diff --git a/modules/bioinspired/include/opencv2/bioinspired/retina.hpp b/modules/bioinspired/include/opencv2/bioinspired/retina.hpp index 8e6eda93cae..478b6a0f75c 100644 --- a/modules/bioinspired/include/opencv2/bioinspired/retina.hpp +++ b/modules/bioinspired/include/opencv2/bioinspired/retina.hpp @@ -94,57 +94,12 @@ enum { 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 + @include default_retina_config.xml 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 + @include realistic_retina_config.xml + */ struct RetinaParameters{ //! Outer Plexiform Layer (OPL) and Inner Plexiform Layer Parvocellular (IplParvo) parameters diff --git a/modules/bioinspired/samples/default_retina_config.xml b/modules/bioinspired/samples/default_retina_config.xml new file mode 100644 index 00000000000..469b5d58f10 --- /dev/null +++ b/modules/bioinspired/samples/default_retina_config.xml @@ -0,0 +1,24 @@ + + + + 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. + + diff --git a/modules/bioinspired/samples/realistic_retina_config.xml b/modules/bioinspired/samples/realistic_retina_config.xml new file mode 100644 index 00000000000..c02e79b3c6d --- /dev/null +++ b/modules/bioinspired/samples/realistic_retina_config.xml @@ -0,0 +1,24 @@ + + + + 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. + + diff --git a/modules/bioinspired/tutorials/retina_model/retina_model.markdown b/modules/bioinspired/tutorials/retina_model/retina_model.markdown index 37285bfa1c7..d71fe797bec 100644 --- a/modules/bioinspired/tutorials/retina_model/retina_model.markdown +++ b/modules/bioinspired/tutorials/retina_model/retina_model.markdown @@ -1,6 +1,8 @@ Retina and real-world vision {#tutorial_bioinspired_retina_model} ============================================================= +@tableofcontents + Goal ---- @@ -382,7 +384,7 @@ need to know if mean luminance information is required or not. If not, the the r significantly reduce its energy thus giving more visibility to higher spatial frequency details. -#### Basic parameters +## Basic parameters The simplest parameters are as follows : @@ -397,7 +399,7 @@ processing. You can expect much faster processing using gray levels : it would r product per pixel for all of the retina processes and it has recently been parallelized for multicore architectures. -#### Photo-receptors parameters +## Photo-receptors parameters The following parameters act on the entry point of the retina - photo-receptors - and has impact on all of the following processes. These sensors are low pass spatio-temporal filters that smooth temporal and @@ -421,7 +423,7 @@ and high frequency noise canceling. A good compromise for color images is a 0.53 value since such choice won't affect too much the color spectrum. Higher values would lead to gray and blurred output images. -#### Horizontal cells parameters +## Horizontal cells parameters This parameter set tunes the neural network connected to the photo-receptors, the horizontal cells. It modulates photo-receptors sensitivity and completes the processing for final spectral whitening @@ -446,7 +448,7 @@ It modulates photo-receptors sensitivity and completes the processing for final and luminance is already partly enhanced. The following parameters act on the last processing stages of the two outing retina signals. -#### Parvo (details channel) dedicated parameter +## Parvo (details channel) dedicated parameter - **ganglionCellsSensitivity** specifies the strength of the final local adaptation occurring at the output of this details' dedicated channel. Parameter values remain between 0 and 1. Low value @@ -455,7 +457,7 @@ of the two outing retina signals. **Note :** this parameter can correct eventual burned images by favoring low energetic details of the visual scene, even in bright areas. -#### IPL Magno (motion/transient channel) parameters +## IPL Magno (motion/transient channel) parameters Once image's information are cleaned, this channel acts as a high pass temporal filter that selects only the signals related to transient signals (events, motion, etc.). A low pass spatial filter diff --git a/modules/cannops/include/opencv2/cann.hpp b/modules/cannops/include/opencv2/cann.hpp index 30555dd8257..bd351481624 100644 --- a/modules/cannops/include/opencv2/cann.hpp +++ b/modules/cannops/include/opencv2/cann.hpp @@ -8,12 +8,12 @@ #include "opencv2/core.hpp" /** - @defgroup cann Ascend-accelerated Computer Vision + @defgroup cannops Ascend-accelerated Computer Vision @{ @defgroup canncore Core part @{ @defgroup cann_struct Data Structures - @defgroup cann_init Initializeation and Information + @defgroup cann_init Initialization and Information @} @} */ diff --git a/modules/cannops/include/opencv2/cann_interface.hpp b/modules/cannops/include/opencv2/cann_interface.hpp index 6667eb58519..6b13090f4f1 100644 --- a/modules/cannops/include/opencv2/cann_interface.hpp +++ b/modules/cannops/include/opencv2/cann_interface.hpp @@ -13,9 +13,9 @@ namespace cann { /** - @addtogroup cann + @addtogroup cannops @{ - @defgroup cannops Operations for Ascend Backend. + @defgroup cannops_ops Operations for Ascend Backend. @{ @defgroup cannops_elem Per-element Operations @defgroup cannops_core Core Operations on Matrices diff --git a/modules/cudaimgproc/include/opencv2/cudaimgproc.hpp b/modules/cudaimgproc/include/opencv2/cudaimgproc.hpp index d72700168cd..01e7c41ca9a 100644 --- a/modules/cudaimgproc/include/opencv2/cudaimgproc.hpp +++ b/modules/cudaimgproc/include/opencv2/cudaimgproc.hpp @@ -844,7 +844,6 @@ cv::Moments cvMoments = convertSpatialMoments(spatialMoments, order); ``` see the \a CUDA_TEST_P(Moments, Async) test inside opencv_contrib_source_code/modules/cudaimgproc/test/test_moments.cpp for an example. -@returns cv::Moments. @sa cuda::moments, cuda::convertSpatialMoments, cuda::numMoments, cuda::MomentsOrder */ CV_EXPORTS_W void spatialMoments(InputArray src, OutputArray moments, const bool binaryImage = false, const MomentsOrder order = MomentsOrder::THIRD_ORDER_MOMENTS, const int momentsType = CV_64F, Stream& stream = Stream::Null()); diff --git a/modules/dnn_superres/tutorials/benchmark/sr_benchmark.markdown b/modules/dnn_superres/tutorials/benchmark/sr_benchmark.markdown index 26244c9f8ae..3a4b88ef81b 100644 --- a/modules/dnn_superres/tutorials/benchmark/sr_benchmark.markdown +++ b/modules/dnn_superres/tutorials/benchmark/sr_benchmark.markdown @@ -50,14 +50,9 @@ Explanation Benchmarking results ----------- -Dataset benchmarking ----- - -###General100 dataset - -
+## General100 dataset -#####2x scaling factor +### 2x scaling factor | | Avg inference time in sec (CPU)| Avg PSNR | Avg SSIM | @@ -70,7 +65,7 @@ Dataset benchmarking | Nearest neighbor | 0.000114 | 29.1665 | 0.9049 | | Lanczos | 0.001094 | 32.4687 | 0.9327 | -#####3x scaling factor +### 3x scaling factor | | Avg inference time in sec (CPU)| Avg PSNR | Avg SSIM | | ------------- |:-------------------:| ---------:|--------:| @@ -83,7 +78,7 @@ Dataset benchmarking | Lanczos | 0.001012 |25.9115 |0.8706 | -#####4x scaling factor +### 4x scaling factor | | Avg inference time in sec (CPU)| Avg PSNR | Avg SSIM | | ------------- |:-------------------:| ---------:|--------:| @@ -96,14 +91,10 @@ Dataset benchmarking | Lanczos | 0.001012 |25.9115 |0.8706 | -
-Images ----- - -
+## Images -####2x scaling factor +### 2x scaling factor |Set5: butterfly.png | size: 256x256 | || |:-------------:|:-------------------:|:-------------:|:----:| @@ -112,7 +103,7 @@ Images ![ESPCN](images/espcn_butterfly.jpg)| ![FSRCNN](images/fsrcnn_butterfly.jpg) | ![LapSRN](images/lapsrn_butterfly.jpg) | ![EDSR](images/edsr_butterfly.jpg) |29.0341 / 0.9354 / **0.004157**| 29.0077 / 0.9345 / 0.006325 | 27.8212 / 0.9230 / 0.037937 | **30.0347** / **0.9453** / 2.077280 | -####3x scaling factor +### 3x scaling factor |Urban100: img_001.png | size: 1024x644 | || |:-------------:|:-------------------:|:-------------:|:----:| @@ -122,7 +113,7 @@ Images |28.0118 / 0.8588 / **0.030748**| 28.0184 / 0.8597 / 0.094173 | | **30.5671** / **0.9019** / 9.517580 | -####4x scaling factor +### 4x scaling factor |Set14: comic.png | size: 250x361 | || |:-------------:|:-------------------:|:-------------:|:----:| @@ -131,7 +122,7 @@ Images |![ESPCN](images/espcn_comic.jpg)| ![FSRCNN](images/fsrcnn_comic.jpg) | ![LapSRN](images/lapsrn_comic.jpg) | ![EDSR](images/edsr_comic.jpg) |20.0417 / 0.6302 / **0.001894**| 20.0885 / 0.6384 / 0.002103 | 20.0676 / 0.6339 / 0.061640 | **20.5233** / **0.6901** / 0.665876 | -####8x scaling factor +### 8x scaling factor |Div2K: 0006.png | size: 1356x2040 | | |:-------------:|:-------------------:|:-------------:| @@ -139,5 +130,3 @@ Images |PSRN / SSIM / Speed (CPU)| 26.3139 / **0.8033** / 0.001107| 23.8291 / 0.7340 / **0.000611** | |![Lanczos interpolation](images/lanczos_div2k.jpg)| ![LapSRN](images/lapsrn_div2k.jpg) | | |26.1565 / 0.7962 / 0.004782| **26.7046** / 0.7987 / 2.274290 | | - -
\ No newline at end of file diff --git a/modules/face/include/opencv2/face/facemark.hpp b/modules/face/include/opencv2/face/facemark.hpp index 86e9384342e..4e66727fe46 100644 --- a/modules/face/include/opencv2/face/facemark.hpp +++ b/modules/face/include/opencv2/face/facemark.hpp @@ -12,12 +12,6 @@ Mentor: Delia Passalacqua #ifndef __OPENCV_FACELANDMARK_HPP__ #define __OPENCV_FACELANDMARK_HPP__ -/** -@defgroup face Face Analysis -- @ref tutorial_table_of_content_facemark -- The Facemark API -*/ - #include "opencv2/core.hpp" #include @@ -25,6 +19,8 @@ Mentor: Delia Passalacqua namespace cv { namespace face { +//! @addtogroup face +//! @{ /** @brief Abstract base class for all facemark models @@ -88,6 +84,7 @@ CV_EXPORTS_W Ptr createFacemarkLBF(); //! construct a Kazemi facemark detector CV_EXPORTS_W Ptr