Java Code Examples for org.opencv.imgproc.Imgproc#GaussianBlur

public void sharpenImage() {
        String fileName = "SharpnessExample2.png";
        fileName = "smoothCat.jpg";
        fileName = "blurredText.jpg";
        fileName = "Blurred Text3.jpg";
        try {
//            Not working that well !!!
            Mat source = Imgcodecs.imread(fileName,
                    //                    Imgcodecs.CV_LOAD_IMAGE_COLOR);
                    Imgcodecs.CV_LOAD_IMAGE_GRAYSCALE);
            Mat destination = new Mat(source.rows(), source.cols(), source.type());
            Imgproc.GaussianBlur(source, destination, new Size(0, 0), 10);
            // The following was used witht he cat
//            Core.addWeighted(source, 1.5, destination, -0.75, 0, destination);
//            Core.addWeighted(source, 2.5, destination, -1.5, 0, destination);
            Core.addWeighted(source, 1.5, destination, -0.75, 0, destination);
            Imgcodecs.imwrite("sharpenedCat.jpg", destination);
        } catch (Exception ex) {
            ex.printStackTrace();
        }
    }
 

public static Mat transEstimate(Mat img, int patchSz, double[] airlight, double lambda, double fTrans, 
		int r, double eps, double gamma) {
	int rows = img.rows();
	int cols = img.cols();
	List<Mat> bgr = new ArrayList<>();
	Core.split(img, bgr);
	int type = bgr.get(0).type();
	// calculate the transmission map
	Mat T = computeTrans(img, patchSz, rows, cols, type, airlight, lambda, fTrans);
	// refine the transmission map
	img.convertTo(img, CvType.CV_8UC1);
	Mat gray = new Mat();
	Imgproc.cvtColor(img, gray, Imgproc.COLOR_BGR2GRAY);
	gray.convertTo(gray, CvType.CV_32F);
	Core.divide(gray, new Scalar(255.0), gray);
	T = Filters.GuidedImageFilter(gray, T, r, eps);
	Mat Tsmooth = new Mat();
	Imgproc.GaussianBlur(T, Tsmooth, new Size(81, 81), 40);
	Mat Tdetails = new Mat();
	Core.subtract(T, Tsmooth, Tdetails);
	Core.multiply(Tdetails, new Scalar(gamma), Tdetails);
	Core.add(Tsmooth, Tdetails, T);
	return T;
}
 

/**
 * Process a image and return a mask
 * @param input - Input image to process
 * @param mask - Output mask
 */
@Override
public void process(Mat input, Mat mask) {
    // Copy the input to working mat
    workingMat = input.clone();
    // Convert the input to HSV color space
    Imgproc.cvtColor(workingMat,workingMat,Imgproc.COLOR_RGB2HSV_FULL);

    // Blur the imgae
    Imgproc.GaussianBlur(workingMat,workingMat,new Size(5,5),0);

    // Run a inRange mask using the color and range
    Scalar lower = new Scalar(perfect.val[0] - (range.val[0]/2), perfect.val[1] - (range.val[1]/2),perfect.val[2] - (range.val[2]/2));
    Scalar upper = new Scalar(perfect.val[0] + (range.val[0]/2), perfect.val[1] + (range.val[1]/2),perfect.val[2] + (range.val[2]/2));
    Core.inRange(workingMat,lower,upper,mask);
}
 

public Bitmap convertToBlackWhite(Bitmap compressImage)
{
    Log.d("CV", "Before converting to black");
    Mat imageMat = new Mat();
    Utils.bitmapToMat(compressImage, imageMat);
    Imgproc.cvtColor(imageMat, imageMat, Imgproc.COLOR_BGR2GRAY);
    Imgproc.GaussianBlur(imageMat, imageMat, new Size(3, 3), 0);
    //Imgproc.adaptiveThreshold(imageMat, imageMat, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY_INV, 5, 4);
    //Imgproc.medianBlur(imageMat, imageMat, 3);
    Imgproc.threshold(imageMat, imageMat, 0, 255, Imgproc.THRESH_OTSU);

    Bitmap newBitmap = compressImage;
    Utils.matToBitmap(imageMat, newBitmap);
    imageView.setImageBitmap(newBitmap);
    Log.d("CV", "After converting to black");


    return newBitmap;

