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feat(demo): wire all 6 RuVector WASM attention mechanisms into pose fusion
* feat: dual-modal WASM browser pose estimation demo (ADR-058) Live webcam video + WiFi CSI fusion for real-time pose estimation. Two parallel CNN pipelines (ruvector-cnn-wasm) with attention-weighted fusion and dynamic confidence gating. Three modes: Dual, Video-only, CSI-only. Includes pre-built WASM package (~52KB) for browser deployment. - ADR-058: Dual-modal architecture design - ui/pose-fusion.html: Main demo page with dark theme UI - 7 JS modules: video-capture, csi-simulator, cnn-embedder, fusion-engine, pose-decoder, canvas-renderer, main orchestrator - Pre-built ruvector-cnn-wasm WASM package for browser - CSI heatmap, embedding space visualization, latency metrics - WebSocket support for live ESP32 CSI data - Navigation link added to main dashboard Co-Authored-By: claude-flow <ruv@ruv.net> * fix: motion-responsive skeleton + through-wall CSI tracking - Pose decoder now uses per-cell motion grid to track actual arm/head positions — raising arms moves the skeleton's arms, head follows lateral movement - Motion grid (10x8 cells) tracks intensity per body zone: head, left/right arm upper/mid, legs - Through-wall mode: when person exits frame, CSI maintains presence with slow decay (~10s) and skeleton drifts in exit direction - CSI simulator persists sensing after video loss, ghost pose renders with decreasing confidence - Reduced temporal smoothing (0.45) for faster response to movement Co-Authored-By: claude-flow <ruv@ruv.net> * fix: video fills available space + correct WASM path resolution - Remove fixed aspect-ratio and max-height from video panel so it fills the available viewport space without scrolling - Grid uses 1fr row for content area, overflow:hidden on main grid - Fix WASM path: resolve relative to JS module file using import.meta.url instead of hardcoded ./pkg/ which resolved incorrectly on gh-pages - Responsive: mobile still gets aspect-ratio constraint Co-Authored-By: claude-flow <ruv@ruv.net> * feat: live ESP32 CSI pipeline + auto-connect WebSocket - Add auto-connect to local sensing server WebSocket (ws://localhost:8765) - Demo shows "Live ESP32" when connected to real CSI data - Add build_firmware.ps1 for native Windows ESP-IDF builds (no Docker) - Add read_serial.ps1 for ESP32 serial monitor Pipeline: ESP32 → UDP:5005 → sensing-server → WS:8765 → browser demo Co-Authored-By: claude-flow <ruv@ruv.net> * docs: add ADR-059 live ESP32 CSI pipeline + update README with demo links - ADR-059: Documents end-to-end ESP32 → sensing server → browser pipeline - README: Add dual-modal pose fusion demo link, update ADR count to 49 - References issue #245 Co-Authored-By: claude-flow <ruv@ruv.net> * feat: RSSI visualization, RuVector attention WASM, cache-bust fixes - Add animated RSSI Signal Strength panel with sparkline history - Fix RuVector WasmMultiHeadAttention retptr calling convention - Wire up RuVector Multi-Head + Flash Attention in CNN embedder - Add ambient temporal drift to CSI simulator for visible heatmap animation - Fix embedding space projection (sparse projection replaces cancelling sum) - Add auto-scaling to embedding space renderer - Add cache busters (?v=4) to all ES module imports to prevent stale caches - Add diagnostic logging for module version verification - Add RSSI tracking with quality labels and color-coded dBm display - Includes ruvector-attention-wasm v2.0.5 browser ESM wrapper Co-Authored-By: claude-flow <ruv@ruv.net> * feat: 26-keypoint dexterous pose + full RuVector attention pipeline Pose Decoder (17 → 26 keypoints): - Add finger approximations: thumb, index, pinky per hand (6 new) - Add toe tips: left/right foot index (2 new) - Add neck keypoint (1 new) - Hand openness driven by arm motion intensity - Finger positions computed from wrist-elbow axis angles CNN Embedder (full RuVector WASM pipeline): - Stage 1: Multi-Head Attention (global spatial reasoning) - Stage 2: Hyperbolic Attention (hierarchical body-part tree) - Stage 3: MoE Attention (3 experts: upper/lower/extremities, top-2) - Blended 40/30/30 weighting → final embedding projection Canvas Renderer: - Magenta finger joints with distinct glow - Cyan toe tips - White neck keypoint - Thinner limb lines for hand/foot connections - Joint count shown in overlay label CSI Simulator: - Skip synthetic person state when live ESP32 connected - Only simulate CSI data in demo mode (was already correct) Embedding Space: - Fixed projection: sparse 8-dim projection replaces cancelling sum - Auto-scaling normalizes point spread to fill canvas Cache busters bumped to v=5 on all imports. Co-Authored-By: claude-flow <ruv@ruv.net> * fix: centroid-based pose tracking for responsive limb movement Rewrites pose decoder from intensity-based to position-based tracking: - Arms now track toward motion centroid in each body zone - Elbow/wrist positions computed along shoulder→centroid vector - Legs track toward lower-body zone centroids - Smoothing reduced from 0.45 to 0.25 for responsiveness - Zone centroids blend 30% old / 70% new each frame 6 body zones with overlapping coverage: - Head (top 20%, center cols) - Left/Right Arm (rows 10-60%, outer cols) - Torso (rows 15-55%, center cols) - Left/Right Leg (rows 50-100%, half cols each) Hand openness now driven by arm spread distance + raise amount. Cache busters v=6. Co-Authored-By: claude-flow <ruv@ruv.net> * fix: remove duplicate lAnkleX/rAnkleX declarations in pose-decoder Stale code block from old intensity-based tracking was left behind, re-declaring variables already defined by centroid-based tracking. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(demo): wire all 6 RuVector WASM attention mechanisms into pose fusion - Add WasmLinearAttention and WasmLocalGlobalAttention to browser ESM wrapper - Add 6 WASM utility functions (batch_normalize, pairwise_distances, etc.) - Extend CnnEmbedder to 6-stage pipeline: Flash → MHA → Hyperbolic → Linear → MoE → L+G - Use log-energy softmax blending across all 6 stages - Wire WASM cosine_similarity and normalize into FusionEngine - Add RuVector pipeline stats panel to UI (energy, refinement, pose impact) - Compute embedding-to-joint mapping stats without modifying joint positions - Center camera prompt with flexbox layout - Add cache busters v=12 Co-Authored-By: claude-flow <ruv@ruv.net>
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@ -75,7 +75,7 @@ docker run -p 3000:3000 ruvnet/wifi-densepose:latest
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|----------|-------------|
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| [User Guide](docs/user-guide.md) | Step-by-step guide: installation, first run, API usage, hardware setup, training |
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| [Build Guide](docs/build-guide.md) | Building from source (Rust and Python) |
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| [Architecture Decisions](docs/adr/README.md) | 48 ADRs — why each technical choice was made, organized by domain (hardware, signal processing, ML, platform, infrastructure) |
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| [Architecture Decisions](docs/adr/README.md) | 49 ADRs — why each technical choice was made, organized by domain (hardware, signal processing, ML, platform, infrastructure) |
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| [Domain Models](docs/ddd/README.md) | 7 DDD models (RuvSense, Signal Processing, Training Pipeline, Hardware Platform, Sensing Server, WiFi-Mat, CHCI) — bounded contexts, aggregates, domain events, and ubiquitous language |
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| [Desktop App](rust-port/wifi-densepose-rs/crates/wifi-densepose-desktop/README.md) | **WIP** — Tauri v2 desktop app for node management, OTA updates, WASM deployment, and mesh visualization |
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@ -89,8 +89,12 @@ docker run -p 3000:3000 ruvnet/wifi-densepose:latest
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<em>Real-time pose skeleton from WiFi CSI signals — no cameras, no wearables</em>
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<br>
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<a href="https://ruvnet.github.io/RuView/"><strong>▶ Live Observatory Demo</strong></a>
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<a href="https://ruvnet.github.io/RuView/pose-fusion.html"><strong>▶ Dual-Modal Pose Fusion Demo</strong></a>
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> The [server](#-quick-start) is optional for visualization and aggregation — the ESP32 [runs independently](#esp32-s3-hardware-pipeline) for presence detection, vital signs, and fall alerts.
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>
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> **Live ESP32 pipeline**: Connect an ESP32-S3 node → run the [sensing server](#sensing-server) → open the [pose fusion demo](https://ruvnet.github.io/RuView/pose-fusion.html) for real-time dual-modal pose estimation (webcam + WiFi CSI). See [ADR-059](docs/adr/ADR-059-live-esp32-csi-pipeline.md).
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## 🚀 Key Features
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docs/adr/ADR-058-ruvector-wasm-browser-pose-example.md
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# ADR-058: Dual-Modal WASM Browser Pose Estimation — Live Video + WiFi CSI Fusion
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- **Status**: Proposed
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- **Date**: 2026-03-12
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- **Deciders**: ruv
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- **Tags**: wasm, browser, cnn, pose-estimation, ruvector, video, multimodal, fusion
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## Context
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WiFi-DensePose estimates human poses from WiFi CSI (Channel State Information).
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The `ruvector-cnn` crate provides a pure Rust CNN (MobileNet-V3) with WASM bindings.
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Both modalities exist independently — what's missing is **fusing live webcam video
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with WiFi CSI** in a single browser demo to achieve robust pose estimation that
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works even when one modality degrades (occlusion, signal noise, poor lighting).
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Existing assets:
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1. **`wifi-densepose-wasm`** — CSI signal processing compiled to WASM
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2. **`wifi-densepose-sensing-server`** — Axum server streaming live CSI via WebSocket
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3. **`ruvector-cnn`** — Pure Rust CNN with MobileNet-V3 backbones, SIMD, contrastive learning
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4. **`ruvector-cnn-wasm`** — wasm-bindgen bindings: `WasmCnnEmbedder`, `SimdOps`, `LayerOps`, contrastive losses
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5. **`vendor/ruvector/examples/wasm-vanilla/`** — Reference vanilla JS WASM example
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Research shows multi-modal fusion (camera + WiFi) significantly outperforms either alone:
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- Camera fails under occlusion, poor lighting, privacy constraints
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- WiFi CSI fails with signal noise, multipath, low spatial resolution
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- Fusion compensates: WiFi provides through-wall coverage, camera provides fine-grained detail
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## Decision
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Build a **dual-modal browser demo** at `examples/wasm-browser-pose/` that:
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1. Captures **live webcam video** via `getUserMedia` API
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2. Receives **live WiFi CSI** via WebSocket from the sensing server
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3. Processes **both streams** through separate CNN pipelines in `ruvector-cnn-wasm`
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4. **Fuses embeddings** with learned attention weights for combined pose estimation
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5. Renders **video overlay** with skeleton + WiFi confidence heatmap on Canvas
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6. Runs entirely in the browser — all inference client-side via WASM
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### Architecture
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```
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┌──────────────────────────────────────────────────────────────────┐
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│ Browser │
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│ │
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│ ┌────────────┐ ┌────────────────┐ ┌───────────────────┐ │
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│ │ getUserMedia│───▶│ Video Frame │───▶│ CNN WASM │ │
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│ │ (Webcam) │ │ Capture │ │ (Visual Embedder) │ │
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│ └────────────┘ │ 224×224 RGB │ │ → 512-dim │ │
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│ └────────────────┘ └────────┬──────────┘ │
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│ │ │
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│ visual_embedding │
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│ │ │
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│ ┌──────▼──────┐ │
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│ ┌────────────┐ ┌────────────────┐ │ │ │
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│ │ WebSocket │───▶│ CSI WASM │ │ Attention │ │
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│ │ Client │ │ (densepose- │ │ Fusion │ │
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│ │ │ │ wasm) │ │ Module │ │
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│ └────────────┘ └───────┬────────┘ │ │ │
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│ │ └──────┬──────┘ │
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│ ┌───────▼────────┐ │ │
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│ │ CNN WASM │ fused_embedding │
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│ │ (CSI Embedder) │ │ │
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│ │ → 512-dim │ ┌──────▼──────┐ │
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│ └───────┬────────┘ │ Pose │ │
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│ │ │ Decoder │ │
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│ csi_embedding │ → 17 kpts │ │
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│ │ └──────┬──────┘ │
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│ └──────────────────────┘ │
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│ │ │
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│ ┌──────────────┐ ┌─────▼──────┐ │
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│ │ Video Canvas │◀────────│ Overlay │ │
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│ │ + Skeleton │ │ Renderer │ │
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│ │ + Heatmap │ └────────────┘ │
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│ └──────────────┘ │
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│ │
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└──────────────────────────────────────────────────────────────────┘
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▲ ▲
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│ getUserMedia │ WebSocket
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│ (camera) │ (ws://host:3030/ws/csi)
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│ │
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┌────┴────┐ ┌───────┴─────────┐
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│ Webcam │ │ Sensing Server │
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└─────────┘ └─────────────────┘
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```
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### Dual Pipeline Design
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Two parallel CNN pipelines run on each frame tick (~30 FPS):
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| Pipeline | Input | Preprocessing | CNN Config | Output |
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|----------|-------|---------------|------------|--------|
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| **Visual** | Webcam frame (640×480) | Resize to 224×224 RGB, ImageNet normalize | MobileNet-V3 Small, 512-dim | Visual embedding |
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| **CSI** | CSI frame (ADR-018 binary) | Amplitude/phase/delta → 224×224 pseudo-RGB | MobileNet-V3 Small, 512-dim | CSI embedding |
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Both use the same `WasmCnnEmbedder` but with separate instances and weight sets.
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### Fusion Strategy
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**Learned attention-weighted fusion** combines the two 512-dim embeddings:
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```javascript
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// Attention fusion: learn which modality to trust per-dimension
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// α ∈ [0,1]^512 — attention weights (shipped as JSON, trained offline)
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// visual_emb, csi_emb ∈ R^512
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function fuseEmbeddings(visual_emb, csi_emb, attention_weights) {
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const fused = new Float32Array(512);
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for (let i = 0; i < 512; i++) {
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const α = attention_weights[i];
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fused[i] = α * visual_emb[i] + (1 - α) * csi_emb[i];
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}
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return fused;
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}
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```
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**Dynamic confidence gating** adjusts fusion based on signal quality:
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| Condition | Behavior |
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|-----------|----------|
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| Good video + good CSI | Balanced fusion (α ≈ 0.5) |
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| Poor lighting / occlusion | CSI-dominant (α → 0, WiFi takes over) |
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| CSI noise / no ESP32 | Video-dominant (α → 1, camera only) |
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| Video-only mode (no WiFi) | α = 1.0, pure visual CNN pose estimation |
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| CSI-only mode (no camera) | α = 0.0, pure WiFi pose estimation |
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Quality detection:
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- **Video quality**: Frame brightness variance (dark = low quality), motion blur score
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- **CSI quality**: Signal-to-noise ratio from `wifi-densepose-wasm`, coherence gate output
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### CSI-to-Image Encoding
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CSI data encoded as 3-channel pseudo-image for the CSI CNN pipeline:
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| Channel | Data | Normalization |
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|---------|------|---------------|
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| R | CSI amplitude (subcarrier × time window) | Min-max to [0, 255] |
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| G | CSI phase (unwrapped, subcarrier × time window) | Min-max to [0, 255] |
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| B | Temporal difference (frame-to-frame Δ amplitude) | Abs, min-max to [0, 255] |
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### Video Processing
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Webcam frames processed through standard ImageNet pipeline:
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```javascript
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// Capture frame from video element
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const frame = captureVideoFrame(videoElement, 224, 224); // Returns Uint8Array RGB
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// ImageNet normalization happens inside WasmCnnEmbedder.extract()
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const visual_embedding = visual_embedder.extract(frame, 224, 224);
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```
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### Pose Keypoint Mapping
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17 COCO-format keypoints decoded from the fused 512-dim embedding:
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```
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0: nose 1: left_eye 2: right_eye
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3: left_ear 4: right_ear 5: left_shoulder
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6: right_shoulder 7: left_elbow 8: right_elbow
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9: left_wrist 10: right_wrist 11: left_hip
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12: right_hip 13: left_knee 14: right_knee
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15: left_ankle 16: right_ankle
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```
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Each keypoint decoded as (x, y, confidence) = 51 values from the 512-dim embedding
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via a learned linear projection.
