opencv_win_ann5.java

package opencvapp;
import org.opencv.core.Core;
import static org.opencv.core.CvType.CV_32F;
import org.opencv.core.Mat;
import org.opencv.ml.ANN_MLP;

public class OpenCVLoadModel {
    public static void main(String[] args) {
        System.load("D:\\FCI\\Programming\\OpenCV\\OpenCV 4.1.0\\opencv-4.1.0-vc14_vc15\\opencv\\build\\java\\x64\\" + Core.NATIVE_LIBRARY_NAME + ".dll");

        double[][] XORTrainArray = {
            {0.0, 0.0},
            {0.0, 1.0},
            {1.0, 0.0},
            {1.0, 1.0}
        };
        Mat XORTrain = new Mat(4, 2, CV_32F);
        XORTrain.put(0, 0, XORTrainArray[0]);
        XORTrain.put(1, 0, XORTrainArray[1]);
        XORTrain.put(2, 0, XORTrainArray[2]);
        XORTrain.put(3, 0, XORTrainArray[3]);
        System.out.println("Train Inputs : \n" + XORTrain.dump());

        double[][] XORTrainOutArray = {
            {0.0},
            {1.0},
            {1.0},
            {0.0}
        };
        Mat XORTrainOut = new Mat(4, 1, CV_32F);
        XORTrainOut.put(0, 0, XORTrainOutArray[0]);
        XORTrainOut.put(1, 0, XORTrainOutArray[1]);
        XORTrainOut.put(2, 0, XORTrainOutArray[2]);
        XORTrainOut.put(3, 0, XORTrainOutArray[3]);
        System.out.println("Train Labels : \n" + XORTrainOut.dump());

        ANN_MLP ANN = ANN_MLP.load("C:\\Users\\Dell\\Documents\\NetBeansProjects\\OpenCVApp\\OpenCV_ANN_XOR.yml");

        double num_correct_predictions = 0;
        for (int i = 0; i < XORTrain.rows(); i++) {
            Mat sample = XORTrain.row(i);
            double correct_label = XORTrainOut.get(i, 0)[0];

            Mat results = new Mat();
            ANN.predict(sample, results, 0);

            double response = results.get(0, 0)[0];
            double predicted_label;

            if (response >= 0.5) {
                predicted_label = 1.0;
            } else {
                predicted_label = 0.0;
            }

            System.out.println("Input Sample : " + sample.dump() + ", Predicted Score : " + response + ", Predicted Label : " + predicted_label + ", Correct Label : " + correct_label);

            if (predicted_label == correct_label) {
                num_correct_predictions += 1;
            }
        }
        double accuracy = (num_correct_predictions / XORTrain.rows()) * 100;
        System.out.println("Accuracy : " + accuracy);
    }    
}