1 #!/bin/python3
2
3 import cv2 as cv
4 import numpy as np
5
6
7 def clamp(pv):
8 if pv > 255:
9 return 255
10 if pv < 0:
11 return 0
12 else:
13 return pv
14
15
16 def gaussian_noise(image):
17 h,w,c = image.shape
18 for row in range(h):
19 for col in range(w):
20 s = np.random.normal(0,20,3)
21 b = image[row,col,0] #blue
22 g = image[row,col,1] #green
23 r = image[row,col,2] #red
24 image[row,col,0] = clamp(b + s[0])
25 image[row,col,1] = clamp(g + s[1])
26 image[row,col,2] = clamp(r + s[2])
27 cv.imshow("noise image",image)
28
29 print("----------Hello Python-----------------")
30 src = cv.imread("/home/laohe/downloads/image.jpeg")
31 cv.namedWindow("input image",cv.WINDOW_AUTOSIZE)
32
33 cv.imshow("input image",src)
34 t1 = cv.getTickCount()
35 gaussian_noise(src)
36 t2 = cv.getTickCount()
37 time = (t2 - t1)/cv.getTickFrequency()
print("time cinsume: %s"%(time*1000))
39 dst = cv.GaussianBlur(src,(5,5),0)
40 cv.imshow("Gaussian Blur",dst)
41 cv.waitKey(0)
42
43 cv.destroyAllWindows()
