-
Notifications
You must be signed in to change notification settings - Fork 19
/
utils.py
183 lines (158 loc) · 6.13 KB
/
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
import os
import random
import numpy as np
import torch
from args_fusion import args
from scipy.misc import imread, imsave, imresize
import matplotlib as mpl
from os import listdir
from os.path import join
EPSILON = 1e-5
def list_images(directory):
images = []
names = []
dir = listdir(directory)
dir.sort()
for file in dir:
name = file
if name.endswith('.png'):
images.append(join(directory, file))
elif name.endswith('.jpg'):
images.append(join(directory, file))
elif name.endswith('.jpeg'):
images.append(join(directory, file))
elif name.endswith('.bmp'):
images.append(join(directory, file))
elif name.endswith('.tif'):
images.append(join(directory, file))
# name1 = name.split('.')
names.append(name)
return images, names
# load training images
def load_dataset(image_path, BATCH_SIZE, num_imgs=None):
if num_imgs is None:
num_imgs = len(image_path)
original_imgs_path = image_path[:num_imgs]
# random
random.shuffle(original_imgs_path)
mod = num_imgs % BATCH_SIZE
print('BATCH SIZE %d.' % BATCH_SIZE)
print('Train images number %d.' % num_imgs)
print('Train images samples %s.' % str(num_imgs / BATCH_SIZE))
if mod > 0:
print('Train set has been trimmed %d samples...\n' % mod)
original_imgs_path = original_imgs_path[:-mod]
batches = int(len(original_imgs_path) // BATCH_SIZE)
return original_imgs_path, batches
def get_image(path, height=256, width=256, flag=False):
if flag is True:
image = imread(path, mode='RGB')
else:
image = imread(path, mode='L')
if height is not None and width is not None:
image = imresize(image, [height, width], interp='nearest')
return image
# load images - test phase
def get_test_image(paths, height=None, width=None, flag=False):
if isinstance(paths, str):
paths = [paths]
images = []
for path in paths:
if flag is True:
image = imread(path, mode='RGB')
else:
image = imread(path, mode='L')
# get saliency part
if height is not None and width is not None:
image = imresize(image, [height, width], interp='nearest')
base_size = 512
h = image.shape[0]
w = image.shape[1]
c = 1
if h > base_size or w > base_size:
c = 4
if flag is True:
image = np.transpose(image, (2, 0, 1))
else:
image = np.reshape(image, [1, h, w])
images = get_img_parts(image, h, w)
else:
if flag is True:
image = np.transpose(image, (2, 0, 1))
else:
image = np.reshape(image, [1, image.shape[0], image.shape[1]])
images.append(image)
images = np.stack(images, axis=0)
images = torch.from_numpy(images).float()
return images, h, w, c
def get_img_parts(image, h, w):
images = []
h_cen = int(np.floor(h / 2))
w_cen = int(np.floor(w / 2))
img1 = image[:, 0:h_cen + 3, 0: w_cen + 3]
img1 = np.reshape(img1, [1, img1.shape[0], img1.shape[1], img1.shape[2]])
img2 = image[:, 0:h_cen + 3, w_cen - 2: w]
img2 = np.reshape(img2, [1, img2.shape[0], img2.shape[1], img2.shape[2]])
img3 = image[:, h_cen - 2:h, 0: w_cen + 3]
img3 = np.reshape(img3, [1, img3.shape[0], img3.shape[1], img3.shape[2]])
img4 = image[:, h_cen - 2:h, w_cen - 2: w]
img4 = np.reshape(img4, [1, img4.shape[0], img4.shape[1], img4.shape[2]])
images.append(torch.from_numpy(img1).float())
images.append(torch.from_numpy(img2).float())
images.append(torch.from_numpy(img3).float())
images.append(torch.from_numpy(img4).float())
return images
def recons_fusion_images(img_lists, h, w):
img_f_list = []
h_cen = int(np.floor(h / 2))
w_cen = int(np.floor(w / 2))
c = img_lists[0][0].shape[1]
ones_temp = torch.ones(1, c, h, w).cuda()
for i in range(len(img_lists[0])):
# img1, img2, img3, img4
img1 = img_lists[0][i]
img2 = img_lists[1][i]
img3 = img_lists[2][i]
img4 = img_lists[3][i]
img_f = torch.zeros(1, c, h, w).cuda()
count = torch.zeros(1, c, h, w).cuda()
img_f[:, :, 0:h_cen + 3, 0: w_cen + 3] += img1
count[:, :, 0:h_cen + 3, 0: w_cen + 3] += ones_temp[:, :, 0:h_cen + 3, 0: w_cen + 3]
img_f[:, :, 0:h_cen + 3, w_cen - 2: w] += img2
count[:, :, 0:h_cen + 3, w_cen - 2: w] += ones_temp[:, :, 0:h_cen + 3, w_cen - 2: w]
img_f[:, :, h_cen - 2:h, 0: w_cen + 3] += img3
count[:, :, h_cen - 2:h, 0: w_cen + 3] += ones_temp[:, :, h_cen - 2:h, 0: w_cen + 3]
img_f[:, :, h_cen - 2:h, w_cen - 2: w] += img4
count[:, :, h_cen - 2:h, w_cen - 2: w] += ones_temp[:, :, h_cen - 2:h, w_cen - 2: w]
img_f = img_f / count
img_f_list.append(img_f)
return img_f_list
def save_image_test(img_fusion, output_path):
img_fusion = img_fusion.float()
if args.cuda:
img_fusion = img_fusion.cpu().data[0].numpy()
# img_fusion = img_fusion.cpu().clamp(0, 255).data[0].numpy()
else:
img_fusion = img_fusion.clamp(0, 255).data[0].numpy()
img_fusion = (img_fusion - np.min(img_fusion)) / (np.max(img_fusion) - np.min(img_fusion) + EPSILON)
img_fusion = img_fusion * 255
img_fusion = img_fusion.transpose(1, 2, 0).astype('uint8')
# cv2.imwrite(output_path, img_fusion)
if img_fusion.shape[2] == 1:
img_fusion = img_fusion.reshape([img_fusion.shape[0], img_fusion.shape[1]])
# img_fusion = imresize(img_fusion, [h, w])
imsave(output_path, img_fusion)
def get_train_images(paths, height=256, width=256, flag=False):
if isinstance(paths, str):
paths = [paths]
images = []
for path in paths:
image = get_image(path, height, width, flag)
if flag is True:
image = np.transpose(image, (2, 0, 1))
else:
image = np.reshape(image, [1, height, width])
images.append(image)
images = np.stack(images, axis=0)
images = torch.from_numpy(images).float()
return images