-
Notifications
You must be signed in to change notification settings - Fork 3
/
Lidar_data_augmentation.py
103 lines (65 loc) · 3.74 KB
/
Lidar_data_augmentation.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
import os
import random
import math
import numpy as np
import open3d as o3d
lidar_path=["/efs/data/preprocess_lidar/Adaptation_dataset_multi_modal/scenario31/",
"/efs/data/preprocess_lidar/Adaptation_dataset_multi_modal/scenario32/",
"/efs/data/preprocess_lidar/Adaptation_dataset_multi_modal/scenario33/"]
lidar_augmentation_path=["/efs/data/preprocess_lidar/Adaptation_dataset_multi_modal/scenario31_aug/",
"/efs/data/preprocess_lidar/Adaptation_dataset_multi_modal/scenario32_aug/",
"/efs/data/preprocess_lidar/Adaptation_dataset_multi_modal/scenario33_aug/"]
################### Lidar data augmentation: downsampling (0.9%) and adding noise (-+0.4)
noise_range=0.4
for scenario_idx in range(len(lidar_path)):
lidar_list=os.listdir(lidar_path[scenario_idx])
for lidar_item in lidar_list:
lidar=lidar_path[scenario_idx]+lidar_item
pcd = o3d.io.read_point_cloud(lidar)
downpcd1 = pcd.random_down_sample(0.9)
lidar_aug_path1=lidar_augmentation_path[scenario_idx]+lidar_item[:-4]+"_1.ply"
o3d.io.write_point_cloud(lidar_aug_path1,downpcd1,write_ascii=True)
for point_item in pcd.points:
point_item[0]=point_item[0]+random.uniform(-noise_range,noise_range)
point_item[1]=point_item[1]+random.uniform(-noise_range,noise_range)
point_item[2]=point_item[2]+random.uniform(-noise_range,noise_range)
lidar_aug_path2=lidar_augmentation_path[scenario_idx]+lidar_item[:-4]+"_2.ply"
o3d.io.write_point_cloud(lidar_aug_path2,pcd,write_ascii=True)
'''
########################### Preprocessing augmented data ##########################################
scenario_list=["scenario31"]
lidar_aug_path=[["/efs/data/Adaptation_dataset_multi_modal/scenario31/unit1/lidar_data_aug/"]]
preprocessed_lidar_aug_path=[["/efs/data/preprocess_lidar/Adaptation_dataset_multi_modal/scenario31_aug/"]]
for scenario_idx in range(len(scenario_list)):
lidar_background="/efs/data/preprocess_lidar/Background/"+scenario_list[scenario_idx]+"_background.ply"
background_pcl = o3d.io.read_point_cloud(lidar_background)
for lidar_path_idx in range(len(lidar_aug_path[scenario_idx])):
lidar_path_item=lidar_aug_path[scenario_idx][lidar_path_idx]
preprocessed_lidar_aug_path_item=preprocessed_lidar_aug_path[scenario_idx][lidar_path_idx]
lidar_list=os.listdir(lidar_path_item)
for lidar_item in lidar_list:
lidar=lidar_path_item+lidar_item
pcd = o3d.io.read_point_cloud(lidar)
pcd_tree = o3d.geometry.KDTreeFlann(background_pcl)
filtered_pcl_list=[]
filter_distance_min=0.3
filter_distance_max=5
lidar_distance_min=40
lidar_distance_cst=30
for point_item in pcd.points:
[k, idx, _]=pcd_tree.search_knn_vector_3d(point_item, 1)
dx=point_item[0]-background_pcl.points[idx[0]][0]
dy=point_item[1]-background_pcl.points[idx[0]][1]
dz=point_item[2]-background_pcl.points[idx[0]][2]
distance=math.sqrt((dx*dx)+(dy*dy))
px=point_item[0]
py=point_item[1]
pz=point_item[2]
point_distance=math.sqrt((px*px)+(py*py))
filter_distance=filter_distance_min+(filter_distance_max-filter_distance_min)*(point_distance/lidar_distance_cst)**4
if (distance>=filter_distance):
filtered_pcl_list.append(point_item)
pcd.points = o3d.utility.Vector3dVector(np.array(filtered_pcl_list))
preprocessed_lidar=preprocessed_lidar_aug_path_item+lidar_item
o3d.io.write_point_cloud(preprocessed_lidar,pcd,write_ascii=True)
'''