-
Notifications
You must be signed in to change notification settings - Fork 5
/
load_scannet_data.py
171 lines (152 loc) · 7.49 KB
/
load_scannet_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
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
161
162
163
164
165
166
167
168
169
170
171
"""
Modified from: https://github.com/facebookresearch/votenet/blob/master/scannet/load_scannet_data.py
Load Scannet scenes with vertices and ground truth labels for semantic and instance segmentations
"""
# python imports
import math
import os, sys, argparse
import inspect
import json
import pdb
import numpy as np
import scannet_utils
def read_aggregation(filename):
object_id_to_segs = {}
label_to_segs = {}
with open(filename) as f:
data = json.load(f)
num_objects = len(data['segGroups'])
for i in range(num_objects):
object_id = data['segGroups'][i]['objectId'] + 1 # instance ids should be 1-indexed
label = data['segGroups'][i]['label']
segs = data['segGroups'][i]['segments']
object_id_to_segs[object_id] = segs
if label in label_to_segs:
label_to_segs[label].extend(segs)
else:
label_to_segs[label] = segs
return object_id_to_segs, label_to_segs
def read_segmentation(filename):
seg_to_verts = {}
with open(filename) as f:
data = json.load(f)
num_verts = len(data['segIndices'])
for i in range(num_verts):
seg_id = data['segIndices'][i]
if seg_id in seg_to_verts:
seg_to_verts[seg_id].append(i)
else:
seg_to_verts[seg_id] = [i]
return seg_to_verts, num_verts
def export(mesh_file, agg_file, seg_file, meta_file, label_map_file, output_file=None):
""" points are XYZ RGB (RGB in 0-255),
semantic label as nyu40 ids,
instance label as 1-#instance,
box as (cx,cy,cz,dx,dy,dz,semantic_label)
"""
label_map = scannet_utils.read_label_mapping(label_map_file, label_from='raw_category', label_to='nyu40id')
# mesh_vertices = scannet_utils.read_mesh_vertices_rgb(mesh_file)
mesh_vertices = scannet_utils.read_mesh_vertices_rgb_normal(mesh_file)
# Load scene axis alignment matrix
lines = open(meta_file).readlines()
axis_align_matrix = None
for line in lines:
if 'axisAlignment' in line:
axis_align_matrix = [float(x) for x in line.rstrip().strip('axisAlignment = ').split(' ')]
if axis_align_matrix != None:
axis_align_matrix = np.array(axis_align_matrix).reshape((4,4))
pts = np.ones((mesh_vertices.shape[0], 4))
pts[:,0:3] = mesh_vertices[:,0:3]
pts = np.dot(pts, axis_align_matrix.transpose()) # Nx4
aligned_vertices = np.copy(mesh_vertices)
aligned_vertices[:,0:3] = pts[:,0:3]
else:
print("No axis alignment matrix found")
aligned_vertices = mesh_vertices
# Load semantic and instance labels
if os.path.isfile(agg_file):
object_id_to_segs, label_to_segs = read_aggregation(agg_file)
seg_to_verts, num_verts = read_segmentation(seg_file)
label_ids = np.zeros(shape=(num_verts), dtype=np.uint32) # 0: unannotated
object_id_to_label_id = {}
for label, segs in label_to_segs.items():
label_id = label_map[label]
for seg in segs:
verts = seg_to_verts[seg]
label_ids[verts] = label_id
instance_ids = np.zeros(shape=(num_verts), dtype=np.uint32) # 0: unannotated
num_instances = len(np.unique(list(object_id_to_segs.keys())))
for object_id, segs in object_id_to_segs.items():
for seg in segs:
verts = seg_to_verts[seg]
instance_ids[verts] = object_id
if object_id not in object_id_to_label_id:
object_id_to_label_id[object_id] = label_ids[verts][0]
instance_bboxes = np.zeros((num_instances,8)) # also include object id
aligned_instance_bboxes = np.zeros((num_instances,8)) # also include object id
for obj_id in object_id_to_segs:
label_id = object_id_to_label_id[obj_id]
# bboxes in the original meshes
obj_pc = mesh_vertices[instance_ids==obj_id, 0:3]
