WPODNet: Build with Torch

Introduction

This repository implements the proposed method from ECCV 2018 paper "License Plate Detection and Recognition in Unconstrained Scenarios" in Torch.

The model in Keras is built by the essay author, see sergiomsilva/alpr-unconstrained.

Example
Annotated
Warp perspective
Confidence	0.9841	0.9945	0.9979

Quick Run

1. Clone this repository

   git clone https://github.com/Pandede/WPODNet-Pytorch.git
   

2. Install PyTorch depends on your environment.
3. Install packages in requirements.txt

   pip3 install -r requirements.txt
   

4. Download the pretrained weight wpodnet.pth from here
5. Predict with an image

   python3 predict.py  docs/sample/original/03009.jpg          # The path to the an image
                       # docs/sample/original                  # OR the path to the directory with bulk of images
                       -w weights/wpodnet.pth                  # The path to the weight
                       --save-annotated docs/sample/annotated  # The directory to save the annotated images
                       --save-warped docs/sample/warped        # The directory to save the warped images
   

Future works

• Inference with GPU
• Inference with bulk of images
• Inference with video
• Introduce training procedure
• The matrix multiplication seems weird in function postprocess, may improve the computation.

Citation

@inproceedings{silva2018license,
  title={License plate detection and recognition in unconstrained scenarios},
  author={Silva, Sergio Montazzolli and Jung, Cl{\'a}udio Rosito},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={580--596},
  year={2018}
}