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:com.sikulix.core.Finder.java

License:Open Source License

public boolean hasChanges(Mat base, Mat current) {
    int PIXEL_DIFF_THRESHOLD = 5;
    int IMAGE_DIFF_THRESHOLD = 5;
    Mat bg = new Mat();
    Mat cg = new Mat();
    Mat diff = new Mat();
    Mat tdiff = new Mat();

    Imgproc.cvtColor(base, bg, Imgproc.COLOR_BGR2GRAY);
    Imgproc.cvtColor(current, cg, Imgproc.COLOR_BGR2GRAY);
    Core.absdiff(bg, cg, diff);//w  w  w  . java2 s  .  co m
    Imgproc.threshold(diff, tdiff, PIXEL_DIFF_THRESHOLD, 0.0, Imgproc.THRESH_TOZERO);
    if (Core.countNonZero(tdiff) <= IMAGE_DIFF_THRESHOLD) {
        return false;
    }

    Imgproc.threshold(diff, diff, PIXEL_DIFF_THRESHOLD, 255, Imgproc.THRESH_BINARY);
    Imgproc.dilate(diff, diff, new Mat());
    Mat se = Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE, new Size(5, 5));
    Imgproc.morphologyEx(diff, diff, Imgproc.MORPH_CLOSE, se);

    List<MatOfPoint> points = new ArrayList<MatOfPoint>();
    Mat contours = new Mat();
    Imgproc.findContours(diff, points, contours, Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
    int n = 0;
    for (Mat pm : points) {
        log.trace("(%d) %s", n++, pm);
        printMatI(pm);
    }
    log.trace("contours: %s", contours);
    printMatI(contours);
    return true;
}

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From source file:com.sikulix.core.SXElement.java

License:Open Source License

protected static Mat makeMat(BufferedImage bImg) {
    Mat aMat = new Mat();
    if (bImg.getType() == BufferedImage.TYPE_INT_RGB) {
        log.trace("makeMat: INT_RGB (%dx%d)", bImg.getWidth(), bImg.getHeight());
        int[] data = ((DataBufferInt) bImg.getRaster().getDataBuffer()).getData();
        ByteBuffer byteBuffer = ByteBuffer.allocate(data.length * 4);
        IntBuffer intBuffer = byteBuffer.asIntBuffer();
        intBuffer.put(data);/* w w w  . jav  a 2  s .  c om*/
        aMat = new Mat(bImg.getHeight(), bImg.getWidth(), CvType.CV_8UC4);
        aMat.put(0, 0, byteBuffer.array());
        Mat oMatBGR = new Mat(bImg.getHeight(), bImg.getWidth(), CvType.CV_8UC3);
        Mat oMatA = new Mat(bImg.getHeight(), bImg.getWidth(), CvType.CV_8UC1);
        java.util.List<Mat> mixIn = new ArrayList<Mat>(Arrays.asList(new Mat[] { aMat }));
        java.util.List<Mat> mixOut = new ArrayList<Mat>(Arrays.asList(new Mat[] { oMatA, oMatBGR }));
        //A 0 - R 1 - G 2 - B 3 -> A 0 - B 1 - G 2 - R 3
        Core.mixChannels(mixIn, mixOut, new MatOfInt(0, 0, 1, 3, 2, 2, 3, 1));
        return oMatBGR;
    } else if (bImg.getType() == BufferedImage.TYPE_3BYTE_BGR) {
        log.error("makeMat: 3BYTE_BGR (%dx%d)", bImg.getWidth(), bImg.getHeight());
        byte[] data = ((DataBufferByte) bImg.getRaster().getDataBuffer()).getData();
        aMat = new Mat(bImg.getHeight(), bImg.getWidth(), CvType.CV_8UC3);
        aMat.put(0, 0, data);
        return aMat;
    } else if (bImg.getType() == BufferedImage.TYPE_4BYTE_ABGR) {
        log.trace("makeMat: TYPE_4BYTE_ABGR (%dx%d)", bImg.getWidth(), bImg.getHeight());
        byte[] data = ((DataBufferByte) bImg.getRaster().getDataBuffer()).getData();
        aMat = new Mat(bImg.getHeight(), bImg.getWidth(), CvType.CV_8UC4);
        aMat.put(0, 0, data);
        Mat oMatBGR = new Mat(bImg.getHeight(), bImg.getWidth(), CvType.CV_8UC3);
        Mat oMatA = new Mat(bImg.getHeight(), bImg.getWidth(), CvType.CV_8UC1);
        java.util.List<Mat> mixIn = new ArrayList<Mat>(Arrays.asList(new Mat[] { aMat }));
        java.util.List<Mat> mixOut = new ArrayList<Mat>(Arrays.asList(new Mat[] { oMatA, oMatBGR }));
        //A 0 - R 1 - G 2 - B 3 -> A 0 - B 1 - G 2 - R 3
        Core.mixChannels(mixIn, mixOut, new MatOfInt(0, 0, 1, 1, 2, 2, 3, 3));
        return oMatBGR;
    } else {
        log.error("makeMat: Type not supported: %d (%dx%d)", bImg.getType(), bImg.getWidth(), bImg.getHeight());
    }
    return aMat;
}

