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6a511755b2
get_hogdescriptor_visu is passed img->clone(), which is not usable as a non-const reference (and it doesn't modify the argument, anyway).
462 lines
15 KiB
C++
462 lines
15 KiB
C++
#include <opencv2/opencv.hpp>
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#include <string>
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#include <iostream>
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#include <fstream>
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#include <vector>
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#include <time.h>
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using namespace cv;
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using namespace std;
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void get_svm_detector(const SVM& svm, vector< float > & hog_detector );
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void convert_to_ml(const std::vector< cv::Mat > & train_samples, cv::Mat& trainData );
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void load_images( const string & prefix, const string & filename, vector< Mat > & img_lst );
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void sample_neg( const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, const Size & size );
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Mat get_hogdescriptor_visu(const Mat& color_origImg, vector<float>& descriptorValues, const Size & size );
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void compute_hog( const vector< Mat > & img_lst, vector< Mat > & gradient_lst, const Size & size );
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void train_svm( const vector< Mat > & gradient_lst, const vector< int > & labels );
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void draw_locations( Mat & img, const vector< Rect > & locations, const Scalar & color );
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void test_it( const Size & size );
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void get_svm_detector(const SVM& svm, vector< float > & hog_detector )
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{
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// get the number of variables
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const int var_all = svm.get_var_count();
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// get the number of support vectors
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const int sv_total = svm.get_support_vector_count();
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// get the decision function
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const CvSVMDecisionFunc* decision_func = svm.get_decision_function();
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// get the support vectors
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const float** sv = new const float*[ sv_total ];
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for( int i = 0 ; i < sv_total ; ++i )
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sv[ i ] = svm.get_support_vector(i);
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CV_Assert( var_all > 0 &&
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sv_total > 0 &&
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decision_func != 0 &&
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decision_func->alpha != 0 &&
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decision_func->sv_count == sv_total );
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float svi = 0.f;
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hog_detector.clear(); //clear stuff in vector.
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hog_detector.reserve( var_all + 1 ); //reserve place for memory efficiency.
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/**
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* hog_detector^i = \sum_j support_vector_j^i * \alpha_j
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* hog_detector^dim = -\rho
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*/
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for( int i = 0 ; i < var_all ; ++i )
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{
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svi = 0.f;
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for( int j = 0 ; j < sv_total ; ++j )
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{
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if( decision_func->sv_index != NULL ) // sometime the sv_index isn't store on YML/XML.
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svi += (float)( sv[decision_func->sv_index[j]][i] * decision_func->alpha[ j ] );
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else
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svi += (float)( sv[j][i] * decision_func->alpha[ j ] );
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}
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hog_detector.push_back( svi );
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}
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hog_detector.push_back( (float)-decision_func->rho );
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delete[] sv;
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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 std::vector< cv::Mat > & train_samples, cv::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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cv::Mat tmp(1, cols, CV_32FC1); //< used for transposition if needed
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trainData = cv::Mat(rows, cols, CV_32FC1 );
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vector< Mat >::const_iterator itr = train_samples.begin();
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vector< Mat >::const_iterator end = train_samples.end();
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for( int i = 0 ; itr != end ; ++itr, ++i )
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{
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CV_Assert( itr->cols == 1 ||
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itr->rows == 1 );
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if( itr->cols == 1 )
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{
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transpose( *(itr), tmp );
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tmp.copyTo( trainData.row( i ) );
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}
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else if( itr->rows == 1 )
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{
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itr->copyTo( trainData.row( i ) );
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}
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}
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}
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void load_images( const string & prefix, const string & filename, vector< Mat > & img_lst )
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{
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string line;
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ifstream file;
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file.open( (prefix+filename).c_str() );
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if( !file.is_open() )
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{
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cerr << "Unable to open the list of images from " << filename << " filename." << endl;
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exit( -1 );
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}
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bool end_of_parsing = false;
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while( !end_of_parsing )
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{
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getline( file, line );
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if( line == "" ) // no more file to read
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{
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end_of_parsing = true;
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break;
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}
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Mat img = imread( (prefix+line).c_str() ); // load the image
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if( !img.data ) // invalid image, just skip it.
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continue;
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#ifdef _DEBUG
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imshow( "image", img );
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waitKey( 10 );
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#endif
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img_lst.push_back( img.clone() );
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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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vector< Mat >::const_iterator img = full_neg_lst.begin();
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vector< Mat >::const_iterator end = full_neg_lst.end();
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for( ; img != end ; ++img )
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{
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box.x = rand() % (img->cols - size_x);
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box.y = rand() % (img->rows - size_y);
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Mat roi = (*img)(box);
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neg_lst.push_back( roi.clone() );
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#ifdef _DEBUG
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imshow( "img", roi.clone() );
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waitKey( 10 );
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#endif
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}
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}
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// From http://www.juergenwiki.de/work/wiki/doku.php?id=public:hog_descriptor_computation_and_visualization
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Mat get_hogdescriptor_visu(const Mat& color_origImg, vector<float>& descriptorValues, const Size & size )
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{
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const int DIMX = size.width;
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const int DIMY = size.height;
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float zoomFac = 3;
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Mat visu;
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resize(color_origImg, visu, Size( (int)(color_origImg.cols*zoomFac), (int)(color_origImg.rows*zoomFac) ) );
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int cellSize = 8;
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int gradientBinSize = 9;
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float radRangeForOneBin = (float)(CV_PI/(float)gradientBinSize); // dividing 180<38> into 9 bins, how large (in rad) is one bin?
