YOLOv8-ObjectTracker

A real-time object detection and tracking application using YOLOv8, OpenCV, and CVZone. This project detects objects from a video feed or webcam and draws bounding boxes with confidence scores around the detected objects.

Features

• Real-Time Object Detection: Uses YOLOv8 for accurate object detection in real-time.
• Object Tracking: Visualizes object bounding boxes and class labels.
• Customizable Classes: Predefined COCO dataset classes.
• Video/Live Feed: Supports webcam or video file inputs.

Demo

Requirements

• Python 3.x
• OpenCV
• Ultralytics YOLOv8
• CVZone

Installation

1. Clone the repository:

   git clone https://github.com/alihassanml/YOLOv8-ObjectTracker.git
   cd YOLOv8-ObjectTracker

2. Create and activate a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Download YOLOv8 weights:

   Place the YOLOv8 model weights (yolov8n.pt) in the weights/ directory.

Usage

Run the following command to start object detection with a webcam:

python app.py

Adjusting the Detection Classes

You can customize the object detection classes by modifying the classNames list in the code.

Code Explanation

The main components of this project include:

• YOLOv8: Used for object detection.
• OpenCV: To handle video input and display.
• CVZone: For better visualization of bounding boxes and labels.

import os
import cv2
import math
import cvzone
from ultralytics import YOLO

# Load the YOLOv8 model
model = YOLO('weights/yolov8n.pt')

# Predefined COCO classes
classNames = ["person", "bicycle", "car", "motorbike", "aeroplane", ...]

Contributing

Feel free to submit issues, fork the repository, and make pull requests. Contributions are welcome!

License

This project is licensed under the MIT License - see the LICENSE file for details.