I implement a tensorflow&keras model described in the papers

• "A Neural Algorithm of Artistic Style".
• "Image Style Transfer Using Convolutional Neural Networks", CVPR 2016. by Leon A. Gatys, Alexander S. Ecker, Matthias Bethge

• Ubuntu 16.04
• Python 3.6

• Numpy
• Opencv2
• Matplotlib
• Tensorflow (1.4 <= x <=1.13)

• images(dir) : images for style transfer.
• neural_style_transfer.py : main code for style transfer.
• neural_style_transfer.ipynb : you can execute this code on jupyternotebook. recommend 'Colab' of Google.

python neural_style_transfer.py --content_image_path <content image path> --style_image_path <style image path>
# ex)
python neural_style_transfer.py --content_image_path images/content/tubingen.jpg --style_image_path images/style/starry-night.jpg

Required :

• --content_image_path: Path of the content image. Default: images/content/tubingen.jpg
• --style_image_path: Path of the style image. Default: images/style/starry-night.jpg

Optional :

• --model_type: Type of pretrained model. Choices: 0(VGG16), 1(VGG19) Default: 0
• --image_resize: Resize(int or tuple) images. Default: 512
• --rescale_image: Rescale the final image to original size. Default: False
• --content_blocks: Layer list for feature vector of Content image. Default: ['block4_conv2']
• --style_blocks: Layer list for feature vector of Style image. Default: ['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1']
• --loss_ratio: Weight of content-loss relative to style-loss. Alpha over beta in the paper. Default: 1e-3
• --total_variation_weight: Total Variation weight. Default: 0 (If you want to use this, then 8.5e-5)
• --initial_type: The initial image to generate image. Choices: content, style, random. Default: 'random'
• --optimizer_type: The optimizer for optimization. Choices: 0(Adam Optimizer), 1(L-BFGS-B Optimizer). Default: 1
• --learning_rate: The value of learning rate for Adam Optimizer. Default: 1e+1
• --beta_1: Beta_1 of Adam Optimizer. Default: 0.9
• --beta_2: Beta_2 of Adam Optimizer. Default: 0.999
• --epsilon: Epsilon of Adam Optimizer. Default: 1e-08
• --iteration: The number of iterations. Default: 150

All of results made from default setting and executed on Colab.

All of results made from default setting and executed on Colab.