This example is I already have explained in past time.
See the this page - >http://feelmare.blogspot.kr/search/label/SVM
But other contents is added in this example.
That is converting of trained xml file to use in Hog.MultiScaleDetection function.
Converting process is started after svm learning.
See the comment of "the second save option" in source code.
Thank you.
---
#include < stdio.h>
#include < opencv2\opencv.hpp>
//#include < opencv2\gpu\gpu.hpp>
using namespace cv;
using namespace std;
#ifdef _DEBUG
#pragma comment(lib, "opencv_core247d.lib")
#pragma comment(lib, "opencv_imgproc247d.lib") //MAT processing
#pragma comment(lib, "opencv_objdetect247d.lib") //HOGDescriptor
//#pragma comment(lib, "opencv_gpu247d.lib")
//#pragma comment(lib, "opencv_features2d247d.lib")
#pragma comment(lib, "opencv_highgui247d.lib")
#pragma comment(lib, "opencv_ml247d.lib")
//#pragma comment(lib, "opencv_stitching247d.lib");
//#pragma comment(lib, "opencv_nonfree247d.lib");
#else
#pragma comment(lib, "opencv_core247.lib")
#pragma comment(lib, "opencv_imgproc247.lib")
#pragma comment(lib, "opencv_objdetect247.lib")
//#pragma comment(lib, "opencv_gpu247.lib")
//#pragma comment(lib, "opencv_features2d247.lib")
#pragma comment(lib, "opencv_highgui247.lib")
#pragma comment(lib, "opencv_ml247.lib")
//#pragma comment(lib, "opencv_stitching247.lib");
//#pragma comment(lib, "opencv_nonfree247.lib");
#endif
class MySvm: public CvSVM
{
public:
int get_alpha_count(){
return this->sv_total;}
int get_sv_dim(){
return this->var_all;}
int get_sv_count(){
return this->decision_func->sv_count;}
double* get_alpha(){
return this->decision_func->alpha;}
float** get_sv(){
return this->sv;}
float get_rho(){
return this->decision_func->rho;}
};
void main()
{
//Read Hog feature from XML file
///////////////////////////////////////////////////////////////////////////
printf("1. Feature data xml load\n");
//create xml to read
FileStorage read_PositiveXml("C:\\POSCO\\Learned\\Positive1643_64_64.xml", FileStorage::READ);
FileStorage read_NegativeXml("C:\\POSCO\\Learned\\Negative16064_64_64.xml", FileStorage::READ);
char SVMSaveFile[100] = "C:\\POSCO\\Learned\\trainedSVM_1643_16064_64_64.xml";
char SVM_HOGDetectorFile[100] = "C:\\POSCO\\Learned\\HogDetectorXML_1643_16064_64_64.xml";
//Positive Mat
Mat pMat;
read_PositiveXml["Descriptor_of_images"] >> pMat;
//Read Row, Cols
int pRow,pCol;
pRow = pMat.rows; pCol = pMat.cols;
//Negative Mat
Mat nMat;
read_NegativeXml["Descriptor_of_images"] >> nMat;
//Read Row, Cols
int nRow,nCol;
nRow = nMat.rows; nCol = nMat.cols;
//Rows, Cols printf
printf(" pRow=%d pCol=%d, nRow=%d nCol=%d\n", pRow, pCol, nRow, nCol );
//release
read_PositiveXml.release();
//release
read_NegativeXml.release();
/////////////////////////////////////////////////////////////////////////////////
//Make training data for SVM
/////////////////////////////////////////////////////////////////////////////////
printf("2. Make training data for SVM\n");
//descriptor data set
Mat PN_Descriptor_mtx( pRow + nRow, pCol, CV_32FC1 ); //in here pCol and nCol is descriptor number, so two value must be same;
memcpy(PN_Descriptor_mtx.data, pMat.data, sizeof(float) * pMat.cols * pMat.rows );
int startP = sizeof(float) * pMat.cols * pMat.rows;
memcpy(&(PN_Descriptor_mtx.data[ startP ]), nMat.data, sizeof(float) * nMat.cols * nMat.rows );
//data labeling
Mat labels( pRow + nRow, 1, CV_32FC1, Scalar(-1.0) );
labels.rowRange( 0, pRow ) = Scalar( 1.0 );
/////////////////////////////////////////////////////////////////////////////////
//Set svm parameter
/////////////////////////////////////////////////////////////////////////////////
printf("4. SVM training\n");
MySvm svm; //CvSVM svm;
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::LINEAR;
params.term_crit = cvTermCriteria( CV_TERMCRIT_ITER, 10000, 1e-6 );
/////////////////////////////////////////////////////////////////////////////////
//Training
/////////////////////////////////////////////////////////////////////////////////
svm.train(PN_Descriptor_mtx, labels, Mat(), Mat(), params);
//Trained data save
/////////////////////////////////////////////////////////////////////////////////
printf("5. SVM xml save\n");
svm.save( SVMSaveFile );
//////////////////////////////////////////////////////////////////////////////////
//Second Save option
//This save file is for Hog.SetSVMDectector() function
//And if we can use this function(SetSVMDectector), we can use detectMultiScale function.
//This function is very easy to detect target, and we also can apply GPU option.
//make firstly, inherited class to access alpha vector and value
int svmVectorSize = svm.get_support_vector_count();
int featureSize = pCol;
//prepare, variables
Mat sv = Mat(svmVectorSize, featureSize, CV_32FC1, 0.0);
Mat alp = Mat(1, svmVectorSize, CV_32FC1, 0.0);
Mat re = Mat(1, featureSize, CV_32FC1, 0.0);
Mat re2 = Mat(1, featureSize+1, CV_32FC1, 0.0);
//set value to variables
for(int i=0; i< svmVectorSize; ++i)
memcpy( (sv.data + i*featureSize), svm.get_support_vector(i), featureSize*sizeof(float) ); //ok
double * alphaArr = svm.get_alpha();
int alphaCount = svm.get_alpha_count();
for(int i=0; i< svmVectorSize; ++i)
{
alp.at< float>(0, i) = (float)alphaArr[i];
//printf("alpha[%d] = %lf \n", i, (float)alphaArr[i] );
}
//cvMatMul(alp, sv, re);
re = alp * sv;
for(int i=0; i< featureSize; ++i)
re2.at< float>(0,i) = re.at< float>(0,i) * -1;
re2.at< float>(0,featureSize) = svm.get_rho();
//save to 1d vector to XML format!!
FileStorage svmSecondXML(SVM_HOGDetectorFile, FileStorage::WRITE);
svmSecondXML << "SecondSVMd" << re2 ;
svmSecondXML.release();
// FileStorage hogXml("testXML.xml", FileStorage::WRITE); //FileStorage::READ
// write(hogXml, "Data", PN_Descriptor_mtx);
// write(hogXml, "Label", labels);
// hogXml.release();
}
...
