From 2aa380752c46e52d52e82b86e073abde9fa6d3bf Mon Sep 17 00:00:00 2001 From: Suleyman TURKMEN Date: Sun, 19 Nov 2017 01:41:24 +0300 Subject: [PATCH] Update train_HOG.cpp --- samples/cpp/train_HOG.cpp | 152 +++++++++++++++++++++----------------- 1 file changed, 84 insertions(+), 68 deletions(-) diff --git a/samples/cpp/train_HOG.cpp b/samples/cpp/train_HOG.cpp index 4d1dcf8e69..1ffac4ce28 100644 --- a/samples/cpp/train_HOG.cpp +++ b/samples/cpp/train_HOG.cpp @@ -10,14 +10,14 @@ using namespace cv; using namespace cv::ml; using namespace std; -void get_svm_detector( const Ptr< SVM > & svm, vector< float > & hog_detector ); +vector< float > get_svm_detector( const Ptr< SVM >& svm ); void convert_to_ml( const std::vector< Mat > & train_samples, Mat& trainData ); void load_images( const String & dirname, vector< Mat > & img_lst, bool showImages ); void sample_neg( const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, const Size & size ); -void computeHOGs( const Size wsize, const vector< Mat > & img_lst, vector< Mat > & gradient_lst ); -int test_trained_detector( String obj_det_filename, String test_dir, String videofilename ); +void computeHOGs( const Size wsize, const vector< Mat > & img_lst, vector< Mat > & gradient_lst, bool use_flip ); +void test_trained_detector( String obj_det_filename, String test_dir, String videofilename ); -void get_svm_detector( const Ptr< SVM >& svm, vector< float > & hog_detector ) +vector< float > get_svm_detector( const Ptr< SVM >& svm ) { // get the support vectors Mat sv = svm->getSupportVectors(); @@ -30,11 +30,11 @@ void get_svm_detector( const Ptr< SVM >& svm, vector< float > & hog_detector ) CV_Assert( (alpha.type() == CV_64F && alpha.at(0) == 1.) || (alpha.type() == CV_32F && alpha.at(0) == 1.f) ); CV_Assert( sv.type() == CV_32F ); - hog_detector.clear(); - hog_detector.resize(sv.cols + 1); + vector< float > hog_detector( sv.cols + 1 ); memcpy( &hog_detector[0], sv.ptr(), sv.cols*sizeof( hog_detector[0] ) ); hog_detector[sv.cols] = (float)-rho; + return hog_detector; } /* @@ -101,35 +101,44 @@ void sample_neg( const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, co srand( (unsigned int)time( NULL ) ); for ( size_t i = 0; i < full_neg_lst.size(); i++ ) - { - box.x = rand() % ( full_neg_lst[i].cols - size_x ); - box.y = rand() % ( full_neg_lst[i].rows - size_y ); - Mat roi = full_neg_lst[i]( box ); - neg_lst.push_back( roi.clone() ); - } + if ( full_neg_lst[i].cols >= box.width && full_neg_lst[i].rows >= box.height ) + { + box.x = rand() % ( full_neg_lst[i].cols - size_x ); + box.y = rand() % ( full_neg_lst[i].rows - size_y ); + Mat roi = full_neg_lst[i]( box ); + neg_lst.push_back( roi.clone() ); + } } -void computeHOGs( const Size wsize, const vector< Mat > & img_lst, vector< Mat > & gradient_lst ) +void computeHOGs( const Size wsize, const vector< Mat > & img_lst, vector< Mat > & gradient_lst, bool use_flip ) { HOGDescriptor hog; hog.winSize = wsize; - - Rect r = Rect( 0, 0, wsize.width, wsize.height ); - r.x += ( img_lst[0].cols - r.width ) / 2; - r.y += ( img_lst[0].rows - r.height ) / 2; - Mat gray; vector< float > descriptors; - for( size_t i=0 ; i< img_lst.size(); i++ ) + for( size_t i = 0 ; i < img_lst.size(); i++ ) { - cvtColor( img_lst[i](r), gray, COLOR_BGR2GRAY ); - hog.compute( gray, descriptors, Size( 8, 8 ), Size( 0, 0 ) ); - gradient_lst.push_back( Mat( descriptors ).clone() ); + if ( img_lst[i].cols >= wsize.width && img_lst[i].rows >= wsize.height ) + { + Rect r = Rect(( img_lst[i].cols - wsize.width ) / 2, + ( img_lst[i].rows - wsize.height ) / 2, + wsize.width, + wsize.height); + cvtColor( img_lst[i](r), gray, COLOR_BGR2GRAY ); + hog.compute( gray, descriptors, Size( 8, 8 ), Size( 0, 0 ) ); + gradient_lst.push_back( Mat( descriptors ).clone() ); + if ( use_flip ) + { + flip( gray, gray, 1 ); + hog.compute( gray, descriptors, Size( 8, 8 ), Size( 0, 0 ) ); + gradient_lst.push_back( Mat( descriptors ).clone() ); + } + } } } -int test_trained_detector( String obj_det_filename, String test_dir, String videofilename ) +void test_trained_detector( String obj_det_filename, String test_dir, String videofilename ) { cout << "Testing trained detector..." << endl; HOGDescriptor hog; @@ -143,7 +152,10 @@ int test_trained_detector( String obj_det_filename, String test_dir, String vide if ( videofilename != "" ) { - cap.open( videofilename ); + if ( videofilename.size() == 1 && isdigit( videofilename[0] ) ) + cap.open( videofilename[0] - '0' ); + else + cap.open( videofilename ); } obj_det_filename = "testing " + obj_det_filename; @@ -165,7 +177,7 @@ int test_trained_detector( String obj_det_filename, String test_dir, String vide if ( img.empty() ) { - return 0; + return; } vector< Rect > detections; @@ -180,12 +192,11 @@ int test_trained_detector( String obj_det_filename, String test_dir, String vide imshow( obj_det_filename, img ); - if( 27 == waitKey( delay ) ) + if( waitKey( delay ) == 27 ) { - return 0; + return; } } - return 0; } int main( int argc, char** argv ) @@ -199,6 +210,7 @@ int main( int argc, char** argv ) "{tv | | test video file name}" "{dw | | width of the detector}" "{dh | | height of the detector}" + "{f |false| indicates if the program will generate and use mirrored samples or not}" "{d |false| train twice}" "{t |false| test a trained detector}" "{v |false| visualize training steps}" @@ -223,6 +235,7 @@ int main( int argc, char** argv ) bool test_detector = parser.get< bool >( "t" ); bool train_twice = parser.get< bool >( "d" ); bool visualization = parser.get< bool >( "v" ); + bool flip_samples = parser.get< bool >( "f" ); if ( test_detector ) { @@ -234,8 +247,8 @@ int main( int argc, char** argv ) { parser.printMessage(); cout << "Wrong number of parameters.\n\n" - << "Example command line:\n" << argv[0] << " -pd=/INRIAPerson/96X160H96/Train/pos -nd=/INRIAPerson/neg -td=/INRIAPerson/Test/pos -fn=HOGpedestrian96x160.yml -d\n" - << "\nExample command line for testing trained detector:\n" << argv[0] << " -t -dw=96 -dh=160 -fn=HOGpedestrian96x160.yml -td=/INRIAPerson/Test/pos"; + << "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" + << "\nExample command line for testing trained detector:\n" << argv[0] << " -t -fn=HOGpedestrian64x128.xml -td=/INRIAPerson/Test/pos"; exit( 1 ); } @@ -256,40 +269,40 @@ int main( int argc, char** argv ) Size pos_image_size = pos_lst[0].size(); - for ( size_t i = 0; i < pos_lst.size(); ++i ) - { - if( pos_lst[i].size() != pos_image_size ) - { - cout << "All positive images should be same size!" << endl; - exit( 1 ); - } - } - - pos_image_size = pos_image_size / 8 * 8; - if ( detector_width && detector_height ) { pos_image_size = Size( detector_width, detector_height ); } - - labels.assign( pos_lst.size(), +1 ); - const unsigned int old = (unsigned int)labels.size(); + else + { + for ( size_t i = 0; i < pos_lst.size(); ++i ) + { + if( pos_lst[i].size() != pos_image_size ) + { + cout << "All positive images should be same size!" << endl; + exit( 1 ); + } + } + pos_image_size = pos_image_size / 8 * 8; + } clog << "Negative images are being loaded..."; load_images( neg_dir, full_neg_lst, false ); sample_neg( full_neg_lst, neg_lst, pos_image_size ); clog << "...[done]" << endl; - labels.insert( labels.end(), neg_lst.size(), -1 ); - CV_Assert( old < labels.size() ); - clog << "Histogram of Gradients are being calculated for positive images..."; - computeHOGs( pos_image_size, pos_lst, gradient_lst ); - clog << "...