Example usage for org.opencv.core Mat put

Introduction

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

Prototype

public int put(int row, int col, byte[] data) 

Source Link

Usage

From source file:org.vinesrobotics.bot.utils.opencv.ColorBlobDetector.java

License:Open Source License

public void setHsvColor(Scalar hsvColor) {
    double minH = (hsvColor.val[0] >= mColorRadius.val[0]) ? hsvColor.val[0] - mColorRadius.val[0] : 0;
    double maxH = (hsvColor.val[0] + mColorRadius.val[0] <= 255) ? hsvColor.val[0] + mColorRadius.val[0] : 255;

    mLowerBound.val[0] = minH;
    mUpperBound.val[0] = maxH;

    mLowerBound.val[1] = hsvColor.val[1] - mColorRadius.val[1];
    mUpperBound.val[1] = hsvColor.val[1] + mColorRadius.val[1];

    mLowerBound.val[2] = hsvColor.val[2] - mColorRadius.val[2];
    mUpperBound.val[2] = hsvColor.val[2] + mColorRadius.val[2];

    mLowerBound.val[3] = 0;
    mUpperBound.val[3] = 255;

    Mat spectrumHsv = new Mat(1, (int) Math.abs(maxH - minH), CvType.CV_8UC3);

    mBaseColor = hsvColor;//from   w w  w .ja va  2  s  . c  o  m

    for (int j = 0; j < maxH - minH; j++) {
        byte[] tmp = { (byte) (minH + j), (byte) 255, (byte) 255 };
        spectrumHsv.put(0, j, tmp);
    }

    Imgproc.cvtColor(spectrumHsv, mSpectrum, Imgproc.COLOR_HSV2RGB_FULL, 4);
}

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From source file:overwatchteampicker.OverwatchTeamPicker.java

public static ReturnValues findImage(String template, String source, int flag) {
    File lib = null;/*  w  ww.ja va2 s. c  om*/
    BufferedImage image = null;
    try {
        image = ImageIO.read(new File(source));
    } catch (Exception e) {
        e.printStackTrace();
    }

    String os = System.getProperty("os.name");
    String bitness = System.getProperty("sun.arch.data.model");

    if (os.toUpperCase().contains("WINDOWS")) {
        if (bitness.endsWith("64")) {
            lib = new File("C:\\Users\\POWERUSER\\Downloads\\opencv\\build\\java\\x64\\"
                    + System.mapLibraryName("opencv_java2413"));
        } else {
            lib = new File("libs//x86//" + System.mapLibraryName("opencv_java2413"));
        }
    }
    System.load(lib.getAbsolutePath());
    String tempObject = "images\\hero_templates\\" + template + ".png";
    String source_pic = source;
    Mat objectImage = Highgui.imread(tempObject, Highgui.CV_LOAD_IMAGE_GRAYSCALE);
    Mat sceneImage = Highgui.imread(source_pic, Highgui.CV_LOAD_IMAGE_GRAYSCALE);

    MatOfKeyPoint objectKeyPoints = new MatOfKeyPoint();
    FeatureDetector featureDetector = FeatureDetector.create(FeatureDetector.SURF);
    featureDetector.detect(objectImage, objectKeyPoints);
    KeyPoint[] keypoints = objectKeyPoints.toArray();
    MatOfKeyPoint objectDescriptors = new MatOfKeyPoint();
    DescriptorExtractor descriptorExtractor = DescriptorExtractor.create(DescriptorExtractor.SURF);
    descriptorExtractor.compute(objectImage, objectKeyPoints, objectDescriptors);

    // Create the matrix for output image.
    Mat outputImage = new Mat(objectImage.rows(), objectImage.cols(), Highgui.CV_LOAD_IMAGE_COLOR);
    Scalar newKeypointColor = new Scalar(255, 0, 0);
    Features2d.drawKeypoints(objectImage, objectKeyPoints, outputImage, newKeypointColor, 0);

