Java OpenCV Tesseract OCR“代码”regocnition

我也学到了很多东西.特别是关于速度.此功能在高分辨率图像上很慢.尺寸为3264 x 1836,最多可能需要10秒钟.

我所做的是为了加快速度,将输入矩阵的大小调整为1/4.这使得它的速度提高了4 ^ 2倍,并且给了我最小的精度损失.下一步是缩放我们发现回到正常大小的四边形.这样我们就可以使用原始源将四边形转换为普通视角.

我为检测最大区域而创建的代码很大程度上基于我在stackoverflow上找到的代码.不幸的是,他们没有像我预期的那样工作,所以我结合了更多的代码片段并进行了大量修改.
这就是我得到的：

private static double angle(Point p1,Point p2,Point p0 ) {
        double dx1 = p1.x - p0.x;
        double dy1 = p1.y - p0.y;
        double dx2 = p2.x - p0.x;
        double dy2 = p2.y - p0.y;
        return (dx1 * dx2 + dy1 * dy2) / Math.sqrt((dx1 * dx1 + dy1 * dy1) * (dx2 * dx2 + dy2 * dy2) + 1e-10);
    }



    private static MatOfPoint find(Mat src) throws Exception {
        Mat blurred = src.clone();
        Imgproc.medianBlur(src,blurred,9);

        Mat gray0 = new Mat(blurred.size(),CvType.CV_8U),gray = new Mat();

        List<MatOfPoint> contours = new ArrayList<>();

        List<Mat> blurredChannel = new ArrayList<>();
        blurredChannel.add(blurred);
        List<Mat> gray0Channel = new ArrayList<>();
        gray0Channel.add(gray0);

        MatOfPoint2f approxCurve;

        double maxArea = 0;
        int maxId = -1;

        for (int c = 0; c < 3; c++) {
            int ch[] = {c,0};
            Core.mixChannels(blurredChannel,gray0Channel,new MatOfInt(ch));

            int thresholdLevel = 1;
            for (int t = 0; t < thresholdLevel; t++) {
                if (t == 0) {
                    Imgproc.Canny(gray0,gray,10,20,3,true); // true ?
                    Imgproc.dilate(gray,new Mat(),new Point(-1,-1),1); // 1 ?
                } else {
                    Imgproc.adaptiveThreshold(gray0,thresholdLevel,Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY,(src.width() + src.height()) / 200,t);
                }

                Imgproc.findContours(gray,contours,Imgproc.RETR_LIST,Imgproc.CHAIN_APPROX_SIMPLE);

                for (MatOfPoint contour : contours) {
                    MatOfPoint2f temp = new MatOfPoint2f(contour.toArray());

                    double area = Imgproc.contourArea(contour);
                    approxCurve = new MatOfPoint2f();
                    Imgproc.approxPolyDP(temp,approxCurve,Imgproc.arcLength(temp,true) * 0.02,true);

                    if (approxCurve.total() == 4 && area >= maxArea) {
                        double maxCosine = 0;

                        List<Point> curves = approxCurve.toList();
                        for (int j = 2; j < 5; j++)
                        {

                            double cosine = Math.abs(angle(curves.get(j % 4),curves.get(j - 2),curves.get(j - 1)));
                            maxCosine = Math.max(maxCosine,cosine);
                        }

                        if (maxCosine < 0.3) {
                            maxArea = area;
                            maxId = contours.indexOf(contour);
                            //contours.set(maxId,getHull(contour));
                        }
                    }
                }
            }
        }

        if (maxId >= 0) {
            return contours.get(maxId);
            //Imgproc.drawContours(src,maxId,new Scalar(255,.8),8);
        }
        return null;
    }

你可以像这样调用它：

MathOfPoint contour = find(src);

从轮廓中查看此四边形检测的答案并将其转换为简单的视角：
Java OpenCV deskewing a contour