创建多线程加速处理这一段for循环int m = m_vpdEquinoxPoints.size();vector<Edge1D_Result>edges;for (int i = 0; i < m; i++) { edges = Extract1DEdgeCircle.Get1DEdge(RoiMat, m_vpdEquinoxPoints[i], m_dMeasureLength, m_dMeasureHeight, m_vdMeasureAngle[i] , m_dSigma, m_nThresholdCircle, m_nTranslationCircle == 1 ? Translation::Poisitive : Translation::Negative, Selection::Strongest); for (int i = 0; i < edges.size(); i++) { m_vpdEdgePoints.push_back(edges[i].m_pdEdgePoint); m_vdEdgeGradient.push_back(edges[i].m_dGradient); } }并且使变量不冲突

可以使用std::thread来创建多线程，每个线程处理一定数量的循环迭代。为了避免变量冲突，可以使用锁（std::mutex）来保证每个线程只访问自己的变量。下面是一个可能的实现：

#include <thread>
#include <mutex>

// 用于保护m_vpdEdgePoints和m_vdEdgeGradient的锁
std::mutex g_mutex;

void process_edges(const cv::Mat& RoiMat,
                   const std::vector<cv::Point2d>& m_vpdEquinoxPoints,
                   const double m_dMeasureLength,
                   const double m_dMeasureHeight,
                   const double m_dSigma,
                   const int m_nThresholdCircle,
                   const int m_nTranslationCircle,
                   const std::vector<double>& m_vdMeasureAngle,
                   std::vector<cv::Point2d>& m_vpdEdgePoints,
                   std::vector<double>& m_vdEdgeGradient,
                   int start_idx, int end_idx)
{
    std::vector<Edge1D_Result> edges;
    for (int i = start_idx; i < end_idx; i++)
    {
        edges = Extract1DEdgeCircle.Get1DEdge(RoiMat, m_vpdEquinoxPoints[i], m_dMeasureLength, m_dMeasureHeight, m_vdMeasureAngle[i], m_dSigma, m_nThresholdCircle, m_nTranslationCircle == 1 ? Translation::Poisitive : Translation::Negative, Selection::Strongest);
        // 使用锁保护m_vpdEdgePoints和m_vdEdgeGradient
        std::lock_guard<std::mutex> lock(g_mutex);
        for (int j = 0; j < edges.size(); j++)
        {
            m_vpdEdgePoints.push_back(edges[j].m_pdEdgePoint);
            m_vdEdgeGradient.push_back(edges[j].m_dGradient);
        }
    }
}

// 在主函数中调用多线程处理
int main()
{
    // ...
    const int num_threads = 4; // 假设有4个线程
    const int m = m_vpdEquinoxPoints.size();
    std::vector<std::thread> threads(num_threads);
    std::vector<std::vector<cv::Point2d>> edge_points(num_threads);
    std::vector<std::vector<double>> edge_gradients(num_threads);
    for (int i = 0; i < num_threads; i++)
    {
        int start_idx = i * m / num_threads;
        int end_idx = (i + 1) * m / num_threads;
        threads[i] = std::thread(process_edges, std::ref(RoiMat), std::ref(m_vpdEquinoxPoints), m_dMeasureLength, m_dMeasureHeight, m_dSigma, m_nThresholdCircle, m_nTranslationCircle, std::ref(m_vdMeasureAngle), std::ref(edge_points[i]), std::ref(edge_gradients[i]), start_idx, end_idx);
    }
    for (int i = 0; i < num_threads; i++)
    {
        threads[i].join();
        // 合并结果
        m_vpdEdgePoints.insert(m_vpdEdgePoints.end(), edge_points[i].begin(), edge_points[i].end());
        m_vdEdgeGradient.insert(m_vdEdgeGradient.end(), edge_gradients[i].begin(), edge_gradients[i].end());
    }
    // ...
}

这个实现中，我们将处理结果存储在每个线程的本地变量edge_points和edge_gradients中，然后在主线程中合并结果。为了避免多个线程同时访问m_vpdEdgePoints和m_vdEdgeGradient，我们使用了一个锁g_mutex来保护这两个变量。同时，我们使用了std::ref函数来将引用传递给子线程，以保证子线程能够修改主线程中的变量。