image. - - Filtering of the input image by means of F-transform. - */ - CV_EXPORTS void filter(const cv::Mat &image, const cv::Mat &kernel, cv::Mat &output); - - //! @} -} -} - -#endif // __OPENCV_FUZZY_IMAGE_H__ diff --git a/IPL/include/opencv/opencv2/fuzzy/types.hpp b/IPL/include/opencv/opencv2/fuzzy/types.hpp deleted file mode 100644 index ec831e6..0000000 --- a/IPL/include/opencv/opencv2/fuzzy/types.hpp +++ /dev/null @@ -1,70 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2015, University of Ostrava, Institute for Research and Applications of Fuzzy Modeling, -// Pavel Vlasanek, all rights reserved. Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_FUZZY_TYPES_H__ -#define __OPENCV_FUZZY_TYPES_H__ - -namespace cv -{ - -namespace ft -{ - //! @addtogroup fuzzy - //! @{ - - enum - { - LINEAR = 1, - SINUS = 2 - }; - - enum - { - ONE_STEP = 1, - MULTI_STEP = 2, - ITERATIVE = 3 - }; - - //! @} -} -} - -#endif // __OPENCV_FUZZY_TYPES_H__ diff --git a/IPL/include/opencv/opencv2/highgui.hpp b/IPL/include/opencv/opencv2/highgui.hpp deleted file mode 100644 index 41bd8af..0000000 --- a/IPL/include/opencv/opencv2/highgui.hpp +++ /dev/null @@ -1,754 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_HIGHGUI_HPP__ -#define __OPENCV_HIGHGUI_HPP__ - -#include "opencv2/core.hpp" -#include "opencv2/imgcodecs.hpp" -#include "opencv2/videoio.hpp" - -/** -@defgroup highgui High-level GUI - -While OpenCV was designed for use in full-scale applications and can be used within functionally -rich UI frameworks (such as Qt\*, WinForms\*, or Cocoa\*) or without any UI at all, sometimes there -it is required to try functionality quickly and visualize the results. This is what the HighGUI -module has been designed for. - -It provides easy interface to: - -- Create and manipulate windows that can display images and "remember" their content (no need to - handle repaint events from OS). -- Add trackbars to the windows, handle simple mouse events as well as keyboard commands. - -@{ - @defgroup highgui_opengl OpenGL support - @defgroup highgui_qt Qt New Functions - - ![image](pics/qtgui.png) - - This figure explains new functionality implemented with Qt\* GUI. The new GUI provides a statusbar, - a toolbar, and a control panel. The control panel can have trackbars and buttonbars attached to it. - If you cannot see the control panel, press Ctrl+P or right-click any Qt window and select **Display - properties window**. - - - To attach a trackbar, the window name parameter must be NULL. - - - To attach a buttonbar, a button must be created. If the last bar attached to the control panel - is a buttonbar, the new button is added to the right of the last button. If the last bar - attached to the control panel is a trackbar, or the control panel is empty, a new buttonbar is - created. Then, a new button is attached to it. - - See below the example used to generate the figure: - @code - int main(int argc, char *argv[]) - { - - int value = 50; - int value2 = 0; - - - namedWindow("main1",WINDOW_NORMAL); - namedWindow("main2",WINDOW_AUTOSIZE | CV_GUI_NORMAL); - createTrackbar( "track1", "main1", &value, 255, NULL); - - String nameb1 = "button1"; - String nameb2 = "button2"; - - createButton(nameb1,callbackButton,&nameb1,QT_CHECKBOX,1); - createButton(nameb2,callbackButton,NULL,QT_CHECKBOX,0); - createTrackbar( "track2", NULL, &value2, 255, NULL); - createButton("button5",callbackButton1,NULL,QT_RADIOBOX,0); - createButton("button6",callbackButton2,NULL,QT_RADIOBOX,1); - - setMouseCallback( "main2",on_mouse,NULL ); - - Mat img1 = imread("files/flower.jpg"); - VideoCapture video; - video.open("files/hockey.avi"); - - Mat img2,img3; - - while( waitKey(33) != 27 ) - { - img1.convertTo(img2,-1,1,value); - video >> img3; - - imshow("main1",img2); - imshow("main2",img3); - } - - destroyAllWindows(); - - return 0; - } - @endcode - - - @defgroup highgui_winrt WinRT support - - This figure explains new functionality implemented with WinRT GUI. The new GUI provides an Image control, - and a slider panel. Slider panel holds trackbars attached to it. - - Sliders are attached below the image control. Every new slider is added below the previous one. - - See below the example used to generate the figure: - @code - void sample_app::MainPage::ShowWindow() - { - static cv::String windowName("sample"); - cv::winrt_initContainer(this->cvContainer); - cv::namedWindow(windowName); // not required - - cv::Mat image = cv::imread("Assets/sample.jpg"); - cv::Mat converted = cv::Mat(image.rows, image.cols, CV_8UC4); - cv::cvtColor(image, converted, COLOR_BGR2BGRA); - cv::imshow(windowName, converted); // this will create window if it hasn't been created before - - int state = 42; - cv::TrackbarCallback callback = [](int pos, void* userdata) - { - if (pos == 0) { - cv::destroyWindow(windowName); - } - }; - cv::TrackbarCallback callbackTwin = [](int pos, void* userdata) - { - if (pos >= 70) { - cv::destroyAllWindows(); - } - }; - cv::createTrackbar("Sample trackbar", windowName, &state, 100, callback); - cv::createTrackbar("Twin brother", windowName, &state, 100, callbackTwin); - } - @endcode - - @defgroup highgui_c C API -@} -*/ - -///////////////////////// graphical user interface ////////////////////////// -namespace cv -{ - -//! @addtogroup highgui -//! @{ - -//! Flags for cv::namedWindow -enum WindowFlags { - WINDOW_NORMAL = 0x00000000, //!< the user can resize the window (no constraint) / also use to switch a fullscreen window to a normal size. - WINDOW_AUTOSIZE = 0x00000001, //!< the user cannot resize the window, the size is constrainted by the image displayed. - WINDOW_OPENGL = 0x00001000, //!< window with opengl support. - - WINDOW_FULLSCREEN = 1, //!< change the window to fullscreen. - WINDOW_FREERATIO = 0x00000100, //!< the image expends as much as it can (no ratio constraint). - WINDOW_KEEPRATIO = 0x00000000 //!< the ratio of the image is respected. - }; - -//! Flags for cv::setWindowProperty / cv::getWindowProperty -enum WindowPropertyFlags { - WND_PROP_FULLSCREEN = 0, //!< fullscreen property (can be WINDOW_NORMAL or WINDOW_FULLSCREEN). - WND_PROP_AUTOSIZE = 1, //!< autosize property (can be WINDOW_NORMAL or WINDOW_AUTOSIZE). - WND_PROP_ASPECT_RATIO = 2, //!< window's aspect ration (can be set to WINDOW_FREERATIO or WINDOW_KEEPRATIO). - WND_PROP_OPENGL = 3 //!< opengl support. - }; - -//! Mouse Events see cv::MouseCallback -enum MouseEventTypes { - EVENT_MOUSEMOVE = 0, //!< indicates that the mouse pointer has moved over the window. - EVENT_LBUTTONDOWN = 1, //!< indicates that the left mouse button is pressed. - EVENT_RBUTTONDOWN = 2, //!< indicates that the right mouse button is pressed. - EVENT_MBUTTONDOWN = 3, //!< indicates that the middle mouse button is pressed. - EVENT_LBUTTONUP = 4, //!< indicates that left mouse button is released. - EVENT_RBUTTONUP = 5, //!< indicates that right mouse button is released. - EVENT_MBUTTONUP = 6, //!< indicates that middle mouse button is released. - EVENT_LBUTTONDBLCLK = 7, //!< indicates that left mouse button is double clicked. - EVENT_RBUTTONDBLCLK = 8, //!< indicates that right mouse button is double clicked. - EVENT_MBUTTONDBLCLK = 9, //!< indicates that middle mouse button is double clicked. - EVENT_MOUSEWHEEL = 10,//!< positive and negative values mean forward and backward scrolling, respectively. - EVENT_MOUSEHWHEEL = 11 //!< positive and negative values mean right and left scrolling, respectively. - }; - -//! Mouse Event Flags see cv::MouseCallback -enum MouseEventFlags { - EVENT_FLAG_LBUTTON = 1, //!< indicates that the left mouse button is down. - EVENT_FLAG_RBUTTON = 2, //!< indicates that the right mouse button is down. - EVENT_FLAG_MBUTTON = 4, //!< indicates that the middle mouse button is down. - EVENT_FLAG_CTRLKEY = 8, //!< indicates that CTRL Key is pressed. - EVENT_FLAG_SHIFTKEY = 16,//!< indicates that SHIFT Key is pressed. - EVENT_FLAG_ALTKEY = 32 //!< indicates that ALT Key is pressed. - }; - -//! Qt font weight -enum QtFontWeights { - QT_FONT_LIGHT = 25, //!< Weight of 25 - QT_FONT_NORMAL = 50, //!< Weight of 50 - QT_FONT_DEMIBOLD = 63, //!< Weight of 63 - QT_FONT_BOLD = 75, //!< Weight of 75 - QT_FONT_BLACK = 87 //!< Weight of 87 - }; - -//! Qt font style -enum QtFontStyles { - QT_STYLE_NORMAL = 0, //!< Normal font. - QT_STYLE_ITALIC = 1, //!< Italic font. - QT_STYLE_OBLIQUE = 2 //!< Oblique font. - }; - -//! Qt "button" type -enum QtButtonTypes { - QT_PUSH_BUTTON = 0, //!< Push button. - QT_CHECKBOX = 1, //!< Checkbox button. - QT_RADIOBOX = 2 //!< Radiobox button. - }; - -/** @brief Callback function for mouse events. see cv::setMouseCallback -@param event one of the cv::MouseEventTypes constants. -@param x The x-coordinate of the mouse event. -@param y The y-coordinate of the mouse event. -@param flags one of the cv::MouseEventFlags constants. -@param userdata The optional parameter. - */ -typedef void (*MouseCallback)(int event, int x, int y, int flags, void* userdata); - -/** @brief Callback function for Trackbar see cv::createTrackbar -@param pos current position of the specified trackbar. -@param userdata The optional parameter. - */ -typedef void (*TrackbarCallback)(int pos, void* userdata); - -/** @brief Callback function defined to be called every frame. See cv::setOpenGlDrawCallback -@param userdata The optional parameter. - */ -typedef void (*OpenGlDrawCallback)(void* userdata); - -/** @brief Callback function for a button created by cv::createButton -@param state current state of the button. It could be -1 for a push button, 0 or 1 for a check/radio box button. -@param userdata The optional parameter. - */ -typedef void (*ButtonCallback)(int state, void* userdata); - -/** @brief Creates a window. - -The function namedWindow creates a window that can be used as a placeholder for images and -trackbars. Created windows are referred to by their names. - -If a window with the same name already exists, the function does nothing. - -You can call cv::destroyWindow or cv::destroyAllWindows to close the window and de-allocate any associated -memory usage. For a simple program, you do not really have to call these functions because all the -resources and windows of the application are closed automatically by the operating system upon exit. - -@note - -Qt backend supports additional flags: - - **WINDOW_NORMAL or WINDOW_AUTOSIZE:** WINDOW_NORMAL enables you to resize the - window, whereas WINDOW_AUTOSIZE adjusts automatically the window size to fit the - displayed image (see imshow ), and you cannot change the window size manually. - - **WINDOW_FREERATIO or WINDOW_KEEPRATIO:** WINDOW_FREERATIO adjusts the image - with no respect to its ratio, whereas WINDOW_KEEPRATIO keeps the image ratio. - - **CV_GUI_NORMAL or CV_GUI_EXPANDED:** CV_GUI_NORMAL is the old way to draw the window - without statusbar and toolbar, whereas CV_GUI_EXPANDED is a new enhanced GUI. -By default, flags == WINDOW_AUTOSIZE | WINDOW_KEEPRATIO | CV_GUI_EXPANDED - -@param winname Name of the window in the window caption that may be used as a window identifier. -@param flags Flags of the window. The supported flags are: (cv::WindowFlags) - */ -CV_EXPORTS_W void namedWindow(const String& winname, int flags = WINDOW_AUTOSIZE); - -/** @brief Destroys the specified window. - -The function destroyWindow destroys the window with the given name. - -@param winname Name of the window to be destroyed. - */ -CV_EXPORTS_W void destroyWindow(const String& winname); - -/** @brief Destroys all of the HighGUI windows. - -The function destroyAllWindows destroys all of the opened HighGUI windows. - */ -CV_EXPORTS_W void destroyAllWindows(); - -CV_EXPORTS_W int startWindowThread(); - -/** @brief Waits for a pressed key. - -The function waitKey waits for a key event infinitely (when \f$\texttt{delay}\leq 0\f$ ) or for delay -milliseconds, when it is positive. Since the OS has a minimum time between switching threads, the -function will not wait exactly delay ms, it will wait at least delay ms, depending on what else is -running on your computer at that time. It returns the code of the pressed key or -1 if no key was -pressed before the specified time had elapsed. - -@note - -This function is the only method in HighGUI that can fetch and handle events, so it needs to be -called periodically for normal event processing unless HighGUI is used within an environment that -takes care of event processing. - -@note - -The function only works if there is at least one HighGUI window created and the window is active. -If there are several HighGUI windows, any of them can be active. - -@param delay Delay in milliseconds. 0 is the special value that means "forever". - */ -CV_EXPORTS_W int waitKey(int delay = 0); - -/** @brief Displays an image in the specified window. - -The function imshow displays an image in the specified window. If the window was created with the -cv::WINDOW_AUTOSIZE flag, the image is shown with its original size, however it is still limited by the screen resolution. -Otherwise, the image is scaled to fit the window. The function may scale the image, depending on its depth: - -- If the image is 8-bit unsigned, it is displayed as is. -- If the image is 16-bit unsigned or 32-bit integer, the pixels are divided by 256. That is, the - value range [0,255\*256] is mapped to [0,255]. -- If the image is 32-bit floating-point, the pixel values are multiplied by 255. That is, the - value range [0,1] is mapped to [0,255]. - -If window was created with OpenGL support, cv::imshow also support ogl::Buffer , ogl::Texture2D and -cuda::GpuMat as input. - -If the window was not created before this function, it is assumed creating a window with cv::WINDOW_AUTOSIZE. - -If you need to show an image that is bigger than the screen resolution, you will need to call namedWindow("", WINDOW_NORMAL) before the imshow. - -@note This function should be followed by cv::waitKey function which displays the image for specified -milliseconds. Otherwise, it won't display the image. For example, **waitKey(0)** will display the window -infinitely until any keypress (it is suitable for image display). **waitKey(25)** will display a frame -for 25 ms, after which display will be automatically closed. (If you put it in a loop to read -videos, it will display the video frame-by-frame) - -@note - -[__Windows Backend Only__] Pressing Ctrl+C will copy the image to the clipboard. - -[__Windows Backend Only__] Pressing Ctrl+S will show a dialog to save the image. - -@param winname Name of the window. -@param mat Image to be shown. - */ -CV_EXPORTS_W void imshow(const String& winname, InputArray mat); - -/** @brief Resizes window to the specified size - -@note - -- The specified window size is for the image area. Toolbars are not counted. -- Only windows created without cv::WINDOW_AUTOSIZE flag can be resized. - -@param winname Window name. -@param width The new window width. -@param height The new window height. - */ -CV_EXPORTS_W void resizeWindow(const String& winname, int width, int height); - -/** @brief Moves window to the specified position - -@param winname Name of the window. -@param x The new x-coordinate of the window. -@param y The new y-coordinate of the window. - */ -CV_EXPORTS_W void moveWindow(const String& winname, int x, int y); - -/** @brief Changes parameters of a window dynamically. - -The function setWindowProperty enables changing properties of a window. - -@param winname Name of the window. -@param prop_id Window property to edit. The supported operation flags are: (cv::WindowPropertyFlags) -@param prop_value New value of the window property. The supported flags are: (cv::WindowFlags) - */ -CV_EXPORTS_W void setWindowProperty(const String& winname, int prop_id, double prop_value); - -/** @brief Updates window title -@param winname Name of the window. -@param title New title. -*/ -CV_EXPORTS_W void setWindowTitle(const String& winname, const String& title); - -/** @brief Provides parameters of a window. - -The function getWindowProperty returns properties of a window. - -@param winname Name of the window. -@param prop_id Window property to retrieve. The following operation flags are available: (cv::WindowPropertyFlags) - -@sa setWindowProperty - */ -CV_EXPORTS_W double getWindowProperty(const String& winname, int prop_id); - -/** @brief Sets mouse handler for the specified window - -@param winname Name of the window. -@param onMouse Mouse callback. See OpenCV samples, such as -, on how to specify and -use the callback. -@param userdata The optional parameter passed to the callback. - */ -CV_EXPORTS void setMouseCallback(const String& winname, MouseCallback onMouse, void* userdata = 0); - -/** @brief Gets the mouse-wheel motion delta, when handling mouse-wheel events cv::EVENT_MOUSEWHEEL and -cv::EVENT_MOUSEHWHEEL. - -For regular mice with a scroll-wheel, delta will be a multiple of 120. The value 120 corresponds to -a one notch rotation of the wheel or the threshold for action to be taken and one such action should -occur for each delta. Some high-precision mice with higher-resolution freely-rotating wheels may -generate smaller values. - -For cv::EVENT_MOUSEWHEEL positive and negative values mean forward and backward scrolling, -respectively. For cv::EVENT_MOUSEHWHEEL, where available, positive and negative values mean right and -left scrolling, respectively. - -With the C API, the macro CV_GET_WHEEL_DELTA(flags) can be used alternatively. - -@note - -Mouse-wheel events are currently supported only on Windows. - -@param flags The mouse callback flags parameter. - */ -CV_EXPORTS int getMouseWheelDelta(int flags); - -/** @brief Creates a trackbar and attaches it to the specified window. - -The function createTrackbar creates a trackbar (a slider or range control) with the specified name -and range, assigns a variable value to be a position synchronized with the trackbar and specifies -the callback function onChange to be called on the trackbar position change. The created trackbar is -displayed in the specified window winname. - -@note - -[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar should be attached to the -control panel. - -Clicking the label of each trackbar enables editing the trackbar values manually. - -@param trackbarname Name of the created trackbar. -@param winname Name of the window that will be used as a parent of the created trackbar. -@param value Optional pointer to an integer variable whose value reflects the position of the -slider. Upon creation, the slider position is defined by this variable. -@param count Maximal position of the slider. The minimal position is always 0. -@param onChange Pointer to the function to be called every time the slider changes position. This -function should be prototyped as void Foo(int,void\*); , where the first parameter is the trackbar -position and the second parameter is the user data (see the next parameter). If the callback is -the NULL pointer, no callbacks are called, but only value is updated. -@param userdata User data that is passed as is to the callback. It can be used to handle trackbar -events without using global variables. - */ -CV_EXPORTS int createTrackbar(const String& trackbarname, const String& winname, - int* value, int count, - TrackbarCallback onChange = 0, - void* userdata = 0); - -/** @brief Returns the trackbar position. - -The function returns the current position of the specified trackbar. - -@note - -[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control -panel. - -@param trackbarname Name of the trackbar. -@param winname Name of the window that is the parent of the trackbar. - */ -CV_EXPORTS_W int getTrackbarPos(const String& trackbarname, const String& winname); - -/** @brief Sets the trackbar position. - -The function sets the position of the specified trackbar in the specified window. - -@note - -[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control -panel. - -@param trackbarname Name of the trackbar. -@param winname Name of the window that is the parent of trackbar. -@param pos New position. - */ -CV_EXPORTS_W void setTrackbarPos(const String& trackbarname, const String& winname, int pos); - -/** @brief Sets the trackbar maximum position. - -The function sets the maximum position of the specified trackbar in the specified window. - -@note - -[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control -panel. - -@param trackbarname Name of the trackbar. -@param winname Name of the window that is the parent of trackbar. -@param maxval New maximum position. - */ -CV_EXPORTS_W void setTrackbarMax(const String& trackbarname, const String& winname, int maxval); - -/** @brief Sets the trackbar minimum position. - -The function sets the minimum position of the specified trackbar in the specified window. - -@note - -[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control -panel. - -@param trackbarname Name of the trackbar. -@param winname Name of the window that is the parent of trackbar. -@param minval New maximum position. - */ -CV_EXPORTS_W void setTrackbarMin(const String& trackbarname, const String& winname, int minval); - -//! @addtogroup highgui_opengl OpenGL support -//! @{ - -/** @brief Displays OpenGL 2D texture in the specified window. - -@param winname Name of the window. -@param tex OpenGL 2D texture data. - */ -CV_EXPORTS void imshow(const String& winname, const ogl::Texture2D& tex); - -/** @brief Sets a callback function to be called to draw on top of displayed image. - -The function setOpenGlDrawCallback can be used to draw 3D data on the window. See the example of -callback function below: -@code - void on_opengl(void* param) - { - glLoadIdentity(); - - glTranslated(0.0, 0.0, -1.0); - - glRotatef( 55, 1, 0, 0 ); - glRotatef( 45, 0, 1, 0 ); - glRotatef( 0, 0, 0, 1 ); - - static const int coords[6][4][3] = { - { { +1, -1, -1 }, { -1, -1, -1 }, { -1, +1, -1 }, { +1, +1, -1 } }, - { { +1, +1, -1 }, { -1, +1, -1 }, { -1, +1, +1 }, { +1, +1, +1 } }, - { { +1, -1, +1 }, { +1, -1, -1 }, { +1, +1, -1 }, { +1, +1, +1 } }, - { { -1, -1, -1 }, { -1, -1, +1 }, { -1, +1, +1 }, { -1, +1, -1 } }, - { { +1, -1, +1 }, { -1, -1, +1 }, { -1, -1, -1 }, { +1, -1, -1 } }, - { { -1, -1, +1 }, { +1, -1, +1 }, { +1, +1, +1 }, { -1, +1, +1 } } - }; - - for (int i = 0; i < 6; ++i) { - glColor3ub( i*20, 100+i*10, i*42 ); - glBegin(GL_QUADS); - for (int j = 0; j < 4; ++j) { - glVertex3d(0.2 * coords[i][j][0], 0.2 * coords[i][j][1], 0.2 * coords[i][j][2]); - } - glEnd(); - } - } -@endcode - -@param winname Name of the window. -@param onOpenGlDraw Pointer to the function to be called every frame. This function should be -prototyped as void Foo(void\*) . -@param userdata Pointer passed to the callback function.(__Optional__) - */ -CV_EXPORTS void setOpenGlDrawCallback(const String& winname, OpenGlDrawCallback onOpenGlDraw, void* userdata = 0); - -/** @brief Sets the specified window as current OpenGL context. - -@param winname Name of the window. - */ -CV_EXPORTS void setOpenGlContext(const String& winname); - -/** @brief Force window to redraw its context and call draw callback ( See cv::setOpenGlDrawCallback ). - -@param winname Name of the window. - */ -CV_EXPORTS void updateWindow(const String& winname); - -//! @} highgui_opengl - -//! @addtogroup highgui_qt -//! @{ - -/** @brief QtFont available only for Qt. See cv::fontQt - */ -struct QtFont -{ - const char* nameFont; //!< Name of the font - Scalar color; //!< Color of the font. Scalar(blue_component, green_component, red_component[, alpha_component]) - int font_face; //!< See cv::QtFontStyles - const int* ascii; //!< font data and metrics - const int* greek; - const int* cyrillic; - float hscale, vscale; - float shear; //!< slope coefficient: 0 - normal, >0 - italic - int thickness; //!< See cv::QtFontWeights - float dx; //!< horizontal interval between letters - int line_type; //!< PointSize -}; - -/** @brief Creates the font to draw a text on an image. - -The function fontQt creates a cv::QtFont object. This cv::QtFont is not compatible with putText . - -A basic usage of this function is the following: : -@code - QtFont font = fontQt("Times"); - addText( img1, "Hello World !", Point(50,50), font); -@endcode - -@param nameFont Name of the font. The name should match the name of a system font (such as -*Times*). If the font is not found, a default one is used. -@param pointSize Size of the font. If not specified, equal zero or negative, the point size of the -font is set to a system-dependent default value. Generally, this is 12 points. -@param color Color of the font in BGRA where A = 255 is fully transparent. Use the macro CV_RGB -for simplicity. -@param weight Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control. -@param style Font style. Available operation flags are : cv::QtFontStyles -@param spacing Spacing between characters. It can be negative or positive. - */ -CV_EXPORTS QtFont fontQt(const String& nameFont, int pointSize = -1, - Scalar color = Scalar::all(0), int weight = QT_FONT_NORMAL, - int style = QT_STYLE_NORMAL, int spacing = 0); - -/** @brief Draws a text on the image. - -The function addText draws *text* on the image *img* using a specific font *font* (see example cv::fontQt -) - -@param img 8-bit 3-channel image where the text should be drawn. -@param text Text to write on an image. -@param org Point(x,y) where the text should start on an image. -@param font Font to use to draw a text. - */ -CV_EXPORTS void addText( const Mat& img, const String& text, Point org, const QtFont& font); - -/** @brief Displays a text on a window image as an overlay for a specified duration. - -The function displayOverlay displays useful information/tips on top of the window for a certain -amount of time *delayms*. The function does not modify the image, displayed in the window, that is, -after the specified delay the original content of the window is restored. - -@param winname Name of the window. -@param text Overlay text to write on a window image. -@param delayms The period (in milliseconds), during which the overlay text is displayed. If this -function is called before the previous overlay text timed out, the timer is restarted and the text -is updated. If this value is zero, the text never disappears. - */ -CV_EXPORTS void displayOverlay(const String& winname, const String& text, int delayms = 0); - -/** @brief Displays a text on the window statusbar during the specified period of time. - -The function displayStatusBar displays useful information/tips on top of the window for a certain -amount of time *delayms* . This information is displayed on the window statusbar (the window must be -created with the CV_GUI_EXPANDED flags). - -@param winname Name of the window. -@param text Text to write on the window statusbar. -@param delayms Duration (in milliseconds) to display the text. If this function is called before -the previous text timed out, the timer is restarted and the text is updated. If this value is -zero, the text never disappears. - */ -CV_EXPORTS void displayStatusBar(const String& winname, const String& text, int delayms = 0); - -/** @brief Saves parameters of the specified window. - -The function saveWindowParameters saves size, location, flags, trackbars value, zoom and panning -location of the window windowName. - -@param windowName Name of the window. - */ -CV_EXPORTS void saveWindowParameters(const String& windowName); - -/** @brief Loads parameters of the specified window. - -The function loadWindowParameters loads size, location, flags, trackbars value, zoom and panning -location of the window windowName. - -@param windowName Name of the window. - */ -CV_EXPORTS void loadWindowParameters(const String& windowName); - -CV_EXPORTS int startLoop(int (*pt2Func)(int argc, char *argv[]), int argc, char* argv[]); - -CV_EXPORTS void stopLoop(); - -/** @brief Attaches a button to the control panel. - -The function createButton attaches a button to the control panel. Each button is added to a -buttonbar to the right of the last button. A new buttonbar is created if nothing was attached to the -control panel before, or if the last element attached to the control panel was a trackbar. - -See below various examples of the cv::createButton function call: : -@code - createButton(NULL,callbackButton);//create a push button "button 0", that will call callbackButton. - createButton("button2",callbackButton,NULL,QT_CHECKBOX,0); - createButton("button3",callbackButton,&value); - createButton("button5",callbackButton1,NULL,QT_RADIOBOX); - createButton("button6",callbackButton2,NULL,QT_PUSH_BUTTON,1); -@endcode - -@param bar_name Name of the button. -@param on_change Pointer to the function to be called every time the button changes its state. -This function should be prototyped as void Foo(int state,\*void); . *state* is the current state -of the button. It could be -1 for a push button, 0 or 1 for a check/radio box button. -@param userdata Pointer passed to the callback function. -@param type Optional type of the button. Available types are: (cv::QtButtonTypes) -@param initial_button_state Default state of the button. Use for checkbox and radiobox. Its -value could be 0 or 1. (__Optional__) -*/ -CV_EXPORTS int createButton( const String& bar_name, ButtonCallback on_change, - void* userdata = 0, int type = QT_PUSH_BUTTON, - bool initial_button_state = false); - -//! @} highgui_qt - -//! @} highgui - -} // cv - -#ifndef DISABLE_OPENCV_24_COMPATIBILITY -#include "opencv2/highgui/highgui_c.h" -#endif - -#endif diff --git a/IPL/include/opencv/opencv2/highgui/highgui.hpp b/IPL/include/opencv/opencv2/highgui/highgui.hpp deleted file mode 100644 index 160c9cf..0000000 --- a/IPL/include/opencv/opencv2/highgui/highgui.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/highgui.hpp" diff --git a/IPL/include/opencv/opencv2/highgui/highgui_c.h b/IPL/include/opencv/opencv2/highgui/highgui_c.h deleted file mode 100644 index 47fdb84..0000000 --- a/IPL/include/opencv/opencv2/highgui/highgui_c.h +++ /dev/null @@ -1,252 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// Intel License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000, Intel Corporation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of Intel Corporation may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_HIGHGUI_H__ -#define __OPENCV_HIGHGUI_H__ - -#include "opencv2/core/core_c.h" -#include "opencv2/imgproc/imgproc_c.h" -#include "opencv2/imgcodecs/imgcodecs_c.h" -#include "opencv2/videoio/videoio_c.h" - -#ifdef __cplusplus -extern "C" { -#endif /* __cplusplus */ - -/** @addtogroup highgui_c - @{ - */ - -/****************************************************************************************\ -* Basic GUI functions * -\****************************************************************************************/ -//YV -//-----------New for Qt -/* For font */ -enum { CV_FONT_LIGHT = 25,//QFont::Light, - CV_FONT_NORMAL = 50,//QFont::Normal, - CV_FONT_DEMIBOLD = 63,//QFont::DemiBold, - CV_FONT_BOLD = 75,//QFont::Bold, - CV_FONT_BLACK = 87 //QFont::Black -}; - -enum { CV_STYLE_NORMAL = 0,//QFont::StyleNormal, - CV_STYLE_ITALIC = 1,//QFont::StyleItalic, - CV_STYLE_OBLIQUE = 2 //QFont::StyleOblique -}; -/* ---------*/ - -//for color cvScalar(blue_component, green_component, red_component[, alpha_component]) -//and alpha= 0 <-> 0xFF (not transparent <-> transparent) -CVAPI(CvFont) cvFontQt(const char* nameFont, int pointSize CV_DEFAULT(-1), CvScalar color CV_DEFAULT(cvScalarAll(0)), int weight CV_DEFAULT(CV_FONT_NORMAL), int style CV_DEFAULT(CV_STYLE_NORMAL), int spacing CV_DEFAULT(0)); - -CVAPI(void) cvAddText(const CvArr* img, const char* text, CvPoint org, CvFont *arg2); - -CVAPI(void) cvDisplayOverlay(const char* name, const char* text, int delayms CV_DEFAULT(0)); -CVAPI(void) cvDisplayStatusBar(const char* name, const char* text, int delayms CV_DEFAULT(0)); - -CVAPI(void) cvSaveWindowParameters(const char* name); -CVAPI(void) cvLoadWindowParameters(const char* name); -CVAPI(int) cvStartLoop(int (*pt2Func)(int argc, char *argv[]), int argc, char* argv[]); -CVAPI(void) cvStopLoop( void ); - -typedef void (CV_CDECL *CvButtonCallback)(int state, void* userdata); -enum {CV_PUSH_BUTTON = 0, CV_CHECKBOX = 1, CV_RADIOBOX = 2}; -CVAPI(int) cvCreateButton( const char* button_name CV_DEFAULT(NULL),CvButtonCallback on_change CV_DEFAULT(NULL), void* userdata CV_DEFAULT(NULL) , int button_type CV_DEFAULT(CV_PUSH_BUTTON), int initial_button_state CV_DEFAULT(0)); -//---------------------- - - -/* this function is used to set some external parameters in case of X Window */ -CVAPI(int) cvInitSystem( int argc, char** argv ); - -CVAPI(int) cvStartWindowThread( void ); - -// --------- YV --------- -enum -{ - //These 3 flags are used by cvSet/GetWindowProperty - CV_WND_PROP_FULLSCREEN = 0, //to change/get window's fullscreen property - CV_WND_PROP_AUTOSIZE = 1, //to change/get window's autosize property - CV_WND_PROP_ASPECTRATIO= 2, //to change/get window's aspectratio property - CV_WND_PROP_OPENGL = 3, //to change/get window's opengl support - - //These 2 flags are used by cvNamedWindow and cvSet/GetWindowProperty - CV_WINDOW_NORMAL = 0x00000000, //the user can resize the window (no constraint) / also use to switch a fullscreen window to a normal size - CV_WINDOW_AUTOSIZE = 0x00000001, //the user cannot resize the window, the size is constrainted by the image displayed - CV_WINDOW_OPENGL = 0x00001000, //window with opengl support - - //Those flags are only for Qt - CV_GUI_EXPANDED = 0x00000000, //status bar and tool bar - CV_GUI_NORMAL = 0x00000010, //old fashious way - - //These 3 flags are used by cvNamedWindow and cvSet/GetWindowProperty - CV_WINDOW_FULLSCREEN = 1,//change the window to fullscreen - CV_WINDOW_FREERATIO = 0x00000100,//the image expends as much as it can (no ratio constraint) - CV_WINDOW_KEEPRATIO = 0x00000000//the ration image is respected. -}; - -/* create window */ -CVAPI(int) cvNamedWindow( const char* name, int flags CV_DEFAULT(CV_WINDOW_AUTOSIZE) ); - -/* Set and Get Property of the window */ -CVAPI(void) cvSetWindowProperty(const char* name, int prop_id, double prop_value); -CVAPI(double) cvGetWindowProperty(const char* name, int prop_id); - -/* display image within window (highgui windows remember their content) */ -CVAPI(void) cvShowImage( const char* name, const CvArr* image ); - -/* resize/move window */ -CVAPI(void) cvResizeWindow( const char* name, int width, int height ); -CVAPI(void) cvMoveWindow( const char* name, int x, int y ); - - -/* destroy window and all the trackers associated with it */ -CVAPI(void) cvDestroyWindow( const char* name ); - -CVAPI(void) cvDestroyAllWindows(void); - -/* get native window handle (HWND in case of Win32 and Widget in case of X Window) */ -CVAPI(void*) cvGetWindowHandle( const char* name ); - -/* get name of highgui window given its native handle */ -CVAPI(const char*) cvGetWindowName( void* window_handle ); - - -typedef void (CV_CDECL *CvTrackbarCallback)(int pos); - -/* create trackbar and display it on top of given window, set callback */ -CVAPI(int) cvCreateTrackbar( const char* trackbar_name, const char* window_name, - int* value, int count, CvTrackbarCallback on_change CV_DEFAULT(NULL)); - -typedef void (CV_CDECL *CvTrackbarCallback2)(int pos, void* userdata); - -CVAPI(int) cvCreateTrackbar2( const char* trackbar_name, const char* window_name, - int* value, int count, CvTrackbarCallback2 on_change, - void* userdata CV_DEFAULT(0)); - -/* retrieve or set trackbar position */ -CVAPI(int) cvGetTrackbarPos( const char* trackbar_name, const char* window_name ); -CVAPI(void) cvSetTrackbarPos( const char* trackbar_name, const char* window_name, int pos ); -CVAPI(void) cvSetTrackbarMax(const char* trackbar_name, const char* window_name, int maxval); -CVAPI(void) cvSetTrackbarMin(const char* trackbar_name, const char* window_name, int minval); - -enum -{ - CV_EVENT_MOUSEMOVE =0, - CV_EVENT_LBUTTONDOWN =1, - CV_EVENT_RBUTTONDOWN =2, - CV_EVENT_MBUTTONDOWN =3, - CV_EVENT_LBUTTONUP =4, - CV_EVENT_RBUTTONUP =5, - CV_EVENT_MBUTTONUP =6, - CV_EVENT_LBUTTONDBLCLK =7, - CV_EVENT_RBUTTONDBLCLK =8, - CV_EVENT_MBUTTONDBLCLK =9, - CV_EVENT_MOUSEWHEEL =10, - CV_EVENT_MOUSEHWHEEL =11 -}; - -enum -{ - CV_EVENT_FLAG_LBUTTON =1, - CV_EVENT_FLAG_RBUTTON =2, - CV_EVENT_FLAG_MBUTTON =4, - CV_EVENT_FLAG_CTRLKEY =8, - CV_EVENT_FLAG_SHIFTKEY =16, - CV_EVENT_FLAG_ALTKEY =32 -}; - - -#define CV_GET_WHEEL_DELTA(flags) ((short)((flags >> 16) & 0xffff)) // upper 16 bits - -typedef void (CV_CDECL *CvMouseCallback )(int event, int x, int y, int flags, void* param); - -/* assign callback for mouse events */ -CVAPI(void) cvSetMouseCallback( const char* window_name, CvMouseCallback on_mouse, - void* param CV_DEFAULT(NULL)); - -/* wait for key event infinitely (delay<=0) or for "delay" milliseconds */ -CVAPI(int) cvWaitKey(int delay CV_DEFAULT(0)); - -// OpenGL support - -typedef void (CV_CDECL *CvOpenGlDrawCallback)(void* userdata); -CVAPI(void) cvSetOpenGlDrawCallback(const char* window_name, CvOpenGlDrawCallback callback, void* userdata CV_DEFAULT(NULL)); - -CVAPI(void) cvSetOpenGlContext(const char* window_name); -CVAPI(void) cvUpdateWindow(const char* window_name); - - -/****************************************************************************************\ - -* Obsolete functions/synonyms * -\****************************************************************************************/ - -#define cvAddSearchPath(path) -#define cvvInitSystem cvInitSystem -#define cvvNamedWindow cvNamedWindow -#define cvvShowImage cvShowImage -#define cvvResizeWindow cvResizeWindow -#define cvvDestroyWindow cvDestroyWindow -#define cvvCreateTrackbar cvCreateTrackbar -#define cvvAddSearchPath cvAddSearchPath -#define cvvWaitKey(name) cvWaitKey(0) -#define cvvWaitKeyEx(name,delay) cvWaitKey(delay) -#define HG_AUTOSIZE CV_WINDOW_AUTOSIZE -#define set_preprocess_func cvSetPreprocessFuncWin32 -#define set_postprocess_func cvSetPostprocessFuncWin32 - -#if defined WIN32 || defined _WIN32 - -CVAPI(void) cvSetPreprocessFuncWin32_(const void* callback); -CVAPI(void) cvSetPostprocessFuncWin32_(const void* callback); -#define cvSetPreprocessFuncWin32(callback) cvSetPreprocessFuncWin32_((const void*)(callback)) -#define cvSetPostprocessFuncWin32(callback) cvSetPostprocessFuncWin32_((const void*)(callback)) - -#endif - -/** @} highgui_c */ - -#ifdef __cplusplus -} -#endif - -#endif diff --git a/IPL/include/opencv/opencv2/imgcodecs.hpp b/IPL/include/opencv/opencv2/imgcodecs.hpp deleted file mode 100644 index ac0fd24..0000000 --- a/IPL/include/opencv/opencv2/imgcodecs.hpp +++ /dev/null @@ -1,267 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_IMGCODECS_HPP__ -#define __OPENCV_IMGCODECS_HPP__ - -#include "opencv2/core.hpp" - -/** - @defgroup imgcodecs Image file reading and writing - @{ - @defgroup imgcodecs_c C API - @defgroup imgcodecs_ios iOS glue - @} -*/ - -//////////////////////////////// image codec //////////////////////////////// -namespace cv -{ - -//! @addtogroup imgcodecs -//! @{ - -//! Imread flags -enum ImreadModes { - IMREAD_UNCHANGED = -1, //!< If set, return the loaded image as is (with alpha channel, otherwise it gets cropped). - IMREAD_GRAYSCALE = 0, //!< If set, always convert image to the single channel grayscale image. - IMREAD_COLOR = 1, //!< If set, always convert image to the 3 channel BGR color image. - IMREAD_ANYDEPTH = 2, //!< If set, return 16-bit/32-bit image when the input has the corresponding depth, otherwise convert it to 8-bit. - IMREAD_ANYCOLOR = 4, //!< If set, the image is read in any possible color format. - IMREAD_LOAD_GDAL = 8, //!< If set, use the gdal driver for loading the image. - IMREAD_REDUCED_GRAYSCALE_2 = 16, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/2. - IMREAD_REDUCED_COLOR_2 = 17, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/2. - IMREAD_REDUCED_GRAYSCALE_4 = 32, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/4. - IMREAD_REDUCED_COLOR_4 = 33, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/4. - IMREAD_REDUCED_GRAYSCALE_8 = 64, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/8. - IMREAD_REDUCED_COLOR_8 = 65 //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/8. - }; - -//! Imwrite flags -enum ImwriteFlags { - IMWRITE_JPEG_QUALITY = 1, //!< For JPEG, it can be a quality from 0 to 100 (the higher is the better). Default value is 95. - IMWRITE_JPEG_PROGRESSIVE = 2, //!< Enable JPEG features, 0 or 1, default is False. - IMWRITE_JPEG_OPTIMIZE = 3, //!< Enable JPEG features, 0 or 1, default is False. - IMWRITE_JPEG_RST_INTERVAL = 4, //!< JPEG restart interval, 0 - 65535, default is 0 - no restart. - IMWRITE_JPEG_LUMA_QUALITY = 5, //!< Separate luma quality level, 0 - 100, default is 0 - don't use. - IMWRITE_JPEG_CHROMA_QUALITY = 6, //!< Separate chroma quality level, 0 - 100, default is 0 - don't use. - IMWRITE_PNG_COMPRESSION = 16, //!< For PNG, it can be the compression level from 0 to 9. A higher value means a smaller size and longer compression time. Default value is 3. - IMWRITE_PNG_STRATEGY = 17, //!< One of cv::ImwritePNGFlags, default is IMWRITE_PNG_STRATEGY_DEFAULT. - IMWRITE_PNG_BILEVEL = 18, //!< Binary level PNG, 0 or 1, default is 0. - IMWRITE_PXM_BINARY = 32, //!< For PPM, PGM, or PBM, it can be a binary format flag, 0 or 1. Default value is 1. - IMWRITE_WEBP_QUALITY = 64 //!< For WEBP, it can be a quality from 1 to 100 (the higher is the better). By default (without any parameter) and for quality above 100 the lossless compression is used. - }; - -//! Imwrite PNG specific flags used to tune the compression algorithm. -/** These flags will be modify the way of PNG image compression and will be passed to the underlying zlib processing stage. - -- The effect of IMWRITE_PNG_STRATEGY_FILTERED is to force more Huffman coding and less string matching; it is somewhat intermediate between IMWRITE_PNG_STRATEGY_DEFAULT and IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY. -- IMWRITE_PNG_STRATEGY_RLE is designed to be almost as fast as IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY, but give better compression for PNG image data. -- The strategy parameter only affects the compression ratio but not the correctness of the compressed output even if it is not set appropriately. -- IMWRITE_PNG_STRATEGY_FIXED prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications. -*/ -enum ImwritePNGFlags { - IMWRITE_PNG_STRATEGY_DEFAULT = 0, //!< Use this value for normal data. - IMWRITE_PNG_STRATEGY_FILTERED = 1, //!< Use this value for data produced by a filter (or predictor).Filtered data consists mostly of small values with a somewhat random distribution. In this case, the compression algorithm is tuned to compress them better. - IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY = 2, //!< Use this value to force Huffman encoding only (no string match). - IMWRITE_PNG_STRATEGY_RLE = 3, //!< Use this value to limit match distances to one (run-length encoding). - IMWRITE_PNG_STRATEGY_FIXED = 4 //!< Using this value prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications. - }; - -/** @brief Loads an image from a file. - -@anchor imread - -The function imread loads an image from the specified file and returns it. If the image cannot be -read (because of missing file, improper permissions, unsupported or invalid format), the function -returns an empty matrix ( Mat::data==NULL ). - -Currently, the following file formats are supported: - -- Windows bitmaps - \*.bmp, \*.dib (always supported) -- JPEG files - \*.jpeg, \*.jpg, \*.jpe (see the *Notes* section) -- JPEG 2000 files - \*.jp2 (see the *Notes* section) -- Portable Network Graphics - \*.png (see the *Notes* section) -- WebP - \*.webp (see the *Notes* section) -- Portable image format - \*.pbm, \*.pgm, \*.ppm \*.pxm, \*.pnm (always supported) -- Sun rasters - \*.sr, \*.ras (always supported) -- TIFF files - \*.tiff, \*.tif (see the *Notes* section) -- OpenEXR Image files - \*.exr (see the *Notes* section) -- Radiance HDR - \*.hdr, \*.pic (always supported) -- Raster and Vector geospatial data supported by Gdal (see the *Notes* section) - -@note - -- The function determines the type of an image by the content, not by the file extension. -- In the case of color images, the decoded images will have the channels stored in **B G R** order. -- On Microsoft Windows\* OS and MacOSX\*, the codecs shipped with an OpenCV image (libjpeg, - libpng, libtiff, and libjasper) are used by default. So, OpenCV can always read JPEGs, PNGs, - and TIFFs. On MacOSX, there is also an option to use native MacOSX image readers. But beware - that currently these native image loaders give images with different pixel values because of - the color management embedded into MacOSX. -- On Linux\*, BSD flavors and other Unix-like open-source operating systems, OpenCV looks for - codecs supplied with an OS image. Install the relevant packages (do not forget the development - files, for example, "libjpeg-dev", in Debian\* and Ubuntu\*) to get the codec support or turn - on the OPENCV_BUILD_3RDPARTY_LIBS flag in CMake. -- In the case you set *WITH_GDAL* flag to true in CMake and @ref IMREAD_LOAD_GDAL to load the image, - then [GDAL](http://www.gdal.org) driver will be used in order to decode the image by supporting - the following formats: [Raster](http://www.gdal.org/formats_list.html), - [Vector](http://www.gdal.org/ogr_formats.html). -@param filename Name of file to be loaded. -@param flags Flag that can take values of cv::ImreadModes -*/ -CV_EXPORTS_W Mat imread( const String& filename, int flags = IMREAD_COLOR ); - -/** @brief Loads a multi-page image from a file. - -The function imreadmulti loads a multi-page image from the specified file into a vector of Mat objects. -@param filename Name of file to be loaded. -@param flags Flag that can take values of cv::ImreadModes, default with cv::IMREAD_ANYCOLOR. -@param mats A vector of Mat objects holding each page, if more than one. -@sa cv::imread -*/ -CV_EXPORTS_W bool imreadmulti(const String& filename, std::vector& mats, int flags = IMREAD_ANYCOLOR); - -/** @brief Saves an image to a specified file. - -The function imwrite saves the image to the specified file. The image format is chosen based on the -filename extension (see cv::imread for the list of extensions). Only 8-bit (or 16-bit unsigned (CV_16U) -in case of PNG, JPEG 2000, and TIFF) single-channel or 3-channel (with 'BGR' channel order) images -can be saved using this function. If the format, depth or channel order is different, use -Mat::convertTo , and cv::cvtColor to convert it before saving. Or, use the universal FileStorage I/O -functions to save the image to XML or YAML format. - -It is possible to store PNG images with an alpha channel using this function. To do this, create -8-bit (or 16-bit) 4-channel image BGRA, where the alpha channel goes last. Fully transparent pixels -should have alpha set to 0, fully opaque pixels should have alpha set to 255/65535. - -The sample below shows how to create such a BGRA image and store to PNG file. It also demonstrates how to set custom -compression parameters : -@code - #include - - using namespace cv; - using namespace std; - - void createAlphaMat(Mat &mat) - { - CV_Assert(mat.channels() == 4); - for (int i = 0; i < mat.rows; ++i) { - for (int j = 0; j < mat.cols; ++j) { - Vec4b& bgra = mat.at(i, j); - bgra[0] = UCHAR_MAX; // Blue - bgra[1] = saturate_cast((float (mat.cols - j)) / ((float)mat.cols) * UCHAR_MAX); // Green - bgra[2] = saturate_cast((float (mat.rows - i)) / ((float)mat.rows) * UCHAR_MAX); // Red - bgra[3] = saturate_cast(0.5 * (bgra[1] + bgra[2])); // Alpha - } - } - } - - int main(int argv, char **argc) - { - // Create mat with alpha channel - Mat mat(480, 640, CV_8UC4); - createAlphaMat(mat); - - vector compression_params; - compression_params.push_back(IMWRITE_PNG_COMPRESSION); - compression_params.push_back(9); - - try { - imwrite("alpha.png", mat, compression_params); - } - catch (cv::Exception& ex) { - fprintf(stderr, "Exception converting image to PNG format: %s\n", ex.what()); - return 1; - } - - fprintf(stdout, "Saved PNG file with alpha data.\n"); - return 0; - } -@endcode -@param filename Name of the file. -@param img Image to be saved. -@param params Format-specific parameters encoded as pairs (paramId_1, paramValue_1, paramId_2, paramValue_2, ... .) see cv::ImwriteFlags -*/ -CV_EXPORTS_W bool imwrite( const String& filename, InputArray img, - const std::vector& params = std::vector()); - -/** @brief Reads an image from a buffer in memory. - -The function imdecode reads an image from the specified buffer in the memory. If the buffer is too short or -contains invalid data, the function returns an empty matrix ( Mat::data==NULL ). - -See cv::imread for the list of supported formats and flags description. - -@note In the case of color images, the decoded images will have the channels stored in **B G R** order. -@param buf Input array or vector of bytes. -@param flags The same flags as in cv::imread, see cv::ImreadModes. -*/ -CV_EXPORTS_W Mat imdecode( InputArray buf, int flags ); - -/** @overload -@param buf -@param flags -@param dst The optional output placeholder for the decoded matrix. It can save the image -reallocations when the function is called repeatedly for images of the same size. -*/ -CV_EXPORTS Mat imdecode( InputArray buf, int flags, Mat* dst); - -/** @brief Encodes an image into a memory buffer. - -The function imencode compresses the image and stores it in the memory buffer that is resized to fit the -result. See cv::imwrite for the list of supported formats and flags description. - -@param ext File extension that defines the output format. -@param img Image to be written. -@param buf Output buffer resized to fit the compressed image. -@param params Format-specific parameters. See cv::imwrite and cv::ImwriteFlags. -*/ -CV_EXPORTS_W bool imencode( const String& ext, InputArray img, - CV_OUT std::vector& buf, - const std::vector& params = std::vector()); - -//! @} imgcodecs - -} // cv - -#endif //__OPENCV_IMGCODECS_HPP__ diff --git a/IPL/include/opencv/opencv2/imgcodecs/imgcodecs.hpp b/IPL/include/opencv/opencv2/imgcodecs/imgcodecs.hpp deleted file mode 100644 index a3cd232..0000000 --- a/IPL/include/opencv/opencv2/imgcodecs/imgcodecs.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/imgcodecs.hpp" diff --git a/IPL/include/opencv/opencv2/imgcodecs/imgcodecs_c.h b/IPL/include/opencv/opencv2/imgcodecs/imgcodecs_c.h deleted file mode 100644 index ad793cc..0000000 --- a/IPL/include/opencv/opencv2/imgcodecs/imgcodecs_c.h +++ /dev/null @@ -1,137 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// Intel License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000, Intel Corporation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of Intel Corporation may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_IMGCODECS_H__ -#define __OPENCV_IMGCODECS_H__ - -#include "opencv2/core/core_c.h" - -#ifdef __cplusplus -extern "C" { -#endif /* __cplusplus */ - -/** @addtogroup imgcodecs_c - @{ - */ - -enum -{ -/* 8bit, color or not */ - CV_LOAD_IMAGE_UNCHANGED =-1, -/* 8bit, gray */ - CV_LOAD_IMAGE_GRAYSCALE =0, -/* ?, color */ - CV_LOAD_IMAGE_COLOR =1, -/* any depth, ? */ - CV_LOAD_IMAGE_ANYDEPTH =2, -/* ?, any color */ - CV_LOAD_IMAGE_ANYCOLOR =4 -}; - -/* load image from file - iscolor can be a combination of above flags where CV_LOAD_IMAGE_UNCHANGED - overrides the other flags - using CV_LOAD_IMAGE_ANYCOLOR alone is equivalent to CV_LOAD_IMAGE_UNCHANGED - unless CV_LOAD_IMAGE_ANYDEPTH is specified images are converted to 8bit -*/ -CVAPI(IplImage*) cvLoadImage( const char* filename, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); -CVAPI(CvMat*) cvLoadImageM( const char* filename, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); - -enum -{ - CV_IMWRITE_JPEG_QUALITY =1, - CV_IMWRITE_JPEG_PROGRESSIVE =2, - CV_IMWRITE_JPEG_OPTIMIZE =3, - CV_IMWRITE_JPEG_RST_INTERVAL =4, - CV_IMWRITE_JPEG_LUMA_QUALITY =5, - CV_IMWRITE_JPEG_CHROMA_QUALITY =6, - CV_IMWRITE_PNG_COMPRESSION =16, - CV_IMWRITE_PNG_STRATEGY =17, - CV_IMWRITE_PNG_BILEVEL =18, - CV_IMWRITE_PNG_STRATEGY_DEFAULT =0, - CV_IMWRITE_PNG_STRATEGY_FILTERED =1, - CV_IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY =2, - CV_IMWRITE_PNG_STRATEGY_RLE =3, - CV_IMWRITE_PNG_STRATEGY_FIXED =4, - CV_IMWRITE_PXM_BINARY =32, - CV_IMWRITE_WEBP_QUALITY =64 -}; - -/* save image to file */ -CVAPI(int) cvSaveImage( const char* filename, const CvArr* image, - const int* params CV_DEFAULT(0) ); - -/* decode image stored in the buffer */ -CVAPI(IplImage*) cvDecodeImage( const CvMat* buf, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); -CVAPI(CvMat*) cvDecodeImageM( const CvMat* buf, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); - -/* encode image and store the result as a byte vector (single-row 8uC1 matrix) */ -CVAPI(CvMat*) cvEncodeImage( const char* ext, const CvArr* image, - const int* params CV_DEFAULT(0) ); - -enum -{ - CV_CVTIMG_FLIP =1, - CV_CVTIMG_SWAP_RB =2 -}; - -/* utility function: convert one image to another with optional vertical flip */ -CVAPI(void) cvConvertImage( const CvArr* src, CvArr* dst, int flags CV_DEFAULT(0)); - -CVAPI(int) cvHaveImageReader(const char* filename); -CVAPI(int) cvHaveImageWriter(const char* filename); - - -/****************************************************************************************\ -* Obsolete functions/synonyms * -\****************************************************************************************/ - -#define cvvLoadImage(name) cvLoadImage((name),1) -#define cvvSaveImage cvSaveImage -#define cvvConvertImage cvConvertImage - -/** @} imgcodecs_c */ - -#ifdef __cplusplus -} -#endif - -#endif // __OPENCV_IMGCODECS_H__ diff --git a/IPL/include/opencv/opencv2/imgcodecs/ios.h b/IPL/include/opencv/opencv2/imgcodecs/ios.h deleted file mode 100644 index fbd6371..0000000 --- a/IPL/include/opencv/opencv2/imgcodecs/ios.h +++ /dev/null @@ -1,57 +0,0 @@ - -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#import -#import -#import -#import -#include "opencv2/core/core.hpp" - -//! @addtogroup imgcodecs_ios -//! @{ - -UIImage* MatToUIImage(const cv::Mat& image); -void UIImageToMat(const UIImage* image, - cv::Mat& m, bool alphaExist = false); - -//! @} diff --git a/IPL/include/opencv/opencv2/imgproc.hpp b/IPL/include/opencv/opencv2/imgproc.hpp deleted file mode 100644 index 1f330f2..0000000 --- a/IPL/include/opencv/opencv2/imgproc.hpp +++ /dev/null @@ -1,4509 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_IMGPROC_HPP__ -#define __OPENCV_IMGPROC_HPP__ - -#include "opencv2/core.hpp" - -/** - @defgroup imgproc Image processing - @{ - @defgroup imgproc_filter Image Filtering - -Functions and classes described in this section are used to perform various linear or non-linear -filtering operations on 2D images (represented as Mat's). It means that for each pixel location -\f$(x,y)\f$ in the source image (normally, rectangular), its neighborhood is considered and used to -compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of -morphological operations, it is the minimum or maximum values, and so on. The computed response is -stored in the destination image at the same location \f$(x,y)\f$. It means that the output image -will be of the same size as the input image. Normally, the functions support multi-channel arrays, -in which case every channel is processed independently. Therefore, the output image will also have -the same number of channels as the input one. - -Another common feature of the functions and classes described in this section is that, unlike -simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For -example, if you want to smooth an image using a Gaussian \f$3 \times 3\f$ filter, then, when -processing the left-most pixels in each row, you need pixels to the left of them, that is, outside -of the image. You can let these pixels be the same as the left-most image pixels ("replicated -border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant -border" extrapolation method), and so on. OpenCV enables you to specify the extrapolation method. -For details, see cv::BorderTypes - -@anchor filter_depths -### Depth combinations -Input depth (src.depth()) | Output depth (ddepth) ---------------------------|---------------------- -CV_8U | -1/CV_16S/CV_32F/CV_64F -CV_16U/CV_16S | -1/CV_32F/CV_64F -CV_32F | -1/CV_32F/CV_64F -CV_64F | -1/CV_64F - -@note when ddepth=-1, the output image will have the same depth as the source. - - @defgroup imgproc_transform Geometric Image Transformations - -The functions in this section perform various geometrical transformations of 2D images. They do not -change the image content but deform the pixel grid and map this deformed grid to the destination -image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from -destination to the source. That is, for each pixel \f$(x, y)\f$ of the destination image, the -functions compute coordinates of the corresponding "donor" pixel in the source image and copy the -pixel value: - -\f[\texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y))\f] - -In case when you specify the forward mapping \f$\left: \texttt{src} \rightarrow -\texttt{dst}\f$, the OpenCV functions first compute the corresponding inverse mapping -\f$\left: \texttt{dst} \rightarrow \texttt{src}\f$ and then use the above formula. - -The actual implementations of the geometrical transformations, from the most generic remap and to -the simplest and the fastest resize, need to solve two main problems with the above formula: - -- Extrapolation of non-existing pixels. Similarly to the filtering functions described in the -previous section, for some \f$(x,y)\f$, either one of \f$f_x(x,y)\f$, or \f$f_y(x,y)\f$, or both -of them may fall outside of the image. In this case, an extrapolation method needs to be used. -OpenCV provides the same selection of extrapolation methods as in the filtering functions. In -addition, it provides the method BORDER_TRANSPARENT. This means that the corresponding pixels in -the destination image will not be modified at all. - -- Interpolation of pixel values. Usually \f$f_x(x,y)\f$ and \f$f_y(x,y)\f$ are floating-point -numbers. This means that \f$\left\f$ can be either an affine or perspective -transformation, or radial lens distortion correction, and so on. So, a pixel value at fractional -coordinates needs to be retrieved. In the simplest case, the coordinates can be just rounded to the -nearest integer coordinates and the corresponding pixel can be used. This is called a -nearest-neighbor interpolation. However, a better result can be achieved by using more -sophisticated [interpolation methods](http://en.wikipedia.org/wiki/Multivariate_interpolation) , -where a polynomial function is fit into some neighborhood of the computed pixel \f$(f_x(x,y), -f_y(x,y))\f$, and then the value of the polynomial at \f$(f_x(x,y), f_y(x,y))\f$ is taken as the -interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See -resize for details. - - @defgroup imgproc_misc Miscellaneous Image Transformations - @defgroup imgproc_draw Drawing Functions - -Drawing functions work with matrices/images of arbitrary depth. The boundaries of the shapes can be -rendered with antialiasing (implemented only for 8-bit images for now). All the functions include -the parameter color that uses an RGB value (that may be constructed with the Scalar constructor ) -for color images and brightness for grayscale images. For color images, the channel ordering is -normally *Blue, Green, Red*. This is what imshow, imread, and imwrite expect. So, if you form a -color using the Scalar constructor, it should look like: - -\f[\texttt{Scalar} (blue \_ component, green \_ component, red \_ component[, alpha \_ component])\f] - -If you are using your own image rendering and I/O functions, you can use any channel ordering. The -drawing functions process each channel independently and do not depend on the channel order or even -on the used color space. The whole image can be converted from BGR to RGB or to a different color -space using cvtColor . - -If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also, -many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means -that the coordinates can be passed as fixed-point numbers encoded as integers. The number of -fractional bits is specified by the shift parameter and the real point coordinates are calculated as -\f$\texttt{Point}(x,y)\rightarrow\texttt{Point2f}(x*2^{-shift},y*2^{-shift})\f$ . This feature is -especially effective when rendering antialiased shapes. - -@note The functions do not support alpha-transparency when the target image is 4-channel. In this -case, the color[3] is simply copied to the repainted pixels. Thus, if you want to paint -semi-transparent shapes, you can paint them in a separate buffer and then blend it with the main -image. - - @defgroup imgproc_colormap ColorMaps in OpenCV - -The human perception isn't built for observing fine changes in grayscale images. Human eyes are more -sensitive to observing changes between colors, so you often need to recolor your grayscale images to -get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your -computer vision application. - -In OpenCV you only need applyColorMap to apply a colormap on a given image. The following sample -code reads the path to an image from command line, applies a Jet colormap on it and shows the -result: - -@code -#include -#include -#include -#include -using namespace cv; - -#include -using namespace std; - -int main(int argc, const char *argv[]) -{ - // We need an input image. (can be grayscale or color) - if (argc < 2) - { - cerr << "We need an image to process here. Please run: colorMap [path_to_image]" << endl; - return -1; - } - Mat img_in = imread(argv[1]); - if(img_in.empty()) - { - cerr << "Sample image (" << argv[1] << ") is empty. Please adjust your path, so it points to a valid input image!" << endl; - return -1; - } - // Holds the colormap version of the image: - Mat img_color; - // Apply the colormap: - applyColorMap(img_in, img_color, COLORMAP_JET); - // Show the result: - imshow("colorMap", img_color); - waitKey(0); - return 0; -} -@endcode - -@see cv::ColormapTypes - - @defgroup imgproc_subdiv2d Planar Subdivision - -The Subdiv2D class described in this section is used to perform various planar subdivision on -a set of 2D points (represented as vector of Point2f). OpenCV subdivides a plane into triangles -using the Delaunay’s algorithm, which corresponds to the dual graph of the Voronoi diagram. -In the figure below, the Delaunay’s triangulation is marked with black lines and the Voronoi -diagram with red lines. - -![Delaunay triangulation (black) and Voronoi (red)](pics/delaunay_voronoi.png) - -The subdivisions can be used for the 3D piece-wise transformation of a plane, morphing, fast -location of points on the plane, building special graphs (such as NNG,RNG), and so forth. - - @defgroup imgproc_hist Histograms - @defgroup imgproc_shape Structural Analysis and Shape Descriptors - @defgroup imgproc_motion Motion Analysis and Object Tracking - @defgroup imgproc_feature Feature Detection - @defgroup imgproc_object Object Detection - @defgroup imgproc_c C API - @} -*/ - -namespace cv -{ - -/** @addtogroup imgproc -@{ -*/ - -//! @addtogroup imgproc_filter -//! @{ - -//! type of morphological operation -enum MorphTypes{ - MORPH_ERODE = 0, //!< see cv::erode - MORPH_DILATE = 1, //!< see cv::dilate - MORPH_OPEN = 2, //!< an opening operation - //!< \f[\texttt{dst} = \mathrm{open} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \mathrm{erode} ( \texttt{src} , \texttt{element} ))\f] - MORPH_CLOSE = 3, //!< a closing operation - //!< \f[\texttt{dst} = \mathrm{close} ( \texttt{src} , \texttt{element} )= \mathrm{erode} ( \mathrm{dilate} ( \texttt{src} , \texttt{element} ))\f] - MORPH_GRADIENT = 4, //!< a morphological gradient - //!< \f[\texttt{dst} = \mathrm{morph\_grad} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \texttt{src} , \texttt{element} )- \mathrm{erode} ( \texttt{src} , \texttt{element} )\f] - MORPH_TOPHAT = 5, //!< "top hat" - //!< \f[\texttt{dst} = \mathrm{tophat} ( \texttt{src} , \texttt{element} )= \texttt{src} - \mathrm{open} ( \texttt{src} , \texttt{element} )\f] - MORPH_BLACKHAT = 6, //!< "black hat" - //!< \f[\texttt{dst} = \mathrm{blackhat} ( \texttt{src} , \texttt{element} )= \mathrm{close} ( \texttt{src} , \texttt{element} )- \texttt{src}\f] - MORPH_HITMISS = 7 //!