Java Mat.convertTo方法代碼示例

import org.opencv.core.Mat; //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.Mat; //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.Mat; //導入方法依賴的package包/類
public static Mat LuminanceWeight(Mat img, Mat L) {
	Mat bCnl = new Mat();
	Core.extractChannel(img, bCnl, 0);
	bCnl.convertTo(bCnl, CvType.CV_32F);
	Mat gCnl = new Mat();
	Core.extractChannel(img, gCnl, 1);
	gCnl.convertTo(gCnl, CvType.CV_32F);
	Mat rCnl = new Mat();
	Core.extractChannel(img, rCnl, 2);
	rCnl.convertTo(rCnl, CvType.CV_32F);
	Mat lum = new Mat(L.rows(), L.cols(), L.type());
	for (int i = 0; i < L.rows(); i++) {
		for (int j = 0; j < L.cols(); j++) {
			double data = Math.sqrt( ( Math.pow(bCnl.get(i, j)[0] / 255.0 - L.get(i, j)[0], 2.0) +
					Math.pow(gCnl.get(i, j)[0] / 255.0 - L.get(i, j)[0], 2.0) +
					Math.pow(rCnl.get(i, j)[0] / 255.0 - L.get(i, j)[0], 2.0) ) / 3 );
			lum.put(i, j, data);
		}
	}
	return lum;
}
 

import org.opencv.core.Mat; //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.Mat; //導入方法依賴的package包/類
public static Mat enhance (Mat image, int blkSize, int patchSize, double lambda, double gamma, int r, double eps, int level) {
	image.convertTo(image, CvType.CV_32F);
	// image decomposition
	Mat[] decomposed = ImgDecompose.illuRefDecompose(image);
	Mat AL = decomposed[0];
	Mat RL = decomposed[1];
	// For RL
	RL = Filters.SimplestColorBalance(RL, colorBalanceRatio);
	// Calculate the air-light
	double[] airlight = AirlightEstimate.estimate(AL, blkSize);
	// estimate the transmission map
	double fTrans = 0.6;
	Mat trans = TransmissionEstimate.transEstimate(AL, patchSize, airlight, lambda, fTrans, r, eps, gamma);
	AL = dehazeProcess(AL, trans, airlight);
	// calculate the weight
	Mat w1 = calWeight(AL);
	Mat w2 = calWeight(RL);
	// Fuse
	return pyramidFuse(w1, w2, AL, RL, level);
}
 

import org.opencv.core.Mat; //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.Mat; //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.Mat; //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.Mat; //導入方法依賴的package包/類
public void enhanceImageBrightness() {
    double alpha = 1;   // Change to 2 for more brightness
    double beta = 50;
    String fileName = "cat.jpg";

    Mat source = Imgcodecs.imread("cat.jpg");
    Mat destination = new Mat(source.rows(), source.cols(),
            source.type());
    source.convertTo(destination, -1, 1, 50);
    Imgcodecs.imwrite("brighterCat.jpg", destination);
}
 

