🛠️ Real-Time Object Detection with YOLOv8 and OpenCV

This repository contains a project for real-time object detection using the YOLOv8 model and OpenCV. The project demonstrates how to leverage a pre-trained YOLO model to detect various objects in a live video stream from a webcam. A special feature highlights knives with a red bounding box for easy identification.

Project Overview

The primary goal of this project is to showcase the real-time object detection capabilities of the YOLOv8 model. YOLO (You Only Look Once) is renowned for its speed and accuracy in object detection tasks. This project includes the following steps:

1. 📹 Initializing the Webcam:

   • Setting up the webcam and configuring the resolution.

2. 🔍 Loading the YOLO Model:

   • Utilizing a pre-trained YOLOv8 model for object detection.

3. 📋 Defining Classes:

   • Listing the objects that the model can detect.

4. 🎥 Capturing and Processing Video Frames:

   • Reading frames from the webcam and processing them with the YOLO model to detect objects.

5. ✏️ Drawing Bounding Boxes:

   • Highlighting detected objects with bounding boxes and displaying their class names and confidence scores.

6. 🔪 Special Handling for Knives:

   • Drawing a red bounding box around knives to make them easily identifiable.

Requirements

• Python 3.x
• OpenCV
• Ultralytics YOLOv8

Feel free to explore the repository and contribute to enhancing this real-time object detection system! 🚀