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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]:
neighborhood.append(a[x - 1][y])
if flag[x - 1][y + 1]:
neighborhood.append(a[x - 1][y + 1])
if flag[x][y + 1]:
neighborhood.append(a[x][y + 1])
if flag[x + 1][y + 1]:
neighborhood.append(a[x + 1][y + 1])
if flag[x + 1][y]:
neighborhood.append(a[x + 1][y])
if flag[x + 1][y - 1]:
neighborhood.append(a[x + 1][y - 1])
if flag[x][y - 1]:
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
"""
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 = []
noises.append(img_noise)
for i in range(10):
new, sub_flag = denoise(noises[i], noise_flag, 1)
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)
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