用C++，openmp，opencv实现连通区域标记的二遍扫描算法，要先将输入的图像二值化，并且用动态二维数组来存储每个像素所属的连通区域标记

以下是用C，OpenMP和OpenCV实现的连通区域标记的二遍扫描算法的示例代码。在此示例中，输入图像被假设为名为“input.png”的PNG文件，并且输出图像被保存为名为“output.png”的PNG文件。

#include <stdio.h>
#include <stdlib.h>
#include <omp.h>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

using namespace cv;

int main(int argc, char** argv)
{
    // Read input image
    Mat input = imread("input.png", CV_LOAD_IMAGE_GRAYSCALE);
    int height = input.rows;
    int width = input.cols;

    // Binarize input image
    Mat binary;
    threshold(input, binary, 128, 255, THRESH_BINARY);

    // Allocate dynamic 2D array to store labels
    int** labels = new int*[height];
    for (int i = 0; i < height; i++)
        labels[i] = new int[width];

    // Initialize labels to -1
    #pragma omp parallel for
    for (int i = 0; i < height; i++)
        for (int j = 0; j < width; j++)
            labels[i][j] = -1;

    // First pass
    int next_label = 0;
    #pragma omp parallel for
    for (int i = 0; i < height; i++) {
        for (int j = 0; j < width; j++) {
            if (binary.at<uchar>(i, j) == 255) {
                int left = (j > 0) ? labels[i][j-1] : -1;
                int up = (i > 0) ? labels[i-1][j] : -1;
                if (left == -1 && up == -1) { // Create new label
                    labels[i][j] = next_label;
                    next_label++;
                } else if (left == -1) { // Use up label
                    labels[i][j] = up;
                } else if (up == -1) { // Use left label
                    labels[i][j] = left;
                } else { // Merge labels
                    int min_label = (left < up) ? left : up;
                    int max_label = (left > up) ? left : up;
                    labels[i][j] = min_label;
                    #pragma omp parallel for
                    for (int ii = 0; ii < height; ii++) {
                        for (int jj = 0; jj < width; jj++) {
                            if (labels[ii][jj] == max_label)
                                labels[ii][jj] = min_label;
                        }
                    }
                }
            }
        }
    }

    // Second pass
    int* counts = new int[next_label];
    for (int i = 0; i < next_label; i++)
        counts[i] = 0;
    #pragma omp parallel for
    for (int i = 0; i < height; i++) {
        for (int j = 0; j < width; j++) {
            if (labels[i][j] >= 0) {
                int label = labels[i][j];
                #pragma omp atomic
                counts[label]++;
            }
        }
    }
    #pragma omp parallel for
    for (int i = 0; i < height; i++) {
        for (int j = 0; j < width; j++) {
            if (labels[i][j] >= 0) {
                int label = labels[i][j];
                int count = counts[label];
                int color = (count > 200) ? 255 : 0; // Threshold count
                binary.at<uchar>(i, j) = color;
            }
        }
    }

    // Save output image
    imwrite("output.png", binary);

    // Deallocate dynamic 2D array
    for (int i = 0; i < height; i++)
        delete[] labels[i];
    delete[] labels;
    delete[] counts;

    return 0;
}

在此示例中，算法的第一遍扫描通过使用OpenMP并行化来加速计算。第二遍扫描也使用OpenMP并行化，但是在计算每个连通区域的像素计数时使用了OpenMP原子操作，以确保线程安全。算法还使用OpenCV中的threshold函数来执行二值化，并使用Mat对象来处理图像。