dearsky/wifi-densepose
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2 Commits

Author SHA1 Message Date
ruv
b7e0f07e6e feat: Sensing-only UI mode with Gaussian splat visualization and Rust migration ADR 
- Add Python WebSocket sensing server (ws_server.py) with ESP32 UDP CSI
  and Windows RSSI auto-detect collectors on port 8765
- Add Three.js Gaussian splat renderer with custom GLSL shaders for
  real-time WiFi signal field visualization (blue→green→red gradient)
- Add SensingTab component with RSSI sparkline, feature meters, and
  motion classification badge
- Add sensing.service.js WebSocket client with reconnect and simulation fallback
- Implement sensing-only mode: suppress all DensePose API calls when
  FastAPI backend (port 8000) is not running, clean console output
- ADR-019: Document sensing-only UI architecture and data flow
- ADR-020: Migrate AI/model inference to Rust with RuVector ONNX Runtime,
  replacing ~2.7GB Python stack with ~50MB static binary
- Add ruvnet/ruvector as upstream remote for RuVector crate ecosystem

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-28 14:37:29 -05:00
Claude
6ed69a3d48 feat: Complete Rust port of WiFi-DensePose with modular crates 
Major changes:
- Organized Python v1 implementation into v1/ subdirectory
- Created Rust workspace with 9 modular crates:
  - wifi-densepose-core: Core types, traits, errors
  - wifi-densepose-signal: CSI processing, phase sanitization, FFT
  - wifi-densepose-nn: Neural network inference (ONNX/Candle/tch)
  - wifi-densepose-api: Axum-based REST/WebSocket API
  - wifi-densepose-db: SQLx database layer
  - wifi-densepose-config: Configuration management
  - wifi-densepose-hardware: Hardware abstraction
  - wifi-densepose-wasm: WebAssembly bindings
  - wifi-densepose-cli: Command-line interface

Documentation:
- ADR-001: Workspace structure
- ADR-002: Signal processing library selection
- ADR-003: Neural network inference strategy
- DDD domain model with bounded contexts

Testing:
- 69 tests passing across all crates
- Signal processing: 45 tests
- Neural networks: 21 tests
- Core: 3 doc tests

Performance targets:
- 10x faster CSI processing (~0.5ms vs ~5ms)
- 5x lower memory usage (~100MB vs ~500MB)
- WASM support for browser deployment
 2026-01-13 03:11:16 +00:00