Java Core.split方法代碼示例

import org.opencv.core.Core; //導入方法依賴的package包/類
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;
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
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;
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
/**
 * Simplest Color Balance. Performs color balancing via histogram
 * normalization.
 *
 * @param img input color or gray scale image
 * @param percent controls the percentage of pixels to clip to white and black. (normally, choose 1~10)
 * @return Balanced image in CvType.CV_32F
 */
public static Mat SimplestColorBalance(Mat img, int percent) {
	if (percent <= 0)
		percent = 5;
	img.convertTo(img, CvType.CV_32F);
	List<Mat> channels = new ArrayList<>();
	int rows = img.rows(); // number of rows of image
	int cols = img.cols(); // number of columns of image
	int chnls = img.channels(); //  number of channels of image
	double halfPercent = percent / 200.0;
	if (chnls == 3) Core.split(img, channels);
	else channels.add(img);
	List<Mat> results = new ArrayList<>();
	for (int i = 0; i < chnls; i++) {
		// find the low and high precentile values (based on the input percentile)
		Mat flat = new Mat();
		channels.get(i).reshape(1, 1).copyTo(flat);
		Core.sort(flat, flat, Core.SORT_ASCENDING);
		double lowVal = flat.get(0, (int) Math.floor(flat.cols() * halfPercent))[0];
		double topVal = flat.get(0, (int) Math.ceil(flat.cols() * (1.0 - halfPercent)))[0];
		// saturate below the low percentile and above the high percentile
		Mat channel = channels.get(i);
		for (int m = 0; m < rows; m++) {
			for (int n = 0; n < cols; n++) {
				if (channel.get(m, n)[0] < lowVal) channel.put(m, n, lowVal);
				if (channel.get(m, n)[0] > topVal) channel.put(m, n, topVal);
			}
		}
		Core.normalize(channel, channel, 0.0, 255.0 / 2, Core.NORM_MINMAX);
		channel.convertTo(channel, CvType.CV_32F);
		results.add(channel);
	}
	Mat outval = new Mat();
	Core.merge(results, outval);
	return outval;
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
public static Mat enhance(Mat image, int blkSize, int patchSize, double lambda, double eps, int krnlSize) {
	image.convertTo(image, CvType.CV_32F);
	// obtain air-light
	double[] airlight = AirlightEstimate.estimate(image, blkSize);
	// obtain coarse transmission map
	double fTrans = 0.5;
	Mat T = TransmissionEstimate.transEstimate(image, patchSize, airlight, lambda, fTrans);
	// refine the transmission map
	Mat gray = new Mat();
	Imgproc.cvtColor(image, gray, Imgproc.COLOR_RGB2GRAY);
	Core.divide(gray, new Scalar(255.0), gray);
	T = Filters.GuidedImageFilter(gray, T, krnlSize, eps);
	// dehaze
	List<Mat> bgr = new ArrayList<>();
	Core.split(image, bgr);
	Mat bChannel = dehaze(bgr.get(0), T, airlight[0]);
	//Core.normalize(bChannel, bChannel, 0, 255, Core.NORM_MINMAX);
	Mat gChannel = dehaze(bgr.get(1), T, airlight[1]);
	//Core.normalize(gChannel, gChannel, 0, 255, Core.NORM_MINMAX);
	Mat rChannel = dehaze(bgr.get(2), T, airlight[2]);
	//Core.normalize(rChannel, rChannel, 0, 255, Core.NORM_MINMAX);
	Mat dehazedImg = new Mat();
	Core.merge(new ArrayList<>(Arrays.asList(bChannel, gChannel, rChannel)), dehazedImg);
	return dehazedImg;
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
private static Mat[] applyCLAHE(Mat img, Mat L) {
	Mat[] result = new Mat[2];
	CLAHE clahe = Imgproc.createCLAHE();
	clahe.setClipLimit(2.0);
	Mat L2 = new Mat();
	clahe.apply(L, L2);
	Mat LabIm2 = new Mat();
	List<Mat> lab = new ArrayList<>();
	Core.split(img, lab);
	Core.merge(new ArrayList<>(Arrays.asList(L2, lab.get(1), lab.get(2))), LabIm2);
	Mat img2 = new Mat();
	Imgproc.cvtColor(LabIm2, img2, Imgproc.COLOR_Lab2BGR);
	result[0] = img2;
	result[1] = L2;
	return result;
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
public static void main(String[] args) {
	String imgPath = "src/main/resources/dcp_images/flash/cave-flash.bmp";
	String guidedImgPath = "src/main/resources/dcp_images/flash/cave-noflash.bmp";
	Mat image = Imgcodecs.imread(imgPath, Imgcodecs.CV_LOAD_IMAGE_COLOR);
	new ImShow("image").showImage(image);
	image.convertTo(image, CvType.CV_32F);
	Mat guide = Imgcodecs.imread(guidedImgPath, Imgcodecs.CV_LOAD_IMAGE_COLOR);
	guide.convertTo(guide, CvType.CV_32F);
	List<Mat> img = new ArrayList<>();
	List<Mat> gid = new ArrayList<>();
	Core.split(image, img);
	Core.split(guide, gid);
	
