Java Code Examples for org.opencv.core.Core.multiply()

The following are Jave code examples for showing how to use multiply() of the org.opencv.core.Core class. You can vote up the examples you like. Your votes will be used in our system to get more good examples.
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Example 1
Project: OptimizedImageEnhance   File: TransmissionEstimate.java   View Source Code Vote up 7 votes
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;
}
 
Example 2
Project: OptimizedImageEnhance   File: ALTMRetinex.java   View Source Code Vote up 6 votes
private static List<Mat> globalAdaptation(Mat b, Mat g, Mat r, int rows, int cols) {
	// Calculate Lw & maximum of Lw
	Mat Lw = new Mat(rows, cols, r.type());
	Core.multiply(r, new Scalar(rParam), r);
	Core.multiply(g, new Scalar(gParam), g);
	Core.multiply(b, new Scalar(bParam), b);
	Core.add(r, g, Lw);
	Core.add(Lw, b, Lw);
	double LwMax = Core.minMaxLoc(Lw).maxVal; // the maximum luminance value
	// Calculate log-average luminance and get global adaptation result
	Mat Lw_ = Lw.clone();
	Core.add(Lw_, new Scalar(0.001), Lw_);
	Core.log(Lw_, Lw_);
	double LwAver = Math.exp(Core.sumElems(Lw_).val[0] / (rows * cols));
	Mat Lg = Lw.clone();
	Core.divide(Lg, new Scalar(LwAver), Lg);
	Core.add(Lg, new Scalar(1.0), Lg);
	Core.log(Lg, Lg);
	Core.divide(Lg, new Scalar(Math.log(LwMax / LwAver + 1.0)), Lg); // Lg is the global adaptation
	List<Mat> list = new ArrayList<>();
	list.add(Lw);
	list.add(Lg);
	return list;
}
 
Example 3
Project: MOAAP   File: MainActivity.java   View Source Code Vote up 6 votes
public void DifferenceOfGaussian() {
    Mat grayMat = new Mat();
    Mat blur1 = new Mat();
    Mat blur2 = new Mat();

    //Converting the image to grayscale
    Imgproc.cvtColor(originalMat, grayMat, Imgproc.COLOR_BGR2GRAY);

    Imgproc.GaussianBlur(grayMat, blur1, new Size(15, 15), 5);
    Imgproc.GaussianBlur(grayMat, blur2, new Size(21, 21), 5);

    //Subtracting the two blurred images
    Mat DoG = new Mat();
    Core.absdiff(blur1, blur2, DoG);

    //Inverse Binary Thresholding
    Core.multiply(DoG, new Scalar(100), DoG);
    Imgproc.threshold(DoG, DoG, 50, 255, Imgproc.THRESH_BINARY_INV);

    //Converting Mat back to Bitmap
    Utils.matToBitmap(DoG, currentBitmap);
    imageView.setImageBitmap(currentBitmap);
}
 
Example 4
Project: fingerblox   File: ImageProcessing.java   View Source Code Vote up 5 votes
/**
 * Calculate ridge frequency.
 */
private double ridgeFrequency(Mat ridgeSegment, Mat segmentMask, Mat ridgeOrientation, Mat frequencies, int blockSize, int windowSize, int minWaveLength, int maxWaveLength) {

    int rows = ridgeSegment.rows();
    int cols = ridgeSegment.cols();

    Mat blockSegment;
    Mat blockOrientation;
    Mat frequency;

    for (int y = 0; y < rows - blockSize; y += blockSize) {
        for (int x = 0; x < cols - blockSize; x += blockSize) {
            blockSegment = ridgeSegment.submat(y, y + blockSize, x, x + blockSize);
            blockOrientation = ridgeOrientation.submat(y, y + blockSize, x, x + blockSize);
            frequency = calculateFrequency(blockSegment, blockOrientation, windowSize, minWaveLength, maxWaveLength);
            frequency.copyTo(frequencies.rowRange(y, y + blockSize).colRange(x, x + blockSize));
        }
    }

    // mask out frequencies calculated for non ridge regions
    Core.multiply(frequencies, segmentMask, frequencies, 1.0, CvType.CV_32FC1);

    // find median frequency over all the valid regions of the image.
    double medianFrequency = medianFrequency(frequencies);

