#include <stdio.h>
#include "utilities.h"
-using namespace cv;
-
-//TODO : All the following methods but ComputeContours use the C API of OpenCV while ComputContours
+// 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
/*!
Computes the corners of the image located at imagePath
*/
-void ShapeRec_FeatureDetector::ComputeCorners(){
-
- // Parameters for the corner detection
- double qualityLevel = 0.2;
- double minDistance = 1;
-
+void ShapeRec_FeatureDetector::ComputeCorners( bool useROI, ShapeRec_Parameters* parameters ){
+ ShapeRec_CornersParameters* aCornersParameters = dynamic_cast<ShapeRec_CornersParameters*>( parameters );
+ if ( !aCornersParameters ) aCornersParameters = new ShapeRec_CornersParameters();
+
// 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 ( rect.width > 1 )
+ if ( useROI )
{
// If a ROI as been set use it for detection
cvSetImageROI( src_img_gray, rect );
// 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, /*quality-level=*/qualityLevel, /*min-distance=*/minDistance);
- cvFindCornerSubPix (src_img_gray, corners, cornerCount,
- cvSize (3, 3), cvSize (-1, -1), cvTermCriteria (CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.03));
+ 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),
+ cvTermCriteria (aCornersParameters->typeCriteria, aCornersParameters->maxIter, aCornersParameters->epsilon));
cvReleaseImage (&eig_img);
cvReleaseImage (&temp_img);
/*!
Computes the contours of the image located at imagePath
*/
-bool ShapeRec_FeatureDetector::ComputeContours( int detection_method ){
+bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters* parameters ){
// Initialising images
- Mat src, src_gray;
- Mat detected_edges;
+ cv::Mat src, src_gray;
+ cv::Mat detected_edges;
// Read image
- src = imread( imagePath.c_str() );
+ src = cv::imread( imagePath.c_str() );
if( !src.data )
return false;
- if ( detection_method == CANNY ) // The problem is that with that filter the detector detects double contours
+ if ( !useROI ) // CANNY: The problem is that with that filter the detector detects double contours
{
- // Thresholds for Canny detector
- int lowThreshold = 100;
- int ratio = 3;
- int kernel_size = 3; // 3,5 or 7
-
// Convert the image to grayscale
if (src.channels() == 3)
- cvtColor( src, src_gray, CV_BGR2GRAY );
+ cv::cvtColor( src, src_gray, CV_BGR2GRAY );
else if (src.channels() == 1)
src_gray = src;
-
- // Reduce noise with a kernel 3x3
- blur( src_gray, detected_edges, Size(3,3) );
+
+ ShapeRec_CannyParameters* aCannyParameters = dynamic_cast<ShapeRec_CannyParameters*>( parameters );
+ if ( !aCannyParameters ) aCannyParameters = new ShapeRec_CannyParameters();
+
+ // Reduce noise
+ blur( src_gray, detected_edges, cv::Size( aCannyParameters->kernelSize, aCannyParameters->kernelSize ) );
// Canny detector
- Canny( detected_edges, detected_edges, lowThreshold, lowThreshold*ratio, kernel_size, /*L2gradient =*/true );
+ Canny( detected_edges, detected_edges, aCannyParameters->lowThreshold, aCannyParameters->lowThreshold * aCannyParameters->ratio,
+ aCannyParameters->kernelSize, aCannyParameters->L2gradient );
}
- else if ( detection_method == COLORFILTER )
+ else //COLORFILTER
{
- if ( !rect.width > 1 )
- return false;
- detected_edges = _colorFiltering();
- }
- else if ( detection_method == RIDGE_DETECTOR ) // Method adapted for engineering drawings (e.g. watershed functionnality could be used here cf.OpenCV documentation and samples)
- {
- // TODO
- return false;
- }
- _detectAndRetrieveContours( detected_edges );
+ IplImage* find_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 );
+
+ // 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);
