The Rust port lived two directories deep (rust-port/wifi-densepose-rs/) without any sibling under rust-port/ that warranted the extra level. Move the whole workspace up to v2/ to match v1/ (Python) at the same depth and shorten every cd / build command across the repo. git mv preserves history for all tracked files. 60 files updated for path references (CI workflows, ADRs, docs, scripts, READMEs, internal .claude-flow state). Two manual fixes for relative-cd paths in CLAUDE.md and ADR-043 that became wrong after the depth change (cd ../.. → cd ..). Validated: - cargo check --workspace --no-default-features → clean (after target/ nuke; the gitignored target/ was carried by the OS rename and had hard-coded old paths in build scripts) - cargo test --workspace --no-default-features → 1,539 passed, 0 failed, 8 ignored (same totals as pre-rename) - ESP32-S3 on COM7 → still streaming live CSI (cb #40300, RSSI -64 dBm) After-merge follow-up: contributors should `rm -rf v2/target` once and let cargo regenerate from the new path. |
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|---|---|---|
| .. | ||
| components | ||
| config | ||
| mobile | ||
| observatory | ||
| pose-fusion | ||
| services | ||
| tests | ||
| utils | ||
| app.js | ||
| index.html | ||
| observatory.html | ||
| pose-fusion.html | ||
| README.md | ||
| start-ui.sh | ||
| style.css | ||
| TEST_REPORT.md | ||
| viz.html | ||
WiFi DensePose UI
A modular, modern web interface for the WiFi DensePose human tracking system. Provides real-time monitoring, WiFi sensing visualization, and pose estimation from CSI (Channel State Information).
Architecture
The UI follows a modular architecture with clear separation of concerns:
ui/
├── app.js # Main application entry point
├── index.html # HTML shell with tab structure
├── style.css # Complete CSS design system
├── config/
│ └── api.config.js # API endpoints and configuration
├── services/
│ ├── api.service.js # HTTP API client
│ ├── websocket.service.js # WebSocket connection manager
│ ├── websocket-client.js # Low-level WebSocket client
│ ├── pose.service.js # Pose estimation API wrapper
│ ├── sensing.service.js # WiFi sensing data service (live + simulation fallback)
│ ├── health.service.js # Health monitoring API wrapper
│ ├── stream.service.js # Streaming API wrapper
│ └── data-processor.js # Signal data processing utilities
├── components/
│ ├── TabManager.js # Tab navigation component
│ ├── DashboardTab.js # Dashboard with live system metrics
│ ├── SensingTab.js # WiFi sensing visualization (3D signal field, metrics)
│ ├── LiveDemoTab.js # Live pose detection with setup guide
│ ├── HardwareTab.js # Hardware configuration
│ ├── SettingsPanel.js # Settings panel
│ ├── PoseDetectionCanvas.js # Canvas-based pose skeleton renderer
│ ├── gaussian-splats.js # 3D Gaussian splat signal field renderer (Three.js)
│ ├── body-model.js # 3D body model
│ ├── scene.js # Three.js scene management
│ ├── signal-viz.js # Signal visualization utilities
│ ├── environment.js # Environment/room visualization
│ └── dashboard-hud.js # Dashboard heads-up display
├── utils/
│ ├── backend-detector.js # Auto-detect backend availability
│ ├── mock-server.js # Mock server for testing
│ └── pose-renderer.js # Pose rendering utilities
└── tests/
├── test-runner.html # Test runner UI
├── test-runner.js # Test framework and cases
└── integration-test.html # Integration testing page
Features
WiFi Sensing Tab
- 3D Gaussian-splat signal field visualization (Three.js)
- Real-time RSSI, variance, motion band, breathing band metrics
- Presence/motion classification with confidence scores
- Data source banner: green "LIVE - ESP32", yellow "RECONNECTING...", or red "SIMULATED DATA"
- Sparkline RSSI history graph
- "About This Data" card explaining CSI capabilities per sensor count
