-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathdeblurring_shoes_algorithm_1.py
143 lines (122 loc) · 5.58 KB
/
deblurring_shoes_algorithm_1.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
import tensorflow as tf
import keras.backend as K
import numpy as np
from Utils import *
from generators.MotionBlurGenerator import *
from generators.ShoeGenerator import *
K.set_learning_phase(0)
from glob import glob
import os
# paths
Orig_Path = './results/Shoes/Original Images/*.png'
Range_Path = './results/Shoes/Range Images/*.png'
Blur_Path = './results/Shoes/Original Blurs/Test Blurs.npy'
# constants
REGULARIZORS = [0.01 , 0.01]
RANDOM_RESTARTS = 10
NOISE_STD = 0.01
STEPS = 10000
IMAGE_RANGE = [-1,1]
def step_size(t):
return 0.01 * np.exp( - t / 1000 )
SAVE_PATH = './results/Shoes/deblurring - alg1 - ' +str(int(NOISE_STD*100)) + 'perc noise - ' +str(RANDOM_RESTARTS) + 'RR/deblurring_'
# -----------------------------------------------------------------------
# loading test blur images
W = np.load(Blur_Path)
BLUR_RES = W.shape[1]
# loading test shoes images
X_Orig = np.array([ imread(path) for path in glob(Orig_Path)])/255
X_Range = np.array([ imread(path) for path in glob(Range_Path)])/255
IMAGE_RES = X_Orig.shape[1]
CHANNELS = X_Orig.shape[-1]
# loading shoe generator
ShoeGen = ShoeGenerator()
ShoeGen.GenerateModel()
ShoeGen.LoadWeights()
ShoeGAN = ShoeGen.GetModels()
shoe_latent_dim = ShoeGen.latent_dim
# loading motion blur generator
BLURGen = MotionBlur()
BLURGen.GenerateModel()
BLURGen.LoadWeights()
blur_vae, blur_encoder, blur_decoder = BLURGen.GetModels()
blur_latent_dim = BLURGen.latent_dim
# check if save dir exists, if not create a new one
try:
os.stat(SAVE_PATH[:-11])
except:
os.mkdir(SAVE_PATH[:-11])
# generating blurry images from test
Y_np = []
Blurry_Images = []
for i in tqdm(range(len(X_Orig)), ascii=True, desc ='Gen-Test-Blurry'):
x_np = X_Orig[i]
w_np = W[i]
y_np, y_f = GenerateBlurry(x_np, w_np, noise_std = NOISE_STD )
Y_np.append(y_np)
for _ in range(RANDOM_RESTARTS):
Blurry_Images.append(y_f)
Y_np = np.array(Y_np)
Blurry_Images = np.array(Blurry_Images)
# generating blurry images from range
Blurry_Images_range = []
Y_np_range = []
for i in tqdm(range(len(X_Orig)), ascii=True, desc ='Gen-Range-Blurry'):
y_np, y_f = GenerateBlurry(X_Range[i], W[i], noise_std = NOISE_STD )
Y_np_range.append(y_np)
for _ in range(RANDOM_RESTARTS):
Blurry_Images_range.append(y_f)
Y_np_range = np.array(Y_np_range)
Blurry_Images_range = np.array(Blurry_Images_range)
# alternating gradient descent for test images
image_gradients, blur_gradients, get_loss = Generate_Gradient_Functions(rr = Blurry_Images.shape[0],
reg = REGULARIZORS, image_range = IMAGE_RANGE,
decoder = ShoeGAN, blur_decoder = blur_decoder,
image_res = IMAGE_RES, blur_res = BLUR_RES,
channels = CHANNELS)
m_hat, h_hat, Loss = Optimize_Parallel(blurry_fourier = Blurry_Images, stepsize=step_size,steps = STEPS,
image_grad = image_gradients , blur_grad = blur_gradients,
getloss = get_loss, latent_image_dim = shoe_latent_dim , latent_blur_dim = blur_latent_dim)
X_hat_test = []
W_hat_test = []
for i in range(len(X_Orig)):
m_hat_i = m_hat[i*RANDOM_RESTARTS:(i+1)*RANDOM_RESTARTS]
h_hat_i = h_hat[i*RANDOM_RESTARTS:(i+1)*RANDOM_RESTARTS]
Loss_i = Loss[i*RANDOM_RESTARTS:(i+1)*RANDOM_RESTARTS]
x_hat_test, w_hat_test, loss_last_iter_test = Get_Min_Loss(Loss_i, m_hat_i, h_hat_i, decoder = ShoeGAN, blur_decoder = blur_decoder,
latent_image_dim = shoe_latent_dim, latent_blur_dim = blur_latent_dim, print_grad=False)
X_hat_test.append(x_hat_test)
W_hat_test.append(w_hat_test)
X_hat_test = np.array(X_hat_test)
W_hat_test = np.array(W_hat_test)
# alternating gradient descent for range images
m_hat, h_hat, Loss = Optimize_Parallel(blurry_fourier = Blurry_Images_range, stepsize=step_size,steps = STEPS,
image_grad = image_gradients , blur_grad = blur_gradients,
getloss = get_loss, latent_image_dim = shoe_latent_dim , latent_blur_dim = blur_latent_dim)
X_hat_range = []
W_hat_range = []
for i in range(len(X_Orig)):
m_hat_i = m_hat[i*RANDOM_RESTARTS:(i+1)*RANDOM_RESTARTS]
h_hat_i = h_hat[i*RANDOM_RESTARTS:(i+1)*RANDOM_RESTARTS]
Loss_i = Loss[i*RANDOM_RESTARTS:(i+1)*RANDOM_RESTARTS]
x_hat_range, w_hat_range, loss_last_iter_range = Get_Min_Loss(Loss_i, m_hat_i, h_hat_i, decoder = ShoeGAN, blur_decoder = blur_decoder,
latent_image_dim = shoe_latent_dim, latent_blur_dim = blur_latent_dim, print_grad=False)
X_hat_range.append(x_hat_range)
W_hat_range.append(w_hat_range)
X_hat_range = np.array(X_hat_range)
W_hat_range = np.array(W_hat_range)
X_hat_test = (X_hat_test + 1)/2
X_hat_range = (X_hat_range + 1)/2
Max = 10**len(str(len(X_Orig)-1))
# saving results
for i in range(len(X_Orig)):
Save_Results(path = SAVE_PATH + str(i+Max)[1:],
x_np = None,
w_np = None,
y_np = Y_np[i],
y_np_range = Y_np_range[i] ,
x_hat_test = X_hat_test[i],
w_hat_test = W_hat_test[i],
x_range = None,
x_hat_range = X_hat_range[i],
w_hat_range = W_hat_range[i], clip=True)