## Inspiration

We love food and we want to try more of it! That's what inspired us to create Global Grub.

## What it does

Every day, you get new selections and recommendations for recipes from all around the world. Start out by choosing a country or region from a map or go with our country of the day. Global Grub will find three tasty recipes for you to try. You'll find all the ingredients, measurements and step by step instructions right on our webpage.

## How we built it

We used ReactJS for the front end and normal Javascript for the back-end. We started with a quick prototype in C# and our project ended up being 70% JavaScript, 19% HTML and 11% CSS. We organized data from the spoonacular API to give recommendations organized by country.

## Challenges we ran into

Our biggest challenge was making the interactive elements, because we didn't have much experience in that. It was also challenging to figure out how to connect the front-end to the back-end.

## Accomplishments that we're proud of

We're super proud of how much we learned during this hackathon. Some of us were writing JavaScript for the very first time. None of us are very experienced web developers, so even though we were all out of our element we perservered, learned, helped each other out and eventually made it to the finish line.

## What we learned

We learned a lot about teamwork. Each of us tried to lean in to our strengths when working on our parts of the project and it was super satisfying to see all the different components come together in the end.

## What's next for Global Grub

We all had a lot of fun making Global Grub and we hope that you enjoy it. We plan to spend some time putting the finishing touches on the project to make it the best place on the internet to find delicious grub around the globe!
## Inspiration

Processing information in the 3rd dimension can be a challenging task for many people. From children in school to adults in the workplace or higher education, being able to interact with a 3 dimensional visual that does not use an excess amount of space can incredibly optimize productivity and use of technology. To bring this idea to life, we came up with the idea of a 3 dimensional white board that displays user-drawn images using light projected in mid-air. To make this innovation more accessible to all, we have developed a system that is intuitive and minimizes contact with external technological devices.

## What it does

Our project consists of 3 elements: 1) a networking support complete with a dashboard web app, 2) the interactive user experience provided by the pen and visual processor technology, and 3) the construction that makes it all happen!

## How we built it

The motion sensor was built using OpenCV's color tracking API which processed two simultaneous data scenes from two perpendicular cameras for the purpose of capturing all three dimensions of space. The laser technology was incorporated by using a custom Galvanometer sensor using acrylic mirrors and servos. The laser was then projected on a board of frosted acrylic glass that was moving back and forth at a speed which allowed the user to visualize just the light and not the object reflecting it.

## Challenges we ran into

With the amount of time we were given to plan and produce our project, we were not able to find the proper materials we needed before our deadline. With half of our team being sick the week prior to the hackathon, our team heavily lacked preparation and research. Additionally, this project is heavily mechanical based, yet our team consisted of members who primarily had experience in Software and Web Development. These obstacles were difficult to overcome, but presented situations that allowed us to grow as a team, both technically and personally.

## Accomplishments that we're proud of

Forming a feasible plan that could be executed within 36 hours was probably one of the most difficult challenges we faced due to our lack of experience in mechanical engineering and the field of optics. However, with the support of professors, mentors, hackathon staff, and friends, we were able to pull together a plan that could come to life with the execution of our project. Building our network, successfully creating a functioning motion sensor with high precision, and producing a website that displayed the feats of this work are all on the list of accomplishments we are proud of as a team. As a group of individuals who primarily attended TreeHacks 2023 as first-time hackers, we are incredibly proud of our ability to work together, allocate tasks, and pull together a completed prototype of our vision.

## What we learned

We, as a team, learned a lot about how mechanics work in correlation with software development. The physics, math, and engineering induced unforeseen challenges, but we pulled ourself together with a strong support system. We also explored the field of light optics and visual illusions to incorporate special effects into our project design. Implementing these features allowed us to achieve things that seemed physically impossible.

## What's next for DarkBoard - 3D Whiteboard

Our prototype for the Darkboard is only at the beginning stages of a long journey. We plan on optimizing the features by improving overall precision, vision quality, incorporating various colors, adding more features to the website, and plenty more!
## Inspiration

We aren't musicians. We can't dance. With AirTunes, we can try to do both! Superheroes are also pretty cool.

## What it does

AirTunes recognizes 10 different popular dance moves (at any given moment) and generates a corresponding sound. The sounds can be looped and added at various times to create an original song with simple gestures.
The user can choose to be one of four different superheroes (Hulk, Superman, Batman, Mr. Incredible) and record their piece with their own personal touch.

## How we built it

In our first attempt, we used OpenCV to maps the arms and face of the user and measure the angles between the body parts to map to a dance move. Although successful with a few gestures, more complex gestures like the "shoot" were not ideal for this method. We ended up training a convolutional neural network in Tensorflow with 1000 samples of each gesture, which worked better. The model works with 98% accuracy on the test data set.

We designed the UI using the kivy library in python. There, we added record functionality, the ability to choose the music and the superhero overlay, which was done with the use of rlib and opencv to detect facial features and map a static image over these features.

## Challenges we ran into

We came in with a completely different idea for the Hack for Resistance Route, and we spent the first day basically working on that until we realized that it was not interesting enough for us to sacrifice our cherished sleep. We abandoned the idea and started experimenting with LeapMotion, which was also unsuccessful because of its limited range. And so, the biggest challenge we faced was time.

It was also tricky to figure out the contour settings and get them 'just right'. To maintain a consistent environment, we even went down to CVS and bought a shower curtain for a plain white background. Afterward, we realized we could have just added a few sliders to adjust the settings based on whatever environment we were in.

## Accomplishments that we're proud of

It was one of our first experiences training an ML model for image recognition and it's a lot more accurate than we had even expected.

## What we learned

All four of us worked with unfamiliar technologies for the majority of the hack, so we each got to learn something new!

## What's next for AirTunes

The biggest feature we see in the future for AirTunes is the ability to add your own gestures. We would also like to create a web app as opposed to a local application and add more customization.