A simple tool for Computer Vision Inference
Reason this release was yanked:
Deprecated version of PeekingDuck
Project description
PeekingDuck
PeekingDuck is a python framework dealing with model inference.
This toolkit provides state of the art computer vision models to make real time inference easy: social distancing, people counting, license plate recognition etc.. Customisability of the node modules ensures flexibility for unique usecases. Attached API server enables real time access to data analysis of the model pipe.
Features
Models
- Yolov4
- EfficientDet
- Blazepose
Use Cases
- Social Distancing
- People Counting
- License Plate Recognition
- Vehicle Counting
- Privacy Filter
How to Use for Developers (Temporary)
git clone
this repocd PeekingDuck
pip install .
- Choose the required nodes in run_config.yml
- To run:
peekingduck run --config_path <path_to_config>
- To create a new node, check out CONTRIBUTING.md
Installation (WIP)
Use python package manager (pip) to install PeekingDuck
pip install pkdk
Usage (WIP)
Start New Projects with init
For new projects, we suggest to use the PeekingDuck cookiecutter starter:
> mkdir <new_project>
> cd <new_project>
> peekingduck init
get-configs
Unless specified, all nodes in peekingduck
will use the default configs for every node. To view and change these configs, you can use:
> peekingduck get-configs
run
You can run the PeekingDuck runner via the following command:
peekingduck run --config_path <path_to_config>
If config_path
is not provided, this command will look for run_config.yml
in the current directory.
For specific information on how to use peekingduck-cli, you can use peekingduck --help
.
Contributing (WIP)
We welcome contributions to the repository through pull requests. When making contributions, first create an issue to describe the problem and the intended changes.
Please note that we have a code of conduct for contributions to the repository.
License
Licensed under Apache License, Version 2.0
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