forked from JoshuaEbenezer/ChipQA-0
-
Notifications
You must be signed in to change notification settings - Fork 1
/
save_stats.py
320 lines (283 loc) · 12.6 KB
/
save_stats.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
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
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
import numpy as np
import cv2
import glob
import os
import skvideo.io
from scipy.special import gamma
from matplotlib import pyplot as plt
import skvideo.utils
import pandas as pd
import math
from joblib import dump
import scipy
from joblib import load
from scipy.fftpack import fft, dct
import scipy.io as sio
from scipy.stats import norm,lognorm,skew,kurtosis
from skvideo.measure import videobliinds,videobliinds_features,niqe
from skimage.util.shape import view_as_windows
win = np.array(skvideo.utils.gen_gauss_window(3, 7.0/6.0))
gamma_range = np.arange(0.2, 10, 0.001)
a = scipy.special.gamma(2.0/gamma_range)
a *= a
b = scipy.special.gamma(1.0/gamma_range)
c = scipy.special.gamma(3.0/gamma_range)
prec_gammas = a/(b*c)
def generate_ggd(x,alphaparam,sigma):
betaparam = sigma*np.sqrt(gamma(1.0/alphaparam)/gamma(3.0/alphaparam))
y = alphaparam/(2*betaparam*gamma(1.0/alphaparam))*np.exp(-(np.abs(x)/betaparam)**alphaparam)
return y
def stat_feats(chroma_mscn):
alpha,sigma = estimateggdparam(chroma_mscn)
skewness = skew(chroma_mscn.flatten())
kurt =kurtosis(chroma_mscn.flatten())
return alpha,sigma,skewness,kurt
def extract_secondord_feats(mscncoefs):
# alpha_m, = extract_ggd_features(mscncoefs)
pps1, pps2, pps3, pps4 = paired_product(mscncoefs)
alpha1, N1, bl1, br1, lsq1, rsq1 = aggd_features(pps1)
alpha2, N2, bl2, br2, lsq2, rsq2 = aggd_features(pps2)
alpha3, N3, bl3, br3, lsq3, rsq3 = aggd_features(pps3)
alpha4, N4, bl4, br4, lsq4, rsq4 = aggd_features(pps4)
return np.array([
alpha1, N1, lsq1**2, rsq1**2, # (V)
alpha2, N2, lsq2**2, rsq2**2, # (H)
alpha3, N3, lsq3**2, rsq3**2, # (D1)
alpha4, N4, lsq4**2, rsq4**2]) # (D2)
def _extract_subband_feats(mscncoefs):
# alpha_m, = extract_ggd_features(mscncoefs)
alpha_m, sigma = estimateggdparam(mscncoefs.copy())
pps1, pps2, pps3, pps4 = paired_product(mscncoefs)
alpha1, N1, bl1, br1, lsq1, rsq1 = aggd_features(pps1)
alpha2, N2, bl2, br2, lsq2, rsq2 = aggd_features(pps2)
alpha3, N3, bl3, br3, lsq3, rsq3 = aggd_features(pps3)
alpha4, N4, bl4, br4, lsq4, rsq4 = aggd_features(pps4)
return np.array([
alpha_m, sigma,
alpha1, N1, lsq1**2, rsq1**2, # (V)
alpha2, N2, lsq2**2, rsq2**2, # (H)
alpha3, N3, lsq3**2, rsq3**2, # (D1)
alpha4, N4, lsq4**2, rsq4**2, # (D2)
])
def estimateggdparam(vec):
gam = np.asarray([x / 1000.0 for x in range(200, 10000, 1)])
r_gam = (gamma(1.0/gam)*gamma(3.0/gam))/((gamma(2.0/gam))**2)
# print(np.mean(vec))
sigma_sq = np.mean(vec**2) #-(np.mean(vec))**2
sigma = np.sqrt(sigma_sq)
E = np.mean(np.abs(vec))
rho = sigma_sq/(E**2+1e-6)
array_position =(np.abs(rho - r_gam)).argmin()
alphaparam = gam[array_position]
return alphaparam,sigma
def all_aggd(y):