}
 

@Override
protected void onActivityResult(int requestCode, int resultCode, Intent imageReturnedIntent) {
    //Put it there, just in case:)
    super.onActivityResult(requestCode, resultCode, imageReturnedIntent);

    switch(requestCode) {
        case SELECT_PHOTO:
            if(resultCode == RESULT_OK && read_external_storage_granted){
                try {
                    final Uri imageUri = imageReturnedIntent.getData();
                    final InputStream imageStream = getContentResolver().openInputStream(imageUri);
                    final Bitmap selectedImage = BitmapFactory.decodeStream(imageStream);
                    src = new Mat(selectedImage.getHeight(), selectedImage.getWidth(), CvType.CV_8UC4);
                    Utils.bitmapToMat(selectedImage, src);
                    src_gray = new Mat(selectedImage.getHeight(), selectedImage.getWidth(), CvType.CV_8UC1);
                    switch (ACTION_MODE) {
                        case HomeActivity.GAUSSIAN_BLUR:
                            Imgproc.GaussianBlur(src, src, new Size(9, 9), 0);
                            break;
                        case HomeActivity.MEAN_BLUR:
                            Imgproc.blur(src, src, new Size(9, 9));
                            break;
                        case HomeActivity.MEDIAN_BLUR:
                            Imgproc.medianBlur(src, src, 9);
                            break;
                        case HomeActivity.SHARPEN:
                            Mat kernel = new Mat(3, 3, CvType.CV_16SC1);
                            //int[] values = {0, -1, 0, -1, 5, -1, 0, -1, 0};
                            Log.d("imageType", CvType.typeToString(src.type()) + "");
                            kernel.put(0, 0, 0, -1, 0, -1, 5, -1, 0, -1, 0);
                            Imgproc.filter2D(src, src, src_gray.depth(), kernel);
                            break;
                        case HomeActivity.DILATE:
                            Imgproc.cvtColor(src, src_gray, Imgproc.COLOR_BGR2GRAY);
                            Imgproc.threshold(src_gray, src_gray, 100, 255, Imgproc.THRESH_BINARY);
                            Mat kernelDilate = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(3, 3));
                            Imgproc.dilate(src_gray, src_gray, kernelDilate);
                            Imgproc.cvtColor(src_gray, src, Imgproc.COLOR_GRAY2RGBA, 4);
                            break;
                        case HomeActivity.ERODE:
                            Imgproc.cvtColor(src, src_gray, Imgproc.COLOR_BGR2GRAY);
                            Imgproc.threshold(src_gray, src_gray, 100, 255, Imgproc.THRESH_BINARY);
                            Mat kernelErode = Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE, new Size(5, 5));
                            Imgproc.erode(src_gray, src_gray, kernelErode);
                            Imgproc.cvtColor(src_gray, src, Imgproc.COLOR_GRAY2RGBA, 4);
                            break;
                        case HomeActivity.THRESHOLD:
                            Imgproc.cvtColor(src, src_gray, Imgproc.COLOR_BGR2GRAY);
                            Imgproc.threshold(src_gray, src_gray, 100, 255, Imgproc.THRESH_BINARY);
                            Imgproc.cvtColor(src_gray, src, Imgproc.COLOR_GRAY2RGBA, 4);
                            break;
                        case HomeActivity.ADAPTIVE_THRESHOLD:
                            Imgproc.cvtColor(src, src_gray, Imgproc.COLOR_BGR2GRAY);
                            Imgproc.adaptiveThreshold(src_gray, src_gray, 255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C, Imgproc.THRESH_BINARY, 3, 0);
                            Imgproc.cvtColor(src_gray, src, Imgproc.COLOR_GRAY2RGBA, 4);
                            break;
                    }
                    Bitmap processedImage = Bitmap.createBitmap(src.cols(), src.rows(), Bitmap.Config.ARGB_8888);
                    Log.i("imageType", CvType.typeToString(src.type()) + "");
                    Utils.matToBitmap(src, processedImage);
                    ivImage.setImageBitmap(selectedImage);
                    ivImageProcessed.setImageBitmap(processedImage);
                    Log.i("process", "process done");
                } catch (FileNotFoundException e) {
                    e.printStackTrace();
                }
            }
            break;
    }
}
 

public void skinSegmentation()
{
    matrix3_skindetection = new Mat(matrix2_grabcut.size(),matrix2_grabcut.type()); 
    matrix3_skindetection.setTo(new Scalar(0,0,255));
    Mat skinMask = new Mat();
    Mat hsvMatrix = new Mat();
    
