Introduction to Anipose#

Anipose is an open-source Python toolkit for robust, markerless 3D pose estimation of animal behavior from multiple camera views.

Documentation: https://anipose.readthedocs.io/en/latest/
Sourcecode: lambdaloop/anipose

Anipose is built on DeepLabCut for markerless tracking, and can be used to expand existing 2D projects with multiple cameras. It consists of a 3D camera calibration module, triangulation of 2D tracked coordinates, and a set of filters to resolve tracking errors both in 2D and 3D models. Synchronized cameras are needed to record behavior from multiple views, and calibration boards such as ChArUco are used to calculate camera parameters such as relative position and orientation in space.

_images/anipose.PNG

Fig. 24 Anipose workflow from Karashchuk et al. 2021.#

Installation#

Unfortunately, the installation of Anipose is still a bit tricky, but checking similar Github issues often helps. Anipose will will be installed on top of your DeepLabCut environment, but I recommend having a new environment with a new DeepLabCut installation. Anipose will analyze your videos using a pre-trained DeepLabCut project, and a GPU will be faster but is not strictly necessary.
If you have Anaconda already installed, follow the steps below:

  1. Download the yaml installation file

  2. cd Downloads

  3. conda env create -f DEEPLABCUT.yaml

  4. conda activate DEEPLABCUT

  5. pip install -U wxPython

  6. pip uninstall opencv-python

  7. install Anipose with pip install anipose

  8. conda install mayavi ffmpeg

  9. pip install --upgrade apptools

  10. check if Anipose was installed with anipose

Note

When you encounter errors (not if, when…), please check the console output carefully and refer to the known Github issues.

Documentation#

Karashchuk, P., Rupp, K. L., Dickinson, E. S., Walling-Bell, S., Sanders, E., Azim, E., Brunton, B. W., & Tuthill, J. C. (2021). Anipose: A toolkit for robust markerless 3D pose estimation. Cell Reports, 36(13), 109730. https://doi.org/10.1016/j.celrep.2021.109730