falpha1,fN1,fbl1,fbr1,flsq1,frsq1 = aggd_features(y.copy())
pps1, pps2, pps3, pps4 = paired_product(y)
alpha1, N1, bl1, br1, lsq1, rsq1 = aggd_features(pps1)
alpha2, N2, bl2, br2, lsq2, rsq2 = aggd_features(pps2)
alpha3, N3, bl3, br3, lsq3, rsq3 = aggd_features(pps3)
alpha4, N4, bl4, br4, lsq4, rsq4 = aggd_features(pps4)
return np.array([
falpha1, fN1, flsq1**2,frsq1**2,
alpha1, N1, lsq1**2, rsq1**2, # (V)
alpha2, N2, lsq2**2, rsq2**2, # (H)
alpha3, N3, lsq3**2, rsq3**2, # (D1)
alpha4, N4, lsq4**2, rsq4**2, # (D2)
])
def brisque(y_mscn):
# half_scale = cv2.resize(y, dsize=(0,0),fx=0.5,fy=0.5, interpolation=cv2.INTER_LANCZOS4)
feats_full = _extract_subband_feats(y_mscn)
# feats_half = _extract_subband_feats(half_scale)
return feats_full#np.concatenate((feats_full,feats_half))
def aggd_features(imdata):
#flatten imdata
imdata.shape = (len(imdata.flat),)
imdata2 = imdata*imdata
left_data = imdata2[imdata<0]
right_data = imdata2[imdata>=0]
left_mean_sqrt = 0
right_mean_sqrt = 0
if len(left_data) > 0:
left_mean_sqrt = np.sqrt(np.average(left_data))
if len(right_data) > 0:
right_mean_sqrt = np.sqrt(np.average(right_data))
if right_mean_sqrt != 0:
gamma_hat = left_mean_sqrt/right_mean_sqrt
else:
gamma_hat = np.inf
#solve r-hat norm
imdata2_mean = np.mean(imdata2)
if imdata2_mean != 0:
r_hat = (np.average(np.abs(imdata))**2) / (np.average(imdata2))
else:
r_hat = np.inf
rhat_norm = r_hat * (((math.pow(gamma_hat, 3) + 1)*(gamma_hat + 1)) / math.pow(math.pow(gamma_hat, 2) + 1, 2))
#solve alpha by guessing values that minimize ro
pos = np.argmin((prec_gammas - rhat_norm)**2);
alpha = gamma_range[pos]
gam1 = scipy.special.gamma(1.0/alpha)
gam2 = scipy.special.gamma(2.0/alpha)
gam3 = scipy.special.gamma(3.0/alpha)
aggdratio = np.sqrt(gam1) / np.sqrt(gam3)
bl = aggdratio * left_mean_sqrt
br = aggdratio * right_mean_sqrt
#mean parameter
N = (br - bl)*(gam2 / gam1)#*aggdratio
return (alpha, N, bl, br, left_mean_sqrt, right_mean_sqrt)
# def ggd_features(imdata):
# nr_gam = 1/prec_gammas
# sigma_sq = np.var(imdata)
# E = np.mean(np.abs(imdata))
# rho = sigma_sq/E**2
# pos = np.argmin(np.abs(nr_gam - rho));
# return gamma_range[pos], sigma_sq
def sigma_map(image):
im = image.astype(np.float32)
mu = cv2.GaussianBlur(im,(7,7),7.0/6.0,7.0/6.0)
mu_sq = mu*mu
sigma = np.sqrt(np.abs(cv2.GaussianBlur(im**2,(7,7),7.0/6.0,7.0/6.0)-mu_sq))
return sigma
def dog(image):
image = image.astype(np.float32)
gauss1 = cv2.GaussianBlur(image,(7,7),7.0/6.0,7.0/6.0)
gauss2 = cv2.GaussianBlur(image,(7,7),7.0*1.5/6.0,7.0*1.5/6.0)
dog = gauss1-gauss2
return dog
def paired_product(new_im):
shift1 = np.roll(new_im.copy(), 1, axis=1)
shift2 = np.roll(new_im.copy(), 1, axis=0)
shift3 = np.roll(np.roll(new_im.copy(), 1, axis=0), 1, axis=1)
shift4 = np.roll(np.roll(new_im.copy(), 1, axis=0), -1, axis=1)
H_img = shift1 * new_im
V_img = shift2 * new_im
D1_img = shift3 * new_im
D2_img = shift4 * new_im
return (H_img, V_img, D1_img, D2_img)
def gen_gauss_window(lw, sigma):
sd = np.float32(sigma)
lw = int(lw)
