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();
CV_Assert( (alpha.type() == CV_64F && alpha.at<double>(0) == 1.) ||
(alpha.type() == CV_32F && alpha.at<float>(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;
}
/*
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;
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;
if ( img.empty() )
{
- return 0;
+ return;
}
vector< Rect > detections;
imshow( obj_det_filename, img );
- if( 27 == waitKey( delay ) )
+ if( waitKey( delay ) == 27 )
{
- return 0;
+ return;
}
}
- return 0;
}
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}"
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 )
{
{
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 );
}
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 );
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 )
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++ )
}
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 );
projects / platform / upstream / opencv.git / commitdiff