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https://github.com/curt-park/style_transfer_keras
Style Transfer with Keras
https://github.com/curt-park/style_transfer_keras
convolutional-neural-networks deep-learning image-processing keras python scipy style-transfer
Last synced: 27 days ago
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Style Transfer with Keras
- Host: GitHub
- URL: https://github.com/curt-park/style_transfer_keras
- Owner: Curt-Park
- Created: 2018-02-25T04:01:00.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2018-03-05T15:18:28.000Z (almost 7 years ago)
- Last Synced: 2024-11-15T19:35:20.032Z (3 months ago)
- Topics: convolutional-neural-networks, deep-learning, image-processing, keras, python, scipy, style-transfer
- Language: Python
- Size: 11.6 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Style Transfer with Keras
This is a Keras implementation of style transfer techniques described in the following paper:
- [Image Style Transfer Using Convolutional Neural Networks](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf) by Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
## Differences from the paper
All differences are marked with comments that start with '! the original paper ...'
- The CNN Model: VGG16 is used instead of VGG19.
- The loss function for style representations: It is divided by (2. * feature map size * channel number).
- The initial canvas: a content image.
They make better results on my implementation rather than the settings suggested by the original paper, empirically.
## Developed with
- Keras 2.1.2
- Python 3.6.3
- Python packages: numpy, scipy, PIL
## How to run
```bash
python style_transfer.py [options]
```
## Options
```bash
$ python style_transfer.py --help
usage: style_transfer.py [-h] [--content CONTENT] [--style STYLE]
[--output OUTPUT] [--iteration ITERATION]
[--loss_ratio LOSS_RATIO]
[--initialization {random,content,style}]
[--save_image_every_nth SAVE_IMAGE_EVERY_NTH]
[--verbose VERBOSE]
optional arguments:
-h, --help show this help message and exit
--content CONTENT The path of the content image (Default: './images/content/tubingen.jpg')
--style STYLE The path of the style image (Default: './images/style/shipwreck.jpg')
--output OUTPUT The directory path for results (Default: './outputs/')
--iteration ITERATION
How many iterations you need to run (Default: 1000)
--loss_ratio LOSS_RATIO
The ratio between content and style -> content / style (Default: 1e-3)
--initialization {random,content,style}
The initial canvas (Default: 'content')
--save_image_every_nth SAVE_IMAGE_EVERY_NTH
Save image every nth iteration (Default: 10)
--verbose VERBOSE Print reports (Default: True)
```
## File descriptions
```bash
├── image/
│ ├── content/ # content images
│ ├── style/ # style images
│ └── results/ # outcomes from style-transfer
├── style_transfer.py
└── utils.py
```
## Sample Results
All examples are obtained by default settings.
### Reproduction
The attempt to reproduce Figure 3 of the paper, which renders a photograph of the Neckarfront in Tübingen, Germany in the style of 5 different paintings. + You can see the generating progress video by clicking the image.
Top Row (left to right): [No style](images/content/tubingen.jpg), [The Shipwreck of the Minotaur](images/style/shipwreck.jpg), [The Starry Night](images/style/starry-night.jpg)
Bottom Row (left to right): [Composition VII](images/style/kandinsky.jpg), [The Scream](images/style/the_scream.jpg), [Seated Nude](images/style/seated-nude.jpg)
### More trials
These are more trials on my son's photo. As above, the generating progress videos will be played by clicking the images.
Top Row (left to right): [No style](images/content/my_son.JPG), [Girl before a mirror](images/style/girl_before_a_mirror.jpg)
Bottom Row (left to right): [Hokusai](images/style/hokusai.jpg), [훈민정음](images/style/hunminjungum.jpg)
## References
### Paper
- [Image Style Transfer Using Convolutional Neural Networks](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf) by Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
### Implementation
- [Keras examples](https://github.com/keras-team/keras/blob/master/examples/neural_style_transfer.py) by Keras Team
- [Keras based Neural Style Transfer](https://github.com/giuseppebonaccorso/Neural_Artistic_Style_Transfer) by giuseppebonaccorso
- [Fast AI's Deep learning course](https://github.com/fastai/courses/blob/master/deeplearning2/neural-style.ipynb) by Fast AI
- [Tensorflow Style-Transfer](https://github.com/hwalsuklee/tensorflow-style-transfer) by Hwalsuk Lee
- [TensorFlow (Python API) implementation of Neural Style](https://github.com/cysmith/neural-style-tf) by cysmith