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// prepare data structure: 9 orientation / gradient strenghts for each cell
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int cells_in_x_dir = DIMX / cellSize;
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int cells_in_y_dir = DIMY / cellSize;
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float*** gradientStrengths = new float**[cells_in_y_dir];
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int** cellUpdateCounter = new int*[cells_in_y_dir];
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for (int y=0; y<cells_in_y_dir; y++)
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{
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gradientStrengths[y] = new float*[cells_in_x_dir];
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cellUpdateCounter[y] = new int[cells_in_x_dir];
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for (int x=0; x<cells_in_x_dir; x++)
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{
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gradientStrengths[y][x] = new float[gradientBinSize];
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cellUpdateCounter[y][x] = 0;
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for (int bin=0; bin<gradientBinSize; bin++)
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gradientStrengths[y][x][bin] = 0.0;
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}
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}
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// nr of blocks = nr of cells - 1
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// since there is a new block on each cell (overlapping blocks!) but the last one
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int blocks_in_x_dir = cells_in_x_dir - 1;
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int blocks_in_y_dir = cells_in_y_dir - 1;
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// compute gradient strengths per cell
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int descriptorDataIdx = 0;
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int cellx = 0;
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int celly = 0;
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for (int blockx=0; blockx<blocks_in_x_dir; blockx++)
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{
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for (int blocky=0; blocky<blocks_in_y_dir; blocky++)
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{
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// 4 cells per block ...
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for (int cellNr=0; cellNr<4; cellNr++)
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{
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// compute corresponding cell nr
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cellx = blockx;
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celly = blocky;
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if (cellNr==1) celly++;
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if (cellNr==2) cellx++;
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if (cellNr==3)
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{
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cellx++;
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celly++;
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}
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for (int bin=0; bin<gradientBinSize; bin++)
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{
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float gradientStrength = descriptorValues[ descriptorDataIdx ];
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descriptorDataIdx++;
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gradientStrengths[celly][cellx][bin] += gradientStrength;
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} // for (all bins)
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// note: overlapping blocks lead to multiple updates of this sum!
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// we therefore keep track how often a cell was updated,
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// to compute average gradient strengths
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cellUpdateCounter[celly][cellx]++;
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} // for (all cells)
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} // for (all block x pos)
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} // for (all block y pos)
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// compute average gradient strengths
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for (celly=0; celly<cells_in_y_dir; celly++)
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{
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for (cellx=0; cellx<cells_in_x_dir; cellx++)
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{
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float NrUpdatesForThisCell = (float)cellUpdateCounter[celly][cellx];
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// compute average gradient strenghts for each gradient bin direction
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for (int bin=0; bin<gradientBinSize; bin++)
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{
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gradientStrengths[celly][cellx][bin] /= NrUpdatesForThisCell;
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}
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}
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}
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// draw cells
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for (celly=0; celly<cells_in_y_dir; celly++)
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{
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for (cellx=0; cellx<cells_in_x_dir; cellx++)
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{
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int drawX = cellx * cellSize;
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int drawY = celly * cellSize;
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int mx = drawX + cellSize/2;
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int my = drawY + cellSize/2;
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rectangle(visu, Point((int)(drawX*zoomFac), (int)(drawY*zoomFac)), Point((int)((drawX+cellSize)*zoomFac), (int)((drawY+cellSize)*zoomFac)), CV_RGB(100,100,100), 1);
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// draw in each cell all 9 gradient strengths
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for (int bin=0; bin<gradientBinSize; bin++)
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{
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float currentGradStrength = gradientStrengths[celly][cellx][bin];
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// no line to draw?
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if (currentGradStrength==0)
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continue;
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float currRad = bin * radRangeForOneBin + radRangeForOneBin/2;
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float dirVecX = cos( currRad );
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float dirVecY = sin( currRad );
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float maxVecLen = (float)(cellSize/2.f);
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float scale = 2.5; // just a visualization scale, to see the lines better
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// compute line coordinates
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float x1 = mx - dirVecX * currentGradStrength * maxVecLen * scale;
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float y1 = my - dirVecY * currentGradStrength * maxVecLen * scale;
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float x2 = mx + dirVecX * currentGradStrength * maxVecLen * scale;
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float y2 = my + dirVecY * currentGradStrength * maxVecLen * scale;
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// draw gradient visualization
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line(visu, Point((int)(x1*zoomFac),(int)(y1*zoomFac)), Point((int)(x2*zoomFac),(int)(y2*zoomFac)), CV_RGB(0,255,0), 1);
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} // for (all bins)
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} // for (cellx)
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} // for (celly)
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// don't forget to free memory allocated by helper data structures!