[done]" << endl; + computeHOGs( pos_image_size, pos_lst, gradient_lst, flip_samples ); + size_t positive_count = gradient_lst.size(); + labels.assign( positive_count, +1 ); + clog << "...[done] ( positive count : " << positive_count << " )" << endl; clog << "Histogram of Gradients are being calculated for negative images..."; - computeHOGs( pos_image_size, neg_lst, gradient_lst ); - clog << "...[done]" << endl; + computeHOGs( pos_image_size, neg_lst, gradient_lst, flip_samples ); + size_t negative_count = gradient_lst.size() - positive_count; + labels.insert( labels.end(), negative_count, -1 ); + CV_Assert( positive_count < labels.size() ); + clog << "...[done] ( negative count : " << negative_count << " )" << endl; Mat train_data; convert_to_ml( gradient_lst, train_data ); @@ -306,7 +319,7 @@ int main( int argc, char** argv ) svm->setP( 0.1 ); // for EPSILON_SVR, epsilon in loss function? svm->setC( 0.01 ); // From paper, soft classifier svm->setType( SVM::EPS_SVR ); // C_SVC; // EPSILON_SVR; // may be also NU_SVR; // do regression task - svm->train( train_data, ROW_SAMPLE, Mat( labels ) ); + svm->train( train_data, ROW_SAMPLE, labels ); clog << "...[done]" << endl; if ( train_twice ) @@ -316,22 +329,25 @@ int main( int argc, char** argv ) my_hog.winSize = pos_image_size; // Set the trained svm to my_hog - vector< float > hog_detector; - get_svm_detector( svm, hog_detector ); - my_hog.setSVMDetector( hog_detector ); + my_hog.setSVMDetector( get_svm_detector( svm ) ); vector< Rect > detections; vector< double > foundWeights; for ( size_t i = 0; i < full_neg_lst.size(); i++ ) { - my_hog.detectMultiScale( full_neg_lst[i], detections, foundWeights ); + if ( full_neg_lst[i].cols >= pos_image_size.width && full_neg_lst[i].rows >= pos_image_size.height ) + my_hog.detectMultiScale( full_neg_lst[i], detections, foundWeights ); + else + detections.clear(); + for ( size_t j = 0; j < detections.size(); j++ ) { Mat detection = full_neg_lst[i]( detections[j] ).clone(); resize( detection, detection, pos_image_size ); neg_lst.push_back( detection ); } + if ( visualization ) { for ( size_t j = 0; j < detections.size(); j++ ) @@ -344,30 +360,30 @@ int main( int argc, char** argv ) } clog << "...[done]" << endl; - labels.clear(); - labels.assign( pos_lst.size(), +1 ); - labels.insert( labels.end(), neg_lst.size(), -1); - gradient_lst.clear(); clog << "Histogram of Gradients are being calculated for positive images..."; - computeHOGs( pos_image_size, pos_lst, gradient_lst ); - clog << "...[done]" << endl; + computeHOGs( pos_image_size, pos_lst, gradient_lst, flip_samples ); + positive_count = gradient_lst.size(); + clog << "...[done] ( positive count : " << positive_count << " )" << endl; clog << "Histogram of Gradients are being calculated for negative images..."; - computeHOGs( pos_image_size, neg_lst, gradient_lst ); - clog << "...[done]" << endl; + computeHOGs( pos_image_size, neg_lst, gradient_lst, flip_samples ); + negative_count = gradient_lst.size() - positive_count; + clog << "...[done] ( negative count : " << negative_count << " )" << endl; + + labels.clear(); + labels.assign(positive_count, +1); + labels.insert(labels.end(), negative_count, -1); clog << "Training SVM again..."; convert_to_ml( gradient_lst, train_data ); - svm->train( train_data, ROW_SAMPLE, Mat( labels ) ); + svm->train( train_data, ROW_SAMPLE, labels ); clog << "...[done]" << endl; } - vector< float > hog_detector; - get_svm_detector( svm, hog_detector ); HOGDescriptor hog; hog.winSize = pos_image_size; - hog.setSVMDetector( hog_detector ); + hog.setSVMDetector( get_svm_detector( svm ) ); hog.save( obj_det_filename ); test_trained_detector( obj_det_filename, test_dir, videofilename );