< "hit and miss" - //!< .- Only supported for CV_8UC1 binary images. Tutorial can be found in [this page](http://opencv-code.com/tutorials/hit-or-miss-transform-in-opencv/) -}; - -//! shape of the structuring element -enum MorphShapes { - MORPH_RECT = 0, //!< a rectangular structuring element: \f[E_{ij}=1\f] - MORPH_CROSS = 1, //!< a cross-shaped structuring element: - //!< \f[E_{ij} = \fork{1}{if i=\texttt{anchor.y} or j=\texttt{anchor.x}}{0}{otherwise}\f] - MORPH_ELLIPSE = 2 //!< an elliptic structuring element, that is, a filled ellipse inscribed - //!< into the rectangle Rect(0, 0, esize.width, 0.esize.height) -}; - -//! @} imgproc_filter - -//! @addtogroup imgproc_transform -//! @{ - -//! interpolation algorithm -enum InterpolationFlags{ - /** nearest neighbor interpolation */ - INTER_NEAREST = 0, - /** bilinear interpolation */ - INTER_LINEAR = 1, - /** bicubic interpolation */ - INTER_CUBIC = 2, - /** resampling using pixel area relation. It may be a preferred method for image decimation, as - it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST - method. */ - INTER_AREA = 3, - /** Lanczos interpolation over 8x8 neighborhood */ - INTER_LANCZOS4 = 4, - /** mask for interpolation codes */ - INTER_MAX = 7, - /** flag, fills all of the destination image pixels. If some of them correspond to outliers in the - source image, they are set to zero */ - WARP_FILL_OUTLIERS = 8, - /** flag, inverse transformation - - For example, polar transforms: - - flag is __not__ set: \f$dst( \phi , \rho ) = src(x,y)\f$ - - flag is set: \f$dst(x,y) = src( \phi , \rho )\f$ - */ - WARP_INVERSE_MAP = 16 -}; - -enum InterpolationMasks { - INTER_BITS = 5, - INTER_BITS2 = INTER_BITS * 2, - INTER_TAB_SIZE = 1 << INTER_BITS, - INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE - }; - -//! @} imgproc_transform - -//! @addtogroup imgproc_misc -//! @{ - -//! Distance types for Distance Transform and M-estimators -//! @see cv::distanceTransform, cv::fitLine -enum DistanceTypes { - DIST_USER = -1, //!< User defined distance - DIST_L1 = 1, //!< distance = |x1-x2| + |y1-y2| - DIST_L2 = 2, //!< the simple euclidean distance - DIST_C = 3, //!< distance = max(|x1-x2|,|y1-y2|) - DIST_L12 = 4, //!< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) - DIST_FAIR = 5, //!< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 - DIST_WELSCH = 6, //!< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 - DIST_HUBER = 7 //!< distance = |x| \texttt{thresh}\)}{0}{otherwise}\f] - THRESH_BINARY_INV = 1, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{maxval}}{otherwise}\f] - THRESH_TRUNC = 2, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{threshold}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f] - THRESH_TOZERO = 3, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{src}(x,y)}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f] - THRESH_TOZERO_INV = 4, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f] - THRESH_MASK = 7, - THRESH_OTSU = 8, //!< flag, use Otsu algorithm to choose the optimal threshold value - THRESH_TRIANGLE = 16 //!< flag, use Triangle algorithm to choose the optimal threshold value -}; - -//! adaptive threshold algorithm -//! see cv::adaptiveThreshold -enum AdaptiveThresholdTypes { - /** the threshold value \f$T(x,y)\f$ is a mean of the \f$\texttt{blockSize} \times - \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ minus C */ - ADAPTIVE_THRESH_MEAN_C = 0, - /** the threshold value \f$T(x, y)\f$ is a weighted sum (cross-correlation with a Gaussian - window) of the \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ - minus C . The default sigma (standard deviation) is used for the specified blockSize . See - cv::getGaussianKernel*/ - ADAPTIVE_THRESH_GAUSSIAN_C = 1 -}; - -//! cv::undistort mode -enum UndistortTypes { - PROJ_SPHERICAL_ORTHO = 0, - PROJ_SPHERICAL_EQRECT = 1 - }; - -//! class of the pixel in GrabCut algorithm -enum GrabCutClasses { - GC_BGD = 0, //!< an obvious background pixels - GC_FGD = 1, //!< an obvious foreground (object) pixel - GC_PR_BGD = 2, //!< a possible background pixel - GC_PR_FGD = 3 //!< a possible foreground pixel -}; - -//! GrabCut algorithm flags -enum GrabCutModes { - /** The function initializes the state and the mask using the provided rectangle. After that it - runs iterCount iterations of the algorithm. */ - GC_INIT_WITH_RECT = 0, - /** The function initializes the state using the provided mask. Note that GC_INIT_WITH_RECT - and GC_INIT_WITH_MASK can be combined. Then, all the pixels outside of the ROI are - automatically initialized with GC_BGD .*/ - GC_INIT_WITH_MASK = 1, - /** The value means that the algorithm should just resume. */ - GC_EVAL = 2 -}; - -//! distanceTransform algorithm flags -enum DistanceTransformLabelTypes { - /** each connected component of zeros in src (as well as all the non-zero pixels closest to the - connected component) will be assigned the same label */ - DIST_LABEL_CCOMP = 0, - /** each zero pixel (and all the non-zero pixels closest to it) gets its own label. */ - DIST_LABEL_PIXEL = 1 -}; - -//! floodfill algorithm flags -enum FloodFillFlags { - /** If set, the difference between the current pixel and seed pixel is considered. Otherwise, - the difference between neighbor pixels is considered (that is, the range is floating). */ - FLOODFILL_FIXED_RANGE = 1 << 16, - /** If set, the function does not change the image ( newVal is ignored), and only fills the - mask with the value specified in bits 8-16 of flags as described above. This option only make - sense in function variants that have the mask parameter. */ - FLOODFILL_MASK_ONLY = 1 << 17 -}; - -//! @} imgproc_misc - -//! @addtogroup imgproc_shape -//! @{ - -//! connected components algorithm output formats -enum ConnectedComponentsTypes { - CC_STAT_LEFT = 0, //!< The leftmost (x) coordinate which is the inclusive start of the bounding - //!< box in the horizontal direction. - CC_STAT_TOP = 1, //!< The topmost (y) coordinate which is the inclusive start of the bounding - //!< box in the vertical direction. - CC_STAT_WIDTH = 2, //!< The horizontal size of the bounding box - CC_STAT_HEIGHT = 3, //!< The vertical size of the bounding box - CC_STAT_AREA = 4, //!< The total area (in pixels) of the connected component - CC_STAT_MAX = 5 -}; - -//! mode of the contour retrieval algorithm -enum RetrievalModes { - /** retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for - all the contours. */ - RETR_EXTERNAL = 0, - /** retrieves all of the contours without establishing any hierarchical relationships. */ - RETR_LIST = 1, - /** retrieves all of the contours and organizes them into a two-level hierarchy. At the top - level, there are external boundaries of the components. At the second level, there are - boundaries of the holes. If there is another contour inside a hole of a connected component, it - is still put at the top level. */ - RETR_CCOMP = 2, - /** retrieves all of the contours and reconstructs a full hierarchy of nested contours.*/ - RETR_TREE = 3, - RETR_FLOODFILL = 4 //!< -}; - -//! the contour approximation algorithm -enum ContourApproximationModes { - /** stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and - (x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is, - max(abs(x1-x2),abs(y2-y1))==1. */ - CHAIN_APPROX_NONE = 1, - /** compresses horizontal, vertical, and diagonal segments and leaves only their end points. - For example, an up-right rectangular contour is encoded with 4 points. */ - CHAIN_APPROX_SIMPLE = 2, - /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */ - CHAIN_APPROX_TC89_L1 = 3, - /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */ - CHAIN_APPROX_TC89_KCOS = 4 -}; - -//! @} imgproc_shape - -//! Variants of a Hough transform -enum HoughModes { - - /** classical or standard Hough transform. Every line is represented by two floating-point - numbers \f$(\rho, \theta)\f$ , where \f$\rho\f$ is a distance between (0,0) point and the line, - and \f$\theta\f$ is the angle between x-axis and the normal to the line. Thus, the matrix must - be (the created sequence will be) of CV_32FC2 type */ - HOUGH_STANDARD = 0, - /** probabilistic Hough transform (more efficient in case if the picture contains a few long - linear segments). It returns line segments rather than the whole line. Each segment is - represented by starting and ending points, and the matrix must be (the created sequence will - be) of the CV_32SC4 type. */ - HOUGH_PROBABILISTIC = 1, - /** multi-scale variant of the classical Hough transform. The lines are encoded the same way as - HOUGH_STANDARD. */ - HOUGH_MULTI_SCALE = 2, - HOUGH_GRADIENT = 3 //!< basically *21HT*, described in @cite Yuen90 -}; - -//! Variants of Line Segment %Detector -//! @ingroup imgproc_feature -enum LineSegmentDetectorModes { - LSD_REFINE_NONE = 0, //!< No refinement applied - LSD_REFINE_STD = 1, //!< Standard refinement is applied. E.g. breaking arches into smaller straighter line approximations. - LSD_REFINE_ADV = 2 //!< Advanced refinement. Number of false alarms is calculated, lines are - //!< refined through increase of precision, decrement in size, etc. -}; - -/** Histogram comparison methods - @ingroup imgproc_hist -*/ -enum HistCompMethods { - /** Correlation - \f[d(H_1,H_2) = \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}\f] - where - \f[\bar{H_k} = \frac{1}{N} \sum _J H_k(J)\f] - and \f$N\f$ is a total number of histogram bins. */ - HISTCMP_CORREL = 0, - /** Chi-Square - \f[d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}\f] */ - HISTCMP_CHISQR = 1, - /** Intersection - \f[d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))\f] */ - HISTCMP_INTERSECT = 2, - /** Bhattacharyya distance - (In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient.) - \f[d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}\f] */ - HISTCMP_BHATTACHARYYA = 3, - HISTCMP_HELLINGER = HISTCMP_BHATTACHARYYA, //!< Synonym for HISTCMP_BHATTACHARYYA - /** Alternative Chi-Square - \f[d(H_1,H_2) = 2 * \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)+H_2(I)}\f] - This alternative formula is regularly used for texture comparison. See e.g. @cite Puzicha1997 */ - HISTCMP_CHISQR_ALT = 4, - /** Kullback-Leibler divergence - \f[d(H_1,H_2) = \sum _I H_1(I) \log \left(\frac{H_1(I)}{H_2(I)}\right)\f] */ - HISTCMP_KL_DIV = 5 -}; - -/** the color conversion code -@see @ref imgproc_color_conversions -@ingroup imgproc_misc - */ -enum ColorConversionCodes { - COLOR_BGR2BGRA = 0, //!< add alpha channel to RGB or BGR image - COLOR_RGB2RGBA = COLOR_BGR2BGRA, - - COLOR_BGRA2BGR = 1, //!< remove alpha channel from RGB or BGR image - COLOR_RGBA2RGB = COLOR_BGRA2BGR, - - COLOR_BGR2RGBA = 2, //!< convert between RGB and BGR color spaces (with or without alpha channel) - COLOR_RGB2BGRA = COLOR_BGR2RGBA, - - COLOR_RGBA2BGR = 3, - COLOR_BGRA2RGB = COLOR_RGBA2BGR, - - COLOR_BGR2RGB = 4, - COLOR_RGB2BGR = COLOR_BGR2RGB, - - COLOR_BGRA2RGBA = 5, - COLOR_RGBA2BGRA = COLOR_BGRA2RGBA, - - COLOR_BGR2GRAY = 6, //!< convert between RGB/BGR and grayscale, @ref color_convert_rgb_gray "color conversions" - COLOR_RGB2GRAY = 7, - COLOR_GRAY2BGR = 8, - COLOR_GRAY2RGB = COLOR_GRAY2BGR, - COLOR_GRAY2BGRA = 9, - COLOR_GRAY2RGBA = COLOR_GRAY2BGRA, - COLOR_BGRA2GRAY = 10, - COLOR_RGBA2GRAY = 11, - - COLOR_BGR2BGR565 = 12, //!< convert between RGB/BGR and BGR565 (16-bit images) - COLOR_RGB2BGR565 = 13, - COLOR_BGR5652BGR = 14, - COLOR_BGR5652RGB = 15, - COLOR_BGRA2BGR565 = 16, - COLOR_RGBA2BGR565 = 17, - COLOR_BGR5652BGRA = 18, - COLOR_BGR5652RGBA = 19, - - COLOR_GRAY2BGR565 = 20, //!< convert between grayscale to BGR565 (16-bit images) - COLOR_BGR5652GRAY = 21, - - COLOR_BGR2BGR555 = 22, //!< convert between RGB/BGR and BGR555 (16-bit images) - COLOR_RGB2BGR555 = 23, - COLOR_BGR5552BGR = 24, - COLOR_BGR5552RGB = 25, - COLOR_BGRA2BGR555 = 26, - COLOR_RGBA2BGR555 = 27, - COLOR_BGR5552BGRA = 28, - COLOR_BGR5552RGBA = 29, - - COLOR_GRAY2BGR555 = 30, //!< convert between grayscale and BGR555 (16-bit images) - COLOR_BGR5552GRAY = 31, - - COLOR_BGR2XYZ = 32, //!< convert RGB/BGR to CIE XYZ, @ref color_convert_rgb_xyz "color conversions" - COLOR_RGB2XYZ = 33, - COLOR_XYZ2BGR = 34, - COLOR_XYZ2RGB = 35, - - COLOR_BGR2YCrCb = 36, //!< convert RGB/BGR to luma-chroma (aka YCC), @ref color_convert_rgb_ycrcb "color conversions" - COLOR_RGB2YCrCb = 37, - COLOR_YCrCb2BGR = 38, - COLOR_YCrCb2RGB = 39, - - COLOR_BGR2HSV = 40, //!< convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions" - COLOR_RGB2HSV = 41, - - COLOR_BGR2Lab = 44, //!< convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions" - COLOR_RGB2Lab = 45, - - COLOR_BGR2Luv = 50, //!< convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions" - COLOR_RGB2Luv = 51, - COLOR_BGR2HLS = 52, //!< convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions" - COLOR_RGB2HLS = 53, - - COLOR_HSV2BGR = 54, //!< backward conversions to RGB/BGR - COLOR_HSV2RGB = 55, - - COLOR_Lab2BGR = 56, - COLOR_Lab2RGB = 57, - COLOR_Luv2BGR = 58, - COLOR_Luv2RGB = 59, - COLOR_HLS2BGR = 60, - COLOR_HLS2RGB = 61, - - COLOR_BGR2HSV_FULL = 66, //!< - COLOR_RGB2HSV_FULL = 67, - COLOR_BGR2HLS_FULL = 68, - COLOR_RGB2HLS_FULL = 69, - - COLOR_HSV2BGR_FULL = 70, - COLOR_HSV2RGB_FULL = 71, - COLOR_HLS2BGR_FULL = 72, - COLOR_HLS2RGB_FULL = 73, - - COLOR_LBGR2Lab = 74, - COLOR_LRGB2Lab = 75, - COLOR_LBGR2Luv = 76, - COLOR_LRGB2Luv = 77, - - COLOR_Lab2LBGR = 78, - COLOR_Lab2LRGB = 79, - COLOR_Luv2LBGR = 80, - COLOR_Luv2LRGB = 81, - - COLOR_BGR2YUV = 82, //!< convert between RGB/BGR and YUV - COLOR_RGB2YUV = 83, - COLOR_YUV2BGR = 84, - COLOR_YUV2RGB = 85, - - //! YUV 4:2:0 family to RGB - COLOR_YUV2RGB_NV12 = 90, - COLOR_YUV2BGR_NV12 = 91, - COLOR_YUV2RGB_NV21 = 92, - COLOR_YUV2BGR_NV21 = 93, - COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21, - COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21, - - COLOR_YUV2RGBA_NV12 = 94, - COLOR_YUV2BGRA_NV12 = 95, - COLOR_YUV2RGBA_NV21 = 96, - COLOR_YUV2BGRA_NV21 = 97, - COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21, - COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21, - - COLOR_YUV2RGB_YV12 = 98, - COLOR_YUV2BGR_YV12 = 99, - COLOR_YUV2RGB_IYUV = 100, - COLOR_YUV2BGR_IYUV = 101, - COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV, - COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV, - COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12, - COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12, - - COLOR_YUV2RGBA_YV12 = 102, - COLOR_YUV2BGRA_YV12 = 103, - COLOR_YUV2RGBA_IYUV = 104, - COLOR_YUV2BGRA_IYUV = 105, - COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV, - COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV, - COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12, - COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12, - - COLOR_YUV2GRAY_420 = 106, - COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420, - COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420, - COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420, - COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420, - COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420, - COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420, - COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420, - - //! YUV 4:2:2 family to RGB - COLOR_YUV2RGB_UYVY = 107, - COLOR_YUV2BGR_UYVY = 108, - //COLOR_YUV2RGB_VYUY = 109, - //COLOR_YUV2BGR_VYUY = 110, - COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY, - COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY, - COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY, - COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY, - - COLOR_YUV2RGBA_UYVY = 111, - COLOR_YUV2BGRA_UYVY = 112, - //COLOR_YUV2RGBA_VYUY = 113, - //COLOR_YUV2BGRA_VYUY = 114, - COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY, - COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY, - COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY, - COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY, - - COLOR_YUV2RGB_YUY2 = 115, - COLOR_YUV2BGR_YUY2 = 116, - COLOR_YUV2RGB_YVYU = 117, - COLOR_YUV2BGR_YVYU = 118, - COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2, - COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2, - COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2, - COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2, - - COLOR_YUV2RGBA_YUY2 = 119, - COLOR_YUV2BGRA_YUY2 = 120, - COLOR_YUV2RGBA_YVYU = 121, - COLOR_YUV2BGRA_YVYU = 122, - COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2, - COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2, - COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2, - COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2, - - COLOR_YUV2GRAY_UYVY = 123, - COLOR_YUV2GRAY_YUY2 = 124, - //CV_YUV2GRAY_VYUY = CV_YUV2GRAY_UYVY, - COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY, - COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY, - COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2, - COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2, - COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2, - - //! alpha premultiplication - COLOR_RGBA2mRGBA = 125, - COLOR_mRGBA2RGBA = 126, - - //! RGB to YUV 4:2:0 family - COLOR_RGB2YUV_I420 = 127, - COLOR_BGR2YUV_I420 = 128, - COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420, - COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420, - - COLOR_RGBA2YUV_I420 = 129, - COLOR_BGRA2YUV_I420 = 130, - COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420, - COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420, - COLOR_RGB2YUV_YV12 = 131, - COLOR_BGR2YUV_YV12 = 132, - COLOR_RGBA2YUV_YV12 = 133, - COLOR_BGRA2YUV_YV12 = 134, - - //! Demosaicing - COLOR_BayerBG2BGR = 46, - COLOR_BayerGB2BGR = 47, - COLOR_BayerRG2BGR = 48, - COLOR_BayerGR2BGR = 49, - - COLOR_BayerBG2RGB = COLOR_BayerRG2BGR, - COLOR_BayerGB2RGB = COLOR_BayerGR2BGR, - COLOR_BayerRG2RGB = COLOR_BayerBG2BGR, - COLOR_BayerGR2RGB = COLOR_BayerGB2BGR, - - COLOR_BayerBG2GRAY = 86, - COLOR_BayerGB2GRAY = 87, - COLOR_BayerRG2GRAY = 88, - COLOR_BayerGR2GRAY = 89, - - //! Demosaicing using Variable Number of Gradients - COLOR_BayerBG2BGR_VNG = 62, - COLOR_BayerGB2BGR_VNG = 63, - COLOR_BayerRG2BGR_VNG = 64, - COLOR_BayerGR2BGR_VNG = 65, - - COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG, - COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG, - COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG, - COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG, - - //! Edge-Aware Demosaicing - COLOR_BayerBG2BGR_EA = 135, - COLOR_BayerGB2BGR_EA = 136, - COLOR_BayerRG2BGR_EA = 137, - COLOR_BayerGR2BGR_EA = 138, - - COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA, - COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA, - COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA, - COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA, - - - COLOR_COLORCVT_MAX = 139 -}; - -/** types of intersection between rectangles -@ingroup imgproc_shape -*/ -enum RectanglesIntersectTypes { - INTERSECT_NONE = 0, //!< No intersection - INTERSECT_PARTIAL = 1, //!< There is a partial intersection - INTERSECT_FULL = 2 //!< One of the rectangle is fully enclosed in the other -}; - -//! finds arbitrary template in the grayscale image using Generalized Hough Transform -class CV_EXPORTS GeneralizedHough : public Algorithm -{ -public: - //! set template to search - virtual void setTemplate(InputArray templ, Point templCenter = Point(-1, -1)) = 0; - virtual void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1)) = 0; - - //! find template on image - virtual void detect(InputArray image, OutputArray positions, OutputArray votes = noArray()) = 0; - virtual void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes = noArray()) = 0; - - //! Canny low threshold. - virtual void setCannyLowThresh(int cannyLowThresh) = 0; - virtual int getCannyLowThresh() const = 0; - - //! Canny high threshold. - virtual void setCannyHighThresh(int cannyHighThresh) = 0; - virtual int getCannyHighThresh() const = 0; - - //! Minimum distance between the centers of the detected objects. - virtual void setMinDist(double minDist) = 0; - virtual double getMinDist() const = 0; - - //! Inverse ratio of the accumulator resolution to the image resolution. - virtual void setDp(double dp) = 0; - virtual double getDp() const = 0; - - //! Maximal size of inner buffers. - virtual void setMaxBufferSize(int maxBufferSize) = 0; - virtual int getMaxBufferSize() const = 0; -}; - -//! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122. -//! Detects position only without traslation and rotation -class CV_EXPORTS GeneralizedHoughBallard : public GeneralizedHough -{ -public: - //! R-Table levels. - virtual void setLevels(int levels) = 0; - virtual int getLevels() const = 0; - - //! The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected. - virtual void setVotesThreshold(int votesThreshold) = 0; - virtual int getVotesThreshold() const = 0; -}; - -//! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038. -//! Detects position, traslation and rotation -class CV_EXPORTS GeneralizedHoughGuil : public GeneralizedHough -{ -public: - //! Angle difference in degrees between two points in feature. - virtual void setXi(double xi) = 0; - virtual double getXi() const = 0; - - //! Feature table levels. - virtual void setLevels(int levels) = 0; - virtual int getLevels() const = 0; - - //! Maximal difference between angles that treated as equal. - virtual void setAngleEpsilon(double angleEpsilon) = 0; - virtual double getAngleEpsilon() const = 0; - - //! Minimal rotation angle to detect in degrees. - virtual void setMinAngle(double minAngle) = 0; - virtual double getMinAngle() const = 0; - - //! Maximal rotation angle to detect in degrees. - virtual void setMaxAngle(double maxAngle) = 0; - virtual double getMaxAngle() const = 0; - - //! Angle step in degrees. - virtual void setAngleStep(double angleStep) = 0; - virtual double getAngleStep() const = 0; - - //! Angle votes threshold. - virtual void setAngleThresh(int angleThresh) = 0; - virtual int getAngleThresh() const = 0; - - //! Minimal scale to detect. - virtual void setMinScale(double minScale) = 0; - virtual double getMinScale() const = 0; - - //! Maximal scale to detect. - virtual void setMaxScale(double maxScale) = 0; - virtual double getMaxScale() const = 0; - - //! Scale step. - virtual void setScaleStep(double scaleStep) = 0; - virtual double getScaleStep() const = 0; - - //! Scale votes threshold. - virtual void setScaleThresh(int scaleThresh) = 0; - virtual int getScaleThresh() const = 0; - - //! Position votes threshold. - virtual void setPosThresh(int posThresh) = 0; - virtual int getPosThresh() const = 0; -}; - - -class CV_EXPORTS_W CLAHE : public Algorithm -{ -public: - CV_WRAP virtual void apply(InputArray src, OutputArray dst) = 0; - - CV_WRAP virtual void setClipLimit(double clipLimit) = 0; - CV_WRAP virtual double getClipLimit() const = 0; - - CV_WRAP virtual void setTilesGridSize(Size tileGridSize) = 0; - CV_WRAP virtual Size getTilesGridSize() const = 0; - - CV_WRAP virtual void collectGarbage() = 0; -}; - - -//! @addtogroup imgproc_subdiv2d -//! @{ - -class CV_EXPORTS_W Subdiv2D -{ -public: - /** Subdiv2D point location cases */ - enum { PTLOC_ERROR = -2, //!< Point location error - PTLOC_OUTSIDE_RECT = -1, //!< Point outside the subdivision bounding rect - PTLOC_INSIDE = 0, //!< Point inside some facet - PTLOC_VERTEX = 1, //!< Point coincides with one of the subdivision vertices - PTLOC_ON_EDGE = 2 //!< Point on some edge - }; - - /** Subdiv2D edge type navigation (see: getEdge()) */ - enum { NEXT_AROUND_ORG = 0x00, - NEXT_AROUND_DST = 0x22, - PREV_AROUND_ORG = 0x11, - PREV_AROUND_DST = 0x33, - NEXT_AROUND_LEFT = 0x13, - NEXT_AROUND_RIGHT = 0x31, - PREV_AROUND_LEFT = 0x20, - PREV_AROUND_RIGHT = 0x02 - }; - - /** creates an empty Subdiv2D object. - To create a new empty Delaunay subdivision you need to use the initDelaunay() function. - */ - CV_WRAP Subdiv2D(); - - /** @overload - - @param rect – Rectangle that includes all of the 2D points that are to be added to the subdivision. - - The function creates an empty Delaunay subdivision where 2D points can be added using the function - insert() . All of the points to be added must be within the specified rectangle, otherwise a runtime - error is raised. - */ - CV_WRAP Subdiv2D(Rect rect); - - /** @brief Creates a new empty Delaunay subdivision - - @param rect – Rectangle that includes all of the 2D points that are to be added to the subdivision. - - */ - CV_WRAP void initDelaunay(Rect rect); - - /** @brief Insert a single point into a Delaunay triangulation. - - @param pt – Point to insert. - - The function inserts a single point into a subdivision and modifies the subdivision topology - appropriately. If a point with the same coordinates exists already, no new point is added. - @returns the ID of the point. - - @note If the point is outside of the triangulation specified rect a runtime error is raised. - */ - CV_WRAP int insert(Point2f pt); - - /** @brief Insert multiple points into a Delaunay triangulation. - - @param ptvec – Points to insert. - - The function inserts a vector of points into a subdivision and modifies the subdivision topology - appropriately. - */ - CV_WRAP void insert(const std::vector& ptvec); - - /** @brief Returns the location of a point within a Delaunay triangulation. - - @param pt – Point to locate. - @param edge – Output edge that the point belongs to or is located to the right of it. - @param vertex – Optional output vertex the input point coincides with. - - The function locates the input point within the subdivision and gives one of the triangle edges - or vertices. - - @returns an integer which specify one of the following five cases for point location: - - The point falls into some facet. The function returns PTLOC_INSIDE and edge will contain one of - edges of the facet. - - The point falls onto the edge. The function returns PTLOC_ON_EDGE and edge will contain this edge. - - The point coincides with one of the subdivision vertices. The function returns PTLOC_VERTEX and - vertex will contain a pointer to the vertex. - - The point is outside the subdivision reference rectangle. The function returns PTLOC_OUTSIDE_RECT - and no pointers are filled. - - One of input arguments is invalid. A runtime error is raised or, if silent or “parent” error - processing mode is selected, CV_PTLOC_ERROR is returnd. - */ - CV_WRAP int locate(Point2f pt, CV_OUT int& edge, CV_OUT int& vertex); - - /** @brief Finds the subdivision vertex closest to the given point. - - @param pt – Input point. - @param nearestPt – Output subdivision vertex point. - - The function is another function that locates the input point within the subdivision. It finds the - subdivision vertex that is the closest to the input point. It is not necessarily one of vertices - of the facet containing the input point, though the facet (located using locate() ) is used as a - starting point. - - @returns vertex ID. - */ - CV_WRAP int findNearest(Point2f pt, CV_OUT Point2f* nearestPt = 0); - - /** @brief Returns a list of all edges. - - @param edgeList – Output vector. - - The function gives each edge as a 4 numbers vector, where each two are one of the edge - vertices. i.e. org_x = v[0], org_y = v[1], dst_x = v[2], dst_y = v[3]. - */ - CV_WRAP void getEdgeList(CV_OUT std::vector& edgeList) const; - - /** @brief Returns a list of all triangles. - - @param triangleList – Output vector. - - The function gives each triangle as a 6 numbers vector, where each two are one of the triangle - vertices. i.e. p1_x = v[0], p1_y = v[1], p2_x = v[2], p2_y = v[3], p3_x = v[4], p3_y = v[5]. - */ - CV_WRAP void getTriangleList(CV_OUT std::vector& triangleList) const; - - /** @brief Returns a list of all Voroni facets. - - @param idx – Vector of vertices IDs to consider. For all vertices you can pass empty vector. - @param facetList – Output vector of the Voroni facets. - @param facetCenters – Output vector of the Voroni facets center points. - - */ - CV_WRAP void getVoronoiFacetList(const std::vector& idx, CV_OUT std::vector >& facetList, - CV_OUT std::vector& facetCenters); - - /** @brief Returns vertex location from vertex ID. - - @param vertex – vertex ID. - @param firstEdge – Optional. The first edge ID which is connected to the vertex. - @returns vertex (x,y) - - */ - CV_WRAP Point2f getVertex(int vertex, CV_OUT int* firstEdge = 0) const; - - /** @brief Returns one of the edges related to the given edge. - - @param edge – Subdivision edge ID. - @param nextEdgeType - Parameter specifying which of the related edges to return. - The following values are possible: - - NEXT_AROUND_ORG next around the edge origin ( eOnext on the picture below if e is the input edge) - - NEXT_AROUND_DST next around the edge vertex ( eDnext ) - - PREV_AROUND_ORG previous around the edge origin (reversed eRnext ) - - PREV_AROUND_DST previous around the edge destination (reversed eLnext ) - - NEXT_AROUND_LEFT next around the left facet ( eLnext ) - - NEXT_AROUND_RIGHT next around the right facet ( eRnext ) - - PREV_AROUND_LEFT previous around the left facet (reversed eOnext ) - - PREV_AROUND_RIGHT previous around the right facet (reversed eDnext ) - - ![sample output](pics/quadedge.png) - - @returns edge ID related to the input edge. - */ - CV_WRAP int getEdge( int edge, int nextEdgeType ) const; - - /** @brief Returns next edge around the edge origin. - - @param edge – Subdivision edge ID. - - @returns an integer which is next edge ID around the edge origin: eOnext on the - picture above if e is the input edge). - */ - CV_WRAP int nextEdge(int edge) const; - - /** @brief Returns another edge of the same quad-edge. - - @param edge – Subdivision edge ID. - @param rotate - Parameter specifying which of the edges of the same quad-edge as the input - one to return. The following values are possible: - - 0 - the input edge ( e on the picture below if e is the input edge) - - 1 - the rotated edge ( eRot ) - - 2 - the reversed edge (reversed e (in green)) - - 3 - the reversed rotated edge (reversed eRot (in green)) - - @returns one of the edges ID of the same quad-edge as the input edge. - */ - CV_WRAP int rotateEdge(int edge, int rotate) const; - CV_WRAP int symEdge(int edge) const; - - /** @brief Returns the edge origin. - - @param edge – Subdivision edge ID. - @param orgpt – Output vertex location. - - @returns vertex ID. - */ - CV_WRAP int edgeOrg(int edge, CV_OUT Point2f* orgpt = 0) const; - - /** @brief Returns the edge destination. - - @param edge – Subdivision edge ID. - @param dstpt – Output vertex location. - - @returns vertex ID. - */ - CV_WRAP int edgeDst(int edge, CV_OUT Point2f* dstpt = 0) const; - -protected: - int newEdge(); - void deleteEdge(int edge); - int newPoint(Point2f pt, bool isvirtual, int firstEdge = 0); - void deletePoint(int vtx); - void setEdgePoints( int edge, int orgPt, int dstPt ); - void splice( int edgeA, int edgeB ); - int connectEdges( int edgeA, int edgeB ); - void swapEdges( int edge ); - int isRightOf(Point2f pt, int edge) const; - void calcVoronoi(); - void clearVoronoi(); - void checkSubdiv() const; - - struct CV_EXPORTS Vertex - { - Vertex(); - Vertex(Point2f pt, bool _isvirtual, int _firstEdge=0); - bool isvirtual() const; - bool isfree() const; - - int firstEdge; - int type; - Point2f pt; - }; - - struct CV_EXPORTS QuadEdge - { - QuadEdge(); - QuadEdge(int edgeidx); - bool isfree() const; - - int next[4]; - int pt[4]; - }; - - //! All of the vertices - std::vector vtx; - //! All of the edges - std::vector qedges; - int freeQEdge; - int freePoint; - bool validGeometry; - - int recentEdge; - //! Top left corner of the bounding rect - Point2f topLeft; - //! Bottom right corner of the bounding rect - Point2f bottomRight; -}; - -//! @} imgproc_subdiv2d - -//! @addtogroup imgproc_feature -//! @{ - -/** @example lsd_lines.cpp -An example using the LineSegmentDetector -*/ - -/** @brief Line segment detector class - -following the algorithm described at @cite Rafael12 . -*/ -class CV_EXPORTS_W LineSegmentDetector : public Algorithm -{ -public: - - /** @brief Finds lines in the input image. - - This is the output of the default parameters of the algorithm on the above shown image. - - ![image](pics/building_lsd.png) - - @param _image A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use: - `lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);` - @param _lines A vector of Vec4i or Vec4f elements specifying the beginning and ending point of a line. Where - Vec4i/Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly - oriented depending on the gradient. - @param width Vector of widths of the regions, where the lines are found. E.g. Width of line. - @param prec Vector of precisions with which the lines are found. - @param nfa Vector containing number of false alarms in the line region, with precision of 10%. The - bigger the value, logarithmically better the detection. - - -1 corresponds to 10 mean false alarms - - 0 corresponds to 1 mean false alarm - - 1 corresponds to 0.1 mean false alarms - This vector will be calculated only when the objects type is LSD_REFINE_ADV. - */ - CV_WRAP virtual void detect(InputArray _image, OutputArray _lines, - OutputArray width = noArray(), OutputArray prec = noArray(), - OutputArray nfa = noArray()) = 0; - - /** @brief Draws the line segments on a given image. - @param _image The image, where the liens will be drawn. Should be bigger or equal to the image, - where the lines were found. - @param lines A vector of the lines that needed to be drawn. - */ - CV_WRAP virtual void drawSegments(InputOutputArray _image, InputArray lines) = 0; - - /** @brief Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels. - - @param size The size of the image, where lines1 and lines2 were found. - @param lines1 The first group of lines that needs to be drawn. It is visualized in blue color. - @param lines2 The second group of lines. They visualized in red color. - @param _image Optional image, where the lines will be drawn. The image should be color(3-channel) - in order for lines1 and lines2 to be drawn in the above mentioned colors. - */ - CV_WRAP virtual int compareSegments(const Size& size, InputArray lines1, InputArray lines2, InputOutputArray _image = noArray()) = 0; - - virtual ~LineSegmentDetector() { } -}; - -/** @brief Creates a smart pointer to a LineSegmentDetector object and initializes it. - -The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want -to edit those, as to tailor it for their own application. - -@param _refine The way found lines will be refined, see cv::LineSegmentDetectorModes -@param _scale The scale of the image that will be used to find the lines. Range (0..1]. -@param _sigma_scale Sigma for Gaussian filter. It is computed as sigma = _sigma_scale/_scale. -@param _quant Bound to the quantization error on the gradient norm. -@param _ang_th Gradient angle tolerance in degrees. -@param _log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advancent refinement -is chosen. -@param _density_th Minimal density of aligned region points in the enclosing rectangle. -@param _n_bins Number of bins in pseudo-ordering of gradient modulus. - */ -CV_EXPORTS_W Ptr createLineSegmentDetector( - int _refine = LSD_REFINE_STD, double _scale = 0.8, - double _sigma_scale = 0.6, double _quant = 2.0, double _ang_th = 22.5, - double _log_eps = 0, double _density_th = 0.7, int _n_bins = 1024); - -//! @} imgproc_feature - -//! @addtogroup imgproc_filter -//! @{ - -/** @brief Returns Gaussian filter coefficients. - -The function computes and returns the \f$\texttt{ksize} \times 1\f$ matrix of Gaussian filter -coefficients: - -\f[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\f] - -where \f$i=0..\texttt{ksize}-1\f$ and \f$\alpha\f$ is the scale factor chosen so that \f$\sum_i G_i=1\f$. - -Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize -smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. -You may also use the higher-level GaussianBlur. -@param ksize Aperture size. It should be odd ( \f$\texttt{ksize} \mod 2 = 1\f$ ) and positive. -@param sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as -`sigma = 0.3\*((ksize-1)\*0.5 - 1) + 0.8`. -@param ktype Type of filter coefficients. It can be CV_32F or CV_64F . -@sa sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur - */ -CV_EXPORTS_W Mat getGaussianKernel( int ksize, double sigma, int ktype = CV_64F ); - -/** @brief Returns filter coefficients for computing spatial image derivatives. - -The function computes and returns the filter coefficients for spatial image derivatives. When -`ksize=CV_SCHARR`, the Scharr \f$3 \times 3\f$ kernels are generated (see cv::Scharr). Otherwise, Sobel -kernels are generated (see cv::Sobel). The filters are normally passed to sepFilter2D or to - -@param kx Output matrix of row filter coefficients. It has the type ktype . -@param ky Output matrix of column filter coefficients. It has the type ktype . -@param dx Derivative order in respect of x. -@param dy Derivative order in respect of y. -@param ksize Aperture size. It can be CV_SCHARR, 1, 3, 5, or 7. -@param normalize Flag indicating whether to normalize (scale down) the filter coefficients or not. -Theoretically, the coefficients should have the denominator \f$=2^{ksize*2-dx-dy-2}\f$. If you are -going to filter floating-point images, you are likely to use the normalized kernels. But if you -compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve -all the fractional bits, you may want to set normalize=false . -@param ktype Type of filter coefficients. It can be CV_32f or CV_64F . - */ -CV_EXPORTS_W void getDerivKernels( OutputArray kx, OutputArray ky, - int dx, int dy, int ksize, - bool normalize = false, int ktype = CV_32F ); - -/** @brief Returns Gabor filter coefficients. - -For more details about gabor filter equations and parameters, see: [Gabor -Filter](http://en.wikipedia.org/wiki/Gabor_filter). - -@param ksize Size of the filter returned. -@param sigma Standard deviation of the gaussian envelope. -@param theta Orientation of the normal to the parallel stripes of a Gabor function. -@param lambd Wavelength of the sinusoidal factor. -@param gamma Spatial aspect ratio. -@param psi Phase offset. -@param ktype Type of filter coefficients. It can be CV_32F or CV_64F . - */ -CV_EXPORTS_W Mat getGaborKernel( Size ksize, double sigma, double theta, double lambd, - double gamma, double psi = CV_PI*0.5, int ktype = CV_64F ); - -//! returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation. -static inline Scalar morphologyDefaultBorderValue() { return Scalar::all(DBL_MAX); } - -/** @brief Returns a structuring element of the specified size and shape for morphological operations. - -The function constructs and returns the structuring element that can be further passed to cv::erode, -cv::dilate or cv::morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as -the structuring element. - -@param shape Element shape that could be one of cv::MorphShapes -@param ksize Size of the structuring element. -@param anchor Anchor position within the element. The default value \f$(-1, -1)\f$ means that the -anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor -position. In other cases the anchor just regulates how much the result of the morphological -operation is shifted. - */ -CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1)); - -/** @brief Blurs an image using the median filter. - -The function smoothes an image using the median filter with the \f$\texttt{ksize} \times -\texttt{ksize}\f$ aperture. Each channel of a multi-channel image is processed independently. -In-place operation is supported. - -@param src input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be -CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U. -@param dst destination array of the same size and type as src. -@param ksize aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ... -@sa bilateralFilter, blur, boxFilter, GaussianBlur - */ -CV_EXPORTS_W void medianBlur( InputArray src, OutputArray dst, int ksize ); - -/** @brief Blurs an image using a Gaussian filter. - -The function convolves the source image with the specified Gaussian kernel. In-place filtering is -supported. - -@param src input image; the image can have any number of channels, which are processed -independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. -@param dst output image of the same size and type as src. -@param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be -positive and odd. Or, they can be zero's and then they are computed from sigma. -@param sigmaX Gaussian kernel standard deviation in X direction. -@param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be -equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, -respectively (see cv::getGaussianKernel for details); to fully control the result regardless of -possible future modifications of all this semantics, it is recommended to specify all of ksize, -sigmaX, and sigmaY. -@param borderType pixel extrapolation method, see cv::BorderTypes - -@sa sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur - */ -CV_EXPORTS_W void GaussianBlur( InputArray src, OutputArray dst, Size ksize, - double sigmaX, double sigmaY = 0, - int borderType = BORDER_DEFAULT ); - -/** @brief Applies the bilateral filter to an image. - -The function applies bilateral filtering to the input image, as described in -http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html -bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is -very slow compared to most filters. - -_Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\< -10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very -strong effect, making the image look "cartoonish". - -_Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time -applications, and perhaps d=9 for offline applications that need heavy noise filtering. - -This filter does not work inplace. -@param src Source 8-bit or floating-point, 1-channel or 3-channel image. -@param dst Destination image of the same size and type as src . -@param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, -it is computed from sigmaSpace. -@param sigmaColor Filter sigma in the color space. A larger value of the parameter means that -farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting -in larger areas of semi-equal color. -@param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that -farther pixels will influence each other as long as their colors are close enough (see sigmaColor -). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is -proportional to sigmaSpace. -@param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes - */ -CV_EXPORTS_W void bilateralFilter( InputArray src, OutputArray dst, int d, - double sigmaColor, double sigmaSpace, - int borderType = BORDER_DEFAULT ); - -/** @brief Blurs an image using the box filter. - -The function smoothes an image using the kernel: - -\f[\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\f] - -where - -\f[\alpha = \fork{\frac{1}{\texttt{ksize.width*ksize.height}}}{when \texttt{normalize=true}}{1}{otherwise}\f] - -Unnormalized box filter is useful for computing various integral characteristics over each pixel -neighborhood, such as covariance matrices of image derivatives (used in dense optical flow -algorithms, and so on). If you need to compute pixel sums over variable-size windows, use cv::integral. - -@param src input image. -@param dst output image of the same size and type as src. -@param ddepth the output image depth (-1 to use src.depth()). -@param ksize blurring kernel size. -@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel -center. -@param normalize flag, specifying whether the kernel is normalized by its area or not. -@param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes -@sa blur, bilateralFilter, GaussianBlur, medianBlur, integral - */ -CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth, - Size ksize, Point anchor = Point(-1,-1), - bool normalize = true, - int borderType = BORDER_DEFAULT ); - -/** @brief Calculates the normalized sum of squares of the pixel values overlapping the filter. - -For every pixel \f$ (x, y) \f$ in the source image, the function calculates the sum of squares of those neighboring -pixel values which overlap the filter placed over the pixel \f$ (x, y) \f$. - -The unnormalized square box filter can be useful in computing local image statistics such as the the local -variance and standard deviation around the neighborhood of a pixel. - -@param _src input image -@param _dst output image of the same size and type as _src -@param ddepth the output image depth (-1 to use src.depth()) -@param ksize kernel size -@param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel -center. -@param normalize flag, specifying whether the kernel is to be normalized by it's area or not. -@param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes -@sa boxFilter -*/ -CV_EXPORTS_W void sqrBoxFilter( InputArray _src, OutputArray _dst, int ddepth, - Size ksize, Point anchor = Point(-1, -1), - bool normalize = true, - int borderType = BORDER_DEFAULT ); - -/** @brief Blurs an image using the normalized box filter. - -The function smoothes an image using the kernel: - -\f[\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\f] - -The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(), -anchor, true, borderType)`. - -@param src input image; it can have any number of channels, which are processed independently, but -the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. -@param dst output image of the same size and type as src. -@param ksize blurring kernel size. -@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel -center. -@param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes -@sa boxFilter, bilateralFilter, GaussianBlur, medianBlur - */ -CV_EXPORTS_W void blur( InputArray src, OutputArray dst, - Size ksize, Point anchor = Point(-1,-1), - int borderType = BORDER_DEFAULT ); - -/** @brief Convolves an image with the kernel. - -The function applies an arbitrary linear filter to an image. In-place operation is supported. When -the aperture is partially outside the image, the function interpolates outlier pixel values -according to the specified border mode. - -The function does actually compute correlation, not the convolution: - -\f[\texttt{dst} (x,y) = \sum _{ \stackrel{0\leq x' < \texttt{kernel.cols},}{0\leq y' < \texttt{kernel.rows}} } \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\f] - -That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip -the kernel using cv::flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - -anchor.y - 1)`. - -The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or -larger) and the direct algorithm for small kernels. - -@param src input image. -@param dst output image of the same size and the same number of channels as src. -@param ddepth desired depth of the destination image, see @ref filter_depths "combinations" -@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point -matrix; if you want to apply different kernels to different channels, split the image into -separate color planes using split and process them individually. -@param anchor anchor of the kernel that indicates the relative position of a filtered point within -the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor -is at the kernel center. -@param delta optional value added to the filtered pixels before storing them in dst. -@param borderType pixel extrapolation method, see cv::BorderTypes -@sa sepFilter2D, dft, matchTemplate - */ -CV_EXPORTS_W void filter2D( InputArray src, OutputArray dst, int ddepth, - InputArray kernel, Point anchor = Point(-1,-1), - double delta = 0, int borderType = BORDER_DEFAULT ); - -/** @brief Applies a separable linear filter to an image. - -The function applies a separable linear filter to the image. That is, first, every row of src is -filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D -kernel kernelY. The final result shifted by delta is stored in dst . - -@param src Source image. -@param dst Destination image of the same size and the same number of channels as src . -@param ddepth Destination image depth, see @ref filter_depths "combinations" -@param kernelX Coefficients for filtering each row. -@param kernelY Coefficients for filtering each column. -@param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor -is at the kernel center. -@param delta Value added to the filtered results before storing them. -@param borderType Pixel extrapolation method, see cv::BorderTypes -@sa filter2D, Sobel, GaussianBlur, boxFilter, blur - */ -CV_EXPORTS_W void sepFilter2D( InputArray src, OutputArray dst, int ddepth, - InputArray kernelX, InputArray kernelY, - Point anchor = Point(-1,-1), - double delta = 0, int borderType = BORDER_DEFAULT ); - -/** @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. - -In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to -calculate the derivative. When \f$\texttt{ksize = 1}\f$, the \f$3 \times 1\f$ or \f$1 \times 3\f$ -kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first -or the second x- or y- derivatives. - -There is also the special value `ksize = CV_SCHARR (-1)` that corresponds to the \f$3\times3\f$ Scharr -filter that may give more accurate results than the \f$3\times3\f$ Sobel. The Scharr aperture is - -\f[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\f] - -for the x-derivative, or transposed for the y-derivative. - -The function calculates an image derivative by convolving the image with the appropriate kernel: - -\f[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\f] - -The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less -resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) -or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first -case corresponds to a kernel of: - -\f[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\f] - -The second case corresponds to a kernel of: - -\f[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\f] - -@param src input image. -@param dst output image of the same size and the same number of channels as src . -@param ddepth output image depth, see @ref filter_depths "combinations"; in the case of - 8-bit input images it will result in truncated derivatives. -@param dx order of the derivative x. -@param dy order of the derivative y. -@param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7. -@param scale optional scale factor for the computed derivative values; by default, no scaling is -applied (see cv::getDerivKernels for details). -@param delta optional delta value that is added to the results prior to storing them in dst. -@param borderType pixel extrapolation method, see cv::BorderTypes -@sa Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar - */ -CV_EXPORTS_W void Sobel( InputArray src, OutputArray dst, int ddepth, - int dx, int dy, int ksize = 3, - double scale = 1, double delta = 0, - int borderType = BORDER_DEFAULT ); - -/** @brief Calculates the first order image derivative in both x and y using a Sobel operator - -Equivalent to calling: - -@code -Sobel( src, dx, CV_16SC1, 1, 0, 3 ); -Sobel( src, dy, CV_16SC1, 0, 1, 3 ); -@endcode - -@param src input image. -@param dx output image with first-order derivative in x. -@param dy output image with first-order derivative in y. -@param ksize size of Sobel kernel. It must be 3. -@param borderType pixel extrapolation method, see cv::BorderTypes - -@sa Sobel - */ - -CV_EXPORTS_W void spatialGradient( InputArray src, OutputArray dx, - OutputArray dy, int ksize = 3, - int borderType = BORDER_DEFAULT ); - -/** @brief Calculates the first x- or y- image derivative using Scharr operator. - -The function computes the first x- or y- spatial image derivative using the Scharr operator. The -call - -\f[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\f] - -is equivalent to - -\f[\texttt{Sobel(src, dst, ddepth, dx, dy, CV\_SCHARR, scale, delta, borderType)} .\f] - -@param src input image. -@param dst output image of the same size and the same number of channels as src. -@param ddepth output image depth, see @ref filter_depths "combinations" -@param dx order of the derivative x. -@param dy order of the derivative y. -@param scale optional scale factor for the computed derivative values; by default, no scaling is -applied (see getDerivKernels for details). -@param delta optional delta value that is added to the results prior to storing them in dst. -@param borderType pixel extrapolation method, see cv::BorderTypes -@sa cartToPolar - */ -CV_EXPORTS_W void Scharr( InputArray src, OutputArray dst, int ddepth, - int dx, int dy, double scale = 1, double delta = 0, - int borderType = BORDER_DEFAULT ); - -/** @example laplace.cpp - An example using Laplace transformations for edge detection -*/ - -/** @brief Calculates the Laplacian of an image. - -The function calculates the Laplacian of the source image by adding up the second x and y -derivatives calculated using the Sobel operator: - -\f[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\f] - -This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image -with the following \f$3 \times 3\f$ aperture: - -\f[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\f] - -@param src Source image. -@param dst Destination image of the same size and the same number of channels as src . -@param ddepth Desired depth of the destination image. -@param ksize Aperture size used to compute the second-derivative filters. See getDerivKernels for -details. The size must be positive and odd. -@param scale Optional scale factor for the computed Laplacian values. By default, no scaling is -applied. See getDerivKernels for details. -@param delta Optional delta value that is added to the results prior to storing them in dst . -@param borderType Pixel extrapolation method, see cv::BorderTypes -@sa Sobel, Scharr - */ -CV_EXPORTS_W void Laplacian( InputArray src, OutputArray dst, int ddepth, - int ksize = 1, double scale = 1, double delta = 0, - int borderType = BORDER_DEFAULT ); - -//! @} imgproc_filter - -//! @addtogroup imgproc_feature -//! @{ - -/** @example edge.cpp - An example on using the canny edge detector -*/ - -/** @brief Finds edges in an image using the Canny algorithm @cite Canny86 . - -The function finds edges in the input image image and marks them in the output map edges using the -Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The -largest value is used to find initial segments of strong edges. See - - -@param image 8-bit input image. -@param edges output edge map; single channels 8-bit image, which has the same size as image . -@param threshold1 first threshold for the hysteresis procedure. -@param threshold2 second threshold for the hysteresis procedure. -@param apertureSize aperture size for the Sobel operator. -@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm -\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude ( -L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough ( -L2gradient=false ). - */ -CV_EXPORTS_W void Canny( InputArray image, OutputArray edges, - double threshold1, double threshold2, - int apertureSize = 3, bool L2gradient = false ); - -/** @brief Calculates the minimal eigenvalue of gradient matrices for corner detection. - -The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal -eigenvalue of the covariance matrix of derivatives, that is, \f$\min(\lambda_1, \lambda_2)\f$ in terms -of the formulae in the cornerEigenValsAndVecs description. - -@param src Input single-channel 8-bit or floating-point image. -@param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as -src . -@param blockSize Neighborhood size (see the details on cornerEigenValsAndVecs ). -@param ksize Aperture parameter for the Sobel operator. -@param borderType Pixel extrapolation method. See cv::BorderTypes. - */ -CV_EXPORTS_W void cornerMinEigenVal( InputArray src, OutputArray dst, - int blockSize, int ksize = 3, - int borderType = BORDER_DEFAULT ); - -/** @brief Harris corner detector. - -The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and -cornerEigenValsAndVecs , for each pixel \f$(x, y)\f$ it calculates a \f$2\times2\f$ gradient covariance -matrix \f$M^{(x,y)}\f$ over a \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood. Then, it -computes the following characteristic: - -\f[\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\f] - -Corners in the image can be found as the local maxima of this response map. - -@param src Input single-channel 8-bit or floating-point image. -@param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same -size as src . -@param blockSize Neighborhood size (see the details on cornerEigenValsAndVecs ). -@param ksize Aperture parameter for the Sobel operator. -@param k Harris detector free parameter. See the formula below. -@param borderType Pixel extrapolation method. See cv::BorderTypes. - */ -CV_EXPORTS_W void cornerHarris( InputArray src, OutputArray dst, int blockSize, - int ksize, double k, - int borderType = BORDER_DEFAULT ); - -/** @brief Calculates eigenvalues and eigenvectors of image blocks for corner detection. - -For every pixel \f$p\f$ , the function cornerEigenValsAndVecs considers a blockSize \f$\times\f$ blockSize -neighborhood \f$S(p)\f$ . It calculates the covariation matrix of derivatives over the neighborhood as: - -\f[M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\f] - -where the derivatives are computed using the Sobel operator. - -After that, it finds eigenvectors and eigenvalues of \f$M\f$ and stores them in the destination image as -\f$(\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\f$ where - -- \f$\lambda_1, \lambda_2\f$ are the non-sorted eigenvalues of \f$M\f$ -- \f$x_1, y_1\f$ are the eigenvectors corresponding to \f$\lambda_1\f$ -- \f$x_2, y_2\f$ are the eigenvectors corresponding to \f$\lambda_2\f$ - -The output of the function can be used for robust edge or corner detection. - -@param src Input single-channel 8-bit or floating-point image. -@param dst Image to store the results. It has the same size as src and the type CV_32FC(6) . -@param blockSize Neighborhood size (see details below). -@param ksize Aperture parameter for the Sobel operator. -@param borderType Pixel extrapolation method. See cv::BorderTypes. - -@sa cornerMinEigenVal, cornerHarris, preCornerDetect - */ -CV_EXPORTS_W void cornerEigenValsAndVecs( InputArray src, OutputArray dst, - int blockSize, int ksize, - int borderType = BORDER_DEFAULT ); - -/** @brief Calculates a feature map for corner detection. - -The function calculates the complex spatial derivative-based function of the source image - -\f[\texttt{dst} = (D_x \texttt{src} )^2 \cdot D_{yy} \texttt{src} + (D_y \texttt{src} )^2 \cdot D_{xx} \texttt{src} - 2 D_x \texttt{src} \cdot D_y \texttt{src} \cdot D_{xy} \texttt{src}\f] - -where \f$D_x\f$,\f$D_y\f$ are the first image derivatives, \f$D_{xx}\f$,\f$D_{yy}\f$ are the second image -derivatives, and \f$D_{xy}\f$ is the mixed derivative. - -The corners can be found as local maximums of the functions, as shown below: -@code - Mat corners, dilated_corners; - preCornerDetect(image, corners, 3); - // dilation with 3x3 rectangular structuring element - dilate(corners, dilated_corners, Mat(), 1); - Mat corner_mask = corners == dilated_corners; -@endcode - -@param src Source single-channel 8-bit of floating-point image. -@param dst Output image that has the type CV_32F and the same size as src . -@param ksize %Aperture size of the Sobel . -@param borderType Pixel extrapolation method. See cv::BorderTypes. - */ -CV_EXPORTS_W void preCornerDetect( InputArray src, OutputArray dst, int ksize, - int borderType = BORDER_DEFAULT ); - -/** @brief Refines the corner locations. - -The function iterates to find the sub-pixel accurate location of corners or radial saddle points, as -shown on the figure below. - -![image](pics/cornersubpix.png) - -Sub-pixel accurate corner locator is based on the observation that every vector from the center \f$q\f$ -to a point \f$p\f$ located within a neighborhood of \f$q\f$ is orthogonal to the image gradient at \f$p\f$ -subject to image and measurement noise. Consider the expression: - -\f[\epsilon _i = {DI_{p_i}}^T \cdot (q - p_i)\f] - -where \f${DI_{p_i}}\f$ is an image gradient at one of the points \f$p_i\f$ in a neighborhood of \f$q\f$ . The -value of \f$q\f$ is to be found so that \f$\epsilon_i\f$ is minimized. A system of equations may be set up -with \f$\epsilon_i\f$ set to zero: - -\f[\sum _i(DI_{p_i} \cdot {DI_{p_i}}^T) - \sum _i(DI_{p_i} \cdot {DI_{p_i}}^T \cdot p_i)\f] - -where the gradients are summed within a neighborhood ("search window") of \f$q\f$ . Calling the first -gradient term \f$G\f$ and the second gradient term \f$b\f$ gives: - -\f[q = G^{-1} \cdot b\f] - -The algorithm sets the center of the neighborhood window at this new center \f$q\f$ and then iterates -until the center stays within a set threshold. - -@param image Input image. -@param corners Initial coordinates of the input corners and refined coordinates provided for -output. -@param winSize Half of the side length of the search window. For example, if winSize=Size(5,5) , -then a \f$5*2+1 \times 5*2+1 = 11 \times 11\f$ search window is used. -@param zeroZone Half of the size of the dead region in the middle of the search zone over which -the summation in the formula below is not done. It is used sometimes to avoid possible -singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such -a size. -@param criteria Criteria for termination of the iterative process of corner refinement. That is, -the process of corner position refinement stops either after criteria.maxCount iterations or when -the corner position moves by less than criteria.epsilon on some iteration. - */ -CV_EXPORTS_W void cornerSubPix( InputArray image, InputOutputArray corners, - Size winSize, Size zeroZone, - TermCriteria criteria ); - -/** @brief Determines strong corners on an image. - -The function finds the most prominent corners in the image or in the specified image region, as -described in @cite Shi94 - -- Function calculates the corner quality measure at every source image pixel using the - cornerMinEigenVal or cornerHarris . -- Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are - retained). -- The corners with the minimal eigenvalue less than - \f$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\f$ are rejected. -- The remaining corners are sorted by the quality measure in the descending order. -- Function throws away each corner for which there is a stronger corner at a distance less than - maxDistance. - -The function can be used to initialize a point-based tracker of an object. - -@note If the function is called with different values A and B of the parameter qualityLevel , and -A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector -with qualityLevel=B . - -@param image Input 8-bit or floating-point 32-bit, single-channel image. -@param corners Output vector of detected corners. -@param maxCorners Maximum number of corners to return. If there are more corners than are found, -the strongest of them is returned. -@param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The -parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue -(see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the -quality measure less than the product are rejected. For example, if the best corner has the -quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure -less than 15 are rejected. -@param minDistance Minimum possible Euclidean distance between the returned corners. -@param mask Optional region of interest. If the image is not empty (it needs to have the type -CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. -@param blockSize Size of an average block for computing a derivative covariation matrix over each -pixel neighborhood. See cornerEigenValsAndVecs . -@param useHarrisDetector Parameter indicating whether to use a Harris detector (see cornerHarris) -or cornerMinEigenVal. -@param k Free parameter of the Harris detector. - -@sa cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform, - */ -CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners, - int maxCorners, double qualityLevel, double minDistance, - InputArray mask = noArray(), int blockSize = 3, - bool useHarrisDetector = false, double k = 0.04 ); - -/** @example houghlines.cpp -An example using the Hough line detector -*/ - -/** @brief Finds lines in a binary image using the standard Hough transform. - -The function implements the standard or standard multi-scale Hough transform algorithm for line -detection. See for a good explanation of Hough -transform. - -@param image 8-bit, single-channel binary source image. The image may be modified by the function. -@param lines Output vector of lines. Each line is represented by a two-element vector -\f$(\rho, \theta)\f$ . \f$\rho\f$ is the distance from the coordinate origin \f$(0,0)\f$ (top-left corner of -the image). \f$\theta\f$ is the line rotation angle in radians ( -\f$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\f$ ). -@param rho Distance resolution of the accumulator in pixels. -@param theta Angle resolution of the accumulator in radians. -@param threshold Accumulator threshold parameter. Only those lines are returned that get enough -votes ( \f$>\texttt{threshold}\f$ ). -@param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho . -The coarse accumulator distance resolution is rho and the accurate accumulator resolution is -rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these -parameters should be positive. -@param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta. -@param min_theta For standard and multi-scale Hough transform, minimum angle to check for lines. -Must fall between 0 and max_theta. -@param max_theta For standard and multi-scale Hough transform, maximum angle to check for lines. -Must fall between min_theta and CV_PI. - */ -CV_EXPORTS_W void HoughLines( InputArray image, OutputArray lines, - double rho, double theta, int threshold, - double srn = 0, double stn = 0, - double min_theta = 0, double max_theta = CV_PI ); - -/** @brief Finds line segments in a binary image using the probabilistic Hough transform. - -The function implements the probabilistic Hough transform algorithm for line detection, described -in @cite Matas00 - -See the line detection example below: - -@code - #include - #include - - using namespace cv; - using namespace std; - - int main(int argc, char** argv) - { - Mat src, dst, color_dst; - if( argc != 2 || !