Trajnet tools.
Project description
Tools
- summary table and plots: python -m trajnettools.summarize <dataset_files>
- plot sample trajectories: python -m trajnettools.trajectories <dataset_file>
- visualize interactions: python -m trajnettools.visualize_interactions <dataset_file> --interaction_type 'ca'
- obtain distribution of trajectory types: python -m trajnettools.dataset_stats <dataset_file>
APIs
- trajnettools.Reader: class to read the dataset_file
- trajnettools.show: module containing contexts for visualizing rows and paths
- trajnettools.writers: write a trajnet dataset file
- trajnettools.metrics: contains unimodal metrics: average_l2(), final_l2() and collision() and multimodal metrics: topk() and nll() implementations
Dataset
Datasets are split into train, val and test set. Every line is a self contained JSON string (ndJSON).
Scene:
{"scene": {"id": 266, "p": 254, "s": 10238, "e": 10358, "fps": 2.5, "tag": 2}}
Track:
{"track": {"f": 10238, "p": 248, "x": 13.2, "y": 5.85}}
with:
- id: scene id
- p: pedestrian id
- s, e: start and end frame id
- fps: frame rate
- tag: trajectory type
- f: frame id
- x, y: x- and y-coordinate in meters
- pred_number: (optional) prediction number for multiple output predictions
- scene_id: (optional) corresponding scene_id for multiple output predictions
Frame numbers are not recomputed. Rows are resampled to about 2.5 rows per second.
Dev
pylint trajnettools
python -m pytest
# optional: mypy trajnettools --disallow-untyped-defs
Dataset Summaries
biwi_hotel:
crowds_students001:
crowds_students003:
crowds_zara02:
crowds_zara03:
dukemtmc:
syi:
wildtrack:
Interactions
leader_follower:
collision_avoidance:
group:
others:
Project details
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