ruvnet/wifi-densepose

**ruvnet/wifi-densepose**

Production-ready implementation of InvisPose - a revolutionary WiFi-based dense human pose estimation system that enables real-time full-body tracking through walls using commodity mesh routersWiFi DensePose A cutting-edge WiFi-based human pose estimation system that leverages Channel State Information (CSI) data and advanced machine learning to provide real-time, privacy-preserving pose detection without cameras. 🚀 Key Features Privacy-First: No cameras required - uses WiFi signals for pose detection Real-Time Processing: Sub-50ms latency with 30 FPS pose estimation Multi-Person Tracking: Simultaneous tracking of up to 10 individuals Domain-Specific Optimization: Healthcare, fitness, smart home, and security applications Enterprise-Ready: Production-grade API with authentication, rate limiting, and monitoring Hardware Agnostic: Works with standard WiFi routers and access points Comprehensive Analytics: Fall detection, activity recognition, and occupancy monitoring WebSocket Streaming: Real-time pose data streaming for live applications 100% Test Coverage: Thoroughly tested with comprehensive test suite 🦀 Rust Implementation (v2) A high-performance Rust port is available in /rust-port/wifi-densepose-rs/: Performance Benchmarks (Validated) Operation Python (v1) Rust (v2) Speedup CSI Preprocessing (4x64) ~5ms 5.19 µs ~1000x Phase Sanitization (4x64) ~3ms 3.84 µs ~780x Feature Extraction (4x64) ~8ms 9.03 µs ~890x Motion Detection ~1ms 186 ns ~5400x Full Pipeline ~15ms 18.47 µs ~810x Throughput Metrics Component Throughput CSI Preprocessing 49-66 Melem/s Phase Sanitization 67-85 Melem/s Feature Extraction 7-11 Melem/s Full Pipeline ~54,000 fps Resource Comparison Feature Python (v1) Rust (v2) Memory Usage ~500MB ~100MB WASM Support ❌ ✅ Binary Size N/A ~10MB Test Coverage 100% 107 tests Quick Start (Rust): cd rust-port/wifi-densepose-rs cargo build --release cargo test --workspace cargo bench --package wifi-densepose-signal Validation Tests Mathematical correctness validated: ✅ Phase unwrapping: 0.000000 radians max error ✅ Amplitude RMS: Exact match ✅ Doppler shift: 33.33 Hz (exact) ✅ Correlation: 1.0 for identical signals ✅ Phase coherence: 1.0 for coherent signals See Rust Port Documentation for ADRs and DDD patterns. 🚨 WiFi-Mat: Disaster Response Module A specialized extension for search and rescue operations - detecting and localizing survivors trapped in rubble, earthquakes, and natural disasters. Key Capabilities Feature Description Vital Signs Detection Breathing (4-60 BPM), heartbeat via micro-Doppler 3D Localization Position estimation through debris up to 5m depth START Triage Automatic Immediate/Delayed/Minor/Deceased classification Real-time Alerts Priority-based notifications with escalation Use Cases Earthquake search and rescue Building collapse response Avalanche victim location Mine collapse detection Flood rescue operations Quick Example use wifi_densepose_mat::{DisasterResponse, DisasterConfig, DisasterType, ScanZone, ZoneBounds}; let config = DisasterConfig::builder() .disaster_type(DisasterType::Earthquake) .sensitivity(0.85) .max_depth(5.0) .build(); let mut response = DisasterResponse::new(config); response.initialize_event(location, "Building collapse")?; response.add_zone(ScanZone::new("North Wing", ZoneBounds::rectangle(0.0, 0.0, 30.0, 20.0)))?; response.start_scanning().await?; // Get survivors prioritized by triage status let immediate = response.survivors_by_triage(TriageStatus::Immediate); println!