    Scalar lower = new Scalar(0,48,80);
    Scalar upper = new Scalar(20,255,255);
    
    Imgproc.cvtColor(matrix2_grabcut, hsvMatrix, Imgproc.COLOR_BGR2HSV);
    Core.inRange(hsvMatrix, lower, upper, skinMask);
    
    Mat kernel =Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE,new Size(11,11));
    Imgproc.erode(skinMask, skinMask, kernel);
    Imgproc.dilate(skinMask, skinMask, kernel);  
    
    Imgproc.GaussianBlur(skinMask,skinMask, new Size(3,3), 0);
    
    Core.bitwise_and(matrix2_grabcut, matrix2_grabcut, matrix3_skindetection,skinMask);
    Imgcodecs.imwrite(resultDirectory+skinDetectionOutput , matrix3_skindetection);
}
 

public static Rect detectBorder(Mat original){
    Mat src = original.clone();
    Log.d(TAG, "1 original: " + src.toString());

    Imgproc.GaussianBlur(src, src, new Size(3, 3), 0);
    Log.d(TAG, "2.1 --> Gaussian blur done\n blur: " + src.toString());

    Imgproc.cvtColor(src, src, Imgproc.COLOR_RGBA2GRAY);
    Log.d(TAG, "2.2 --> Grayscaling done\n gray: " + src.toString());

    Mat sobelX = new Mat();
    Mat sobelY = new Mat();

    Imgproc.Sobel(src, sobelX, CvType.CV_32FC1, 2, 0, 5, 1, 0);
    Log.d(TAG, "3.1 --> Sobel done.\n X: " + sobelX.toString());
    Imgproc.Sobel(src, sobelY, CvType.CV_32FC1, 0, 2, 5, 1, 0);
    Log.d(TAG, "3.2 --> Sobel done.\n Y: " + sobelY.toString());

    Mat sum_img = new Mat();
    Core.addWeighted(sobelX, 0.5, sobelY, 0.5, 0.5, sum_img);
    //Core.add(sobelX, sobelY, sum_img);
    Log.d(TAG, "4 --> Addition done. sum: " + sum_img.toString());

    sobelX.release();
    sobelY.release();

    Mat gray = new Mat();
    Core.normalize(sum_img, gray, 0, 255, Core.NORM_MINMAX, CvType.CV_8UC1);
    Log.d(TAG, "5 --> Normalization done. gray: " + gray.toString());
    sum_img.release();

    Mat row_proj = new Mat();
    Mat col_proj = new Mat();
    Core.reduce(gray, row_proj, 1, Core.REDUCE_AVG, CvType.CV_8UC1);
    Log.d(TAG, "6.1 --> Reduce done. row: " + row_proj.toString());

    Core.reduce(gray, col_proj, 0, Core.REDUCE_AVG, CvType.CV_8UC1);
    Log.d(TAG, "6.2 --> Reduce done. col: " + col_proj.toString());
    gray.release();

    Imgproc.Sobel(row_proj, row_proj, CvType.CV_8UC1, 0, 2);
    Log.d(TAG, "7.1 --> Sobel done. row: " + row_proj.toString());

    Imgproc.Sobel(col_proj, col_proj, CvType.CV_8UC1, 2, 0);
    Log.d(TAG, "7.2 --> Sobel done. col: " + col_proj.toString());

    Rect result = new Rect();

    int half_pos = (int) (row_proj.total()/2);
    Mat row_sub = new Mat(row_proj, new Range(0, half_pos), new Range(0, 1));
    Log.d(TAG, "8.1 --> Copy sub matrix done. row: " + row_sub.toString());
    result.y = (int) Core.minMaxLoc(row_sub).maxLoc.y;
    Log.d(TAG, "8.2 --> Minmax done. Y: " + result.y);
    row_sub.release();
    Mat row_sub2 = new Mat(row_proj, new Range(half_pos, (int) row_proj.total()), new Range(0, 1));
    Log.d(TAG, "8.3 --> Copy sub matrix done. row: " + row_sub2.toString());
    result.height = (int) (Core.minMaxLoc(row_sub2).maxLoc.y + half_pos - result.y);
    Log.d(TAG, "8.4 --> Minmax done. Height: " + result.height);
    row_sub2.release();

