## Inspiration

/<http://www.firmoo.com/answer/tag_img/blurry-eyes.jpg>

You know that someone is clearly in front of you. Who is it? You know that both Emily and Jane have shortish hair, a roundish face, but the world is much too blurry for you to correctly discern what is happening You wish there were a way for you to know instantly who was in front of you without always having to ask. But instead, you're left in constant darkness with what is going on around you.

Because of that need, our team developed a cool and interesting way to connect the visually impaired with the world around them using Android phone technology and Facebook.

## What it does

Spot-A-Friend allows visually impaired members of the community to better see the world around them. The process is simple. You open up the Spot-a-friend App and place the phone into the VR headset. From there, the view the user sees will be that of the Android camera view. The person can then look around and, when they feel that there are people in the area whom they can't identify, they can press a button on the headset and potentially utilize a voice command. "Spot a friend".

From there, the friend is identified and their general location

## How we built it

We built it by interfacing the Android and a computer back end system. The android app takes cares of the image by capturing it via button command. The entire system will be attached to a VR headset and mounted in the eye region. It interfaces via python flask back end that utilizes socket technology. It re-purposes Facebook's tagging algorithm to be used as a facial recognition. Then, the back end returns information on what the algorithm was able to identify.

## Challenges we ran into

Not enough food and not enough sleep! Also trying to do facial detection while streaming the camera was difficult. Syncing up speech recognition and text to speech functions also posed a great challenge!

## Accomplishments that we're proud of

Communication between the back end and the android app was difficult. Also, maintaining both systems in syncs and having the back end bypass csrf on Facebook's domain and internal endpoints was a huge success!

## What we learned

Teamwork and how to balance work and play :) !

## What's next for Spot-a-Friend

Use Microsoft's Azure services to incorporate sentiment analysis so that the user can understand the context and emotion of the people around them. Additionally we would like to iron out the kinks with our speech recognition feature, and allow more commands from it. Perhaps, we can even reduce the form factor of the entire device so that people can wear it around with ease.
## Inspiration

Millions of people around the world are either blind, or partially sighted. For those who's vision is impaired, but not lost, there are tools that can help them see better. By increasing contrast and detecting lines in an image, some people might be able to see clearer.

## What it does

We developed an AR headset that processes the view in front of it and displays a high contrast image. It also has the capability to recognize certain images and can bring their existence to the attention of the wearer (one example we used was looking for crosswalk signs) with an outline and a vocal alert.

## How we built it

OpenCV was used to process the image stream from a webcam mounted on the VR headset, the image is processed with a Canny edge detector to find edges and contours. Further a BFMatcher is used to find objects that resemble a given image file, which is highlighted if found.

## Challenges we ran into

We originally hoped to use an oculus rift, but we were not able to drive the headset with the available hardware. We opted to use an Adafruit display mounted inside a Samsung VR headset instead, and it worked quite well!

## Accomplishments that we're proud of

Our development platform was based on macOS 10.12, Python 3.5 and OpenCV 3.1.0, and OpenCV would not cooperate with our OS. We spent many hours compiling and configuring our environment until it finally worked. This was no small feat. We were also able to create a smooth interface using multiprocessing, which operated much better than we expected.

## What we learned

Without the proper environment, your code is useless.

## What's next for EyeSee

Existing solutions exist, and are better suited for general use. However a DIY solution is endlessly customizable, we this project inspires other developers to create projects that help other people.

## Links

Feel free to read more about visual impairment, and how to help;
<https://w3c.github.io/low-vision-a11y-tf/requirements.html>
If we take a moment to stop and think of those who can't speak or hear, we will realize and be thankful for what we have. To make the lives of these differently ables people, we needed to come up with a solution and here we present you with Proximity.

Proximity uses the Myo armband for sign recognition and an active voice for speech recognition. The armband is trained on a ML model reads the signs made by human hands and interprets them, thereby, helping the speech impaired to share their ideas and communicate with people and digital assistants alike. The service is also for those who are hearing impaired, so that they can know when somebody is calling their name or giving them a task.

We're proud of successfully recognizing a few gestures and setting up a web app that understands and learns the name of a person. Apart from that we have calibrated a to-do list that can enable the hearing impaired people to actively note down tasks assigned to them.

We learned an entirely new language, Lua to set up and use the Myo Armband SDK. Apart from that we used vast array of languages, scripts, APIs, and products for different parts of the product including Python, C++, Lua, Js, NodeJs, HTML, CSS, the Azure Machine Learning Studio, and Google Firebase.

We look forward to explore the unlimited opportunities with Proximity. From training it to recognize the entire American Sign Language using the powerful computing capabilities of the Azure Machine Learning Studio to advancing our speech recognition app for it to understand more complex conversations. Proximity should integrate seamlessly into the lives of the differently abled.