    // Match object image with the scene image
    MatOfKeyPoint sceneKeyPoints = new MatOfKeyPoint();
    MatOfKeyPoint sceneDescriptors = new MatOfKeyPoint();
    featureDetector.detect(sceneImage, sceneKeyPoints);
    descriptorExtractor.compute(sceneImage, sceneKeyPoints, sceneDescriptors);

    Mat matchoutput = new Mat(sceneImage.rows() * 2, sceneImage.cols() * 2, Highgui.CV_LOAD_IMAGE_COLOR);
    Scalar matchestColor = new Scalar(0, 255, 25);

    List<MatOfDMatch> matches = new LinkedList<MatOfDMatch>();
    DescriptorMatcher descriptorMatcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED);
    descriptorMatcher.knnMatch(objectDescriptors, sceneDescriptors, matches, 2);

    LinkedList<DMatch> goodMatchesList = new LinkedList<DMatch>();

    float nndrRatio = .78f;

    for (int i = 0; i < matches.size(); i++) {
        MatOfDMatch matofDMatch = matches.get(i);
        DMatch[] dmatcharray = matofDMatch.toArray();
        DMatch m1 = dmatcharray[0];
        DMatch m2 = dmatcharray[1];

        if (m1.distance <= m2.distance * nndrRatio) {
            goodMatchesList.addLast(m1);

        }
    }

    if (goodMatchesList.size() >= 4) {

        List<KeyPoint> objKeypointlist = objectKeyPoints.toList();
        List<KeyPoint> scnKeypointlist = sceneKeyPoints.toList();

        LinkedList<Point> objectPoints = new LinkedList<>();
        LinkedList<Point> scenePoints = new LinkedList<>();

        for (int i = 0; i < goodMatchesList.size(); i++) {
            objectPoints.addLast(objKeypointlist.get(goodMatchesList.get(i).queryIdx).pt);
            scenePoints.addLast(scnKeypointlist.get(goodMatchesList.get(i).trainIdx).pt);
        }

        MatOfPoint2f objMatOfPoint2f = new MatOfPoint2f();
        objMatOfPoint2f.fromList(objectPoints);
        MatOfPoint2f scnMatOfPoint2f = new MatOfPoint2f();
        scnMatOfPoint2f.fromList(scenePoints);

        Mat homography = Calib3d.findHomography(objMatOfPoint2f, scnMatOfPoint2f, Calib3d.RANSAC, 3);

        Mat obj_corners = new Mat(4, 1, CvType.CV_32FC2);
        Mat scene_corners = new Mat(4, 1, CvType.CV_32FC2);

        obj_corners.put(0, 0, new double[] { 0, 0 });
        obj_corners.put(1, 0, new double[] { objectImage.cols(), 0 });
        obj_corners.put(2, 0, new double[] { objectImage.cols(), objectImage.rows() });
        obj_corners.put(3, 0, new double[] { 0, objectImage.rows() });

        Core.perspectiveTransform(obj_corners, scene_corners, homography);

        Mat img = Highgui.imread(source_pic, Highgui.CV_LOAD_IMAGE_COLOR);

        Core.line(img, new Point(scene_corners.get(0, 0)), new Point(scene_corners.get(1, 0)),
                new Scalar(0, 255, 255), 4);
        Core.line(img, new Point(scene_corners.get(1, 0)), new Point(scene_corners.get(2, 0)),
                new Scalar(255, 255, 0), 4);
        Core.line(img, new Point(scene_corners.get(2, 0)), new Point(scene_corners.get(3, 0)),
                new Scalar(0, 255, 0), 4);
        Core.line(img, new Point(scene_corners.get(3, 0)), new Point(scene_corners.get(0, 0)),
                new Scalar(0, 255, 0), 4);

        MatOfDMatch goodMatches = new MatOfDMatch();
        goodMatches.fromList(goodMatchesList);

