图片处理完整流程（包含二值化处理、对黑白照片降噪、边缘去黑像素、三通道转为一通道、图片转array、图片转成任意像素等功能）——可满足一般图片处理要求

因为注释给的很详细，所以直接给代码：

 1 from PIL import Image
 2 import numpy as np
 3 # 二值化处理
 4 
 5 
 6 def 二值化处理(image):
 7     for i in range(1, 5):
 8         # 灰度图
 9         lim = image.convert('L')
10         # 灰度阈值设为165，低于这个值的点全部填白色
11         threshold = 165
12         table = []
13         for j in range(256):
14             if j < threshold:
15                 table.append(0)
16             else:
17                 table.append(1)
18         bim = lim.point(table, '1')
19     return bim
20 def 对黑白图片进行降噪(im):
21     # 图像二值化
22     data = im.getdata()
23     w, h = im.size
24     black_point = 0
25     for x in range(1, w - 1):
26         for y in range(1, h - 1):
27             mid_pixel = data[w * y + x]  # 中央像素点像素值
28             if mid_pixel < 50:  # 找出上下左右四个方向像素点像素值
29                 top_pixel = data[w * (y - 1) + x]
30                 left_pixel = data[w * y + (x - 1)]
31                 down_pixel = data[w * (y + 1) + x]
32                 right_pixel = data[w * y + (x + 1)]
33                 # 判断上下左右的黑色像素点总个数
34                 if top_pixel < 10:
35                     black_point += 1
36                 if left_pixel < 10:
37                     black_point += 1
38                 if down_pixel < 10:
39                     black_point += 1
40                 if right_pixel < 10:
41                     black_point += 1
42                 if black_point < 1:
43                     im.putpixel((x, y), 255)
44                 # print(black_point)
45                 black_point = 0
46     return im
47 
48 def 边缘黑像素(im):
49     # 去除干扰线
50 
51     # 图像二值化
52     data = im.getdata()
53     w, h = im.size
54     black_point = 0
55     for x in range(1, w - 1):
56         for y in range(1, h - 1):
57             if x < 2 or y < 2:
58                 im.putpixel((x - 1, y - 1), 255)
59             if x > w - 3 or y > h - 3:
60                 im.putpixel((x + 1, y + 1), 255)
61     return im
62 def 处理照片(image):
63     im_1=二值化处理(image)
64     im_2=对黑白图片进行降噪(im_1)
65     im_3=边缘黑像素(im_2)
66     return im_3
67 
68 def 切图片(im):
69     gray = im.convert('L')
70     (x, y) = gray.size  # read image size
71     x_s = 28  # define standard width
72     y_s = 28  # calc height based on standard width
73     out = gray.resize((x_s, y_s), Image.ANTIALIAS)  # resize image with high-quality
74     return out
75 def 转array(im):
76     im2 = 1 - np.array(im) / 255
77     print(im2)
78     print(im2.shape)
79     return im2
80 def GET_true_img(url_name):
81     image = Image.open('../data/test/'+url_name+'.png')
82     im_1=处理照片(image)
83     im_1.save("../data/test/去噪/"+url_name+"_out_处理.png")
84     im_2=切图片(im_1)
85     im_2.save("../data/test/去噪/"+url_name+"_out.png")
86     im_narray=转array(im_2)
87 
88     return im_narray
89 if __name__ =='__main__':
90     image = Image.open('../data/test/8.png')
91     im_1=处理照片(image)
92     im_1.save("../data/test/去噪/8_out_处理.png")
93     im_2=切图片(im_1)
94     im_2.save("../data/test/去噪/8_out.png")
95     im_narray=转array(im_2)