(src=imread(argv[1], 0)).data) - return -1; - - Canny( src, dst, 50, 200, 3 ); - cvtColor( dst, color_dst, COLOR_GRAY2BGR ); - - #if 0 - vector lines; - HoughLines( dst, lines, 1, CV_PI/180, 100 ); - - for( size_t i = 0; i < lines.size(); i++ ) - { - float rho = lines[i][0]; - float theta = lines[i][1]; - double a = cos(theta), b = sin(theta); - double x0 = a*rho, y0 = b*rho; - Point pt1(cvRound(x0 + 1000*(-b)), - cvRound(y0 + 1000*(a))); - Point pt2(cvRound(x0 - 1000*(-b)), - cvRound(y0 - 1000*(a))); - line( color_dst, pt1, pt2, Scalar(0,0,255), 3, 8 ); - } - #else - vector lines; - HoughLinesP( dst, lines, 1, CV_PI/180, 80, 30, 10 ); - for( size_t i = 0; i < lines.size(); i++ ) - { - line( color_dst, Point(lines[i][0], lines[i][1]), - Point(lines[i][2], lines[i][3]), Scalar(0,0,255), 3, 8 ); - } - #endif - namedWindow( "Source", 1 ); - imshow( "Source", src ); - - namedWindow( "Detected Lines", 1 ); - imshow( "Detected Lines", color_dst ); - - waitKey(0); - return 0; - } -@endcode -This is a sample picture the function parameters have been tuned for: - -![image](pics/building.jpg) - -And this is the output of the above program in case of the probabilistic Hough transform: - -![image](pics/houghp.png) - -@param image 8-bit, single-channel binary source image. The image may be modified by the function. -@param lines Output vector of lines. Each line is represented by a 4-element vector -\f$(x_1, y_1, x_2, y_2)\f$ , where \f$(x_1,y_1)\f$ and \f$(x_2, y_2)\f$ are the ending points of each detected -line segment. -@param rho Distance resolution of the accumulator in pixels. -@param theta Angle resolution of the accumulator in radians. -@param threshold Accumulator threshold parameter. Only those lines are returned that get enough -votes ( \f$>\texttt{threshold}\f$ ). -@param minLineLength Minimum line length. Line segments shorter than that are rejected. -@param maxLineGap Maximum allowed gap between points on the same line to link them. - -@sa LineSegmentDetector - */ -CV_EXPORTS_W void HoughLinesP( InputArray image, OutputArray lines, - double rho, double theta, int threshold, - double minLineLength = 0, double maxLineGap = 0 ); - -/** @example houghcircles.cpp -An example using the Hough circle detector -*/ - -/** @brief Finds circles in a grayscale image using the Hough transform. - -The function finds circles in a grayscale image using a modification of the Hough transform. - -Example: : -@code - #include - #include - #include - - using namespace cv; - using namespace std; - - int main(int argc, char** argv) - { - Mat img, gray; - if( argc != 2 || !(img=imread(argv[1], 1)).data) - return -1; - cvtColor(img, gray, COLOR_BGR2GRAY); - // smooth it, otherwise a lot of false circles may be detected - GaussianBlur( gray, gray, Size(9, 9), 2, 2 ); - vector circles; - HoughCircles(gray, circles, HOUGH_GRADIENT, - 2, gray.rows/4, 200, 100 ); - for( size_t i = 0; i < circles.size(); i++ ) - { - Point center(cvRound(circles[i][0]), cvRound(circles[i][1])); - int radius = cvRound(circles[i][2]); - // draw the circle center - circle( img, center, 3, Scalar(0,255,0), -1, 8, 0 ); - // draw the circle outline - circle( img, center, radius, Scalar(0,0,255), 3, 8, 0 ); - } - namedWindow( "circles", 1 ); - imshow( "circles", img ); - - waitKey(0); - return 0; - } -@endcode - -@note Usually the function detects the centers of circles well. However, it may fail to find correct -radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if -you know it. Or, you may ignore the returned radius, use only the center, and find the correct -radius using an additional procedure. - -@param image 8-bit, single-channel, grayscale input image. -@param circles Output vector of found circles. Each vector is encoded as a 3-element -floating-point vector \f$(x, y, radius)\f$ . -@param method Detection method, see cv::HoughModes. Currently, the only implemented method is HOUGH_GRADIENT -@param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if -dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has -half as big width and height. -@param minDist Minimum distance between the centers of the detected circles. If the parameter is -too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is -too large, some circles may be missed. -@param param1 First method-specific parameter. In case of CV_HOUGH_GRADIENT , it is the higher -threshold of the two passed to the Canny edge detector (the lower one is twice smaller). -@param param2 Second method-specific parameter. In case of CV_HOUGH_GRADIENT , it is the -accumulator threshold for the circle centers at the detection stage. The smaller it is, the more -false circles may be detected. Circles, corresponding to the larger accumulator values, will be -returned first. -@param minRadius Minimum circle radius. -@param maxRadius Maximum circle radius. - -@sa fitEllipse, minEnclosingCircle - */ -CV_EXPORTS_W void HoughCircles( InputArray image, OutputArray circles, - int method, double dp, double minDist, - double param1 = 100, double param2 = 100, - int minRadius = 0, int maxRadius = 0 ); - -//! @} imgproc_feature - -//! @addtogroup imgproc_filter -//! @{ - -/** @example morphology2.cpp - An example using the morphological operations -*/ - -/** @brief Erodes an image by using a specific structuring element. - -The function erodes the source image using the specified structuring element that determines the -shape of a pixel neighborhood over which the minimum is taken: - -\f[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f] - -The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In -case of multi-channel images, each channel is processed independently. - -@param src input image; the number of channels can be arbitrary, but the depth should be one of -CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. -@param dst output image of the same size and type as src. -@param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular -structuring element is used. Kernel can be created using getStructuringElement. -@param anchor position of the anchor within the element; default value (-1, -1) means that the -anchor is at the element center. -@param iterations number of times erosion is applied. -@param borderType pixel extrapolation method, see cv::BorderTypes -@param borderValue border value in case of a constant border -@sa dilate, morphologyEx, getStructuringElement - */ -CV_EXPORTS_W void erode( InputArray src, OutputArray dst, InputArray kernel, - Point anchor = Point(-1,-1), int iterations = 1, - int borderType = BORDER_CONSTANT, - const Scalar& borderValue = morphologyDefaultBorderValue() ); - -/** @brief Dilates an image by using a specific structuring element. - -The function dilates the source image using the specified structuring element that determines the -shape of a pixel neighborhood over which the maximum is taken: -\f[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f] - -The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In -case of multi-channel images, each channel is processed independently. - -@param src input image; the number of channels can be arbitrary, but the depth should be one of -CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. -@param dst output image of the same size and type as src\`. -@param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular -structuring element is used. Kernel can be created using getStructuringElement -@param anchor position of the anchor within the element; default value (-1, -1) means that the -anchor is at the element center. -@param iterations number of times dilation is applied. -@param borderType pixel extrapolation method, see cv::BorderTypes -@param borderValue border value in case of a constant border -@sa erode, morphologyEx, getStructuringElement - */ -CV_EXPORTS_W void dilate( InputArray src, OutputArray dst, InputArray kernel, - Point anchor = Point(-1,-1), int iterations = 1, - int borderType = BORDER_CONSTANT, - const Scalar& borderValue = morphologyDefaultBorderValue() ); - -/** @brief Performs advanced morphological transformations. - -The function morphologyEx can perform advanced morphological transformations using an erosion and dilation as -basic operations. - -Any of the operations can be done in-place. In case of multi-channel images, each channel is -processed independently. - -@param src Source image. The number of channels can be arbitrary. The depth should be one of -CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. -@param dst Destination image of the same size and type as source image. -@param op Type of a morphological operation, see cv::MorphTypes -@param kernel Structuring element. It can be created using cv::getStructuringElement. -@param anchor Anchor position with the kernel. Negative values mean that the anchor is at the -kernel center. -@param iterations Number of times erosion and dilation are applied. -@param borderType Pixel extrapolation method, see cv::BorderTypes -@param borderValue Border value in case of a constant border. The default value has a special -meaning. -@sa dilate, erode, getStructuringElement - */ -CV_EXPORTS_W void morphologyEx( InputArray src, OutputArray dst, - int op, InputArray kernel, - Point anchor = Point(-1,-1), int iterations = 1, - int borderType = BORDER_CONSTANT, - const Scalar& borderValue = morphologyDefaultBorderValue() ); - -//! @} imgproc_filter - -//! @addtogroup imgproc_transform -//! @{ - -/** @brief Resizes an image. - -The function resize resizes the image src down to or up to the specified size. Note that the -initial dst type or size are not taken into account. Instead, the size and type are derived from -the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst, -you may call the function as follows: -@code - // explicitly specify dsize=dst.size(); fx and fy will be computed from that. - resize(src, dst, dst.size(), 0, 0, interpolation); -@endcode -If you want to decimate the image by factor of 2 in each direction, you can call the function this -way: -@code - // specify fx and fy and let the function compute the destination image size. - resize(src, dst, Size(), 0.5, 0.5, interpolation); -@endcode -To shrink an image, it will generally look best with cv::INTER_AREA interpolation, whereas to -enlarge an image, it will generally look best with cv::INTER_CUBIC (slow) or cv::INTER_LINEAR -(faster but still looks OK). - -@param src input image. -@param dst output image; it has the size dsize (when it is non-zero) or the size computed from -src.size(), fx, and fy; the type of dst is the same as of src. -@param dsize output image size; if it equals zero, it is computed as: - \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f] - Either dsize or both fx and fy must be non-zero. -@param fx scale factor along the horizontal axis; when it equals 0, it is computed as -\f[\texttt{(double)dsize.width/src.cols}\f] -@param fy scale factor along the vertical axis; when it equals 0, it is computed as -\f[\texttt{(double)dsize.height/src.rows}\f] -@param interpolation interpolation method, see cv::InterpolationFlags - -@sa warpAffine, warpPerspective, remap - */ -CV_EXPORTS_W void resize( InputArray src, OutputArray dst, - Size dsize, double fx = 0, double fy = 0, - int interpolation = INTER_LINEAR ); - -/** @brief Applies an affine transformation to an image. - -The function warpAffine transforms the source image using the specified matrix: - -\f[\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\f] - -when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted -with cv::invertAffineTransform and then put in the formula above instead of M. The function cannot -operate in-place. - -@param src input image. -@param dst output image that has the size dsize and the same type as src . -@param M \f$2\times 3\f$ transformation matrix. -@param dsize size of the output image. -@param flags combination of interpolation methods (see cv::InterpolationFlags) and the optional -flag WARP_INVERSE_MAP that means that M is the inverse transformation ( -\f$\texttt{dst}\rightarrow\texttt{src}\f$ ). -@param borderMode pixel extrapolation method (see cv::BorderTypes); when -borderMode=BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to -the "outliers" in the source image are not modified by the function. -@param borderValue value used in case of a constant border; by default, it is 0. - -@sa warpPerspective, resize, remap, getRectSubPix, transform - */ -CV_EXPORTS_W void warpAffine( InputArray src, OutputArray dst, - InputArray M, Size dsize, - int flags = INTER_LINEAR, - int borderMode = BORDER_CONSTANT, - const Scalar& borderValue = Scalar()); - -/** @brief Applies a perspective transformation to an image. - -The function warpPerspective transforms the source image using the specified matrix: - -\f[\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , - \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\f] - -when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert -and then put in the formula above instead of M. The function cannot operate in-place. - -@param src input image. -@param dst output image that has the size dsize and the same type as src . -@param M \f$3\times 3\f$ transformation matrix. -@param dsize size of the output image. -@param flags combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the -optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( -\f$\texttt{dst}\rightarrow\texttt{src}\f$ ). -@param borderMode pixel extrapolation method (BORDER_CONSTANT or BORDER_REPLICATE). -@param borderValue value used in case of a constant border; by default, it equals 0. - -@sa warpAffine, resize, remap, getRectSubPix, perspectiveTransform - */ -CV_EXPORTS_W void warpPerspective( InputArray src, OutputArray dst, - InputArray M, Size dsize, - int flags = INTER_LINEAR, - int borderMode = BORDER_CONSTANT, - const Scalar& borderValue = Scalar()); - -/** @brief Applies a generic geometrical transformation to an image. - -The function remap transforms the source image using the specified map: - -\f[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\f] - -where values of pixels with non-integer coordinates are computed using one of available -interpolation methods. \f$map_x\f$ and \f$map_y\f$ can be encoded as separate floating-point maps -in \f$map_1\f$ and \f$map_2\f$ respectively, or interleaved floating-point maps of \f$(x,y)\f$ in -\f$map_1\f$, or fixed-point maps created by using convertMaps. The reason you might want to -convert from floating to fixed-point representations of a map is that they can yield much faster -(\~2x) remapping operations. In the converted case, \f$map_1\f$ contains pairs (cvFloor(x), -cvFloor(y)) and \f$map_2\f$ contains indices in a table of interpolation coefficients. - -This function cannot operate in-place. - -@param src Source image. -@param dst Destination image. It has the same size as map1 and the same type as src . -@param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 , -CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point -representation to fixed-point for speed. -@param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map -if map1 is (x,y) points), respectively. -@param interpolation Interpolation method (see cv::InterpolationFlags). The method INTER_AREA is -not supported by this function. -@param borderMode Pixel extrapolation method (see cv::BorderTypes). When -borderMode=BORDER_TRANSPARENT, it means that the pixels in the destination image that -corresponds to the "outliers" in the source image are not modified by the function. -@param borderValue Value used in case of a constant border. By default, it is 0. - */ -CV_EXPORTS_W void remap( InputArray src, OutputArray dst, - InputArray map1, InputArray map2, - int interpolation, int borderMode = BORDER_CONSTANT, - const Scalar& borderValue = Scalar()); - -/** @brief Converts image transformation maps from one representation to another. - -The function converts a pair of maps for remap from one representation to another. The following -options ( (map1.type(), map2.type()) \f$\rightarrow\f$ (dstmap1.type(), dstmap2.type()) ) are -supported: - -- \f$\texttt{(CV\_32FC1, CV\_32FC1)} \rightarrow \texttt{(CV\_16SC2, CV\_16UC1)}\f$. This is the -most frequently used conversion operation, in which the original floating-point maps (see remap ) -are converted to a more compact and much faster fixed-point representation. The first output array -contains the rounded coordinates and the second array (created only when nninterpolation=false ) -contains indices in the interpolation tables. - -- \f$\texttt{(CV\_32FC2)} \rightarrow \texttt{(CV\_16SC2, CV\_16UC1)}\f$. The same as above but -the original maps are stored in one 2-channel matrix. - -- Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same -as the originals. - -@param map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 . -@param map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), -respectively. -@param dstmap1 The first output map that has the type dstmap1type and the same size as src . -@param dstmap2 The second output map. -@param dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or -CV_32FC2 . -@param nninterpolation Flag indicating whether the fixed-point maps are used for the -nearest-neighbor or for a more complex interpolation. - -@sa remap, undistort, initUndistortRectifyMap - */ -CV_EXPORTS_W void convertMaps( InputArray map1, InputArray map2, - OutputArray dstmap1, OutputArray dstmap2, - int dstmap1type, bool nninterpolation = false ); - -/** @brief Calculates an affine matrix of 2D rotation. - -The function calculates the following matrix: - -\f[\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}\f] - -where - -\f[\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}\f] - -The transformation maps the rotation center to itself. If this is not the target, adjust the shift. - -@param center Center of the rotation in the source image. -@param angle Rotation angle in degrees. Positive values mean counter-clockwise rotation (the -coordinate origin is assumed to be the top-left corner). -@param scale Isotropic scale factor. - -@sa getAffineTransform, warpAffine, transform - */ -CV_EXPORTS_W Mat getRotationMatrix2D( Point2f center, double angle, double scale ); - -//! returns 3x3 perspective transformation for the corresponding 4 point pairs. -CV_EXPORTS Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[] ); - -/** @brief Calculates an affine transform from three pairs of the corresponding points. - -The function calculates the \f$2 \times 3\f$ matrix of an affine transform so that: - -\f[\begin{bmatrix} x'_i \\ y'_i \end{bmatrix} = \texttt{map\_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f] - -where - -\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2\f] - -@param src Coordinates of triangle vertices in the source image. -@param dst Coordinates of the corresponding triangle vertices in the destination image. - -@sa warpAffine, transform - */ -CV_EXPORTS Mat getAffineTransform( const Point2f src[], const Point2f dst[] ); - -/** @brief Inverts an affine transformation. - -The function computes an inverse affine transformation represented by \f$2 \times 3\f$ matrix M: - -\f[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ a_{21} & a_{22} & b_2 \end{bmatrix}\f] - -The result is also a \f$2 \times 3\f$ matrix of the same type as M. - -@param M Original affine transformation. -@param iM Output reverse affine transformation. - */ -CV_EXPORTS_W void invertAffineTransform( InputArray M, OutputArray iM ); - -/** @brief Calculates a perspective transform from four pairs of the corresponding points. - -The function calculates the \f$3 \times 3\f$ matrix of a perspective transform so that: - -\f[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map\_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f] - -where - -\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\f] - -@param src Coordinates of quadrangle vertices in the source image. -@param dst Coordinates of the corresponding quadrangle vertices in the destination image. - -@sa findHomography, warpPerspective, perspectiveTransform - */ -CV_EXPORTS_W Mat getPerspectiveTransform( InputArray src, InputArray dst ); - -CV_EXPORTS_W Mat getAffineTransform( InputArray src, InputArray dst ); - -/** @brief Retrieves a pixel rectangle from an image with sub-pixel accuracy. - -The function getRectSubPix extracts pixels from src: - -\f[dst(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\f] - -where the values of the pixels at non-integer coordinates are retrieved using bilinear -interpolation. Every channel of multi-channel images is processed independently. While the center of -the rectangle must be inside the image, parts of the rectangle may be outside. In this case, the -replication border mode (see cv::BorderTypes) is used to extrapolate the pixel values outside of -the image. - -@param image Source image. -@param patchSize Size of the extracted patch. -@param center Floating point coordinates of the center of the extracted rectangle within the -source image. The center must be inside the image. -@param patch Extracted patch that has the size patchSize and the same number of channels as src . -@param patchType Depth of the extracted pixels. By default, they have the same depth as src . - -@sa warpAffine, warpPerspective - */ -CV_EXPORTS_W void getRectSubPix( InputArray image, Size patchSize, - Point2f center, OutputArray patch, int patchType = -1 ); - -/** @example polar_transforms.cpp -An example using the cv::linearPolar and cv::logPolar operations -*/ - -/** @brief Remaps an image to log-polar space. - -transforms the source image using the following transformation: -\f[dst( \phi , \rho ) = src(x,y)\f] -where -\f[\rho = M \cdot \log{\sqrt{x^2 + y^2}} , \phi =atan(y/x)\f] - -The function emulates the human "foveal" vision and can be used for fast scale and -rotation-invariant template matching, for object tracking and so forth. The function can not operate -in-place. - -@param src Source image -@param dst Destination image -@param center The transformation center; where the output precision is maximal -@param M Magnitude scale parameter. -@param flags A combination of interpolation methods, see cv::InterpolationFlags - */ -CV_EXPORTS_W void logPolar( InputArray src, OutputArray dst, - Point2f center, double M, int flags ); - -/** @brief Remaps an image to polar space. - -transforms the source image using the following transformation: -\f[dst( \phi , \rho ) = src(x,y)\f] -where -\f[\rho = (src.width/maxRadius) \cdot \sqrt{x^2 + y^2} , \phi =atan(y/x)\f] - -The function can not operate in-place. - -@param src Source image -@param dst Destination image -@param center The transformation center; -@param maxRadius Inverse magnitude scale parameter -@param flags A combination of interpolation methods, see cv::InterpolationFlags - */ -CV_EXPORTS_W void linearPolar( InputArray src, OutputArray dst, - Point2f center, double maxRadius, int flags ); - -//! @} imgproc_transform - -//! @addtogroup imgproc_misc -//! @{ - -/** @overload */ -CV_EXPORTS_W void integral( InputArray src, OutputArray sum, int sdepth = -1 ); - -/** @overload */ -CV_EXPORTS_AS(integral2) void integral( InputArray src, OutputArray sum, - OutputArray sqsum, int sdepth = -1, int sqdepth = -1 ); - -/** @brief Calculates the integral of an image. - -The functions calculate one or more integral images for the source image as follows: - -\f[\texttt{sum} (X,Y) = \sum _{x - -Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed -with getOptimalDFTSize. - -The function performs the following equations: -- First it applies a Hanning window (see ) to each -image to remove possible edge effects. This window is cached until the array size changes to speed -up processing time. -- Next it computes the forward DFTs of each source array: -\f[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\f] -where \f$\mathcal{F}\f$ is the forward DFT. -- It then computes the cross-power spectrum of each frequency domain array: -\f[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\f] -- Next the cross-correlation is converted back into the time domain via the inverse DFT: -\f[r = \mathcal{F}^{-1}\{R\}\f] -- Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to -achieve sub-pixel accuracy. -\f[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\f] -- If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 -centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single -peak) and will be smaller when there are multiple peaks. - -@param src1 Source floating point array (CV_32FC1 or CV_64FC1) -@param src2 Source floating point array (CV_32FC1 or CV_64FC1) -@param window Floating point array with windowing coefficients to reduce edge effects (optional). -@param response Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional). -@returns detected phase shift (sub-pixel) between the two arrays. - -@sa dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow - */ -CV_EXPORTS_W Point2d phaseCorrelate(InputArray src1, InputArray src2, - InputArray window = noArray(), CV_OUT double* response = 0); - -/** @brief This function computes a Hanning window coefficients in two dimensions. - -See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function) -for more information. - -An example is shown below: -@code - // create hanning window of size 100x100 and type CV_32F - Mat hann; - createHanningWindow(hann, Size(100, 100), CV_32F); -@endcode -@param dst Destination array to place Hann coefficients in -@param winSize The window size specifications -@param type Created array type - */ -CV_EXPORTS_W void createHanningWindow(OutputArray dst, Size winSize, int type); - -//! @} imgproc_motion - -//! @addtogroup imgproc_misc -//! @{ - -/** @brief Applies a fixed-level threshold to each array element. - -The function applies fixed-level thresholding to a single-channel array. The function is typically -used to get a bi-level (binary) image out of a grayscale image ( cv::compare could be also used for -this purpose) or for removing a noise, that is, filtering out pixels with too small or too large -values. There are several types of thresholding supported by the function. They are determined by -type parameter. - -Also, the special values cv::THRESH_OTSU or cv::THRESH_TRIANGLE may be combined with one of the -above values. In these cases, the function determines the optimal threshold value using the Otsu's -or Triangle algorithm and uses it instead of the specified thresh . The function returns the -computed threshold value. Currently, the Otsu's and Triangle methods are implemented only for 8-bit -images. - -@param src input array (single-channel, 8-bit or 32-bit floating point). -@param dst output array of the same size and type as src. -@param thresh threshold value. -@param maxval maximum value to use with the THRESH_BINARY and THRESH_BINARY_INV thresholding -types. -@param type thresholding type (see the cv::ThresholdTypes). - -@sa adaptiveThreshold, findContours, compare, min, max - */ -CV_EXPORTS_W double threshold( InputArray src, OutputArray dst, - double thresh, double maxval, int type ); - - -/** @brief Applies an adaptive threshold to an array. - -The function transforms a grayscale image to a binary image according to the formulae: -- **THRESH_BINARY** - \f[dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\f] -- **THRESH_BINARY_INV** - \f[dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\f] -where \f$T(x,y)\f$ is a threshold calculated individually for each pixel (see adaptiveMethod parameter). - -The function can process the image in-place. - -@param src Source 8-bit single-channel image. -@param dst Destination image of the same size and the same type as src. -@param maxValue Non-zero value assigned to the pixels for which the condition is satisfied -@param adaptiveMethod Adaptive thresholding algorithm to use, see cv::AdaptiveThresholdTypes -@param thresholdType Thresholding type that must be either THRESH_BINARY or THRESH_BINARY_INV, -see cv::ThresholdTypes. -@param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the -pixel: 3, 5, 7, and so on. -@param C Constant subtracted from the mean or weighted mean (see the details below). Normally, it -is positive but may be zero or negative as well. - -@sa threshold, blur, GaussianBlur - */ -CV_EXPORTS_W void adaptiveThreshold( InputArray src, OutputArray dst, - double maxValue, int adaptiveMethod, - int thresholdType, int blockSize, double C ); - -//! @} imgproc_misc - -//! @addtogroup imgproc_filter -//! @{ - -/** @brief Blurs an image and downsamples it. - -By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in -any case, the following conditions should be satisfied: - -\f[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\f] - -The function performs the downsampling step of the Gaussian pyramid construction. First, it -convolves the source image with the kernel: - -\f[\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\f] - -Then, it downsamples the image by rejecting even rows and columns. - -@param src input image. -@param dst output image; it has the specified size and the same type as src. -@param dstsize size of the output image. -@param borderType Pixel extrapolation method, see cv::BorderTypes (BORDER_CONSTANT isn't supported) - */ -CV_EXPORTS_W void pyrDown( InputArray src, OutputArray dst, - const Size& dstsize = Size(), int borderType = BORDER_DEFAULT ); - -/** @brief Upsamples an image and then blurs it. - -By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any -case, the following conditions should be satisfied: - -\f[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\f] - -The function performs the upsampling step of the Gaussian pyramid construction, though it can -actually be used to construct the Laplacian pyramid. First, it upsamples the source image by -injecting even zero rows and columns and then convolves the result with the same kernel as in -pyrDown multiplied by 4. - -@param src input image. -@param dst output image. It has the specified size and the same type as src . -@param dstsize size of the output image. -@param borderType Pixel extrapolation method, see cv::BorderTypes (only BORDER_DEFAULT is supported) - */ -CV_EXPORTS_W void pyrUp( InputArray src, OutputArray dst, - const Size& dstsize = Size(), int borderType = BORDER_DEFAULT ); - -/** @brief Constructs the Gaussian pyramid for an image. - -The function constructs a vector of images and builds the Gaussian pyramid by recursively applying -pyrDown to the previously built pyramid layers, starting from `dst[0]==src`. - -@param src Source image. Check pyrDown for the list of supported types. -@param dst Destination vector of maxlevel+1 images of the same type as src. dst[0] will be the -same as src. dst[1] is the next pyramid layer, a smoothed and down-sized src, and so on. -@param maxlevel 0-based index of the last (the smallest) pyramid layer. It must be non-negative. -@param borderType Pixel extrapolation method, see cv::BorderTypes (BORDER_CONSTANT isn't supported) - */ -CV_EXPORTS void buildPyramid( InputArray src, OutputArrayOfArrays dst, - int maxlevel, int borderType = BORDER_DEFAULT ); - -//! @} imgproc_filter - -//! @addtogroup imgproc_transform -//! @{ - -/** @brief Transforms an image to compensate for lens distortion. - -The function transforms an image to compensate radial and tangential lens distortion. - -The function is simply a combination of cv::initUndistortRectifyMap (with unity R ) and cv::remap -(with bilinear interpolation). See the former function for details of the transformation being -performed. - -Those pixels in the destination image, for which there is no correspondent pixels in the source -image, are filled with zeros (black color). - -A particular subset of the source image that will be visible in the corrected image can be regulated -by newCameraMatrix. You can use cv::getOptimalNewCameraMatrix to compute the appropriate -newCameraMatrix depending on your requirements. - -The camera matrix and the distortion parameters can be determined using cv::calibrateCamera. If -the resolution of images is different from the resolution used at the calibration stage, \f$f_x, -f_y, c_x\f$ and \f$c_y\f$ need to be scaled accordingly, while the distortion coefficients remain -the same. - -@param src Input (distorted) image. -@param dst Output (corrected) image that has the same size and type as src . -@param cameraMatrix Input camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . -@param distCoeffs Input vector of distortion coefficients -\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ -of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. -@param newCameraMatrix Camera matrix of the distorted image. By default, it is the same as -cameraMatrix but you may additionally scale and shift the result by using a different matrix. - */ -CV_EXPORTS_W void undistort( InputArray src, OutputArray dst, - InputArray cameraMatrix, - InputArray distCoeffs, - InputArray newCameraMatrix = noArray() ); - -/** @brief Computes the undistortion and rectification transformation map. - -The function computes the joint undistortion and rectification transformation and represents the -result in the form of maps for remap. The undistorted image looks like original, as if it is -captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a -monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by -cv::getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera, -newCameraMatrix is normally set to P1 or P2 computed by cv::stereoRectify . - -Also, this new camera is oriented differently in the coordinate space, according to R. That, for -example, helps to align two heads of a stereo camera so that the epipolar lines on both images -become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera). - -The function actually builds the maps for the inverse mapping algorithm that is used by remap. That -is, for each pixel \f$(u, v)\f$ in the destination (corrected and rectified) image, the function -computes the corresponding coordinates in the source image (that is, in the original image from -camera). The following process is applied: -\f[ -\begin{array}{l} -x \leftarrow (u - {c'}_x)/{f'}_x \\ -y \leftarrow (v - {c'}_y)/{f'}_y \\ -{[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\ -x' \leftarrow X/W \\ -y' \leftarrow Y/W \\ -r^2 \leftarrow x'^2 + y'^2 \\ -x'' \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} -+ 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\ -y'' \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} -+ p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ -s\vecthree{x'''}{y'''}{1} = -\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} -{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} -{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ -map_x(u,v) \leftarrow x''' f_x + c_x \\ -map_y(u,v) \leftarrow y''' f_y + c_y -\end{array} -\f] -where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ -are the distortion coefficients. - -In case of a stereo camera, this function is called twice: once for each camera head, after -stereoRectify, which in its turn is called after cv::stereoCalibrate. But if the stereo camera -was not calibrated, it is still possible to compute the rectification transformations directly from -the fundamental matrix using cv::stereoRectifyUncalibrated. For each camera, the function computes -homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D -space. R can be computed from H as -\f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f] -where cameraMatrix can be chosen arbitrarily. - -@param cameraMatrix Input camera matrix \f$A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . -@param distCoeffs Input vector of distortion coefficients -\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ -of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. -@param R Optional rectification transformation in the object space (3x3 matrix). R1 or R2 , -computed by stereoRectify can be passed here. If the matrix is empty, the identity transformation -is assumed. In cvInitUndistortMap R assumed to be an identity matrix. -@param newCameraMatrix New camera matrix \f$A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\f$. -@param size Undistorted image size. -@param m1type Type of the first output map that can be CV_32FC1 or CV_16SC2, see cv::convertMaps -@param map1 The first output map. -@param map2 The second output map. - */ -CV_EXPORTS_W void initUndistortRectifyMap( InputArray cameraMatrix, InputArray distCoeffs, - InputArray R, InputArray newCameraMatrix, - Size size, int m1type, OutputArray map1, OutputArray map2 ); - -//! initializes maps for cv::remap() for wide-angle -CV_EXPORTS_W float initWideAngleProjMap( InputArray cameraMatrix, InputArray distCoeffs, - Size imageSize, int destImageWidth, - int m1type, OutputArray map1, OutputArray map2, - int projType = PROJ_SPHERICAL_EQRECT, double alpha = 0); - -/** @brief Returns the default new camera matrix. - -The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when -centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true). - -In the latter case, the new camera matrix will be: - -\f[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\f] - -where \f$f_x\f$ and \f$f_y\f$ are \f$(0,0)\f$ and \f$(1,1)\f$ elements of cameraMatrix, respectively. - -By default, the undistortion functions in OpenCV (see initUndistortRectifyMap, undistort) do not -move the principal point. However, when you work with stereo, it is important to move the principal -points in both views to the same y-coordinate (which is required by most of stereo correspondence -algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for -each view where the principal points are located at the center. - -@param cameraMatrix Input camera matrix. -@param imgsize Camera view image size in pixels. -@param centerPrincipalPoint Location of the principal point in the new camera matrix. The -parameter indicates whether this location should be at the image center or not. - */ -CV_EXPORTS_W Mat getDefaultNewCameraMatrix( InputArray cameraMatrix, Size imgsize = Size(), - bool centerPrincipalPoint = false ); - -/** @brief Computes the ideal point coordinates from the observed point coordinates. - -The function is similar to cv::undistort and cv::initUndistortRectifyMap but it operates on a -sparse set of points instead of a raster image. Also the function performs a reverse transformation -to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a -planar object, it does, up to a translation vector, if the proper R is specified. -@code - // (u,v) is the input point, (u', v') is the output point - // camera_matrix=[fx 0 cx; 0 fy cy; 0 0 1] - // P=[fx' 0 cx' tx; 0 fy' cy' ty; 0 0 1 tz] - x" = (u - cx)/fx - y" = (v - cy)/fy - (x',y') = undistort(x",y",dist_coeffs) - [X,Y,W]T = R*[x' y' 1]T - x = X/W, y = Y/W - // only performed if P=[fx' 0 cx' [tx]; 0 fy' cy' [ty]; 0 0 1 [tz]] is specified - u' = x*fx' + cx' - v' = y*fy' + cy', -@endcode -where cv::undistort is an approximate iterative algorithm that estimates the normalized original -point coordinates out of the normalized distorted point coordinates ("normalized" means that the -coordinates do not depend on the camera matrix). - -The function can be used for both a stereo camera head or a monocular camera (when R is empty). - -@param src Observed point coordinates, 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2). -@param dst Output ideal point coordinates after undistortion and reverse perspective -transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates. -@param cameraMatrix Camera matrix \f$\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . -@param distCoeffs Input vector of distortion coefficients -\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ -of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. -@param R Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by -cv::stereoRectify can be passed here. If the matrix is empty, the identity transformation is used. -@param P New camera matrix (3x3) or new projection matrix (3x4). P1 or P2 computed by -cv::stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used. - */ -CV_EXPORTS_W void undistortPoints( InputArray src, OutputArray dst, - InputArray cameraMatrix, InputArray distCoeffs, - InputArray R = noArray(), InputArray P = noArray()); - -//! @} imgproc_transform - -//! @addtogroup imgproc_hist -//! @{ - -/** @example demhist.cpp -An example for creating histograms of an image -*/ - -/** @brief Calculates a histogram of a set of arrays. - -The functions calcHist calculate the histogram of one or more arrays. The elements of a tuple used -to increment a histogram bin are taken from the corresponding input arrays at the same location. The -sample below shows how to compute a 2D Hue-Saturation histogram for a color image. : -@code - #include - #include - - using namespace cv; - - int main( int argc, char** argv ) - { - Mat src, hsv; - if( argc != 2 || !(src=imread(argv[1], 1)).data ) - return -1; - - cvtColor(src, hsv, COLOR_BGR2HSV); - - // Quantize the hue to 30 levels - // and the saturation to 32 levels - int hbins = 30, sbins = 32; - int histSize[] = {hbins, sbins}; - // hue varies from 0 to 179, see cvtColor - float hranges[] = { 0, 180 }; - // saturation varies from 0 (black-gray-white) to - // 255 (pure spectrum color) - float sranges[] = { 0, 256 }; - const float* ranges[] = { hranges, sranges }; - MatND hist; - // we compute the histogram from the 0-th and 1-st channels - int channels[] = {0, 1}; - - calcHist( &hsv, 1, channels, Mat(), // do not use mask - hist, 2, histSize, ranges, - true, // the histogram is uniform - false ); - double maxVal=0; - minMaxLoc(hist, 0, &maxVal, 0, 0); - - int scale = 10; - Mat histImg = Mat::zeros(sbins*scale, hbins*10, CV_8UC3); - - for( int h = 0; h < hbins; h++ ) - for( int s = 0; s < sbins; s++ ) - { - float binVal = hist.at(h, s); - int intensity = cvRound(binVal*255/maxVal); - rectangle( histImg, Point(h*scale, s*scale), - Point( (h+1)*scale - 1, (s+1)*scale - 1), - Scalar::all(intensity), - CV_FILLED ); - } - - namedWindow( "Source", 1 ); - imshow( "Source", src ); - - namedWindow( "H-S Histogram", 1 ); - imshow( "H-S Histogram", histImg ); - waitKey(); - } -@endcode - -@param images Source arrays. They all should have the same depth, CV_8U or CV_32F , and the same -size. Each of them can have an arbitrary number of channels. -@param nimages Number of source images. -@param channels List of the dims channels used to compute the histogram. The first array channels -are numerated from 0 to images[0].channels()-1 , the second array channels are counted from -images[0].channels() to images[0].channels() + images[1].channels()-1, and so on. -@param mask Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size -as images[i] . The non-zero mask elements mark the array elements counted in the histogram. -@param hist Output histogram, which is a dense or sparse dims -dimensional array. -@param dims Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS -(equal to 32 in the current OpenCV version). -@param histSize Array of histogram sizes in each dimension. -@param ranges Array of the dims arrays of the histogram bin boundaries in each dimension. When the -histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower -(inclusive) boundary \f$L_0\f$ of the 0-th histogram bin and the upper (exclusive) boundary -\f$U_{\texttt{histSize}[i]-1}\f$ for the last histogram bin histSize[i]-1 . That is, in case of a -uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform ( -uniform=false ), then each of ranges[i] contains histSize[i]+1 elements: -\f$L_0, U_0=L_1, U_1=L_2, ..., U_{\texttt{histSize[i]}-2}=L_{\texttt{histSize[i]}-1}, U_{\texttt{histSize[i]}-1}\f$ -. The array elements, that are not between \f$L_0\f$ and \f$U_{\texttt{histSize[i]}-1}\f$ , are not -counted in the histogram. -@param uniform Flag indicating whether the histogram is uniform or not (see above). -@param accumulate Accumulation flag. If it is set, the histogram is not cleared in the beginning -when it is allocated. This feature enables you to compute a single histogram from several sets of -arrays, or to update the histogram in time. -*/ -CV_EXPORTS void calcHist( const Mat* images, int nimages, - const int* channels, InputArray mask, - OutputArray hist, int dims, const int* histSize, - const float** ranges, bool uniform = true, bool accumulate = false ); - -/** @overload - -this variant uses cv::SparseMat for output -*/ -CV_EXPORTS void calcHist( const Mat* images, int nimages, - const int* channels, InputArray mask, - SparseMat& hist, int dims, - const int* histSize, const float** ranges, - bool uniform = true, bool accumulate = false ); - -/** @overload */ -CV_EXPORTS_W void calcHist( InputArrayOfArrays images, - const std::vector& channels, - InputArray mask, OutputArray hist, - const std::vector& histSize, - const std::vector& ranges, - bool accumulate = false ); - -/** @brief Calculates the back projection of a histogram. - -The functions calcBackProject calculate the back project of the histogram. That is, similarly to -cv::calcHist , at each location (x, y) the function collects the values from the selected channels -in the input images and finds the corresponding histogram bin. But instead of incrementing it, the -function reads the bin value, scales it by scale , and stores in backProject(x,y) . In terms of -statistics, the function computes probability of each element value in respect with the empirical -probability distribution represented by the histogram. See how, for example, you can find and track -a bright-colored object in a scene: - -- Before tracking, show the object to the camera so that it covers almost the whole frame. -Calculate a hue histogram. The histogram may have strong maximums, corresponding to the dominant -colors in the object. - -- When tracking, calculate a back projection of a hue plane of each input video frame using that -pre-computed histogram. Threshold the back projection to suppress weak colors. It may also make -sense to suppress pixels with non-sufficient color saturation and too dark or too bright pixels. - -- Find connected components in the resulting picture and choose, for example, the largest -component. - -This is an approximate algorithm of the CamShift color object tracker. - -@param images Source arrays. They all should have the same depth, CV_8U or CV_32F , and the same -size. Each of them can have an arbitrary number of channels. -@param nimages Number of source images. -@param channels The list of channels used to compute the back projection. The number of channels -must match the histogram dimensionality. The first array channels are numerated from 0 to -images[0].channels()-1 , the second array channels are counted from images[0].channels() to -images[0].channels() + images[1].channels()-1, and so on. -@param hist Input histogram that can be dense or sparse. -@param backProject Destination back projection array that is a single-channel array of the same -size and depth as images[0] . -@param ranges Array of arrays of the histogram bin boundaries in each dimension. See calcHist . -@param scale Optional scale factor for the output back projection. -@param uniform Flag indicating whether the histogram is uniform or not (see above). - -@sa cv::calcHist, cv::compareHist - */ -CV_EXPORTS void calcBackProject( const Mat* images, int nimages, - const int* channels, InputArray hist, - OutputArray backProject, const float** ranges, - double scale = 1, bool uniform = true ); - -/** @overload */ -CV_EXPORTS void calcBackProject( const Mat* images, int nimages, - const int* channels, const SparseMat& hist, - OutputArray backProject, const float** ranges, - double scale = 1, bool uniform = true ); - -/** @overload */ -CV_EXPORTS_W void calcBackProject( InputArrayOfArrays images, const std::vector& channels, - InputArray hist, OutputArray dst, - const std::vector& ranges, - double scale ); - -/** @brief Compares two histograms. - -The function compare two dense or two sparse histograms using the specified method. - -The function returns \f$d(H_1, H_2)\f$ . - -While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable -for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling -problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms -or more general sparse configurations of weighted points, consider using the cv::EMD function. - -@param H1 First compared histogram. -@param H2 Second compared histogram of the same size as H1 . -@param method Comparison method, see cv::HistCompMethods - */ -CV_EXPORTS_W double compareHist( InputArray H1, InputArray H2, int method ); - -/** @overload */ -CV_EXPORTS double compareHist( const SparseMat& H1, const SparseMat& H2, int method ); - -/** @brief Equalizes the histogram of a grayscale image. - -The function equalizes the histogram of the input image using the following algorithm: - -- Calculate the histogram \f$H\f$ for src . -- Normalize the histogram so that the sum of histogram bins is 255. -- Compute the integral of the histogram: -\f[H'_i = \sum _{0 \le j < i} H(j)\f] -- Transform the image using \f$H'\f$ as a look-up table: \f$\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\f$ - -The algorithm normalizes the brightness and increases the contrast of the image. - -@param src Source 8-bit single channel image. -@param dst Destination image of the same size and type as src . - */ -CV_EXPORTS_W void equalizeHist( InputArray src, OutputArray dst ); - -/** @brief Computes the "minimal work" distance between two weighted point configurations. - -The function computes the earth mover distance and/or a lower boundary of the distance between the -two weighted point configurations. One of the applications described in @cite RubnerSept98, -@cite Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation -problem that is solved using some modification of a simplex algorithm, thus the complexity is -exponential in the worst case, though, on average it is much faster. In the case of a real metric -the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used -to determine roughly whether the two signatures are far enough so that they cannot relate to the -same object. - -@param signature1 First signature, a \f$\texttt{size1}\times \texttt{dims}+1\f$ floating-point matrix. -Each row stores the point weight followed by the point coordinates. The matrix is allowed to have -a single column (weights only) if the user-defined cost matrix is used. -@param signature2 Second signature of the same format as signature1 , though the number of rows -may be different. The total weights may be different. In this case an extra "dummy" point is added -to either signature1 or signature2 . -@param distType Used metric. See cv::DistanceTypes. -@param cost User-defined \f$\texttt{size1}\times \texttt{size2}\f$ cost matrix. Also, if a cost matrix -is used, lower boundary lowerBound cannot be calculated because it needs a metric function. -@param lowerBound Optional input/output parameter: lower boundary of a distance between the two -signatures that is a distance between mass centers. The lower boundary may not be calculated if -the user-defined cost matrix is used, the total weights of point configurations are not equal, or -if the signatures consist of weights only (the signature matrices have a single column). You -**must** initialize \*lowerBound . If the calculated distance between mass centers is greater or -equal to \*lowerBound (it means that the signatures are far enough), the function does not -calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on -return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound -should be set to 0. -@param flow Resultant \f$\texttt{size1} \times \texttt{size2}\f$ flow matrix: \f$\texttt{flow}_{i,j}\f$ is -a flow from \f$i\f$ -th point of signature1 to \f$j\f$ -th point of signature2 . - */ -CV_EXPORTS float EMD( InputArray signature1, InputArray signature2, - int distType, InputArray cost=noArray(), - float* lowerBound = 0, OutputArray flow = noArray() ); - -//! @} imgproc_hist - -/** @example watershed.cpp -An example using the watershed algorithm - */ - -/** @brief Performs a marker-based image segmentation using the watershed algorithm. - -The function implements one of the variants of watershed, non-parametric marker-based segmentation -algorithm, described in @cite Meyer92 . - -Before passing the image to the function, you have to roughly outline the desired regions in the -image markers with positive (\>0) indices. So, every region is represented as one or more connected -components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary -mask using findContours and drawContours (see the watershed.cpp demo). The markers are "seeds" of -the future image regions. All the other pixels in markers , whose relation to the outlined regions -is not known and should be defined by the algorithm, should be set to 0's. In the function output, -each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the -regions. - -@note Any two neighbor connected components are not necessarily separated by a watershed boundary -(-1's pixels); for example, they can touch each other in the initial marker image passed to the -function. - -@param image Input 8-bit 3-channel image. -@param markers Input/output 32-bit single-channel image (map) of markers. It should have the same -size as image . - -@sa findContours - -@ingroup imgproc_misc - */ -CV_EXPORTS_W void watershed( InputArray image, InputOutputArray markers ); - -//! @addtogroup imgproc_filter -//! @{ - -/** @brief Performs initial step of meanshift segmentation of an image. - -The function implements the filtering stage of meanshift segmentation, that is, the output of the -function is the filtered "posterized" image with color gradients and fine-grain texture flattened. -At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes -meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is -considered: - -\f[(x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}\f] - -where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively -(though, the algorithm does not depend on the color space used, so any 3-component color space can -be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector -(R',G',B') are found and they act as the neighborhood center on the next iteration: - -\f[(X,Y)~(X',Y'), (R,G,B)~(R',G',B').