Inspiration
What were the 2000s known for? The Great Depression? No. High-rise jeans? Absolutely not.
Undoubtedly, they belonged to Gwyneth Paltrow. It was defined by her rise to power; of course, we’re not talking about her acting career (her artistic talents precede the decade).
Cool, calm, and calculating. This describes Gwyneth’s attitude all throughout the 2000s. She knew that her influence was inevitable; it was not a question of if, but when. Centuries from now, no one will remember the crash of the stock and housing markets. What they will remember is the gift so generously bestowed on humanity: in September 2008, Gwyneth launched goop.
Quickly, Paltrow’s influence has reached billions and her empire, backed by an army of #goopmoms, stands steadfast against all adversity. She has developed technology that rivals Jeffrey Bezos. Moreover, she has granted us wisdom that transcends works everywhere. Her legacy would be cemented in a mere matter of years.
What people never really questioned was how she was able to achieve all this. The answer to this is simple: Gwyneth Paltrow is using Gwyn Cam. Today, we bring you the technology that restores people to collaborative spaces before the COVID-19 pandemic and uses filters and Computer Vision to have fun, elevate and bring back this sense of camaraderie during online calls, gaming and more video applications!
What it does
Gwyn Cam is a free to use online application that attempts to bring people closer to the metaverse. Using computer vision technologies to facial detection, and simple javascript, this app is a camera enabled filter system with the possibility of customization! With this app, you can add in any picture you want, and filters you want and bring back this idea of collaboration and interactiveness, even though it is through a screen! We had an incredible amount of fun building this app, even if we had little to no knowledge about adding filters to cameras with moving individuals, and even to add a camera onto a Django Website! Overall, this app is both a learning experience, and a way to interact with others while trying out different filters and customizing your own!
How we built it
This project was created using the Django framework in Python and implemented OpenCV in the backend to create a facial-recognition algorithm. This allowed us to create a JavaScript video stream object that is sent to the HTML frontend. Next, we used JavaScript (specifically, the J5 library) to upload images of Gwyneth Paltrow and overlay them with the user’s face on the live video stream. Later, non-Gwyneth facial filters were also added as options.
Challenges we ran into
We ran into many challenges, from conceptualizing our project to the tools sometimes not working on different devices to a hosting challenge on heroku, but through everything, our team's passion for technology and willingness to open our minds to each other, our respect for one another and our patience helped us concretize this project in only 1 day and a few hours, which we are quite proud of! The team communication, having a list of requirements and starting with the mindset of an MVP helped us reach our goal to submit a project at this hackathon!
Accomplishments that we're proud of
For the majority of the group, this was the first time using image-recognition tools, this was also by far our largest challenge as well. This project helped us explore how these tools work in Python specifically, and it was a great starting point for bringing future ideas to life. At a project management level, we have tended to run short on time at previous Hackathon events; this time, we were able to get a well-designed app together and achieve our MVP goals within the time limits. Finally, we can say that we were very creative with the theme; we can say with the utmost confidence that probably no other team at this event was able to draw the connection between restoration and Gwyneth Paltrow.
What's next for So You Wanna Be Gwyneth Paltrow
The next step here would really be to have a place where people can take pictures with filters on their faces, take screen recordings and simply have call conversations with their custom filters, which a lot of applications such as microsoft teams does not enable. This project could be expanded while hosting it to add the possibility of collaborating on filters together with friends before uploading them!
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