# Anipose

[![PyPI version](https://badge.fury.io/py/anipose.svg)](https://badge.fury.io/py/anipose)

Anipose is an open-source toolkit for robust, markerless 3D pose estimation of animal behavior from multiple camera views. It leverages the machine learning toolbox [DeepLabCut](https://github.com/AlexEMG/DeepLabCut) to track keypoints in 2D, then triangulates across camera views to estimate 3D pose.

Check out the [Anipose paper](https://www.cell.com/cell-reports/fulltext/S2211-1247(21)01179-7) for more information.

The name Anipose comes from **Ani**mal **Pose**, but it also sounds like "any pose".

## Documentation

Up to date documentation may be found at [anipose.org](http://anipose.org) .

## Demos


<img src="https://raw.githubusercontent.com/lambdaloop/anipose-docs/master/tracking_3cams_full_slower5.gif" width="70%" >


Videos of flies by Evyn Dickinson (slowed 5x), <a href=http://faculty.washington.edu/tuthill/>Tuthill Lab</a>



<img src="https://raw.githubusercontent.com/lambdaloop/anipose-docs/master/hand-demo.gif" width="70%" >


Videos of hand by Katie Rupp




## References

Here are some references for DeepLabCut and other things this project relies upon:
- Mathis et al, 2018, "DeepLabCut: markerless pose estimation of user-defined body parts with deep learning"
- Romero-Ramirez et al, 2018, "Speeded up detection of squared fiducial markers"

Framework for scalable DeepLabCut based analysis including 3D tracking. 

anipose

Framework for scalable DeepLabCut based analysis including 3D tracking

lambdaloop

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