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From source file:com.sikulix.core.SXElement.java

License:Open Source License

public static BufferedImage getBufferedImage(Mat mat, String type) {
    BufferedImage bImg = null;/* w  ww.  j a va 2 s.c om*/
    MatOfByte bytemat = new MatOfByte();
    if (SX.isNull(mat)) {
        mat = new Mat();
    }
    Imgcodecs.imencode(type, mat, bytemat);
    byte[] bytes = bytemat.toArray();
    InputStream in = new ByteArrayInputStream(bytes);
    try {
        bImg = ImageIO.read(in);
    } catch (IOException ex) {
        log.error("getBufferedImage: %s error(%s)", mat, ex.getMessage());
    }
    return bImg;
}

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From source file:com.sikulix.core.Visual.java

License:Open Source License

protected byte[] getImageBytes(String dotType) {
    MatOfByte bytemat = new MatOfByte();
    if (SX.isNull(content)) {
        content = new Mat();
    }/*www.java2 s  . com*/
    Highgui.imencode(dotType, content, bytemat);
    return bytemat.toArray();
}

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From source file:com.superbool.easylpr.model.Transformation.java

public Mat remapSmallPointstoCrop(Mat smallMat, Mat transformationMatrix) {
    Mat remappedMat = new Mat();

    Core.perspectiveTransform(smallMat, remappedMat, transformationMatrix);

    return remappedMat;
}

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From source file:com.trandi.opentld.tld.Tld.java

License:Apache License

private TrackingStruct track(final Mat lastImg, final Mat currentImg, final BoundingBox lastBox) {
    Log.i(Util.TAG, "[TRACK]");

    // Generate points
    final Point[] lastPoints = lastBox.points();
    if (lastPoints.length == 0) {
        Log.e(Util.TAG, "Points not generated from lastBox: " + lastBox);
        return null;
    }//from   w  w w. j a  v a  2s  .com

    // Frame-to-frame tracking with forward-backward error checking
    final Pair<Point[], Point[]> trackedPoints = _tracker.track(lastImg, currentImg, lastPoints);
    if (trackedPoints == null) {
        Log.e(Util.TAG, "No points could be tracked.");
        return null;
    }
    if (_tracker.getMedianErrFB() > _params.tracker_stability_FBerrMax) {
        Log.w(Util.TAG, "TRACKER too unstable. FB Median error: " + _tracker.getMedianErrFB() + " > "
                + _params.tracker_stability_FBerrMax);
        // return null;  // we hope the detection will find the pattern again
    }

    // bounding box prediction
    final BoundingBox predictedBB = lastBox.predict(trackedPoints.first, trackedPoints.second);
    if (predictedBB.x > currentImg.cols() || predictedBB.y > currentImg.rows() || predictedBB.br().x < 1
            || predictedBB.br().y < 1) {
        Log.e(Util.TAG, "TRACKER Predicted bounding box out of range !");
        return null;
    }

    // estimate Confidence
    Mat pattern = new Mat();
    try {
        resizeZeroMeanStdev(currentImg.submat(predictedBB.intersect(currentImg)), pattern, _params.patch_size);
    } catch (Throwable t) {
        Log.e(Util.TAG, "PredBB when failed: " + predictedBB);
    }
    //Log.i(Util.TAG, "Confidence " + pattern.dump());      

    //Conservative Similarity
    final NNConfStruct nnConf = _classifierNN.nnConf(pattern);
    Log.i(Util.TAG, "Tracking confidence: " + nnConf.conservativeSimilarity);

    Log.i(Util.TAG, "[TRACK END]");
    return new TrackingStruct(nnConf.conservativeSimilarity, predictedBB, trackedPoints.first,
            trackedPoints.second);
}

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From source file:com.trandi.opentld.tld.Tld.java

