mirror of
https://github.com/opencv/opencv.git
synced 2025-06-11 11:45:30 +08:00
Update train_HOG.cpp
This commit is contained in:
Suleyman TURKMEN
parent
0e436c3fe9
commit
f6daaccccf
@ -1,34 +1,30 @@
|
||||
#include <opencv2/opencv.hpp>
|
||||
#include "opencv2/imgproc.hpp"
|
||||
#include "opencv2/highgui.hpp"
|
||||
#include "opencv2/ml.hpp"
|
||||
#include "opencv2/objdetect.hpp"
|
||||
|
||||
#include <string>
|
||||
#include <iostream>
|
||||
#include <fstream>
|
||||
#include <vector>
|
||||
|
||||
#include <time.h>
|
||||
|
||||
using namespace cv;
|
||||
using namespace cv::ml;
|
||||
using namespace std;
|
||||
|
||||
void get_svm_detector(const Ptr<SVM>& svm, vector< float > & hog_detector );
|
||||
void convert_to_ml(const std::vector< cv::Mat > & train_samples, cv::Mat& trainData );
|
||||
void load_images( const string & prefix, const string & filename, vector< Mat > & img_lst );
|
||||
void get_svm_detector( const Ptr< SVM > & svm, vector< float > & hog_detector );
|
||||
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 );
|
||||
Mat get_hogdescriptor_visu(const Mat& color_origImg, vector<float>& descriptorValues, const Size & size );
|
||||
void compute_hog( const vector< Mat > & img_lst, vector< Mat > & gradient_lst, const Size & size );
|
||||
void train_svm( const vector< Mat > & gradient_lst, const vector< int > & labels );
|
||||
void draw_locations( Mat & img, const vector< Rect > & locations, const Scalar & color );
|
||||
void test_it( 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 get_svm_detector(const Ptr<SVM>& svm, vector< float > & hog_detector )
|
||||
void get_svm_detector( const Ptr< SVM >& svm, vector< float > & hog_detector )
|
||||
{
|
||||
// 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);
|
||||
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.) ||
|
||||
@ -37,70 +33,59 @@ void get_svm_detector(const Ptr<SVM>& svm, vector< float > & hog_detector )
|
||||
hog_detector.clear();
|
||||
|
||||
hog_detector.resize(sv.cols + 1);
|
||||
memcpy(&hog_detector[0], sv.ptr(), sv.cols*sizeof(hog_detector[0]));
|
||||
memcpy( &hog_detector[0], sv.ptr(), sv.cols*sizeof( hog_detector[0] ) );
|
||||
hog_detector[sv.cols] = (float)-rho;
|
||||
}
|
||||
|
||||
|
||||
/*
|
||||
* 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 std::vector< cv::Mat > & train_samples, cv::Mat& trainData )
|
||||
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 );
|
||||
cv::Mat tmp(1, cols, CV_32FC1); //< used for transposition if needed
|
||||
trainData = cv::Mat(rows, cols, CV_32FC1 );
|
||||
vector< Mat >::const_iterator itr = train_samples.begin();
|
||||
vector< Mat >::const_iterator end = train_samples.end();
|
||||
for( int i = 0 ; itr != end ; ++itr, ++i )
|
||||
Mat tmp( 1, cols, CV_32FC1 ); //< used for transposition if needed
|
||||
trainData = Mat( rows, cols, CV_32FC1 );
|
||||
|
||||
for( size_t i = 0 ; i < train_samples.size(); ++i )
|
||||
{
|
||||
CV_Assert( itr->cols == 1 ||
|
||||
itr->rows == 1 );
|
||||
if( itr->cols == 1 )
|
||||
CV_Assert( train_samples[i].cols == 1 || train_samples[i].rows == 1 );
|
||||
|
||||
if( train_samples[i].cols == 1 )
|
||||
{
|
||||
