PokerFace

Inspiration

The inspiration for PokerFace came from the frustration many players experience during home poker games. The process of buying in and cashing out chips can be tedious and prone to errors, especially when the game gets intense. We wanted to streamline this aspect of poker, making it easier and more efficient for players to focus on the game rather than the logistics of handling chips.

What it does

PokerFace automates the calculation and distribution of poker winnings. By using a combination of image recognition and real-time calculations, it detects the poker chips and handles the payouts, making the entire process smoother. Players no longer have to manually count or sort chips at the end of a game—it’s all done for them automatically!

How we built it

We developed PokerFace using a modern tech stack, which includes:

React for building a user-friendly, responsive frontend interface. Flask as our backend framework for handling API calls and processing logic. OpenCV for implementing the chip detection and recognition algorithm, allowing the app to visually identify different poker chips and their values. Supabase for managing our database needs, handling player accounts and game data in a scalable, real-time environment. Stripe for secure and seamless payment integration, allowing players to handle buy-ins and payouts directly through the app.

Challenges we ran into

Building PokerFace came with its fair share of challenges: Payment Integration: Incorporating Stripe's payment API was trickier than expected, especially when dealing with different currencies and ensuring secure transactions. Refining the OpenCV Algorithm: Getting the chip detection to work consistently was a tough nut to crack. We had to fine-tune the algorithm to accurately differentiate between chips of various colors and values, even in low lighting or with slightly damaged chips. Chip Recognition: One of the biggest hurdles was training the system to correctly recognize different chip types across different environments, taking into account variations like lighting, camera angles, and background noise.

Accomplishments that we're proud of

Creating a prototype for the chip detection algorithm!

What we learned

The development of PokerFace was a huge learning experience for our team: We gained deep insights into how payment APIs work, particularly when it comes to handling complex transactions in a gaming context. We learned a lot about object detection, specifically using OpenCV to recognize poker chips in a dynamic environment. Understanding the nuances of image processing and algorithm refinement was a big takeaway from this project.