Metadata-Version: 2.1
Name: basicsr
Version: 1.3.1
Summary: Open Source Image and Video Super-Resolution Toolbox
Home-page: https://github.com/xinntao/BasicSR
Author: Xintao Wang
Author-email: xintao.wang@outlook.com
License: Apache License 2.0
Description: # :rocket: BasicSR
        
        [English](README.md) **|** [简体中文](README_CN.md) &emsp; [GitHub](https://github.com/xinntao/BasicSR) **|** [Gitee码云](https://gitee.com/xinntao/BasicSR)
        
        <a href="https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" height="18" alt="google colab logo"></a> Google Colab: [GitHub Link](colab) **|** [Google Drive Link](https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing) <br>
        :m: [Model Zoo](docs/ModelZoo.md) :arrow_double_down: Google Drive: [Pretrained Models](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing) **|** [Reproduced Experiments](https://drive.google.com/drive/folders/1XN4WXKJ53KQ0Cu0Yv-uCt8DZWq6uufaP?usp=sharing)
        :arrow_double_down: 百度网盘: [预训练模型](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g) **|** [复现实验](https://pan.baidu.com/s/1UElD6q8sVAgn_cxeBDOlvQ) <br>
        :file_folder: [Datasets](docs/DatasetPreparation.md) :arrow_double_down: [Google Drive](https://drive.google.com/drive/folders/1gt5eT293esqY0yr1Anbm36EdnxWW_5oH?usp=sharing) :arrow_double_down: [百度网盘](https://pan.baidu.com/s/1AZDcEAFwwc1OC3KCd7EDnQ) (提取码:basr)<br>
        :chart_with_upwards_trend: [Training curves in wandb](https://app.wandb.ai/xintao/basicsr) <br>
        :computer: [Commands for training and testing](docs/TrainTest.md) <br>
        :zap: [HOWTOs](#zap-howtos)
        
        ---
        
        BasicSR (**Basic** **S**uper **R**estoration) is an open source **image and video restoration** toolbox based on PyTorch, such as super-resolution, denoise, deblurring, JPEG artifacts removal, *etc*.<br>
        <sub>([ESRGAN](https://github.com/xinntao/ESRGAN), [EDVR](https://github.com/xinntao/EDVR), [DNI](https://github.com/xinntao/DNI), [SFTGAN](https://github.com/xinntao/SFTGAN))</sub>
        <sub>([HandyView](https://github.com/xinntao/HandyView), [HandyFigure](https://github.com/xinntao/HandyFigure), [HandyCrawler](https://github.com/xinntao/HandyCrawler), [HandyWriting](https://github.com/xinntao/HandyWriting))</sub>
        
        ## :sparkles: New Features
        
        - Nov 29, 2020. Add **ESRGAN** and **DFDNet** [colab demo](colab).
        - Sep 8, 2020. Add **blind face restoration** inference codes: [DFDNet](https://github.com/csxmli2016/DFDNet).
        - Aug 27, 2020. Add **StyleGAN2 training and testing** codes: [StyleGAN2](https://github.com/rosinality/stylegan2-pytorch).
        
        <details>
          <summary>More</summary>
        <ul>
          <li> Sep 8, 2020. Add <b>blind face restoration</b> inference codes: <b>DFDNet</b>. <br> <i><font color="#DCDCDC">ECCV20: Blind Face Restoration via Deep Multi-scale Component Dictionaries</font></i> <br> <i><font color="#DCDCDC">Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, Wangmeng Zuo and Lei Zhang</font></i> </li>
          <li> Aug 27, 2020. Add <b>StyleGAN2</b> training and testing codes. <br> <i><font color="#DCDCDC">CVPR20: Analyzing and Improving the Image Quality of StyleGAN</font></i> <br> <i><font color="#DCDCDC">Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen and Timo Aila</font></i> </li>
          <li>Aug 19, 2020. A <b>brand-new</b> BasicSR v1.0.0 online.</li>
        </ul>
        </details>
        
        ## :zap: HOWTOs
        
        We provides simple pipelines to train/test/inference models for quick start.
        These pipelines/commands cannot cover all the cases and more details are in the following sections.
        
        | GAN |  |  |  | | |
        | :--- | :---:        |     :---:      | :--- | :---:        |     :---:      |
        | StyleGAN2   | [Train](docs/HOWTOs.md#How-to-train-StyleGAN2) | [Inference](docs/HOWTOs.md#How-to-inference-StyleGAN2) | | | |
        | **Face Restoration** |  |  |  | | |
        | DFDNet | - | [Inference](docs/HOWTOs.md#How-to-inference-DFDNet) | | | |
        | **Super Resolution** |  |  |  | | |
        | ESRGAN | *TODO* | *TODO* | SRGAN | *TODO* | *TODO*|
        | EDSR | *TODO* | *TODO* | SRResNet | *TODO* | *TODO*|
        | RCAN | *TODO* | *TODO* |  |  | |
        | EDVR | *TODO* | *TODO* | DUF | - | *TODO* |
        | BasicVSR | *TODO* | *TODO* | TOF | - | *TODO* |
        | **Deblurring** |  |  |  | | |
        | DeblurGANv2 | - | *TODO* |  | | |
        | **Denoise** |  |  |  | | |
        | RIDNet | - | *TODO* | CBDNet | - | *TODO*|
        
        ## :wrench: Dependencies and Installation
        
        - Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
        - [PyTorch >= 1.3](https://pytorch.org/)
        - NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
        
        ### Pip install
        
        ```bash
        pip install basicsr
        ```
        
        - pip installation does not compile cuda extensions.
        - If you want to use cuda extensions, set environment variable `BASICSR_JIT=True`. Note that every time you run the model, it will compile the extensions just time.
          - Example: StyleGAN2 inference colab.
        