\f] - -After the iterations over, the color components of the initial pixel (that is, the pixel from where -the iterations started) are set to the final value (average color at the last iteration): - -\f[I(X,Y) <- (R*,G*,B*)\f] - -When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is -run on the smallest layer first. After that, the results are propagated to the larger layer and the -iterations are run again only on those pixels where the layer colors differ by more than sr from the -lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the -results will be actually different from the ones obtained by running the meanshift procedure on the -whole original image (i.e. when maxLevel==0). - -@param src The source 8-bit, 3-channel image. -@param dst The destination image of the same format and the same size as the source. -@param sp The spatial window radius. -@param sr The color window radius. -@param maxLevel Maximum level of the pyramid for the segmentation. -@param termcrit Termination criteria: when to stop meanshift iterations. - */ -CV_EXPORTS_W void pyrMeanShiftFiltering( InputArray src, OutputArray dst, - double sp, double sr, int maxLevel = 1, - TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) ); - -//! @} - -//! @addtogroup imgproc_misc -//! @{ - -/** @example grabcut.cpp -An example using the GrabCut algorithm - */ - -/** @brief Runs the GrabCut algorithm. - -The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut). - -@param img Input 8-bit 3-channel image. -@param mask Input/output 8-bit single-channel mask. The mask is initialized by the function when -mode is set to GC_INIT_WITH_RECT. Its elements may have one of the cv::GrabCutClasses. -@param rect ROI containing a segmented object. The pixels outside of the ROI are marked as -"obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT . -@param bgdModel Temporary array for the background model. Do not modify it while you are -processing the same image. -@param fgdModel Temporary arrays for the foreground model. Do not modify it while you are -processing the same image. -@param iterCount Number of iterations the algorithm should make before returning the result. Note -that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or -mode==GC_EVAL . -@param mode Operation mode that could be one of the cv::GrabCutModes - */ -CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect, - InputOutputArray bgdModel, InputOutputArray fgdModel, - int iterCount, int mode = GC_EVAL ); - -/** @example distrans.cpp -An example on using the distance transform\ -*/ - - -/** @brief Calculates the distance to the closest zero pixel for each pixel of the source image. - -The functions distanceTransform calculate the approximate or precise distance from every binary -image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. - -When maskSize == DIST_MASK_PRECISE and distanceType == DIST_L2 , the function runs the -algorithm described in @cite Felzenszwalb04 . This algorithm is parallelized with the TBB library. - -In other cases, the algorithm @cite Borgefors86 is used. This means that for a pixel the function -finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, -diagonal, or knight's move (the latest is available for a \f$5\times 5\f$ mask). The overall -distance is calculated as a sum of these basic distances. Since the distance function should be -symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all -the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the -same cost (denoted as `c`). For the cv::DIST_C and cv::DIST_L1 types, the distance is calculated -precisely, whereas for cv::DIST_L2 (Euclidean distance) the distance can be calculated only with a -relative error (a \f$5\times 5\f$ mask gives more accurate results). For `a`,`b`, and `c`, OpenCV -uses the values suggested in the original paper: -- DIST_L1: `a = 1, b = 2` -- DIST_L2: - - `3 x 3`: `a=0.955, b=1.3693` - - `5 x 5`: `a=1, b=1.4, c=2.1969` -- DIST_C: `a = 1, b = 1` - -Typically, for a fast, coarse distance estimation DIST_L2, a \f$3\times 3\f$ mask is used. For a -more accurate distance estimation DIST_L2, a \f$5\times 5\f$ mask or the precise algorithm is used. -Note that both the precise and the approximate algorithms are linear on the number of pixels. - -This variant of the function does not only compute the minimum distance for each pixel \f$(x, y)\f$ -but also identifies the nearest connected component consisting of zero pixels -(labelType==DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==DIST_LABEL_PIXEL). Index of the -component/pixel is stored in `labels(x, y)`. When labelType==DIST_LABEL_CCOMP, the function -automatically finds connected components of zero pixels in the input image and marks them with -distinct labels. When labelType==DIST_LABEL_CCOMP, the function scans through the input image and -marks all the zero pixels with distinct labels. - -In this mode, the complexity is still linear. That is, the function provides a very fast way to -compute the Voronoi diagram for a binary image. Currently, the second variant can use only the -approximate distance transform algorithm, i.e. maskSize=DIST_MASK_PRECISE is not supported -yet. - -@param src 8-bit, single-channel (binary) source image. -@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, -single-channel image of the same size as src. -@param labels Output 2D array of labels (the discrete Voronoi diagram). It has the type -CV_32SC1 and the same size as src. -@param distanceType Type of distance, see cv::DistanceTypes -@param maskSize Size of the distance transform mask, see cv::DistanceTransformMasks. -DIST_MASK_PRECISE is not supported by this variant. In case of the DIST_L1 or DIST_C distance type, -the parameter is forced to 3 because a \f$3\times 3\f$ mask gives the same result as \f$5\times -5\f$ or any larger aperture. -@param labelType Type of the label array to build, see cv::DistanceTransformLabelTypes. - */ -CV_EXPORTS_AS(distanceTransformWithLabels) void distanceTransform( InputArray src, OutputArray dst, - OutputArray labels, int distanceType, int maskSize, - int labelType = DIST_LABEL_CCOMP ); - -/** @overload -@param src 8-bit, single-channel (binary) source image. -@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, -single-channel image of the same size as src . -@param distanceType Type of distance, see cv::DistanceTypes -@param maskSize Size of the distance transform mask, see cv::DistanceTransformMasks. In case of the -DIST_L1 or DIST_C distance type, the parameter is forced to 3 because a \f$3\times 3\f$ mask gives -the same result as \f$5\times 5\f$ or any larger aperture. -@param dstType Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for -the first variant of the function and distanceType == DIST_L1. -*/ -CV_EXPORTS_W void distanceTransform( InputArray src, OutputArray dst, - int distanceType, int maskSize, int dstType=CV_32F); - -/** @example ffilldemo.cpp - An example using the FloodFill technique -*/ - -/** @overload - -variant without `mask` parameter -*/ -CV_EXPORTS int floodFill( InputOutputArray image, - Point seedPoint, Scalar newVal, CV_OUT Rect* rect = 0, - Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), - int flags = 4 ); - -/** @brief Fills a connected component with the given color. - -The functions floodFill fill a connected component starting from the seed point with the specified -color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The -pixel at \f$(x,y)\f$ is considered to belong to the repainted domain if: - -- in case of a grayscale image and floating range -\f[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\f] - - -- in case of a grayscale image and fixed range -\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\f] - - -- in case of a color image and floating range -\f[\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\f] -\f[\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\f] -and -\f[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\f] - - -- in case of a color image and fixed range -\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\f] -\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\f] -and -\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\f] - - -where \f$src(x',y')\f$ is the value of one of pixel neighbors that is already known to belong to the -component. That is, to be added to the connected component, a color/brightness of the pixel should -be close enough to: -- Color/brightness of one of its neighbors that already belong to the connected component in case -of a floating range. -- Color/brightness of the seed point in case of a fixed range. - -Use these functions to either mark a connected component with the specified color in-place, or build -a mask and then extract the contour, or copy the region to another image, and so on. - -@param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the -function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See -the details below. -@param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels -taller than image. Since this is both an input and output parameter, you must take responsibility -of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, -an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the -mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags -as described below. It is therefore possible to use the same mask in multiple calls to the function -to make sure the filled areas do not overlap. -@param seedPoint Starting point. -@param newVal New value of the repainted domain pixels. -@param loDiff Maximal lower brightness/color difference between the currently observed pixel and -one of its neighbors belonging to the component, or a seed pixel being added to the component. -@param upDiff Maximal upper brightness/color difference between the currently observed pixel and -one of its neighbors belonging to the component, or a seed pixel being added to the component. -@param rect Optional output parameter set by the function to the minimum bounding rectangle of the -repainted domain. -@param flags Operation flags. The first 8 bits contain a connectivity value. The default value of -4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A -connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) -will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill -the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest -neighbours and fill the mask with a value of 255. The following additional options occupy higher -bits and therefore may be further combined with the connectivity and mask fill values using -bit-wise or (|), see cv::FloodFillFlags. - -@note Since the mask is larger than the filled image, a pixel \f$(x, y)\f$ in image corresponds to the -pixel \f$(x+1, y+1)\f$ in the mask . - -@sa findContours - */ -CV_EXPORTS_W int floodFill( InputOutputArray image, InputOutputArray mask, - Point seedPoint, Scalar newVal, CV_OUT Rect* rect=0, - Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), - int flags = 4 ); - -/** @brief Converts an image from one color space to another. - -The function converts an input image from one color space to another. In case of a transformation -to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note -that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the -bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue -component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and -sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on. - -The conventional ranges for R, G, and B channel values are: -- 0 to 255 for CV_8U images -- 0 to 65535 for CV_16U images -- 0 to 1 for CV_32F images - -In case of linear transformations, the range does not matter. But in case of a non-linear -transformation, an input RGB image should be normalized to the proper value range to get the correct -results, for example, for RGB \f$\rightarrow\f$ L\*u\*v\* transformation. For example, if you have a -32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will -have the 0..255 value range instead of 0..1 assumed by the function. So, before calling cvtColor , -you need first to scale the image down: -@code - img *= 1./255; - cvtColor(img, img, COLOR_BGR2Luv); -@endcode -If you use cvtColor with 8-bit images, the conversion will have some information lost. For many -applications, this will not be noticeable but it is recommended to use 32-bit images in applications -that need the full range of colors or that convert an image before an operation and then convert -back. - -If conversion adds the alpha channel, its value will set to the maximum of corresponding channel -range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F. - -@param src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision -floating-point. -@param dst output image of the same size and depth as src. -@param code color space conversion code (see cv::ColorConversionCodes). -@param dstCn number of channels in the destination image; if the parameter is 0, the number of the -channels is derived automatically from src and code. - -@see @ref imgproc_color_conversions - */ -CV_EXPORTS_W void cvtColor( InputArray src, OutputArray dst, int code, int dstCn = 0 ); - -//! @} imgproc_misc - -// main function for all demosaicing procceses -CV_EXPORTS_W void demosaicing(InputArray _src, OutputArray _dst, int code, int dcn = 0); - -//! @addtogroup imgproc_shape -//! @{ - -/** @brief Calculates all of the moments up to the third order of a polygon or rasterized shape. - -The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The -results are returned in the structure cv::Moments. - -@param array Raster image (single-channel, 8-bit or floating-point 2D array) or an array ( -\f$1 \times N\f$ or \f$N \times 1\f$ ) of 2D points (Point or Point2f ). -@param binaryImage If it is true, all non-zero image pixels are treated as 1's. The parameter is -used for images only. -@returns moments. - -@sa contourArea, arcLength - */ -CV_EXPORTS_W Moments moments( InputArray array, bool binaryImage = false ); - -/** @brief Calculates seven Hu invariants. - -The function calculates seven Hu invariants (introduced in @cite Hu62; see also -) defined as: - -\f[\begin{array}{l} hu[0]= \eta _{20}+ \eta _{02} \\ hu[1]=( \eta _{20}- \eta _{02})^{2}+4 \eta _{11}^{2} \\ hu[2]=( \eta _{30}-3 \eta _{12})^{2}+ (3 \eta _{21}- \eta _{03})^{2} \\ hu[3]=( \eta _{30}+ \eta _{12})^{2}+ ( \eta _{21}+ \eta _{03})^{2} \\ hu[4]=( \eta _{30}-3 \eta _{12})( \eta _{30}+ \eta _{12})[( \eta _{30}+ \eta _{12})^{2}-3( \eta _{21}+ \eta _{03})^{2}]+(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ hu[5]=( \eta _{20}- \eta _{02})[( \eta _{30}+ \eta _{12})^{2}- ( \eta _{21}+ \eta _{03})^{2}]+4 \eta _{11}( \eta _{30}+ \eta _{12})( \eta _{21}+ \eta _{03}) \\ hu[6]=(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}]-( \eta _{30}-3 \eta _{12})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ \end{array}\f] - -where \f$\eta_{ji}\f$ stands for \f$\texttt{Moments::nu}_{ji}\f$ . - -These values are proved to be invariants to the image scale, rotation, and reflection except the -seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of -infinite image resolution. In case of raster images, the computed Hu invariants for the original and -transformed images are a bit different. - -@param moments Input moments computed with moments . -@param hu Output Hu invariants. - -@sa matchShapes - */ -CV_EXPORTS void HuMoments( const Moments& moments, double hu[7] ); - -/** @overload */ -CV_EXPORTS_W void HuMoments( const Moments& m, OutputArray hu ); - -//! @} imgproc_shape - -//! @addtogroup imgproc_object -//! @{ - -//! type of the template matching operation -enum TemplateMatchModes { - TM_SQDIFF = 0, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2\f] - TM_SQDIFF_NORMED = 1, //!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y')-I(x+x',y+y'))^2}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f] - TM_CCORR = 2, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y'))\f] - TM_CCORR_NORMED = 3, //!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I(x+x',y+y'))}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f] - TM_CCOEFF = 4, //!< \f[R(x,y)= \sum _{x',y'} (T'(x',y') \cdot I'(x+x',y+y'))\f] - //!< where - //!< \f[\begin{array}{l} T'(x',y')=T(x',y') - 1/(w \cdot h) \cdot \sum _{x'',y''} T(x'',y'') \\ I'(x+x',y+y')=I(x+x',y+y') - 1/(w \cdot h) \cdot \sum _{x'',y''} I(x+x'',y+y'') \end{array}\f] - TM_CCOEFF_NORMED = 5 //!< \f[R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} }\f] -}; - -/** @brief Compares a template against overlapped image regions. - -The function slides through image , compares the overlapped patches of size \f$w \times h\f$ against -templ using the specified method and stores the comparison results in result . Here are the formulae -for the available comparison methods ( \f$I\f$ denotes image, \f$T\f$ template, \f$R\f$ result ). The summation -is done over template and/or the image patch: \f$x' = 0...w-1, y' = 0...h-1\f$ - -After the function finishes the comparison, the best matches can be found as global minimums (when -TM_SQDIFF was used) or maximums (when TM_CCORR or TM_CCOEFF was used) using the -minMaxLoc function. In case of a color image, template summation in the numerator and each sum in -the denominator is done over all of the channels and separate mean values are used for each channel. -That is, the function can take a color template and a color image. The result will still be a -single-channel image, which is easier to analyze. - -@param image Image where the search is running. It must be 8-bit or 32-bit floating-point. -@param templ Searched template. It must be not greater than the source image and have the same -data type. -@param result Map of comparison results. It must be single-channel 32-bit floating-point. If image -is \f$W \times H\f$ and templ is \f$w \times h\f$ , then result is \f$(W-w+1) \times (H-h+1)\f$ . -@param method Parameter specifying the comparison method, see cv::TemplateMatchModes -@param mask Mask of searched template. It must have the same datatype and size with templ. It is -not set by default. - */ -CV_EXPORTS_W void matchTemplate( InputArray image, InputArray templ, - OutputArray result, int method, InputArray mask = noArray() ); - -//! @} - -//! @addtogroup imgproc_shape -//! @{ - -/** @brief computes the connected components labeled image of boolean image - -image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 -represents the background label. ltype specifies the output label image type, an important -consideration based on the total number of labels or alternatively the total number of pixels in -the source image. - -@param image the 8-bit single-channel image to be labeled -@param labels destination labeled image -@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively -@param ltype output image label type. Currently CV_32S and CV_16U are supported. - */ -CV_EXPORTS_W int connectedComponents(InputArray image, OutputArray labels, - int connectivity = 8, int ltype = CV_32S); - -/** @overload -@param image the 8-bit single-channel image to be labeled -@param labels destination labeled image -@param stats statistics output for each label, including the background label, see below for -available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of -cv::ConnectedComponentsTypes. The data type is CV_32S. -@param centroids centroid output for each label, including the background label. Centroids are -accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. -@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively -@param ltype output image label type. Currently CV_32S and CV_16U are supported. -*/ -CV_EXPORTS_W int connectedComponentsWithStats(InputArray image, OutputArray labels, - OutputArray stats, OutputArray centroids, - int connectivity = 8, int ltype = CV_32S); - - -/** @brief Finds contours in a binary image. - -The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours -are a useful tool for shape analysis and object detection and recognition. See squares.c in the -OpenCV sample directory. - -@note Source image is modified by this function. Also, the function does not take into account -1-pixel border of the image (it's filled with 0's and used for neighbor analysis in the algorithm), -therefore the contours touching the image border will be clipped. - -@param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero -pixels remain 0's, so the image is treated as binary . You can use compare , inRange , threshold , -adaptiveThreshold , Canny , and others to create a binary image out of a grayscale or color one. -The function modifies the image while extracting the contours. If mode equals to RETR_CCOMP -or RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1). -@param contours Detected contours. Each contour is stored as a vector of points. -@param hierarchy Optional output vector, containing information about the image topology. It has -as many elements as the number of contours. For each i-th contour contours[i] , the elements -hierarchy[i][0] , hiearchy[i][1] , hiearchy[i][2] , and hiearchy[i][3] are set to 0-based indices -in contours of the next and previous contours at the same hierarchical level, the first child -contour and the parent contour, respectively. If for the contour i there are no next, previous, -parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. -@param mode Contour retrieval mode, see cv::RetrievalModes -@param method Contour approximation method, see cv::ContourApproximationModes -@param offset Optional offset by which every contour point is shifted. This is useful if the -contours are extracted from the image ROI and then they should be analyzed in the whole image -context. - */ -CV_EXPORTS_W void findContours( InputOutputArray image, OutputArrayOfArrays contours, - OutputArray hierarchy, int mode, - int method, Point offset = Point()); - -/** @overload */ -CV_EXPORTS void findContours( InputOutputArray image, OutputArrayOfArrays contours, - int mode, int method, Point offset = Point()); - -/** @brief Approximates a polygonal curve(s) with the specified precision. - -The functions approxPolyDP approximate a curve or a polygon with another curve/polygon with less -vertices so that the distance between them is less or equal to the specified precision. It uses the -Douglas-Peucker algorithm - -@param curve Input vector of a 2D point stored in std::vector or Mat -@param approxCurve Result of the approximation. The type should match the type of the input curve. -@param epsilon Parameter specifying the approximation accuracy. This is the maximum distance -between the original curve and its approximation. -@param closed If true, the approximated curve is closed (its first and last vertices are -connected). Otherwise, it is not closed. - */ -CV_EXPORTS_W void approxPolyDP( InputArray curve, - OutputArray approxCurve, - double epsilon, bool closed ); - -/** @brief Calculates a contour perimeter or a curve length. - -The function computes a curve length or a closed contour perimeter. - -@param curve Input vector of 2D points, stored in std::vector or Mat. -@param closed Flag indicating whether the curve is closed or not. - */ -CV_EXPORTS_W double arcLength( InputArray curve, bool closed ); - -/** @brief Calculates the up-right bounding rectangle of a point set. - -The function calculates and returns the minimal up-right bounding rectangle for the specified point set. - -@param points Input 2D point set, stored in std::vector or Mat. - */ -CV_EXPORTS_W Rect boundingRect( InputArray points ); - -/** @brief Calculates a contour area. - -The function computes a contour area. Similarly to moments , the area is computed using the Green -formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using -drawContours or fillPoly , can be different. Also, the function will most certainly give a wrong -results for contours with self-intersections. - -Example: -@code - vector contour; - contour.push_back(Point2f(0, 0)); - contour.push_back(Point2f(10, 0)); - contour.push_back(Point2f(10, 10)); - contour.push_back(Point2f(5, 4)); - - double area0 = contourArea(contour); - vector approx; - approxPolyDP(contour, approx, 5, true); - double area1 = contourArea(approx); - - cout << "area0 =" << area0 << endl << - "area1 =" << area1 << endl << - "approx poly vertices" << approx.size() << endl; -@endcode -@param contour Input vector of 2D points (contour vertices), stored in std::vector or Mat. -@param oriented Oriented area flag. If it is true, the function returns a signed area value, -depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can -determine orientation of a contour by taking the sign of an area. By default, the parameter is -false, which means that the absolute value is returned. - */ -CV_EXPORTS_W double contourArea( InputArray contour, bool oriented = false ); - -/** @brief Finds a rotated rectangle of the minimum area enclosing the input 2D point set. - -The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a -specified point set. See the OpenCV sample minarea.cpp . Developer should keep in mind that the -returned rotatedRect can contain negative indices when data is close to the containing Mat element -boundary. - -@param points Input vector of 2D points, stored in std::vector\<\> or Mat - */ -CV_EXPORTS_W RotatedRect minAreaRect( InputArray points ); - -/** @brief Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle. - -The function finds the four vertices of a rotated rectangle. This function is useful to draw the -rectangle. In C++, instead of using this function, you can directly use box.points() method. Please -visit the [tutorial on bounding -rectangle](http://docs.opencv.org/doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.html#bounding-rects-circles) -for more information. - -@param box The input rotated rectangle. It may be the output of -@param points The output array of four vertices of rectangles. - */ -CV_EXPORTS_W void boxPoints(RotatedRect box, OutputArray points); - -/** @brief Finds a circle of the minimum area enclosing a 2D point set. - -The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm. See -the OpenCV sample minarea.cpp . - -@param points Input vector of 2D points, stored in std::vector\<\> or Mat -@param center Output center of the circle. -@param radius Output radius of the circle. - */ -CV_EXPORTS_W void minEnclosingCircle( InputArray points, - CV_OUT Point2f& center, CV_OUT float& radius ); - -/** @example minarea.cpp - */ - -/** @brief Finds a triangle of minimum area enclosing a 2D point set and returns its area. - -The function finds a triangle of minimum area enclosing the given set of 2D points and returns its -area. The output for a given 2D point set is shown in the image below. 2D points are depicted in -*red* and the enclosing triangle in *yellow*. - -![Sample output of the minimum enclosing triangle function](pics/minenclosingtriangle.png) - -The implementation of the algorithm is based on O'Rourke's @cite ORourke86 and Klee and Laskowski's -@cite KleeLaskowski85 papers. O'Rourke provides a \f$\theta(n)\f$ algorithm for finding the minimal -enclosing triangle of a 2D convex polygon with n vertices. Since the minEnclosingTriangle function -takes a 2D point set as input an additional preprocessing step of computing the convex hull of the -2D point set is required. The complexity of the convexHull function is \f$O(n log(n))\f$ which is higher -than \f$\theta(n)\f$. Thus the overall complexity of the function is \f$O(n log(n))\f$. - -@param points Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector\<\> or Mat -@param triangle Output vector of three 2D points defining the vertices of the triangle. The depth -of the OutputArray must be CV_32F. - */ -CV_EXPORTS_W double minEnclosingTriangle( InputArray points, CV_OUT OutputArray triangle ); - -/** @brief Compares two shapes. - -The function compares two shapes. All three implemented methods use the Hu invariants (see cv::HuMoments) - -@param contour1 First contour or grayscale image. -@param contour2 Second contour or grayscale image. -@param method Comparison method, see ::ShapeMatchModes -@param parameter Method-specific parameter (not supported now). - */ -CV_EXPORTS_W double matchShapes( InputArray contour1, InputArray contour2, - int method, double parameter ); - -/** @example convexhull.cpp -An example using the convexHull functionality -*/ - -/** @brief Finds the convex hull of a point set. - -The functions find the convex hull of a 2D point set using the Sklansky's algorithm @cite Sklansky82 -that has *O(N logN)* complexity in the current implementation. See the OpenCV sample convexhull.cpp -that demonstrates the usage of different function variants. - -@param points Input 2D point set, stored in std::vector or Mat. -@param hull Output convex hull. It is either an integer vector of indices or vector of points. In -the first case, the hull elements are 0-based indices of the convex hull points in the original -array (since the set of convex hull points is a subset of the original point set). In the second -case, hull elements are the convex hull points themselves. -@param clockwise Orientation flag. If it is true, the output convex hull is oriented clockwise. -Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing -to the right, and its Y axis pointing upwards. -@param returnPoints Operation flag. In case of a matrix, when the flag is true, the function -returns convex hull points. Otherwise, it returns indices of the convex hull points. When the -output array is std::vector, the flag is ignored, and the output depends on the type of the -vector: std::vector\ implies returnPoints=true, std::vector\ implies -returnPoints=false. - */ -CV_EXPORTS_W void convexHull( InputArray points, OutputArray hull, - bool clockwise = false, bool returnPoints = true ); - -/** @brief Finds the convexity defects of a contour. - -The figure below displays convexity defects of a hand contour: - -![image](pics/defects.png) - -@param contour Input contour. -@param convexhull Convex hull obtained using convexHull that should contain indices of the contour -points that make the hull. -@param convexityDefects The output vector of convexity defects. In C++ and the new Python/Java -interface each convexity defect is represented as 4-element integer vector (a.k.a. cv::Vec4i): -(start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices -in the original contour of the convexity defect beginning, end and the farthest point, and -fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the -farthest contour point and the hull. That is, to get the floating-point value of the depth will be -fixpt_depth/256.0. - */ -CV_EXPORTS_W void convexityDefects( InputArray contour, InputArray convexhull, OutputArray convexityDefects ); - -/** @brief Tests a contour convexity. - -The function tests whether the input contour is convex or not. The contour must be simple, that is, -without self-intersections. Otherwise, the function output is undefined. - -@param contour Input vector of 2D points, stored in std::vector\<\> or Mat - */ -CV_EXPORTS_W bool isContourConvex( InputArray contour ); - -//! finds intersection of two convex polygons -CV_EXPORTS_W float intersectConvexConvex( InputArray _p1, InputArray _p2, - OutputArray _p12, bool handleNested = true ); - -/** @example fitellipse.cpp - An example using the fitEllipse technique -*/ - -/** @brief Fits an ellipse around a set of 2D points. - -The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of -all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by @cite Fitzgibbon95 -is used. Developer should keep in mind that it is possible that the returned -ellipse/rotatedRect data contains negative indices, due to the data points being close to the -border of the containing Mat element. - -@param points Input 2D point set, stored in std::vector\<\> or Mat - */ -CV_EXPORTS_W RotatedRect fitEllipse( InputArray points ); - -/** @brief Fits a line to a 2D or 3D point set. - -The function fitLine fits a line to a 2D or 3D point set by minimizing \f$\sum_i \rho(r_i)\f$ where -\f$r_i\f$ is a distance between the \f$i^{th}\f$ point, the line and \f$\rho(r)\f$ is a distance function, one -of the following: -- DIST_L2 -\f[\rho (r) = r^2/2 \quad \text{(the simplest and the fastest least-squares method)}\f] -- DIST_L1 -\f[\rho (r) = r\f] -- DIST_L12 -\f[\rho (r) = 2 \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\f] -- DIST_FAIR -\f[\rho \left (r \right ) = C^2 \cdot \left ( \frac{r}{C} - \log{\left(1 + \frac{r}{C}\right)} \right ) \quad \text{where} \quad C=1.3998\f] -- DIST_WELSCH -\f[\rho \left (r \right ) = \frac{C^2}{2} \cdot \left ( 1 - \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right ) \quad \text{where} \quad C=2.9846\f] -- DIST_HUBER -\f[\rho (r) = \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\f] - -The algorithm is based on the M-estimator ( ) technique -that iteratively fits the line using the weighted least-squares algorithm. After each iteration the -weights \f$w_i\f$ are adjusted to be inversely proportional to \f$\rho(r_i)\f$ . - -@param points Input vector of 2D or 3D points, stored in std::vector\<\> or Mat. -@param line Output line parameters. In case of 2D fitting, it should be a vector of 4 elements -(like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and -(x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like -Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line -and (x0, y0, z0) is a point on the line. -@param distType Distance used by the M-estimator, see cv::DistanceTypes -@param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value -is chosen. -@param reps Sufficient accuracy for the radius (distance between the coordinate origin and the line). -@param aeps Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps. - */ -CV_EXPORTS_W void fitLine( InputArray points, OutputArray line, int distType, - double param, double reps, double aeps ); - -/** @brief Performs a point-in-contour test. - -The function determines whether the point is inside a contour, outside, or lies on an edge (or -coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) -value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. -Otherwise, the return value is a signed distance between the point and the nearest contour edge. - -See below a sample output of the function where each image pixel is tested against the contour: - -![sample output](pics/pointpolygon.png) - -@param contour Input contour. -@param pt Point tested against the contour. -@param measureDist If true, the function estimates the signed distance from the point to the -nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not. - */ -CV_EXPORTS_W double pointPolygonTest( InputArray contour, Point2f pt, bool measureDist ); - -/** @brief Finds out if there is any intersection between two rotated rectangles. - -If there is then the vertices of the interesecting region are returned as well. - -Below are some examples of intersection configurations. The hatched pattern indicates the -intersecting region and the red vertices are returned by the function. - -![intersection examples](pics/intersection.png) - -@param rect1 First rectangle -@param rect2 Second rectangle -@param intersectingRegion The output array of the verticies of the intersecting region. It returns -at most 8 vertices. Stored as std::vector\ or cv::Mat as Mx1 of type CV_32FC2. -@returns One of cv::RectanglesIntersectTypes - */ -CV_EXPORTS_W int rotatedRectangleIntersection( const RotatedRect& rect1, const RotatedRect& rect2, OutputArray intersectingRegion ); - -//! @} imgproc_shape - -CV_EXPORTS_W Ptr createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8)); - -//! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122. -//! Detects position only without traslation and rotation -CV_EXPORTS Ptr createGeneralizedHoughBallard(); - -//! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038. -//! Detects position, traslation and rotation -CV_EXPORTS Ptr createGeneralizedHoughGuil(); - -//! Performs linear blending of two images -CV_EXPORTS void blendLinear(InputArray src1, InputArray src2, InputArray weights1, InputArray weights2, OutputArray dst); - -//! @addtogroup imgproc_colormap -//! @{ - -//! GNU Octave/MATLAB equivalent colormaps -enum ColormapTypes -{ - COLORMAP_AUTUMN = 0, //!< ![autumn](pics/colormaps/colorscale_autumn.jpg) - COLORMAP_BONE = 1, //!< ![bone](pics/colormaps/colorscale_bone.jpg) - COLORMAP_JET = 2, //!< ![jet](pics/colormaps/colorscale_jet.jpg) - COLORMAP_WINTER = 3, //!< ![winter](pics/colormaps/colorscale_winter.jpg) - COLORMAP_RAINBOW = 4, //!< ![rainbow](pics/colormaps/colorscale_rainbow.jpg) - COLORMAP_OCEAN = 5, //!< ![ocean](pics/colormaps/colorscale_ocean.jpg) - COLORMAP_SUMMER = 6, //!< ![summer](pics/colormaps/colorscale_summer.jpg) - COLORMAP_SPRING = 7, //!< ![spring](pics/colormaps/colorscale_spring.jpg) - COLORMAP_COOL = 8, //!< ![cool](pics/colormaps/colorscale_cool.jpg) - COLORMAP_HSV = 9, //!< ![HSV](pics/colormaps/colorscale_hsv.jpg) - COLORMAP_PINK = 10, //!< ![pink](pics/colormaps/colorscale_pink.jpg) - COLORMAP_HOT = 11, //!< ![hot](pics/colormaps/colorscale_hot.jpg) - COLORMAP_PARULA = 12 //!< ![parula](pics/colormaps/colorscale_parula.jpg) -}; - -/** @brief Applies a GNU Octave/MATLAB equivalent colormap on a given image. - -@param src The source image, grayscale or colored does not matter. -@param dst The result is the colormapped source image. Note: Mat::create is called on dst. -@param colormap The colormap to apply, see cv::ColormapTypes - */ -CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, int colormap); - -//! @} imgproc_colormap - -//! @addtogroup imgproc_draw -//! @{ - -/** @brief Draws a line segment connecting two points. - -The function line draws the line segment between pt1 and pt2 points in the image. The line is -clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected -or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased -lines are drawn using Gaussian filtering. - -@param img Image. -@param pt1 First point of the line segment. -@param pt2 Second point of the line segment. -@param color Line color. -@param thickness Line thickness. -@param lineType Type of the line, see cv::LineTypes. -@param shift Number of fractional bits in the point coordinates. - */ -CV_EXPORTS_W void line(InputOutputArray img, Point pt1, Point pt2, const Scalar& color, - int thickness = 1, int lineType = LINE_8, int shift = 0); - -/** @brief Draws a arrow segment pointing from the first point to the second one. - -The function arrowedLine draws an arrow between pt1 and pt2 points in the image. See also cv::line. - -@param img Image. -@param pt1 The point the arrow starts from. -@param pt2 The point the arrow points to. -@param color Line color. -@param thickness Line thickness. -@param line_type Type of the line, see cv::LineTypes -@param shift Number of fractional bits in the point coordinates. -@param tipLength The length of the arrow tip in relation to the arrow length - */ -CV_EXPORTS_W void arrowedLine(InputOutputArray img, Point pt1, Point pt2, const Scalar& color, - int thickness=1, int line_type=8, int shift=0, double tipLength=0.1); - -/** @brief Draws a simple, thick, or filled up-right rectangle. - -The function rectangle draws a rectangle outline or a filled rectangle whose two opposite corners -are pt1 and pt2. - -@param img Image. -@param pt1 Vertex of the rectangle. -@param pt2 Vertex of the rectangle opposite to pt1 . -@param color Rectangle color or brightness (grayscale image). -@param thickness Thickness of lines that make up the rectangle. Negative values, like CV_FILLED , -mean that the function has to draw a filled rectangle. -@param lineType Type of the line. See the line description. -@param shift Number of fractional bits in the point coordinates. - */ -CV_EXPORTS_W void rectangle(InputOutputArray img, Point pt1, Point pt2, - const Scalar& color, int thickness = 1, - int lineType = LINE_8, int shift = 0); - -/** @overload - -use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and -r.br()-Point(1,1)` are opposite corners -*/ -CV_EXPORTS void rectangle(CV_IN_OUT Mat& img, Rect rec, - const Scalar& color, int thickness = 1, - int lineType = LINE_8, int shift = 0); - -/** @brief Draws a circle. - -The function circle draws a simple or filled circle with a given center and radius. -@param img Image where the circle is drawn. -@param center Center of the circle. -@param radius Radius of the circle. -@param color Circle color. -@param thickness Thickness of the circle outline, if positive. Negative thickness means that a -filled circle is to be drawn. -@param lineType Type of the circle boundary. See the line description. -@param shift Number of fractional bits in the coordinates of the center and in the radius value. - */ -CV_EXPORTS_W void circle(InputOutputArray img, Point center, int radius, - const Scalar& color, int thickness = 1, - int lineType = LINE_8, int shift = 0); - -/** @brief Draws a simple or thick elliptic arc or fills an ellipse sector. - -The functions ellipse with less parameters draw an ellipse outline, a filled ellipse, an elliptic -arc, or a filled ellipse sector. A piecewise-linear curve is used to approximate the elliptic arc -boundary. If you need more control of the ellipse rendering, you can retrieve the curve using -ellipse2Poly and then render it with polylines or fill it with fillPoly . If you use the first -variant of the function and want to draw the whole ellipse, not an arc, pass startAngle=0 and -endAngle=360 . The figure below explains the meaning of the parameters. - -![Parameters of Elliptic Arc](pics/ellipse.png) - -@param img Image. -@param center Center of the ellipse. -@param axes Half of the size of the ellipse main axes. -@param angle Ellipse rotation angle in degrees. -@param startAngle Starting angle of the elliptic arc in degrees. -@param endAngle Ending angle of the elliptic arc in degrees. -@param color Ellipse color. -@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that -a filled ellipse sector is to be drawn. -@param lineType Type of the ellipse boundary. See the line description. -@param shift Number of fractional bits in the coordinates of the center and values of axes. - */ -CV_EXPORTS_W void ellipse(InputOutputArray img, Point center, Size axes, - double angle, double startAngle, double endAngle, - const Scalar& color, int thickness = 1, - int lineType = LINE_8, int shift = 0); - -/** @overload -@param img Image. -@param box Alternative ellipse representation via RotatedRect. This means that the function draws -an ellipse inscribed in the rotated rectangle. -@param color Ellipse color. -@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that -a filled ellipse sector is to be drawn. -@param lineType Type of the ellipse boundary. See the line description. -*/ -CV_EXPORTS_W void ellipse(InputOutputArray img, const RotatedRect& box, const Scalar& color, - int thickness = 1, int lineType = LINE_8); - -/* ----------------------------------------------------------------------------------------- */ -/* ADDING A SET OF PREDEFINED MARKERS WHICH COULD BE USED TO HIGHLIGHT POSITIONS IN AN IMAGE */ -/* ----------------------------------------------------------------------------------------- */ - -//! Possible set of marker types used for the cv::drawMarker function -enum MarkerTypes -{ - MARKER_CROSS = 0, //!< A crosshair marker shape - MARKER_TILTED_CROSS = 1, //!< A 45 degree tilted crosshair marker shape - MARKER_STAR = 2, //!< A star marker shape, combination of cross and tilted cross - MARKER_DIAMOND = 3, //!< A diamond marker shape - MARKER_SQUARE = 4, //!< A square marker shape - MARKER_TRIANGLE_UP = 5, //!< An upwards pointing triangle marker shape - MARKER_TRIANGLE_DOWN = 6 //!< A downwards pointing triangle marker shape -}; - -/** @brief Draws a marker on a predefined position in an image. - -The function drawMarker draws a marker on a given position in the image. For the moment several -marker types are supported, see cv::MarkerTypes for more information. - -@param img Image. -@param position The point where the crosshair is positioned. -@param color Line color. -@param markerType The specific type of marker you want to use, see cv::MarkerTypes -@param thickness Line thickness. -@param line_type Type of the line, see cv::LineTypes -@param markerSize The length of the marker axis [default = 20 pixels] - */ -CV_EXPORTS_W void drawMarker(CV_IN_OUT Mat& img, Point position, const Scalar& color, - int markerType = MARKER_CROSS, int markerSize=20, int thickness=1, - int line_type=8); - -/* ----------------------------------------------------------------------------------------- */ -/* END OF MARKER SECTION */ -/* ----------------------------------------------------------------------------------------- */ - -/** @overload */ -CV_EXPORTS void fillConvexPoly(Mat& img, const Point* pts, int npts, - const Scalar& color, int lineType = LINE_8, - int shift = 0); - -/** @brief Fills a convex polygon. - -The function fillConvexPoly draws a filled convex polygon. This function is much faster than the -function cv::fillPoly . It can fill not only convex polygons but any monotonic polygon without -self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) -twice at the most (though, its top-most and/or the bottom edge could be horizontal). - -@param img Image. -@param points Polygon vertices. -@param color Polygon color. -@param lineType Type of the polygon boundaries. See the line description. -@param shift Number of fractional bits in the vertex coordinates. - */ -CV_EXPORTS_W void fillConvexPoly(InputOutputArray img, InputArray points, - const Scalar& color, int lineType = LINE_8, - int shift = 0); - -/** @overload */ -CV_EXPORTS void fillPoly(Mat& img, const Point** pts, - const int* npts, int ncontours, - const Scalar& color, int lineType = LINE_8, int shift = 0, - Point offset = Point() ); - -/** @brief Fills the area bounded by one or more polygons. - -The function fillPoly fills an area bounded by several polygonal contours. The function can fill -complex areas, for example, areas with holes, contours with self-intersections (some of their -parts), and so forth. - -@param img Image. -@param pts Array of polygons where each polygon is represented as an array of points. -@param color Polygon color. -@param lineType Type of the polygon boundaries. See the line description. -@param shift Number of fractional bits in the vertex coordinates. -@param offset Optional offset of all points of the contours. - */ -CV_EXPORTS_W void fillPoly(InputOutputArray img, InputArrayOfArrays pts, - const Scalar& color, int lineType = LINE_8, int shift = 0, - Point offset = Point() ); - -/** @overload */ -CV_EXPORTS void polylines(Mat& img, const Point* const* pts, const int* npts, - int ncontours, bool isClosed, const Scalar& color, - int thickness = 1, int lineType = LINE_8, int shift = 0 ); - -/** @brief Draws several polygonal curves. - -@param img Image. -@param pts Array of polygonal curves. -@param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed, -the function draws a line from the last vertex of each curve to its first vertex. -@param color Polyline color. -@param thickness Thickness of the polyline edges. -@param lineType Type of the line segments. See the line description. -@param shift Number of fractional bits in the vertex coordinates. - -The function polylines draws one or more polygonal curves. - */ -CV_EXPORTS_W void polylines(InputOutputArray img, InputArrayOfArrays pts, - bool isClosed, const Scalar& color, - int thickness = 1, int lineType = LINE_8, int shift = 0 ); - -/** @example contours2.cpp - An example using the drawContour functionality -*/ - -/** @example segment_objects.cpp -An example using drawContours to clean up a background segmentation result - */ - -/** @brief Draws contours outlines or filled contours. - -The function draws contour outlines in the image if \f$\texttt{thickness} \ge 0\f$ or fills the area -bounded by the contours if \f$\texttt{thickness}<0\f$ . The example below shows how to retrieve -connected components from the binary image and label them: : -@code - #include "opencv2/imgproc.hpp" - #include "opencv2/highgui.hpp" - - using namespace cv; - using namespace std; - - int main( int argc, char** argv ) - { - Mat src; - // the first command-line parameter must be a filename of the binary - // (black-n-white) image - if( argc != 2 || !(src=imread(argv[1], 0)).data) - return -1; - - Mat dst = Mat::zeros(src.rows, src.cols, CV_8UC3); - - src = src > 1; - namedWindow( "Source", 1 ); - imshow( "Source", src ); - - vector > contours; - vector hierarchy; - - findContours( src, contours, hierarchy, - RETR_CCOMP, CHAIN_APPROX_SIMPLE ); - - // iterate through all the top-level contours, - // draw each connected component with its own random color - int idx = 0; - for( ; idx >= 0; idx = hierarchy[idx][0] ) - { - Scalar color( rand()&255, rand()&255, rand()&255 ); - drawContours( dst, contours, idx, color, FILLED, 8, hierarchy ); - } - - namedWindow( "Components", 1 ); - imshow( "Components", dst ); - waitKey(0); - } -@endcode - -@param image Destination image. -@param contours All the input contours. Each contour is stored as a point vector. -@param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. -@param color Color of the contours. -@param thickness Thickness of lines the contours are drawn with. If it is negative (for example, -thickness=CV_FILLED ), the contour interiors are drawn. -@param lineType Line connectivity. See cv::LineTypes. -@param hierarchy Optional information about hierarchy. It is only needed if you want to draw only -some of the contours (see maxLevel ). -@param maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn. -If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function -draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This -parameter is only taken into account when there is hierarchy available. -@param offset Optional contour shift parameter. Shift all the drawn contours by the specified -\f$\texttt{offset}=(dx,dy)\f$ . - */ -CV_EXPORTS_W void drawContours( InputOutputArray image, InputArrayOfArrays contours, - int contourIdx, const Scalar& color, - int thickness = 1, int lineType = LINE_8, - InputArray hierarchy = noArray(), - int maxLevel = INT_MAX, Point offset = Point() ); - -/** @brief Clips the line against the image rectangle. - -The functions clipLine calculate a part of the line segment that is entirely within the specified -rectangle. They return false if the line segment is completely outside the rectangle. Otherwise, -they return true . -@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . -@param pt1 First line point. -@param pt2 Second line point. - */ -CV_EXPORTS bool clipLine(Size imgSize, CV_IN_OUT Point& pt1, CV_IN_OUT Point& pt2); - -/** @overload -@param imgRect Image rectangle. -@param pt1 First line point. -@param pt2 Second line point. -*/ -CV_EXPORTS_W bool clipLine(Rect imgRect, CV_OUT CV_IN_OUT Point& pt1, CV_OUT CV_IN_OUT Point& pt2); - -/** @brief Approximates an elliptic arc with a polyline. - -The function ellipse2Poly computes the vertices of a polyline that approximates the specified -elliptic arc. It is used by cv::ellipse. - -@param center Center of the arc. -@param axes Half of the size of the ellipse main axes. See the ellipse for details. -@param angle Rotation angle of the ellipse in degrees. See the ellipse for details. -@param arcStart Starting angle of the elliptic arc in degrees. -@param arcEnd Ending angle of the elliptic arc in degrees. -@param delta Angle between the subsequent polyline vertices. It defines the approximation -accuracy. -@param pts Output vector of polyline vertices. - */ -CV_EXPORTS_W void ellipse2Poly( Point center, Size axes, int angle, - int arcStart, int arcEnd, int delta, - CV_OUT std::vector& pts ); - -/** @brief Draws a text string. - -The function putText renders the specified text string in the image. Symbols that cannot be rendered -using the specified font are replaced by question marks. See getTextSize for a text rendering code -example. - -@param img Image. -@param text Text string to be drawn. -@param org Bottom-left corner of the text string in the image. -@param fontFace Font type, see cv::HersheyFonts. -@param fontScale Font scale factor that is multiplied by the font-specific base size. -@param color Text color. -@param thickness Thickness of the lines used to draw a text. -@param lineType Line type. See the line for details. -@param bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise, -it is at the top-left corner. - */ -CV_EXPORTS_W void putText( InputOutputArray img, const String& text, Point org, - int fontFace, double fontScale, Scalar color, - int thickness = 1, int lineType = LINE_8, - bool bottomLeftOrigin = false ); - -/** @brief Calculates the width and height of a text string. - -The function getTextSize calculates and returns the size of a box that contains the specified text. -That is, the following code renders some text, the tight box surrounding it, and the baseline: : -@code - String text = "Funny text inside the box"; - int fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX; - double fontScale = 2; - int thickness = 3; - - Mat img(600, 800, CV_8UC3, Scalar::all(0)); - - int baseline=0; - Size textSize = getTextSize(text, fontFace, - fontScale, thickness, &baseline); - baseline += thickness; - - // center the text - Point textOrg((img.cols - textSize.width)/2, - (img.rows + textSize.height)/2); - - // draw the box - rectangle(img, textOrg + Point(0, baseline), - textOrg + Point(textSize.width, -textSize.height), - Scalar(0,0,255)); - // ... and the baseline first - line(img, textOrg + Point(0, thickness), - textOrg + Point(textSize.width, thickness), - Scalar(0, 0, 255)); - - // then put the text itself - putText(img, text, textOrg, fontFace, fontScale, - Scalar::all(255), thickness, 8); -@endcode - -@param text Input text string. -@param fontFace Font to use, see cv::HersheyFonts. -@param fontScale Font scale factor that is multiplied by the font-specific base size. -@param thickness Thickness of lines used to render the text. See putText for details. -@param[out] baseLine y-coordinate of the baseline relative to the bottom-most text -point. -@return The size of a box that contains the specified text. - -@see cv::putText - */ -CV_EXPORTS_W Size getTextSize(const String& text, int fontFace, - double fontScale, int thickness, - CV_OUT int* baseLine); - -/** @brief Line iterator - -The class is used to iterate over all the pixels on the raster line -segment connecting two specified points. - -The class LineIterator is used to get each pixel of a raster line. It -can be treated as versatile implementation of the Bresenham algorithm -where you can stop at each pixel and do some extra processing, for -example, grab pixel values along the line or draw a line with an effect -(for example, with XOR operation). - -The number of pixels along the line is stored in LineIterator::count. -The method LineIterator::pos returns the current position in the image: - -@code{.cpp} -// grabs pixels along the line (pt1, pt2) -// from 8-bit 3-channel image to the buffer -LineIterator it(img, pt1, pt2, 8); -LineIterator it2 = it; -vector buf(it.count); - -for(int i = 0; i < it.count; i++, ++it) - buf[i] = *(const Vec3b)*it; - -// alternative way of iterating through the line -for(int i = 0; i < it2.count; i++, ++it2) -{ - Vec3b val = img.at(it2.pos()); - CV_Assert(buf[i] == val); -} -@endcode -*/ -class CV_EXPORTS LineIterator -{ -public: - /** @brief intializes the iterator - - creates iterators for the line connecting pt1 and pt2 - the line will be clipped on the image boundaries - the line is 8-connected or 4-connected - If leftToRight=true, then the iteration is always done - from the left-most point to the right most, - not to depend on the ordering of pt1 and pt2 parameters - */ - LineIterator( const Mat& img, Point pt1, Point pt2, - int connectivity = 8, bool leftToRight = false ); - /** @brief returns pointer to the current pixel - */ - uchar* operator *(); - /** @brief prefix increment operator (++it). shifts iterator to the next pixel - */ - LineIterator& operator ++(); - /** @brief postfix increment operator (it++). shifts iterator to the next pixel - */ - LineIterator operator ++(int); - /** @brief returns coordinates of the current pixel - */ - Point pos() const; - - uchar* ptr; - const uchar* ptr0; - int step, elemSize; - int err, count; - int minusDelta, plusDelta; - int minusStep, plusStep; -}; - -//! @cond IGNORED - -// === LineIterator implementation === - -inline -uchar* LineIterator::operator *() -{ - return ptr; -} - -inline -LineIterator& LineIterator::operator ++() -{ - int mask = err < 0 ? -1 : 0; - err += minusDelta + (plusDelta & mask); - ptr += minusStep + (plusStep & mask); - return *this; -} - -inline -LineIterator LineIterator::operator ++(int) -{ - LineIterator it = *this; - ++(*this); - return it; -} - -inline -Point LineIterator::pos() const -{ - Point p; - p.y = (int)((ptr - ptr0)/step); - p.x = (int)(((ptr - ptr0) - p.y*step)/elemSize); - return p; -} - -//! @endcond - -//! @} imgproc_draw - -//! @} imgproc - -} // cv - -#ifndef DISABLE_OPENCV_24_COMPATIBILITY -#include "opencv2/imgproc/imgproc_c.h" -#endif - -#endif diff --git a/IPL/include/opencv/opencv2/imgproc/detail/distortion_model.hpp b/IPL/include/opencv/opencv2/imgproc/detail/distortion_model.hpp deleted file mode 100644 index ca29304..0000000 --- a/IPL/include/opencv/opencv2/imgproc/detail/distortion_model.hpp +++ /dev/null @@ -1,123 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP__ -#define __OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP__ - -//! @cond IGNORED - -namespace cv { namespace detail { -/** -Computes the matrix for the projection onto a tilted image sensor -\param tauX angular parameter rotation around x-axis -\param tauY angular parameter rotation around y-axis -\param matTilt if not NULL returns the matrix -\f[ -\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} -{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} -{0}{0}{1} R(\tau_x, \tau_y) -\f] -where -\f[ -R(\tau_x, \tau_y) = -\vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)} -\vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} = -\vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)} -{0}{\cos(\tau_x)}{\sin(\tau_x)} -{\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}. -\f] -\param dMatTiltdTauX if not NULL it returns the derivative of matTilt with -respect to \f$\tau_x\f$. -\param dMatTiltdTauY if not NULL it returns the derivative of matTilt with -respect to \f$\tau_y\f$. -\param invMatTilt if not NULL it returns the inverse of matTilt -**/ -template -void computeTiltProjectionMatrix(FLOAT tauX, - FLOAT tauY, - Matx* matTilt = 0, - Matx* dMatTiltdTauX = 0, - Matx* dMatTiltdTauY = 0, - Matx* invMatTilt = 0) -{ - FLOAT cTauX = cos(tauX); - FLOAT sTauX = sin(tauX); - FLOAT cTauY = cos(tauY); - FLOAT sTauY = sin(tauY); - Matx matRotX = Matx(1,0,0,0,cTauX,sTauX,0,-sTauX,cTauX); - Matx matRotY = Matx(cTauY,0,-sTauY,0,1,0,sTauY,0,cTauY); - Matx matRotXY = matRotY * matRotX; - Matx matProjZ = Matx(matRotXY(2,2),0,-matRotXY(0,2),0,matRotXY(2,2),-matRotXY(1,2),0,0,1); - if (matTilt) - { - // Matrix for trapezoidal distortion of tilted image sensor - *matTilt = matProjZ * matRotXY; - } - if (dMatTiltdTauX) - { - // Derivative with respect to tauX - Matx dMatRotXYdTauX = matRotY * Matx(0,0,0,0,-sTauX,cTauX,0,-cTauX,-sTauX); - Matx dMatProjZdTauX = Matx(dMatRotXYdTauX(2,2),0,-dMatRotXYdTauX(0,2), - 0,dMatRotXYdTauX(2,2),-dMatRotXYdTauX(1,2),0,0,0); - *dMatTiltdTauX = (matProjZ * dMatRotXYdTauX) + (dMatProjZdTauX * matRotXY); - } - if (dMatTiltdTauY) - { - // Derivative with respect to tauY - Matx dMatRotXYdTauY = Matx(-sTauY,0,-cTauY,0,0,0,cTauY,0,-sTauY) * matRotX; - Matx dMatProjZdTauY = Matx(dMatRotXYdTauY(2,2),0,-dMatRotXYdTauY(0,2), - 0,dMatRotXYdTauY(2,2),-dMatRotXYdTauY(1,2),0,0,0); - *dMatTiltdTauY = (matProjZ * dMatRotXYdTauY) + (dMatProjZdTauY * matRotXY); - } - if (invMatTilt) - { - FLOAT inv = 1./matRotXY(2,2); - Matx invMatProjZ = Matx(inv,0,inv*matRotXY(0,2),0,inv,inv*matRotXY(1,2),0,0,1); - *invMatTilt = matRotXY.t()*invMatProjZ; - } -} -}} // namespace detail, cv - - -//! @endcond - -#endif // __OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP__ diff --git a/IPL/include/opencv/opencv2/imgproc/imgproc.hpp b/IPL/include/opencv/opencv2/imgproc/imgproc.hpp deleted file mode 100644 index 4175bd0..0000000 --- a/IPL/include/opencv/opencv2/imgproc/imgproc.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/imgproc.hpp" diff --git a/IPL/include/opencv/opencv2/imgproc/imgproc_c.h b/IPL/include/opencv/opencv2/imgproc/imgproc_c.h deleted file mode 100644 index 87518d7..0000000 --- a/IPL/include/opencv/opencv2/imgproc/imgproc_c.h +++ /dev/null @@ -1,1210 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_IMGPROC_IMGPROC_C_H__ -#define __OPENCV_IMGPROC_IMGPROC_C_H__ - -#include "opencv2/imgproc/types_c.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @addtogroup imgproc_c -@{ -*/ - -/*********************** Background statistics accumulation *****************************/ - -/** @brief Adds image to accumulator -@see cv::accumulate -*/ -CVAPI(void) cvAcc( const CvArr* image, CvArr* sum, - const CvArr* mask CV_DEFAULT(NULL) ); - -/** @brief Adds squared image to accumulator -@see cv::accumulateSquare -*/ -CVAPI(void) cvSquareAcc( const CvArr* image, CvArr* sqsum, - const CvArr* mask CV_DEFAULT(NULL) ); - -/** @brief Adds a product of two images to accumulator -@see cv::accumulateProduct -*/ -CVAPI(void) cvMultiplyAcc( const CvArr* image1, const CvArr* image2, CvArr* acc, - const CvArr* mask CV_DEFAULT(NULL) ); - -/** @brief Adds image to accumulator with weights: acc = acc*(1-alpha) + image*alpha -@see cv::accumulateWeighted -*/ -CVAPI(void) cvRunningAvg( const CvArr* image, CvArr* acc, double alpha, - const CvArr* mask CV_DEFAULT(NULL) ); - -/****************************************************************************************\ -* Image Processing * -\****************************************************************************************/ - -/** Copies source 2D array inside of the larger destination array and - makes a border of the specified type (IPL_BORDER_*) around the copied area. */ -CVAPI(void) cvCopyMakeBorder( const CvArr* src, CvArr* dst, CvPoint offset, - int bordertype, CvScalar value CV_DEFAULT(cvScalarAll(0))); - -/** @brief Smooths the image in one of several ways. - -@param src The source image -@param dst The destination image -@param smoothtype Type of the smoothing, see SmoothMethod_c -@param size1 The first parameter of the smoothing operation, the aperture width. Must be a -positive odd number (1, 3, 5, ...) -@param size2 The second parameter of the smoothing operation, the aperture height. Ignored by -CV_MEDIAN and CV_BILATERAL methods. In the case of simple scaled/non-scaled and Gaussian blur if -size2 is zero, it is set to size1. Otherwise it must be a positive odd number. -@param sigma1 In the case of a Gaussian parameter this parameter may specify Gaussian \f$\sigma\f$ -(standard deviation). If it is zero, it is calculated from the kernel size: -\f[\sigma = 0.3 (n/2 - 1) + 0.8 \quad \text{where} \quad n= \begin{array}{l l} \mbox{\texttt{size1} for horizontal kernel} \\ \mbox{\texttt{size2} for vertical kernel} \end{array}\f] -Using standard sigma for small kernels ( \f$3\times 3\f$ to \f$7\times 7\f$ ) gives better speed. If -sigma1 is not zero, while size1 and size2 are zeros, the kernel size is calculated from the -sigma (to provide accurate enough operation). -@param sigma2 additional parameter for bilateral filtering - -@see cv::GaussianBlur, cv::blur, cv::medianBlur, cv::bilateralFilter. - */ -CVAPI(void) cvSmooth( const CvArr* src, CvArr* dst, - int smoothtype CV_DEFAULT(CV_GAUSSIAN), - int size1 CV_DEFAULT(3), - int size2 CV_DEFAULT(0), - double sigma1 CV_DEFAULT(0), - double sigma2 CV_DEFAULT(0)); - -/** @brief Convolves an image with the kernel. - -@param src input image. -@param dst output image of the same size and the same number of channels as src. -@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point -matrix; if you want to apply different kernels to different channels, split the image into -separate color planes using split and process them individually. -@param anchor anchor of the kernel that indicates the relative position of a filtered point within -the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor -is at the kernel center. - -@see cv::filter2D - */ -CVAPI(void) cvFilter2D( const CvArr* src, CvArr* dst, const CvMat* kernel, - CvPoint anchor CV_DEFAULT(cvPoint(-1,-1))); - -/** @brief Finds integral image: SUM(X,Y) = sum(x \texttt{hist1}(I)\)}{\frac{\texttt{hist2}(I) \cdot \texttt{scale}}{\texttt{hist1}(I)}}{if \(\texttt{hist1}(I) \ne 0\) and \(\texttt{hist2}(I) \le \texttt{hist1}(I)\)}\f] - -@param hist1 First histogram (the divisor). -@param hist2 Second histogram. -@param dst_hist Destination histogram. -@param scale Scale factor for the destination histogram. - */ -CVAPI(void) cvCalcProbDensity( const CvHistogram* hist1, const CvHistogram* hist2, - CvHistogram* dst_hist, double scale CV_DEFAULT(255) ); - -/** @brief equalizes histogram of 8-bit single-channel image -@see cv::equalizeHist -*/ -CVAPI(void) cvEqualizeHist( const CvArr* src, CvArr* dst ); - - -/** @brief Applies distance transform to binary image -@see cv::distanceTransform -*/ -CVAPI(void) cvDistTransform( const CvArr* src, CvArr* dst, - int distance_type CV_DEFAULT(CV_DIST_L2), - int mask_size CV_DEFAULT(3), - const float* mask CV_DEFAULT(NULL), - CvArr* labels CV_DEFAULT(NULL), - int labelType CV_DEFAULT(CV_DIST_LABEL_CCOMP)); - - -/** @brief Applies fixed-level threshold to grayscale image. - - This is a basic operation applied before retrieving contours -@see cv::threshold -*/ -CVAPI(double) cvThreshold( const CvArr* src, CvArr* dst, - double threshold, double max_value, - int threshold_type ); - -/** @brief Applies adaptive threshold to grayscale image. - - The two parameters for methods CV_ADAPTIVE_THRESH_MEAN_C and - CV_ADAPTIVE_THRESH_GAUSSIAN_C are: - neighborhood size (3, 5, 7 etc.), - and a constant subtracted from mean (...,-3,-2,-1,0,1,2,3,...) -@see cv::adaptiveThreshold -*/ -CVAPI(void) cvAdaptiveThreshold( const CvArr* src, CvArr* dst, double max_value, - int adaptive_method CV_DEFAULT(CV_ADAPTIVE_THRESH_MEAN_C), - int threshold_type CV_DEFAULT(CV_THRESH_BINARY), - int block_size CV_DEFAULT(3), - double param1 CV_DEFAULT(5)); - -/** @brief Fills the connected component until the color difference gets large enough -@see cv::floodFill -*/ -CVAPI(void) cvFloodFill( CvArr* image, CvPoint seed_point, - CvScalar new_val, CvScalar lo_diff CV_DEFAULT(cvScalarAll(0)), - CvScalar up_diff CV_DEFAULT(cvScalarAll(0)), - CvConnectedComp* comp CV_DEFAULT(NULL), - int flags CV_DEFAULT(4), - CvArr* mask CV_DEFAULT(NULL)); - -/****************************************************************************************\ -* Feature detection * -\****************************************************************************************/ - -/** @brief Runs canny edge detector -@see cv::Canny -*/ -CVAPI(void) cvCanny( const CvArr* image, CvArr* edges, double threshold1, - double threshold2, int aperture_size CV_DEFAULT(3) ); - -/** @brief Calculates constraint image for corner detection - - Dx^2 * Dyy + Dxx * Dy^2 - 2 * Dx * Dy * Dxy. - Applying threshold to the result gives coordinates of corners -@see cv::preCornerDetect -*/ -CVAPI(void) cvPreCornerDetect( const CvArr* image, CvArr* corners, - int aperture_size CV_DEFAULT(3) ); - -/** @brief Calculates eigen values and vectors of 2x2 - gradient covariation matrix at every image pixel -@see cv::cornerEigenValsAndVecs -*/ -CVAPI(void) cvCornerEigenValsAndVecs( const CvArr* image, CvArr* eigenvv, - int block_size, int aperture_size CV_DEFAULT(3) ); - -/** @brief Calculates minimal eigenvalue for 2x2 gradient covariation matrix at - every image pixel -@see cv::cornerMinEigenVal -*/ -CVAPI(void) cvCornerMinEigenVal( const CvArr* image, CvArr* eigenval, - int block_size, int aperture_size CV_DEFAULT(3) ); - -/** @brief Harris corner detector: - - Calculates det(M) - k*(trace(M)^2), where M is 2x2 gradient covariation matrix for each pixel -@see cv::cornerHarris -*/ -CVAPI(void) cvCornerHarris( const CvArr* image, CvArr* harris_response, - int block_size, int aperture_size CV_DEFAULT(3), - double k CV_DEFAULT(0.04) ); - -/** @brief Adjust corner position using some sort of gradient search -@see cv::cornerSubPix -*/ -CVAPI(void) cvFindCornerSubPix( const CvArr* image, CvPoint2D32f* corners, - int count, CvSize win, CvSize zero_zone, - CvTermCriteria criteria ); - -/** @brief Finds a sparse set of points within the selected region - that seem to be easy to track -@see cv::goodFeaturesToTrack -*/ -CVAPI(void) cvGoodFeaturesToTrack( const CvArr* image, CvArr* eig_image, - CvArr* temp_image, CvPoint2D32f* corners, - int* corner_count, double quality_level, - double min_distance, - const CvArr* mask CV_DEFAULT(NULL), - int block_size CV_DEFAULT(3), - int use_harris CV_DEFAULT(0), - double k CV_DEFAULT(0.04) ); - -/** @brief Finds lines on binary image using one of several methods. - - line_storage is either memory storage or 1 x _max number of lines_ CvMat, its - number of columns is changed by the function. - method is one of CV_HOUGH_*; - rho, theta and threshold are used for each of those methods; - param1 ~ line length, param2 ~ line gap - for probabilistic, - param1 ~ srn, param2 ~ stn - for multi-scale -@see cv::HoughLines -*/ -CVAPI(CvSeq*) cvHoughLines2( CvArr* image, void* line_storage, int method, - double rho, double theta, int threshold, - double param1 CV_DEFAULT(0), double param2 CV_DEFAULT(0), - double min_theta CV_DEFAULT(0), double max_theta CV_DEFAULT(CV_PI)); - -/** @brief Finds circles in the image -@see cv::HoughCircles -*/ -CVAPI(CvSeq*) cvHoughCircles( CvArr* image, void* circle_storage, - int method, double dp, double min_dist, - double param1 CV_DEFAULT(100), - double param2 CV_DEFAULT(100), - int min_radius CV_DEFAULT(0), - int max_radius CV_DEFAULT(0)); - -/** @brief Fits a line into set of 2d or 3d points in a robust way (M-estimator technique) -@see cv::fitLine -*/ -CVAPI(void) cvFitLine( const CvArr* points, int dist_type, double param, - double reps, double aeps, float* line ); - -/****************************************************************************************\ -* Drawing * -\****************************************************************************************/ - -/****************************************************************************************\ -* Drawing functions work with images/matrices of arbitrary type. * -* For color images the channel order is BGR[A] * -* Antialiasing is supported only for 8-bit image now. * -* All the functions include parameter color that means rgb value (that may be * -* constructed with CV_RGB macro) for color images and brightness * -* for grayscale images. * -* If a drawn figure is partially or completely outside of the image, it is clipped.