Training and prediction of class using SVM in OpenCv

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Hi,

I have three classes (0,1 and 2),and for each class I have set of x,y data. Now my training dataset is considered like this. For class 0, I have some 30 x,y coordinates data.In case of class 1, some 40 x,y coordinates data and for class2, some 50 x,y coordinates data. I want to implement SVM using openCv library. I went through the following link https://docs.opencv.org/2.4/doc/tutor.... I want to modify the code to accomodate my own data set. I would be really glad,if someone can let me know the changes needs to be done in the below code,so that,I can train my data for the three classes and also,if I pass a contour( consisting of a list of x,y values), I want to predict is classification using SVM.

The code from the above link is as follows:

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/ml/ml.hpp>

using namespace cv;

int main()
{
// Data for visual representation
int width = 512, height = 512;
Mat image = Mat::zeros(height, width, CV_8UC3);

// Set up training data
float labels[4] = {1.0, -1.0, -1.0, -1.0};
Mat labelsMat(4, 1, CV_32FC1, labels);

float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
Mat trainingDataMat(4, 2, CV_32FC1, trainingData);

// Set up SVM's parameters
CvSVMParams params;
params.svm_type    = CvSVM::C_SVC;
params.kernel_type = CvSVM::LINEAR;
params.term_crit   = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);

// Train the SVM
CvSVM SVM;
SVM.train(trainingDataMat, labelsMat, Mat(), Mat(), params);

Vec3b green(0,255,0), blue (255,0,0);
// Show the decision regions given by the SVM
for (int i = 0; i < image.rows; ++i)
    for (int j = 0; j < image.cols; ++j)
    {
        Mat sampleMat = (Mat_<float>(1,2) << j,i);
        float response = SVM.predict(sampleMat);

        if (response == 1)
            image.at<Vec3b>(i,j)  = green;
        else if (response == -1)
             image.at<Vec3b>(i,j)  = blue;
    }

// Show the training data
int thickness = -1;
int lineType = 8;
circle( image, Point(501,  10), 5, Scalar(  0,   0,   0), thickness, lineType);
circle( image, Point(255,  10), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness, lineType);

// Show support vectors
thickness = 2;
lineType  = 8;
int c     = SVM.get_support_vector_count();

for (int i = 0; i < c; ++i)
{
    const float* v = SVM.get_support_vector(i);
    circle( image,  Point( (int) v[0], (int) v[1]),   6,  Scalar(128, 128, 128), thickness, lineType);
}

imwrite("result.png", image);        // save the image

imshow("SVM Simple Example", image); // show it to the user
waitKey(0);

}