opencv中通道合并和通道分离CV：：merge CV：：mean

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本文介绍如何使用OpenCV的cv::split()和cv::merge()函数来分离和合并多通道图像。通过示例代码展示了如何将图像分解为单独的颜色通道，并重新组合这些通道以创建纯红、绿、蓝视图。

在图像处理中，尤其是处理多通道图像时，有时需要对各个通道进行分离，分别处理；有时还需要对分离处理后的各个通道进行合并，重新合并成一个多通道的图像。opencv2和opencv3中实现图像通道的合并与分离的函数分别是cv::split()和cv::merge()。

1、多通道图像的分离

cv::split()的具体调用方法如下：

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void cv::split(
const cv::Mat& mtx, //输入图像
vector<Mat>& mv // 输出的多通道序列（n个单通道序列）
);

void cv::split(
    const cv::Mat& mtx, //输入图像
    vector<Mat>& mv // 输出的多通道序列（n个单通道序列）
);

2、图像多个通道的合并

cv::merge()的具体调用方法如下：

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void merge(
const vector<cv::Mat>& mv, // 输入的多通道序列(n个单通道序列)
cv::OutputArray dst // 输出图像，包含mv
);

void merge(
    const vector<cv::Mat>& mv, // 输入的多通道序列(n个单通道序列)
    cv::OutputArray dst // 输出图像，包含mv
);

代码示例如下：

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#include <opencv2/opencv.hpp>
int main()
{
cv::Mat src = imread(”lenna.jpg”, cv::IMREAD_COLOR);
cv::imshow(”src”, src);
// Split the image into different channels
std::vector<cv::Mat> rgbChannels(3);
split(src, rgbChannels);
// Show individual channels
cv::Mat blank_ch, fin_img;
blank_ch = cv::Mat::zeros(cv::Size(src.cols, src.rows), CV_8UC1);
// Showing Red Channel
// G and B channels are kept as zero matrix for visual perception
std::vector<cv::Mat> channels_r;
channels_r.push_back(blank_ch);
channels_r.push_back(blank_ch);
channels_r.push_back(rgbChannels[2]);
/// Merge the three channels
cv::merge(channels_r, fin_img);
cv::imshow(”R”, fin_img);
// Showing Green Channel
std::vector<cv::Mat> channels_g;
channels_g.push_back(blank_ch);
channels_g.push_back(rgbChannels[1]);
channels_g.push_back(blank_ch);
cv::merge(channels_g, fin_img);
cv::imshow(”G”, fin_img);
// Showing Blue Channel
std::vector<cv::Mat> channels_b;
channels_b.push_back(rgbChannels[0]);
channels_b.push_back(blank_ch);
channels_b.push_back(blank_ch);
cv::merge(channels_b, fin_img);
cv::imshow(”B”, fin_img);
cv::waitKey(0);
return 0;
}

#include <opencv2/opencv.hpp>

int main()
{
    cv::Mat src = imread("lenna.jpg", cv::IMREAD_COLOR);
    cv::imshow("src", src);

    // Split the image into different channels
    std::vector<cv::Mat> rgbChannels(3);
    split(src, rgbChannels);

    // Show individual channels
    cv::Mat blank_ch, fin_img;
    blank_ch = cv::Mat::zeros(cv::Size(src.cols, src.rows), CV_8UC1);

    // Showing Red Channel
    // G and B channels are kept as zero matrix for visual perception
    std::vector<cv::Mat> channels_r;
    channels_r.push_back(blank_ch);
    channels_r.push_back(blank_ch);
    channels_r.push_back(rgbChannels[2]);

    /// Merge the three channels
    cv::merge(channels_r, fin_img);
    cv::imshow("R", fin_img);


    // Showing Green Channel
    std::vector<cv::Mat> channels_g;
    channels_g.push_back(blank_ch);
    channels_g.push_back(rgbChannels[1]);
    channels_g.push_back(blank_ch);
    cv::merge(channels_g, fin_img);
    cv::imshow("G", fin_img);


    // Showing Blue Channel
    std::vector<cv::Mat> channels_b;
    channels_b.push_back(rgbChannels[0]);
    channels_b.push_back(blank_ch);
    channels_b.push_back(blank_ch);
    cv::merge(channels_b, fin_img);
    cv::imshow("B", fin_img);

    cv::waitKey(0);
    return 0;
}

显示结果：