From: Pascal Obry Date: Mon, 11 May 2020 09:23:49 +0000 (+0200) Subject: Add support for OpenCV 4.x needed for Debian/sid (will be Debian 11). X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=1b786186384dc4c8d45897d83f287e52118735a6;p=modules%2Fgeom.git Add support for OpenCV 4.x needed for Debian/sid (will be Debian 11). --- diff --git a/src/ShapeRecognition/ShapeRec_FeatureDetector.cxx b/src/ShapeRecognition/ShapeRec_FeatureDetector.cxx index fbe3c27e7..210382e0a 100644 --- a/src/ShapeRecognition/ShapeRec_FeatureDetector.cxx +++ b/src/ShapeRecognition/ShapeRec_FeatureDetector.cxx @@ -28,13 +28,12 @@ #include "utilities.h" #include +#include - -#if CV_MAJOR_VERSION == 3 -#define cvCvtPixToPlane cvSplit +#if CV_MAJOR_VERSION < 3 +#error OpenCV version below 3 is not supported #endif - // 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 @@ -65,10 +64,16 @@ void ShapeRec_FeatureDetector::SetPath( const std::string& thePath ) imagePath = thePath; if (imagePath != "") { +#if CV_MAJOR_VERSION > 3 + cv::Mat src = cv::imread(imagePath.c_str(), cv::IMREAD_COLOR); + imgHeight = src.size().height; + imgWidth = src.size().width; +#else IplImage* src = cvLoadImage(imagePath.c_str(),CV_LOAD_IMAGE_COLOR); imgHeight = src->height; imgWidth = src->width; cvReleaseImage(&src); +#endif } } @@ -80,6 +85,31 @@ void ShapeRec_FeatureDetector::ComputeCorners( bool useROI, ShapeRec_Parameters* ShapeRec_CornersParameters* aCornersParameters = dynamic_cast( parameters ); if ( !aCornersParameters ) aCornersParameters = new ShapeRec_CornersParameters(); +#if CV_MAJOR_VERSION > 3 + cv::Mat src_img_gray = cv::imread (imagePath.c_str(), cv::IMREAD_GRAYSCALE); + + if ( useROI ) + { + // If a ROI as been set use it for detection + src_img_gray = src_img_gray(rect); + } + + std::vector corners; + // image height and width + imgHeight = src_img_gray.size().height; + imgWidth = src_img_gray.size().width; + + cv::goodFeaturesToTrack (src_img_gray, corners, cornerCount, + aCornersParameters->qualityLevel, aCornersParameters->minDistance, + cv::noArray(), 3, false); + + cv::cornerSubPix + (src_img_gray, corners, + cvSize (aCornersParameters->kernelSize, aCornersParameters->kernelSize), + cvSize (-1, -1), + cvTermCriteria (aCornersParameters->typeCriteria, aCornersParameters->maxIter, aCornersParameters->epsilon)); + +#else // Images to be used for detection IplImage *eig_img, *temp_img, *src_img_gray; @@ -109,7 +139,7 @@ void ShapeRec_FeatureDetector::ComputeCorners( bool useROI, ShapeRec_Parameters* cvReleaseImage (&eig_img); cvReleaseImage (&temp_img); cvReleaseImage (&src_img_gray); - +#endif } /*! @@ -145,6 +175,68 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters } else //COLORFILTER { +#if CV_MAJOR_VERSION > 3 + cv::Mat input_image = cv::imread(imagePath.c_str(), cv::IMREAD_COLOR); + + ShapeRec_ColorFilterParameters* aColorFilterParameters = dynamic_cast( parameters ); + if ( !aColorFilterParameters ) aColorFilterParameters = new ShapeRec_ColorFilterParameters(); + + cv::GaussianBlur( input_image, input_image, + cvSize (aColorFilterParameters->smoothSize, aColorFilterParameters->smoothSize), 0 ); + + cv::Mat sample_image = input_image(rect); + + cv::Mat sample_hsv; + + cv::cvtColor(sample_image, sample_hsv, CV_BGR2HSV); + + /// Separate the image in 3 places ( H, S and V ) + std::vector hsv_planes; + cv::split( sample_image, hsv_planes ); + + cv::Mat sample_planes[] = { hsv_planes[0], hsv_planes[1] }; + + // Create the hue / saturation histogram of the SAMPLE image. + // This histogramm will be representative of