CV.java

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package svm_train;

import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.Scalar;
import org.opencv.core.TermCriteria;
import org.opencv.highgui.Highgui;
import org.opencv.ml.CvSVMParams;
import org.opencv.ml.CvSVM;

/**
 *
 * @author arumoy.chakraborty
 */
public class Main {
    static {
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    }
    private static final String TRAINING_DATA_ROOT = 
            "C:\\Users\\arumoy.chakraborty\\Desktop\\SVM_Train\\";
    /**
     * @param args the command line arguments
     */
    public static void main(String[] args) {
        CvSVMParams params = new CvSVMParams();

        params.set_svm_type(CvSVM.C_SVC);
        params.set_kernel_type(CvSVM.LINEAR);
        params.set_gamma(1);
        params.set_degree(0);
        params.set_coef0(0);
        params.set_C(1);
        params.set_p(0);
        params.set_nu(0);
        params.set_term_crit(
                new TermCriteria(TermCriteria.MAX_ITER, 1000, 0.01));

        Mat imr = Highgui.imread(acquireTrainingDataColor("orange")+"1.bmp",
                Highgui.IMREAD_GRAYSCALE);
        Mat bir_ = imr.reshape(0, 1);
        Mat bir = new Mat();
        bir_.convertTo(bir, CvType.CV_32F);
        
        Mat imb = Highgui.imread(acquireTrainingDataColor("orange")+"1.bmp",
                Highgui.IMREAD_GRAYSCALE);
        Mat bib_ = imb.reshape(0, 1);
        Mat bib = new Mat();
        bib_.convertTo(bib, CvType.CV_32F);
        
        Mat trainData = new Mat(), labels = new Mat();

        for (int i = 0; i < 10; i++) {
            trainData.push_back(bir);
            labels.push_back(new Mat(1, 1, CvType.CV_32F, new Scalar(1.0)));
            trainData.push_back(bib);
            labels.push_back(new Mat(1, 1, CvType.CV_32F, new Scalar(2.0)));
        }
        
//        CvSVM svm = new CvSVM(trainData, labels);
        CvSVM svm = new CvSVM();   
        boolean train = false;
        try {
            train = svm.train(trainData, labels, new Mat(), new Mat(), params);
        } catch (Exception e) {
            System.out.println(e.getMessage());
        }
        
        if (train) {
            System.out.println(svm.predict(bir));
        }
    }

    private static String acquireTrainingDataColor(String col) {
	return TRAINING_DATA_ROOT + col + "\\";
    }
}