Face Detection Application

A real-time face detection application built with Streamlit and OpenCV Haar Cascades, featuring image processing filters and webcam support.

Features

• 📸 Image Upload Mode: Upload and process images with face detection
• 🎥 Real-time Webcam Mode: Live face detection from your camera
• 🎨 Image Processing Filters: Gaussian blur, edge detection, brightness/contrast adjustment
• ⚙️ Adjustable Detection Parameters: Fine-tune scale factor and min neighbors
• 💾 Save Captured Frames: Export processed images with detections

Quick Start

Prerequisites

• Python 3.8 or higher
• Webcam (optional, for real-time detection)

Installation

1. Clone the repository:

   git clone <your-repo-url>
   cd Face-Detection-App

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   streamlit run app.py

The app will open in your default browser at http://localhost:8501.

Docker Installation (Alternative)

docker build -t face-detection-app .
docker run -p 8501:8501 face-detection-app

Usage

Image Mode

1. Click "📷 Image Mode" in the sidebar
2. Upload an image (JPG, JPEG, or PNG)
3. Adjust detection parameters and filters
4. Click "Detect Faces" to see results

Webcam Mode

1. Click "🎥 Webcam Mode" in the sidebar
2. Choose processing mode:
   • Continuous Detection: Real-time face detection
   • Detection on Demand: Detect only when you click
   • Filter Only: Apply filters without detection
3. Click "Start Webcam"
4. Use "📸 Save Current Frame" to export images

Configuration

Adjust detection sensitivity in the sidebar:

• Scale Factor (1.05-1.5): Lower = more sensitive but slower
• Min Neighbors (1-10): Higher = fewer false positives

Troubleshooting

Webcam Issues

• Camera not detected: Check permissions and connections
• Multiple cameras: The app tries indices 0-4 automatically
• Linux: Ensure user is in video group: sudo usermod -a -G video $USER

Performance

• Use smaller images for faster processing
• Reduce webcam resolution in .streamlit/config.toml
• Try simpler filters (Gaussian blur vs. edge detection)

Missing Haar Cascades

If cascade files aren't found, they'll fall back to OpenCV's built-in paths automatically.

Development

Running Tests

pip install pytest pytest-cov
pytest tests/ -v
pytest tests/ --cov=src --cov-report=html

Code Formatting

pip install black mypy
black src/ tests/
mypy src/

Project Structure

DIP_Project/
├── app.py                  # Entry point
├── src/
│   ├── config.py          # Configuration and paths
│   ├── detector.py        # Face detection logic
│   ├── main.py            # Streamlit UI
│   ├── style.py           # CSS styling
│   └── utils/
│       └── visualization.py  # Drawing utilities
├── haarcascades/          # Haar cascade XML files
├── tests/                 # Unit tests
├── .streamlit/            # Streamlit config
├── requirements.txt       # Dependencies
└── Dockerfile            # Container setup

Dependencies

• streamlit: Web UI framework
• opencv-python-headless: Computer vision (no GUI dependencies)
• numpy: Numerical operations
• pillow: Image processing

See requirements.txt for pinned versions.

Future Enhancements

• DNN-based face detection (more accurate)
• MediaPipe face mesh integration
• Video file upload and processing
• Face recognition capabilities
• Export detection metrics (CSV/JSON)

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new features
4. Submit a pull request

Acknowledgments

• OpenCV Haar Cascades for face detection
• Streamlit for the interactive UI framework