Portfolio Details

Each project in this portfolio reflects my hands-on experience in solving real-world problems using data science, machine learning, and full-stack development. From predictive models to intelligent web applications, these solutions are built with a focus on scalability, functionality, and impact.

Project Information

  • Project Name: AI Color Detection Studio
  • Category: Computer Vision & Real-time Detection
  • Model Type: HSV Color Space Detection with OpenCV
  • Github Repo: Link
  • Live Link: Link

Technologies Used

This project is built using Python with OpenCV for computer vision processing and Streamlit for the interactive web interface. It leverages HSV color space for accurate color detection, with special handling for red hue wrap-around. The system includes real-time video processing through WebRTC, contour detection for precise object tracking, and a responsive UI with custom CSS styling for professional presentation.

Details of Project

Tired of manually identifying colors in images or videos? This advanced tool automates the process with computer vision precision.

The AI Color Detection Studio is a sophisticated real-time system that detects and tracks specific colors in live video feeds. Whether you're a designer verifying color schemes, a quality inspector checking product colors, or an educator demonstrating color theory, this tool provides instant visual feedback with bounding boxes and detection metrics.

The system handles challenging lighting conditions and includes special logic for detecting red hues (which wrap around in HSV space). With multiple color selection methods and real-time statistics, it's a versatile solution for professional color analysis.

Project Features

  • Real-time color detection in live video streams with WebRTC integration
  • Multiple color selection methods: RGB sliders, color picker, and preset colors
  • Advanced HSV color space processing with special handling for red hues
  • Contour detection for precise object localization and area calculation
  • Comprehensive detection statistics (counts, frame rate, detection percentage)
  • Professional UI with interactive elements and visual feedback
  • Responsive design that works on both desktop and mobile devices

Technical Implementation

The system converts camera frames to HSV color space for more consistent color detection under varying lighting conditions. It uses OpenCV's inRange function to create a mask of the target color, then applies contour detection to identify and highlight objects. The WebRTC integration enables efficient real-time video streaming directly in the browser, while Streamlit provides the interactive web interface with minimal latency.

Special algorithms handle the red color challenge (where hues wrap around 0/180 in HSV space) by implementing dual-range detection when needed. The system also includes sensitivity controls and visual feedback mechanisms to optimize detection for different use cases.