Java MinMaxLocResult类代码示例

import org.opencv.core.Core.MinMaxLocResult; //导入依赖的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.MinMaxLocResult; //导入依赖的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);
}
 

import org.opencv.core.Core.MinMaxLocResult; //导入依赖的package包/类
private boolean minMaxLocResultIsValid(MinMaxLocResult minMaxLocRes) {
    if (minMaxLocRes.minVal == 1
            && minMaxLocRes.maxVal == 1
            && minMaxLocRes.maxLoc.x == 0
            && minMaxLocRes.maxLoc.y == 0
            && minMaxLocRes.minLoc.x == 0
            && minMaxLocRes.minLoc.y == 0) {

        return false;
    } else {
        return true;
    }
}
 

import org.opencv.core.Core.MinMaxLocResult; //导入依赖的package包/类
/**
 * Checks if the pattern can be found in the given area of interest and sets up feedback.
 * @param match_method Match Methods supported by OpenCV.
 * @param res ID of the resource pattern in the res directory.
 * @param resName Name of the resource to be returned.
 * @param thresh Threshold the best detection has to pass in order to be a successful detection.
 */
public void templateMatching(int match_method, int res, String resName, double thresh) {
	// Pattern Matching
	Point matchLocCode; double matchValCode;
	Log.i("HERE", "" + resName);
	Rect roiCodeArea = new Rect(new Point(showBit.cols() * card.getPattern(resName).getTl().x,showBit.rows() * card.getPattern(resName).getTl().y), new Point(showBit.cols() * card.getPattern(resName).getBr().x, showBit.rows() * card.getPattern(resName).getBr().y));
	Mat cropedCodeArea = showBit.submat(roiCodeArea); 
	Bitmap bmCode = BitmapFactory.decodeResource(getResources(), res);
	Mat cropedCode = new Mat ( bmCode.getHeight(), bmCode.getWidth(), CvType.CV_8U, new Scalar(4));
	Utils.bitmapToMat(bmCode, cropedCode);

	int result_cols_code = cropedCodeArea.cols() - cropedCode.cols() + 1;
	int result_rows_code = cropedCodeArea.rows() - cropedCode.rows() + 1;
	Mat resultCode = new Mat(result_rows_code, result_cols_code, CvType.CV_32FC1);

	Imgproc.matchTemplate(cropedCodeArea, cropedCode, resultCode, match_method);
	MinMaxLocResult mmrCode = Core.minMaxLoc(resultCode);

	if (match_method == Imgproc.TM_SQDIFF || match_method == Imgproc.TM_SQDIFF_NORMED) {
		matchLocCode = mmrCode.minLoc;
		matchValCode = mmrCode.minVal;
	} else {
		matchLocCode = mmrCode.maxLoc;
		matchValCode = mmrCode.maxVal;
	}
	Log.w("matchValCode", "" + matchValCode);
	// Pattern passes Detection
	if(matchValCode >= thresh) {
		// If detecting card and pattern passes: get the card that pattern belongs to.
		if(cardType.equals("-DETECT-")) {
			cardType = getFoundCard(resName);
			runOnUiThread(new Runnable() {
				@Override
				public void run() {
					Toast.makeText(
							CardValidationActivity.this,
							"Card detected: " + cardType,
							Toast.LENGTH_LONG
							).show();
				}
			});
		}
		else {
			Core.rectangle( cropedCodeArea, matchLocCode, new Point( matchLocCode.x + cropedCode.cols() , matchLocCode.y + cropedCode.rows() ), new Scalar(0, 255, 0, 255), 4 );
			theText.put("Pattern: " + resName, "PASSED");
		}
	}
	else {
		theText.put("Pattern: " + resName, "FAILED");
		failureCount++;
	}
}
 

import org.opencv.core.Core.MinMaxLocResult; //导入依赖的package包/类
public SingleScaleMatch(double fingerprintMatch, MinMaxLocResult minMaxLocResult, Rectangle.Int result) {
	this.fingerprintMatch = fingerprintMatch;
	this.minMaxLocResult = minMaxLocResult;
	this.result = result;
}
 

import org.opencv.core.Core.MinMaxLocResult; //导入依赖的package包/类
private static ScanMatch findMatch(Mat searchImageMat, Mat templateMat, double templateStdDev, double scale,
		Image.Int searchImageScaled, BigDecimal s) {		
	
	double templateScale = s.doubleValue() * scale;
	int w = (int) Math.round(templateMat.width() * templateScale);
	int h = (int) Math.round(templateMat.height() * templateScale);
	
	// early exit - template is bigger than search image
	if (templateMat.cols() * templateScale >= searchImageMat.cols() || templateMat.rows() * templateScale >= searchImageMat.rows()) {
		return null;
	}
	
	if (isTemplateTooSmall(w, h, s)) {
		return null;
	}			
	
	// scale
	Mat scaledTemplateMat = new MatOfFloat();
	resize(templateMat, scaledTemplateMat, new Size(w, h), 0, 0, CV_INTER_AREA);
	
