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 cv2
import numpy
import numpy as np

img=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 cv2
import numpy as np

img=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.strides
print merged

mergedByNp=np.dstack([b,g,r])
print "Merge by Numpy"
print mergedByNp.strides
print mergedByNp

cv2.imshow("Merged",merged)
cv2.imshow("MergedByNp",mergedByNp)

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

cv2.waitKey(0)
cv2.destroyAllWindows()