-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathSSIM.py
69 lines (52 loc) · 1.97 KB
/
SSIM.py
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
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 12 12:59:56 2018
@author: zhang
"""
# we use psrn, ssim, dice score to evaluate the
import numpy as np
from PIL import Image
from scipy.signal import convolve2d
def matlab_style_gauss2D(shape=(3,3),sigma=0.5):
"""
2D gaussian mask - should give the same result as MATLAB's
fspecial('gaussian',[shape],[sigma])
"""
m,n = [(ss-1.)/2. for ss in shape]
y,x = np.ogrid[-m:m+1,-n:n+1]
h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )
h[ h < np.finfo(h.dtype).eps*h.max() ] = 0
sumh = h.sum()
if sumh != 0:
h /= sumh
return h
def filter2(x, kernel, mode='same'):
return convolve2d(x, np.rot90(kernel, 2), mode=mode)
def compute_ssim(im1, im2, k1=0.01, k2=0.03, win_size=11, L=255):
if not im1.shape == im2.shape:
raise ValueError("Input Imagees must have the same dimensions")
if len(im1.shape) > 2:
raise ValueError("Please input the images with 1 channel")
M, N = im1.shape
C1 = (k1*L)**2
C2 = (k2*L)**2
window = matlab_style_gauss2D(shape=(win_size,win_size), sigma=1.5)
window = window/np.sum(np.sum(window))
if im1.dtype == np.uint8:
im1 = np.double(im1)
if im2.dtype == np.uint8:
im2 = np.double(im2)
mu1 = filter2(im1, window, 'valid')
mu2 = filter2(im2, window, 'valid')
mu1_sq = mu1 * mu1
mu2_sq = mu2 * mu2
mu1_mu2 = mu1 * mu2
sigma1_sq = filter2(im1*im1, window, 'valid') - mu1_sq
sigma2_sq = filter2(im2*im2, window, 'valid') - mu2_sq
sigmal2 = filter2(im1*im2, window, 'valid') - mu1_mu2
ssim_map = ((2*mu1_mu2+C1) * (2*sigmal2+C2)) / ((mu1_sq+mu2_sq+C1) * (sigma1_sq+sigma2_sq+C2))
return np.mean(np.mean(ssim_map))
if __name__ == "__main__":
im1 = Image.open("results//coastal_mansion2.png")
im2 = Image.open("results//coastal_mansion3.png")
print(compute_ssim(np.array(im1),np.array(im2)))