import org.opencv.core.Mat; //導入方法依賴的package包/類
/**
 * 其主要思路為：
	1、求取源圖I的平均灰度，並記錄rows和cols；
	2、按照一定大小，分為N*M個方塊，求出每塊的平均值，得到子塊的亮度矩陣D；
	3、用矩陣D的每個元素減去源圖的平均灰度，得到子塊的亮度差值矩陣E；
	4、用雙立方差值法，將矩陣E差值成與源圖一樣大小的亮度分布矩陣R；
	5、得到矯正後的圖像result=I-R；
* @Title: unevenLightCompensate 
* @Description: 光線補償 
* @param image
* @param blockSize
* void 
* @throws
 */
public static void unevenLightCompensate(Mat image, int blockSize) {
	if(image.channels() == 3) {
		Imgproc.cvtColor(image, image, 7);
	}
	double average = Core.mean(image).val[0];
	Scalar scalar = new Scalar(average);
	int rowsNew = (int) Math.ceil((double)image.rows() / (double)blockSize);
	int colsNew = (int) Math.ceil((double)image.cols() / (double)blockSize);
	Mat blockImage = new Mat();
	blockImage = Mat.zeros(rowsNew, colsNew, CvType.CV_32FC1);
	for(int i = 0; i < rowsNew; i ++) {
		for(int j = 0; j < colsNew; j ++) {
			int rowmin = i * blockSize;
			int rowmax = (i + 1) * blockSize;
			if(rowmax > image.rows()) rowmax = image.rows();
			int colmin = j * blockSize;
			int colmax = (j +1) * blockSize;
			if(colmax > image.cols()) colmax = image.cols();
			Range rangeRow = new Range(rowmin, rowmax);
			Range rangeCol = new Range(colmin, colmax);
			Mat imageROI = new Mat(image, rangeRow, rangeCol);
			double temaver = Core.mean(imageROI).val[0];
			blockImage.put(i, j, temaver);
		}
	}
	
	Core.subtract(blockImage, scalar, blockImage);
	Mat blockImage2 = new Mat();
	int INTER_CUBIC = 2;
	Imgproc.resize(blockImage, blockImage2, image.size(), 0, 0, INTER_CUBIC);
	Mat image2 = new Mat();
	image.convertTo(image2, CvType.CV_32FC1);
	Mat dst = new Mat();
	Core.subtract(image2, blockImage2, dst);
	dst.convertTo(image, CvType.CV_8UC1);
}
 

import org.opencv.core.Mat; //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.Mat; //導入方法依賴的package包/類
/**
 * 
* @Title: gammaAdjust 
* @Description: gamma校正
* @param grayImg
* @return
* Mat 
* @throws
 */
public static Mat gammaAdjust(Mat grayImg) {
	
	Mat X = new Mat();
	grayImg.convertTo(X, CvType.CV_32FC1);
	Mat I = new Mat();
    float gamma = 1/2.2f;
    Core.pow(X, gamma, I);
    
    Mat result = norm_0_255(I);
    return result;
}
 

import org.opencv.core.Mat; //導入方法依賴的package包/類
public static void main (String[] args) {
    String imgPath = "src/main/resources/haze_images/canon_2.jpg";
    Mat image = Imgcodecs.imread(imgPath, Imgcodecs.CV_LOAD_IMAGE_COLOR);
    new ImShow("org-image").showImage(image);
    Mat result = OptimizedContrastEnhance.enhance(image, blkSize, patchSize, lambda, eps, krnlSize);
    result.convertTo(result, CvType.CV_8UC1);
    new ImShow("dehaze-image").showImage(result);
}
 

import org.opencv.core.Mat; //導入方法依賴的package包/類
public static void main (String[] args) {
    String imgPath = "src/main/resources/underwater_images/underwater_scene.jpg";
    Mat image = Imgcodecs.imread(imgPath, Imgcodecs.CV_LOAD_IMAGE_COLOR);
    new ImShow("original").showImage(image); // show image
    Mat fusion = RemoveBackScatter.enhance(image, blkSize, patchSize, lambda, gamma, r, eps, level);
    fusion.convertTo(fusion, CvType.CV_8UC1);
    new ImShow("fusion").showImage(fusion); // show fusion result
}
 

import org.opencv.core.Mat; //導入方法依賴的package包/類
public static void main (String[] args) {
    String imgPath = "src/main/resources/underwater_images/underwater_scene.jpg";
    Mat image = Imgcodecs.imread(imgPath, Imgcodecs.CV_LOAD_IMAGE_COLOR);
    new ImShow("original").showImage(image);
    Mat fusion = FusionEnhance.enhance(image, level);
    fusion.convertTo(fusion, CvType.CV_8UC1);
    new ImShow("fusion").showImage(fusion);
}