        Features2d.drawMatches(objectImage, objectKeyPoints, sceneImage, sceneKeyPoints, goodMatches,
                matchoutput, matchestColor, newKeypointColor, new MatOfByte(), 2);
        if (new Point(scene_corners.get(0, 0)).x < new Point(scene_corners.get(1, 0)).x
                && new Point(scene_corners.get(0, 0)).y < new Point(scene_corners.get(2, 0)).y) {
            System.out.println("found " + template);
            Highgui.imwrite("points.jpg", outputImage);
            Highgui.imwrite("matches.jpg", matchoutput);
            Highgui.imwrite("final.jpg", img);

            if (flag == 0) {
                ReturnValues retVal = null;
                int y = (int) new Point(scene_corners.get(3, 0)).y;
                int yHeight = (int) new Point(scene_corners.get(3, 0)).y
                        - (int) new Point(scene_corners.get(2, 0)).y;
                if (y < image.getHeight() * .6) { //if found hero is in upper half of image then return point 3,0
                    retVal = new ReturnValues(y + (int) (image.getHeight() * .01), yHeight);
                } else { //if found hero is in lower half of image then return point 2,0
                    y = (int) new Point(scene_corners.get(2, 0)).y;
                    retVal = new ReturnValues(y + (int) (image.getHeight() * .3), yHeight);
                }
                return retVal;
            } else if (flag == 1) {
                int[] xPoints = new int[4];
                int[] yPoints = new int[4];

                xPoints[0] = (int) (new Point(scene_corners.get(0, 0)).x);
                xPoints[1] = (int) (new Point(scene_corners.get(1, 0)).x);
                xPoints[2] = (int) (new Point(scene_corners.get(2, 0)).x);
                xPoints[3] = (int) (new Point(scene_corners.get(3, 0)).x);

                yPoints[0] = (int) (new Point(scene_corners.get(0, 0)).y);
                yPoints[1] = (int) (new Point(scene_corners.get(1, 0)).y);
                yPoints[2] = (int) (new Point(scene_corners.get(2, 0)).y);
                yPoints[3] = (int) (new Point(scene_corners.get(3, 0)).y);

                ReturnValues retVal = new ReturnValues(xPoints, yPoints);
                return retVal;

            }
        }
    }
    return null;

}

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From source file:pipeline.TextRegion.java

public static String SplitFiles(File fileIn) {
    String result = "";
    try {/*  w  w  w .j a va2s  .  co m*/
        String nomeFile = fileIn.getName();
        //System.out.println("il nome del file  "+nomeFile);
        FileInputStream in = new FileInputStream("src/pipeline/receivedImg/" + nomeFile);
        JPEGImageDecoder decoder = JPEGCodec.createJPEGDecoder(in);
        BufferedImage image = decoder.decodeAsBufferedImage();
        in.close();

        TextRecognition myget = new TextRecognition(image);
        LinkedList boxes = myget.getTextBoxes();

        String nomeFileOut = "src/pipeline/outputImg/" + Global.getJPGNameFile() + " out.jpg";
        FileOutputStream out = new FileOutputStream(nomeFileOut);
        JPEGImageEncoder encoder = JPEGCodec.createJPEGEncoder(out);
        encoder.encode(myget.isolateText(boxes));
        out.close();

        //parte con opencv

        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
        File f = new File("src/pipeline/receivedImg/" + nomeFile);
        BufferedImage imageFile = ImageIO.read(f);

        byte[] data = ((DataBufferByte) imageFile.getRaster().getDataBuffer()).getData();
        Mat mat = new Mat(imageFile.getHeight(), imageFile.getWidth(), CvType.CV_8UC3);
        mat.put(0, 0, data);
        int tolleranza = 15;

        for (int i = 0; i < boxes.size(); i++) {
            TextRegion app = (TextRegion) boxes.get(i);
            //             System.out.println("RIGA: "+i+"  -> "+app.x1 +" "+app.x2 +" "+app.y1 +" "+app.y2 +" ");
            Rect roi1 = new Rect(app.x1 - tolleranza, app.y1 - tolleranza, app.x2 - app.x1 + tolleranza,
                    app.y2 - app.y1 + 2 * tolleranza);
            Mat mat1 = new Mat(mat, roi1);

            byte[] data1 = new byte[mat1.rows() * mat1.cols() * (int) (mat1.elemSize())];
            mat1.get(0, 0, data1);
            BufferedImage image1 = new BufferedImage(mat1.cols(), mat1.rows(), BufferedImage.TYPE_3BYTE_BGR);
            image1.getRaster().setDataElements(0, 0, mat1.cols(), mat1.rows(), data1);

