📸 Auto-Selfie with OpenCV

This Python project uses OpenCV to capture selfies automatically when it detects a smiling face in the camera frame. It provides a fun and interactive way to take pictures with just a smile!

✨ Features

• Detects faces and smiles in real-time using OpenCV.
• Captures selfies when a smiling face is detected.
• Saves selfies with timestamps in PNG format.
• Simple and interactive.

📋 Prerequisites

Before you begin, ensure you have met the following requirements:

• Python (version 3.x recommended)
• OpenCV (pip install opencv-python)
• Haar cascades for face and smile detection (haarcascade_frontalface_default.xml and haarcascade_smile.xml)

🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/sid-lakhani/auto-selfie.git
   cd auto-selfie

2. Install the required Python packages:

   pip install opencv-python

3. Download the Haarcascade XML files for face and smile detection and place them in the project directory.
4. Run the script:

   python main.py

📷 Usage Steps

1. Start the script – Launch the program by running:

   python main.py

2. Smile at the camera – The program continuously detects faces and smiles in real time.
3. Selfie Capture – As soon as a smile is detected, the selfie is automatically taken.
4. Save & View – The image is saved in the project directory with a timestamp.
5. Exit the program – To close the camera and stop the script, press the q key.

🔮 Future Scope

• Pose Estimation – Integrate pose detection to capture selfies only when a user strikes a specific pose.
• Gesture-Based Capture – Allow users to trigger selfies using hand gestures instead of just smiles.
• AI-Based Image Enhancement – Apply AI-powered enhancements like automatic brightness correction and noise reduction.
• Multi-Person Smart Framing – Automatically adjust the frame and focus based on the number of people in view.

🤝 Contributing

Contributions are welcome! Here’s how you can contribute:

1. Fork the repository on GitHub.
2. Create a new branch (git checkout -b feature-name).
3. Make your changes and commit (git commit -m "Add new feature").
4. Push to GitHub (git push origin feature-name).
5. Open a pull request to merge your changes.

📜 License

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