* -\****************************************************************************************/ - -#define CV_RGB( r, g, b ) cvScalar( (b), (g), (r), 0 ) -#define CV_FILLED -1 - -#define CV_AA 16 - -/** @brief Draws 4-connected, 8-connected or antialiased line segment connecting two points -@see cv::line -*/ -CVAPI(void) cvLine( CvArr* img, CvPoint pt1, CvPoint pt2, - CvScalar color, int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); - -/** @brief Draws a rectangle given two opposite corners of the rectangle (pt1 & pt2) - - if thickness<0 (e.g. thickness == CV_FILLED), the filled box is drawn -@see cv::rectangle -*/ -CVAPI(void) cvRectangle( CvArr* img, CvPoint pt1, CvPoint pt2, - CvScalar color, int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8), - int shift CV_DEFAULT(0)); - -/** @brief Draws a rectangle specified by a CvRect structure -@see cv::rectangle -*/ -CVAPI(void) cvRectangleR( CvArr* img, CvRect r, - CvScalar color, int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8), - int shift CV_DEFAULT(0)); - - -/** @brief Draws a circle with specified center and radius. - - Thickness works in the same way as with cvRectangle -@see cv::circle -*/ -CVAPI(void) cvCircle( CvArr* img, CvPoint center, int radius, - CvScalar color, int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); - -/** @brief Draws ellipse outline, filled ellipse, elliptic arc or filled elliptic sector - - depending on _thickness_, _start_angle_ and _end_angle_ parameters. The resultant figure - is rotated by _angle_. All the angles are in degrees -@see cv::ellipse -*/ -CVAPI(void) cvEllipse( CvArr* img, CvPoint center, CvSize axes, - double angle, double start_angle, double end_angle, - CvScalar color, int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); - -CV_INLINE void cvEllipseBox( CvArr* img, CvBox2D box, CvScalar color, - int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ) -{ - CvSize axes; - axes.width = cvRound(box.size.width*0.5); - axes.height = cvRound(box.size.height*0.5); - - cvEllipse( img, cvPointFrom32f( box.center ), axes, box.angle, - 0, 360, color, thickness, line_type, shift ); -} - -/** @brief Fills convex or monotonous polygon. -@see cv::fillConvexPoly -*/ -CVAPI(void) cvFillConvexPoly( CvArr* img, const CvPoint* pts, int npts, CvScalar color, - int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); - -/** @brief Fills an area bounded by one or more arbitrary polygons -@see cv::fillPoly -*/ -CVAPI(void) cvFillPoly( CvArr* img, CvPoint** pts, const int* npts, - int contours, CvScalar color, - int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); - -/** @brief Draws one or more polygonal curves -@see cv::polylines -*/ -CVAPI(void) cvPolyLine( CvArr* img, CvPoint** pts, const int* npts, int contours, - int is_closed, CvScalar color, int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); - -#define cvDrawRect cvRectangle -#define cvDrawLine cvLine -#define cvDrawCircle cvCircle -#define cvDrawEllipse cvEllipse -#define cvDrawPolyLine cvPolyLine - -/** @brief Clips the line segment connecting *pt1 and *pt2 - by the rectangular window - - (0<=xptr will point to pt1 (or pt2, see left_to_right description) location in -the image. Returns the number of pixels on the line between the ending points. -@see cv::LineIterator -*/ -CVAPI(int) cvInitLineIterator( const CvArr* image, CvPoint pt1, CvPoint pt2, - CvLineIterator* line_iterator, - int connectivity CV_DEFAULT(8), - int left_to_right CV_DEFAULT(0)); - -#define CV_NEXT_LINE_POINT( line_iterator ) \ -{ \ - int _line_iterator_mask = (line_iterator).err < 0 ? -1 : 0; \ - (line_iterator).err += (line_iterator).minus_delta + \ - ((line_iterator).plus_delta & _line_iterator_mask); \ - (line_iterator).ptr += (line_iterator).minus_step + \ - ((line_iterator).plus_step & _line_iterator_mask); \ -} - - -#define CV_FONT_HERSHEY_SIMPLEX 0 -#define CV_FONT_HERSHEY_PLAIN 1 -#define CV_FONT_HERSHEY_DUPLEX 2 -#define CV_FONT_HERSHEY_COMPLEX 3 -#define CV_FONT_HERSHEY_TRIPLEX 4 -#define CV_FONT_HERSHEY_COMPLEX_SMALL 5 -#define CV_FONT_HERSHEY_SCRIPT_SIMPLEX 6 -#define CV_FONT_HERSHEY_SCRIPT_COMPLEX 7 - -#define CV_FONT_ITALIC 16 - -#define CV_FONT_VECTOR0 CV_FONT_HERSHEY_SIMPLEX - - -/** Font structure */ -typedef struct CvFont -{ - const char* nameFont; //Qt:nameFont - CvScalar color; //Qt:ColorFont -> cvScalar(blue_component, green_component, red_component[, alpha_component]) - int font_face; //Qt: bool italic /** =CV_FONT_* */ - const int* ascii; //!< font data and metrics - const int* greek; - const int* cyrillic; - float hscale, vscale; - float shear; //!< slope coefficient: 0 - normal, >0 - italic - int thickness; //!< Qt: weight /** letters thickness */ - float dx; //!< horizontal interval between letters - int line_type; //!< Qt: PointSize -} -CvFont; - -/** @brief Initializes font structure (OpenCV 1.x API). - -The function initializes the font structure that can be passed to text rendering functions. - -@param font Pointer to the font structure initialized by the function -@param font_face Font name identifier. See cv::HersheyFonts and corresponding old CV_* identifiers. -@param hscale Horizontal scale. If equal to 1.0f , the characters have the original width -depending on the font type. If equal to 0.5f , the characters are of half the original width. -@param vscale Vertical scale. If equal to 1.0f , the characters have the original height depending -on the font type. If equal to 0.5f , the characters are of half the original height. -@param shear Approximate tangent of the character slope relative to the vertical line. A zero -value means a non-italic font, 1.0f means about a 45 degree slope, etc. -@param thickness Thickness of the text strokes -@param line_type Type of the strokes, see line description - -@sa cvPutText - */ -CVAPI(void) cvInitFont( CvFont* font, int font_face, - double hscale, double vscale, - double shear CV_DEFAULT(0), - int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8)); - -CV_INLINE CvFont cvFont( double scale, int thickness CV_DEFAULT(1) ) -{ - CvFont font; - cvInitFont( &font, CV_FONT_HERSHEY_PLAIN, scale, scale, 0, thickness, CV_AA ); - return font; -} - -/** @brief Renders text stroke with specified font and color at specified location. - CvFont should be initialized with cvInitFont -@see cvInitFont, cvGetTextSize, cvFont, cv::putText -*/ -CVAPI(void) cvPutText( CvArr* img, const char* text, CvPoint org, - const CvFont* font, CvScalar color ); - -/** @brief Calculates bounding box of text stroke (useful for alignment) -@see cv::getTextSize -*/ -CVAPI(void) cvGetTextSize( const char* text_string, const CvFont* font, - CvSize* text_size, int* baseline ); - -/** @brief Unpacks color value - -if arrtype is CV_8UC?, _color_ is treated as packed color value, otherwise the first channels -(depending on arrtype) of destination scalar are set to the same value = _color_ -*/ -CVAPI(CvScalar) cvColorToScalar( double packed_color, int arrtype ); - -/** @brief Returns the polygon points which make up the given ellipse. - -The ellipse is define by the box of size 'axes' rotated 'angle' around the 'center'. A partial -sweep of the ellipse arc can be done by spcifying arc_start and arc_end to be something other than -0 and 360, respectively. The input array 'pts' must be large enough to hold the result. The total -number of points stored into 'pts' is returned by this function. -@see cv::ellipse2Poly -*/ -CVAPI(int) cvEllipse2Poly( CvPoint center, CvSize axes, - int angle, int arc_start, int arc_end, CvPoint * pts, int delta ); - -/** @brief Draws contour outlines or filled interiors on the image -@see cv::drawContours -*/ -CVAPI(void) cvDrawContours( CvArr *img, CvSeq* contour, - CvScalar external_color, CvScalar hole_color, - int max_level, int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8), - CvPoint offset CV_DEFAULT(cvPoint(0,0))); - -/** @} */ - -#ifdef __cplusplus -} -#endif - -#endif diff --git a/IPL/include/opencv/opencv2/imgproc/types_c.h b/IPL/include/opencv/opencv2/imgproc/types_c.h deleted file mode 100644 index 5ecb460..0000000 --- a/IPL/include/opencv/opencv2/imgproc/types_c.h +++ /dev/null @@ -1,626 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_IMGPROC_TYPES_C_H__ -#define __OPENCV_IMGPROC_TYPES_C_H__ - -#include "opencv2/core/core_c.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @addtogroup imgproc_c - @{ -*/ - -/** Connected component structure */ -typedef struct CvConnectedComp -{ - double area; /** DBL_EPSILON ? 1./std::sqrt(am00) : 0; - } - operator cv::Moments() const - { - return cv::Moments(m00, m10, m01, m20, m11, m02, m30, m21, m12, m03); - } -#endif -} -CvMoments; - -/** Hu invariants */ -typedef struct CvHuMoments -{ - double hu1, hu2, hu3, hu4, hu5, hu6, hu7; /**< Hu invariants */ -} -CvHuMoments; - -/** Template matching methods */ -enum -{ - CV_TM_SQDIFF =0, - CV_TM_SQDIFF_NORMED =1, - CV_TM_CCORR =2, - CV_TM_CCORR_NORMED =3, - CV_TM_CCOEFF =4, - CV_TM_CCOEFF_NORMED =5 -}; - -typedef float (CV_CDECL * CvDistanceFunction)( const float* a, const float* b, void* user_param ); - -/** Contour retrieval modes */ -enum -{ - CV_RETR_EXTERNAL=0, - CV_RETR_LIST=1, - CV_RETR_CCOMP=2, - CV_RETR_TREE=3, - CV_RETR_FLOODFILL=4 -}; - -/** Contour approximation methods */ -enum -{ - CV_CHAIN_CODE=0, - CV_CHAIN_APPROX_NONE=1, - CV_CHAIN_APPROX_SIMPLE=2, - CV_CHAIN_APPROX_TC89_L1=3, - CV_CHAIN_APPROX_TC89_KCOS=4, - CV_LINK_RUNS=5 -}; - -/* -Internal structure that is used for sequential retrieving contours from the image. -It supports both hierarchical and plane variants of Suzuki algorithm. -*/ -typedef struct _CvContourScanner* CvContourScanner; - -/** Freeman chain reader state */ -typedef struct CvChainPtReader -{ - CV_SEQ_READER_FIELDS() - char code; - CvPoint pt; - schar deltas[8][2]; -} -CvChainPtReader; - -/** initializes 8-element array for fast access to 3x3 neighborhood of a pixel */ -#define CV_INIT_3X3_DELTAS( deltas, step, nch ) \ - ((deltas)[0] = (nch), (deltas)[1] = -(step) + (nch), \ - (deltas)[2] = -(step), (deltas)[3] = -(step) - (nch), \ - (deltas)[4] = -(nch), (deltas)[5] = (step) - (nch), \ - (deltas)[6] = (step), (deltas)[7] = (step) + (nch)) - - -/** Contour approximation algorithms */ -enum -{ - CV_POLY_APPROX_DP = 0 -}; - -/** @brief Shape matching methods - -\f$A\f$ denotes object1,\f$B\f$ denotes object2 - -\f$\begin{array}{l} m^A_i = \mathrm{sign} (h^A_i) \cdot \log{h^A_i} \\ m^B_i = \mathrm{sign} (h^B_i) \cdot \log{h^B_i} \end{array}\f$ - -and \f$h^A_i, h^B_i\f$ are the Hu moments of \f$A\f$ and \f$B\f$ , respectively. -*/ -enum ShapeMatchModes -{ - CV_CONTOURS_MATCH_I1 =1, //!< \f[I_1(A,B) = \sum _{i=1...7} \left | \frac{1}{m^A_i} - \frac{1}{m^B_i} \right |\f] - CV_CONTOURS_MATCH_I2 =2, //!< \f[I_2(A,B) = \sum _{i=1...7} \left | m^A_i - m^B_i \right |\f] - CV_CONTOURS_MATCH_I3 =3 //!< \f[I_3(A,B) = \max _{i=1...7} \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }\f] -}; - -/** Shape orientation */ -enum -{ - CV_CLOCKWISE =1, - CV_COUNTER_CLOCKWISE =2 -}; - - -/** Convexity defect */ -typedef struct CvConvexityDefect -{ - CvPoint* start; /**< point of the contour where the defect begins */ - CvPoint* end; /**< point of the contour where the defect ends */ - CvPoint* depth_point; /**< the farthest from the convex hull point within the defect */ - float depth; /**< distance between the farthest point and the convex hull */ -} CvConvexityDefect; - - -/** Histogram comparison methods */ -enum -{ - CV_COMP_CORREL =0, - CV_COMP_CHISQR =1, - CV_COMP_INTERSECT =2, - CV_COMP_BHATTACHARYYA =3, - CV_COMP_HELLINGER =CV_COMP_BHATTACHARYYA, - CV_COMP_CHISQR_ALT =4, - CV_COMP_KL_DIV =5 -}; - -/** Mask size for distance transform */ -enum -{ - CV_DIST_MASK_3 =3, - CV_DIST_MASK_5 =5, - CV_DIST_MASK_PRECISE =0 -}; - -/** Content of output label array: connected components or pixels */ -enum -{ - CV_DIST_LABEL_CCOMP = 0, - CV_DIST_LABEL_PIXEL = 1 -}; - -/** Distance types for Distance Transform and M-estimators */ -enum -{ - CV_DIST_USER =-1, /**< User defined distance */ - CV_DIST_L1 =1, /**< distance = |x1-x2| + |y1-y2| */ - CV_DIST_L2 =2, /**< the simple euclidean distance */ - CV_DIST_C =3, /**< distance = max(|x1-x2|,|y1-y2|) */ - CV_DIST_L12 =4, /**< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) */ - CV_DIST_FAIR =5, /**< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 */ - CV_DIST_WELSCH =6, /**< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 */ - CV_DIST_HUBER =7 /**< distance = |x| threshold ? max_value : 0 */ - CV_THRESH_BINARY_INV =1, /**< value = value > threshold ? 0 : max_value */ - CV_THRESH_TRUNC =2, /**< value = value > threshold ? threshold : value */ - CV_THRESH_TOZERO =3, /**< value = value > threshold ? value : 0 */ - CV_THRESH_TOZERO_INV =4, /**< value = value > threshold ? 0 : value */ - CV_THRESH_MASK =7, - CV_THRESH_OTSU =8, /**< use Otsu algorithm to choose the optimal threshold value; - combine the flag with one of the above CV_THRESH_* values */ - CV_THRESH_TRIANGLE =16 /**< use Triangle algorithm to choose the optimal threshold value; - combine the flag with one of the above CV_THRESH_* values, but not - with CV_THRESH_OTSU */ -}; - -/** Adaptive threshold methods */ -enum -{ - CV_ADAPTIVE_THRESH_MEAN_C =0, - CV_ADAPTIVE_THRESH_GAUSSIAN_C =1 -}; - -/** FloodFill flags */ -enum -{ - CV_FLOODFILL_FIXED_RANGE =(1 << 16), - CV_FLOODFILL_MASK_ONLY =(1 << 17) -}; - - -/** Canny edge detector flags */ -enum -{ - CV_CANNY_L2_GRADIENT =(1 << 31) -}; - -/** Variants of a Hough transform */ -enum -{ - CV_HOUGH_STANDARD =0, - CV_HOUGH_PROBABILISTIC =1, - CV_HOUGH_MULTI_SCALE =2, - CV_HOUGH_GRADIENT =3 -}; - - -/* Fast search data structures */ -struct CvFeatureTree; -struct CvLSH; -struct CvLSHOperations; - -/** @} */ - -#ifdef __cplusplus -} -#endif - -#endif diff --git a/IPL/include/opencv/opencv2/line_descriptor.hpp b/IPL/include/opencv/opencv2/line_descriptor.hpp deleted file mode 100644 index cb2969f..0000000 --- a/IPL/include/opencv/opencv2/line_descriptor.hpp +++ /dev/null @@ -1,119 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// - // - // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. - // - // By downloading, copying, installing or using the software you agree to this license. - // If you do not agree to this license, do not download, install, - // copy or use the software. - // - // - // License Agreement - // For Open Source Computer Vision Library - // - // Copyright (C) 2013, OpenCV Foundation, all rights reserved. - // Third party copyrights are property of their respective owners. - // - // Redistribution and use in source and binary forms, with or without modification, - // are permitted provided that the following conditions are met: - // - // * Redistribution's of source code must retain the above copyright notice, - // this list of conditions and the following disclaimer. - // - // * Redistribution's in binary form must reproduce the above copyright notice, - // this list of conditions and the following disclaimer in the documentation - // and/or other materials provided with the distribution. - // - // * The name of the copyright holders may not be used to endorse or promote products - // derived from this software without specific prior written permission. - // - // This software is provided by the copyright holders and contributors "as is" and - // any express or implied warranties, including, but not limited to, the implied - // warranties of merchantability and fitness for a particular purpose are disclaimed. - // In no event shall the Intel Corporation or contributors be liable for any direct, - // indirect, incidental, special, exemplary, or consequential damages - // (including, but not limited to, procurement of substitute goods or services; - // loss of use, data, or profits; or business interruption) however caused - // and on any theory of liability, whether in contract, strict liability, - // or tort (including negligence or otherwise) arising in any way out of - // the use of this software, even if advised of the possibility of such damage. - // - //M*/ - -#ifndef __OPENCV_LINE_DESCRIPTOR_HPP__ -#define __OPENCV_LINE_DESCRIPTOR_HPP__ - -#include "opencv2/line_descriptor/descriptor.hpp" - -/** @defgroup line_descriptor Binary descriptors for lines extracted from an image - -Introduction ------------- - -One of the most challenging activities in computer vision is the extraction of useful information -from a given image. Such information, usually comes in the form of points that preserve some kind of -property (for instance, they are scale-invariant) and are actually representative of input image. - -The goal of this module is seeking a new kind of representative information inside an image and -providing the functionalities for its extraction and representation. In particular, differently from -previous methods for detection of relevant elements inside an image, lines are extracted in place of -points; a new class is defined ad hoc to summarize a line's properties, for reuse and plotting -purposes. - -Computation of binary descriptors ---------------------------------- - -To obtatin a binary descriptor representing a certain line detected from a certain octave of an -image, we first compute a non-binary descriptor as described in @cite LBD . Such algorithm works on -lines extracted using EDLine detector, as explained in @cite EDL . Given a line, we consider a -rectangular region centered at it and called *line support region (LSR)*. Such region is divided -into a set of bands \f$\{B_1, B_2, ..., B_m\}\f$, whose length equals the one of line. - -If we indicate with \f$\bf{d}_L\f$ the direction of line, the orthogonal and clockwise direction to line -\f$\bf{d}_{\perp}\f$ can be determined; these two directions, are used to construct a reference frame -centered in the middle point of line. The gradients of pixels \f$\bf{g'}\f$ inside LSR can be projected -to the newly determined frame, obtaining their local equivalent -\f$\bf{g'} = (\bf{g}^T \cdot \bf{d}_{\perp}, \bf{g}^T \cdot \bf{d}_L)^T \triangleq (\bf{g'}_{d_{\perp}}, \bf{g'}_{d_L})^T\f$. - -Later on, a Gaussian function is applied to all LSR's pixels along \f$\bf{d}_\perp\f$ direction; first, -we assign a global weighting coefficient \f$f_g(i) = (1/\sqrt{2\pi}\sigma_g)e^{-d^2_i/2\sigma^2_g}\f$ to -*i*-th row in LSR, where \f$d_i\f$ is the distance of *i*-th row from the center row in LSR, -\f$\sigma_g = 0.5(m \cdot w - 1)\f$ and \f$w\f$ is the width of bands (the same for every band). Secondly, -considering a band \f$B_j\f$ and its neighbor bands \f$B_{j-1}, B_{j+1}\f$, we assign a local weighting -\f$F_l(k) = (1/\sqrt{2\pi}\sigma_l)e^{-d'^2_k/2\sigma_l^2}\f$, where \f$d'_k\f$ is the distance of *k*-th -row from the center row in \f$B_j\f$ and \f$\sigma_l = w\f$. Using the global and local weights, we obtain, -at the same time, the reduction of role played by gradients far from line and of boundary effect, -respectively. - -Each band \f$B_j\f$ in LSR has an associated *band descriptor(BD)* which is computed considering -previous and next band (top and bottom bands are ignored when computing descriptor for first and -last band). Once each band has been assignen its BD, the LBD descriptor of line is simply given by - -\f[LBD = (BD_1^T, BD_2^T, ... , BD^T_m)^T.\f] - -To compute a band descriptor \f$B_j\f$, each *k*-th row in it is considered and the gradients in such -row are accumulated: - -\f[\begin{matrix} \bf{V1}^k_j = \lambda \sum\limits_{\bf{g}'_{d_\perp}>0}\bf{g}'_{d_\perp}, & \bf{V2}^k_j = \lambda \sum\limits_{\bf{g}'_{d_\perp}<0} -\bf{g}'_{d_\perp}, \\ \bf{V3}^k_j = \lambda \sum\limits_{\bf{g}'_{d_L}>0}\bf{g}'_{d_L}, & \bf{V4}^k_j = \lambda \sum\limits_{\bf{g}'_{d_L}<0} -\bf{g}'_{d_L}\end{matrix}.\f] - -with \f$\lambda = f_g(k)f_l(k)\f$. - -By stacking previous results, we obtain the *band description matrix (BDM)* - -\f[BDM_j = \left(\begin{matrix} \bf{V1}_j^1 & \bf{V1}_j^2 & \ldots & \bf{V1}_j^n \\ \bf{V2}_j^1 & \bf{V2}_j^2 & \ldots & \bf{V2}_j^n \\ \bf{V3}_j^1 & \bf{V3}_j^2 & \ldots & \bf{V3}_j^n \\ \bf{V4}_j^1 & \bf{V4}_j^2 & \ldots & \bf{V4}_j^n \end{matrix} \right) \in \mathbb{R}^{4\times n},\f] - -with \f$n\f$ the number of rows in band \f$B_j\f$: - -\f[n = \begin{cases} 2w, & j = 1||m; \\ 3w, & \mbox{else}. \end{cases}\f] - -Each \f$BD_j\f$ can be obtained using the standard deviation vector \f$S_j\f$ and mean vector \f$M_j\f$ of -\f$BDM_J\f$. Thus, finally: - -\f[LBD = (M_1^T, S_1^T, M_2^T, S_2^T, \ldots, M_m^T, S_m^T)^T \in \mathbb{R}^{8m}\f] - -Once the LBD has been obtained, it must be converted into a binary form. For such purpose, we -consider 32 possible pairs of BD inside it; each couple of BD is compared bit by bit and comparison -generates an 8 bit string. Concatenating 32 comparison strings, we get the 256-bit final binary -representation of a single LBD. -*/ - -#endif diff --git a/IPL/include/opencv/opencv2/line_descriptor/descriptor.hpp b/IPL/include/opencv/opencv2/line_descriptor/descriptor.hpp deleted file mode 100644 index 65c4395..0000000 --- a/IPL/include/opencv/opencv2/line_descriptor/descriptor.hpp +++ /dev/null @@ -1,1366 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// - // - // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. - // - // By downloading, copying, installing or using the software you agree to this license. - // If you do not agree to this license, do not download, install, - // copy or use the software. - // - // - // License Agreement - // For Open Source Computer Vision Library - // - // Copyright (C) 2014, Biagio Montesano, all rights reserved. - // Third party copyrights are property of their respective owners. - // - // Redistribution and use in source and binary forms, with or without modification, - // are permitted provided that the following conditions are met: - // - // * Redistribution's of source code must retain the above copyright notice, - // this list of conditions and the following disclaimer. - // - // * Redistribution's in binary form must reproduce the above copyright notice, - // this list of conditions and the following disclaimer in the documentation - // and/or other materials provided with the distribution. - // - // * The name of the copyright holders may not be used to endorse or promote products - // derived from this software without specific prior written permission. - // - // This software is provided by the copyright holders and contributors "as is" and - // any express or implied warranties, including, but not limited to, the implied - // warranties of merchantability and fitness for a particular purpose are disclaimed. - // In no event shall the Intel Corporation or contributors be liable for any direct, - // indirect, incidental, special, exemplary, or consequential damages - // (including, but not limited to, procurement of substitute goods or services; - // loss of use, data, or profits; or business interruption) however caused - // and on any theory of liability, whether in contract, strict liability, - // or tort (including negligence or otherwise) arising in any way out of - // the use of this software, even if advised of the possibility of such damage. - // - //M*/ - -#ifndef __OPENCV_DESCRIPTOR_HPP__ -#define __OPENCV_DESCRIPTOR_HPP__ - -#include -#include -#include - -#if defined _MSC_VER && _MSC_VER <= 1700 -#include -#else -#include -#endif - -#include -#include - -#include "opencv2/core/utility.hpp" -//#include "opencv2/core/private.hpp" -#include -#include -#include -#include "opencv2/core.hpp" - -/* define data types */ -typedef uint64_t UINT64; -typedef uint32_t UINT32; -typedef uint16_t UINT16; -typedef uint8_t UINT8; - -/* define constants */ -#define UINT64_1 ((UINT64)0x01) -#define UINT32_1 ((UINT32)0x01) - -namespace cv -{ -namespace line_descriptor -{ - -//! @addtogroup line_descriptor -//! @{ - -/** @brief A class to represent a line - -As aformentioned, it is been necessary to design a class that fully stores the information needed to -characterize completely a line and plot it on image it was extracted from, when required. - -*KeyLine* class has been created for such goal; it is mainly inspired to Feature2d's KeyPoint class, -since KeyLine shares some of *KeyPoint*'s fields, even if a part of them assumes a different -meaning, when speaking about lines. In particular: - -- the *class_id* field is used to gather lines extracted from different octaves which refer to - same line inside original image (such lines and the one they represent in original image share - the same *class_id* value) -- the *angle* field represents line's slope with respect to (positive) X axis -- the *pt* field represents line's midpoint -- the *response* field is computed as the ratio between the line's length and maximum between - image's width and height -- the *size* field is the area of the smallest rectangle containing line - -Apart from fields inspired to KeyPoint class, KeyLines stores information about extremes of line in -original image and in octave it was extracted from, about line's length and number of pixels it -covers. - */ -struct CV_EXPORTS KeyLine -{ - public: - /** orientation of the line */ - float angle; - - /** object ID, that can be used to cluster keylines by the line they represent */ - int class_id; - - /** octave (pyramid layer), from which the keyline has been extracted */ - int octave; - - /** coordinates of the middlepoint */ - Point2f pt; - - /** the response, by which the strongest keylines have been selected. - It's represented by the ratio between line's length and maximum between - image's width and height */ - float response; - - /** minimum area containing line */ - float size; - - /** lines's extremes in original image */ - float startPointX; - float startPointY; - float endPointX; - float endPointY; - - /** line's extremes in image it was extracted from */ - float sPointInOctaveX; - float sPointInOctaveY; - float ePointInOctaveX; - float ePointInOctaveY; - - /** the length of line */ - float lineLength; - - /** number of pixels covered by the line */ - int numOfPixels; - - /** Returns the start point of the line in the original image */ - Point2f getStartPoint() const - { - return Point2f(startPointX, startPointY); - } - - /** Returns the end point of the line in the original image */ - Point2f getEndPoint() const - { - return Point2f(endPointX, endPointY); - } - - /** Returns the start point of the line in the octave it was extracted from */ - Point2f getStartPointInOctave() const - { - return Point2f(sPointInOctaveX, sPointInOctaveY); - } - - /** Returns the end point of the line in the octave it was extracted from */ - Point2f getEndPointInOctave() const - { - return Point2f(ePointInOctaveX, ePointInOctaveY); - } - - /** constructor */ - KeyLine() - { - } -}; - -/** @brief Class implements both functionalities for detection of lines and computation of their -binary descriptor. - -Class' interface is mainly based on the ones of classical detectors and extractors, such as -Feature2d's @ref features2d_main and @ref features2d_match. Retrieved information about lines is -stored in line_descriptor::KeyLine objects. - */ -class CV_EXPORTS BinaryDescriptor : public Algorithm -{ - - public: - /** @brief List of BinaryDescriptor parameters: - */ - struct CV_EXPORTS Params - { - /*CV_WRAP*/ - Params(); - - /** the number of image octaves (default = 1) */ - - int numOfOctave_; - - /** the width of band; (default: 7) */ - - int widthOfBand_; - - /** image's reduction ratio in construction of Gaussian pyramids */ - int reductionRatio; - - int ksize_; - - /** read parameters from a FileNode object and store them (struct function) */ - void read( const FileNode& fn ); - - /** store parameters to a FileStorage object (struct function) */ - void write( FileStorage& fs ) const; - - }; - - /** @brief Constructor - - @param parameters configuration parameters BinaryDescriptor::Params - - If no argument is provided, constructor sets default values (see comments in the code snippet in - previous section). Default values are strongly reccomended. - */ - BinaryDescriptor( const BinaryDescriptor::Params ¶meters = BinaryDescriptor::Params() ); - - /** @brief Create a BinaryDescriptor object with default parameters (or with the ones provided) - and return a smart pointer to it - */ - static Ptr createBinaryDescriptor(); - static Ptr createBinaryDescriptor( Params parameters ); - - /** destructor */ - ~BinaryDescriptor(); - - /** @brief Get current number of octaves - */ - int getNumOfOctaves();/*CV_WRAP*/ - /** @brief Set number of octaves - @param octaves number of octaves - */ - void setNumOfOctaves( int octaves );/*CV_WRAP*/ - /** @brief Get current width of bands - */ - int getWidthOfBand();/*CV_WRAP*/ - /** @brief Set width of bands - @param width width of bands - */ - void setWidthOfBand( int width );/*CV_WRAP*/ - /** @brief Get current reduction ratio (used in Gaussian pyramids) - */ - int getReductionRatio();/*CV_WRAP*/ - /** @brief Set reduction ratio (used in Gaussian pyramids) - @param rRatio reduction ratio - */ - void setReductionRatio( int rRatio ); - - /** @brief Read parameters from a FileNode object and store them - - @param fn source FileNode file - */ - virtual void read( const cv::FileNode& fn ); - - /** @brief Store parameters to a FileStorage object - - @param fs output FileStorage file - */ - virtual void write( cv::FileStorage& fs ) const; - - /** @brief Requires line detection - - @param image input image - @param keypoints vector that will store extracted lines for one or more images - @param mask mask matrix to detect only KeyLines of interest - */ - void detect( const Mat& image, CV_OUT std::vector& keypoints, const Mat& mask = Mat() ); - - /** @overload - - @param images input images - @param keylines set of vectors that will store extracted lines for one or more images - @param masks vector of mask matrices to detect only KeyLines of interest from each input image - */ - void detect( const std::vector& images, std::vector >& keylines, const std::vector& masks = - std::vector() ) const; - - /** @brief Requires descriptors computation - - @param image input image - @param keylines vector containing lines for which descriptors must be computed - @param descriptors - @param returnFloatDescr flag (when set to true, original non-binary descriptors are returned) - */ - void compute( const Mat& image, CV_OUT CV_IN_OUT std::vector& keylines, CV_OUT Mat& descriptors, bool returnFloatDescr = false ) const; - - /** @overload - - @param images input images - @param keylines set of vectors containing lines for which descriptors must be computed - @param descriptors - @param returnFloatDescr flag (when set to true, original non-binary descriptors are returned) - */ - void compute( const std::vector& images, std::vector >& keylines, std::vector& descriptors, bool returnFloatDescr = - false ) const; - - /** @brief Return descriptor size - */ - int descriptorSize() const; - - /** @brief Return data type - */ - int descriptorType() const; - - /** returns norm mode */ - /*CV_WRAP*/ - int defaultNorm() const; - - /** @brief Define operator '()' to perform detection of KeyLines and computation of descriptors in a row. - - @param image input image - @param mask mask matrix to select which lines in KeyLines must be accepted among the ones - extracted (used when *keylines* is not empty) - @param keylines vector that contains input lines (when filled, the detection part will be skipped - and input lines will be passed as input to the algorithm computing descriptors) - @param descriptors matrix that will store final descriptors - @param useProvidedKeyLines flag (when set to true, detection phase will be skipped and only - computation of descriptors will be executed, using lines provided in *keylines*) - @param returnFloatDescr flag (when set to true, original non-binary descriptors are returned) - */ - virtual void operator()( InputArray image, InputArray mask, CV_OUT std::vector& keylines, OutputArray descriptors, - bool useProvidedKeyLines = false, bool returnFloatDescr = false ) const; - - protected: - /** implementation of line detection */ - virtual void detectImpl( const Mat& imageSrc, std::vector& keylines, const Mat& mask = Mat() ) const; - - /** implementation of descriptors' computation */ - virtual void computeImpl( const Mat& imageSrc, std::vector& keylines, Mat& descriptors, bool returnFloatDescr, - bool useDetectionData ) const; - - private: - /** struct to represent lines extracted from an octave */ - struct OctaveLine - { - unsigned int octaveCount; //the octave which this line is detected - unsigned int lineIDInOctave; //the line ID in that octave image - unsigned int lineIDInScaleLineVec; //the line ID in Scale line vector - float lineLength; //the length of line in original image scale - }; - - // A 2D line (normal equation parameters). - struct SingleLine - { - //note: rho and theta are based on coordinate origin, i.e. the top-left corner of image - double rho; //unit: pixel length - double theta; //unit: rad - double linePointX; // = rho * cos(theta); - double linePointY; // = rho * sin(theta); - //for EndPoints, the coordinate origin is the top-left corner of image. - double startPointX; - double startPointY; - double endPointX; - double endPointY; - //direction of a line, the angle between positive line direction (dark side is in the left) and positive X axis. - double direction; - //mean gradient magnitude - double gradientMagnitude; - //mean gray value of pixels in dark side of line - double darkSideGrayValue; - //mean gray value of pixels in light side of line - double lightSideGrayValue; - //the length of line - double lineLength; - //the width of line; - double width; - //number of pixels - int numOfPixels; - //the decriptor of line - std::vector descriptor; - }; - - // Specifies a vector of lines. - typedef std::vector Lines_list; - - struct OctaveSingleLine - { - /*endPoints, the coordinate origin is the top-left corner of the original image. - *startPointX = sPointInOctaveX * (factor)^octaveCount; */ - float startPointX; - float startPointY; - float endPointX; - float endPointY; - //endPoints, the coordinate origin is the top-left corner of the octave image. - float sPointInOctaveX; - float sPointInOctaveY; - float ePointInOctaveX; - float ePointInOctaveY; - //direction of a line, the angle between positive line direction (dark side is in the left) and positive X axis. - float direction; - //the summation of gradient magnitudes of pixels on lines - float salience; - //the length of line - float lineLength; - //number of pixels - unsigned int numOfPixels; - //the octave which this line is detected - unsigned int octaveCount; - //the decriptor of line - std::vector descriptor; - }; - - struct Pixel - { - unsigned int x; //X coordinate - unsigned int y; //Y coordinate - }; - struct EdgeChains - { - std::vector xCors; //all the x coordinates of edge points - std::vector yCors; //all the y coordinates of edge points - std::vector sId; //the start index of each edge in the coordinate arrays - unsigned int numOfEdges; //the number of edges whose length are larger than minLineLen; numOfEdges < sId.size; - }; - - struct LineChains - { - std::vector xCors; //all the x coordinates of line points - std::vector yCors; //all the y coordinates of line points - std::vector sId; //the start index of each line in the coordinate arrays - unsigned int numOfLines; //the number of lines whose length are larger than minLineLen; numOfLines < sId.size; - }; - - typedef std::list PixelChain; //each edge is a pixel chain - - struct EDLineParam - { - int ksize; - float sigma; - float gradientThreshold; - float anchorThreshold; - int scanIntervals; - int minLineLen; - double lineFitErrThreshold; - }; - - #define RELATIVE_ERROR_FACTOR 100.0 - #define MLN10 2.30258509299404568402 - #define log_gamma(x) ((x)>15.0?log_gamma_windschitl(x):log_gamma_lanczos(x)) - - /** This class is used to detect lines from input image. - * First, edges are extracted from input image following the method presented in Cihan Topal and - * Cuneyt Akinlar's paper:"Edge Drawing: A Heuristic Approach to Robust Real-Time Edge Detection", 2010. - * Then, lines are extracted from the edge image following the method presented in Cuneyt Akinlar and - * Cihan Topal's paper:"EDLines: A real-time line segment detector with a false detection control", 2011 - * PS: The linking step of edge detection has a little bit difference with the Edge drawing algorithm - * described in the paper. The edge chain doesn't stop when the pixel direction is changed. - */ - class EDLineDetector - { - public: - EDLineDetector(); - EDLineDetector( EDLineParam param ); - ~EDLineDetector(); - - /*extract edges from image - *image: In, gray image; - *edges: Out, store the edges, each edge is a pixel chain - *return -1: error happen - */ - int EdgeDrawing( cv::Mat &image, EdgeChains &edgeChains ); - - /*extract lines from image - *image: In, gray image; - *lines: Out, store the extracted lines, - *return -1: error happen - */ - int EDline( cv::Mat &image, LineChains &lines ); - - /** extract line from image, and store them */ - int EDline( cv::Mat &image ); - - cv::Mat dxImg_; //store the dxImg; - - cv::Mat dyImg_; //store the dyImg; - - cv::Mat gImgWO_; //store the gradient image without threshold; - - LineChains lines_; //store the detected line chains; - - //store the line Equation coefficients, vec3=[w1,w2,w3] for line w1*x + w2*y + w3=0; - std::vector > lineEquations_; - - //store the line endpoints, [x1,y1,x2,y3] - std::vector > lineEndpoints_; - - //store the line direction - std::vector lineDirection_; - - //store the line salience, which is the summation of gradients of pixels on line - std::vector lineSalience_; - - // image sizes - unsigned int imageWidth; - unsigned int imageHeight; - - /*The threshold of line fit error; - *If lineFitErr is large than this threshold, then - *the pixel chain is not accepted as a single line segment.*/ - double lineFitErrThreshold_; - - /*the threshold of pixel gradient magnitude. - *Only those pixel whose gradient magnitude are larger than this threshold will be - *taken as possible edge points. Default value is 36*/ - short gradienThreshold_; - - /*If the pixel's gradient value is bigger than both of its neighbors by a - *certain threshold (ANCHOR_THRESHOLD), the pixel is marked to be an anchor. - *Default value is 8*/ - unsigned char anchorThreshold_; - - /*anchor testing can be performed at different scan intervals, i.e., - *every row/column, every second row/column etc. - *Default value is 2*/ - unsigned int scanIntervals_; - - int minLineLen_; //minimal acceptable line length - - private: - void InitEDLine_(); - - /*For an input edge chain, find the best fit line, the default chain length is minLineLen_ - *xCors: In, pointer to the X coordinates of pixel chain; - *yCors: In, pointer to the Y coordinates of pixel chain; - *offsetS:In, start index of this chain in vector; - *lineEquation: Out, [a,b] which are the coefficient of lines y=ax+b(horizontal) or x=ay+b(vertical); - *return: line fit error; -1:error happens; - */ - double LeastSquaresLineFit_( unsigned int *xCors, unsigned int *yCors, unsigned int offsetS, std::vector &lineEquation ); - - /*For an input pixel chain, find the best fit line. Only do the update based on new points. - *For A*x=v, Least square estimation of x = Inv(A^T * A) * (A^T * v); - *If some new observations are added, i.e, [A; A'] * x = [v; v'], - *then x' = Inv(A^T * A + (A')^T * A') * (A^T * v + (A')^T * v'); - *xCors: In, pointer to the X coordinates of pixel chain; - *yCors: In, pointer to the Y coordinates of pixel chain; - *offsetS:In, start index of this chain in vector; - *newOffsetS: In, start index of extended part; - *offsetE:In, end index of this chain in vector; - *lineEquation: Out, [a,b] which are the coefficient of lines y=ax+b(horizontal) or x=ay+b(vertical); - *return: line fit error; -1:error happens; - */ - double LeastSquaresLineFit_( unsigned int *xCors, unsigned int *yCors, unsigned int offsetS, unsigned int newOffsetS, unsigned int offsetE, - std::vector &lineEquation ); - - /** Validate line based on the Helmholtz principle, which basically states that - * for a structure to be perceptually meaningful, the expectation of this structure - * by chance must be very low. - */ - bool LineValidation_( unsigned int *xCors, unsigned int *yCors, unsigned int offsetS, unsigned int offsetE, std::vector &lineEquation, - float &direction ); - - bool bValidate_; //flag to decide whether line will be validated - - int ksize_; //the size of Gaussian kernel: ksize X ksize, default value is 5. - - float sigma_; //the sigma of Gaussian kernal, default value is 1.0. - - /*For example, there two edges in the image: - *edge1 = [(7,4), (8,5), (9,6),| (10,7)|, (11, 8), (12,9)] and - *edge2 = [(14,9), (15,10), (16,11), (17,12),| (18, 13)|, (19,14)] ; then we store them as following: - *pFirstPartEdgeX_ = [10, 11, 12, 18, 19];//store the first part of each edge[from middle to end] - *pFirstPartEdgeY_ = [7, 8, 9, 13, 14]; - *pFirstPartEdgeS_ = [0,3,5];// the index of start point of first part of each edge - *pSecondPartEdgeX_ = [10, 9, 8, 7, 18, 17, 16, 15, 14];//store the second part of each edge[from middle to front] - *pSecondPartEdgeY_ = [7, 6, 5, 4, 13, 12, 11, 10, 9];//anchor points(10, 7) and (18, 13) are stored again - *pSecondPartEdgeS_ = [0, 4, 9];// the index of start point of second part of each edge - *This type of storage order is because of the order of edge detection process. - *For each edge, start from one anchor point, first go right, then go left or first go down, then go up*/ - - //store the X coordinates of the first part of the pixels for chains - unsigned int *pFirstPartEdgeX_; - - //store the Y coordinates of the first part of the pixels for chains - unsigned int *pFirstPartEdgeY_; - - //store the start index of every edge chain in the first part arrays - unsigned int *pFirstPartEdgeS_; - - //store the X coordinates of the second part of the pixels for chains - unsigned int *pSecondPartEdgeX_; - - //store the Y coordinates of the second part of the pixels for chains - unsigned int *pSecondPartEdgeY_; - - //store the start index of every edge chain in the second part arrays - unsigned int *pSecondPartEdgeS_; - - //store the X coordinates of anchors - unsigned int *pAnchorX_; - - //store the Y coordinates of anchors - unsigned int *pAnchorY_; - - //edges - cv::Mat edgeImage_; - - cv::Mat gImg_; //store the gradient image; - - cv::Mat dirImg_; //store the direction image - - double logNT_; - - cv::Mat_ ATA; //the previous matrix of A^T * A; - - cv::Mat_ ATV; //the previous vector of A^T * V; - - cv::Mat_ fitMatT; //the matrix used in line fit function; - - cv::Mat_ fitVec; //the vector used in line fit function; - - cv::Mat_ tempMatLineFit; //the matrix used in line fit function; - - cv::Mat_ tempVecLineFit; //the vector used in line fit function; - - /** Compare doubles by relative error. - The resulting rounding error after floating point computations - depend on the specific operations done. The same number computed by - different algorithms could present different rounding errors. For a - useful comparison, an estimation of the relative rounding error - should be considered and compared to a factor times EPS. The factor - should be related to the accumulated rounding error in the chain of - computation. Here, as a simplification, a fixed factor is used. - */ - static int double_equal( double a, double b ) - { - double abs_diff, aa, bb, abs_max; - /* trivial case */ - if( a == b ) - return true; - abs_diff = fabs( a - b ); - aa = fabs( a ); - bb = fabs( b ); - abs_max = aa > bb ? aa : bb; - - /* DBL_MIN is the smallest normalized number, thus, the smallest - number whose relative error is bounded by DBL_EPSILON. For - smaller numbers, the same quantization steps as for DBL_MIN - are used. Then, for smaller numbers, a meaningful "relative" - error should be computed by dividing the difference by DBL_MIN. */ - if( abs_max < DBL_MIN ) - abs_max = DBL_MIN; - - /* equal if relative error <= factor x eps */ - return ( abs_diff / abs_max ) <= ( RELATIVE_ERROR_FACTOR * DBL_EPSILON ); - } - - /** Computes the natural logarithm of the absolute value of - the gamma function of x using the Lanczos approximation. - See http://www.rskey.org/gamma.htm - The formula used is - @f[ - \Gamma(x) = \frac{ \sum_{n=0}^{N} q_n x^n }{ \Pi_{n=0}^{N} (x+n) } - (x+5.5)^{x+0.5} e^{-(x+5.5)} - @f] - so - @f[ - \log\Gamma(x) = \log\left( \sum_{n=0}^{N} q_n x^n \right) - + (x+0.5) \log(x+5.5) - (x+5.5) - \sum_{n=0}^{N} \log(x+n) - @f] - and - q0 = 75122.6331530, - q1 = 80916.6278952, - q2 = 36308.2951477, - q3 = 8687.24529705, - q4 = 1168.92649479, - q5 = 83.8676043424, - q6 = 2.50662827511. - */ - static double log_gamma_lanczos( double x ) - { - static double q[7] = - { 75122.6331530, 80916.6278952, 36308.2951477, 8687.24529705, 1168.92649479, 83.8676043424, 2.50662827511 }; - double a = ( x + 0.5 ) * log( x + 5.5 ) - ( x + 5.5 ); - double b = 0.0; - int n; - for ( n = 0; n < 7; n++ ) - { - a -= log( x + (double) n ); - b += q[n] * pow( x, (double) n ); - } - return a + log( b ); - } - - /** Computes the natural logarithm of the absolute value of - the gamma function of x using Windschitl method. - See http://www.rskey.org/gamma.htm - The formula used is - @f[ - \Gamma(x) = \sqrt{\frac{2\pi}{x}} \left( \frac{x}{e} - \sqrt{ x\sinh(1/x) + \frac{1}{810x^6} } \right)^x - @f] - so - @f[ - \log\Gamma(x) = 0.5\log(2\pi) + (x-0.5)\log(x) - x - + 0.5x\log\left( x\sinh(1/x) + \frac{1}{810x^6} \right). - @f] - This formula is a good approximation when x > 15. - */ - static double log_gamma_windschitl( double x ) - { - return 0.918938533204673 + ( x - 0.5 ) * log( x ) - x + 0.5 * x * log( x * sinh( 1 / x ) + 1 / ( 810.0 * pow( x, 6.0 ) ) ); - } - - /** Computes -log10(NFA). - NFA stands for Number of False Alarms: - @f[ - \mathrm{NFA} = NT \cdot B(n,k,p) - @f] - - NT - number of tests - - B(n,k,p) - tail of binomial distribution with parameters n,k and p: - @f[ - B(n,k,p) = \sum_{j=k}^n - \left(\begin{array}{c}n\\j\end{array}\right) - p^{j} (1-p)^{n-j} - @f] - The value -log10(NFA) is equivalent but more intuitive than NFA: - - -1 corresponds to 10 mean false alarms - - 0 corresponds to 1 mean false alarm - - 1 corresponds to 0.1 mean false alarms - - 2 corresponds to 0.01 mean false alarms - - ... - Used this way, the bigger the value, better the detection, - and a logarithmic scale is used. - @param n,k,p binomial parameters. - @param logNT logarithm of Number of Tests - The computation is based in the gamma function by the following - relation: - @f[ - \left(\begin{array}{c}n\\k\end{array}\right) - = \frac{ \Gamma(n+1) }{ \Gamma(k+1) \cdot \Gamma(n-k+1) }. - @f] - We use efficient algorithms to compute the logarithm of - the gamma function. - To make the computation faster, not all the sum is computed, part - of the terms are neglected based on a bound to the error obtained - (an error of 10% in the result is accepted). - */ - static double nfa( int n, int k, double p, double logNT ) - { - double tolerance = 0.1; /* an error of 10% in the result is accepted */ - double log1term, term, bin_term, mult_term, bin_tail, err, p_term; - int i; - - /* check parameters */ - if( n < 0 || k < 0 || k > n || p <= 0.0 || p >= 1.0 ) - { - std::cout << "nfa: wrong n, k or p values." << std::endl; - exit( 0 ); - } - /* trivial cases */ - if( n == 0 || k == 0 ) - return -logNT; - if( n == k ) - return -logNT - (double) n * log10( p ); - - /* probability term */ - p_term = p / ( 1.0 - p ); - - /* compute the first term of the series */ - /* - binomial_tail(n,k,p) = sum_{i=k}^n bincoef(n,i) * p^i * (1-p)^{n-i} - where bincoef(n,i) are the binomial coefficients. - But - bincoef(n,k) = gamma(n+1) / ( gamma(k+1) * gamma(n-k+1) ). - We use this to compute the first term. Actually the log of it. - */ - log1term = log_gamma( (double) n + 1.0 )- log_gamma( (double ) k + 1.0 )- log_gamma( (double ) ( n - k ) + 1.0 ) -+ (double) k * log( p ) -+ (double) ( n - k ) * log( 1.0 - p ); -term = exp( log1term ); - -/* in some cases no more computations are needed */ -if( double_equal( term, 0.0 ) ) -{ /* the first term is almost zero */ - if( (double) k > (double) n * p ) /* at begin or end of the tail? */ - return -log1term / MLN10 - logNT; /* end: use just the first term */ - else - return -logNT; /* begin: the tail is roughly 1 */ -} - -/* compute more terms if needed */ -bin_tail = term; -for ( i = k + 1; i <= n; i++ ) -{ - /* As - term_i = bincoef(n,i) * p^i * (1-p)^(n-i) - and - bincoef(n,i)/bincoef(n,i-1) = n-i+1 / i, - then, - term_i / term_i-1 = (n-i+1)/i * p/(1-p) - and - term_i = term_i-1 * (n-i+1)/i * p/(1-p). - p/(1-p) is computed only once and stored in 'p_term'. - */ - bin_term = (double) ( n - i + 1 ) / (double) i; - mult_term = bin_term * p_term; - term *= mult_term; - bin_tail += term; - if( bin_term < 1.0 ) - { - /* When bin_term<1 then mult_term_ji. - Then, the error on the binomial tail when truncated at - the i term can be bounded by a geometric series of form - term_i * sum mult_term_i^j. */ - err = term * ( ( 1.0 - pow( mult_term, (double) ( n - i + 1 ) ) ) / ( 1.0 - mult_term ) - 1.0 ); - /* One wants an error at most of tolerance*final_result, or: - tolerance * abs(-log10(bin_tail)-logNT). - Now, the error that can be accepted on bin_tail is - given by tolerance*final_result divided by the derivative - of -log10(x) when x=bin_tail. that is: - tolerance * abs(-log10(bin_tail)-logNT) / (1/bin_tail) - Finally, we truncate the tail if the error is less than: - tolerance * abs(-log10(bin_tail)-logNT) * bin_tail */ - if( err < tolerance * fabs( -log10( bin_tail ) - logNT ) * bin_tail ) - break; - } -} -return -log10( bin_tail ) - logNT; -} -}; - - // Specifies a vector of lines. -typedef std::vector LinesVec; - -// each element in ScaleLines is a vector of lines -// which corresponds the same line detected in different octave images. -typedef std::vector ScaleLines; - -/* compute Gaussian pyramids */ -void computeGaussianPyramid( const Mat& image, const int numOctaves ); - -/* compute Sobel's derivatives */ -void computeSobel( const Mat& image, const int numOctaves ); - -/* conversion of an LBD descriptor to its binary representation */ -unsigned char binaryConversion( float* f1, float* f2 ); - -/* compute LBD descriptors using EDLine extractor */ -int computeLBD( ScaleLines &keyLines, bool useDetectionData = false ); - -/* gathers lines in groups using EDLine extractor. - Each group contains the same line, detected in different octaves */ -int OctaveKeyLines( cv::Mat& image, ScaleLines &keyLines ); - -/* the local gaussian coefficient applied to the orthogonal line direction within each band */ -std::vector gaussCoefL_; - -/* the global gaussian coefficient applied to each row within line support region */ -std::vector gaussCoefG_; - -/* descriptor parameters */ -Params params; - -/* vector of sizes of downsampled and blurred images */ -std::vector images_sizes; - -/*For each octave of image, we define an EDLineDetector, because we can get gradient images (dxImg, dyImg, gImg) - *from the EDLineDetector class without extra computation cost. Another reason is that, if we use - *a single EDLineDetector to detect lines in different octave of images, then we need to allocate and release - *memory for gradient images (dxImg, dyImg, gImg) repeatedly for their varying size*/ -std::vector > edLineVec_; - -/* Sobel's derivatives */ -std::vector dxImg_vector, dyImg_vector; - -/* Gaussian pyramid */ -std::vector octaveImages; - -}; - -/** -Lines extraction methodology ----------------------------- - -The lines extraction methodology described in the following is mainly based on @cite EDL . The -extraction starts with a Gaussian pyramid generated from an original image, downsampled N-1 times, -blurred N times, to obtain N layers (one for each octave), with layer 0 corresponding to input -image. Then, from each layer (octave) in the pyramid, lines are extracted using LSD algorithm. - -Differently from EDLine lines extractor used in original article, LSD furnishes information only -about lines extremes; thus, additional information regarding slope and equation of line are computed -via analytic methods. The number of pixels is obtained using *LineIterator*. Extracted lines are -returned in the form of KeyLine objects, but since extraction is based on a method different from -the one used in *BinaryDescriptor* class, data associated to a line's extremes in original image and -in octave it was extracted from, coincide. KeyLine's field *class_id* is used as an index to -indicate the order of extraction of a line inside a single octave. -*/ -class CV_EXPORTS LSDDetector : public Algorithm -{ -public: - -/* constructor */ -/*CV_WRAP*/ -LSDDetector() -{ -} -; - -/** @brief Creates ad LSDDetector object, using smart pointers. - */ -static Ptr createLSDDetector(); - -/** @brief Detect lines inside an image. - -@param image input image -@param keypoints vector that will store extracted lines for one or more images -@param scale scale factor used in pyramids generation -@param numOctaves number of octaves inside pyramid -@param mask mask matrix to detect only KeyLines of interest - */ -void detect( const Mat& image, CV_OUT std::vector& keypoints, int scale, int numOctaves, const Mat& mask = Mat() ); - -/** @overload -@param images input images -@param keylines set of vectors that will store extracted lines for one or more images -@param scale scale factor used in pyramids generation -@param numOctaves number of octaves inside pyramid -@param masks vector of mask matrices to detect only KeyLines of interest from each input image -*/ -void detect( const std::vector& images, std::vector >& keylines, int scale, int numOctaves, -const std::vector& masks = std::vector() ) const; - -private: -/* compute Gaussian pyramid of input image */ -void computeGaussianPyramid( const Mat& image, int numOctaves, int scale ); - -/* implementation of line detection */ -void detectImpl( const Mat& imageSrc, std::vector& keylines, int numOctaves, int scale, const Mat& mask ) const; - -/* matrices for Gaussian pyramids */ -std::vector gaussianPyrs; -}; - -/** @brief furnishes all functionalities for querying a dataset provided by user or internal to -class (that user must, anyway, populate) on the model of @ref features2d_match - - -Once descriptors have been extracted from an image (both they represent lines and points), it -becomes interesting to be able to match a descriptor with another one extracted from a different -image and representing the same line or point, seen from a differente perspective or on a different -scale. In reaching such goal, the main headache is designing an efficient search algorithm to -associate a query descriptor to one extracted from a dataset. In the following, a matching modality -based on *Multi-Index Hashing (MiHashing)* will be described. - -Multi-Index Hashing -------------------- - -The theory described in this section is based on @cite MIH . Given a dataset populated with binary -codes, each code is indexed *m* times into *m* different hash tables, according to *m* substrings it -has been divided into. Thus, given a query code, all the entries close to it at least in one -substring are returned by search as *neighbor candidates*. Returned entries are then checked for -validity by verifying that their full codes are not distant (in Hamming space) more than *r* bits -from query code. In details, each binary code **h** composed of *b* bits is divided into *m* -disjoint substrings \f$\mathbf{h}^{(1)}, ..., \mathbf{h}^{(m)}\f$, each with length -\f$\lfloor b/m \rfloor\f$ or \f$\lceil b/m \rceil\f$ bits. Formally, when two codes **h** and **g** differ -by at the most *r* bits, in at the least one of their *m* substrings they differ by at the most -\f$\lfloor r/m \rfloor\f$ bits. In particular, when \f$||\mathbf{h}-\mathbf{g}||_H \le r\f$ (where \f$||.||_H\f$ -is the Hamming norm), there must exist a substring *k* (with \f$1 \le k \le m\f$) such that - -\f[||\mathbf{h}^{(k)} - \mathbf{g}^{(k)}||_H \le \left\lfloor \frac{r}{m} \right\rfloor .\f] - -That means that if Hamming distance between each of the *m* substring is strictly greater than -\f$\lfloor r/m \rfloor\f$, then \f$||\mathbf{h}-\mathbf{g}||_H\f$ must be larger that *r* and that is a -contradiction. If the codes in dataset are divided into *m* substrings, then *m* tables will be -built. Given a query **q** with substrings \f$\{\mathbf{q}^{(i)}\}^m_{i=1}\f$, *i*-th hash table is -searched for entries distant at the most \f$\lfloor r/m \rfloor\f$ from \f$\mathbf{q}^{(i)}\f$ and a set of -candidates \f$\mathcal{N}_i(\mathbf{q})\f$ is obtained. The union of sets -\f$\mathcal{N}(\mathbf{q}) = \bigcup_i \mathcal{N}_i(\mathbf{q})\f$ is a superset of the *r*-neighbors -of **q**. Then, last step of algorithm is computing the Hamming distance between **q** and each -element in \f$\mathcal{N}(\mathbf{q})\f$, deleting the codes that are distant more that *r* from **q**. -*/ -class CV_EXPORTS BinaryDescriptorMatcher : public Algorithm -{ - -public: -/** @brief For every input query descriptor, retrieve the best matching one from a dataset provided from user -or from the one internal to class - -@param queryDescriptors query descriptors -@param trainDescriptors dataset of descriptors furnished by user -@param matches vector to host retrieved matches -@param mask mask to select which input descriptors must be matched to one in dataset - */ -void match( const Mat& queryDescriptors, const Mat& trainDescriptors, std::vector& matches, const Mat& mask = Mat() ) const; - -/** @overload -@param queryDescriptors query descriptors -@param matches vector to host retrieved matches -@param masks vector of masks to select which input descriptors must be matched to one in dataset -(the *i*-th mask in vector indicates whether each input query can be matched with descriptors in -dataset relative to *i*-th image) -*/ -void match( const Mat& queryDescriptors, std::vector& matches, const std::vector& masks = std::vector() ); - -/** @brief For every input query descriptor, retrieve the best *k* matching ones from a dataset provided from -user or from the one internal to class - -@param queryDescriptors query descriptors -@param trainDescriptors dataset of descriptors furnished by user -@param matches vector to host retrieved matches -@param k number of the closest descriptors to be returned for every input query -@param mask mask to select which input descriptors must be matched to ones in dataset -@param compactResult flag to obtain a compact result (if true, a vector that doesn't contain any -matches for a given query is not inserted in final result) - */ -void knnMatch( const Mat& queryDescriptors, const Mat& trainDescriptors, std::vector >& matches, int k, const Mat& mask = Mat(), -bool compactResult = false ) const; - -/** @overload -@param queryDescriptors query descriptors -@param matches vector to host retrieved matches -@param k number of the closest descriptors to be returned for every input query -@param masks vector of masks to select which input descriptors must be matched to ones in dataset -(the *i*-th mask in vector indicates whether each input query can be matched with descriptors in -dataset relative to *i*-th image) -@param compactResult flag to obtain a compact result (if true, a vector that doesn't contain any -matches for a given query is not inserted in final result) -*/ -void knnMatch( const Mat& queryDescriptors, std::vector >& matches, int k, const std::vector& masks = std::vector(), -bool compactResult = false ); - -/** @brief For every input query descriptor, retrieve, from a dataset provided from user or from the one -internal to class, all the descriptors that are not further than *maxDist* from input query - -@param queryDescriptors query descriptors -@param trainDescriptors dataset of descriptors furnished by user -@param matches vector to host retrieved matches -@param maxDistance search radius -@param mask mask to select which input descriptors must be matched to ones in dataset -@param compactResult flag to obtain a compact result (if true, a vector that doesn't contain any -matches for a given query is not inserted in final result) - */ -void radiusMatch( const Mat& queryDescriptors, const Mat& trainDescriptors, std::vector >& matches, float maxDistance, -const Mat& mask = Mat(), bool compactResult = false ) const; - -/** @overload -@param queryDescriptors query descriptors -@param matches vector to host retrieved matches -@param maxDistance search radius -@param masks vector of masks to select which input descriptors must be matched to ones in dataset -(the *i*-th mask in vector indicates whether each input query can be matched with descriptors in -dataset relative to *i*-th image) -@param compactResult flag to obtain a compact result (if true, a vector that doesn't contain any -matches for a given query is not inserted in final result) -*/ -void radiusMatch( const Mat& queryDescriptors, std::vector >& matches, float maxDistance, const std::vector& masks = -std::vector(), -bool compactResult = false ); - -/** @brief Store locally new descriptors to be inserted in dataset, without updating dataset. - -@param descriptors matrices containing descriptors to be inserted into dataset - -@note Each matrix *i* in **descriptors** should contain descriptors relative to lines extracted from -*i*-th image. - */ -void add( const std::vector& descriptors ); - -/** @brief Update dataset by inserting into it all descriptors that were stored locally by *add* function. - -@note Every time this function is invoked, current dataset is deleted and locally stored descriptors -are inserted into dataset. The locally stored copy of just inserted descriptors is then removed. - */ -void train(); - -/** @brief Create a BinaryDescriptorMatcher object and return a smart pointer to it. - */ -static Ptr createBinaryDescriptorMatcher(); - -/** @brief Clear dataset and internal data - */ -void clear(); - -/** @brief Constructor. - -The BinaryDescriptorMatcher constructed is able to store and manage 256-bits long entries. - */ -BinaryDescriptorMatcher(); - -/** destructor */ -~BinaryDescriptorMatcher() -{ -} - -private: -class BucketGroup -{ - -public: -/** constructor */ -BucketGroup(); - -/** destructor */ -~BucketGroup(); - -/** insert data into the bucket */ -void insert( int subindex, UINT32 data ); - -/** perform a query to the bucket */ -UINT32* query( int subindex, int *size ); - -/** utility functions */ -void insert_value( std::vector& vec, int index, UINT32 data ); -void push_value( std::vector& vec, UINT32 Data ); - -/** data fields */ -UINT32 empty; -std::vector group; - - -}; - -class SparseHashtable -{ - -private: - -/** Maximum bits per key before folding the table */ -static const int MAX_B; - -/** Bins (each bin is an Array object for duplicates of the same key) */ -BucketGroup *table; - -public: - -/** constructor */ -SparseHashtable(); - -/** destructor */ -~SparseHashtable(); - -/** initializer */ -int init( int _b ); - -/** insert data */ -void insert( UINT64 index, UINT32 data ); - -/** query data */ -UINT32* query( UINT64 index, int* size ); - -/** Bits per index */ -int b; - -/** Number of bins */ -UINT64 size; - -}; - -/** class defining a sequence of bits */ -class bitarray -{ - -public: -/** pointer to bits sequence and sequence's length */ -UINT32 *arr; -UINT32 length; - -/** constructor setting default values */ -bitarray() -{ -arr = NULL; -length = 0; -} - -/** constructor setting sequence's length */ -bitarray( UINT64 _bits ) -{ -init( _bits ); -} - -/** initializer of private fields */ -void init( UINT64 _bits ) -{ -length = (UINT32) ceil( _bits / 32.00 ); -arr = new UINT32[length]; -erase(); -} - -/** destructor */ -~bitarray() -{ -if( arr ) -delete[] arr; -} - -inline void flip( UINT64 index ) -{ -arr[index >> 5] ^= ( (UINT32) 0x01 ) << ( index % 32 ); -} - -inline void set( UINT64 index ) -{ -arr[index >> 5] |= ( (UINT32) 0x01 ) << ( index % 32 ); -} - -inline UINT8 get( UINT64 index ) -{ -return ( arr[index >> 5] & ( ( (UINT32) 0x01 ) << ( index % 32 ) ) ) != 0; -} - -/** reserve menory for an UINT32 */ -inline void erase() -{ -memset( arr, 0, sizeof(UINT32) * length ); -} - -}; - -class Mihasher -{ - -public: -/** Bits per code */ -int B; - -/** B/8 */ -int B_over_8; - -/** Bits per chunk (must be less than 64) */ -int b; - -/** Number of chunks */ -int m; - -/** Number of chunks with b bits (have 1 bit more than others) */ -int mplus; - -/** Maximum hamming search radius (we use B/2 by default) */ -int D; - -/** Maximum hamming search radius per substring */ -int d; - -/** Maximum results to return */ -int K; - -/** Number of codes */ -UINT64 N; - -/** Table of original full-length codes */ -cv::Mat codes; - -/** Counter for eliminating duplicate results (it is not thread safe) */ -bitarray *counter; - -/** Array of m hashtables */ -SparseHashtable *H; - -/** Volume of a b-bit Hamming ball with radius s (for s = 0 to d) */ -UINT32 *xornum; - -/** Used within generation of binary codes at a certain Hamming distance */ -int power[100]; - -/** constructor */ -Mihasher(); - -/** desctructor */ -~Mihasher(); - -/** constructor 2 */ -Mihasher( int B, int m ); - -/** K setter */ -void setK( int K ); - -/** populate tables */ -void populate( cv::Mat & codes, UINT32 N, int dim1codes ); - -/** execute a batch query */ -void batchquery( UINT32 * results, UINT32 *numres/*, qstat *stats*/, const cv::Mat & q, UINT32 numq, int dim1queries ); - -private: - -/** execute a single query */ -void query( UINT32 * results, UINT32* numres/*, qstat *stats*/, UINT8 *q, UINT64 * chunks, UINT32 * res ); -}; - -/** retrieve Hamming distances */ -void checkKDistances( UINT32 * numres, int k, std::vector& k_distances, int row, int string_length ) const; - -/** matrix to store new descriptors */ -Mat descriptorsMat; - -/** map storing where each bunch of descriptors benins in DS */ -std::map indexesMap; - -/** internal MiHaser representing dataset */ -Mihasher* dataset; - -/** index from which next added descriptors' bunch must begin */ -int nextAddedIndex; - -/** number of images whose descriptors are stored in DS */ -int numImages; - -/** number of descriptors in dataset */ -int descrInDS; - -}; - -/* -------------------------------------------------------------------------------------------- - UTILITY FUNCTIONS - -------------------------------------------------------------------------------------------- */ - -/** struct for drawing options */ -struct CV_EXPORTS DrawLinesMatchesFlags -{ -enum -{ -DEFAULT = 0, //!