OpenCV Java 实现票据、纸张的四边形边缘检测与提取、摆正

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    最近公司让研究用opencv来做发票的提取摆正，简单的说就是如下图所示

原图：

结果：

说说过程吧，网上找了很多范例，也试了很多。很多贴的代码都不全，要么就不是用java来实现的，下面是实现如上功能的具体java代码：

public static void main(String[] args) {
    logger.info("测试票据、纸张的四边形边缘检测与提取、摆正程序开始....");
    //这个必须配置，否则会报错
    System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    Mat img = Imgcodecs.imread("D:\\orc\\7.jpg");
    if(img.empty()){
        logger.info("读取的图片信息为空");
        return;
    }
    Mat greyImg = img.clone();
    //1.彩色转灰色
    Imgproc.cvtColor(img, greyImg, Imgproc.COLOR_BGR2GRAY);
    createImage(greyImg, "D:\\orc\\huise.jpg");

    Mat gaussianBlurImg = greyImg.clone();
    // 2.高斯滤波，降噪
    Imgproc.GaussianBlur(greyImg, gaussianBlurImg, new Size(3,3),2,2);
    createImage(gaussianBlurImg, "D:\\orc\\jiangzao.jpg");

    Mat cannyImg = gaussianBlurImg.clone();
    // 3.Canny边缘检测
    Imgproc.Canny(gaussianBlurImg, cannyImg, 20, 60, 3, false);
    createImage(cannyImg, "D:\\orc\\bianyuanjiance.jpg");
    // 4.膨胀，连接边缘
    Mat dilateImg = cannyImg.clone();
    Imgproc.dilate(cannyImg, dilateImg, new Mat(), new Point(-1, -1), 2, 1, new Scalar(1));
    createImage(dilateImg, "D:\\orc\\bianyuanjiance2.jpg");
    //5.对边缘检测的结果图再进行轮廓提取
    List<MatOfPoint> contours = new ArrayList<>();
    List<MatOfPoint> drawContours = new ArrayList<>();
    Mat hierarchy = new Mat();
    Imgproc.findContours(dilateImg, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
    Mat linePic = Mat.zeros(dilateImg.rows(), dilateImg.cols(), CvType.CV_8UC3);

    //6.找出轮廓对应凸包的四边形拟合
    List<MatOfPoint> squares = new ArrayList<>();
    List<MatOfPoint> hulls = new ArrayList<>();
    MatOfInt hull = new MatOfInt();
    MatOfPoint2f approx = new MatOfPoint2f();
    approx.convertTo(approx, CvType.CV_32F);

    for (int i = 0; i < contours.size(); i++) {
        MatOfPoint contour = contours.get(i);
        // 边框的凸包
        Imgproc.convexHull(contour, hull);
        // 用凸包计算出新的轮廓点
        Point[] contourPoints = contour.toArray();
        int[] indices = hull.toArray();
        List<Point> newPoints = new ArrayList<>();
        for (int index : indices) {
            newPoints.add(contourPoints[index]);
        }
        MatOfPoint2f contourHull = new MatOfPoint2f();
        contourHull.fromList(newPoints);
        // 多边形拟合凸包边框(此时的拟合的精度较低)
        Imgproc.approxPolyDP(contourHull, approx, Imgproc.arcLength(contourHull, true)*0.02, true);
        MatOfPoint mat = new MatOfPoint();
        mat.fromArray(approx.toArray());
        drawContours.add(mat);
        // 筛选出面积大于某一阈值的，且四边形的各个角度都接近直角的凸四边形
        MatOfPoint approxf1 = new MatOfPoint();
        approx.convertTo(approxf1, CvType.CV_32S);
        if (approx.rows() == 4 && Math.abs(Imgproc.contourArea(approx)) > 40000 &&
                Imgproc.isContourConvex(approxf1)) {
            double maxCosine = 0;
            for (int j = 2; j < 5; j++) {
                double cosine = Math.abs(getAngle(approxf1.toArray()[j%4], approxf1.toArray()[j-2], approxf1.toArray()[j-1]));
                maxCosine = Math.max(maxCosine, cosine);
            }
            // 角度大概72度
            if (maxCosine < 0.3) {
                MatOfPoint tmp = new MatOfPoint();
                contourHull.convertTo(tmp, CvType.CV_32S);
                squares.add(approxf1);
                hulls.add(tmp);
            }
        }
    }
    //这里是把提取出来的轮廓通过不同颜色的线描述出来，具体效果可以自己去看
    Random r = new Random();
    for (int i = 0; i < drawContours.size(); i++) {
        Imgproc.drawContours(linePic, drawContours, i, new Scalar(r.nextInt(255),r.nextInt(255), r.nextInt(255)));
    }
    createImage(linePic, "D:\\orc\\drawContours1.jpg");
    //7.找出最大的矩形
    int index = findLargestSquare(squares);
    MatOfPoint largest_square = squares.get(index);
    Mat polyPic = Mat.zeros(img.size(), CvType.CV_8UC3);
    Imgproc.drawContours(polyPic, squares, index, new Scalar(0, 0,255), 2);
    createImage(polyPic, "D:\\orc\\drawContours2.jpg");
    //存储矩形的四个凸点
    hull = new MatOfInt();
    Imgproc.convexHull(largest_square, hull, false);
    List<Integer> hullList =  hull.toList();
    List<Point> polyContoursList = largest_square.toList();
    List<Point> hullPointList = new ArrayList<>();
    List<Point> lastHullPointList = new ArrayList<>();
    for(int i = 0; i < hullList.size();i++){
        Imgproc.circle(polyPic, polyContoursList.get(hullList.get(i)), 10, new Scalar(r.nextInt(255),r.nextInt(255), r.nextInt(255), 3));
        hullPointList.add(polyContoursList.get(hullList.get(i)));
        System.out.println(hullList.get(i));
    }
    Core.addWeighted(polyPic, 0.5, img, 0.5, 0, img);
    createImage(img, "D:\\orc\\addWeighted.jpg");
    for(int i = 0; i < hullPointList.size(); i++){
        lastHullPointList.add(hullPointList.get(i));
    }
    //dstPoints储存的是变换后各点的坐标，依次为左上，右上，右下， 左下
    //srcPoints储存的是上面得到的四个角的坐标
    Point[] dstPoints = {new Point(0,0), new Point(img.cols(),0), new Point(img.cols(),img.rows()), new Point(0,img.rows())};
    Point[] srcPoints = new Point[4];
    boolean sorted = false;
    int n = 4;
    //对四个点进行排序 分出左上 右上 右下 左下
    while (!sorted){
        for (int i = 1; i < n; i++){
            sorted = true;
            if (lastHullPointList.get(i-1).x > lastHullPointList.get(i).x){
                Point tempp1 = lastHullPointList.get(i);
                Point tempp2 = lastHullPointList.get(i-1);
                lastHullPointList.set(i, tempp2);
                lastHullPointList.set(i-1, tempp1);
                sorted = false;
            }
        }
        n--;
    }

