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 )
{
itr->copyTo( trainData.row( i ) );
}
- }
+ }
}
void load_images( const string & prefix, const string & filename, vector< Mat > & img_lst )
cerr << "Unable to open the list of images from " << filename << " filename." << endl;
exit( -1 );
}
-
+
while( 1 )
{
getline( file, line );
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;
{
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++)
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++)
{
}
}
}
-
+
// draw cells
for (int celly=0; celly<cells_in_y_dir; celly++)
{
{
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 )
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;
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() );
cerr << "Unable to open the device 0" << endl;
exit( -1 );
}
-
+
while( true )
{
video >> img;
break;
draw = img.clone();
-
+
locations.clear();
hog.detectMultiScale( img, locations );
draw_locations( draw, locations, reference );
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;
projects / profile / ivi / opencv.git / commitdiff