+ 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;
+
+ cvCvtColor(test_image, test_hsv, CV_BGR2HSV);
+
+ cvCvtPixToPlane(test_hsv, h_plane, s_plane, 0, 0);
+ IplImage* planes[] = { h_plane, s_plane };
+
+ //create hist
+ float hranges[] = { 0, 180 };
+ float sranges[] = { 0, 256 };
+ float* ranges[] = { hranges, sranges };
+ hist = cvCreateHist( 2, aColorFilterParameters->histSize, aColorFilterParameters->histType, ranges );
+
+ //calculate hue /saturation histogram
+ cvCalcHist(planes, 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;
+// cvGetMinMaxHistValue(hist, 0 , &max_value, 0, 0);
+// int bin_w = hist_image->width/size_hist;
+// for(int i = 0; i < size_hist; i++ )
+// {
+// //prevent overflow
+// int val = cvRound( cvGetReal1D(hist->bins,i)*hist_image->
+// height/max_value);
+// CvScalar color = CV_RGB(200,0,0);
+// //hsv2rgb(i*180.f/size_hist);
+// cvRectangle( hist_image, cvPoint(i*bin_w,hist_image->height),
+// cvPoint((i+1)*bin_w,hist_image->height - val),
+// color, -1, 8, 0 );
+// }
+//
+//
+// 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);
+
+ // Threshold in order to obtain 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(&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)
+ // {
+ // // 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) );
+
return true;
}
bool ShapeRec_FeatureDetector::ComputeLines(){
MESSAGE("ShapeRec_FeatureDetector::ComputeLines()")
// Initialising images
- Mat src, src_gray, detected_edges, dst;
+ cv::Mat src, src_gray, detected_edges, dst;
- src=imread(imagePath.c_str(), 0);
+ src=cv::imread(imagePath.c_str(), 0);
Canny( src, dst, 50, 200, 3 );
HoughLinesP( dst, lines, 1, CV_PI/180, 80, 30, 10 );
return "/tmp/cropped_image.bmp";
}
+/*!
+ \class ShapeRec_CornersParameters
+ \brief Parameters for the corners detection
+*/
+ShapeRec_CornersParameters::ShapeRec_CornersParameters()
+{
+ qualityLevel = 0.2;
+ minDistance = 1;
+ typeCriteria = CV_TERMCRIT_ITER | CV_TERMCRIT_EPS;
+ maxIter = 20;
+ epsilon = 0.03;
+}
+ShapeRec_CornersParameters::~ShapeRec_CornersParameters()
+{
+}
/*!
- Performs contours detection and store them in contours
- \param binaryImg - src image to find contours of
+ \class ShapeRec_Parameters
+ \brief Parameters for the contour/corners detection
*/
-void ShapeRec_FeatureDetector::_detectAndRetrieveContours( Mat binaryImg )
+ShapeRec_Parameters::ShapeRec_Parameters()
+{
+ kernelSize = 3;
+ findContoursMethod = CV_CHAIN_APPROX_NONE;
+}
+ShapeRec_Parameters::~ShapeRec_Parameters()
{
- binaryImg = binaryImg > 1;
- int method = CV_CHAIN_APPROX_NONE;
- findContours( binaryImg, contours, hierarchy,CV_RETR_CCOMP, method);
- // Other possible approximations CV_CHAIN_APPROX_TC89_KCOS, CV_CHAIN_APPROX_TC89_L1, CV_CHAIN_APPROX_SIMPLE cf. OpenCV documentation
- // for precise information
}
/*!
- Performs color filtering from the image sample contained in the ROI rect of the image
- located at imagePath
- Thresholds the result in order ot obtain a binary image
- \return binary image resulting from filtering and thersholding
+ \class ShapeRec_CannyParameters
+ \brief Parameters for the contour detection
*/
-Mat ShapeRec_FeatureDetector::_colorFiltering()
-{
- IplImage* find_image = cvLoadImage(imagePath.c_str(),CV_LOAD_IMAGE_COLOR);
- // Reduce noise with a kernel 3x3
- cvSmooth( find_image, find_image, CV_GAUSSIAN, 3, 3 );
-
- if ( !rect.width > 1 )
- return Mat(find_image);
-
- // 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);
-
- 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;
+ShapeRec_CannyParameters::ShapeRec_CannyParameters()
+{
+ lowThreshold = 100; // is used for edge linking.