    // the median frequency value used across the whole fingerprint gives a more satisfactory result
    Core.multiply(segmentMask, Scalar.all(medianFrequency), frequencies, 1.0, CvType.CV_32FC1);

    return medianFrequency;
}
 
Example 5
Project: fingerblox   File: ImageProcessing.java   View Source Code Vote up 5 votes
/**
 * Enhance the image after ridge filter.
 * Apply mask, binary threshold, thinning, ..., etc.
 */
private void enhancement(Mat source, Mat result, int blockSize, int rows, int cols, int padding) {
    Mat MatSnapShotMask = snapShotMask(rows, cols, padding);
    Mat paddedMask = imagePadding(MatSnapShotMask, blockSize);

    if (BuildConfig.DEBUG && !paddedMask.size().equals(source.size())) {
        throw new RuntimeException("Incompatible sizes of image and mask");
    }

    // apply the original mask to get rid of extras
    Core.multiply(source, paddedMask, result, 1.0, CvType.CV_8UC1);

    // apply binary threshold
    Imgproc.threshold(result, result, 0, 255, Imgproc.THRESH_BINARY);
}
 
Example 6
Project: OptimizedImageEnhance   File: BlkTransEstimate.java   View Source Code Vote up 5 votes
private static Mat preDehaze(Mat img, double a, double nTrans) {
	// nOut = ( (blkIm - a) * nTrans + 128 * a ) / 128;
	Core.subtract(img, new Scalar(a), img);
	Core.multiply(img, new Scalar(nTrans), img);
	Core.add(img, new Scalar(128.0 * a), img);
	Core.divide(img, new Scalar(128.0), img);
	return img;
}
 
Example 7
Project: OptimizedImageEnhance   File: OptimizedContrastEnhance.java   View Source Code Vote up 5 votes
@SuppressWarnings("unused")
public static Mat enhanceEachChannel(Mat image, int blkSize, int patchSize, double lambda, double eps, int krnlSize) {
	image.convertTo(image, CvType.CV_32F);
	// split image to three channels
	List<Mat> bgr = new ArrayList<>();
	Core.split(image, bgr);
	Mat bChannel = bgr.get(0);
	Mat gChannel = bgr.get(1);
	Mat rChannel = bgr.get(2);
	// obtain air-light
	double[] airlight = AirlightEstimate.estimate(image, blkSize);
	// obtain coarse transmission map and refine it for each channel
	double fTrans = 0.3;
	Mat T = TransmissionEstimate.transEstimateEachChannel(bChannel, patchSize, airlight[0], lambda, fTrans);
	Core.subtract(T, new Scalar(1.0), T);
	Core.multiply(T, new Scalar(-1.0), T);
	Mat Tb = Filters.GuidedImageFilter(bChannel, T, krnlSize, eps);
	T = TransmissionEstimate.transEstimateEachChannel(gChannel, patchSize, airlight[1], lambda, fTrans);
	Core.subtract(T, new Scalar(1.0), T);
	Core.multiply(T, new Scalar(-1.0), T);
	Mat Tg = Filters.GuidedImageFilter(gChannel, T, krnlSize, eps);
	T = TransmissionEstimate.transEstimateEachChannel(rChannel, patchSize, airlight[2], lambda, fTrans);
	Core.subtract(T, new Scalar(1.0), T);
	Core.multiply(T, new Scalar(-1.0), T);
	Mat Tr = Filters.GuidedImageFilter(rChannel, T, krnlSize, eps);
	// dehaze
	bChannel = dehaze(bChannel, Tb, airlight[0]);
	gChannel = dehaze(gChannel, Tg, airlight[1]);
	rChannel = dehaze(rChannel, Tr, airlight[2]);
	Mat outval = new Mat();
	Core.merge(new ArrayList<>(Arrays.asList(bChannel, gChannel, rChannel)), outval);
	return outval;
}
 
Example 8
Project: OptimizedImageEnhance   File: DarkChannelPriorDehaze.java   View Source Code Vote up 5 votes
private static Mat dehaze(Mat channel, Mat t, double minAtmosLight) {
	Mat t_ = new Mat();
	Core.subtract(t, new Scalar(1.0), t_);
	Core.multiply(t_, new Scalar(-1.0 * minAtmosLight), t_);
	Core.subtract(channel, t_, channel);
	Core.divide(channel, t, channel);
	return channel;
}
 
Example 9
Project: OptimizedImageEnhance   File: RemoveBackScatter.java   View Source Code Vote up 5 votes
private static Mat pyramidFuse(Mat w1, Mat w2, Mat img1, Mat img2, int level) {
	// Normalized weight
	Mat sumW = new Mat();
	Core.add(w1, w2, sumW);
	Core.divide(w1, sumW, w1);
	Core.multiply(w1, new Scalar(2.0), w1);
	Core.divide(w2, sumW, w2);
	Core.multiply(w2, new Scalar(2.0), w2);
	// Pyramid decomposition and reconstruct
	return ImgDecompose.fuseTwoImage(w1, img1, w2, img2, level);
}
 
