org.opencv.core.MatOfFloat Java Examples

public void predict_hair_color()
{
    Mat hsv_input = matrix9_finalOutput.clone();
    List<Mat> channels= new ArrayList<>();
    Mat hsv_histogram = new Mat();
    MatOfFloat  ranges = new MatOfFloat(0,180);
    MatOfInt histSize = new MatOfInt(255);
    Imgproc.cvtColor(hsv_input, hsv_input, Imgproc.COLOR_BGR2HSV);
    Core.split(hsv_input, channels);
    Imgproc.calcHist(channels.subList(0,1), new MatOfInt(0), new Mat(), hsv_histogram, histSize, ranges);
    int hist_w =256;
    int hist_h = 150;
    
    int bin_w =(int)Math.round(hist_w/histSize.get(0,0)[0]);
    Mat histImage= new Mat(hist_h,hist_w,CvType.CV_8UC3,new Scalar(0,0,0));
    
    for(int i=1;i < histSize.get(0,0)[0];i++)
    {
        Imgproc.line(histImage, new Point(bin_w * (i - 1), hist_h - Math.round(hsv_histogram.get(i - 1, 0)[0])), new Point(bin_w * (i), hist_h - Math.round(hsv_histogram.get(i, 0)[0])), new Scalar(255,0,0),2);			    
    }
    Imgcodecs.imwrite(resultDirectory+"histogram_image.png",histImage);
}
 

public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err)
{
    Mat prevPts_mat = prevPts;
    Mat nextPts_mat = nextPts;
    Mat status_mat = status;
    Mat err_mat = err;
    calcOpticalFlowPyrLK_2(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj);
    
    return;
}
 

public  void compute(Mat img, MatOfFloat descriptors)
{
    Mat descriptors_mat = descriptors;
    compute_1(nativeObj, img.nativeObj, descriptors_mat.nativeObj);
    
    return;
}
 

public static BRISK create(MatOfFloat radiusList, MatOfInt numberList, float dMax)
{
    Mat radiusList_mat = radiusList;
    Mat numberList_mat = numberList;
    BRISK retVal = BRISK.__fromPtr__(create_10(radiusList_mat.nativeObj, numberList_mat.nativeObj, dMax));
    
    return retVal;
}
 

public  void compute(Mat img, MatOfFloat descriptors)
{
    Mat descriptors_mat = descriptors;
    compute_1(nativeObj, img.nativeObj, descriptors_mat.nativeObj);

    return;
}
 

public static void calcHist(List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges, boolean accumulate)
{
    Mat images_mat = Converters.vector_Mat_to_Mat(images);
    Mat channels_mat = channels;
    Mat histSize_mat = histSize;
    Mat ranges_mat = ranges;
    calcHist_0(images_mat.nativeObj, channels_mat.nativeObj, mask.nativeObj, hist.nativeObj, histSize_mat.nativeObj, ranges_mat.nativeObj, accumulate);
    
    return;
}
 

public static void fastNlMeansDenoising(Mat src, Mat dst, MatOfFloat h, int templateWindowSize, int searchWindowSize, int normType)
{
    Mat h_mat = h;
    fastNlMeansDenoising_2(src.nativeObj, dst.nativeObj, h_mat.nativeObj, templateWindowSize, searchWindowSize, normType);
    
    return;
}
 

public static void calcBackProject(List<Mat> images, MatOfInt channels, Mat hist, Mat dst, MatOfFloat ranges, double scale)
{
    Mat images_mat = Converters.vector_Mat_to_Mat(images);
    Mat channels_mat = channels;
    Mat ranges_mat = ranges;
    calcBackProject_0(images_mat.nativeObj, channels_mat.nativeObj, hist.nativeObj, dst.nativeObj, ranges_mat.nativeObj, scale);
    
    return;
}
 

public static void fastNlMeansDenoisingMulti(List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h)
{
    Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs);
    Mat h_mat = h;
    fastNlMeansDenoisingMulti_3(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h_mat.nativeObj);
    
    return;
}
 

public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria)
{
    Mat prevPts_mat = prevPts;
    Mat nextPts_mat = nextPts;
    Mat status_mat = status;
    Mat err_mat = err;
    calcOpticalFlowPyrLK_2(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height, maxLevel, criteria.type, criteria.maxCount, criteria.epsilon);
    