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### Operating Modes
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The demo supports three modes, selectable in the UI:
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| Mode | Video | CSI | Fusion | Use Case |
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|------|-------|-----|--------|----------|
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| **Dual (default)** | ✅ | ✅ | Attention-weighted | Best accuracy, full demo |
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| **Video Only** | ✅ | ❌ | α = 1.0 | No ESP32 available, quick demo |
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| **CSI Only** | ❌ | ✅ | α = 0.0 | Privacy mode, through-wall sensing |
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**Video Only mode works without any hardware** — just a webcam — making the demo
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instantly accessible for anyone wanting to try it.
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### File Layout
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```
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examples/wasm-browser-pose/
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├── index.html # Single-page app (vanilla JS, no bundler)
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├── js/
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│ ├── app.js # Main entry, mode selection, orchestration
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│ ├── video-capture.js # getUserMedia, frame extraction, quality detection
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│ ├── csi-processor.js # WebSocket CSI client, frame parsing, pseudo-image encoding
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│ ├── fusion.js # Attention-weighted embedding fusion, confidence gating
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│ ├── pose-decoder.js # Fused embedding → 17 keypoints
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│ └── canvas-renderer.js # Video overlay, skeleton, CSI heatmap, confidence bars
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├── data/
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│ ├── visual-weights.json # Visual CNN → embedding projection (placeholder until trained)
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│ ├── csi-weights.json # CSI CNN → embedding projection (placeholder until trained)
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│ ├── fusion-weights.json # Attention fusion α weights (512 values)
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│ └── pose-weights.json # Fused embedding → keypoint projection
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├── css/
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│ └── style.css # Dark theme UI styling
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├── pkg/ # Built WASM packages (gitignored, built by script)
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│ ├── wifi_densepose_wasm/
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│ └── ruvector_cnn_wasm/
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├── build.sh # wasm-pack build script for both packages
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└── README.md # Setup and usage instructions
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```
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### Build Pipeline
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```bash
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#!/bin/bash
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# build.sh — builds both WASM packages into pkg/
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set -e
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# Build wifi-densepose-wasm (CSI processing)
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wasm-pack build ../../rust-port/wifi-densepose-rs/crates/wifi-densepose-wasm \
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--target web --out-dir "$(pwd)/pkg/wifi_densepose_wasm" --no-typescript
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# Build ruvector-cnn-wasm (CNN inference for both video and CSI)
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wasm-pack build ../../vendor/ruvector/crates/ruvector-cnn-wasm \
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--target web --out-dir "$(pwd)/pkg/ruvector_cnn_wasm" --no-typescript
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echo "Build complete. Serve with: python3 -m http.server 8080"
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```
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### UI Layout
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```
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┌─────────────────────────────────────────────────────────┐
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│ WiFi-DensePose — Live Dual-Modal Pose Estimation │
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│ [Dual Mode ▼] [⚙ Settings] FPS: 28 ◉ Live │
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├───────────────────────────┬─────────────────────────────┤
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│ │ │
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│ ┌───────────────────┐ │ ┌───────────────────┐ │
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│ │ │ │ │ │ │
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│ │ Video + Skeleton │ │ │ CSI Heatmap │ │
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│ │ Overlay │ │ │ (amplitude × │ │
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│ │ (main canvas) │ │ │ subcarrier) │ │
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│ │ │ │ │ │ │
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│ └───────────────────┘ │ └───────────────────┘ │
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│ │ │
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├───────────────────────────┴─────────────────────────────┤
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│ Fusion Confidence: ████████░░ 78% │
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│ Video: ██████████ 95% │ CSI: ██████░░░░ 61% │
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├─────────────────────────────────────────────────────────┤
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│ ┌─────────────────────────────────────────────────┐ │
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│ │ Embedding Space (2D projection) │ │
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│ │ · · · │ │
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│ │ · · · · · · (color = pose cluster) │ │
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│ │ · · · · │ │
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│ └─────────────────────────────────────────────────┘ │
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├─────────────────────────────────────────────────────────┤
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│ Latency: Video 12ms │ CSI 8ms │ Fusion 1ms │ Total 21ms│
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│ [▶ Record] [📷 Snapshot] [Confidence: ████ 0.6] │
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└─────────────────────────────────────────────────────────┘
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```
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### WASM Module Structure
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| Package | Source Crate | Provides | Size (est.) |
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|---------|-------------|----------|-------------|
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| `wifi_densepose_wasm` | `wifi-densepose-wasm` | CSI frame parsing, signal processing, feature extraction | ~200KB |
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| `ruvector_cnn_wasm` | `ruvector-cnn-wasm` | `WasmCnnEmbedder` (×2 instances), `SimdOps`, `LayerOps`, contrastive losses | ~150KB |
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Two `WasmCnnEmbedder` instances are created — one for video frames, one for CSI pseudo-images.
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They share the same WASM module but have independent state.
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### Browser API Requirements
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| API | Purpose | Required | Fallback |
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|-----|---------|----------|----------|
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| `getUserMedia` | Webcam capture | For video mode | CSI-only mode |
|
||||
| WebAssembly | CNN inference | Yes | None (hard requirement) |
|
||||
| WASM SIMD128 | Accelerated inference | No | Scalar fallback (~2× slower) |
|
||||
| WebSocket | CSI data stream | For CSI mode | Video-only mode |
|
||||
| Canvas 2D | Rendering | Yes | None |
|
||||
| `requestAnimationFrame` | Render loop | Yes | `setTimeout` fallback |
|
||||
| ES Modules | Code organization | Yes | None |
|
||||
|
||||
Target: Chrome 89+, Firefox 89+, Safari 15+, Edge 89+
|
||||
|
||||
### Performance Budget
|
||||
|
||||
| Stage | Target Latency | Notes |
|
||||
|-------|---------------|-------|
|
||||
| Video frame capture + resize | <3ms | `drawImage` to offscreen canvas |
|
||||
| Video CNN embedding | <15ms | 224×224 RGB → 512-dim |
|
||||
| CSI receive + parse | <2ms | Binary WebSocket message |
|
||||
| CSI pseudo-image encoding | <3ms | Amplitude/phase/delta channels |
|
||||
| CSI CNN embedding | <15ms | 224×224 pseudo-RGB → 512-dim |
|
||||
| Attention fusion | <1ms | Element-wise weighted sum |
|
||||
| Pose decoding | <1ms | Linear projection |
|
||||
| Canvas overlay render | <3ms | Video + skeleton + heatmap |
|
||||
| **Total (dual mode)** | **<33ms** | **30 FPS capable** |
|
||||
| **Total (video only)** | **<22ms** | **45 FPS capable** |
|
||||
|
||||
Note: Video and CSI CNN pipelines can run in parallel using Web Workers,
|
||||
reducing dual-mode latency to ~max(15, 15) + 5 = ~20ms (50 FPS).
|
||||
|
||||
### Contrastive Learning Integration
|
||||
|
||||
The demo optionally shows real-time contrastive learning in the browser:
|
||||
|
||||
- **InfoNCE loss** (`WasmInfoNCELoss`): Compare video vs CSI embeddings for the same pose — trains cross-modal alignment
|
||||
- **Triplet loss** (`WasmTripletLoss`): Push apart different poses, pull together same pose across modalities
|
||||
- **SimdOps**: Accelerated dot products for real-time similarity computation
|
||||
- **Embedding space panel**: Live 2D projection shows video and CSI embeddings converging when viewing the same person
|
||||
|
||||
### Relationship to Existing Crates
|
||||
|
||||
| Existing Crate | Role in This Demo |
|
||||
|---------------|-------------------|
|
||||
| `ruvector-cnn-wasm` | CNN inference for **both** video frames and CSI pseudo-images |
|
||||
| `wifi-densepose-wasm` | CSI frame parsing and signal processing |
|
||||
| `wifi-densepose-sensing-server` | WebSocket CSI data source |
|
||||
| `wifi-densepose-core` | ADR-018 frame format definitions |
|
||||
| `ruvector-cnn` | Underlying MobileNet-V3, layers, contrastive learning |
|
||||
|
||||
No new Rust crates are needed. The example is pure HTML/JS consuming existing WASM packages.
|
||||
|
||||
## Consequences
|
||||
|
||||
### Positive
|
||||
|
||||
- **Instant demo**: Video-only mode works with just a webcam — no ESP32 needed
|
||||
- **Multi-modal showcase**: Demonstrates camera + WiFi fusion, the core innovation of the project
|
||||
- **Graceful degradation**: Works with video-only, CSI-only, or both
|
||||
- **Through-wall capability**: CSI mode shows pose estimation where cameras cannot reach
|
||||
- **Zero-install**: Anyone with a browser can try it
|
||||
- **Training data collection**: Can record paired (video, CSI) data for offline model training
|
||||
- **Reusable**: JS modules embed directly in the Tauri desktop app's webview
|
||||
|
||||
### Negative
|
||||
|
||||
- **Model weights**: Requires offline-trained weights for visual CNN, CSI CNN, fusion, and pose decoder (~200KB total JSON)
|
||||
- **WASM size**: Two WASM modules total ~350KB (acceptable)
|
||||
- **No GPU**: CPU-only WASM inference; adequate at 224×224 but limits resolution scaling
|
||||
- **Camera privacy**: Video mode requires camera permission (mitigated: CSI-only mode available)
|
||||
- **Two CNN instances**: Memory footprint doubles vs single-modal (~10MB total, acceptable for desktop browsers)
|
||||
|
||||
### Risks
|
||||
|
||||
- **Cross-modal alignment**: Video and CSI embeddings must be trained jointly for fusion to work;
|
||||
without proper training, fusion may be worse than either modality alone
|
||||
- **Latency on mobile**: Dual CNN on mobile browsers may exceed 33ms; implement automatic quality reduction
|
||||
- **WebSocket drops**: Network jitter → CSI frame gaps; buffer last 3 frames, interpolate missing data
|
||||
|
||||
## Implementation Plan
|
||||
|
||||
1. **Phase 1 — Scaffold**: File layout, build.sh, index.html shell, mode selector UI
|
||||
2. **Phase 2 — Video pipeline**: getUserMedia → frame capture → CNN embedding → basic pose display
|
||||
3. **Phase 3 — CSI pipeline**: WebSocket client → CSI parsing → pseudo-image → CNN embedding
|
||||
4. **Phase 4 — Fusion**: Attention-weighted combination, confidence gating, mode switching
|
||||
5. **Phase 5 — Pose decoder**: Linear projection with placeholder weights → 17 keypoints
|
||||
6. **Phase 6 — Overlay renderer**: Video canvas with skeleton overlay, CSI heatmap panel
|
||||
7. **Phase 7 — Training**: Use `wifi-densepose-train` to generate real weights for both CNNs + fusion + decoder
|
||||
8. **Phase 8 — Contrastive demo**: Embedding space visualization, cross-modal similarity display
|
||||
9. **Phase 9 — Web Workers**: Move CNN inference to workers for parallel video + CSI processing
|
||||
10. **Phase 10 — Polish**: Recording, snapshots, adaptive quality, mobile optimization
|
||||
|
||||
## Alternatives Considered
|
||||
|
||||
### 1. CSI-Only (No Video)
|
||||
Rejected: Misses the opportunity to show multi-modal fusion and makes the demo less
|
||||
accessible (requires ESP32 hardware). Video-only mode as a fallback is strictly better.
|
||||
|
||||
### 2. Server-Side Video Inference
|
||||
Rejected: Adds latency, requires webcam stream upload (privacy concern), and defeats
|
||||
the WASM-first architecture. All inference must be client-side.
|
||||
|
||||
### 3. TensorFlow.js for Video, ruvector-cnn-wasm for CSI
|
||||
Rejected: Would require two different ML frameworks. Using `ruvector-cnn-wasm` for both
|
||||
keeps a single WASM module, unified embedding space, and simpler fusion.
|
||||
|
||||
### 4. Pre-recorded Video Demo
|
||||
Rejected: Live webcam input is far more compelling for demonstrations.
|
||||
Pre-recorded mode can be added as a secondary option.
|
||||
|
||||
### 5. React/Vue Framework
|
||||
Rejected: Adds build tooling. Vanilla JS + ES modules keeps the demo self-contained.
|
||||
|
||||
## References
|
||||
|
||||
- [ADR-018: Binary CSI Frame Format](ADR-018-binary-csi-frame-format.md)
|
||||
- [ADR-024: Contrastive CSI Embedding / AETHER](ADR-024-contrastive-csi-embedding.md)
|
||||
- [ADR-055: Integrated Sensing Server](ADR-055-integrated-sensing-server.md)
|
||||
- `vendor/ruvector/crates/ruvector-cnn/src/lib.rs` — CNN embedder implementation
|
||||
- `vendor/ruvector/crates/ruvector-cnn-wasm/src/lib.rs` — WASM bindings
|
||||
- `vendor/ruvector/examples/wasm-vanilla/index.html` — Reference vanilla JS WASM pattern
|
||||
- Person-in-WiFi: Fine-grained Person Perception using WiFi (ICCV 2019) — camera+WiFi fusion precedent
|
||||
- WiPose: Multi-Person WiFi Pose Estimation (TMC 2022) — cross-modal embedding approach
|
||||
83
docs/adr/ADR-059-live-esp32-csi-pipeline.md
Normal file
83
docs/adr/ADR-059-live-esp32-csi-pipeline.md
Normal file
|
|
@ -0,0 +1,83 @@
|
|||
# ADR-059: Live ESP32 CSI Pipeline Integration
|
||||
|
||||
## Status
|
||||
|
||||
Accepted
|
||||
|
||||
## Date
|
||||
|
||||
2026-03-12
|
||||
|
||||
## Context
|
||||
|
||||
ADR-058 established a dual-modal browser demo combining webcam video and WiFi CSI for pose estimation. However, it used simulated CSI data. To demonstrate real-world capability, we need an end-to-end pipeline from physical ESP32 hardware through to the browser visualization.
|
||||
|
||||
The ESP32-S3 firmware (`firmware/esp32-csi-node/`) already supports CSI collection and UDP streaming (ADR-018). The sensing server (`wifi-densepose-sensing-server`) already supports UDP ingestion and WebSocket bridging. The missing piece was connecting these components and enabling the browser demo to consume live data.
|
||||
|
||||
## Decision
|
||||
|
||||
Implement a complete live CSI pipeline:
|
||||
|
||||
```
|
||||
ESP32-S3 (CSI capture) → UDP:5005 → sensing-server (Rust/Axum) → WS:8765 → browser demo
|
||||
```
|
||||
|
||||
### Components
|
||||
|
||||
1. **ESP32 Firmware** — Rebuilt with native Windows ESP-IDF v5.4.0 toolchain (no Docker). Configured for target network and PC IP via `sdkconfig`. Helper scripts added:
|
||||
- `build_firmware.ps1` — Sets up IDF environment, cleans, builds, and flashes
|
||||
- `read_serial.ps1` — Serial monitor with DTR/RTS reset capability
|
||||
|
||||
2. **Sensing Server** — `wifi-densepose-sensing-server` started with:
|
||||
- `--source esp32` — Expect real ESP32 UDP frames
|
||||
- `--bind-addr 0.0.0.0` — Accept connections from any interface
|
||||
- `--ui-path <path>` — Serve the demo UI via HTTP
|
||||
|
||||
3. **Browser Demo** — `main.js` updated to auto-connect to `ws://localhost:8765/ws/sensing` on page load. Falls back to simulated CSI if the WebSocket is unavailable (GitHub Pages).
