Merge pull request #48 from kartik2112/kartik-interfacing

Kartik interfacing

.gitattributes

@@ -1 +1,2 @@
 training-images.zip filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text

GetMyHand/handDetection.cpp

@@ -7,8 +7,8 @@
 #include <opencv2/opencv.hpp>
 #include <opencv2/imgproc.hpp>
 #include "opencv2/highgui.hpp"
-#include "opencv2/cudafilters.hpp"
-#include "opencv2/cudaimgproc.hpp"
+// #include "opencv2/cudafilters.hpp"
+// #include "opencv2/cudaimgproc.hpp"
 
 #include "skinColorSegmentation.hpp"
 #include "trackBarHandling.hpp"

Jugaad/GetMyHand/handDetection.cpp

@@ -0,0 +1,198 @@
+#include <opencv2/opencv.hpp>
+#include <opencv2/imgproc.hpp>
+#include "opencv2/highgui.hpp"
+
+// #include "skinColorSegmentation.hpp"
+// #include "trackBarHandling.hpp"
+// #include "predictionsHandler.hpp"
+// #include "Classification/classifyPythonAPI.hpp"
+
+#include <iostream>
+#include <fstream>
+#include <cmath>
+#include <cstdlib>
+
+#include <experimental/filesystem>
+
+
+// #define OVERALL 0
+// #define SKIN_COLOR_EXTRACTION 1
+// #define MORPHOLOGY_OPERATIONS 2
+// #define MODIFIED_IMAGE_GENERATION 3
+// #define HAND_CONTOURS_GENERATION 4
+// #define CONTOURS_PRE_PROCESSING 5
+// #define CONTOURS_IMPROVEMENT 6
+// #define CONTOURS_POST_PROCESSING 7
+// #define CONTOUR_CLASSIFICATION_IN_PY 8
+
+using namespace std;
+using namespace cv;
+// namespace fs = std::experimental::filesystem;
+
+Mat getMyHand(Mat& image);
+// Mat findHandContours(Mat& src);
+// Mat combineExtractedWithMain(Mat& maskedImg,Mat& image);
+// void prepareWindows();
+// void connectContours(vector<vector<Point> > &contours);
+// void reduceClusterPoints(vector< vector< Point > > &contours, vector<vector<Point> > &hull);
+// void findClassUsingPythonModels( vector<float> &distVector );
+void detectAndEliminateFace(Mat frame);
+
+
+
+
+int morphOpenKernSize=2,morphCloseKernSize=3;
+int morphCloseNoOfIterations=3;
+
+int kernSize=2;
+int thresh=100;
+int contourDistThreshold = 30;
+double startTime;
+
+// extern int lH,lS,lV,hH,hS,hV;
+extern string subDirName;
+
+// extern vector<double> frameStepsTimes;
+
+extern char** args_v;
+extern int args_c;
+
+extern bool wrapperModeOn;
+
+extern long long predictedSign;
+
+int YMax=255,YMin=0,CrMax=200,CrMin=137,CbMax=150,CbMin=100;
+
+
+/*
+This is the main entry point function of this file
+*/
+Mat getMyHand(Mat& imageOG){
+	// startTime=(double)getTickCount();  //---Timing related part
+
+	// imshow("Original Image",imageOG);
+
+	detectAndEliminateFace(imageOG);
+
+	Mat image,imageYCrCb;
+
+	GaussianBlur(imageOG,image,Size(2*kernSize+1,2*kernSize+1),0,0);
+
+	medianBlur(image,image,2*kernSize+1);
+
+	/* Convert BGR Image into YCrCb Image */
+	cvtColor(image,imageYCrCb,CV_BGR2YCrCb);
+
+    Mat dst;
+	inRange(imageYCrCb,Scalar(YMin,CrMin,CbMin),Scalar(YMax,CrMax,CbMax),dst);
+
+
+	// frameStepsTimes[ SKIN_COLOR_EXTRACTION ] = (getTickCount()-(double)startTime)/getTickFrequency();   //---Timing related part
+	// startTime=(double)getTickCount();  //---Timing related part
+
+
+	Mat morphOpenElement = getStructuringElement(MORPH_CROSS,Size(morphOpenKernSize*2+1,morphOpenKernSize*2+1),Point(morphOpenKernSize,morphOpenKernSize));
+	Mat morphCloseElement = getStructuringElement(MORPH_CROSS,Size(morphCloseKernSize*2+1,morphCloseKernSize*2+1),Point(morphCloseKernSize,morphCloseKernSize));
+	Mat dstEroded;
+
+	morphologyEx(dst,dstEroded,MORPH_OPEN,morphOpenElement);
+
+	/* 
+	This will act make white areas larger then make them smaller
+	i.e. this will do erode(dilate(img))
+	Thus filling out missing spots in big white areas.
+	 */
+	morphologyEx(dstEroded,dstEroded,MORPH_CLOSE,morphCloseElement);
+
+
+	Mat dilateElement = getStructuringElement(MORPH_ELLIPSE,Size(3,3),Point(1,1));
+	/* This will enlarge white areas */
+	dilate(dstEroded,dstEroded,dilateElement,Point(-1,-1),1);
+
+
+	// frameStepsTimes[ MORPHOLOGY_OPERATIONS ] = (getTickCount()-(double)startTime)/getTickFrequency();   //---Timing related part
+	// startTime=(double)getTickCount();  //---Timing related part
+
+	// Mat maskedImg;
+	// cvtColor(dstEroded,dstEroded,CV_GRAY2BGR);
+	// bitwise_and(dstEroded,imageOG,maskedImg);
+
+	// Mat finImg=combineExtractedWithMain(maskedImg,image);
+	// Mat finImg = dstEroded;
+
+	// frameStepsTimes[ MODIFIED_IMAGE_GENERATION ] = (getTickCount()-(double)startTime)/getTickFrequency();   //---Timing related part
+	// startTime=(double)getTickCount();  //---Timing related part
+
+	// Mat contouredImg=findHandContours(finImg);
+
+	// frameStepsTimes[ HAND_CONTOURS_GENERATION ] = (getTickCount()-(double)startTime)/getTickFrequency();   //---Timing related part
+	// startTime=(double)getTickCount();  //---Timing related part
+
+	/// Show in a window  
+	// imshow("Contours", contouredImg );	
+	// imwrite("./ContourImages/img.png",contouredImg);
+	// imshow("Morphed Mask",dstEroded);
+	// imshow("Masked Image",maskedImg);
+	// imshow("Final Image",finImg);
+	// imshow("HSV + BGR Mask",dst);
+	// imshow("HSV Mask",dstHSV);
+
+
+
+	// Py_SetProgramName();
+	// Py_Initialize();
+	// PyRun_SimpleString("print '\nThis is first successful Python Statement being run from C++' ");
+	// Py_Finalize();
+
+
+
+
+	return dstEroded;	
+    // return maskedImg;
+}
+
+
+void detectAndEliminateFace(Mat frame){
+	String face_cascade_name = "./HaarCascades/haarcascade_frontalface_default.xml";
+	CascadeClassifier face_cascade;
+	if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading\n"); };
+	std::vector<Rect> faces;
+	Mat frame_gray;
+
+	cvtColor( frame, frame_gray, CV_BGR2GRAY );
+	equalizeHist( frame_gray, frame_gray );
+
+	//-- Detect faces
+	face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );
+
+	long long maxArea = 0;
+	int maxAreaRectInd = -1;
+
+	for( size_t i = 0; i < faces.size(); i++ )
+	{
+		long long area1 = (faces[i].width * faces[i].height);
+		if( area1 > maxArea){
+			maxArea = area1;
+			maxAreaRectInd = i;
+		}
+		// Point center( faces[i].x + faces[i].width*0.5, faces[i].y + faces[i].height*0.5 );
+		// ellipse( frame, center, Size( faces[i].width*0.5, faces[i].height*0.5), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );
+		// rectangle(frame,faces[i],Scalar(0,0,0),-1);
+
+		// Mat faceROI = frame_gray( faces[i] );
+	}
+
+	if(maxAreaRectInd==-1){
+		return;
+	}
+
+	//Modify face rectangle to eliminate neck and cover bigger part of face so as to eliminate possibility of leaving out some skin area
+	faces[maxAreaRectInd].x -= 10;
+	faces[maxAreaRectInd].y -= 10;
+	faces[maxAreaRectInd].width += 20;
+	faces[maxAreaRectInd].height += 45;
+	rectangle(frame,faces[maxAreaRectInd],Scalar(0,0,0),-1);
+
+	//-- Show what you got
+	// imshow( "Framed", frame );
+}

