对图片进行操作时经常会涉及到不同通道数据提取，在OpenCV中提供了很多比较便捷的操作函数，本文涉及函数如下：

1.cvtColor 颜色空间转换
2.convertTo 图片数据类型转换
3.split 图片通道分离
4.merge 图片不同通道合并
5.extractChannel 抽取图片某一个通道
6.applyColorMap 灰度图转伪彩色图

原文：OpenCV图片颜色通道数据转换

1.函数cvtColor

定义：

void cvtColor( InputArray src, OutputArray dst, int code, int dstCn = 0 );
def cvtColor(src, code, dst=None, dstCn=None)

作用:图像颜色转换。
参数：
src：输入图像
dst：输出图像
dstCn:转换后目标图像通道，通常采用默认值
code:转换类型，可以取下列枚举值

enum ColorConversionCodes {COLOR_BGR2BGRA     = 0, //!< add alpha channel to RGB or BGR imageCOLOR_RGB2RGBA     = COLOR_BGR2BGRA,COLOR_BGRA2BGR     = 1, //!< remove alpha channel from RGB or BGR imageCOLOR_RGBA2RGB     = COLOR_BGRA2BGR,COLOR_BGR2RGBA     = 2, //!< convert between RGB and BGR color spaces (with or without alpha channel)COLOR_RGB2BGRA     = COLOR_BGR2RGBA,COLOR_RGBA2BGR     = 3,COLOR_BGRA2RGB     = COLOR_RGBA2BGR,COLOR_BGR2RGB      = 4,COLOR_RGB2BGR      = COLOR_BGR2RGB,COLOR_BGRA2RGBA    = 5,COLOR_RGBA2BGRA    = COLOR_BGRA2RGBA,COLOR_BGR2GRAY     = 6, //!< convert between RGB/BGR and grayscale, @ref color_convert_rgb_gray "color conversions"COLOR_RGB2GRAY     = 7,COLOR_GRAY2BGR     = 8,COLOR_GRAY2RGB     = COLOR_GRAY2BGR,COLOR_GRAY2BGRA    = 9,COLOR_GRAY2RGBA    = COLOR_GRAY2BGRA,COLOR_BGRA2GRAY    = 10,COLOR_RGBA2GRAY    = 11,COLOR_BGR2BGR565   = 12, //!< convert between RGB/BGR and BGR565 (16-bit images)COLOR_RGB2BGR565   = 13,COLOR_BGR5652BGR   = 14,COLOR_BGR5652RGB   = 15,COLOR_BGRA2BGR565  = 16,COLOR_RGBA2BGR565  = 17,COLOR_BGR5652BGRA  = 18,COLOR_BGR5652RGBA  = 19,COLOR_GRAY2BGR565  = 20, //!< convert between grayscale to BGR565 (16-bit images)COLOR_BGR5652GRAY  = 21,COLOR_BGR2BGR555   = 22,  //!< convert between RGB/BGR and BGR555 (16-bit images)COLOR_RGB2BGR555   = 23,COLOR_BGR5552BGR   = 24,COLOR_BGR5552RGB   = 25,COLOR_BGRA2BGR555  = 26,COLOR_RGBA2BGR555  = 27,COLOR_BGR5552BGRA  = 28,COLOR_BGR5552RGBA  = 29,COLOR_GRAY2BGR555  = 30, //!< convert between grayscale and BGR555 (16-bit images)COLOR_BGR5552GRAY  = 31,COLOR_BGR2XYZ      = 32, //!< convert RGB/BGR to CIE XYZ, @ref color_convert_rgb_xyz "color conversions"COLOR_RGB2XYZ      = 33,COLOR_XYZ2BGR      = 34,COLOR_XYZ2RGB      = 35,COLOR_BGR2YCrCb    = 36, //!< convert RGB/BGR to luma-chroma (aka YCC), @ref color_convert_rgb_ycrcb "color conversions"COLOR_RGB2YCrCb    = 37,COLOR_YCrCb2BGR    = 38,COLOR_YCrCb2RGB    = 39,COLOR_BGR2HSV      = 40, //!< convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions"COLOR_RGB2HSV      = 41,COLOR_BGR2Lab      = 44, //!< convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions"COLOR_RGB2Lab      = 45,COLOR_BGR2Luv      = 50, //!< convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions"COLOR_RGB2Luv      = 51,COLOR_BGR2HLS      = 52, //!< convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions"COLOR_RGB2HLS      = 53,COLOR_HSV2BGR      = 54, //!< backward conversions to RGB/BGRCOLOR_HSV2RGB      = 55,COLOR_Lab2BGR      = 56,COLOR_Lab2RGB      = 57,COLOR_Luv2BGR      = 58,COLOR_Luv2RGB      = 59,COLOR_HLS2BGR      = 60,COLOR_HLS2RGB      = 61,COLOR_BGR2HSV_FULL = 66,COLOR_RGB2HSV_FULL = 67,COLOR_BGR2HLS_FULL = 68,COLOR_RGB2HLS_FULL = 69,COLOR_HSV2BGR_FULL = 70,COLOR_HSV2RGB_FULL = 71,COLOR_HLS2BGR_FULL = 72,COLOR_HLS2RGB_FULL = 73,COLOR_LBGR2Lab     = 74,COLOR_LRGB2Lab     = 75,COLOR_LBGR2Luv     = 76,COLOR_LRGB2Luv     = 77,COLOR_Lab2LBGR     = 78,COLOR_Lab2LRGB     = 79,COLOR_Luv2LBGR     = 80,COLOR_Luv2LRGB     = 81,COLOR_BGR2YUV      = 82, //!< convert between RGB/BGR and YUVCOLOR_RGB2YUV      = 83,COLOR_YUV2BGR      = 84,COLOR_YUV2RGB      = 85,//! YUV 4:2:0 family to RGBCOLOR_YUV2RGB_NV12  = 90,COLOR_YUV2BGR_NV12  = 91,COLOR_YUV2RGB_NV21  = 92,COLOR_YUV2BGR_NV21  = 93,COLOR_YUV420sp2RGB  = COLOR_YUV2RGB_NV21,COLOR_YUV420sp2BGR  = COLOR_YUV2BGR_NV21,COLOR_YUV2RGBA_NV12 = 94,COLOR_YUV2BGRA_NV12 = 95,COLOR_YUV2RGBA_NV21 = 96,COLOR_YUV2BGRA_NV21 = 97,COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21,COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21,COLOR_YUV2RGB_YV12  = 98,COLOR_YUV2BGR_YV12  = 99,COLOR_YUV2RGB_IYUV  = 100,COLOR_YUV2BGR_IYUV  = 101,COLOR_YUV2RGB_I420  = COLOR_YUV2RGB_IYUV,COLOR_YUV2BGR_I420  = COLOR_YUV2BGR_IYUV,COLOR_YUV420p2RGB   = COLOR_YUV2RGB_YV12,COLOR_YUV420p2BGR   = COLOR_YUV2BGR_YV12,COLOR_YUV2RGBA_YV12 = 102,COLOR_YUV2BGRA_YV12 = 103,COLOR_YUV2RGBA_IYUV = 