Live Demo Tab
- WebSocket-based real-time pose skeleton rendering
- Estimation Mode badge: green "Signal-Derived" or blue "Model Inference"
- Setup Guide panel showing what each ESP32 count provides:
- 1 ESP32: presence, breathing, gross motion
- 2-3 ESP32s: body localization, motion direction
- 4+ ESP32s + trained model: individual limb tracking, full pose
- Debug mode with log export
- Zone selection and force-reconnect controls
- Performance metrics sidebar (frames, uptime, errors)
Dashboard
- Live system health monitoring
- Real-time pose detection statistics
- Zone occupancy tracking
- System metrics (CPU, memory, disk)
- API status indicators
Hardware Configuration
- Interactive antenna array visualization
- Real-time CSI data display
- Configuration panels
- Hardware status monitoring
Data Sources
The sensing service (sensing.service.js) supports three connection states:
| State | Banner Color | Description |
|---|---|---|
| LIVE - ESP32 | Green | Connected to the Rust sensing server receiving real CSI data |
| RECONNECTING | Yellow (pulsing) | WebSocket disconnected, retrying (up to 20 attempts) |
| SIMULATED DATA | Red | Fallback to client-side simulation after 5+ failed reconnects |
Simulated frames include a _simulated: true marker so code can detect synthetic data.
Backends
Rust Sensing Server (primary)
The Rust-based wifi-densepose-sensing-server serves the UI and provides:
GET /health— server healthGET /api/v1/sensing/latest— latest sensing featuresGET /api/v1/vital-signs— vital sign estimates (HR/RR)GET /api/v1/model/info— RVF model container infoWS /ws/sensing— real-time sensing data streamWS /api/v1/stream/pose— real-time pose keypoint stream
Python FastAPI (legacy)
The original Python backend on port 8000 is still supported. The UI auto-detects which backend is available via backend-detector.js.
Quick Start
With Docker (recommended)
cd docker/
# Default: auto-detects ESP32 on UDP 5005, falls back to simulation
docker-compose up
# Force real ESP32 data
CSI_SOURCE=esp32 docker-compose up
# Force simulation (no hardware needed)
CSI_SOURCE=simulated docker-compose up
Open http://localhost:3000/ui/index.html
With local Rust binary
cd v2
cargo build -p wifi-densepose-sensing-server --no-default-features
# Run with simulated data
../../target/debug/sensing-server --source simulated --tick-ms 100 --ui-path ../../ui --http-port 3000
# Run with real ESP32
../../target/debug/sensing-server --source esp32 --tick-ms 100 --ui-path ../../ui --http-port 3000
Open http://localhost:3000/ui/index.html
With Python HTTP server (legacy)
# Start FastAPI backend on port 8000
wifi-densepose start
# Serve the UI on port 3000
cd ui/
python -m http.server 3000
Pose Estimation Modes
| Mode | Badge | Requirements | Accuracy |
|---|---|---|---|
| Signal-Derived | Green | 1+ ESP32, no model needed | Presence, breathing, gross motion |
| Model Inference | Blue | 4+ ESP32s + trained .rvf model |
Full 17-keypoint COCO pose |
To use model inference, start the server with a trained model:
sensing-server --source esp32 --model path/to/model.rvf --ui-path ./ui
Configuration
API Configuration
Edit config/api.config.js:
export const API_CONFIG = {
BASE_URL: window.location.origin,
API_VERSION: '/api/v1',
WS_CONFIG: {
RECONNECT_DELAY: 5000,
MAX_RECONNECT_ATTEMPTS: 20,
PING_INTERVAL: 30000
}
};
Testing
Open tests/test-runner.html to run the test suite:
cd ui/
python -m http.server 3000
# Open http://localhost:3000/tests/test-runner.html
Test categories: API configuration, API service, WebSocket, pose service, health service, UI components, integration.
Styling
Uses a CSS design system with custom properties, dark/light mode, responsive layout, and component-based styling. Key variables in :root of style.css.
License
Part of the WiFi-DensePose system. See the main project LICENSE file.