Example usage for org.opencv.core Mat cols

Introduction

In this page you can find the example usage for org.opencv.core Mat cols.

Prototype

public int cols() 

Source Link

Usage

From source file:net.semanticmetadata.lire.imageanalysis.features.local.opencvfeatures.CvSiftExtractor.java

License:Open Source License

@Override
public void extract(BufferedImage img) {
    MatOfKeyPoint keypoints = new MatOfKeyPoint();
    Mat descriptors = new Mat();
    List<KeyPoint> myKeys;//from  ww w .j  a va 2s  .  com
    //        Mat img_object = Highgui.imread(image, 0); //0 = CV_LOAD_IMAGE_GRAYSCALE
    //        detector.detect(img_object, keypoints);
    byte[] data = ((DataBufferByte) img.getRaster().getDataBuffer()).getData();
    Mat matRGB = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC3);
    matRGB.put(0, 0, data);
    Mat matGray = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC1);
    Imgproc.cvtColor(matRGB, matGray, Imgproc.COLOR_BGR2GRAY); //TODO: RGB or BGR?
    byte[] dataGray = new byte[matGray.rows() * matGray.cols() * (int) (matGray.elemSize())];
    matGray.get(0, 0, dataGray);

    detector.detect(matGray, keypoints);
    extractor.compute(matGray, keypoints, descriptors);
    myKeys = keypoints.toList();

    features = new LinkedList<CvSiftFeature>();
    KeyPoint key;
    CvSiftFeature feat;
    double[] desc;
    int cols, rows = myKeys.size();
    for (int i = 0; i < rows; i++) {
        cols = (descriptors.row(i)).cols();
        desc = new double[cols];
        key = myKeys.get(i);
        for (int j = 0; j < cols; j++) {
            desc[j] = descriptors.get(i, j)[0];
        }
        feat = new CvSiftFeature(key.pt.x, key.pt.y, key.size, desc);
        features.add(feat);
    }
}

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From source file:net.semanticmetadata.lire.imageanalysis.features.local.opencvfeatures.CvSiftExtractor.java

License:Open Source License

public LinkedList<CvSiftFeature> computeSiftKeypoints(BufferedImage img) {
    MatOfKeyPoint keypoints = new MatOfKeyPoint();
    List<KeyPoint> myKeys;/*from  w w  w.jav a2  s .  c  om*/
    //        Mat img_object = Highgui.imread(image, 0); //0 = CV_LOAD_IMAGE_GRAYSCALE
    //        detector.detect(img_object, keypoints);
    byte[] data = ((DataBufferByte) img.getRaster().getDataBuffer()).getData();
    Mat matRGB = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC3);
    matRGB.put(0, 0, data);
    Mat matGray = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC1);
    Imgproc.cvtColor(matRGB, matGray, Imgproc.COLOR_BGR2GRAY); //TODO: RGB or BGR?
    byte[] dataGray = new byte[matGray.rows() * matGray.cols() * (int) (matGray.elemSize())];
    matGray.get(0, 0, dataGray);

    detector.detect(matGray, keypoints);
    myKeys = keypoints.toList();

    LinkedList<CvSiftFeature> myKeypoints = new LinkedList<CvSiftFeature>();
    KeyPoint key;
    CvSiftFeature feat;
    for (Iterator<KeyPoint> iterator = myKeys.iterator(); iterator.hasNext();) {
        key = iterator.next();
        feat = new CvSiftFeature(key.pt.x, key.pt.y, key.size, null);
        myKeypoints.add(feat);
    }

    return myKeypoints;
}

---

From source file:net.semanticmetadata.lire.imageanalysis.features.local.opencvfeatures.CvSurfExtractor.java

License:Open Source License

@Override
public void extract(BufferedImage img) {
    MatOfKeyPoint keypoints = new MatOfKeyPoint();
    Mat descriptors = new Mat();
    List<KeyPoint> myKeys;//  ww w  .ja  v  a 2 s.  co  m
    //        Mat img_object = Highgui.imread(image, 0); //0 = CV_LOAD_IMAGE_GRAYSCALE
    //        detector.detect(img_object, keypoints);
    byte[] data = ((DataBufferByte) img.getRaster().getDataBuffer()).getData();
    Mat matRGB = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC3);
    matRGB.put(0, 0, data);
    Mat matGray = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC1);
    Imgproc.cvtColor(matRGB, matGray, Imgproc.COLOR_BGR2GRAY); //TODO: RGB or BGR?
    byte[] dataGray = new byte[matGray.rows() * matGray.cols() * (int) (matGray.elemSize())];
    matGray.get(0, 0, dataGray);

