While coming home after a hackathon, hackers are often left sleepy, tired, and flooded with stickers. However, sometimes they care about the arrangements of their stickers, but post-hackathon they are swamped with work and unable to find the time to properly arrange their stickers onto their laptop covers. By generating layouts for hackathon stickers, we wanted to turn the cluttered messes that are the laptops of hackathon goers into something more organized, and find a good use of hackathon stickers.

What it does

Our web app lets you take pictures of your laptop and stickers, and it will find an arrangement that covers a lot of space without overlapping stickers. It also lets you scan stickers and get descriptions about the company it's about, along with links to their home and careers websites.

How we built it

For sticker scanning, we used the Google Cloud Vision API to detect both web entities and character sequences present in the image of a sticker to try to find what company it's for. We then search a database we created of many common companies who give out stickers at hackathons in order to filter out irrelevant results from the api.

To arrange the stickers while taking in account their shape, we used OpenCV to trace the edge of the sticker, then dilated and eroded the image to fix the edge of the image. After that, we produced a filled-vector image by flood filling the fixed edges of an inverted copy of the image.

To retrieve information on the companies, we created a Custom Search Engine and then pulled details from Google Search's API.

Challenges we ran into

We ran into several Javascript bugs. We also had no knowledge of how to use OpenCV prior to this hackathon, so it was pretty difficult to figure out. In addition, there were many issues as well with the Pillow module's naming consistency.

Google also deprecated their normal Search API so we had to figure out how to use a Custom Search Engine to make a workaround for it.

Accomplishments that we're proud of

We figured out OpenCV.

What we learned


What's next for StickerStorm

Convolutional neural networks and blockchain as well as an additional, flashy static site.

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