🖼️ YOLOv8 Object Detector

Welcome to the YOLOv8 Object Detector project! This application utilizes YOLOv8 for real-time object detection with a user-friendly GUI built using Tkinter.

✨ Features

• Real-Time Object Detection: Capture video feed from your webcam and detect objects in real time.
• Customizable Settings: Easily adjust model parameters, confidence thresholds, frame rate, and bounding box color.
• Capture Still Images: Take snapshots of the detected objects.

🛠️ Installation

Follow these steps to set up the project on your local machine:

1. Clone the Repository

   git clone https://github.com/yourusername/YOLOv8-Object-Detector.git
   cd YOLOv8-Object-Detector

2. Install Dependencies

   Ensure you have Python installed, then run:

   pip install -r requirements.txt

3. Download YOLOv8 Models

   Download the YOLOv8 models you wish to use (e.g., yolov8n.pt, yolov8s.pt, yolov8m.pt) from the official YOLOv8 GitHub repository.

🚀 Usage

1. Run the Application

   python object_detector.py

2. Interact with the GUI

   • Capture Still Image: Click the "Capture Still Image" button to take a snapshot.
   • Configuration: Click the "Configuration" button to open the settings window where you can adjust:
     • Model
     • Confidence Threshold
     • Frame Rate
     • Box Color
   • Quit: Click the "Quit" button to exit the application.

⚙️ Configuration

Configuration settings are stored in a config.json file. You can manually edit this file or use the in-app configuration window to adjust settings. Here is an example of the config.json file:

{
  "model": "yolov8n.pt",
  "confidence_threshold": 0.5,
  "fps": 30,
  "box_color": "#00FF00"
}

📁 Code Structure

• object_detector.py: Main script that initializes the GUI and handles the detection logic.
• config_window.py: Contains the ConfigWindow class for the configuration GUI.
• requirements.txt: List of Python dependencies.

📦 Dependencies

• OpenCV: For video capture and image processing.
• Torch: Deep learning framework.
• Ultralytics YOLO: Pretrained YOLO models.
• Tkinter: GUI framework.
• Pillow: Image handling.

🤝 Contributing

Contributions are welcome! Feel free to submit a pull request or open an issue.

📄 License

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