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Update sample and code with external computation of HOG detector.

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pull/1320/head
Mathieu Barnachon 12 years ago
parent 2fe340bf8a
commit 0934344a3d
3 changed files with 100 additions and 91 deletions
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  1. 3
      modules/ml/include/opencv2/ml.hpp
  2. 32
      modules/ml/src/svm.cpp
  3. 156
      samples/cpp/train_HOG.cpp

3
modules/ml/include/opencv2/ml.hpp
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@ -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;

156
samples/cpp/train_HOG.cpp
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@ -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 )
@ -52,7 +94,7 @@ void load_images( const string & prefix, const string & filename, vector< Mat >
cerr << "Unable to open the list of images from " << filename << " filename." << endl;
exit( -1 );
}
while( 1 )
{
getline( file, line );
@ -102,12 +144,12 @@ Mat get_hogdescriptor_visu(Mat& color_origImg, vector<float>& descriptorValues,
float zoomFac = 3;
Mat visu;
resize(color_origImg, visu, Size(color_origImg.cols*zoomFac, color_origImg.rows*zoomFac));
int blockSize = 16;
int cellSize = 8;
int gradientBinSize = 9;
float radRangeForOneBin = 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;
@ -122,22 +164,22 @@ Mat get_hogdescriptor_visu(Mat& color_origImg, vector<float>& descriptorValues,
{
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++)
@ -155,37 +197,37 @@ Mat get_hogdescriptor_visu(Mat& color_origImg, vector<float>& descriptorValues,
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 (int celly=0; celly<cells_in_y_dir; celly++)
{
for (int 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++)
{
@ -193,7 +235,7 @@ Mat get_hogdescriptor_visu(Mat& color_origImg, vector<float>& descriptorValues,
}
}
}
// draw cells
for (int celly=0; celly<cells_in_y_dir; celly++)
{
@ -201,58 +243,58 @@ Mat get_hogdescriptor_visu(Mat& color_origImg, vector<float>& descriptorValues,
{
int drawX = cellx * cellSize;
int drawY = celly * cellSize;
int mx = drawX + cellSize/2;
int my = drawY + cellSize/2;
rectangle(visu, Point(drawX*zoomFac,drawY*zoomFac), Point((drawX+cellSize)*zoomFac,(drawY+cellSize)*zoomFac), CV_RGB(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 = cellSize/2;
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(x1*zoomFac,y1*zoomFac), Point(x2*zoomFac,y2*zoomFac), CV_RGB(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];
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 )
@ -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() );
@ -344,7 +386,7 @@ void test_it( const Size & size )
cerr << "Unable to open the device 0" << endl;
exit( -1 );
}
while( true )
{
video >> img;
@ -352,7 +394,7 @@ void test_it( const Size & size )
break;
draw = img.clone();
locations.clear();
hog.detectMultiScale( img, locations );
draw_locations( draw, locations, reference );
@ -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;

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