transpose( *(itr), tmp );
|
||||
tmp.copyTo( trainData.row( i ) );
|
||||
transpose( train_samples[i], tmp );
|
||||
tmp.copyTo( trainData.row( (int)i ) );
|
||||
}
|
||||
else if( itr->rows == 1 )
|
||||
else if( train_samples[i].rows == 1 )
|
||||
{
|
||||
itr->copyTo( trainData.row( i ) );
|
||||
train_samples[i].copyTo( trainData.row( (int)i ) );
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void load_images( const string & prefix, const string & filename, vector< Mat > & img_lst )
|
||||
void load_images( const String & dirname, vector< Mat > & img_lst, bool showImages = false )
|
||||
{
|
||||
string line;
|
||||
ifstream file;
|
||||
vector< String > files;
|
||||
glob( dirname, files );
|
||||
|
||||
file.open( (prefix+filename).c_str() );
|
||||
if( !file.is_open() )
|
||||
for ( size_t i = 0; i < files.size(); ++i )
|
||||
{
|
||||
cerr << "Unable to open the list of images from " << filename << " filename." << endl;
|
||||
exit( -1 );
|
||||
}
|
||||
|
||||
bool end_of_parsing = false;
|
||||
while( !end_of_parsing )
|
||||
{
|
||||
getline( file, line );
|
||||
if( line.empty() ) // no more file to read
|
||||
Mat img = imread( files[i] ); // load the image
|
||||
if ( img.empty() ) // invalid image, skip it.
|
||||
{
|
||||
end_of_parsing = true;
|
||||
break;
|
||||
}
|
||||
Mat img = imread( (prefix+line).c_str() ); // load the image
|
||||
if( img.empty() ) // invalid image, just skip it.
|
||||
cout << files[i] << " is invalid!" << endl;
|
||||
continue;
|
||||
#ifdef _DEBUG
|
||||
imshow( "image", img );
|
||||
waitKey( 10 );
|
||||
#endif
|
||||
img_lst.push_back( img.clone() );
|
||||
}
|
||||
|
||||
if ( showImages )
|
||||
{
|
||||
imshow( "image", img );
|
||||
waitKey( 1 );
|
||||
}
|
||||
img_lst.push_back( img );
|
||||
}
|
||||
}
|
||||
|
||||
@ -115,331 +100,277 @@ void sample_neg( const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, co
|
||||
|
||||
srand( (unsigned int)time( NULL ) );
|
||||
|
||||
vector< Mat >::const_iterator img = full_neg_lst.begin();
|
||||
vector< Mat >::const_iterator end = full_neg_lst.end();
|
||||
for( ; img != end ; ++img )
|
||||
for ( size_t i = 0; i < full_neg_lst.size(); i++ )
|
||||
{
|
||||
box.x = rand() % (img->cols - size_x);
|
||||
box.y = rand() % (img->rows - size_y);
|
||||
Mat roi = (*img)(box);
|
||||
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() );
|
||||
#ifdef _DEBUG
|
||||
imshow( "img", roi.clone() );
|
||||
waitKey( 10 );
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
// From http://www.juergenwiki.de/work/wiki/doku.php?id=public:hog_descriptor_computation_and_visualization
|
||||
Mat get_hogdescriptor_visu(const Mat& color_origImg, vector<float>& descriptorValues, const Size & size )
|
||||
{
|
||||
const int DIMX = size.width;
|
||||
const int DIMY = size.height;
|
||||
float zoomFac = 3;
|
||||
Mat visu;
|
||||
resize(color_origImg, visu, Size( (int)(color_origImg.cols*zoomFac), (int)(color_origImg.rows*zoomFac) ) );
|
||||
|
||||
int cellSize = 8;
|
||||
int gradientBinSize = 9;
|
||||
float radRangeForOneBin = (float)(CV_PI/(float)gradientBinSize); // dividing 180 into 9 bins, how large (in rad) is one bin?
