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BUG: ShapeRecognition: The ROI concept in contour detection was missused

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In the contour detection part the ROI is used to get a representative sample of a zone where we want to find a contour. The aim is NOT to apply the contour detection only in a Region Of Interest. The current implementation broke the use of color filtering to find a contour in a picture
This commit is contained in:
rnc 2013-12-06 17:03:27 +00:00
parent 9c631c67b1
commit 1d52aa3422
1 changed files with 58 additions and 33 deletions
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91
src/ShapeRecognition/ShapeRec_FeatureDetector.cxx
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@ -66,7 +66,8 @@ void ShapeRec_FeatureDetector::SetPath( const std::string& thePath )
/*!
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();
@ -105,8 +106,8 @@ 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 ){
bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters* parameters )
{
// Initialising images
cv::Mat src, src_gray;
cv::Mat detected_edges;
@ -135,40 +136,44 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters
}
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);
@ -194,18 +199,38 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters
// 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_image);
cvReleaseImage(&input_hsv);
cvReleaseImage(&input_hplane);
cvReleaseImage(&input_splane);
cvReleaseImage(&backproject);
detected_edges = cv::Mat(binary_backproject);
@ -218,7 +243,7 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters
// _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;