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
- Number Plate Recognition on a Live 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 following command:
pip install requests
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. The JSON output can easily be consumed by your application.
[
{
"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
You can also blur the license plate with the following parameters: --blur-amount AMOUNT. AMOUNT is a number between 0 and 50. you should also supply a directory to save the blurred images. use:
pip install pillow
python plate_recognition.py --api-key MY_API_KEY --blur-amount 4 --blur-dir /path/to/save/blurred/images /path/to/vehicle.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
To do ANPR on videos, you will also need to install OpenCV. Here are the installation instructions. Those 2 python packages are also needed:
pip install requests
pip install pillow
The script alpr_video.py lets you perform license plate recognition on a video file. It looks at each frame of the video and reads license plates. If you are only interested in one part of the video, you may use the --start and --end arguments. Additionally, you can use the --skip argument to read 1 in every N frames. It will speed up the analysis of large videos. Here's an example:
python alpr_video.py --api MY_API_KEY --start 900 --end 2000 --skip 3 /path/to/cars.mp4
OpenCV is also capable of reading live video streams. See this page for an example.
Follow the instructions above to install OpenCV including the installation of dependencies requests and pillow. Then run the script as shown below.
Usage:
python anpr_campera_stream.py --help
For example:
python anpr_camera_stream.py --show-image --camera rtsp://192.168.x.x:5554/camera --api-key MY_TOKEN --regions fr --output /path/to/save.csv
CSV output example:
date,license_plate,score,dscore,vehicle_type
12/19/19 05:33:10,nwk652,0.675,0.704,Car
12/19/19 05:33:12,nmk669,0.625,0.823,Car
For testing purposes when you don't have a camera, you can install CamOn Live Streaming app from the Google Play Store and use its RTSP url to stream your mobile phone's camera.
Monitor a folder and automatically process images (Cloud or SDK) as they are added. 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.
Have questions? Let us know how we can help.
Provided by Plate Recognizer, a subsidiary of ParkPow.