Metadata-Version: 2.1
Name: timst
Version: 0.2.0
Summary: Image style transfer using Torch
Home-page: https://github.com/Radonirinaunimi/Style-Transfer
Author: Tanjona R. Rabemananjara
Author-email: tanjona.rabemananjara@mi.infn.it
License: UNKNOWN
Platform: UNKNOWN
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Dist: numpy
Requires-Dist: torch
Requires-Dist: tqdm
Requires-Dist: torchvision
Provides-Extra: docs
Requires-Dist: recommonmark ; extra == 'docs'
Requires-Dist: sphinx-rtd-theme ; extra == 'docs'
Requires-Dist: sphinxcontrib-bibtex ; extra == 'docs'
Provides-Extra: tests
Requires-Dist: pytest ; extra == 'tests'
Requires-Dist: pytest-cov ; extra == 'tests'

![license](https://img.shields.io/github/license/Radonirinaunimi/Style-Transfer?style=flat-square)
![repo size](https://img.shields.io/github/repo-size/Radonirinaunimi/Style-Transfer?style=flat-square)
#### Description
----------------

**Timst** is a python package based on [pyTorch]() that extracts the features of an image and tranfers them into another; such a technique is known as *image style transfer*. The following implementation is a re-implementation of this [code]() that is based on the following [scientific paper](https://arxiv.org/pdf/1508.06576.pdf). The architecture is based on [Convolutional Neural Network](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf) (CNN) which is one of the applications of [Deep Learning](https://en.wikipedia.org/wiki/Deep_learning).


#### How to install
-------------------

There are different ways the package can be installed: 
* By clonning this repository and running the following command in the terminal (you might require <kbd>sudo</kbd> privilege)
```bash
git clone https://github.com/Radonirinaunimi/Style-Transfer
cd Style-Transfer/
python setup.py install --user
```
* By installing it through the Python Package Index (PyPI)
```bash
pip install timst --upgrade
```

#### How to use
---------------

To use **timst**, just run the following:
```bash
timst -i [IMAGE_TO_BE_STYLED] -s [STYLE_TO_BE_APPLIED] [-n NUMBER_OF_ITERATIONS]
```

#### For bugs and feature request
---------------------------------

Open an [issue](https://github.com/Radonirinaunimi/Style-Transfer/issues/new/choose) or a [pull request](https://github.com/Radonirinaunimi/Style-Transfer/compare).


