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Update sample and code with external computation of HOG detector.

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This commit is contained in:
Mathieu Barnachon 2013-09-12 18:38:49 +02:00
parent 2fe340bf8a
commit 0934344a3d
3 changed files with 100 additions and 91 deletions
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3
modules/ml/include/opencv2/ml.hpp
View File

@ -518,8 +518,7 @@ public:
virtual CvSVMParams get_params() const { return params; };
CV_WRAP virtual void clear();
// return a single vector for HOG detector.
virtual void get_svm_detector( std::vector< float > & detector ) const;
virtual const CvSVMDecisionFunc* get_decision_function() const { return decision_func; }
static CvParamGrid get_default_grid( int param_id );

32
modules/ml/src/svm.cpp
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@ -1245,38 +1245,6 @@ const float* CvSVM::get_support_vector(int i) const
return sv && (unsigned)i < (unsigned)sv_total ? sv[i] : 0;
}
void CvSVM::get_svm_detector( std::vector< float > & detector ) const
{
CV_Assert( var_all > 0 &&
sv_total > 0 &&
sv != 0 &&
decision_func != 0 &&
decision_func->alpha != 0 &&
decision_func->sv_count == sv_total );
float svi = 0.f;
detector.clear(); //clear stuff in vector.
detector.reserve( var_all + 1 ); //reserve place for memory efficiency.
/**
* detector^i = \sum_j support_vector_j^i * \alpha_j
* detector^dim = -\rho
*/
for( int i = 0 ; i < var_all ; ++i )
{
svi = 0.f;
for( int j = 0 ; j < sv_total ; ++j )
{
if( decision_func->sv_index != NULL ) // sometime the sv_index isn't store on YML/XML.
svi += (float)( sv[decision_func->sv_index[j]][i] * decision_func->alpha[ j ] );
else
svi += (float)( sv[j][i] * decision_func->alpha[ j ] );
}
detector.push_back( svi );
}
detector.push_back( (float)-decision_func->rho );
}
bool CvSVM::set_params( const CvSVMParams& _params )
{
bool ok = false;

84
samples/cpp/train_HOG.cpp
View File

@ -11,22 +11,64 @@ using namespace cv;
using namespace std;
void get_svm_detector(const SVM& svm, vector< float > & hog_detector )
{
// get the number of variables
const int var_all = svm.get_var_count();
// get the number of support vectors
const int sv_total = svm.get_support_vector_count();
// get the decision function
const CvSVMDecisionFunc* decision_func = svm.get_decision_function();
// get the support vectors
const float** sv = &(svm.get_support_vector(0));
CV_Assert( var_all > 0 &&
sv_total > 0 &&
decision_func != 0 &&
decision_func->alpha != 0 &&
decision_func->sv_count == sv_total );
float svi = 0.f;
hog_detector.clear(); //clear stuff in vector.
hog_detector.reserve( var_all + 1 ); //reserve place for memory efficiency.
/**
* hog_detector^i = \sum_j support_vector_j^i * \alpha_j
* hog_detector^dim = -\rho
*/
for( int i = 0 ; i < var_all ; ++i )
{
svi = 0.f;
for( int j = 0 ; j < sv_total ; ++j )
{
if( decision_func->sv_index != NULL ) // sometime the sv_index isn't store on YML/XML.
svi += (float)( sv[decision_func->sv_index[j]][i] * decision_func->alpha[ j ] );
else
svi += (float)( sv[j][i] * decision_func->alpha[ j ] );
}
hog_detector.push_back( svi );
}
hog_detector.push_back( (float)-decision_func->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.
*/
* 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 )
{
//--Convert data
//--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 );
auto& itr = train_samples.begin();
auto& end = train_samples.end();
for( int i = 0 ; itr != end ; ++itr, ++i )
{
trainData = cv::Mat(rows, cols, CV_32FC1 );
auto& itr = train_samples.begin();
auto& end = train_samples.end();
for( int i = 0 ; itr != end ; ++itr, ++i )
{
CV_Assert( itr->cols == 1 ||
itr->rows == 1 );
if( itr->cols == 1 )
@ -38,7 +80,7 @@ void convert_to_ml(const std::vector< cv::Mat > & train_samples, cv::Mat& trainD
{
itr->copyTo( trainData.row( i ) );
}
}
}
}
void load_images( const string & prefix, const string & filename, vector< Mat > & img_lst )
@ -241,12 +283,12 @@ Mat get_hogdescriptor_visu(Mat& color_origImg, vector<float>& descriptorValues,
// 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];
for (int x=0; x<cells_in_x_dir; x++)
{
delete[] gradientStrengths[y][x];
}
delete[] gradientStrengths[y];
delete[] cellUpdateCounter[y];
}
delete[] gradientStrengths;
delete[] cellUpdateCounter;
@ -322,7 +364,7 @@ void test_it( const Size & size )
Scalar reference( 0, 255, 0 );
Scalar trained( 0, 0, 255 );
Mat img, draw;
SVM svm;
MySVM svm;
HOGDescriptor hog;
HOGDescriptor my_hog;
my_hog.winSize = size;
@ -333,7 +375,7 @@ void test_it( const Size & size )
svm.load( "my_people_detector.yml" );
// Set the trained svm to my_hog
vector< float > hog_detector;
svm.get_svm_detector( hog_detector );
get_svm_detector( svm, hog_detector );
my_hog.setSVMDetector( hog_detector );
// Set the people detector.
hog.setSVMDetector( hog.getDefaultPeopleDetector() );
@ -373,8 +415,8 @@ int main( int argc, char** argv )
if( argc != 4 )
{
cout << "Wrong number of parameters." << endl
<< "Usage: " << argv[0] << " pos_dir pos.lst neg_dir neg.lst" << endl
<< "example: " << argv[0] << " /INRIA_dataset/ Train/pos.lst /INRIA_dataset/ Train/neg.lst" << endl;
<< "Usage: " << argv[0] << " pos_dir pos.lst neg_dir neg.lst" << endl
<< "example: " << argv[0] << " /INRIA_dataset/ Train/pos.lst /INRIA_dataset/ Train/neg.lst" << endl;
exit( -1 );
}
vector< Mat > pos_lst;