neurartist 0.4.1
Ready-to-use artistic deep learning algorithms
Requires Python: >=3

Neurartist
==========

A ready-to-use implementation of various Artistic Deep Learning
Algorithms.

-  *Image Style Transfer Using Convolutional Neural Networks*, Gatys et.
   al, 2016
-  *Controlling Perceptual Factors in Neural Style Transfer*, Gatys et.
   al, 2016

Installation
============

::

   # It is recommended to install torch/torchvision manually before this command, according to your hardware configuration (see below)
   pip install neurartist

Please note that the use of a GPU is recommended, as CNN computations
are pretty slow on a CPU.

NB for GPU users: pip ships ``torch``/``torchvision`` with the Cuda
Toolkit 9.0. If you use a more recent version of the Cuda Toolkit, see
the `PyTorch website <https://pytorch.org/get-started/locally/>`__ for
instructions on PyTorch installation with another version of the
toolkit.

Usage
=====

Console entrypoint
------------------

::

   # Then see the builtin help for usage details
   neurartist --help

See the examples below for the most common use cases.

Library
-------

::

   import neurartist

To be added.

Examples
========

-  Basic usage: apply the style of an image to a content image, while
   preserving the semantic content.

::

   neurartist -c content.jpg -s style.jpg

-  Color control: apply a style, but preserve the color of the content
   image.

::

   # Luminance only
   neurartist -c content.jpg -s style.jpg --color-control luminance_only
   # Luminance only, luma normalized
   neurartist -c content.jpg -s style.jpg --color-control luminance_only --cc-luminance-only-normalize
   # Color histogram matching
   neurartist -c content.jpg -s style.jpg --color-control histogram_matching

-  Style mixin: mix the coarse scale information of style1 (higher
   layers) with the fine scale information of style2 (lower layers), to
   create a mixed style to apply to a content image.

::

   neurartist -c style1.jpg -s style2.jpg -o mixed.png --content-layers [22,29] --style-layers [1,6]
   neurartist -c content.jpg -s mixed.png

-  Efficient high resolution: first pass is a low resolution style
   transfer that efficiently catches coarse scale style features, second
   pass is a high resolution style transfer that upscales the result of
   the first pass and fills the lost fine information using fine scale
   style features.

::

   neurartist -c content.jpg -s style.jpg -o lowres.png -S 500
   neurartist -c content.jpg -s style.jpg -o highres.png -S 1000 --init-image-path lowres.png

-  Spatial control: Guided gram matrices with guidance channels.
   Guidance paths should contain black and white guidance images (with
   the same size ratio as content and style images), defining the
   boundaries of semantic regions of each image. Style guidance images
   and content guidance images should have the same name, in
   correspondance to a semantic region. Segmentation of the image should
   be exhaustive.

::

   neurartist -c content.jpg -s style.jpg --content-guidance content_image_guidance_path/ --style-guidance style_image_guidance_path/ --guidance-propagation-method inside

Development
===========

Anaconda is strongly recommended:

::

   conda create python=3.7 --name neurartist_env
   conda activate neurartist_env

   # with gpu
   conda install pytorch torchvision cudatoolkit=<your cudatoolkit version> -c pytorch
   conda install --file requirements.txt

   # with cpu
   conda install pytorch-cpu torchvision-cpu -c pytorch
   conda install --file requirements.txt

You can then run the main entrypoint directly using:

::

   python -m neurartist --help

Or build and install the wheel file with the ``--editable`` flag.

TODO
====

-  Documentation.
-  Implement total variation loss (see
   `this <https://www.tensorflow.org/beta/tutorials/generative/style_transfer>`__).
-  Implement guided sums.
-  Examine if we need to add a fallback global guidance channel for
   pixels that aren’t covered by any channel (briefly mentioned in the
   article but very vague). Short answer: yes, it is needed, with bigger
   kernel sizes the style transfer isn’t performed at the boundary.
-  For guidance channels propagation: investigate what they mean by
   “erosion” (see supplementary material document linked in the original
   article).
-  `Semantic segmentation as described in this article as to limit
   spillovers <https://arxiv.org/pdf/1703.07511.pdf>`__: different
   approach than guided gram matrices, but same idea of using spatial
   guidance channels that describe a semantic segmentation of our
   images.
-  More deep-artistic algorithms.