if len(obj_pc) == 0: continue
# Compute axis aligned box
# An axis aligned bounding box is parameterized by
# (cx,cy,cz) and (dx,dy,dz) and label id
# where (cx,cy,cz) is the center point of the box,
# dx is the x-axis length of the box.
xmin = np.min(obj_pc[:,0])
ymin = np.min(obj_pc[:,1])
zmin = np.min(obj_pc[:,2])
xmax = np.max(obj_pc[:,0])
ymax = np.max(obj_pc[:,1])
zmax = np.max(obj_pc[:,2])
bbox = np.array([(xmin+xmax)/2, (ymin+ymax)/2, (zmin+zmax)/2, xmax-xmin, ymax-ymin, zmax-zmin, label_id, obj_id-1]) # also include object id
# NOTE: this assumes obj_id is in 1,2,3,.,,,.NUM_INSTANCES
instance_bboxes[obj_id-1,:] = bbox
# bboxes in the aligned meshes
obj_pc = aligned_vertices[instance_ids==obj_id, 0:3]
if len(obj_pc) == 0: continue
# Compute axis aligned box
# An axis aligned bounding box is parameterized by
# (cx,cy,cz) and (dx,dy,dz) and label id
# where (cx,cy,cz) is the center point of the box,
# dx is the x-axis length of the box.
xmin = np.min(obj_pc[:,0])
ymin = np.min(obj_pc[:,1])
zmin = np.min(obj_pc[:,2])
xmax = np.max(obj_pc[:,0])
ymax = np.max(obj_pc[:,1])
zmax = np.max(obj_pc[:,2])
bbox = np.array([(xmin+xmax)/2, (ymin+ymax)/2, (zmin+zmax)/2, xmax-xmin, ymax-ymin, zmax-zmin, label_id, obj_id-1]) # also include object id
# NOTE: this assumes obj_id is in 1,2,3,.,,,.NUM_INSTANCES
aligned_instance_bboxes[obj_id-1,:] = bbox
else:
# use zero as placeholders for the test scene
print("use placeholders")
num_verts = mesh_vertices.shape[0]
label_ids = np.zeros(shape=(num_verts), dtype=np.uint32) # 0: unannotated
instance_ids = np.zeros(shape=(num_verts), dtype=np.uint32) # 0: unannotated
instance_bboxes = np.zeros((1, 8)) # also include object id
aligned_instance_bboxes = np.zeros((1, 8)) # also include object id
if output_file is not None:
np.save(output_file+'_vert.npy', mesh_vertices)
np.save(output_file+'_aligned_vert.npy', aligned_vertices)
np.save(output_file+'_sem_label.npy', label_ids)
np.save(output_file+'_ins_label.npy', instance_ids)
np.save(output_file+'_bbox.npy', instance_bboxes)
np.save(output_file+'_aligned_bbox.npy', instance_bboxes)
return mesh_vertices, aligned_vertices, label_ids, instance_ids, instance_bboxes, aligned_instance_bboxes
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--scan_path', required=True, help='path to scannet scene (e.g., data/ScanNet/v2/scene0000_00')
parser.add_argument('--output_file', required=True, help='output file')
parser.add_argument('--label_map_file', required=True, help='path to scannetv2-labels.combined.tsv')
opt = parser.parse_args()
scan_name = os.path.split(opt.scan_path)[-1]
mesh_file = os.path.join(opt.scan_path, scan_name + '_vh_clean_2.ply')
agg_file = os.path.join(opt.scan_path, scan_name + '.aggregation.json')
seg_file = os.path.join(opt.scan_path, scan_name + '_vh_clean_2.0.010000.segs.json')
meta_file = os.path.join(opt.scan_path, scan_name + '.txt') # includes axisAlignment info for the train set scans.
export(mesh_file, agg_file, seg_file, meta_file, opt.label_map_file, opt.output_file)
if __name__ == '__main__':
main()