    detector.detect(matGray, keypoints);
    extractor.compute(matGray, keypoints, descriptors);
    myKeys = keypoints.toList();

    features = new LinkedList<CvSurfFeature>();
    KeyPoint key;
    CvSurfFeature feat;
    double[] desc;
    int cols, rows = myKeys.size();
    for (int i = 0; i < rows; i++) {
        cols = (descriptors.row(i)).cols();
        desc = new double[cols];
        key = myKeys.get(i);
        for (int j = 0; j < cols; j++) {
            desc[j] = descriptors.get(i, j)[0];
        }
        feat = new CvSurfFeature(key.pt.x, key.pt.y, key.size, desc);
        features.add(feat);
    }
}

---

From source file:net.semanticmetadata.lire.imageanalysis.features.local.opencvfeatures.CvSurfExtractor.java

License:Open Source License

public LinkedList<CvSurfFeature> computeSurfKeypoints(BufferedImage img) {
    MatOfKeyPoint keypoints = new MatOfKeyPoint();
    List<KeyPoint> myKeys;/*from ww  w.  j a v  a 2  s .  c  o m*/
    //        Mat img_object = Highgui.imread(image, 0); //0 = CV_LOAD_IMAGE_GRAYSCALE
    //        detector.detect(img_object, keypoints);
    byte[] data = ((DataBufferByte) img.getRaster().getDataBuffer()).getData();
    Mat matRGB = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC3);
    matRGB.put(0, 0, data);
    Mat matGray = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC1);
    Imgproc.cvtColor(matRGB, matGray, Imgproc.COLOR_BGR2GRAY); //TODO: RGB or BGR?
    byte[] dataGray = new byte[matGray.rows() * matGray.cols() * (int) (matGray.elemSize())];
    matGray.get(0, 0, dataGray);

    detector.detect(matGray, keypoints);
    myKeys = keypoints.toList();

    LinkedList<CvSurfFeature> myKeypoints = new LinkedList<CvSurfFeature>();
    KeyPoint key;
    CvSurfFeature feat;
    for (Iterator<KeyPoint> iterator = myKeys.iterator(); iterator.hasNext();) {
        key = iterator.next();
        feat = new CvSurfFeature(key.pt.x, key.pt.y, key.size, null);
        myKeypoints.add(feat);
    }

    return myKeypoints;
}

---

From source file:nz.ac.auckland.lablet.vision.CamShiftTracker.java

License:Open Source License

/**
 * Finds the dominant colour in an image, and returns two values in HSV colour space to represent similar colours,
 * e.g. so you can keep all colours similar to the dominant colour.
 *
 * How the algorithm works://  ww w. j av a  2  s  .  com
 *
 * 1. Scale the frame down so that algorithm doesn't take too long.
 * 2. Segment the frame into different colours (number of colours determined by k)
 * 3. Find dominant cluster (largest area) and get its central colour point.
 * 4. Get range (min max) to represent similar colours.
 *
 * @param bgr The input frame, in BGR colour space.
 * @param k The number of segments to use (2 works well).
 * @return The min and max HSV colour values, which represent the colours similar to the dominant colour.
 */
private Pair<Scalar, Scalar> getMinMaxHsv(Mat bgr, int k) {
    //Convert to HSV
    Mat input = new Mat();
    Imgproc.cvtColor(bgr, input, Imgproc.COLOR_BGR2BGRA, 3);

    //Scale image
    Size bgrSize = bgr.size();
    Size newSize = new Size();

    if (bgrSize.width > CamShiftTracker.KMEANS_IMG_SIZE || bgrSize.height > CamShiftTracker.KMEANS_IMG_SIZE) {

        if (bgrSize.width > bgrSize.height) {
            newSize.width = CamShiftTracker.KMEANS_IMG_SIZE;
            newSize.height = CamShiftTracker.KMEANS_IMG_SIZE / bgrSize.width * bgrSize.height;
        } else {
            newSize.width = CamShiftTracker.KMEANS_IMG_SIZE / bgrSize.height * bgrSize.width;
            newSize.height = CamShiftTracker.KMEANS_IMG_SIZE;
        }

