1
import math
2
from PIL import Image
3
import matplotlib.pyplot as plt
4

5
img = Image.open("CNN.png") # 打开为图像对象
6
img = img.convert("1") # 转灰度图
7

8
raw = img.load() # 加载图像矩阵
9

10
pngV = [[raw[w, h] for w in range(img.size[1])] for h in range(img.size[0])] # 转为一个宽高相同的二维数组用来存放灰度值
11

12
kernel = [  #卷积核
13
    [0, 0, 0],
14
    [0, 1, 0],
15
    [1, 0, 1],
16
]
17

18
kernel1 = [ #无用的卷积核
19
    [1]
20
]
21

22

23
def conv(v, w, step=1, pad=0, fill_value=1, bias=0):#卷积过程，参数：灰度矩阵，权重（卷积核），步长，边缘填充，边缘填充的值，偏置
24
    v_row = len(v)
25
    v_col = len(v[0])
26
    if pad:
27
        for _ in range(pad):#上边缘填充
28
            v.insert(0, [fill_value for _ in range(v_col + pad * 2)])
29
        for i in range(pad, v_row + pad):#中间两侧边缘填充
30
            for _ in range(pad):
31
                v[i].insert(0, fill_value)
32
                v[i].append(fill_value)
33
        for _ in range(pad):#下边缘填充
34
            v.append([fill_value for _ in range(v_col + pad * 2)])
35
        v_row += pad * 2
36
        v_col += pad * 2
37

38
    w_row = len(w)
39
    w_col = len(w[0])
40
    res = []
41
    for x in range(w_row // 2, v_row - w_row // 2, step):#受卷积核宽高的影响需要计算起止位置
42
        line = []
43
        for y in range(w_col // 2, v_col - w_col // 2, step):
44
            temp = 0
45
            for i in range(w_row):
46
                for j in range(w_col):
47
                    t = v[x + i - w_row // 2][y + j - w_col // 2] * w[i][j] # 灰度 乘 权重
48
                    temp += t # 累加乘积
49
            temp += bias # 加偏置
50
            line.append(temp)
51
        res.append(line)
52
    return res
53

54
def relu(v): # 线性整流函数
55
    for i in range(len(v)):
56
        for j in range(len(v[i])):
57
            v[i][j]=v[i][j] if v[i][j] > 0 else 0
58
    return v
59

60
def max_pooling(v,size=2): # 最大池化
61
    v_row = math.ceil(len(v) / size)
62
    v_col = math.ceil(len(v[0]) / size)
63
    r = []
64
    for y in range(v_row):
65
        line = []
66
        for x in range(v_col):
67
            compare = []
68
            for j in range(y*size, y*size + size if y*size + size < len(v) else len(v)): # 防止边缘越界
69
                for i in range(x*size, x*size + size if x*size+size < len(v) else len(v[0])):
70
                    compare.append(v[j][i])
71
            line.append(max(compare))
72
        r.append(line)
73
    return r
74

75

76

77
if __name__ == '__main__':
78
    v = conv(pngV, kernel, step=9, pad=0, bias=-763)
79
    v = relu(v)
80
    v = max_pooling(v)
81
    plt.imshow(v)
82
    plt.show()
83
    plt.imsave("2.png", v)