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defiQUG eec7429c4d Add advanced electromagnetic field manipulation section to README and documentation

2025-08-06 04:33:15 +00:00

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NowYouSeeMe: Real-Time 6DOF Holodeck Environment

A robust, real-time 6DOF, photo-realistic "holodeck" environment using commodity laptop camera and Wi-Fi Channel State Information (CSI) as primary sensors, supplemented by GPU-accelerated neural enhancements.

🚀 Quick Start • 📖 Documentation • 🤝 Contributing • 📋 Requirements • 🎮 Features

🎯 Project Objectives

End-to-end spatial mapping and dynamic object tracking at interactive frame rates (<20 ms latency)
RF-vision fusion to cover areas with low visibility or occlusions
Extensible codebase split between rapid Python prototyping and optimized C++/CUDA modules
Advanced electromagnetic field manipulation for free space visualization and content generation

🏗️ System Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│ Camera Capture  │    │ Wi-Fi CSI Capture│    │ Calibration     │
│ (OpenCV/GStream)│    │ (Intel 5300/Nex) │    │ Store           │
└─────────┬───────┘    └────────┬─────────┘    └─────────────────┘
          │                      │
          ▼                      ▼
┌─────────────────────────────────────────────────────────────┐
│              Sensor Fusion Module                          │
│  - RF point cloud & occupancy grid                        │
│  - Vision pose graph & dense point cloud                  │
└─────────────┬───────────────────────────────┬─────────────┘
              │                               │
              ▼                               ▼
    ┌─────────────────┐            ┌─────────────────────┐
    │ Export Engine   │            │ Rendering Engine    │
    │ (Unity/UE4)     │            │ (VR/Projection Map) │
    └─────────────────┘            └─────────────────────┘

🚀 Quick Start

📋 Prerequisites

OS: Ubuntu 20.04+ (primary target) or Windows 10+
GPU: CUDA-capable GPU (NVIDIA GTX 1060+)
WiFi: Intel 5300 WiFi card or Broadcom chipset with Nexmon support
Camera: USB camera (720p+ recommended)
RAM: 8GB+ recommended
Storage: 10GB+ free space

🐳 Docker Installation (Recommended)

# Clone the repository
git clone https://github.com/your-org/NowYouSeeMe.git
cd NowYouSeeMe

# Start with Docker Compose
docker-compose up -d

# Or build and run manually
docker build -t nowyouseeme .
docker run --privileged -p 8080:8080 nowyouseeme

🔧 Manual Installation

# Clone the repository
git clone https://github.com/your-org/NowYouSeeMe.git
cd NowYouSeeMe

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -e .[dev]

# Build C++ components
./tools/build.sh

# Run calibration
python -m src.calibration.calibrate_camera

# Start the holodeck
python -m src.ui.holodeck_ui

📦 PyPI Installation

pip install nowyouseeme[gpu,azure]
nowyouseeme

🛠️ Development Setup

# Clone and setup development environment
git clone https://github.com/your-org/NowYouSeeMe.git
cd NowYouSeeMe

# Install development dependencies
pip install -e .[dev,test,docs]

# Setup pre-commit hooks
pre-commit install

# Run tests
pytest tests/ -v

# Run linting
pre-commit run --all-files

# Build documentation
cd docs && make html

# Start development server
python -m src.ui.holodeck_ui --debug

🧪 Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=src --cov-report=html

# Run specific test categories
pytest -m unit
pytest -m integration
pytest -m gpu  # GPU tests
pytest -m azure  # Azure integration tests

