PyPlateX
Simple , Scalable and Ready to use ANPR package for Automatic Number Plate Recognition
MIT License
PyPlateX
High-Performance Scalable ANPR Package: Ready-to-Use, Simple, and Efficient License Plate Recognition
Unlock top-tier accuracy and scalability with cutting-edge ANPR solution in 3 line of code. Designed for seamless integration and ease of use, it delivers robust performance and reliability for all your license plate recognition needs.
Simple ready to use ANPR
Note: The ANPR.detect function is asynchronous, so ensure you use the await keyword when calling it within an async function.
Install from pypi.org
pip install pyplatex
from pyplatex import ANPR
anpr = ANPR()
det = await anpr.detect('./demo/plate-1.jpg')
print(det)
or
from pyplatex import ANPR
import asyncio
async def main():
anpr = ANPR()
plates = await anpr.detect('./demo/plate-1.jpg')
print(plates)
# Run the async main function
asyncio.run(main())
the output would be like
{
'is_plate': True,
'is_plate_confidence': 0.78,
'plate_number': 'MUN389',
'plate_number_confidence': 1.0
}
Args for anpr.detect()
| Parameter | Default Value | Description |
|---|---|---|
image_path |
None | Path to the image file to be processed. |
max_detections |
1 | Maximum number of license plates to detect in the image. |
confidence |
0.6 | Confidence threshold for detecting a license plate. Only detections with confidence above this value will be considered. |
save_image |
False | If True, the detected plate image will be saved to disk. |
padding |
5 | Padding around the detected license plate when saving the image. |
folder_name |
None | Directory name where the detected images will be saved. If save_image is True, this folder will be created if it does not exist. |
use_ocr |
True | If True, Optical Character Recognition (OCR) will be performed on the detected license plates. |
return_tensor |
False | If True, returns the image tensor of the detected license plates. |
verbose |
True | If True, logs detailed information during processing. |
Dev TODO:
- Release a Inital Version
- Add a plate detection model
- Read and detect Plates
- Format output
- Integrate Cv2filters
- Change Cofidence to a round number
- Add a ocr Model
- Release a Initial Version
- Add a option to accept image as Tensor / numpy array
- Add auto filters tag
This is a pre-release version; there might be some bugs. If you encounter any issues or performance-related problems, please report them here. If you'd like to contribute to this project, you can create a pull request here.
Warning: Use this pre-release with caution as it may still have unresolved issues.
If you like this project, please consider giving it a star on Github! Your support is appreciated. If you want to contribute further, you can also sponsor the project through GitHub Sponsors. Every contribution helps improve and maintain the project for the community.
Happy Coding 🚀 ...
