forked from ahmetgunduz/Real-time-GesRec
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathdataset.py
158 lines (147 loc) · 5.2 KB
/
dataset.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
from datasets.jester import Jester
from datasets.egogesture import EgoGesture
from datasets.nv import NV
from datasets.egogesture_online import EgoGestureOnline
from datasets.nv_online import NVOnline
def get_training_set(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['jester', 'egogesture', 'nv']
if opt.train_validate:
subset = ['training', 'validation']
else:
subset = 'training'
if opt.dataset == 'jester':
training_data = Jester(
opt.video_path,
opt.annotation_path,
subset,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration,
modality=opt.modality)
elif opt.dataset == 'egogesture':
training_data = EgoGesture(
opt.video_path,
opt.annotation_path,
subset,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration,
modality=opt.modality)
elif opt.dataset == 'nv':
training_data = NV(
opt.video_path,
opt.annotation_path,
subset,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration,
modality=opt.modality)
return training_data
def get_validation_set(opt, spatial_transform, temporal_transform,
target_transform):
assert opt.dataset in ['jester', 'egogesture', 'nv']
if opt.dataset == 'jester':
validation_data = Jester(
opt.video_path,
opt.annotation_path,
'validation',
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
sample_duration=opt.sample_duration)
elif opt.dataset == 'egogesture':
validation_data = EgoGesture(
opt.video_path,
opt.annotation_path,
'validation',
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
sample_duration=opt.sample_duration)
elif opt.dataset == 'nv':
validation_data = NV(
opt.video_path,
opt.annotation_path,
'validation',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration,
modality=opt.modality)
return validation_data
def get_test_set(opt, spatial_transform, temporal_transform, target_transform):
assert opt.dataset in ['jester', 'egogesture', 'nv']
assert opt.test_subset in ['val', 'test']
if opt.test_subset == 'val':
subset = 'validation'
else:
subset = 'testing'
if opt.dataset == 'jester':
test_data = Jester(
opt.video_path,
opt.annotation_path,
subset,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
sample_duration=opt.sample_duration)
elif opt.dataset == 'egogesture':
test_data = EgoGesture(
opt.video_path,
opt.annotation_path,
subset,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality=opt.modality,
sample_duration=opt.sample_duration)
elif opt.dataset == 'nv':
test_data = NV(
opt.video_path,
opt.annotation_path,
'validation',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
sample_duration=opt.sample_duration,
modality=opt.modality)
return test_data
def get_online_data(opt, spatial_transform, temporal_transform, target_transform):
assert opt.dataset in [ 'egogesture', 'nv']
whole_path = opt.whole_path
if opt.dataset == 'egogesture':
online_data = EgoGestureOnline(
opt.annotation_path,
opt.video_path,
opt.whole_path,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality="RGB-D",
stride_len = opt.stride_len,
sample_duration=opt.sample_duration)
if opt.dataset == 'nv':
online_data = NVOnline(
opt.annotation_path,
opt.video_path,
opt.whole_path,
opt.n_val_samples,
spatial_transform,
temporal_transform,
target_transform,
modality="RGB-D",
stride_len = opt.stride_len,
sample_duration=opt.sample_duration)
return online_data