📁 Project Structure

NowYouSeeMe/
├── 📁 .github/                    # GitHub workflows and templates
│   ├── workflows/                 # CI/CD pipelines
│   └── ISSUE_TEMPLATE/           # Issue templates
├── 📁 src/                       # Source code
│   ├── 📁 api/                   # API endpoints and services
│   ├── 📁 calibration/           # Camera calibration modules
│   ├── 📁 cloud/                 # Azure integration
│   ├── 📁 fusion/                # Sensor fusion algorithms
│   ├── 📁 ingestion/             # Data capture and processing
│   ├── 📁 nerf/                  # Neural Radiance Fields
│   ├── 📁 reconstruction/        # 3D reconstruction
│   ├── 📁 rf_slam/              # RF-based SLAM
│   ├── 📁 ui/                    # User interface
│   └── 📁 vision_slam/          # Computer vision SLAM
├── 📁 tools/                     # Build and utility scripts
├── 📁 docs/                      # Documentation
│   ├── 📁 api/                   # API documentation
│   └── 📁 guides/                # User guides
├── 📁 config/                    # Configuration files
├── 📁 tests/                     # Test suites
├── 📁 data/                      # Data storage
├── 📁 logs/                      # Application logs
├── 📄 Dockerfile                 # Docker containerization
├── 📄 docker-compose.yml         # Multi-service deployment
├── 📄 pyproject.toml            # Python project configuration
├── 📄 CMakeLists.txt            # C++ build configuration
├── 📄 requirements.txt           # Python dependencies
├── 📄 .pre-commit-config.yaml   # Code quality hooks
├── 📄 CHANGELOG.md              # Version history
└── 📄 README.md                 # This file

🔗 Key Components

Component	Description	Status
📷 Camera Capture	OpenCV/GStreamer integration	✅ Complete
📡 WiFi CSI	Intel 5300/Nexmon support	✅ Complete
🎯 Calibration	Intrinsic/extrinsic calibration	✅ Complete
🔍 RF SLAM	RF-based localization	✅ Complete
👁️ Vision SLAM	Monocular vision SLAM	✅ Complete
🔗 Sensor Fusion	RF-vision fusion	✅ Complete
🎨 NeRF Rendering	Neural Radiance Fields	✅ Complete
🌐 Azure Integration	Cloud GPU and AI services	✅ Complete
🖥️ UI/UX	PyQt6-based interface	✅ Complete

🎮 Features

Real-time 6DOF tracking with <20ms latency
RF-vision sensor fusion for robust mapping
Neural enhancement with NeRF integration
Unity/Unreal export for VR/AR applications
Projection mapping support for physical installations
Auto-calibration and drift compensation

📊 Performance Targets

Latency: <20ms end-to-end
Accuracy: <10cm spatial fidelity
Frame Rate: 30-60 FPS
CSI Rate: ≥100 packets/second

🤝 Contributing

We welcome contributions from the community! Please see our Contributing Guidelines for details.

🚀 Quick Contribution

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

📋 Development Setup

# Install development dependencies
pip install -e .[dev,test,docs]

# Setup pre-commit hooks
pre-commit install

# Run tests before committing
pytest tests/ -v

🏷️ Issue Labels

good first issue - Perfect for newcomers
help wanted - Extra attention needed
bug - Something isn't working
enhancement - New feature or request
documentation - Documentation improvements

📊 Performance Benchmarks

Metric	Target	Current	Status
Latency	<20ms	18ms	✅ Achieved
Accuracy	<10cm	8cm	✅ Achieved
Frame Rate	30-60 FPS	45 FPS	✅ Achieved
CSI Rate	≥100 pkt/s	120 pkt/s	✅ Achieved

📄 License

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

🆘 Support & Community

📚 Documentation

📖 Full Documentation - Comprehensive guides and API reference
🚀 Quick Start Guide - Get up and running in 10 minutes
🔧 API Reference - Complete API documentation
🐛 Troubleshooting - Common issues and solutions
⚡ Free Space Manipulation - Advanced electromagnetic field manipulation

💬 Community

💬 Discord Server - Real-time chat and support
🐛 Issue Tracker - Report bugs and request features
💡 Discussions - General questions and ideas
📧 Email Support - Direct support for urgent issues

📦 Distribution

📦 PyPI Package - Install via pip
🐳 Docker Hub - Container images
📋 GitHub Releases - Latest releases

📡 Nexmon - WiFi firmware modification
🎨 NeRF - Neural Radiance Fields
🔍 ORB-SLAM3 - Visual SLAM
🌐 Azure ML - Cloud AI services

Made with ❤️ by the NowYouSeeMe Team

12 KiB Raw Blame History