java+opencv4.5.0 实现获取照片所有红色角点。再用线条连接

实现这个功能，可以按照以下步骤进行：

1. 导入 OpenCV Java 库，创建 Mat 对象读取照片。

System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
Mat image = Imgcodecs.imread("image.jpg");

2. 将图片转换为 HSV 色彩空间，在 HSV 色彩空间中，红色的范围是两个区间：[0,10] 和 [170,180]，使用 inRange 函数提取出红色区域。

Mat hsvImage = new Mat();
Imgproc.cvtColor(image, hsvImage, Imgproc.COLOR_BGR2HSV);

Mat redMask1 = new Mat();
Mat redMask2 = new Mat();
Core.inRange(hsvImage, new Scalar(0, 70, 50), new Scalar(10, 255, 255), redMask1);
Core.inRange(hsvImage, new Scalar(170, 70, 50), new Scalar(180, 255, 255), redMask2);
Core.bitwise_or(redMask1, redMask2, redMask);

3. 对提取出来的红色区域进行形态学操作，将小的噪点去除，只留下角点。

Mat kernel = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5, 5));
Imgproc.morphologyEx(redMask, redMask, Imgproc.MORPH_CLOSE, kernel);

Mat corners = new Mat();
Imgproc.cornerHarris(redMask, corners, 2, 3, 0.04);
Core.normalize(corners, corners, 0, 255, Core.NORM_MINMAX);

4. 在角点位置绘制圆点，并用线条连接所有角点。

for (int i = 0; i < corners.rows(); i++) {
    for (int j = 0; j < corners.cols(); j++) {
        double[] corner = corners.get(i, j);
        if (corner[0] > threshold) {
            Imgproc.circle(image, new Point(j, i), 3, new Scalar(0, 255, 0), 2);
            cornersList.add(new Point(j, i));
        }
    }
}

Mat lines = new Mat();
Imgproc.HoughLinesP(redMask, lines, 1, Math.PI / 180, 50, 50, 10);
for (int i = 0; i < lines.rows(); i++) {
    double[] line = lines.get(i, 0);
    Point pt1 = new Point(line[0], line[1]);
    Point pt2 = new Point(line[2], line[3]);
    Imgproc.line(image, pt1, pt2, new Scalar(0, 0, 255), 2);
}

完整代码如下：

import org.opencv.core.*;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.highgui.HighGui;

import java.util.ArrayList;
import java.util.List;

public class RedCornerDetection {
    public static void main(String[] args) {
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

        Mat image = Imgcodecs.imread("image.jpg");
        Mat hsvImage = new Mat();
        Imgproc.cvtColor(image, hsvImage, Imgproc.COLOR_BGR2HSV);

        Mat redMask1 = new Mat();
        Mat redMask2 = new Mat();
        Core.inRange(hsvImage, new Scalar(0, 70, 50), new Scalar(10, 255, 255), redMask1);
        Core.inRange(hsvImage, new Scalar(170, 70, 50), new Scalar(180, 255, 255), redMask2);
        Mat redMask = new Mat();
        Core.bitwise_or(redMask1, redMask2, redMask);

        Mat kernel = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5, 5));
        Imgproc.morphologyEx(redMask, redMask, Imgproc.MORPH_CLOSE, kernel);

        Mat corners = new Mat();
        Imgproc.cornerHarris(redMask, corners, 2, 3, 0.04);
        Core.normalize(corners, corners, 0, 255, Core.NORM_MINMAX);

        List<Point> cornersList = new ArrayList<>();
        double threshold = 100000;
        for (int i = 0; i < corners.rows(); i++) {
            for (int j = 0; j < corners.cols(); j++) {
                double[] corner = corners.get(i, j);
                if (corner[0] > threshold) {
                    Imgproc.circle(image, new Point(j, i), 3, new Scalar(0, 255, 0), 2);
                    cornersList.add(new Point(j, i));
                }
            }
        }

        Mat lines = new Mat();
        Imgproc.HoughLinesP(redMask, lines, 1, Math.PI / 180, 50, 50, 10);
        for (int i = 0; i < lines.rows(); i++) {
            double[] line = lines.get(i, 0);
            Point pt1 = new Point(line[0], line[1]);
            Point pt2 = new Point(line[2], line[3]);
            Imgproc.line(image, pt1, pt2, new Scalar(0, 0, 255), 2);
        }

        HighGui.imshow("Red Corner Detection", image);
        HighGui.waitKey();
    }
}