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DIPLOMAT

Deep learning-based Identity Preserving Labeled-Object Multi-Animal Tracking.

NOTE: DIPLOMAT is currently early beta software, there may be minor bugs and usability issues.

If you are a user (not a developer contributing code), you may want to visit:

About

DIPLOMAT provides a multi-animal tracking/pose-estimation interface that (1) reduces identity swaps and body part losses, and (2) simplifies correction of automated tracking errors. Provided with a video and a pose estimation model (trained using SLEAP or DeepLabCut packages), DIPLOMAT computes a multi-animal maximum-probability trace to Track multiple animals (and their body parts). DIPLOMAT then provides an intuitive and memory/time efficient Interact interface to edit and re-track as needed. DIPLOMAT differs from other multi-animal tracking packages by working directly off of confidence maps instead of running peak detection, allowing for more accurate tracking results.

UI Demo Showing user correcting tracking in a video

Correcting and re-tracing on the Interact Interface

Example of tracking 2 Degus in a Box Example of tracking 3 Rats
Example of tracking 2 Degus Example of tracking 3 Rats

Installation

For detailed installation instructions, please refer to the documentation.

Usage

Below, we provide a few simple instructions to remind users how to run DIPLOMAT commands. New users should read through the documentation to understand project setup, etc.

Running DIPLOMAT

To run DIPLOMAT on a video once it is installed, simply use DIPLOMAT's track and track_and_interact commands to track a video:

# Run DIPLOMAT with no UI, for 4 animals ...
diplomat track -c path/to/config -v path/to/video -no 4
# Run DIPLOMAT with UI, for 3 animals ...
diplomat track_and_interact -c path/to/config -v path/to/video -no 3

Multiple videos can be tracked by passing them as a list:

diplomat track -c path/to/config -v [path/to/video1, path/to/video2, "path/to/video3"] -no <num_animals>

Once tracking is done, DIPLOMAT can create labeled videos via it's annotate subcommand:

diplomat annotate -v path/to/video -c path/to/csv

If you need to reopen the UI to make further major modifications, you can do so using the interact subcommand:

diplomat interact -s path/to/ui_state.dipui

This displays the full UI again for making further edits. Results are saved back to the same files.

If you need to make minor modifications after tracking a video, you can do so using the tweak subcommand:

diplomat tweak -v path/to/video -c path/to/csv

This will display a stripped down version of the interactive editing UI, allowing for minor tweaks to be made to the tracks, and then saved back to the same file.

For a list of additional ways DIPLOMAT can be used, see the documentation.

Additional Help

All DIPLOMAT commands are documented via help strings. To get more information about a diplomat subcommand or command, simply run it with the -h or --help flag.

# Help for all of diplomat (lists sub commands of diplomat):
diplomat --help 
# Help for the track subcommand:
diplomat track --help
# Help for the predictors subcommand space:
diplomat predictors --help

Some commands support passing frontend-specific arguments. To get frontend specific help, you can use the --help_extra flag.

diplomat track -c /path/to/config --help_extra
diplomat track_and_interact -c /path/to/config --help_extra
diplomat track_with -c /path/to/config -p Predictor --help_extra

Documentation

DIPLOMAT has documentation on ReadTheDocs at https://diplomat.readthedocs.io/en/latest.

Development

DIPLOMAT is written entirely in python. To set up an environment for developing DIPLOMAT, you can simply pull down this repository and install its requirements using pip. For a further description of how to set up DIPLOMAT for development, see the Development Usage section in the documentation.

Contributing

We welcome external contributions, although it is a good idea to contact the maintainers before embarking on any significant development work to make sure the proposed changes are a good fit.

Contributors agree to license their code under the license in use by this project (see LICENSE).

To contribute:

  1. Fork the repo
  2. Make changes on a branch
  3. Create a pull request

License

See LICENSE for details.

Authors

If you have any questions, feel free to reach out to George Glidden, at [email protected]

See AUTHORS for the full list of authors.

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Deep learning-based Identity Preserving Labeled-Object Multi-Animal Tracking (Beta)

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