|
||||
|
||||
// prepare data structure: 9 orientation / gradient strenghts for each cell
|
||||
int cells_in_x_dir = DIMX / cellSize;
|
||||
int cells_in_y_dir = DIMY / cellSize;
|
||||
float*** gradientStrengths = new float**[cells_in_y_dir];
|
||||
int** cellUpdateCounter = new int*[cells_in_y_dir];
|
||||
for (int y=0; y<cells_in_y_dir; y++)
|
||||
{
|
||||
gradientStrengths[y] = new float*[cells_in_x_dir];
|
||||
cellUpdateCounter[y] = new int[cells_in_x_dir];
|
||||
for (int x=0; x<cells_in_x_dir; x++)
|
||||
{
|
||||
gradientStrengths[y][x] = new float[gradientBinSize];
|
||||
cellUpdateCounter[y][x] = 0;
|
||||
|
||||
for (int bin=0; bin<gradientBinSize; bin++)
|
||||
gradientStrengths[y][x][bin] = 0.0;
|
||||
}
|
||||
}
|
||||
|
||||
// nr of blocks = nr of cells - 1
|
||||
// since there is a new block on each cell (overlapping blocks!) but the last one
|
||||
int blocks_in_x_dir = cells_in_x_dir - 1;
|
||||
int blocks_in_y_dir = cells_in_y_dir - 1;
|
||||
|
||||
// compute gradient strengths per cell
|
||||
int descriptorDataIdx = 0;
|
||||
int cellx = 0;
|
||||
int celly = 0;
|
||||
|
||||
for (int blockx=0; blockx<blocks_in_x_dir; blockx++)
|
||||
{
|
||||
for (int blocky=0; blocky<blocks_in_y_dir; blocky++)
|
||||
{
|
||||
// 4 cells per block ...
|
||||
for (int cellNr=0; cellNr<4; cellNr++)
|
||||
{
|
||||
// compute corresponding cell nr
|
||||
cellx = blockx;
|
||||
celly = blocky;
|
||||
if (cellNr==1) celly++;
|
||||
if (cellNr==2) cellx++;
|
||||
if (cellNr==3)
|
||||
{
|
||||
cellx++;
|
||||
celly++;
|
||||
}
|
||||
|
||||
for (int bin=0; bin<gradientBinSize; bin++)
|
||||
{
|
||||
float gradientStrength = descriptorValues[ descriptorDataIdx ];
|
||||
descriptorDataIdx++;
|
||||
|
||||
gradientStrengths[celly][cellx][bin] += gradientStrength;
|
||||
|
||||
} // for (all bins)
|
||||
|
||||
|
||||
// note: overlapping blocks lead to multiple updates of this sum!
|
||||
// we therefore keep track how often a cell was updated,
|
||||
// to compute average gradient strengths
|
||||
cellUpdateCounter[celly][cellx]++;
|
||||
|
||||
} // for (all cells)
|
||||
|
||||
|
||||
} // for (all block x pos)
|
||||
} // for (all block y pos)
|
||||
|
||||
|
||||
// compute average gradient strengths
|
||||
for (celly=0; celly<cells_in_y_dir; celly++)
|
||||
{
|
||||
for (cellx=0; cellx<cells_in_x_dir; cellx++)
|
||||
{
|
||||
|
||||
float NrUpdatesForThisCell = (float)cellUpdateCounter[celly][cellx];
|
||||
|
||||
// compute average gradient strenghts for each gradient bin direction
|
||||
for (int bin=0; bin<gradientBinSize; bin++)
|
||||
{
|
||||
gradientStrengths[celly][cellx][bin] /= NrUpdatesForThisCell;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// draw cells
|
||||
for (celly=0; celly<cells_in_y_dir; celly++)
|
||||
{
|
||||
for (cellx=0; cellx<cells_in_x_dir; cellx++)
|
||||
{
|
||||
int drawX = cellx * cellSize;
|
||||
int drawY = celly * cellSize;
|
||||
|
||||
int mx = drawX + cellSize/2;
|
||||
int my = drawY + cellSize/2;
|
||||
|
||||
rectangle(visu, Point((int)(drawX*zoomFac), (int)(drawY*zoomFac)), Point((int)((drawX+cellSize)*zoomFac), (int)((drawY+cellSize)*zoomFac)), Scalar(100,100,100), 1);
|
||||
|
||||
// draw in each cell all 9 gradient strengths
|
||||
for (int bin=0; bin<gradientBinSize; bin++)
|
||||
{
|
||||
float currentGradStrength = gradientStrengths[celly][cellx][bin];
|
||||
|
||||
// no line to draw?