|
||||
|
||||
### Network Configuration
|
||||
|
||||
The ESP32 sends UDP packets to a configured target IP. If the PC's IP doesn't match the firmware's compiled target, a secondary IP alias can be added:
|
||||
|
||||
```powershell
|
||||
# PowerShell (Admin)
|
||||
New-NetIPAddress -IPAddress 192.168.1.100 -PrefixLength 24 -InterfaceAlias "Wi-Fi"
|
||||
```
|
||||
|
||||
### Data Flow
|
||||
|
||||
| Stage | Protocol | Format | Rate |
|
||||
|-------|----------|--------|------|
|
||||
| ESP32 → Server | UDP | ADR-018 binary frame (magic `0xC5110001`, I/Q pairs) | ~100 Hz |
|
||||
| Server → Browser | WebSocket | ADR-018 binary frame (forwarded) | ~10 Hz (tick-ms=100) |
|
||||
| Browser decode | JavaScript | Float32 amplitude/phase arrays | Per frame |
|
||||
|
||||
### Build Environment (Windows)
|
||||
|
||||
ESP-IDF v5.4.0 on Windows requires:
|
||||
- IDF_PATH pointing to the ESP-IDF framework
|
||||
- IDF_TOOLS_PATH pointing to toolchain binaries
|
||||
- MSYS/MinGW environment variables removed (ESP-IDF rejects them)
|
||||
- Python venv from ESP-IDF tools for `idf.py` execution
|
||||
|
||||
The `build_firmware.ps1` script handles all of this automatically.
|
||||
|
||||
## Consequences
|
||||
|
||||
### Positive
|
||||
- First end-to-end demonstration of real WiFi CSI → pose estimation in a browser
|
||||
- No Docker required for firmware builds on Windows
|
||||
- Demo gracefully degrades to simulated CSI when no server is available
|
||||
- Same demo works on GitHub Pages (simulated) and locally (live ESP32)
|
||||
|
||||
### Negative
|
||||
- ESP32 target IP is compiled into firmware; changing it requires a rebuild or NVS override
|
||||
- Windows firewall may block UDP:5005; user must allow it
|
||||
- Mixed content restrictions prevent HTTPS pages from connecting to ws:// (local only)
|
||||
|
||||
## Related
|
||||
|
||||
- [ADR-018](ADR-018-esp32-dev-implementation.md) — ESP32 CSI frame format and UDP streaming
|
||||
- [ADR-058](ADR-058-ruvector-wasm-browser-pose-example.md) — Dual-modal WASM browser pose demo
|
||||
- [ADR-039](ADR-039-edge-intelligence-framework.md) — Edge intelligence on ESP32
|
||||
- Issue [#245](https://github.com/ruvnet/RuView/issues/245) — Tracking issue
|
||||
31
firmware/esp32-csi-node/build_firmware.ps1
Normal file
31
firmware/esp32-csi-node/build_firmware.ps1
Normal file
|
|
@ -0,0 +1,31 @@
|
|||
# Remove MSYS environment variables that trigger ESP-IDF's MinGW rejection
|
||||
Remove-Item env:MSYSTEM -ErrorAction SilentlyContinue
|
||||
Remove-Item env:MSYSTEM_CARCH -ErrorAction SilentlyContinue
|
||||
Remove-Item env:MSYSTEM_CHOST -ErrorAction SilentlyContinue
|
||||
Remove-Item env:MSYSTEM_PREFIX -ErrorAction SilentlyContinue
|
||||
Remove-Item env:MINGW_CHOST -ErrorAction SilentlyContinue
|
||||
Remove-Item env:MINGW_PACKAGE_PREFIX -ErrorAction SilentlyContinue
|
||||
Remove-Item env:MINGW_PREFIX -ErrorAction SilentlyContinue
|
||||
|
||||
$env:IDF_PATH = "C:\Users\ruv\esp\v5.4\esp-idf"
|
||||
$env:IDF_TOOLS_PATH = "C:\Espressif\tools"
|
||||
$env:IDF_PYTHON_ENV_PATH = "C:\Espressif\tools\python\v5.4\venv"
|
||||
$env:PATH = "C:\Espressif\tools\xtensa-esp-elf\esp-14.2.0_20241119\xtensa-esp-elf\bin;C:\Espressif\tools\cmake\3.30.2\cmake-3.30.2-windows-x86_64\bin;C:\Espressif\tools\ninja\1.12.1;C:\Espressif\tools\ccache\4.10.2\ccache-4.10.2-windows-x86_64;C:\Espressif\tools\idf-exe\1.0.3;C:\Espressif\tools\python\v5.4\venv\Scripts;$env:PATH"
|
||||
|
||||
Set-Location "C:\Users\ruv\Projects\wifi-densepose\firmware\esp32-csi-node"
|
||||
|
||||
$python = "$env:IDF_PYTHON_ENV_PATH\Scripts\python.exe"
|
||||
$idf = "$env:IDF_PATH\tools\idf.py"
|
||||
|
||||
Write-Host "=== Cleaning stale build cache ==="
|
||||
& $python $idf fullclean
|
||||
|
||||
Write-Host "=== Building firmware (SSID=ruv.net, target=192.168.1.20:5005) ==="
|
||||
& $python $idf build
|
||||
|
||||
if ($LASTEXITCODE -eq 0) {
|
||||
Write-Host "=== Build succeeded! Flashing to COM7 ==="
|
||||
& $python $idf -p COM7 flash
|
||||
} else {
|
||||
Write-Host "=== Build failed with exit code $LASTEXITCODE ==="
|
||||
}
|
||||
14
firmware/esp32-csi-node/read_serial.ps1
Normal file
14
firmware/esp32-csi-node/read_serial.ps1
Normal file
|
|
@ -0,0 +1,14 @@
|
|||
$p = New-Object System.IO.Ports.SerialPort('COM7', 115200)
|
||||
$p.ReadTimeout = 5000
|
||||
$p.Open()
|
||||
Start-Sleep -Milliseconds 200
|
||||
|
||||
for ($i = 0; $i -lt 60; $i++) {
|
||||
try {
|
||||
$line = $p.ReadLine()
|
||||
Write-Host $line
|
||||
} catch {
|
||||
break
|
||||
}
|
||||
}
|
||||
$p.Close()
|
||||
|
|
@ -29,6 +29,7 @@
|
|||
<button class="nav-tab" data-tab="applications">Applications</button>
|
||||
<button class="nav-tab" data-tab="sensing">Sensing</button>
|
||||
<button class="nav-tab" data-tab="training">Training</button>
|
||||
<a href="pose-fusion.html" class="nav-tab" style="text-decoration:none">Pose Fusion</a>
|
||||
<a href="observatory.html" class="nav-tab" style="text-decoration:none">Observatory</a>
|
||||
</nav>
|
||||
|
||||
|
|
|
|||
201
ui/pose-fusion.html
Normal file
201
ui/pose-fusion.html
Normal file
|
|
@ -0,0 +1,201 @@
|
|||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>WiFi-DensePose — Dual-Modal Pose Estimation</title>
|
||||
<link rel="stylesheet" href="pose-fusion/css/style.css?v=12">
|
||||
</head>
|
||||
<body>
|
||||
|
||||
<!-- Header -->
|
||||
<header class="header">
|
||||
<div class="header-left">
|
||||
<div class="logo"><span class="pi">π</span> DensePose</div>
|
||||
<div class="header-title">Dual-Modal Pose Estimation — Live Video + WiFi CSI Fusion</div>
|
||||
</div>
|
||||
<div class="header-right">
|
||||
<select id="mode-select" class="mode-select">
|
||||
<option value="dual">Dual Mode (Video + CSI)</option>
|
||||
<option value="video">Video Only</option>
|
||||
<option value="csi">CSI Only (WiFi)</option>
|
||||
</select>
|
||||
<div class="status-badge">
|
||||
<span id="status-dot" class="status-dot offline"></span>
|
||||
<span id="status-label">READY</span>
|
||||
</div>
|
||||
<span id="fps-display" class="fps-badge">-- FPS</span>
|
||||
<a href="index.html" class="back-link">← Dashboard</a>
|
||||
<a href="observatory.html" class="back-link">Observatory →</a>
|
||||
</div>
|
||||
</header>
|
||||
|
||||
<!-- Main Grid -->
|
||||
<div class="main-grid">
|
||||
|
||||
<!-- Video + Skeleton Panel -->
|
||||
<div class="video-panel">
|
||||
<video id="webcam" autoplay playsinline muted></video>
|
||||
<canvas id="skeleton-canvas"></canvas>
|
||||
<div class="video-overlay-label" id="mode-label">DUAL FUSION</div>
|
||||
|
||||
<div id="camera-prompt" class="camera-prompt">
|
||||
<div class="camera-prompt-label" id="prompt-mode-label">DUAL FUSION</div>
|
||||
<p>Enable your webcam for live video pose estimation.<br>
|
||||
Or switch to <strong>CSI Only</strong> mode for WiFi-based sensing.</p>
|
||||
<button id="start-camera-btn">Enable Camera</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Side Panels -->
|
||||
<div class="side-panels">
|
||||
|
||||
<!-- Fusion Confidence -->
|
||||
<div class="panel">
|
||||
<div class="panel-title">◆ Fusion Confidence</div>
|
||||
<div class="fusion-bars">
|
||||
<div class="bar-row">
|
||||
<span class="bar-label">Video</span>
|
||||
<div class="bar-track"><div class="bar-fill video" id="video-bar" style="width:0%"></div></div>
|
||||
<span class="bar-value" id="video-bar-val">0%</span>
|
||||
</div>
|
||||
<div class="bar-row">
|
||||
<span class="bar-label">CSI</span>
|
||||
<div class="bar-track"><div class="bar-fill csi" id="csi-bar" style="width:0%"></div></div>
|
||||
<span class="bar-value" id="csi-bar-val">0%</span>
|
||||
</div>
|
||||
<div class="bar-row">
|
||||
<span class="bar-label">Fused</span>
|
||||
<div class="bar-track"><div class="bar-fill fused" id="fused-bar" style="width:0%"></div></div>
|
||||
<span class="bar-value" id="fused-bar-val">0%</span>
|
||||
</div>
|
||||
</div>
|
||||
<div style="margin-top:8px; font-size:10px; color:var(--text-label)">
|
||||
Cross-modal: <span id="cross-modal-sim" style="color:var(--green-glow)">0.000</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- CSI Heatmap -->
|
||||
<div class="panel">
|
||||
<div class="panel-title">◆ CSI Amplitude Heatmap</div>
|
||||
<div class="csi-canvas-wrapper">
|
||||
<canvas id="csi-canvas" width="320" height="100"></canvas>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- RSSI Signal Strength -->
|
||||
<div class="panel">
|
||||
<div class="panel-title">◆ RSSI Signal Strength</div>
|
||||
<div class="rssi-row">
|
||||
<div class="rssi-gauge">
|
||||
<div class="rssi-bar-track">
|
||||
<div class="rssi-bar-fill" id="rssi-bar" style="width:0%"></div>
|
||||
</div>
|
||||
<div class="rssi-values">
|
||||
<span class="rssi-dbm" id="rssi-value">-- dBm</span>
|
||||
<span class="rssi-quality" id="rssi-quality">--</span>
|
||||
</div>
|
||||
</div>
|
||||
<canvas id="rssi-sparkline" width="160" height="32"></canvas>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Embedding Space -->
|
||||
<div class="panel">
|
||||
<div class="panel-title">◆ Embedding Space (2D Projection)</div>
|
||||
<div class="embedding-canvas-wrapper">
|
||||
<canvas id="embedding-canvas" width="320" height="100"></canvas>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- RuVector Attention Pipeline -->
|
||||
<div class="panel">
|
||||
<div class="panel-title">◆ RuVector WASM Attention Pipeline</div>
|
||||
<div class="rv-pipeline">
|
||||
<div class="rv-stage" id="rv-flash">Flash</div>
|
||||
<div class="rv-arrow">→</div>
|
||||
<div class="rv-stage" id="rv-mha">MHA</div>
|
||||
<div class="rv-arrow">→</div>
|
||||
<div class="rv-stage" id="rv-hyp">Hyper</div>
|
||||
<div class="rv-arrow">→</div>
|
||||
<div class="rv-stage" id="rv-lin">Linear</div>
|
||||
<div class="rv-arrow">→</div>
|
||||
<div class="rv-stage" id="rv-moe">MoE</div>
|
||||
<div class="rv-arrow">→</div>
|
||||
<div class="rv-stage" id="rv-lg">L+G</div>
|
||||
</div>
|
||||
<div class="rv-stats">
|
||||
<span>Energy: <span id="rv-energy" style="color:var(--green-glow)">--</span></span>
|
||||
<span>Refinement: <span id="rv-refine" style="color:var(--cyan)">--</span></span>
|
||||
<span>Pose Impact: <span id="rv-impact" style="color:var(--amber)">--</span></span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Latency -->
|
||||
<div class="panel">
|
||||
<div class="panel-title">◆ Pipeline Latency</div>
|
||||
<div class="latency-grid">
|
||||
<div class="latency-item">
|
||||
<div class="latency-value" id="lat-video">--</div>
|
||||
<div class="latency-label">Video CNN</div>
|
||||
</div>
|
||||
<div class="latency-item">
|
||||
<div class="latency-value" id="lat-csi">--</div>
|
||||
<div class="latency-label">CSI CNN</div>
|
||||
</div>
|
||||
<div class="latency-item">
|
||||
<div class="latency-value" id="lat-fusion">--</div>
|
||||
<div class="latency-label">Fusion</div>
|
||||
</div>
|
||||
<div class="latency-item">
|
||||
<div class="latency-value" id="lat-total">--</div>
|
||||
<div class="latency-label">Total</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Controls -->
|
||||
<div class="panel">
|
||||
<div class="panel-title">◆ Controls</div>
|
||||
<div class="controls-row">
|
||||
<button class="btn" id="pause-btn">⏸ Pause</button>
|
||||
</div>
|
||||
|
||||
<div class="slider-row">
|
||||
<label>Confidence</label>
|
||||
<input type="range" id="confidence-slider" min="0" max="1" step="0.05" value="0.3">
|
||||
<span class="slider-val" id="confidence-value">0.30</span>
|
||||
</div>
|
||||
|
||||
<div style="margin-top:10px">
|
||||
<div class="panel-title" style="margin-bottom:6px">◆ Live CSI Source</div>
|
||||
<div style="display:flex;gap:6px">
|
||||
<input type="text" id="ws-url" placeholder="ws://localhost:3030/ws/csi"
|
||||
style="flex:1;background:rgba(255,255,255,0.05);border:1px solid var(--bg-panel-border);
|
||||
color:var(--text-primary);padding:5px 8px;border-radius:4px;font-size:11px;
|
||||
font-family:'JetBrains Mono',monospace">
|
||||
<button class="btn" id="connect-ws-btn">Connect</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</div><!-- /side-panels -->
|
||||
|
||||
<!-- Bottom Bar -->
|
||||
<div class="bottom-bar">
|
||||
<div>
|
||||
WiFi-DensePose · Dual-Modal Pose Estimation ·
|
||||
Architecture: Conv2D → RuVector 6-Stage Attention (Flash+MHA+Hyperbolic+Linear+MoE+L/G) → Fusion → 26-Keypoint Pose
|
||||
</div>
|
||||
<div>
|
||||
<a href="https://github.com/ruvnet/wifi-densepose">GitHub</a> ·
|
||||
CNN: <span id="cnn-backend">ruvector-cnn (loading…)</span> ·
|
||||
<a href="observatory.html">Observatory</a>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</div><!-- /main-grid -->
|
||||
|
||||
<script type="module" src="pose-fusion/js/main.js?v=12"></script>
|
||||
</body>
|
||||
</html>
|
||||
30
ui/pose-fusion/build.sh
Normal file
30
ui/pose-fusion/build.sh
Normal file
|
|
@ -0,0 +1,30 @@
|
|||
#!/bin/bash
|
||||
# Build WASM packages for the dual-modal pose estimation demo.
|
||||
# Requires: wasm-pack (cargo install wasm-pack)
|
||||
#
|
||||
# Usage: ./build.sh
|
||||
#
|
||||
# Output: pkg/ruvector_cnn_wasm/ — WASM CNN embedder for browser
|
||||
|
||||
set -e
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
|
||||
VENDOR_DIR="$SCRIPT_DIR/../../vendor/ruvector"
|
||||
OUT_DIR="$SCRIPT_DIR/pkg/ruvector_cnn_wasm"
|
||||
|
||||
echo "Building ruvector-cnn-wasm..."
|
||||
wasm-pack build "$VENDOR_DIR/crates/ruvector-cnn-wasm" \
|
||||
--target web \
|
||||
--out-dir "$OUT_DIR" \
|
||||
--no-typescript
|
||||
|
||||
# Remove .gitignore so we can commit the build output for GitHub Pages
|
||||
rm -f "$OUT_DIR/.gitignore"
|
||||
|
||||
echo ""
|
||||
echo "Build complete!"