< Output image matrix will be created (Mat::create), - //!< i.e. existing memory of output image may be reused. - //!< Two source images, matches, and single keylines - //!< will be drawn. -DRAW_OVER_OUTIMG = 1,//!< Output image matrix will not be -//!< created (using Mat::create). Matches will be drawn -//!< on existing content of output image. -NOT_DRAW_SINGLE_LINES = 2//!< Single keylines will not be drawn. -}; -}; - -/** @brief Draws the found matches of keylines from two images. - -@param img1 first image -@param keylines1 keylines extracted from first image -@param img2 second image -@param keylines2 keylines extracted from second image -@param matches1to2 vector of matches -@param outImg output matrix to draw on -@param matchColor drawing color for matches (chosen randomly in case of default value) -@param singleLineColor drawing color for keylines (chosen randomly in case of default value) -@param matchesMask mask to indicate which matches must be drawn -@param flags drawing flags, see DrawLinesMatchesFlags - -@note If both *matchColor* and *singleLineColor* are set to their default values, function draws -matched lines and line connecting them with same color - */ -CV_EXPORTS void drawLineMatches( const Mat& img1, const std::vector& keylines1, const Mat& img2, const std::vector& keylines2, - const std::vector& matches1to2, Mat& outImg, const Scalar& matchColor = Scalar::all( -1 ), - const Scalar& singleLineColor = Scalar::all( -1 ), const std::vector& matchesMask = std::vector(), - int flags = DrawLinesMatchesFlags::DEFAULT ); - -/** @brief Draws keylines. - -@param image input image -@param keylines keylines to be drawn -@param outImage output image to draw on -@param color color of lines to be drawn (if set to defaul value, color is chosen randomly) -@param flags drawing flags - */ -CV_EXPORTS void drawKeylines( const Mat& image, const std::vector& keylines, Mat& outImage, const Scalar& color = Scalar::all( -1 ), - int flags = DrawLinesMatchesFlags::DEFAULT ); - -//! @} - -} -} - -#endif diff --git a/IPL/include/opencv/opencv2/ml.hpp b/IPL/include/opencv/opencv2/ml.hpp deleted file mode 100644 index 0b90269..0000000 --- a/IPL/include/opencv/opencv2/ml.hpp +++ /dev/null @@ -1,1527 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000, Intel Corporation, all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2014, Itseez Inc, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_ML_HPP__ -#define __OPENCV_ML_HPP__ - -#ifdef __cplusplus -# include "opencv2/core.hpp" -#endif - -#ifdef __cplusplus - -#include -#include -#include - -/** - @defgroup ml Machine Learning - - The Machine Learning Library (MLL) is a set of classes and functions for statistical - classification, regression, and clustering of data. - - Most of the classification and regression algorithms are implemented as C++ classes. As the - algorithms have different sets of features (like an ability to handle missing measurements or - categorical input variables), there is a little common ground between the classes. This common - ground is defined by the class cv::ml::StatModel that all the other ML classes are derived from. - - See detailed overview here: @ref ml_intro. - */ - -namespace cv -{ - -namespace ml -{ - -//! @addtogroup ml -//! @{ - -/** @brief Variable types */ -enum VariableTypes -{ - VAR_NUMERICAL =0, //!< same as VAR_ORDERED - VAR_ORDERED =0, //!< ordered variables - VAR_CATEGORICAL =1 //!< categorical variables -}; - -/** @brief %Error types */ -enum ErrorTypes -{ - TEST_ERROR = 0, - TRAIN_ERROR = 1 -}; - -/** @brief Sample types */ -enum SampleTypes -{ - ROW_SAMPLE = 0, //!< each training sample is a row of samples - COL_SAMPLE = 1 //!< each training sample occupies a column of samples -}; - -/** @brief The structure represents the logarithmic grid range of statmodel parameters. - -It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate -being computed by cross-validation. - */ -class CV_EXPORTS ParamGrid -{ -public: - /** @brief Default constructor */ - ParamGrid(); - /** @brief Constructor with parameters */ - ParamGrid(double _minVal, double _maxVal, double _logStep); - - double minVal; //!< Minimum value of the statmodel parameter. Default value is 0. - double maxVal; //!< Maximum value of the statmodel parameter. Default value is 0. - /** @brief Logarithmic step for iterating the statmodel parameter. - - The grid determines the following iteration sequence of the statmodel parameter values: - \f[(minVal, minVal*step, minVal*{step}^2, \dots, minVal*{logStep}^n),\f] - where \f$n\f$ is the maximal index satisfying - \f[\texttt{minVal} * \texttt{logStep} ^n < \texttt{maxVal}\f] - The grid is logarithmic, so logStep must always be greater then 1. Default value is 1. - */ - double logStep; -}; - -/** @brief Class encapsulating training data. - -Please note that the class only specifies the interface of training data, but not implementation. -All the statistical model classes in _ml_ module accepts Ptr\ as parameter. In other -words, you can create your own class derived from TrainData and pass smart pointer to the instance -of this class into StatModel::train. - -@sa @ref ml_intro_data - */ -class CV_EXPORTS_W TrainData -{ -public: - static inline float missingValue() { return FLT_MAX; } - virtual ~TrainData(); - - CV_WRAP virtual int getLayout() const = 0; - CV_WRAP virtual int getNTrainSamples() const = 0; - CV_WRAP virtual int getNTestSamples() const = 0; - CV_WRAP virtual int getNSamples() const = 0; - CV_WRAP virtual int getNVars() const = 0; - CV_WRAP virtual int getNAllVars() const = 0; - - CV_WRAP virtual void getSample(InputArray varIdx, int sidx, float* buf) const = 0; - CV_WRAP virtual Mat getSamples() const = 0; - CV_WRAP virtual Mat getMissing() const = 0; - - /** @brief Returns matrix of train samples - - @param layout The requested layout. If it's different from the initial one, the matrix is - transposed. See ml::SampleTypes. - @param compressSamples if true, the function returns only the training samples (specified by - sampleIdx) - @param compressVars if true, the function returns the shorter training samples, containing only - the active variables. - - In current implementation the function tries to avoid physical data copying and returns the - matrix stored inside TrainData (unless the transposition or compression is needed). - */ - CV_WRAP virtual Mat getTrainSamples(int layout=ROW_SAMPLE, - bool compressSamples=true, - bool compressVars=true) const = 0; - - /** @brief Returns the vector of responses - - The function returns ordered or the original categorical responses. Usually it's used in - regression algorithms. - */ - CV_WRAP virtual Mat getTrainResponses() const = 0; - - /** @brief Returns the vector of normalized categorical responses - - The function returns vector of responses. Each response is integer from `0` to `-1`. The actual label value can be retrieved then from the class label vector, see - TrainData::getClassLabels. - */ - CV_WRAP virtual Mat getTrainNormCatResponses() const = 0; - CV_WRAP virtual Mat getTestResponses() const = 0; - CV_WRAP virtual Mat getTestNormCatResponses() const = 0; - CV_WRAP virtual Mat getResponses() const = 0; - CV_WRAP virtual Mat getNormCatResponses() const = 0; - CV_WRAP virtual Mat getSampleWeights() const = 0; - CV_WRAP virtual Mat getTrainSampleWeights() const = 0; - CV_WRAP virtual Mat getTestSampleWeights() const = 0; - CV_WRAP virtual Mat getVarIdx() const = 0; - CV_WRAP virtual Mat getVarType() const = 0; - CV_WRAP virtual int getResponseType() const = 0; - CV_WRAP virtual Mat getTrainSampleIdx() const = 0; - CV_WRAP virtual Mat getTestSampleIdx() const = 0; - CV_WRAP virtual void getValues(int vi, InputArray sidx, float* values) const = 0; - virtual void getNormCatValues(int vi, InputArray sidx, int* values) const = 0; - CV_WRAP virtual Mat getDefaultSubstValues() const = 0; - - CV_WRAP virtual int getCatCount(int vi) const = 0; - - /** @brief Returns the vector of class labels - - The function returns vector of unique labels occurred in the responses. - */ - CV_WRAP virtual Mat getClassLabels() const = 0; - - CV_WRAP virtual Mat getCatOfs() const = 0; - CV_WRAP virtual Mat getCatMap() const = 0; - - /** @brief Splits the training data into the training and test parts - @sa TrainData::setTrainTestSplitRatio - */ - CV_WRAP virtual void setTrainTestSplit(int count, bool shuffle=true) = 0; - - /** @brief Splits the training data into the training and test parts - - The function selects a subset of specified relative size and then returns it as the training - set. If the function is not called, all the data is used for training. Please, note that for - each of TrainData::getTrain\* there is corresponding TrainData::getTest\*, so that the test - subset can be retrieved and processed as well. - @sa TrainData::setTrainTestSplit - */ - CV_WRAP virtual void setTrainTestSplitRatio(double ratio, bool shuffle=true) = 0; - CV_WRAP virtual void shuffleTrainTest() = 0; - - /** @brief Returns matrix of test samples */ - CV_WRAP Mat getTestSamples() const; - - CV_WRAP static Mat getSubVector(const Mat& vec, const Mat& idx); - - /** @brief Reads the dataset from a .csv file and returns the ready-to-use training data. - - @param filename The input file name - @param headerLineCount The number of lines in the beginning to skip; besides the header, the - function also skips empty lines and lines staring with `#` - @param responseStartIdx Index of the first output variable. If -1, the function considers the - last variable as the response - @param responseEndIdx Index of the last output variable + 1. If -1, then there is single - response variable at responseStartIdx. - @param varTypeSpec The optional text string that specifies the variables' types. It has the - format `ord[n1-n2,n3,n4-n5,...]cat[n6,n7-n8,...]`. That is, variables from `n1 to n2` - (inclusive range), `n3`, `n4 to n5` ... are considered ordered and `n6`, `n7 to n8` ... are - considered as categorical. The range `[n1..n2] + [n3] + [n4..n5] + ... + [n6] + [n7..n8]` - should cover all the variables. If varTypeSpec is not specified, then algorithm uses the - following rules: - - all input variables are considered ordered by default. If some column contains has non- - numerical values, e.g. 'apple', 'pear', 'apple', 'apple', 'mango', the corresponding - variable is considered categorical. - - if there are several output variables, they are all considered as ordered. Error is - reported when non-numerical values are used. - - if there is a single output variable, then if its values are non-numerical or are all - integers, then it's considered categorical. Otherwise, it's considered ordered. - @param delimiter The character used to separate values in each line. - @param missch The character used to specify missing measurements. It should not be a digit. - Although it's a non-numerical value, it surely does not affect the decision of whether the - variable ordered or categorical. - @note If the dataset only contains input variables and no responses, use responseStartIdx = -2 - and responseEndIdx = 0. The output variables vector will just contain zeros. - */ - static Ptr loadFromCSV(const String& filename, - int headerLineCount, - int responseStartIdx=-1, - int responseEndIdx=-1, - const String& varTypeSpec=String(), - char delimiter=',', - char missch='?'); - - /** @brief Creates training data from in-memory arrays. - - @param samples matrix of samples. It should have CV_32F type. - @param layout see ml::SampleTypes. - @param responses matrix of responses. If the responses are scalar, they should be stored as a - single row or as a single column. The matrix should have type CV_32F or CV_32S (in the - former case the responses are considered as ordered by default; in the latter case - as - categorical) - @param varIdx vector specifying which variables to use for training. It can be an integer vector - (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of - active variables. - @param sampleIdx vector specifying which samples to use for training. It can be an integer - vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask - of training samples. - @param sampleWeights optional vector with weights for each sample. It should have CV_32F type. - @param varType optional vector of type CV_8U and size ` + - `, containing types of each input and output variable. See - ml::VariableTypes. - */ - CV_WRAP static Ptr create(InputArray samples, int layout, InputArray responses, - InputArray varIdx=noArray(), InputArray sampleIdx=noArray(), - InputArray sampleWeights=noArray(), InputArray varType=noArray()); -}; - -/** @brief Base class for statistical models in OpenCV ML. - */ -class CV_EXPORTS_W StatModel : public Algorithm -{ -public: - /** Predict options */ - enum Flags { - UPDATE_MODEL = 1, - RAW_OUTPUT=1, //!< makes the method return the raw results (the sum), not the class label - COMPRESSED_INPUT=2, - PREPROCESSED_INPUT=4 - }; - - /** @brief Returns the number of variables in training samples */ - CV_WRAP virtual int getVarCount() const = 0; - - CV_WRAP virtual bool empty() const; - - /** @brief Returns true if the model is trained */ - CV_WRAP virtual bool isTrained() const = 0; - /** @brief Returns true if the model is classifier */ - CV_WRAP virtual bool isClassifier() const = 0; - - /** @brief Trains the statistical model - - @param trainData training data that can be loaded from file using TrainData::loadFromCSV or - created with TrainData::create. - @param flags optional flags, depending on the model. Some of the models can be updated with the - new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP). - */ - CV_WRAP virtual bool train( const Ptr& trainData, int flags=0 ); - - /** @brief Trains the statistical model - - @param samples training samples - @param layout See ml::SampleTypes. - @param responses vector of responses associated with the training samples. - */ - CV_WRAP virtual bool train( InputArray samples, int layout, InputArray responses ); - - /** @brief Computes error on the training or test dataset - - @param data the training data - @param test if true, the error is computed over the test subset of the data, otherwise it's - computed over the training subset of the data. Please note that if you loaded a completely - different dataset to evaluate already trained classifier, you will probably want not to set - the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so - that the error is computed for the whole new set. Yes, this sounds a bit confusing. - @param resp the optional output responses. - - The method uses StatModel::predict to compute the error. For regression models the error is - computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%). - */ - CV_WRAP virtual float calcError( const Ptr& data, bool test, OutputArray resp ) const; - - /** @brief Predicts response(s) for the provided sample(s) - - @param samples The input samples, floating-point matrix - @param results The optional output matrix of results. - @param flags The optional flags, model-dependent. See cv::ml::StatModel::Flags. - */ - CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0; - - /** @brief Create and train model with default parameters - - The class must implement static `create()` method with no parameters or with all default parameter values - */ - template static Ptr<_Tp> train(const Ptr& data, int flags=0) - { - Ptr<_Tp> model = _Tp::create(); - return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>(); - } -}; - -/****************************************************************************************\ -* Normal Bayes Classifier * -\****************************************************************************************/ - -/** @brief Bayes classifier for normally distributed data. - -@sa @ref ml_intro_bayes - */ -class CV_EXPORTS_W NormalBayesClassifier : public StatModel -{ -public: - /** @brief Predicts the response for sample(s). - - The method estimates the most probable classes for input vectors. Input vectors (one or more) - are stored as rows of the matrix inputs. In case of multiple input vectors, there should be one - output vector outputs. The predicted class for a single input vector is returned by the method. - The vector outputProbs contains the output probabilities corresponding to each element of - result. - */ - CV_WRAP virtual float predictProb( InputArray inputs, OutputArray outputs, - OutputArray outputProbs, int flags=0 ) const = 0; - - /** Creates empty model - Use StatModel::train to train the model after creation. */ - CV_WRAP static Ptr create(); -}; - -/****************************************************************************************\ -* K-Nearest Neighbour Classifier * -\****************************************************************************************/ - -/** @brief The class implements K-Nearest Neighbors model - -@sa @ref ml_intro_knn - */ -class CV_EXPORTS_W KNearest : public StatModel -{ -public: - - /** Default number of neighbors to use in predict method. */ - /** @see setDefaultK */ - CV_WRAP virtual int getDefaultK() const = 0; - /** @copybrief getDefaultK @see getDefaultK */ - CV_WRAP virtual void setDefaultK(int val) = 0; - - /** Whether classification or regression model should be trained. */ - /** @see setIsClassifier */ - CV_WRAP virtual bool getIsClassifier() const = 0; - /** @copybrief getIsClassifier @see getIsClassifier */ - CV_WRAP virtual void setIsClassifier(bool val) = 0; - - /** Parameter for KDTree implementation. */ - /** @see setEmax */ - CV_WRAP virtual int getEmax() const = 0; - /** @copybrief getEmax @see getEmax */ - CV_WRAP virtual void setEmax(int val) = 0; - - /** %Algorithm type, one of KNearest::Types. */ - /** @see setAlgorithmType */ - CV_WRAP virtual int getAlgorithmType() const = 0; - /** @copybrief getAlgorithmType @see getAlgorithmType */ - CV_WRAP virtual void setAlgorithmType(int val) = 0; - - /** @brief Finds the neighbors and predicts responses for input vectors. - - @param samples Input samples stored by rows. It is a single-precision floating-point matrix of - ` * k` size. - @param k Number of used nearest neighbors. Should be greater than 1. - @param results Vector with results of prediction (regression or classification) for each input - sample. It is a single-precision floating-point vector with `` elements. - @param neighborResponses Optional output values for corresponding neighbors. It is a single- - precision floating-point matrix of ` * k` size. - @param dist Optional output distances from the input vectors to the corresponding neighbors. It - is a single-precision floating-point matrix of ` * k` size. - - For each input vector (a row of the matrix samples), the method finds the k nearest neighbors. - In case of regression, the predicted result is a mean value of the particular vector's neighbor - responses. In case of classification, the class is determined by voting. - - For each input vector, the neighbors are sorted by their distances to the vector. - - In case of C++ interface you can use output pointers to empty matrices and the function will - allocate memory itself. - - If only a single input vector is passed, all output matrices are optional and the predicted - value is returned by the method. - - The function is parallelized with the TBB library. - */ - CV_WRAP virtual float findNearest( InputArray samples, int k, - OutputArray results, - OutputArray neighborResponses=noArray(), - OutputArray dist=noArray() ) const = 0; - - /** @brief Implementations of KNearest algorithm - */ - enum Types - { - BRUTE_FORCE=1, - KDTREE=2 - }; - - /** @brief Creates the empty model - - The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method. - */ - CV_WRAP static Ptr create(); -}; - -/****************************************************************************************\ -* Support Vector Machines * -\****************************************************************************************/ - -/** @brief Support Vector Machines. - -@sa @ref ml_intro_svm - */ -class CV_EXPORTS_W SVM : public StatModel -{ -public: - - class CV_EXPORTS Kernel : public Algorithm - { - public: - virtual int getType() const = 0; - virtual void calc( int vcount, int n, const float* vecs, const float* another, float* results ) = 0; - }; - - /** Type of a %SVM formulation. - See SVM::Types. Default value is SVM::C_SVC. */ - /** @see setType */ - CV_WRAP virtual int getType() const = 0; - /** @copybrief getType @see getType */ - CV_WRAP virtual void setType(int val) = 0; - - /** Parameter \f$\gamma\f$ of a kernel function. - For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1. */ - /** @see setGamma */ - CV_WRAP virtual double getGamma() const = 0; - /** @copybrief getGamma @see getGamma */ - CV_WRAP virtual void setGamma(double val) = 0; - - /** Parameter _coef0_ of a kernel function. - For SVM::POLY or SVM::SIGMOID. Default value is 0.*/ - /** @see setCoef0 */ - CV_WRAP virtual double getCoef0() const = 0; - /** @copybrief getCoef0 @see getCoef0 */ - CV_WRAP virtual void setCoef0(double val) = 0; - - /** Parameter _degree_ of a kernel function. - For SVM::POLY. Default value is 0. */ - /** @see setDegree */ - CV_WRAP virtual double getDegree() const = 0; - /** @copybrief getDegree @see getDegree */ - CV_WRAP virtual void setDegree(double val) = 0; - - /** Parameter _C_ of a %SVM optimization problem. - For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0. */ - /** @see setC */ - CV_WRAP virtual double getC() const = 0; - /** @copybrief getC @see getC */ - CV_WRAP virtual void setC(double val) = 0; - - /** Parameter \f$\nu\f$ of a %SVM optimization problem. - For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0. */ - /** @see setNu */ - CV_WRAP virtual double getNu() const = 0; - /** @copybrief getNu @see getNu */ - CV_WRAP virtual void setNu(double val) = 0; - - /** Parameter \f$\epsilon\f$ of a %SVM optimization problem. - For SVM::EPS_SVR. Default value is 0. */ - /** @see setP */ - CV_WRAP virtual double getP() const = 0; - /** @copybrief getP @see getP */ - CV_WRAP virtual void setP(double val) = 0; - - /** Optional weights in the SVM::C_SVC problem, assigned to particular classes. - They are multiplied by _C_ so the parameter _C_ of class _i_ becomes `classWeights(i) * C`. Thus - these weights affect the misclassification penalty for different classes. The larger weight, - the larger penalty on misclassification of data from the corresponding class. Default value is - empty Mat. */ - /** @see setClassWeights */ - CV_WRAP virtual cv::Mat getClassWeights() const = 0; - /** @copybrief getClassWeights @see getClassWeights */ - CV_WRAP virtual void setClassWeights(const cv::Mat &val) = 0; - - /** Termination criteria of the iterative %SVM training procedure which solves a partial - case of constrained quadratic optimization problem. - You can specify tolerance and/or the maximum number of iterations. Default value is - `TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON )`; */ - /** @see setTermCriteria */ - CV_WRAP virtual cv::TermCriteria getTermCriteria() const = 0; - /** @copybrief getTermCriteria @see getTermCriteria */ - CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0; - - /** Type of a %SVM kernel. - See SVM::KernelTypes. Default value is SVM::RBF. */ - CV_WRAP virtual int getKernelType() const = 0; - - /** Initialize with one of predefined kernels. - See SVM::KernelTypes. */ - CV_WRAP virtual void setKernel(int kernelType) = 0; - - /** Initialize with custom kernel. - See SVM::Kernel class for implementation details */ - virtual void setCustomKernel(const Ptr &_kernel) = 0; - - //! %SVM type - enum Types { - /** C-Support Vector Classification. n-class classification (n \f$\geq\f$ 2), allows - imperfect separation of classes with penalty multiplier C for outliers. */ - C_SVC=100, - /** \f$\nu\f$-Support Vector Classification. n-class classification with possible - imperfect separation. Parameter \f$\nu\f$ (in the range 0..1, the larger the value, the smoother - the decision boundary) is used instead of C. */ - NU_SVC=101, - /** Distribution Estimation (One-class %SVM). All the training data are from - the same class, %SVM builds a boundary that separates the class from the rest of the feature - space. */ - ONE_CLASS=102, - /** \f$\epsilon\f$-Support Vector Regression. The distance between feature vectors - from the training set and the fitting hyper-plane must be less than p. For outliers the - penalty multiplier C is used. */ - EPS_SVR=103, - /** \f$\nu\f$-Support Vector Regression. \f$\nu\f$ is used instead of p. - See @cite LibSVM for details. */ - NU_SVR=104 - }; - - /** @brief %SVM kernel type - - A comparison of different kernels on the following 2D test case with four classes. Four - SVM::C_SVC SVMs have been trained (one against rest) with auto_train. Evaluation on three - different kernels (SVM::CHI2, SVM::INTER, SVM::RBF). The color depicts the class with max score. - Bright means max-score \> 0, dark means max-score \< 0. - ![image](pics/SVM_Comparison.png) - */ - enum KernelTypes { - /** Returned by SVM::getKernelType in case when custom kernel has been set */ - CUSTOM=-1, - /** Linear kernel. No mapping is done, linear discrimination (or regression) is - done in the original feature space. It is the fastest option. \f$K(x_i, x_j) = x_i^T x_j\f$. */ - LINEAR=0, - /** Polynomial kernel: - \f$K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0\f$. */ - POLY=1, - /** Radial basis function (RBF), a good choice in most cases. - \f$K(x_i, x_j) = e^{-\gamma ||x_i - x_j||^2}, \gamma > 0\f$. */ - RBF=2, - /** Sigmoid kernel: \f$K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\f$. */ - SIGMOID=3, - /** Exponential Chi2 kernel, similar to the RBF kernel: - \f$K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\f$. */ - CHI2=4, - /** Histogram intersection kernel. A fast kernel. \f$K(x_i, x_j) = min(x_i,x_j)\f$. */ - INTER=5 - }; - - //! %SVM params type - enum ParamTypes { - C=0, - GAMMA=1, - P=2, - NU=3, - COEF=4, - DEGREE=5 - }; - - /** @brief Trains an %SVM with optimal parameters. - - @param data the training data that can be constructed using TrainData::create or - TrainData::loadFromCSV. - @param kFold Cross-validation parameter. The training set is divided into kFold subsets. One - subset is used to test the model, the others form the train set. So, the %SVM algorithm is - executed kFold times. - @param Cgrid grid for C - @param gammaGrid grid for gamma - @param pGrid grid for p - @param nuGrid grid for nu - @param coeffGrid grid for coeff - @param degreeGrid grid for degree - @param balanced If true and the problem is 2-class classification then the method creates more - balanced cross-validation subsets that is proportions between classes in subsets are close - to such proportion in the whole train dataset. - - The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p, - nu, coef0, degree. Parameters are considered optimal when the cross-validation - estimate of the test set error is minimal. - - If there is no need to optimize a parameter, the corresponding grid step should be set to any - value less than or equal to 1. For example, to avoid optimization in gamma, set `gammaGrid.step - = 0`, `gammaGrid.minVal`, `gamma_grid.maxVal` as arbitrary numbers. In this case, the value - `Gamma` is taken for gamma. - - And, finally, if the optimization in a parameter is required but the corresponding grid is - unknown, you may call the function SVM::getDefaultGrid. To generate a grid, for example, for - gamma, call `SVM::getDefaultGrid(SVM::GAMMA)`. - - This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the - regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and - the usual %SVM with parameters specified in params is executed. - */ - virtual bool trainAuto( const Ptr& data, int kFold = 10, - ParamGrid Cgrid = SVM::getDefaultGrid(SVM::C), - ParamGrid gammaGrid = SVM::getDefaultGrid(SVM::GAMMA), - ParamGrid pGrid = SVM::getDefaultGrid(SVM::P), - ParamGrid nuGrid = SVM::getDefaultGrid(SVM::NU), - ParamGrid coeffGrid = SVM::getDefaultGrid(SVM::COEF), - ParamGrid degreeGrid = SVM::getDefaultGrid(SVM::DEGREE), - bool balanced=false) = 0; - - /** @brief Retrieves all the support vectors - - The method returns all the support vectors as a floating-point matrix, where support vectors are - stored as matrix rows. - */ - CV_WRAP virtual Mat getSupportVectors() const = 0; - - /** @brief Retrieves all the uncompressed support vectors of a linear %SVM - - The method returns all the uncompressed support vectors of a linear %SVM that the compressed - support vector, used for prediction, was derived from. They are returned in a floating-point - matrix, where the support vectors are stored as matrix rows. - */ - CV_WRAP Mat getUncompressedSupportVectors() const; - - /** @brief Retrieves the decision function - - @param i the index of the decision function. If the problem solved is regression, 1-class or - 2-class classification, then there will be just one decision function and the index should - always be 0. Otherwise, in the case of N-class classification, there will be \f$N(N-1)/2\f$ - decision functions. - @param alpha the optional output vector for weights, corresponding to different support vectors. - In the case of linear %SVM all the alpha's will be 1's. - @param svidx the optional output vector of indices of support vectors within the matrix of - support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear - %SVM each decision function consists of a single "compressed" support vector. - - The method returns rho parameter of the decision function, a scalar subtracted from the weighted - sum of kernel responses. - */ - CV_WRAP virtual double getDecisionFunction(int i, OutputArray alpha, OutputArray svidx) const = 0; - - /** @brief Generates a grid for %SVM parameters. - - @param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is - generated for the parameter with this ID. - - The function generates a grid for the specified parameter of the %SVM algorithm. The grid may be - passed to the function SVM::trainAuto. - */ - static ParamGrid getDefaultGrid( int param_id ); - - /** Creates empty model. - Use StatModel::train to train the model. Since %SVM has several parameters, you may want to - find the best parameters for your problem, it can be done with SVM::trainAuto. */ - CV_WRAP static Ptr create(); - - /** @brief Loads and creates a serialized svm from a file - * - * Use SVM::save to serialize and store an SVM to disk. - * Load the SVM from this file again, by calling this function with the path to the file. - * - * @param filepath path to serialized svm - */ - CV_WRAP static Ptr load(const String& filepath); -}; - -/****************************************************************************************\ -* Expectation - Maximization * -\****************************************************************************************/ - -/** @brief The class implements the Expectation Maximization algorithm. - -@sa @ref ml_intro_em - */ -class CV_EXPORTS_W EM : public StatModel -{ -public: - //! Type of covariation matrices - enum Types { - /** A scaled identity matrix \f$\mu_k * I\f$. There is the only - parameter \f$\mu_k\f$ to be estimated for each matrix. The option may be used in special cases, - when the constraint is relevant, or as a first step in the optimization (for example in case - when the data is preprocessed with PCA). The results of such preliminary estimation may be - passed again to the optimization procedure, this time with - covMatType=EM::COV_MAT_DIAGONAL. */ - COV_MAT_SPHERICAL=0, - /** A diagonal matrix with positive diagonal elements. The number of - free parameters is d for each matrix. This is most commonly used option yielding good - estimation results. */ - COV_MAT_DIAGONAL=1, - /** A symmetric positively defined matrix. The number of free - parameters in each matrix is about \f$d^2/2\f$. It is not recommended to use this option, unless - there is pretty accurate initial estimation of the parameters and/or a huge number of - training samples. */ - COV_MAT_GENERIC=2, - COV_MAT_DEFAULT=COV_MAT_DIAGONAL - }; - - //! Default parameters - enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100}; - - //! The initial step - enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0}; - - /** The number of mixture components in the Gaussian mixture model. - Default value of the parameter is EM::DEFAULT_NCLUSTERS=5. Some of %EM implementation could - determine the optimal number of mixtures within a specified value range, but that is not the - case in ML yet. */ - /** @see setClustersNumber */ - CV_WRAP virtual int getClustersNumber() const = 0; - /** @copybrief getClustersNumber @see getClustersNumber */ - CV_WRAP virtual void setClustersNumber(int val) = 0; - - /** Constraint on covariance matrices which defines type of matrices. - See EM::Types. */ - /** @see setCovarianceMatrixType */ - CV_WRAP virtual int getCovarianceMatrixType() const = 0; - /** @copybrief getCovarianceMatrixType @see getCovarianceMatrixType */ - CV_WRAP virtual void setCovarianceMatrixType(int val) = 0; - - /** The termination criteria of the %EM algorithm. - The %EM algorithm can be terminated by the number of iterations termCrit.maxCount (number of - M-steps) or when relative change of likelihood logarithm is less than termCrit.epsilon. Default - maximum number of iterations is EM::DEFAULT_MAX_ITERS=100. */ - /** @see setTermCriteria */ - CV_WRAP virtual TermCriteria getTermCriteria() const = 0; - /** @copybrief getTermCriteria @see getTermCriteria */ - CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0; - - /** @brief Returns weights of the mixtures - - Returns vector with the number of elements equal to the number of mixtures. - */ - CV_WRAP virtual Mat getWeights() const = 0; - /** @brief Returns the cluster centers (means of the Gaussian mixture) - - Returns matrix with the number of rows equal to the number of mixtures and number of columns - equal to the space dimensionality. - */ - CV_WRAP virtual Mat getMeans() const = 0; - /** @brief Returns covariation matrices - - Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures, - each matrix is a square floating-point matrix NxN, where N is the space dimensionality. - */ - CV_WRAP virtual void getCovs(CV_OUT std::vector& covs) const = 0; - - /** @brief Returns a likelihood logarithm value and an index of the most probable mixture component - for the given sample. - - @param sample A sample for classification. It should be a one-channel matrix of - \f$1 \times dims\f$ or \f$dims \times 1\f$ size. - @param probs Optional output matrix that contains posterior probabilities of each component - given the sample. It has \f$1 \times nclusters\f$ size and CV_64FC1 type. - - The method returns a two-element double vector. Zero element is a likelihood logarithm value for - the sample. First element is an index of the most probable mixture component for the given - sample. - */ - CV_WRAP virtual Vec2d predict2(InputArray sample, OutputArray probs) const = 0; - - /** @brief Estimate the Gaussian mixture parameters from a samples set. - - This variation starts with Expectation step. Initial values of the model parameters will be - estimated by the k-means algorithm. - - Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take - responses (class labels or function values) as input. Instead, it computes the *Maximum - Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the - parameters inside the structure: \f$p_{i,k}\f$ in probs, \f$a_k\f$ in means , \f$S_k\f$ in - covs[k], \f$\pi_k\f$ in weights , and optionally computes the output "class label" for each - sample: \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most - probable mixture component for each sample). - - The trained model can be used further for prediction, just like any other classifier. The - trained model is similar to the NormalBayesClassifier. - - @param samples Samples from which the Gaussian mixture model will be estimated. It should be a - one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type - it will be converted to the inner matrix of such type for the further computing. - @param logLikelihoods The optional output matrix that contains a likelihood logarithm value for - each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type. - @param labels The optional output "class label" for each sample: - \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable - mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type. - @param probs The optional output matrix that contains posterior probabilities of each Gaussian - mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and - CV_64FC1 type. - */ - CV_WRAP virtual bool trainEM(InputArray samples, - OutputArray logLikelihoods=noArray(), - OutputArray labels=noArray(), - OutputArray probs=noArray()) = 0; - - /** @brief Estimate the Gaussian mixture parameters from a samples set. - - This variation starts with Expectation step. You need to provide initial means \f$a_k\f$ of - mixture components. Optionally you can pass initial weights \f$\pi_k\f$ and covariance matrices - \f$S_k\f$ of mixture components. - - @param samples Samples from which the Gaussian mixture model will be estimated. It should be a - one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type - it will be converted to the inner matrix of such type for the further computing. - @param means0 Initial means \f$a_k\f$ of mixture components. It is a one-channel matrix of - \f$nclusters \times dims\f$ size. If the matrix does not have CV_64F type it will be - converted to the inner matrix of such type for the further computing. - @param covs0 The vector of initial covariance matrices \f$S_k\f$ of mixture components. Each of - covariance matrices is a one-channel matrix of \f$dims \times dims\f$ size. If the matrices - do not have CV_64F type they will be converted to the inner matrices of such type for the - further computing. - @param weights0 Initial weights \f$\pi_k\f$ of mixture components. It should be a one-channel - floating-point matrix with \f$1 \times nclusters\f$ or \f$nclusters \times 1\f$ size. - @param logLikelihoods The optional output matrix that contains a likelihood logarithm value for - each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type. - @param labels The optional output "class label" for each sample: - \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable - mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type. - @param probs The optional output matrix that contains posterior probabilities of each Gaussian - mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and - CV_64FC1 type. - */ - CV_WRAP virtual bool trainE(InputArray samples, InputArray means0, - InputArray covs0=noArray(), - InputArray weights0=noArray(), - OutputArray logLikelihoods=noArray(), - OutputArray labels=noArray(), - OutputArray probs=noArray()) = 0; - - /** @brief Estimate the Gaussian mixture parameters from a samples set. - - This variation starts with Maximization step. You need to provide initial probabilities - \f$p_{i,k}\f$ to use this option. - - @param samples Samples from which the Gaussian mixture model will be estimated. It should be a - one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type - it will be converted to the inner matrix of such type for the further computing. - @param probs0 - @param logLikelihoods The optional output matrix that contains a likelihood logarithm value for - each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type. - @param labels The optional output "class label" for each sample: - \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable - mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type. - @param probs The optional output matrix that contains posterior probabilities of each Gaussian - mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and - CV_64FC1 type. - */ - CV_WRAP virtual bool trainM(InputArray samples, InputArray probs0, - OutputArray logLikelihoods=noArray(), - OutputArray labels=noArray(), - OutputArray probs=noArray()) = 0; - - /** Creates empty %EM model. - The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you - can use one of the EM::train\* methods or load it from file using Algorithm::load\(filename). - */ - CV_WRAP static Ptr create(); -}; - -/****************************************************************************************\ -* Decision Tree * -\****************************************************************************************/ - -/** @brief The class represents a single decision tree or a collection of decision trees. - -The current public interface of the class allows user to train only a single decision tree, however -the class is capable of storing multiple decision trees and using them for prediction (by summing -responses or using a voting schemes), and the derived from DTrees classes (such as RTrees and Boost) -use this capability to implement decision tree ensembles. - -@sa @ref ml_intro_trees -*/ -class CV_EXPORTS_W DTrees : public StatModel -{ -public: - /** Predict options */ - enum Flags { PREDICT_AUTO=0, PREDICT_SUM=(1<<8), PREDICT_MAX_VOTE=(2<<8), PREDICT_MASK=(3<<8) }; - - /** Cluster possible values of a categorical variable into K\<=maxCategories clusters to - find a suboptimal split. - If a discrete variable, on which the training procedure tries to make a split, takes more than - maxCategories values, the precise best subset estimation may take a very long time because the - algorithm is exponential. Instead, many decision trees engines (including our implementation) - try to find sub-optimal split in this case by clustering all the samples into maxCategories - clusters that is some categories are merged together. The clustering is applied only in n \> - 2-class classification problems for categorical variables with N \> max_categories possible - values. In case of regression and 2-class classification the optimal split can be found - efficiently without employing clustering, thus the parameter is not used in these cases. - Default value is 10.*/ - /** @see setMaxCategories */ - CV_WRAP virtual int getMaxCategories() const = 0; - /** @copybrief getMaxCategories @see getMaxCategories */ - CV_WRAP virtual void setMaxCategories(int val) = 0; - - /** The maximum possible depth of the tree. - That is the training algorithms attempts to split a node while its depth is less than maxDepth. - The root node has zero depth. The actual depth may be smaller if the other termination criteria - are met (see the outline of the training procedure @ref ml_intro_trees "here"), and/or if the - tree is pruned. Default value is INT_MAX.*/ - /** @see setMaxDepth */ - CV_WRAP virtual int getMaxDepth() const = 0; - /** @copybrief getMaxDepth @see getMaxDepth */ - CV_WRAP virtual void setMaxDepth(int val) = 0; - - /** If the number of samples in a node is less than this parameter then the node will not be split. - - Default value is 10.*/ - /** @see setMinSampleCount */ - CV_WRAP virtual int getMinSampleCount() const = 0; - /** @copybrief getMinSampleCount @see getMinSampleCount */ - CV_WRAP virtual void setMinSampleCount(int val) = 0; - - /** If CVFolds \> 1 then algorithms prunes the built decision tree using K-fold - cross-validation procedure where K is equal to CVFolds. - Default value is 10.*/ - /** @see setCVFolds */ - CV_WRAP virtual int getCVFolds() const = 0; - /** @copybrief getCVFolds @see getCVFolds */ - CV_WRAP virtual void setCVFolds(int val) = 0; - - /** If true then surrogate splits will be built. - These splits allow to work with missing data and compute variable importance correctly. - Default value is false. - @note currently it's not implemented.*/ - /** @see setUseSurrogates */ - CV_WRAP virtual bool getUseSurrogates() const = 0; - /** @copybrief getUseSurrogates @see getUseSurrogates */ - CV_WRAP virtual void setUseSurrogates(bool val) = 0; - - /** If true then a pruning will be harsher. - This will make a tree more compact and more resistant to the training data noise but a bit less - accurate. Default value is true.*/ - /** @see setUse1SERule */ - CV_WRAP virtual bool getUse1SERule() const = 0; - /** @copybrief getUse1SERule @see getUse1SERule */ - CV_WRAP virtual void setUse1SERule(bool val) = 0; - - /** If true then pruned branches are physically removed from the tree. - Otherwise they are retained and it is possible to get results from the original unpruned (or - pruned less aggressively) tree. Default value is true.*/ - /** @see setTruncatePrunedTree */ - CV_WRAP virtual bool getTruncatePrunedTree() const = 0; - /** @copybrief getTruncatePrunedTree @see getTruncatePrunedTree */ - CV_WRAP virtual void setTruncatePrunedTree(bool val) = 0; - - /** Termination criteria for regression trees. - If all absolute differences between an estimated value in a node and values of train samples - in this node are less than this parameter then the node will not be split further. Default - value is 0.01f*/ - /** @see setRegressionAccuracy */ - CV_WRAP virtual float getRegressionAccuracy() const = 0; - /** @copybrief getRegressionAccuracy @see getRegressionAccuracy */ - CV_WRAP virtual void setRegressionAccuracy(float val) = 0; - - /** @brief The array of a priori class probabilities, sorted by the class label value. - - The parameter can be used to tune the decision tree preferences toward a certain class. For - example, if you want to detect some rare anomaly occurrence, the training base will likely - contain much more normal cases than anomalies, so a very good classification performance - will be achieved just by considering every case as normal. To avoid this, the priors can be - specified, where the anomaly probability is artificially increased (up to 0.5 or even - greater), so the weight of the misclassified anomalies becomes much bigger, and the tree is - adjusted properly. - - You can also think about this parameter as weights of prediction categories which determine - relative weights that you give to misclassification. That is, if the weight of the first - category is 1 and the weight of the second category is 10, then each mistake in predicting - the second category is equivalent to making 10 mistakes in predicting the first category. - Default value is empty Mat.*/ - /** @see setPriors */ - CV_WRAP virtual cv::Mat getPriors() const = 0; - /** @copybrief getPriors @see getPriors */ - CV_WRAP virtual void setPriors(const cv::Mat &val) = 0; - - /** @brief The class represents a decision tree node. - */ - class CV_EXPORTS Node - { - public: - Node(); - double value; //!< Value at the node: a class label in case of classification or estimated - //!< function value in case of regression. - int classIdx; //!< Class index normalized to 0..class_count-1 range and assigned to the - //!< node. It is used internally in classification trees and tree ensembles. - int parent; //!< Index of the parent node - int left; //!< Index of the left child node - int right; //!< Index of right child node - int defaultDir; //!< Default direction where to go (-1: left or +1: right). It helps in the - //!< case of missing values. - int split; //!< Index of the first split - }; - - /** @brief The class represents split in a decision tree. - */ - class CV_EXPORTS Split - { - public: - Split(); - int varIdx; //!< Index of variable on which the split is created. - bool inversed; //!< If true, then the inverse split rule is used (i.e. left and right - //!< branches are exchanged in the rule expressions below). - float quality; //!< The split quality, a positive number. It is used to choose the best split. - int next; //!< Index of the next split in the list of splits for the node - float c; /**< The threshold value in case of split on an ordered variable. - The rule is: - @code{.none} - if var_value < c - then next_node <- left - else next_node <- right - @endcode */ - int subsetOfs; /**< Offset of the bitset used by the split on a categorical variable. - The rule is: - @code{.none} - if bitset[var_value] == 1 - then next_node <- left - else next_node <- right - @endcode */ - }; - - /** @brief Returns indices of root nodes - */ - virtual const std::vector& getRoots() const = 0; - /** @brief Returns all the nodes - - all the node indices are indices in the returned vector - */ - virtual const std::vector& getNodes() const = 0; - /** @brief Returns all the splits - - all the split indices are indices in the returned vector - */ - virtual const std::vector& getSplits() const = 0; - /** @brief Returns all the bitsets for categorical splits - - Split::subsetOfs is an offset in the returned vector - */ - virtual const std::vector& getSubsets() const = 0; - - /** @brief Creates the empty model - - The static method creates empty decision tree with the specified parameters. It should be then - trained using train method (see StatModel::train). Alternatively, you can load the model from - file using Algorithm::load\(filename). - */ - CV_WRAP static Ptr create(); -}; - -/****************************************************************************************\ -* Random Trees Classifier * -\****************************************************************************************/ - -/** @brief The class implements the random forest predictor. - -@sa @ref ml_intro_rtrees - */ -class CV_EXPORTS_W RTrees : public DTrees -{ -public: - - /** If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance. - Default value is false.*/ - /** @see setCalculateVarImportance */ - CV_WRAP virtual bool getCalculateVarImportance() const = 0; - /** @copybrief getCalculateVarImportance @see getCalculateVarImportance */ - CV_WRAP virtual void setCalculateVarImportance(bool val) = 0; - - /** The size of the randomly selected subset of features at each tree node and that are used - to find the best split(s). - If you set it to 0 then the size will be set to the square root of the total number of - features. Default value is 0.*/ - /** @see setActiveVarCount */ - CV_WRAP virtual int getActiveVarCount() const = 0; - /** @copybrief getActiveVarCount @see getActiveVarCount */ - CV_WRAP virtual void setActiveVarCount(int val) = 0; - - /** The termination criteria that specifies when the training algorithm stops. - Either when the specified number of trees is trained and added to the ensemble or when - sufficient accuracy (measured as OOB error) is achieved. Typically the more trees you have the - better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes - pass a certain number of trees. Also to keep in mind, the number of tree increases the - prediction time linearly. Default value is TermCriteria(TermCriteria::MAX_ITERS + - TermCriteria::EPS, 50, 0.1)*/ - /** @see setTermCriteria */ - CV_WRAP virtual TermCriteria getTermCriteria() const = 0; - /** @copybrief getTermCriteria @see getTermCriteria */ - CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0; - - /** Returns the variable importance array. - The method returns the variable importance vector, computed at the training stage when - CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is - returned. - */ - CV_WRAP virtual Mat getVarImportance() const = 0; - - /** Creates the empty model. - Use StatModel::train to train the model, StatModel::train to create and train the model, - Algorithm::load to load the pre-trained model. - */ - CV_WRAP static Ptr create(); -}; - -/****************************************************************************************\ -* Boosted tree classifier * -\****************************************************************************************/ - -/** @brief Boosted tree classifier derived from DTrees - -@sa @ref ml_intro_boost - */ -class CV_EXPORTS_W Boost : public DTrees -{ -public: - /** Type of the boosting algorithm. - See Boost::Types. Default value is Boost::REAL. */ - /** @see setBoostType */ - CV_WRAP virtual int getBoostType() const = 0; - /** @copybrief getBoostType @see getBoostType */ - CV_WRAP virtual void setBoostType(int val) = 0; - - /** The number of weak classifiers. - Default value is 100. */ - /** @see setWeakCount */ - CV_WRAP virtual int getWeakCount() const = 0; - /** @copybrief getWeakCount @see getWeakCount */ - CV_WRAP virtual void setWeakCount(int val) = 0; - - /** A threshold between 0 and 1 used to save computational time. - Samples with summary weight \f$\leq 1 - weight_trim_rate\f$ do not participate in the *next* - iteration of training. Set this parameter to 0 to turn off this functionality. Default value is 0.95.*/ - /** @see setWeightTrimRate */ - CV_WRAP virtual double getWeightTrimRate() const = 0; - /** @copybrief getWeightTrimRate @see getWeightTrimRate */ - CV_WRAP virtual void setWeightTrimRate(double val) = 0; - - /** Boosting type. - Gentle AdaBoost and Real AdaBoost are often the preferable choices. */ - enum Types { - DISCRETE=0, //!< Discrete AdaBoost. - REAL=1, //!< Real AdaBoost. It is a technique that utilizes confidence-rated predictions - //!< and works well with categorical data. - LOGIT=2, //!< LogitBoost. It can produce good regression fits. - GENTLE=3 //!< Gentle AdaBoost. It puts less weight on outlier data points and for that - //!(filename) to load the pre-trained model. */ - CV_WRAP static Ptr create(); -}; - -/****************************************************************************************\ -* Gradient Boosted Trees * -\****************************************************************************************/ - -/*class CV_EXPORTS_W GBTrees : public DTrees -{ -public: - struct CV_EXPORTS_W_MAP Params : public DTrees::Params - { - CV_PROP_RW int weakCount; - CV_PROP_RW int lossFunctionType; - CV_PROP_RW float subsamplePortion; - CV_PROP_RW float shrinkage; - - Params(); - Params( int lossFunctionType, int weakCount, float shrinkage, - float subsamplePortion, int maxDepth, bool useSurrogates ); - }; - - enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS}; - - virtual void setK(int k) = 0; - - virtual float predictSerial( InputArray samples, - OutputArray weakResponses, int flags) const = 0; - - static Ptr create(const Params& p); -};*/ - -/****************************************************************************************\ -* Artificial Neural Networks (ANN) * -\****************************************************************************************/ - -/////////////////////////////////// Multi-Layer Perceptrons ////////////////////////////// - -/** @brief Artificial Neural Networks - Multi-Layer Perceptrons. - -Unlike many other models in ML that are constructed and trained at once, in the MLP model these -steps are separated. First, a network with the specified topology is created using the non-default -constructor or the method ANN_MLP::create. All the weights are set to zeros. Then, the network is -trained using a set of input and output vectors. The training procedure can be repeated more than -once, that is, the weights can be adjusted based on the new training data. - -Additional flags for StatModel::train are available: ANN_MLP::TrainFlags. - -@sa @ref ml_intro_ann - */ -class CV_EXPORTS_W ANN_MLP : public StatModel -{ -public: - /** Available training methods */ - enum TrainingMethods { - BACKPROP=0, //!< The back-propagation algorithm. - RPROP=1 //!< The RPROP algorithm. See @cite RPROP93 for details. - }; - - /** Sets training method and common parameters. - @param method Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods. - @param param1 passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP - @param param2 passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP. - */ - CV_WRAP virtual void setTrainMethod(int method, double param1 = 0, double param2 = 0) = 0; - - /** Returns current training method */ - CV_WRAP virtual int getTrainMethod() const = 0; - - /** Initialize the activation function for each neuron. - Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM. - @param type The type of activation function. See ANN_MLP::ActivationFunctions. - @param param1 The first parameter of the activation function, \f$\alpha\f$. Default value is 0. - @param param2 The second parameter of the activation function, \f$\beta\f$. Default value is 0. - */ - CV_WRAP virtual void setActivationFunction(int type, double param1 = 0, double param2 = 0) = 0; - - /** Integer vector specifying the number of neurons in each layer including the input and output layers. - The very first element specifies the number of elements in the input layer. - The last element - number of elements in the output layer. Default value is empty Mat. - @sa getLayerSizes */ - CV_WRAP virtual void setLayerSizes(InputArray _layer_sizes) = 0; - - /** Integer vector specifying the number of neurons in each layer including the input and output layers. - The very first element specifies the number of elements in the input layer. - The last element - number of elements in the output layer. - @sa setLayerSizes */ - CV_WRAP virtual cv::Mat getLayerSizes() const = 0; - - /** Termination criteria of the training algorithm. - You can specify the maximum number of iterations (maxCount) and/or how much the error could - change between the iterations to make the algorithm continue (epsilon). Default value is - TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 0.01).*/ - /** @see setTermCriteria */ - CV_WRAP virtual TermCriteria getTermCriteria() const = 0; - /** @copybrief getTermCriteria @see getTermCriteria */ - CV_WRAP virtual void setTermCriteria(TermCriteria val) = 0; - - /** BPROP: Strength of the weight gradient term. - The recommended value is about 0.1. Default value is 0.1.*/ - /** @see setBackpropWeightScale */ - CV_WRAP virtual double getBackpropWeightScale() const = 0; - /** @copybrief getBackpropWeightScale @see getBackpropWeightScale */ - CV_WRAP virtual void setBackpropWeightScale(double val) = 0; - - /** BPROP: Strength of the momentum term (the difference between weights on the 2 previous iterations). - This parameter provides some inertia to smooth the random fluctuations of the weights. It can - vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. - Default value is 0.1.*/ - /** @see setBackpropMomentumScale */ - CV_WRAP virtual double getBackpropMomentumScale() const = 0; - /** @copybrief getBackpropMomentumScale @see getBackpropMomentumScale */ - CV_WRAP virtual void setBackpropMomentumScale(double val) = 0; - - /** RPROP: Initial value \f$\Delta_0\f$ of update-values \f$\Delta_{ij}\f$. - Default value is 0.1.*/ - /** @see setRpropDW0 */ - CV_WRAP virtual double getRpropDW0() const = 0; - /** @copybrief getRpropDW0 @see getRpropDW0 */ - CV_WRAP virtual void setRpropDW0(double val) = 0; - - /** RPROP: Increase factor \f$\eta^+\f$. - It must be \>1. Default value is 1.2.*/ - /** @see setRpropDWPlus */ - CV_WRAP virtual double getRpropDWPlus() const = 0; - /** @copybrief getRpropDWPlus @see getRpropDWPlus */ - CV_WRAP virtual void setRpropDWPlus(double val) = 0; - - /** RPROP: Decrease factor \f$\eta^-\f$. - It must be \<1. Default value is 0.5.*/ - /** @see setRpropDWMinus */ - CV_WRAP virtual double getRpropDWMinus() const = 0; - /** @copybrief getRpropDWMinus @see getRpropDWMinus */ - CV_WRAP virtual void setRpropDWMinus(double val) = 0; - - /** RPROP: Update-values lower limit \f$\Delta_{min}\f$. - It must be positive. Default value is FLT_EPSILON.*/ - /** @see setRpropDWMin */ - CV_WRAP virtual double getRpropDWMin() const = 0; - /** @copybrief getRpropDWMin @see getRpropDWMin */ - CV_WRAP virtual void setRpropDWMin(double val) = 0; - - /** RPROP: Update-values upper limit \f$\Delta_{max}\f$. - It must be \>1. Default value is 50.*/ - /** @see setRpropDWMax */ - CV_WRAP virtual double getRpropDWMax() const = 0; - /** @copybrief getRpropDWMax @see getRpropDWMax */ - CV_WRAP virtual void setRpropDWMax(double val) = 0; - - /** possible activation functions */ - enum ActivationFunctions { - /** Identity function: \f$f(x)=x\f$ */ - IDENTITY = 0, - /** Symmetrical sigmoid: \f$f(x)=\beta*(1-e^{-\alpha x})/(1+e^{-\alpha x}\f$ - @note - If you are using the default sigmoid activation function with the default parameter values - fparam1=0 and fparam2=0 then the function used is y = 1.7159\*tanh(2/3 \* x), so the output - will range from [-1.7159, 1.7159], instead of [0,1].*/ - SIGMOID_SYM = 1, - /** Gaussian function: \f$f(x)=\beta e^{-\alpha x*x}\f$ */ - GAUSSIAN = 2 - }; - - /** Train options */ - enum TrainFlags { - /** Update the network weights, rather than compute them from scratch. In the latter case - the weights are initialized using the Nguyen-Widrow algorithm. */ - UPDATE_WEIGHTS = 1, - /** Do not normalize the input vectors. If this flag is not set, the training algorithm - normalizes each input feature independently, shifting its mean value to 0 and making the - standard deviation equal to 1. If the network is assumed to be updated frequently, the new - training data could be much different from original one. In this case, you should take care - of proper normalization. */ - NO_INPUT_SCALE = 2, - /** Do not normalize the output vectors. If the flag is not set, the training algorithm - normalizes each output feature independently, by transforming it to the certain range - depending on the used activation function. */ - NO_OUTPUT_SCALE = 4 - }; - - CV_WRAP virtual Mat getWeights(int layerIdx) const = 0; - - /** @brief Creates empty model - - Use StatModel::train to train the model, Algorithm::load\(filename) to load the pre-trained model. - Note that the train method has optional flags: ANN_MLP::TrainFlags. - */ - CV_WRAP static Ptr create(); - - /** @brief Loads and creates a serialized ANN from a file - * - * Use ANN::save to serialize and store an ANN to disk. - * Load the ANN from this file again, by calling this function with the path to the file. - * - * @param filepath path to serialized ANN - */ - CV_WRAP static Ptr load(const String& filepath); - -}; - -/****************************************************************************************\ -* Logistic Regression * -\****************************************************************************************/ - -/** @brief Implements Logistic Regression classifier. - -@sa @ref ml_intro_lr - */ -class CV_EXPORTS_W LogisticRegression : public StatModel -{ -public: - - /** Learning rate. */ - /** @see setLearningRate */ - CV_WRAP virtual double getLearningRate() const = 0; - /** @copybrief getLearningRate @see getLearningRate */ - CV_WRAP virtual void setLearningRate(double val) = 0; - - /** Number of iterations. */ - /** @see setIterations */ - CV_WRAP virtual int getIterations() const = 0; - /** @copybrief getIterations @see getIterations */ - CV_WRAP virtual void setIterations(int val) = 0; - - /** Kind of regularization to be applied. See LogisticRegression::RegKinds. */ - /** @see setRegularization */ - CV_WRAP virtual int getRegularization() const = 0; - /** @copybrief getRegularization @see getRegularization */ - CV_WRAP virtual void setRegularization(int val) = 0; - - /** Kind of training method used. See LogisticRegression::Methods. */ - /** @see setTrainMethod */ - CV_WRAP virtual int getTrainMethod() const = 0; - /** @copybrief getTrainMethod @see getTrainMethod */ - CV_WRAP virtual void setTrainMethod(int val) = 0; - - /** Specifies the number of training samples taken in each step of Mini-Batch Gradient - Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm. It - has to take values less than the total number of training samples. */ - /** @see setMiniBatchSize */ - CV_WRAP virtual int getMiniBatchSize() const = 0; - /** @copybrief getMiniBatchSize @see getMiniBatchSize */ - CV_WRAP virtual void setMiniBatchSize(int val) = 0; - - /** Termination criteria of the algorithm. */ - /** @see setTermCriteria */ - CV_WRAP virtual TermCriteria getTermCriteria() const = 0; - /** @copybrief getTermCriteria @see getTermCriteria */ - CV_WRAP virtual void setTermCriteria(TermCriteria val) = 0; - - //! Regularization kinds - enum RegKinds { - REG_DISABLE = -1, //!