The implementation of ECCV 2018 paper "License Plate Detection and Recognition in Unconstrained Scenarios" in PyTorch
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Project description
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 |  |
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| Annotated |  |
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| Warp perspective |  |
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| Confidence | 0.9841 | 0.9945 | 0.9979 |
Quick Run
- Clone this repository
git clone https://github.com/Pandede/WPODNet-Pytorch.git
- Install PyTorch depends on your environment.
- Install packages in
requirements.txtpip3 install -r requirements.txt
- Download the pretrained weight
wpodnet.pthfrom here - 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}
}
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