weights = [0.0] * (2 * lw + 1)
weights[lw] = 1.0
sum = 1.0
sd *= sd
for ii in range(1, lw + 1):
tmp = np.exp(-0.5 * np.float32(ii * ii) / sd)
weights[lw + ii] = tmp
weights[lw - ii] = tmp
sum += 2.0 * tmp
for ii in range(2 * lw + 1):
weights[ii] /= sum
return weights
def compute_image_mscn_transform(image, C=1, avg_window=None, extend_mode='constant'):
if avg_window is None:
avg_window = gen_gauss_window(3, 7.0/6.0)
assert len(np.shape(image)) == 2
h, w = np.shape(image)
mu_image = np.zeros((h, w), dtype=np.float32)
var_image = np.zeros((h, w), dtype=np.float32)
image = np.array(image).astype('float32')
scipy.ndimage.correlate1d(image, avg_window, 0, mu_image, mode=extend_mode)
scipy.ndimage.correlate1d(mu_image, avg_window, 1, mu_image, mode=extend_mode)
scipy.ndimage.correlate1d(image**2, avg_window, 0, var_image, mode=extend_mode)
scipy.ndimage.correlate1d(var_image, avg_window, 1, var_image, mode=extend_mode)
var_image = np.sqrt(np.abs(var_image - mu_image**2))
return (image - mu_image)/(var_image + C), var_image, mu_image
def generate_aggd(x1,x2,alpha,sigma_l,sigma_r):
beta_l = sigma_l*np.sqrt(gamma(1/alpha)/gamma(3/alpha))
beta_r= sigma_r*np.sqrt(gamma(1/alpha)/gamma(3/alpha))
f1 = alpha/((beta_l+beta_r)*gamma(1/alpha))*np.exp(-(-x1/beta_l)**alpha)
f2 = alpha/((beta_l+beta_r)*gamma(1/alpha))*np.exp(-(x2/beta_r)**alpha)
f = np.concatenate((f1,f2),axis=0)
return f
def chroma_feats(lab):
# lab = cv2.cvtColor(bgr,cv2.COLOR_BGR2Lab)
a = lab[:,:,1]
b = lab[:,:,2]
chroma = np.sqrt(a**2+b**2)
chroma_mscn,sigma_map,_ = compute_image_mscn_transform(chroma)
sigma_mscn,_,_ =compute_image_mscn_transform(sigma_map)
alpha,sigma,skewness,kurt= stat_feats(chroma_mscn)
salpha,ssigma,sskewness,skurt= stat_feats(sigma_mscn)
half_scale = cv2.resize(chroma.astype(np.uint8), dsize=(0,0),fx=0.5,fy=0.5, interpolation=cv2.INTER_CUBIC)
half_chroma_mscn,half_sigma_map,_ = compute_image_mscn_transform(half_scale)
half_sigma_mscn,_,_ = compute_image_mscn_transform(half_sigma_map)
halpha,hsigma,hskewness,hkurt= stat_feats(half_chroma_mscn)
hsalpha,hssigma,hsskewness,hskurt= stat_feats(half_sigma_mscn)
first_order_feats = np.asarray([alpha,sigma,skewness,kurt,halpha,hsigma,hskewness,hkurt,salpha,ssigma,sskewness,skurt,hsalpha,hssigma,hsskewness,hskurt])
return first_order_feats
def estimate_log_deri_ggd(image):
log_im = np.log(image+0.5)
log_feats = []
shifts= [(0,1),(1,0),(1,1),(1,-1)]
for i in range(len(shifts)):
rolled = np.roll(log_im, shift=shifts[i],axis=(0,1))
log_deri = log_im - rolled
alpha,sigma = estimateggdparam(log_deri)
log_feats.append(np.asarray([alpha,sigma]))
D5 = log_im + np.roll(log_im,shift=(1,1),axis=(0,1))-np.roll(log_im,shift=(0,1),axis=(0,1))-np.roll(log_im,shift=(1,0),axis=(0,1))
D6 = np.roll(log_im,shift=(-1,0),axis=(0,1))+np.roll(log_im,shift=(1,0),axis=(0,1))-np.roll(log_im,shift=(0,-1),axis=(0,1))-np.roll(log_im,shift=(0,1),axis=(0,1))
D7 = np.roll(log_im,shift=(-1,-1),axis=(0,1))+np.roll(log_im,shift=(1,1),axis=(0,1))-np.roll(log_im,shift=(-1,1),axis=(0,1))-np.roll(log_im,shift=(1,-1),axis=(0,1))