104,COLOR_YUV2BGRA_IYUV = 105,COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV,COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV,COLOR_YUV420p2RGBA  = COLOR_YUV2RGBA_YV12,COLOR_YUV420p2BGRA  = COLOR_YUV2BGRA_YV12,COLOR_YUV2GRAY_420  = 106,COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420,COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420,COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420,COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420,COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420,COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420,COLOR_YUV420p2GRAY  = COLOR_YUV2GRAY_420,//! YUV 4:2:2 family to RGBCOLOR_YUV2RGB_UYVY = 107,COLOR_YUV2BGR_UYVY = 108,//COLOR_YUV2RGB_VYUY = 109,//COLOR_YUV2BGR_VYUY = 110,COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY,COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY,COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY,COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY,COLOR_YUV2RGBA_UYVY = 111,COLOR_YUV2BGRA_UYVY = 112,//COLOR_YUV2RGBA_VYUY = 113,//COLOR_YUV2BGRA_VYUY = 114,COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY,COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY,COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY,COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY,COLOR_YUV2RGB_YUY2 = 115,COLOR_YUV2BGR_YUY2 = 116,COLOR_YUV2RGB_YVYU = 117,COLOR_YUV2BGR_YVYU = 118,COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2,COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2,COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2,COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2,COLOR_YUV2RGBA_YUY2 = 119,COLOR_YUV2BGRA_YUY2 = 120,COLOR_YUV2RGBA_YVYU = 121,COLOR_YUV2BGRA_YVYU = 122,COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2,COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2,COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2,COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2,COLOR_YUV2GRAY_UYVY = 123,COLOR_YUV2GRAY_YUY2 = 124,//CV_YUV2GRAY_VYUY    = CV_YUV2GRAY_UYVY,COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY,COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY,COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2,COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2,COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2,//! alpha premultiplicationCOLOR_RGBA2mRGBA    = 125,COLOR_mRGBA2RGBA    = 126,//! RGB to YUV 4:2:0 familyCOLOR_RGB2YUV_I420  = 127,COLOR_BGR2YUV_I420  = 128,COLOR_RGB2YUV_IYUV  = COLOR_RGB2YUV_I420,COLOR_BGR2YUV_IYUV  = COLOR_BGR2YUV_I420,COLOR_RGBA2YUV_I420 = 129,COLOR_BGRA2YUV_I420 = 130,COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420,COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420,COLOR_RGB2YUV_YV12  = 131,COLOR_BGR2YUV_YV12  = 132,COLOR_RGBA2YUV_YV12 = 133,COLOR_BGRA2YUV_YV12 = 134,//! DemosaicingCOLOR_BayerBG2BGR = 46,COLOR_BayerGB2BGR = 47,COLOR_BayerRG2BGR = 48,COLOR_BayerGR2BGR = 49,COLOR_BayerBG2RGB = COLOR_BayerRG2BGR,COLOR_BayerGB2RGB = COLOR_BayerGR2BGR,COLOR_BayerRG2RGB = COLOR_BayerBG2BGR,COLOR_BayerGR2RGB = COLOR_BayerGB2BGR,COLOR_BayerBG2GRAY = 86,COLOR_BayerGB2GRAY = 87,COLOR_BayerRG2GRAY = 88,COLOR_BayerGR2GRAY = 89,//! Demosaicing using Variable Number of GradientsCOLOR_BayerBG2BGR_VNG = 62,COLOR_BayerGB2BGR_VNG = 63,COLOR_BayerRG2BGR_VNG = 64,COLOR_BayerGR2BGR_VNG = 65,COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG,COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG,COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG,COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG,//! Edge-Aware DemosaicingCOLOR_BayerBG2BGR_EA  = 135,COLOR_BayerGB2BGR_EA  = 136,COLOR_BayerRG2BGR_EA  = 137,COLOR_BayerGR2BGR_EA  = 138,COLOR_BayerBG2RGB_EA  = COLOR_BayerRG2BGR_EA,COLOR_BayerGB2RGB_EA  = COLOR_BayerGR2BGR_EA,COLOR_BayerRG2RGB_EA  = COLOR_BayerBG2BGR_EA,COLOR_BayerGR2RGB_EA  = COLOR_BayerGB2BGR_EA,//! Demosaicing with alpha channelCOLOR_BayerBG2BGRA = 139,COLOR_BayerGB2BGRA = 140,COLOR_BayerRG2BGRA = 141,COLOR_BayerGR2BGRA = 142,COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA,COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA,COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA,COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA,COLOR_COLORCVT_MAX  = 143
};