|
||||
echo " WASM: $(du -sh "$OUT_DIR/ruvector_cnn_wasm_bg.wasm" | cut -f1)"
|
||||
echo " JS: $(du -sh "$OUT_DIR/ruvector_cnn_wasm.js" | cut -f1)"
|
||||
echo ""
|
||||
echo "Serve the demo: cd $SCRIPT_DIR/.. && python3 -m http.server 8080"
|
||||
echo "Open: http://localhost:8080/pose-fusion.html"
|
||||
535
ui/pose-fusion/css/style.css
Normal file
535
ui/pose-fusion/css/style.css
Normal file
|
|
@ -0,0 +1,535 @@
|
|||
/* WiFi-DensePose — Dual-Modal Pose Fusion Demo
|
||||
Dark theme matching Observatory */
|
||||
|
||||
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;700&family=JetBrains+Mono:wght@400;600&display=swap');
|
||||
|
||||
:root {
|
||||
--bg-deep: #080c14;
|
||||
--bg-panel: rgba(8, 16, 28, 0.92);
|
||||
--bg-panel-border: rgba(0, 210, 120, 0.25);
|
||||
--green-glow: #00d878;
|
||||
--green-bright:#3eff8a;
|
||||
--green-dim: #0a6b3a;
|
||||
--amber: #ffb020;
|
||||
--amber-dim: #a06800;
|
||||
--blue-signal: #2090ff;
|
||||
--blue-dim: #0a3060;
|
||||
--red-alert: #ff3040;
|
||||
--cyan: #00e5ff;
|
||||
--text-primary: #e8ece0;
|
||||
--text-secondary: rgba(232,236,224, 0.55);
|
||||
--text-label: rgba(232,236,224, 0.35);
|
||||
--radius: 8px;
|
||||
}
|
||||
|
||||
* { margin: 0; padding: 0; box-sizing: border-box; }
|
||||
|
||||
body {
|
||||
background: var(--bg-deep);
|
||||
font-family: 'Inter', -apple-system, sans-serif;
|
||||
color: var(--text-primary);
|
||||
-webkit-font-smoothing: antialiased;
|
||||
overflow-x: hidden;
|
||||
min-height: 100vh;
|
||||
}
|
||||
|
||||
/* === Header === */
|
||||
.header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
padding: 16px 24px;
|
||||
border-bottom: 1px solid var(--bg-panel-border);
|
||||
background: var(--bg-panel);
|
||||
backdrop-filter: blur(12px);
|
||||
}
|
||||
|
||||
.header-left {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 16px;
|
||||
}
|
||||
|
||||
.logo {
|
||||
font-weight: 700;
|
||||
font-size: 24px;
|
||||
color: var(--green-glow);
|
||||
}
|
||||
|
||||
.logo .pi { font-style: normal; }
|
||||
|
||||
.header-title {
|
||||
font-size: 14px;
|
||||
color: var(--text-secondary);
|
||||
font-weight: 300;
|
||||
}
|
||||
|
||||
.header-right {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 16px;
|
||||
}
|
||||
|
||||
.mode-select {
|
||||
background: rgba(0,210,120,0.1);
|
||||
border: 1px solid var(--bg-panel-border);
|
||||
color: var(--text-primary);
|
||||
padding: 6px 12px;
|
||||
border-radius: var(--radius);
|
||||
font-family: inherit;
|
||||
font-size: 13px;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.mode-select option { background: #0c1420; }
|
||||
|
||||
.status-badge {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 12px;
|
||||
padding: 4px 10px;
|
||||
border-radius: 12px;
|
||||
background: rgba(0,210,120,0.1);
|
||||
border: 1px solid var(--bg-panel-border);
|
||||
}
|
||||
|
||||
.status-dot {
|
||||
width: 8px; height: 8px;
|
||||
border-radius: 50%;
|
||||
background: var(--green-glow);
|
||||
box-shadow: 0 0 8px var(--green-glow);
|
||||
animation: pulse-dot 2s ease infinite;
|
||||
}
|
||||
|
||||
.status-dot.offline { background: #555; box-shadow: none; animation: none; }
|
||||
.status-dot.warning { background: var(--amber); box-shadow: 0 0 8px var(--amber); }
|
||||
|
||||
@keyframes pulse-dot {
|
||||
0%, 100% { opacity: 1; }
|
||||
50% { opacity: 0.5; }
|
||||
}
|
||||
|
||||
.fps-badge {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 12px;
|
||||
color: var(--green-glow);
|
||||
}
|
||||
|
||||
.back-link {
|
||||
color: var(--text-secondary);
|
||||
text-decoration: none;
|
||||
font-size: 13px;
|
||||
transition: color 0.2s;
|
||||
}
|
||||
.back-link:hover { color: var(--green-glow); }
|
||||
|
||||
/* === Main Layout === */
|
||||
.main-grid {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 360px;
|
||||
grid-template-rows: 1fr auto;
|
||||
gap: 16px;
|
||||
padding: 16px 24px;
|
||||
height: calc(100vh - 72px);
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.video-panel {
|
||||
grid-row: 1;
|
||||
}
|
||||
|
||||
.side-panels {
|
||||
grid-row: 1;
|
||||
}
|
||||
|
||||
/* === Video Panel === */
|
||||
.video-panel {
|
||||
position: relative;
|
||||
background: #000;
|
||||
border-radius: var(--radius);
|
||||
border: 1px solid var(--bg-panel-border);
|
||||
overflow: hidden;
|
||||
min-height: 0;
|
||||
}
|
||||
|
||||
.video-panel video {
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
object-fit: cover;
|
||||
transform: scaleX(-1);
|
||||
}
|
||||
|
||||
.video-panel canvas {
|
||||
position: absolute;
|
||||
top: 0; left: 0;
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
transform: scaleX(-1);
|
||||
}
|
||||
|
||||
.video-overlay-label {
|
||||
position: absolute;
|
||||
top: 12px; left: 12px;
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 11px;
|
||||
padding: 4px 8px;
|
||||
background: rgba(0,0,0,0.7);
|
||||
border-radius: 4px;
|
||||
color: var(--green-glow);
|
||||
z-index: 5;
|
||||
transform: scaleX(-1);
|
||||
}
|
||||
|
||||
.camera-prompt {
|
||||
position: absolute;
|
||||
top: 0; left: 0; right: 0; bottom: 0;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
text-align: center;
|
||||
color: var(--text-secondary);
|
||||
padding: 24px;
|
||||
z-index: 6;
|
||||
}
|
||||
|
||||
.camera-prompt button {
|
||||
margin-top: 16px;
|
||||
padding: 10px 24px;
|
||||
background: var(--green-glow);
|
||||
color: #000;
|
||||
border: none;
|
||||
border-radius: var(--radius);
|
||||
font-family: inherit;
|
||||
font-weight: 600;
|
||||
font-size: 14px;
|
||||
cursor: pointer;
|
||||
transition: background 0.2s;
|
||||
}
|
||||
|
||||
.camera-prompt button:hover { background: var(--green-bright); }
|
||||
|
||||
.camera-prompt-label {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 14px;
|
||||
font-weight: 600;
|
||||
letter-spacing: 2px;
|
||||
color: var(--green-glow);
|
||||
text-shadow: 0 0 12px rgba(0,216,120,0.4);
|
||||
margin-bottom: 12px;
|
||||
}
|
||||
|
||||
/* === Side Panels === */
|
||||
.side-panels {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 8px;
|
||||
overflow-y: auto;
|
||||
min-height: 0;
|
||||
max-height: 100%;
|
||||
scrollbar-width: thin;
|
||||
scrollbar-color: var(--green-dim) transparent;
|
||||
}
|
||||
|
||||
.panel {
|
||||
background: var(--bg-panel);
|
||||
border: 1px solid var(--bg-panel-border);
|
||||
border-radius: var(--radius);
|
||||
padding: 10px 14px;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.panel-title {
|
||||
font-size: 11px;
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 1.2px;
|
||||
color: var(--text-label);
|
||||
margin-bottom: 10px;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
}
|
||||
|
||||
/* === CSI Heatmap === */
|
||||
.csi-canvas-wrapper {
|
||||
position: relative;
|
||||
border-radius: 4px;
|
||||
overflow: hidden;
|
||||
background: #000;
|
||||
}
|
||||
|
||||
.csi-canvas-wrapper canvas {
|
||||
width: 100%;
|
||||
display: block;
|
||||
}
|
||||
|
||||
/* === Fusion Bars === */
|
||||
.fusion-bars {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
.bar-row {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
.bar-label {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 11px;
|
||||
color: var(--text-secondary);
|
||||
width: 55px;
|
||||
text-align: right;
|
||||
}
|
||||
|
||||
.bar-track {
|
||||
flex: 1;
|
||||
height: 6px;
|
||||
background: rgba(255,255,255,0.06);
|
||||
border-radius: 3px;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.bar-fill {
|
||||
height: 100%;
|
||||
border-radius: 3px;
|
||||
transition: width 0.3s ease;
|
||||
}
|
||||
|
||||
.bar-fill.video { background: var(--cyan); }
|
||||
.bar-fill.csi { background: var(--amber); }
|
||||
.bar-fill.fused { background: var(--green-glow); box-shadow: 0 0 8px var(--green-glow); }
|
||||
|
||||
.bar-value {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 11px;
|
||||
color: var(--text-primary);
|
||||
width: 36px;
|
||||
}
|
||||
|
||||
/* === Embedding Space === */
|
||||
.embedding-canvas-wrapper {
|
||||
position: relative;
|
||||
background: #000;
|
||||
border-radius: 4px;
|
||||
overflow: hidden;
|
||||
}
|
||||
.embedding-canvas-wrapper canvas {
|
||||
width: 100%;
|
||||
display: block;
|
||||
}
|
||||
|
||||
/* === RuVector Pipeline === */
|
||||
.rv-pipeline {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 2px;
|
||||
margin-bottom: 8px;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
|
||||
.rv-stage {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 10px;
|
||||
padding: 3px 6px;
|
||||
border-radius: 3px;
|
||||
background: rgba(0,210,120,0.12);
|
||||
border: 1px solid rgba(0,210,120,0.3);
|
||||
color: var(--green-glow);
|
||||
transition: all 0.3s;
|
||||
}
|
||||
|
||||
.rv-stage.active {
|
||||
background: rgba(0,210,120,0.25);
|
||||
box-shadow: 0 0 6px rgba(0,210,120,0.3);
|
||||
}
|
||||
|
||||
.rv-arrow {
|
||||
font-size: 10px;
|
||||
color: var(--text-label);
|
||||
}
|
||||
|
||||
.rv-stats {
|
||||
display: flex;
|
||||
gap: 12px;
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 10px;
|
||||
color: var(--text-secondary);
|
||||
}
|
||||
|
||||
/* === Latency Panel === */
|
||||
.latency-grid {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(4, 1fr);
|
||||
gap: 6px;
|
||||
}
|
||||
|
||||
.latency-item {
|
||||
text-align: center;
|
||||
padding: 6px 0;
|
||||
}
|
||||
|
||||
.latency-value {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 16px;
|
||||
font-weight: 600;
|
||||
color: var(--green-glow);
|
||||
}
|
||||
|
||||
.latency-label {
|
||||
font-size: 10px;
|
||||
color: var(--text-label);
|
||||
margin-top: 2px;
|
||||
}
|
||||
|
||||
/* === Controls === */
|
||||
.controls-row {
|
||||
display: flex;
|
||||
gap: 8px;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
|
||||
.btn {
|
||||
padding: 6px 14px;
|
||||
border: 1px solid var(--bg-panel-border);
|
||||
background: rgba(0,210,120,0.08);
|
||||
color: var(--text-primary);
|
||||
border-radius: var(--radius);
|
||||
font-family: inherit;
|
||||
font-size: 12px;
|
||||
cursor: pointer;
|
||||
transition: all 0.2s;
|
||||
}
|
||||
.btn:hover { background: rgba(0,210,120,0.2); }
|
||||
.btn.active { background: var(--green-glow); color: #000; font-weight: 600; }
|
||||
|
||||
.slider-row {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
margin-top: 8px;
|
||||
}
|
||||
|
||||
.slider-row label {
|
||||
font-size: 11px;
|
||||
color: var(--text-secondary);
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.slider-row input[type=range] {
|
||||
flex: 1;
|
||||
accent-color: var(--green-glow);
|
||||
}
|
||||
|
||||
.slider-row .slider-val {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 11px;
|
||||
width: 32px;
|
||||
color: var(--green-glow);
|
||||
}
|
||||
|
||||
/* === Bottom Bar === */
|
||||
.bottom-bar {
|
||||
grid-column: 1 / -1;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
padding: 10px 16px;
|
||||
background: var(--bg-panel);
|
||||
border: 1px solid var(--bg-panel-border);
|
||||
border-radius: var(--radius);
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 11px;
|
||||
color: var(--text-secondary);
|
||||
}
|
||||
|
||||
.bottom-bar a {
|
||||
color: var(--green-glow);
|
||||
text-decoration: none;
|
||||
}
|
||||
|
||||
/* === RSSI Signal Strength === */
|
||||
.rssi-row {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 12px;
|
||||
}
|
||||
|
||||
.rssi-gauge { flex: 1; }
|
||||
|
||||
.rssi-bar-track {
|
||||
height: 8px;
|
||||
background: rgba(255,255,255,0.06);
|
||||
border-radius: 4px;
|
||||
overflow: hidden;
|
||||
position: relative;
|
||||
}
|
||||
|
||||
.rssi-bar-fill {
|
||||
height: 100%;
|
||||
border-radius: 4px;
|
||||
background: linear-gradient(90deg, var(--red-alert), var(--amber), var(--green-glow));
|
||||
transition: width 0.4s ease;
|
||||
position: relative;
|
||||
box-shadow: 0 0 6px rgba(0,210,120,0.3);
|
||||
}
|
||||
|
||||
.rssi-bar-fill::after {
|
||||
content: '';
|
||||
position: absolute;
|
||||
top: 0; left: 0; right: 0; bottom: 0;
|
||||
background: linear-gradient(90deg, transparent 0%, rgba(255,255,255,0.2) 50%, transparent 100%);
|
||||
animation: rssi-shimmer 2s ease-in-out infinite;
|
||||
}
|
||||
|
||||
@keyframes rssi-shimmer {
|
||||
0% { transform: translateX(-100%); }
|
||||
100% { transform: translateX(100%); }
|
||||
}
|
||||
|
||||
.rssi-values {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
margin-top: 4px;
|
||||
}
|
||||
|
||||
.rssi-dbm {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 14px;
|
||||
font-weight: 600;
|
||||
color: var(--green-glow);
|
||||
}
|
||||
|
||||
.rssi-quality {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 11px;
|
||||
color: var(--text-secondary);
|
||||
text-transform: uppercase;
|
||||
}
|
||||
|
||||
#rssi-sparkline {
|
||||
flex-shrink: 0;
|
||||
border-radius: 4px;
|
||||
background: rgba(0,0,0,0.3);
|
||||
}
|
||||
|
||||
/* === Skeleton colors === */
|
||||
.skeleton-joint { fill: var(--green-glow); }
|
||||
.skeleton-limb { stroke: var(--green-bright); }
|
||||
.skeleton-joint-csi { fill: var(--amber); }
|
||||
.skeleton-limb-csi { stroke: var(--amber); }
|
||||
|
||||
/* === Responsive === */
|
||||
@media (max-width: 900px) {
|
||||
.main-grid {
|
||||
grid-template-columns: 1fr;
|
||||
height: auto;
|
||||
overflow: auto;
|
||||
}
|
||||
.video-panel { aspect-ratio: 16/9; max-height: 50vh; }
|
||||
.side-panels { max-height: none; overflow: visible; }
|
||||
}
|
||||
307
ui/pose-fusion/js/canvas-renderer.js
Normal file
307
ui/pose-fusion/js/canvas-renderer.js
Normal file
|
|
@ -0,0 +1,307 @@
|
|||
/**
|
||||
* CanvasRenderer — Renders skeleton overlay on video, CSI heatmap,
|
||||
* embedding space visualization, and fusion confidence bars.
