Stephen Smith's Blog

Musings on Machine Learning…

An Introduction to Image Style Transfer

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Introduction

Image Style Transfer is an AI technique that is becoming quite popular for enhancing or stylizing photos. It takes one picture (often a classical painting) and then applies the style of that picture to another picture. For example I could take this photo of the Queen of Surrey passing Hopkins Landing:

Combined with the style of Vincent van Gogh’s Starry Night:

To then feed these through the AI algorithm to get:

In this article, we’ll be look at some of the ways you can accomplish this yourself either through using online services or running your own Neural Network with TensorFlow.

Playing with Image Style Transfer

There are lots of services that let you play with this. Generally to apply a canned style to your own picture is quite fast (a few seconds). To provide your own photo as the style photo is more involved, since it involves “training” the style and this can take 30 minutes (or more).

Probably the most popular program is the Prisma app for either iPhone or Android. This app has a large number of pre-trained styles and can apply any of them to any photo on your phone. This app works quite well and gives plenty of variety to play with. Plus its free. Here is the ferry in Prisma’s comic theme:

If you want to provide your own photo as the style reference then deepart.io is a good choice. This is available as a web app as well as either an iPhone or Android app. The good part about this for photographers is that you can copy photos from your good camera to your computer and then use this program’s website, no phone required. This site has some pre-programmed styles based on Vincent van Gogh which work really quickly and produce good results. Then it has the ability to upload a style photo. Processing a style is more work and typically takes 25 minutes (you can pay to have it processed quicker, but not that much quicker). If you don’t mind the wait this site is free and works quite well. Here is an example of the ferry picture above van Gogh’ized by deepart.io (sorry they don’t label the styles so I don’t know which painting this is styled from):

Playing More Directly

These programs are great fun, but I like to tinker with things myself on my computer. So can I run these programs myself? Can I get the source code? Fortunately the answer to both is yes. This turns out to be a bit easier than you first might think, largely due to a project out of the Visual Geometry Group (VGG) at the University of Oxford. They created an exceptional image recognition neural network that they trained and won several competitions with. It turns out that the backbone to doing Image Style Transfer is to have a good image recognition Neural Network. This Neural Net is 19 layers deep and Oxford released the fully trained network for anyone to use. Several people have then taken this network, figured out how to load it into TensorFlow and created some really good Image Style Transfer programs based on this. The first program I played with was Anish Athalye’s program posted on GitHub here. This program uses VGG and can train a neural network for a given style picture. Anish has quite a good write up on his blog here.

Then I played with a program that expanded on Anish’s by Shafeen Tejani which is on GitHub here along with a blog post here. This program lets you keep the trained network so you can perform the transformation quickly on any picture you like. This is similar to how Prisma works. The example up in the introduction was created with this picture. To train the network you require a training set of image like the Microsoft COCO collection.

Running these programs isn’t for everyone. You have to be used to running Python programs and have TensorFlow installed and working on your system. You need a few other dependent Python libraries and of course you need the VGG saved Neural Network. But if you already have Python and TensorFlow, I found both of these programs just ran and I could play with them quite easily.

The writeups on all these programs highly recommend having a good GPU to speed up the calculations. I’m playing on an older MacBook Air with no GPU and was able to get quite good results. One trick I found that helped is to play with reduced resolution images to help speed up the process, then run the algorithm on a higher resolution version when you have things right. I found I couldn’t use the full resolution from my DLSR (12meg), but had to use the Apple’s “large” size (286KB).

Summary

This was a quick introduction to Image Style Transfer. We are seeing this in more and more places. There are applications that can apply this same technique to videos. I expect this will become a standard part of all image processing software like PhotoShop or Gimp. It also might remain the domain of specialty programs like HDR has, since it is quite technical and resource intensive. In the meantime projects like VGG have made this technology quite accessible for anyone to play with.

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Written by smist08

August 14, 2017 at 6:48 pm

2 Responses

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  1. […] time we introduced Image Style Transfer, an AI algorithm that combines the contents of one image with the style of another image. In this […]

  2. […] This is a great building block for other image manipulation projects like Image Style Transfer that we looked at previously. […]


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