Example usage for org.opencv.core Mat Mat

Introduction

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

Prototype

public Mat() 

Source Link

Usage

From source file:classes.TextRecognitionPreparer.java

public static ArrayList<BufferedImage> generateRecognizableBufferedImages(Mat img, Scalar backgroundColor,
        Scalar userPickedColor) {/* www  .  jav  a2 s.c  o  m*/

    ArrayList<BufferedImage> images = new ArrayList<BufferedImage>();

    Mat filledImage = img.clone();
    Scalar newVal = new Scalar(userPickedColor.val[2], userPickedColor.val[1], userPickedColor.val[0]);

    Imgproc.floodFill(filledImage, new Mat(), new Point(0, 0), newVal);
    images.add(Util.mat2Img(filledImage));

    Mat filledGrayImage = new Mat();
    Imgproc.cvtColor(filledImage, filledGrayImage, Imgproc.COLOR_BGR2GRAY);
    images.add(Util.mat2Img(filledGrayImage));

    Mat gaussianGrayImage = new Mat();
    Imgproc.GaussianBlur(filledGrayImage, gaussianGrayImage, new Size(0, 0), 3);
    Core.addWeighted(filledGrayImage, 3.5, gaussianGrayImage, -1, 0, gaussianGrayImage);
    images.add(Util.mat2Img(gaussianGrayImage));

    Mat filledBinarizedImage2 = new Mat();
    Imgproc.adaptiveThreshold(filledGrayImage, filledBinarizedImage2, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C,
            Imgproc.THRESH_BINARY, 75, 10);
    images.add(Util.mat2Img(filledBinarizedImage2));

    Mat filledBinarizedImage1 = new Mat();
    Imgproc.adaptiveThreshold(filledGrayImage, filledBinarizedImage1, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C,
            Imgproc.THRESH_BINARY, 15, 4);
    images.add(Util.mat2Img(filledBinarizedImage1));

    return images;
}

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From source file:classes.TextRecognitionPreparer.java

public static ArrayList<Mat> generateRecognizableImages(Mat img, Scalar backgroundColor,
        Scalar userPickedColor) {/*from  ww  w .j a v a 2 s  .c  om*/

    ArrayList<Mat> images = new ArrayList<Mat>();

    Mat filledImage = img.clone();
    Scalar newVal = new Scalar(userPickedColor.val[2], userPickedColor.val[1], userPickedColor.val[0]);
    Imgproc.floodFill(filledImage, new Mat(), new Point(0, 0), newVal);
    String file1 = "filledImage.png";
    //        Highgui.imwrite(file1, filledImage);
    images.add(filledImage);

    Mat filledGrayImage = new Mat();
    Imgproc.cvtColor(filledImage, filledGrayImage, Imgproc.COLOR_BGR2GRAY);
    String file2 = "filledGrayImage.png";
    //        Highgui.imwrite(file2, filledGrayImage);
    images.add(filledGrayImage);

    Mat gaussianGrayImage = new Mat();
    Imgproc.GaussianBlur(filledGrayImage, gaussianGrayImage, new Size(0, 0), 3);
    Core.addWeighted(filledGrayImage, 3.5, gaussianGrayImage, -1, 0, gaussianGrayImage);
    //        Core.addWeighted(filledGrayImage, 2.5, gaussianGrayImage, -0.5, 0, gaussianGrayImage);
    String file3 = "sharpenedImage.png";
    //        Highgui.imwrite(file3, gaussianGrayImage);
    images.add(gaussianGrayImage);

    Mat filledBinarizedImage = new Mat();
    Imgproc.adaptiveThreshold(filledGrayImage, filledBinarizedImage, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C,
            Imgproc.THRESH_BINARY, 15, 4);
    String file4 = "filledBinarizedImage.png";
    //        Highgui.imwrite(file4, filledBinarizedImage);
    images.add(filledBinarizedImage);

    //        BackgroundSubtractorMOG2 backgroundSubtractorMOG2 = new BackgroundSubtractorMOG2();
    //        Mat foregroundMask = new Mat();
    //        backgroundSubtractorMOG2.apply(img, foregroundMask);
    //        Highgui.imwrite("mFGMask.png", foregroundMask);

    Scalar fillingColor = cluster(userPickedColor, img, 3);

    Mat replacedColor = replaceColor(img, backgroundColor, fillingColor);
    String file5 = "replacedColor.png";
    //        Highgui.imwrite(file5, replacedColor);
    images.add(replacedColor);

