-// Copyright (C) 2007-2013 CEA/DEN, EDF R&D, OPEN CASCADE
+// Copyright (C) 2007-2019 CEA/DEN, EDF R&D, OPEN CASCADE
//
// Copyright (C) 2003-2007 OPEN CASCADE, EADS/CCR, LIP6, CEA/DEN,
// CEDRAT, EDF R&D, LEG, PRINCIPIA R&D, BUREAU VERITAS
// This library is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
-// version 2.1 of the License.
+// version 2.1 of the License, or (at your option) any later version.
//
// This library is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
{
IplImage* src = cvLoadImage(imagePath.c_str(),CV_LOAD_IMAGE_COLOR);
imgHeight = src->height;
- imgWidth = src->width;
+ imgWidth = src->width;
+ cvReleaseImage(&src);
}
}
/*!
Computes the corners of the image located at imagePath
*/
-void ShapeRec_FeatureDetector::ComputeCorners( bool useROI, ShapeRec_Parameters* parameters ){
+void ShapeRec_FeatureDetector::ComputeCorners( bool useROI, ShapeRec_Parameters* parameters )
+{
ShapeRec_CornersParameters* aCornersParameters = dynamic_cast<ShapeRec_CornersParameters*>( parameters );
if ( !aCornersParameters ) aCornersParameters = new ShapeRec_CornersParameters();
/*!
Computes the contours of the image located at imagePath
*/
-bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters* parameters ){
-
+bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters* parameters )
+{
// Initialising images
cv::Mat src, src_gray;
cv::Mat detected_edges;
}
else //COLORFILTER
{
- IplImage* find_image = cvLoadImage(imagePath.c_str(),CV_LOAD_IMAGE_COLOR);
+ // Load the input image where we want to detect contours
+ IplImage* input_image = cvLoadImage(imagePath.c_str(),CV_LOAD_IMAGE_COLOR);
ShapeRec_ColorFilterParameters* aColorFilterParameters = dynamic_cast<ShapeRec_ColorFilterParameters*>( parameters );
if ( !aColorFilterParameters ) aColorFilterParameters = new ShapeRec_ColorFilterParameters();
// Reduce noise
- cvSmooth( find_image, find_image, CV_GAUSSIAN, aColorFilterParameters->smoothSize, aColorFilterParameters->smoothSize );
+ cvSmooth( input_image, input_image, CV_GAUSSIAN, aColorFilterParameters->smoothSize, aColorFilterParameters->smoothSize );
- // Crop the image to build an histogram from the selected part
- cvSetImageROI(find_image, rect);
- IplImage* test_image = cvCreateImage(cvGetSize(find_image),
- find_image->depth,
- find_image->nChannels);
- cvCopy(find_image, test_image, NULL);
- cvResetImageROI(find_image);
+ // Crop the image to the selected part only (sample_image)
+ cvSetImageROI(input_image, rect);
+ IplImage* sample_image = cvCreateImage(cvGetSize(input_image),
+ input_image->depth,
+ input_image->nChannels);
+ cvCopy(input_image, sample_image, NULL);
+ cvResetImageROI(input_image);
- IplImage* test_hsv = cvCreateImage(cvGetSize(test_image),8,3);
- IplImage* h_plane = cvCreateImage( cvGetSize(test_image), 8, 1 );
- IplImage* s_plane = cvCreateImage( cvGetSize(test_image), 8, 1 );
- CvHistogram* hist;
+ 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 );
+ CvHistogram* sample_hist;
- cvCvtColor(test_image, test_hsv, CV_BGR2HSV);
+ cvCvtColor(sample_image, sample_hsv, CV_BGR2HSV);
- cvCvtPixToPlane(test_hsv, h_plane, s_plane, 0, 0);
- IplImage* planes[] = { h_plane, s_plane };
+ cvCvtPixToPlane(sample_hsv, sample_h_plane, sample_s_plane, 0, 0);
+ IplImage* sample_planes[] = { sample_h_plane, sample_s_plane };
- //create hist
+ // 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
float hranges[] = { 0, 180 };
float sranges[] = { 0, 256 };
float* ranges[] = { hranges, sranges };
- hist = cvCreateHist( 2, aColorFilterParameters->histSize, aColorFilterParameters->histType, ranges );
+ sample_hist = cvCreateHist( 2, aColorFilterParameters->histSize, aColorFilterParameters->histType, ranges );
//calculate hue /saturation histogram
- cvCalcHist(planes, hist, 0 ,0);
+ cvCalcHist(sample_planes, sample_hist, 0 ,0);
// // TEST print of the histogram for debugging
// IplImage* hist_image = cvCreateImage(cvSize(320,300),8,3);
// cvNamedWindow("hist", 1); cvShowImage("hist",hist_image);
- //calculate back projection of hue and saturation planes of input image
- IplImage* backproject = cvCreateImage(cvGetSize(test_image), 8, 1);
- IplImage* binary_backproject = cvCreateImage(cvGetSize(test_image), 8, 1);
- cvCalcBackProject(planes, backproject, hist);
+ // 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.
+ 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);
- // Threshold in order to obtain binary image
+ // 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(&test_image);
- cvReleaseImage(&test_hsv);
- cvReleaseImage(&h_plane);
- cvReleaseImage(&s_plane);
- cvReleaseImage(&find_image);
+ cvReleaseImage(&sample_image);
+ cvReleaseImage(&sample_hsv);
+ cvReleaseImage(&sample_h_plane);
+ cvReleaseImage(&sample_s_plane);
+ cvReleaseImage(&input_image);
+ cvReleaseImage(&input_hsv);
+ cvReleaseImage(&input_hplane);
+ cvReleaseImage(&input_splane);
cvReleaseImage(&backproject);
detected_edges = cv::Mat(binary_backproject);
}
- // else if ( detection_method == RIDGE_DETECTOR ) // Method adapted for engineering drawings (e.g. watershed functionnality could be used here cf.OpenCV documentation and samples)
+ // else if ( detection_method == RIDGE_DETECTOR ) // Method adapted for engineering drawings (e.g. watershed functionality could be used here cf.OpenCV documentation and samples)
// {
// // TODO
// return false;
// _detectAndRetrieveContours( detected_edges, parameters->findContoursMethod );
detected_edges = detected_edges > 1;
- findContours( detected_edges, contours, hierarchy, CV_RETR_CCOMP, parameters->findContoursMethod, useROI ? cvPoint(rect.x,rect.y) : cvPoint(0,0) );
+ findContours( detected_edges, contours, hierarchy, CV_RETR_CCOMP, parameters->findContoursMethod);
return true;
cvSaveImage ("/tmp/cropped_image.bmp", cropped_image);
+ cvReleaseImage(&src);
+ cvReleaseImage(&cropped_image);
+
return "/tmp/cropped_image.bmp";
}
