samples/cpp/train_HOG.cpp

#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/ml.hpp"
#include "opencv2/objdetect.hpp"
#include "opencv2/videoio.hpp"
#include <iostream>
#include <time.h>
 
using namespace cv;
using namespace cv::ml;
using namespace std;
 
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, bool use_flip );
void test_trained_detector( String obj_det_filename, String test_dir, String videofilename );
 
vector< float > get_svm_detector( const Ptr< SVM >& svm )
{
 // get the support vectors
 Mat sv = svm->getSupportVectors();
 const int sv_total = sv.rows;
 // get the decision function
 Mat alpha, svidx;
 double rho = svm->getDecisionFunction( 0, alpha, svidx );
 
 CV_Assert( alpha.total() == 1 && svidx.total() == 1 && sv_total == 1 );
 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 );
 
 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;
}
 
/*
* Convert training/testing set to be used by OpenCV Machine Learning algorithms.
* TrainData is a matrix of size (#samples x max(#cols,#rows) per samples), in 32FC1.
* Transposition of samples are made if needed.
*/
void convert_to_ml( const vector< Mat > & train_samples, Mat& trainData )
{
 //--Convert data
 const int rows = (int)train_samples.size();
 const int cols = (int)std::max( train_samples[0].cols, train_samples[0].rows );
 Mat tmp( 1, cols, CV_32FC1 ); 
 trainData = Mat( rows, cols, CV_32FC1 );
 
 for( size_t i = 0 ; i < train_samples.size(); ++i )
 {
 CV_Assert( train_samples[i].cols == 1 || train_samples[i].rows == 1 );
 
 if( train_samples[i].cols == 1 )
 {
 transpose( train_samples[i], tmp );
 tmp.copyTo( trainData.row( (int)i ) );
 }
 else if( train_samples[i].rows == 1 )
 {
 train_samples[i].copyTo( trainData.row( (int)i ) );
 }
 }
}
 
void load_images( const String & dirname, vector< Mat > & img_lst, bool showImages = false )
{
 vector< String > files;
 glob( dirname, files );
 
 for ( size_t i = 0; i < files.size(); ++i )
 {
 Mat img = imread( files[i] ); // load the image
 if ( img.empty() )
 {
 cout << files[i] << " is invalid!" << endl; // invalid image, skip it.
 continue;
 }
 
 if ( showImages )
 {
 imshow( "image", img );
 waitKey( 1 );
 }
 img_lst.push_back( img );
 }
}
 
void sample_neg( const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, const Size & size )
{
 Rect box;
 box.width = size.width;
 box.height = size.height;
 
 srand( (unsigned int)time( NULL ) );
 
 for ( size_t i = 0; i < full_neg_lst.size(); i++ )
 if ( full_neg_lst[i].cols > box.width && full_neg_lst[i].rows > box.height )
 {
 box.x = rand() % ( full_neg_lst[i].cols - box.width );
 box.y = rand() % ( full_neg_lst[i].rows - box.height );
 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, bool use_flip )
{
 HOGDescriptor hog;
 hog.winSize = wsize;
 Mat gray;
 vector< float > descriptors;
 
 for( size_t i = 0 ; i < img_lst.size(); i++ )
 {
 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() );
 }
 }
 }
}
 
void test_trained_detector( String obj_det_filename, String test_dir, String videofilename )
{
 cout << "Testing trained detector..." << endl;
 HOGDescriptor hog;
 hog.load( obj_det_filename );
 
 vector< String > files;
 glob( test_dir, files );
 
 int delay = 0;
 VideoCapture cap;
 
 if ( videofilename != "" )
 {
 if ( videofilename.size() == 1 && isdigit( videofilename[0] ) )
 cap.open( videofilename[0] - '0' );
 else
 cap.open( videofilename );
 }
 
 obj_det_filename = "testing " + obj_det_filename;
 namedWindow( obj_det_filename, WINDOW_NORMAL );
 
 for( size_t i=0;; i++ )
 {
 Mat img;
 
 if ( cap.isOpened() )
 {
 cap >> img;
 delay = 1;
 }
 else if( i < files.size() )
 {
 img = imread( files[i] );
 }
 
 if ( img.empty() )
 {
 return;
 }
 
 vector< Rect > detections;
 vector< double > foundWeights;
 
 hog.detectMultiScale( img, detections, foundWeights );
 for ( size_t j = 0; j < detections.size(); j++ )
 {
 Scalar color = Scalar( 0, foundWeights[j] * foundWeights[j] * 200, 0 );
 rectangle( img, detections[j], color, img.cols / 400 + 1 );
 }
 
 imshow( obj_det_filename, img );
 
 if( waitKey( delay ) == 27 )
 {
 return;
 }
 }
}
 
int main( int argc, char** argv )
{
 const char* keys =
 {
 "{help h| | show help message}"
 "{pd | | path of directory contains positive images}"
 "{nd | | path of directory contains negative images}"
 "{td | | path of directory contains test images}"
 "{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}"
 "{fn |my_detector.yml| file name of trained SVM}"
 };
 
