opencvsharp 多线程NCC亚像素匹配

OpenCvSharp 是一个基于 OpenCV 的 C# 封装库，可以帮助开发者使用 C# 编写 OpenCV 应用程序。多线程 NCC 亚像素匹配可以提高匹配的速度和精度。以下是一个示例代码：

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

namespace OpenCvSharp_MultiThreaded_NCC_SubPixel_Matching
{
    class Program
    {
        static void Main(string[] args)
        {
            Mat image1 = Cv2.ImRead("image1.png", ImreadModes.GrayScale);
            Mat image2 = Cv2.ImRead("image2.png", ImreadModes.GrayScale);

            // 创建输出图像
            Mat result = Mat.Zeros(image1.Rows, image1.Cols, MatType.CV_32F);

            // 定义块大小
            int block_size = 21;

            // 定义搜索范围
            int search_range = 25;

            // 定义阈值
            double threshold = 0.98;

            // 创建 NCCMatcher 对象
            NccMatcher ncc_matcher = new NccMatcher(image1, block_size, search_range, threshold);

            // 定义线程数
            int num_threads = 4;

            // 创建线程池
            Task[] tasks = new Task[num_threads];

            // 定义线程块大小
            int block_height = image1.Rows / num_threads;

            // 分配任务
            for (int i = 0; i < num_threads; i++)
            {
                int start_row = i * block_height;
                int end_row = (i == num_threads - 1) ? image1.Rows : (i + 1) * block_height;

                tasks[i] = Task.Factory.StartNew(() => {
                    for (int row = start_row; row < end_row; row++)
                    {
                        for (int col = 0; col < image1.Cols; col++)
                        {
                            // 计算亚像素匹配
                            Point2f match_location = ncc_matcher.Match(image2, new Point2f(col, row));

                            // 设置匹配结果
                            result.At<float>(row, col) = match_location.X;
                        }
                    }
                });
            }

            // 等待所有任务完成
            Task.WaitAll(tasks);

            // 显示结果
            Cv2.ImShow("Result", result);
            Cv2.WaitKey(0);
        }
    }

    class NccMatcher
    {
        private Mat image1;
        private int block_size;
        private int search_range;
        private double threshold;

        public NccMatcher(Mat image1, int block_size, int search_range, double threshold)
        {
            this.image1 = image1;
            this.block_size = block_size;
            this.search_range = search_range;
            this.threshold = threshold;
        }

        public Point2f Match(Mat image2, Point2f location)
        {
            // 定义搜索范围
            Rect search_window = new Rect(
                (int)(location.X - search_range / 2),
                (int)(location.Y - search_range / 2),
                search_range,
                search_range);

            // 检查搜索范围是否越界
            search_window.X = Math.Max(search_window.X, 0);
            search_window.Y = Math.Max(search_window.Y, 0);
            search_window.Width = Math.Min(search_window.Width, image2.Cols - search_window.X);
            search_window.Height = Math.Min(search_window.Height, image2.Rows - search_window.Y);

            // 获取模板块
            Mat template_block = image1.SubMat(
                new Rect(
                    (int)(location.X - block_size / 2),
                    (int)(location.Y - block_size / 2),
                    block_size,
                    block_size));

            // 初始化最大相关系数和匹配位置
            double max_ncc = -1;
            Point2f max_location = new Point2f(-1, -1);

            // 遍历搜索范围
            for (int row = search_window.Y; row < search_window.Y + search_window.Height - block_size; row++)
            {
                for (int col = search_window.X; col < search_window.X + search_window.Width - block_size; col++)
                {
                    // 获取窗口块
                    Mat window_block = image2.SubMat(
                        new Rect(col, row, block_size, block_size));

                    // 计算 NCC
                    double ncc = CalculateNcc(template_block, window_block);

                    // 如果 NCC 大于阈值并且大于当前最大值，则更新最大值和最大位置
                    if (ncc > threshold && ncc > max_ncc)
                    {
                        max_ncc = ncc;
                        max_location.X = col + block_size / 2.0f;
                        max_location.Y = row + block_size / 2.0f;
                    }
                }
            }

            return max_location;
        }

        private double CalculateNcc(Mat image1, Mat image2)
        {
            // 计算均值
            Scalar mean1 = Cv2.Mean(image1);
            Scalar mean2 = Cv2.Mean(image2);

            // 计算标准差
            Scalar std1, std2;
            Cv2.MeanStdDev(image1, out mean1, out std1);
            Cv2.MeanStdDev(image2, out mean2, out std2);

            // 计算协方差矩阵
            Mat covar = new Mat();
            Cv2.CalcCovarMatrix(new Mat[] { image1, image2 }, covar, 0, CovarFlags.Normal | CovarFlags.Scale);

            // 计算相关系数
            double ncc = covar.At<double>(0, 1) / (std1.Val0 * std2.Val0);

            return ncc;
        }
    }
}

在这个示例代码中，我们使用了多线程来加速 NCC 亚像素匹配。我们将图像分成若干个块，在不同的线程中处理不同的块。由于每个线程只处理一部分图像，因此可以大大提高匹配的速度。