🌟 MoodLyft Mirror Input Emotion Analyzer 🌟

Elevating your mood with intelligent emotion detection

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📱 What is MoodLyft Mirror Input Emotion Analyzer?

The MoodLyft Mirror Input Emotion Analyzer is a comprehensive emotion detection tool that leverages AI to:

• Analyze Emotions in Media: Detect emotions in static images and videos by processing files placed in designated input folders.
• Provide Uplifting and Personalized Compliments: Generate tailored compliments based on detected emotions to enhance user experience.
• Organized Input/Output Workflow: Easily manage your media by placing inputs in specific directories and accessing analyzed outputs effortlessly.

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

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📚 Table of Contents

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

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✨ Features

Emotion Detection

• Analyze Emotions in Media: Detect emotions in uploaded images and videos using advanced AI algorithms.
• Displays Dominant Emotions: Identify and display emotions such as happiness, sadness, anger, and more.

Personalized Compliments

• Tailored Uplifting Messages: Generate intelligent compliments based on the detected emotions.
• Text-to-Speech (TTS) Functionality: Deliver compliments audibly for an enhanced interactive experience.

Organized Input/Output Workflow

• Structured Directories: Place your input media in designated folders and retrieve analyzed outputs from organized directories.
• Seamless Processing: Easily manage and process multiple media files with minimal effort.

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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 TTS functionality.
• NumPy: For numerical operations and efficient data processing.

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📸 Screenshots

Input 1	Output 1
Input 2	Output 2
Input 3	Output 3
Input 4	Output 4
Input 5	Output 5
Input 6	Output 6
Input 6	Output 6

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🧩 Try the App

Want to Experience MoodLyft Mirror?

Clone the repository and follow the setup instructions to run the project locally.
Stay tuned for future releases!

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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-Input-Emotion-Analyzer.git
   cd MoodLyft-Mirror-Input-Emotion-Analyzer
   cd MoodLyft-Mirror-Input-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. Add Your Media

• Images: Place your input images in the Input/Images directory.
• Videos: Place your input videos in the Input/Videos directory.

5. Run the Application

   python main.py

6. View Results

   • Processed images will be saved in Output/analyzedImages.
   • Processed videos will be saved in Output/analyzedVideos.

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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!