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for (int y=0; y<cells_in_y_dir; y++)
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{
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for (int x=0; x<cells_in_x_dir; x++)
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{
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delete[] gradientStrengths[y][x];
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}
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delete[] gradientStrengths[y];
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delete[] cellUpdateCounter[y];
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}
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delete[] gradientStrengths;
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delete[] cellUpdateCounter;
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return visu;
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} // get_hogdescriptor_visu
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void compute_hog( const vector< Mat > & img_lst, vector< Mat > & gradient_lst, const Size & size )
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{
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HOGDescriptor hog;
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hog.winSize = size;
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Mat gray;
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vector< Point > location;
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vector< float > descriptors;
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vector< Mat >::const_iterator img = img_lst.begin();
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vector< Mat >::const_iterator end = img_lst.end();
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for( ; img != end ; ++img )
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{
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cvtColor( *img, gray, COLOR_BGR2GRAY );
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hog.compute( gray, descriptors, Size( 8, 8 ), Size( 0, 0 ), location );
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gradient_lst.push_back( Mat( descriptors ).clone() );
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#ifdef _DEBUG
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imshow( "gradient", get_hogdescriptor_visu( img->clone(), descriptors, size ) );
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waitKey( 10 );
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#endif
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}
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}
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void train_svm( const vector< Mat > & gradient_lst, const vector< int > & labels )
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{
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SVM svm;
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/* Default values to train SVM */
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SVMParams params;
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params.coef0 = 0.0;
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params.degree = 3;
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params.term_crit.epsilon = 1e-3;
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params.gamma = 0;
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params.kernel_type = SVM::LINEAR;
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params.nu = 0.5;
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params.p = 0.1; // for EPSILON_SVR, epsilon in loss function?
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params.C = 0.01; // From paper, soft classifier
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params.svm_type = SVM::EPS_SVR; // C_SVC; // EPSILON_SVR; // may be also NU_SVR; // do regression task
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Mat train_data;
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convert_to_ml( gradient_lst, train_data );
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clog << "Start training...";
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svm.train( train_data, Mat( labels ), Mat(), Mat(), params );
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clog << "...[done]" << endl;
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svm.save( "my_people_detector.yml" );
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}
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void draw_locations( Mat & img, const vector< Rect > & locations, const Scalar & color )
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{
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if( !locations.empty() )
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{
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vector< Rect >::const_iterator loc = locations.begin();
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vector< Rect >::const_iterator end = locations.end();
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for( ; loc != end ; ++loc )
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{
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rectangle( img, *loc, color, 2 );
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}
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}
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}
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void test_it( const Size & size )
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{
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char key = 27;
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Scalar reference( 0, 255, 0 );
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Scalar trained( 0, 0, 255 );
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Mat img, draw;
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SVM svm;
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HOGDescriptor hog;
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HOGDescriptor my_hog;
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my_hog.winSize = size;
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VideoCapture video;
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vector< Rect > locations;
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// Load the trained SVM.
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svm.load( "my_people_detector.yml" );
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// Set the trained svm to my_hog
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vector< float > hog_detector;
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get_svm_detector( svm, hog_detector );
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my_hog.setSVMDetector( hog_detector );
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// Set the people detector.
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hog.setSVMDetector( hog.getDefaultPeopleDetector() );
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// Open the camera.
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video.open(0);
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if( !video.isOpened() )
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{
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cerr << "Unable to open the device 0" << endl;
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exit( -1 );
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}
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bool end_of_process = false;
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while( !end_of_process )
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{
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video >> img;
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if( !img.data )
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break;
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draw = img.clone();
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locations.clear();
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hog.detectMultiScale( img, locations );
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draw_locations( draw, locations, reference );
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locations.clear();
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my_hog.detectMultiScale( img, locations );
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draw_locations( draw, locations, trained );
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imshow( "Video", draw );
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key = (char)waitKey( 10 );
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if( 27 == key )
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end_of_process = true;
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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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if( argc != 4 )
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{
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cout << "Wrong number of parameters." << endl
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<< "Usage: " << argv[0] << " pos_dir pos.lst neg_dir neg.lst" << endl
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<< "example: " << argv[0] << " /INRIA_dataset/ Train/pos.lst /INRIA_dataset/ Train/neg.lst" << endl;
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exit( -1 );
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}
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vector< Mat > pos_lst;
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vector< Mat > full_neg_lst;
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vector< Mat > neg_lst;
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vector< Mat > gradient_lst;
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vector< int > labels;
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load_images( argv[1], argv[2], pos_lst );
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labels.assign( pos_lst.size(), +1 );
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const unsigned int old = (unsigned int)labels.size();
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load_images( argv[3], argv[4], full_neg_lst );
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sample_neg( full_neg_lst, neg_lst, Size( 96,160 ) );
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labels.insert( labels.end(), neg_lst.size(), -1 );
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CV_Assert( old < labels.size() );
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compute_hog( pos_lst, gradient_lst, Size( 96, 160 ) );
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compute_hog( neg_lst, gradient_lst, Size( 96, 160 ) );
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train_svm( gradient_lst, labels );
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test_it( Size( 96, 160 ) ); // change with your parameters
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return 0;
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}
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