pencvsharp4.6 编写采用并行计算一个亚像素找圆心函数， 这个函数会输入一张图、一个圆心中心位置的大约位置、输入找圆扇形区域的最小半径和最大半径、设定扇形区域起始角度、扇形区域结束角度、扇形区域的法线上找交点个数 、找边沿点宽度、从内到外找圆心、从黑到白找圆惦、找圆分数、 canny参数 根据给定的圆心位置和半径范围定义一个圆环区域。 为了加快找圆心速度，将圆环区域分成多份，可设定，并行计算找出边沿点 在边缘图像中找到最长的轮廓。 对轮廓上的点进行拟合，得到圆心和半径。请编写函数代码 ，

以下是使用OpenCVSharp4.6实现的函数代码：

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using OpenCvSharp;

namespace CircleDetector
{
    class Program
    {
        static void Main(string[] args)
        {
            Mat image = Cv2.ImRead("test.jpg", ImreadModes.GrayScale);

            Point2f center = new Point2f(200, 200);
            int minRadius = 50;
            int maxRadius = 150;
            int startAngle = 0;
            int endAngle = 360;
            int intersectionCount = 10;
            int edgeWidth = 5;
            bool fromInside = true;
            bool fromBlackToWhite = true;
            double minScore = 0.7;
            double cannyThreshold1 = 50;
            double cannyThreshold2 = 200;

            Point2f detectedCenter = FindCircleCenter(image, center, minRadius, maxRadius, startAngle, endAngle, intersectionCount, edgeWidth, fromInside, fromBlackToWhite, minScore, cannyThreshold1, cannyThreshold2);

            Console.WriteLine("Detected Circle Center: ({0}, {1})", detectedCenter.X, detectedCenter.Y);
            Console.ReadKey();
        }

        static Point2f FindCircleCenter(Mat image, Point2f center, int minRadius, int maxRadius, int startAngle, int endAngle, int intersectionCount, int edgeWidth, bool fromInside, bool fromBlackToWhite, double minScore, double cannyThreshold1, double cannyThreshold2)
        {
            int partitionCount = 4; //分区数目
            int angleStep = (endAngle - startAngle) / partitionCount;
            List<Point>[] edgePoints = new List<Point>[partitionCount];

            Parallel.For(0, partitionCount, (i) =>
            {
                int currentStartAngle = startAngle + i * angleStep;
                int currentEndAngle = currentStartAngle + angleStep;

                List<Point> points = new List<Point>();

                for (int j = minRadius; j < maxRadius; j++)
                {
                    for (int k = currentStartAngle; k < currentEndAngle; k++)
                    {
                        int x = (int)(center.X + j * Math.Cos(k * Math.PI / 180));
                        int y = (int)(center.Y + j * Math.Sin(k * Math.PI / 180));

                        if (x >= 0 && x < image.Width && y >= 0 && y < image.Height)
                        {
                            points.Add(new Point(x, y));
                        }
                    }
                }

                edgePoints[i] = FindEdgePoints(image, points, intersectionCount, edgeWidth, fromInside, fromBlackToWhite, cannyThreshold1, cannyThreshold2);
            });

            List<Point> allEdgePoints = new List<Point>();

            for (int i = 0; i < partitionCount; i++)
            {
                allEdgePoints.AddRange(edgePoints[i]);
            }

            RotatedRect ellipse = Cv2.FitEllipse(allEdgePoints.ToArray());

            if (ellipse.Size.Width / ellipse.Size.Height < 1 - minScore || ellipse.Size.Width / ellipse.Size.Height > 1 + minScore)
            {
                throw new Exception("Detected ellipse is not a circle!");
            }

            return ellipse.Center;
        }

        static List<Point> FindEdgePoints(Mat image, List<Point> points, int intersectionCount, int edgeWidth, bool fromInside, bool fromBlackToWhite, double cannyThreshold1, double cannyThreshold2)
        {
            List<Point> result = new List<Point>();

            foreach (Point point in points)
            {
                List<Point> intersectPoints = new List<Point>();
                int currentAngle = 0;
                int angleStep = 360 / intersectionCount;

                for (int i = 0; i < intersectionCount; i++)
                {
                    int x1 = (int)(point.X + edgeWidth * Math.Cos(currentAngle * Math.PI / 180));
                    int y1 = (int)(point.Y + edgeWidth * Math.Sin(currentAngle * Math.PI / 180));
                    int x2 = (int)(point.X + (fromInside ? -1 : 1) * edgeWidth * Math.Cos(currentAngle * Math.PI / 180));
                    int y2 = (int)(point.Y + (fromInside ? -1 : 1) * edgeWidth * Math.Sin(currentAngle * Math.PI / 180));

                    if (x1 >= 0 && x1 < image.Width && y1 >= 0 && y1 < image.Height && x2 >= 0 && x2 < image.Width && y2 >= 0 && y2 < image.Height)
                    {
                        LineIterator iterator = new LineIterator(image, new Point(x1, y1), new Point(x2, y2));

                        for (int j = 0; j < iterator.Count; j++)
                        {
                            Vec3b intensity = iterator.GetPixel(j);
                            bool isPixelBlack = intensity.Item0 < 127;

                            if ((isPixelBlack && fromBlackToWhite) || (!isPixelBlack && !fromBlackToWhite))
                            {
                                intersectPoints.Add(iterator.Pos());
                                break;
                            }
                        }
                    }

                    currentAngle += angleStep;
                }

                if (intersectPoints.Count > 0)
                {
                    result.Add(intersectPoints[0]);
                }
            }

            return result;
        }
    }
}

这个函数接受一个灰度图像作为输入，以及一些参数，包括圆心位置的大约位置、输入找圆扇形区域的最小半径和最大半径、设定扇形区域起始角度、扇形区域结束角度、扇形区域的法线上找交点个数、找边沿点宽度、从内到外找圆心、从黑到白找圆惦、找圆分数、canny参数等。函数使用并行计算将圆环区域分成多份，并在每个分区上寻找边缘点。最后，对所有边缘点进行拟合，得到圆心和半径。