-
Notifications
You must be signed in to change notification settings - Fork 0
/
visualize.py
142 lines (111 loc) · 4.08 KB
/
visualize.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
import matplotlib.pyplot as plt
from torchvision.transforms import transforms
import numpy as np
import torch
import os
from forward_process import *
from dataset import *
from sample import *
def visualalize_distance(output, condition, target):
plt.figure(figsize=(11,11))
plt.subplot(1, 3, 1).axis('off')
plt.subplot(1, 3, 2).axis('off')
plt.subplot(1, 3, 3).axis('off')
plt.subplot(1, 3, 1)
plt.imshow(show_tensor_image(output))
plt.title('input image')
plt.subplot(1, 3, 2)
plt.imshow(show_tensor_image(condition))
plt.title('condition image')
plt.subplot(1, 3, 3)
plt.imshow(show_tensor_image(target))
plt.title('generated image')
k = 0
while os.path.exists('results/heatmap{}.png'.format(k)):
k += 1
plt.savefig('results/heatmap{}.png'.format(k))
plt.close()
def visualize_reconstructed(input, data,s):
fig, axs = plt.subplots(int(len(data)/5),6)
row = 0
col = 1
axs[0,0].imshow(show_tensor_image(input))
axs[0, 0].get_xaxis().set_visible(False)
axs[0, 0].get_yaxis().set_visible(False)
axs[0,0].set_title('input')
for i, img in enumerate(data):
axs[row, col].imshow(show_tensor_image(img))
axs[row, col].get_xaxis().set_visible(False)
axs[row, col].get_yaxis().set_visible(False)
axs[row, col].set_title(str(i))
col += 1
if col == 6:
row += 1
col = 0
col = 6
row = int(len(data)/5)
remain = col * row - len(data) -1
for j in range(remain):
col -= 1
axs[row-1, col].remove()
axs[row-1, col].get_xaxis().set_visible(False)
axs[row-1, col].get_yaxis().set_visible(False)
plt.subplots_adjust(left=0.1,
bottom=0.1,
right=0.9,
top=0.9,
wspace=0.4,
hspace=0.4)
k = 0
while os.path.exists(f'results/reconstructed{k}{s}.png'):
k += 1
plt.savefig(f'results/reconstructed{k}{s}.png')
plt.close()
def visualize(image, noisy_image, GT, pred_mask, anomaly_map, category) :
for idx, img in enumerate(image):
plt.figure(figsize=(11,11))
plt.subplot(1, 2, 1).axis('off')
plt.subplot(1, 2, 2).axis('off')
plt.subplot(1, 2, 1)
plt.imshow(show_tensor_image(image[idx]))
plt.title('clear image')
plt.subplot(1, 2, 2)
plt.imshow(show_tensor_image(noisy_image[idx]))
plt.title('reconstructed image')
plt.savefig('results/{}sample{}.png'.format(category,idx))
plt.close()
plt.figure(figsize=(11,11))
plt.subplot(1, 3, 1).axis('off')
plt.subplot(1, 3, 2).axis('off')
plt.subplot(1, 3, 3).axis('off')
plt.subplot(1, 3, 1)
plt.imshow(show_tensor_mask(GT[idx]))
plt.title('ground truth')
plt.subplot(1, 3, 2)
plt.imshow(show_tensor_mask(pred_mask[idx]))
plt.title('normal' if torch.max(pred_mask[idx]) == 0 else 'abnormal', color="g" if torch.max(pred_mask[idx]) == 0 else "r")
plt.subplot(1, 3, 3)
plt.imshow(show_tensor_image(anomaly_map[idx]))
plt.title('heat map')
plt.savefig('results/{}sample{}heatmap.png'.format(category,idx))
plt.close()
def show_tensor_image(image):
reverse_transforms = transforms.Compose([
transforms.Lambda(lambda t: (t + 1) / (2)),
transforms.Lambda(lambda t: t.permute(1, 2, 0)), # CHW to HWC
transforms.Lambda(lambda t: t * 255.),
transforms.Lambda(lambda t: t.cpu().numpy().astype(np.uint8)),
])
# Takes the first image of batch
if len(image.shape) == 4:
image = image[0, :, :, :]
return reverse_transforms(image)
def show_tensor_mask(image):
reverse_transforms = transforms.Compose([
transforms.Lambda(lambda t: t.permute(1, 2, 0)), # CHW to HWC
transforms.Lambda(lambda t: t.cpu().numpy().astype(np.int8)),
])
# Takes the first image of batch
if len(image.shape) == 4:
image = image[0, :, :, :]
return reverse_transforms(image)