The Spring Show is over and it was a big success. If you are reading this and you attended the event, thank you for coming! I hope you were inspired by our hard work.
I enjoyed the show very much and am grateful for the people who stopped by to see what I am working on and ask questions. People seemed to like my project and had great feedback. I feel encouraged and even more motivated to continue working on this.
I'm going to keep developing these ideas over the summer and will continue posting to this blog. Check back often!
I created a software program to apply a Neural Style Transfer algorithm to Google Street View data to make an animation that looks like you are traveling inside a painting.
Google collects panorama photos from all around the world and makes them available through the Google Street View service. Although they visit many interesting locations, the photos often look dull. My project involves obtaining these photos through their API and using a coherent style transfer algorithm to make the sequence look like a painted animation.
The semester is over and I am giving my end of class presentation tomorrow. I decided I'm not going to call it my "final" presentation because this project is nowhere near over. I've built a great platform for working with Google Street View data but haven't done anything with it that demonstrates its potential. I'm going to continue working on this over the summer and beyond.
Two weeks remaining in the class and I finally was able to make progress on writing my own style transfer code. I didn't write it from scratch, I rewrote the existing code I found online to improve the code quality and make it fit my workflow. I'm going to continue working to make further improvements over the next few days.
Today I am meeting with ITP's Feedback Collective to discuss this project. I'm going to use this blog post to present what my project is about so this post will provide a review of what I've accomplished.
The basic idea is to take Google Street View photos and stylize them to make them more visually appealing. Although the Google Street View tool is an amazing application the photos taken can be fairly dull looking. Google is working to address this but they are aiming for a high degree of realism. My goal is to take a much more artistic approach.
Our class is half over and it is time for our midterm full-class feedback session. I've made a lot of progress recently and am excited to share my work with my fellow students. I will definitely achieve my original goals for this class, however, if that's the only thing I do with this, it would be a crime. This project is absolutely going to live on long after this class is over.
Milestone #2: Data Assembly¶
I'm comfortable saying I've completed this milestone. I've finished all the major features and have a nice interface for interacting with the downloaded data.
There are a few minor issues but none require a lot of time or brainpower to implement. Mostly nice-to-have enhancements like better error checking in my code that I feel compelled to do but aren't critical right now. I'll complete them as time allows.
The important thing is that I can now begin downloading the data I need without fear that I will need to download everything a second time later.
I made an interactive tool in matplotlib to visualize a spatial map of the locations I've downloaded data for. It looks like this:
Milestone #2: Data Assembly¶
The second step of this project is to access the Google Street View data and organize it in a suitable format. In my project plan my target was to reach this goal by February 21st (last Wednesday). Although I have accomplished a lot, I have not achieved all of the things I wanted to achieve for this milestone. I expect to hit it by next week at the latest.
Here's what I have achieved.
First, I can download all of the relevant data from Google. This includes all of the panorama image data and meta data. I can also access the panorama ids for the neighboring locations. All of the metadata is stored in a database.