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pwais/gist:da265eb73ce1f891266a7094ae244c0d

Created October 12, 2019 23:20
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gistfile1.txt
def print_sensor_sample_rates(self, scene_names=None):
if not scene_names:
scene_names = [s['name'] for s in self.scene]
scene_names = set(scene_names)
def to_sec(timestamp):
# NuScenes and Lyft Level 5 timestamps are in microseconds
return timestamp * 1e-6
scene_tokens = set(
s['token'] for s in self.scene if s['name'] in scene_names)
for scene_token in scene_tokens:
scene_samples = [
s for s in self.sample if s['scene_token'] == scene_token
]
# Samples are supposed to be 'best groupings' of lidar and camera data.
# Let's measure their sample rate.
from collections import defaultdict
name_to_tss = defaultdict(list)
for sample in scene_samples:
name_to_tss['sample (annos)'].append(to_sec(sample['timestamp']))
# Now measure individual sensor sample rates.
sample_toks = set(s['token'] for s in scene_samples)
scene_sample_datas = [
sd for sd in self.sample_data if sd['sample_token'] in sample_toks
]
for sd in scene_sample_datas:
name_to_tss[sd['channel']].append(to_sec(sd['timestamp']))
ego_pose = self.get('ego_pose', sd['ego_pose_token'])
name_to_tss['ego_pose'].append(to_sec(ego_pose['timestamp']))
name_to_tss['sample_data'].append(to_sec(sd['timestamp']))
annos = [
a for a in self.sample_annotation if a['sample_token'] in sample_toks
]
num_tracks = len(set(a['instance_token'] for a in annos))
import itertools
all_ts = sorted(itertools.chain.from_iterable(name_to_tss.values()))
from datetime import datetime
dt = datetime.utcfromtimestamp(all_ts[0])
start = dt.strftime('%Y-%m-%d %H:%M:%S')
duration = (all_ts[-1] - all_ts[0])
# Print a report
print('---')
print('---')
scene = self.get('scene', scene_token)
print('Scene %s %s' % (scene['name'], scene['token']))
print('Start %s \tDuration %s sec' % (start, duration))
print('Num Annos %s (Tracks %s)' % (len(annos), num_tracks))
import pandas as pd
from collections import OrderedDict
rows = []
for name in sorted(name_to_tss.keys()):
def get_series(name):
return np.array(sorted(name_to_tss[name]))
series = get_series(name)
freqs = series[1:] - series[:-1]
lidar_series = get_series('LIDAR_TOP')
diff_lidar_ms = 1e3 * np.mean(
[np.abs(lidar_series - t).min() for t in series])
rows.append(OrderedDict((
('Series', name),
('Freq Hz', 1. / np.mean(freqs)),
('Diff Lidar (msec)', diff_lidar_ms),
('Duration', series[-1] - series[0]),
('Support', len(series)),
)))
print(pd.DataFrame(rows))
print()
print()
# NuScenes:
# ---
# ---
# Scene scene-1000 09f67057dd8346388b28f79d9bb1cf04
# Start 2018-11-14 11:01:41 Duration 19.922956943511963 sec
# Num Annos 493 (Tracks 27)
# Series Freq Hz Diff Lidar (msec) Duration Support
# 0 CAM_BACK 11.738035 10.554540 19.850000 234
# 1 CAM_BACK_LEFT 11.536524 0.918133 19.850000 230
# 2 CAM_BACK_RIGHT 11.788413 20.070969 19.850000 235
# 3 CAM_FRONT 11.637280 14.986247 19.850000 232
# 4 CAM_FRONT_LEFT 11.536524 7.586523 19.850000 230
# 5 CAM_FRONT_RIGHT 11.536524 22.528135 19.850000 230
# 6 LIDAR_TOP 19.747937 0.000000 19.850175 393
# 7 RADAR_BACK_LEFT 12.593898 12.635898 19.850883 251
# 8 RADAR_BACK_RIGHT 13.211602 12.786931 19.831054 263
# 9 RADAR_FRONT 12.976290 12.728528 19.882416 259
# 10 RADAR_FRONT_LEFT 13.510020 12.710849 19.911148 270
# 11 RADAR_FRONT_RIGHT 13.724216 12.571387 19.891846 274
# 12 ego_pose 155.599393 11.128198 19.922957 3101
# 13 sample (annos) 2.015096 0.000000 19.850175 41
# 14 sample_data 155.599393 11.128198 19.922957 3101
# ---
# ---
# Scene scene-0293 6308d6d934074a028fc3145eedf3e65f
# Start 2018-08-31 15:25:42 Duration 19.525898933410645 sec
# Num Annos 3548 (Tracks 277)
# Series Freq Hz Diff Lidar (msec) Duration Support
# 0 CAM_BACK 11.773779 10.441362 19.450000 230
# 1 CAM_BACK_LEFT 11.825193 1.573582 19.450000 231
# 2 CAM_BACK_RIGHT 11.928021 19.624993 19.450000 233
# 3 CAM_FRONT 11.876607 14.872485 19.450000 232
# 4 CAM_FRONT_LEFT 11.876607 7.329719 19.450000 232
# 5 CAM_FRONT_RIGHT 11.979434 22.875966 19.450000 234
# 6 LIDAR_TOP 19.844216 