/
data_prepare_semantickitti.py
87 lines (79 loc) · 4.06 KB
/
data_prepare_semantickitti.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
import os
import yaml
import pickle
import argparse
import numpy as np
from os.path import join, exists
from sklearn.neighbors import KDTree
from utils.data_process import DataProcessing as DP
from tqdm import tqdm
parser = argparse.ArgumentParser()
parser.add_argument('--src_path', default='/home/chx/Work/semantic-kitti/sequences', help='source dataset path [default: None]')
parser.add_argument('--dst_path', default='/home/chx/Work/semantic-kitti-randla/sequences', help='destination dataset path [default: None]')
parser.add_argument('--grid_size', type=float, default=0.06, help='Subsample Grid Size [default: 0.06]')
parser.add_argument('--yaml_config', default='utils/semantic-kitti.yaml', help='semantic-kitti.yaml path')
FLAGS = parser.parse_args()
data_config = FLAGS.yaml_config
DATA = yaml.safe_load(open(data_config, 'r'))
remap_dict = DATA["learning_map"]
max_key = max(remap_dict.keys())
remap_lut = np.zeros((max_key + 100), dtype=np.int32)
remap_lut[list(remap_dict.keys())] = list(remap_dict.values())
grid_size = FLAGS.grid_size
dataset_path = FLAGS.src_path
output_path = FLAGS.dst_path
seq_list = np.sort(os.listdir(dataset_path))
for seq_id in tqdm(seq_list, total=len(seq_list)):
print('sequence ' + seq_id + ' start')
seq_path = join(dataset_path, seq_id)
seq_path_out = join(output_path, seq_id)
pc_path = join(seq_path, 'velodyne')
pc_path_out = join(seq_path_out, 'velodyne')
KDTree_path_out = join(seq_path_out, 'KDTree')
os.makedirs(seq_path_out) if not exists(seq_path_out) else None
os.makedirs(pc_path_out) if not exists(pc_path_out) else None
os.makedirs(KDTree_path_out) if not exists(KDTree_path_out) else None
if int(seq_id) < 11:
# continue
label_path = join(seq_path, 'labels')
label_path_out = join(seq_path_out, 'labels')
os.makedirs(label_path_out) if not exists(label_path_out) else None
scan_list = np.sort(os.listdir(pc_path))
# scan_list = scan_list[0:len(scan_list):4]
for scan_id in tqdm(scan_list, total=len(scan_list)):
# print(scan_id)
points = DP.load_pc_kitti(join(pc_path, scan_id))
labels = DP.load_label_kitti(join(label_path, str(scan_id[:-4]) + '.label'), remap_lut)
sub_points, sub_labels = DP.grid_sub_sampling(points, labels=labels, grid_size=grid_size)
search_tree = KDTree(sub_points)
KDTree_save = join(KDTree_path_out, str(scan_id[:-4]) + '.pkl')
np.save(join(pc_path_out, scan_id)[:-4], sub_points)
np.save(join(label_path_out, scan_id)[:-4], sub_labels)
with open(KDTree_save, 'wb') as f:
pickle.dump(search_tree, f)
if seq_id == '08':
proj_path = join(seq_path_out, 'proj')
os.makedirs(proj_path) if not exists(proj_path) else None
proj_inds = np.squeeze(search_tree.query(points, return_distance=False))
proj_inds = proj_inds.astype(np.int32)
proj_save = join(proj_path, str(scan_id[:-4]) + '_proj.pkl')
with open(proj_save, 'wb') as f:
pickle.dump([proj_inds], f)
else:
proj_path = join(seq_path_out, 'proj')
os.makedirs(proj_path) if not exists(proj_path) else None
scan_list = np.sort(os.listdir(pc_path))
for scan_id in tqdm(scan_list, total=len(scan_list)):
# print(scan_id)
points = DP.load_pc_kitti(join(pc_path, scan_id))
sub_points = DP.grid_sub_sampling(points, grid_size=0.06)
search_tree = KDTree(sub_points)
proj_inds = np.squeeze(search_tree.query(points, return_distance=False))
proj_inds = proj_inds.astype(np.int32)
KDTree_save = join(KDTree_path_out, str(scan_id[:-4]) + '.pkl')
proj_save = join(proj_path, str(scan_id[:-4]) + '_proj.pkl')
np.save(join(pc_path_out, scan_id)[:-4], sub_points)
with open(KDTree_save, 'wb') as f:
pickle.dump(search_tree, f)
with open(proj_save, 'wb') as f:
pickle.dump([proj_inds], f)