SVM in OpenCV

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Hello,

I have written a code that fetches HOG features from a given set of data and trains accordingly. Now remember to change the parameters as per the comments in the code. Also I am mentioning a code that will allow you to test the svm_training. Both the codes have been written and tested in Ubuntu(linux)

#################svmTrain.cpp

#include "cv.h" 
#include "highgui.h"
#include "ml.h"
#include <stdio.h>
#include <iostream>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <vector>
#include <sstream>
#include <string>
#include <cstring>
#include <stdlib.h>

using namespace cv; 
using namespace std;

void reverse(char str[], int length)
{
    int start = 0;
    int end = length -1;
    while (start < end)
    {
        swap(*(str+start), *(str+end));
        start++;
        end--;
    }
}

// Implementation of itoa()
char* itoa(int num, char* str, int base)
{
    int i = 0;
    bool isNegative = false;

    /* Handle 0 explicitely, otherwise empty string is printed for 0 */
    if (num == 0)
    {
        str[i++] = '0';
        str[i] = '\0';
        return str;
    }

    // In standard itoa(), negative numbers are handled only with
    // base 10. Otherwise numbers are considered unsigned.
    if (num < 0 && base == 10)
    {
        isNegative = true;
        num = -num;
    }

    // Process individual digits
    while (num != 0)
    {
        int rem = num % base;
        str[i++] = (rem > 9)? (rem-10) + 'a' : rem + '0';
        num = num/base;
    }

    // If number is negative, append '-'
    if (isNegative)
        str[i++] = '-';

    str[i] = '\0'; // Append string terminator

    // Reverse the string
    reverse(str, i);

    return str;
}

int main()  
{  
 //variables  

 char FirstFileName[100]="train/";                  //the location of the training images.Both positives and negatives must be in same folder
 char lastname[100] = ".JPG";                       //type of the images
 float data[1000][3];                               
 int FileNum=5781;                                  //Number of images(Positives + negatives)

 vector< vector < float> > v_descriptorsValues;  
 vector< vector < Point> > v_locations;  
 Mat Hogfeat; 
 float labelsMat[5781];                              //Length of array must be equal to number of training images

 int img_area = 1*61236;                    //61236 is the number of features detected. Make sure you mention the number of features detected.
 Mat training_mat(FileNum,img_area,CV_32FC1);

//  Mat training_mat(4, 2, CV_32FC1, trainingData);
 for(int i=1; i<=FileNum; i++)  
 {  

  char FullFileName[100] = "";
  char number[100] = "";
  strcat(FullFileName,FirstFileName);
    itoa(i,number,10);
    strcat(FullFileName,number);
    strcat(FullFileName,lastname);

  //read image file  

  Mat img, img_gray;  
  img = imread(FullFileName);  

  //resizing  
  resize(img, img, Size(64,48) ); //Size(64,48) ); //Size(32*2,16*2)); //Size(80,72) );   
  //gray  
  cvtColor(img, img_gray, CV_RGB2GRAY);

    if(i < 1761)                                 // i must be less han number of positives
        {
            labelsMat[i-1] = 1.0;
        }
        else
        {
            labelsMat[i-1] = -1.0;
        }
//      cout << "values of " << d << " filled in training_mat" << endl;  

  //extract feature  
  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);  

  v_descriptorsValues.push_back( descriptorsValues );  
  v_locations.push_back( locations );

    Hogfeat.create(descriptorsValues.size(),1,CV_32FC1);
//  cout << descriptorsValues.size() << endl;
    for(int j=0;j<descriptorsValues.size();j++)
    {
     Hogfeat.at<float>(0,j)=descriptorsValues.at(j);
    }

/*  int ii = 0; // Current column in training_mat
        for (int p = 0; p<Hogfeat.rows; p++) 
        {
            for ...