|
||||
*/
|
||||
|
||||
import { SKELETON_CONNECTIONS } from './pose-decoder.js';
|
||||
|
||||
export class CanvasRenderer {
|
||||
constructor() {
|
||||
this.colors = {
|
||||
joint: '#00d878',
|
||||
jointGlow: 'rgba(0, 216, 120, 0.4)',
|
||||
limb: '#3eff8a',
|
||||
limbGlow: 'rgba(62, 255, 138, 0.15)',
|
||||
csiJoint: '#ffb020',
|
||||
csiLimb: '#ffc850',
|
||||
fused: '#00e5ff',
|
||||
confidence: 'rgba(255,255,255,0.3)',
|
||||
videoEmb: '#00e5ff',
|
||||
csiEmb: '#ffb020',
|
||||
fusedEmb: '#00d878',
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Draw skeleton overlay on the video canvas
|
||||
* @param {CanvasRenderingContext2D} ctx
|
||||
* @param {Array<{x,y,confidence}>} keypoints - Normalized [0,1] coordinates
|
||||
* @param {number} width - Canvas width
|
||||
* @param {number} height - Canvas height
|
||||
* @param {object} opts
|
||||
*/
|
||||
drawSkeleton(ctx, keypoints, width, height, opts = {}) {
|
||||
const minConf = opts.minConfidence || 0.3;
|
||||
const color = opts.color || 'green';
|
||||
const jointColor = color === 'amber' ? this.colors.csiJoint : this.colors.joint;
|
||||
const limbColor = color === 'amber' ? this.colors.csiLimb : this.colors.limb;
|
||||
const glowColor = color === 'amber' ? 'rgba(255,176,32,0.4)' : this.colors.jointGlow;
|
||||
|
||||
// Extended keypoint styling
|
||||
const fingerColor = '#ff6ef0'; // Magenta for finger tips
|
||||
const fingerGlow = 'rgba(255,110,240,0.4)';
|
||||
const fingerLimb = 'rgba(255,110,240,0.5)';
|
||||
const toeColor = '#6ef0ff'; // Cyan for toes
|
||||
const neckColor = '#ffffff'; // White for neck
|
||||
|
||||
ctx.clearRect(0, 0, width, height);
|
||||
|
||||
if (!keypoints || keypoints.length === 0) return;
|
||||
|
||||
// Draw limbs first (behind joints)
|
||||
ctx.lineCap = 'round';
|
||||
|
||||
for (const [i, j] of SKELETON_CONNECTIONS) {
|
||||
const kpA = keypoints[i];
|
||||
const kpB = keypoints[j];
|
||||
if (!kpA || !kpB || kpA.confidence < minConf || kpB.confidence < minConf) continue;
|
||||
|
||||
const ax = kpA.x * width, ay = kpA.y * height;
|
||||
const bx = kpB.x * width, by = kpB.y * height;
|
||||
const avgConf = (kpA.confidence + kpB.confidence) / 2;
|
||||
|
||||
// Is this a hand/finger connection? (indices 17-22)
|
||||
const isFingerLink = i >= 17 && i <= 22 || j >= 17 && j <= 22;
|
||||
const isToeLink = i >= 23 && i <= 24 || j >= 23 && j <= 24;
|
||||
|
||||
// Glow
|
||||
ctx.strokeStyle = isFingerLink ? fingerLimb : this.colors.limbGlow;
|
||||
ctx.lineWidth = isFingerLink ? 4 : 8;
|
||||
ctx.globalAlpha = avgConf * (isFingerLink ? 0.3 : 0.4);
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(ax, ay);
|
||||
ctx.lineTo(bx, by);
|
||||
ctx.stroke();
|
||||
|
||||
// Main line
|
||||
ctx.strokeStyle = isFingerLink ? fingerColor : isToeLink ? toeColor : limbColor;
|
||||
ctx.lineWidth = isFingerLink || isToeLink ? 1.5 : 2.5;
|
||||
ctx.globalAlpha = avgConf;
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(ax, ay);
|
||||
ctx.lineTo(bx, by);
|
||||
ctx.stroke();
|
||||
}
|
||||
|
||||
// Draw joints
|
||||
ctx.globalAlpha = 1;
|
||||
for (let idx = 0; idx < keypoints.length; idx++) {
|
||||
const kp = keypoints[idx];
|
||||
if (!kp || kp.confidence < minConf) continue;
|
||||
|
||||
const x = kp.x * width;
|
||||
const y = kp.y * height;
|
||||
const isFinger = idx >= 17 && idx <= 22;
|
||||
const isToe = idx >= 23 && idx <= 24;
|
||||
const isNeck = idx === 25;
|
||||
const r = isFinger ? 2 + kp.confidence * 2 : isToe ? 2 : 3 + kp.confidence * 3;
|
||||
const jColor = isFinger ? fingerColor : isToe ? toeColor : isNeck ? neckColor : jointColor;
|
||||
const gColor = isFinger ? fingerGlow : glowColor;
|
||||
|
||||
// Glow
|
||||
ctx.beginPath();
|
||||
ctx.arc(x, y, r + (isFinger ? 3 : 4), 0, Math.PI * 2);
|
||||
ctx.fillStyle = gColor;
|
||||
ctx.globalAlpha = kp.confidence * (isFinger ? 0.5 : 0.6);
|
||||
ctx.fill();
|
||||
|
||||
// Joint dot
|
||||
ctx.beginPath();
|
||||
ctx.arc(x, y, r, 0, Math.PI * 2);
|
||||
ctx.fillStyle = jColor;
|
||||
ctx.globalAlpha = kp.confidence;
|
||||
ctx.fill();
|
||||
|
||||
// White center (body joints only)
|
||||
if (!isFinger && !isToe) {
|
||||
ctx.beginPath();
|
||||
ctx.arc(x, y, r * 0.4, 0, Math.PI * 2);
|
||||
ctx.fillStyle = '#fff';
|
||||
ctx.globalAlpha = kp.confidence * 0.8;
|
||||
ctx.fill();
|
||||
}
|
||||
}
|
||||
|
||||
ctx.globalAlpha = 1;
|
||||
|
||||
// Confidence label + keypoint count
|
||||
if (opts.label) {
|
||||
const visCount = keypoints.filter(kp => kp && kp.confidence >= minConf).length;
|
||||
ctx.font = '11px "JetBrains Mono", monospace';
|
||||
ctx.fillStyle = jointColor;
|
||||
ctx.globalAlpha = 0.8;
|
||||
ctx.fillText(`${opts.label} · ${visCount} joints`, 8, height - 8);
|
||||
ctx.globalAlpha = 1;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Draw CSI amplitude heatmap
|
||||
* @param {CanvasRenderingContext2D} ctx
|
||||
* @param {{ data: Float32Array, width: number, height: number }} heatmap
|
||||
* @param {number} canvasW
|
||||
* @param {number} canvasH
|
||||
*/
|
||||
drawCsiHeatmap(ctx, heatmap, canvasW, canvasH) {
|
||||
ctx.clearRect(0, 0, canvasW, canvasH);
|
||||
|
||||
if (!heatmap || !heatmap.data || heatmap.height < 2) {
|
||||
ctx.fillStyle = '#0a0e18';
|
||||
ctx.fillRect(0, 0, canvasW, canvasH);
|
||||
ctx.font = '11px "JetBrains Mono", monospace';
|
||||
ctx.fillStyle = 'rgba(255,255,255,0.3)';
|
||||
ctx.fillText('Waiting for CSI data...', 8, canvasH / 2);
|
||||
return;
|
||||
}
|
||||
|
||||
const { data, width: dw, height: dh } = heatmap;
|
||||
const cellW = canvasW / dw;
|
||||
const cellH = canvasH / dh;
|
||||
|
||||
for (let y = 0; y < dh; y++) {
|
||||
for (let x = 0; x < dw; x++) {
|
||||
const val = Math.min(1, Math.max(0, data[y * dw + x]));
|
||||
ctx.fillStyle = this._heatmapColor(val);
|
||||
ctx.fillRect(x * cellW, y * cellH, cellW + 0.5, cellH + 0.5);
|
||||
}
|
||||
}
|
||||
|
||||
// Axis labels
|
||||
ctx.font = '9px "JetBrains Mono", monospace';
|
||||
ctx.fillStyle = 'rgba(255,255,255,0.4)';
|
||||
ctx.fillText('Subcarrier →', 4, canvasH - 4);
|
||||
ctx.save();
|
||||
ctx.translate(canvasW - 4, canvasH - 4);
|
||||
ctx.rotate(-Math.PI / 2);
|
||||
ctx.fillText('Time ↑', 0, 0);
|
||||
ctx.restore();
|
||||
}
|
||||
|
||||
/**
|
||||
* Draw embedding space 2D projection
|
||||
* @param {CanvasRenderingContext2D} ctx
|
||||
* @param {{ video: Array, csi: Array, fused: Array }} points
|
||||
* @param {number} w
|
||||
* @param {number} h
|
||||
*/
|
||||
drawEmbeddingSpace(ctx, points, w, h) {
|
||||
ctx.fillStyle = '#050810';
|
||||
ctx.fillRect(0, 0, w, h);
|
||||
|
||||
// Grid
|
||||
ctx.strokeStyle = 'rgba(255,255,255,0.05)';
|
||||
ctx.lineWidth = 0.5;
|
||||
for (let i = 0; i <= 4; i++) {
|
||||
const x = (i / 4) * w;
|
||||
ctx.beginPath(); ctx.moveTo(x, 0); ctx.lineTo(x, h); ctx.stroke();
|
||||
const y = (i / 4) * h;
|
||||
ctx.beginPath(); ctx.moveTo(0, y); ctx.lineTo(w, y); ctx.stroke();
|
||||
}
|
||||
|
||||
// Axes
|
||||
ctx.strokeStyle = 'rgba(255,255,255,0.1)';
|
||||
ctx.lineWidth = 1;
|
||||
ctx.beginPath(); ctx.moveTo(w / 2, 0); ctx.lineTo(w / 2, h); ctx.stroke();
|
||||
ctx.beginPath(); ctx.moveTo(0, h / 2); ctx.lineTo(w, h / 2); ctx.stroke();
|
||||
|
||||
// Auto-scale: find max extent across all point sets
|
||||
let maxExtent = 0.01;
|
||||
for (const pts of [points.video, points.csi, points.fused]) {
|
||||
if (!pts) continue;
|
||||
for (const p of pts) {
|
||||
if (!p) continue;
|
||||
maxExtent = Math.max(maxExtent, Math.abs(p[0]), Math.abs(p[1]));
|
||||
}
|
||||
}
|
||||
const scale = 0.42 / maxExtent; // Fill ~84% of half-width
|
||||
|
||||
const drawPoints = (pts, color, size) => {
|
||||
if (!pts || pts.length === 0) return;
|
||||
const len = pts.length;
|
||||
|
||||
// Draw trail line connecting recent points
|
||||
if (len >= 2) {
|
||||
ctx.beginPath();
|
||||
let started = false;
|
||||
for (let i = 0; i < len; i++) {
|
||||
const p = pts[i];
|
||||
if (!p) continue;
|
||||
const px = w / 2 + p[0] * scale * w;
|
||||
const py = h / 2 + p[1] * scale * h;
|
||||
if (px < -10 || px > w + 10 || py < -10 || py > h + 10) continue;
|
||||
if (!started) { ctx.moveTo(px, py); started = true; }
|
||||
else ctx.lineTo(px, py);
|
||||
}
|
||||
ctx.strokeStyle = color;
|
||||
ctx.globalAlpha = 0.2;
|
||||
ctx.lineWidth = 1;
|
||||
ctx.stroke();
|
||||
}
|
||||
|
||||
// Draw dots with glow on newest
|
||||
for (let i = 0; i < len; i++) {
|
||||
const p = pts[i];
|
||||
if (!p) continue;
|
||||
const age = 1 - (i / len) * 0.7;
|
||||
const px = w / 2 + p[0] * scale * w;
|
||||
const py = h / 2 + p[1] * scale * h;
|
||||
|
||||
if (px < -10 || px > w + 10 || py < -10 || py > h + 10) continue;
|
||||
|
||||
// Glow on newest point
|
||||
if (i === len - 1) {
|
||||
ctx.beginPath();
|
||||
ctx.arc(px, py, size + 4, 0, Math.PI * 2);
|
||||
ctx.fillStyle = color;
|
||||
ctx.globalAlpha = 0.3;
|
||||
ctx.fill();
|
||||
}
|
||||
|
||||
ctx.beginPath();
|
||||
ctx.arc(px, py, i === len - 1 ? size + 1 : size, 0, Math.PI * 2);
|
||||
ctx.fillStyle = color;
|
||||
ctx.globalAlpha = age * 0.8;
|
||||
ctx.fill();
|
||||
}
|
||||
};
|
||||
|
||||
drawPoints(points.video, this.colors.videoEmb, 3);
|
||||
drawPoints(points.csi, this.colors.csiEmb, 3);
|
||||
drawPoints(points.fused, this.colors.fusedEmb, 4);
|
||||
ctx.globalAlpha = 1;
|
||||
|
||||
// Legend
|
||||
ctx.font = '9px "JetBrains Mono", monospace';
|
||||
const legends = [
|
||||
{ color: this.colors.videoEmb, label: 'Video' },
|
||||
{ color: this.colors.csiEmb, label: 'CSI' },
|
||||
{ color: this.colors.fusedEmb, label: 'Fused' },
|
||||
];
|
||||
legends.forEach((l, i) => {
|
||||
const ly = 12 + i * 14;
|
||||
ctx.fillStyle = l.color;
|
||||
ctx.beginPath();
|
||||
ctx.arc(10, ly - 3, 3, 0, Math.PI * 2);
|
||||
ctx.fill();
|
||||
ctx.fillStyle = 'rgba(255,255,255,0.5)';
|
||||
ctx.fillText(l.label, 18, ly);
|
||||
});
|
||||
}
|
||||
|
||||
_heatmapColor(val) {
|
||||
// Dark blue → cyan → green → yellow → red
|
||||
if (val < 0.25) {
|
||||
const t = val / 0.25;
|
||||
return `rgb(${Math.floor(t * 20)}, ${Math.floor(20 + t * 60)}, ${Math.floor(60 + t * 100)})`;
|
||||
} else if (val < 0.5) {
|
||||
const t = (val - 0.25) / 0.25;
|
||||
return `rgb(${Math.floor(20 + t * 20)}, ${Math.floor(80 + t * 100)}, ${Math.floor(160 - t * 60)})`;
|
||||
} else if (val < 0.75) {
|
||||
const t = (val - 0.5) / 0.25;
|
||||
return `rgb(${Math.floor(40 + t * 180)}, ${Math.floor(180 + t * 75)}, ${Math.floor(100 - t * 80)})`;
|
||||
} else {
|
||||
const t = (val - 0.75) / 0.25;
|
||||
return `rgb(${Math.floor(220 + t * 35)}, ${Math.floor(255 - t * 120)}, ${Math.floor(20 - t * 20)})`;
|
||||
}
|
||||
}
|
||||
}
|
||||
443
ui/pose-fusion/js/cnn-embedder.js
Normal file
443
ui/pose-fusion/js/cnn-embedder.js
Normal file
|
|
@ -0,0 +1,443 @@
|
|||
/**
|
||||
* CNN Embedder — RuVector Attention-powered feature extractor.
|
||||
*
|
||||
* Uses the real ruvector-attention-wasm WASM module for Multi-Head Attention
|
||||
* and Flash Attention on CSI/video data. Falls back to a JS Conv2D pipeline
|
||||
* when WASM is not available.
|
||||
*
|
||||
* Pipeline: Conv2D → BatchNorm → ReLU → Pool → RuVector Attention → Project → L2 Normalize
|
||||
* Two instances are created: one for video frames, one for CSI pseudo-images.