    half_pos = (int) (col_proj.total()/2);
    Mat col_sub = new Mat(col_proj, new Range(0, 1), new Range(0, half_pos));
    Log.d(TAG, "9.1 --> Copy sub matrix done. col: " + col_sub.toString());
    result.x = (int) Core.minMaxLoc(col_sub).maxLoc.x;
    Log.d(TAG, "9.2 --> Minmax done. X: " + result.x);
    col_sub.release();
    Mat col_sub2 = new Mat(col_proj, new Range(0, 1), new Range(half_pos, (int) col_proj.total()));
    Log.d(TAG, "9.3 --> Copy sub matrix done. col: " + col_sub2.toString());
    result.width = (int) (Core.minMaxLoc(col_sub2).maxLoc.x + half_pos - result.x);
    Log.d(TAG, "9.4 --> Minmax done. Width: " + result.width);
    col_sub2.release();

    row_proj.release();
    col_proj.release();
    src.release();

    return result;
}
 

public static Mat Saliency(Mat img) {
	// blur image with a 3x3 or 5x5 Gaussian filter
	Mat gfbgr = new Mat();
	Imgproc.GaussianBlur(img, gfbgr, new Size(3, 3), 3);
	// Perform sRGB to CIE Lab color space conversion
	Mat LabIm = new Mat();
	Imgproc.cvtColor(gfbgr, LabIm, Imgproc.COLOR_BGR2Lab);
	// Compute Lab average values (note that in the paper this average is found from the
	// un-blurred original image, but the results are quite similar)
	List<Mat> lab = new ArrayList<>();
	Core.split(LabIm, lab);
	Mat l = lab.get(0);
	l.convertTo(l, CvType.CV_32F);
	Mat a = lab.get(1);
	a.convertTo(a, CvType.CV_32F);
	Mat b = lab.get(2);
	b.convertTo(b, CvType.CV_32F);
	double lm = Core.mean(l).val[0];
	double am = Core.mean(a).val[0];
	double bm = Core.mean(b).val[0];
	// Finally compute the saliency map
	Mat sm = Mat.zeros(l.rows(), l.cols(), l.type());
	Core.subtract(l, new Scalar(lm), l);
	Core.subtract(a, new Scalar(am), a);
	Core.subtract(b, new Scalar(bm), b);
	Core.add(sm, l.mul(l), sm);
	Core.add(sm, a.mul(a), sm);
	Core.add(sm, b.mul(b), sm);
	return sm;
}
 

public static Mat Saliency(Mat img) {
	// blur image with a 3x3 or 5x5 Gaussian filter
	Mat gfbgr = new Mat();
	Imgproc.GaussianBlur(img, gfbgr, new Size(3, 3), 3);
	// Perform sRGB to CIE Lab color space conversion
	Mat LabIm = new Mat();
	Imgproc.cvtColor(gfbgr, LabIm, Imgproc.COLOR_BGR2Lab);
	// Compute Lab average values (note that in the paper this average is found from the
	// un-blurred original image, but the results are quite similar)
	List<Mat> lab = new ArrayList<Mat>();
	Core.split(LabIm, lab);
	Mat l = lab.get(0);
	l.convertTo(l, CvType.CV_32F);
	Mat a = lab.get(1);
	a.convertTo(a, CvType.CV_32F);
	Mat b = lab.get(2);
	b.convertTo(b, CvType.CV_32F);
	double lm = Core.mean(l).val[0];
	double am = Core.mean(a).val[0];
	double bm = Core.mean(b).val[0];
	// Finally compute the saliency map
	Mat sm = Mat.zeros(l.rows(), l.cols(), l.type());
	Core.subtract(l, new Scalar(lm), l);
	Core.subtract(a, new Scalar(am), a);
	Core.subtract(b, new Scalar(bm), b);
	Core.add(sm, l.mul(l), sm);
	Core.add(sm, a.mul(a), sm);
	Core.add(sm, b.mul(b), sm);
	return sm;
}
 

public PreProcessor preprocessImage(PreProcessor preProcessor) {
    List<Mat> images = preProcessor.getImages();
    List<Mat> processed = new ArrayList<Mat>();
    for (Mat img : images){
        Mat gauss1 = new Mat();
        Mat gauss2 = new Mat();
        Imgproc.GaussianBlur(img, gauss1, size1, sigma1);
        Imgproc.GaussianBlur(img, gauss2, size2, sigma2);
        Core.absdiff(gauss1, gauss2, img);
        processed.add(img);
    }
    preProcessor.setImages(processed);
    return preProcessor;
}
 