createFacemarkKazemi(); +//! @} } // face } // cv diff --git a/modules/face/include/opencv2/face/facemark_train.hpp b/modules/face/include/opencv2/face/facemark_train.hpp index d6e27e9face..591c079a0d6 100644 --- a/modules/face/include/opencv2/face/facemark_train.hpp +++ b/modules/face/include/opencv2/face/facemark_train.hpp @@ -12,12 +12,6 @@ Mentor: Delia Passalacqua #ifndef __OPENCV_FACELANDMARKTRAIN_HPP__ #define __OPENCV_FACELANDMARKTRAIN_HPP__ -/** -@defgroup face Face Analysis -- @ref tutorial_table_of_content_facemark -- The Facemark API -*/ - #include "opencv2/face/facemark.hpp" #include "opencv2/objdetect.hpp" #include diff --git a/modules/face/tutorials/face_landmark/face_landmark_trainer.markdown b/modules/face/tutorials/face_landmark/face_landmark_trainer.markdown index 601a6b4c428..8fdeaa611d5 100644 --- a/modules/face/tutorials/face_landmark/face_landmark_trainer.markdown +++ b/modules/face/tutorials/face_landmark/face_landmark_trainer.markdown @@ -21,7 +21,7 @@ The above format is similar to HELEN dataset which is used for training the mode ./sample_train_landmark_detector -annotations=/home/sukhad/Downloads/code/trainset/ -config=config.xml -face_cascade=lbpcascadefrontalface.xml -model=trained_model.dat -width=460 -height=460 ``` -### Description of command parameters +## Description of command parameters > * **annotations** a : (REQUIRED) Path to annotations txt file [example - /data/annotations.txt] > * **config** c : (REQUIRED) Path to configuration xml file containing parameters for training.[ example - /data/config.xml] @@ -30,7 +30,7 @@ The above format is similar to HELEN dataset which is used for training the mode > * **height** h : (OPTIONAL) The height which you want all images to get to scale the annotations. Large images are slow to process [default = 460] > * **face_cascade** f (REQUIRED) Path to the face cascade xml file which you want to use as a detector. -### Description of training parameters +## Description of training parameters The configuration file described above which is used while training contains the training parameters which are required for training. @@ -49,7 +49,7 @@ The configuration file described above which is used while training contains the To get more detailed description about the training parameters you can refer to the [Research paper](https://pdfs.semanticscholar.org/d78b/6a5b0dcaa81b1faea5fb0000045a62513567.pdf). -### Understanding code +## Understanding code ![](images/3.jpg) diff --git a/modules/fuzzy/include/opencv2/fuzzy.hpp b/modules/fuzzy/include/opencv2/fuzzy.hpp index d660cc3615c..59f2a3f2a1f 100644 --- a/modules/fuzzy/include/opencv2/fuzzy.hpp +++ b/modules/fuzzy/include/opencv2/fuzzy.hpp @@ -52,19 +52,19 @@ Namespace for all functions is `ft`. The module brings implementation of the last image processing algorithms based on fuzzy mathematics. Method are named based on the pattern `FT`_degree_dimension`_`method. - @{ +@{ @defgroup f0_math Math with F0-transform support -Fuzzy transform (\f$F^0\f$-transform) of the 0th degree transforms whole image to a matrix of its components. These components are used in latter computation where each of them represents average color of certain subarea. + Fuzzy transform (\f$F^0\f$-transform) of the 0th degree transforms whole image to a matrix of its components. These components are used in latter computation where each of them represents average color of certain subarea. @defgroup f1_math Math with F1-transform support -Fuzzy transform (\f$F^1\f$-transform) of the 1th degree transforms whole image to a matrix of its components. Each component is polynomial of the 1th degree carrying information about average color and average gradient of certain subarea. + Fuzzy transform (\f$F^1\f$-transform) of the 1th degree transforms whole image to a matrix of its components. Each component is polynomial of the 1th degree carrying information about average color and average gradient of certain subarea. @defgroup f_image Fuzzy image processing -Image proceesing based on fuzzy mathematics namely F-transform. - @} + Image proceesing based on fuzzy mathematics namely F-transform. +@} */ diff --git a/modules/hdf/include/opencv2/hdf.hpp b/modules/hdf/include/opencv2/hdf.hpp index ff40426ff65..ac48e4b9ac8 100644 --- a/modules/hdf/include/opencv2/hdf.hpp +++ b/modules/hdf/include/opencv2/hdf.hpp @@ -41,17 +41,15 @@ This module provides storage routines for Hierarchical Data Format objects. - @{ +@{ @defgroup hdf5 Hierarchical Data Format version 5 -Hierarchical Data Format version 5 --------------------------------------------------------- + Hierarchical Data Format version 5 + -------------------------------------------------------- -In order to use it, the hdf5 library has to be installed, which -means cmake should find it using `find_package(HDF5)` . - - - @} + In order to use it, the hdf5 library