用C和C++，opencv实现连通区域标记的两遍扫描算法，最后结果以窗口显示

以下是用C++和OpenCV实现连通区域标记的两遍扫描算法的示例代码：

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

using namespace cv;
using namespace std;

int main()
{
    Mat img = imread("image.jpg", CV_LOAD_IMAGE_GRAYSCALE);

    if (img.empty())
    {
        cout << "Could not open or find the image" << endl;
        return -1;
    }

    int label = 1;
    int num_labels = 0;
    vector<int> labels(1000000, 0); // assuming max 1000x1000 image size

    // First pass
    for (int y = 0; y < img.rows; y++)
    {
        for (int x = 0; x < img.cols; x++)
        {
            if (img.at<uchar>(y, x) == 255) // if pixel is white
            {
                if (x == 0 && y == 0)
                {
                    labels[label] = label;
                    label++;
                }
                else if (x == 0 && y != 0)
                {
                    if (img.at<uchar>(y - 1, x) == 255)
                        labels[y * img.cols + x] = labels[(y - 1) * img.cols + x];
                    else
                    {
                        labels[label] = label;
                        labels[y * img.cols + x] = label;
                        label++;
                    }
                }
                else if (x != 0 && y == 0)
                {
                    if (img.at<uchar>(y, x - 1) == 255)
                        labels[y * img.cols + x] = labels[y * img.cols + x - 1];
                    else
                    {
                        labels[label] = label;
                        labels[y * img.cols + x] = label;
                        label++;
                    }
                }
                else
                {
                    if (img.at<uchar>(y, x - 1) == 255 && img.at<uchar>(y - 1, x) == 255)
                    {
                        labels[y * img.cols + x] = min(labels[y * img.cols + x - 1], labels[(y - 1) * img.cols + x]);
                        if (labels[y * img.cols + x - 1] != labels[(y - 1) * img.cols + x])
                        {
                            // Merge labels
                            int min_label = min(labels[y * img.cols + x - 1], labels[(y - 1) * img.cols + x]);
                            int max_label = max(labels[y * img.cols + x - 1], labels[(y - 1) * img.cols + x]);
                            for (int i = 0; i < img.rows * img.cols; i++)
                            {
                                if (labels[i] == max_label)
                                    labels[i] = min_label;
                            }
                        }
                    }
                    else if (img.at<uchar>(y, x - 1) == 255)
                        labels[y * img.cols + x] = labels[y * img.cols + x - 1];
                    else if (img.at<uchar>(y - 1, x) == 255)
                        labels[y * img.cols + x] = labels[(y - 1) * img.cols + x];
                    else
                    {
                        labels[label] = label;
                        labels[y * img.cols + x] = label;
                        label++;
                    }
                }
            }
        }
    }

    // Second pass
    for (int y = 0; y < img.rows; y++)
    {
        for (int x = 0; x < img.cols; x++)
        {
            if (img.at<uchar>(y, x) == 255) // if pixel is white
            {
                int l = labels[y * img.cols + x];
                if (l > 0)
                {
                    if (labels[l] == l)
                    {
                        num_labels++;
                        labels[l] = num_labels;
                    }
                    else
                        labels[y * img.cols + x] = labels[l];
                }
            }
        }
    }

    // Display result
    Mat result(img.rows, img.cols, CV_8UC3, Scalar(0, 0, 0));
    vector<Vec3b> colors(num_labels + 1);
    for (int i = 0; i <= num_labels; i++)
    {
        colors[i] = Vec3b(rand() & 255, rand() & 255, rand() & 255);
    }

    for (int y = 0; y < img.rows; y++)
    {
        for (int x = 0; x < img.cols; x++)
        {
            if (img.at<uchar>(y, x) == 255) // if pixel is white
            {
                int l = labels[y * img.cols + x];
                if (l > 0)
                {
                    result.at<Vec3b>(y, x) = colors[l];
                }
            }
        }
    }

    namedWindow("Result", WINDOW_NORMAL);
    imshow("Result", result);
    waitKey(0);

    return 0;
}

该代码中的第一遍扫描使用了4个条件语句处理边界和邻域情况，第二遍扫描将标签替换为最小等价标签或新标签。最后，将每个区域分配一个随机颜色，并将结果显示在窗口中。