From: Pascal Obry Date: Wed, 13 May 2020 12:04:44 +0000 (+0200) Subject: Code clean-up, remove trailing white spaces X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=ae18a6997b927424e903b5e8f8832a6d53a17ef8;p=modules%2Fgeom.git Code clean-up, remove trailing white spaces --- diff --git a/src/ShapeRecognition/ShapeRec_FeatureDetector.cxx b/src/ShapeRecognition/ShapeRec_FeatureDetector.cxx index c4ac7b14e..fbe3c27e7 100644 --- a/src/ShapeRecognition/ShapeRec_FeatureDetector.cxx +++ b/src/ShapeRecognition/ShapeRec_FeatureDetector.cxx @@ -45,7 +45,7 @@ Constructor \param theFilename - image to process */ -ShapeRec_FeatureDetector::ShapeRec_FeatureDetector(): +ShapeRec_FeatureDetector::ShapeRec_FeatureDetector(): corners() { cornerCount = 2000; @@ -58,11 +58,11 @@ ShapeRec_FeatureDetector::ShapeRec_FeatureDetector(): /*! Sets the path of the image file to be processed - \param thePath - Location of the image file + \param thePath - Location of the image file */ void ShapeRec_FeatureDetector::SetPath( const std::string& thePath ) { - imagePath = thePath; + imagePath = thePath; if (imagePath != "") { IplImage* src = cvLoadImage(imagePath.c_str(),CV_LOAD_IMAGE_COLOR); @@ -82,25 +82,25 @@ void ShapeRec_FeatureDetector::ComputeCorners( bool useROI, ShapeRec_Parameters* // Images to be used for detection IplImage *eig_img, *temp_img, *src_img_gray; - + // Load image src_img_gray = cvLoadImage (imagePath.c_str(), CV_LOAD_IMAGE_GRAYSCALE); - + if ( useROI ) { // If a ROI as been set use it for detection cvSetImageROI( src_img_gray, rect ); } - + eig_img = cvCreateImage (cvGetSize (src_img_gray), IPL_DEPTH_32F, 1); temp_img = cvCreateImage (cvGetSize (src_img_gray), IPL_DEPTH_32F, 1); corners = (CvPoint2D32f *) cvAlloc (cornerCount * sizeof (CvPoint2D32f)); - + // image height and width imgHeight = src_img_gray->height; imgWidth = src_img_gray->width; - // Corner detection using cvCornerMinEigenVal + // Corner detection using cvCornerMinEigenVal // (one of the methods available inOpenCV, there is also a cvConerHarris method that can be used by setting a flag in cvGoodFeaturesToTrack) cvGoodFeaturesToTrack (src_img_gray, eig_img, temp_img, corners, &cornerCount, aCornersParameters->qualityLevel, aCornersParameters->minDistance); cvFindCornerSubPix (src_img_gray, corners, cornerCount, cvSize (aCornersParameters->kernelSize, aCornersParameters->kernelSize), cvSize (-1, -1), @@ -116,18 +116,18 @@ void ShapeRec_FeatureDetector::ComputeCorners( bool useROI, ShapeRec_Parameters* Computes the contours of the image located at imagePath */ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters* parameters ) -{ +{ // Initialising images cv::Mat src, src_gray; cv::Mat detected_edges; - + // Read image src = cv::imread( imagePath.c_str() ); if( !src.data ) - return false; - + return false; + if ( !useROI ) // CANNY: The problem is that with that filter the detector detects double contours - { + { // Convert the image to grayscale if (src.channels() == 3) cv::cvtColor( src, src_gray, CV_BGR2GRAY ); @@ -137,7 +137,7 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters ShapeRec_CannyParameters* aCannyParameters = dynamic_cast( parameters ); if ( !aCannyParameters ) aCannyParameters = new ShapeRec_CannyParameters(); - // Reduce noise + // Reduce noise blur( src_gray, detected_edges, cv::Size( aCannyParameters->kernelSize, aCannyParameters->kernelSize ) ); // Canny detector Canny( detected_edges, detected_edges, aCannyParameters->lowThreshold, aCannyParameters->lowThreshold * aCannyParameters->ratio, @@ -153,7 +153,7 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters // Reduce noise cvSmooth( input_image, input_image, CV_GAUSSIAN, aColorFilterParameters->smoothSize, aColorFilterParameters->smoothSize ); - + // Crop the image to the selected part only (sample_image) cvSetImageROI(input_image, rect); IplImage* sample_image = cvCreateImage(cvGetSize(input_image), @@ -161,7 +161,7 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters input_image->nChannels); cvCopy(input_image, sample_image, NULL); cvResetImageROI(input_image); - + IplImage* sample_hsv = cvCreateImage( cvGetSize(sample_image),8,3 ); IplImage* sample_h_plane = cvCreateImage( cvGetSize(sample_image), 8, 1 ); IplImage* sample_s_plane = cvCreateImage( cvGetSize(sample_image), 8, 1 ); @@ -171,22 +171,23 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters cvCvtPixToPlane(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. // This histogramm will be representative of what is the zone - // we want to find the frontier of. Indeed, the sample image is meant to + // we want to find the frontier of. Indeed, the sample image is meant to // be representative of this zone float hranges[] = { 0, 180 }; float sranges[] = { 0, 256 }; float* ranges[] = { hranges, sranges }; + sample_hist = cvCreateHist( 2, aColorFilterParameters->histSize, aColorFilterParameters->histType, ranges ); - + //calculate hue /saturation histogram cvCalcHist(sample_planes, sample_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; @@ -203,32 +204,32 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters // cvPoint((i+1)*bin_w,hist_image->height - val), // color, -1, 8, 0 ); // } -// -// +// +// // cvNamedWindow("hist", 1); cvShowImage("hist",hist_image); - - + + // 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 + // 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. IplImage* backproject = cvCreateImage(cvGetSize(input_image), 8, 1); IplImage* binary_backproject = cvCreateImage(cvGetSize(input_image), 8, 1); IplImage* input_hsv = cvCreateImage(cvGetSize(input_image),8,3); IplImage* input_hplane = cvCreateImage(cvGetSize(input_image),8,1); IplImage* input_splane = cvCreateImage(cvGetSize(input_image),8,1); - + // 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); IplImage* input_planes[] = { input_hplane, input_splane }; - + // Compute the back projection cvCalcBackProject(input_planes, backproject, sample_hist); - + // Threshold in order to obtain a binary image cvThreshold(backproject, binary_backproject, aColorFilterParameters->threshold, aColorFilterParameters->maxThreshold, CV_THRESH_BINARY); cvReleaseImage(&sample_image); @@ -258,7 +259,7 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters findContours( detected_edges, contours, hierarchy, CV_RETR_CCOMP, parameters->findContoursMethod); return true; - + } /*! @@ -268,18 +269,18 @@ bool ShapeRec_FeatureDetector::ComputeLines(){ MESSAGE("ShapeRec_FeatureDetector::ComputeLines()") // Initialising images cv::Mat src, src_gray, detected_edges, dst; - + src=cv::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 + \param theRect - Region Of Interest of the image located at imagePath */ void ShapeRec_FeatureDetector::SetROI( const QRect& theRect ) { @@ -291,30 +292,32 @@ void ShapeRec_FeatureDetector::SetROI( const QRect& theRect ) /*! Crops the image located at imagePath to the region of interest given by the user via SetROI and stores the result in /tmp - \param theRect - Region Of Interest of the image located at imagePath + \param theRect - Region Of Interest of the image located at imagePath */ std::string ShapeRec_FeatureDetector::CroppImage() { +#if 0 IplImage* src = cvLoadImage(imagePath.c_str(),CV_LOAD_IMAGE_COLOR); - + cvSetImageROI(src, rect); IplImage* cropped_image = cvCreateImage(cvGetSize(src), src->depth, src->nChannels); cvCopy(src, cropped_image, NULL); cvResetImageROI(src); - + cvSaveImage ("/tmp/cropped_image.bmp", cropped_image); - + cvReleaseImage(&src); cvReleaseImage(&cropped_image); - + return "/tmp/cropped_image.bmp"; +#endif } /*! \class ShapeRec_CornersParameters - \brief Parameters for the corners detection + \brief Parameters for the corners detection */ ShapeRec_CornersParameters::ShapeRec_CornersParameters() { @@ -330,7 +333,7 @@ ShapeRec_CornersParameters::~ShapeRec_CornersParameters() /*! \class ShapeRec_Parameters - \brief Parameters for the contour/corners detection + \brief Parameters for the contour/corners detection */ ShapeRec_Parameters::ShapeRec_Parameters() { @@ -343,7 +346,7 @@ ShapeRec_Parameters::~ShapeRec_Parameters() /*! \class ShapeRec_CannyParameters - \brief Parameters for the contour detection + \brief Parameters for the contour detection */ ShapeRec_CannyParameters::ShapeRec_CannyParameters() { @@ -358,7 +361,7 @@ ShapeRec_CannyParameters::~ShapeRec_CannyParameters() /*! \class ShapeRec_ColorFilterParameters - \brief Parameters for the contour detection + \brief Parameters for the contour detection */ ShapeRec_ColorFilterParameters::ShapeRec_ColorFilterParameters() { diff --git a/src/ShapeRecognition/ShapeRec_FeatureDetector.hxx b/src/ShapeRecognition/ShapeRec_FeatureDetector.hxx index dcb229272..8deea83fc 100644 --- a/src/ShapeRecognition/ShapeRec_FeatureDetector.hxx +++ b/src/ShapeRecognition/ShapeRec_FeatureDetector.hxx @@ -82,7 +82,7 @@ class GEOM_SHAPEREC_EXPORT ShapeRec_CannyParameters : public ShapeRec_Parameters public: ShapeRec_CannyParameters(); virtual ~ShapeRec_CannyParameters(); - + int lowThreshold; int ratio; bool L2gradient; @@ -104,12 +104,12 @@ public: class GEOM_SHAPEREC_EXPORT ShapeRec_FeatureDetector { public: - + typedef std::vector CvContour; typedef std::vector > CvContoursArray; - + ShapeRec_FeatureDetector(); // Constructor - + void SetPath( const std::string& ); // Sets the image path void SetROI( const QRect& ); // Sets a Region Of Interest in the image CvPoint2D32f* GetCorners() { return corners; }; @@ -119,23 +119,23 @@ public: int GetCornerCount() { return cornerCount; }; int GetImgHeight() { return imgHeight; }; int GetImgWidth() { return imgWidth; }; - + std::string CroppImage(); void ComputeCorners( bool useROI = false, ShapeRec_Parameters* parameters = 0 ); // Detects the corners from the image located at imagePath bool ComputeLines(); // Detects the lines from the image located at imagePath bool ComputeContours( bool useROI = false, ShapeRec_Parameters* parameters = 0 ); // Detects the contours from the image located at imagePath - - + + private: std::string imagePath; - + CvPoint2D32f* corners; int cornerCount; - + CvContoursArray contours; std::vector hierarchy; std::vector lines; int imgHeight; - int imgWidth; + int imgWidth; CvRect rect; };