< Regularization disabled - REG_L1 = 0, //!< %L1 norm - REG_L2 = 1 //!< %L2 norm - }; - - //! Training methods - enum Methods { - BATCH = 0, - MINI_BATCH = 1 //!< Set MiniBatchSize to a positive integer when using this method. - }; - - /** @brief Predicts responses for input samples and returns a float type. - - @param samples The input data for the prediction algorithm. Matrix [m x n], where each row - contains variables (features) of one object being classified. Should have data type CV_32F. - @param results Predicted labels as a column matrix of type CV_32S. - @param flags Not used. - */ - CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0; - - /** @brief This function returns the trained paramters arranged across rows. - - For a two class classifcation problem, it returns a row matrix. It returns learnt paramters of - the Logistic Regression as a matrix of type CV_32F. - */ - CV_WRAP virtual Mat get_learnt_thetas() const = 0; - - /** @brief Creates empty model. - - Creates Logistic Regression model with parameters given. - */ - CV_WRAP static Ptr create(); -}; - -/****************************************************************************************\ -* Auxilary functions declarations * -\****************************************************************************************/ - -/** @brief Generates _sample_ from multivariate normal distribution - -@param mean an average row vector -@param cov symmetric covariation matrix -@param nsamples returned samples count -@param samples returned samples array -*/ -CV_EXPORTS void randMVNormal( InputArray mean, InputArray cov, int nsamples, OutputArray samples); - -/** @brief Creates test set */ -CV_EXPORTS void createConcentricSpheresTestSet( int nsamples, int nfeatures, int nclasses, - OutputArray samples, OutputArray responses); - -//! @} ml - -} -} - -#endif // __cplusplus -#endif // __OPENCV_ML_HPP__ - -/* End of file. */ diff --git a/IPL/include/opencv/opencv2/ml/ml.hpp b/IPL/include/opencv/opencv2/ml/ml.hpp deleted file mode 100644 index f6f9cd8..0000000 --- a/IPL/include/opencv/opencv2/ml/ml.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/ml.hpp" diff --git a/IPL/include/opencv/opencv2/objdetect.hpp b/IPL/include/opencv/opencv2/objdetect.hpp deleted file mode 100644 index 6587b3d..0000000 --- a/IPL/include/opencv/opencv2/objdetect.hpp +++ /dev/null @@ -1,466 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OBJDETECT_HPP__ -#define __OPENCV_OBJDETECT_HPP__ - -#include "opencv2/core.hpp" - -/** -@defgroup objdetect Object Detection - -Haar Feature-based Cascade Classifier for Object Detection ----------------------------------------------------------- - -The object detector described below has been initially proposed by Paul Viola @cite Viola01 and -improved by Rainer Lienhart @cite Lienhart02 . - -First, a classifier (namely a *cascade of boosted classifiers working with haar-like features*) is -trained with a few hundred sample views of a particular object (i.e., a face or a car), called -positive examples, that are scaled to the same size (say, 20x20), and negative examples - arbitrary -images of the same size. - -After a classifier is trained, it can be applied to a region of interest (of the same size as used -during the training) in an input image. The classifier outputs a "1" if the region is likely to show -the object (i.e., face/car), and "0" otherwise. To search for the object in the whole image one can -move the search window across the image and check every location using the classifier. The -classifier is designed so that it can be easily "resized" in order to be able to find the objects of -interest at different sizes, which is more efficient than resizing the image itself. So, to find an -object of an unknown size in the image the scan procedure should be done several times at different -scales. - -The word "cascade" in the classifier name means that the resultant classifier consists of several -simpler classifiers (*stages*) that are applied subsequently to a region of interest until at some -stage the candidate is rejected or all the stages are passed. The word "boosted" means that the -classifiers at every stage of the cascade are complex themselves and they are built out of basic -classifiers using one of four different boosting techniques (weighted voting). Currently Discrete -Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported. The basic classifiers are -decision-tree classifiers with at least 2 leaves. Haar-like features are the input to the basic -classifiers, and are calculated as described below. The current algorithm uses the following -Haar-like features: - -![image](pics/haarfeatures.png) - -The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within -the region of interest and the scale (this scale is not the same as the scale used at the detection -stage, though these two scales are multiplied). For example, in the case of the third line feature -(2c) the response is calculated as the difference between the sum of image pixels under the -rectangle covering the whole feature (including the two white stripes and the black stripe in the -middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to -compensate for the differences in the size of areas. The sums of pixel values over a rectangular -regions are calculated rapidly using integral images (see below and the integral description). - -To see the object detector at work, have a look at the facedetect demo: - - -The following reference is for the detection part only. There is a separate application called -opencv_traincascade that can train a cascade of boosted classifiers from a set of samples. - -@note In the new C++ interface it is also possible to use LBP (local binary pattern) features in -addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection -using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. The paper is available online at - - -@{ - @defgroup objdetect_c C API -@} - */ - -typedef struct CvHaarClassifierCascade CvHaarClassifierCascade; - -namespace cv -{ - -//! @addtogroup objdetect -//! @{ - -///////////////////////////// Object Detection //////////////////////////// - -//! class for grouping object candidates, detected by Cascade Classifier, HOG etc. -//! instance of the class is to be passed to cv::partition (see cxoperations.hpp) -class CV_EXPORTS SimilarRects -{ -public: - SimilarRects(double _eps) : eps(_eps) {} - inline bool operator()(const Rect& r1, const Rect& r2) const - { - double delta = eps*(std::min(r1.width, r2.width) + std::min(r1.height, r2.height))*0.5; - return std::abs(r1.x - r2.x) <= delta && - std::abs(r1.y - r2.y) <= delta && - std::abs(r1.x + r1.width - r2.x - r2.width) <= delta && - std::abs(r1.y + r1.height - r2.y - r2.height) <= delta; - } - double eps; -}; - -/** @brief Groups the object candidate rectangles. - -@param rectList Input/output vector of rectangles. Output vector includes retained and grouped -rectangles. (The Python list is not modified in place.) -@param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a -group of rectangles to retain it. -@param eps Relative difference between sides of the rectangles to merge them into a group. - -The function is a wrapper for the generic function partition . It clusters all the input rectangles -using the rectangle equivalence criteria that combines rectangles with similar sizes and similar -locations. The similarity is defined by eps. When eps=0 , no clustering is done at all. If -\f$\texttt{eps}\rightarrow +\inf\f$ , all the rectangles are put in one cluster. Then, the small -clusters containing less than or equal to groupThreshold rectangles are rejected. In each other -cluster, the average rectangle is computed and put into the output rectangle list. - */ -CV_EXPORTS void groupRectangles(std::vector& rectList, int groupThreshold, double eps = 0.2); -/** @overload */ -CV_EXPORTS_W void groupRectangles(CV_IN_OUT std::vector& rectList, CV_OUT std::vector& weights, - int groupThreshold, double eps = 0.2); -/** @overload */ -CV_EXPORTS void groupRectangles(std::vector& rectList, int groupThreshold, - double eps, std::vector* weights, std::vector* levelWeights ); -/** @overload */ -CV_EXPORTS void groupRectangles(std::vector& rectList, std::vector& rejectLevels, - std::vector& levelWeights, int groupThreshold, double eps = 0.2); -/** @overload */ -CV_EXPORTS void groupRectangles_meanshift(std::vector& rectList, std::vector& foundWeights, - std::vector& foundScales, - double detectThreshold = 0.0, Size winDetSize = Size(64, 128)); - -template<> CV_EXPORTS void DefaultDeleter::operator ()(CvHaarClassifierCascade* obj) const; - -enum { CASCADE_DO_CANNY_PRUNING = 1, - CASCADE_SCALE_IMAGE = 2, - CASCADE_FIND_BIGGEST_OBJECT = 4, - CASCADE_DO_ROUGH_SEARCH = 8 - }; - -class CV_EXPORTS_W BaseCascadeClassifier : public Algorithm -{ -public: - virtual ~BaseCascadeClassifier(); - virtual bool empty() const = 0; - virtual bool load( const String& filename ) = 0; - virtual void detectMultiScale( InputArray image, - CV_OUT std::vector& objects, - double scaleFactor, - int minNeighbors, int flags, - Size minSize, Size maxSize ) = 0; - - virtual void detectMultiScale( InputArray image, - CV_OUT std::vector& objects, - CV_OUT std::vector& numDetections, - double scaleFactor, - int minNeighbors, int flags, - Size minSize, Size maxSize ) = 0; - - virtual void detectMultiScale( InputArray image, - CV_OUT std::vector& objects, - CV_OUT std::vector& rejectLevels, - CV_OUT std::vector& levelWeights, - double scaleFactor, - int minNeighbors, int flags, - Size minSize, Size maxSize, - bool outputRejectLevels ) = 0; - - virtual bool isOldFormatCascade() const = 0; - virtual Size getOriginalWindowSize() const = 0; - virtual int getFeatureType() const = 0; - virtual void* getOldCascade() = 0; - - class CV_EXPORTS MaskGenerator - { - public: - virtual ~MaskGenerator() {} - virtual Mat generateMask(const Mat& src)=0; - virtual void initializeMask(const Mat& /*src*/) { } - }; - virtual void setMaskGenerator(const Ptr& maskGenerator) = 0; - virtual Ptr getMaskGenerator() = 0; -}; - -/** @brief Cascade classifier class for object detection. - */ -class CV_EXPORTS_W CascadeClassifier -{ -public: - CV_WRAP CascadeClassifier(); - /** @brief Loads a classifier from a file. - - @param filename Name of the file from which the classifier is loaded. - */ - CV_WRAP CascadeClassifier(const String& filename); - ~CascadeClassifier(); - /** @brief Checks whether the classifier has been loaded. - */ - CV_WRAP bool empty() const; - /** @brief Loads a classifier from a file. - - @param filename Name of the file from which the classifier is loaded. The file may contain an old - HAAR classifier trained by the haartraining application or a new cascade classifier trained by the - traincascade application. - */ - CV_WRAP bool load( const String& filename ); - /** @brief Reads a classifier from a FileStorage node. - - @note The file may contain a new cascade classifier (trained traincascade application) only. - */ - CV_WRAP bool read( const FileNode& node ); - - /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list - of rectangles. - - @param image Matrix of the type CV_8U containing an image where objects are detected. - @param objects Vector of rectangles where each rectangle contains the detected object, the - rectangles may be partially outside the original image. - @param scaleFactor Parameter specifying how much the image size is reduced at each image scale. - @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have - to retain it. - @param flags Parameter with the same meaning for an old cascade as in the function - cvHaarDetectObjects. It is not used for a new cascade. - @param minSize Minimum possible object size. Objects smaller than that are ignored. - @param maxSize Maximum possible object size. Objects larger than that are ignored. - - The function is parallelized with the TBB library. - - @note - - (Python) A face detection example using cascade classifiers can be found at - opencv_source_code/samples/python/facedetect.py - */ - CV_WRAP void detectMultiScale( InputArray image, - CV_OUT std::vector& objects, - double scaleFactor = 1.1, - int minNeighbors = 3, int flags = 0, - Size minSize = Size(), - Size maxSize = Size() ); - - /** @overload - @param image Matrix of the type CV_8U containing an image where objects are detected. - @param objects Vector of rectangles where each rectangle contains the detected object, the - rectangles may be partially outside the original image. - @param numDetections Vector of detection numbers for the corresponding objects. An object's number - of detections is the number of neighboring positively classified rectangles that were joined - together to form the object. - @param scaleFactor Parameter specifying how much the image size is reduced at each image scale. - @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have - to retain it. - @param flags Parameter with the same meaning for an old cascade as in the function - cvHaarDetectObjects. It is not used for a new cascade. - @param minSize Minimum possible object size. Objects smaller than that are ignored. - @param maxSize Maximum possible object size. Objects larger than that are ignored. - */ - CV_WRAP_AS(detectMultiScale2) void detectMultiScale( InputArray image, - CV_OUT std::vector& objects, - CV_OUT std::vector& numDetections, - double scaleFactor=1.1, - int minNeighbors=3, int flags=0, - Size minSize=Size(), - Size maxSize=Size() ); - - /** @overload - if `outputRejectLevels` is `true` returns `rejectLevels` and `levelWeights` - */ - CV_WRAP_AS(detectMultiScale3) void detectMultiScale( InputArray image, - CV_OUT std::vector& objects, - CV_OUT std::vector& rejectLevels, - CV_OUT std::vector& levelWeights, - double scaleFactor = 1.1, - int minNeighbors = 3, int flags = 0, - Size minSize = Size(), - Size maxSize = Size(), - bool outputRejectLevels = false ); - - CV_WRAP bool isOldFormatCascade() const; - CV_WRAP Size getOriginalWindowSize() const; - CV_WRAP int getFeatureType() const; - void* getOldCascade(); - - CV_WRAP static bool convert(const String& oldcascade, const String& newcascade); - - void setMaskGenerator(const Ptr& maskGenerator); - Ptr getMaskGenerator(); - - Ptr cc; -}; - -CV_EXPORTS Ptr createFaceDetectionMaskGenerator(); - -//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector ////////////// - -//! struct for detection region of interest (ROI) -struct DetectionROI -{ - //! scale(size) of the bounding box - double scale; - //! set of requrested locations to be evaluated - std::vector locations; - //! vector that will contain confidence values for each location - std::vector confidences; -}; - -struct CV_EXPORTS_W HOGDescriptor -{ -public: - enum { L2Hys = 0 - }; - enum { DEFAULT_NLEVELS = 64 - }; - - CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8), - cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1), - histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true), - free_coef(-1.f), nlevels(HOGDescriptor::DEFAULT_NLEVELS), signedGradient(false) - {} - - CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, - Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1, - int _histogramNormType=HOGDescriptor::L2Hys, - double _L2HysThreshold=0.2, bool _gammaCorrection=false, - int _nlevels=HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient=false) - : winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize), - nbins(_nbins), derivAperture(_derivAperture), winSigma(_winSigma), - histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold), - gammaCorrection(_gammaCorrection), free_coef(-1.f), nlevels(_nlevels), signedGradient(_signedGradient) - {} - - CV_WRAP HOGDescriptor(const String& filename) - { - load(filename); - } - - HOGDescriptor(const HOGDescriptor& d) - { - d.copyTo(*this); - } - - virtual ~HOGDescriptor() {} - - CV_WRAP size_t getDescriptorSize() const; - CV_WRAP bool checkDetectorSize() const; - CV_WRAP double getWinSigma() const; - - CV_WRAP virtual void setSVMDetector(InputArray _svmdetector); - - virtual bool read(FileNode& fn); - virtual void write(FileStorage& fs, const String& objname) const; - - CV_WRAP virtual bool load(const String& filename, const String& objname = String()); - CV_WRAP virtual void save(const String& filename, const String& objname = String()) const; - virtual void copyTo(HOGDescriptor& c) const; - - CV_WRAP virtual void compute(InputArray img, - CV_OUT std::vector& descriptors, - Size winStride = Size(), Size padding = Size(), - const std::vector& locations = std::vector()) const; - - //! with found weights output - CV_WRAP virtual void detect(const Mat& img, CV_OUT std::vector& foundLocations, - CV_OUT std::vector& weights, - double hitThreshold = 0, Size winStride = Size(), - Size padding = Size(), - const std::vector& searchLocations = std::vector()) const; - //! without found weights output - virtual void detect(const Mat& img, CV_OUT std::vector& foundLocations, - double hitThreshold = 0, Size winStride = Size(), - Size padding = Size(), - const std::vector& searchLocations=std::vector()) const; - - //! with result weights output - CV_WRAP virtual void detectMultiScale(InputArray img, CV_OUT std::vector& foundLocations, - CV_OUT std::vector& foundWeights, double hitThreshold = 0, - Size winStride = Size(), Size padding = Size(), double scale = 1.05, - double finalThreshold = 2.0,bool useMeanshiftGrouping = false) const; - //! without found weights output - virtual void detectMultiScale(InputArray img, CV_OUT std::vector& foundLocations, - double hitThreshold = 0, Size winStride = Size(), - Size padding = Size(), double scale = 1.05, - double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const; - - CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs, - Size paddingTL = Size(), Size paddingBR = Size()) const; - - CV_WRAP static std::vector getDefaultPeopleDetector(); - CV_WRAP static std::vector getDaimlerPeopleDetector(); - - CV_PROP Size winSize; - CV_PROP Size blockSize; - CV_PROP Size blockStride; - CV_PROP Size cellSize; - CV_PROP int nbins; - CV_PROP int derivAperture; - CV_PROP double winSigma; - CV_PROP int histogramNormType; - CV_PROP double L2HysThreshold; - CV_PROP bool gammaCorrection; - CV_PROP std::vector svmDetector; - UMat oclSvmDetector; - float free_coef; - CV_PROP int nlevels; - CV_PROP bool signedGradient; - - - //! evaluate specified ROI and return confidence value for each location - virtual void detectROI(const cv::Mat& img, const std::vector &locations, - CV_OUT std::vector& foundLocations, CV_OUT std::vector& confidences, - double hitThreshold = 0, cv::Size winStride = Size(), - cv::Size padding = Size()) const; - - //! evaluate specified ROI and return confidence value for each location in multiple scales - virtual void detectMultiScaleROI(const cv::Mat& img, - CV_OUT std::vector& foundLocations, - std::vector& locations, - double hitThreshold = 0, - int groupThreshold = 0) const; - - //! read/parse Dalal's alt model file - void readALTModel(String modelfile); - void groupRectangles(std::vector& rectList, std::vector& weights, int groupThreshold, double eps) const; -}; - -//! @} objdetect - -} - -#include "opencv2/objdetect/detection_based_tracker.hpp" - -#ifndef DISABLE_OPENCV_24_COMPATIBILITY -#include "opencv2/objdetect/objdetect_c.h" -#endif - -#endif diff --git a/IPL/include/opencv/opencv2/objdetect/detection_based_tracker.hpp b/IPL/include/opencv/opencv2/objdetect/detection_based_tracker.hpp deleted file mode 100644 index 1f5f1d3..0000000 --- a/IPL/include/opencv/opencv2/objdetect/detection_based_tracker.hpp +++ /dev/null @@ -1,225 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OBJDETECT_DBT_HPP__ -#define __OPENCV_OBJDETECT_DBT_HPP__ - -// After this condition removal update blacklist for bindings: modules/python/common.cmake -#if defined(__linux__) || defined(LINUX) || defined(__APPLE__) || defined(__ANDROID__) || \ - (defined(__cplusplus) && __cplusplus > 201103L) || (defined(_MSC_VER) && _MSC_VER >= 1700) - -#include - -namespace cv -{ - -//! @addtogroup objdetect -//! @{ - -class CV_EXPORTS DetectionBasedTracker -{ - public: - struct Parameters - { - int maxTrackLifetime; - int minDetectionPeriod; //the minimal time between run of the big object detector (on the whole frame) in ms (1000 mean 1 sec), default=0 - - Parameters(); - }; - - class IDetector - { - public: - IDetector(): - minObjSize(96, 96), - maxObjSize(INT_MAX, INT_MAX), - minNeighbours(2), - scaleFactor(1.1f) - {} - - virtual void detect(const cv::Mat& image, std::vector& objects) = 0; - - void setMinObjectSize(const cv::Size& min) - { - minObjSize = min; - } - void setMaxObjectSize(const cv::Size& max) - { - maxObjSize = max; - } - cv::Size getMinObjectSize() const - { - return minObjSize; - } - cv::Size getMaxObjectSize() const - { - return maxObjSize; - } - float getScaleFactor() - { - return scaleFactor; - } - void setScaleFactor(float value) - { - scaleFactor = value; - } - int getMinNeighbours() - { - return minNeighbours; - } - void setMinNeighbours(int value) - { - minNeighbours = value; - } - virtual ~IDetector() {} - - protected: - cv::Size minObjSize; - cv::Size maxObjSize; - int minNeighbours; - float scaleFactor; - }; - - DetectionBasedTracker(cv::Ptr mainDetector, cv::Ptr trackingDetector, const Parameters& params); - virtual ~DetectionBasedTracker(); - - virtual bool run(); - virtual void stop(); - virtual void resetTracking(); - - virtual void process(const cv::Mat& imageGray); - - bool setParameters(const Parameters& params); - const Parameters& getParameters() const; - - - typedef std::pair Object; - virtual void getObjects(std::vector& result) const; - virtual void getObjects(std::vector& result) const; - - enum ObjectStatus - { - DETECTED_NOT_SHOWN_YET, - DETECTED, - DETECTED_TEMPORARY_LOST, - WRONG_OBJECT - }; - struct ExtObject - { - int id; - cv::Rect location; - ObjectStatus status; - ExtObject(int _id, cv::Rect _location, ObjectStatus _status) - :id(_id), location(_location), status(_status) - { - } - }; - virtual void getObjects(std::vector& result) const; - - - virtual int addObject(const cv::Rect& location); //returns id of the new object - - protected: - class SeparateDetectionWork; - cv::Ptr separateDetectionWork; - friend void* workcycleObjectDetectorFunction(void* p); - - struct InnerParameters - { - int numLastPositionsToTrack; - int numStepsToWaitBeforeFirstShow; - int numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown; - int numStepsToShowWithoutDetecting; - - float coeffTrackingWindowSize; - float coeffObjectSizeToTrack; - float coeffObjectSpeedUsingInPrediction; - - InnerParameters(); - }; - Parameters parameters; - InnerParameters innerParameters; - - struct TrackedObject - { - typedef std::vector PositionsVector; - - PositionsVector lastPositions; - - int numDetectedFrames; - int numFramesNotDetected; - int id; - - TrackedObject(const cv::Rect& rect):numDetectedFrames(1), numFramesNotDetected(0) - { - lastPositions.push_back(rect); - id=getNextId(); - }; - - static int getNextId() - { - static int _id=0; - return _id++; - } - }; - - int numTrackedSteps; - std::vector trackedObjects; - - std::vector weightsPositionsSmoothing; - std::vector weightsSizesSmoothing; - - cv::Ptr cascadeForTracking; - - void updateTrackedObjects(const std::vector& detectedObjects); - cv::Rect calcTrackedObjectPositionToShow(int i) const; - cv::Rect calcTrackedObjectPositionToShow(int i, ObjectStatus& status) const; - void detectInRegion(const cv::Mat& img, const cv::Rect& r, std::vector& detectedObjectsInRegions); -}; - -//! @} objdetect - -} //end of cv namespace -#endif - -#endif diff --git a/IPL/include/opencv/opencv2/objdetect/objdetect.hpp b/IPL/include/opencv/opencv2/objdetect/objdetect.hpp deleted file mode 100644 index 3ee284f..0000000 --- a/IPL/include/opencv/opencv2/objdetect/objdetect.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/objdetect.hpp" diff --git a/IPL/include/opencv/opencv2/objdetect/objdetect_c.h b/IPL/include/opencv/opencv2/objdetect/objdetect_c.h deleted file mode 100644 index 632a438..0000000 --- a/IPL/include/opencv/opencv2/objdetect/objdetect_c.h +++ /dev/null @@ -1,165 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OBJDETECT_C_H__ -#define __OPENCV_OBJDETECT_C_H__ - -#include "opencv2/core/core_c.h" - -#ifdef __cplusplus -#include -#include - -extern "C" { -#endif - -/** @addtogroup objdetect_c - @{ - */ - -/****************************************************************************************\ -* Haar-like Object Detection functions * -\****************************************************************************************/ - -#define CV_HAAR_MAGIC_VAL 0x42500000 -#define CV_TYPE_NAME_HAAR "opencv-haar-classifier" - -#define CV_IS_HAAR_CLASSIFIER( haar ) \ - ((haar) != NULL && \ - (((const CvHaarClassifierCascade*)(haar))->flags & CV_MAGIC_MASK)==CV_HAAR_MAGIC_VAL) - -#define CV_HAAR_FEATURE_MAX 3 - -typedef struct CvHaarFeature -{ - int tilted; - struct - { - CvRect r; - float weight; - } rect[CV_HAAR_FEATURE_MAX]; -} CvHaarFeature; - -typedef struct CvHaarClassifier -{ - int count; - CvHaarFeature* haar_feature; - float* threshold; - int* left; - int* right; - float* alpha; -} CvHaarClassifier; - -typedef struct CvHaarStageClassifier -{ - int count; - float threshold; - CvHaarClassifier* classifier; - - int next; - int child; - int parent; -} CvHaarStageClassifier; - -typedef struct CvHidHaarClassifierCascade CvHidHaarClassifierCascade; - -typedef struct CvHaarClassifierCascade -{ - int flags; - int count; - CvSize orig_window_size; - CvSize real_window_size; - double scale; - CvHaarStageClassifier* stage_classifier; - CvHidHaarClassifierCascade* hid_cascade; -} CvHaarClassifierCascade; - -typedef struct CvAvgComp -{ - CvRect rect; - int neighbors; -} CvAvgComp; - -/* Loads haar classifier cascade from a directory. - It is obsolete: convert your cascade to xml and use cvLoad instead */ -CVAPI(CvHaarClassifierCascade*) cvLoadHaarClassifierCascade( - const char* directory, CvSize orig_window_size); - -CVAPI(void) cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** cascade ); - -#define CV_HAAR_DO_CANNY_PRUNING 1 -#define CV_HAAR_SCALE_IMAGE 2 -#define CV_HAAR_FIND_BIGGEST_OBJECT 4 -#define CV_HAAR_DO_ROUGH_SEARCH 8 - -CVAPI(CvSeq*) cvHaarDetectObjects( const CvArr* image, - CvHaarClassifierCascade* cascade, CvMemStorage* storage, - double scale_factor CV_DEFAULT(1.1), - int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0), - CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0))); - -/* sets images for haar classifier cascade */ -CVAPI(void) cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade, - const CvArr* sum, const CvArr* sqsum, - const CvArr* tilted_sum, double scale ); - -/* runs the cascade on the specified window */ -CVAPI(int) cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade, - CvPoint pt, int start_stage CV_DEFAULT(0)); - -/** @} objdetect_c */ - -#ifdef __cplusplus -} - -CV_EXPORTS CvSeq* cvHaarDetectObjectsForROC( const CvArr* image, - CvHaarClassifierCascade* cascade, CvMemStorage* storage, - std::vector& rejectLevels, std::vector& levelWeightds, - double scale_factor = 1.1, - int min_neighbors = 3, int flags = 0, - CvSize min_size = cvSize(0, 0), CvSize max_size = cvSize(0, 0), - bool outputRejectLevels = false ); - -#endif - -#endif /* __OPENCV_OBJDETECT_C_H__ */ diff --git a/IPL/include/opencv/opencv2/opencv.hpp b/IPL/include/opencv/opencv2/opencv.hpp deleted file mode 100644 index 49b6a66..0000000 --- a/IPL/include/opencv/opencv2/opencv.hpp +++ /dev/null @@ -1,80 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009-2010, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_ALL_HPP__ -#define __OPENCV_ALL_HPP__ - -#include "opencv2/opencv_modules.hpp" - -#include "opencv2/core.hpp" -#ifdef HAVE_OPENCV_IMGPROC -#include "opencv2/imgproc.hpp" -#endif -#ifdef HAVE_OPENCV_PHOTO -#include "opencv2/photo.hpp" -#endif -#ifdef HAVE_OPENCV_VIDEO -#include "opencv2/video.hpp" -#endif -#ifdef HAVE_OPENCV_FEATURES2D -#include "opencv2/features2d.hpp" -#endif -#ifdef HAVE_OPENCV_OBJDETECT -#include "opencv2/objdetect.hpp" -#endif -#ifdef HAVE_OPENCV_CALIB3D -#include "opencv2/calib3d.hpp" -#endif -#ifdef HAVE_OPENCV_IMGCODECS -#include "opencv2/imgcodecs.hpp" -#endif -#ifdef HAVE_OPENCV_VIDEOIO -#include "opencv2/videoio.hpp" -#endif -#ifdef HAVE_OPENCV_HIGHGUI -#include "opencv2/highgui.hpp" -#endif -#ifdef HAVE_OPENCV_ML -#include "opencv2/ml.hpp" -#endif - -#endif diff --git a/IPL/include/opencv/opencv2/opencv_modules.hpp b/IPL/include/opencv/opencv2/opencv_modules.hpp deleted file mode 100644 index 9b85f33..0000000 --- a/IPL/include/opencv/opencv2/opencv_modules.hpp +++ /dev/null @@ -1,50 +0,0 @@ -/* - * ** File generated automatically, do not modify ** - * - * This file defines the list of modules available in current build configuration - * - * -*/ - -#define HAVE_OPENCV_ARUCO -#define HAVE_OPENCV_BGSEGM -#define HAVE_OPENCV_BIOINSPIRED -#define HAVE_OPENCV_CALIB3D -#define HAVE_OPENCV_CCALIB -#define HAVE_OPENCV_CORE -#define HAVE_OPENCV_DATASETS -#define HAVE_OPENCV_DNN -#define HAVE_OPENCV_DPM -#define HAVE_OPENCV_FACE -#define HAVE_OPENCV_FEATURES2D -#define HAVE_OPENCV_FLANN -#define HAVE_OPENCV_FUZZY -#define HAVE_OPENCV_HIGHGUI -#define HAVE_OPENCV_IMGCODECS -#define HAVE_OPENCV_IMGPROC -#define HAVE_OPENCV_LINE_DESCRIPTOR -#define HAVE_OPENCV_ML -#define HAVE_OPENCV_OBJDETECT -#define HAVE_OPENCV_OPTFLOW -#define HAVE_OPENCV_PHOTO -#define HAVE_OPENCV_PLOT -#define HAVE_OPENCV_REG -#define HAVE_OPENCV_RGBD -#define HAVE_OPENCV_SALIENCY -#define HAVE_OPENCV_SHAPE -#define HAVE_OPENCV_STEREO -#define HAVE_OPENCV_STITCHING -#define HAVE_OPENCV_STRUCTURED_LIGHT -#define HAVE_OPENCV_SUPERRES -#define HAVE_OPENCV_SURFACE_MATCHING -#define HAVE_OPENCV_TEXT -#define HAVE_OPENCV_TRACKING -#define HAVE_OPENCV_VIDEO -#define HAVE_OPENCV_VIDEOIO -#define HAVE_OPENCV_VIDEOSTAB -#define HAVE_OPENCV_XFEATURES2D -#define HAVE_OPENCV_XIMGPROC -#define HAVE_OPENCV_XOBJDETECT -#define HAVE_OPENCV_XPHOTO - - diff --git a/IPL/include/opencv/opencv2/photo.hpp b/IPL/include/opencv/opencv2/photo.hpp deleted file mode 100644 index c093f65..0000000 --- a/IPL/include/opencv/opencv2/photo.hpp +++ /dev/null @@ -1,870 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_PHOTO_HPP__ -#define __OPENCV_PHOTO_HPP__ - -#include "opencv2/core.hpp" -#include "opencv2/imgproc.hpp" - -/** -@defgroup photo Computational Photography -@{ - @defgroup photo_denoise Denoising - @defgroup photo_hdr HDR imaging - -This section describes high dynamic range imaging algorithms namely tonemapping, exposure alignment, -camera calibration with multiple exposures and exposure fusion. - - @defgroup photo_clone Seamless Cloning - @defgroup photo_render Non-Photorealistic Rendering - @defgroup photo_c C API -@} - */ - -namespace cv -{ - -//! @addtogroup photo -//! @{ - -//! the inpainting algorithm -enum -{ - INPAINT_NS = 0, // Navier-Stokes algorithm - INPAINT_TELEA = 1 // A. Telea algorithm -}; - -enum -{ - NORMAL_CLONE = 1, - MIXED_CLONE = 2, - MONOCHROME_TRANSFER = 3 -}; - -enum -{ - RECURS_FILTER = 1, - NORMCONV_FILTER = 2 -}; - -/** @brief Restores the selected region in an image using the region neighborhood. - -@param src Input 8-bit 1-channel or 3-channel image. -@param inpaintMask Inpainting mask, 8-bit 1-channel image. Non-zero pixels indicate the area that -needs to be inpainted. -@param dst Output image with the same size and type as src . -@param inpaintRadius Radius of a circular neighborhood of each point inpainted that is considered -by the algorithm. -@param flags Inpainting method that could be one of the following: -- **INPAINT_NS** Navier-Stokes based method [Navier01] -- **INPAINT_TELEA** Method by Alexandru Telea @cite Telea04 . - -The function reconstructs the selected image area from the pixel near the area boundary. The -function may be used to remove dust and scratches from a scanned photo, or to remove undesirable -objects from still images or video. See for more details. - -@note - - An example using the inpainting technique can be found at - opencv_source_code/samples/cpp/inpaint.cpp - - (Python) An example using the inpainting technique can be found at - opencv_source_code/samples/python/inpaint.py - */ -CV_EXPORTS_W void inpaint( InputArray src, InputArray inpaintMask, - OutputArray dst, double inpaintRadius, int flags ); - -//! @addtogroup photo_denoise -//! @{ - -/** @brief Perform image denoising using Non-local Means Denoising algorithm - with several computational -optimizations. Noise expected to be a gaussian white noise - -@param src Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image. -@param dst Output image with the same size and type as src . -@param templateWindowSize Size in pixels of the template patch that is used to compute weights. -Should be odd. Recommended value 7 pixels -@param searchWindowSize Size in pixels of the window that is used to compute weighted average for -given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater -denoising time. Recommended value 21 pixels -@param h Parameter regulating filter strength. Big h value perfectly removes noise but also -removes image details, smaller h value preserves details but also preserves some noise - -This function expected to be applied to grayscale images. For colored images look at -fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored -image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting -image to CIELAB colorspace and then separately denoise L and AB components with different h -parameter. - */ -CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst, float h = 3, - int templateWindowSize = 7, int searchWindowSize = 21); - -/** @brief Perform image denoising using Non-local Means Denoising algorithm - with several computational -optimizations. Noise expected to be a gaussian white noise - -@param src Input 8-bit or 16-bit (only with NORM_L1) 1-channel, -2-channel, 3-channel or 4-channel image. -@param dst Output image with the same size and type as src . -@param templateWindowSize Size in pixels of the template patch that is used to compute weights. -Should be odd. Recommended value 7 pixels -@param searchWindowSize Size in pixels of the window that is used to compute weighted average for -given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater -denoising time. Recommended value 21 pixels -@param h Array of parameters regulating filter strength, either one -parameter applied to all channels or one per channel in dst. Big h value -perfectly removes noise but also removes image details, smaller h -value preserves details but also preserves some noise -@param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 - -This function expected to be applied to grayscale images. For colored images look at -fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored -image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting -image to CIELAB colorspace and then separately denoise L and AB components with different h -parameter. - */ -CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst, - const std::vector& h, - int templateWindowSize = 7, int searchWindowSize = 21, - int normType = NORM_L2); - -/** @brief Modification of fastNlMeansDenoising function for colored images - -@param src Input 8-bit 3-channel image. -@param dst Output image with the same size and type as src . -@param templateWindowSize Size in pixels of the template patch that is used to compute weights. -Should be odd. Recommended value 7 pixels -@param searchWindowSize Size in pixels of the window that is used to compute weighted average for -given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater -denoising time. Recommended value 21 pixels -@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly -removes noise but also removes image details, smaller h value preserves details but also preserves -some noise -@param hColor The same as h but for color components. For most images value equals 10 -will be enough to remove colored noise and do not distort colors - -The function converts image to CIELAB colorspace and then separately denoise L and AB components -with given h parameters using fastNlMeansDenoising function. - */ -CV_EXPORTS_W void fastNlMeansDenoisingColored( InputArray src, OutputArray dst, - float h = 3, float hColor = 3, - int templateWindowSize = 7, int searchWindowSize = 21); - -/** @brief Modification of fastNlMeansDenoising function for images sequence where consequtive images have been -captured in small period of time. For example video. This version of the function is for grayscale -images or for manual manipulation with colorspaces. For more details see - - -@param srcImgs Input 8-bit 1-channel, 2-channel, 3-channel or -4-channel images sequence. All images should have the same type and -size. -@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence -@param temporalWindowSize Number of surrounding images to use for target image denoising. Should -be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to -imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise -srcImgs[imgToDenoiseIndex] image. -@param dst Output image with the same size and type as srcImgs images. -@param templateWindowSize Size in pixels of the template patch that is used to compute weights. -Should be odd. Recommended value 7 pixels -@param searchWindowSize Size in pixels of the window that is used to compute weighted average for -given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater -denoising time. Recommended value 21 pixels -@param h Parameter regulating filter strength. Bigger h value -perfectly removes noise but also removes image details, smaller h -value preserves details but also preserves some noise - */ -CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst, - int imgToDenoiseIndex, int temporalWindowSize, - float h = 3, int templateWindowSize = 7, int searchWindowSize = 21); - -/** @brief Modification of fastNlMeansDenoising function for images sequence where consequtive images have been -captured in small period of time. For example video. This version of the function is for grayscale -images or for manual manipulation with colorspaces. For more details see - - -@param srcImgs Input 8-bit or 16-bit (only with NORM_L1) 1-channel, -2-channel, 3-channel or 4-channel images sequence. All images should -have the same type and size. -@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence -@param temporalWindowSize Number of surrounding images to use for target image denoising. Should -be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to -imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise -srcImgs[imgToDenoiseIndex] image. -@param dst Output image with the same size and type as srcImgs images. -@param templateWindowSize Size in pixels of the template patch that is used to compute weights. -Should be odd. Recommended value 7 pixels -@param searchWindowSize Size in pixels of the window that is used to compute weighted average for -given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater -denoising time. Recommended value 21 pixels -@param h Array of parameters regulating filter strength, either one -parameter applied to all channels or one per channel in dst. Big h value -perfectly removes noise but also removes image details, smaller h -value preserves details but also preserves some noise -@param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 - */ -CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst, - int imgToDenoiseIndex, int temporalWindowSize, - const std::vector& h, - int templateWindowSize = 7, int searchWindowSize = 21, - int normType = NORM_L2); - -/** @brief Modification of fastNlMeansDenoisingMulti function for colored images sequences - -@param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and -size. -@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence -@param temporalWindowSize Number of surrounding images to use for target image denoising. Should -be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to -imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise -srcImgs[imgToDenoiseIndex] image. -@param dst Output image with the same size and type as srcImgs images. -@param templateWindowSize Size in pixels of the template patch that is used to compute weights. -Should be odd. Recommended value 7 pixels -@param searchWindowSize Size in pixels of the window that is used to compute weighted average for -given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater -denoising time. Recommended value 21 pixels -@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly -removes noise but also removes image details, smaller h value preserves details but also preserves -some noise. -@param hColor The same as h but for color components. - -The function converts images to CIELAB colorspace and then separately denoise L and AB components -with given h parameters using fastNlMeansDenoisingMulti function. - */ -CV_EXPORTS_W void fastNlMeansDenoisingColoredMulti( InputArrayOfArrays srcImgs, OutputArray dst, - int imgToDenoiseIndex, int temporalWindowSize, - float h = 3, float hColor = 3, - int templateWindowSize = 7, int searchWindowSize = 21); - -/** @brief Primal-dual algorithm is an algorithm for solving special types of variational problems (that is, -finding a function to minimize some functional). As the image denoising, in particular, may be seen -as the variational problem, primal-dual algorithm then can be used to perform denoising and this is -exactly what is implemented. - -It should be noted, that this implementation was taken from the July 2013 blog entry -@cite MA13 , which also contained (slightly more general) ready-to-use source code on Python. -Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end -of July 2013 and finally it was slightly adapted by later authors. - -Although the thorough discussion and justification of the algorithm involved may be found in -@cite ChambolleEtAl, it might make sense to skim over it here, following @cite MA13 . To begin -with, we consider the 1-byte gray-level images as the functions from the rectangular domain of -pixels (it may be seen as set -\f$\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\f$ for some -\f$m,\;n\in\mathbb{N}\f$) into \f$\{0,1,\dots,255\}\f$. We shall denote the noised images as \f$f_i\f$ and with -this view, given some image \f$x\f$ of the same size, we may measure how bad it is by the formula - -\f[\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\f] - -\f$\|\|\cdot\|\|\f$ here denotes \f$L_2\f$-norm and as you see, the first addend states that we want our -image to be smooth (ideally, having zero gradient, thus being constant) and the second states that -we want our result to be close to the observations we've got. If we treat \f$x\f$ as a function, this is -exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play. - -@param observations This array should contain one or more noised versions of the image that is to -be restored. -@param result Here the denoised image will be stored. There is no need to do pre-allocation of -storage space, as it will be automatically allocated, if necessary. -@param lambda Corresponds to \f$\lambda\f$ in the formulas above. As it is enlarged, the smooth -(blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly -speaking, as it becomes smaller, the result will be more blur but more sever outliers will be -removed. -@param niters Number of iterations that the algorithm will run. Of course, as more iterations as -better, but it is hard to quantitatively refine this statement, so just use the default and -increase it if the results are poor. - */ -CV_EXPORTS_W void denoise_TVL1(const std::vector& observations,Mat& result, double lambda=1.0, int niters=30); - -//! @} photo_denoise - -//! @addtogroup photo_hdr -//! @{ - -enum { LDR_SIZE = 256 }; - -/** @brief Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range. - */ -class CV_EXPORTS_W Tonemap : public Algorithm -{ -public: - /** @brief Tonemaps image - - @param src source image - 32-bit 3-channel Mat - @param dst destination image - 32-bit 3-channel Mat with values in [0, 1] range - */ - CV_WRAP virtual void process(InputArray src, OutputArray dst) = 0; - - CV_WRAP virtual float getGamma() const = 0; - CV_WRAP virtual void setGamma(float gamma) = 0; -}; - -/** @brief Creates simple linear mapper with gamma correction - -@param gamma positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma -equal to 2.2f is suitable for most displays. -Generally gamma \> 1 brightens the image and gamma \< 1 darkens it. - */ -CV_EXPORTS_W Ptr createTonemap(float gamma = 1.0f); - -/** @brief Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in -logarithmic domain. - -Since it's a global operator the same function is applied to all the pixels, it is controlled by the -bias parameter. - -Optional saturation enhancement is possible as described in @cite FL02 . - -For more information see @cite DM03 . - */ -class CV_EXPORTS_W TonemapDrago : public Tonemap -{ -public: - - CV_WRAP virtual float getSaturation() const = 0; - CV_WRAP virtual void setSaturation(float saturation) = 0; - - CV_WRAP virtual float getBias() const = 0; - CV_WRAP virtual void setBias(float bias) = 0; -}; - -/** @brief Creates TonemapDrago object - -@param gamma gamma value for gamma correction. See createTonemap -@param saturation positive saturation enhancement value. 1.0 preserves saturation, values greater -than 1 increase saturation and values less than 1 decrease it. -@param bias value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best -results, default value is 0.85. - */ -CV_EXPORTS_W Ptr createTonemapDrago(float gamma = 1.0f, float saturation = 1.0f, float bias = 0.85f); - -/** @brief This algorithm decomposes image into two layers: base layer and detail layer using bilateral filter -and compresses contrast of the base layer thus preserving all the details. - -This implementation uses regular bilateral filter from opencv. - -Saturation enhancement is possible as in ocvTonemapDrago. - -For more information see @cite DD02 . - */ -class CV_EXPORTS_W TonemapDurand : public Tonemap -{ -public: - - CV_WRAP virtual float getSaturation() const = 0; - CV_WRAP virtual void setSaturation(float saturation) = 0; - - CV_WRAP virtual float getContrast() const = 0; - CV_WRAP virtual void setContrast(float contrast) = 0; - - CV_WRAP virtual float getSigmaSpace() const = 0; - CV_WRAP virtual void setSigmaSpace(float sigma_space) = 0; - - CV_WRAP virtual float getSigmaColor() const = 0; - CV_WRAP virtual void setSigmaColor(float sigma_color) = 0; -}; - -/** @brief Creates TonemapDurand object - -@param gamma gamma value for gamma correction. See createTonemap -@param contrast resulting contrast on logarithmic scale, i. e. log(max / min), where max and min -are maximum and minimum luminance values of the resulting image. -@param saturation saturation enhancement value. See createTonemapDrago -@param sigma_space bilateral filter sigma in color space -@param sigma_color bilateral filter sigma in coordinate space - */ -CV_EXPORTS_W Ptr -createTonemapDurand(float gamma = 1.0f, float contrast = 4.0f, float saturation = 1.0f, float sigma_space = 2.0f, float sigma_color = 2.0f); - -/** @brief This is a global tonemapping operator that models human visual system. - -Mapping function is controlled by adaptation parameter, that is computed using light adaptation and -color adaptation. - -For more information see @cite RD05 . - */ -class CV_EXPORTS_W TonemapReinhard : public Tonemap -{ -public: - CV_WRAP virtual float getIntensity() const = 0; - CV_WRAP virtual void setIntensity(float intensity) = 0; - - CV_WRAP virtual float getLightAdaptation() const = 0; - CV_WRAP virtual void setLightAdaptation(float light_adapt) = 0; - - CV_WRAP virtual float getColorAdaptation() const = 0; - CV_WRAP virtual void setColorAdaptation(float color_adapt) = 0; -}; - -/** @brief Creates TonemapReinhard object - -@param gamma gamma value for gamma correction. See createTonemap -@param intensity result intensity in [-8, 8] range. Greater intensity produces brighter results. -@param light_adapt light adaptation in [0, 1] range. If 1 adaptation is based only on pixel -value, if 0 it's global, otherwise it's a weighted mean of this two cases. -@param color_adapt chromatic adaptation in [0, 1] range. If 1 channels are treated independently, -if 0 adaptation level is the same for each channel. - */ -CV_EXPORTS_W Ptr -createTonemapReinhard(float gamma = 1.0f, float intensity = 0.0f, float light_adapt = 1.0f, float color_adapt = 0.0f); - -/** @brief This algorithm transforms image to contrast using gradients on all levels of gaussian pyramid, -transforms contrast values to HVS response and scales the response. After this the image is -reconstructed from new contrast values. - -For more information see @cite MM06 . - */ -class CV_EXPORTS_W TonemapMantiuk : public Tonemap -{ -public: - CV_WRAP virtual float getScale() const = 0; - CV_WRAP virtual void setScale(float scale) = 0; - - CV_WRAP virtual float getSaturation() const = 0; - CV_WRAP virtual void setSaturation(float saturation) = 0; -}; - -/** @brief Creates TonemapMantiuk object - -@param gamma gamma value for gamma correction. See createTonemap -@param scale contrast scale factor. HVS response is multiplied by this parameter, thus compressing -dynamic range. Values from 0.6 to 0.9 produce best results. -@param saturation saturation enhancement value. See createTonemapDrago - */ -CV_EXPORTS_W Ptr -createTonemapMantiuk(float gamma = 1.0f, float scale = 0.7f, float saturation = 1.0f); - -/** @brief The base class for algorithms that align images of the same scene with different exposures - */ -class CV_EXPORTS_W AlignExposures : public Algorithm -{ -public: - /** @brief Aligns images - - @param src vector of input images - @param dst vector of aligned images - @param times vector of exposure time values for each image - @param response 256x1 matrix with inverse camera response function for each pixel value, it should - have the same number of channels as images. - */ - CV_WRAP virtual void process(InputArrayOfArrays src, std::vector& dst, - InputArray times, InputArray response) = 0; -}; - -/** @brief This algorithm converts images to median threshold bitmaps (1 for pixels brighter than median -luminance and 0 otherwise) and than aligns the resulting bitmaps using bit operations. - -It is invariant to exposure, so exposure values and camera response are not necessary. - -In this implementation new image regions are filled with zeros. - -For more information see @cite GW03 . - */ -class CV_EXPORTS_W AlignMTB : public AlignExposures -{ -public: - CV_WRAP virtual void process(InputArrayOfArrays src, std::vector& dst, - InputArray times, InputArray response) = 0; - - /** @brief Short version of process, that doesn't take extra arguments. - - @param src vector of input images - @param dst vector of aligned images - */ - CV_WRAP virtual void process(InputArrayOfArrays src, std::vector& dst) = 0; - - /** @brief Calculates shift between two images, i. e. how to shift the second image to correspond it with the - first. - - @param img0 first image - @param img1 second image - */ - CV_WRAP virtual Point calculateShift(InputArray img0, InputArray img1) = 0; - /** @brief Helper function, that shift Mat filling new regions with zeros. - - @param src input image - @param dst result image - @param shift shift value - */ - CV_WRAP virtual void shiftMat(InputArray src, OutputArray dst, const Point shift) = 0; - /** @brief Computes median threshold and exclude bitmaps of given image. - - @param img input image - @param tb median threshold bitmap - @param eb exclude bitmap - */ - CV_WRAP virtual void computeBitmaps(InputArray img, OutputArray tb, OutputArray eb) = 0; - - CV_WRAP virtual int getMaxBits() const = 0; - CV_WRAP virtual void setMaxBits(int max_bits) = 0; - - CV_WRAP virtual int getExcludeRange() const = 0; - CV_WRAP virtual void setExcludeRange(int exclude_range) = 0; - - CV_WRAP virtual bool getCut() const = 0; - CV_WRAP virtual void setCut(bool value) = 0; -}; - -/** @brief Creates AlignMTB object - -@param max_bits logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are -usually good enough (31 and 63 pixels shift respectively). -@param exclude_range range for exclusion bitmap that is constructed to suppress noise around the -median value. -@param cut if true cuts images, otherwise fills the new regions with zeros. - */ -CV_EXPORTS_W Ptr createAlignMTB(int max_bits = 6, int exclude_range = 4, bool cut = true); - -/** @brief The base class for camera response calibration algorithms. - */ -class CV_EXPORTS_W CalibrateCRF : public Algorithm -{ -public: - /** @brief Recovers inverse camera response. - - @param src vector of input images - @param dst 256x1 matrix with inverse camera response function - @param times vector of exposure time values for each image - */ - CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0; -}; - -/** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective -function as linear system. Objective function is constructed using pixel values on the same position -in all images, extra term is added to make the result smoother. - -For more information see @cite DM97 . - */ -class CV_EXPORTS_W CalibrateDebevec : public CalibrateCRF -{ -public: - CV_WRAP virtual float getLambda() const = 0; - CV_WRAP virtual void setLambda(float lambda) = 0; - - CV_WRAP virtual int getSamples() const = 0; - CV_WRAP virtual void setSamples(int samples) = 0; - - CV_WRAP virtual bool getRandom() const = 0; - CV_WRAP virtual void setRandom(bool random) = 0; -}; - -/** @brief Creates CalibrateDebevec object - -@param samples number of pixel locations to use -@param lambda smoothness term weight. Greater values produce smoother results, but can alter the -response. -@param random if true sample pixel locations are chosen at random, otherwise the form a -rectangular grid. - */ -CV_EXPORTS_W Ptr createCalibrateDebevec(int samples = 70, float lambda = 10.0f, bool random = false); - -/** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective -function as linear system. This algorithm uses all image pixels. - -For more information see @cite RB99 . - */ -class CV_EXPORTS_W CalibrateRobertson : public CalibrateCRF -{ -public: - CV_WRAP virtual int getMaxIter() const = 0; - CV_WRAP virtual void setMaxIter(int max_iter) = 0; - - CV_WRAP virtual float getThreshold() const = 0; - CV_WRAP virtual void setThreshold(float threshold) = 0; - - CV_WRAP virtual Mat getRadiance() const = 0; -}; - -/** @brief Creates CalibrateRobertson object - -@param max_iter maximal number of Gauss-Seidel solver iterations. -@param threshold target difference between results of two successive steps of the minimization. - */ -CV_EXPORTS_W Ptr createCalibrateRobertson(int max_iter = 30, float threshold = 0.01f); - -/** @brief The base class algorithms that can merge exposure sequence to a single image. - */ -class CV_EXPORTS_W MergeExposures : public Algorithm -{ -public: - /** @brief Merges images. - - @param src vector of input images - @param dst result image - @param times vector of exposure time values for each image - @param response 256x1 matrix with inverse camera response function for each pixel value, it should - have the same number of channels as images. - */ - CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, - InputArray times, InputArray response) = 0; -}; - -/** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure -values and camera response. - -For more information see @cite DM97 . - */ -class CV_EXPORTS_W MergeDebevec : public MergeExposures -{ -public: - CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, - InputArray times, InputArray response) = 0; - CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0; -}; - -/** @brief Creates MergeDebevec object - */ -CV_EXPORTS_W Ptr createMergeDebevec(); - -/** @brief Pixels are weighted using contrast, saturation and well-exposedness measures, than images are -combined using laplacian pyramids. - -The resulting image weight is constructed as weighted average of contrast, saturation and -well-exposedness measures. - -The resulting image doesn't require tonemapping and can be converted to 8-bit image by multiplying -by 255, but it's recommended to apply gamma correction and/or linear tonemapping. - -For more information see @cite MK07 . - */ -class CV_EXPORTS_W MergeMertens : public MergeExposures -{ -public: - CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, - InputArray times, InputArray response) = 0; - /** @brief Short version of process, that doesn't take extra arguments. - - @param src vector of input images - @param dst result image - */ - CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst) = 0; - - CV_WRAP virtual float getContrastWeight() const = 0; - CV_WRAP virtual void setContrastWeight(float contrast_weiht) = 0; - - CV_WRAP virtual float getSaturationWeight() const = 0; - CV_WRAP virtual void setSaturationWeight(float saturation_weight) = 0; - - CV_WRAP virtual float getExposureWeight() const = 0; - CV_WRAP virtual void setExposureWeight(float exposure_weight) = 0; -}; - -/** @brief Creates MergeMertens object - -@param contrast_weight contrast measure weight. See MergeMertens. -@param saturation_weight saturation measure weight -@param exposure_weight well-exposedness measure weight - */ -CV_EXPORTS_W Ptr -createMergeMertens(float contrast_weight = 1.0f, float saturation_weight = 1.0f, float exposure_weight = 0.0f); - -/** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure -values and camera response. - -For more information see @cite RB99 . - */ -class CV_EXPORTS_W MergeRobertson : public MergeExposures -{ -public: - CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, - InputArray times, InputArray response) = 0; - CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0; -}; - -/** @brief Creates MergeRobertson object - */ -CV_EXPORTS_W Ptr createMergeRobertson(); - -//! @} photo_hdr - -/** @brief Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized -black-and-white photograph rendering, and in many single channel image processing applications -@cite CL12 . - -@param src Input 8-bit 3-channel image. -@param grayscale Output 8-bit 1-channel image. -@param color_boost Output 8-bit 3-channel image. - -This function is to be applied on color images. - */ -CV_EXPORTS_W void decolor( InputArray src, OutputArray grayscale, OutputArray color_boost); - -//! @addtogroup photo_clone -//! @{ - -/** @brief Image editing tasks concern either global changes (color/intensity corrections, filters, -deformations) or local changes concerned to a selection. Here we are interested in achieving local -changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless -manner. The extent of the changes ranges from slight distortions to complete replacement by novel -content @cite PM03 . - -@param src Input 8-bit 3-channel image. -@param dst Input 8-bit 3-channel image. -@param mask Input 8-bit 1 or 3-channel image. -@param p Point in dst image where object is placed. -@param blend Output image with the same size and type as dst. -@param flags Cloning method that could be one of the following: -- **NORMAL_CLONE** The power of the method is fully expressed when inserting objects with -complex outlines into a new background -- **MIXED_CLONE** The classic method, color-based selection and alpha masking might be time -consuming and often leaves an undesirable halo. Seamless cloning, even averaged with the -original image, is not effective. Mixed seamless cloning based on a loose selection proves -effective. -- **FEATURE_EXCHANGE** Feature exchange allows the user to easily replace certain features of -one object by alternative features. - */ -CV_EXPORTS_W void seamlessClone( InputArray src, InputArray dst, InputArray mask, Point p, - OutputArray blend, int flags); - -/** @brief Given an original color image, two differently colored versions of this image can be mixed -seamlessly. - -@param src Input 8-bit 3-channel image. -@param mask Input 8-bit 1 or 3-channel image. -@param dst Output image with the same size and type as src . -@param red_mul R-channel multiply factor. -@param green_mul G-channel multiply factor. -@param blue_mul B-channel multiply factor. - -Multiplication factor is between .5 to 2.5. - */ -CV_EXPORTS_W void colorChange(InputArray src, InputArray mask, OutputArray dst, float red_mul = 1.0f, - float green_mul = 1.0f, float blue_mul = 1.0f); - -/** @brief Applying an appropriate non-linear transformation to the gradient field inside the selection and -then integrating back with a Poisson solver, modifies locally the apparent illumination of an image. - -@param src Input 8-bit 3-channel image. -@param mask Input 8-bit 1 or 3-channel image. -@param dst Output image with the same size and type as src. -@param alpha Value ranges between 0-2. -@param beta Value ranges between 0-2. - -This is useful to highlight under-exposed foreground objects or to reduce specular reflections. - */ -CV_EXPORTS_W void illuminationChange(InputArray src, InputArray mask, OutputArray dst, - float alpha = 0.2f, float beta = 0.4f); - -/** @brief By retaining only the gradients at edge locations, before integrating with the Poisson solver, one -washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge -Detector is used. - -@param src Input 8-bit 3-channel image. -@param mask Input 8-bit 1 or 3-channel image. -@param dst Output image with the same size and type as src. -@param low_threshold Range from 0 to 100. -@param high_threshold Value \> 100. -@param kernel_size The size of the Sobel kernel to be used. - -**NOTE:** - -The algorithm assumes that the color of the source image is close to that of the destination. This -assumption means that when the colors don't match, the source image color gets tinted toward the -color of the destination image. - */ -CV_EXPORTS_W void textureFlattening(InputArray src, InputArray mask, OutputArray dst, - float low_threshold = 30, float high_threshold = 45, - int kernel_size = 3); - -//! @} photo_clone - -//! @addtogroup photo_render -//! @{ - -/** @brief Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing -filters are used in many different applications @cite EM11 . - -@param src Input 8-bit 3-channel image. -@param dst Output 8-bit 3-channel image. -@param flags Edge preserving filters: -- **RECURS_FILTER** = 1 -- **NORMCONV_FILTER** = 2 -@param sigma_s Range between 0 to 200. -@param sigma_r Range between 0 to 1. - */ -CV_EXPORTS_W void edgePreservingFilter(InputArray src, OutputArray dst, int flags = 1, - float sigma_s = 60, float sigma_r = 0.4f); - -/** @brief This filter enhances the details of a particular image. - -@param src Input 8-bit 3-channel image. -@param dst Output image with the same size and type as src. -@param sigma_s Range between 0 to 200. -@param sigma_r Range between 0 to 1. - */ -CV_EXPORTS_W void detailEnhance(InputArray src, OutputArray dst, float sigma_s = 10, - float sigma_r = 0.15f); - -/** @brief Pencil-like non-photorealistic line drawing - -@param src Input 8-bit 3-channel image. -@param dst1 Output 8-bit 1-channel image. -@param dst2 Output image with the same size and type as src. -@param sigma_s Range between 0 to 200. -@param sigma_r Range between 0 to 1. -@param shade_factor Range between 0 to 0.1. - */ -CV_EXPORTS_W void pencilSketch(InputArray src, OutputArray dst1, OutputArray dst2, - float sigma_s = 60, float sigma_r = 0.07f, float shade_factor = 0.02f); - -/** @brief Stylization aims to produce digital imagery with a wide variety of effects not focused on -photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low -contrast while preserving, or enhancing, high-contrast features. - -@param src Input 8-bit 3-channel image. -@param dst Output image with the same size and type as src. -@param sigma_s Range between 0 to 200. -@param sigma_r Range between 0 to 1. - */ -CV_EXPORTS_W void stylization(InputArray src, OutputArray dst, float sigma_s = 60, - float sigma_r = 0.45f); - -//! @} photo_render - -//! @} photo - -} // cv - -#ifndef DISABLE_OPENCV_24_COMPATIBILITY -#include "opencv2/photo/photo_c.h" -#endif - -#endif diff --git a/IPL/include/opencv/opencv2/photo/cuda.hpp b/IPL/include/opencv/opencv2/photo/cuda.hpp deleted file mode 100644 index aeac1fa..0000000 --- a/IPL/include/opencv/opencv2/photo/cuda.hpp +++ /dev/null @@ -1,132 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_PHOTO_CUDA_HPP__ -#define __OPENCV_PHOTO_CUDA_HPP__ - -#include "opencv2/core/cuda.hpp" - -namespace cv { namespace cuda { - -//! @addtogroup photo_denoise -//! @{ - -/** @brief Performs pure non local means denoising without any simplification, and thus it is not fast. - -@param src Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3. -@param dst Destination image. -@param h Filter sigma regulating filter strength for color. -@param search_window Size of search window. -@param block_size Size of block used for computing weights. -@param borderMode Border type. See borderInterpolate for details. BORDER_REFLECT101 , -BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now. -@param stream Stream for the asynchronous version. - -@sa - fastNlMeansDenoising - */ -CV_EXPORTS void nonLocalMeans(InputArray src, OutputArray dst, - float h, - int search_window = 21, - int block_size = 7, - int borderMode = BORDER_DEFAULT, - Stream& stream = Stream::Null()); - -/** @brief Perform image denoising using Non-local Means Denoising algorithm - with several computational -optimizations. Noise expected to be a gaussian white noise - -@param src Input 8-bit 1-channel, 2-channel or 3-channel image. -@param dst Output image with the same size and type as src . -@param h Parameter regulating filter strength. Big h value perfectly removes noise but also -removes image details, smaller h value preserves details but also preserves some noise -@param search_window Size in pixels of the window that is used to compute weighted average for -given pixel. Should be odd. Affect performance linearly: greater search_window - greater -denoising time. Recommended value 21 pixels -@param block_size Size in pixels of the template patch that is used to compute weights. Should be -odd. Recommended value 7 pixels -@param stream Stream for the asynchronous invocations. - -This function expected to be applied to grayscale images. For colored images look at -FastNonLocalMeansDenoising::labMethod. - -@sa - fastNlMeansDenoising - */ -CV_EXPORTS void fastNlMeansDenoising(InputArray src, OutputArray dst, - float h, - int search_window = 21, - int block_size = 7, - Stream& stream = Stream::Null()); - -/** @brief Modification of fastNlMeansDenoising function for colored images - -@param src Input 8-bit 3-channel image. -@param dst Output image with the same size and type as src . -@param h_luminance Parameter regulating filter strength. Big h value perfectly removes noise but -also removes image details, smaller h value preserves details but also preserves some noise -@param photo_render float The same as h but for color components. For most images value equals 10 will be -enough to remove colored noise and do not distort colors -@param search_window Size in pixels of the window that is used to compute weighted average for -given pixel. Should be odd. Affect performance linearly: greater search_window - greater -denoising time. Recommended value 21 pixels -@param block_size Size in pixels of the template patch that is used to compute weights. Should be -odd. Recommended value 7 pixels -@param stream Stream for the asynchronous invocations. - -The function converts image to CIELAB colorspace and then separately denoise L and AB components -with given h parameters using FastNonLocalMeansDenoising::simpleMethod function. - -@sa - fastNlMeansDenoisingColored - */ -CV_EXPORTS void fastNlMeansDenoisingColored(InputArray src, OutputArray dst, - float h_luminance, float photo_render, - int search_window = 21, - int block_size = 7, - Stream& stream = Stream::Null()); - -//! @} photo - -}} // namespace cv { namespace cuda { - -#endif /* __OPENCV_PHOTO_CUDA_HPP__ */ diff --git a/IPL/include/opencv/opencv2/photo/photo.hpp b/IPL/include/opencv/opencv2/photo/photo.hpp deleted file mode 100644 index 8af5e9f..0000000 --- a/IPL/include/opencv/opencv2/photo/photo.