Real-Time Object Detection

This project demonstrates real-time object detection using the YOLO (You Only Look Once) model. It allows you to perform object detection on a video file and draw bounding boxes around detected objects with their class labels and confidence scores.

Table of Contents

• Installation
• Usage
• Project Structure
• Requirements
• License

Installation

To run this project locally, follow these steps:

1. Clone the repository to your local machine:

   git clone https://github.com/umutonuryasar/Real-Time-Object-Detection.git
   cd Real-Time-Object-Detection
   

2. Install the required Python packages using pip:

   pip install -r requirements.txt
   

Usage

1. Make sure you have your video file (sample_video.mp4) and class names file (classes.txt) in the data/ directory. You can customize these files as needed.

2. Run the object detection script from the project root:

   python main.py
   

3. The object detection results will be displayed in a 'Object Detection' window. Press 'q' to exit the application.

Project Structure

The project follows this directory structure:

Real-Time-Object-Detection/
│
├── data/
│   ├── sample_video.mp4
│   ├── classes.txt
│   └── yolo-Weights/
│       └── yolov8n.pt
│
├── src/
│   ├── __init__.py
│   ├── object_detection.py
│   └── utils.py
│
├── requirements.txt
├── README.md
└── main.py

• data/: Contains video and class names files.
• src/: Contains the project source code.
• requirements.txt: Lists project dependencies.
• main.py: Entry point for the project.

Requirements

• Python 3.x
• OpenCV (opencv-python)
• Ultralytics (ultralytics)

You can install the required packages using pip as mentioned in the installation steps.

License

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