OpenCvSharp 编写函数 RansacCircleFiler(List<Point2f> points

)，实现圆形拟合和 RANSAC 算法去除异常点的完整代码如下：

using System.Collections.Generic;
using OpenCvSharp;

public static class RansacCircleFilter
{
    public static bool FitCircle(List<Point2f> points, out Point2f center, out float radius)
    {
        // Convert points to Mat
        Mat pointsMat = new Mat(points.Count, 2, MatType.CV_32FC1);
        for (int i = 0; i < points.Count; i++)
        {
            pointsMat.Set<float>(i, 0, points[i].X);
            pointsMat.Set<float>(i, 1, points[i].Y);
        }

        // RANSAC parameters
        int iterations = 1000; // Number of iterations
        float threshold = 10; // Distance threshold for inliers
        int inlierCount = 0; // Best inlier count
        Point2f[] bestPoints = new Point2f[3]; // Best inlier points
        bool success = false; // Whether a good model was found

        // RANSAC loop
        for (int i = 0; i < iterations; i++)
        {
            // Randomly select three points
            Point2f[] samplePoints = new Point2f[3];
            for (int j = 0; j < 3; j++)
            {
                int index = Cv2.RandInt(0, points.Count);
                samplePoints[j] = points[index];
            }

            // Fit a circle to the sample points
            Point2f sampleCenter;
            float sampleRadius;
            bool sampleSuccess = Cv2.MinEnclosingCircle(samplePoints, out sampleCenter, out sampleRadius);

            if (sampleSuccess)
            {
                // Count inliers
                int sampleInlierCount = 0;
                Point2f[] sampleInliers = new Point2f[points.Count];
                for (int j = 0; j < points.Count; j++)
                {
                    float distance = Cv2.Norm(points[j] - sampleCenter);
                    if (distance < threshold)
                    {
                        sampleInliers[sampleInlierCount] = points[j];
                        sampleInlierCount++;
                    }
                }

                // Check if this is the best model so far
                if (sampleInlierCount > inlierCount)
                {
                    inlierCount = sampleInlierCount;
                    bestPoints = sampleInliers;
                    center = sampleCenter;
                    radius = sampleRadius;

                    // Check if we have enough inliers to declare success
                    if (inlierCount >= points.Count * 0.5)
                    {
                        success = true;
                        break;
                    }
                }
            }
        }

        // If no good model was found, return false
        if (!success)
        {
            center = new Point2f();
            radius = 0;
            return false;
        }

        // Refine the circle using all inliers
        Mat inliersMat = new Mat(inlierCount, 2, MatType.CV_32FC1);
        for (int i = 0; i < inlierCount; i++)
        {
            inliersMat.Set<float>(i, 0, bestPoints[i].X);
            inliersMat.Set<float>(i, 1, bestPoints[i].Y);
        }
        Cv2.MinEnclosingCircle(inliersMat, out center, out radius);

        return true;
    }
}

在这个代码中，我们首先将点集转换为 `Mat` 类型，并且设置 RANSAC 算法的一些参数，如迭代次数、距离阈值等。然后，在 RANSAC 的每一轮中，我们随机选取三个点进行拟合，然后计算出所有距离这个模型的距离小于阈值的点的个数，并且记录下这些点。最后，在所有轮中选取拟合效果最好的模型，然后再对选取的所有点进行最小外接圆拟合，得到最终的圆形模型。

这个函数返回一个布尔值，表示是否成功拟合出圆形。如果成功，函数会将圆心和半径输出到参数 `center` 和 `radius` 中。这个函数还支持 RANSAC 算法去除异常点，从而提高拟合的精度。