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/photo.hpp" diff --git a/IPL/include/opencv/opencv2/photo/photo_c.h b/IPL/include/opencv/opencv2/photo/photo_c.h deleted file mode 100644 index 07ca9b3..0000000 --- a/IPL/include/opencv/opencv2/photo/photo_c.h +++ /dev/null @@ -1,74 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_PHOTO_C_H__ -#define __OPENCV_PHOTO_C_H__ - -#include "opencv2/core/core_c.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @addtogroup photo_c - @{ - */ - -/* Inpainting algorithms */ -enum InpaintingModes -{ - CV_INPAINT_NS =0, - CV_INPAINT_TELEA =1 -}; - - -/* Inpaints the selected region in the image */ -CVAPI(void) cvInpaint( const CvArr* src, const CvArr* inpaint_mask, - CvArr* dst, double inpaintRange, int flags ); - -/** @} */ - -#ifdef __cplusplus -} //extern "C" -#endif - -#endif //__OPENCV_PHOTO_C_H__ diff --git a/IPL/include/opencv/opencv2/plot.hpp b/IPL/include/opencv/opencv2/plot.hpp deleted file mode 100644 index 8243985..0000000 --- a/IPL/include/opencv/opencv2/plot.hpp +++ /dev/null @@ -1,86 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009-2012, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ -//################################################################################ -// -// Created by Nuno Moutinho -// -//################################################################################ - -#ifndef _OPENCV_PLOT_H_ -#define _OPENCV_PLOT_H_ -#ifdef __cplusplus - -#include - -/** -@defgroup plot Plot function for Mat data -*/ - -namespace cv -{ - namespace plot - { - class CV_EXPORTS_W Plot2d : public Algorithm - { - public: - - CV_WRAP virtual void setMinX(double _plotMinX) = 0; - CV_WRAP virtual void setMinY(double _plotMinY) = 0; - CV_WRAP virtual void setMaxX(double _plotMaxX) = 0; - CV_WRAP virtual void setMaxY(double _plotMaxY) = 0; - CV_WRAP virtual void setPlotLineWidth(int _plotLineWidth) = 0; - CV_WRAP virtual void setPlotLineColor(Scalar _plotLineColor) = 0; - CV_WRAP virtual void setPlotBackgroundColor(Scalar _plotBackgroundColor) = 0; - CV_WRAP virtual void setPlotAxisColor(Scalar _plotAxisColor) = 0; - CV_WRAP virtual void setPlotGridColor(Scalar _plotGridColor) = 0; - CV_WRAP virtual void setPlotTextColor(Scalar _plotTextColor) = 0; - CV_WRAP virtual void setPlotSize(int _plotSizeWidth, int _plotSizeHeight) = 0; - CV_WRAP virtual void render(Mat &_plotResult) = 0; - }; - - CV_EXPORTS_W Ptr createPlot2d(Mat data); - CV_EXPORTS_W Ptr createPlot2d(Mat dataX, Mat dataY); - } -} - -#endif -#endif diff --git a/IPL/include/opencv/opencv2/reg/map.hpp b/IPL/include/opencv/opencv2/reg/map.hpp deleted file mode 100644 index 26b29e3..0000000 --- a/IPL/include/opencv/opencv2/reg/map.hpp +++ /dev/null @@ -1,175 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef MAP_H_ -#define MAP_H_ - -#include // Basic OpenCV structures (cv::Mat, Scalar) - -/** @defgroup reg Image Registration - -The Registration module implements parametric image registration. The implemented method is direct -alignment, that is, it uses directly the pixel values for calculating the registration between a -pair of images, as opposed to feature-based registration. The implementation follows essentially the -corresponding part of @cite Szeliski06 . - -Feature based methods have some advantages over pixel based methods when we are trying to register -pictures that have been shoot under different lighting conditions or exposition times, or when the -images overlap only partially. On the other hand, the main advantage of pixel-based methods when -compared to feature based methods is their better precision for some pictures (those shoot under -similar lighting conditions and that have a significative overlap), due to the fact that we are -using all the information available in the image, which allows us to achieve subpixel accuracy. This -is particularly important for certain applications like multi-frame denoising or super-resolution. - -In fact, pixel and feature registration methods can complement each other: an application could -first obtain a coarse registration using features and then refine the registration using a pixel -based method on the overlapping area of the images. The code developed allows this use case. - -The module implements classes derived from the abstract classes cv::reg::Map or cv::reg::Mapper. The -former models a coordinate transformation between two reference frames, while the later encapsulates -a way of invoking a method that calculates a Map between two images. Although the objective has been -to implement pixel based methods, the module can be extended to support other methods that can -calculate transformations between images (feature methods, optical flow, etc.). - -Each class derived from Map implements a motion model, as follows: - -- MapShift: Models a simple translation -- MapAffine: Models an affine transformation -- MapProjec: Models a projective transformation - -MapProject can also be used to model affine motion or translations, but some operations on it are -more costly, and that is the reason for defining the other two classes. - -The classes derived from Mapper are - -- MapperGradShift: Gradient based alignment for calculating translations. It produces a MapShift - (two parameters that correspond to the shift vector). -- MapperGradEuclid: Gradient based alignment for euclidean motions, that is, rotations and - translations. It calculates three parameters (angle and shift vector), although the result is - stored in a MapAffine object for convenience. -- MapperGradSimilar: Gradient based alignment for calculating similarities, which adds scaling to - the euclidean motion. It calculates four parameters (two for the anti-symmetric matrix and two - for the shift vector), although the result is stored in a MapAffine object for better - convenience. -- MapperGradAffine: Gradient based alignment for an affine motion model. The number of parameters - is six and the result is stored in a MapAffine object. -- MapperGradProj: Gradient based alignment for calculating projective transformations. The number - of parameters is eight and the result is stored in a MapProject object. -- MapperPyramid: It implements hyerarchical motion estimation using a Gaussian pyramid. Its - constructor accepts as argument any other object that implements the Mapper interface, and it is - that mapper the one called by MapperPyramid for each scale of the pyramid. - -If the motion between the images is not very small, the normal way of using these classes is to -create a MapperGrad\* object and use it as input to create a MapperPyramid, which in turn is called -to perform the calculation. However, if the motion between the images is small enough, we can use -directly the MapperGrad\* classes. Another possibility is to use first a feature based method to -perform a coarse registration and then do a refinement through MapperPyramid or directly a -MapperGrad\* object. The "calculate" method of the mappers accepts an initial estimation of the -motion as input. - -When deciding which MapperGrad to use we must take into account that mappers with more parameters -can handle more complex motions, but involve more calculations and are therefore slower. Also, if we -are confident on the motion model that is followed by the sequence, increasing the number of -parameters beyond what we need will decrease the accuracy: it is better to use the least number of -degrees of freedom that we can. - -In the module tests there are examples that show how to register a pair of images using any of the -implemented mappers. -*/ - -namespace cv { -namespace reg { - -//! @addtogroup reg -//! @{ - -/** @brief Base class for modelling a Map between two images. - -The class is only used to define the common interface for any possible map. - */ -class CV_EXPORTS Map -{ -public: - /*! - * Virtual destructor - */ - virtual ~Map(void); - - /*! - * Warps image to a new coordinate frame. The calculation is img2(x)=img1(T^{-1}(x)), as we - * have to apply the inverse transformation to the points to move them to were the values - * of img2 are. - * \param[in] img1 Original image - * \param[out] img2 Warped image - */ - virtual void warp(const cv::Mat& img1, cv::Mat& img2) const; - - /*! - * Warps image to a new coordinate frame. The calculation is img2(x)=img1(T(x)), so in fact - * this is the inverse warping as we are taking the value of img1 with the forward - * transformation of the points. - * \param[in] img1 Original image - * \param[out] img2 Warped image - */ - virtual void inverseWarp(const cv::Mat& img1, cv::Mat& img2) const = 0; - - /*! - * Calculates the inverse map - * \return Inverse map - */ - virtual cv::Ptr inverseMap(void) const = 0; - - /*! - * Changes the map composing the current transformation with the one provided in the call. - * The order is first the current transformation, then the input argument. - * \param[in] map Transformation to compose with. - */ - virtual void compose(const Map& map) = 0; - - /*! - * Scales the map by a given factor as if the coordinates system is expanded/compressed - * by that factor. - * \param[in] factor Expansion if bigger than one, compression if smaller than one - */ - virtual void scale(double factor) = 0; -}; - -//! @} - -}} // namespace cv::reg - -#endif // MAP_H_ diff --git a/IPL/include/opencv/opencv2/reg/mapaffine.hpp b/IPL/include/opencv/opencv2/reg/mapaffine.hpp deleted file mode 100644 index 1c91326..0000000 --- a/IPL/include/opencv/opencv2/reg/mapaffine.hpp +++ /dev/null @@ -1,105 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef MAPAFFINE_H_ -#define MAPAFFINE_H_ - -#include "map.hpp" - -namespace cv { -namespace reg { - -//! @addtogroup reg -//! @{ - -/*! - * Defines an affine transformation - */ -class CV_EXPORTS MapAffine : public Map -{ -public: - /*! - * Default constructor builds an identity map - */ - MapAffine(void); - - /*! - * Constructor providing explicit values - * \param[in] linTr Linear part of the affine transformation - * \param[in] shift Displacement part of the affine transformation - */ - MapAffine(const cv::Matx& linTr, const cv::Vec& shift); - - /*! - * Destructor - */ - ~MapAffine(void); - - void inverseWarp(const cv::Mat& img1, cv::Mat& img2) const; - - cv::Ptr inverseMap(void) const; - - void compose(const Map& map); - - void scale(double factor); - - /*! - * Return linear part of the affine transformation - * \return Linear part of the affine transformation - */ - const cv::Matx& getLinTr() const { - return linTr_; - } - - /*! - * Return displacement part of the affine transformation - * \return Displacement part of the affine transformation - */ - const cv::Vec& getShift() const { - return shift_; - } - -private: - cv::Matx linTr_; - cv::Vec shift_; -}; - -//! @} - -}} // namespace cv::reg - -#endif // MAPAFFINE_H_ diff --git a/IPL/include/opencv/opencv2/reg/mapper.hpp b/IPL/include/opencv/opencv2/reg/mapper.hpp deleted file mode 100644 index 8abadd1..0000000 --- a/IPL/include/opencv/opencv2/reg/mapper.hpp +++ /dev/null @@ -1,113 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef MAPPER_H_ -#define MAPPER_H_ - -#include // Basic OpenCV structures (cv::Mat, Scalar) -#include "map.hpp" - -namespace cv { -namespace reg { - -//! @addtogroup reg -//! @{ - -/** @brief Base class for modelling an algorithm for calculating a - -The class is only used to define the common interface for any possible mapping algorithm. - */ -class CV_EXPORTS Mapper -{ -public: - virtual ~Mapper(void) {} - - /* - * Calculate mapping between two images - * \param[in] img1 Reference image - * \param[in] img2 Warped image - * \param[in,out] res Map from img1 to img2, stored in a smart pointer. If present as input, - * it is an initial rough estimation that the mapper will try to refine. - */ - virtual void calculate(const cv::Mat& img1, const cv::Mat& img2, cv::Ptr& res) const = 0; - - /* - * Returns a map compatible with the Mapper class - * \return Pointer to identity Map - */ - virtual cv::Ptr getMap(void) const = 0; - -protected: - /* - * Calculates gradient and difference between images - * \param[in] img1 Image one - * \param[in] img2 Image two - * \param[out] Ix Gradient x-coordinate - * \param[out] Iy Gradient y-coordinate - * \param[out] It Difference of images - */ - void gradient(const cv::Mat& img1, const cv::Mat& img2, - cv::Mat& Ix, cv::Mat& Iy, cv::Mat& It) const; - - /* - * Fills matrices with pixel coordinates of an image - * \param[in] img Image - * \param[out] grid_r Row (y-coordinate) - * \param[out] grid_c Column (x-coordinate) - */ - void grid(const Mat& img, Mat& grid_r, Mat& grid_c) const; - - /* - * Per-element square of a matrix - * \param[in] mat1 Input matrix - * \return mat1[i,j]^2 - */ - cv::Mat sqr(const cv::Mat& mat1) const - { - cv::Mat res; - res.create(mat1.size(), mat1.type()); - res = mat1.mul(mat1); - return res; - } -}; - -//! @} - -}} // namespace cv::reg - -#endif // MAPPER_H_ - diff --git a/IPL/include/opencv/opencv2/reg/mappergradaffine.hpp b/IPL/include/opencv/opencv2/reg/mappergradaffine.hpp deleted file mode 100644 index 08d5397..0000000 --- a/IPL/include/opencv/opencv2/reg/mappergradaffine.hpp +++ /dev/null @@ -1,67 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef MAPPERGRADAFFINE_H_ -#define MAPPERGRADAFFINE_H_ - -#include "mapper.hpp" - -namespace cv { -namespace reg { - -//! @addtogroup reg -//! @{ - -/*! - * Mapper for affine motion - */ -class CV_EXPORTS MapperGradAffine: public Mapper -{ -public: - MapperGradAffine(void); - ~MapperGradAffine(void); - - virtual void calculate(const cv::Mat& img1, const cv::Mat& img2, cv::Ptr& res) const; - - cv::Ptr getMap(void) const; -}; - -//! @} - -}} // namespace cv::reg - -#endif // MAPPERGRADAFFINE_H_ diff --git a/IPL/include/opencv/opencv2/reg/mappergradeuclid.hpp b/IPL/include/opencv/opencv2/reg/mappergradeuclid.hpp deleted file mode 100644 index 29c49cb..0000000 --- a/IPL/include/opencv/opencv2/reg/mappergradeuclid.hpp +++ /dev/null @@ -1,67 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef MAPPERGRADEUCLID_H_ -#define MAPPERGRADEUCLID_H_ - -#include "mapper.hpp" - -namespace cv { -namespace reg { - -//! @addtogroup reg -//! @{ - -/*! - * Mapper for euclidean motion: rotation plus shift - */ -class CV_EXPORTS MapperGradEuclid: public Mapper -{ -public: - MapperGradEuclid(void); - ~MapperGradEuclid(void); - - virtual void calculate(const cv::Mat& img1, const cv::Mat& img2, cv::Ptr& res) const; - - cv::Ptr getMap(void) const; -}; - -//! @} - -}} // namespace cv::reg - -#endif // MAPPERGRADEUCLID_H_ diff --git a/IPL/include/opencv/opencv2/reg/mappergradproj.hpp b/IPL/include/opencv/opencv2/reg/mappergradproj.hpp deleted file mode 100644 index f1721e8..0000000 --- a/IPL/include/opencv/opencv2/reg/mappergradproj.hpp +++ /dev/null @@ -1,67 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef MAPPERGRADPROJ_H_ -#define MAPPERGRADPROJ_H_ - -#include "mapper.hpp" - -namespace cv { -namespace reg { - -//! @addtogroup reg -//! @{ - -/*! - * Gradient mapper for a projective transformation - */ -class CV_EXPORTS MapperGradProj: public Mapper -{ -public: - MapperGradProj(void); - ~MapperGradProj(void); - - virtual void calculate(const cv::Mat& img1, const cv::Mat& img2, cv::Ptr& res) const; - - cv::Ptr getMap(void) const; -}; - -//! @} - -}} // namespace cv::reg - -#endif // MAPPERGRADPROJ_H_ diff --git a/IPL/include/opencv/opencv2/reg/mappergradshift.hpp b/IPL/include/opencv/opencv2/reg/mappergradshift.hpp deleted file mode 100644 index a9f75b3..0000000 --- a/IPL/include/opencv/opencv2/reg/mappergradshift.hpp +++ /dev/null @@ -1,67 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef MAPPERGRADSHIFT_H_ -#define MAPPERGRADSHIFT_H_ - -#include "mapper.hpp" - -namespace cv { -namespace reg { - -//! @addtogroup reg -//! @{ - -/*! - * Gradient mapper for a translation - */ -class CV_EXPORTS MapperGradShift: public Mapper -{ -public: - MapperGradShift(void); - virtual ~MapperGradShift(void); - - virtual void calculate(const cv::Mat& img1, const cv::Mat& img2, cv::Ptr& res) const; - - cv::Ptr getMap(void) const; -}; - -//! @} - -}} // namespace cv::reg - -#endif // MAPPERGRADSHIFT_H_ diff --git a/IPL/include/opencv/opencv2/reg/mappergradsimilar.hpp b/IPL/include/opencv/opencv2/reg/mappergradsimilar.hpp deleted file mode 100644 index ea45ab9..0000000 --- a/IPL/include/opencv/opencv2/reg/mappergradsimilar.hpp +++ /dev/null @@ -1,67 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef MAPPERGRADSIMILAR_H_ -#define MAPPERGRADSIMILAR_H_ - -#include "mapper.hpp" - -namespace cv { -namespace reg { - -//! @addtogroup reg -//! @{ - -/*! - * Calculates a similarity transformation between to images (scale, rotation, and shift) - */ -class CV_EXPORTS MapperGradSimilar: public Mapper -{ -public: - MapperGradSimilar(void); - ~MapperGradSimilar(void); - - virtual void calculate(const cv::Mat& img1, const cv::Mat& img2, cv::Ptr& res) const; - - cv::Ptr getMap(void) const; -}; - -//! @} - -}} // namespace cv::reg - -#endif // MAPPERGRADSIMILAR_H_ diff --git a/IPL/include/opencv/opencv2/reg/mapperpyramid.hpp b/IPL/include/opencv/opencv2/reg/mapperpyramid.hpp deleted file mode 100644 index 33440bd..0000000 --- a/IPL/include/opencv/opencv2/reg/mapperpyramid.hpp +++ /dev/null @@ -1,78 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef MAPPERPYRAMID_H_ -#define MAPPERPYRAMID_H_ - -#include "mapper.hpp" - - -namespace cv { -namespace reg { - -//! @addtogroup reg -//! @{ - -/*! - * Calculates a map using a gaussian pyramid - */ -class CV_EXPORTS MapperPyramid: public Mapper -{ -public: - /* - * Constructor - * \param[in] baseMapper Base mapper used for the refinements - */ - MapperPyramid(const Mapper& baseMapper); - - void calculate(const cv::Mat& img1, const cv::Mat& img2, cv::Ptr& res) const; - - cv::Ptr getMap(void) const; - - unsigned numLev_; /*!< Number of levels of the pyramid */ - unsigned numIterPerScale_; /*!< Number of iterations at a given scale of the pyramid */ - -private: - MapperPyramid& operator=(const MapperPyramid&); - const Mapper& baseMapper_; /*!< Mapper used in inner level */ -}; - -//! @} - -}} // namespace cv::reg - -#endif // MAPPERPYRAMID_H_ diff --git a/IPL/include/opencv/opencv2/reg/mapprojec.hpp b/IPL/include/opencv/opencv2/reg/mapprojec.hpp deleted file mode 100644 index 57ef146..0000000 --- a/IPL/include/opencv/opencv2/reg/mapprojec.hpp +++ /dev/null @@ -1,105 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef MAPPROJEC_H_ -#define MAPPROJEC_H_ - -#include "map.hpp" - - -namespace cv { -namespace reg { - -//! @addtogroup reg -//! @{ - -/*! - * Defines an transformation that consists on a projective transformation - */ -class CV_EXPORTS MapProjec : public Map -{ -public: - /*! - * Default constructor builds an identity map - */ - MapProjec(void); - - /*! - * Constructor providing explicit values - * \param[in] projTr Projective transformation - */ - MapProjec(const cv::Matx& projTr); - - /*! - * Destructor - */ - ~MapProjec(void); - - void inverseWarp(const cv::Mat& img1, cv::Mat& img2) const; - - cv::Ptr inverseMap(void) const; - - void compose(const Map& map); - - void scale(double factor); - - /*! - * Returns projection matrix - * \return Projection matrix - */ - const cv::Matx& getProjTr() const { - return projTr_; - } - - /*! - * Normalizes object's homography - */ - void normalize(void) { - double z = 1./projTr_(2, 2); - for(size_t v_i = 0; v_i < sizeof(projTr_.val)/sizeof(projTr_.val[0]); ++v_i) - projTr_.val[v_i] *= z; - } - -private: - cv::Matx projTr_; /*< Projection matrix */ -}; - -//! @} - -}} // namespace cv::reg - -#endif // MAPPROJEC_H_ diff --git a/IPL/include/opencv/opencv2/reg/mapshift.hpp b/IPL/include/opencv/opencv2/reg/mapshift.hpp deleted file mode 100644 index e5f54a4..0000000 --- a/IPL/include/opencv/opencv2/reg/mapshift.hpp +++ /dev/null @@ -1,96 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef MAPSHIFT_H_ -#define MAPSHIFT_H_ - -#include "map.hpp" - - -namespace cv { -namespace reg { - -//! @addtogroup reg -//! @{ - -/*! - * Defines an transformation that consists on a simple displacement - */ -class CV_EXPORTS MapShift : public Map -{ -public: - /*! - * Default constructor builds an identity map - */ - MapShift(void); - - /*! - * Constructor providing explicit values - * \param[in] shift Displacement - */ - MapShift(const cv::Vec& shift); - - /*! - * Destructor - */ - ~MapShift(void); - - void inverseWarp(const cv::Mat& img1, cv::Mat& img2) const; - - cv::Ptr inverseMap(void) const; - - void compose(const Map& map); - - void scale(double factor); - - /*! - * Return displacement - * \return Displacement - */ - const cv::Vec& getShift() const { - return shift_; - } - -private: - cv::Vec shift_; /*< Displacement */ -}; - -//! @} - -}} // namespace cv::reg - -#endif // MAPSHIFT_H_ diff --git a/IPL/include/opencv/opencv2/rgbd.hpp b/IPL/include/opencv/opencv2/rgbd.hpp deleted file mode 100644 index b25bd3d..0000000 --- a/IPL/include/opencv/opencv2/rgbd.hpp +++ /dev/null @@ -1,1049 +0,0 @@ -/* - * Software License Agreement (BSD License) - * - * Copyright (c) 2009, Willow Garage, Inc. - * All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above - * copyright notice, this list of conditions and the following - * disclaimer in the documentation and/or other materials provided - * with the distribution. - * * Neither the name of Willow Garage, Inc. nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS - * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE - * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, - * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER - * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT - * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN - * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE - * POSSIBILITY OF SUCH DAMAGE. - * - */ - -#ifndef __OPENCV_RGBD_HPP__ -#define __OPENCV_RGBD_HPP__ - -#ifdef __cplusplus - -#include -#include - -/** @defgroup rgbd RGB-Depth Processing -*/ - -namespace cv -{ -namespace rgbd -{ - -//! @addtogroup rgbd -//! @{ - - /** Checks if the value is a valid depth. For CV_16U or CV_16S, the convention is to be invalid if it is - * a limit. For a float/double, we just check if it is a NaN - * @param depth the depth to check for validity - */ - CV_EXPORTS - inline bool - isValidDepth(const float & depth) - { - return !cvIsNaN(depth); - } - CV_EXPORTS - inline bool - isValidDepth(const double & depth) - { - return !cvIsNaN(depth); - } - CV_EXPORTS - inline bool - isValidDepth(const short int & depth) - { - return (depth != std::numeric_limits::min()) && (depth != std::numeric_limits::max()); - } - CV_EXPORTS - inline bool - isValidDepth(const unsigned short int & depth) - { - return (depth != std::numeric_limits::min()) - && (depth != std::numeric_limits::max()); - } - CV_EXPORTS - inline bool - isValidDepth(const int & depth) - { - return (depth != std::numeric_limits::min()) && (depth != std::numeric_limits::max()); - } - CV_EXPORTS - inline bool - isValidDepth(const unsigned int & depth) - { - return (depth != std::numeric_limits::min()) && (depth != std::numeric_limits::max()); - } - - /** Object that can compute the normals in an image. - * It is an object as it can cache data for speed efficiency - * The implemented methods are either: - * - FALS (the fastest) and SRI from - * ``Fast and Accurate Computation of Surface Normals from Range Images`` - * by H. Badino, D. Huber, Y. Park and T. Kanade - * - the normals with bilateral filtering on a depth image from - * ``Gradient Response Maps for Real-Time Detection of Texture-Less Objects`` - * by S. Hinterstoisser, C. Cagniart, S. Ilic, P. Sturm, N. Navab, P. Fua, and V. Lepetit - */ - class CV_EXPORTS RgbdNormals: public Algorithm - { - public: - enum RGBD_NORMALS_METHOD - { - RGBD_NORMALS_METHOD_FALS, RGBD_NORMALS_METHOD_LINEMOD, RGBD_NORMALS_METHOD_SRI - }; - - RgbdNormals() - : - rows_(0), - cols_(0), - depth_(0), - K_(Mat()), - window_size_(0), - method_(RGBD_NORMALS_METHOD_FALS), - rgbd_normals_impl_(0) - { - } - - /** Constructor - * @param rows the number of rows of the depth image normals will be computed on - * @param cols the number of cols of the depth image normals will be computed on - * @param depth the depth of the normals (only CV_32F or CV_64F) - * @param K the calibration matrix to use - * @param window_size the window size to compute the normals: can only be 1,3,5 or 7 - * @param method one of the methods to use: RGBD_NORMALS_METHOD_SRI, RGBD_NORMALS_METHOD_FALS - */ - RgbdNormals(int rows, int cols, int depth, InputArray K, int window_size = 5, int method = - RGBD_NORMALS_METHOD_FALS); - - ~RgbdNormals(); - - /** Given a set of 3d points in a depth image, compute the normals at each point. - * @param points a rows x cols x 3 matrix of CV_32F/CV64F or a rows x cols x 1 CV_U16S - * @param normals a rows x cols x 3 matrix - */ - void - operator()(InputArray points, OutputArray normals) const; - - /** Initializes some data that is cached for later computation - * If that function is not called, it will be called the first time normals are computed - */ - void - initialize() const; - - int getRows() const - { - return rows_; - } - void setRows(int val) - { - rows_ = val; - } - int getCols() const - { - return cols_; - } - void setCols(int val) - { - cols_ = val; - } - int getWindowSize() const - { - return window_size_; - } - void setWindowSize(int val) - { - window_size_ = val; - } - int getDepth() const - { - return depth_; - } - void setDepth(int val) - { - depth_ = val; - } - cv::Mat getK() const - { - return K_; - } - void setK(const cv::Mat &val) - { - K_ = val; - } - int getMethod() const - { - return method_; - } - void setMethod(int val) - { - method_ = val; - } - - protected: - void - initialize_normals_impl(int rows, int cols, int depth, const Mat & K, int window_size, int method) const; - - int rows_, cols_, depth_; - Mat K_; - int window_size_; - int method_; - mutable void* rgbd_normals_impl_; - }; - - /** Object that can clean a noisy depth image - */ - class CV_EXPORTS DepthCleaner: public Algorithm - { - public: - /** NIL method is from - * ``Modeling Kinect Sensor Noise for Improved 3d Reconstruction and Tracking`` - * by C. Nguyen, S. Izadi, D. Lovel - */ - enum DEPTH_CLEANER_METHOD - { - DEPTH_CLEANER_NIL - }; - - DepthCleaner() - : - depth_(0), - window_size_(0), - method_(DEPTH_CLEANER_NIL), - depth_cleaner_impl_(0) - { - } - - /** Constructor - * @param depth the depth of the normals (only CV_32F or CV_64F) - * @param window_size the window size to compute the normals: can only be 1,3,5 or 7 - * @param method one of the methods to use: RGBD_NORMALS_METHOD_SRI, RGBD_NORMALS_METHOD_FALS - */ - DepthCleaner(int depth, int window_size = 5, int method = DEPTH_CLEANER_NIL); - - ~DepthCleaner(); - - /** Given a set of 3d points in a depth image, compute the normals at each point. - * @param points a rows x cols x 3 matrix of CV_32F/CV64F or a rows x cols x 1 CV_U16S - * @param depth a rows x cols matrix of the cleaned up depth - */ - void - operator()(InputArray points, OutputArray depth) const; - - /** Initializes some data that is cached for later computation - * If that function is not called, it will be called the first time normals are computed - */ - void - initialize() const; - - int getWindowSize() const - { - return window_size_; - } - void setWindowSize(int val) - { - window_size_ = val; - } - int getDepth() const - { - return depth_; - } - void setDepth(int val) - { - depth_ = val; - } - int getMethod() const - { - return method_; - } - void setMethod(int val) - { - method_ = val; - } - - protected: - void - initialize_cleaner_impl() const; - - int depth_; - int window_size_; - int method_; - mutable void* depth_cleaner_impl_; - }; - - - /** Registers depth data to an external camera - * Registration is performed by creating a depth cloud, transforming the cloud by - * the rigid body transformation between the cameras, and then projecting the - * transformed points into the RGB camera. - * - * uv_rgb = K_rgb * [R | t] * z * inv(K_ir) * uv_ir - * - * Currently does not check for negative depth values. - * - * @param unregisteredCameraMatrix the camera matrix of the depth camera - * @param registeredCameraMatrix the camera matrix of the external camera - * @param registeredDistCoeffs the distortion coefficients of the external camera - * @param Rt the rigid body transform between the cameras. Transforms points from depth camera frame to external camera frame. - * @param unregisteredDepth the input depth data - * @param outputImagePlaneSize the image plane dimensions of the external camera (width, height) - * @param registeredDepth the result of transforming the depth into the external camera - * @param depthDilation whether or not the depth is dilated to avoid holes and occlusion errors (optional) - */ - CV_EXPORTS - void - registerDepth(InputArray unregisteredCameraMatrix, InputArray registeredCameraMatrix, InputArray registeredDistCoeffs, - InputArray Rt, InputArray unregisteredDepth, const Size& outputImagePlaneSize, - OutputArray registeredDepth, bool depthDilation=false); - - /** - * @param depth the depth image - * @param in_K - * @param in_points the list of xy coordinates - * @param points3d the resulting 3d points - */ - CV_EXPORTS - void - depthTo3dSparse(InputArray depth, InputArray in_K, InputArray in_points, OutputArray points3d); - - /** Converts a depth image to an organized set of 3d points. - * The coordinate system is x pointing left, y down and z away from the camera - * @param depth the depth image (if given as short int CV_U, it is assumed to be the depth in millimeters - * (as done with the Microsoft Kinect), otherwise, if given as CV_32F or CV_64F, it is assumed in meters) - * @param K The calibration matrix - * @param points3d the resulting 3d points. They are of depth the same as `depth` if it is CV_32F or CV_64F, and the - * depth of `K` if `depth` is of depth CV_U - * @param mask the mask of the points to consider (can be empty) - */ - CV_EXPORTS - void - depthTo3d(InputArray depth, InputArray K, OutputArray points3d, InputArray mask = noArray()); - - /** If the input image is of type CV_16UC1 (like the Kinect one), the image is converted to floats, divided - * by 1000 to get a depth in meters, and the values 0 are converted to std::numeric_limits::quiet_NaN() - * Otherwise, the image is simply converted to floats - * @param in the depth image (if given as short int CV_U, it is assumed to be the depth in millimeters - * (as done with the Microsoft Kinect), it is assumed in meters) - * @param depth the desired output depth (floats or double) - * @param out The rescaled float depth image - */ - CV_EXPORTS - void - rescaleDepth(InputArray in, int depth, OutputArray out); - - /** Object that can compute planes in an image - */ - class CV_EXPORTS RgbdPlane: public Algorithm - { - public: - enum RGBD_PLANE_METHOD - { - RGBD_PLANE_METHOD_DEFAULT - }; - - RgbdPlane(RGBD_PLANE_METHOD method = RGBD_PLANE_METHOD_DEFAULT) - : - method_(method), - block_size_(40), - min_size_(block_size_*block_size_), - threshold_(0.01), - sensor_error_a_(0), - sensor_error_b_(0), - sensor_error_c_(0) - { - } - - /** Find The planes in a depth image - * @param points3d the 3d points organized like the depth image: rows x cols with 3 channels - * @param normals the normals for every point in the depth image - * @param mask An image where each pixel is labeled with the plane it belongs to - * and 255 if it does not belong to any plane - * @param plane_coefficients the coefficients of the corresponding planes (a,b,c,d) such that ax+by+cz+d=0, norm(a,b,c)=1 - * and c < 0 (so that the normal points towards the camera) - */ - void - operator()(InputArray points3d, InputArray normals, OutputArray mask, - OutputArray plane_coefficients); - - /** Find The planes in a depth image but without doing a normal check, which is faster but less accurate - * @param points3d the 3d points organized like the depth image: rows x cols with 3 channels - * @param mask An image where each pixel is labeled with the plane it belongs to - * and 255 if it does not belong to any plane - * @param plane_coefficients the coefficients of the corresponding planes (a,b,c,d) such that ax+by+cz+d=0 - */ - void - operator()(InputArray points3d, OutputArray mask, OutputArray plane_coefficients); - - int getBlockSize() const - { - return block_size_; - } - void setBlockSize(int val) - { - block_size_ = val; - } - int getMinSize() const - { - return min_size_; - } - void setMinSize(int val) - { - min_size_ = val; - } - int getMethod() const - { - return method_; - } - void setMethod(int val) - { - method_ = val; - } - double getThreshold() const - { - return threshold_; - } - void setThreshold(double val) - { - threshold_ = val; - } - double getSensorErrorA() const - { - return sensor_error_a_; - } - void setSensorErrorA(double val) - { - sensor_error_a_ = val; - } - double getSensorErrorB() const - { - return sensor_error_b_; - } - void setSensorErrorB(double val) - { - sensor_error_b_ = val; - } - double getSensorErrorC() const - { - return sensor_error_c_; - } - void setSensorErrorC(double val) - { - sensor_error_c_ = val; - } - - private: - /** The method to use to compute the planes */ - int method_; - /** The size of the blocks to look at for a stable MSE */ - int block_size_; - /** The minimum size of a cluster to be considered a plane */ - int min_size_; - /** How far a point can be from a plane to belong to it (in meters) */ - double threshold_; - /** coefficient of the sensor error with respect to the. All 0 by default but you want a=0.0075 for a Kinect */ - double sensor_error_a_, sensor_error_b_, sensor_error_c_; - }; - - /** Object that contains a frame data. - */ - struct CV_EXPORTS RgbdFrame - { - RgbdFrame(); - RgbdFrame(const Mat& image, const Mat& depth, const Mat& mask=Mat(), const Mat& normals=Mat(), int ID=-1); - virtual ~RgbdFrame(); - - virtual void - release(); - - int ID; - Mat image; - Mat depth; - Mat mask; - Mat normals; - }; - - /** Object that contains a frame data that is possibly needed for the Odometry. - * It's used for the efficiency (to pass precomputed/cached data of the frame that participates - * in the Odometry processing several times). - */ - struct CV_EXPORTS OdometryFrame : public RgbdFrame - { - /** These constants are used to set a type of cache which has to be prepared depending on the frame role: - * srcFrame or dstFrame (see compute method of the Odometry class). For the srcFrame and dstFrame different cache data may be required, - * some part of a cache may be common for both frame roles. - * @param CACHE_SRC The cache data for the srcFrame will be prepared. - * @param CACHE_DST The cache data for the dstFrame will be prepared. - * @param CACHE_ALL The cache data for both srcFrame and dstFrame roles will be computed. - */ - enum - { - CACHE_SRC = 1, CACHE_DST = 2, CACHE_ALL = CACHE_SRC + CACHE_DST - }; - - OdometryFrame(); - OdometryFrame(const Mat& image, const Mat& depth, const Mat& mask=Mat(), const Mat& normals=Mat(), int ID=-1); - - virtual void - release(); - - void - releasePyramids(); - - std::vector pyramidImage; - std::vector pyramidDepth; - std::vector pyramidMask; - - std::vector pyramidCloud; - - std::vector pyramid_dI_dx; - std::vector pyramid_dI_dy; - std::vector pyramidTexturedMask; - - std::vector pyramidNormals; - std::vector pyramidNormalsMask; - }; - - /** Base class for computation of odometry. - */ - class CV_EXPORTS Odometry: public Algorithm - { - public: - - /** A class of transformation*/ - enum - { - ROTATION = 1, TRANSLATION = 2, RIGID_BODY_MOTION = 4 - }; - - static inline float - DEFAULT_MIN_DEPTH() - { - return 0.f; // in meters - } - static inline float - DEFAULT_MAX_DEPTH() - { - return 4.f; // in meters - } - static inline float - DEFAULT_MAX_DEPTH_DIFF() - { - return 0.07f; // in meters - } - static inline float - DEFAULT_MAX_POINTS_PART() - { - return 0.07f; // in [0, 1] - } - static inline float - DEFAULT_MAX_TRANSLATION() - { - return 0.15f; // in meters - } - static inline float - DEFAULT_MAX_ROTATION() - { - return 15; // in degrees - } - - /** Method to compute a transformation from the source frame to the destination one. - * Some odometry algorithms do not used some data of frames (eg. ICP does not use images). - * In such case corresponding arguments can be set as empty Mat. - * The method returns true if all internal computions were possible (e.g. there were enough correspondences, - * system of equations has a solution, etc) and resulting transformation satisfies some test if it's provided - * by the Odometry inheritor implementation (e.g. thresholds for maximum translation and rotation). - * @param srcImage Image data of the source frame (CV_8UC1) - * @param srcDepth Depth data of the source frame (CV_32FC1, in meters) - * @param srcMask Mask that sets which pixels have to be used from the source frame (CV_8UC1) - * @param dstImage Image data of the destination frame (CV_8UC1) - * @param dstDepth Depth data of the destination frame (CV_32FC1, in meters) - * @param dstMask Mask that sets which pixels have to be used from the destination frame (CV_8UC1) - * @param Rt Resulting transformation from the source frame to the destination one (rigid body motion): - dst_p = Rt * src_p, where dst_p is a homogeneous point in the destination frame and src_p is - homogeneous point in the source frame, - Rt is 4x4 matrix of CV_64FC1 type. - * @param initRt Initial transformation from the source frame to the destination one (optional) - */ - bool - compute(const Mat& srcImage, const Mat& srcDepth, const Mat& srcMask, const Mat& dstImage, const Mat& dstDepth, - const Mat& dstMask, Mat& Rt, const Mat& initRt = Mat()) const; - - /** One more method to compute a transformation from the source frame to the destination one. - * It is designed to save on computing the frame data (image pyramids, normals, etc.). - */ - bool - compute(Ptr& srcFrame, Ptr& dstFrame, Mat& Rt, const Mat& initRt = Mat()) const; - - /** Prepare a cache for the frame. The function checks the precomputed/passed data (throws the error if this data - * does not satisfy) and computes all remaining cache data needed for the frame. Returned size is a resolution - * of the prepared frame. - * @param frame The odometry which will process the frame. - * @param cacheType The cache type: CACHE_SRC, CACHE_DST or CACHE_ALL. - */ - virtual Size prepareFrameCache(Ptr& frame, int cacheType) const; - - static Ptr create(const String & odometryType); - - /** @see setCameraMatrix */ - virtual cv::Mat getCameraMatrix() const = 0; - /** @copybrief getCameraMatrix @see getCameraMatrix */ - virtual void setCameraMatrix(const cv::Mat &val) = 0; - /** @see setTransformType */ - virtual int getTransformType() const = 0; - /** @copybrief getTransformType @see getTransformType */ - virtual void setTransformType(int val) = 0; - - protected: - virtual void - checkParams() const = 0; - - virtual bool - computeImpl(const Ptr& srcFrame, const Ptr& dstFrame, Mat& Rt, - const Mat& initRt) const = 0; - }; - - /** Odometry based on the paper "Real-Time Visual Odometry from Dense RGB-D Images", - * F. Steinbucker, J. Strum, D. Cremers, ICCV, 2011. - */ - class CV_EXPORTS RgbdOdometry: public Odometry - { - public: - RgbdOdometry(); - /** Constructor. - * @param cameraMatrix Camera matrix - * @param minDepth Pixels with depth less than minDepth will not be used (in meters) - * @param maxDepth Pixels with depth larger than maxDepth will not be used (in meters) - * @param maxDepthDiff Correspondences between pixels of two given frames will be filtered out - * if their depth difference is larger than maxDepthDiff (in meters) - * @param iterCounts Count of iterations on each pyramid level. - * @param minGradientMagnitudes For each pyramid level the pixels will be filtered out - * if they have gradient magnitude less than minGradientMagnitudes[level]. - * @param maxPointsPart The method uses a random pixels subset of size frameWidth x frameHeight x pointsPart - * @param transformType Class of transformation - */ - RgbdOdometry(const Mat& cameraMatrix, float minDepth = DEFAULT_MIN_DEPTH(), float maxDepth = DEFAULT_MAX_DEPTH(), - float maxDepthDiff = DEFAULT_MAX_DEPTH_DIFF(), const std::vector& iterCounts = std::vector(), - const std::vector& minGradientMagnitudes = std::vector(), float maxPointsPart = DEFAULT_MAX_POINTS_PART(), - int transformType = RIGID_BODY_MOTION); - - virtual Size prepareFrameCache(Ptr& frame, int cacheType) const; - - cv::Mat getCameraMatrix() const - { - return cameraMatrix; - } - void setCameraMatrix(const cv::Mat &val) - { - cameraMatrix = val; - } - double getMinDepth() const - { - return minDepth; - } - void setMinDepth(double val) - { - minDepth = val; - } - double getMaxDepth() const - { - return maxDepth; - } - void setMaxDepth(double val) - { - maxDepth = val; - } - double getMaxDepthDiff() const - { - return maxDepthDiff; - } - void setMaxDepthDiff(double val) - { - maxDepthDiff = val; - } - cv::Mat getIterationCounts() const - { - return iterCounts; - } - void setIterationCounts(const cv::Mat &val) - { - iterCounts = val; - } - cv::Mat getMinGradientMagnitudes() const - { - return minGradientMagnitudes; - } - void setMinGradientMagnitudes(const cv::Mat &val) - { - minGradientMagnitudes = val; - } - double getMaxPointsPart() const - { - return maxPointsPart; - } - void setMaxPointsPart(double val) - { - maxPointsPart = val; - } - int getTransformType() const - { - return transformType; - } - void setTransformType(int val) - { - transformType = val; - } - double getMaxTranslation() const - { - return maxTranslation; - } - void setMaxTranslation(double val) - { - maxTranslation = val; - } - double getMaxRotation() const - { - return maxRotation; - } - void setMaxRotation(double val) - { - maxRotation = val; - } - - protected: - virtual void - checkParams() const; - - virtual bool - computeImpl(const Ptr& srcFrame, const Ptr& dstFrame, Mat& Rt, - const Mat& initRt) const; - - // Some params have commented desired type. It's due to AlgorithmInfo::addParams does not support it now. - /*float*/ - double minDepth, maxDepth, maxDepthDiff; - /*vector*/ - Mat iterCounts; - /*vector*/ - Mat minGradientMagnitudes; - double maxPointsPart; - - Mat cameraMatrix; - int transformType; - - double maxTranslation, maxRotation; - }; - - /** Odometry based on the paper "KinectFusion: Real-Time Dense Surface Mapping and Tracking", - * Richard A. Newcombe, Andrew Fitzgibbon, at al, SIGGRAPH, 2011. - */ - class ICPOdometry: public Odometry - { - public: - ICPOdometry(); - /** Constructor. - * @param cameraMatrix Camera matrix - * @param minDepth Pixels with depth less than minDepth will not be used - * @param maxDepth Pixels with depth larger than maxDepth will not be used - * @param maxDepthDiff Correspondences between pixels of two given frames will be filtered out - * if their depth difference is larger than maxDepthDiff - * @param maxPointsPart The method uses a random pixels subset of size frameWidth x frameHeight x pointsPart - * @param iterCounts Count of iterations on each pyramid level. - * @param transformType Class of trasformation - */ - ICPOdometry(const Mat& cameraMatrix, float minDepth = DEFAULT_MIN_DEPTH(), float maxDepth = DEFAULT_MAX_DEPTH(), - float maxDepthDiff = DEFAULT_MAX_DEPTH_DIFF(), float maxPointsPart = DEFAULT_MAX_POINTS_PART(), - const std::vector& iterCounts = std::vector(), int transformType = RIGID_BODY_MOTION); - - virtual Size prepareFrameCache(Ptr& frame, int cacheType) const; - - cv::Mat getCameraMatrix() const - { - return cameraMatrix; - } - void setCameraMatrix(const cv::Mat &val) - { - cameraMatrix = val; - } - double getMinDepth() const - { - return minDepth; - } - void setMinDepth(double val) - { - minDepth = val; - } - double getMaxDepth() const - { - return maxDepth; - } - void setMaxDepth(double val) - { - maxDepth = val; - } - double getMaxDepthDiff() const - { - return maxDepthDiff; - } - void setMaxDepthDiff(double val) - { - maxDepthDiff = val; - } - cv::Mat getIterationCounts() const - { - return iterCounts; - } - void setIterationCounts(const cv::Mat &val) - { - iterCounts = val; - } - double getMaxPointsPart() const - { - return maxPointsPart; - } - void setMaxPointsPart(double val) - { - maxPointsPart = val; - } - int getTransformType() const - { - return transformType; - } - void setTransformType(int val) - { - transformType = val; - } - double getMaxTranslation() const - { - return maxTranslation; - } - void setMaxTranslation(double val) - { - maxTranslation = val; - } - double getMaxRotation() const - { - return maxRotation; - } - void setMaxRotation(double val) - { - maxRotation = val; - } - Ptr getNormalsComputer() const - { - return normalsComputer; - } - - protected: - virtual void - checkParams() const; - - virtual bool - computeImpl(const Ptr& srcFrame, const Ptr& dstFrame, Mat& Rt, - const Mat& initRt) const; - - // Some params have commented desired type. It's due to AlgorithmInfo::addParams does not support it now. - /*float*/ - double minDepth, maxDepth, maxDepthDiff; - /*float*/ - double maxPointsPart; - /*vector*/ - Mat iterCounts; - - Mat cameraMatrix; - int transformType; - - double maxTranslation, maxRotation; - - mutable Ptr normalsComputer; - }; - - /** Odometry that merges RgbdOdometry and ICPOdometry by minimize sum of their energy functions. - */ - - class RgbdICPOdometry: public Odometry - { - public: - RgbdICPOdometry(); - /** Constructor. - * @param cameraMatrix Camera matrix - * @param minDepth Pixels with depth less than minDepth will not be used - * @param maxDepth Pixels with depth larger than maxDepth will not be used - * @param maxDepthDiff Correspondences between pixels of two given frames will be filtered out - * if their depth difference is larger than maxDepthDiff - * @param maxPointsPart The method uses a random pixels subset of size frameWidth x frameHeight x pointsPart - * @param iterCounts Count of iterations on each pyramid level. - * @param minGradientMagnitudes For each pyramid level the pixels will be filtered out - * if they have gradient magnitude less than minGradientMagnitudes[level]. - * @param transformType Class of trasformation - */ - RgbdICPOdometry(const Mat& cameraMatrix, float minDepth = DEFAULT_MIN_DEPTH(), float maxDepth = DEFAULT_MAX_DEPTH(), - float maxDepthDiff = DEFAULT_MAX_DEPTH_DIFF(), float maxPointsPart = DEFAULT_MAX_POINTS_PART(), - const std::vector& iterCounts = std::vector(), - const std::vector& minGradientMagnitudes = std::vector(), - int transformType = RIGID_BODY_MOTION); - - virtual Size prepareFrameCache(Ptr& frame, int cacheType) const; - - cv::Mat getCameraMatrix() const - { - return cameraMatrix; - } - void setCameraMatrix(const cv::Mat &val) - { - cameraMatrix = val; - } - double getMinDepth() const - { - return minDepth; - } - void setMinDepth(double val) - { - minDepth = val; - } - double getMaxDepth() const - { - return maxDepth; - } - void setMaxDepth(double val) - { - maxDepth = val; - } - double getMaxDepthDiff() const - { - return maxDepthDiff; - } - void setMaxDepthDiff(double val) - { - maxDepthDiff = val; - } - double getMaxPointsPart() const - { - return maxPointsPart; - } - void setMaxPointsPart(double val) - { - maxPointsPart = val; - } - cv::Mat getIterationCounts() const - { - return iterCounts; - } - void setIterationCounts(const cv::Mat &val) - { - iterCounts = val; - } - cv::Mat getMinGradientMagnitudes() const - { - return minGradientMagnitudes; - } - void setMinGradientMagnitudes(const cv::Mat &val) - { - minGradientMagnitudes = val; - } - int getTransformType() const - { - return transformType; - } - void setTransformType(int val) - { - transformType = val; - } - double getMaxTranslation() const - { - return maxTranslation; - } - void setMaxTranslation(double val) - { - maxTranslation = val; - } - double getMaxRotation() const - { - return maxRotation; - } - void setMaxRotation(double val) - { - maxRotation = val; - } - Ptr getNormalsComputer() const - { - return normalsComputer; - } - - protected: - virtual void - checkParams() const; - - virtual bool - computeImpl(const Ptr& srcFrame, const Ptr& dstFrame, Mat& Rt, - const Mat& initRt) const; - - // Some params have commented desired type. It's due to AlgorithmInfo::addParams does not support it now. - /*float*/ - double minDepth, maxDepth, maxDepthDiff; - /*float*/ - double maxPointsPart; - /*vector*/ - Mat iterCounts; - /*vector*/ - Mat minGradientMagnitudes; - - Mat cameraMatrix; - int transformType; - - double maxTranslation, maxRotation; - - mutable Ptr normalsComputer; - }; - - /** Warp the image: compute 3d points from the depth, transform them using given transformation, - * then project color point cloud to an image plane. - * This function can be used to visualize results of the Odometry algorithm. - * @param image The image (of CV_8UC1 or CV_8UC3 type) - * @param depth The depth (of type used in depthTo3d fuction) - * @param mask The mask of used pixels (of CV_8UC1), it can be empty - * @param Rt The transformation that will be applied to the 3d points computed from the depth - * @param cameraMatrix Camera matrix - * @param distCoeff Distortion coefficients - * @param warpedImage The warped image. - * @param warpedDepth The warped depth. - * @param warpedMask The warped mask. - */ - CV_EXPORTS - void - warpFrame(const Mat& image, const Mat& depth, const Mat& mask, const Mat& Rt, const Mat& cameraMatrix, - const Mat& distCoeff, Mat& warpedImage, Mat* warpedDepth = 0, Mat* warpedMask = 0); - -// TODO Depth interpolation -// Curvature -// Get rescaleDepth return dubles if asked for - -//! @} - -} /* namespace rgbd */ -} /* namespace cv */ - -#include "opencv2/rgbd/linemod.hpp" - -#endif /* __cplusplus */ -#endif - -/* End of file. */ - diff --git a/IPL/include/opencv/opencv2/rgbd/linemod.hpp b/IPL/include/opencv/opencv2/rgbd/linemod.hpp deleted file mode 100644 index ac56291..0000000 --- a/IPL/include/opencv/opencv2/rgbd/linemod.hpp +++ /dev/null @@ -1,458 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OBJDETECT_LINEMOD_HPP__ -#define __OPENCV_OBJDETECT_LINEMOD_HPP__ - -#include "opencv2/core.hpp" -#include - -/****************************************************************************************\ -* LINE-MOD * -\****************************************************************************************/ - -namespace cv { -namespace linemod { - -//! @addtogroup rgbd -//! @{ - -/** - * \brief Discriminant feature described by its location and label. - */ -struct CV_EXPORTS Feature -{ - int x; ///< x offset - int y; ///< y offset - int label; ///< Quantization - - Feature() : x(0), y(0), label(0) {} - Feature(int x, int y, int label); - - void read(const FileNode& fn); - void write(FileStorage& fs) const; -}; - -inline Feature::Feature(int _x, int _y, int _label) : x(_x), y(_y), label(_label) {} - -struct CV_EXPORTS Template -{ - int width; - int height; - int pyramid_level; - std::vector features; - - void read(const FileNode& fn); - void write(FileStorage& fs) const; -}; - -/** - * \brief Represents a modality operating over an image pyramid. - */ -class QuantizedPyramid -{ -public: - // Virtual destructor - virtual ~QuantizedPyramid() {} - - /** - * \brief Compute quantized image at current pyramid level for online detection. - * - * \param[out] dst The destination 8-bit image. For each pixel at most one bit is set, - * representing its classification. - */ - virtual void quantize(Mat& dst) const =0; - - /** - * \brief Extract most discriminant features at current pyramid level to form a new template. - * - * \param[out] templ The new template. - */ - virtual bool extractTemplate(Template& templ) const =0; - - /** - * \brief Go to the next pyramid level. - * - * \todo Allow pyramid scale factor other than 2 - */ - virtual void pyrDown() =0; - -protected: - /// Candidate feature with a score - struct Candidate - { - Candidate(int x, int y, int label, float score); - - /// Sort candidates with high score to the front - bool operator<(const Candidate& rhs) const - { - return score > rhs.score; - } - - Feature f; - float score; - }; - - /** - * \brief Choose candidate features so that they are not bunched together. - * - * \param[in] candidates Candidate features sorted by score. - * \param[out] features Destination vector of selected features. - * \param[in] num_features Number of candidates to select. - * \param[in] distance Hint for desired distance between features. - */ - static void selectScatteredFeatures(const std::vector& candidates, - std::vector& features, - size_t num_features, float distance); -}; - -inline QuantizedPyramid::Candidate::Candidate(int x, int y, int label, float _score) : f(x, y, label), score(_score) {} - -/** - * \brief Interface for modalities that plug into the LINE template matching representation. - * - * \todo Max response, to allow optimization of summing (255/MAX) features as uint8 - */ -class CV_EXPORTS Modality -{ -public: - // Virtual destructor - virtual ~Modality() {} - - /** - * \brief Form a quantized image pyramid from a source image. - * - * \param[in] src The source image. Type depends on the modality. - * \param[in] mask Optional mask. If not empty, unmasked pixels are set to zero - * in quantized image and cannot be extracted as features. - */ - Ptr process(const Mat& src, - const Mat& mask = Mat()) const - { - return processImpl(src, mask); - } - - virtual String name() const =0; - - virtual void read(const FileNode& fn) =0; - virtual void write(FileStorage& fs) const =0; - - /** - * \brief Create modality by name. - * - * The following modality types are supported: - * - "ColorGradient" - * - "DepthNormal" - */ - static Ptr create(const String& modality_type); - - /** - * \brief Load a modality from file. - */ - static Ptr create(const FileNode& fn); - -protected: - // Indirection is because process() has a default parameter. - virtual Ptr processImpl(const Mat& src, - const Mat& mask) const =0; -}; - -/** - * \brief Modality that computes quantized gradient orientations from a color image. - */ -class CV_EXPORTS ColorGradient : public Modality -{ -public: - /** - * \brief Default constructor. Uses reasonable default parameter values. - */ - ColorGradient(); - - /** - * \brief Constructor. - * - * \param weak_threshold When quantizing, discard gradients with magnitude less than this. - * \param num_features How many features a template must contain. - * \param strong_threshold Consider as candidate features only gradients whose norms are - * larger than this. - */ - ColorGradient(float weak_threshold, size_t num_features, float strong_threshold); - - virtual String name() const; - - virtual void read(const FileNode& fn); - virtual void write(FileStorage& fs) const; - - float weak_threshold; - size_t num_features; - float strong_threshold; - -protected: - virtual Ptr processImpl(const Mat& src, - const Mat& mask) const; -}; - -/** - * \brief Modality that computes quantized surface normals from a dense depth map. - */ -class CV_EXPORTS DepthNormal : public Modality -{ -public: - /** - * \brief Default constructor. Uses reasonable default parameter values. - */ - DepthNormal(); - - /** - * \brief Constructor. - * - * \param distance_threshold Ignore pixels beyond this distance. - * \param difference_threshold When computing normals, ignore contributions of pixels whose - * depth difference with the central pixel is above this threshold. - * \param num_features How many features a template must contain. - * \param extract_threshold Consider as candidate feature only if there are no differing - * orientations within a distance of extract_threshold. - */ - DepthNormal(int distance_threshold, int difference_threshold, size_t num_features, - int extract_threshold); - - virtual String name() const; - - virtual void read(const FileNode& fn); - virtual void write(FileStorage& fs) const; - - int distance_threshold; - int difference_threshold; - size_t num_features; - int extract_threshold; - -protected: - virtual Ptr processImpl(const Mat& src, - const Mat& mask) const; -}; - -/** - * \brief Debug function to colormap a quantized image for viewing. - */ -void colormap(const Mat& quantized, Mat& dst); - -/** - * \brief Represents a successful template match. - */ -struct CV_EXPORTS Match -{ - Match() - { - } - - Match(int x, int y, float similarity, const String& class_id, int template_id); - - /// Sort matches with high similarity to the front - bool operator<(const Match& rhs) const - { - // Secondarily sort on template_id for the sake of duplicate removal - if (similarity != rhs.similarity) - return similarity > rhs.similarity; - else - return template_id < rhs.template_id; - } - - bool operator==(const Match& rhs) const - { - return x == rhs.x && y == rhs.y && similarity == rhs.similarity && class_id == rhs.class_id; - } - - int x; - int y; - float similarity; - String class_id; - int template_id; -}; - -inline -Match::Match(int _x, int _y, float _similarity, const String& _class_id, int _template_id) - : x(_x), y(_y), similarity(_similarity), class_id(_class_id), template_id(_template_id) -{} - -/** - * \brief Object detector using the LINE template matching algorithm with any set of - * modalities. - */ -class CV_EXPORTS Detector -{ -public: - /** - * \brief Empty constructor, initialize with read(). - */ - Detector(); - - /** - * \brief Constructor. - * - * \param modalities Modalities to use (color gradients, depth normals, ...). - * \param T_pyramid Value of the sampling step T at each pyramid level. The - * number of pyramid levels is T_pyramid.size(). - */ - Detector(const std::vector< Ptr >& modalities, const std::vector& T_pyramid); - - /** - * \brief Detect objects by template matching. - * - * Matches globally at the lowest pyramid level, then refines locally stepping up the pyramid. - * - * \param sources Source images, one for each modality. - * \param threshold Similarity threshold, a percentage between 0 and 100. - * \param[out] matches Template matches, sorted by similarity score. - * \param class_ids If non-empty, only search for the desired object classes. - * \param[out] quantized_images Optionally return vector of quantized images. - * \param masks The masks for consideration during matching. The masks should be CV_8UC1 - * where 255 represents a valid pixel. If non-empty, the vector must be - * the same size as sources. Each element must be - * empty or the same size as its corresponding source. - */ - void match(const std::vector& sources, float threshold, std::vector& matches, - const std::vector& class_ids = std::vector(), - OutputArrayOfArrays quantized_images = noArray(), - const std::vector& masks = std::vector()) const; - - /** - * \brief Add new object template. - * - * \param sources Source images, one for each modality. - * \param class_id Object class ID. - * \param object_mask Mask separating object from background. - * \param[out] bounding_box Optionally return bounding box of the extracted features. - * - * \return Template ID, or -1 if failed to extract a valid template. - */ - int addTemplate(const std::vector& sources, const String& class_id, - const Mat& object_mask, Rect* bounding_box = NULL); - - /** - * \brief Add a new object template computed by external means. - */ - int addSyntheticTemplate(const std::vector