@Override
public void process(Mat input, Mat mask) {
    Imgproc.cvtColor(input, input, Imgproc.COLOR_RGB2YCrCb);

    // Blur it
    Imgproc.GaussianBlur(input,input,new Size(5,5),0);
    // Run in range check
    Core.inRange(input, lower, upper, mask);
    input.release();
}
 

/**
 * Process a image and return a mask
 * @param input - Input image to process
 * @param mask - Output mask
 */
@Override
public void process(Mat input, Mat mask) {
    // Convert the input to HSV
    Imgproc.cvtColor(input,input,Imgproc.COLOR_RGB2HSV_FULL);

    // Blur it
    Imgproc.GaussianBlur(input,input,new Size(5,5),0);
    // Run in range check
    Core.inRange(input,lower,upper,mask);
    input.release();
}
 

/**
	 * 
	 * @param image
	 * @param T
	 * @param patch OUTPUT
	 * @param patchSize
	 */
	void generate(final Mat image, final Mat T, Mat patch, Size patchSize, final RNG rng){
	    patch.create( patchSize, image.type() );
	    if( backgroundMin != backgroundMax ) {
	    	Core.randu(patch, backgroundMin, backgroundMax);
	    	// TODO if that null scalar OK or should it be new Scalar(0) ?
	    	Imgproc.warpAffine(image, patch, T, patchSize, Imgproc.INTER_LINEAR, Imgproc.BORDER_TRANSPARENT, null);
	    } else {
	    	Imgproc.warpAffine(image, patch, T, patchSize, Imgproc.INTER_LINEAR, Imgproc.BORDER_CONSTANT, new Scalar(backgroundMin));
	    }

	    int ksize = randomBlur ? rng.nextInt() % 9 - 5 : 0;
	    if( ksize > 0 ) {
	        ksize = ksize * 2 + 1;
	        Imgproc.GaussianBlur(patch, patch, new Size(ksize, ksize), 0, 0);
	    }

	    if( noiseRange > 0 ) {
	        final Mat noise = new Mat(patchSize, image.type());
	        int delta = (image.depth() == CvType.CV_8U ? 128 : (image.depth() == CvType.CV_16U ? 32768 : 0));
	        Core.randn(noise, delta, noiseRange);
	        
	        // TODO this was different !!
	        Core.addWeighted(patch, 1, noise, 1, -delta, patch);
	        
//	        if( backgroundMin != backgroundMax )
//	            addWeighted(patch, 1, noise, 1, -delta, patch);
//	        else
//	        {
//	            for( int i = 0; i < patchSize.height; i++ )
//	            {
//	                uchar* prow = patch.ptr<uchar>(i);
//	                const uchar* nrow =  noise.ptr<uchar>(i);
//	                for( int j = 0; j < patchSize.width; j++ )
//	                    if( prow[j] != backgroundMin )
//	                        prow[j] = saturate_cast<uchar>(prow[j] + nrow[j] - delta);
//	            }
//	        }
	    }		
	}
 

public static Mat sharpenImage(Mat src){
    Mat sharped = new Mat();
    Imgproc.GaussianBlur(src, sharped, new Size(0, 0), 3);
    Core.addWeighted(src, 1.5, sharped, -0.5, 0, sharped);

    return sharped;
}
 

@Override
public Mat process(@NonNull Mat frame) {
    if (frame.empty()) {
        Log.e("Invalid input frame.");
        return null;
    }
    Mat tmp = frame.clone();
    // Apply gaussian blur
    for (int i = 0; i < REPETITIONS; i++) {
        Imgproc.GaussianBlur(tmp, tmp, new Size(KERNEL_SIZE, KERNEL_SIZE), 0);
    }
    return tmp;
}
 