has to be installed, which + means cmake should find it using `find_package(HDF5)`. +@} */ #endif diff --git a/modules/mcc/include/opencv2/mcc/checker_model.hpp b/modules/mcc/include/opencv2/mcc/checker_model.hpp index c13d5afc585..0768c691e05 100644 --- a/modules/mcc/include/opencv2/mcc/checker_model.hpp +++ b/modules/mcc/include/opencv2/mcc/checker_model.hpp @@ -116,7 +116,6 @@ class CV_EXPORTS_W CCheckerDraw virtual ~CCheckerDraw() {} /** \brief Draws the checker to the given image. * \param img image in color space BGR - * \return void */ CV_WRAP virtual void draw(InputOutputArray img) = 0; /** \brief Create a new CCheckerDraw object. diff --git a/modules/rgbd/include/opencv2/rgbd/dynafu.hpp b/modules/rgbd/include/opencv2/rgbd/dynafu.hpp index 32875ad5ac7..e5ad3447778 100644 --- a/modules/rgbd/include/opencv2/rgbd/dynafu.hpp +++ b/modules/rgbd/include/opencv2/rgbd/dynafu.hpp @@ -114,7 +114,6 @@ class CV_EXPORTS_W DynaFu virtual void renderSurface(OutputArray depthImage, OutputArray vertImage, OutputArray normImage, bool warp=true) = 0; }; -//! @} -} -} -#endif +} // dynafu:: +} // cv:: +#endif // __OPENCV_RGBD_DYNAFU_HPP__ diff --git a/modules/sfm/include/opencv2/sfm.hpp b/modules/sfm/include/opencv2/sfm.hpp index 25a3b10da5d..52c1af07e8e 100644 --- a/modules/sfm/include/opencv2/sfm.hpp +++ b/modules/sfm/include/opencv2/sfm.hpp @@ -75,7 +75,7 @@ This module has been originally developed as a project for Google Summer of Code - Notice that it is compiled only when Eigen, GLog and GFlags are correctly installed.\n Check installation instructions in the following tutorial: @ref tutorial_sfm_installation - @{ +@{ @defgroup conditioning Conditioning @defgroup fundamental Fundamental @defgroup io Input/Output @@ -85,18 +85,17 @@ This module has been originally developed as a project for Google Summer of Code @defgroup triangulation Triangulation @defgroup reconstruction Reconstruction - @note - - Notice that it is compiled only when Ceres Solver is correctly installed.\n - Check installation instructions in the following tutorial: @ref tutorial_sfm_installation + @note + - Notice that it is compiled only when Ceres Solver is correctly installed.\n + Check installation instructions in the following tutorial: @ref tutorial_sfm_installation @defgroup simple_pipeline Simple Pipeline - @note - - Notice that it is compiled only when Ceres Solver is correctly installed.\n - Check installation instructions in the following tutorial: @ref tutorial_sfm_installation - - @} + @note + - Notice that it is compiled only when Ceres Solver is correctly installed.\n + Check installation instructions in the following tutorial: @ref tutorial_sfm_installation +@} */ #endif diff --git a/modules/stereo/include/opencv2/stereo/quasi_dense_stereo.hpp b/modules/stereo/include/opencv2/stereo/quasi_dense_stereo.hpp index b2290e3768c..469c46f72ea 100644 --- a/modules/stereo/include/opencv2/stereo/quasi_dense_stereo.hpp +++ b/modules/stereo/include/opencv2/stereo/quasi_dense_stereo.hpp @@ -18,6 +18,7 @@ namespace cv { namespace stereo { + /** \addtogroup stereo * @{ */ @@ -190,9 +191,8 @@ class CV_EXPORTS_W QuasiDenseStereo CV_PROP_RW PropagationParameters Param; }; -} //namespace cv -} //namespace stereo - /** @}*/ +} //namespace cv +} //namespace stereo #endif // __OPENCV_QUASI_DENSE_STEREO_H__ diff --git a/modules/text/include/opencv2/text.hpp b/modules/text/include/opencv2/text.hpp index 86ce3ec6e80..2b84451c23f 100644 --- a/modules/text/include/opencv2/text.hpp +++ b/modules/text/include/opencv2/text.hpp @@ -52,49 +52,49 @@ scene images. @{ @defgroup text_detect Scene Text Detection -Class-specific Extremal Regions for Scene Text Detection --------------------------------------------------------- - -The scene text detection algorithm described below has been initially proposed by Lukás Neumann & -Jiri Matas @cite Neumann11. The main idea behind Class-specific Extremal Regions is similar to the MSER -in that suitable Extremal Regions (ERs) are selected from the whole component tree of the image. -However, this technique differs from MSER in that selection of suitable ERs is done by a sequential -classifier trained for character detection, i.e. dropping the stability requirement of MSERs and -selecting class-specific (not necessarily stable) regions. - -The component tree of an image is constructed by thresholding by an increasing value step-by-step -from 0 to 255 and then linking the obtained connected components from successive levels in a -hierarchy by their inclusion relation: - -![image](pics/component_tree.png) - -The component tree may contain a huge number of regions even for a very simple image as shown in -the previous image. This number can easily reach the order of 1 x 10\^6 regions for an average 1 -Megapixel image. In order to efficiently select suitable regions among all the ERs the algorithm -make use of a sequential classifier with two differentiated stages. - -In the first stage incrementally computable descriptors (area, perimeter, bounding box, and Euler's -number) are computed (in O(1)) for each region r and used as features for a classifier which -estimates the class-conditional probability p(r|character). Only the ERs which correspond to local -maximum of the probability p(r|character) are selected (if their probability is above a global limit -p_min and the difference between local maximum and local minimum is greater than a delta_min -value). - -In the second stage, the ERs that passed the first stage are classified into character and -non-character classes using more informative but also more computationally expensive features. (Hole -area ratio, convex hull ratio, and the number of outer boundary inflexion points). - -This ER filtering process is done in different single-channel projections of the input image in -order to increase the character localization recall. - -After the ER filtering is done on each input channel, character candidates must be grouped in -high-level text blocks (i.e. words, text lines, paragraphs, ...). The opencv_text module implements -two different grouping algorithms: the Exhaustive Search algorithm proposed in @cite Neumann12 for -grouping horizontally aligned text, and the method proposed by Lluis Gomez and Dimosthenis Karatzas -in @cite Gomez13 @cite Gomez14 for grouping arbitrary oriented text (see erGrouping). - -To see the text detector at work, have a look at the textdetection demo: - + Class-specific Extremal Regions for Scene Text Detection + -------------------------------------------------------- + + The scene text detection algorithm described below has been initially proposed by Lukás Neumann & + Jiri Matas @cite Neumann11. The main idea behind Class-specific Extremal Regions is similar to the MSER + in that suitable Extremal Regions (ERs) are selected from the whole component tree of the image. + However, this technique differs from MSER in that selection of suitable ERs is done by a sequential + classifier trained for character detection, i.e. dropping the stability requirement of MSERs and + selecting class-specific (not necessarily stable) regions. + + The component tree of an image is constructed by thresholding by an increasing value step-by-step + from 0 to 255 and then linking the obtained connected components from successive levels in a + hierarchy by their inclusion relation: + + ![image](pics/component_tree.png) + + The component tree may contain a huge number of regions even for a very simple image as shown in + the previous image. This number can easily reach the order of 1 x 10\^6 regions for an average 1 + Megapixel image. In order to efficiently select suitable regions among all the ERs the algorithm + make use of a sequential classifier with two differentiated stages. + + In the first stage incrementally computable descriptors (area, perimeter, bounding box, and Euler's + number) are computed (in O(1)) for each region r and used as features for a classifier which + estimates the class-conditional probability p(r|character). Only the ERs which correspond to local + maximum of the probability p(r|character) are selected (if their probability is above a global limit + p_min and the difference between local maximum and local minimum is greater than a delta_min + value). + + In the second stage, the ERs that passed the first stage are classified into character and + non-character classes using more informative but also more computationally expensive features. (Hole + area ratio, convex hull ratio, and the number of outer boundary inflexion points). + + This ER filtering process is done in different single-channel projections of the input image in + order to increase the character localization recall. + + After the ER filtering is done on each input channel, character candidates must be grouped in + high-level text blocks (i.e. words, text lines, paragraphs, ...). The opencv_text module implements + two different grouping algorithms: the Exhaustive Search algorithm proposed in @cite Neumann12 for + grouping horizontally aligned text, and the method proposed by Lluis Gomez and Dimosthenis Karatzas + in @cite Gomez13 @cite Gomez14 for grouping arbitrary oriented text (see erGrouping). + + To see the text detector at work, have a look at the textdetection demo: + @defgroup text_recognize Scene Text Recognition @} diff --git a/modules/text/include/opencv2/text/ocr.hpp b/modules/text/include/opencv2/text/ocr.hpp index a0c967e87bd..083fc7a5aba 100644 --- a/modules/text/include/opencv2/text/ocr.hpp +++ b/modules/text/include/opencv2/text/ocr.hpp @@ -363,7 +363,6 @@ CV_EXPORTS_W Ptr loadOCRHMMClassifierCNN(cons */ CV_EXPORTS_W Ptr loadOCRHMMClassifier(const String& filename, int classifier); -//! @} /** @brief Utility