Java Core.minMaxLoc方法代码示例

import org.opencv.core.Core; //导入方法依赖的package包/类
public MatchResult match(Mat scene, Mat templ, Method method, Mat img) {
	
	int result_cols = scene.cols() - templ.cols() + 1;
	int result_rows = scene.rows() - templ.rows() + 1;
	Mat result = new Mat(result_rows, result_cols, CV_32FC1);
	Imgproc.matchTemplate(scene, templ, result, method.ordinal());
	//Core.normalize(result, result, 0, 1, 32,-1,new Mat());
		
	MinMaxLocResult mmr = Core.minMaxLoc(result);

	
	Point matchLoc;
	double maxVal;
	if (method.ordinal() == Imgproc.TM_SQDIFF
	        || method.ordinal() == Imgproc.TM_SQDIFF_NORMED) {
	    
		matchLoc = mmr.minLoc;
		maxVal = mmr.minVal;
	}
	else {
	    matchLoc = mmr.maxLoc;
	    maxVal = mmr.maxVal;
	}
	
	MatchResult currResult = new MatchResult(matchLoc.x +(templ.cols()/2),matchLoc.y +(templ.rows()/2),0,maxVal);
	return currResult;
}
 

import org.opencv.core.Core; //导入方法依赖的package包/类
public static Pair<Point, Double> getBestMatched(Mat tmResult, int matchMethod, float threshold) {
    TimingLogger logger = new TimingLogger(LOG_TAG, "best_matched_point");
    // FIXME: 2017/11/26 正交化?
    //   Core.normalize(tmResult, tmResult, 0, 1, Core.NORM_MINMAX, -1, new Mat());
    Core.MinMaxLocResult mmr = Core.minMaxLoc(tmResult);
    logger.addSplit("minMaxLoc");
    double value;
    Point pos;
    if (matchMethod == Imgproc.TM_SQDIFF || matchMethod == Imgproc.TM_SQDIFF_NORMED) {
        pos = mmr.minLoc;
        value = -mmr.minVal;
    } else {
        pos = mmr.maxLoc;
        value = mmr.maxVal;
    }
    logger.addSplit("value:" + value);
    logger.dumpToLog();
    return new Pair<>(pos, value);
}
 

import org.opencv.core.Core; //导入方法依赖的package包/类
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; //导入方法依赖的package包/类
/**
 * <p>Build a template from a specific eye area previously substracted
 * uses detectMultiScale for this area, then uses minMaxLoc method to
 * detect iris from the detected eye</p>
 *
 * @param area Preformatted Area
 * @param size minimum iris size
 * @param grayMat image in gray
 * @param rgbaMat image in color
 * @param detectorEye Haar Cascade classifier
 * @return built template
 */
@NonNull
private static Mat buildTemplate(Rect area, final int size,
                                 @NonNull Mat grayMat,
                                 @NonNull Mat rgbaMat,
                                 CascadeClassifier detectorEye) {
    Mat template = new Mat();
    Mat graySubMatEye = grayMat.submat(area);
    MatOfRect eyes = new MatOfRect();
    Rect eyeTemplate;
    detectorEye.detectMultiScale(graySubMatEye, eyes, 1.15, 2,
            Objdetect.CASCADE_FIND_BIGGEST_OBJECT
                    | Objdetect.CASCADE_SCALE_IMAGE, new Size(EYE_MIN_SIZE, EYE_MIN_SIZE),
            new Size());

    Rect[] eyesArray = eyes.toArray();
    if (eyesArray.length > 0) {
        Rect e = eyesArray[0];
        e.x = area.x + e.x;
        e.y = area.y + e.y;
        Rect eyeRectangle = getEyeArea((int) e.tl().x,
                (int) (e.tl().y + e.height * 0.4),
                e.width,
                (int) (e.height * 0.6));
        graySubMatEye = grayMat.submat(eyeRectangle);
        Mat rgbaMatEye = rgbaMat.submat(eyeRectangle);


        Core.MinMaxLocResult minMaxLoc = Core.minMaxLoc(graySubMatEye);

        FaceDrawerOpenCV.drawIrisCircle(rgbaMatEye, minMaxLoc);
        Point iris = new Point();
        iris.x = minMaxLoc.minLoc.x + eyeRectangle.x;
        iris.y = minMaxLoc.minLoc.y + eyeRectangle.y;
        eyeTemplate = getEyeArea((int) iris.x - size / 2,
                (int) iris.y
                        - size / 2, size, size);
        FaceDrawerOpenCV.drawEyeRectangle(eyeTemplate, rgbaMat);
        template = (grayMat.submat(eyeTemplate)).clone();
    }
    return template;
}
 

import org.opencv.core.Core; //导入方法依赖的package包/类
@SuppressWarnings("unused")
private static Mat adjustment(Mat alpha, double a) {
	double b = Core.minMaxLoc(alpha).maxVal;
	int rows = alpha.rows();
	int cols = alpha.cols();
	for (int i = 0; i < rows; i++) {
		for (int j = 0; j < cols; j++) {
			//double val = alpha.get(i, j)[0];
			alpha.put(i, j, (2 * Math.atan(a * alpha.get(i, j)[0] / b) / Math.PI * b));
		}
	}
	return alpha;
}
 

import org.opencv.core.Core; //导入方法依赖的package包/类
private ImageFinderResult findImage(Mat sourceMat, Mat templateMat, double desiredAccuracy) {
    if (sourceMat.width() < templateMat.width() || sourceMat.height() < templateMat.height()) {
        throw new UnsupportedOperationException("The template image is larger than the source image. Ensure that the width and/or height of the image you are trying to find do not exceed the dimensions of the source image.");
    }

    Mat result = new Mat(sourceMat.rows() - templateMat.rows() + 1, sourceMat.rows() - templateMat.rows() + 1, CvType.CV_32FC1);
    int intMatchingMethod;

    switch (this.matchingMethod) {
        case MM_CORELLATION_COEFF:
            intMatchingMethod = Imgproc.TM_CCOEFF_NORMED;
            break;
        case MM_CROSS_CORELLATION:
            intMatchingMethod = Imgproc.TM_CCORR_NORMED;
            break;
        default:
            intMatchingMethod = Imgproc.TM_SQDIFF_NORMED;
    }

    Imgproc.matchTemplate(sourceMat, templateMat, result, intMatchingMethod);
    MinMaxLocResult minMaxLocRes = Core.minMaxLoc(result);

    double accuracy = 0;
    Point location = null;

    if (this.matchingMethod == MatchingMethod.MM_SQUARE_DIFFERENCE) {
        accuracy = 1 - minMaxLocRes.minVal;
        location = minMaxLocRes.minLoc;
    } else {
        accuracy = minMaxLocRes.maxVal;
        location = minMaxLocRes.maxLoc;
    }

    if (accuracy < desiredAccuracy) {
        throw new ImageNotFoundException(
                String.format(
                        "Failed to find template image in the source image. The accuracy was %.2f and the desired accuracy was %.2f",
                        accuracy,
                        desiredAccuracy),
                new Rectangle((int) location.x, (int) location.y, templateMat.width(), templateMat.height()),
                accuracy);
    }

    if (!minMaxLocResultIsValid(minMaxLocRes)) {
        throw new ImageNotFoundException(
                "Image find result (MinMaxLocResult) was invalid. This usually happens when the source image is covered in one solid color.",
                null,
                null);
    }

    Rectangle foundRect = new Rectangle(
            (int) location.x,
            (int) location.y,
            templateMat.width(),
            templateMat.height());

    return new ImageFinderResult(foundRect, accuracy);
}