    Mat grayImage = new Mat();
    Imgproc.cvtColor(replacedColor, grayImage, Imgproc.COLOR_BGR2GRAY);
    String file6 = "grayImage.png";
    //        Highgui.imwrite(file6, grayImage);
    images.add(grayImage);

    Mat binarized = new Mat();
    Imgproc.adaptiveThreshold(grayImage, binarized, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY,
            15, 4);
    String file7 = "binarized.png";
    //        Highgui.imwrite(file7, binarized);
    images.add(binarized);

    Mat colorReplacedEqualized = equalizeIntensity(replacedColor);
    String file8 = "colorReplacedEqualized.png";
    //        Highgui.imwrite(file8, colorReplacedEqualized);
    images.add(colorReplacedEqualized);

    Mat colorReducedImage = reduceColor(replacedColor, 64);
    String file9 = "replacedColorColorReduced.png";
    //        Highgui.imwrite(file9, colorReducedImage);
    images.add(colorReducedImage);

    // Equalizing image
    Mat colorReducedEqualized = equalizeIntensity(colorReducedImage);
    String file10 = "colorReducedEqualized.png";
    //        Highgui.imwrite(file10, colorReducedEqualized);
    images.add(colorReducedEqualized);

    return images;

}

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From source file:classes.TextRecognitionPreparer.java

public static Scalar cluster(Scalar userColor, Mat cutout, int k) {

    Mat samples = cutout.reshape(1, cutout.cols() * cutout.rows());
    Mat samples32f = new Mat();
    samples.convertTo(samples32f, CvType.CV_32F, 1.0 / 255.0);

    Mat labels = new Mat();
    TermCriteria criteria = new TermCriteria(TermCriteria.COUNT, 100, 1);
    Mat centers = new Mat();
    Core.kmeans(samples32f, k, labels, criteria, 1, Core.KMEANS_PP_CENTERS, centers);

    Scalar fillingColor = getFillingColor(userColor, cutout, labels, centers);

    return fillingColor;
}

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From source file:classes.TextRecognitionPreparer.java

public static Mat equalizeIntensity(Mat inputImage) {

    if (inputImage.channels() >= 3) {

        Mat ycrcb = new Mat();

        Imgproc.cvtColor(inputImage, ycrcb, Imgproc.COLOR_BGR2YUV);

        ArrayList<Mat> channels = new ArrayList<Mat>();

        Core.split(ycrcb, channels);/*from w ww  .j a va  2 s. co  m*/

        Mat equalized = new Mat();

        Imgproc.equalizeHist(channels.get(0), equalized);

        channels.set(0, equalized);
        Core.merge(channels, ycrcb);

        Mat result = new Mat();
        Imgproc.cvtColor(ycrcb, result, Imgproc.COLOR_YUV2BGR);

        return result;
    }

    return null;
}

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From source file:cmib_4_4.Countour.java

public static void main(String args[]) {

    System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    Mat image = Highgui.imread("input1.jpg", Highgui.CV_LOAD_IMAGE_GRAYSCALE);
    Mat image1 = Highgui.imread("input1.jpg", Highgui.CV_LOAD_IMAGE_GRAYSCALE);
    Mat image4 = Highgui.imread("input1.jpg");
    Imgproc.threshold(image1, image1, 0, 255, THRESH_OTSU);
    Imgproc.Canny(image1, image1, Imgproc.THRESH_BINARY_INV + Imgproc.THRESH_OTSU,
            Imgproc.THRESH_BINARY_INV + Imgproc.THRESH_OTSU);
    Mat image2 = Mat.zeros(image.rows() + 2, image.cols() + 2, CV_8U);
    List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
    Imgproc.findContours(image1, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);

    for (int i = 0; i < contours.size(); i++) {

        if (Imgproc.contourArea(contours.get(i)) > 100) {

            Rect rect = Imgproc.boundingRect(contours.get(i));
            Imgproc.floodFill(image1, image2, new Point(150, 150), new Scalar(255));
            Rect rectCrop = new Rect(rect.x, rect.y, rect.width, rect.height);
            Mat image_roi_rgb = new Mat(image4, rectCrop);
            Highgui.imwrite("crop2.jpg", image_roi_rgb);
            if (rect.height > 28) {

                Core.rectangle(image, new Point(rect.x, rect.y),
                        new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 0, 255));
            }//  w  w w  .  j  a  v  a  2 s  . c  o  m
        }
    }
    Highgui.imwrite("falciparum2.jpg", image);

