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https://github.com/ankandrew/fast-alpr
Fast Automatic License Plate Recognition (ALPR) framework.
https://github.com/ankandrew/fast-alpr
alpr alpr-sdk anpr automatic-license-plate-recognition europe-plate-ocr fast-alpr hf-space license-plate-detection license-plate-reader license-plate-recognition plate-detection plate-recognition real-time
Last synced: 27 minutes ago
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Fast Automatic License Plate Recognition (ALPR) framework.
- Host: GitHub
- URL: https://github.com/ankandrew/fast-alpr
- Owner: ankandrew
- License: mit
- Created: 2020-09-14T03:17:02.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-12-09T02:37:42.000Z (13 days ago)
- Last Synced: 2024-12-17T10:12:52.824Z (5 days ago)
- Topics: alpr, alpr-sdk, anpr, automatic-license-plate-recognition, europe-plate-ocr, fast-alpr, hf-space, license-plate-detection, license-plate-reader, license-plate-recognition, plate-detection, plate-recognition, real-time
- Language: Python
- Homepage: https://ankandrew.github.io/fast-alpr/
- Size: 131 MB
- Stars: 63
- Watchers: 4
- Forks: 24
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: docs/contributing.md
- License: LICENSE
Awesome Lists containing this project
README
# FastALPR
[](https://github.com/ankandrew/fast-alpr/actions)
[](https://github.com/astral-sh/ruff)
[](https://github.com/pylint-dev/pylint)
[](http://mypy-lang.org/)
[](https://onnx.ai/)
[](https://huggingface.co/spaces/ankandrew/fast-alpr)
[](https://ankandrew.github.io/fast-alpr/)
[](https://www.python.org/)
[](https://github.com/ankandrew/fast-alpr/releases)
[](./LICENSE)
[](https://youtu.be/-TPJot7-HTs?t=652)
**FastALPR** is a high-performance, customizable Automatic License Plate Recognition (ALPR) system. We offer fast and
efficient ONNX models by default, but you can easily swap in your own models if needed.
For Optical Character Recognition (**OCR**), we use [fast-plate-ocr](https://github.com/ankandrew/fast-plate-ocr) by
default, and for **license plate detection**, we
use [open-image-models](https://github.com/ankandrew/open-image-models). However, you can integrate any OCR or detection
model of your choice.
## 📋 Table of Contents
* [✨ Features](#-features)
* [📦 Installation](#-installation)
* [🚀 Quick Start](#-quick-start)
* [🛠️ Customization and Flexibility](#-customization-and-flexibility)
* [📖 Documentation](#-documentation)
* [🤝 Contributing](#-contributing)
* [🙏 Acknowledgements](#-acknowledgements)
* [📫 Contact](#-contact)
## ✨ Features
- **High Accuracy**: Uses advanced models for precise license plate detection and OCR.
- **Customizable**: Easily switch out detection and OCR models.
- **Easy to Use**: Quick setup with a simple API.
- **Out-of-the-Box Models**: Includes ready-to-use detection and OCR models
- **Fast Performance**: Optimized with ONNX Runtime for speed.
## 📦 Installation
```bash
pip install fast-alpr
```
## 🚀 Quick Start
> [!TIP]
> Try `fast-plate-ocr` pre-trained models in [Hugging Spaces](https://huggingface.co/spaces/ankandrew/fast-alpr).
Here's how to get started with FastALPR:
```python
from fast_alpr import ALPR
# You can also initialize the ALPR with custom plate detection and OCR models.
alpr = ALPR(
detector_model="yolo-v9-t-384-license-plate-end2end",
ocr_model="global-plates-mobile-vit-v2-model",
)
# The "assets/test_image.png" can be found in repo root dit
alpr_results = alpr.predict("assets/test_image.png")
print(alpr_results)
```
Output:
You can also draw the predictions directly on the image:
```python
import cv2
from fast_alpr import ALPR
# Initialize the ALPR
alpr = ALPR(
detector_model="yolo-v9-t-384-license-plate-end2end",
ocr_model="global-plates-mobile-vit-v2-model",
)
# Load the image
image_path = "assets/test_image.png"
frame = cv2.imread(image_path)
# Draw predictions on the image
annotated_frame = alpr.draw_predictions(frame)
# Display the result
cv2.imshow("ALPR Result", annotated_frame)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
Output:
## 🛠️ Customization and Flexibility
FastALPR is designed to be flexible. You can customize the detector and OCR models according to your requirements.
You can very easily integrate with **Tesseract** OCR to leverage its capabilities:
```python
import re
from statistics import mean
import numpy as np
import pytesseract
from fast_alpr.alpr import ALPR, BaseOCR, OcrResult
class PytesseractOCR(BaseOCR):
def __init__(self) -> None:
"""
Init PytesseractOCR.
"""
def predict(self, cropped_plate: np.ndarray) -> OcrResult | None:
if cropped_plate is None:
return None
# You can change 'eng' to the appropriate language code as needed
data = pytesseract.image_to_data(
cropped_plate,
lang="eng",
config="--oem 3 --psm 6",
output_type=pytesseract.Output.DICT,
)
plate_text = " ".join(data["text"]).strip()
plate_text = re.sub(r"[^A-Za-z0-9]", "", plate_text)
avg_confidence = mean(conf for conf in data["conf"] if conf > 0) / 100.0
return OcrResult(text=plate_text, confidence=avg_confidence)
alpr = ALPR(detector_model="yolo-v9-t-384-license-plate-end2end", ocr=PytesseractOCR())
alpr_results = alpr.predict("assets/test_image.png")
print(alpr_results)
```
> [!TIP]
> See the [docs](https://ankandrew.github.io/fast-alpr/) for more examples!
## 📖 Documentation
Comprehensive documentation is available [here](https://ankandrew.github.io/fast-alpr/), including detailed API
references and additional examples.
## 🤝 Contributing
Contributions to the repo are greatly appreciated. Whether it's bug fixes, feature enhancements, or new models,
your contributions are warmly welcomed.
To start contributing or to begin development, you can follow these steps:
1. Clone repo
```shell
git clone https://github.com/ankandrew/fast-alpr.git
```
2. Install all dependencies using [Poetry](https://python-poetry.org/docs/#installation):
```shell
poetry install --all-extras
```
3. To ensure your changes pass linting and tests before submitting a PR:
```shell
make checks
```
## 🙏 Acknowledgements
- [fast-plate-ocr](https://github.com/ankandrew/fast-plate-ocr) for default **OCR** models.
- [open-image-models](https://github.com/ankandrew/open-image-models) for default plate **detection** models.
## 📫 Contact
For questions or suggestions, feel free to open an issue or reach out through social networks.