OpenCVSharp分块的图像上并行计算Canny亚像素级别的匹配c#代码

以下是使用OpenCVSharp分块的图像上并行计算Canny亚像素级别的匹配的C#代码示例：

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

namespace ParallelCanny
{
    class Program
    {
        static void Main(string[] args)
        {
            // Load the image
            Mat image = Cv2.ImRead("test.jpg", ImreadModes.Grayscale);

            // Split the image into blocks
            List<Mat> blocks = SplitImage(image, 8);

            // Create a parallel loop to process each block
            Parallel.For(0, blocks.Count, i =>
            {
                Mat block = blocks[i];

                // Apply Canny edge detection to the block with sub-pixel accuracy
                Mat canny = new Mat();
                Cv2.Canny(block, canny, 100, 200, 3, true);

                // Match template using normalized cross-correlation
                Mat result = new Mat();
                Cv2.MatchTemplate(block, canny, result, TemplateMatchModes.CCoeffNormed);

                // Find the max value and location
                double minVal, maxVal;
                Point minLoc, maxLoc;
                Cv2.MinMaxLoc(result, out minVal, out maxVal, out minLoc, out maxLoc);

                // Output the result
                Console.WriteLine("Block {0}: Max value = {1}, Max location = ({2}, {3})", i, maxVal, maxLoc.X, maxLoc.Y);
            });
        }

        static List<Mat> SplitImage(Mat image, int numBlocks)
        {
            List<Mat> blocks = new List<Mat>();

            int blockWidth = image.Width / numBlocks;
            int blockHeight = image.Height / numBlocks;

            for (int i = 0; i < numBlocks; i++)
            {
                for (int j = 0; j < numBlocks; j++)
                {
                    Rect roi = new Rect(j * blockWidth, i * blockHeight, blockWidth, blockHeight);
                    Mat block = new Mat(image, roi);
                    blocks.Add(block);
                }
            }

            return blocks;
        }
    }
}

在此示例中，我们首先加载图像，然后将其分成8个块。接下来，我们使用并行循环处理每个块，对每个块应用Canny边缘检测，并使用归一化的互相关匹配模板来查找亚像素级别的匹配。最后，我们输出每个块中最大值的位置和值。