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Joachim
392
samples/cpp/train_HOG.cpp
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392
samples/cpp/train_HOG.cpp
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#include "opencv2/imgproc.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/ml.hpp"
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#include "opencv2/objdetect.hpp"
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#include "opencv2/videoio.hpp"
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#include <iostream>
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#include <time.h>
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using namespace cv;
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using namespace cv::ml;
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using namespace std;
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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, bool use_flip );
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void test_trained_detector( String obj_det_filename, String test_dir, String videofilename );
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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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const int sv_total = sv.rows;
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// get the decision function
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Mat alpha, svidx;
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double rho = svm->getDecisionFunction( 0, alpha, svidx );
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CV_Assert( alpha.total() == 1 && svidx.total() == 1 && sv_total == 1 );
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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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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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* Convert training/testing set to be used by OpenCV Machine Learning algorithms.
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* TrainData is a matrix of size (#samples x max(#cols,#rows) per samples), in 32FC1.
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* Transposition of samples are made if needed.
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*/
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void convert_to_ml( const vector< Mat > & train_samples, Mat& trainData )
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{
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//--Convert data
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const int rows = (int)train_samples.size();
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const int cols = (int)std::max( train_samples[0].cols, train_samples[0].rows );
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Mat tmp( 1, cols, CV_32FC1 ); //< used for transposition if needed
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trainData = Mat( rows, cols, CV_32FC1 );
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for( size_t i = 0 ; i < train_samples.size(); ++i )
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{
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CV_Assert( train_samples[i].cols == 1 || train_samples[i].rows == 1 );
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if( train_samples[i].cols == 1 )
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{
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transpose( train_samples[i], tmp );
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tmp.copyTo( trainData.row( (int)i ) );
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}
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else if( train_samples[i].rows == 1 )
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{
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train_samples[i].copyTo( trainData.row( (int)i ) );
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}
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}
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}
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void load_images( const String & dirname, vector< Mat > & img_lst, bool showImages = false )
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{
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vector< String > files;
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glob( dirname, files );
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for ( size_t i = 0; i < files.size(); ++i )
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{
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Mat img = imread( files[i] ); // load the image
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if ( img.empty() ) // invalid image, skip it.
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{
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cout << files[i] << " is invalid!" << endl;
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continue;
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}
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if ( showImages )
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{
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imshow( "image", img );
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waitKey( 1 );
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}
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img_lst.push_back( img );
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}
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}
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void sample_neg( const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, const Size & size )
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{
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Rect box;
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box.width = size.width;
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box.height = size.height;
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const int size_x = box.width;
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const int size_y = box.height;
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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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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, bool use_flip )
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{
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HOGDescriptor hog;
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hog.winSize = wsize;
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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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{
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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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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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hog.load( obj_det_filename );
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vector< String > files;
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glob( test_dir, files );
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int delay = 0;
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VideoCapture cap;
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if ( videofilename != "" )
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{
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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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namedWindow( obj_det_filename, WINDOW_NORMAL );
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for( size_t i=0;; i++ )
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{
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Mat img;
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if ( cap.isOpened() )
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{
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cap >> img;
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delay = 1;
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}
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else if( i < files.size() )
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{
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img = imread( files[i] );
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}
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if ( img.empty() )
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{
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return;
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}
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vector< Rect > detections;
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vector< double > foundWeights;
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hog.detectMultiScale( img, detections, foundWeights );
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for ( size_t j = 0; j < detections.size(); j++ )
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{
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Scalar color = Scalar( 0, foundWeights[j] * foundWeights[j] * 200, 0 );
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rectangle( img, detections[j], color, img.cols / 400 + 1 );
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}
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imshow( obj_det_filename, img );
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if( waitKey( delay ) == 27 )
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{
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return;
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}
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}
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}
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int main( int argc, char** argv )
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{
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const char* keys =
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{
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"{help h| | show help message}"
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"{pd | | path of directory contains positive images}"
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"{nd | | path of directory contains negative images}"
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"{td | | path of directory contains test images}"
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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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"{fn |my_detector.yml| file name of trained SVM}"
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};
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CommandLineParser parser( argc, argv, keys );
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if ( parser.has( "help" ) )
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{
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parser.printMessage();
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exit( 0 );
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}
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String pos_dir = parser.get< String >( "pd" );
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String neg_dir = parser.get< String >( "nd" );
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String test_dir = parser.get< String >( "td" );
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String obj_det_filename = parser.get< String >( "fn" );
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String videofilename = parser.get< String >( "tv" );
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int detector_width = parser.get< int >( "dw" );
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int detector_height = parser.get< int >( "dh" );
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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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test_trained_detector( obj_det_filename, test_dir, videofilename );
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exit( 0 );
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}
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if( pos_dir.empty() || neg_dir.empty() )
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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] << " -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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vector< Mat > pos_lst, full_neg_lst, neg_lst, gradient_lst;
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vector< int > labels;
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clog << "Positive images are being loaded..." ;
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load_images( pos_dir, pos_lst, visualization );
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if ( pos_lst.size() > 0 )
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{
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clog << "...[done]" << endl;
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}
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else
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{
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clog << "no image in " << pos_dir <<endl;
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return 1;
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}
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Size pos_image_size = pos_lst[0].size();
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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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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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clog << "Histogram of Gradients are being calculated for positive images...";
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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, 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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clog << "Training SVM...";
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Ptr< SVM > svm = SVM::create();
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/* Default values to train SVM */
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svm->setCoef0( 0.0 );
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svm->setDegree( 3 );
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svm->setTermCriteria( TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 1e-3 ) );
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svm->setGamma( 0 );
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svm->setKernel( SVM::LINEAR );
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svm->setNu( 0.5 );
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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, labels );
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clog << "...[done]" << endl;
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if ( train_twice )
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{
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clog << "Testing trained detector on negative images. This may take a few minutes...";
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HOGDescriptor my_hog;
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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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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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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, 0, 0, INTER_LINEAR_EXACT);
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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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{
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rectangle( full_neg_lst[i], detections[j], Scalar( 0, 255, 0 ), 2 );
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}
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imshow( "testing trained detector on negative images", full_neg_lst[i] );
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waitKey( 5 );
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}
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}
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clog << "...[done]" << endl;
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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, 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, 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, labels );
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clog << "...[done]" << endl;
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}
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HOGDescriptor hog;
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hog.winSize = pos_image_size;
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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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return 0;
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}
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