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Update train_HOG.cpp

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pull/10176/head
Suleyman TURKMEN 7 years ago
parent c7f1843584
commit 2aa380752c
1 changed files with 84 additions and 68 deletions
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  1. 152
      samples/cpp/train_HOG.cpp

152
samples/cpp/train_HOG.cpp
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@ -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<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;
}
/*
@ -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 );

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