            String nomeFileUscrita = "src/pipeline/outputImg/" + i + Global.getJPGNameFile() + " uscita.jpg";
            File tmp = new File(nomeFileUscrita);
            File output = new File(nomeFileUscrita);
            ImageIO.write(image1, "jpg", output);
            result += (i + 1) + ")" + OCR_Processing.performOCR_String2Text(output);
            tmp.delete();

        }
        f.delete();
        File foo = new File(nomeFileOut);
        foo.delete();

    } catch (Exception e) {
        System.out.println("Exception: " + e);
    }

    return result;

}

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From source file:processdata.depthDataProcessingUtilities.java

/**
* converts depth data to opencv Mat object leaving depth values that are only within min and max thresholds
* @param path/*from w  w  w.  j a va  2s . c  o  m*/
* @param minThreshold
* @param maxThreshold
* @return
* @throws FileNotFoundException
*/
public static Mat processDepthDataFile(String path, int minThreshold, int maxThreshold)
        throws FileNotFoundException {
    File depthData = new File(path);

    double[][] depthDataArray = new double[1][217088];

    //read depth data into array
    int count = 0;

    inDepthDataFile = new Scanner(depthData);//.useDelimiter(",\\s*");

    while (inDepthDataFile.hasNext()) {
        String currentStr = inDepthDataFile.nextLine();
        if (!currentStr.isEmpty())
            depthDataArray[0][count++] = Double.parseDouble(currentStr);
    }

    double depthDataMatrix[][] = new double[512][424];

    depthDataMatrix = reshape(depthDataArray, 512, 424);

    Mat matDepthDataMatrix = new Mat(512, 424, CvType.CV_64F);

    //cut-off the remaining depth values
    for (int i = 0; i < depthDataMatrix.length; i++) {
        for (int j = 0; j < depthDataMatrix[0].length; j++) {
            if (depthDataMatrix[i][j] > maxThreshold || depthDataMatrix[i][j] < minThreshold)
                depthDataMatrix[i][j] = 0;
        }
    }

    //find max value
    double max = 0;

    for (int i = 0; i < depthDataMatrix.length; i++) {
        for (int j = 0; j < depthDataMatrix[0].length; j++) {
            if (depthDataMatrix[i][j] > max)
                max = depthDataMatrix[i][j];
        }
    }

    //FILL THE DEPTH MATRIX
    //System.out.println("Max Element "+ max);

    for (int i = 0; i < depthDataMatrix.length; i++) {
        for (int j = 0; j < depthDataMatrix[0].length; j++) {
            matDepthDataMatrix.put(i, j, depthDataMatrix[i][j] / max * 255.0);
        }
    }

    //      //printout the depth matrix
    //      for(int i = 0;i<depthDataMatrix.length;i++){
    //          for(int j = 0;j<depthDataMatrix[0].length;j++){
    //              System.out.print(depthDataMatrix[i][j]+"\t");
    //          }
    //        System.out.println();
    //      }
    //      

    //apply colormap to visualize
    Mat processedMathDepthImage = new Mat(matDepthDataMatrix.size(), CvType.CV_8U);
    matDepthDataMatrix.convertTo(processedMathDepthImage, CvType.CV_8UC1);

    Core.transpose(processedMathDepthImage, processedMathDepthImage);
    org.opencv.contrib.Contrib.applyColorMap(processedMathDepthImage, processedMathDepthImage,
            org.opencv.contrib.Contrib.COLORMAP_JET);

    return processedMathDepthImage;
}

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From source file:processdata.ExperimentalDataProcessingUI.java

public static Mat BufferedImage2Mat(BufferedImage image) {
    //source: http://stackoverflow.com/questions/18581633/fill-in-and-detect-contour-rectangles-in-java-opencv
    byte[] data = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
    Mat mat = new Mat(image.getHeight(), image.getWidth(), CvType.CV_8UC3);
    mat.put(0, 0, data);
    return mat;//from   w  ww.  j a v  a  2  s  .  co  m
}