        Imgproc.resize(input, input, newSize);
    }

    //Image quantization using k-means, see here for details of k-means algorithm: http://bit.ly/1JIvrlB
    Mat clusterData = new Mat();

    Mat reshaped = input.reshape(1, input.rows() * input.cols());
    reshaped.convertTo(clusterData, CvType.CV_32F, 1.0 / 255.0);
    Mat labels = new Mat();
    Mat centres = new Mat();
    TermCriteria criteria = new TermCriteria(TermCriteria.COUNT, 50, 1);
    Core.kmeans(clusterData, k, labels, criteria, 1, Core.KMEANS_PP_CENTERS, centres);

    //Get num hits for each category
    int[] counts = new int[k];

    for (int i = 0; i < labels.rows(); i++) {
        int label = (int) labels.get(i, 0)[0];
        counts[label] += 1;
    }

    //Get cluster index with maximum number of members
    int maxCluster = 0;
    int index = -1;

    for (int i = 0; i < counts.length; i++) {
        int value = counts[i];

        if (value > maxCluster) {
            maxCluster = value;
            index = i;
        }
    }

    //Get cluster centre point hsv
    int r = (int) (centres.get(index, 2)[0] * 255.0);
    int g = (int) (centres.get(index, 1)[0] * 255.0);
    int b = (int) (centres.get(index, 0)[0] * 255.0);
    int sum = (r + g + b) / 3;

    //Get colour range
    Scalar min;
    Scalar max;

    int rg = Math.abs(r - g);
    int gb = Math.abs(g - b);
    int rb = Math.abs(r - b);
    int maxDiff = Math.max(Math.max(rg, gb), rb);

    if (maxDiff < 35 && sum > 120) { //white
        min = new Scalar(0, 0, 0);
        max = new Scalar(180, 40, 255);
    } else if (sum < 50 && maxDiff < 35) { //black
        min = new Scalar(0, 0, 0);
        max = new Scalar(180, 255, 40);
    } else {
        Mat bgrColour = new Mat(1, 1, CvType.CV_8UC3, new Scalar(r, g, b));
        Mat hsvColour = new Mat();

        Imgproc.cvtColor(bgrColour, hsvColour, Imgproc.COLOR_BGR2HSV, 3);
        double[] hsv = hsvColour.get(0, 0);

        int addition = 0;
        int minHue = (int) hsv[0] - colourRange;
        if (minHue < 0) {
            addition = Math.abs(minHue);
        }

        int maxHue = (int) hsv[0] + colourRange;

        min = new Scalar(Math.max(minHue, 0), 60, Math.max(35, hsv[2] - 30));
        max = new Scalar(Math.min(maxHue + addition, 180), 255, 255);
    }

    return new Pair<>(min, max);
}

---

From source file:objectdetection.Mat2Image.java

BufferedImage getImage(Mat mat) {
    getSpace(mat);
    mat.get(0, 0, dat);
    img.getRaster().setDataElements(0, 0, mat.cols(), mat.rows(), dat);
    return img;
}

---

From source file:opencv.CaptchaDetection.java

public static String goDetect(String filename) throws CvException {
    final Mat src = Imgcodecs.imread(filename);

    Mat src_gray = thres_rgb(src);

    Mat noise = k_means_spilter(src);/*from   ww  w .j av  a 2 s. c  o  m*/

    Mat line = check_is_line(src_gray);

    delete_target(src_gray, noise, line);

    delete_point(src_gray);

    Mat src_resize = new Mat();
    Imgproc.resize(src_gray, src_resize, new Size(src_gray.cols() * 2, src_gray.rows() * 2));

    List<Mat> num_rio = find_number(src_resize);

    String answer = dect_number(num_rio);

    return answer;
}

---

From source file:opencv.CaptchaDetection.java

private static Mat thres_rgb(Mat src) {
    Mat gray = Mat.zeros(src.size(), CvType.CV_8UC1);

