python多進程共享變量，附共享圖像內存實例

因爲python多線程只能在單核上跑，所以須要cpu多核處理只能用多進程。

python多進程通常用multiprocessing。但是用multiprocessing的array或者value對內存的讀寫速度特別慢。緣由以及解決方法以下連接：

http://stackoverflow.com/questions/37705974/why-are-multiprocessing-sharedctypes-assignments-so-slow

 

針對numpy的圖像多進程共享的示例代碼以下：

 1 import numpy as np
 2 import cv2, multiprocessing, sharedmem
 3 
 4 def show_image(image_in):
 5     while 1:
 6         cv2.imshow("avi",image_in)
 7         cv2.waitKey(1)
 8 
 9 def aa(images):
10     while 1:
11         for i in range(20):
12             cv2.imshow("a",images[i])
13             cv2.waitKey(1)
14 
15 if __name__ == '__main__':
16     cap = cv2.VideoCapture('1.avi')
17     assert cap.isOpened(), 'Cannot capture source'
18 
19     _, image = cap.read()
20     shape = np.shape(image)
21     dtype = image.dtype
22     images = multiprocessing.Manager().dict()
23     image_in = sharedmem.empty(shape, dtype)
24     image_in[:] = image.copy()
25 
26     a = multiprocessing.Process(target=show_image,args=(image_in,))
27     a.start()
28     count = 0
29     for i in range(20):
30         ret, image = cap.read()
31         if not ret:
32             break
33         image_in[:] = image.copy()
34         images[count] = image_in
35         count += 1
36     multiprocessing.Process(target=aa, args=(images,)).start()
37     aa.join()