	int r = 8;
	double eps = 0.02 * 0.02;
	Mat q_r = Filters.GuidedImageFilter(img.get(0), gid.get(0), r, eps);
	Mat q_g = Filters.GuidedImageFilter(img.get(1), gid.get(1), r, eps);
	Mat q_b = Filters.GuidedImageFilter(img.get(2), gid.get(2), r, eps);
	Mat q = new Mat();
	Core.merge(new ArrayList<>(Arrays.asList(q_r, q_g, q_b)), q);
	q.convertTo(q, CvType.CV_8UC1);
	new ImShow("q").showImage(q);
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
public static void main(String[] args) {
	String imgPath = "src/main/resources/dcp_images/enhancement/tulips.bmp";
	Mat image = Imgcodecs.imread(imgPath, Imgcodecs.CV_LOAD_IMAGE_COLOR);
	new ImShow("image").showImage(image);
	image.convertTo(image, CvType.CV_32F);
	List<Mat> img = new ArrayList<>();
	Core.split(image, img);
	int r = 16;
	double eps = 0.01;
	
	Mat q_r = Filters.GuidedImageFilter(img.get(0), img.get(0), r, eps);
	Mat q_g = Filters.GuidedImageFilter(img.get(1), img.get(1), r, eps);
	Mat q_b = Filters.GuidedImageFilter(img.get(2), img.get(2), r, eps);
	
	Mat q = new Mat();
	Core.merge(new ArrayList<>(Arrays.asList(q_r, q_g, q_b)), q);
	q.convertTo(q, CvType.CV_8UC1);
	new ImShow("q").showImage(q);
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
private static Mat[] applyCLAHE(Mat img, Mat L) {
	Mat[] result = new Mat[2];
	CLAHE clahe = Imgproc.createCLAHE();
	clahe.setClipLimit(2.0);
	Mat L2 = new Mat();
	clahe.apply(L, L2);
	Mat LabIm2 = new Mat();
	List<Mat> lab = new ArrayList<Mat>();
	Core.split(img, lab);
	Core.merge(new ArrayList<Mat>(Arrays.asList(L2, lab.get(1), lab.get(2))), LabIm2);
	Mat img2 = new Mat();
	Imgproc.cvtColor(LabIm2, img2, Imgproc.COLOR_Lab2BGR);
	result[0] = img2;
	result[1] = L2;
	return result;
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
public void leviRedFilter (Mat input, Mat mask, double threshold){


        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);

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

import org.opencv.core.Core; //導入方法依賴的package包/類
public void leviBlueFilter (Mat input, Mat mask){
    List<Mat> channels = new ArrayList<>();

    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, 145, 255, Imgproc.THRESH_BINARY);

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

import org.opencv.core.Core; //導入方法依賴的package包/類
@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;
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
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;
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
@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 YELLOW:
            if(threshold == -1){
                threshold = 95;
            }

            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_INV);
            break;
    }

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

    input.release();