|
||||
if (currentGradStrength==0)
|
||||
continue;
|
||||
|
||||
float currRad = bin * radRangeForOneBin + radRangeForOneBin/2;
|
||||
|
||||
float dirVecX = cos( currRad );
|
||||
float dirVecY = sin( currRad );
|
||||
float maxVecLen = (float)(cellSize/2.f);
|
||||
float scale = 2.5; // just a visualization scale, to see the lines better
|
||||
|
||||
// compute line coordinates
|
||||
float x1 = mx - dirVecX * currentGradStrength * maxVecLen * scale;
|
||||
float y1 = my - dirVecY * currentGradStrength * maxVecLen * scale;
|
||||
float x2 = mx + dirVecX * currentGradStrength * maxVecLen * scale;
|
||||
float y2 = my + dirVecY * currentGradStrength * maxVecLen * scale;
|
||||
|
||||
// draw gradient visualization
|
||||
line(visu, Point((int)(x1*zoomFac),(int)(y1*zoomFac)), Point((int)(x2*zoomFac),(int)(y2*zoomFac)), Scalar(0,255,0), 1);
|
||||
|
||||
} // for (all bins)
|
||||
|
||||
} // for (cellx)
|
||||
} // for (celly)
|
||||
|
||||
|
||||
// don't forget to free memory allocated by helper data structures!
|
||||
for (int y=0; y<cells_in_y_dir; y++)
|
||||
{
|
||||
for (int x=0; x<cells_in_x_dir; x++)
|
||||
{
|
||||
delete[] gradientStrengths[y][x];
|
||||
}
|
||||
delete[] gradientStrengths[y];
|
||||
delete[] cellUpdateCounter[y];
|
||||
}
|
||||
delete[] gradientStrengths;
|
||||
delete[] cellUpdateCounter;
|
||||
|
||||
return visu;
|
||||
|
||||
} // get_hogdescriptor_visu
|
||||
|
||||
void compute_hog( const vector< Mat > & img_lst, vector< Mat > & gradient_lst, const Size & size )
|
||||
void computeHOGs( const Size wsize, const vector< Mat > & img_lst, vector< Mat > & gradient_lst )
|
||||
{
|
||||
HOGDescriptor hog;
|
||||
hog.winSize = size;
|
||||
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< Point > location;
|
||||
vector< float > descriptors;
|
||||
|
||||
vector< Mat >::const_iterator img = img_lst.begin();
|
||||
vector< Mat >::const_iterator end = img_lst.end();
|
||||
for( ; img != end ; ++img )
|
||||
for( size_t i=0 ; i< img_lst.size(); i++ )
|
||||
{
|
||||
cvtColor( *img, gray, COLOR_BGR2GRAY );
|
||||
hog.compute( gray, descriptors, Size( 8, 8 ), Size( 0, 0 ), location );
|
||||
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() );
|
||||
#ifdef _DEBUG
|
||||
imshow( "gradient", get_hogdescriptor_visu( img->clone(), descriptors, size ) );
|
||||
waitKey( 10 );
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