}

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From source file:cmib_4_4.Feature.java

public int[] featureVector(Mat image1) {

    Mat image = new Mat();
    Size sz = new Size(30, 30);
    Imgproc.resize(image1, image, sz);//from   w w w  .  j  ava  2  s  .co m
    int size = (int) (image1.total() * image1.channels());
    int size2 = (image.width() * image.height());
    double[][] spec1 = new double[size2][3];

    FeatureVector A = new FeatureVector();
    int k = 0;
    for (int i = 0; i < image.rows(); i++) {

        for (int j = 0; j < image.cols(); j++) {

            //image.get(i, j, rgb);
            double[] rgb = image.get(i, j);
            double[] a = A.cartToSpec(rgb[0], rgb[1], rgb[2]);
            double x = Math.toRadians(90);
            spec1[k][0] = a[0] / x;
            spec1[k][1] = a[1] / x;
            spec1[k][2] = a[2] / x;
            //System.out.println(rgb[0]);
            //System.out.println(spec1[k][2]);
            k++;
        }
    }

    int[][] b = new int[11][11];
    for (int i = 0; i < 11; i++) {

        for (int j = 0; j < 11; j++) {

            b[i][j] = 0;
        }
    }

    for (int i = 0; i < 900; i++) {

        int x1 = (int) (Math.round(spec1[i][1] * 10));
        int y1 = (int) (Math.round(spec1[i][2] * 10));

        b[x1][y1] = b[x1][y1] + 1;
        //System.out.println(x1+"and"+y1);
    }
    int l = 0;
    int[] c = new int[121];
    for (int i = 0; i < 11; i++) {

        for (int j = 0; j < 11; j++) {

            c[l] = b[i][j];
            l++;
            //System.out.println(c[l-1]);
        }
    }
    return c;

}

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From source file:com.android.cts.verifier.sensors.RVCVXCheckAnalyzer.java

License:Apache License

/**
 * Analyze video frames using computer vision approach and generate a ArrayList<AttitudeRec>
 *
 * @param recs  output ArrayList of AttitudeRec
 * @return total number of frame of the video
 *//*from   www  . jav a  2 s .  c o  m*/
private int analyzeVideo(ArrayList<AttitudeRec> recs) {
    VideoMetaInfo meta = new VideoMetaInfo(new File(mPath, "videometa.json"));

    int decimation = 1;
    boolean use_timestamp = true;

    // roughly determine if decimation is necessary
    if (meta.fps > DECIMATION_FPS_TARGET) {
        decimation = (int) (meta.fps / DECIMATION_FPS_TARGET);
        meta.fps /= decimation;
    }

    VideoDecoderForOpenCV videoDecoder = new VideoDecoderForOpenCV(new File(mPath, "video.mp4"), decimation);

    Mat frame;
    Mat gray = new Mat();
    int i = -1;

    Size frameSize = videoDecoder.getSize();

    if (frameSize.width != meta.frameWidth || frameSize.height != meta.frameHeight) {
        // this is very unlikely
        return -1;
    }

    if (TRACE_VIDEO_ANALYSIS) {
        Debug.startMethodTracing("cvprocess");
    }

    Size patternSize = new Size(4, 11);

    float fc = (float) (meta.frameWidth / 2.0 / Math.tan(meta.fovWidth / 2.0));
    Mat camMat = cameraMatrix(fc, new Size(frameSize.width / 2, frameSize.height / 2));
    MatOfDouble coeff = new MatOfDouble(); // dummy

    MatOfPoint2f centers = new MatOfPoint2f();
    MatOfPoint3f grid = asymmetricalCircleGrid(patternSize);
    Mat rvec = new MatOfFloat();
    Mat tvec = new MatOfFloat();

    MatOfPoint2f reprojCenters = new MatOfPoint2f();

    if (LOCAL_LOGV) {
        Log.v(TAG, "Camera Mat = \n" + camMat.dump());
    }

    long startTime = System.nanoTime();
    long[] ts = new long[1];

    while ((frame = videoDecoder.getFrame(ts)) != null) {
        if (LOCAL_LOGV) {
            Log.v(TAG, "got a frame " + i);
        }

        if (use_timestamp && ts[0] == -1) {
            use_timestamp = false;
        }

        // has to be in front, as there are cases where execution
        // will skip the later part of this while
        i++;

        // convert to gray manually as by default findCirclesGridDefault uses COLOR_BGR2GRAY
        Imgproc.cvtColor(frame, gray, Imgproc.COLOR_RGB2GRAY);

        boolean foundPattern = Calib3d.findCirclesGrid(gray, patternSize, centers,
                Calib3d.CALIB_CB_ASYMMETRIC_GRID);

        if (!foundPattern) {
            // skip to next frame
            continue;
        }

        if (OUTPUT_DEBUG_IMAGE) {
            Calib3d.drawChessboardCorners(frame, patternSize, centers, true);
        }