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From source file:processdata.ProcessImages.java

/**
 * //  w w w.j  a  v  a  2  s.c o  m
 * @param imgFile
 * @return
 * @throws IOException
 * @throws ClassNotFoundException
 */
public static Mat reconstructImage(File imgFile, int index) {
    Mat mat = new Mat();

    FileInputStream fis;
    try {
        fis = new FileInputStream(imgFile.getPath());
        ObjectInputStream ois = new ObjectInputStream(fis);
        byte[] data = (byte[]) ois.readObject();

        if (index == 1) {
            mat = new Mat(1080, 1920, CvType.CV_8UC3);
        }
        if (index == 2) {
            mat = new Mat(424, 512, CvType.CV_8UC3);
        }

        mat.put(0, 0, data);

        ois.close();
        fis.close();

    } catch (IOException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();

    } catch (ClassNotFoundException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }

    return mat;

}

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From source file:qupath.opencv.classify.NeuralNetworksClassifier.java

License:Open Source License

@Override
protected void createAndTrainClassifier() {
    // Create the required Mats
    int nMeasurements = measurements.size();
    Mat matTraining = new Mat(arrayTraining.length / nMeasurements, nMeasurements, CvType.CV_32FC1);
    matTraining.put(0, 0, arrayTraining);

    // Parse parameters
    ParameterList params = getParameterList();
    int nHidden = Math.max(2, params.getIntParameterValue("nHidden"));
    int termIterations = params.getIntParameterValue("termCritMaxIterations");
    double termEPS = params.getDoubleParameterValue("termCritEPS");
    TermCriteria crit = createTerminationCriteria(termIterations, termEPS);

    // Create & train the classifier
    classifier = createClassifier();/*  w  ww. ja va2 s  .  com*/
    ANN_MLP nnet = (ANN_MLP) classifier;
    System.out.println(nnet.getLayerSizes());
    Mat layers = new Mat(3, 1, CvType.CV_32F);
    int n = arrayTraining.length / nMeasurements;
    //      layers.put(0, 0, new float[]{nMeasurements, nHidden, pathClasses.size()});
    layers.put(0, 0, nMeasurements);
    layers.put(1, 0, nHidden); // Number of hidden layers
    layers.put(2, 0, pathClasses.size());
    if (crit != null)
        nnet.setTermCriteria(crit);
    else
        crit = nnet.getTermCriteria();
    nnet.setLayerSizes(layers);
    //         matResponses.convertTo(matResponses, CvType.CV_32F);
    Mat matResponses = new Mat(n, pathClasses.size(), CvType.CV_32F);
    matResponses.setTo(new Scalar(0));
    for (int i = 0; i < n; i++) {
        matResponses.put(i, arrayResponses[i], 1);
    }
    nnet.setActivationFunction(ANN_MLP.SIGMOID_SYM, 1, 1);
    nnet.train(matTraining, Ml.ROW_SAMPLE, matResponses);

    //      lastDescription = getName() + "\n\nMain parameters:\n  " + DefaultPluginWorkflowStep.getParameterListJSON(params, "\n  ") + "\n\nTermination criteria:\n  " + crit.toString();
}

---

From source file:qupath.opencv.classify.OpenCvClassifier.java

License:Open Source License

protected void createAndTrainClassifier() {

    // Create the required Mats
    int nMeasurements = measurements.size();

    Mat matTraining = new Mat(arrayTraining.length / nMeasurements, nMeasurements, CvType.CV_32FC1);
    matTraining.put(0, 0, arrayTraining);
    Mat matResponses = new Mat(arrayResponses.length, 1, CvType.CV_32SC1);
    matResponses.put(0, 0, arrayResponses);

    //      // Clear any existing classifier
    //      if (classifier != null)
    //         classifier.clear();

    logger.info("Training size: " + matTraining.size());
    logger.info("Responses size: " + matResponses.size());