function to create a tailored language model transitions table from a given list of words (lexicon). * diff --git a/modules/videostab/include/opencv2/videostab.hpp b/modules/videostab/include/opencv2/videostab.hpp index ca3f5adef2b..14c52ebaf1b 100644 --- a/modules/videostab/include/opencv2/videostab.hpp +++ b/modules/videostab/include/opencv2/videostab.hpp @@ -44,7 +44,7 @@ #define OPENCV_VIDEOSTAB_HPP /** - @defgroup videostab Video Stabilization +@defgroup videostab Video Stabilization The video stabilization module contains a set of functions and classes that can be used to solve the problem of video stabilization. There are a few methods implemented, most of them are described in @@ -53,26 +53,24 @@ paper methods. ### References - 1. "Full-Frame Video Stabilization with Motion Inpainting" - Yasuyuki Matsushita, Eyal Ofek, Weina Ge, Xiaoou Tang, Senior Member, and Heung-Yeung Shum - 2. "Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths" - Matthias Grundmann, Vivek Kwatra, Irfan Essa +1. "Full-Frame Video Stabilization with Motion Inpainting" + Yasuyuki Matsushita, Eyal Ofek, Weina Ge, Xiaoou Tang, Senior Member, and Heung-Yeung Shum +2. "Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths" + Matthias Grundmann, Vivek Kwatra, Irfan Essa - @{ - @defgroup videostab_motion Global Motion Estimation +@{ + @defgroup videostab_motion Global Motion Estimation -The video stabilization module contains a set of functions and classes for global motion estimation -between point clouds or between images. In the last case features are extracted and matched -internally. For the sake of convenience the motion estimation functions are wrapped into classes. -Both the functions and the classes are available. + The video stabilization module contains a set of functions and classes for global motion estimation + between point clouds or between images. In the last case features are extracted and matched + internally. For the sake of convenience the motion estimation functions are wrapped into classes. + Both the functions and the classes are available. - @defgroup videostab_marching Fast Marching Method - -The Fast Marching Method @cite Telea04 is used in of the video stabilization routines to do motion and -color inpainting. The method is implemented is a flexible way and it's made public for other users. - - @} + @defgroup videostab_marching Fast Marching Method + The Fast Marching Method @cite Telea04 is used in of the video stabilization routines to do motion and + color inpainting. The method is implemented is a flexible way and it's made public for other users. +@} */ #include "opencv2/videostab/stabilizer.hpp" diff --git a/modules/viz/include/opencv2/viz.hpp b/modules/viz/include/opencv2/viz.hpp index fc79b8b60e7..c31ed342ab1 100644 --- a/modules/viz/include/opencv2/viz.hpp +++ b/modules/viz/include/opencv2/viz.hpp @@ -60,25 +60,24 @@ interact with it. 3D visualization window (see Viz3d) is used to display widgets (see Widget), and it provides several methods to interact with scene and widgets. - @{ +@{ @defgroup viz_widget Widget -In this section, the widget framework is explained. Widgets represent 2D or 3D objects, varying from -simple ones such as lines to complex ones such as point clouds and meshes. + In this section, the widget framework is explained. Widgets represent 2D or 3D objects, varying from + simple ones such as lines to complex ones such as point clouds and meshes. -Widgets are **implicitly shared**. Therefore, one can add a widget to the scene, and modify the -widget without re-adding the widget. + Widgets are **implicitly shared**. Therefore, one can add a widget to the scene, and modify the + widget without re-adding the widget. -@code -// Create a cloud widget -viz::WCloud cw(cloud, viz::Color::red()); -// Display it in a window -myWindow.showWidget("CloudWidget1", cw); -// Modify it, and it will be modified in the window. -cw.setColor(viz::Color::yellow()); -@endcode - - @} + @code + // Create a cloud widget + viz::WCloud cw(cloud, viz::Color::red()); + // Display it in a window + myWindow.showWidget("CloudWidget1", cw); + // Modify it, and it will be modified in the window. + cw.setColor(viz::Color::yellow()); + @endcode +@} */ #endif /* OPENCV_VIZ_HPP */ diff --git a/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp b/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp index 3313a38348a..3793541c238 100644 --- a/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp +++ b/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp @@ -46,19 +46,18 @@ the use of this software, even if advised of the possibility of such damage. @{ @defgroup xfeatures2d_experiment Experimental 2D Features Algorithms -This section describes experimental algorithms for 2d feature detection. + This section describes experimental algorithms for 2d feature detection. @defgroup