-
Notifications
You must be signed in to change notification settings - Fork 120
/
pre_process_kitti.py
160 lines (135 loc) · 6.72 KB
/
pre_process_kitti.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
import argparse
import pdb
import cv2
import numpy as np
import os
from tqdm import tqdm
import sys
CUR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(CUR)
from utils import read_points, write_points, read_calib, read_label, \
write_pickle, remove_outside_points, get_points_num_in_bbox, \
points_in_bboxes_v2
def judge_difficulty(annotation_dict):
truncated = annotation_dict['truncated']
occluded = annotation_dict['occluded']
bbox = annotation_dict['bbox']
height = bbox[:, 3] - bbox[:, 1]
MIN_HEIGHTS = [40, 25, 25]
MAX_OCCLUSION = [0, 1, 2]
MAX_TRUNCATION = [0.15, 0.30, 0.50]
difficultys = []
for h, o, t in zip(height, occluded, truncated):
difficulty = -1
for i in range(2, -1, -1):
if h > MIN_HEIGHTS[i] and o <= MAX_OCCLUSION[i] and t <= MAX_TRUNCATION[i]:
difficulty = i
difficultys.append(difficulty)
return np.array(difficultys, dtype=np.int32)
def create_data_info_pkl(data_root, data_type, prefix, label=True, db=False):
sep = os.path.sep
print(f"Processing {data_type} data..")
ids_file = os.path.join(CUR, 'dataset', 'ImageSets', f'{data_type}.txt')
with open(ids_file, 'r') as f:
ids = [id.strip() for id in f.readlines()]
split = 'training' if label else 'testing'
kitti_infos_dict = {}
if db:
kitti_dbinfos_train = {}
db_points_saved_path = os.path.join(data_root, f'{prefix}_gt_database')
os.makedirs(db_points_saved_path, exist_ok=True)
for id in tqdm(ids):
cur_info_dict={}
img_path = os.path.join(data_root, split, 'image_2', f'{id}.png')
lidar_path = os.path.join(data_root, split, 'velodyne', f'{id}.bin')
calib_path = os.path.join(data_root, split, 'calib', f'{id}.txt')
cur_info_dict['velodyne_path'] = sep.join(lidar_path.split(sep)[-3:])
img = cv2.imread(img_path)
image_shape = img.shape[:2]
cur_info_dict['image'] = {
'image_shape': image_shape,
'image_path': sep.join(img_path.split(sep)[-3:]),
'image_idx': int(id),
}
calib_dict = read_calib(calib_path)
cur_info_dict['calib'] = calib_dict
lidar_points = read_points(lidar_path)
reduced_lidar_points = remove_outside_points(
points=lidar_points,
r0_rect=calib_dict['R0_rect'],
tr_velo_to_cam=calib_dict['Tr_velo_to_cam'],
P2=calib_dict['P2'],
image_shape=image_shape)
saved_reduced_path = os.path.join(data_root, split, 'velodyne_reduced')
os.makedirs(saved_reduced_path, exist_ok=True)
saved_reduced_points_name = os.path.join(saved_reduced_path, f'{id}.bin')
write_points(reduced_lidar_points, saved_reduced_points_name)
if label:
label_path = os.path.join(data_root, split, 'label_2', f'{id}.txt')
annotation_dict = read_label(label_path)
annotation_dict['difficulty'] = judge_difficulty(annotation_dict)
annotation_dict['num_points_in_gt'] = get_points_num_in_bbox(
points=reduced_lidar_points,
r0_rect=calib_dict['R0_rect'],
tr_velo_to_cam=calib_dict['Tr_velo_to_cam'],
dimensions=annotation_dict['dimensions'],
location=annotation_dict['location'],
rotation_y=annotation_dict['rotation_y'],
name=annotation_dict['name'])
cur_info_dict['annos'] = annotation_dict
if db:
indices, n_total_bbox, n_valid_bbox, bboxes_lidar, name = \
points_in_bboxes_v2(
points=lidar_points,
r0_rect=calib_dict['R0_rect'].astype(np.float32),
tr_velo_to_cam=calib_dict['Tr_velo_to_cam'].astype(np.float32),
dimensions=annotation_dict['dimensions'].astype(np.float32),
location=annotation_dict['location'].astype(np.float32),
rotation_y=annotation_dict['rotation_y'].astype(np.float32),
name=annotation_dict['name']
)
for j in range(n_valid_bbox):
db_points = lidar_points[indices[:, j]]
db_points[:, :3] -= bboxes_lidar[j, :3]
db_points_saved_name = os.path.join(db_points_saved_path, f'{int(id)}_{name[j]}_{j}.bin')
write_points(db_points, db_points_saved_name)
db_info={
'name': name[j],
'path': os.path.join(os.path.basename(db_points_saved_path), f'{int(id)}_{name[j]}_{j}.bin'),
'box3d_lidar': bboxes_lidar[j],
'difficulty': annotation_dict['difficulty'][j],
'num_points_in_gt': len(db_points),
}
if name[j] not in kitti_dbinfos_train:
kitti_dbinfos_train[name[j]] = [db_info]
else:
kitti_dbinfos_train[name[j]].append(db_info)
kitti_infos_dict[int(id)] = cur_info_dict
saved_path = os.path.join(data_root, f'{prefix}_infos_{data_type}.pkl')
write_pickle(kitti_infos_dict, saved_path)
if db:
saved_db_path = os.path.join(data_root, f'{prefix}_dbinfos_train.pkl')
write_pickle(kitti_dbinfos_train, saved_db_path)
return kitti_infos_dict
def main(args):
data_root = args.data_root
prefix = args.prefix
## 1. train: create data infomation pkl file && create reduced point clouds
## && create database(points in gt bbox) for data aumentation
kitti_train_infos_dict = create_data_info_pkl(data_root, 'train', prefix, db=True)
## 2. val: create data infomation pkl file && create reduced point clouds
kitti_val_infos_dict = create_data_info_pkl(data_root, 'val', prefix)
## 3. trainval: create data infomation pkl file
kitti_trainval_infos_dict = {**kitti_train_infos_dict, **kitti_val_infos_dict}
saved_path = os.path.join(data_root, f'{prefix}_infos_trainval.pkl')
write_pickle(kitti_trainval_infos_dict, saved_path)
## 4. test: create data infomation pkl file && create reduced point clouds
kitti_test_infos_dict = create_data_info_pkl(data_root, 'test', prefix, label=False)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Dataset infomation')
parser.add_argument('--data_root', default='/mnt/ssd1/lifa_rdata/det/kitti',
help='your data root for kitti')
parser.add_argument('--prefix', default='kitti',
help='the prefix name for the saved .pkl file')
args = parser.parse_args()
main(args)