Constructor
\param theFilename - image to process
*/
-ShapeRec_FeatureDetector::ShapeRec_FeatureDetector():
+ShapeRec_FeatureDetector::ShapeRec_FeatureDetector():
corners()
{
cornerCount = 2000;
/*!
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);
// 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),
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 );
ShapeRec_CannyParameters* aCannyParameters = dynamic_cast<ShapeRec_CannyParameters*>( 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,
// 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),
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 );
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;
// 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);
findContours( detected_edges, contours, hierarchy, CV_RETR_CCOMP, parameters->findContoursMethod);
return true;
-
+
}
/*!
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 )
{
/*!
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()
{
/*!
\class ShapeRec_Parameters
- \brief Parameters for the contour/corners detection
+ \brief Parameters for the contour/corners detection
*/
ShapeRec_Parameters::ShapeRec_Parameters()
{
/*!
\class ShapeRec_CannyParameters
- \brief Parameters for the contour detection
+ \brief Parameters for the contour detection
*/
ShapeRec_CannyParameters::ShapeRec_CannyParameters()
{
/*!
\class ShapeRec_ColorFilterParameters
- \brief Parameters for the contour detection
+ \brief Parameters for the contour detection
*/
ShapeRec_ColorFilterParameters::ShapeRec_ColorFilterParameters()
{
