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Update train_HOG.cpp
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Suleyman TURKMEN
parent
c7f1843584
commit
2aa380752c
@ -10,14 +10,14 @@ using namespace cv;
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using namespace cv::ml;
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using namespace std;
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void get_svm_detector( const Ptr< SVM > & svm, vector< float > & hog_detector );
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vector< float > get_svm_detector( const Ptr< SVM >& svm );
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void convert_to_ml( const std::vector< Mat > & train_samples, Mat& trainData );
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void load_images( const String & dirname, vector< Mat > & img_lst, bool showImages );
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void sample_neg( const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, const Size & size );
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void computeHOGs( const Size wsize, const vector< Mat > & img_lst, vector< Mat > & gradient_lst );
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int test_trained_detector( String obj_det_filename, String test_dir, String videofilename );
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void computeHOGs( const Size wsize, const vector< Mat > & img_lst, vector< Mat > & gradient_lst, bool use_flip );
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void test_trained_detector( String obj_det_filename, String test_dir, String videofilename );
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void get_svm_detector( const Ptr< SVM >& svm, vector< float > & hog_detector )
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vector< float > get_svm_detector( const Ptr< SVM >& svm )
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{
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// get the support vectors
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Mat sv = svm->getSupportVectors();
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@ -30,11 +30,11 @@ void get_svm_detector( const Ptr< SVM >& svm, vector< float > & hog_detector )
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CV_Assert( (alpha.type() == CV_64F && alpha.at<double>(0) == 1.) ||
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(alpha.type() == CV_32F && alpha.at<float>(0) == 1.f) );
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CV_Assert( sv.type() == CV_32F );
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hog_detector.clear();
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hog_detector.resize(sv.cols + 1);
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vector< float > hog_detector( sv.cols + 1 );
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memcpy( &hog_detector[0], sv.ptr(), sv.cols*sizeof( hog_detector[0] ) );
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hog_detector[sv.cols] = (float)-rho;
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return hog_detector;
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}
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/*
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@ -101,35 +101,44 @@ void sample_neg( const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, co
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srand( (unsigned int)time( NULL ) );
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for ( size_t i = 0; i < full_neg_lst.size(); i++ )
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{
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box.x = rand() % ( full_neg_lst[i].cols - size_x );
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box.y = rand() % ( full_neg_lst[i].rows - size_y );
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Mat roi = full_neg_lst[i]( box );
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neg_lst.push_back( roi.clone() );
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}
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if ( full_neg_lst[i].cols >= box.width && full_neg_lst[i].rows >= box.height )
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{
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box.x = rand() % ( full_neg_lst[i].cols - size_x );
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box.y = rand() % ( full_neg_lst[i].rows - size_y );
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Mat roi = full_neg_lst[i]( box );
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neg_lst.push_back( roi.clone() );
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}
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}
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void computeHOGs( const Size wsize, const vector< Mat > & img_lst, vector< Mat > & gradient_lst )
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void computeHOGs( const Size wsize, const vector< Mat > & img_lst, vector< Mat > & gradient_lst, bool use_flip )
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{
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HOGDescriptor hog;
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hog.winSize = wsize;
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Rect r = Rect( 0, 0, wsize.width, wsize.height );
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r.x += ( img_lst[0].cols - r.width ) / 2;
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r.y += ( img_lst[0].rows - r.height ) / 2;
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Mat gray;
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vector< float > descriptors;
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for( size_t i=0 ; i< img_lst.size(); i++ )
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for( size_t i = 0 ; i < img_lst.size(); i++ )
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{
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cvtColor( img_lst[i](r), gray, COLOR_BGR2GRAY );
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hog.compute( gray, descriptors, Size( 8, 8 ), Size( 0, 0 ) );
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gradient_lst.push_back( Mat( descriptors ).clone() );
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if ( img_lst[i].cols >= wsize.width && img_lst[i].rows >= wsize.height )
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{
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Rect r = Rect(( img_lst[i].cols - wsize.width ) / 2,
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( img_lst[i].rows - wsize.height ) / 2,
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wsize.width,
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wsize.height);
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cvtColor( img_lst[i](r), gray, COLOR_BGR2GRAY );
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hog.compute( gray, descriptors, Size( 8, 8 ), Size( 0, 0 ) );
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gradient_lst.push_back( Mat( descriptors ).clone() );
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if ( use_flip )
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{
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flip( gray, gray, 1 );
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hog.compute( gray, descriptors, Size( 8, 8 ), Size( 0, 0 ) );
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gradient_lst.push_back( Mat( descriptors ).clone() );
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}
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}
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}
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}
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int test_trained_detector( String obj_det_filename, String test_dir, String videofilename )
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void test_trained_detector( String obj_det_filename, String test_dir, String videofilename )
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{
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cout << "Testing trained detector..." << endl;
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HOGDescriptor hog;
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@ -143,7 +152,10 @@ int test_trained_detector( String obj_det_filename, String test_dir, String vide
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if ( videofilename != "" )
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{
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cap.open( videofilename );
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if ( videofilename.size() == 1 && isdigit( videofilename[0] ) )
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cap.open( videofilename[0] - '0' );
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else
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cap.open( videofilename );
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}