alpha,sigma = estimateggdparam(D6)
log_feats.append(np.asarray([alpha,sigma]))
alpha,sigma = estimateggdparam(D7)
log_feats.append(np.asarray([alpha,sigma]))
alpha,sigma = estimateggdparam(D5)
log_feats.append(np.asarray([alpha,sigma]))
log_feats = np.asarray(log_feats)
log_feats = np.reshape(log_feats,(14,))
return log_feats
def estimate_extralogderis(image):
log_im = np.log(image+0.5)
log_feats =[]
D6 = np.roll(log_im,shift=(-1,0),axis=(0,1))+np.roll(log_im,shift=(1,0),axis=(0,1))-np.roll(log_im,shift=(0,-1),axis=(0,1))-np.roll(log_im,shift=(0,1),axis=(0,1))
D7 = np.roll(log_im,shift=(-1,-1),axis=(0,1))+np.roll(log_im,shift=(1,1),axis=(0,1))-np.roll(log_im,shift=(-1,1),axis=(0,1))-np.roll(log_im,shift=(1,-1),axis=(0,1))
alpha,sigma = estimateggdparam(D6)
log_feats.append(np.asarray([alpha,sigma]))
alpha,sigma = estimateggdparam(D7)
log_feats.append(np.asarray([alpha,sigma]))
log_feats = np.asarray(log_feats)
log_feats = np.reshape(log_feats,(4,))
return log_feats
def chroma_gradients(lab):
# lab = cv2.cvtColor(bgr,cv2.COLOR_BGR2Lab)
# a = lab[:,:,1]
# b = lab[:,:,2]
chroma_grad_feats = []
gradient_x = cv2.Sobel(lab,ddepth=-1,dx=1,dy=0)
gradient_y = cv2.Sobel(lab,ddepth=-1,dx=0,dy=1)
gradient_mag = np.sqrt(gradient_x**2+gradient_y**2)
return [gradient_mag[:,:,0],gradient_mag[:,:,1],gradient_mag[:,:,2]]
def chroma_gradient_feats(lab):
gradient_mag = chroma_gradients(lab)
for i in range(3):
gradient_mscn,_,_ = compute_image_mscn_transform(gradient_mag[i])
alpha,sigma = estimateggdparam(gradient_mscn)
# log_ggd_params = estimate_log_ggd(gradient_mag[:,:,i])
grad_sigma = strided_variance(gradient_mag[:,:,i],5)
grad_sigma_mean = np.mean(grad_sigma.flatten())
grad_sigma_var = np.std(grad_sigma.flatten())
dispersion = grad_sigma_var/grad_sigma_mean
log_sigma_params = np.asarray([alpha,sigma,dispersion,grad_sigma_mean])
# chroma_grad_feats.append(np.asarray([sigma_alpha,sigma_var]))
chroma_grad_feats.append(log_sigma_params)
chroma_grad_feats= np.asarray(chroma_grad_feats)
chroma_grad_feats = np.reshape(chroma_grad_feats,(12,))
return chroma_grad_feats
def colorfulness(image):
rg = image[:,:,2]-image[:,:,1]
yb = 0.5*(image[:,:,2]+image[:,:,1])-image[:,:,0]
mu = np.sqrt(np.mean(rg.flatten())**2+np.mean(yb.flatten())**2)
sigma = np.sqrt(np.std(rg.flatten())**2+np.std(yb.flatten())**2)
c = sigma+0.3*mu
return c
def main():
dataset = 'vqc'
if(dataset=='konvid'):
folder = '/mnt/b9f5646b-2c64-4699-8766-c4bba45fb442/konvid/konvid_sts_mscn_down_videos'
results_folder = '/mnt/b9f5646b-2c64-4699-8766-c4bba45fb442/konvid/konvid_sts_mscn_down_features'
csv_file = "/mnt/b9f5646b-2c64-4699-8766-c4bba45fb442/konvid/KoNViD_1k_metadata/KoNViD_1k_mos.csv"
elif(dataset=='vqc'):
folder = '/mnt/b9f5646b-2c64-4699-8766-c4bba45fb442/VQC/vqc_sts_medianof'
results_folder = '/mnt/b9f5646b-2c64-4699-8766-c4bba45fb442/VQC/vqc_sts_medianof_feats'
elif(dataset=='vqa'):
folder = '/mnt/b9f5646b-2c64-4699-8766-c4bba45fb442/VQA/sts_mscn_down'