Example 10
Project: OptimizedImageEnhance   File: RemoveBackScatter.java   View Source Code Vote up 5 votes
private static Mat dehazeProcess(Mat img, Mat trans, double[] airlight) {
	Mat balancedImg = Filters.SimplestColorBalance(img, 5);
	Mat bCnl = new Mat();
	Core.extractChannel(balancedImg, bCnl, 0);
	Mat gCnl = new Mat();
	Core.extractChannel(balancedImg, gCnl, 1);
	Mat rCnl = new Mat();
	Core.extractChannel(balancedImg, rCnl, 2);
	// get mean value
	double bMean = Core.mean(bCnl).val[0];
	double gMean = Core.mean(gCnl).val[0];
	double rMean = Core.mean(rCnl).val[0];
	// get transmission map for each channel
	Mat Tb = trans.clone();
	Core.multiply(Tb, new Scalar(Math.max(bMean, Math.max(gMean, rMean)) / bMean * 0.8), Tb);
	Mat Tg = trans.clone();
	Core.multiply(Tg, new Scalar(Math.max(bMean, Math.max(gMean, rMean)) / gMean * 0.9), Tg);
	Mat Tr = trans.clone();
	Core.multiply(Tr, new Scalar(Math.max(bMean, Math.max(gMean, rMean)) / rMean * 0.8), Tr);
	// dehaze by formula
	// blue channel
	Mat bChannel = new Mat();
	Core.subtract(bCnl, new Scalar(airlight[0]), bChannel);
	Core.divide(bChannel, Tb, bChannel);
	Core.add(bChannel, new Scalar(airlight[0]), bChannel);
	// green channel
	Mat gChannel = new Mat();
	Core.subtract(gCnl, new Scalar(airlight[1]), gChannel);
	Core.divide(gChannel, Tg, gChannel);
	Core.add(gChannel, new Scalar(airlight[1]), gChannel);
	// red channel
	Mat rChannel = new Mat();
	Core.subtract(rCnl, new Scalar(airlight[2]), rChannel);
	Core.divide(rChannel, Tr, rChannel);
	Core.add(rChannel, new Scalar(airlight[2]), rChannel);
	Mat dehazed = new Mat();
	Core.merge(new ArrayList<>(Arrays.asList(bChannel, gChannel, rChannel)), dehazed);
	return dehazed;
}
 
Example 11
Project: RobotIGS   File: ColorBlobDetector.java   View Source Code Vote up 5 votes
/**
 * Process an rgba image. The results can be drawn on retrieved later.
 * This method does not modify the image.
 *
 * @param rgbaImage An RGBA image matrix
 */
public void process(Mat rgbaImage) {
    Imgproc.pyrDown(rgbaImage, mPyrDownMat);
    Imgproc.pyrDown(mPyrDownMat, mPyrDownMat);

    Imgproc.cvtColor(mPyrDownMat, mHsvMat, Imgproc.COLOR_RGB2HSV_FULL);

    //Test whether we need two inRange operations (only if the hue crosses over 255)
    if (upperBound.getScalar().val[0] <= 255) {
        Core.inRange(mHsvMat, lowerBound.getScalar(), upperBound.getScalar(), mMask);
    } else {
        //We need two operations - we're going to OR the masks together
        Scalar lower = lowerBound.getScalar().clone();
        Scalar upper = upperBound.getScalar().clone();
        while (upper.val[0] > 255)
            upper.val[0] -= 255;
        double tmp = lower.val[0];
        lower.val[0] = 0;
        //Mask 1 - from 0 to n
        Core.inRange(mHsvMat, lower, upper, mMaskOne);
        //Mask 2 - from 255-n to 255
        lower.val[0] = tmp;
        upper.val[0] = 255;

        Core.inRange(mHsvMat, lower, upper, mMask);
        //OR the two masks
        Core.bitwise_or(mMaskOne, mMask, mMask);
    }

    //Dilate (blur) the mask to decrease processing power
    Imgproc.dilate(mMask, mDilatedMask, new Mat());

    List<MatOfPoint> contourListTemp = new ArrayList<>();

    Imgproc.findContours(mDilatedMask, contourListTemp, mHierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);

    // Filter contours by area and resize to fit the original image size
    contours.clear();
    for (MatOfPoint c : contourListTemp) {
        Core.multiply(c, new Scalar(4, 4), c);
        contours.add(new Contour(c));
    }
}
 
