platerec

platerec is a lightweight package for reading license plates using an ONNX model. It is designed to be part of a pipeline for detecting, cropping, and reading license plates. The underlying model is a mobilenetv2 as encoder and a light gpt for decoder. The training data comprises primarily Brazilian license plates, sourced from internet images, also synthetic data generated in the same font with transforms. The model repository can be found here.

Installation

To install the required dependencies, use the following command:

For cpu

pip install "platerec[cpu]"

For cuda 11.X

pip install "platerec[gpu]"

For cuda 12.X

pip install "platerec[gpu]" --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/

Usage

Command Line Interface

You can use the command line interface to detect license plates in an image:

platerec image_path [--encoder_path ENCODER_PATH] [--decoder_path DECODER_PATH] [--return_types RETURN_TYPE] [--providers PROVIDERS] [--platedet]

Arguments

• image_path: Path to the input image. Could be more than one image.
• --encoder_path: Path to the ONNX encoder model (default: artifacts/encoder.onnx).
• --decoder_path: Path to the ONNX decoder model (default: artifacts/decoder.onnx).
• --return_type: Output formats (choices: word, char). Word return the plate text and confidence detected, char return the plate chars detected with confidences for each char.
• --providers: ONNX Runtime providers (default: CPUExecutionProvider).
• --platedet: Use platedet to detect plates first. Will first detect plates and then read them.

Example

To just read an already cropped image:

python3 platerec/cli.py examples/1.jpg --return_type word

To detect license plates and read them:

python3 platerec/cli.py examples/1.jpg --return_type word --platedet

Using in Code

To just read an already cropped image:

from PIL import Image
from platerec import Platerec

platerec = Platerec()
image = Image.open('examples/1.jpg')
pred = platerec.read(image)

pred will be something like:

{'word': 'ZZZ1Z11', 'confidence': 0.98828125}

To detect license plates and read them:

from PIL import Image
from platerec import Platerec

platerec = Platerec()
image = Image.open('examples/1.jpg')
crops = platerec.detect_read(image)
for idx, crop in enumerate(crops['pil']['images']):
    crop.save(f'{idx}.jpg')

pred will be something like:

{'images': [<PIL.Image.Image image mode=RGB size=105x40 at 0x7FEE25B67AD0>], 'confidences': array([0.72949219]), 'words': ['AAA1A11'], 'boxes': array([[ 393, 1188,  498, 1228]], dtype=int32), 'words_confidences': [0.95263671875]}

If you want to use CUDA:

from platerec import Platerec

platerec = Platerec(providers=["CUDAExecutionProvider"])

Check all execution providers here.