Style AI

In recent years, online shopping has become quite popular. However, it's hard to tell how clothes look and fit on you. Style AI recommends a list of clothes and lets you try them on virtually.

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Inspiration

In recent years, especially post-COVID, online shopping has become extremely common. One big issue when shopping online is that users are unable to try on clothes before ordering them. This results in people getting clothes that end up not fitting or not looking great, which is something nobody wants. In addition, many people face constant difficulties in their life that limit their This gave us the inspiration to create Style AI as a way to let people try on clothes virtually before ordering them online.

What it does

Style AI takes a photo of you and analyzes the clothes you are currently wearing and gives detailed clothing recommendations of specific brands, shirt types, and colors. Then, the user has the option to try on each of the recommendations virtually.

How we built it

We used OpenCV to capture a photo of the user. Then the image is inputted to Gemini API to generate a list of clothing recommendations. These recommendations are then passed into google shopping API, which uses google search to find where the user can buy the recommended clothes. Then, we filter through the results to find clothes that have the correct image format.

The image of the shirt is superimposed onto a live OpenCV video stream of the user. To overlay the shirt on the user, we segmented the shirt image into 3 sections: left sleeve, center, and right sleeve. We also perform segmentation on the user using MediaPipe. Then, we warp each segment of the shirt onto the user's body in the video stream.

We made the website using Reflex.

Challenges we ran into

The shirt overlay aspect was much more challenging than expected. At first, we planned to use a semantic segmentation model for the shirt of the user because then we could warp and transform the shape of the real shirt to the shirt mask on the user. The issue was that semantic segmentation was very slow so the shirt wasn't able to overlay on the user in real-time. We solved this by using a combination of various OpenCV functions so the shirt could be overlaid in real-time.

Accomplishments that we're proud of

We are proud of every part of our project, since each required lots of research, and we are all proud of the individual contributions to the project. We are also proud that we were able to overcome many challenges and adapt to things that went wrong. Specifically, we were proud that we were able to use a completely new framework, reflex, which allowed us to work in python natively across both the frontend and the backend.

What we learned

We learned how to use Reflex to create websites. We also learned how to use APIs. Also, we learned about more functionalities of MediaPipe and OpenCV when writing the shirt overlay code.

What's next for Style AI

Expand Style AI for all types of clothing such as pants and shoes. Implementation of a "bulk order" functionality allowing users to order across online retailers. Add more personalized recommendations. Enable real-time voice assisted chat bot conversations to simulate talking to a fashion expert in-person.

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