pyplatex 0.0.4

A scalable and versatile ANPR package leveraging YOLO for detection and multiple OCR options to accurately recognize license plates.

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
}

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.

Happy Coding 🚀 ...