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modified sample find_obj_calonder

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pull/13383/head
Maria Dimashova 15 years ago
parent 62fa7f6620
commit 6cf6a22772
1 changed files with 89 additions and 92 deletions
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  1. 181
      samples/c/find_obj_calonder.cpp

181
samples/c/find_obj_calonder.cpp
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@ -8,7 +8,9 @@
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);
@ -25,110 +27,85 @@ void warpPerspectiveRand( const Mat& src, Mat& dst, Mat& H, RNG& rng )
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;
@ -136,23 +113,43 @@ int main( int argc, char **argv )
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;
}

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