🌟 MoodLyft Mirror RealTime Emotion Analyzer 🌟

Elevating your mood with intelligent emotion detection

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📱 What is MoodLyft Mirror RealTime Emotion Analyzer?

The MoodLyft Mirror is an advanced emotion detection project that leverages AI to:

• Recognize emotions in real-time through facial analysis.
• Provide uplifting and personalized compliments based on detected emotions.
• Utilize a sleek and modern UI to enhance user experience.

"Enhance your day by visualizing and understanding your emotions!"

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📚 Table of Contents

1. ✨ Features
2. 🦾 Tech Stack
3. 📸 Screenshots
4. 👨‍🔧 Setup Instructions
5. 🎯 Target Audience
6. 🤝 Contributing
7. 📜 License

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✨ Features

Advanced Emotion Detection

• Real-time emotion recognition using advanced AI algorithms with MTCNN face detection
• Displays dominant emotions like happiness, sadness, anger, surprise, fear, and disgust
• Confidence levels and emotion history tracking
• Intelligent frame skipping for optimal performance

Personalized Compliments

• Intelligent compliments tailored to your mood with 5+ variations per emotion
• Advanced text-to-speech (TTS) with voice selection and non-blocking audio
• Smart cooldown system for natural interaction timing
• Emoji integration for enhanced visual feedback

Modern Glassmorphism UI

• Sleek, translucent interface with modern glassmorphism effects
• Smooth animations including pulsing borders and color transitions
• Dynamic gradient backgrounds that respond to emotions
• Real-time performance metrics with color-coded FPS display
• Animated confidence bars and emotion history graphs

Performance Optimizations

• Intelligent frame processing with configurable skip rates
• Threaded audio processing for smooth operation
• Auto-hardware detection with optimized presets
• Memory management with circular buffers
• Cross-platform font optimization

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🦾 Tech Stack

🌐 Core Technologies

• Python: Core programming language.
• OpenCV: For real-time video processing and face detection.
• FER: Facial Expression Recognition library for emotion analysis.
• Pillow: For enhanced text rendering and UI effects.

Additional Libraries

• Pyttsx3: For advanced TTS functionality with voice selection
• NumPy: For numerical operations and efficient data processing
• SciPy: For advanced mathematical computations and optimizations
• Matplotlib: For real-time emotion history visualization
• Psutil: For system monitoring and auto-performance tuning

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📸 Screenshots

Emotion Detection	Modern UI	Compliments in Action

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👨‍🔧 Setup Instructions

Prerequisites

• Python 3.11 or higher installed on your system.
• A webcam for real-time emotion detection.
• Install required Python packages listed in requirements.txt.

Steps to Run the Project

1. Clone the Repository

   git clone https://github.com/alienx5499/MoodLyft-Mirror-RealTime-Emotion-Analyzer-RealTime-Emotion-Analyzer.git
   cd MoodLyft-Mirror-RealTime-Emotion-Analyzer

2. Set Up a Virtual Environment Setting up a virtual environment ensures that your project's dependencies are isolated from your global Python installation, preventing version conflicts and promoting a clean development environment.

   For macOS/Linux

   1. Create a virtual environment:

   python3 -m venv moodlyft_env

   2. Activate the virtual environment:

   source moodlyft_env/bin/activate

   For Windows

   1. Create a virtual environment:

   python3 -m venv moodlyft_env

   2. Activate the virtual environment:

   moodlyft_env\Scripts\activate 

3. Install Dependencies For macOS/Linux

   pip install -r requirements-macos.txt

   For Windows

   pip install -r requirements-windows.txt

4. Run the Setup Script (Recommended)

   python setup.py

   This will automatically detect your system and apply optimal settings

   OR manually run the application:

   python main.py

5. Experience the App

   • Ensure your webcam is connected and accessible
   • Use keyboard controls: 'q' to quit, 's' for screenshot, 'r' to reset history
   • Try the interactive demo: python demo.py

⚙️ Performance Configuration

The application automatically detects your hardware and applies optimal settings. You can customize performance in config.py:

# Performance presets available:
from config import HardwarePresets

HardwarePresets.high_performance()  # For powerful systems
HardwarePresets.balanced()          # Default balanced mode  
HardwarePresets.performance_mode()  # For older hardware
HardwarePresets.battery_saver()     # For laptops/mobile

🎮 Controls

Key	Action
q	Quit application
s	Save screenshot
r	Reset emotion history

🎯 Performance Improvements

Metric	Original	Optimized	Improvement
FPS	15-20	25-30	+40-50%
Memory Usage	~300MB	~200MB	-33%
CPU Usage	~40%	~25%	-37%
TTS Blocking	Yes	No	Non-blocking

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🎯 Target Audience

1. Individuals: Track your mood and uplift your spirits daily.
2. Therapists: Utilize emotion detection as part of therapy sessions.
3. Developers: Enhance and expand the project with additional features.

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🤝 Contributing

We ❤️ open source! Contributions are welcome to make this project even better.

1. Fork the repository.
2. Create your feature branch.

   git checkout -b feature/new-feature

3. Commit your changes.

   git commit -m "Add a new feature"

4. Push to the branch and open a pull request.

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📜 License

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

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📬 Feedback & Suggestions

We value your input! Share your thoughts through GitHub Issues.

💡 Let's work together to uplift emotions and create positivity!