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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

Bringing AI and computer vision to real-world applications