使用案例

#python code
#彩色图转灰度读
cvtColorImage = cv2.cvtColor('imagePath',cv2.COLOR_RGB2GRAY)
plt.figure("cvtColorImage")
plt.title("cvtColorImage")
plt.imshow(cvtColorImage)
plt.show()#C code
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{Mat src = imread("youImagePath", 1);Mat cvtMat;cvtColor(src, cvtMat, COLOR_RGB2GRAY);namedWindow("src", 0);imshow("src", src);namedWindow("cvtMat", 0);imshow("cvtMat", cvtMat);waitKey(0);return 0;
}

效果图

2.函数convertTo

定义：

void cv::Mat::convertTo(OutputArray dst, int rtype, double alpha=1.0, double beta=0.0) const

参数：
dst：输出图像
rtype:转换的数据类型
alpha：尺度变换因子
beta: 附加到尺度变换后的值上的偏移量
作用:将图像从一种数据类型转换为另一种数据类型。

(该函数在opencv-python中并没有体现，可通过numpy函数.astype(np.uint8)来实现)

#python code
import numpy as np
#数据类型 np.uint8/np.uint16/np.uint32/np.uint64
stdImage = np.asarray(stdImage).astype(np.uint8)#C++ code
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{Mat src = imread("xx.jpg", 0);Mat convertToMat;src.convertTo(convertToMat, CV_32F);int srcDepth = src.depth();int newDepth = convertToMat.depth();cout << "srcDepth=" << srcDepth << endl;cout << "newDepth=" << newDepth << endl;system("pause");return 0;
}

3.函数split (将多通道图像分离为每个通道)

定义：

void split(const Mat& src, Mat* mvbegin);
void split(InputArray m, OutputArrayOfArrays mv);
def split(m, mv=None)