/**
 * Structure the classifier into 3 stages:
 * a) patch variance
 * b) ensemble of ferns classifier
 * c) nearest neighbour
 */
private Pair<List<DetectionStruct>, List<DetectionStruct>> detect(final Mat frame){
	Log.i(Util.TAG, "[DETECT]");
	
	final List<DetectionStruct> fernClassDetected = new ArrayList<Tld.DetectionStruct>(); //dt
	final List<DetectionStruct> nnMatches = new ArrayList<Tld.DetectionStruct>(); //dbb
	
	
	// 0. Cleaning
	_boxClusterMap.clear();
	
	// 1. DETECTION
	final Mat img = new Mat(frame.rows(), frame.cols(), CvType.CV_8U);
	updateIntegralImgs(frame);
	Imgproc.GaussianBlur(frame, img, new Size(9, 9), 1.5);
	
	// Apply the Variance filter TODO : Bottleneck
	int a=0;
	for(BoundingBox box : _grid){
		// a) speed up by doing the features/ferns check ONLY if the variance is high enough !
		if(Util.getVar(box, _iisumJava, _iisqsumJava, _iiCols) >= _var ){
			a++;
			final Mat patch = img.submat(box);
			final int[] allFernsHashCodes = _classifierFern.getAllFernsHashCodes(patch, box.scaleIdx);
			final double averagePosterior = _classifierFern.averagePosterior(allFernsHashCodes);
			_fernDetectionNegDataForLearning.put(box,  allFernsHashCodes);// store for later use in learning
			
			// b)
			if(averagePosterior > _classifierFern.getFernPosThreshold()){
				fernClassDetected.add(new DetectionStruct(box, allFernsHashCodes, averagePosterior, patch));
			}
		}
	}
	
	Log.i(Util.TAG, a + " Bounding boxes passed the variance filter (" + _var + ")");
	Log.i(Util.TAG, fernClassDetected.size() + " Initial detected from Fern Classifier");
	if(fernClassDetected.size() == 0){
		Log.i(Util.TAG, "[DETECT END]");
		return null;
	}
	
	// keep only the best
	Util.keepBestN(fernClassDetected, MAX_DETECTED, new Comparator<DetectionStruct>() {
		@Override
		public int compare(DetectionStruct detS1, DetectionStruct detS2) {
			return Double.compare(detS1.averagePosterior, detS2.averagePosterior);
		}
	});
	
	
	// 2. MATCHING using the NN classifier  c)
	for(DetectionStruct detStruct : fernClassDetected){
		// update detStruct.patch to params.patch_size and normalise it
		Mat pattern = new Mat();
		resizeZeroMeanStdev(detStruct.patch, pattern, _params.patch_size);
		detStruct.nnConf = _classifierNN.nnConf(pattern);
		
		Log.i(Util.TAG, "NNConf: " + detStruct.nnConf.relativeSimilarity + " / " + detStruct.nnConf.conservativeSimilarity + " Threshold: " + _classifierNN.getNNThreshold());
		// only keep valid boxes
		if(detStruct.nnConf.relativeSimilarity > _classifierNN.getNNThreshold()){
			nnMatches.add(detStruct); 
		}
	}
	
	Log.i(Util.TAG, "[DETECT END]");
	return new Pair<List<DetectionStruct>, List<DetectionStruct>>(fernClassDetected, nnMatches);
}
 

public static  double findMarkerWidth(String imgPath){
    Mat frame = Highgui.imread(imgPath);
    Mat gscale = new Mat();
    Mat blur = new Mat();
    Mat edged = new Mat();

    // convert the image to grayscale, blur it, and detect edges
    if(frame.channels()>1)
        Imgproc.cvtColor(frame, gscale, Imgproc.COLOR_BGR2GRAY);
    else
        gscale = frame;

    Imgproc.GaussianBlur(gscale, blur, new Size(5, 5), 0);
    Imgproc.Canny(blur, edged, 35, 125);

    // find the contours in the edged image and keep the largest one;
    // we'll assume that this is our piece of paper in the image
    List<MatOfPoint> contours = new ArrayList<>();
    Mat hierarchy = new Mat(edged.width(), edged.height(), CvType.CV_8UC1);
    Imgproc.findContours(edged.clone(), contours, hierarchy, Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
    int max_idx = 0;