Jugaad/GetMyHand/handDetection.hpp

@@ -0,0 +1,39 @@
+#include <opencv2/opencv.hpp>
+#include <opencv2/imgproc.hpp>
+#include "opencv2/highgui.hpp"
+
+// #include "skinColorSegmentation.hpp"
+// #include "trackBarHandling.hpp"
+// #include "predictionsHandler.hpp"
+// #include "Classification/classifyPythonAPI.hpp"
+
+#include <iostream>
+#include <fstream>
+#include <cmath>
+#include <cstdlib>
+
+#include <experimental/filesystem>
+
+
+// #define OVERALL 0
+// #define SKIN_COLOR_EXTRACTION 1
+// #define MORPHOLOGY_OPERATIONS 2
+// #define MODIFIED_IMAGE_GENERATION 3
+// #define HAND_CONTOURS_GENERATION 4
+// #define CONTOURS_PRE_PROCESSING 5
+// #define CONTOURS_IMPROVEMENT 6
+// #define CONTOURS_POST_PROCESSING 7
+// #define CONTOUR_CLASSIFICATION_IN_PY 8
+
+using namespace std;
+using namespace cv;
+// namespace fs = std::experimental::filesystem;
+
+Mat getMyHand(Mat& image);
+// Mat findHandContours(Mat& src);
+// Mat combineExtractedWithMain(Mat& maskedImg,Mat& image);
+// void prepareWindows();
+// void connectContours(vector<vector<Point> > &contours);
+// void reduceClusterPoints(vector< vector< Point > > &contours, vector<vector<Point> > &hull);
+// void findClassUsingPythonModels( vector<float> &distVector );
+void detectAndEliminateFace(Mat frame);