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made everything compile and even run somehow

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This commit is contained in:
Vadim Pisarevsky
2014-08-03 01:41:09 +04:00
parent 10b60f8d16
commit c20ff6ce19
31 changed files with 11910 additions and 9061 deletions
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78
samples/cpp/train_HOG.cpp
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@@ -8,9 +8,10 @@
#include <time.h>
using namespace cv;
using namespace cv::ml;
using namespace std;
void get_svm_detector(const SVM& svm, vector< float > & hog_detector );
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 sample_neg( const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, const Size & size );
@@ -20,49 +21,24 @@ 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 get_svm_detector(const SVM& svm, vector< float > & hog_detector )
void get_svm_detector(const Ptr<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 = new const float*[ sv_total ];
for( int i = 0 ; i < sv_total ; ++i )
sv[ i ] = svm.get_support_vector(i);
Mat sv = svm->getSupportVectors();
const int sv_total = sv.rows;
// get the decision function
Mat alpha, svidx;
double rho = svm->getDecisionFunction(0, alpha, svidx);
CV_Assert( var_all > 0 &&
sv_total > 0 &&
decision_func != 0 &&
decision_func->alpha != 0 &&
decision_func->sv_count == sv_total );
CV_Assert( alpha.total() == 1 && svidx.total() == 1 && sv_total == 1 );
CV_Assert( (alpha.type() == CV_64F && alpha.at<double>(0) == 1.) ||
(alpha.type() == CV_32F && alpha.at<float>(0) == 1.f) );
CV_Assert( sv.type() == CV_32F );
hog_detector.clear();
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 );
delete[] sv;
hog_detector.resize(sv.cols + 1);
memcpy(&hog_detector[0], sv.data, sv.cols*sizeof(hog_detector[0]));
hog_detector[sv.cols] = (float)-rho;
}
@@ -263,7 +239,7 @@ Mat get_hogdescriptor_visu(const Mat& color_origImg, vector<float>& descriptorVa
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)), CV_RGB(100,100,100), 1);
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++)
@@ -288,7 +264,7 @@ Mat get_hogdescriptor_visu(const Mat& color_origImg, vector<float>& descriptorVa
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)), CV_RGB(0,255,0), 1);
line(visu, Point((int)(x1*zoomFac),(int)(y1*zoomFac)), Point((int)(x2*zoomFac),(int)(y2*zoomFac)), Scalar(0,255,0), 1);
} // for (all bins)
@@ -337,28 +313,26 @@ void compute_hog( const vector< Mat > & img_lst, vector< Mat > & gradient_lst, c
void train_svm( const vector< Mat > & gradient_lst, const vector< int > & labels )
{
SVM svm;
/* Default values to train SVM */
SVMParams params;
SVM::Params params;
params.coef0 = 0.0;
params.degree = 3;
params.term_crit.epsilon = 1e-3;
params.termCrit.epsilon = 1e-3;
params.gamma = 0;
params.kernel_type = SVM::LINEAR;
params.kernelType = SVM::LINEAR;
params.nu = 0.5;
params.p = 0.1; // for EPSILON_SVR, epsilon in loss function?
params.C = 0.01; // From paper, soft classifier
params.svm_type = SVM::EPS_SVR; // C_SVC; // EPSILON_SVR; // may be also NU_SVR; // do regression task
params.svmType = SVM::EPS_SVR; // C_SVC; // EPSILON_SVR; // may be also NU_SVR; // do regression task
Mat train_data;
convert_to_ml( gradient_lst, train_data );
clog << "Start training...";
svm.train( train_data, Mat( labels ), Mat(), Mat(), params );
Ptr<SVM> svm = StatModel::train<SVM>(train_data, ROW_SAMPLE, Mat(labels), params);
clog << "...[done]" << endl;
svm.save( "my_people_detector.yml" );
svm->save( "my_people_detector.yml" );
}
void draw_locations( Mat & img, const vector< Rect > & locations, const Scalar & color )
@@ -380,7 +354,7 @@ void test_it( const Size & size )
Scalar reference( 0, 255, 0 );
Scalar trained( 0, 0, 255 );
Mat img, draw;
SVM svm;
Ptr<SVM> svm;
HOGDescriptor hog;
HOGDescriptor my_hog;
my_hog.winSize = size;
@@ -388,7 +362,7 @@ void test_it( const Size & size )
vector< Rect > locations;
// Load the trained SVM.
svm.load( "my_people_detector.yml" );
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 );