    return;
}
 

public  void compute(Mat img, MatOfFloat descriptors, Size winStride, Size padding, MatOfPoint locations)
{
    Mat descriptors_mat = descriptors;
    Mat locations_mat = locations;
    compute_0(nativeObj, img.nativeObj, descriptors_mat.nativeObj, winStride.width, winStride.height, padding.width, padding.height, locations_mat.nativeObj);
    
    return;
}
 

public static int chamerMatching(Mat img, Mat templ, List<MatOfPoint> results, MatOfFloat cost, double templScale, int maxMatches, double minMatchDistance, int padX, int padY, int scales, double minScale, double maxScale, double orientationWeight, double truncate)
{
    Mat results_mat = new Mat();
    Mat cost_mat = cost;
    int retVal = chamerMatching_0(img.nativeObj, templ.nativeObj, results_mat.nativeObj, cost_mat.nativeObj, templScale, maxMatches, minMatchDistance, padX, padY, scales, minScale, maxScale, orientationWeight, truncate);
    Converters.Mat_to_vector_vector_Point(results_mat, results);
    return retVal;
}
 

public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel)
{
    Mat prevPts_mat = prevPts;
    Mat nextPts_mat = nextPts;
    Mat status_mat = status;
    Mat err_mat = err;
    calcOpticalFlowPyrLK_1(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height, maxLevel);
    
    return;
}
 

public static void calcHist(List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges, boolean accumulate)
{
    Mat images_mat = Converters.vector_Mat_to_Mat(images);
    Mat channels_mat = channels;
    Mat histSize_mat = histSize;
    Mat ranges_mat = ranges;
    calcHist_0(images_mat.nativeObj, channels_mat.nativeObj, mask.nativeObj, hist.nativeObj, histSize_mat.nativeObj, ranges_mat.nativeObj, accumulate);
    
    return;
}
 

/**
 * @return Pair of new, FILTERED, last and current POINTS, or null if it hasn't managed to track anything.
 */
Pair<Point[], Point[]> track(final Mat lastImg, final Mat currentImg, Point[] lastPoints){
	final int size = lastPoints.length;
	final MatOfPoint2f currentPointsMat = new MatOfPoint2f();
	final MatOfPoint2f pointsFBMat = new MatOfPoint2f();
	final MatOfByte statusMat = new MatOfByte();
	final MatOfFloat errSimilarityMat = new MatOfFloat();
	final MatOfByte statusFBMat = new MatOfByte();
	final MatOfFloat errSimilarityFBMat = new MatOfFloat();
	
	//Forward-Backward tracking
	Video.calcOpticalFlowPyrLK(lastImg, currentImg, new MatOfPoint2f(lastPoints), currentPointsMat, 
			statusMat, errSimilarityMat, WINDOW_SIZE, MAX_LEVEL, termCriteria, 0, LAMBDA);
	Video.calcOpticalFlowPyrLK(currentImg, lastImg, currentPointsMat, pointsFBMat, 
			statusFBMat, errSimilarityFBMat, WINDOW_SIZE, MAX_LEVEL, termCriteria, 0, LAMBDA);
	
	final byte[] status = statusMat.toArray();
	float[] errSimilarity = new float[lastPoints.length]; 
	//final byte[] statusFB = statusFBMat.toArray();
	final float[] errSimilarityFB = errSimilarityFBMat.toArray();	
	
	// compute the real FB error (relative to LAST points not the current ones...
	final Point[] pointsFB = pointsFBMat.toArray();
	for(int i = 0; i < size; i++){
		errSimilarityFB[i] = Util.norm(pointsFB[i], lastPoints[i]);
	}
	
	final Point[] currPoints = currentPointsMat.toArray();
	// compute real similarity error
	errSimilarity = normCrossCorrelation(lastImg, currentImg, lastPoints, currPoints, status);
	
	
	//TODO  errSimilarityFB has problem != from C++
	// filter out points with fwd-back error > the median AND points with similarity error > median
	return filterPts(lastPoints, currPoints, errSimilarity, errSimilarityFB, status);
}
 

public static BRISK create(MatOfFloat radiusList, MatOfInt numberList)
{
    Mat radiusList_mat = radiusList;
    Mat numberList_mat = numberList;
    BRISK retVal = BRISK.__fromPtr__(create_11(radiusList_mat.nativeObj, numberList_mat.nativeObj));
    
    return retVal;
}
 

public static void fastNlMeansDenoisingMulti(List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h, int templateWindowSize, int searchWindowSize, int normType)
{
    Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs);
    Mat h_mat = h;
    fastNlMeansDenoisingMulti_4(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h_mat.nativeObj, templateWindowSize, searchWindowSize, normType);
    
