Note: npy file in vgg_conv.npy.zip in case the box link ever goes down.

Tried deploying to Lambda via Zappa and Serverless. File was too big but when I finally got it under 512MB on Zappa I had to set "use_precompiled_packages": false which disabled C extensions. This affected _imaging from Pillow and

TODO

• README
• Google bucket to upload to Imgur Note: couldn't figure out how to upload file objects to Imgur Figured out
• Imgur create gallery to store input
• Create frontend rather than Flask restplus
• Style all the pages
• Feedback form with star rating and description
• Separate pages explaining how it works, link to repo, etc
• Output
• Hook into React gallery
• Calculate estimated time based on tf loss
• Email user when output is finished
• Make separate Imgur account
• Hook up Imgur account to app
• Check to see if requirements.txt works. Used pipreqs to get a cleaner file
• Account for bad input
• Make Imgur images public when uploaded

Below is Vince's original README explaining how this version of style transfer works

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Style Transfer as Optimal Transport

An algorithm that transfers the distribution of visual characteristics, or style, of a reference image onto a subject image via an Optimal Transport plan.

How it Works:

• For a general description and pictures: style-transfer-theory.pdf.
• For an explanation of the use of L2-Wasserstein distance instead of the loss function from the Gatys et. al framework see why_wasserstein.ipynb.
• Demo video

tl;dr - Passes a subject and style image through the convolution layers of the vgg19 network. Extracts probabalistic descriptions (first two moments) of the convolution filter activations. Calculates L2-Wasserstein distance between these probability distributions and then modifies the subject image optimally to minimize this distance.

Running

Requires:

1. Python 3 (w/ NumPy and PIL)
2. Tensorflow (tested w/ version 1.3)
3. 'vgg_conv.npy' binary (115.5 MB) which contains calibrated convolution filters from vgg19 network. Can be downloaded here [md5sum: bf8a930fec201a0a2ade13d3f7274d0e]

Basic Usage from Command Line:

python basic_styletrans.py --subject media/wave_small.jpg --style media/kngwa_small.jpg --output media/wave_kngwa.jpg --vggnet vgg_conv.npy

Output:

loss: 118,500,040.00
ALL DONE 
post clip | time: 49.0 final loss: 2,771,486.50
synthesized image saved: media/wave_kngwa.jpg