        ### Git clone and compile
        
        1. Clone repo
        
            ```bash
            git clone https://github.com/xinntao/BasicSR.git
            ```
        
        1. Install dependent packages
        
            ```bash
            cd BasicSR
            pip install -r requirements.txt
            ```
        
        1. Install BasicSR
        
            Please run the following commands in the **BasicSR root path** to install BasicSR:<br>
            (Make sure that your GCC version: gcc >= 5) <br>
            If you do need the cuda extensions: <br>
            &emsp;[*dcn* for EDVR](basicsr/ops)<br>
            &emsp;[*upfirdn2d* and *fused_act* for StyleGAN2](basicsr/ops)<br>
            please add `--cuda_ext` when installing.<br>
            If you use the EDVR and StyleGAN2 model, the above cuda extensions are necessary.
        
            ```bash
            python setup.py develop --cuda_ext
            ```
        
            Otherwise, install without compiling cuda extensions
        
            ```bash
            python setup.py develop
            ```
        
            You may also want to specify the CUDA paths:
        
              ```bash
              CUDA_HOME=/usr/local/cuda \
              CUDNN_INCLUDE_DIR=/usr/local/cuda \
              CUDNN_LIB_DIR=/usr/local/cuda \
              python setup.py develop
              ```
        
        Note that BasicSR is only tested in Ubuntu, and may be not suitable for Windows. You may try [Windows WSL with CUDA supports](https://docs.microsoft.com/en-us/windows/win32/direct3d12/gpu-cuda-in-wsl) :-) (It is now only available for insider build with Fast ring).
        
        ## :hourglass_flowing_sand: TODO List
        
        Please see [project boards](https://github.com/xinntao/BasicSR/projects).
        
        ## :turtle: Dataset Preparation
        
        - Please refer to **[DatasetPreparation.md](docs/DatasetPreparation.md)** for more details.
        - The descriptions of currently supported datasets (`torch.utils.data.Dataset` classes) are in [Datasets.md](docs/Datasets.md).
        
        ## :computer: Train and Test
        
        - **Training and testing commands**: Please see **[TrainTest.md](docs/TrainTest.md)** for the basic usage.
        - **Options/Configs**: Please refer to [Config.md](docs/Config.md).
        - **Logging**: Please refer to [Logging.md](docs/Logging.md).
        
        ## :european_castle: Model Zoo and Baselines
        
        - The descriptions of currently supported models are in [Models.md](docs/Models.md).
        - **Pre-trained models and log examples** are available in **[ModelZoo.md](docs/ModelZoo.md)**.
        - We also provide **training curves** in [wandb](https://app.wandb.ai/xintao/basicsr):
        
        <p align="center">
        <a href="https://app.wandb.ai/xintao/basicsr" target="_blank">
           <img src="./assets/wandb.jpg" height="280">
        </a></p>
        
        ## :memo: Codebase Designs and Conventions
        
        Please see [DesignConvention.md](docs/DesignConvention.md) for the designs and conventions of the BasicSR codebase.<br>
        The figure below shows the overall framework. More descriptions for each component: <br>
        **[Datasets.md](docs/Datasets.md)**&emsp;|&emsp;**[Models.md](docs/Models.md)**&emsp;|&emsp;**[Config.md](Config.md)**&emsp;|&emsp;**[Logging.md](docs/Logging.md)**
        
        ![overall_structure](./assets/overall_structure.png)
        
        ## :scroll: License and Acknowledgement
        
        This project is released under the Apache 2.0 license.<br>
        More details about **license** and **acknowledgement** are in [LICENSE](LICENSE/README.md).
        
        ## :earth_asia: Citations
        
        If BasicSR helps your research or work, please consider citing BasicSR.<br>
        The following is a BibTeX reference. The BibTeX entry requires the `url` LaTeX package.
        
        ``` latex
        @misc{wang2020basicsr,
          author =       {Xintao Wang and Ke Yu and Kelvin C.K. Chan and
                          Chao Dong and Chen Change Loy},
          title =        {BasicSR},
          howpublished = {\url{https://github.com/xinntao/BasicSR}},
          year =         {2020}
        }
        ```
        
        > Xintao Wang, Ke Yu, Kelvin C.K. Chan, Chao Dong and Chen Change Loy. BasicSR. https://github.com/xinntao/BasicSR, 2020.
        
        ## :e-mail: Contact
        
        If you have any question, please email `xintao.wang@outlook.com`.
        
Keywords: computer vision,restoration,super resolution
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
