Introduction

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, for recognizing text in detected number plates.

Installation

You can install the package using pip:

pip install fastanpr

Requirements

• Windows:
  • Python >=3.8, <3.11
• Ubuntu, macOS, Raspbian:
  • Python >=3.8, <3.12

Usage

import cv2
from fastanpr import FastANPR

# Create an instance of FastANPR
fast_anpr = FastANPR()

# Load images (images should be of type numpy ndarray)
files = [...]
images = [cv2.cvtColor(cv2.imread(file), cv2.COLOR_BGR2RGB) for file in files]

# Run ANPR on the images
number_plates = await fast_anpr.run(images)

# Print out results
for file, plates in zip(files, number_plates):
    print(file)
    for plate in plates:
        print("Plate Attributes:")
        print("Detection bounding box:", plate.det_box)
        print("Detection confidence:", plate.det_conf)
        print("Recognition text:", plate.rec_text)
        print("Recognition polygon:", plate.rec_poly)
        print("Recognition confidence:", plate.rec_conf)
        print()
    print()

Class: FastANPR

Methods

run(images: List[np.ndarray] -> List[List[NumberPlate]]

Runs ANPR on a list of images and return a list of detected number plates.

• Parameters:

  • images (List[np.ndarray]): A list of images represented as numpy ndarray.

• Returns:

  • List[List[NumberPlate]]: A list of detected number plates for every image.

Class: NumberPlate

Attributes

• det_box (List[int]): Bounding box coordinates of detected number plate.
• det_conf (float): Confidence score of number plate detection.
• rec_text (str): Recognized plate number.
• rec_poly (List[List[int]]): Polygon coordinates of detected texts.
• rec_conf (float): Confidence score of recognition.

FastAPI

To start a FastAPI server locally from your console:

uvicorn api:app

Usage

import base64
import requests

# Step 1: Read the image file
image_path = 'tests/images/image001.jpg'
with open(image_path, 'rb') as image_file:
    image_data = image_file.read()

# Step 2: Convert the image to a base64 encoded string
base64_image_str = base64.b64encode(image_data).decode('utf-8')

# Prepare the data for the POST request (assuming the API expects JSON)
data = {'image': base64_image_str}

# Step 3: Send a POST request
response = requests.post(url='http://127.0.0.1:8000/recognise', json=data)

# Check the response
if response.status_code == 200:
    # 'number_plates': [
    #       {
    #           'det_box': [682, 414, 779, 455], 
    #           'det_conf': 0.29964497685432434, 
    #           'rec_poly': [[688, 420], [775, 420], [775, 451], [688, 451]], 
    #           'rec_text': 'BVH826', 
    #           'rec_conf': 0.940690815448761
    #       }
    # ]
    print(response.json())
else:
    print(f"Request failed with status code {response.status_code}.")

Docker

Hosting a FastAPI server can also be done by building a docker file as from console:

docker build -t fastanpr-app .
docker run -p 8000:8000 fastanpr-app

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.