void train_svm( const vector< Mat > & gradient_lst, const vector< int > & labels )
|
||||
int test_trained_detector( String obj_det_filename, String test_dir, String videofilename )
|
||||
{
|
||||
cout << "Testing trained detector..." << endl;
|
||||
HOGDescriptor hog;
|
||||
hog.load( obj_det_filename );
|
||||
|
||||
Mat train_data;
|
||||
convert_to_ml( gradient_lst, train_data );
|
||||
vector< String > files;
|
||||
glob( test_dir, files );
|
||||
|
||||
clog << "Start training...";
|
||||
Ptr<SVM> svm = SVM::create();
|
||||
/* Default values to train SVM */
|
||||
svm->setCoef0(0.0);
|
||||
svm->setDegree(3);
|
||||
svm->setTermCriteria(TermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_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, Mat(labels));
|
||||
clog << "...[done]" << endl;
|
||||
int delay = 0;
|
||||
VideoCapture cap;
|
||||
|
||||
svm->save( "my_people_detector.yml" );
|
||||
}
|
||||
|
||||
void draw_locations( Mat & img, const vector< Rect > & locations, const Scalar & color )
|
||||
{
|
||||
if( !locations.empty() )
|
||||
if ( videofilename != "" )
|
||||
{
|
||||
vector< Rect >::const_iterator loc = locations.begin();
|
||||
vector< Rect >::const_iterator end = locations.end();
|
||||
for( ; loc != end ; ++loc )
|
||||
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() )
|
||||
{
|
||||
rectangle( img, *loc, color, 2 );
|
||||
cap >> img;
|
||||
delay = 1;
|
||||
}
|
||||
else if( i < files.size() )
|
||||
{
|
||||
img = imread( files[i] );
|
||||
}
|
||||
|
||||
if ( img.empty() )
|
||||
{
|
||||
return 0;
|
||||
}
|
||||
|
||||
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( 27 == waitKey( delay ) )
|
||||
{
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void test_it( const Size & size )
|
||||
{
|
||||
char key = 27;
|
||||
Scalar reference( 0, 255, 0 );
|
||||
Scalar trained( 0, 0, 255 );
|
||||
Mat img, draw;
|
||||
Ptr<SVM> svm;
|
||||
HOGDescriptor hog;
|
||||
HOGDescriptor my_hog;
|
||||
my_hog.winSize = size;
|
||||
VideoCapture video;
|
||||
vector< Rect > locations;
|
||||
|
||||
// Load the trained SVM.
|
||||
svm = StatModel::load<SVM>( "my_people_detector.yml" );
|
||||
// Set the trained svm to my_hog
|
||||
vector< float > hog_detector;
|
||||
get_svm_detector( svm, hog_detector );
|
||||
my_hog.setSVMDetector( hog_detector );
|
||||
// Set the people detector.
|
||||
hog.setSVMDetector( hog.getDefaultPeopleDetector() );
|
||||
// Open the camera.
