【Python+OpenCV之五】 分离颜色通道&多通道图像混合

参考：浅墨_毛星云

原始图像：

 

#(五)分离颜色通道&多通道图像混合
import cv2
'''
cv2.split(m, mv):将一个多通道数组分离成几个单通道数组
m：我们需要进行分离的多通道数组
mv：函数的输出数组或者输出的vector容器
'''
import  argparse
import numpy as np

img1 = cv2.imread("2.jpg")
(B,G,R) = cv2.split(img1)
cv2.imshow("Red", R)
cv2.imshow("Green", G)
cv2.imshow("Blue",B)
print("R_shape:",G.shape,"  G_shape:",G.shape,"  B_shape:",B.shape)

cv2.waitKey()
cv2.destroyAllWindows()

三个通道的图像：

R_shape: (150, 150)   G_shape: (150, 150)   B_shape: (150, 150)

 

分离出来的各个通道并不是RGB本身的颜色的，彩色图像都是三个通道的。要想得到有颜色需要扩展通道，如下所示：

#(五)分离颜色通道&多通道图像混合
import cv2
'''
cv2.split(m, mv):将一个多通道数组分离成几个单通道数组
m：我们需要进行分离的多通道数组
mv：函数的输出数组或者输出的vector容器
'''
import  argparse
import numpy as np

img1 = cv2.imread("2.jpg")
(B,G,R) = cv2.split(img1)
cv2.imshow("Red", R)
cv2.imshow("Green", G)
cv2.imshow("Blue",B)
print("R_shape:",G.shape,"  G_shape:",G.shape,"  B_shape:",B.shape)

#通道扩展
zeros = np.zeros(img1.shape[:2], np.uint8)
img_B = cv2.merge([B,zeros,zeros])
cv2.imshow("B_", img_B)
img_G = cv2.merge([zeros,G,zeros])
cv2.imshow("G_", img_G)
img_R = cv2.merge([zeros,zeros,R])
cv2.imshow("R_", img_R)

cv2.waitKey()
cv2.destroyAllWindows()