sleap-io 0.0.9

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.

Installation

pip install sleap-io

For development, use one of the following syntaxes:

conda env create -f environment.yml

pip install -e .[dev]

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_slp("predictions.slp")

# Save to NWB file.
sio.save_nwb(labels, "predictions.nwb")

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.Video.from_filename("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])

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.