opencvsharp 分块并行计算canny找直线边编写函数，参数从白到黑 从左到右,将所有找到的边合并

以下是使用OpenCvSharp进行分块并行计算canny找直线边，并将所有找到的边合并的函数代码：

using System;
using OpenCvSharp;

public static class EdgeDetection
{
    public static Mat FindLines(Mat src, int blockSize)
    {
        // Convert to grayscale
        Mat gray = new Mat();
        Cv2.CvtColor(src, gray, ColorConversionCodes.BGR2GRAY);

        // Split image into blocks
        int rows = src.Rows / blockSize;
        int cols = src.Cols / blockSize;
        Mat[] blocks = new Mat[rows * cols];
        for (int i = 0; i < rows; i++)
        {
            for (int j = 0; j < cols; j++)
            {
                Rect roi = new Rect(j * blockSize, i * blockSize, blockSize, blockSize);
                blocks[i * cols + j] = new Mat(gray, roi);
            }
        }

        // Find edges in each block using Canny algorithm
        Parallel.For(0, blocks.Length, i =>
        {
            Cv2.Canny(blocks[i], blocks[i], 50, 150);
        });

        // Merge all edges into one image
        Mat edges = new Mat(gray.Size(), MatType.CV_8UC1, Scalar.All(0));
        for (int i = 0; i < rows; i++)
        {
            for (int j = 0; j < cols; j++)
            {
                Rect roi = new Rect(j * blockSize, i * blockSize, blockSize, blockSize);
                Mat blockEdges = new Mat(edges, roi);
                Cv2.BitwiseOr(blocks[i * cols + j], blockEdges, blockEdges);
            }
        }

        // Find lines in merged edges using Hough transform
        LineSegmentPoint[] lines = Cv2.HoughLinesP(edges, 1, Math.PI / 180, 50, 50, 10);
        foreach (LineSegmentPoint line in lines)
        {
            Cv2.Line(src, line.P1, line.P2, Scalar.Red, 2);
        }

        return src;
    }
}

这个函数接受两个参数：输入图像src和块大小blockSize。它首先将输入图像转换为灰度图像，然后将其分成大小为blockSize x blockSize的块。然后，它并行处理每个块，使用Canny算法找到边缘。接下来，它将所有块的边缘合并为一个图像，使用Hough变换找到线段，并在输入图像上绘制这些线段。最后，它返回绘制了线段的输入图像。