 CommandLineParser parser( argc, argv, keys );
 
 if ( parser.has( "help" ) )
 {
 parser.printMessage();
 exit( 0 );
 }
 
 String pos_dir = parser.get< String >( "pd" );
 String neg_dir = parser.get< String >( "nd" );
 String test_dir = parser.get< String >( "td" );
 String obj_det_filename = parser.get< String >( "fn" );
 String videofilename = parser.get< String >( "tv" );
 int detector_width = parser.get< int >( "dw" );
 int detector_height = parser.get< int >( "dh" );
 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 )
 {
 test_trained_detector( obj_det_filename, test_dir, videofilename );
 exit( 0 );
 }
 
 if( pos_dir.empty() || neg_dir.empty() )
 {
 parser.printMessage();
 cout << "Wrong number of parameters.\n\n"
 << "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 );
 }
 
 vector< Mat > pos_lst, full_neg_lst, neg_lst, gradient_lst;
 vector< int > labels;
 
 clog << "Positive images are being loaded..." ;
 load_images( pos_dir, pos_lst, visualization );
 if ( pos_lst.size() > 0 )
 {
 clog << "...[done] " << pos_lst.size() << " files." << endl;
 }
 else
 {
 clog << "no image in " << pos_dir <<endl;
 return 1;
 }
 
 Size pos_image_size = pos_lst[0].size();
 
 if ( detector_width && detector_height )
 {
 pos_image_size = Size( detector_width, detector_height );
 }
 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, visualization );
 clog << "...[done] " << full_neg_lst.size() << " files." << endl;
 
 clog << "Negative images are being processed...";
 sample_neg( full_neg_lst, neg_lst, pos_image_size );
 clog << "...[done] " << neg_lst.size() << " files." << endl;
 
 clog << "Histogram of Gradients are being calculated for positive images...";
 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 images count : " << positive_count << " )" << endl;
 
 clog << "Histogram of Gradients are being calculated for negative images...";
 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 images count : " << negative_count << " )" << endl;
 
 Mat train_data;
 convert_to_ml( gradient_lst, train_data );
 
 clog << "Training SVM...";
 Ptr< SVM > svm = SVM::create();
 /* Default values to train SVM */
 svm->setCoef0( 0.0 );
 svm->setDegree( 3 );
 svm->setTermCriteria( TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 1e-3 ) );
 svm->setGamma( 0 );
 svm->setKernel( SVM::LINEAR );
 svm->setNu( 0.5 );
 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, labels );
 clog << "...[done]" << endl;
 
 if ( train_twice )
 {
 clog << "Testing trained detector on negative images. This might take a few minutes...";
 HOGDescriptor my_hog;
 my_hog.winSize = pos_image_size;
 
 // Set the trained svm to my_hog
 my_hog.setSVMDetector( get_svm_detector( svm ) );
 
 vector< Rect > detections;
 vector< double > foundWeights;
 
 for ( size_t i = 0; i < full_neg_lst.size(); i++ )
 {
 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, 0, 0, INTER_LINEAR_EXACT);
 neg_lst.push_back( detection );
 }
 
 if ( visualization )
 {
 for ( size_t j = 0; j < detections.size(); j++ )
 {
 rectangle( full_neg_lst[i], detections[j], Scalar( 0, 255, 0 ), 2 );
 }
 imshow( "testing trained detector on negative images", full_neg_lst[i] );
 waitKey( 5 );
 }
 }
 clog << "...[done]" << endl;
 
 gradient_lst.clear();
 clog << "Histogram of Gradients are being calculated for positive images...";
 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, 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, labels );
 clog << "...[done]" << endl;
 }
 
 HOGDescriptor hog;
 hog.winSize = pos_image_size;
 hog.setSVMDetector( get_svm_detector( svm ) );
 hog.save( obj_det_filename );
 
 test_trained_detector( obj_det_filename, test_dir, videofilename );
 
 return 0;
}