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ID Year Detection Project

Overview

This project utilizes computer vision and audio processing to detect specific colors in real-time using a webcam. When a color is detected, the application highlights the area containing that color in the video feed and provides an auditory output of the detected color.

Objectives

  • Real-time Color Detection: Capture video frames from a webcam and convert them to the HSV color space for effective color detection.
  • Bounding Box Visualization: Draw bounding boxes around detected colors to visually indicate their locations in the video feed.
  • Audio Feedback: Use text-to-speech technology to announce the detected color, providing an interactive experience.

Key Features

  1. Color Range Detection: The application is capable of detecting multiple colors including yellow, brown, green, and blue.
  2. Bounding Box Highlighting: When a color is detected, a green rectangle is drawn around the area in the video feed where the color is found.
  3. Text-to-Speech Integration: Each detected color is announced via a speech synthesis engine, enhancing user interaction.

Technologies Used

  • OpenCV: For video capture and image processing.
  • Pillow (PIL): For image manipulation and bounding box calculations.
  • NumPy: For array operations and mask generation.
  • pyttsx3: For converting text to speech, providing audio feedback.

Installation

Prerequisites

Ensure you have Python installed on your machine.

Dependencies

Install the required libraries using pip:

pip install opencv-python pillow numpy pyttsx3

About

This project utilizes computer vision and audio processing to detect specific colors in real-time using a webcam. When a color is detected, the application highlights the area containing that color in the video feed and provides an auditory output of the detected color.

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