Vibrant Vision

Project Description: Color Detection and Tracking System

Links

GitHub repo -> https://github.com/Diya0297/VibrantVision.git

Overview

Our project is a real-time Color Detection and Tracking System using OpenCV and Python. This system leverages computer vision to identify, track, and highlight objects of specific colors through a webcam feed. By analyzing the HSV (Hue, Saturation, Value) color space, the program accurately detects various colors and labels them accordingly, making it an excellent tool for applications in robotics, accessibility tools for the visually impaired, and educational purposes.

Features

• Real-time Detection: Processes video frames in real-time to detect and track colored objects.
• Multi-Color Support: Identifies multiple predefined colors, including Black, Brown, Red, Orange, Yellow, Green, Cyan, Blue, and Violet.
• Largest Object Focus: Highlights and labels the largest detected object of each specified color.
• User-Friendly Interface: Displays bounding boxes and color names directly on the video feed for easy identification.

Technical Details

1. Color Boundaries Definition: Predefined HSV ranges for different colors.
2. Color Identification: Function to determine color name based on HSV values.
3. Contour Detection: Identifies contours of objects matching the color boundaries.
4. Real-time Processing: Captures video feed from the webcam, applies Gaussian blur, and converts frames to HSV color space for processing.
5. Largest Contour Highlighting: Draws bounding boxes around the largest detected object of each color and labels it.

How It Works

1. Video Capture: The system captures video from the webcam.
2. Frame Processing: Each frame is blurred and converted to the HSV color space.
3. Masking and Contours: Masks are created for each color, and contours are identified.
4. Largest Object Detection: The largest contour for each color is found and highlighted.
5. Display: The processed frame is displayed with bounding boxes and color labels.

Applications

• Accessibility Tools: Assists colorblind individuals by labeling and identifying colors in their environment.
• Educational Tools: Aids in teaching computer vision and color theory concepts.
• Quality Control: Used in manufacturing for color-based quality inspection.

Learning

• We made this project to help people with color blindness and in this process we got to learn about openCV library frame work of python. We had to learn the way the frame works and do lot of math so that the frame identifies the object color with maximum accuracy.