|
||||
video.open(0);
|
||||
if( !video.isOpened() )
|
||||
{
|
||||
cerr << "Unable to open the device 0" << endl;
|
||||
exit( -1 );
|
||||
}
|
||||
|
||||
bool end_of_process = false;
|
||||
while( !end_of_process )
|
||||
{
|
||||
video >> img;
|
||||
if( img.empty() )
|
||||
break;
|
||||
|
||||
draw = img.clone();
|
||||
|
||||
locations.clear();
|
||||
hog.detectMultiScale( img, locations );
|
||||
draw_locations( draw, locations, reference );
|
||||
|
||||
locations.clear();
|
||||
my_hog.detectMultiScale( img, locations );
|
||||
draw_locations( draw, locations, trained );
|
||||
|
||||
imshow( "Video", draw );
|
||||
key = (char)waitKey( 10 );
|
||||
if( 27 == key )
|
||||
end_of_process = true;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
int main( int argc, char** argv )
|
||||
{
|
||||
cv::CommandLineParser parser(argc, argv, "{help h|| show help message}"
|
||||
"{pd||pos_dir}{p||pos.lst}{nd||neg_dir}{n||neg.lst}");
|
||||
if (parser.has("help"))
|
||||
const char* keys =
|
||||
{
|
||||
"{help h| | show help message}"
|
||||
"{pd | | path of directory contains possitive 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}"
|
||||
"{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);
|
||||
exit( 0 );
|
||||
}
|
||||
vector< Mat > pos_lst;
|
||||
vector< Mat > full_neg_lst;
|
||||
vector< Mat > neg_lst;
|
||||
vector< Mat > gradient_lst;
|
||||
vector< int > labels;
|
||||
string pos_dir = parser.get<string>("pd");
|
||||
string pos = parser.get<string>("p");
|
||||
string neg_dir = parser.get<string>("nd");
|
||||
string neg = parser.get<string>("n");
|
||||
if( pos_dir.empty() || pos.empty() || neg_dir.empty() || neg.empty() )
|
||||
|
||||
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" );
|
||||
|
||||
if ( test_detector )
|
||||
{
|
||||
cout << "Wrong number of parameters." << endl
|
||||
<< "Usage: " << argv[0] << " --pd=pos_dir -p=pos.lst --nd=neg_dir -n=neg.lst" << endl
|
||||
<< "example: " << argv[0] << " --pd=/INRIA_dataset/ -p=Train/pos.lst --nd=/INRIA_dataset/ -n=Train/neg.lst" << endl;
|
||||
exit( -1 );
|
||||
test_trained_detector( obj_det_filename, test_dir, videofilename );
|
||||
exit( 0 );
|
||||
}
|
||||
load_images( pos_dir, pos, pos_lst );
|
||||
|
||||
if( pos_dir.empty() || neg_dir.empty() )
|
||||
{
|
||||
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";
|
||||
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]" << endl;
|
||||
}
|
||||
else
|
||||
{
|
||||
clog << "no image in " << pos_dir <<endl;
|
||||
return 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();
|
||||
load_images( neg_dir, neg, full_neg_lst );
|
||||
sample_neg( full_neg_lst, neg_lst, Size( 96,160 ) );
|
||||
|
||||
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() );
|
||||
|
||||
compute_hog( pos_lst, gradient_lst, Size( 96, 160 ) );
|
||||
compute_hog( neg_lst, gradient_lst, Size( 96, 160 ) );
|
||||
clog << "Histogram of Gradients are being calculated for positive images...";
|
||||
computeHOGs( pos_image_size, pos_lst, gradient_lst );
|
||||
clog << "...[done]" << endl;
|
||||
|
||||
train_svm( gradient_lst, labels );
|
||||
clog << "Histogram of Gradients are being calculated for negative images...";
|
||||
computeHOGs( pos_image_size, neg_lst, gradient_lst );
|
||||
clog << "...[done]" << endl;
|
||||
|
||||
test_it( Size( 96, 160 ) ); // change with your parameters
|
||||
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( CV_TERMCRIT_ITER + CV_TERMCRIT_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, Mat( labels ) );
|
||||
clog << "...[done]" << endl;
|
||||
|
||||
if ( train_twice )
|
||||
{
|
||||
clog << "Testing trained detector on negative images. This may take a few minutes...";
|
||||
HOGDescriptor my_hog;
|
||||
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 );
|
||||
|
||||
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 );
|
||||
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++ )
|
||||
{
|
||||
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;
|
||||
|
||||
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;
|
||||
|
||||
clog << "Histogram of Gradients are being calculated for negative images...";
|
||||
computeHOGs( pos_image_size, neg_lst, gradient_lst );
|
||||
clog << "...[done]" << endl;
|
||||
|
||||
clog << "Training SVM again...";
|
||||
convert_to_ml( gradient_lst, train_data );
|
||||
svm->train( train_data, ROW_SAMPLE, Mat( 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.save( obj_det_filename );
|
||||
|
||||
test_trained_detector( obj_det_filename, test_dir, videofilename );
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user