using namespace std;
using namespace cv;
-
+/*
+ * Generates random perspective transform of image
+ */
void warpPerspectiveRand( const Mat& src, Mat& dst, Mat& H, RNG& rng )
{
H.create(3, 3, CV_32FC1);
warpPerspective( src, dst, H, src.size() );
}
-int main( int argc, char **argv )
+/*
+ * 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 )
{
- #if 0
- if( argc != 4 && argc != 3 )
+ // Reads train images
+ ifstream is( imgFilename.c_str(), ifstream::in );
+ vector<Mat> trainImgs;
+ while( !is.eof() )
{
- cout << "Format:" << endl <<
- " classifier(xml to write) test_image file_with_train_images_filenames(txt)" <<
- " or" << endl <<
- " classifier(xml to read) test_image" << endl;
- return -1;
+ 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;
- CalonderClassifier classifier;
- if( argc == 4 ) // Train
+ // Extracts keypoints from train images
+ SurfFeatureDetector detector;
+ vector<BaseKeypoint> trainPoints;
+ vector<IplImage> iplTrainImgs(trainImgs.size());
+ for( size_t imgIdx = 0; imgIdx < trainImgs.size(); imgIdx++ )
{
- // Read train images and test image
- ifstream fst( argv[3], ifstream::in );
- vector<Mat> trainImgs;
- while( !fst.eof() )
- {
- string str;
- getline( fst, 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;
- return -1;
- }
- cout << trainImgs.size() << " train images were read." << endl;
+ iplTrainImgs[imgIdx] = trainImgs[imgIdx];
+ vector<KeyPoint> kps; detector.detect( trainImgs[imgIdx], kps );
- // Extract keypoints from train images
- SurfFeatureDetector detector;
- vector<vector<Point2f> > trainPoints( trainImgs.size() );
- for( size_t i = 0; i < trainImgs.size(); i++ )
+ for( size_t pointIdx = 0; pointIdx < kps.size(); pointIdx++ )
{
- vector<KeyPoint> kps;
- detector.detect( trainImgs[i], kps );
- KeyPoint::convert( kps, trainPoints[i] );
+ Point2f p = kps[pointIdx].pt;
+ trainPoints.push_back( BaseKeypoint(cvRound(p.x), cvRound(p.y), &iplTrainImgs[imgIdx]) );
}
-
- // Train Calonder classifier on extracted points
- classifier.setVerbose( true);
- classifier.train( trainPoints, trainImgs );
-
- // Write Calonder classifier
- FileStorage fs( argv[1], FileStorage::WRITE );
- if( fs.isOpened() ) classifier.write( fs );
- }
- else
- {
- // Read Calonder classifier
- FileStorage fs( argv[1], FileStorage::READ );
- if( fs.isOpened() ) classifier.read( fs.root() );
}
- if( classifier.empty() )
- {
- cout << "Calonder classifier is empty" << endl;
- return -1;
- }
+ // 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 on test image and warped one
- Mat testImg1 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE ), testImg2, H12;
- if( testImg1.empty() )
+/*
+ * 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;
- return -1;
+ exit(-1);
}
- warpPerspectiveRand( testImg1, testImg2, H12, theRNG() );
-
+ warpPerspectiveRand( img1, img2, H12, theRNG() );
// Exstract keypoints from test images
SurfFeatureDetector detector;
- vector<KeyPoint> testKeypoints1; detector.detect( testImg1, testKeypoints1 );
- vector<KeyPoint> testKeypoints2; detector.detect( testImg2, testKeypoints2 );
- vector<Point2f> testPoints1; KeyPoint::convert( testKeypoints1, testPoints1 );
- vector<Point2f> testPoints2; KeyPoint::convert( testKeypoints2, testPoints2 );
-
- // Calculate Calonder descriptors
- int signatureSize = classifier.getSignatureSize();
- vector<float> r1(testPoints1.size()*signatureSize), r2(testPoints2.size()*signatureSize);
- vector<float>::iterator rit = r1.begin();
- for( size_t i = 0; i < testPoints1.size(); i++ )
- {
- vector<float> s;
- classifier( testImg1, testPoints1[i], s );
- copy( s.begin(), s.end(), rit );
- rit += s.size();
- }
- rit = r2.begin();
- for( size_t i = 0; i < testPoints2.size(); i++ )
- {
- vector<float> s;
- classifier( testImg2, testPoints2[i], s );
- copy( s.begin(), s.end(), rit );
- rit += s.size();
- }
+ vector<KeyPoint> keypoints1; detector.detect( img1, keypoints1 );
+ vector<KeyPoint> keypoints2; detector.detect( img2, keypoints2 );
- Mat descriptors1(testPoints1.size(), classifier.getSignatureSize(), CV_32FC1, &r1[0] ),
- descriptors2(testPoints2.size(), classifier.getSignatureSize(), CV_32FC1, &r2[0] );
+ // Compute descriptors
+ CalonderDescriptorExtractor<float> de( classifierFilename );
+ Mat descriptors1; de.compute( img1, keypoints1, descriptors1 );
+ Mat descriptors2; de.compute( img2, keypoints2, descriptors2 );
// Match descriptors
BruteForceMatcher<L1<float> > matcher;
vector<int> matches;
matcher.match( descriptors1, matches );
- // Draw results
// Prepare inlier mask
vector<char> matchesMask( matches.size(), 0 );
- Mat points1t; perspectiveTransform(Mat(testPoints1), points1t, H12);
+ vector<Point2f> points1; KeyPoint::convert( keypoints1, points1 );
+ vector<Point2f> points2; KeyPoint::convert( keypoints2, points2 );
+ Mat points1t; perspectiveTransform(Mat(points1), points1t, H12);
vector<int>::const_iterator mit = matches.begin();
for( size_t mi = 0; mi < matches.size(); mi++ )
{
- if( norm(testPoints2[matches[mi]] - points1t.at<Point2f>(mi,0)) < 4 ) // inlier
+ if( norm(points2[matches[mi]] - points1t.at<Point2f>(mi,0)) < 4 ) // inlier
matchesMask[mi] = 1;
}
+
// Draw
Mat drawImg;
- drawMatches( testImg1, testKeypoints1, testImg2, testKeypoints2, matches, drawImg, CV_RGB(0, 255, 0), CV_RGB(0, 0, 255), matchesMask );
+ 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();
-#endif
+}
+
+
+int main( int argc, char **argv )
+{
+ if( argc != 4 && argc != 3 )
+ {
+ cout << "Format:" << endl <<
+ " classifier_file(to write) test_image file_with_train_images_filenames(txt)" <<
+ " or" << endl <<
+ " classifier_file(to read) test_image" << endl;
+ return -1;
+ }
+
+ if( argc == 4 )
+ trainCalonderClassifier( argv[1], argv[3] );
+
+ testCalonderClassifier( argv[1], argv[2] );
+
return 0;
}