1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
|
import cv2 as cv
import numpy as np
def _2d_2_1d(x, y):
if x == 0 and y == 0:
return int(0)
elif x == 0 and y == 1:
return int(1)
elif x == 0 and y == 2:
return int(2)
elif x == 1 and y == 2:
return int(3)
elif x == 2 and y == 2:
return int(4)
elif x == 2 and y == 1:
return int(5)
elif x == 2 and y == 0:
return int(6)
elif x == 1 and y == 0:
return int(7)
elif x == 1 and y == 1:
return int(8)
def _1d_2_2d(x):
if x == 0:
return 0, 0
elif x == 1:
return 0, 1
elif x == 2:
return 0, 2
elif x == 3:
return 1, 2
elif x == 4:
return 2, 2
elif x == 5:
return 2, 1
elif x == 6:
return 2, 0
elif x == 7:
return 1, 0
def division(_8):
result = [0] * 8
p1 = [_8[0][0], _8[0][1], _8[0][2], _8[1][2], _8[2][2], _8[2][1], _8[2][0],
_8[1][0]]
p = sorted(p1)
q = [abs(int(p[0]) - int(p[1])), abs(int(p[1]) - int(p[2])), abs(int(p[2]) - int(p[3])), abs(int(p[3]) - int(p[4])),
abs(int(p[4]) - int(p[5])), abs(int(p[5]) - int(p[6])), abs(int(p[6]) - int(p[7]))]
if max(q) < 10:
return result, False
max_index = q.index(max(q))
for i in range(0, max_index + 1):
for j in range(len(q)):
if p1[j] == p[i]:
result[j] = 1
for i in range(8):
if result[i] != 1:
result[i] = 2
return result, True
def find_best(a, x, y, flag):
p = a[x][y]
neighborhood = []
if flag[x - 1][y - 1] == 0:
neighborhood.append(a[x - 1][y - 1])
if flag[x - 1][y] == 0:
neighborhood.append(a[x - 1][y])
if flag[x - 1][y + 1] == 0:
neighborhood.append(a[x - 1][y + 1])
if flag[x][y + 1] == 0:
neighborhood.append(a[x][y + 1])
if flag[x + 1][y + 1] == 0:
neighborhood.append(a[x + 1][y + 1])
if flag[x + 1][y] == 0:
neighborhood.append(a[x + 1][y])
if flag[x + 1][y - 1] == 0:
neighborhood.append(a[x + 1][y - 1])
if flag[x][y - 1] == 0:
neighborhood.append(a[x][y - 1])
d = []
for i in range(len(neighborhood)):
d.append(abs(int(neighborhood[i]) - int(p)))
if len(d) == 0:
return p
min_index = d.index(min(d))
return neighborhood[min_index]
def denoise_1_7(channel, flag, k):
"""
:param channel: 待检测通道
:param flag: 噪声标记
:param k: 多尺度指数
:return: 被找到的噪声个数
"""
if k == 1:
x, y = channel.shape
flag0 = np.zeros((x, y))
sub_flag = 0
for i in range(1, x - 1):
for j in range(1, y - 1):
a = [[0] * 3 for ix in range(3)]
for i0 in range(3):
for j0 in range(3):
a[i0][j0] = channel[i - 1 + i0][j - 1 + j0]
div, div_flag = division(a)
if not div_flag:
continue
cnt = 0
for i0 in range(8):
if div[i0] == 1:
cnt += 1
if cnt == 1:
for i0 in range(8):
if div[i0] == 1:
x0, y0 = _1d_2_2d(i0)
if flag[i - 1 + x0][j - 1 + y0] == 1 and flag0[i - 1 + x0][j - 1 + y0] == 0:
sub_flag += 1
flag0[i - 1 + x0][j - 1 + y0] = 1
elif cnt == 7:
for i0 in range(8):
if div[i0] == 2:
x0, y0 = _1d_2_2d(i0)
if flag[i - 1 + x0][j - 1 + y0] == 1 and flag0[i - 1 + x0][j - 1 + y0] == 0:
sub_flag += 1
flag0[i - 1 + x0][j - 1 + y0] = 1
for i in range(1, x - 1):
for j in range(1, y - 1):
if flag[i][j] == 1:
channel[i][j] = find_best(channel, i, j, flag0)
return channel, sub_flag
def denoise(noise, noise_flag, k):
"""
:param noise: 噪声图像
:param noise_flag: 噪声标记
:param k: 多尺度指数
:return: 修改后的噪声图像， 被找到的噪声
"""
b_noise, g_noise, r_noise = cv.split(noise)
sub_flag = 0
b_noise, f = denoise_1_7(b_noise, noise_flag[0], k)
sub_flag += f
g_noise, f = denoise_1_7(g_noise, noise_flag[1], k)
sub_flag += f
r_noise, f = denoise_1_7(r_noise, noise_flag[2], k)
sub_flag += f
new_noise = cv.merge((b_noise, g_noise, r_noise))
return new_noise, sub_flag
def get_noise_flag(origin, noise):
"""
:param origin: Mat原图
:param noise: Mat噪声图
:return: flag矩阵
"""
b_origin, g_origin, r_origin = cv.split(origin)
b_noise, g_noise, r_noise = cv.split(noise)
row, col = b_origin.shape
flag = np.zeros((3, row, col))
cnt_flag = 0
for i in range(row):
for j in range(col):
if b_origin[i][j] != b_noise[i][j]:
flag[0][i][j] = 1
cnt_flag += 1
if g_origin[i][j] != g_noise[i][j]:
flag[1][i][j] = 1
cnt_flag += 1
if r_origin[i][j] != r_noise[i][j]:
flag[2][i][j] = 1
cnt_flag += 1
return flag, cnt_flag
def main(origin, noise, k):
"""
:param origin: 原始标准图片路径
:param noise: 噪声图路径
:param k: 多尺度指数
:return:
"""
img_origin = cv.imread(origin, 1)
img_noise = cv.imread(noise, 1)
noise_flag, cnt_flag = get_noise_flag(img_origin, img_noise)
print('原噪声个数：', cnt_flag)
noises = [img_noise]
for i in range(10):
new, sub_flag = denoise(noises[i], noise_flag, k)
noises.append(new)
print('[' + str(i + 1) + ']', sub_flag / cnt_flag)
cv.imwrite('new.png', noises[9])
if __name__ == '__main__':
origin_addr = 'img.png'
noise_addr = 'img_1.png'
main(origin_addr, noise_addr, 1)
|
wechat
alipay
