pyplatex 0.0.5

A high-performance, scalable, and ready-to-use Automatic Number Plate Recognition (ANPR) package.

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.

Happy Coding 🚀 ...