tracktech
Real-time tracking of objects on multiple cameras.
Science Score: 49.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 3 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (10.6%) to scientific vocabulary
Keywords
Repository
Real-time tracking of objects on multiple cameras.
Basic Info
- Host: GitHub
- Owner: UU-tracktech
- License: gpl-3.0
- Language: Python
- Default Branch: master
- Homepage: https://uu-tracktech.github.io/tracktech/
- Size: 248 MB
Statistics
- Stars: 7
- Watchers: 3
- Forks: 4
- Open Issues: 0
- Releases: 1
Topics
Metadata Files
README.md
TrackTech: Real-time tracking of subjects and objects on multiple cameras
This project is part of the 2021 spring bachelor final project of the Bachelor of Computer Science at Utrecht University. The team that worked on the project consists of eleven students from the Bachelor of Computer Science and Bachelor of Game Technology. This project has been done for educational purposes. All code is open-source, and proper credit is given to respective parties.
GPU support
Updating/Installing drivers
Update the GPU drivers and restart the system for changes to take effect.
Optionally, use a different driver listed after running ubuntu-drivers devices
bash
sudo apt install nvidia-driver-460
sudo reboot
Installing the container toolkit
Add the distribution, update the package manager, install NVIDIA for Docker, and restart Docker for changes to take effect. For more information, look at the install guide
bash
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt update
sudo apt install -y nvidia-docker2
sudo systemctl restart docker
Acquire the GPU ID
According to this read the GPU UUID like GPU-a1b2c3d (just the first part) from
bash
nvidia-smi -a
Add the resource
Add the GPU UUID from the last step to the Docker engine configuration file typically at /etc/docker/daemon.json. Create the file if it does not exist yet.
json
{
"runtimes": {
"nvidia": {
"path": "/usr/bin/nvidia-container-runtime",
"runtimeArgs": []
}
},
"default-runtime": "nvidia",
"node-generic-resources": ["gpu=GPU-a1b2c3d"]
}
Pylint
We use Pylint for python code quality assurance.
Installation
Input following command terminal:
pip install pylint
Run
To run linting on the entire repository, run the following command from the root:
pylint CameraProcessor docs Interface ProcessorOrchestrator utility VideoForwarder --rcfile=.pylintrc --reports=n
Explanation
pylint <Subsystem> --rcfile=.pylintrc --reports=n
<Subsystem> is the Python module to run.
--rcfile is the linting specification used by Pylint.
--reports sets whether the full report should be displayed or not.
Our recommendation would be n since this only displays linting errors/warnings and the eventual score.
Constraints
Pylint needs an __init__.py file in the subsystem root to parse all folders to lint.
This run must be a subsystem since the root does not contain an __init__.py file.
Ignoring folders from linting
Some folders should be excluded from linting.
The exclusion could be for multiple reasons like,
the symlinked algorithms in the CameraProcessor folder or
the Python virtual environment folder.
Add folder name to ignore= in .pylintrc.