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https://github.com/nuhmanpk/pyplatex

Simple , Scalable and Ready to use ANPR package for Automatic Number Plate Recognition
https://github.com/nuhmanpk/pyplatex

anpr anpr-system hacktoberfest license-plate-detection license-plate-reader license-plate-recognition ocr-recognition plate-recognition pypi ultralytics yolo

Last synced: about 2 months ago
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Simple , Scalable and Ready to use ANPR package for Automatic Number Plate Recognition

Host: GitHub
URL: https://github.com/nuhmanpk/pyplatex
Owner: nuhmanpk
License: mit
Created: 2024-07-13T09:59:11.000Z (5 months ago)
Default Branch: main
Last Pushed: 2024-09-08T07:53:01.000Z (4 months ago)
Last Synced: 2024-10-12T20:05:12.551Z (2 months ago)
Topics: anpr, anpr-system, hacktoberfest, license-plate-detection, license-plate-reader, license-plate-recognition, ocr-recognition, plate-recognition, pypi, ultralytics, yolo
Language: Python
Homepage: https://pypi.org/project/pyplatex/
Size: 10.7 MB
Stars: 4
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE

Awesome Lists containing this project

README

        # 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.

[![Downloads](https://static.pepy.tech/personalized-badge/pyplatex?period=total&units=abbreviation&left_color=grey&right_color=yellow&left_text=Total-Downloads)](https://pepy.tech/project/pyplatex)

[![Supported Versions](https://img.shields.io/pypi/pyversions/pyplatex.svg)](https://pypi.org/project/pyplatex)

![GitHub](https://img.shields.io/github/license/nuhmanpk/pyplatex)

![PyPI](https://img.shields.io/pypi/v/pyplatex)

![PyPI - Downloads](https://img.shields.io/pypi/dm/pyplatex)

[![Downloads](https://static.pepy.tech/personalized-badge/pyplatex?period=week&units=international_system&left_color=grey&right_color=brightgreen&left_text=Downloads/Week)](https://pepy.tech/project/pyplatex)

![PyPI - Format](https://img.shields.io/pypi/format/pyplatex)

## 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

```sh

pip install pyplatex

```

```py

from pyplatex import ANPR

anpr = ANPR()

det = await anpr.detect('./demo/plate-1.jpg')

print(det)

```

or

```py

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

![https://github.com/nuhmanpk/pyplatex](https://raw.githubusercontent.com/nuhmanpk/PyPlateX/main/demo/plate-1.jpg)

```

{

    '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:

- [x] Release a Inital Version

- [x] Add a plate detection model

- [x] Read and detect Plates

- [x] Format output

- [x] Integrate Cv2filters

- [x] Change Cofidence to a round number

- [x] Add a ocr Model

- [x] 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](https://github.com/nuhmanpk/pyplatex/issues). If you'd like to contribute to this project, you can create a pull request [here](https://github.com/nuhmanpk/pyplatex/pulls).**

**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](https://github.com/nuhmanpk/pyplatex)! Your support is appreciated. If you want to contribute further, you can also sponsor the project through [GitHub Sponsors](https://github.com/sponsors/nuhmanpk). Every contribution helps improve and maintain the project for the community.

Happy Coding 🚀 ...