xfeatures2d_nonfree Non-free 2D Features Algorithms -This section describes two popular algorithms for 2d feature detection, SIFT and SURF, that are -known to be patented. You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk. + This section describes two popular algorithms for 2d feature detection, SIFT and SURF, that are + known to be patented. You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk. @defgroup xfeatures2d_match Experimental 2D Features Matching Algorithm -This section describes the following matching strategies: - - GMS: Grid-based Motion Statistics, @cite Bian2017gms - - LOGOS: Local geometric support for high-outlier spatial verification, @cite Lowry2018LOGOSLG - + This section describes the following matching strategies: + - GMS: Grid-based Motion Statistics, @cite Bian2017gms + - LOGOS: Local geometric support for high-outlier spatial verification, @cite Lowry2018LOGOSLG @} */ diff --git a/modules/xfeatures2d/include/opencv2/xfeatures2d/nonfree.hpp b/modules/xfeatures2d/include/opencv2/xfeatures2d/nonfree.hpp index 8eb11aa6653..5fb299f20f4 100644 --- a/modules/xfeatures2d/include/opencv2/xfeatures2d/nonfree.hpp +++ b/modules/xfeatures2d/include/opencv2/xfeatures2d/nonfree.hpp @@ -50,6 +50,9 @@ namespace cv namespace xfeatures2d { +//! @addtogroup xfeatures2d_nonfree +//! @{ + /** @brief Class for extracting Speeded Up Robust Features from an image @cite Bay06 . The algorithm parameters: diff --git a/modules/ximgproc/include/opencv2/ximgproc.hpp b/modules/ximgproc/include/opencv2/ximgproc.hpp index dca0443c0ad..099205126cb 100644 --- a/modules/ximgproc/include/opencv2/ximgproc.hpp +++ b/modules/ximgproc/include/opencv2/ximgproc.hpp @@ -65,12 +65,13 @@ #include "ximgproc/find_ellipses.hpp" -/** @defgroup ximgproc Extended Image Processing - @{ +/** +@defgroup ximgproc Extended Image Processing +@{ @defgroup ximgproc_edge Structured forests for fast edge detection -This module contains implementations of modern structured edge detection algorithms, -i.e. algorithms which somehow takes into account pixel affinities in natural images. + This module contains implementations of modern structured edge detection algorithms, + i.e. algorithms which somehow takes into account pixel affinities in natural images. @defgroup ximgproc_edgeboxes EdgeBoxes @@ -84,16 +85,16 @@ i.e. algorithms which somehow takes into account pixel affinities in natural ima @defgroup ximgproc_edge_drawing EdgeDrawing -EDGE DRAWING LIBRARY FOR GEOMETRIC FEATURE EXTRACTION AND VALIDATION + EDGE DRAWING LIBRARY FOR GEOMETRIC FEATURE EXTRACTION AND VALIDATION -Edge Drawing (ED) algorithm is an proactive approach on edge detection problem. In contrast to many other existing edge detection algorithms which follow a subtractive -approach (i.e. after applying gradient filters onto an image eliminating pixels w.r.t. several rules, e.g. non-maximal suppression and hysteresis in Canny), ED algorithm -works via an additive strategy, i.e. it picks edge pixels one by one, hence the name Edge Drawing. Then we process those random shaped edge segments to extract higher level -edge features, i.e. lines, circles, ellipses, etc. The popular method of extraction edge pixels from the thresholded gradient magnitudes is non-maximal supression that tests -every pixel whether it has the maximum gradient response along its gradient direction and eliminates if it does not. However, this method does not check status of the -neighboring pixels, and therefore might result low quality (in terms of edge continuity, smoothness, thinness, localization) edge segments. Instead of non-maximal supression, -ED points a set of edge pixels and join them by maximizing the total gradient response of edge segments. Therefore it can extract high quality edge segments without need for -an additional hysteresis step. + Edge Drawing (ED) algorithm is an proactive approach on edge detection problem. In contrast to many other existing edge detection algorithms which follow a subtractive + approach (i.e. after applying gradient filters onto an image eliminating pixels w.r.t. several rules, e.g. non-maximal suppression and hysteresis in Canny), ED algorithm + works via an additive strategy, i.e. it picks edge pixels one by one, hence the name Edge Drawing. Then we process those random shaped edge segments to extract higher level + edge features, i.e. lines, circles, ellipses, etc. The popular method of extraction edge pixels from the thresholded gradient magnitudes is non-maximal supression that tests + every pixel whether it has the maximum gradient response along its gradient direction and eliminates if it does not. However, this method does not check status of the + neighboring pixels, and therefore might result low quality (in terms of edge continuity, smoothness, thinness, localization) edge segments. Instead of non-maximal