    //  , ?/* w w  w . j a va  2  s .  co m*/
    int thres = 150;
    double gamma = 2.5;

    for (int row = 0; row < src.rows(); row++) {
        for (int col = 0; col < src.cols(); col++) {
            double[] s_data = src.get(row, col);

            byte[] s_buff = new byte[3];
            byte[] g_buff = new byte[1];

            double color_sum = s_data[0] + s_data[1] + s_data[2];

            if (color_sum / 3 > thres) {
                for (int channel = 0; channel < 3; channel++)
                    s_buff[channel] = (byte) 255;

                g_buff[0] = 0;
            } else {
                //   gamma 
                for (int channel = 0; channel < 3; channel++) {
                    double tmp = s_data[channel];
                    tmp = Math.pow(tmp / 255, gamma) * 255;

                    if (tmp < 0)
                        s_buff[channel] = 0;
                    else if (tmp > 255)
                        s_buff[channel] = (byte) 255;
                    else
                        s_buff[channel] = (byte) tmp;
                }

                g_buff[0] = (byte) 255;
            }
            src.put(row, col, s_buff);
            gray.put(row, col, g_buff);
        }
    }
    return gray;
}

---

From source file:opencv.CaptchaDetection.java

private static Mat k_means_spilter(Mat src) {
    Mat dst = Mat.zeros(src.size(), CvType.CV_8UC1);

    int width = src.cols();
    int height = src.rows();
    int dims = src.channels();

    //   /*from w w  w  .j  a va  2s  .  com*/
    int clusterCount = 3;

    Mat points = new Mat(width * height, dims, CvType.CV_32F, new Scalar(0));
    Mat centers = new Mat(clusterCount, dims, CvType.CV_32F);
    Mat labels = new Mat(width * height, 1, CvType.CV_32S);

    //    points
    for (int row = 0; row < height; row++) {
        for (int col = 0; col < width; col++) {
            int index = row * width + col;
            double[] s_data = src.get(row, col);

            for (int channel = 0; channel < 3; channel++) {
                float[] f_buff = new float[1];
                f_buff[0] = (float) s_data[channel];

                points.put(index, channel, f_buff);
            }
        }
    }

    //  knn ?
    TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 10, 0.1);
    Core.kmeans(points, clusterCount, labels, criteria, 3, Core.KMEANS_PP_CENTERS, centers);

    //  ??? label index
    Map<Integer, Integer> tmp = new TreeMap<>();
    for (int i = 0; i < clusterCount; i++) {
        int sum = 0;
        for (int j = 0; j < dims; j++) {
            sum += centers.get(i, j)[0];
        }
        while (tmp.containsKey(sum))
            sum++;
        tmp.put(sum, i);
    }

    int count = 0;
    int[] label_order = new int[clusterCount];
    for (Map.Entry<Integer, Integer> iter : tmp.entrySet()) {
        label_order[count++] = iter.getValue();
    }

    for (int row = 0; row < height; row++) {
        for (int col = 0; col < width; col++) {
            int index = row * width + col;
            int label = (int) labels.get(index, 0)[0];

            if (label == label_order[1]) {
                byte[] d_buff = new byte[1];
                d_buff[0] = (byte) 255;
                dst.put(row, col, d_buff);
            }
        }
    }

    return dst;
}

---

From source file:opencv.CaptchaDetection.java

private static Mat check_is_line(Mat src) {
    Mat dst = Mat.zeros(src.size(), CvType.CV_8UC1);

    int min_length = 3;

    //  ?/*from ww w  .  j ava 2  s.  com*/
    for (int row = 0; row < src.rows(); row++) {
        for (int col = 0; col < src.cols(); col++) {
            if (src.get(row, col)[0] == 0)
                continue;

            //  ??
            boolean left_black = false, right_black = false;

            if (col == 0 || src.get(row, col - 1)[0] == 0)
                left_black = true;

            if (col == src.cols() - 1 || src.get(row, col + 1)[0] == 0)
                right_black = true;

            if (!left_black || !right_black)
                continue;

            //   
            int length = col_length(src, row, col);
            if (length > min_length) {
                byte[] d_buff = new byte[1];
                d_buff[0] = (byte) 255;
                dst.put(row, col, d_buff);
            }

        }
    }

    //  ?
    for (int row = 0; row < src.rows(); row++) {
        for (int col = 0; col < src.cols(); col++) {
            if (src.get(row, col)[0] == 0)
                continue;

            //  ?
            boolean up_black = false, down_black = false;

            if (row == 0 || src.get(row - 1, col)[0] == 0)
                up_black = true;

            if (row == src.rows() - 1 || src.get(row + 1, col)[0] == 0)
                down_black = true;

            if (!up_black || !down_black)
                continue;

            //  
            int length = row_length(src, row, col);
            if (length > min_length) {
                byte[] d_buff = new byte[1];
                d_buff[0] = (byte) 255;
                dst.put(row, col, d_buff);
            }
        }
    }

    return dst;
}

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