https://github.com/sudochris/squashfeettoolkit

Toolkit to evaluate the squash feet dataset
https://github.com/sudochris/squashfeettoolkit

Last synced: about 2 months ago
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Toolkit to evaluate the squash feet dataset

• Host: GitHub
• URL: https://github.com/sudochris/squashfeettoolkit
• Owner: sudochris
• License: gpl-3.0
• Created: 2020-10-07T14:52:20.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2021-07-07T15:38:31.000Z (over 3 years ago)
• Last Synced: 2023-10-20T07:48:51.077Z (about 1 year ago)
• Language: Python
• Size: 269 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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## Feet Analysis Toolkit

This application provides a toolkit and scripts used in "Evaluation of pre-trained and open-source deep convolutional neural networks suitable for player detection and motion analysis in squash". 

## Getting started (Linux):

### Setup application

1. Create virtual environment

    `python3 -m venv /path/to/new/virtual/environment`

1. Activate the environment

    `source /path/to/new/virtual/environment/bin/activate`

1. Install requirements from `requirements.txt`

    `pip install -r requirements.txt`

### Setup data folder

1. Create a softlink for the dataset folder

    `ln -s ~/Projects/released-datasets/ ./dataset`

1. Create a clean output folder for results. 

    `mkdir output`

### Run

1. Run the application inside your virtual environment and provide dataset description file and your output folder

    `python run_toolkit.py --description=/dataset_description.json --output=./output`

### Other useful parameters (optional)

| Parameter | Description                                   |

|-----------|-----------------------------------------------|

| --debug   | Prints debugging log output during evaluation |

| --render  | Enables rendering while processing (**very slow**) |

### Citation

Please cite in your publications if it helps your research:

    

    @Article{Brumann2021,

        AUTHOR         = {Brumann, Christopher and Kukuk, Markus and Reinsberger, Claus},

        TITLE          = {Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash},

        JOURNAL        = {Sensors},

        VOLUME         = {21},

        YEAR           = {2021},

        NUMBER         = {13},

        ARTICLE-NUMBER = {4550},

        URL            = {https://www.mdpi.com/1424-8220/21/13/4550},

        ISSN           = {1424-8220},

        DOI            = {10.3390/s21134550}

    }

### License

This program is free software: you can redistribute it and/or modify

it under the terms of the GNU General Public License as published by

the Free Software Foundation, either version 3 of the License, or

(at your option) any later version.

This program is distributed in the hope that it will be useful,

but WITHOUT ANY WARRANTY; without even the implied warranty of

MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the

GNU General Public License for more details.

You should have received a copy of the GNU General Public License

along with this program.  If not, see .