Example 12
Project: FTC2016   File: ColorBlobDetector.java   View Source Code Vote up 4 votes
/**
 * Process an rgba image. The results can be drawn on retrieved later.
 * This method does not modify the image.
 *
 * @param rgbaImage An RGBA image matrix
 */
public void process(Mat rgbaImage) {
    Imgproc.pyrDown(rgbaImage, mPyrDownMat);
    Imgproc.pyrDown(mPyrDownMat, mPyrDownMat);

    Imgproc.cvtColor(mPyrDownMat, mHsvMat, Imgproc.COLOR_RGB2HSV_FULL);

    //Test whether we need two inRange operations (only if the hue crosses over 255)
    if (upperBound.getScalar().val[0] <= 255) {
        Core.inRange(mHsvMat, lowerBound.getScalar(), upperBound.getScalar(), mMask);
    } else {
        //We need two operations - we're going to OR the masks together
        Scalar lower = lowerBound.getScalar().clone();
        Scalar upper = upperBound.getScalar().clone();
        while (upper.val[0] > 255)
            upper.val[0] -= 255;
        double tmp = lower.val[0];
        lower.val[0] = 0;
        //Mask 1 - from 0 to n
        Core.inRange(mHsvMat, lower, upper, mMaskOne);
        //Mask 2 - from 255-n to 255
        lower.val[0] = tmp;
        upper.val[0] = 255;

        Core.inRange(mHsvMat, lower, upper, mMask);
        //OR the two masks
        Core.bitwise_or(mMaskOne, mMask, mMask);
    }

    //Dilate (blur) the mask to decrease processing power
    Imgproc.dilate(mMask, mDilatedMask, new Mat());

    List<MatOfPoint> contourListTemp = new ArrayList<>();

    Imgproc.findContours(mDilatedMask, contourListTemp, mHierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);

    // Filter contours by area and resize to fit the original image size
    contours.clear();
    for (MatOfPoint c : contourListTemp) {
        Core.multiply(c, new Scalar(4, 4), c);
        contours.add(new Contour(c));
    }
}
 
Example 13
Project: OptimizedImageEnhance   File: DarkChannelPriorDehaze.java   View Source Code Vote up 4 votes
public static Mat enhance(Mat image, double krnlRatio, double minAtmosLight, double eps) {
	image.convertTo(image, CvType.CV_32F);
	// extract each color channel
	List<Mat> rgb = new ArrayList<>();
	Core.split(image, rgb);
	Mat rChannel = rgb.get(0);
	Mat gChannel = rgb.get(1);
	Mat bChannel = rgb.get(2);
	int rows = rChannel.rows();
	int cols = rChannel.cols();
	// derive the dark channel from original image
	Mat dc = rChannel.clone();
	for (int i = 0; i < image.rows(); i++) {
		for (int j = 0; j < image.cols(); j++) {
			double min = Math.min(rChannel.get(i, j)[0], Math.min(gChannel.get(i, j)[0], bChannel.get(i, j)[0]));
			dc.put(i, j, min);
		}
	}
	// minimum filter
	int krnlSz = Double.valueOf(Math.max(Math.max(rows * krnlRatio, cols * krnlRatio), 3.0)).intValue();
	Mat kernel = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(krnlSz, krnlSz), new Point(-1, -1));
	Imgproc.erode(dc, dc, kernel);
	// get coarse transmission map
	Mat t = dc.clone();
	Core.subtract(t, new Scalar(255.0), t);
	Core.multiply(t, new Scalar(-1.0), t);
	Core.divide(t, new Scalar(255.0), t);
	// obtain gray scale image
	Mat gray = new Mat();
	Imgproc.cvtColor(image, gray, Imgproc.COLOR_RGB2GRAY);
	Core.divide(gray, new Scalar(255.0), gray);
	// refine transmission map
	int r = krnlSz * 4;
	t = Filters.GuidedImageFilter(gray, t, r, eps);
	// get minimum atmospheric light
	minAtmosLight = Math.min(minAtmosLight, Core.minMaxLoc(dc).maxVal);
	// dehaze each color channel
	rChannel = dehaze(rChannel, t, minAtmosLight);
	gChannel = dehaze(gChannel, t, minAtmosLight);
	bChannel = dehaze(bChannel, t, minAtmosLight);
	// merge three color channels to a image
	Mat outval = new Mat();
	Core.merge(new ArrayList<>(Arrays.asList(rChannel, gChannel, bChannel)), outval);
	outval.convertTo(outval, CvType.CV_8UC1);
	return outval;
}
 
Example 14
Project: SudoCAM-Ku   File: Thresholding.java   View Source Code Vote up 4 votes
public static Mat InvertImageColor(Mat img){
	Mat im = new Mat();;
	Core.bitwise_not(normalThresholding(img),im);
    Core.multiply(im,new Scalar(255),im);
	return im;
}