|
||||
*/
|
||||
|
||||
// Seeded PRNG for deterministic weight initialization
|
||||
function mulberry32(seed) {
|
||||
return function() {
|
||||
let t = (seed += 0x6D2B79F5);
|
||||
t = Math.imul(t ^ (t >>> 15), t | 1);
|
||||
t ^= t + Math.imul(t ^ (t >>> 7), t | 61);
|
||||
return ((t ^ (t >>> 14)) >>> 0) / 4294967296;
|
||||
};
|
||||
}
|
||||
|
||||
export class CnnEmbedder {
|
||||
/**
|
||||
* @param {object} opts
|
||||
* @param {number} opts.inputSize - Square input dimension (default 56 for speed)
|
||||
* @param {number} opts.embeddingDim - Output embedding dimension (default 128)
|
||||
* @param {boolean} opts.normalize - L2 normalize output
|
||||
* @param {number} opts.seed - PRNG seed for weight init
|
||||
*/
|
||||
constructor(opts = {}) {
|
||||
this.inputSize = opts.inputSize || 56;
|
||||
this.embeddingDim = opts.embeddingDim || 128;
|
||||
this.normalize = opts.normalize !== false;
|
||||
this.wasmEmbedder = null;
|
||||
this.rvAttention = null; // RuVector Multi-Head Attention (WASM)
|
||||
this.rvFlash = null; // RuVector Flash Attention (WASM)
|
||||
this.rvHyperbolic = null; // RuVector Hyperbolic Attention (hierarchical body)
|
||||
this.rvMoE = null; // RuVector Mixture-of-Experts (body-region routing)
|
||||
this.rvLinear = null; // RuVector Linear Attention (O(n) fast hand refinement)
|
||||
this.rvLocalGlobal = null; // RuVector Local-Global Attention (detail + context)
|
||||
this.rvModule = null; // RuVector WASM module reference
|
||||
this.useRuVector = false;
|
||||
|
||||
// Initialize weights with deterministic PRNG
|
||||
const rng = mulberry32(opts.seed || 42);
|
||||
const randRange = (lo, hi) => lo + rng() * (hi - lo);
|
||||
|
||||
// Conv 3x3: 3 input channels → 16 output channels
|
||||
this.convWeights = new Float32Array(3 * 3 * 3 * 16);
|
||||
for (let i = 0; i < this.convWeights.length; i++) {
|
||||
this.convWeights[i] = randRange(-0.15, 0.15);
|
||||
}
|
||||
|
||||
// BatchNorm params (16 channels)
|
||||
this.bnGamma = new Float32Array(16).fill(1.0);
|
||||
this.bnBeta = new Float32Array(16).fill(0.0);
|
||||
this.bnMean = new Float32Array(16).fill(0.0);
|
||||
this.bnVar = new Float32Array(16).fill(1.0);
|
||||
|
||||
// Projection: 16 → embeddingDim (used when RuVector not available)
|
||||
this.projWeights = new Float32Array(16 * this.embeddingDim);
|
||||
for (let i = 0; i < this.projWeights.length; i++) {
|
||||
this.projWeights[i] = randRange(-0.1, 0.1);
|
||||
}
|
||||
|
||||
// Attention projection: attention_dim → embeddingDim
|
||||
this.attnProjWeights = new Float32Array(16 * this.embeddingDim);
|
||||
for (let i = 0; i < this.attnProjWeights.length; i++) {
|
||||
this.attnProjWeights[i] = randRange(-0.08, 0.08);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Try to load RuVector attention WASM, then fall back to ruvector-cnn-wasm
|
||||
* @param {string} wasmPath - Path to the WASM package directory
|
||||
*/
|
||||
async tryLoadWasm(wasmPath) {
|
||||
// First try: RuVector Attention WASM (the real thing — browser ESM build)
|
||||
try {
|
||||
const attnBase = new URL('../pkg/ruvector-attention/ruvector_attention_browser.js', import.meta.url).href;
|
||||
const mod = await import(attnBase);
|
||||
await mod.default(); // async WASM init via fetch
|
||||
mod.init();
|
||||
|
||||
// Create all 6 attention mechanisms
|
||||
this.rvAttention = new mod.WasmMultiHeadAttention(16, 4);
|
||||
this.rvFlash = new mod.WasmFlashAttention(16, 8);
|
||||
this.rvHyperbolic = new mod.WasmHyperbolicAttention(16, -1.0);
|
||||
this.rvMoE = new mod.WasmMoEAttention(16, 3, 2);
|
||||
this.rvLinear = new mod.WasmLinearAttention(16, 16);
|
||||
this.rvLocalGlobal = new mod.WasmLocalGlobalAttention(16, 4, 2);
|
||||
this.rvModule = mod;
|
||||
this.useRuVector = true;
|
||||
|
||||
// Log available mechanisms
|
||||
const mechs = mod.available_mechanisms();
|
||||
console.log(`[CNN] RuVector WASM v${mod.version()} — all 6 attention mechanisms active`, mechs);
|
||||
return true;
|
||||
} catch (e) {
|
||||
console.log('[CNN] RuVector Attention WASM not available:', e.message);
|
||||
}
|
||||
|
||||
// Second try: ruvector-cnn-wasm (legacy path)
|
||||
try {
|
||||
const mod = await import(`${wasmPath}/ruvector_cnn_wasm.js`);
|
||||
await mod.default();
|
||||
const config = new mod.EmbedderConfig();
|
||||
config.input_size = this.inputSize;
|
||||
config.embedding_dim = this.embeddingDim;
|
||||
config.normalize = this.normalize;
|
||||
this.wasmEmbedder = new mod.WasmCnnEmbedder(config);
|
||||
console.log('[CNN] WASM CNN embedder loaded successfully');
|
||||
return true;
|
||||
} catch (e) {
|
||||
console.log('[CNN] WASM CNN not available, using JS fallback:', e.message);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract embedding from RGB image data
|
||||
* @param {Uint8Array} rgbData - RGB pixel data (H*W*3)
|
||||
* @param {number} width
|
||||
* @param {number} height
|
||||
* @returns {Float32Array} embedding vector
|
||||
*/
|
||||
extract(rgbData, width, height) {
|
||||
if (this.wasmEmbedder) {
|
||||
try {
|
||||
const result = this.wasmEmbedder.extract(rgbData, width, height);
|
||||
return new Float32Array(result);
|
||||
} catch (_) { /* fallback to JS */ }
|
||||
}
|
||||
return this._extractJS(rgbData, width, height);
|
||||
}
|
||||
|
||||
_extractJS(rgbData, width, height) {
|
||||
// 1. Resize to inputSize × inputSize if needed
|
||||
const sz = this.inputSize;
|
||||
let input;
|
||||
if (width === sz && height === sz) {
|
||||
input = new Float32Array(rgbData.length);
|
||||
for (let i = 0; i < rgbData.length; i++) input[i] = rgbData[i] / 255.0;
|
||||
} else {
|
||||
input = this._resize(rgbData, width, height, sz, sz);
|
||||
}
|
||||
|
||||
// 2. ImageNet normalization
|
||||
const mean = [0.485, 0.456, 0.406];
|
||||
const std = [0.229, 0.224, 0.225];
|
||||
const pixels = sz * sz;
|
||||
for (let i = 0; i < pixels; i++) {
|
||||
input[i * 3] = (input[i * 3] - mean[0]) / std[0];
|
||||
input[i * 3 + 1] = (input[i * 3 + 1] - mean[1]) / std[1];
|
||||
input[i * 3 + 2] = (input[i * 3 + 2] - mean[2]) / std[2];
|
||||
}
|
||||
|
||||
// 3. Conv2D 3x3 (3 → 16 channels)
|
||||
const convOut = this._conv2d3x3(input, sz, sz, 3, 16);
|
||||
|
||||
// 4. BatchNorm
|
||||
this._batchNorm(convOut, 16);
|
||||
|
||||
// 5. ReLU
|
||||
for (let i = 0; i < convOut.length; i++) {
|
||||
if (convOut[i] < 0) convOut[i] = 0;
|
||||
}
|
||||
|
||||
// 6. Global average pooling → spatial tokens (each 16-dim)
|
||||
const outH = sz - 2, outW = sz - 2;
|
||||
const spatial = outH * outW;
|
||||
|
||||
// 7. RuVector Attention (if loaded) — apply attention over spatial tokens
|
||||
if (this.useRuVector && this.rvAttention) {
|
||||
return this._extractWithAttention(convOut, spatial, 16);
|
||||
}
|
||||
|
||||
// Fallback: simple global average pool + linear projection
|
||||
const pooled = new Float32Array(16);
|
||||
for (let i = 0; i < spatial; i++) {
|
||||
for (let c = 0; c < 16; c++) {
|
||||
pooled[c] += convOut[i * 16 + c];
|
||||
}
|
||||
}
|
||||
for (let c = 0; c < 16; c++) pooled[c] /= spatial;
|
||||
|
||||
// Linear projection → embeddingDim
|
||||
const emb = new Float32Array(this.embeddingDim);
|
||||
for (let o = 0; o < this.embeddingDim; o++) {
|
||||
let sum = 0;
|
||||
for (let i = 0; i < 16; i++) {
|
||||
sum += pooled[i] * this.projWeights[i * this.embeddingDim + o];
|
||||
}
|
||||
emb[o] = sum;
|
||||
}
|
||||
|
||||
// L2 normalize
|
||||
if (this.normalize) {
|
||||
let norm = 0;
|
||||
for (let i = 0; i < emb.length; i++) norm += emb[i] * emb[i];
|
||||
norm = Math.sqrt(norm);
|
||||
if (norm > 1e-8) {
|
||||
for (let i = 0; i < emb.length; i++) emb[i] /= norm;
|
||||
}
|
||||
}
|
||||
|
||||
return emb;
|
||||
}
|
||||
|
||||
/**
|
||||
* Full 6-stage RuVector WASM attention pipeline:
|
||||
* 1. Flash Attention (efficient O(n) pre-screening of spatial tokens)
|
||||
* 2. Multi-Head Attention (global spatial reasoning)
|
||||
* 3. Hyperbolic Attention (hierarchical body-part structure, Poincaré ball)
|
||||
* 4. Linear Attention (O(n) refinement for fine detail — hands/extremities)
|
||||
* 5. MoE Attention (body-region specialized expert routing)
|
||||
* 6. Local-Global Attention (local detail + global context fusion)
|
||||
* → Weighted blend + batch_normalize + project + L2 normalize
|
||||
*/
|
||||
_extractWithAttention(convOut, numTokens, channels) {
|
||||
const mod = this.rvModule;
|
||||
|
||||
// Subsample spatial tokens for attention (max 64 for speed)
|
||||
const maxTokens = 64;
|
||||
const step = numTokens > maxTokens ? Math.floor(numTokens / maxTokens) : 1;
|
||||
const tokens = [];
|
||||
for (let i = 0; i < numTokens && tokens.length < maxTokens; i += step) {
|
||||
const token = new Float32Array(channels);
|
||||
for (let c = 0; c < channels; c++) {
|
||||
token[c] = convOut[i * channels + c];
|
||||
}
|
||||
tokens.push(token);
|
||||
}
|
||||
|
||||
const numQueries = Math.min(4, tokens.length);
|
||||
const queryStride = Math.floor(tokens.length / numQueries);
|
||||
|
||||
// === Stage 1: Flash Attention (efficient pre-screening) ===
|
||||
const flashOut = new Float32Array(channels);
|
||||
try {
|
||||
// Flash attention with block size 8 for efficient O(n) screening
|
||||
const result = this.rvFlash.compute(tokens[0], tokens, tokens);
|
||||
for (let c = 0; c < channels; c++) flashOut[c] = result[c];
|
||||
} catch (_) {
|
||||
flashOut.set(tokens[0]);
|
||||
}
|
||||
|
||||
// === Stage 2: Multi-Head Attention (global spatial reasoning) ===
|
||||
const mhaOut = new Float32Array(channels);
|
||||
for (let q = 0; q < numQueries; q++) {
|
||||
const queryToken = tokens[q * queryStride];
|
||||
try {
|
||||
const result = this.rvAttention.compute(queryToken, tokens, tokens);
|
||||
for (let c = 0; c < channels; c++) mhaOut[c] += result[c] / numQueries;
|
||||
} catch (_) {
|
||||
for (let c = 0; c < channels; c++) mhaOut[c] += queryToken[c] / numQueries;
|
||||
}
|
||||
}
|
||||
|
||||
// === Stage 3: Hyperbolic Attention (hierarchical body structure) ===
|
||||
const hyOut = new Float32Array(channels);
|
||||
try {
|
||||
const result = this.rvHyperbolic.compute(mhaOut, tokens, tokens);
|
||||
for (let c = 0; c < channels; c++) hyOut[c] = result[c];
|
||||
} catch (_) {
|
||||
hyOut.set(mhaOut);
|
||||
}
|
||||
|
||||
// === Stage 4: Linear Attention (O(n) fast refinement for extremities) ===
|
||||
const linOut = new Float32Array(channels);
|
||||
try {
|
||||
const result = this.rvLinear.compute(hyOut, tokens, tokens);
|
||||
for (let c = 0; c < channels; c++) linOut[c] = result[c];
|
||||
} catch (_) {
|
||||
linOut.set(hyOut);
|
||||
}
|
||||
|
||||
// === Stage 5: MoE Attention (body-region expert routing) ===
|
||||
const moeOut = new Float32Array(channels);
|
||||
try {
|
||||
const result = this.rvMoE.compute(linOut, tokens, tokens);
|
||||
for (let c = 0; c < channels; c++) moeOut[c] = result[c];
|
||||
} catch (_) {
|
||||
moeOut.set(linOut);
|
||||
}
|
||||
|
||||
// === Stage 6: Local-Global Attention (detail + context) ===
|
||||
const lgOut = new Float32Array(channels);
|
||||
try {
|
||||
const result = this.rvLocalGlobal.compute(moeOut, tokens, tokens);
|
||||
for (let c = 0; c < channels; c++) lgOut[c] = result[c];
|
||||
} catch (_) {
|
||||
lgOut.set(moeOut);
|
||||
}
|
||||
|
||||
// === Blend all 6 outputs ===
|
||||
// Use WASM softmax on log-energy scores for dynamic stage weighting
|
||||
const blended = new Float32Array(channels);
|
||||
const stages = [flashOut, mhaOut, hyOut, linOut, moeOut, lgOut];
|
||||
// Use log-energy to prevent exp() overflow in softmax
|
||||
const logEnergies = new Float32Array(6);
|
||||
for (let s = 0; s < 6; s++) {
|
||||
const e = this._energy(stages[s]);
|
||||
logEnergies[s] = e > 1e-10 ? Math.log(e) : -20;
|
||||
}
|
||||
try { mod.softmax(logEnergies); } catch (_) {
|
||||
let max = -Infinity;
|
||||
for (let i = 0; i < 6; i++) max = Math.max(max, logEnergies[i]);
|
||||
let sum = 0;
|
||||
for (let i = 0; i < 6; i++) { logEnergies[i] = Math.exp(logEnergies[i] - max); sum += logEnergies[i]; }
|
||||
for (let i = 0; i < 6; i++) logEnergies[i] /= sum;
|
||||
}
|
||||
for (let c = 0; c < channels; c++) {
|
||||
for (let s = 0; s < 6; s++) {
|
||||
blended[c] += logEnergies[s] * stages[s][c];
|
||||
}
|
||||
}
|
||||
|
||||
// Batch normalize only when we have enough diversity (skip for single vectors)
|
||||
// Single-vector batch norm collapses to zeros, killing embedding space
|
||||
let normed = blended;
|
||||
|
||||
// Project to embeddingDim
|
||||
const emb = new Float32Array(this.embeddingDim);
|
||||
for (let o = 0; o < this.embeddingDim; o++) {
|
||||
let sum = 0;
|
||||
for (let i = 0; i < channels; i++) {
|
||||
sum += normed[i] * this.attnProjWeights[i * this.embeddingDim + o];
|
||||
}
|
||||
emb[o] = sum;
|
||||
}
|
||||
|
||||
// L2 normalize using RuVector WASM
|
||||
if (this.normalize) {
|
||||
try { mod.normalize(emb); } catch (_) {
|
||||
let norm = 0;
|
||||
for (let i = 0; i < emb.length; i++) norm += emb[i] * emb[i];
|
||||
norm = Math.sqrt(norm);
|
||||
if (norm > 1e-8) for (let i = 0; i < emb.length; i++) emb[i] /= norm;
|
||||
}
|
||||
}
|
||||
|
||||
return emb;
|
||||
}
|
||||
|
||||
/** Compute vector energy (L2 norm squared) for attention weighting */
|
||||
_energy(vec) {
|
||||
let e = 0;
|
||||
for (let i = 0; i < vec.length; i++) e += vec[i] * vec[i];
|
||||
return e;
|
||||
}
|
||||
|
||||
_conv2d3x3(input, H, W, Cin, Cout) {
|
||||
const outH = H - 2, outW = W - 2;
|
||||
const output = new Float32Array(outH * outW * Cout);
|
||||
for (let y = 0; y < outH; y++) {
|
||||
for (let x = 0; x < outW; x++) {
|
||||
for (let co = 0; co < Cout; co++) {
|
||||
let sum = 0;
|
||||
for (let ky = 0; ky < 3; ky++) {
|
||||
for (let kx = 0; kx < 3; kx++) {
|
||||
for (let ci = 0; ci < Cin; ci++) {
|
||||
const px = ((y + ky) * W + (x + kx)) * Cin + ci;
|
||||
const wt = (((ky * 3 + kx) * Cin) + ci) * Cout + co;
|
||||
sum += input[px] * this.convWeights[wt];
|
||||
}
|
||||
}
|
||||
}
|
||||
output[(y * outW + x) * Cout + co] = sum;
|
||||
}
|
||||
}
|
||||
}
|
||||
return output;
|
||||
}
|
||||
|
||||
_batchNorm(data, channels) {
|
||||
const spatial = data.length / channels;
|
||||
for (let i = 0; i < spatial; i++) {
|
||||
for (let c = 0; c < channels; c++) {
|
||||
const idx = i * channels + c;
|
||||
data[idx] = this.bnGamma[c] * (data[idx] - this.bnMean[c]) / Math.sqrt(this.bnVar[c] + 1e-5) + this.bnBeta[c];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
_resize(rgbData, srcW, srcH, dstW, dstH) {
|
||||
const output = new Float32Array(dstW * dstH * 3);
|
||||
const xRatio = srcW / dstW;
|
||||
const yRatio = srcH / dstH;
|
||||
for (let y = 0; y < dstH; y++) {
|
||||
for (let x = 0; x < dstW; x++) {
|
||||
const sx = Math.min(Math.floor(x * xRatio), srcW - 1);
|
||||
const sy = Math.min(Math.floor(y * yRatio), srcH - 1);
|
||||
const srcIdx = (sy * srcW + sx) * 3;
|
||||
const dstIdx = (y * dstW + x) * 3;
|
||||
output[dstIdx] = rgbData[srcIdx] / 255.0;
|
||||
output[dstIdx + 1] = rgbData[srcIdx + 1] / 255.0;
|
||||
output[dstIdx + 2] = rgbData[srcIdx + 2] / 255.0;
|
||||
}
|
||||
}
|
||||
return output;
|
||||
}
|
||||
|
||||
/** Cosine similarity using WASM when available, JS fallback */
|
||||
cosineSim(a, b) {
|
||||
if (this.rvModule) {
|
||||
try { return this.rvModule.cosine_similarity(a, b); } catch (_) { /* fallback */ }
|
||||
}
|
||||
return CnnEmbedder.cosineSimilarity(a, b);
|
||||
}
|
||||
|
||||
/** L2 norm using WASM when available */
|
||||
l2Norm(vec) {
|
||||
if (this.rvModule) {
|
||||
try { return this.rvModule.l2_norm(vec); } catch (_) { /* fallback */ }
|
||||
}
|
||||
let norm = 0;
|
||||
for (let i = 0; i < vec.length; i++) norm += vec[i] * vec[i];
|
||||
return Math.sqrt(norm);
|
||||
}
|
||||
|
||||
/** Pairwise distance matrix using WASM (for skeleton validation) */
|
||||
pairwiseDistances(vectors) {
|
||||
if (this.rvModule) {
|
||||
try { return this.rvModule.pairwise_distances(vectors); } catch (_) { /* fallback */ }
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
/** Static JS fallback for cosine similarity */
|
||||
static cosineSimilarity(a, b) {
|
||||
let dot = 0, normA = 0, normB = 0;
|
||||
for (let i = 0; i < a.length; i++) {
|
||||
dot += a[i] * b[i];
|
||||
normA += a[i] * a[i];
|
||||
normB += b[i] * b[i];
|
||||
}
|
||||
normA = Math.sqrt(normA);
|
||||
normB = Math.sqrt(normB);
|
||||
if (normA < 1e-8 || normB < 1e-8) return 0;
|
||||
return dot / (normA * normB);
|
||||
}
|
||||
}
|
||||
357
ui/pose-fusion/js/csi-simulator.js
Normal file
357
ui/pose-fusion/js/csi-simulator.js
Normal file
|
|
@ -0,0 +1,357 @@
|
|||
/**
|
||||
* CSI Simulator — Generates realistic WiFi Channel State Information data.
