Java Core.add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
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;
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
private static Mat calWeight(Mat img) {
	Mat L = new Mat();
	img.convertTo(img, CvType.CV_8UC1);
	Imgproc.cvtColor(img, L, Imgproc.COLOR_BGR2GRAY);
	L.convertTo(L, CvType.CV_32F);
	Core.divide(L, new Scalar(255.0), L);
	// calculate Luminance weight
	Mat WC = FeatureWeight.LuminanceWeight(img, L);
	WC.convertTo(WC, L.type());
	// calculate the Saliency weight
	Mat WS = FeatureWeight.Saliency(img);
	WS.convertTo(WS, L.type());
	// calculate the Exposedness weight
	Mat WE = FeatureWeight.Exposedness(L);
	WE.convertTo(WE, L.type());
	// sum
	Mat weight = WC.clone();
	Core.add(weight, WS, weight);
	Core.add(weight, WE, weight);
	return weight;
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
public static Mat enhance (Mat image, int level) {
	// color balance
	Mat img1 = Filters.SimplestColorBalance(image, 5);
	img1.convertTo(img1, CvType.CV_8UC1);
	// Perform sRGB to CIE Lab color space conversion
	Mat LabIm1 = new Mat();
	Imgproc.cvtColor(img1, LabIm1, Imgproc.COLOR_BGR2Lab);
	Mat L1 = new Mat();
	Core.extractChannel(LabIm1, L1, 0);
	// apply CLAHE
	Mat[] result = applyCLAHE(LabIm1, L1);
	Mat img2 = result[0];
	Mat L2 = result[1];
	// calculate normalized weight
	Mat w1 = calWeight(img1, L1);
	Mat w2 = calWeight(img2, L2);
	Mat sumW = new Mat();
	Core.add(w1, w2, sumW);
	Core.divide(w1, sumW, w1);
	Core.divide(w2, sumW, w2);
	// merge image1 and image2
	return ImgDecompose.fuseTwoImage(w1, img1, w2, img2, level);
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
private static Mat calWeight(Mat img, Mat L) {
	Core.divide(L, new Scalar(255.0), L);
	L.convertTo(L, CvType.CV_32F);
	// calculate laplacian contrast weight
	Mat WL = FeatureWeight.LaplacianContrast(L);
	WL.convertTo(WL, L.type());
	// calculate Local contrast weight
	Mat WC = FeatureWeight.LocalContrast(L);
	WC.convertTo(WC, L.type());
	// calculate the saliency weight
	Mat WS = FeatureWeight.Saliency(img);
	WS.convertTo(WS, L.type());
	// calculate the exposedness weight
	Mat WE = FeatureWeight.Exposedness(L);
	WE.convertTo(WE, L.type());
	// sum
	Mat weight = WL.clone();
	Core.add(weight, WC, weight);
	Core.add(weight, WS, weight);
	Core.add(weight, WE, weight);
	return weight;
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
private static Mat calWeight(Mat img, Mat L) {
	Core.divide(L, new Scalar(255.0), L);
	L.convertTo(L, CvType.CV_32F);
	// calculate laplacian contrast weight
	Mat WL = WeightCalculate.LaplacianContrast(L);
	WL.convertTo(WL, L.type());
	// calculate Local contrast weight
	Mat WC = WeightCalculate.LocalContrast(L);
	WC.convertTo(WC, L.type());
	// calculate the saliency weight
	Mat WS = WeightCalculate.Saliency(img);
	WS.convertTo(WS, L.type());
	// calculate the exposedness weight
	Mat WE = WeightCalculate.Exposedness(L);
	WE.convertTo(WE, L.type());
	// sum
	Mat weight = WL.clone();
	Core.add(weight, WC, weight);
	Core.add(weight, WS, weight);
	Core.add(weight, WE, weight);
	return weight;
}
 

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

import org.opencv.core.Core; //導入方法依賴的package包/類
private static Mat dehaze(Mat img, Mat T, double airlight) {
	// J = (img - airlight) ./ T + airlight;
	Core.subtract(img, new Scalar(airlight), img);
	Core.divide(img, T, img);
	Core.add(img, new Scalar(airlight), img);
	return img;
}
 

