face-mood-analyzer 1.0.2

AI-powered emotion analyzer that detects faces in photos and generates corresponding music

Face Mood Analyzer

A stable, production-ready AI application that analyzes emotions in photos and generates corresponding musical journeys. Built with well-tested, stable versions of industry-standard libraries.

Features

• Advanced face detection and recognition using RetinaFace
• Emotion analysis across multiple photos (7 basic emotions)
• Face quality assessment and filtering
• Emotion-based music generation
• Interactive web interface
• Video generation with emotional music
• Support for multiple input photos
• Real-time processing and analysis

Technical Stack

• TensorFlow 2.12.0
• PyTorch 2.0.1
• OpenCV 4.8.0
• DeepFace 0.0.75
• Flask 2.3.3

Setup

1. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

3. Place your photos in the uploads directory

4. Run the application:

python app.py

Project Structure

• app.py: Main Flask application and web interface
• emotion_analyzer.py: Face detection and emotion analysis module
• music_generator.py: Music generation based on emotional patterns
• static/: Web assets (CSS, JavaScript, images)
• templates/: HTML templates for the web interface
• uploads/: Directory for input photos
• output/: Directory for generated content

Usage

1. Place your photos in the uploads directory
2. Run the application
3. Access the web interface at http://localhost:5000
4. Upload reference photos of the person to track
5. Upload photos to analyze
6. The system will process your photos and generate a musical journey

Requirements

• Python 3.8+
• CUDA-capable GPU (recommended for faster processing)
• Modern web browser
• 4GB+ RAM
• 10GB+ disk space

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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

Acknowledgments

• Built with Flask
• Uses deep learning for emotion detection
• Music generation powered by machine learning algorithms