-
Notifications
You must be signed in to change notification settings - Fork 2
/
uqim_utils.py
192 lines (175 loc) · 5.64 KB
/
uqim_utils.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
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
"""
# > Modules for computing the Underwater Image Quality Measure (UIQM)
# Maintainer: Jahid (email: islam034@umn.edu)
"""
from scipy import ndimage
from PIL import Image
import numpy as np
import math
def mu_a(x, alpha_L=0.1, alpha_R=0.1):
"""
Calculates the asymetric alpha-trimmed mean
"""
# sort pixels by intensity - for clipping
x = sorted(x)
# get number of pixels
K = len(x)
# calculate T alpha L and T alpha R
T_a_L = math.ceil(alpha_L*K)
T_a_R = math.floor(alpha_R*K)
# calculate mu_alpha weight
weight = (1/(K-T_a_L-T_a_R))
# loop through flattened image starting at T_a_L+1 and ending at K-T_a_R
s = int(T_a_L+1)
e = int(K-T_a_R)
val = sum(x[s:e])
val = weight*val
return val
def s_a(x, mu):
val = 0
for pixel in x:
val += math.pow((pixel-mu), 2)
return val/len(x)
def _uicm(x):
R = x[:,:,0].flatten()
G = x[:,:,1].flatten()
B = x[:,:,2].flatten()
RG = R-G
YB = ((R+G)/2)-B
mu_a_RG = mu_a(RG)
mu_a_YB = mu_a(YB)
s_a_RG = s_a(RG, mu_a_RG)
s_a_YB = s_a(YB, mu_a_YB)
l = math.sqrt( (math.pow(mu_a_RG,2)+math.pow(mu_a_YB,2)) )
r = math.sqrt(s_a_RG+s_a_YB)
return (-0.0268*l)+(0.1586*r)
def sobel(x):
dx = ndimage.sobel(x,0)
dy = ndimage.sobel(x,1)
mag = np.hypot(dx, dy)
mag *= 255.0 / np.max(mag)
return mag
def eme(x, window_size):
"""
Enhancement measure estimation
x.shape[0] = height
x.shape[1] = width
"""
# if 4 blocks, then 2x2...etc.
k1 = int(x.shape[1]/window_size)
k2 = int(x.shape[0]/window_size)
# weight
w = 2./(k1*k2)
blocksize_x = window_size
blocksize_y = window_size
# make sure image is divisible by window_size - doesn't matter if we cut out some pixels
x = x[:blocksize_y*k2, :blocksize_x*k1]
val = 0
for l in range(k1):
for k in range(k2):
block = x[k*window_size:window_size*(k+1), l*window_size:window_size*(l+1)]
max_ = np.max(block)
min_ = np.min(block)
# bound checks, can't do log(0)
if min_ == 0.0: val += 0
elif max_ == 0.0: val += 0
else: val += math.log(max_/min_)
return w*val
def _uism(x):
"""
Underwater Image Sharpness Measure
"""
# get image channels
R = x[:,:,0]
G = x[:,:,1]
B = x[:,:,2]
# first apply Sobel edge detector to each RGB component
Rs = sobel(R)
Gs = sobel(G)
Bs = sobel(B)
# multiply the edges detected for each channel by the channel itself
R_edge_map = np.multiply(Rs, R)
G_edge_map = np.multiply(Gs, G)
B_edge_map = np.multiply(Bs, B)
# get eme for each channel
r_eme = eme(R_edge_map, 10)
g_eme = eme(G_edge_map, 10)
b_eme = eme(B_edge_map, 10)
# coefficients
lambda_r = 0.299
lambda_g = 0.587
lambda_b = 0.144
return (lambda_r*r_eme) + (lambda_g*g_eme) + (lambda_b*b_eme)
def plip_g(x,mu=1026.0):
return mu-x
def plip_theta(g1, g2, k):
g1 = plip_g(g1)
g2 = plip_g(g2)
return k*((g1-g2)/(k-g2))
def plip_cross(g1, g2, gamma):
g1 = plip_g(g1)
g2 = plip_g(g2)
return g1+g2-((g1*g2)/(gamma))
def plip_diag(c, g, gamma):
g = plip_g(g)
return gamma - (gamma * math.pow((1 - (g/gamma) ), c) )
def plip_multiplication(g1, g2):
return plip_phiInverse(plip_phi(g1) * plip_phi(g2))
#return plip_phiInverse(plip_phi(plip_g(g1)) * plip_phi(plip_g(g2)))
def plip_phiInverse(g):
plip_lambda = 1026.0
plip_beta = 1.0
return plip_lambda * (1 - math.pow(math.exp(-g / plip_lambda), 1 / plip_beta));
def plip_phi(g):
plip_lambda = 1026.0
plip_beta = 1.0
return -plip_lambda * math.pow(math.log(1 - g / plip_lambda), plip_beta)
def _uiconm(x, window_size):
"""
Underwater image contrast measure
https://github.com/tkrahn108/UIQM/blob/master/src/uiconm.cpp
https://ieeexplore.ieee.org/abstract/document/5609219
"""
plip_lambda = 1026.0
plip_gamma = 1026.0
plip_beta = 1.0
plip_mu = 1026.0
plip_k = 1026.0
# if 4 blocks, then 2x2...etc.
k1 = int(x.shape[1]/window_size)
k2 = int(x.shape[0]/window_size)
# weight
w = -1./(k1*k2)
blocksize_x = window_size
blocksize_y = window_size
# make sure image is divisible by window_size - doesn't matter if we cut out some pixels
x = x[:blocksize_y*k2, :blocksize_x*k1]
# entropy scale - higher helps with randomness
alpha = 1
val = 0
for l in range(k1):
for k in range(k2):
block = x[k*window_size:window_size*(k+1), l*window_size:window_size*(l+1), :]
max_ = np.max(block)
min_ = np.min(block)
top = max_-min_
bot = max_+min_
if math.isnan(top) or math.isnan(bot) or bot == 0.0 or top == 0.0: val += 0.0
else: val += alpha*math.pow((top/bot),alpha) * math.log(top/bot)
#try: val += plip_multiplication((top/bot),math.log(top/bot))
return w*val
def getUIQM(x):
"""
Function to return UIQM to be called from other programs
x: image
"""
x = x.astype(np.float32)
### UCIQE: https://ieeexplore.ieee.org/abstract/document/7300447
#c1 = 0.4680; c2 = 0.2745; c3 = 0.2576
### UIQM https://ieeexplore.ieee.org/abstract/document/7305804
c1 = 0.0282; c2 = 0.2953; c3 = 3.5753
uicm = _uicm(x)
uism = _uism(x)
uiconm = _uiconm(x, 10)
uiqm = (c1*uicm) + (c2*uism) + (c3*uiconm)
return uiqm,uicm,uism,uiconm