    //即先对四个点的x坐标进行冒泡排序分出左右，再根据两对坐标的y值比较分出上下
    if (lastHullPointList.get(0).y < lastHullPointList.get(1).y){
        srcPoints[0] = lastHullPointList.get(0);
        srcPoints[3] = lastHullPointList.get(1);
    }else{
        srcPoints[0] = lastHullPointList.get(1);
        srcPoints[3] = lastHullPointList.get(0);
    }
    if (lastHullPointList.get(2).y < lastHullPointList.get(3).y){
        srcPoints[1] = lastHullPointList.get(2);
        srcPoints[2] = lastHullPointList.get(3);
    }else{
        srcPoints[1] = lastHullPointList.get(3);
        srcPoints[2] = lastHullPointList.get(2);
    }
    List<Point> listSrcs = java.util.Arrays.asList(srcPoints[0], srcPoints[1], srcPoints[2], srcPoints[3]);
    Mat srcPointsMat = Converters.vector_Point_to_Mat(listSrcs, CvType.CV_32F);

    List<Point> dstSrcs = java.util.Arrays.asList(dstPoints[0], dstPoints[1], dstPoints[2], dstPoints[3]);
    Mat dstPointsMat = Converters.vector_Point_to_Mat(dstSrcs, CvType.CV_32F);
    //参数分别为输入输出图像、变换矩阵、大小。
    //坐标变换后就得到了我们要的最终图像。
    Mat transMat = Imgproc.getPerspectiveTransform(srcPointsMat, dstPointsMat);    //得到变换矩阵
    Mat outPic = new Mat();
    Imgproc.warpPerspective(img, outPic, transMat, img.size());
    createImage(outPic, "D:\\orc\\outPic.jpg");

    logger.info("测试票据、纸张的四边形边缘检测与提取、摆正程序结束....");
}

// 根据三个点计算中间那个点的夹角   pt1 pt0 pt2
private static double getAngle(Point pt1, Point pt2, Point pt0)
{
    double dx1 = pt1.x - pt0.x;
    double dy1 = pt1.y - pt0.y;
    double dx2 = pt2.x - pt0.x;
    double dy2 = pt2.y - pt0.y;
    return (dx1*dx2 + dy1*dy2)/Math.sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10);
}

// 找到最大的正方形轮廓
private static int findLargestSquare(List<MatOfPoint> squares) {
    if (squares.size() == 0)
        return -1;
    int max_width = 0;
    int max_height = 0;
    int max_square_idx = 0;
    int currentIndex = 0;
    for (MatOfPoint square : squares) {
        Rect rectangle = Imgproc.boundingRect(square);
        if (rectangle.width >= max_width && rectangle.height >= max_height) {
            max_width = rectangle.width;
            max_height = rectangle.height;
            max_square_idx = currentIndex;
        }
        currentIndex++;
    }
    return max_square_idx;
}


/**
 * 创建一张图片到指定位置
 * @param mat
 * @param fileName
 */
public static void createImage(Mat mat, String fileName) {
    Mat dst = mat.clone();
    Imgcodecs.imwrite(fileName, dst);
}

代码是经过本机测试的，使用如上图片是没有问题的，这里只是提供了一种大体思路，本文参考了：https://www.cnblogs.com/josephkim/p/8319069.html 

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