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

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

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
public static Mat PyramidReconstruct(Mat[] pyramid) {
	int level = pyramid.length;
	for (int i = level - 1; i > 0; i--) {
		Mat tmpPyr = new Mat();
		Imgproc.resize(pyramid[i], tmpPyr, pyramid[i - 1].size(), 0, 0, Imgproc.INTER_LINEAR);
		Core.add(pyramid[i - 1], tmpPyr, pyramid[i - 1]);
	}
	return pyramid[0];
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
public static Mat subspaceReconstruct(Mat W, Mat mean, Mat src) {
	int n = src.rows();
	int d = src.cols();
	Mat X = new Mat();
	Mat Y = new Mat();
	src.convertTo(Y, W.type());
	Core.gemm(Y, W, 1.0, new Mat(), 0.0, X, 2);
	if(!mean.empty()) {
		for(int i = 0; i < n; i ++) {
			Mat r_i = X.row(i);
			Core.add(r_i, mean.reshape(1, 1), r_i);
		}
	}
	return X;
}
 

import org.opencv.core.Core; //導入方法依賴的package包/類
/**
 * Guided Image Filter for grayscale image, O(1) time implementation of guided filter
 *
 * @param I guidance image (should be a gray-scale/single channel image)
 * @param p filtering input image (should be a gray-scale/single channel image)
 * @param r local window radius
 * @param eps regularization parameter
 * @return filtered image
 */
public static Mat GuidedImageFilter(Mat I, Mat p, int r, double eps) {
	I.convertTo(I, CvType.CV_64FC1);
	p.convertTo(p, CvType.CV_64FC1);
	//[hei, wid] = size(I);
	int rows = I.rows();
	int cols = I.cols();
	// N = boxfilter(ones(hei, wid), r); % the size of each local patch; N=(2r+1)^2 except for boundary pixels.
	Mat N = new Mat();
	Imgproc.boxFilter(Mat.ones(rows, cols, I.type()), N, -1, new Size(r, r));
	// mean_I = boxfilter(I, r) ./ N;
	Mat mean_I = new Mat();
	Imgproc.boxFilter(I, mean_I, -1, new Size(r, r));
	// mean_p = boxfilter(p, r) ./ N
	Mat mean_p = new Mat();
	Imgproc.boxFilter(p, mean_p, -1, new Size(r, r));
	// mean_Ip = boxfilter(I.*p, r) ./ N;
	Mat mean_Ip = new Mat();
	Imgproc.boxFilter(I.mul(p), mean_Ip, -1, new Size(r, r));
	// cov_Ip = mean_Ip - mean_I .* mean_p; % this is the covariance of (I, p) in each local patch.
	Mat cov_Ip = new Mat();
	Core.subtract(mean_Ip, mean_I.mul(mean_p), cov_Ip);
	// mean_II = boxfilter(I.*I, r) ./ N;
	Mat mean_II = new Mat();
	Imgproc.boxFilter(I.mul(I), mean_II, -1, new Size(r, r));
	// var_I = mean_II - mean_I .* mean_I;
	Mat var_I = new Mat();
	Core.subtract(mean_II, mean_I.mul(mean_I), var_I);
	// a = cov_Ip ./ (var_I + eps); % Eqn. (5) in the paper;
	Mat a = new Mat();
	Core.add(var_I, new Scalar(eps), a);
	Core.divide(cov_Ip, a, a);
	//b = mean_p - a .* mean_I; % Eqn. (6) in the paper;
	Mat b = new Mat();
	Core.subtract(mean_p, a.mul(mean_I), b);
	// mean_a = boxfilter(a, r) ./ N;
	Mat mean_a = new Mat();
	Imgproc.boxFilter(a, mean_a, -1, new Size(r, r));
	Core.divide(mean_a, N, mean_a);
	// mean_b = boxfilter(b, r) ./ N;
	Mat mean_b = new Mat();
	Imgproc.boxFilter(b, mean_b, -1, new Size(r, r));
	Core.divide(mean_b, N, mean_b);
	// q = mean_a .* I + mean_b; % Eqn. (8) in the paper;
	Mat q = new Mat();
	Core.add(mean_a.mul(I), mean_b, q);
	q.convertTo(q, CvType.CV_32F);
	return q;
}