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".
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Create virtual environment
python3 -m venv /path/to/new/virtual/environment
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Activate the environment
source /path/to/new/virtual/environment/bin/activate
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Install requirements from
requirements.txt
pip install -r requirements.txt
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Create a softlink for the dataset folder
ln -s ~/Projects/released-datasets/ ./dataset
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Create a clean output folder for results.
mkdir output
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Run the application inside your virtual environment and provide dataset description file and your output folder
python run_toolkit.py --description=<DATASET_FOLDER>/dataset_description.json --output=./output
Parameter | Description |
---|---|
--debug | Prints debugging log output during evaluation |
--render | Enables rendering while processing (very slow) |
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}
}
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 https://www.gnu.org/licenses/.