实现一个多线程并行的canny边缘检测

以下是一个使用OpenCV库实现多线程并行的canny边缘检测的示例代码：

#include <iostream>
#include <thread>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

void cannyEdgeDetection(Mat& image, Mat& result, int lowThreshold, int highThreshold) {
    Canny(image, result, lowThreshold, highThreshold);
}

int main(int argc, char** argv) {
    if (argc != 2) {
        cerr << "Usage: ./canny_parallel [image_path]" << endl;
        return -1;
    }

    Mat image = imread(argv[1], IMREAD_GRAYSCALE);

    if (image.empty()) {
        cerr << "Failed to read image" << endl;
        return -1;
    }

    int numThreads = thread::hardware_concurrency();
    vector<thread> threads(numThreads);
    vector<Mat> results(numThreads);

    int lowThreshold = 50;
    int highThreshold = 150;

    int rowsPerThread = image.rows / numThreads;
    int remainingRows = image.rows % numThreads;

    int startRow = 0;
    for (int i = 0; i < numThreads; i++) {
        int rows = rowsPerThread;
        if (remainingRows > 0) {
            rows++;
            remainingRows--;
        }

        Mat subImage = image.rowRange(startRow, startRow + rows);
        results[i].create(rows, image.cols, CV_8UC1);

        threads[i] = thread(cannyEdgeDetection, subImage, ref(results[i]), lowThreshold, highThreshold);

        startRow += rows;
    }

    for (int i = 0; i < numThreads; i++) {
        threads[i].join();
    }

    Mat edgeImage;
    vconcat(results, edgeImage);

    imshow("Edge detection result", edgeImage);
    waitKey(0);

    return 0;
}

该程序的核心思想是将输入图像分成多个部分，然后将每个部分分配给不同的线程进行canny边缘检测。每个线程将其分配的部分图像处理成一个Mat对象，并将其结果存储在另一个Mat对象中。最后，使用OpenCV库中的vconcat()函数将所有部分的结果合并成最终的边缘检测结果。在本示例中，线程的数量由系统硬件决定，可以使用thread::hardware_concurrency()函数获取。