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obj_det_filename = "testing " + obj_det_filename;
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@ -165,7 +177,7 @@ int test_trained_detector( String obj_det_filename, String test_dir, String vide
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if ( img.empty() )
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{
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return 0;
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return;
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}
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vector< Rect > detections;
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@ -180,12 +192,11 @@ int test_trained_detector( String obj_det_filename, String test_dir, String vide
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imshow( obj_det_filename, img );
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if( 27 == waitKey( delay ) )
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if( waitKey( delay ) == 27 )
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{
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return 0;
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return;
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}
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}
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return 0;
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}
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int main( int argc, char** argv )
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@ -199,6 +210,7 @@ int main( int argc, char** argv )
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"{tv | | test video file name}"
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"{dw | | width of the detector}"
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"{dh | | height of the detector}"
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"{f |false| indicates if the program will generate and use mirrored samples or not}"
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"{d |false| train twice}"
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"{t |false| test a trained detector}"
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"{v |false| visualize training steps}"
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@ -223,6 +235,7 @@ int main( int argc, char** argv )
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bool test_detector = parser.get< bool >( "t" );
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bool train_twice = parser.get< bool >( "d" );
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bool visualization = parser.get< bool >( "v" );
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bool flip_samples = parser.get< bool >( "f" );
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if ( test_detector )
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{
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@ -234,8 +247,8 @@ int main( int argc, char** argv )
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{
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parser.printMessage();
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cout << "Wrong number of parameters.\n\n"
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<< "Example command line:\n" << argv[0] << " -pd=/INRIAPerson/96X160H96/Train/pos -nd=/INRIAPerson/neg -td=/INRIAPerson/Test/pos -fn=HOGpedestrian96x160.yml -d\n"
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<< "\nExample command line for testing trained detector:\n" << argv[0] << " -t -dw=96 -dh=160 -fn=HOGpedestrian96x160.yml -td=/INRIAPerson/Test/pos";
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<< "Example command line:\n" << argv[0] << " -dw=64 -dh=128 -pd=/INRIAPerson/96X160H96/Train/pos -nd=/INRIAPerson/neg -td=/INRIAPerson/Test/pos -fn=HOGpedestrian64x128.xml -d\n"
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<< "\nExample command line for testing trained detector:\n" << argv[0] << " -t -fn=HOGpedestrian64x128.xml -td=/INRIAPerson/Test/pos";
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exit( 1 );
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}
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@ -256,40 +269,40 @@ int main( int argc, char** argv )
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Size pos_image_size = pos_lst[0].size();
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for ( size_t i = 0; i < pos_lst.size(); ++i )
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{
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if( pos_lst[i].size() != pos_image_size )
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{
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cout << "All positive images should be same size!" << endl;
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exit( 1 );
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}
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}
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pos_image_size = pos_image_size / 8 * 8;
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if ( detector_width && detector_height )
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{
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pos_image_size = Size( detector_width, detector_height );
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}
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labels.assign( pos_lst.size(), +1 );
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const unsigned int old = (unsigned int)labels.size();
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else
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{
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for ( size_t i = 0; i < pos_lst.size(); ++i )
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{
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if( pos_lst[i].size() != pos_image_size )
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{
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cout << "All positive images should be same size!" << endl;
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exit( 1 );
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}
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}
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pos_image_size = pos_image_size / 8 * 8;
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}
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clog << "Negative images are being loaded...";
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load_images( neg_dir, full_neg_lst, false );
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sample_neg( full_neg_lst, neg_lst, pos_image_size );
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clog << "...[done]" << endl;
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labels.insert( labels.end(), neg_lst.size(), -1 );
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CV_Assert( old < labels.size() );
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clog << "Histogram of Gradients are being calculated for positive images...";
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computeHOGs( pos_image_size, pos_lst, gradient_lst );
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clog << "...[done]" << endl;
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computeHOGs( pos_image_size, pos_lst, gradient_lst, flip_samples );
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size_t positive_count = gradient_lst.size();
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labels.assign( positive_count, +1 );
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clog << "...[done] ( positive count : " << positive_count << " )" << endl;
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clog << "Histogram of Gradients are being calculated for negative images...";
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computeHOGs( pos_image_size, neg_lst, gradient_lst );
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clog << "...[done]" << endl;
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computeHOGs( pos_image_size, neg_lst, gradient_lst, flip_samples );
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size_t negative_count = gradient_lst.size() - positive_count;
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labels.insert( labels.end(), negative_count, -1 );
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CV_Assert( positive_count < labels.size() );
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clog << "...[done] ( negative count : " << negative_count << " )" << endl;
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Mat train_data;
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convert_to_ml( gradient_lst, train_data );
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@ -306,7 +319,7 @@ int main( int argc, char** argv )
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svm->setP( 0.1 ); // for EPSILON_SVR, epsilon in loss function?
