Real-Time-Emotion-Detector-with-Voice-Feedback

This project is a real-time emotion detection system that captures emotions through your Webcam, recognizes various facial expressions (like happy, sad, angry, etc.), and speaks out the detected emotion using text-to-speech.

Features

• Detects seven emotions: happy, sad, angry, fear, surprise, disgust, and neutral.
• Real-time webcam feed with bounding boxes around detected faces.
• Speaks the detected dominant emotion using the text-to-speech feature.
• Displays confidence levels for all emotions detected on screen.

Demo

example of detecting and speaking emotions in real-time.

Prerequisites

Make sure you have Python 3.6+ installed on your system. You can download it from python.org.

Libraries

You need the following Python libraries to run the project:

• opencv-python
• fer
• pyttsx3
• tensorflow (required for fer)

You can install these using pip:

pip install opencv-python fer pyttsx3 tensorflow

How to Run

1. Clone this repository to your local machine:

   git clone: https://github.com/zouraiz523/Real-Time-Emotion-Detector-with-Voice-Feedback.git

2. Navigate to the project directory:

   cd real-time-emotion-detector

3. Run the Python script:

   python emotion_detector.py

4. Allow access to your webcam, and the emotion detection will start in real-time!

Usage

• The application will open a window displaying your webcam feed.
• It will recognize emotions in real-time and draw a bounding box around each detected face.
• The dominant emotion will be displayed above each face.
• The program will speak the detected emotion out loud.
• Press 'q' to quit the program.

Project Structure

.
├── emotion_detector.py     # Main script to run the emotion detection
├── README.md               # Project documentation
└── requirements.txt        # List of required dependencies

Potential Improvements

• Add support for more emotions by using advanced machine learning models.
• Improve speech by adding more voice options and customizing text-to-speech speed and tone.
• Integrate face recognition to map emotions to specific individuals.

Contributing

Contributions are welcome! If you have any ideas or improvements, feel free to create a pull request. Please make sure your contributions are well-documented and tested.

1. Fork the project.
2. Create your feature branch: git checkout -b feature/your-feature.
3. Commit your changes: git commit -m 'Add some feature'.
4. Push to the branch: git push origin feature/your-feature.
5. Open a pull request.