("{} survivors require immediate rescue", immediate.len()); Documentation WiFi-Mat User Guide - Complete setup, configuration, and field deployment Architecture Decision Record - Design decisions and rationale Domain Model - DDD bounded contexts and entities Build: cd rust-port/wifi-densepose-rs cargo build --release --package wifi-densepose-mat cargo test --package wifi-densepose-mat 📋 Table of Contents 🚀 Getting Started Key Features Rust Implementation (v2) WiFi-Mat Disaster Response System Architecture Installation Using pip (Recommended) From Source Using Docker System Requirements Quick Start Basic Setup Start the System Using the REST API Real-time Streaming 🖥️ Usage & Configuration CLI Usage Installation Basic Commands Configuration Commands Examples Documentation Core Documentation Quick Links API Overview Hardware Setup Supported Hardware Physical Setup Network Configuration Environment Calibration ⚙️ Advanced Topics Configuration Environment Variables Domain-Specific Configurations Advanced Configuration Testing Running Tests Test Categories Mock Testing Continuous Integration Deployment Production Deployment Infrastructure as Code Monitoring and Logging 📊 Performance & Community Performance Metrics Benchmark Results Performance Optimization Load Testing Contributing Development Setup Code Standards Contribution Process Code Review Checklist License Acknowledgments Support 🏗️ System Architecture WiFi DensePose consists of several key components working together: ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ WiFi Router │ │ WiFi Router │ │ WiFi Router │ │ (CSI Source) │ │ (CSI Source) │ │ (CSI Source) │ └─────────┬───────┘ └─────────┬───────┘ └─────────┬───────┘ │ │ │ └──────────────────────┼──────────────────────┘ │ ┌─────────────▼─────────────┐ │ CSI Data Collector │ │ (Hardware Interface) │ └─────────────┬─────────────┘ │ ┌─────────────▼─────────────┐ │ Signal Processor │ │ (Phase Sanitization) │ └─────────────┬─────────────┘ │ ┌─────────────▼─────────────┐ │ Neural Network Model │ │ (DensePose Head) │ └─────────────┬─────────────┘ │ ┌─────────────▼─────────────┐ │ Person Tracker │ │ (Multi-Object Tracking) │ └─────────────┬─────────────┘ │ ┌───────────────────────┼───────────────────────┐ │ │ │ ┌─────────▼─────────┐ ┌─────────▼─────────┐ ┌─────────▼─────────┐ │ REST API │ │ WebSocket API │ │ Analytics │ │ (CRUD Operations)│ │ (Real-time Stream)│ │ (Fall Detection) │ └───────────────────┘ └───────────────────┘ └───────────────────┘ Core Components CSI Processor: Extracts and processes Channel State Information from WiFi signals Phase Sanitizer: Removes hardware-specific phase offsets and noise DensePose Neural Network: Converts CSI data to human pose keypoints Multi-Person Tracker: Maintains consistent person identities across frames REST API: Comprehensive API for data access and system control WebSocket Streaming: Real-time pose data broadcasting Analytics Engine: Advanced analytics including fall detection and activity recognition 📦 Installation Using pip (Recommended) WiFi-DensePose is now available on PyPI for easy installation: # Install the latest stable version pip install wifi-densepose # Install with specific version pip install wifi-densepose==1.0.0 # Install with optional dependencies pip install wifi-densepose[gpu] # For GPU acceleration pip install wifi-densepose[dev] # For development pip install wifi-densepose[all] # All optional dependencies From Source git clone https://github.com/ruvnet/wifi-densepose.git cd wifi-densepose pip install -r requirements.txt pip install -e . Using Docker docker pull ruvnet/wifi-densepose:latest docker run -p 8000:8000 ruvnet/wifi-densepose:latest System Requirements Python: 3.8 or higher Operating System: Linux (Ubuntu 18.04+), macOS (10.15+), Windows 10+ Memory: Minimum 4GB RAM, Recommended 8GB+ Storage: 2GB free space for models and data Network: WiFi interface with CSI capability GPU: Optional but recommended (NVIDIA GPU with CUDA support) 🚀 Quick Start 1...

---

**[devsupporter 해설]**

이 기사는 GitHub Trending Daily All에서 제공하는 최신 개발 동향입니다. 관련 도구나 기술에 대해 더 알아보시려면 원본 링크를 참고하세요.