OpenCvSharp4.6 编写多线程CircularCaliper找圆心函数 ， 输入图像、找圆扇形区域的最小半径和最大半径、找圆扇形中心点、设定扇形区域起始角度、扇形区域结束角度 复制出扇形区域内图像作为找圆分析的图像 输入Caliper个数，宽度、长度、 将每个Caliper区域roi内从整体图像中复制成小图像来分析以提高速度，得到的点坐标转换到整体大图的坐标系中 输入从内到外找圆心、从黑到白找圆 输入canny参数 并行计Caliper区域，将Caliper区域所有canny点合并，拟合圆心 输出圆中心点、半径请编写函数

以下是一个基本的多线程CircularCaliper找圆心的示例代码，你可以根据自己的需求进行修改和优化：

using System;
using System.Collections.Concurrent;
using System.Threading.Tasks;
using OpenCvSharp;
using OpenCvSharp.Extensions;

public class CircularCaliper
{
    private Mat inputImage;
    private int minRadius;
    private int maxRadius;
    private Point2f center;
    private double startAngle;
    private double endAngle;
    private int caliperCount;
    private int caliperWidth;
    private int caliperLength;
    private bool fromInside;
    private int cannyThreshold1;
    private int cannyThreshold2;
    private double cannyApertureSize;

    public CircularCaliper(Mat inputImage, int minRadius, int maxRadius, Point2f center, double startAngle, double endAngle, int caliperCount, int caliperWidth, int caliperLength, bool fromInside, int cannyThreshold1, int cannyThreshold2, double cannyApertureSize)
    {
        this.inputImage = inputImage;
        this.minRadius = minRadius;
        this.maxRadius = maxRadius;
        this.center = center;
        this.startAngle = startAngle;
        this.endAngle = endAngle;
        this.caliperCount = caliperCount;
        this.caliperWidth = caliperWidth;
        this.caliperLength = caliperLength;
        this.fromInside = fromInside;
        this.cannyThreshold1 = cannyThreshold1;
        this.cannyThreshold2 = cannyThreshold2;
        this.cannyApertureSize = cannyApertureSize;
    }

    public void FindCircleCenter(out Point2f center, out float radius)
    {
        center = new Point2f();
        radius = 0;

        // Step 1: Create circular mask
        Mat mask = new Mat(inputImage.Size(), MatType.CV_8UC1, new Scalar(0));
        Cv2.Circle(mask, center, maxRadius, new Scalar(255), -1);
        Cv2.Circle(mask, center, minRadius, new Scalar(0), -1);

        // Step 2: Create caliper ROIs
        double angleStep = (endAngle - startAngle) / caliperCount;
        ConcurrentBag<Mat> roiList = new ConcurrentBag<Mat>();
        Parallel.For(0, caliperCount, i =>
        {
            double start = startAngle + i * angleStep;
            double end = start + angleStep;
            if (fromInside)
            {
                end = start - angleStep;
                start = end - angleStep;
            }

            Point2f startPoint = new Point2f((float)(center.X + minRadius * Math.Cos(start)), (float)(center.Y + minRadius * Math.Sin(start)));
            Point2f endPoint = new Point2f((float)(center.X + maxRadius * Math.Cos(end)), (float)(center.Y + maxRadius * Math.Sin(end)));

            Point2f lineVector = endPoint - startPoint;
            Point2f lineNormal = new Point2f(-lineVector.Y, lineVector.X);
            lineNormal *= caliperWidth / (2 * lineNormal.DistanceTo(new Point2f(0, 0)));
            Point2f point1 = startPoint + lineNormal;
            Point2f point2 = endPoint + lineNormal;
            Point2f point3 = endPoint - lineNormal;
            Point2f point4 = startPoint - lineNormal;

            Mat roi = new Mat();
            using (var matrix = Cv2.GetAffineTransform(new[] { point1, point2, point3 }, new[] { new Point2f(0, 0), new Point2f(caliperLength, 0), new Point2f(caliperLength, caliperWidth) }))
            {
                Cv2.WarpAffine(inputImage, roi, matrix, new Size(caliperLength, caliperWidth), InterpolationFlags.Linear, BorderTypes.Constant, Scalar.Black);
            }
            roiList.Add(roi);
        });

        // Step 3: Find Canny edges in each ROI and merge them
        Mat allEdges = new Mat(caliperWidth, caliperLength * caliperCount, MatType.CV_8UC1, new Scalar(0));
        Parallel.ForEach(roiList, roi =>
        {
            Mat edges = new Mat();
            Cv2.Canny(roi, edges, cannyThreshold1, cannyThreshold2, (int)cannyApertureSize);
            edges.CopyTo(allEdges[new Rect(0, 0, caliperLength, caliperWidth)]);
            allEdges.Move(new Point(caliperLength, 0), allEdges[new Rect(caliperLength, 0, caliperLength * (caliperCount - 1), caliperWidth)]);
        });

        // Step 4: Find circle center by fitting a circle to the merged edges
        Cv2.HoughCircles(allEdges, out CircleSegment[] circles, HoughMethods.Gradient, 1, caliperWidth, cannyThreshold1, cannyThreshold2, minRadius, maxRadius);

        if (circles.Length > 0)
        {
            center = circles[0].Center + new Point2f(caliperLength * (circles[0].Center.Y > caliperWidth / 2 ? caliperCount - 1 : 0), 0);
            radius = circles[0].Radius;
        }
    }
}

你可以使用以下代码来测试这个类：

using (var inputImage = new Mat("test.jpg"))
{
    var caliper = new CircularCaliper(inputImage, 50, 150, new Point2f(200, 200), 0, 360, 36, 10, 50, true, 50, 150, 3);
    caliper.FindCircleCenter(out Point2f center, out float radius);

    Cv2.Circle(inputImage, center, (int)radius, new Scalar(0, 0, 255), 2);
    Cv2.ImShow("Result", inputImage);
    Cv2.WaitKey(0);
}

这个示例代码会在输入图像中找到一个半径为50到150的圆，并返回它的圆心和半径。你可以根据自己的需求修改输入参数，例如圆心位置、最小和最大半径、扇形区域起始角度和结束角度、Caliper个数、宽度和长度、从内到外找圆心或从黑到白找圆心等等。