Metadata-Version: 2.1
Name: rudi
Version: 1.0
Summary: Small, fast and simple Python CLI image converter for CNNs.
Home-page: https://github.com/liashchynskyi/rudi
Author: Petro Liashchynskyi
License: UNKNOWN
Description: 
        # Rudi
        
        If you want to train a CNN, the custom dataset must be a collection of images of the same aspect ratio, extension, etc.
        This script is gonna do that for you. Rudi is a command line tool for converting and augmenting your dataset of images.
        
        # Installation
        
        Install `Python3` and then run the following command:
        ```sh
        pip install rudi
        ```
        
        # Usage
        ![Imgur](https://i.imgur.com/KIi431Z.png)
        ---
        * [Convert a dataset](#convert-a-dataset)
        * [Dataset augmentation](#dataset-augmentation)
        * [Donate](#donate)
        
        # Convert a dataset
        
        For example, you have a basic tree of the root directory (the script will also work if the root containt only images without other dirs).
        ```
        root    
        в””в”Ђв”Ђв”Ђclass1
        в”‚   в”‚   image_c1.png
        в”‚   в”‚   image_c2.png
        в”‚   в””в”Ђв”Ђв”Ђsubdirectory    
        в””в”Ђв”Ђв”Ђclass2
            в”‚   image_c1.png
            в”‚   image_c2.png
        ```
        
        Just run `rudi convert --help`
        ![Imgur](https://i.imgur.com/GAWRBja.png)
        
        Let's convert images in current directory to `jpg` format and set new aspect ratio to `224px`.
        ```sh
        rudi convert -t jpg --target-size=224 ./
        ```
        Output images will be saved in `output` dir of the root.
        
        # Dataset augmentation
        
        Command: `rudi augment --help`
        ![Imgur](https://i.imgur.com/64Ijbjr.png)
        
        There are a few supported operations:
        * `flip` - random image flipping followed by `-p` option
        * `rotate` - random image rotation followed by `-p`,`-mlr` and `-mrr` options
        * `distortion` - random image distortion followed by `-p`,`-mg` and `-gwh` options
        * `skew` - random image skewing followed by `-p` option and constant `magnitude` value of `0.7`
        * `zoom` - random image zooming followed by `-p`,`-minf` and `-maxf` options
        
        Output images will be saved in `output` dir of the root.
        
        # Donate
        Just put a star on this repository рџЊћ Thanks!
        
        <div align="center">
        made by <a href="https://github.com/liashchynskyi">@liashchynskyi</a>
        </div>
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
