Metadata-Version: 2.1
Name: styletx
Version: 1.0.1
Summary: Initial release with Style Transfer
Home-page: UNKNOWN
Author: Dinesh Kumar Gnanasekaran
Author-email: dinesh.gna111@gmail.com
License: MIT
Description: # styletx
        **styletx** is a python package that applies effects of an image to another image using machine learning.
        
        ## Installation
        You can install the styletx package using the command given below
        
        `pip install styletx`
        
        ## Requirements
        Python3
        
        **styletx** depends on the following python packages
        ```
        torch==1.5.0
        torchvision==0.6.0
        numpy==1.19.0
        pillow==7.1.2
        tqdm==4.46.1
        ```
        All the requirements stated above are used in creating the project, lower versions of the requirements may or may not work.\
        Also, using GPU will significantly reduce the run time of the script. Make sure to get `torch` and `torchvision` version that supports GPU.
        
        ## Implementation
        
        ```
        # 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)
        
        # plot 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
        ![alt text](https://github.com/dinesh-GDK/StyleTx/blob/master/images/Result.png)
        
        ## References
        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).
        
        ## License
        [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](info/../styletx/info/LICENSE.txt)
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