        // figure out the extrinsic parameters using real ground truth 3D points and the pixel
        // position of blobs found in findCircleGrid, an estimated camera matrix and
        // no-distortion are assumed.
        boolean foundSolution = Calib3d.solvePnP(grid, centers, camMat, coeff, rvec, tvec, false,
                Calib3d.CV_ITERATIVE);

        if (!foundSolution) {
            // skip to next frame
            if (LOCAL_LOGV) {
                Log.v(TAG, "cannot find pnp solution in frame " + i + ", skipped.");
            }
            continue;
        }

        // reproject points to for evaluation of result accuracy of solvePnP
        Calib3d.projectPoints(grid, rvec, tvec, camMat, coeff, reprojCenters);

        // error is evaluated in norm2, which is real error in pixel distance / sqrt(2)
        double error = Core.norm(centers, reprojCenters, Core.NORM_L2);

        if (LOCAL_LOGV) {
            Log.v(TAG, "Found attitude, re-projection error = " + error);
        }

        // if error is reasonable, add it into the results. use ratio to frame height to avoid
        // discriminating higher definition videos
        if (error < REPROJECTION_THREASHOLD_RATIO * frameSize.height) {
            double[] rv = new double[3];
            double timestamp;

            rvec.get(0, 0, rv);
            if (use_timestamp) {
                timestamp = (double) ts[0] / 1e6;
            } else {
                timestamp = (double) i / meta.fps;
            }
            if (LOCAL_LOGV)
                Log.v(TAG, String.format("Added frame %d  ts = %f", i, timestamp));
            recs.add(new AttitudeRec(timestamp, rodr2rpy(rv)));
        }

        if (OUTPUT_DEBUG_IMAGE) {
            Calib3d.drawChessboardCorners(frame, patternSize, reprojCenters, true);
            Imgcodecs.imwrite(Environment.getExternalStorageDirectory().getPath() + "/RVCVRecData/DebugCV/img"
                    + i + ".png", frame);
        }
    }

    if (LOCAL_LOGV) {
        Log.v(TAG, "Finished decoding");
    }

    if (TRACE_VIDEO_ANALYSIS) {
        Debug.stopMethodTracing();
    }

    if (LOCAL_LOGV) {
        // time analysis
        double totalTime = (System.nanoTime() - startTime) / 1e9;
        Log.i(TAG, "Total time: " + totalTime + "s, Per frame time: " + totalTime / i);
    }
    return i;
}

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From source file:com.astrocytes.core.operationsengine.CoreOperations.java

License:Open Source License

/**
 * Invert image.//w  w  w . ja v a  2  s  . c o m
 *
 * @param src - source image.
 * @return inverted image.
 */
public static Mat invert(Mat src) {
    Mat dest = new Mat();
    Core.bitwise_not(src, dest);
    return dest;
}

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From source file:com.astrocytes.core.operationsengine.CoreOperations.java

License:Open Source License

/**
 * Applies thresholding for gray image.//from w  w w  .ja  v a2  s  . c  om
 *
 * @param src - gray source image.
 * @param thresh - the level of threshold.
 * @return thresholded gray image.
 */
public static Mat threshold(Mat src, int thresh) {
    Mat dest = new Mat();
    Imgproc.threshold(src, dest, thresh, 255, Imgproc.THRESH_BINARY);
    return dest;
}

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From source file:com.astrocytes.core.operationsengine.CoreOperations.java

License:Open Source License

/**
 * Applies thresholding for color image.
 *
 * @param src - color source image./* w w  w. j  av  a2s.  c o m*/
 * @param r - the value for red value in threshold.
 * @param g - the value for green value in threshold.
 * @param b - the value for blue value in threshold.
 * @return thresholded color image.
 */
public static Mat threshold(Mat src, int r, int g, int b) {
    if (src.channels() < 3)
        return src;

    Mat dest = new Mat();
    Mat srcBin = new Mat();

    Imgproc.threshold(src, srcBin, 1, 255, Imgproc.THRESH_BINARY);
    Core.inRange(src, new Scalar(0), new Scalar(r, g, b), dest);
    dest = invert(dest);
    cvtColor(dest, dest, Imgproc.COLOR_GRAY2BGR);

    dest = xor(srcBin, dest);
    dest = and(src, dest);
    return dest;
}

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