supression, + ED points a set of edge pixels and join them by maximizing the total gradient response of edge segments. Therefore it can extract high quality edge segments without need for + an additional hysteresis step. @defgroup ximgproc_fourier Fourier descriptors @@ -115,8 +116,7 @@ an additional hysteresis step. The size of the original image is required for compatibility with the imgproc functions when the boundary handling requires that pixel outside the image boundary are "on". - - @} +@} */ namespace cv @@ -124,6 +124,9 @@ namespace cv namespace ximgproc { +//! @addtogroup ximgproc +//! @{ + enum ThinningTypes{ THINNING_ZHANGSUEN = 0, // Thinning technique of Zhang-Suen THINNING_GUOHALL = 1 // Thinning technique of Guo-Hall @@ -139,9 +142,6 @@ enum LocalBinarizationMethods{ BINARIZATION_NICK = 3 //!< NICK technique. See @cite Khurshid2009 . }; -//! @addtogroup ximgproc -//! @{ - /** @brief Performs thresholding on input images using Niblack's technique or some of the popular variations it inspired. diff --git a/modules/ximgproc/include/opencv2/ximgproc/color_match.hpp b/modules/ximgproc/include/opencv2/ximgproc/color_match.hpp index c18390d4ac6..8408b5b2331 100644 --- a/modules/ximgproc/include/opencv2/ximgproc/color_match.hpp +++ b/modules/ximgproc/include/opencv2/ximgproc/color_match.hpp @@ -61,6 +61,8 @@ CV_EXPORTS_W void qdft(InputArray img, OutputArray qimg, int flags, bool sideL */ CV_EXPORTS_W void colorMatchTemplate(InputArray img, InputArray templ, OutputArray result); +//! @} + } } #endif diff --git a/modules/ximgproc/include/opencv2/ximgproc/deriche_filter.hpp b/modules/ximgproc/include/opencv2/ximgproc/deriche_filter.hpp index 26d3b6759da..18adade6f90 100644 --- a/modules/ximgproc/include/opencv2/ximgproc/deriche_filter.hpp +++ b/modules/ximgproc/include/opencv2/ximgproc/deriche_filter.hpp @@ -71,6 +71,8 @@ CV_EXPORTS_W void GradientDericheY(InputArray op, OutputArray dst, double alpha, */ CV_EXPORTS_W void GradientDericheX(InputArray op, OutputArray dst, double alpha,double omega); +//! @} + } } #endif diff --git a/modules/ximgproc/include/opencv2/ximgproc/edgepreserving_filter.hpp b/modules/ximgproc/include/opencv2/ximgproc/edgepreserving_filter.hpp index f5685ce39bb..758b61b4349 100644 --- a/modules/ximgproc/include/opencv2/ximgproc/edgepreserving_filter.hpp +++ b/modules/ximgproc/include/opencv2/ximgproc/edgepreserving_filter.hpp @@ -26,8 +26,8 @@ namespace cv { namespace ximgproc { */ CV_EXPORTS_W void edgePreservingFilter( InputArray src, OutputArray dst, int d, double threshold ); -}} // namespace - //! @} +}} // namespace + #endif diff --git a/modules/ximgproc/include/opencv2/ximgproc/fast_hough_transform.hpp b/modules/ximgproc/include/opencv2/ximgproc/fast_hough_transform.hpp index adfbf543b57..94668b06520 100644 --- a/modules/ximgproc/include/opencv2/ximgproc/fast_hough_transform.hpp +++ b/modules/ximgproc/include/opencv2/ximgproc/fast_hough_transform.hpp @@ -82,8 +82,7 @@ enum AngleRangeOption * two operands. Formally, a binary operation @f$ f @f$ on a set @f$ S @f$ * is a binary relation that maps elements of the Cartesian product * @f$ S \times S @f$ to @f$ S @f$: -* @f[ f: S \times S \to S @f] - * @ingroup MinUtils_MathOper + * @f[ f: S \times S \to S @f] */ enum HoughOp { diff --git a/modules/ximgproc/include/opencv2/ximgproc/paillou_filter.hpp b/modules/ximgproc/include/opencv2/ximgproc/paillou_filter.hpp index 03754a1119d..56fcd3c9618 100644 --- a/modules/ximgproc/include/opencv2/ximgproc/paillou_filter.hpp +++ b/modules/ximgproc/include/opencv2/ximgproc/paillou_filter.hpp @@ -61,6 +61,8 @@ namespace ximgproc { CV_EXPORTS void GradientPaillouY(InputArray op, OutputArray _dst, double alpha, double omega); CV_EXPORTS void GradientPaillouX(InputArray op, OutputArray _dst, double alpha, double omega); +//! @} + } } #endif diff --git a/modules/ximgproc/include/opencv2/ximgproc/peilin.hpp b/modules/ximgproc/include/opencv2/ximgproc/peilin.hpp index 1b224aaf88b..194f12e1196 100644 --- a/modules/ximgproc/include/opencv2/ximgproc/peilin.hpp +++ b/modules/ximgproc/include/opencv2/ximgproc/peilin.hpp @@ -27,6 +27,8 @@ namespace cv { namespace ximgproc { /** @overload */ CV_EXPORTS_W void PeiLinNormalization ( InputArray I, OutputArray T ); + //! @} + }} // namespace #endif diff --git a/modules/ximgproc/include/opencv2/ximgproc/run_length_morphology.hpp b/modules/ximgproc/include/opencv2/ximgproc/run_length_morphology.hpp index c19e2d858db..6cf2eb663c1 100644 --- a/modules/ximgproc/include/opencv2/ximgproc/run_length_morphology.hpp +++ b/modules/ximgproc/include/opencv2/ximgproc/run_length_morphology.hpp @@ -113,6 +113,8 @@ CV_EXPORTS void createRLEImage(const std::vector& runs, OutputArray CV_EXPORTS void morphologyEx(InputArray rlSrc, OutputArray rlDest, int op, InputArray rlKernel, bool bBoundaryOnForErosion = true, Point anchor = Point(0,0)); +//! @} + } } }