Java Mat.clone方法代碼示例

import org.opencv.core.Mat; //導入方法依賴的package包/類
@SuppressWarnings("unused")
public MatchResult scaleTemplateMatch(Mat scene, Mat templ, Method method, double scaleFactor){
	int tw = templ.width();
	int th = templ.height();
	double currScaleFactor = scaleFactor;
	MatchResult bestScore = null;
	
	for(Mat img = templ.clone(); img.width() > tw*0.25;
			CvProcessing.resize(img, scaleFactor)){
		
		MatchResult currResult = match(scene, img, method, img);
        
        if(bestScore == null || bestScore.maxVal < currResult.maxVal){
        	bestScore = currResult;
        	bestScore.scaleFactor = currScaleFactor;
        }
        currScaleFactor  *= scaleFactor;
     }
	
	
      	return bestScore;
}
 

import org.opencv.core.Mat; //導入方法依賴的package包/類
/**
 * Convert a matrix in one color space to another
 *
 * @param in       Input matrix
 * @param spaceIn  Input colorspace
 * @param spaceOut Output colorspace
 * @return Matrix in output colorspace
 */
public static Mat convertColorMat(Mat in, ColorSpace spaceIn, ColorSpace spaceOut) {
    if (spaceIn == spaceOut)
        return in;
    if (!spaceIn.canConvertTo(spaceOut))
        throw new IllegalArgumentException("Cannot convert color to the desired color space.");

    Mat output = in.clone();

    try {
        for (int i = 0; i < spaceIn.getConversionsTo(spaceOut).length; i += 3) {
            int conversion = spaceIn.getConversionsTo(spaceOut)[i];
            int inputDim = spaceIn.getConversionsTo(spaceOut)[i + 1];
            int outputDim = spaceIn.getConversionsTo(spaceOut)[i + 2];

            Imgproc.cvtColor(output, output, conversion, outputDim);
        }
    } catch (Exception ignored) {
        throw new IllegalArgumentException("Cannot convert color to the desired color space.");
    }

    return output;
}
 

import org.opencv.core.Mat; //導入方法依賴的package包/類
private static ArrayList<Rect> getContourArea(Mat mat) {
	Mat hierarchy = new Mat();
	Mat image = mat.clone();
	List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
	Imgproc.findContours(image, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
	Rect rect = null;
	double maxArea = 300;
	ArrayList<Rect> arr = new ArrayList<Rect>();
	for (int i = 0; i < contours.size(); i++) {
		Mat contour = contours.get(i);
		double contourArea = Imgproc.contourArea(contour);
		if (contourArea > maxArea) {
			rect = Imgproc.boundingRect(contours.get(i));
			arr.add(rect);
		}
	}
	return arr;
}
 

import org.opencv.core.Mat; //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.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
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
private static Mat dobj(CascadeClassifier objDetector, Mat src) {
	final Mat dst = src.clone();

	final MatOfRect objDetections = new MatOfRect();

	objDetector.detectMultiScale(dst, objDetections);

	if (objDetections.toArray().length <= 0) {
		return src;
	}
	for (final Rect rect : objDetections.toArray()) {
		Imgproc.rectangle(dst, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
				new Scalar(0, 0, 255), 2);
	}
	return dst;
}
 

import org.opencv.core.Mat; //導入方法依賴的package包/類
public void smoothImage() {
    // Smoothing, also called blurring, will make the edges soother.
    Mat source = Imgcodecs.imread("cat.jpg");
    Mat destination = source.clone();
    for (int i = 0; i < 25; i++) {
        Mat sourceImage = destination.clone();
        Imgproc.blur(sourceImage, destination, new Size(3.0, 3.0));
    }
    Imgcodecs.imwrite("smoothCat.jpg", destination);
}
 

import org.opencv.core.Mat; //導入方法依賴的package包/類
private void readCameraParameters() {
    Mat camM = new Mat(3, 3, CvType.CV_64FC1);
    Mat difC = new Mat(1, 5, CvType.CV_64FC1);
    CalibrationResultInitializer.tryLoad(activity, camM, difC);
    this.cameraMatrix = camM.clone();
    this.distCoeff = difC.clone();
}
 

import org.opencv.core.Mat; //導入方法依賴的package包/類
private void resizeAndConvertOneImage() {
    Size size = new Size(IMAGE.cols() * scaleSize, IMAGE.rows() * scaleSize);
    Mat imgTMP = new Mat();
    Imgproc.resize(IMAGE, imgTMP, size);
    IMAGE = imgTMP.clone();
    Imgproc.cvtColor(IMAGE, imgTMP, Imgproc.COLOR_BGR2GRAY);
    IMAGE = imgTMP.clone();
}
 

import org.opencv.core.Mat; //導入方法依賴的package包/類
public static Mat LSD(Mat in) {
	final LineSegmentDetector lsd = Imgproc.createLineSegmentDetector();
	final Mat gray = new Mat();
	Imgproc.cvtColor(in, gray, Imgproc.COLOR_RGB2GRAY);
	final Mat out = gray.clone();
	final Mat _lines = new Mat();
	lsd.detect(gray, _lines);
	lsd.drawSegments(out, _lines);
	return out;
}
 

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

import org.opencv.core.Mat; //導入方法依賴的package包/類
public static Mat copiarImagem(Mat imagem) {
	return imagem.clone();
}