Metadata-Version: 2.1
Name: textbsr
Version: 0.0.24
Summary: a simple version for blind text image super-resolution (current version is only for English and Chinese)
Home-page: https://github.com/csxmli2016/MARCONet
Author: Xiaoming Li
Author-email: csxmli@gmail.com
License: S-Lab License 1.0
Description: ## This is a simple text image super-resolution package.
        
        More details can be found in our Project Page: https://github.com/csxmli2016/textbsr
        > It can post-process the text region with a simple commond, i.e.,
        ``` 
        textbsr -i [LR_TEXT_PATH] -b [BACKGROUND_SR_PATH]
        ```
        
        
        ## Quick Start
        ### Dependencies and Installation
        - numpy
        - opencv-python
        - torch>=1.8.1
        - torchvision>=0.9
        
        ``` 
        # Install with pip
        pip install textbsr
        ```
        
        
        ### Basic Usage
        
        ```
        # On the terminal command
        textbsr -i [LR_TEXT_PATH]
        ```
        or
        ```
        # On the python environment
        from textbsr import textbsr
        textbsr.bsr(input_path='./testsets/LQs')
        ```
        
        Parameter details:
        
        | parameter name | default | description  |
        | :-----  | :-----:  | :-----  |
        | <span style="white-space:nowrap">-i, --input_path </span>| - | The lr text image path. It can be a full image or a text region only |
        | <span style="white-space:nowrap">-b, --bg_path</span> | None | The background sr path from other methods. If None, we only super-resolve the text region.|
        | <span style="white-space:nowrap">-o, --output_path</span> | None | The save path for text sr result. If None, we save the results on the same path with [input_path]_TIMESTAMP|
        | <span style="white-space:nowrap">-a, --aligned </span>| False | action='store_true'. If True, the input text image contains only text region. If False, we use CnSTD to detect and restore the text region.|
        | <span style="white-space:nowrap">-s, --save_text </span>| False | action='store_true'. If True, save the LR and SR text layout.|
        | <span style="white-space:nowrap">-d, --device</span> | None | Device, use 'gpu' or 'cpu'. If None, we use torch.cuda.is_available to select the device. |
        
        
        ### Example for post-processing the text region
        ```
        # On the terminal command
        textbsr -i [LR_TEXT_PATH] -b [BACKGROUND_SR_PATH] -s
        ```
        or
        ```
        # On the python environment
        from textbsr import textbsr
        textbsr.bsr(input_path='./testsets/LQs', bg_path='./testsets/RealESRGANResults', save_text=True)
        ```
        > When [BACKGROUND_SR_PATH] is None, we only restore the text region and paste back to the LR input, with the background region unchanged.
        
        
        ---
        
        ### Example for super-resolving the aligned text region
        ```
        # On the terminal command
        textbsr -i [LR_TEXT_PATH] -a
        ```
        or
        ```
        # On the python environment
        from textbsr import textbsr
        textbsr.bsr(input_path='./testsets/LQs', aligned=True)
        ```
        
        
        
        > If you find this package helpful, please kindly consider to cite our paper:
        ```
        @InProceedings{li2023marconet,
        author = {Li, Xiaoming and Zuo, Wangmeng and Loy, Chen Change},
        title = {Learning Generative Structure Prior for Blind Text Image Super-resolution},
        booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
        year = {2023}
        }
        ```
        
Keywords: blind text image super-resolution
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
