Metadata-Version: 2.1
Name: styletx
Version: 1.0.4
Summary: Initial release with StyleTransfer
Home-page: https://github.com/dinesh-GDK/StyleTx
Author: Dinesh Kumar Gnanasekaran
Author-email: dinesh.gna111@gmail.com
License: MIT
Description: # StyleTx
        **StyleTx** is a python project that applies effects of an image to another image using machine learning.
        
        ## Installation
        You can install the StyleTx package using the command given below
        
        ```
        pip3 install styletx
        ```
        
        ## Requirements
        Requires Python >=3.8
        
        Required packages are specified in [requirements.txt](https://github.com/dinesh-GDK/StyleTx/blob/master/requirements.txt) file, which you can install using
        
        ```
        pip3 install -r requirements.txt
        ```
        
        `torch` and `torchvision` versions in `requirements.txt` are CPU only, if you want to use the GPU versions that suit your hardware requirements visit this [link](https://pytorch.org/).
        
        
        ## Implementation
        
        ```python
        # import necessary packages
        from styletx import StyleTransfer
        from PIL import Image
        import matlibplot.pyplot as plt
        
        # import the images
        content_image = Image.open('path/filename')
        style_image = Image.open('path/filename')
        
        # implement StyleTransfer
        output_image = StyleTransfer(content_image, style_image, alpha=1, beta=10, epochs=500)
        
        # display the results
        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
        ax1.imshow(content_image)
        ax2.imshow(output_image)
        plt.show()
        ```
        The above code will apply the effects of the `style_image` to `content_image`.
        
        # Inputs
        `content_image` - a PIL object\
        `style_image` - a PIL object\
        `alpha` - a positive integer\
        `beta` - a positive integer\
        `epochs` - a positive integer
        
        By default `alpha` = 1, `beta` = 10 and `epochs` = 500.
        You can play around these values to get desired output image.
        
        ## Example
        ![](https://raw.githubusercontent.com/dinesh-GDK/StyleTx/master/images/Result.png)
        
        ## License
        [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/dinesh-GDK/StyleTx/blob/master/LICENSE.txt)
        
        ## Resources
        - The complete theory behind the **StyleTransfer** can be found in this [link](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf).
        
        - https://github.com/udacity/deep-learning-v2-pytorch/tree/master/style-transfer
        
Platform: UNKNOWN
Requires-Python: >3.8
Description-Content-Type: text/markdown