    // Create & train the classifier
    try {//from   w  ww  .  jav  a 2  s.  com
        classifier = createClassifier();
        classifier.train(matTraining, Ml.ROW_SAMPLE, matResponses);
    } catch (CvException e) {
        // For reasons I haven't yet discerned, sometimes OpenCV throws an exception with the following message:
        // OpenCV Error: Assertion failed ((int)_sleft.size() < n && (int)_sright.size() < n) in calcDir, file /tmp/opencv320150620-1681-1u5iwhh/opencv-3.0.0/modules/ml/src/tree.cpp, line 1190
        // With one sample fewer, it can often recover... so attempt that, rather than failing miserably...
        //         logger.error("Classifier training error", e);
        logger.info("Will attempt retraining classifier with one sample fewer...");
        matTraining = matTraining.rowRange(0, matTraining.rows() - 1);
        matResponses = matResponses.rowRange(0, matResponses.rows() - 1);
        classifier = createClassifier();
        classifier.train(matTraining, Ml.ROW_SAMPLE, matResponses);
    }

    matTraining.release();
    matResponses.release();

    logger.info("Classifier trained with " + arrayResponses.length + " samples");
}

---

From source file:qupath.opencv.classify.OpenCvClassifier.java

License:Open Source License

@Override
public int classifyPathObjects(Collection<PathObject> pathObjects) {

    int counter = 0;
    float[] array = new float[measurements.size()];
    Mat samples = new Mat(1, array.length, CvType.CV_32FC1);

    Mat results = new Mat();

    for (PathObject pathObject : pathObjects) {
        MeasurementList measurementList = pathObject.getMeasurementList();
        int idx = 0;
        for (String m : measurements) {
            double value = measurementList.getMeasurementValue(m);

            if (normScale != null && normOffset != null)
                value = (value + normOffset[idx]) * normScale[idx];

            array[idx] = (float) value;
            idx++;/* w  w w  . ja v  a 2  s  .c o  m*/
        }

        samples.put(0, 0, array);

        try {
            setPredictedClass(classifier, pathClasses, samples, results, pathObject);
            //            float prediction = classifier.predict(samples);
            //            
            ////            float prediction2 = classifier.predict(samples, results, StatModel.RAW_OUTPUT);
            //            float prediction2 = classifier.predict(samples, results, StatModel.RAW_OUTPUT);
            //            
            //            pathObject.setPathClass(pathClasses.get((int)prediction), prediction2);
        } catch (Exception e) {
            pathObject.setPathClass(null);
            logger.trace("Error with samples: " + samples.dump());
            //               e.printStackTrace();
        }
        //         }
        counter++;
    }

    samples.release();
    results.release();

    return counter;
}

---

From source file:qupath.opencv.processing.OpenCVTools.java

License:Open Source License

/**
 * Convert an RGB image to an OpenCV Mat.
 * //  w ww .  ja v  a2s  .  com
 * @param img
 * @return
 */
public static Mat imageToMat(BufferedImage img) {
    //      img.getRaster().getDataBuffer()
    //      img.getRaster().getDataBuffer().getDataType()
    //      byte[] data = ((DataBufferByte) img.getRaster().getDataBuffer()).getData();
    int[] data = ((DataBufferInt) img.getRaster().getDataBuffer()).getData();

    ByteBuffer byteBuffer = ByteBuffer.allocate(data.length * 4);
    IntBuffer intBuffer = byteBuffer.asIntBuffer();
    intBuffer.put(data);
    byte[] dataBytes = byteBuffer.array();

    //      byte[] dataBytes = new byte[data.length * 4];
    //      int j = 0;
    //       for (int x : data) {
    //          dataBytes[j] = (byte) ((x >>> 0) & 0xff);           
    //          dataBytes[j] = (byte) ((x >>> 8) & 0xff);
    //          dataBytes[j] = (byte) ((x >>> 16) & 0xff);
    //          dataBytes[j] = (byte) ((x >>> 24) & 0xff);
    //           j++;
    //       }

    //      Mat mat = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC3);
    Mat mat = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC4);
    mat.put(0, 0, dataBytes);
    return mat;
}

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