Accurate, fast and easy to use API for license plate recognition. Trained on data from over 100 countries and regions around the world. The core of our license plate detection system is based on state of the art deep neural networks architectures.
Integrate with our ALPR API in a few lines of code. Get an easy to use JSON response with the number plate value of vehicles and the bounding boxes.
- Reading License Plates from Images
- Number Plate Recognition on a Video or Camera Stream
- Automatic Image Transfer
- Code Samples
Get your API key from Plate Recognizer. Replace MY_API_KEY with your API key and run the command below.
# Getting started!
git clone https://github.com/marcbelmont/deep-license-plate-recognition.git
cd deep-license-plate-recognition
pip install requests pillow
python plate_recognition.py --api-key MY_API_KEY /path/to/vehicle.jpg
The result includes the bounding boxes (rectangle around object) and the plate value for each plate. View the details of the results on our documentation.
[
{
"version": 1,
"results": [
{
"box": {
"xmin": 85,
"ymin": 85,
"ymax": 211,
"xmax": 331
},
"plate": "ABC123",
"score": 0.904,
"dscore": 0.92
}
],
"filename": "car.jpg"
}
]You can match the license plate patterns of a specific region.
python plate_recognition.py --api-key MY_API_KEY --regions fr --regions it /path/to/car.jpg
You can also run the license plate reader on many files at once. To run the script on all the images of a directory, use:
python plate_recognition.py --api-key MY_API_KEY /path/to/car1.jpg /path/to/car2.jpg /path/to/trucks*.jpg
To use a locally hosted sdk, pass the url to the docker container as follows:
python plate_recognition.py --sdk-url http://localhost:8080 /path/to/vehicle.jpg
The script number_plate_redaction.py differs from the default prediction script because it also detects plates that are barely readable and/or very small. It returns the bounding boxes of all the license plates. They can be used to blur or mask the license plates.
In addition, it can split the image in smaller images to better deal with high resolution images. This will use 3 lookups instead of just 1. To use this options add --split-image.
The option --save-blurred lets you save blurred images. They are saved to a new file with _blurred suffix.
The option --ignore-regexp lets you specify a regex of plates to ignore from blur. This option can be specified multiple times.
The option --ignore-no-bb lets you ignore recognitions without a vehicle bounding box from blur.
python number_plate_redaction.py --help
python number_plate_redaction.py --api-key API_KEY vehicels.jpg
python number_plate_redaction.py --sdk-url http://localhost:8080 --split-image vehicels.jpg
python number_plate_redaction.py --api-key 77c### 58C5A57_14965463.jpg --save-blurred --ignore-regexp ^58c5a57$ --ignore-regexp ^[0-9][0-9]c5a57$
To process images from the FTP server run ftp_processor script:
python ftp_processor.py --api-key MY_API_KEY --ftp-host hostname --ftp-user user --ftp-password password --folder /path/to/server_folder
To remove images from FTP server after processing, add the argument --delete.
Automatic Image Transfer is a command line tool that runs our ALPR Engine. It monitors a folder and automatically process images (Cloud or SDK) as they are added. Once processed, images are moved to an archive directory. It can also forward the results to our parking management service Parkpow.
To get started: python transfer.py --help
See our sample projects to easily get started with the API.
- Example program in C++.
- Example program in C#.
- Example program in Java.
- Android App. It lets you take a picture and send it to our API.
- View how to integrate with other languages in our documentation.
- A Node-RED node for license plate recognition.
Have questions? Let us know how we can help.
Provided by Plate Recognizer, a subsidiary of ParkPow.