Tag: image

Building a $86 million car theft AI in 57 lines of JavaScript

Tait Brown was annoyed at the Victoria Police who had spent $86 million Australian dollars on developing the BlueNet system which basically consists of an license-plate OCR which crosschecks against a car theft database.

Tait was so disgruntled as he thought he could easily replicate this system without spending millions and millions of tax dollars. And so he did. In only 57 lines of JavaScript, though, to be honest, there are many more lines of code hidden away in abstraction and APIs…

Anyway, he built a system that can identify license plates, read them, and should be able to cross check them with a criminal database.

I really liked reading about this project, so please do so if you’re curious via the links below:

Part 1: How I replicated an $86 million project in 57 lines of code

Part 2: Remember the $86 million license plate scanner I replicated?

Part X: the code on Github

Cover image via Medium via Freepik

Creating plots with custom icons for data points

Data visualizations that make smart use of icons have a way of conveying information that sticks. Dataviz professionals like Moritz Stefaner know this and use the practice in their daily work.

A recent #tidytuesday entry by Georgios Karamanis demonstrates how easy it is to integrate visual icons in your data figures when you write code in R. You can simply store the URL location of an icon as a data column, and map it to an aesthetic using the ggplot2::geom_image function.

Via https://github.com/gkaramanis/tidytuesday/tree/master/week-21

Do have a closer look at Georgios’ github repository for week 21 of tidytuesday. You will probably have to alter the code a bit to get it to work. though!

For those who haven’t moved away from base R plotting functions yet, here’s a good StackOverflow item showing how to use icons in both base R and tidyverse.

Now that I think of it, the above probably uses the same methods that were used to make this amazing Game of Thrones map in R.

R Image Art, by Michael Freeman

Michael Freeman — information researcher at the University of Washington — was asked whether he could manipulate images with only R programming and he thought to give it a try. In his blog, Michael demonstrates how he used ggplot2 and the imager packages, among others, to go from this original photo:

Spain, Vitoria-Gasteiz, Graffiti, Painting, Art — Via https://pixabay.com/photos/spain-vitoria-gasteiz-graffiti-88385/

To this dot representation:

And this voronoi diagram:

Generating Pusheen with AI

Zack Nado wrote the best machine learning application I’ve seen so far: a neural network architecture that generates new Pusheen pictures.

Image result for pusheen — This is an orginal Pusheen picture.

In his blog, Zack describes his generative adversarial network (GAN) , a special type of machine learning architecture where two neural networks try to fool each other. Zack first gave the discriminator network some real Pusheen images, so it gets an idea of what Pusheen looks like. Next, the generator network gets a bunch of random numbers so it can generate completely new (fake) images. These generated images are then fed back into the discriminator, so it knows what generated images look like. Zack repeated this process several hundred thousand times, so he obtained a generator network that’s great at making new Pusheen images which the discriminator (nearly) can’t dinstinguish from the original, real ones. Below is the learning process of the generator network visualized:

ezgif.com-video-to-gif — Samples output by the generator network. It learns distinctive features of “real” Pusheen (e.g., tail, eyes, ears) over time [original]

In the end, the generated images are very much like the real Pusheen. Zack added an interactive module (using Tensorflow.js) to the blog so you can generate some Pusheens yourself. (it didn’t work for me though…) On a final note, Zack wrote the orginal blog both in plain English, for non-experts, and in jargon, for the more experienced data scientists. I highly recommend you read either one of those versions!