	// normalized cross-corr
	Mat resultMatrix = new MatOfFloat();
	matchTemplate(searchImageMat, scaledTemplateMat, resultMatrix, TM_CCORR_NORMED);			
	MinMaxLocResult minMaxResult = minMaxLoc(resultMatrix);			
	
	// compute fingerprint for scaled template
	Image.Int templateForFingerprint = ImageUtil.Convert.toImage(OpenCV.matToBufferedImage(scaledTemplateMat));
	ImageFingerprint templateFingerprint = new ImageFingerprint(ImageUtil.toSquare(templateForFingerprint), 0xf2, 0xf1, 0xf0, FINGERPRINT_SIZE);
	
	// if template has low contrast bump it up
	if (templateStdDev < STDDEV_THRESHOLD) {
		
		Image.Int contrastedImage = ImageUtil.Convert.toImageInt(Contrast.autoContrast(ImageUtil.Convert.toImageByte(templateForFingerprint)));
		templateFingerprint = new ImageFingerprint(ImageUtil.toSquare(contrastedImage), 0xf2, 0xf1, 0xf0, FINGERPRINT_SIZE);
	}
	
	// cut the possible area from the image and get fingerprint probability for it		
	Rectangle.Int resultRectangle = new Rectangle.Int((int) minMaxResult.maxLoc.x, (int) minMaxResult.maxLoc.y, w, h);
	SingleScaleMatch singleScaleMatch = getMatchForRectangle(searchImageScaled, templateFingerprint, templateStdDev, minMaxResult, resultRectangle);
		
	// free
	resultMatrix.release();
	
	return new ScanMatch(singleScaleMatch.fingerprintMatch, scaleRectangle(singleScaleMatch.result, 1 / scale), s);
}
 

import org.opencv.core.Core.MinMaxLocResult; //导入依赖的package包/类
private static SingleScaleMatch getMatchForRectangle(Image.Int searchImage, ImageFingerprint templateFingerprint,
		double templateStdDev, MinMaxLocResult result, Rectangle.Int resultRectangle) {
	Image.Int crop = ImageUtil.Cut.crop(searchImage, resultRectangle);
	
	// also increase contrast for crop of search image
	Image.Int contrast = applyContrast(crop, templateStdDev < STDDEV_THRESHOLD);
	ImageFingerprint resultFingerprint = new ImageFingerprint(ImageUtil.toSquare(contrast), 0xf2, 0xf1, 0xf0, FINGERPRINT_SIZE);		
	
	double stddev1 = stddev(templateFingerprint);
	double stddev2 = stddev(resultFingerprint);
	double stddevMatch = Math.min(stddev1, stddev2) / Math.max(stddev1, stddev2);
	
	
	double fingerprintProbability = fingerprintMatch(templateFingerprint, resultFingerprint);			
	
	double matchProbability = stddevMatch > 0.8 ? fingerprintProbability : fingerprintProbability * stddevMatch;		
	
	return new SingleScaleMatch(matchProbability, result, resultRectangle);
}
 

import org.opencv.core.Core.MinMaxLocResult; //导入依赖的package包/类
@Override
	public void execute(Raster raster, Map<Key, Serializable> params,Hints hints, ProgressListener listener) {
		
		
		double[] minMax = null;
		if(params != null){
			if(params.containsKey(KEY_MINMAX)){
				 minMax = (double[]) params.get(KEY_MINMAX);									
			}
		}
		
		final int raster_width  = raster.getDimension().width();
		final int raster_height = raster.getDimension().height();
		
		final int pixelAmount = raster_width * raster_height;
		
		if(minMax == null){
			final Mat srcMat = matAccordingToDatatype(
					raster.getBands().get(0).datatype(),
					raster.getData(),
					raster_width,
					raster_height);

			MinMaxLocResult result = Core.minMaxLoc(srcMat);
			minMax = new double[]{result.minVal, result.maxVal};
		}
		
		int[] pixels = new int[pixelAmount];
 
		final ByteBufferReader reader = new ByteBufferReader(raster.getData().array(), ByteOrder.nativeOrder());

//    	Log.d(OpenCVAmplitudeRescaler.class.getSimpleName(), "rawdata min "+minMax[0] +" max "+minMax[1]);


    	for (int i = 0; i < pixelAmount; i++) {
        	
        	double d = ByteBufferReaderUtil.getValue(reader, raster.getBands().get(0).datatype());

    		pixels[i] = pixelValueForGrayScale(d, minMax[0], minMax[1]);

        }

		ByteBuffer buffer = ByteBuffer.allocate(pixels.length * 4);
		
		buffer.asIntBuffer().put(pixels);
		
		raster.setData(buffer);
	}