# sleap-io
[](https://github.com/talmolab/sleap-io/actions/workflows/ci.yml)
[](https://codecov.io/gh/talmolab/sleap-io)
[](https://github.com/talmolab/sleap-io/releases/)
[](https://pypi.org/project/sleap-io)
Standalone utilities for working with animal pose tracking data.
This is intended to be a complement to the core [SLEAP](https://github.com/talmolab/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`](CONTRIBUTING.md) for more information on development.
## Usage
### Load and save in different formats
```py
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
```py
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
```py
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
```py
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](https://github.com/talmolab/sleap-io/issues)
with a description of your problem or request.
For general SLEAP usage, see the [main website](https://sleap.ai).
Other questions? Reach out to `talmo@salk.edu`.
## License
This package is distributed under a BSD 3-Clause License and can be used without
restrictions. See [`LICENSE`](LICENSE) for details.
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"description": "# sleap-io\n\n[](https://github.com/talmolab/sleap-io/actions/workflows/ci.yml)\n[](https://codecov.io/gh/talmolab/sleap-io)\n[](https://github.com/talmolab/sleap-io/releases/)\n[](https://pypi.org/project/sleap-io)\n\n\nStandalone utilities for working with animal pose tracking data.\n\nThis is intended to be a complement to the core [SLEAP](https://github.com/talmolab/sleap)\npackage that aims to provide functionality for interacting with pose tracking-related\ndata structures and file formats with minimal dependencies. This package *does not*\nhave any functionality related to labeling, training, or inference.\n\n## Installation\n```\npip install sleap-io\n```\n\nFor development, use one of the following syntaxes:\n```\nconda env create -f environment.yml\n```\n```\npip install -e .[dev]\n```\nSee [`CONTRIBUTING.md`](CONTRIBUTING.md) for more information on development.\n\n## Usage\n\n### Load and save in different formats\n\n```py\nimport sleap_io as sio\n\n# Load from SLEAP file.\nlabels = sio.load_file(\"predictions.slp\")\n\n# Save to NWB file.\nsio.save_file(labels, \"predictions.nwb\")\n# Or:\n# labels.save(\"predictions.nwb\")\n```\n\n### Convert labels to raw arrays\n\n```py\nimport sleap_io as sio\n\nlabels = sio.load_slp(\"tests/data/slp/centered_pair_predictions.slp\")\n\n# Convert predictions to point coordinates in a single array.\ntrx = labels.numpy()\nn_frames, n_tracks, n_nodes, xy = trx.shape\nassert xy == 2\n\n# Convert to array with confidence scores appended.\ntrx_with_scores = labels.numpy(return_confidence=True)\nn_frames, n_tracks, n_nodes, xy_score = trx.shape \nassert xy_score == 3\n```\n\n### Read video data\n\n```py\nimport sleap_io as sio\n\nvideo = sio.load_video(\"test.mp4\")\nn_frames, height, width, channels = video.shape\n\nframe = video[0]\nheight, width, channels = frame.shape\n```\n\n### Create labels from raw data\n\n```py\nimport sleap_io as sio\nimport numpy as np\n\n# Create skeleton.\nskeleton = sio.Skeleton(\n nodes=[\"head\", \"thorax\", \"abdomen\"],\n edges=[(\"head\", \"thorax\"), (\"thorax\", \"abdomen\")]\n)\n\n# Create video.\nvideo = sio.load_video(\"test.mp4\")\n\n# Create instance.\ninstance = sio.Instance.from_numpy(\n points=np.array([\n [10.2, 20.4],\n [5.8, 15.1],\n [0.3, 10.6],\n ]),\n skeleton=skeleton\n)\n\n# Create labeled frame.\nlf = sio.LabeledFrame(video=video, frame_idx=0, instances=[instance])\n\n# Create labels.\nlabels = sio.Labels(videos=[video], skeletons=[skeleton], labeled_frames=[lf])\n\n# Save.\nlabels.save(\"labels.slp\")\n```\n\n## Support\nFor technical inquiries specific to this package, please [open an Issue](https://github.com/talmolab/sleap-io/issues)\nwith a description of your problem or request.\n\nFor general SLEAP usage, see the [main website](https://sleap.ai).\n\nOther questions? Reach out to `talmo@salk.edu`.\n\n## License\nThis package is distributed under a BSD 3-Clause License and can be used without\nrestrictions. See [`LICENSE`](LICENSE) for details.\n",
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