0.000000 19.451512 387
# 7 RADAR_BACK_LEFT 13.157897 12.698666 19.455997 257
# 8 RADAR_BACK_RIGHT 13.490288 12.647669 19.421380 263
# 9 RADAR_FRONT 13.082394 12.616018 19.491845 256
# 10 RADAR_FRONT_LEFT 13.305139 12.709008 19.466163 260
# 11 RADAR_FRONT_RIGHT 13.209300 12.723777 19.455989 258
# 12 ego_pose 157.329504 11.144872 19.525899 3073
# 13 sample (annos) 2.004986 0.000000 19.451512 40
# 14 sample_data 157.329504 11.144872 19.525899 3073
# ---
# ---
# Scene scene-1107 89f20737ec344aa48b543a9e005a38ca
# Start 2018-11-21 11:59:53 Duration 19.820924997329712 sec
# Num Annos 496 (Tracks 47)
# Series Freq Hz Diff Lidar (msec) Duration Support
# 0 CAM_BACK 11.696203 11.014590 19.750000 232
# 1 CAM_BACK_LEFT 11.848101 1.382666 19.750000 235
# 2 CAM_BACK_RIGHT 11.746835 20.483792 19.750000 233
# 3 CAM_FRONT 11.848103 14.481831 19.749997 235
# 4 CAM_FRONT_LEFT 11.898734 7.063236 19.750000 236
# 5 CAM_FRONT_RIGHT 11.898734 22.159666 19.750000 236
# 6 LIDAR_TOP 19.797377 0.000000 19.750091 392
# 7 RADAR_BACK_LEFT 13.578124 12.646209 19.811279 270
# 8 RADAR_BACK_RIGHT 13.271911 12.721395 19.740940 263
# 9 RADAR_FRONT 13.198245 12.553021 19.775357 262
# 10 RADAR_FRONT_LEFT 13.730931 12.645984 19.736462 272
# 11 RADAR_FRONT_RIGHT 13.778595 12.488227 19.740764 273
# 12 ego_pose 158.317536 11.102567 19.820925 3139
# 13 sample (annos) 2.025307 0.000000 19.750091 41
# 14 sample_data 158.317536 11.102567 19.820925 3139
# Lyft Level 5:
# ---
# ---
# Scene host-a015-lidar0-1235423635198474636-1235423660098038666 755e4564756ad5c92243b7f77039d07ab1cce40662a6a19b67c820647666a3ef
# Start 2019-02-28 21:13:55 Duration 24.99979877471924 sec
# Num Annos 1637 (Tracks 44)
# Series Freq Hz Diff Lidar (msec) Duration Support
# 0 CAM_BACK 5.020080 98.882582 24.900000 126
# 1 CAM_BACK_LEFT 5.020080 16.919276 24.900000 126
# 2 CAM_BACK_RIGHT 5.020080 82.887411 24.900000 126
# 3 CAM_FRONT 5.020080 50.027272 24.900000 126
# 4 CAM_FRONT_LEFT 5.020080 33.542296 24.900000 126
# 5 CAM_FRONT_RIGHT 5.020080 66.427920 24.900000 126
# 6 CAM_FRONT_ZOOMED 5.020080 50.096270 24.900000 126
# 7 LIDAR_TOP 5.020156 0.000000 24.899626 126
# 8 ego_pose 40.442375 0.000000 24.899626 1008
# 9 sample (annos) 5.020156 0.000000 24.899626 126
# 10 sample_data 40.280324 49.847878 24.999799 1008
# ---
# ---
# Scene host-a004-lidar0-1233947108297817786-1233947133198765096 114b780b2efd6f73f134fc3a8f9db628e43131dc47f90e9b5dfdb886400d70f2
# Start 2019-02-11 19:05:08 Duration 25.000741004943848 sec
# Num Annos 4155 (Tracks 137)
# Series Freq Hz Diff Lidar (msec) Duration Support
# 0 CAM_BACK 5.020080 98.790201 24.900000 126
# 1 CAM_BACK_LEFT 5.020080 17.030725 24.900000 126
# 2 CAM_BACK_RIGHT 5.020080 83.033195 24.900000 126
# 3 CAM_FRONT 5.020080 50.151564 24.900000 126
# 4 CAM_FRONT_LEFT 5.020080 33.667718 24.900000 126
# 5 CAM_FRONT_RIGHT 5.020080 66.649443 24.900000 126
# 6 CAM_FRONT_ZOOMED 5.020080 50.265691 24.900000 126
# 7 LIDAR_TOP 5.019934 0.000000 24.900724 126
# 8 ego_pose 40.440592 0.000000 24.900724 1008
# 9 sample (annos) 5.019934 0.000000 24.900724 126
# 10 sample_data 40.278806 49.948567 25.000741 1008
# ---
# ---
# Scene host-a101-lidar0-1241886983298988182-1241887008198992182 7b4640d63a9c62d07a8551d4b430d0acd88eaba8249c843248feb888f4630070
# Start 2019-05-14 16:36:23 Duration 25.002139806747437 sec
# Num Annos 4777 (Tracks 173)
# Series Freq Hz Diff Lidar (msec) Duration Support
# 0 CAM_BACK 5.020080 93.825297 24.900000 126
# 1 CAM_BACK_LEFT 5.020080 85.205165 24.900000 126
# 2 CAM_BACK_RIGHT 5.020080 19.099243 24.900000 126
# 3 CAM_FRONT 5.020080 52.394347 24.900000 126
# 4 CAM_FRONT_LEFT 5.020080 68.799780 24.900000 126
# 5 CAM_FRONT_RIGHT 5.020080 35.769232 24.900000 126
# 6 CAM_FRONT_ZOOMED 5.020080 52.394347 24.900000 126
# 7 LIDAR_FRONT_LEFT 5.020060 0.000000 24.900101 126
# 8 LIDAR_FRONT_RIGHT 5.020060 0.000000 24.900101 126
# 9 LIDAR_TOP 5.020060 0.000000 24.900101 126
# 10 ego_pose 50.562044 0.000000 24.900101 1260
# 11 sample (annos) 5.020060 0.000000 24.900101 126
# 12 sample_data 50.355690 40.748741 25.002140 1260
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