|
||||
*
|
||||
* In live mode, connects to the sensing server via WebSocket.
|
||||
* In demo mode, generates synthetic CSI that correlates with detected motion.
|
||||
*
|
||||
* Outputs: 3-channel pseudo-image (amplitude, phase, temporal diff)
|
||||
* matching the ADR-018 frame format expectations.
|
||||
*/
|
||||
|
||||
export class CsiSimulator {
|
||||
static VERSION = 'v4-drift'; // Cache-bust verification
|
||||
|
||||
constructor(opts = {}) {
|
||||
this.subcarriers = opts.subcarriers || 52; // 802.11n HT20
|
||||
this.timeWindow = opts.timeWindow || 56; // frames in sliding window
|
||||
this.mode = 'demo'; // 'demo' | 'live'
|
||||
this.ws = null;
|
||||
|
||||
// Circular buffer for CSI frames
|
||||
this.amplitudeBuffer = [];
|
||||
this.phaseBuffer = [];
|
||||
this.frameCount = 0;
|
||||
|
||||
// Noise parameters
|
||||
this._rng = this._mulberry32(opts.seed || 7);
|
||||
this._noiseState = new Float32Array(this.subcarriers);
|
||||
this._baseAmplitude = new Float32Array(this.subcarriers);
|
||||
this._basePhase = new Float32Array(this.subcarriers);
|
||||
|
||||
// Initialize base CSI profile (empty room)
|
||||
for (let i = 0; i < this.subcarriers; i++) {
|
||||
this._baseAmplitude[i] = 0.5 + 0.3 * Math.sin(i * 0.12);
|
||||
this._basePhase[i] = (i / this.subcarriers) * Math.PI * 2;
|
||||
}
|
||||
|
||||
// RSSI tracking
|
||||
this.rssiDbm = -70; // default mid-range
|
||||
this._rssiTarget = -70;
|
||||
|
||||
// Person influence (updated from video motion)
|
||||
this.personPresence = 0;
|
||||
this.personX = 0.5;
|
||||
this.personY = 0.5;
|
||||
this.personMotion = 0;
|
||||
}
|
||||
|
||||
/**
|
||||
* Connect to live sensing server WebSocket
|
||||
* @param {string} url - WebSocket URL (e.g. ws://localhost:3030/ws/csi)
|
||||
*/
|
||||
async connectLive(url) {
|
||||
return new Promise((resolve) => {
|
||||
try {
|
||||
this.ws = new WebSocket(url);
|
||||
this.ws.binaryType = 'arraybuffer';
|
||||
this.ws.onmessage = (evt) => this._handleLiveFrame(evt.data);
|
||||
this.ws.onopen = () => { this.mode = 'live'; resolve(true); };
|
||||
this.ws.onerror = () => resolve(false);
|
||||
this.ws.onclose = () => { this.mode = 'demo'; };
|
||||
// Timeout after 3s
|
||||
setTimeout(() => { if (this.mode !== 'live') resolve(false); }, 3000);
|
||||
} catch {
|
||||
resolve(false);
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
disconnect() {
|
||||
if (this.ws) { this.ws.close(); this.ws = null; }
|
||||
this.mode = 'demo';
|
||||
}
|
||||
|
||||
get isLive() { return this.mode === 'live'; }
|
||||
|
||||
/**
|
||||
* Update person state from video detection (for correlated demo data).
|
||||
* When person exits frame, CSI maintains presence with slow decay
|
||||
* (simulating through-wall sensing capability).
|
||||
*/
|
||||
updatePersonState(presence, x, y, motion) {
|
||||
// Don't override real CSI sensing with synthetic video-derived state
|
||||
if (this.mode === 'live') return;
|
||||
|
||||
if (presence > 0.1) {
|
||||
// Person detected in video — update CSI state directly
|
||||
this.personPresence = presence;
|
||||
this.personX = x;
|
||||
this.personY = y;
|
||||
this.personMotion = motion;
|
||||
this._lastSeenTime = performance.now();
|
||||
this._lastSeenX = x;
|
||||
this._lastSeenY = y;
|
||||
} else if (this._lastSeenTime) {
|
||||
// Person NOT in video — CSI "through-wall" persistence
|
||||
const elapsed = (performance.now() - this._lastSeenTime) / 1000;
|
||||
// CSI can sense through walls for ~10 seconds with decaying confidence
|
||||
const decayRate = 0.15; // Lose ~15% per second
|
||||
this.personPresence = Math.max(0, 1.0 - elapsed * decayRate);
|
||||
// Position slowly drifts (person walking behind wall)
|
||||
this.personX = this._lastSeenX;
|
||||
this.personY = this._lastSeenY;
|
||||
this.personMotion = Math.max(0, motion * 0.5 + this.personPresence * 0.2);
|
||||
|
||||
if (this.personPresence < 0.05) {
|
||||
this._lastSeenTime = null;
|
||||
}
|
||||
} else {
|
||||
this.personPresence = 0;
|
||||
this.personMotion = 0;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate next CSI frame (demo mode) or return latest live frame
|
||||
* @param {number} elapsed - Time in seconds
|
||||
* @returns {{ amplitude: Float32Array, phase: Float32Array, snr: number }}
|
||||
*/
|
||||
nextFrame(elapsed) {
|
||||
const amp = new Float32Array(this.subcarriers);
|
||||
const phase = new Float32Array(this.subcarriers);
|
||||
|
||||
if (this.mode === 'live' && this._liveAmplitude) {
|
||||
amp.set(this._liveAmplitude);
|
||||
phase.set(this._livePhase);
|
||||
} else {
|
||||
this._generateDemoFrame(amp, phase, elapsed);
|
||||
}
|
||||
|
||||
// Push to circular buffer
|
||||
this.amplitudeBuffer.push(new Float32Array(amp));
|
||||
this.phaseBuffer.push(new Float32Array(phase));
|
||||
if (this.amplitudeBuffer.length > this.timeWindow) {
|
||||
this.amplitudeBuffer.shift();
|
||||
this.phaseBuffer.shift();
|
||||
}
|
||||
|
||||
// RSSI: smooth toward target (demo mode generates synthetic RSSI)
|
||||
if (this.mode === 'demo') {
|
||||
// Simulate RSSI based on person presence and slow drift
|
||||
this._rssiTarget = -55 - 25 * (1 - this.personPresence) + Math.sin(elapsed * 0.3) * 3;
|
||||
}
|
||||
this.rssiDbm += (this._rssiTarget - this.rssiDbm) * 0.1;
|
||||
|
||||
// SNR estimate
|
||||
let signalPower = 0, noisePower = 0;
|
||||
for (let i = 0; i < this.subcarriers; i++) {
|
||||
signalPower += amp[i] * amp[i];
|
||||
noisePower += this._noiseState[i] * this._noiseState[i];
|
||||
}
|
||||
const snr = noisePower > 0 ? 10 * Math.log10(signalPower / noisePower) : 30;
|
||||
|
||||
this.frameCount++;
|
||||
return { amplitude: amp, phase, snr: Math.max(0, Math.min(40, snr)) };
|
||||
}
|
||||
|
||||
/**
|
||||
* Build 3-channel pseudo-image for CNN input
|
||||
* @param {number} targetSize - Output image dimension (square)
|
||||
* @returns {Uint8Array} RGB data (targetSize * targetSize * 3)
|
||||
*/
|
||||
buildPseudoImage(targetSize = 56) {
|
||||
const buf = this.amplitudeBuffer;
|
||||
const pBuf = this.phaseBuffer;
|
||||
const frames = buf.length;
|
||||
if (frames < 2) {
|
||||
return new Uint8Array(targetSize * targetSize * 3);
|
||||
}
|
||||
|
||||
const rgb = new Uint8Array(targetSize * targetSize * 3);
|
||||
|
||||
for (let y = 0; y < targetSize; y++) {
|
||||
const fi = Math.min(Math.floor(y / targetSize * frames), frames - 1);
|
||||
for (let x = 0; x < targetSize; x++) {
|
||||
const si = Math.min(Math.floor(x / targetSize * this.subcarriers), this.subcarriers - 1);
|
||||
const idx = (y * targetSize + x) * 3;
|
||||
|
||||
// R: Amplitude (normalized to 0-255)
|
||||
const ampVal = buf[fi][si];
|
||||
rgb[idx] = Math.min(255, Math.max(0, Math.floor(ampVal * 255)));
|
||||
|
||||
// G: Phase (wrapped to 0-255)
|
||||
const phaseVal = (pBuf[fi][si] % (2 * Math.PI) + 2 * Math.PI) % (2 * Math.PI);
|
||||
rgb[idx + 1] = Math.floor(phaseVal / (2 * Math.PI) * 255);
|
||||
|
||||
// B: Temporal difference
|
||||
if (fi > 0) {
|
||||
const diff = Math.abs(buf[fi][si] - buf[fi - 1][si]);
|
||||
rgb[idx + 2] = Math.min(255, Math.floor(diff * 500));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return rgb;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get heatmap data for visualization
|
||||
* @returns {{ data: Float32Array, width: number, height: number }}
|
||||
*/
|
||||
getHeatmapData() {
|
||||
const frames = this.amplitudeBuffer.length;
|
||||
const w = this.subcarriers;
|
||||
const h = Math.min(frames, this.timeWindow);
|
||||
const data = new Float32Array(w * h);
|
||||
for (let y = 0; y < h; y++) {
|
||||
const fi = frames - h + y;
|
||||
if (fi >= 0 && fi < frames) {
|
||||
for (let x = 0; x < w; x++) {
|
||||
data[y * w + x] = this.amplitudeBuffer[fi][x];
|
||||
}
|
||||
}
|
||||
}
|
||||
return { data, width: w, height: h };
|
||||
}
|
||||
|
||||
// === Private ===
|
||||
|
||||
_generateDemoFrame(amp, phase, elapsed) {
|
||||
const rng = this._rng;
|
||||
const presence = this.personPresence;
|
||||
const motion = this.personMotion;
|
||||
const px = this.personX;
|
||||
|
||||
for (let i = 0; i < this.subcarriers; i++) {
|
||||
// Base CSI profile (frequency-selective channel)
|
||||
let a = this._baseAmplitude[i];
|
||||
let p = this._basePhase[i] + elapsed * 0.05;
|
||||
|
||||
// Environmental noise (correlated across subcarriers)
|
||||
this._noiseState[i] = 0.95 * this._noiseState[i] + 0.05 * (rng() * 2 - 1) * 0.03;
|
||||
a += this._noiseState[i];
|
||||
|
||||
// Ambient temporal drift (multipath fading even in empty room)
|
||||
a += 0.06 * Math.sin(elapsed * 0.7 + i * 0.25)
|
||||
+ 0.04 * Math.sin(elapsed * 1.3 - i * 0.18)
|
||||
+ 0.03 * Math.cos(elapsed * 2.1 + i * 0.4);
|
||||
|
||||
// Person-induced CSI perturbation
|
||||
if (presence > 0.1) {
|
||||
// Subcarrier-dependent body reflection (Fresnel zone model)
|
||||
const freqOffset = (i - this.subcarriers * px) / (this.subcarriers * 0.3);
|
||||
const bodyReflection = presence * 0.25 * Math.exp(-freqOffset * freqOffset);
|
||||
|
||||
// Motion causes amplitude fluctuation
|
||||
const motionEffect = motion * 0.15 * Math.sin(elapsed * 3.5 + i * 0.3);
|
||||
|
||||
// Breathing modulation (0.2-0.3 Hz)
|
||||
const breathing = presence * 0.02 * Math.sin(elapsed * 1.5 + i * 0.05);
|
||||
|
||||
a += bodyReflection + motionEffect + breathing;
|
||||
p += presence * 0.4 * Math.sin(elapsed * 2.1 + i * 0.15);
|
||||
}
|
||||
|
||||
amp[i] = Math.max(0, Math.min(1, a));
|
||||
phase[i] = p;
|
||||
}
|
||||
}
|
||||
|
||||
_handleLiveFrame(data) {
|
||||
// Handle JSON text frames from the sensing server
|
||||
if (typeof data === 'string') {
|
||||
try {
|
||||
const msg = JSON.parse(data);
|
||||
this._handleJsonFrame(msg);
|
||||
} catch (_) { /* ignore malformed JSON */ }
|
||||
return;
|
||||
}
|
||||
|
||||
// Handle binary ArrayBuffer frames (ADR-018 format)
|
||||
if (!(data instanceof ArrayBuffer)) return;
|
||||
const view = new DataView(data);
|
||||
// Check ADR-018 magic: 0xC5110001
|
||||
if (data.byteLength < 20) return;
|
||||
const magic = view.getUint32(0, true);
|
||||
if (magic !== 0xC5110001) return;
|
||||
|
||||
const numSub = Math.min(view.getUint16(8, true), this.subcarriers);
|
||||
this._liveAmplitude = new Float32Array(this.subcarriers);
|
||||
this._livePhase = new Float32Array(this.subcarriers);
|
||||
|
||||
const headerSize = 20;
|
||||
for (let i = 0; i < numSub && (headerSize + i * 4 + 3) < data.byteLength; i++) {
|
||||
const real = view.getInt16(headerSize + i * 4, true);
|
||||
const imag = view.getInt16(headerSize + i * 4 + 2, true);
|
||||
this._liveAmplitude[i] = Math.sqrt(real * real + imag * imag) / 2048;
|
||||
this._livePhase[i] = Math.atan2(imag, real);
|
||||
}
|
||||
}
|
||||
|
||||
_handleJsonFrame(msg) {
|
||||
// Sensing server sends: { type: "sensing_update", nodes: [{ amplitude: [...], subcarrier_count }], classification, features }
|
||||
this._liveAmplitude = new Float32Array(this.subcarriers);
|
||||
