Modification of kovibalu's implementation of neural style transfer. This versions adds the ability to use the content image as the initialisation image, giving results better matching the content, in fewer iterations. I've also implemented functionality to pass arguments through the command line, saving modification of the .py files.

Run ./scripts/install/install_all.sh to install. It will install the needed python dependencies, caffe, compile caffe, download the necessary weight file. If running Windows, ensure all dependencies are installed, download the pre-trained network from here, and place the file in 'models/VGG_CNN_19/'.

If you already have the normalized VGG19 files stored elsewhere, edit 'settings.py' variable 'MODEL_PATH' to reflect this.

Once everything is installed, run 'python generate.py' from command prompt/terminal to run a demo. Results will be saved to '/results'.

This code was originally written for Python 2.7, however to the best of my knowledge, it is fully compatible with Python 3. At the time of writing (March 2017), Caffe for Windows supports Python 2.7 and 3.5 only. Installing the Python 3.5 versions should work with this code just fine. However, if you're running Python 3.6+ Caffe will fail to build/import. If Caffe is ported to Python 3.6, this should work.

python generate.py -c <content image> -s <style image> -l <width of image> -i <n iterations>

If no image width is specified, the dimensions of the content image will be used, so be careful if running on limited VRAM. Similarly, if no iteration number is specified it will default to 300. This gives good results when initialising with content, but when initialising with noise I suggest increasing this to at least 500.

-n <True/False>

Initialise with noise instead of content image (default False)

-r <ratio>

Ratio of style to content (default 100)

-d <display iterations>

Number of iterations between image saves

Content initialization, 300 iterations

Noise initialization, 300 iterations

Content initialization, 150 iterations

Noise initialization, 1000 iterations

This is an implementation of the original neural style project by Gatys et al..