使用visual studio，基于opencv完成一个直方图匹配算法，输入两个图片，完成彩色图片（三通道）的直方图匹配,要求处理两张图片的颜色分布不同的情况，并且可以处理两张图片的对比度不同的情况

以下是基于OpenCV的直方图匹配算法的代码，可以处理两张图片的颜色分布不同和对比度不同的情况：

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

using namespace cv;
using namespace std;

Mat histogram_matching(Mat src, Mat ref)
{
    Mat src_channels[3], ref_channels[3];
    split(src, src_channels);
    split(ref, ref_channels);

    Mat result(src.size(), CV_8UC3);

    for (int i = 0; i < 3; i++)
    {
        // 计算源图和参考图的直方图
        int histSize = 256;
        float range[] = { 0, 256 };
        const float* histRange = { range };
        bool uniform = true, accumulate = false;
        Mat src_hist, ref_hist;
        calcHist(&src_channels[i], 1, 0, Mat(), src_hist, 1, &histSize, &histRange, uniform, accumulate);
        calcHist(&ref_channels[i], 1, 0, Mat(), ref_hist, 1, &histSize, &histRange, uniform, accumulate);

        // 计算源图和参考图的累积直方图
        Mat src_cum_hist(histSize, 1, CV_32FC1);
        Mat ref_cum_hist(histSize, 1, CV_32FC1);
        src_cum_hist.at<float>(0, 0) = src_hist.at<float>(0, 0);
        ref_cum_hist.at<float>(0, 0) = ref_hist.at<float>(0, 0);
        for (int j = 1; j < histSize; j++)
        {
            src_cum_hist.at<float>(j, 0) = src_cum_hist.at<float>(j - 1, 0) + src_hist.at<float>(j, 0);
            ref_cum_hist.at<float>(j, 0) = ref_cum_hist.at<float>(j - 1, 0) + ref_hist.at<float>(j, 0);
        }

        // 计算源图和参考图的累积直方图归一化
        src_cum_hist /= src_cum_hist.at<float>(histSize - 1, 0);
        ref_cum_hist /= ref_cum_hist.at<float>(histSize - 1, 0);

        // 直方图匹配
        Mat lookup_table(1, histSize, CV_8UC1);
        for (int j = 0; j < histSize; j++)
        {
            int k = histSize - 1;
            while (k >= 0 && ref_cum_hist.at<float>(j, 0) <= src_cum_hist.at<float>(k, 0))
                k--;
            lookup_table.at<uchar>(j, 0) = k;
        }

        // 应用查找表
        LUT(src_channels[i], lookup_table, result.channels()[i]);
    }

    return result;
}

int main()
{
    Mat src = imread("img1.jpg");
    Mat ref = imread("img2.jpg");

    if (src.empty() || ref.empty())
    {
        cout << "Could not open or find the images!\n";
        return -1;
    }

    // 调整对比度
    float alpha = 1.5;
    float beta = 50;
    src.convertTo(src, -1, alpha, beta);

    Mat result = histogram_matching(src, ref);

    namedWindow("Source", WINDOW_NORMAL);
    namedWindow("Reference", WINDOW_NORMAL);
    namedWindow("Result", WINDOW_NORMAL);

    imshow("Source", src);
    imshow("Reference", ref);
    imshow("Result", result);

    waitKey(0);

    return 0;
}

首先将源图和参考图分离成三个通道。然后对于每个通道，计算源图和参考图的直方图和累积直方图，并计算直方图匹配的查找表。最后将查找表应用到源图的每个通道上，得到匹配后的结果。

在实现中，还加入了对比度的调整，可以通过alpha和beta参数来控制。