vim+python+OpenCV学习四 ： 像素通道分割



#-*-coding=UTF-8-*-
#使用OpenCV自带的split函数,输出为黑白图像
import cv2
import numpy as np

img=cv2.imread("E:\\testpictures\\meizi4.jpg")

'''
下面一句话可以这样
b = cv2.split(img)[0] 
g = cv2.split(img)[1] 
r = cv2.split(img)[2] 
'''
b,g,r=cv2.split(img)

cv2.imshow("Blue",r)
cv2.imshow("Red",g)
cv2.imshow("Green",b)

cv2.waitKey(0)
cv2.destroyAllWindows()

#第二：用numpy数组来实现：

#-*-coding=UTF-8-*-#直接操作NumPy数组来达到这一目的import cv2import numpyimport numpy as npimg=cv2.imread("E:\\testpictures\\meizi9.jpg")b=np.zeros((img.shape[0],img.shape[1]),dtype=img.dtype)g=np.zeros((img.shape[0],img.shape[1]),dtype=img.dtype)r=np.zeros((img.shape[0],img.shape[1]),dtype=img.dtype)b[:,:]=img[:,:,0]g[:,:]=img[:,:,1]r[:,:]=img[:,:,2]cv2.imshow("Blue",r)cv2.imshow("Red",g)cv2.imshow("Green",b)cv2.waitKey(0)cv2.destroyAllWindows()

从上面的图可以看出，颜色通道的分离能让有些条纹变得非常清晰有助于处理。

一下合并各通道：

#-*-coding=UTF-8-*-#合并三个通道的图像'''注意：这里只是演示，实际使用时请用OpenCV自带的merge函数！用NumPy组合的结果不能在OpenCV中其他函数使用，因为其组合方式与OpenCV自带的不一样.'''import cv2import numpy as npimg=cv2.imread("E:\\testpictures\\meizi9.jpg")b=np.zeros((img.shape[0],img.shape[1]),dtype=img.dtype)g=np.zeros((img.shape[0],img.shape[1]),dtype=img.dtype)r=np.zeros((img.shape[0],img.shape[1]),dtype=img.dtype)b[:,:]=img[:,:,0]g[:,:]=img[:,:,1]r[:,:]=img[:,:,2]#用opencv自带的merge函数merged=cv2.merge([b,g,r])print "Merge by OpenCV"print merged.stridesprint mergedmergedByNp=np.dstack([b,g,r])print "Merge by Numpy"print mergedByNp.stridesprint mergedByNpcv2.imshow("Merged",merged)cv2.imshow("MergedByNp",mergedByNp)cv2.imshow("Blue",r)cv2.imshow("Red",g)cv2.imshow("Green",b)cv2.waitKey(0)cv2.destroyAllWindows()