License:Apache License

private boolean learn(final Mat img, final List<DetectionStruct> fernClassDetected) {
    Log.i(Util.TAG, "[LEARN]");
    Mat pattern = new Mat();
    final double stdev = resizeZeroMeanStdev(img.submat(_lastbox.intersect(img)), pattern, _params.patch_size);
    final NNConfStruct confStruct = _classifierNN.nnConf(pattern);

    if (confStruct.relativeSimilarity < 0.5) {
        Log.w(Util.TAG, "Fast change, NOT learning");
        return false;
    }//from   www .  j  a  v  a  2s.  c  o  m
    if (Math.pow(stdev, 2) < _var) {
        Log.w(Util.TAG, "Low variance, NOT learning");
        return false;
    }
    if (confStruct.isin.inNegSet) {
        Log.w(Util.TAG, "Patch in negative data, NOT learning");
        return false;
    }

    // Data generation
    _grid.updateGoodBadBoxes(_lastbox, _params.num_closest_update);
    if (_grid.getGoodBoxes().length > 0) {
        generatePositiveData(img, _params.num_warps_update, _grid);
    } else {
        Log.w(Util.TAG, "NO good boxes, NOT learning.");
        return false;
    }

    // TODO why don't we learn from the GOOD boxes too !?
    final List<Pair<int[], Boolean>> fernExamples = new ArrayList<Util.Pair<int[], Boolean>>(_pFerns);
    for (BoundingBox badBox : _grid.getBadBoxes()) {
        final int[] allFernsHashCodes = _fernDetectionNegDataForLearning.get(badBox);
        if (allFernsHashCodes != null) {
            // these are NEGATIVE examples !
            fernExamples.add(new Pair<int[], Boolean>(allFernsHashCodes, false));
        }
    }

    final List<Mat> nnExamples = new ArrayList<Mat>();
    if (fernClassDetected != null) {
        for (DetectionStruct detStruct : fernClassDetected) {
            if (_lastbox.calcOverlap(detStruct.detectedBB) < Grid.BAD_OVERLAP) {
                nnExamples.add(detStruct.patch);
            }
        }
    }

    // Classifiers update
    _classifierFern.trainF(fernExamples, 2);
    _classifierNN.trainNN(_pExample, _nExamples);

    Log.i(Util.TAG, "[LEARN END]");
    return true;
}

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From source file:com.trandi.opentld.tld.Tld.java

License:Apache License

/** Inputs:
 * - Image/*from  w  w  w  .  j  a v  a  2  s .co m*/
 * - bad_boxes (Boxes far from the bounding box)
 * - variance (pEx variance)
 * Outputs
 * - Negative fern features (nFerns)
 * - Negative NN examples (nExample)
 */
private Pair<List<Pair<int[], Boolean>>, List<Mat>> generateNegativeData(final Mat frame) {
    final List<Pair<int[], Boolean>> negFerns = new ArrayList<Pair<int[], Boolean>>();
    final List<Mat> negExamples = new ArrayList<Mat>();

    final List<BoundingBox> badBoxes = Arrays.asList(_grid.getBadBoxes());
    Collections.shuffle(badBoxes);
    Log.w(Util.TAG, "ST");
    // Get Fern Features of the boxes with big variance (calculated using integral images)
    for (BoundingBox badBox : badBoxes) {
        if (Util.getVar(badBox, _iisumJava, _iisqsumJava, _iiCols) >= _var * 0.5f) {
            final Mat patch = frame.submat(badBox);
            final int[] allFernsHashCodes = _classifierFern.getAllFernsHashCodes(patch, badBox.scaleIdx);
            negFerns.add(new Pair<int[], Boolean>(allFernsHashCodes, false));
        }
    }

    // select a hard coded number of negative examples
    Iterator<BoundingBox> bbIt = badBoxes.iterator();
    for (int i = 0; i < _params.num_bad_patches && bbIt.hasNext(); i++) {
        final Mat pattern = new Mat();
        final Mat patch = frame.submat(bbIt.next());
        resizeZeroMeanStdev(patch, pattern, _params.patch_size);
        negExamples.add(pattern);
    }

    Log.i(Util.TAG, "Negative examples generated. Ferns count: " + negFerns.size() + ". negEx count: "
            + negExamples.size());

    return new Pair<List<Pair<int[], Boolean>>, List<Mat>>(negFerns, negExamples);
}

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From source file:com.trandi.opentld.tld.Tld.java

