A fast automatic number-plate recognition (ANPR) library
Project description
FastANPR
A fast automatic number-plate recognition (ANPR) library. This package employs YOLOv8, a lightweight model, for detection, and Paddle OCR, a lightweight optical character recognition (OCR) library.
Installation
pip install fastanpr
Usage
import cv2
import asyncio
from fastanpr import FastANPR
# To use cuda, replace 'cpu' with 'cuda' or device id, e.g., '0'. Default is set to 'cpu'.
fast_anpr = FastANPR(device='cpu')
async def show_anpr_result(file: str) -> None:
image = cv2.cvtColor(cv2.imread(file), cv2.COLOR_BGR2RGB)
anpr_result = await fast_anpr.run(image)
print(anpr_result)
files = [...]
_ = await asyncio.gather(*[show_anpr_result(file) for file in files])
Licence
This project incorporates the YOLOv8 model from Ultralytics, which is licensed under the AGPL-3.0 license. As such, this project is also distributed under the GNU Affero General Public License v3.0 (AGPL-3.0) to comply with the licensing requirements.
For more details on the YOLOv8 model and its license, please visit the Ultralytics GitHub repository.
Contributing
We warmly welcome contributions from the community! If you're interested in contributing to this project, please start by reading our CONTRIBUTING.md guide.
Whether you're looking to submit a bug report, propose a new feature, or contribute code, we're excited to see what you have to offer. Please don't hesitate to reach out by opening an issue or submitting a pull request.
Thank you for considering contributing to our project. Your support helps us make the software better for everyone.
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