Examples with MatVector org.bytedeco.javacpp.opencv_core.MatVector used on opensource projects

Example 1 with MatVector

use of org.bytedeco.javacpp.opencv_core.MatVector in project javacv by bytedeco.

the class ImageSegmentation method main.

public static void main(String[] args) {
    // Load the image
    Mat src = imread(args[0]);
    // Check if everything was fine
    if (src.data().isNull())
        return;
    // Show source image
    imshow("Source Image", src);
    // Change the background from white to black, since that will help later to extract
    // better results during the use of Distance Transform
    UByteIndexer srcIndexer = src.createIndexer();
    for (int x = 0; x < srcIndexer.rows(); x++) {
        for (int y = 0; y < srcIndexer.cols(); y++) {
            int[] values = new int[3];
            srcIndexer.get(x, y, values);
            if (Arrays.equals(values, WHITE)) {
                srcIndexer.put(x, y, BLACK);
            }
        }
    }
    // Show output image
    imshow("Black Background Image", src);
    // Create a kernel that we will use for accuting/sharpening our image
    Mat kernel = Mat.ones(3, 3, CV_32F).asMat();
    FloatIndexer kernelIndexer = kernel.createIndexer();
    // an approximation of second derivative, a quite strong kernel
    kernelIndexer.put(1, 1, -8);
    // do the laplacian filtering as it is
    // well, we need to convert everything in something more deeper then CV_8U
    // because the kernel has some negative values,
    // and we can expect in general to have a Laplacian image with negative values
    // BUT a 8bits unsigned int (the one we are working with) can contain values from 0 to 255
    // so the possible negative number will be truncated
    Mat imgLaplacian = new Mat();
    // copy source image to another temporary one
    Mat sharp = src;
    filter2D(sharp, imgLaplacian, CV_32F, kernel);
    src.convertTo(sharp, CV_32F);
    Mat imgResult = subtract(sharp, imgLaplacian).asMat();
    // convert back to 8bits gray scale
    imgResult.convertTo(imgResult, CV_8UC3);
    imgLaplacian.convertTo(imgLaplacian, CV_8UC3);
    // imshow( "Laplace Filtered Image", imgLaplacian );
    imshow("New Sharped Image", imgResult);
    // copy back
    src = imgResult;
    // Create binary image from source image
    Mat bw = new Mat();
    cvtColor(src, bw, CV_BGR2GRAY);
    threshold(bw, bw, 40, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
    imshow("Binary Image", bw);
    // Perform the distance transform algorithm
    Mat dist = new Mat();
    distanceTransform(bw, dist, CV_DIST_L2, 3);
    // Normalize the distance image for range = {0.0, 1.0}
    // so we can visualize and threshold it
    normalize(dist, dist, 0, 1., NORM_MINMAX, -1, null);
    imshow("Distance Transform Image", dist);
    // Threshold to obtain the peaks
    // This will be the markers for the foreground objects
    threshold(dist, dist, .4, 1., CV_THRESH_BINARY);
    // Dilate a bit the dist image
    Mat kernel1 = Mat.ones(3, 3, CV_8UC1).asMat();
    dilate(dist, dist, kernel1);
    imshow("Peaks", dist);
    // Create the CV_8U version of the distance image
    // It is needed for findContours()
    Mat dist_8u = new Mat();
    dist.convertTo(dist_8u, CV_8U);
    // Find total markers
    MatVector contours = new MatVector();
    findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
    // Create the marker image for the watershed algorithm
    Mat markers = Mat.zeros(dist.size(), CV_32SC1).asMat();
    // Draw the foreground markers
    for (int i = 0; i < contours.size(); i++) drawContours(markers, contours, i, Scalar.all((i) + 1));
    // Draw the background marker
    circle(markers, new Point(5, 5), 3, RGB(255, 255, 255));
    imshow("Markers", multiply(markers, 10000).asMat());
    // Perform the watershed algorithm
    watershed(src, markers);
    Mat mark = Mat.zeros(markers.size(), CV_8UC1).asMat();
    markers.convertTo(mark, CV_8UC1);
    bitwise_not(mark, mark);
    // imshow("Markers_v2", mark); // uncomment this if you want to see how the mark
    // image looks like at that point
    // Generate random colors
    List<int[]> colors = new ArrayList<int[]>();
    for (int i = 0; i < contours.size(); i++) {
        int b = theRNG().uniform(0, 255);
        int g = theRNG().uniform(0, 255);
        int r = theRNG().uniform(0, 255);
        int[] color = { b, g, r };
        colors.add(color);
    }
    // Create the result image
    Mat dst = Mat.zeros(markers.size(), CV_8UC3).asMat();
    // Fill labeled objects with random colors
    IntIndexer markersIndexer = markers.createIndexer();
    UByteIndexer dstIndexer = dst.createIndexer();
    for (int i = 0; i < markersIndexer.rows(); i++) {
        for (int j = 0; j < markersIndexer.cols(); j++) {
            int index = markersIndexer.get(i, j);
            if (index > 0 && index <= contours.size())
                dstIndexer.put(i, j, colors.get(index - 1));
            else
                dstIndexer.put(i, j, BLACK);
        }
    }
    // Visualize the final image
    imshow("Final Result", dst);
}

Example 2 with MatVector

use of org.bytedeco.javacpp.opencv_core.MatVector in project javacv by bytedeco.

the class OpenCVFaceRecognizer method main.

public static void main(String[] args) {
    String trainingDir = args[0];
    Mat testImage = imread(args[1], CV_LOAD_IMAGE_GRAYSCALE);
    File root = new File(trainingDir);
    FilenameFilter imgFilter = new FilenameFilter() {

        public boolean accept(File dir, String name) {
            name = name.toLowerCase();
            return name.endsWith(".jpg") || name.endsWith(".pgm") || name.endsWith(".png");
        }
    };
    File[] imageFiles = root.listFiles(imgFilter);
    MatVector images = new MatVector(imageFiles.length);
    Mat labels = new Mat(imageFiles.length, 1, CV_32SC1);
    IntBuffer labelsBuf = labels.createBuffer();
    int counter = 0;
    for (File image : imageFiles) {
        Mat img = imread(image.getAbsolutePath(), CV_LOAD_IMAGE_GRAYSCALE);
        int label = Integer.parseInt(image.getName().split("\\-")[0]);
        images.put(counter, img);
        labelsBuf.put(counter, label);
        counter++;
    }
    FaceRecognizer faceRecognizer = FisherFaceRecognizer.create();
    // FaceRecognizer faceRecognizer = EigenFaceRecognizer.create();
    // FaceRecognizer faceRecognizer = LBPHFaceRecognizer.create();
    faceRecognizer.train(images, labels);
    IntPointer label = new IntPointer(1);
    DoublePointer confidence = new DoublePointer(1);
    faceRecognizer.predict(testImage, label, confidence);
    int predictedLabel = label.get(0);
    System.out.println("Predicted label: " + predictedLabel);
}