Color Detection with OpenCV and PIL

📝 Overview

This Color Detection project uses OpenCV and PIL to identify and track specific colors (yellow, red, blue, and green) in real-time using a webcam feed. The system converts the webcam feed to HSV color space and detects colors based on predefined ranges. When a color is detected, the project highlights the object on the screen and labels it.

✨ Features

• 🎥 Real-time Color Detection: Detect and highlight objects based on their color in the webcam feed.
• 📊 Multiple Colors: The system is configured to detect four colors: Yellow, Red, Blue, and Green.
• 🔍 Dynamic Bounding Box: A bounding box is drawn around the detected color, and the object is labeled accordingly.
• 🎨 Adjustable Color Ranges: The HSV limits for each color can be customized for fine-tuning color detection.

📋 Requirements

• 🐍 Python 3.x
• 👁️ OpenCV 4.8.0.74
• 🖼️ Pillow (PIL) 9.3.0
• ➗ Numpy 1.23.5

🎨 Detected Colors

The program is configured to detect the following colors:

• Yellow: Objects colored yellow will be highlighted with a bounding box and labeled "Yellow".
• Red: Objects colored red will be detected, outlined, and labeled "Red".
• Blue: Blue objects will be tracked with a bounding box and labeled "Blue".
• Green: Green objects will be detected, highlighted, and labeled "Green".

🔧 Code Breakdown

Main Script

The script opens a webcam feed using OpenCV and continuously processes frames to detect colors:

• HSV Conversion: Each frame from the webcam is converted to HSV color space for better color detection.
• Color Masks: Based on predefined HSV ranges, a mask is created for each color (Yellow, Red, Blue, Green).
• Bounding Boxes: When a color is detected in the frame, a bounding box is drawn around the object using the mask.

get_limits(color) Function

This utility function takes an RGB color and returns the HSV lower and upper bounds for detecting that color:

• Converts the given color to HSV.
• Defines the lower and upper bounds with some tolerance to detect colors in a range.