Emotion and Object Detection with YOLO

Overview

This project utilizes a YOLOv8 model for real-time object detection and a TensorFlow-based emotion model for emotion recognition. The system captures video from a webcam, detects objects, specifically faces, and predicts emotions for the detected faces.

Features

• Real-time object detection using YOLOv8 model.
• Emotion detection from faces using a TensorFlow-based emotion model.
• Annotates video frames with detected objects and emotions.

Prerequisites

• Python 3.x
• OpenCV
• TensorFlow
• Ultralyitcs (for YOLOv8)

Setup

1. Clone the repository:

   git clone https://github.com/your-repo/emotion-object-detection.git
   cd emotion-object-detection

2. Create a virtual environment and activate it:

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

3. Install the required dependencies:

   pip install -r requirements.txt

4. Download the YOLO model:

   ultralytics download yolov8n.pt

Usage

1. Run the script:

   python main.py

2. The webcam feed with object and emotion detection will be displayed in real-time.

3. Press 'q' to exit the application.

Models Used

• YOLOv8: For object detection.
• TensorFlow: For emotion detection using a simple CNN model.

Contributions

Contributions are welcome! Feel free to fork this repository and submit pull requests.

License

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