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:
- 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:
- 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:
- #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:
2017.04.27
</div>
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:
- 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:
- 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:
- #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:
2017.04.27
</div>