linger_loaders
Python tools for the LiNGER dataset: loaders for the ceiling-LiDAR tracklets, the recorded point-cloud segments and the environment files (static map, furniture, study area), the experimental flicker filter, and a replay tool that turns tracklets into a Rerun recording. The loaders need only the standard library and NumPy and return NumPy arrays in the shared coordinate frame (meters, z up), with field names as in the paper's appendix.
Install
pip install -e . # from this directory: loaders and flicker filter (NumPy only)
pip install -e ".[quickstart]" # adds matplotlib for examples/quickstart.py
pip install -e ".[replay]" # adds rerun-sdk 0.38.1 for the replay tool (needs Python 3.10 or newer)
Quickstart
python examples/quickstart.py /path/to/linger busy quickstart_busy.png
This plots the busy benchmark session's offline tracks over the static map and the study area.
Loaders
The paths below are relative to the dataset root (the folder that holds README.md, CONFIG.json and
environment/), as laid out in the review sample.
import csv, json
from linger_loaders import (load_tracklets, track_runs, load_segment, iter_frames,
load_static_map, load_furniture, load_study_area)
off = load_tracklets("benchmark/busy/tracks_final_5hz.csv.gz") # t_unix, track_id, x, y, state, coasting
win = json.load(open("benchmark/busy/window.json"))["window_unix"] # [start, end) in Unix seconds
live = load_tracklets("track_log/tracks_day15_000000.jsonl.gz", session=win)
starts = [float(r["started_unix"]) for r in csv.DictReader(open("metadata/tracker_runs.csv"))]
run = track_runs(live["stamp"], starts) # key live tracks by (run[i], live["id"][i])
seg = load_segment("benchmark/busy/clouds/geo_day15_080014/node1__seg033")
for t_ms, pts in iter_frames(seg): # pts: n x 3 float32, shared coordinate frame
...
sm = load_static_map("environment/static_map.npz") # points (m), node, floor_z
furn = load_furniture("environment/furniture.json") # list of dicts: id, label, x, y, len_m, wid_m, yaw_rad, shape
area = load_study_area("environment/study_area.json") # n x 2 polygon
Live track IDs restart with the tracker, so they are unique only within one run. A state ending in ~coasting
marks a row the tracker predicted without a detection.
Flicker filter
linger_loaders.flicker is the experimental flicker filter. It writes a list of flagged live tracks and never
deletes any; the rule is described at the top of linger_loaders/flicker.py.
python -m linger_loaders.flicker --logs track_log/tracks_day8_000000.jsonl.gz track_log/tracks_day15_000000.jsonl.gz \
benchmark/quiet/tracks_live_day13_120200.jsonl.gz --runs metadata/tracker_runs.csv \
--sessions metadata/flicker/session_spans.json --out flags.json --flagged-tsv flagged.tsv
The released flags were computed on the full logs, so running this command on the sample's logs gives nearly the same
set: a track cut off at the edge of a sample log can count differently. The tracker_run column holds the run's
position in metadata/tracker_runs.csv.
linger-flicker is the same command once the package is installed.
Replay
linger_loaders.replay writes one Rerun recording per tracklet file: the static floor map with the study area and
the node positions, each current track as a point labeled with its ID, a trail of its last 3 s, and the number of
tracks present per frame. It reads only the released files (environment/, CONFIG.json and the tracklets).
python -m linger_loaders.replay . track_log/tracks_day15_000000.jsonl.gz -o day15.rrd --title "Day 15"
python -m linger_loaders.replay . benchmark/busy/tracks_live_day15_133000.jsonl.gz \
benchmark/busy/tracks_final_5hz.csv.gz -o busy.rrd --title "Busy benchmark"
rerun day15.rrd
With several tracklet files, the live and offline recordings share one file; switch between them in the viewer's
recording list. linger-replay is the same command once the package is installed.
License
The code in this package is licensed under the MIT License (see LICENSE). The data it reads are licensed
separately, under CC BY-NC 4.0 (see the dataset's LICENSE).