参数：
src：输入图像
m：输入图像
mvbegin:输出每个通道
mv：输出每个通道

使用案例

#python code
image=cv2.imread('imagepath')
SplitImage1,SplitImage2,SplitImage3 = cv2.split(image)#C++ code
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{Mat src = imread("xxx.jpg", 1);vector<Mat> vecMat;split(src,vecMat);namedWindow("src", 0);imshow("src", src);for (int i = 0; i < vecMat.size(); i++){namedWindow(to_string(i), 0);imshow(to_string(i), vecMat[i]);}waitKey(0);return 0;
}

效果图

4.函数merge 将图片不同通道合并

定义：

void merge(const Mat* mv, size_t count, OutputArray dst);
void merge(InputArrayOfArrays mv, OutputArray dst);
def merge(mv, dst=None)

参数：
mv：输入的每个单通道图像
count：输入图像的数量
dst：输出的多通道
一般用于多个单通图片合并成多通道彩色图

python code
#python code
image=cv2.imread('imagepath')
SplitImage1,SplitImage2,SplitImage3 = cv2.split(image)
new_image_merge=cv2.merge([SplitImage1,SplitImage2,SplitImage3])
plt.figure("new_image_merge")
plt.title("new_image_merge")
plt.imshow(new_image_merge)
plt.show()#C++ code
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{Mat src = imread("xxxx.jpg", 1);vector<Mat> vecMat;split(src, vecMat);Mat mergeMat;merge(vecMat, mergeMat);namedWindow("mergeMat", 0);imshow("mergeMat", mergeMat);waitKey(0);return 0;
}

5.函数extractChannel 指定抽取多通道图像的某个通道

定义：

void extractChannel(InputArray src, OutputArray dst, int coi);
def extractChannel(src, coi, dst=None)

参数：
src：输入的多通道图像
dst：输出指定通道图像
coi：输入图像的指定通道

使用案例

#python code
image=cv2.imread('imagepath')
channel0=cv2.extractChannel(image,0)
channel1=cv2.extractChannel(image,1)
channel2=cv2.extractChannel(image,2)#C++ code
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{Mat src = imread("xxx.jpg", 1);Mat dst;extractChannel(src, dst, 1);namedWindow("dst", 0);imshow("dst", dst);waitKey(0);return 0;
}

6.函数applyColorMap 灰度图转伪彩色图

定义：

void applyColorMap(InputArray src, OutputArray dst, int colormap);
def applyColorMap(src, colormap, dst=None)

参数：
src：输入的多通道图像
dst：输出指定通道图像
colormap：颜色模式，可以取下列枚举值(具体效果大家自己可以手动尝试)

enum ColormapTypes
{COLORMAP_AUTUMN = 0, //!< ![autumn](pics/colormaps/colorscale_autumn.jpg)COLORMAP_BONE = 1, //!< ![bone](pics/colormaps/colorscale_bone.jpg)COLORMAP_JET = 2, //!< ![jet](pics/colormaps/colorscale_jet.jpg)COLORMAP_WINTER = 3, //!< ![winter](pics/colormaps/colorscale_winter.jpg)COLORMAP_RAINBOW = 4, //!< ![rainbow](pics/colormaps/colorscale_rainbow.jpg)COLORMAP_OCEAN = 5, //!< ![ocean](pics/colormaps/colorscale_ocean.jpg)COLORMAP_SUMMER = 6, //!< ![summer](pics/colormaps/colorscale_summer.jpg)COLORMAP_SPRING = 7, //!< ![spring](pics/colormaps/colorscale_spring.jpg)COLORMAP_COOL = 8, //!< ![cool](pics/colormaps/colorscale_cool.jpg)COLORMAP_HSV = 9, //!< ![HSV](pics/colormaps/colorscale_hsv.jpg)COLORMAP_PINK = 10, //!< ![pink](pics/colormaps/colorscale_pink.jpg)COLORMAP_HOT = 11, //!< ![hot](pics/colormaps/colorscale_hot.jpg)COLORMAP_PARULA = 12 //!< ![parula](pics/colormaps/colorscale_parula.jpg)
};

使用案例

#python code
image=cv2.imread('imagepath')
SplitImage1,SplitImage2,SplitImage3 = cv2.split(image)
applyColorMapImage = cv2.applyColorMap(SplitImage1,cv2.COLORMAP_AUTUMN)
plt.figure("applyColorMapImage")
plt.title("applyColorMapImage")
plt.imshow(applyColorMapImage)
plt.show()#C++ code
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{Mat src = imread("xxx.jpg", 0);Mat dst(src.size(), src.depth());applyColorMap(src, dst, 1);namedWindow("dst", 0);imshow("dst", dst);waitKey(0);return 0;
}

效果图

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