About the Project

Inspiration

ColorPi was born out of my first hackathon experience, which inspired me to dive into the vast range of tech tools available today. I was eager to explore ways of organizing data through OCR (Optical Character Recognition) technology and applying it to image processing. The project combines my curiosity for data organization and the potential for OCR in extracting meaningful information from visuals.

What it Does

ColorPi is designed to take images from various sources, analyze their color profiles, and output these insights in a user-friendly format on a web interface. It extracts specific color metrics (such as RGB, CMYK, HEX, and CIELAB) from images and displays this information alongside the original visuals, enabling users to understand and compare color characteristics effectively. ColorPi is especially valuable for applications in laboratory analysis, design, and other fields where color precision matters.

How We Built It

This project was developed using Streamlit for the web interface and Python for backend image processing. Streamlit allowed us to create an interactive, easy-to-use web app with a focus on rapid data display and user accessibility. The app currently reads images, extracts color data using OCR (which interprets textual information from visuals), and displays the data dynamically on a local or cloud-hosted website. The app connects to external file storage systems, including Google Drive, for retrieving images.

Challenges We Ran Into

This project presented several challenges, particularly as I navigated new territory. Bugs in the code were frequent and sometimes hard to debug, especially with OCR inconsistencies and handling complex image processing tasks. Another challenge was working with Google Drive implementation, where integrating image retrieval with consistent results required experimentation and troubleshooting. Developing this app also pushed me to balance functionality with interface design, making it both informative and intuitive to users.

Accomplishments That We’re Proud Of

One of the biggest accomplishments for me was the resilience to keep pushing forward, especially during long hours of debugging and learning new tools. Despite setbacks, I was able to create something tangible that I can share and continue to improve. Staying awake, committed, and seeing this project through to completion is a major personal achievement.

What We Learned

Through ColorPi, I learned about the innovative possibilities in computer science, especially in the fields of image processing and data organization. Participating in this project showed me the depth and speed of development possible with the right tools and ideas. Seeing the creativity and potential in these tools has opened my eyes to a world of opportunities in this industry.

What's Next for ColorPi

ColorPi is just the beginning. Moving forward, I aim to make the app more functional and practical by refining the OCR process to handle errors more gracefully, improving Google Drive integration, and implementing better photo storage solutions. The goal is to enhance ColorPi to become a robust, reliable color analysis tool for a wider audience.

Built With

• python
• streamlit