To test SVM trained data is whether reliable or not. (example source code)

After training SVM, we should test the trained XML data is reliable or not..

The method to extract HOG feature is refer to -> http://feelmare.blogspot.kr/2014/04/example-source-code-of-extract-hog.html
The method to training SVM of HOG feature is refer to -> http://feelmare.blogspot.kr/2014/04/example-source-code-hog-feature-to.html

The method is using training data.
Again training data make HOG feature, and check the feature is positive or not using trained SVM data.

The example source code is like that.
...

#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 


void main()
{

 //variables
 char FullFileName[100];
 char FirstFileName[100]="./images/upperbody"; //"./NegaImages/Negative";      // 
 int FileNum=96; //262;

 //Load trained SVM xml data
 CvSVM svm;
 svm.load("trainedSVM.xml");

 //count variable
 int nnn=0, ppp=0;

 for(int i=0; i< FileNum; ++i)
 {
  sprintf_s(FullFileName, "%s%d.png", FirstFileName, i+1);
  //printf("%s\n", FullFileName);

  //read image file
  Mat img, img_gray;
  img = imread(FullFileName);
  
  //resizing
  //resize(img, img, Size(16,8) ); //Size(64,48) ); //Size(32*2,16*2)); //Size(80,72) ); 
  resize(img, img, Size(64,48) ); //Size(32*2,16*2)); //Size(80,72) ); 
  //gray
  cvtColor(img, img_gray, CV_RGB2GRAY);

  //Extract HogFeature
  HOGDescriptor d( Size(32,16), Size(8,8), Size(4,4), Size(4,4), 9);
  vector< float> descriptorsValues;
  vector< Point> locations;
  d.compute( img_gray, descriptorsValues, Size(0,0), Size(0,0), locations);
  //vector to Mat
  Mat fm = Mat(descriptorsValues);
  
  //Classification whether data is positive or negative
  int result = svm.predict(fm);
  printf("%s - > %d\n", FullFileName, result);

  //Count data
  if(result == 1)
   ppp++;
  else
   nnn++;

  //show image
  imshow("origin", img);

  waitKey(5);
 }

 printf(" positive/negative = (%d/%d) \n", ppp, nnn);

}

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