Ready-to-use artistic deep learning algorithms
Project description
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 for instructions on PyTorch installation with another version of the toolkit.
Usage
Console entrypoint
# Then see the builtin help for usage details neurartist --help
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
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
Be more consistent with batchsize/no batchsize, especially in covariance_matrix(), add squeeze/unsqueeze steps in transforms
Option to initialize the optimizer with another image (and depreciate –hr options, because with this one we can do the lowres and highres passes with two separate console calls)
Documentation.
Implement the remaining parts of the jupyter notebook.
Semantic segmentation as described in this article as to limit spillovers: 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.
Project details
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