travisddavies / number_plate_recognition

That detects and extracts number plates from images and videos

Number Plate Recognition

Introduction

This project aims to accurately detect number plates on cars and extract the number plate from an image/video.

Note

This model is specific to Chinese number plates. This model will likely not work well on number plates from other countries.

In the Works

• Deploying a web interface onto a raspberry pi

How to Use

Clone the Repo and Install Dependencies

git clone [e-mail]:travisddavies/number_plate_recognition.git

cd number_plate_recognition

pip install -r requirements.txt

Run the software

For image recognition:

python3 image_recognition.py -c <au or ch> -i <input image> -o <saved image>

For video recognition:

python3 video_recognition.py -c <au or ch> -i <input video> -o <saved video>

Example:

python3 video_recognition.py -i samples/IMG_0467.MOV -o result.MOV

Deployment onto Raspberry Pis

A yaml script has been written to deploy this model onto a Raspi OS Bookworm Raspberry Pi. This deployment will run a script that will deploy a live recording model that accesses your Pi Camera and sends the data of passing number plates to a CouchDB database.

To update the information for your particular Raspberry Pi, change the information in hosts.ini to your information. The username to access the database is "admin" and the password is "password".

Note:

A bug exists due a deprecation in the paddleocr code in PaddleOCR/ppocr/postprocess/db_postprocess.py at line 188 to 191. To fix this, simply change the "np.int" part of each line to "np.int64" (I know this doesn't sound very professional but unfortunately deployment of PaddleOCR on a Raspberry Pi was not easy).

To deploy the model, simple run the following:

ansible-playbook pi_deployment.yaml

This should deploy all dependencies, open ports, deploy the docker containers, set up venvs, etc to run the model on a Raspberry Pi.