## Inspiration

Imagine you're a new student in Montreal. You know those first days where everything and everywhere seems picture perfect! You just want to capture those post-worthy pics to admire for days after. Unfortunately though, you can never figure out a pose that does justice to your beauty - it's always those folded hands. That's where Posely comes in - the app that'll help you pose like a Broadway star! Our inspiration for Posely stems from the desire to revolutionize the way people approach and enjoy photography. We observed the common struggle individuals face when posing for pictures, and we were motivated to create a solution that empowers everyone to effortlessly enhance their photo-taking experience.

## What it Does

Posely is a revolutionary app designed to guide users in striking the perfect pose for their photos. Using advanced AI algorithms, the app analyzes the user's body language in real-time and provides instant, personalized suggestions to optimize their pose. From casual selfies to professional photoshoots, Posely ensures that every moment is captured with grace and confidence, leaving ever

## How We Built It

Posely came to life through the integration of computer vision technologies and a meticulously curated dataset. Employing OpenCV for landmark detection, Mediapipe for enhanced pose capabilities, and PyTorch for a robust custom-built Convolutional Neural Network (CNN), our team ensured the development of a responsive and highly accurate AI model. The app's user interface was designed using React Native, guaranteeing a seamless and intuitive experience.

## Challenges We Ran Into

Developing Posely came with its set of challenges. Achieving a delicate balance between accuracy and responsiveness in the AI model, as well as integrating it smoothly into the user interface, required meticulous attention. Additionally, we encountered a obstacle stemming from variations in sizes between dataset images and those captured on mobile devices, so this called for the need to scale the predicted landmarks to best fit the captured picture.

## Accomplishments That We're Proud Of

We are proud to have successfully implemented a cutting-edge AI model that provides valuable and instant feedback on poses. The seamless integration of Posely into various photography scenarios and the positive feedback received during testing are accomplishments that highlight the effectiveness and user satisfaction of our solution.

## What We Learned

The development of Posely provided us with invaluable insights into the intricacies of computer vision, user experience design, and real-time AI applications. We learned the importance of user feedback in refining our model and the significance of a user-centric approach in creating a successful and impactful product.

## What's Next for Posely

Looking ahead, we plan to enhance Posely by incorporating additional features such as allowing users to customize and save their preferred poses for different scenarios, and suggesting more than one pose at a time. We will actively seek user feedback to refine our AI model further and expand the app's capabilities. Posely is not just a pose guide; it's a platform for continuous improvement and empowerment in photography.
## Inspiration

I wanted a platform that incentivizes going outside and taking photos. This also helps with people who want to build a portfolio in photography

## What it does

Every morning, a random photography prompt will appear on the app/website. The users will be able to then go out and take a photo of said prompt. The photo will be objectively rated upon focus quality, which is basically asking if the subject is in focus, plus if there is any apparent motion blur. The photo will also be rated upon correct exposure (lighting). They will be marked out of 100, using interesting code to determine the scores. We would also like to implement a leaderboard of best photos taken of said subject.

## How we built it

Bunch of python, and little bit of HTML. The future holds react coding to make everything run and look much better.

## Challenges we ran into

everything.

## Accomplishments that we're proud of

Managed to get a decent scoring method for both categories, which had pretty fair outcomes. Also I got to learn a lot about flask.

## What we learned

A lot of fun flask information, and how to connect backend with frontend.

## What's next for PictureDay

Many things mentioned above, such as:

* Leaderboard
* Photo gallery/portfolio
* pretty website
* social aspects such as adding friends.
## Inspiration

Being sport and fitness buffs, we understand the importance of right form. Incidentally, suffering from a wrist injury himself, Mayank thought of this idea while in a gym where he could see almost everyone following wrong form for a wide variety of exercises. He knew that it couldn't be impossible to make something that easily accessible yet accurate in recognizing wrong exercise form and most of all, be free. He was sick of watching YouTube videos and just trying to emulate the guys in it with no real guidance. That's when the idea for Fi(t)nesse was born, and luckily, he met an equally health passionate group of people at PennApps which led to this hack, an entirely functional prototype that provides real-time feedback on pushup form. It also lays down an API that allows expansion to a whole array of exercises or even sports movements.

## What it does

A user is recorded doing the push-up twice, from two different angles. Any phone with a camera can fulfill this task.
The data is then analyzed and within a minute, the user has access to detailed feedback pertaining to the 4 most common push-up mistakes. The application uses custom algorithms to detect these mistakes and also their extent and uses this information to provide a custom numerical score to the user for each category.

## How we built it

Human Pose detection with a simple camera was achieved with OpenCV and deep neural nets. We tried using both the COCO and the MPI datasets for training data and ultimately went with COCO. We then setup an Apache server running Flask using the Google Computing Engine to serve as an endpoint for the input videos. Due to lack of access to GPUs, a 24 core machine on the Google Cloud Platform was used to run the neural nets and generate pose estimations.
The Fi(t)nesse website was coded in HTML+CSS while all the backend was written in Python.

## Challenges we ran into

Getting the Pose Detection right and consistent was a huge challenge. After a lot of tries, we ended and a model that works surprisingly accurately. Combating the computing power requirements of a large neural network was also a big challenge. We were initially planning to do the entire project on our local machines but when they kept slowing down to a crawl, we decided to shift everything to a VM.
The algorithms to detect form mistakes and generate scores for them were also a challenge since we could find no mathematical information about the right form for push-ups, or any of the other popular exercises for that matter. We had to come up with the algorithms and tweak them ourselves which meant we had to do a LOT of pushups. But to our pleasant surprise, the application worked better than we expected.
Getting a reliable data pipeline setup was also a challenge since everyone on our team was new to deployed systems. A lot of hours and countless tutorials later, even though we couldn't reach exactly the level of integration we were hoping for, we were able to create something fairly streamlined. Every hour of the struggle taught us new things though so it was all worth it.

## Accomplishments that we're proud of

-- Achieving accurate single body human pose detection with support for multiple bodies as well from a simple camera feed.
-- Detecting the right frames to analyze from the video since running every frame through our processing pipeline was too resource intensive
-- Developing algorithms that can detect the most common push-up mistakes.

-- Deploying a functioning app

## What we learned

Almost every part of this project involved a massive amount of learning for all of us. Right from deep neural networks to using huge datasets like COCO and MPI, to learning how deployed app systems work and learning the ins and outs of the Google Cloud Service.

## What's next for Fi(t)nesse

There is an immense amount of expandability to this project.
Adding more exercises/movements is definitely an obvious next step. Also interesting to consider is the 'gameability' of an app like this. By giving you a score and sassy feedback on your exercises, it has the potential to turn exercise into a fun activity where people want to exercise not just with higher weights but also with just as good form.
We also see this as being able to be turned into a full-fledged phone app with the right optimizations done to the neural nets.