From: gdd Date: Fri, 28 Oct 2011 13:46:41 +0000 (+0000) Subject: rnc: some simplifications in the code X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=eb648c97a41a5b4783c8139dceaabef408fa5f77;p=modules%2Fgui.git rnc: some simplifications in the code --- diff --git a/src/OCCViewer/OCCViewer_FeatureDetector.cxx b/src/OCCViewer/OCCViewer_FeatureDetector.cxx index b3ae5a321..31e406c63 100644 --- a/src/OCCViewer/OCCViewer_FeatureDetector.cxx +++ b/src/OCCViewer/OCCViewer_FeatureDetector.cxx @@ -28,6 +28,12 @@ using namespace cv; +//TODO : All the following methods but ComputeContours use the C API of OpenCV while ComputContours +// uses the C++ API of the library. +// This should be homogenized and preferably by using the C++ API (which is more recent for all the methods + +// The code has to be "cleaned up" too + /*! Constructor \param theFilename - image to process @@ -88,54 +94,68 @@ void OCCViewer_FeatureDetector::ComputeCorners(){ /*! Computes the contours of the image located at imagePath */ -bool OCCViewer_FeatureDetector::ComputeContours(){ +bool OCCViewer_FeatureDetector::ComputeContours( int detection_method ){ // Initialising images Mat src, src_gray; Mat detected_edges; - - // Thresholds for Canny detector - int lowThreshold = 100; - int ratio = 3; - int kernel_size = 3; // 3,5 or 7 - bool isRectSelected = (rect.width > 1); - if (isRectSelected) - src = _colorFiltering(); - else - { - src = imread( imagePath.c_str() ); - } - + // Read image + src = imread( imagePath.c_str() ); if( !src.data ) - { return false; } - - // Convert the image to grayscale - if (src.channels() == 3) - cvtColor( src, src_gray, CV_BGR2GRAY ); - else if (src.channels() == 1) - src_gray = src; + return false; + + if ( detection_method == CANNY ) // The problem is that with that filter the detector detects double contours + { + // Thresholds for Canny detector + int lowThreshold = 100; + int ratio = 3; + int kernel_size = 3; // 3,5 or 7 + + // Convert the image to grayscale + if (src.channels() == 3) + cvtColor( src, src_gray, CV_BGR2GRAY ); + else if (src.channels() == 1) + src_gray = src; - if(isRectSelected) - { - _detectAndRetrieveContours( src_gray ); - } - else - { // Reduce noise with a kernel 3x3 blur( src_gray, detected_edges, Size(3,3) ); - // Canny detector - Canny( detected_edges, detected_edges, lowThreshold, lowThreshold*ratio, kernel_size, /*L2gradient =*/true ); // The problem is that with that filter the detector detects double contours - - // Retrieve contours on the Canny result - _detectAndRetrieveContours( detected_edges ); + Canny( detected_edges, detected_edges, lowThreshold, lowThreshold*ratio, kernel_size, /*L2gradient =*/true ); + } + else if ( detection_method == COLORFILTER ) + { + if ( !rect.width > 1 ) + return false; + detected_edges = _colorFiltering(); } + else if ( detection_method == RIDGE_DETECTOR ) // Method adapted for engineering drawings (e.g. watershed functionnality could be used here cf.OpenCV documentation and samples) + { + // TODO + return false; + } + _detectAndRetrieveContours( detected_edges ); return true; } +/*! + Computes the lines in the image located at imagePath +*/ +bool OCCViewer_FeatureDetector::ComputeLines(){ + MESSAGE("OCCViewer_FeatureDetector::ComputeLines()") + // Initialising images + Mat src, src_gray, detected_edges, dst; + + src=imread(imagePath.c_str(), 0); + + Canny( src, dst, 50, 200, 3 ); + HoughLinesP( dst, lines, 1, CV_PI/180, 80, 30, 10 ); + return true; + +} + /*! Stores a region of interest given by user in rect \param theRect - Region Of Interest of the image located at imagePath @@ -154,11 +174,9 @@ void OCCViewer_FeatureDetector::SetROI( const QRect& theRect ) void OCCViewer_FeatureDetector::_detectAndRetrieveContours( Mat src ) { src = src > 1; - int method ;//= CV_CHAIN_APPROX_SIMPLE; -// if ( rect.width > 1 ) - method = CV_CHAIN_APPROX_NONE;//CV_CHAIN_APPROX_TC89_KCOS;//CV_CHAIN_APPROX_TC89_L1; + int method = CV_CHAIN_APPROX_NONE; findContours( src, contours, hierarchy,CV_RETR_CCOMP, method); - // Other possible approximations CV_CHAIN_APPROX_TC89_L1, CV_CHAIN_APPROX_SIMPLE cf. OpenCV documentation + // Other possible approximations CV_CHAIN_APPROX_TC89_KCOS, CV_CHAIN_APPROX_TC89_L1, CV_CHAIN_APPROX_SIMPLE cf. OpenCV documentation // for precise information } @@ -171,6 +189,9 @@ void OCCViewer_FeatureDetector::_detectAndRetrieveContours( Mat src ) Mat OCCViewer_FeatureDetector::_colorFiltering() { IplImage* find_image = cvLoadImage(imagePath.c_str(),CV_LOAD_IMAGE_COLOR); + // Reduce noise with a kernel 3x3 + cvSmooth( find_image, find_image, CV_GAUSSIAN, 3, 3 ); + if ( !rect.width > 1 ) return Mat(find_image); @@ -184,7 +205,6 @@ Mat OCCViewer_FeatureDetector::_colorFiltering() IplImage* test_hsv = cvCreateImage(cvGetSize(test_image),8,3); IplImage* test_hue = cvCreateImage(cvGetSize(test_image),8,1); - CvHistogram* hist; cvCvtColor(test_image, test_hsv, CV_BGR2HSV); @@ -199,6 +219,30 @@ Mat OCCViewer_FeatureDetector::_colorFiltering() //calculate hue` histogram cvCalcHist(&test_hue, hist, 0 ,0); +// // TEST print of the histogram for debugging +// IplImage* hist_image = cvCreateImage(cvSize(320,300),8,3); +// +// //draw hist on hist_test image. +// cvZero(hist_image); +// float max_value = 0; +// cvGetMinMaxHistValue(hist, 0 , &max_value, 0, 0); +// int bin_w = hist_image->width/size_hist; +// for(int i = 0; i < size_hist; i++ ) +// { +// //prevent overflow +// int val = cvRound( cvGetReal1D(hist->bins,i)*hist_image-> +// height/max_value); +// CvScalar color = CV_RGB(200,0,0); +// //hsv2rgb(i*180.f/size_hist); +// cvRectangle( hist_image, cvPoint(i*bin_w,hist_image->height), +// cvPoint((i+1)*bin_w,hist_image->height - val), +// color, -1, 8, 0 ); +// } +// +// +// cvNamedWindow("hist", 1); cvShowImage("hist",hist_image); + + //calculate back projection of hue plane of input image IplImage* backproject = cvCreateImage(cvGetSize(find_image), 8, 1); IplImage* binary_backproject = cvCreateImage(cvGetSize(find_image), 8, 1); @@ -210,7 +254,7 @@ Mat OCCViewer_FeatureDetector::_colorFiltering() cvCalcBackProject(&find_hue, backproject, hist); // Threshold in order to obtain binary image - cvThreshold(backproject, binary_backproject, 1, 255, CV_THRESH_BINARY); // NOTE it would be good to think about the best threshold to use (1 here) + cvThreshold(backproject, binary_backproject, 1, 255, CV_THRESH_BINARY); // NOTE it would be good to think about the best threshold to use (it's 1 for now) cvReleaseImage(&test_image); cvReleaseImage(&test_hsv); cvReleaseImage(&test_hue); diff --git a/src/OCCViewer/OCCViewer_FeatureDetector.h b/src/OCCViewer/OCCViewer_FeatureDetector.h index b05e526c3..3fc8b15d9 100644 --- a/src/OCCViewer/OCCViewer_FeatureDetector.h +++ b/src/OCCViewer/OCCViewer_FeatureDetector.h @@ -32,6 +32,13 @@ #include #include +enum // Method used for contour detection +{ + CANNY, + COLORFILTER, + RIDGE_DETECTOR +}; + class OCCViewer_FeatureDetector { public: @@ -42,11 +49,13 @@ public: OCCViewer_FeatureDetector( const QString& ); // Constructor void ComputeCorners(); // Detects the corners from the image located at imagePath - bool ComputeContours(); // Detects the corners from the image located at imagePath + bool ComputeLines(); // Detects the lines from the image located at imagePath + bool ComputeContours( int method ); // Detects the contours from the image located at imagePath void SetROI( const QRect& ); // Sets a Region Of Interest in the image CvPoint2D32f* GetCorners() { return corners; }; CvContoursArray GetContours() { return contours; }; + std::vector GetLines() { return lines; }; std::vector GetContoursHierarchy() { return hierarchy; }; int GetCornerCount() { return cornerCount; }; int GetImgHeight() { return imgHeight; }; @@ -61,6 +70,7 @@ private: CvContoursArray contours; std::vector hierarchy; + std::vector lines; int imgHeight; int imgWidth; CvRect rect;