Computer Vision Projects

A collection of computer vision and machine learning projects showcasing real-world applications of AI in healthcare and real-time detection systems.

🚀 Projects Overview

1. Face Detection System

A real-time face detection web application built with OpenCV and FastAPI.

Key Features:

• Real-time face detection using Haar cascade classifiers
• Modern web interface with live video feed
• Video recording capabilities
• Cross-platform compatibility

Technologies: Python, OpenCV, FastAPI, HTML5, JavaScript

2. Pneumonia Detection

A medical AI application for detecting pneumonia from pediatric chest X-ray images using deep learning.

Key Features:

• CNN-based pneumonia classification
• Web interface for medical image upload
• Confidence scoring for predictions
• FastAPI backend with CORS support

Technologies: Python, TensorFlow, Keras, FastAPI, HTML5

🛠️ Installation & Setup

Prerequisites

• Python 3.8+
• pip package manager
• Web browser with camera access (for face detection)

Quick Start

1. Clone the repository

   git clone https://github.com/zer-art/Computer-Vision-Projects.git
   cd Computer-Vision-Projects

2. Choose a project to run

   For Face Detection:

   cd Face-Detection
   pip install -r requirements.txt
   python main.py

   Then open http://localhost:8000 in your browser.

   For Pneumonia Detection:

   cd pneumonia-detection
   pip install -r requirements.txt
   uvicorn main:app --reload

   Then open index.html in your browser.

📁 Repository Structure

Computer-Vision-Projects/
├── Face-Detection/                 # Real-time face detection system
│   ├── data/                      # Haar cascade classifier files
│   ├── src/                       # Core detection algorithms
│   ├── static/                    # Web assets (CSS, JS)
│   ├── index.html                 # Web interface
│   ├── main.py                    # FastAPI server
│   ├── requirements.txt           # Python dependencies
│   └── Readme.md                  # Project documentation
├── pneumonia-detection/           # Medical image classification
│   ├── index.html                 # Upload interface
│   ├── main.py                    # FastAPI server
│   ├── requirements.txt           # Python dependencies
│   └── Readme.md                  # Project documentation
└── README.md                      # This file

🎯 Use Cases

Face Detection System

• Security Systems: Access control and surveillance
• Photography: Automatic face focusing and detection
• Social Media: Automatic tagging and photo organization
• Healthcare: Patient monitoring and identification
• Education: Attendance tracking systems

Pneumonia Detection

• Medical Diagnosis: Assist radiologists in pneumonia screening
• Remote Healthcare: Telemedicine applications
• Research: Medical image analysis studies
• Education: Training medical students and residents
• Public Health: Mass screening programs

🔧 Technical Details

Face Detection

• Algorithm: Haar Cascade Classifiers (OpenCV)
• Real-time Processing: WebRTC integration
• Backend: FastAPI with async support
• Frontend: Vanilla JavaScript with modern web APIs

Pneumonia Detection

• Algorithm: Convolutional Neural Network (CNN)
• Framework: TensorFlow/Keras
• Data: Pediatric chest X-ray images
• Deployment: FastAPI RESTful API

🌟 Features

• Web-based Interfaces: No desktop application installation required
• Cross-platform: Works on Windows, macOS, and Linux
• Responsive Design: Mobile and desktop friendly
• API-first: RESTful APIs for easy integration
• Real-time Processing: Low latency detection and classification
• Extensible: Modular architecture for easy feature additions

📊 Performance

Face Detection

• Accuracy: High detection rate with minimal false positives
• Speed: Real-time processing at 30+ FPS
• Compatibility: Works with most web cameras

Pneumonia Detection

• Accuracy: Trained on validated medical datasets
• Processing Time: < 2 seconds per image
• Input Format: JPEG, PNG chest X-ray images

🤝 Contributing

We welcome contributions! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Guidelines

• Follow PEP 8 coding standards
• Add unit tests for new features
• Update documentation as needed
• Ensure cross-platform compatibility

📋 Requirements

Face Detection

• opencv-python>=4.8.0
• fastapi>=0.100.0
• uvicorn>=0.20.0
• numpy>=1.24.0

Pneumonia Detection

• tensorflow>=2.10.0
• fastapi>=0.100.0
• pillow>=9.0.0
• numpy>=1.24.0

🔮 Future Enhancements

Planned Features

• Multi-disease Detection: Expand beyond pneumonia
• Mobile Applications: Native iOS and Android apps
• Real-time Emotion Detection: Facial expression analysis
• 3D Face Reconstruction: Advanced facial modeling
• Edge Deployment: TensorFlow Lite optimization
• Database Integration: Patient record management
• Advanced Analytics: Detection statistics and reporting

Research Directions

• Integration with larger medical imaging datasets
• Federated learning for privacy-preserving model training
• Explainable AI for medical decision support
• Multi-modal analysis combining different imaging techniques

📄 License

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

🙏 Acknowledgments

• OpenCV Community for computer vision tools and algorithms
• TensorFlow Team for deep learning framework
• FastAPI Developers for the modern web framework
• Medical Imaging Researchers for providing datasets and insights
• Open Source Community for continuous support and contributions

📞 Support

If you encounter any issues or have questions:

1. Check the individual project READMEs for specific documentation
2. Open an issue in the GitHub repository
3. Review existing issues for similar problems
4. Contact the maintainers for collaboration opportunities

🏆 Awards & Recognition

This repository showcases practical applications of computer vision in:

• Healthcare technology
• Real-time systems
• Web-based AI applications
• Medical image analysis

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Bringing AI and computer vision to real-world applications