forked from princeton-vl/DPVO
-
Notifications
You must be signed in to change notification settings - Fork 0
/
evaluate_kitti.py
168 lines (123 loc) · 5.71 KB
/
evaluate_kitti.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
from itertools import count
from multiprocessing import Process, Queue
from pathlib import Path
import cv2
import evo.main_ape as main_ape
import numpy as np
import torch
from evo.core import sync
from evo.core.metrics import PoseRelation
from evo.core.trajectory import PoseTrajectory3D
from evo.tools import file_interface, plot
from dpvo.config import cfg
from dpvo.dpvo import DPVO
from dpvo.plot_utils import plot_trajectory
from dpvo.utils import Timer
SKIP = 0
def show_image(image, t=0):
image = image.permute(1, 2, 0).cpu().numpy()
cv2.imshow('image', image / 255.0)
cv2.waitKey(t)
# From https://github.com/utiasSTARS/pykitti/blob/d3e1bb81676e831886726cc5ed79ce1f049aef2c/pykitti/utils.py#L68
def read_calib_file(filepath):
"""Read in a calibration file and parse into a dictionary."""
data = {}
with open(filepath, 'r') as f:
for line in f.readlines():
key, value = line.split(':', 1)
# The only non-float values in these files are dates, which
# we don't care about anyway
try:
data[key] = np.array([float(x) for x in value.split()])
except ValueError:
pass
return data
def kitti_image_stream(queue, kittidir, sequence, stride, skip=0):
""" image generator """
images_dir = kittidir / "dataset" / "sequences" / sequence
image_list = sorted((images_dir / "image_2").glob("*.png"))[skip::stride]
calib = read_calib_file(images_dir / "calib.txt")
intrinsics = calib['P0'][[0, 5, 2, 6]]
for t, imfile in enumerate(image_list):
image_left = cv2.imread(str(imfile))
H, W, _ = image_left.shape
H, W = (H - H%4, W - W%4)
image_left = image_left[..., :H, :W, :]
queue.put((t, image_left, intrinsics))
queue.put((-1, image_left, intrinsics))
@torch.no_grad()
def run(cfg, network, kittidir, sequence, stride=1, viz=False, show_img=False):
slam = None
queue = Queue(maxsize=8)
reader = Process(target=kitti_image_stream, args=(queue, kittidir, sequence, stride, 0))
reader.start()
for step in count(start=1):
(t, image, intrinsics) = queue.get()
if t < 0: break
image = torch.as_tensor(image, device='cuda').permute(2,0,1)
intrinsics = torch.as_tensor(intrinsics, dtype=torch.float, device='cuda')
if show_img:
show_image(image, 1)
if slam is None:
slam = DPVO(cfg, network, ht=image.shape[-2], wd=image.shape[-1], viz=viz)
intrinsics = intrinsics.cuda()
with Timer("SLAM", enabled=False):
slam(t, image, intrinsics)
reader.join()
return slam.terminate()
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--network', type=str, default='dpvo.pth')
parser.add_argument('--config', default="config/default.yaml")
parser.add_argument('--stride', type=int, default=2)
parser.add_argument('--viz', action="store_true")
parser.add_argument('--show_img', action="store_true")
parser.add_argument('--trials', type=int, default=1)
parser.add_argument('--kittidir', type=Path, default="datasets/KITTI")
parser.add_argument('--backend_thresh', type=float, default=32.0)
parser.add_argument('--plot', action="store_true")
parser.add_argument('--opts', nargs='+', default=[])
parser.add_argument('--save_trajectory', action="store_true")
args = parser.parse_args()
cfg.merge_from_file(args.config)
cfg.BACKEND_THRESH = args.backend_thresh
cfg.merge_from_list(args.opts)
print("\nRunning with config...")
print(cfg, "\n")
torch.manual_seed(1234)
kitti_scenes = [f"{i:02d}" for i in range(11)]
results = {}
for scene in kitti_scenes:
groundtruth = args.kittidir / "dataset" / "poses" / f"{scene}.txt"
poses_ref = file_interface.read_kitti_poses_file(groundtruth)
print(f"Evaluating KITTI {scene} with {poses_ref.num_poses // args.stride} poses")
scene_results = []
for trial_num in range(args.trials):
traj_est, timestamps = run(cfg, args.network, args.kittidir, scene, args.stride, args.viz, args.show_img)
traj_est = PoseTrajectory3D(
positions_xyz=traj_est[:,:3],
orientations_quat_wxyz=traj_est[:, [6, 3, 4, 5]],
timestamps=timestamps * args.stride)
traj_ref = PoseTrajectory3D(
positions_xyz=poses_ref.positions_xyz,
orientations_quat_wxyz=poses_ref.orientations_quat_wxyz,
timestamps=np.arange(poses_ref.num_poses, dtype=np.float64))
traj_ref, traj_est = sync.associate_trajectories(traj_ref, traj_est)
result = main_ape.ape(traj_ref, traj_est, est_name='traj',
pose_relation=PoseRelation.translation_part, align=True, correct_scale=True)
ate_score = result.stats["rmse"]
if args.plot:
plot_trajectory(traj_est, traj_ref, f"kitti sequence {scene} Trial #{trial_num+1}", f"trajectory_plots/kitti_seq{scene}_trial{trial_num+1:02d}.pdf", align=True, correct_scale=True)
if args.save_trajectory:
Path("saved_trajectories").mkdir(exist_ok=True)
file_interface.write_tum_trajectory_file(f"saved_trajectories/KITTI_{scene}.txt", traj_est)
# file_interface.write_kitti_poses_file(f"saved_trajectories/{scene}.txt", traj_est) # standard kitti format
scene_results.append(ate_score)
results[scene] = np.median(scene_results)
print(scene, sorted(scene_results))
xs = []
for scene in results:
print(scene, results[scene])
xs.append(results[scene])
print("AVG: ", np.mean(xs))