what is the zone + // we want to find the frontier of. Indeed, the sample image is meant to + // be representative of this zone + const float hranges[] = { 0, 180 }; + const float sranges[] = { 0, 256 }; + const float* ranges[] = { hranges, sranges }; + + cv::Mat sample_hist; + cv::calcHist( sample_planes, 2, 0, cv::Mat(), sample_hist, 1, aColorFilterParameters->histSize, ranges ); + + // Calculate the back projection of hue and saturation planes of the INPUT image + // by mean of the histogram of the SAMPLE image. + // + // The pixels which (h,s) coordinates correspond to high values in the histogram + // will have high values in the grey image result. It means that a pixel of the INPUT image + // which is more probably in the zone represented by the SAMPLE image, will be whiter + // in the back projection. + + // Get hue and saturation planes of the INPUT image + cv::Mat input_hsv; + cv::cvtColor(input_image, input_hsv, CV_BGR2HSV); + + /// Separate the image in 3 places ( H, S and V ) + std::vector input_hsv_planes; + cv::split( input_hsv, input_hsv_planes ); + + cv::Mat input_planes[] = { input_hsv_planes[0], input_hsv_planes[1] }; + + cv::Mat backproject, binary_backproject; + + // Compute the back projection + cv::calcBackProject(input_planes, 2, 0, sample_hist, backproject, ranges); + + // Threshold in order to obtain a binary image + cv::threshold(backproject, binary_backproject, + aColorFilterParameters->threshold, aColorFilterParameters->maxThreshold, cv::THRESH_BINARY); + + detected_edges = cv::Mat(binary_backproject); + +#else // Load the input image where we want to detect contours IplImage* input_image = cvLoadImage(imagePath.c_str(),CV_LOAD_IMAGE_COLOR); @@ -168,8 +260,8 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters CvHistogram* sample_hist; cvCvtColor(sample_image, sample_hsv, CV_BGR2HSV); - - cvCvtPixToPlane(sample_hsv, sample_h_plane, sample_s_plane, 0, 0); + + cvSplit(sample_hsv, sample_h_plane, sample_s_plane, 0, 0); IplImage* sample_planes[] = { sample_h_plane, sample_s_plane }; // Create the hue / saturation histogram of the SAMPLE image. @@ -224,7 +316,7 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters // Get hue and saturation planes of the INPUT image cvCvtColor(input_image, input_hsv, CV_BGR2HSV); - cvCvtPixToPlane(input_hsv, input_hplane, input_splane, 0, 0); + cvSplit(input_hsv, input_hplane, input_splane, 0, 0); IplImage* input_planes[] = { input_hplane, input_splane }; // Compute the back projection @@ -242,10 +334,7 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters cvReleaseImage(&input_splane); cvReleaseImage(&backproject); -#if CV_MAJOR_VERSION == 3 detected_edges = cv::cvarrToMat(binary_backproject); -#else - detected_edges = cv::Mat(binary_backproject); #endif } // else if ( detection_method == RIDGE_DETECTOR ) // Method adapted for engineering drawings (e.g. watershed functionality could be used here cf.OpenCV documentation and samples) diff --git a/src/ShapeRecognition/ShapeRec_FeatureDetector.hxx b/src/ShapeRecognition/ShapeRec_FeatureDetector.hxx index 8deea83fc..2c1e5a438 100644 --- a/src/ShapeRecognition/ShapeRec_FeatureDetector.hxx +++ b/src/ShapeRecognition/ShapeRec_FeatureDetector.hxx @@ -29,16 +29,18 @@ // https://tracker.dev.opencascade.org/view.php?id=28457 issue. #ifdef HAVE_TBB #undef HAVE_TBB - #include - #include - #include - #include + + #include + #include + #include + #include #define HAVE_TBB -#else - #include - #include - #include - #include + +#else // HAVE_TBB + #include + #include + #include + #include #endif // Qt