    // if any contour exist...
    if (hierarchy.size().height > 0 && hierarchy.size().width > 0)
    {
        double max_area = 0;
        double area;
        // find the contour with largest area
        for (int idx = 0; idx >= 0; idx = (int) hierarchy.get(0, idx)[0])
        {
            area = Imgproc.contourArea(contours.get(idx));
            if(area > max_area){
                max_area = area;
                max_idx = idx;
            }
            Imgproc.drawContours(frame, contours, idx, new Scalar(0, 0, 255));
        }

        //Riz: Save File
        //Imgproc.drawContours(frame, contours, max_idx, new Scalar(250, 0, 0));
        byte[] bytes = new byte[ frame.rows() * frame.cols() * frame.channels() ];


        File file = new File(CameraActivity.activity.getExternalFilesDir(null), "pic_contour"+ Integer.toString(pic_count) + ".jpg");
        pic_count++;

        Boolean bool = null;
        String filename = file.toString();
        bool = Highgui.imwrite(filename, frame);

        if (bool == true)
            Log.d(LOG_TAG, "SUCCESS writing image to external storage");
        else
            Log.d(LOG_TAG, "Fail writing image to external storage");

        Log.i(LOG_TAG, "Max Area: " + Double.toString(max_area));
    }
    else{
        Log.e(LOG_TAG, "No Contour Found!");
    }

    MatOfPoint2f newPoint = new MatOfPoint2f(contours.get(max_idx).toArray());

    return Imgproc.arcLength(newPoint, true);
}
 

/**
 * Process a image and return a mask
 * @param input - Input image to process
 * @param mask - Output mask
 */
@Override
public void process(Mat input, Mat mask) {
    channels = new ArrayList<>();

    switch(color){
        case RED:
            if(threshold == -1){
                threshold = 164;
            }

            Imgproc.cvtColor(input, input, Imgproc.COLOR_RGB2Lab);
            Imgproc.GaussianBlur(input,input,new Size(3,3),0);
            Core.split(input, channels);
            Imgproc.threshold(channels.get(1), mask, threshold, 255, Imgproc.THRESH_BINARY);
            break;
        case BLUE:
            if(threshold == -1){
                threshold = 145;
            }

            Imgproc.cvtColor(input, input, Imgproc.COLOR_RGB2YUV);
            Imgproc.GaussianBlur(input,input,new Size(3,3),0);
            Core.split(input, channels);
            Imgproc.threshold(channels.get(1), mask, threshold, 255, Imgproc.THRESH_BINARY);
            break;
        case WHITE:
            if(threshold == -1) {
                threshold = 150;
            }

            Imgproc.cvtColor(input, input, Imgproc.COLOR_RGB2Lab);
            Imgproc.GaussianBlur(input,input,new Size(3,3),0);
            Core.split(input, channels);
            Core.inRange(channels.get(0), new Scalar(threshold, 150, 40), new Scalar(255, 150, 150), mask);
            break;
        case YELLOW:
            if(threshold == -1){
                threshold = 70;
            }
            
            Mat lab = new Mat(input.size(), 0);
            Imgproc.cvtColor(input, lab, Imgproc.COLOR_RGB2Lab);
            Mat temp = new Mat();
            Core.inRange(input, new Scalar(0,0,0), new Scalar(255,255,164), temp);
            Mat mask2 = new Mat(input.size(), 0);
            temp.copyTo(mask2);
            input.copyTo(input, mask2);
            mask2.release();
            temp.release();
            lab.release();
            
            Imgproc.cvtColor(input, input, Imgproc.COLOR_RGB2YUV);
            Imgproc.GaussianBlur(input,input,new Size(3,3),0);
            Core.split(input, channels);
            if(channels.size() > 0){
                Imgproc.threshold(channels.get(1), mask, threshold, 255, Imgproc.THRESH_BINARY_INV);
            }

            break;
    }

    for(int i=0;i<channels.size();i++){
        channels.get(i).release();
    }

    input.release();

}
 

private void makeDefault() {
            HighlightView hv = new HighlightView(mImageView);

            int width = mBitmap.getWidth();
            int height = mBitmap.getHeight();

            Rect imageRect = new Rect(0, 0, width, height);

