#NeuralStyleTransfer

A Chainer implementation of A Neural Algorithm of Artistic Style. In short, this is an algorithm that transfers the artistic style of one image onto another with the help from a convolutional neural network.

To run this code you need Chainer:

pip install chainer

Versions tested are v1.14.0 and v1.18.0, should work with any versions inbetween.

A VGG-19 Caffe model is also required for this implementation to work. You can use the normalized version used by the authors of the article: vgg_normalised.caffemodel, or use the original one made by VGG team: VGG_ILSVRC_19_layers.caffemodel.

With minor modifications, other CNNs (e.g. NIN, GoogLeNet) can be used as well. See jcjohnson's explanation and mattya's implementation.

A helper function generate_image() was created to do the transfer. Simply call it in main() to generate images. An example is given below:

def main():
    # set up global flag to run this program on gpu
    use_gpu(True)
    
    # instantiate a VGG19 model object
    cnn = VGG19()
    
    # begin transfer style
    generate_image(cnn, 'content.jpg', 'style.jpg', alpha=150.0, beta=12000.0,
                   init_image='noise', optimizer='rmsprop', iteration=1600, lr=0.25, prefix='temp')

The parameters generate_image() uses are:

• cnn: A CNN model object. Currently only VGG19 is implemented.
• content, style: Strings. Filenames of the content and style images to use.
• alpha, beta: Floats. Weighting factors for content and style reconstruction.
• color: String. Scheme of color preserving to use, choose between none (no color preserving), historgram (for histogram matching), and luminance (for luminance-only transfer).
• a: Boolean. Whether to match the luminance channel of the style image to the content image before transfering, only work if color is luminance of course.
• init_image: String. Choose between noise, content, and style.
• optimizer: String. Optimizer to use, you can choose between adam (ADAM) and rmsprop (Alex Graves’s RMSprop).
• iteration: Int, number of iterations to run.
• lr: Float. Learning rate of the optimizer.
• save: Int. The optimizer will write an output to file after every save iterations.
• filename: String. Prefix of filename when saving output. The saved files will have name in format prefix_iterations.
• contrast: Boolean. Wether to boost the contrast when saving the output. Default to True. Sometimes the output has less saturation than expected, so I insert few lines to give the contrast a kick when saving to file.

Here we demostrate the effect of transformation using a photo (original file) of Grainger Engineering Library at UIUC. Titles of the artwork we used are also given:

Original Image	The Shipwreck of the Minotaur
De Sterrennacht	Der Schrei
Femme Nue Assise	Композиция 7
夢入靑城天下幽人間仙境圖	神奈川沖浪裏

under construction ᕕ( ᐛ )ᕗ

Transfer without Color Preserving	Transfer with histogram matching
Luminance-only transfer, histogram matched	Luminance-only transfer

Leon A. Gatys, Alexander S. Ecker & Matthias Bethge (2015). A Neural Algorithm of Artistic Style. In: CoRR.

Leon A. Gatys, Matthias Bethge, Aaron Hertzmann & Eli Shechtman (2016). Preserving Color in Neural Artistic Style Transfer. In: CoRR.

Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, Aaron Hertzmann & Eli Shechtman (2016). Controlling Perceptual Factors in Neural Style Transfer. In: CoRR.

Gatys, Leon A., Ecker, Alexander S. & Bethge, Matthias (2016). Image Style Transfer Using Convolutional Neural Networks. In: The IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414-2423.

F. Pitie & A. Kokaram (2007). The linear Monge-Kantorovitch linear colour mapping for example-based colour transfer. In: IETCVMP. 4th European Conference on Visual Media Production, 2007, pp. 1-9.

In making this program, I referred to helpful works of jcjohnson, apple2373, mattya, and andersbll.

Francis Hsu, University of Illinois at Urbana–Champaign.