-
Notifications
You must be signed in to change notification settings - Fork 5
/
split_dataset.py
176 lines (158 loc) · 8.15 KB
/
split_dataset.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
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
from path import Path
import pandas as pd
from tqdm import tqdm
import numpy as np
from scipy.spatial.transform import Rotation
import random
parser = ArgumentParser(description='Convert dataset to KITTI format, optionnally create a visualization video',
formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument('--dataset_dir', metavar='DIR', type=Path, required=True,
help='folder containing the converted dataset')
parser.add_argument('--output_dir', metavar='DIR', type=Path, required=True,
help='foutput folder which will be used as a dataset for unsupervised depth training')
parser.add_argument('--scenes_list', type=Path, default=None,
help="List of folders containing videos to split, useful if some are used for testing")
parser.add_argument('--verbose', '-v', action='count', default=0)
parser.add_argument('--min_displacement', default=0, type=float,
help='Minimum displacement between two frames')
parser.add_argument('--max_rotation', default=1, type=float,
help='Maximum rotation between two frames. If above, frame is discarded and video is split')
parser.add_argument('--min_num_frames', default=5, type=int,
help='minimum number of frames in a video split. If below, split is removed')
parser.add_argument('--allow_interpolated_frames', action='store_true',
help='If set, will consider frames with interpolated odometry to be valid')
parser.add_argument('--adapt_min_disp_to_depth', action='store_true')
parser.add_argument('--train_split', type=float, default=0.9,
help='proportion of videos taken for training split, the other videos are taken for validation')
parser.add_argument('--seed', type=int, default=0)
def sample_splits(sequence, min_displacement, max_rot, min_num_frames):
def get_rotation(row):
flat_matrix = row[["pose00", "pose01", "pose02",
"pose10", "pose11", "pose12",
"pose20", "pose21", "pose22"]].values
rotation = Rotation.from_matrix(flat_matrix.reshape(3, 3))
return rotation
current_split = [0]
last_rot = None
last_intrinsics = None
for k in sample_frames(sequence, min_displacement):
row = sequence.iloc[k]
current_rot = get_rotation(row)
current_intrinsics = row[["fx", "fy", "cx", "cy"]].values.astype(float)
if last_rot is not None:
rot_diff_mag = (current_rot.inv() * last_rot).magnitude()
else:
rot_diff_mag = 0
if last_intrinsics is None:
last_intrinsics = current_intrinsics
if rot_diff_mag > max_rot or not np.allclose(current_intrinsics, last_intrinsics):
if len(current_split) > min_num_frames:
yield current_split
current_split = []
last_rot = None
last_intrinsics = None
else:
last_rot = current_rot
current_split.append(k)
if len(current_split) > min_num_frames:
yield current_split
def sample_frames(sequence, min_displacement):
tvec = sequence[["pose03", "pose13", "pose23"]].values
origin = tvec[0]
current_disp = 0
for j, pos in enumerate(tvec):
current_disp = np.linalg.norm(pos - origin)
if current_disp > min_displacement:
yield j
origin = pos
def get_min_depth(row):
depth_path = row["image_path"].splitext() + ".npy"
depth = np.load(depth_path)
return depth.min()
def sample_frames_depth(sequence, min_displacement, min_depth):
tvec = sequence[["pose03", "pose13", "pose23"]].values
origin = tvec[0]
current_disp = 0
last_min_depth = get_min_depth(sequence.iloc[0])
for i, pos in enumerate(tvec):
current_min_depth = get_min_depth(sequence.iloc[i])
if current_disp * np.min(last_min_depth, current_min_depth) / min_depth > min_displacement:
yield i
origin = pos
current_disp = np.lialg.norm(pos - origin)
def main():
args = parser.parse_args()
random.seed(args.seed)
args.output_dir.makedirs_p()
total_scenes = 0
total_frames = 0
frames_per_video = {}
dirs_per_video = {}
if args.scenes_list is not None:
with open(args.scenes_list, 'r') as f:
dataset_folders = [args.dataset_dir / path[:-1] for path in f.readlines()]
else:
dataset_folders = [f for f in args.dataset_dir.walkdirs() if f.files("*.jpg")]
for v in tqdm(dataset_folders):
frames_per_video[v] = 0
dirs_per_video[v] = []
metadata = pd.read_csv(v / "metadata.csv")
metadata["full_image_path"] = [v / Path(f).basename()
for f in metadata["image_path"].values]
valid_odometry_frames = metadata["registered"]
if not args.allow_interpolated_frames:
valid_odometry_frames = valid_odometry_frames & ~metadata["interpolated"]
# Construct valid sequences
valid_diff = valid_odometry_frames.astype(float).diff()
invalidity_start = valid_diff.index[valid_diff == -1].tolist()
validity_start = valid_diff.index[valid_diff == 1].tolist()
if valid_odometry_frames.iloc[0]:
validity_start = [0] + validity_start
if valid_odometry_frames.iloc[-1]:
invalidity_start.append(len(valid_odometry_frames))
valid_sequences = [metadata.iloc[s:e].copy() for s, e in zip(validity_start, invalidity_start)]
for i, seq in enumerate(valid_sequences):
splits = sample_splits(seq, args.min_displacement, args.max_rotation, args.min_num_frames)
for j, s in enumerate(splits):
total_scenes += 1
total_frames += len(s)
frames_per_video[v] += len(s)
final_seq = seq.iloc[s]
relative_dir = args.dataset_dir.relpathto(v)
folder_tree = relative_dir.splitall()[1:]
folder_name = '_'.join(folder_tree) + '_{:02d}_{:02d}'.format(i, j)
output_dir = args.output_dir / folder_name
dirs_per_video[v].append(folder_name)
output_dir.makedirs_p()
for _, row in final_seq.iterrows():
image_name = Path(row["image_path"]).basename()
src_img_path = v / image_name
tgt_img_path = output_dir / image_name
src_depth_path = src_img_path.stripext() + '.npy'
tgt_depth_path = tgt_img_path.stripext() + '.npy'
src_img_path.copy(tgt_img_path)
src_depth_path.copy(tgt_depth_path)
poses = final_seq[["pose00", "pose01", "pose02", "pose03",
"pose10", "pose11", "pose12", "pose13",
"pose20", "pose21", "pose22", "pose23"]]
np.savetxt(output_dir / "poses.txt", poses)
final_seq.to_csv(output_dir / "metadata.csv")
intrinsics = final_seq[["fx", "fy", "cx", "cy"]].values[0]
cam = np.array([[intrinsics[0], 0, intrinsics[2]],
[0, intrinsics[1], intrinsics[3]],
[0, 0, 1]])
np.savetxt(output_dir / "cam.txt", cam)
print("constructed a training set of {} frames in {} scenes".format(total_frames, total_scenes))
random.shuffle(dataset_folders)
train_frames = total_frames * args.train_split
cumulative_num_frames = np.cumsum(np.array([frames_per_video[v] for v in dataset_folders]))
used_for_train = (cumulative_num_frames - train_frames) < 0
train_folders = [dirs_per_video[dataset_folders[i]] for i in np.where(used_for_train)[0]]
val_folders = [dirs_per_video[dataset_folders[i]] for i in np.where(~used_for_train)[0]]
with open(args.output_dir / "train.txt", 'w') as f:
f.writelines([scene + '\n' for tf in train_folders for scene in tf])
with open(args.output_dir / "val.txt", 'w') as f:
f.writelines([scene + '\n' for vf in val_folders for scene in vf])
if __name__ == '__main__':
main()