            // make the default size about 4/5 of the width or height
//            int cropWidth = Math.min(width, height) * 4 / 5;
//            int cropHeight = cropWidth;
            int cropWidth = width;
            int cropHeight = height;

            if (mAspectX != 0 && mAspectY != 0) {
                if (mAspectX > mAspectY) {
                	// �����辩缉���
                    cropHeight = cropWidth * mAspectY ;// mAspectX;
                } else {
                    cropWidth = cropHeight * mAspectX ;// mAspectY;
                }
            }

            int x = (width - cropWidth) / 2;
            int y = (height - cropHeight) / 2;
            
            Mat imgSource = new Mat();
            Utils.bitmapToMat(mBitmap, imgSource);
            //convert the image to black and white 
            Imgproc.cvtColor(imgSource, imgSource, Imgproc.COLOR_BGR2GRAY);
            //convert the image to black and white does (8 bit)
            Imgproc.Canny(imgSource, imgSource, 50, 50);

            //apply gaussian blur to smoothen lines of dots
            Imgproc.GaussianBlur(imgSource, imgSource, new  org.opencv.core.Size(5, 5), 5);

            //find the contours
            List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
            Imgproc.findContours(imgSource, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);

            double maxArea = -1;
            int maxAreaIdx = -1;
            Log.d("size",Integer.toString(contours.size()));
            MatOfPoint temp_contour = contours.get(0); //the largest is at the index 0 for starting point
            MatOfPoint2f approxCurve = new MatOfPoint2f();
            MatOfPoint largest_contour = contours.get(0);
            //largest_contour.ge
            List<MatOfPoint> largest_contours = new ArrayList<MatOfPoint>();
            //Imgproc.drawContours(imgSource,contours, -1, new Scalar(0, 255, 0), 1);

            for (int idx = 0; idx < contours.size(); idx++) {
                temp_contour = contours.get(idx);
                double contourarea = Imgproc.contourArea(temp_contour);
                //compare this contour to the previous largest contour found
                if (contourarea > maxArea) {
                    //check if this contour is a square
                    MatOfPoint2f new_mat = new MatOfPoint2f( temp_contour.toArray() );
                    int contourSize = (int)temp_contour.total();
                    MatOfPoint2f approxCurve_temp = new MatOfPoint2f();
                    Imgproc.approxPolyDP(new_mat, approxCurve_temp, contourSize*0.05, true);
                    if (approxCurve_temp.total() == 4) {
                        maxArea = contourarea;
                        maxAreaIdx = idx;
                        approxCurve=approxCurve_temp;
                        largest_contour = temp_contour;
                    }
                }
            }

           Imgproc.cvtColor(imgSource, imgSource, Imgproc.COLOR_BayerBG2RGB);
           double[] temp_double;
           float x1, y1, x2, y2;
           temp_double = approxCurve.get(0,0);       
           Point p1 = new Point(temp_double[0], temp_double[1]);
           x1 = (float)temp_double[0];
           y1 = (float)temp_double[1];
           //Core.circle(imgSource,p1,55,new Scalar(0,0,255));
           //Imgproc.warpAffine(sourceImage, dummy, rotImage,sourceImage.size());
           temp_double = approxCurve.get(1,0);       
           Point p2 = new Point(temp_double[0], temp_double[1]);
          // Core.circle(imgSource,p2,150,new Scalar(255,255,255));
           temp_double = approxCurve.get(2,0);       
           Point p3 = new Point(temp_double[0], temp_double[1]);
           x2 = (float)temp_double[0];
           y2 = (float)temp_double[1];
           //Core.circle(imgSource,p3,200,new Scalar(255,0,0));
           temp_double = approxCurve.get(3,0);       
           Point p4 = new Point(temp_double[0], temp_double[1]);

            RectF cropRect = new RectF(x, y, x + cropWidth, y + cropHeight);
            //RectF cropRect = new RectF(x1, y1, x2, y2);
         // �����辩缉���
            
            hv.setup(mImageMatrix, imageRect, cropRect, mCircleCrop,false
                     /*mAspectX != 0 && mAspectY != 0*/);
            mImageView.add(hv);
        }
 

/**
 * Blur the image using a Gaussian blur
 *
 * @param img    Image matrix
 * @param amount Amount >= 0
 */
public static void blur(Mat img, int amount) {
    Imgproc.GaussianBlur(img, img, new Size(2 * amount + 1, 2 * amount + 1), 0, 0);
}