+ ratio = 3; // lowThreshold*ratio is used to find initial segments of strong edges
+ L2gradient = true; // norm L2 or L1
+}
- cvCvtColor(test_image, test_hsv, CV_BGR2HSV);
-
- cvCvtPixToPlane(test_hsv, h_plane, s_plane, 0, 0);
- IplImage* planes[] = { h_plane, s_plane };
-
- //create hist
- int hbins = 30, sbins = 32; // TODO think to the best values here
- int hist_size[] = { hbins, sbins };
- float hranges[] = { 0, 180 };
- float sranges[] = { 0, 255 };
- float* ranges[] = { hranges, sranges };
- hist = cvCreateHist(2, hist_size, CV_HIST_ARRAY, ranges, 1);
-
- //calculate hue /saturation histogram
- cvCalcHist(planes, hist, 0 ,0);
+ShapeRec_CannyParameters::~ShapeRec_CannyParameters()
+{
+}
-// // 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;
-// cvGetMinMaxHistValue(hist, 0 , &max_value, 0, 0);
-// int bin_w = hist_image->width/size_hist;
-// for(int i = 0; i < size_hist; i++ )
-// {
-// //prevent overflow
-// int val = cvRound( cvGetReal1D(hist->bins,i)*hist_image->
-// height/max_value);
-// CvScalar color = CV_RGB(200,0,0);
-// //hsv2rgb(i*180.f/size_hist);
-// cvRectangle( hist_image, cvPoint(i*bin_w,hist_image->height),
-// cvPoint((i+1)*bin_w,hist_image->height - val),
-// color, -1, 8, 0 );
-// }
-//
-//
-// cvNamedWindow("hist", 1); cvShowImage("hist",hist_image);
-
-
- //calculate back projection of hue and saturation planes of input image
- IplImage* backproject = cvCreateImage(cvGetSize(find_image), 8, 1);
- IplImage* binary_backproject = cvCreateImage(cvGetSize(find_image), 8, 1);
- IplImage* find_hsv = cvCreateImage(cvGetSize(find_image),8,3);
- IplImage* find_hplane = cvCreateImage(cvGetSize(find_image),8,1);
- IplImage* find_splane = cvCreateImage(cvGetSize(find_image),8,1);
-
- cvCvtColor(find_image, find_hsv, CV_BGR2HSV);
- cvCvtPixToPlane(find_hsv, find_hplane, find_splane, 0, 0);
- IplImage* find_planes[] = { find_hplane, find_splane };
- cvCalcBackProject(find_planes, backproject, hist);
-
- // Threshold in order to obtain binary image
- cvThreshold(backproject, binary_backproject, 1, 255, CV_THRESH_BINARY); // NOTE it would be good to think about the best threshold to use (it's 1 for now)
- cvReleaseImage(&test_image);
- cvReleaseImage(&test_hsv);
- cvReleaseImage(&h_plane);
- cvReleaseImage(&s_plane);
- cvReleaseImage(&find_image);
- cvReleaseImage(&find_hsv);
- cvReleaseImage(&find_hplane);
- cvReleaseImage(&find_splane);
- cvReleaseImage(&backproject);
-
- return Mat(binary_backproject);
+/*!
+ \class ShapeRec_ColorFilterParameters
+ \brief Parameters for the contour detection
+*/
+ShapeRec_ColorFilterParameters::ShapeRec_ColorFilterParameters()
+{
+ smoothSize = 3; // The parameter of the smoothing operation, the aperture width. Must be a positive odd number
+ histSize = new int[2]; // array of the histogram dimension sizes
+ histSize[0] = 30; // hbins
+ histSize[1] = 32; // sbins
+ histType = CV_HIST_ARRAY; // histogram representation format
+ threshold = 128; // threshold value
+ maxThreshold = 255; // maximum value to use with the THRESH_BINARY thresholding types
+}
+
+ShapeRec_ColorFilterParameters::~ShapeRec_ColorFilterParameters()
+{
}
