Standalone utilities for working with animal pose tracking data.
This is intended to be a complement to the core SLEAP package that aims to provide functionality for interacting with pose tracking-related data structures and file formats with minimal dependencies. This package does not have any functionality related to labeling, training, or inference.
📚 Documentation - Comprehensive guides and API reference
pip install sleap-io
pip install git+https://github.com/talmolab/sleap-io.git@main
For video backend support, install with extras:
pip install sleap-io[opencv] # For OpenCV backend
pip install sleap-io[pyav] # For PyAV backend
pip install sleap-io[all] # For all video backends
For development, use one of the following:
uv sync --all-extras # Recommended: install with uv
conda env create -f environment.yml
pip install -e .[dev,all] # Install with all extras for development
See CONTRIBUTING.md for more information on development.
import sleap_io as sio
# Load from SLEAP file.
labels = sio.load_file("predictions.slp")
# Save to NWB file.
sio.save_file(labels, "predictions.nwb")
# Or:
# labels.save("predictions.nwb")import sleap_io as sio
labels = sio.load_slp("tests/data/slp/centered_pair_predictions.slp")
# Convert predictions to point coordinates in a single array.
trx = labels.numpy()
n_frames, n_tracks, n_nodes, xy = trx.shape
assert xy == 2
# Convert to array with confidence scores appended.
trx_with_scores = labels.numpy(return_confidence=True)
n_frames, n_tracks, n_nodes, xy_score = trx.shape
assert xy_score == 3import sleap_io as sio
video = sio.load_video("test.mp4")
n_frames, height, width, channels = video.shape
frame = video[0]
height, width, channels = frame.shapeimport sleap_io as sio
import numpy as np
# Create skeleton.
skeleton = sio.Skeleton(
nodes=["head", "thorax", "abdomen"],
edges=[("head", "thorax"), ("thorax", "abdomen")]
)
# Create video.
video = sio.load_video("test.mp4")
# Create instance.
instance = sio.Instance.from_numpy(
points=np.array([
[10.2, 20.4],
[5.8, 15.1],
[0.3, 10.6],
]),
skeleton=skeleton
)
# Create labeled frame.
lf = sio.LabeledFrame(video=video, frame_idx=0, instances=[instance])
# Create labels.
labels = sio.Labels(videos=[video], skeletons=[skeleton], labeled_frames=[lf])
# Save.
labels.save("labels.slp")For technical inquiries specific to this package, please open an Issue with a description of your problem or request.
For general SLEAP usage, see the main website.
Other questions? Reach out to talmo@salk.edu.
This package is distributed under a BSD 3-Clause License and can be used without
restrictions. See LICENSE for details.