License:Apache License

/**
 * Generate Positive data //w ww.j av a  2 s.  c o  m
 * Inputs: 
 * - good_boxes 
 * - best_box 
 * - bbhull
 * Outputs: 
 * - Positive fern features (pFerns) 
 * - Positive NN examples (pExample)
 */
void generatePositiveData(final Mat frame, final int numWarps, final Grid aGrid) {
    resizeZeroMeanStdev(frame.submat(aGrid.getBestBox()), _pExample, _params.patch_size);
    //Get Fern features on warped patches
    final Mat img = new Mat();
    Imgproc.GaussianBlur(frame, img, new Size(9, 9), 1.5);
    final BoundingBox bbhull = aGrid.getBBhull();
    final Mat warped = img.submat(bbhull);
    // centre of the hull
    final Point pt = new Point(bbhull.x + (bbhull.width - 1) * 0.5f, bbhull.y + (bbhull.height - 1) * 0.5f);

    _pFerns.clear();
    _pPatterns.clear();

    for (int i = 0; i < numWarps; i++) {
        if (i > 0) {
            // this is important as it introduces the necessary noise / fuziness in the initial examples such that the Fern classifier recognises similar shapes not only Exact ones ! 
            // warped is a reference to a subset of the img data, so this will affect the img object
            _patchGenerator.generate(frame, pt, warped, bbhull.size(), _rng);
        }

        final BoundingBox[] goodBoxes = aGrid.getGoodBoxes();
        for (BoundingBox goodBox : goodBoxes) {
            final Mat patch = img.submat(goodBox);
            final int[] allFernsHashCodes = _classifierFern.getAllFernsHashCodes(patch, goodBox.scaleIdx);
            _pFerns.add(new Pair<int[], Boolean>(allFernsHashCodes, true));

            //            // this will be used for display only
            //            final Mat tempPattern = new Mat();
            //            Imgproc.resize(patch, tempPattern, new Size(_params.patch_size, _params.patch_size));
            //            _pPatterns.add(tempPattern);
        }
    }

    Log.i(Util.TAG, "Positive examples generated( ferns: " + _pFerns.size() + " NN: 1/n )");
}

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From source file:com.ttolley.pongbot.opencv.CvWorker.java

@Override
protected Void doInBackground() throws Exception {
    try {/*from  ww w  . jav a2  s . c o  m*/
        //-- 2. Read the video stream  
        Mat webcam_image = new Mat();

        if (capture.isOpened()) {
            while (true) {
                capture.read(webcam_image);
                if (!webcam_image.empty()) {
                    PublishObject publishObject = new PublishObject(webcam_image);
                    for (Map.Entry<FilterType, Filter> entry : filters.entrySet()) {
                        Mat hsv_image = new Mat();
                        Mat thresholded = new Mat();
                        Filter filter = entry.getValue();
                        // One way to select a range of colors by Hue  
                        Imgproc.cvtColor(webcam_image, hsv_image, Imgproc.COLOR_BGR2HSV);
                        Core.inRange(hsv_image, filter.hsv_min, filter.hsv_max, thresholded);
                        // Morph open
                        final Size erodeSizeObj = new Size(filter.erodeSize, filter.erodeSize);
                        final Size dilateSizeObj = new Size(filter.dilateSize, filter.dilateSize);
                        Imgproc.erode(thresholded, thresholded,
                                Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE, erodeSizeObj));
                        Imgproc.dilate(thresholded, thresholded,
                                Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE, erodeSizeObj));
                        // Morph close
                        Imgproc.dilate(thresholded, thresholded,
                                Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE, dilateSizeObj));
                        Imgproc.erode(thresholded, thresholded,
                                Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE, dilateSizeObj));

                        Mat temp = new Mat();
                        thresholded.copyTo(temp);
                        List<MatOfPoint> contours = new ArrayList();
                        Mat heirarchy = new Mat();
                        Imgproc.findContours(temp, contours, heirarchy, Imgproc.RETR_TREE,
                                Imgproc.CHAIN_APPROX_SIMPLE);
                        FilteredObject.Target largestTarget = findTarget(contours, webcam_image, filter);
                        publishObject.addObject(entry.getKey(), new FilteredObject(largestTarget, thresholded));
                    }
                    publish(publishObject);

                } else {
                    System.out.println(" --(!) No captured frame -- Break!");
                    break;
                }
            }
        }
    } catch (Exception ex) {
        System.out.println("Unable to loop");
        System.out.println(getStackTrace(ex));
    }
    return null;
}

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