opencvsharp编写一个高精度找圆心函数， 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。

以下是一个使用 OpenCVSharp 编写的高精度找圆心函数的示例代码：

using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Linq;

namespace CircleDetector
{
    class CircleDetector
    {
        private Mat _src;
        private Point _approxCenter;
        private int _minRadius;
        private int _maxRadius;
        private double _startAngle;
        private double _endAngle;
        private int _numIntersections;
        private int _edgeWidth;
        private int _numSectors;
        private double _minScore;
        private int _cannyThreshold1;
        private int _cannyThreshold2;

        public CircleDetector(Mat src, Point approxCenter, int minRadius, int maxRadius,
            double startAngle, double endAngle, int numIntersections, int edgeWidth,
            int numSectors, double minScore, int cannyThreshold1, int cannyThreshold2)
        {
            _src = src;
            _approxCenter = approxCenter;
            _minRadius = minRadius;
            _maxRadius = maxRadius;
            _startAngle = startAngle;
            _endAngle = endAngle;
            _numIntersections = numIntersections;
            _edgeWidth = edgeWidth;
            _numSectors = numSectors;
            _minScore = minScore;
            _cannyThreshold1 = cannyThreshold1;
            _cannyThreshold2 = cannyThreshold2;
        }

        public CircleResult FindCircle()
        {
            // Define the circular region of interest
            int roiRadius = (_maxRadius - _minRadius) / 2;
            int roiRadiusSquared = roiRadius * roiRadius;
            Point roiCenter = new Point(_approxCenter.X - roiRadius, _approxCenter.Y - roiRadius);
            Rect roiRect = new Rect(roiCenter, new Size(roiRadius * 2, roiRadius * 2));

            // Create a mask for the circular region of interest
            Mat mask = new Mat(_src.Size(), MatType.CV_8UC1, Scalar.All(0));
            Cv2.Circle(mask, roiCenter + new Point(roiRadius, roiRadius), roiRadius, Scalar.All(255), -1);

            // Apply Canny edge detection to the region of interest
            Mat edges = new Mat();
            Cv2.Canny(_src, edges, _cannyThreshold1, _cannyThreshold2);
            edges = edges.And(mask);

            // Divide the circular region of interest into sectors
            List<Point>[] sectorPoints = new List<Point>[_numSectors];
            for (int i = 0; i < _numSectors; i++)
            {
                sectorPoints[i] = new List<Point>();
            }
            for (int y = roiCenter.Y; y < roiCenter.Y + roiRadius * 2; y++)
            {
                for (int x = roiCenter.X; x < roiCenter.X + roiRadius * 2; x++)
                {
                    Point p = new Point(x, y) - roiCenter - new Point(roiRadius, roiRadius);
                    int rSquared = p.X * p.X + p.Y * p.Y;
                    if (rSquared >= roiRadiusSquared - _edgeWidth && rSquared <= roiRadiusSquared)
                    {
                        double angle = Math.Atan2(p.Y, p.X) * 180 / Math.PI;
                        if (angle < 0)
                        {
                            angle += 360;
                        }
                        int sectorIndex = (int)Math.Floor(angle / 360 * _numSectors);
                        sectorPoints[sectorIndex].Add(new Point(x, y));
                    }
                }
            }

            // Find the longest contour in each sector
            List<Point>[] sectorContours = new List<Point>[_numSectors];
            for (int i = 0; i < _numSectors; i++)
            {
                List<Point> sectorEdges = new List<Point>();
                foreach (Point p in sectorPoints[i])
                {
                    if (edges.At<byte>(p.Y, p.X) != 0)
                    {
                        sectorEdges.Add(p);
                    }
                }
                if (sectorEdges.Count > 0)
                {
                    Mat sectorEdgesMat = new Mat(sectorEdges.Count, 1, MatType.CV_32SC2, sectorEdges.Select(p => new Point2i(p.X, p.Y)).ToArray());
                    Mat hull = new Mat();
                    Cv2.ConvexHull(sectorEdgesMat, hull);
                    List<Point> contour = Cv2.Polylines(hull, true).ToList();
                    sectorContours[i] = contour;
                }
            }

            // Find the best circle using the intersections of the longest contours
            double bestScore = 0;
            CircleResult bestCircle = null;
            for (int i = 0; i < _numSectors; i++)
            {
                List<Point> contour = sectorContours[i];
                if (contour != null && contour.Count >= _numIntersections)
                {
                    double sectorStartAngle = i * 360.0 / _numSectors;
                    double sectorEndAngle = (i + 1) * 360.0 / _numSectors;
                    double sectorScore = ScoreContour(contour, sectorStartAngle, sectorEndAngle);
                    if (sectorScore >= bestScore && sectorScore >= _minScore)
                    {
                        CircleResult circle = FitCircle(contour);
                        if (circle != null)
                        {
                            bestScore = sectorScore;
                            bestCircle = circle;
                        }
                    }
                }
            }

            return bestCircle;
        }

        private double ScoreContour(List<Point> contour, double startAngle, double endAngle)
        {
            int numSamples = _numIntersections;
            double angleIncrement = (endAngle - startAngle) / numSamples;
            double score = 0;
            for (int i = 0; i < numSamples; i++)
            {
                double angle = startAngle + i * angleIncrement;
                double closestDistance = double.MaxValue;
                foreach (Point p in contour)
                {
                    double pAngle = Math.Atan2(p.Y - _approxCenter.Y, p.X - _approxCenter.X) * 180 / Math.PI;
                    if (pAngle < 0)
                    {
                        pAngle += 360;
                    }
                    if (pAngle >= angle && pAngle < angle + angleIncrement)
                    {
                        double distance = Math.Sqrt((p.X - _approxCenter.X) * (p.X - _approxCenter.X) + (p.Y - _approxCenter.Y) * (p.Y - _approxCenter.Y));
                        if (distance < closestDistance)
                        {
                            closestDistance = distance;
                        }
                    }
                }
                score += closestDistance;
            }
            return score;
        }

        private CircleResult FitCircle(List<Point> contour)
        {
            if (contour.Count < 5)
            {
                return null;
            }
            Mat contourMat = new Mat(contour.Count, 1, MatType.CV_32SC2, contour.Select(p => new Point2i(p.X, p.Y)).ToArray());
            double[] center = new double[2];
            double[] radius = new double[1];
            Cv2.MinEnclosingCircle(contourMat, center, radius);
            CircleResult circle = new CircleResult(new Point((int)Math.Round(center[0]), (int)Math.Round(center[1])), (int)Math.Round(radius[0]));
            int numInliers = 0;
            for (int i = 0; i < contour.Count; i++)
            {
                double distance = Math.Sqrt((contour[i].X - circle.Center.X) * (contour[i].X - circle.Center.X) + (contour[i].Y - circle.Center.Y) * (contour[i].Y - circle.Center.Y));
                if (distance < circle.Radius)
                {
                    numInliers++;
                }
            }
            if (numInliers < 5)
            {
                return null;
            }
            return circle;
        }
    }

    class CircleResult
    {
        public Point Center { get; private set; }
        public int Radius { get; private set; }

        public CircleResult(Point center, int radius)
        {
            Center = center;
            Radius = radius;
        }
    }
}

这个 `CircleDetector` 类的构造函数需要传入许多参数，包括输入图像、圆心的大约位置、最小半径和最大半径、扇形区域的起始角度和结束角度、扇形区域的法线上找交点的个数、找边沿点的宽度、分割圆环区域的数量等等。然后，你可以调用 `FindCircle` 方法来执行圆心检测。这个方法将返回一个 `CircleResult` 对象，其中包含检测到的圆心位置和半径。