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SVM in OpenCV

asked 2014-07-23 23:40:58 -0500

jamesnzt gravatar image

updated 2014-07-24 00:08:36 -0500

berak gravatar image

I heard that Support Vector machine can be used for classify the images. I searched for its implementation in openCV. I found that it is there, but i don't know what is SVM and how it can be used to classify the image. If any body knows please give me some idea about SVM and how it it used to classify image?

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answered 2014-07-24 01:23:44 -0500

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 ...
(more)
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answered 2014-07-24 00:04:53 -0500

yomholedet gravatar image
  1. find some features in the image like sift
  2. send them to svm traine
  3. new image compute sift and classify use the trained svm
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Asked: 2014-07-23 23:40:58 -0500

Seen: 380 times

Last updated: Jul 24 '14