sleap-io 0.6.5

Standalone utilities for working with pose data from SLEAP and other tools.

sleap-io

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

Installation

From PyPI

pip install sleap-io

From source (latest version)

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 (fastest)
pip install sleap-io[ffmpeg]   # For FFMPEG backend (most reliable)
pip install sleap-io[pyav]     # For PyAV backend (balanced)
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.

Usage

Load and save in different formats

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")

Convert labels to raw arrays

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 == 3

Read video data

import sleap_io as sio

video = sio.load_video("test.mp4")
n_frames, height, width, channels = video.shape

frame = video[0]
height, width, channels = frame.shape

Create labels from raw data

import 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")

Support

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 [e-mail].

License

This package is distributed under a BSD 3-Clause License and can be used without restrictions. See LICENSE for details.