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svm->setC( 0.01 ); // From paper, soft classifier
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svm->setType( SVM::EPS_SVR ); // C_SVC; // EPSILON_SVR; // may be also NU_SVR; // do regression task
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svm->train( train_data, ROW_SAMPLE, Mat( labels ) );
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svm->train( train_data, ROW_SAMPLE, labels );
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clog << "...[done]" << endl;
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if ( train_twice )
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@ -316,22 +329,25 @@ int main( int argc, char** argv )
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my_hog.winSize = pos_image_size;
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// Set the trained svm to my_hog
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vector< float > hog_detector;
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get_svm_detector( svm, hog_detector );
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my_hog.setSVMDetector( hog_detector );
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my_hog.setSVMDetector( get_svm_detector( svm ) );
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vector< Rect > detections;
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vector< double > foundWeights;
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for ( size_t i = 0; i < full_neg_lst.size(); i++ )
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{
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my_hog.detectMultiScale( full_neg_lst[i], detections, foundWeights );
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if ( full_neg_lst[i].cols >= pos_image_size.width && full_neg_lst[i].rows >= pos_image_size.height )
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my_hog.detectMultiScale( full_neg_lst[i], detections, foundWeights );
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else
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detections.clear();
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for ( size_t j = 0; j < detections.size(); j++ )
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{
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Mat detection = full_neg_lst[i]( detections[j] ).clone();
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resize( detection, detection, pos_image_size );
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neg_lst.push_back( detection );
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}
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if ( visualization )
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{
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for ( size_t j = 0; j < detections.size(); j++ )
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@ -344,30 +360,30 @@ int main( int argc, char** argv )
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}
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clog << "...[done]" << endl;
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labels.clear();
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labels.assign( pos_lst.size(), +1 );
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labels.insert( labels.end(), neg_lst.size(), -1);
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gradient_lst.clear();
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clog << "Histogram of Gradients are being calculated for positive images...";
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computeHOGs( pos_image_size, pos_lst, gradient_lst );
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clog << "...[done]" << endl;
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computeHOGs( pos_image_size, pos_lst, gradient_lst, flip_samples );
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positive_count = gradient_lst.size();
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clog << "...[done] ( positive count : " << positive_count << " )" << endl;
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clog << "Histogram of Gradients are being calculated for negative images...";
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computeHOGs( pos_image_size, neg_lst, gradient_lst );
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clog << "...[done]" << endl;
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computeHOGs( pos_image_size, neg_lst, gradient_lst, flip_samples );
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negative_count = gradient_lst.size() - positive_count;
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clog << "...[done] ( negative count : " << negative_count << " )" << endl;
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labels.clear();
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labels.assign(positive_count, +1);
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labels.insert(labels.end(), negative_count, -1);
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clog << "Training SVM again...";
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convert_to_ml( gradient_lst, train_data );
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svm->train( train_data, ROW_SAMPLE, Mat( labels ) );
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svm->train( train_data, ROW_SAMPLE, labels );
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clog << "...[done]" << endl;
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}
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vector< float > hog_detector;
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get_svm_detector( svm, hog_detector );
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HOGDescriptor hog;
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hog.winSize = pos_image_size;
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hog.setSVMDetector( hog_detector );
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hog.setSVMDetector( get_svm_detector( svm ) );
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hog.save( obj_det_filename );
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test_trained_detector( obj_det_filename, test_dir, videofilename );
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