-
Notifications
You must be signed in to change notification settings - Fork 23
/
process_transfer_indoor_data.py
43 lines (33 loc) · 1.32 KB
/
process_transfer_indoor_data.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
import cv2
import os
CARLA_data_root = '../data/Carla_Dataset_v1'
dataset_root = '../data/nuScenes'
scene_list = sorted(os.listdir(CARLA_data_root))
train_list = scene_list[:int(5*len(scene_list)/6)]
# val_list = scene_list[int(5*len(scene_list)/6):]
train_output = []
# val_output = []
# data_type = ['rgb', 'sem', 'depth', 'ins', ]
for scene_id in train_list:
print('Processing ' + scene_id)
coor_set = sorted(os.listdir(os.path.join(CARLA_data_root, scene_id, 'CAM_RGB_FRONT')))
for coor in coor_set:
coor_path = os.path.join(scene_id, 'CAM_RGB_FRONT', coor)
train_output.append(coor_path)
with open('train_source_list.txt', 'w') as f:
print('Writing train_source_list file ...')
f.write('\n'.join(train_output))
nuScenes_data_root = '../data/nuScenes'
scene_list = sorted(os.listdir(nuScenes_data_root))
train_list = scene_list
train_output = []
for scene_id in train_list:
print('Processing ' + scene_id)
coor_set = sorted(os.listdir(os.path.join(nuScenes_data_root, scene_id, 'CAM_RGB_FRONT')))
coor_set = coor_set[:18220]
for coor in coor_set:
coor_path = os.path.join(scene_id, 'CAM_RGB_FRONT', coor)
train_output.append(coor_path)
with open('train_target_list.txt', 'w') as f:
print('Writing train_target_list file ...')
f.write('\n'.join(train_output))