{"id":21970208,"url":"https://github.com/pandede/wpodnet-pytorch","last_synced_at":"2025-04-28T11:16:01.102Z","repository":{"id":166909268,"uuid":"475487051","full_name":"Pandede/WPODNet-Pytorch","owner":"Pandede","description":"The implementation of ECCV 2018 paper \"License Plate Detection and Recognition in Unconstrained Scenarios\" in 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WPODNet: Build with Torch\n## Introduction\nThis repository implements the proposed method from **ECCV 2018 paper [\"License Plate Detection and Recognition in Unconstrained Scenarios\"](https://openaccess.thecvf.com/content_ECCV_2018/papers/Sergio_Silva_License_Plate_Detection_ECCV_2018_paper.pdf)** in Torch.\n\nThe model in Keras is built by the essay author, see [sergiomsilva/alpr-unconstrained](https://github.com/sergiomsilva/alpr-unconstrained).\n\n\n\u003ctable\u003e\n    \u003ctr\u003e\n        \u003ctd\u003e Example \u003c/td\u003e\n        \u003ctd\u003e \u003cimg src=\"./docs/sample/original/03009.jpg\" width=\"300px\"\u003e\u003c/td\u003e\n        \u003ctd\u003e \u003cimg src=\"./docs/sample/original/03016.jpg\" width=\"300px\"\u003e\u003c/td\u003e\n        \u003ctd\u003e \u003cimg src=\"./docs/sample/original/03025.jpg\" width=\"300px\"\u003e\u003c/td\u003e\n    \u003c/tr\u003e \n    \u003ctr\u003e\n        \u003ctd\u003e Annotated \u003c/td\u003e\n        \u003ctd\u003e\u003cimg src=\"./docs/sample/annotated/03009.jpg\" width=\"300px\"\u003e\u003c/td\u003e\n        \u003ctd\u003e\u003cimg src=\"./docs/sample/annotated/03016.jpg\" width=\"300px\"\u003e\u003c/td\u003e\n        \u003ctd\u003e\u003cimg src=\"./docs/sample/annotated/03025.jpg\" width=\"300px\"\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n        \u003ctd\u003e Warp perspective \u003c/td\u003e\n        \u003ctd\u003e\u003cimg src=\"./docs/sample/warped/03009.jpg\" width=\"300px\"\u003e\u003c/td\u003e\n        \u003ctd\u003e\u003cimg src=\"./docs/sample/warped/03016.jpg\" width=\"300px\"\u003e\u003c/td\u003e\n        \u003ctd\u003e\u003cimg src=\"./docs/sample/warped/03025.jpg\" width=\"300px\"\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n        \u003ctd\u003e Confidence \u003c/td\u003e\n        \u003ctd\u003e 0.9841 \u003c/td\u003e\n        \u003ctd\u003e 0.9945 \u003c/td\u003e\n        \u003ctd\u003e 0.9979 \u003c/td\u003e\n    \u003c/tr\u003e\n\u003c/table\u003e\n\n## Quick Run\n1. Clone this repository\n    ```bash\n    git clone https://github.com/Pandede/WPODNet-Pytorch.git\n    ```\n2. Install [PyTorch](https://pytorch.org) depends on your environment.\n3. Install packages in `requirements.txt`\n    ```bash\n    pip3 install -r requirements.txt\n    ```\n4. Download the pretrained weight `wpodnet.pth` from [here](https://github.com/Pandede/WPODNet-Pytorch/releases/download/1.0.0/wpodnet.pth)\n5. Predict with an image\n    ```bash\n    python3 predict.py  docs/sample/original/03009.jpg          # The path to the an image\n                        # docs/sample/original                  # OR the path to the directory with bulk of images\n                        -w weights/wpodnet.pth                  # The path to the weight\n                        --save-annotated docs/sample/annotated  # The directory to save the annotated images\n                        --save-warped docs/sample/warped        # The directory to save the warped images\n    ```\n\n## Future works\n- [x] Inference with GPU\n- [x] Inference with bulk of images\n- [ ] Inference with video\n- [ ] Introduce training procedure\n- [x] The matrix multiplication seems weird in function `postprocess`, may improve the computation.\n\n## Citation\n```bibtex\n@inproceedings{silva2018license,\n  title={License plate detection and recognition in unconstrained scenarios},\n  author={Silva, Sergio Montazzolli and Jung, Cl{\\'a}udio Rosito},\n  booktitle={Proceedings of the European conference on computer vision (ECCV)},\n  pages={580--596},\n  year={2018}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpandede%2Fwpodnet-pytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpandede%2Fwpodnet-pytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpandede%2Fwpodnet-pytorch/lists"}