#include "opencv2/highgui/highgui.hpp" #include "opencv2/core/core.hpp" #include "opencv2/imgproc/imgproc.hpp" #include "opencv2/features2d/features2d.hpp" #include #include using namespace std; using namespace cv; void help() { cout << "This program shows the use of the Calonder point descriptor classifier" "SURF is used to detect interest points, Calonder is used to describe/match these points\n" "Format:" << endl << " classifier_file(to write) test_image file_with_train_images_filenames(txt)" << " or" << endl << " classifier_file(to read) test_image" << endl; } /* * Generates random perspective transform of image */ void warpPerspectiveRand( const Mat& src, Mat& dst, Mat& H, RNG& rng ) { H.create(3, 3, CV_32FC1); H.at(0,0) = rng.uniform( 0.8f, 1.2f); H.at(0,1) = rng.uniform(-0.1f, 0.1f); H.at(0,2) = rng.uniform(-0.1f, 0.1f)*src.cols; H.at(1,0) = rng.uniform(-0.1f, 0.1f); H.at(1,1) = rng.uniform( 0.8f, 1.2f); H.at(1,2) = rng.uniform(-0.1f, 0.1f)*src.rows; H.at(2,0) = rng.uniform( -1e-4f, 1e-4f); H.at(2,1) = rng.uniform( -1e-4f, 1e-4f); H.at(2,2) = rng.uniform( 0.8f, 1.2f); warpPerspective( src, dst, H, src.size() ); } /* * Trains Calonder classifier and writes trained classifier in file: * imgFilename - name of .txt file which contains list of full filenames of train images, * classifierFilename - name of binary file in which classifier will be written. * * To train Calonder classifier RTreeClassifier class need to be used. */ void trainCalonderClassifier( const string& classifierFilename, const string& imgFilename ) { // Reads train images ifstream is( imgFilename.c_str(), ifstream::in ); vector trainImgs; while( !is.eof() ) { string str; getline( is, str ); if (str.empty()) break; Mat img = imread( str, CV_LOAD_IMAGE_GRAYSCALE ); if( !img.empty() ) trainImgs.push_back( img ); } if( trainImgs.empty() ) { cout << "All train images can not be read." << endl; exit(-1); } cout << trainImgs.size() << " train images were read." << endl; // Extracts keypoints from train images SurfFeatureDetector detector; vector trainPoints; vector iplTrainImgs(trainImgs.size()); for( size_t imgIdx = 0; imgIdx < trainImgs.size(); imgIdx++ ) { iplTrainImgs[imgIdx] = trainImgs[imgIdx]; vector kps; detector.detect( trainImgs[imgIdx], kps ); for( size_t pointIdx = 0; pointIdx < kps.size(); pointIdx++ ) { Point2f p = kps[pointIdx].pt; trainPoints.push_back( BaseKeypoint(cvRound(p.x), cvRound(p.y), &iplTrainImgs[imgIdx]) ); } } // Trains Calonder classifier on extracted points RTreeClassifier classifier; classifier.train( trainPoints, theRNG(), 48, 9, 100 ); // Writes classifier classifier.write( classifierFilename.c_str() ); } /* * Test Calonder classifier to match keypoints on given image: * classifierFilename - name of file from which classifier will be read, * imgFilename - test image filename. * * To calculate keypoint descriptors you may use RTreeClassifier class (as to train), * but it is convenient to use CalonderDescriptorExtractor class which is wrapper of * RTreeClassifier. */ void testCalonderClassifier( const string& classifierFilename, const string& imgFilename ) { Mat img1 = imread( imgFilename, CV_LOAD_IMAGE_GRAYSCALE ), img2, H12; if( img1.empty() ) { cout << "Test image can not be read." << endl; exit(-1); } warpPerspectiveRand( img1, img2, H12, theRNG() ); // Exstract keypoints from test images SurfFeatureDetector detector; vector keypoints1; detector.detect( img1, keypoints1 ); vector keypoints2; detector.detect( img2, keypoints2 ); // Compute descriptors CalonderDescriptorExtractor de( classifierFilename ); Mat descriptors1; de.compute( img1, keypoints1, descriptors1 ); Mat descriptors2; de.compute( img2, keypoints2, descriptors2 ); // Match descriptors BruteForceMatcher > matcher; vector matches; matcher.match( descriptors1, descriptors2, matches ); // Prepare inlier mask vector matchesMask( matches.size(), 0 ); vector points1; KeyPoint::convert( keypoints1, points1 ); vector points2; KeyPoint::convert( keypoints2, points2 ); Mat points1t; perspectiveTransform(Mat(points1), points1t, H12); for( size_t mi = 0; mi < matches.size(); mi++ ) { if( norm(points2[matches[mi].trainIdx] - points1t.at(mi,0)) < 4 ) // inlier matchesMask[mi] = 1; } // Draw Mat drawImg; drawMatches( img1, keypoints1, img2, keypoints2, matches, drawImg, CV_RGB(0, 255, 0), CV_RGB(0, 0, 255), matchesMask ); string winName = "Matches"; namedWindow( winName, WINDOW_AUTOSIZE ); imshow( winName, drawImg ); waitKey(); } int main( int argc, char **argv ) { if( argc != 4 && argc != 3 ) { help(); return -1; } if( argc == 4 ) trainCalonderClassifier( argv[1], argv[3] ); testCalonderClassifier( argv[1], argv[2] ); return 0; }