https://github.com/cheind/py-style-transfer
:art: Artistic neural style transfer with tweaks in PyTorch
Science Score: 26.0%
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:art: Artistic neural style transfer with tweaks in PyTorch
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Readme.md
Neural style transfer
Style transfer applied to a picture of the Golden Gate bridge using on Vincent van Gogh's 'The Starry Night' artistic style. A result using Picasso's work can be found here.
py-style-transfer implements image style transfer as proposed by [1,4,5] using PyTorch. Given an artistic image and a content image, the method iteratively generates an image that is similar to the content but drawn in the desired artistic style. While the method is not real-time capable, it is the most flexible approach, not requiring any style pre-training expect for a readily available pre-trained convolutional architecture such as VGG. While this implementation is based on [1,4,5] we also incorporate ideas from [2,3].
We also extend the approach to two more use-cases - Seamless mode generates tiles that can be stacked vertically/horizontally without visual seams. - Tiled mode allows generation of very large images that would otherwise not fit into memory. Like this 8192x8192 10Mb/JPEG pure Picasso artistic style image.
See the interactive StyleTransfer.ipynb notebook for usage and examples.
Features
- Various style losses such as gram-based, patch-based, semantic-based.
- Capability to process on multiple scales.
- Support for generating huge image sizes through tiling.
- Support for generating images that stitch seamlessly.
- Easily add new losses or modify the optimization through plugins.
References
[1] Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576 (2015). [2] Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Perceptual losses for real-time style transfer and super-resolution." European Conference on Computer Vision. Springer, Cham, 2016. [3] Gatys, Leon A., et al. "Controlling perceptual factors in neural style transfer." IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. [4] Li, Chuan, and Michael Wand. "Combining markov random fields and convolutional neural networks for image synthesis." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. [5] Champandard, Alex J. "Semantic style transfer and turning two-bit doodles into fine artworks." arXiv preprint arXiv:1603.01768 (2016).
License
``` Copyright 2018 Christoph Heindl
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ```
Owner
- Name: Christoph Heindl
- Login: cheind
- Kind: user
- Location: Austrian area
- Website: https://cheind.github.io/
- Repositories: 88
- Profile: https://github.com/cheind
I am a computer scientist working at the interface of perception, robotics and deep learning.
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Dependencies
- Pillow >=5.1.0
- numpy >=1.14.2
- torch >=0.4.0
- torchvision >=0.2.1
- tqdm >=4.23.1