Java Code Examples for org.opencv.core.MatOfPoint2f#toArray()

static public Quadrilateral getQuadrilateral(List<MatOfPoint> contours, Size srcSize){
    double ratio = getScaleRatio(srcSize);
    int height = Double.valueOf(srcSize.height / ratio).intValue();
    int width = Double.valueOf(srcSize.width / ratio).intValue();
    Size size = new Size(width,height);

    for ( MatOfPoint c: contours ) {
        MatOfPoint2f c2f = new MatOfPoint2f(c.toArray());
        double peri = Imgproc.arcLength(c2f, true);
        MatOfPoint2f approx = new MatOfPoint2f();
        Imgproc.approxPolyDP(c2f, approx, 0.02 * peri, true);

        Point[] points = approx.toArray();
        Log.d("SCANNER", "approx size: " + points.length);

        // select biggest 4 angles polygon
        if (points.length == 4) {
            Point[] foundPoints = sortPoints(points);

            if (isInside(foundPoints, size) && isLargeEnough(foundPoints, size, 0.25)) {
                return new Quadrilateral( c , foundPoints );
            }
            else{
                //showToast(context, "Try getting closer to the ID");
                Log.d("SCANNER", "Not inside defined area");
            }
        }
    }

    //showToast(context, "Make sure the ID is on a contrasting background");
    return null;
}
 

private MatOfPoint2f sortPointsForWarpPerspective(final MatOfPoint2f boardRect, final Point[] corners) {
	final Point[] cornerArray = new Point[4];
	final Double[] cornerED = new Double[4];
	final Point[] boardRectArray = boardRect.toArray();
	for (int i = 0; i < 4; i++)
		cornerED[i] = -1.0;
	for (final Point cpt : corners) {
		for (int i = 0; i < 4; i++) {

			final double tempED = euclideanDistance(cpt, boardRectArray[i]);
			if (cornerED[i] == -1.0 || tempED < cornerED[i]) {
				cornerArray[i] = cpt;
				cornerED[i] = tempED;
			}
		}
	}

	final MatOfPoint2f corners2f = new MatOfPoint2f();
	corners2f.fromArray(cornerArray);
	return corners2f;
}
 

private boolean isRectangle(MatOfPoint2f polygon, int srcArea) {
    MatOfPoint polygonInt = MathUtils.toMatOfPointInt(polygon);

    if (polygon.rows() != 4) {
        return false;
    }

    double area = Math.abs(Imgproc.contourArea(polygon));
    if (area < srcArea * AREA_LOWER_THRESHOLD || area > srcArea * AREA_UPPER_THRESHOLD) {
        return false;
    }

    if (!Imgproc.isContourConvex(polygonInt)) {
        return false;
    }

    // Check if the all angles are more than 72.54 degrees (cos 0.3).
    double maxCosine = 0;
    Point[] approxPoints = polygon.toArray();

    for (int i = 2; i < 5; i++) {
        double cosine = Math.abs(MathUtils.angle(approxPoints[i % 4], approxPoints[i - 2], approxPoints[i - 1]));
        maxCosine = Math.max(cosine, maxCosine);
    }

    return !(maxCosine >= 0.3);
}
 

private Size getRectangleSize(MatOfPoint2f rectangle) {
    Point[] corners = rectangle.toArray();

    double top = getDistance(corners[0], corners[1]);
    double right = getDistance(corners[1], corners[2]);
    double bottom = getDistance(corners[2], corners[3]);
    double left = getDistance(corners[3], corners[0]);

    double averageWidth = (top + bottom) / 2f;
    double averageHeight = (right + left) / 2f;

    return new Size(new Point(averageWidth, averageHeight));
}
 

/**
 * @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);
}
 

/**
 * Locate rectangles in an image
 *
 * @param grayImage Grayscale image
 * @return Rectangle locations
 */
public RectangleLocationResult locateRectangles(Mat grayImage) {
    Mat gray = grayImage.clone();

    //Filter out some noise by halving then doubling size
    Filter.downsample(gray, 2);
    Filter.upsample(gray, 2);

    //Mat is short for Matrix, and here is used to store an image.
    //it is n-dimensional, but as an image, is two-dimensional
    Mat cacheHierarchy = new Mat();
    Mat grayTemp = new Mat();
    List<Rectangle> rectangles = new ArrayList<>();
    List<Contour> contours = new ArrayList<>();