}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
public static double blkEstimate(Mat blkIm, double[] airlight, double lambda, double fTrans) {
	double Trans = 0.0;
	double nTrans = Math.floor(1.0 / fTrans * 128);
	double fMinCost = Double.MAX_VALUE;
	int numberOfPixels = blkIm.rows() * blkIm.cols() * blkIm.channels();
	double nCounter = 0.0;
	List<Mat> bgr = new ArrayList<>();
	Core.split(blkIm, bgr);
	while (nCounter < (1.0 - fTrans) * 10) {
		// initial dehazing process to calculate the loss information
		Mat bChannel = bgr.get(0).clone();
		bChannel = preDehaze(bChannel, airlight[0], nTrans);
		Mat gChannel = bgr.get(1).clone();
		gChannel = preDehaze(gChannel, airlight[1], nTrans);
		Mat rChannel = bgr.get(2).clone();
		rChannel = preDehaze(rChannel, airlight[2], nTrans);
		// find the pixels with over-255 value and below-0 value, and
		// calculate the sum of information loss
		double nSumOfLoss = 0.0;
		for (int i = 0; i < bChannel.rows(); i++) {
			for (int j = 0; j < bChannel.cols(); j++) {
				if (bChannel.get(i, j)[0] > 255.0) nSumOfLoss += (bChannel.get(i, j)[0] - 255.0) * (bChannel.get(i, j)[0] - 255.0);
				else if (bChannel.get(i, j)[0] < 0.0) nSumOfLoss += bChannel.get(i, j)[0] * bChannel.get(i, j)[0];
				if (gChannel.get(i, j)[0] > 255.0) nSumOfLoss += (gChannel.get(i, j)[0] - 255.0) * (gChannel.get(i, j)[0] - 255.0);
				else if (gChannel.get(i, j)[0] < 0.0) nSumOfLoss += gChannel.get(i, j)[0] * gChannel.get(i, j)[0];
				if (rChannel.get(i, j)[0] > 255.0) nSumOfLoss += (rChannel.get(i, j)[0] - 255.0) * (rChannel.get(i, j)[0] - 255.0);
				else if (rChannel.get(i, j)[0] < 0.0) nSumOfLoss += rChannel.get(i, j)[0] * rChannel.get(i, j)[0];
			}
		}
		// calculate the value of sum of square out
		double nSumOfSquareOuts = Core.sumElems(bChannel.mul(bChannel)).val[0] + Core.sumElems(gChannel.mul(gChannel)).val[0] + Core.sumElems(rChannel.mul(rChannel)).val[0];
		// calculate the value of sum of out
		double nSumOfOuts = Core.sumElems(bChannel).val[0] + Core.sumElems(gChannel).val[0] + Core.sumElems(rChannel).val[0];
		// calculate the mean value of the block image
		double fMean = nSumOfOuts / numberOfPixels;
		// calculate the cost function
		double fCost = lambda * nSumOfLoss / numberOfPixels - (nSumOfSquareOuts / numberOfPixels - fMean * fMean);
		// find the minimum cost and the related transmission
		if (nCounter == 0 || fMinCost > fCost) {
			fMinCost = fCost;
			Trans = fTrans;
		}
		fTrans = fTrans + 0.1;
		nTrans = 1.0 / fTrans * 128.0;
		nCounter = nCounter + 1;
	}
	return Trans;
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
public static Mat enhance(Mat image, int r, double eps, double eta, double lambda, double krnlRatio) {
	image.convertTo(image, CvType.CV_32F);
	// extract each color channel
	List<Mat> bgr = new ArrayList<>();
	Core.split(image, bgr);
	Mat bChannel = bgr.get(0);
	Mat gChannel = bgr.get(1);
	Mat rChannel = bgr.get(2);
	int m = rChannel.rows();
	int n = rChannel.cols();
	// Global Adaptation
	List<Mat> list = globalAdaptation(bChannel, gChannel, rChannel, m, n);
	Mat Lw = list.get(0);
	Mat Lg = list.get(1);
	// Local Adaptation
	Mat Hg = localAdaptation(Lg, m, n, r, eps, krnlRatio);
	Lg.convertTo(Lg, CvType.CV_32F);
	// process
	Mat alpha = new Mat(m, n, rChannel.type());
	Core.divide(Lg, new Scalar(Core.minMaxLoc(Lg).maxVal / eta), alpha);
	//Core.multiply(alpha, new Scalar(eta), alpha);
	Core.add(alpha, new Scalar(1.0), alpha);
	//alpha = adjustment(alpha, 1.25);
	Mat Lg_ = new Mat(m, n, rChannel.type());
	Core.add(Lg, new Scalar(1.0 / 255.0), Lg_);
	Core.log(Lg_, Lg_);
	double beta = Math.exp(Core.sumElems(Lg_).val[0] / (m * n)) * lambda;
	Mat Lout = new Mat(m, n, rChannel.type());
	Core.divide(Lg, Hg, Lout);
	Core.add(Lout, new Scalar(beta), Lout);
	Core.log(Lout, Lout);
	Core.normalize(alpha.mul(Lout), Lout, 0, 255, Core.NORM_MINMAX);
	Mat gain = obtainGain(Lout, Lw, m, n);
	// output
	Core.divide(rChannel.mul(gain), new Scalar(Core.minMaxLoc(rChannel).maxVal / 255.0), rChannel); // Red Channel
	Core.divide(gChannel.mul(gain), new Scalar(Core.minMaxLoc(gChannel).maxVal / 255.0), gChannel); // Green Channel
	Core.divide(bChannel.mul(gain), new Scalar(Core.minMaxLoc(bChannel).maxVal / 255.0), bChannel); // Blue Channel
	// merge three color channels to a image
	Mat outval = new Mat();
	Core.merge(new ArrayList<>(Arrays.asList(bChannel, gChannel, rChannel)), outval);
	outval.convertTo(outval, CvType.CV_8UC1);
	return outval;
}