实现效果
实现代码
|
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
|
from skimage import img_as_float
import matplotlib.pyplot as plt
from skimage import io
import numpy as np
import numpy.matlib
file_name='D:/2020121173119242.png' # 图片路径
img=io.imread(file_name)
img = img_as_float(img)
img_out = img.copy()
row, col, channel = img.shape
xx = np.arange (col)
yy = np.arange (row)
x_mask = numpy.matlib.repmat (xx, row, 1)
y_mask = numpy.matlib.repmat (yy, col, 1)
y_mask = np.transpose(y_mask)
center_y = (row -1) / 2.0
center_x = (col -1) / 2.0
R = np.sqrt((x_mask - center_x) **2 + (y_mask - center_y) ** 2)
angle = np.arctan2(y_mask - center_y , x_mask - center_x)
Num = 20
arr = np.arange(Num)
for i in range (row):
for j in range (col):
R_arr = R[i, j] - arr
R_arr[R_arr < 0] = 0
new_x = R_arr * np.cos(angle[i,j]) + center_x
new_y = R_arr * np.sin(angle[i,j]) + center_y
int_x = new_x.astype(int)
int_y = new_y.astype(int)
int_x[int_x > col-1] = col - 1
int_x[int_x < 0] = 0
int_y[int_y < 0] = 0
int_y[int_y > row -1] = row -1
img_out[i,j,0] = img[int_y, int_x, 0].sum()/Num
img_out[i,j,1] = img[int_y, int_x, 1].sum()/Num
img_out[i,j,2] = img[int_y, int_x, 2].sum()/Num
plt.figure(1)
plt.imshow(img)
plt.axis('off')
plt.figure(2)
plt.imshow(img_out)
plt.axis('off')
plt.show()
|
以上就是Python 实现 PS 滤镜中的径向模糊特效的详细内容，更多关于python 图片模糊滤镜的资料请关注服务器之家其它相关文章！
原文链接：https://www.cnblogs.com/mtcnn/p/9412386.html