    //This finds the edges using a Canny Edge Detector
    //It is sent the grayscale Image, a temp Mat, the lower detection threshold for an edge,
    //the higher detection threshold, the Aperture (blurring) of the image - higher is better
    //for long, smooth edges, and whether a more accurate version (but time-expensive) version
    //should be used (true = more accurate)
    //Note: the edges are stored in "grayTemp", which is an image where everything
    //is black except for gray-scale lines delineating the edges.
    Imgproc.Canny(gray, grayTemp, 0, THRESHOLD_CANNY, APERTURE_CANNY, true);
    //make the white lines twice as big, while leaving the image size constant
    Filter.dilate(gray, 2);

    List<MatOfPoint> contoursTemp = new ArrayList<>();
    //Find contours - the parameters here are very important to compression and retention
    //grayTemp is the image from which the contours are found,
    //contoursTemp is where the resultant contours are stored (note: color is not retained),
    //cacheHierarchy is the parent-child relationship between the contours (e.g. a contour
    //inside of another is its child),
    //Imgproc.CV_RETR_LIST disables the hierarchical relationships being returned,
    //Imgproc.CHAIN_APPROX_SIMPLE means that the contour is compressed from a massive chain of
    //paired coordinates to just the endpoints of each segment (e.g. an up-right rectangular
    //contour is encoded with 4 points.)
    Imgproc.findContours(grayTemp, contoursTemp, cacheHierarchy, Imgproc.CV_RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
    //MatOfPoint2f means that is a MatofPoint (Matrix of Points) represented by floats instead of ints
    MatOfPoint2f approx = new MatOfPoint2f();
    //For each contour, test whether the contour is a rectangle
    //List<Contour> contours = new ArrayList<>()
    for (MatOfPoint co : contoursTemp) {
        //converting the MatOfPoint to MatOfPoint2f
        MatOfPoint2f matOfPoint2f = new MatOfPoint2f(co.toArray());
        //converting the matrix to a Contour
        Contour c = new Contour(co);

        //Attempt to fit the contour to the best polygon
        //input: matOfPoint2f, which is the contour found earlier
        //output: approx, which is the MatOfPoint2f that holds the new polygon that has less vertices
        //basically, it smooths out the edges using the third parameter as its approximation accuracy
        //final parameter determines whether the new approximation must be closed (true=closed)
        Imgproc.approxPolyDP(matOfPoint2f, approx,
                c.arcLength(true) * EPLISON_APPROX_TOLERANCE_FACTOR, true);

        //converting the MatOfPoint2f to a contour
        Contour approxContour = new Contour(approx);

        //Make sure the contour is big enough, CLOSED (convex), and has exactly 4 points
        if (approx.toArray().length == 4 &&
                Math.abs(approxContour.area()) > 1000 &&
                approxContour.isClosed()) {

            //TODO contours and rectangles array may not match up, but why would they?
            contours.add(approxContour);

            //Check each angle to be approximately 90 degrees
            //Done by comparing the three points constituting the angle of each corner
            double maxCosine = 0;
            for (int j = 2; j < 5; j++) {
                double cosine = Math.abs(MathUtil.angle(approx.toArray()[j % 4],
                        approx.toArray()[j - 2], approx.toArray()[j - 1]));
                maxCosine = Math.max(maxCosine, cosine);
            }

            if (maxCosine < MAX_COSINE_VALUE) {
                //Convert the points to a rectangle instance
                rectangles.add(new Rectangle(approx.toArray()));
            }
        }
    }

    return new RectangleLocationResult(contours, rectangles);
}
 

/**
 * 利用函数approxPolyDP来对指定的点集进行逼近 精确度设置好，效果还是比较好的
 *
 * @param cannyMat
 * @param threshold
 *            阀值(精确度)
 * @return
 */
public static Point[] useApproxPolyDPFindPoints(Mat cannyMat, double threshold) {

	MatOfPoint maxContour = findMaxContour(cannyMat);

	MatOfPoint2f approxCurve = new MatOfPoint2f();
	MatOfPoint2f matOfPoint2f = new MatOfPoint2f(maxContour.toArray());

	// 原始曲线与近似曲线之间的最大距离设置为0.01，true表示是闭合的曲线
	Imgproc.approxPolyDP(matOfPoint2f, approxCurve, threshold, true);

	Point[] points = approxCurve.toArray();

	return points;
}
 

/**
 * Instantiate a contour from an OpenCV matrix of points (double)
 *
 * @param data OpenCV matrix of points
 */
public Contour(MatOfPoint2f data) {
    this.mat = new MatOfPoint(data.toArray());
}