1 __author__ = "WSX"
2 import cv2 as cv
3 import numpy as np
4 #高斯模糊 基于权重（卷积）
5 #高斯模糊 去燥效果很好
6
7 #高斯模糊
8 def guss_blur(img): #去燥 基于卷积
9 dst = cv.blur(img , (5 ,5)) #卷积核大小（1*3）
10 cv.imshow("Show", dst)
11
12 def deal( number ):
13 if number > 255:
14 return 255
15 elif number < 0:
16 return 0
17 else:
18 return number
19
20 def guss_noise(img): #加燥,自己写的
21 h,w,c = img.shape
22 for i in range(h):
23 for j in range(w):
24 s = np.random.normal(0 , 20 ,3)
25 b = img[i ,j ,0]
26 g = img[i, j, 1]
27 r = img[i, j, 2]
28 img[i, j, 0] = deal(b + s[0])
29 img[i, j, 1] = deal(g + s[1])
30 img[i, j, 2] = deal(r + s[2])
31 cv.imshow("noise", img)
32
33 #cv.GaussianBlur(img ,(5,5) ,15) 高斯模糊 5*5
34
35 def main():
36 img = cv.imread("1.JPG")
37 cv.namedWindow("Show", cv.WINDOW_AUTOSIZE)
38 #guss_blur(img)
39 guss_noise(img)
40 cv.waitKey(0)
41 cv.destroyAllWindows()
42
43 main()
