-
Notifications
You must be signed in to change notification settings - Fork 1.6k
/
extract_posed_images.py
180 lines (153 loc) · 6.65 KB
/
extract_posed_images.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
172
173
174
175
176
177
178
179
180
# Modified from https://github.com/ScanNet/ScanNet/blob/master/SensReader/python/SensorData.py # noqa
import os
import struct
import zlib
from argparse import ArgumentParser
from functools import partial
import imageio
import mmcv
import numpy as np
COMPRESSION_TYPE_COLOR = {-1: 'unknown', 0: 'raw', 1: 'png', 2: 'jpeg'}
COMPRESSION_TYPE_DEPTH = {
-1: 'unknown',
0: 'raw_ushort',
1: 'zlib_ushort',
2: 'occi_ushort'
}
class RGBDFrame:
"""Class for single ScanNet RGB-D image processing."""
def load(self, file_handle):
self.camera_to_world = np.asarray(
struct.unpack('f' * 16, file_handle.read(16 * 4)),
dtype=np.float32).reshape(4, 4)
self.timestamp_color = struct.unpack('Q', file_handle.read(8))[0]
self.timestamp_depth = struct.unpack('Q', file_handle.read(8))[0]
self.color_size_bytes = struct.unpack('Q', file_handle.read(8))[0]
self.depth_size_bytes = struct.unpack('Q', file_handle.read(8))[0]
self.color_data = b''.join(
struct.unpack('c' * self.color_size_bytes,
file_handle.read(self.color_size_bytes)))
self.depth_data = b''.join(
struct.unpack('c' * self.depth_size_bytes,
file_handle.read(self.depth_size_bytes)))
def decompress_depth(self, compression_type):
assert compression_type == 'zlib_ushort'
return zlib.decompress(self.depth_data)
def decompress_color(self, compression_type):
assert compression_type == 'jpeg'
return imageio.imread(self.color_data)
class SensorData:
"""Class for single ScanNet scene processing.
Single scene file contains multiple RGB-D images.
"""
def __init__(self, filename, limit):
self.version = 4
self.load(filename, limit)
def load(self, filename, limit):
with open(filename, 'rb') as f:
version = struct.unpack('I', f.read(4))[0]
assert self.version == version
strlen = struct.unpack('Q', f.read(8))[0]
self.sensor_name = b''.join(
struct.unpack('c' * strlen, f.read(strlen)))
self.intrinsic_color = np.asarray(
struct.unpack('f' * 16, f.read(16 * 4)),
dtype=np.float32).reshape(4, 4)
self.extrinsic_color = np.asarray(
struct.unpack('f' * 16, f.read(16 * 4)),
dtype=np.float32).reshape(4, 4)
self.intrinsic_depth = np.asarray(
struct.unpack('f' * 16, f.read(16 * 4)),
dtype=np.float32).reshape(4, 4)
self.extrinsic_depth = np.asarray(
struct.unpack('f' * 16, f.read(16 * 4)),
dtype=np.float32).reshape(4, 4)
self.color_compression_type = COMPRESSION_TYPE_COLOR[struct.unpack(
'i', f.read(4))[0]]
self.depth_compression_type = COMPRESSION_TYPE_DEPTH[struct.unpack(
'i', f.read(4))[0]]
self.color_width = struct.unpack('I', f.read(4))[0]
self.color_height = struct.unpack('I', f.read(4))[0]
self.depth_width = struct.unpack('I', f.read(4))[0]
self.depth_height = struct.unpack('I', f.read(4))[0]
self.depth_shift = struct.unpack('f', f.read(4))[0]
num_frames = struct.unpack('Q', f.read(8))[0]
self.frames = []
if limit > 0 and limit < num_frames:
index = np.random.choice(
np.arange(num_frames), limit, replace=False).tolist()
else:
index = list(range(num_frames))
for i in range(num_frames):
frame = RGBDFrame()
frame.load(f)
if i in index:
self.frames.append(frame)
def export_depth_images(self, output_path):
if not os.path.exists(output_path):
os.makedirs(output_path)
for f in range(len(self.frames)):
depth_data = self.frames[f].decompress_depth(
self.depth_compression_type)
depth = np.fromstring(
depth_data, dtype=np.uint16).reshape(self.depth_height,
self.depth_width)
imageio.imwrite(
os.path.join(output_path,
self.index_to_str(f) + '.png'), depth)
def export_color_images(self, output_path):
if not os.path.exists(output_path):
os.makedirs(output_path)
for f in range(len(self.frames)):
color = self.frames[f].decompress_color(
self.color_compression_type)
imageio.imwrite(
os.path.join(output_path,
self.index_to_str(f) + '.jpg'), color)
@staticmethod
def index_to_str(index):
return str(index).zfill(5)
@staticmethod
def save_mat_to_file(matrix, filename):
with open(filename, 'w') as f:
for line in matrix:
np.savetxt(f, line[np.newaxis], fmt='%f')
def export_poses(self, output_path):
if not os.path.exists(output_path):
os.makedirs(output_path)
for f in range(len(self.frames)):
self.save_mat_to_file(
self.frames[f].camera_to_world,
os.path.join(output_path,
self.index_to_str(f) + '.txt'))
def export_intrinsics(self, output_path):
if not os.path.exists(output_path):
os.makedirs(output_path)
self.save_mat_to_file(self.intrinsic_color,
os.path.join(output_path, 'intrinsic.txt'))
def process_scene(path, limit, idx):
"""Process single ScanNet scene.
Extract RGB images, poses and camera intrinsics.
"""
data = SensorData(os.path.join(path, idx, f'{idx}.sens'), limit)
output_path = os.path.join('posed_images', idx)
data.export_color_images(output_path)
data.export_intrinsics(output_path)
data.export_poses(output_path)
def process_directory(path, limit, nproc):
print(f'processing {path}')
mmcv.track_parallel_progress(
func=partial(process_scene, path, limit),
tasks=os.listdir(path),
nproc=nproc)
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('--max-images-per-scene', type=int, default=300)
parser.add_argument('--nproc', type=int, default=8)
args = parser.parse_args()
# process train and val scenes
if os.path.exists('scans'):
process_directory('scans', args.max_images_per_scene, args.nproc)
# process test scenes
if os.path.exists('scans_test'):
process_directory('scans_test', args.max_images_per_scene, args.nproc)