    return;
}
 

public static void fastNlMeansDenoisingMulti(List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h)
{
    Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs);
    Mat h_mat = h;
    fastNlMeansDenoisingMulti_3(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h_mat.nativeObj);
    
    return;
}
 

public static BRISK create(MatOfFloat radiusList, MatOfInt numberList)
{
    Mat radiusList_mat = radiusList;
    Mat numberList_mat = numberList;
    BRISK retVal = new BRISK(create_5(radiusList_mat.nativeObj, numberList_mat.nativeObj));
    
    return retVal;
}
 

public static void calcHist(List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges)
{
    Mat images_mat = Converters.vector_Mat_to_Mat(images);
    Mat channels_mat = channels;
    Mat histSize_mat = histSize;
    Mat ranges_mat = ranges;
    calcHist_1(images_mat.nativeObj, channels_mat.nativeObj, mask.nativeObj, hist.nativeObj, histSize_mat.nativeObj, ranges_mat.nativeObj);
    
    return;
}
 

public static void calcHist(List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges, boolean accumulate)
{
    Mat images_mat = Converters.vector_Mat_to_Mat(images);
    Mat channels_mat = channels;
    Mat histSize_mat = histSize;
    Mat ranges_mat = ranges;
    calcHist_0(images_mat.nativeObj, channels_mat.nativeObj, mask.nativeObj, hist.nativeObj, histSize_mat.nativeObj, ranges_mat.nativeObj, accumulate);
    
    return;
}
 

public static void fastNlMeansDenoisingMulti(List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h)
{
    Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs);
    Mat h_mat = h;
    fastNlMeansDenoisingMulti_3(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h_mat.nativeObj);
    
    return;
}
 

public static void calcHist(List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges)
{
    Mat images_mat = Converters.vector_Mat_to_Mat(images);
    Mat channels_mat = channels;
    Mat histSize_mat = histSize;
    Mat ranges_mat = ranges;
    calcHist_1(images_mat.nativeObj, channels_mat.nativeObj, mask.nativeObj, hist.nativeObj, histSize_mat.nativeObj, ranges_mat.nativeObj);
    
    return;
}
 

public static void fastNlMeansDenoisingMulti(List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h, int templateWindowSize, int searchWindowSize, int normType)
{
    Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs);
    Mat h_mat = h;
    fastNlMeansDenoisingMulti_2(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h_mat.nativeObj, templateWindowSize, searchWindowSize, normType);
    
    return;
}
 

public  void compute(Mat img, MatOfFloat descriptors, Size winStride)
{
    Mat descriptors_mat = descriptors;
    compute_2(nativeObj, img.nativeObj, descriptors_mat.nativeObj, winStride.width, winStride.height);
    
    return;
}
 

public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria, int flags, double minEigThreshold)
{
    Mat prevPts_mat = prevPts;
    Mat nextPts_mat = nextPts;
    Mat status_mat = status;
    Mat err_mat = err;
    calcOpticalFlowPyrLK_0(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height, maxLevel, criteria.type, criteria.maxCount, criteria.epsilon, flags, minEigThreshold);
    
    return;
}
 

public static void fastNlMeansDenoising(Mat src, Mat dst, MatOfFloat h, int templateWindowSize, int searchWindowSize, int normType)
{
    Mat h_mat = h;
    fastNlMeansDenoising_2(src.nativeObj, dst.nativeObj, h_mat.nativeObj, templateWindowSize, searchWindowSize, normType);
    
    return;
}
 

public static BRISK create(MatOfFloat radiusList, MatOfInt numberList, float dMax, float dMin)
{
    Mat radiusList_mat = radiusList;
    Mat numberList_mat = numberList;
    BRISK retVal = BRISK.__fromPtr__(create_9(radiusList_mat.nativeObj, numberList_mat.nativeObj, dMax, dMin));
    
    return retVal;
}
 

public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel)
{
    Mat prevPts_mat = prevPts;
    Mat nextPts_mat = nextPts;
    Mat status_mat = status;
    Mat err_mat = err;
    calcOpticalFlowPyrLK_1(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height, maxLevel);
    
    return;
}
 

public  void compute(Mat img, MatOfFloat descriptors)
{
    Mat descriptors_mat = descriptors;
    compute_1(nativeObj, img.nativeObj, descriptors_mat.nativeObj);

    return;
}