使用OpenCV进行Android Paper Detection

时间:2017-08-24 11:38:40

标签: android opencv image-processing object-detection

我正在尝试通过OpenCV实现Paper检测。我能够理解如何获得它的概念,

输入 - > Canny-> Blur->找到Conture->搜索(关闭)四边形 - > Draw Conture

但是,我还是OpenCV编程的新手。因此在实施它时遇到问题。我通过这个答案找到了帮助

Android OpenCV Paper Sheet detection

但是它在每个可能的衬里上绘制轮廓。这是我试图实现的代码。

public Mat onCameraFrame(CvCameraViewFrame inputFrame) {

    mRgba = inputFrame.rgba(); 
    Imgproc.drawContours(mRgba,findContours(mRgba), 0, new Scalar(0 , 255, 0), 5);
    return mRgba; 
}

public static class Quadrilateral {
    public MatOfPoint contour;
    public Point[] points;

    public Quadrilateral(MatOfPoint contour, Point[] points) {
        this.contour = contour;
        this.points = points;
    }
}

public static Quadrilateral findDocument( Mat inputRgba ) {
    ArrayList<MatOfPoint> contours = findContours(inputRgba);
    Quadrilateral quad = getQuadrilateral(contours);
    return quad;
}

private static ArrayList<MatOfPoint> findContours(Mat src) {

    double ratio = src.size().height / 500;
    int height = Double.valueOf(src.size().height / ratio).intValue();
    int width = Double.valueOf(src.size().width / ratio).intValue();
    Size size = new Size(width,height);

    Mat resizedImage = new Mat(size, CvType.CV_8UC4);
    Mat grayImage = new Mat(size, CvType.CV_8UC4);
    Mat cannedImage = new Mat(size, CvType.CV_8UC1);

    Imgproc.resize(src,resizedImage,size);
    Imgproc.cvtColor(resizedImage, grayImage, Imgproc.COLOR_RGBA2GRAY, 4);
    Imgproc.GaussianBlur(grayImage, grayImage, new Size(5, 5), 0);
    Imgproc.Canny(grayImage, cannedImage, 75, 200);

    ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
    Mat hierarchy = new Mat();

    Imgproc.findContours(cannedImage, contours, hierarchy, Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);

    hierarchy.release();

    Collections.sort(contours, new Comparator<MatOfPoint>() {

        @Override
        public int compare(MatOfPoint lhs, MatOfPoint rhs) {
            return Double.valueOf(Imgproc.contourArea(rhs)).compareTo(Imgproc.contourArea(lhs));
        }
    });

    resizedImage.release();
    grayImage.release();
    cannedImage.release();

    return contours;
}

private static Quadrilateral getQuadrilateral(ArrayList<MatOfPoint> contours) {

    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();

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

            return new Quadrilateral(c, foundPoints);
        }
    }

    return null;
}

private static Point[] sortPoints(Point[] src) {

    ArrayList<Point> srcPoints = new ArrayList<>(Arrays.asList(src));

    Point[] result = { null , null , null , null };

    Comparator<Point> sumComparator = new Comparator<Point>() {
        @Override
        public int compare(Point lhs, Point rhs) {
            return Double.valueOf(lhs.y + lhs.x).compareTo(rhs.y + rhs.x);
        }
    };

    Comparator<Point> diffComparator = new Comparator<Point>() {

        @Override
        public int compare(Point lhs, Point rhs) {
            return Double.valueOf(lhs.y - lhs.x).compareTo(rhs.y - rhs.x);
        }
    };

    // top-left corner = minimal sum
    result[0] = Collections.min(srcPoints, sumComparator);

    // bottom-right corner = maximal sum
    result[2] = Collections.max(srcPoints, sumComparator);

    // top-right corner = minimal diference
    result[1] = Collections.min(srcPoints, diffComparator);

    // bottom-left corner = maximal diference
    result[3] = Collections.max(srcPoints, diffComparator);

    return result;
}

答案表明我应该使用Quadrilateral Object并使用Imgproc.drawContours()调用它,但是此函数将ArrayList作为参数,其中Quadrilateral对象包含MatofPoint和Point []。有人可以帮我解决这个问题。我正在使用OpenCV(3.3)和Android(1.5.1)?

以下是示例应该是什么样子 Expected Output

0 个答案:

没有答案