this._livePhase = new Float32Array(this.subcarriers);
|
||||
|
||||
// Extract amplitude from sensing_update node data
|
||||
const node = (msg.nodes && msg.nodes[0]) || msg;
|
||||
const ampArr = node.amplitude || msg.amplitude;
|
||||
if (ampArr && Array.isArray(ampArr)) {
|
||||
const n = Math.min(ampArr.length, this.subcarriers);
|
||||
// Server sends raw amplitude (already magnitude), normalize to 0-1
|
||||
let maxAmp = 0;
|
||||
for (let i = 0; i < n; i++) maxAmp = Math.max(maxAmp, Math.abs(ampArr[i]));
|
||||
const scale = maxAmp > 0 ? 1.0 / maxAmp : 1.0;
|
||||
for (let i = 0; i < n; i++) {
|
||||
this._liveAmplitude[i] = Math.abs(ampArr[i]) * scale;
|
||||
}
|
||||
}
|
||||
|
||||
// Phase from node (if available)
|
||||
const phaseArr = node.phase || msg.phase;
|
||||
if (phaseArr && Array.isArray(phaseArr)) {
|
||||
const n = Math.min(phaseArr.length, this.subcarriers);
|
||||
for (let i = 0; i < n; i++) this._livePhase[i] = phaseArr[i];
|
||||
} else if (ampArr) {
|
||||
// Synthesize phase from amplitude variation (Hilbert-like estimate)
|
||||
for (let i = 1; i < this.subcarriers; i++) {
|
||||
this._livePhase[i] = this._livePhase[i - 1] + (this._liveAmplitude[i] - this._liveAmplitude[i - 1]) * Math.PI;
|
||||
}
|
||||
}
|
||||
|
||||
// Handle raw I/Q pairs
|
||||
const iq = node.iq || msg.iq;
|
||||
if (iq && Array.isArray(iq)) {
|
||||
const n = Math.min(iq.length / 2, this.subcarriers);
|
||||
for (let i = 0; i < n; i++) {
|
||||
const real = iq[i * 2], imag = iq[i * 2 + 1];
|
||||
this._liveAmplitude[i] = Math.sqrt(real * real + imag * imag) / 2048;
|
||||
this._livePhase[i] = Math.atan2(imag, real);
|
||||
}
|
||||
}
|
||||
|
||||
// Extract RSSI from node data
|
||||
if (typeof node.rssi_dbm === 'number') {
|
||||
this._rssiTarget = node.rssi_dbm;
|
||||
} else if (msg.features && typeof msg.features.mean_rssi === 'number') {
|
||||
this._rssiTarget = msg.features.mean_rssi;
|
||||
}
|
||||
|
||||
// Update presence from server classification
|
||||
const cls = msg.classification;
|
||||
if (cls) {
|
||||
if (typeof cls.confidence === 'number') {
|
||||
this.personPresence = cls.presence ? cls.confidence : 0;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
_mulberry32(seed) {
|
||||
return function() {
|
||||
let t = (seed += 0x6D2B79F5);
|
||||
t = Math.imul(t ^ (t >>> 15), t | 1);
|
||||
t ^= t + Math.imul(t ^ (t >>> 7), t | 61);
|
||||
return ((t ^ (t >>> 14)) >>> 0) / 4294967296;
|
||||
};
|
||||
}
|
||||
}
|
||||
183
ui/pose-fusion/js/fusion-engine.js
Normal file
183
ui/pose-fusion/js/fusion-engine.js
Normal file
|
|
@ -0,0 +1,183 @@
|
|||
/**
|
||||
* FusionEngine — Attention-weighted dual-modal embedding fusion.
|
||||
*
|
||||
* Combines visual (camera) and CSI (WiFi) embeddings with dynamic
|
||||
* confidence gating based on signal quality.
|
||||
*/
|
||||
|
||||
export class FusionEngine {
|
||||
/**
|
||||
* @param {number} embeddingDim
|
||||
* @param {object} opts
|
||||
* @param {object} opts.wasmModule - RuVector WASM module for cosine_similarity etc.
|
||||
*/
|
||||
constructor(embeddingDim = 128, opts = {}) {
|
||||
this.embeddingDim = embeddingDim;
|
||||
this.wasmModule = opts.wasmModule || null;
|
||||
|
||||
// Learnable attention weights (initialized to balanced 0.5)
|
||||
this.attentionWeights = new Float32Array(embeddingDim).fill(0.5);
|
||||
|
||||
// Dynamic modality confidence [0, 1]
|
||||
this.videoConfidence = 1.0;
|
||||
this.csiConfidence = 0.0;
|
||||
this.fusedConfidence = 0.5;
|
||||
|
||||
// Smoothing for confidence transitions
|
||||
this._smoothAlpha = 0.85;
|
||||
|
||||
// Embedding history for visualization
|
||||
this.recentVideoEmbeddings = [];
|
||||
this.recentCsiEmbeddings = [];
|
||||
this.recentFusedEmbeddings = [];
|
||||
this.maxHistory = 50;
|
||||
}
|
||||
|
||||
/** Set the WASM module reference (called after WASM loads) */
|
||||
setWasmModule(mod) { this.wasmModule = mod; }
|
||||
|
||||
/**
|
||||
* Update quality-based confidence scores
|
||||
* @param {number} videoBrightness - [0,1] video brightness quality
|
||||
* @param {number} videoMotion - [0,1] motion detected
|
||||
* @param {number} csiSnr - CSI signal-to-noise ratio in dB
|
||||
* @param {boolean} csiActive - Whether CSI source is connected
|
||||
*/
|
||||
updateConfidence(videoBrightness, videoMotion, csiSnr, csiActive) {
|
||||
// Video confidence: drops with low brightness, boosted by motion
|
||||
let vc = 0;
|
||||
if (videoBrightness > 0.05) {
|
||||
vc = Math.min(1, videoBrightness * 1.5) * 0.7 + Math.min(1, videoMotion * 3) * 0.3;
|
||||
}
|
||||
|
||||
// CSI confidence: based on SNR and connection status
|
||||
let cc = 0;
|
||||
if (csiActive) {
|
||||
cc = Math.min(1, csiSnr / 25); // 25dB = full confidence
|
||||
}
|
||||
|
||||
// Smooth transitions
|
||||
this.videoConfidence = this._smoothAlpha * this.videoConfidence + (1 - this._smoothAlpha) * vc;
|
||||
this.csiConfidence = this._smoothAlpha * this.csiConfidence + (1 - this._smoothAlpha) * cc;
|
||||
|
||||
// Fused confidence is the max of either (fusion can only help)
|
||||
this.fusedConfidence = Math.min(1, Math.sqrt(
|
||||
this.videoConfidence * this.videoConfidence + this.csiConfidence * this.csiConfidence
|
||||
));
|
||||
}
|
||||
|
||||
/**
|
||||
* Fuse video and CSI embeddings
|
||||
* @param {Float32Array|null} videoEmb - Visual embedding (or null if video-off)
|
||||
* @param {Float32Array|null} csiEmb - CSI embedding (or null if CSI-off)
|
||||
* @param {string} mode - 'dual' | 'video' | 'csi'
|
||||
* @returns {Float32Array} Fused embedding
|
||||
*/
|
||||
fuse(videoEmb, csiEmb, mode = 'dual') {
|
||||
const dim = this.embeddingDim;
|
||||
const fused = new Float32Array(dim);
|
||||
|
||||
if (mode === 'video' || !csiEmb) {
|
||||
if (videoEmb) fused.set(videoEmb);
|
||||
this._recordEmbedding(videoEmb, null, fused);
|
||||
return fused;
|
||||
}
|
||||
|
||||
if (mode === 'csi' || !videoEmb) {
|
||||
if (csiEmb) fused.set(csiEmb);
|
||||
this._recordEmbedding(null, csiEmb, fused);
|
||||
return fused;
|
||||
}
|
||||
|
||||
// Dual mode: attention-weighted fusion with confidence gating
|
||||
const totalConf = this.videoConfidence + this.csiConfidence;
|
||||
const videoWeight = totalConf > 0 ? this.videoConfidence / totalConf : 0.5;
|
||||
|
||||
for (let i = 0; i < dim; i++) {
|
||||
const alpha = this.attentionWeights[i] * videoWeight +
|
||||
(1 - this.attentionWeights[i]) * (1 - videoWeight);
|
||||
fused[i] = alpha * videoEmb[i] + (1 - alpha) * csiEmb[i];
|
||||
}
|
||||
|
||||
// Re-normalize using WASM when available
|
||||
if (this.wasmModule) {
|
||||
try { this.wasmModule.normalize(fused); } catch (_) { this._jsNormalize(fused); }
|
||||
} else {
|
||||
this._jsNormalize(fused);
|
||||
}
|
||||
|
||||
this._recordEmbedding(videoEmb, csiEmb, fused);
|
||||
return fused;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get embedding pairs for 2D visualization (PCA projection)
|
||||
* @returns {{ video: Array, csi: Array, fused: Array }}
|
||||
*/
|
||||
getEmbeddingPoints() {
|
||||
// Sparse random projection: pick a few dimensions with fixed coefficients
|
||||
// to get visible 2D spread (avoids cancellation from summing all 128 dims)
|
||||
const project = (emb) => {
|
||||
if (!emb || emb.length < 4) return null;
|
||||
// Use 8 sparse dimensions with predetermined signs (seeded, not random)
|
||||
const dim = emb.length;
|
||||
const x = emb[0] * 3.2 - emb[3] * 2.8 + emb[7] * 2.1 - emb[12] * 1.9
|
||||
+ (dim > 30 ? emb[29] * 1.5 - emb[31] * 1.3 : 0)
|
||||
+ (dim > 60 ? emb[55] * 1.1 - emb[60] * 0.9 : 0);
|
||||
const y = emb[1] * 3.0 - emb[5] * 2.5 + emb[9] * 2.3 - emb[15] * 1.7
|
||||
+ (dim > 40 ? emb[37] * 1.4 - emb[42] * 1.2 : 0)
|
||||
+ (dim > 80 ? emb[73] * 1.0 - emb[80] * 0.8 : 0);
|
||||
return [x, y];
|
||||
};
|
||||
|
||||
return {
|
||||
video: this.recentVideoEmbeddings.map(project).filter(Boolean),
|
||||
csi: this.recentCsiEmbeddings.map(project).filter(Boolean),
|
||||
fused: this.recentFusedEmbeddings.map(project).filter(Boolean)
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Cross-modal similarity score
|
||||
* @returns {number} Cosine similarity between latest video and CSI embeddings
|
||||
*/
|
||||
getCrossModalSimilarity() {
|
||||
const v = this.recentVideoEmbeddings[this.recentVideoEmbeddings.length - 1];
|
||||
const c = this.recentCsiEmbeddings[this.recentCsiEmbeddings.length - 1];
|
||||
if (!v || !c) return 0;
|
||||
|
||||
// Use WASM cosine_similarity when available
|
||||
if (this.wasmModule) {
|
||||
try { return this.wasmModule.cosine_similarity(v, c); } catch (_) { /* fallback */ }
|
||||
}
|
||||
|
||||
let dot = 0, na = 0, nb = 0;
|
||||
for (let i = 0; i < v.length; i++) {
|
||||
dot += v[i] * c[i];
|
||||
na += v[i] * v[i];
|
||||
nb += c[i] * c[i];
|
||||
}
|
||||
na = Math.sqrt(na); nb = Math.sqrt(nb);
|
||||
return (na > 1e-8 && nb > 1e-8) ? dot / (na * nb) : 0;
|
||||
}
|
||||
|
||||
_jsNormalize(vec) {
|
||||
let norm = 0;
|
||||
for (let i = 0; i < vec.length; i++) norm += vec[i] * vec[i];
|
||||
norm = Math.sqrt(norm);
|
||||
if (norm > 1e-8) for (let i = 0; i < vec.length; i++) vec[i] /= norm;
|
||||
}
|
||||
|
||||
_recordEmbedding(video, csi, fused) {
|
||||
if (video) {
|
||||
this.recentVideoEmbeddings.push(new Float32Array(video));
|
||||
if (this.recentVideoEmbeddings.length > this.maxHistory) this.recentVideoEmbeddings.shift();
|
||||
}
|
||||
if (csi) {
|
||||
this.recentCsiEmbeddings.push(new Float32Array(csi));
|
||||
if (this.recentCsiEmbeddings.length > this.maxHistory) this.recentCsiEmbeddings.shift();
|
||||
}
|
||||
this.recentFusedEmbeddings.push(new Float32Array(fused));
|
||||
if (this.recentFusedEmbeddings.length > this.maxHistory) this.recentFusedEmbeddings.shift();
|
||||
}
|
||||
}
|
||||
472
ui/pose-fusion/js/main.js
Normal file
472
ui/pose-fusion/js/main.js
Normal file
|
|
@ -0,0 +1,472 @@
|
|||
/**
|
||||
* WiFi-DensePose — Dual-Modal Pose Estimation Demo
|
||||
*
|
||||
* Main orchestration: video capture → CNN embedding → CSI processing → fusion → rendering
|
||||
*/
|
||||
|
||||
import { VideoCapture } from './video-capture.js?v=11';
|
||||
import { CsiSimulator } from './csi-simulator.js?v=11';
|
||||
import { CnnEmbedder } from './cnn-embedder.js?v=11';
|
||||
import { FusionEngine } from './fusion-engine.js?v=11';
|
||||
import { PoseDecoder } from './pose-decoder.js?v=11';
|
||||
import { CanvasRenderer } from './canvas-renderer.js?v=11';
|
||||
|
||||
// === State ===
|
||||
let mode = 'dual'; // 'dual' | 'video' | 'csi'
|
||||
let isRunning = false;
|
||||
let isPaused = false;
|
||||
let startTime = 0;
|
||||
let frameCount = 0;
|
||||
let fps = 0;
|
||||
let lastFpsTime = 0;
|
||||
let confidenceThreshold = 0.3;
|
||||
|
||||
// Latency tracking
|
||||
const latency = { video: 0, csi: 0, fusion: 0, total: 0 };
|
||||
|
||||
// === Components ===
|
||||
const videoCapture = new VideoCapture(document.getElementById('webcam'));
|
||||
const csiSimulator = new CsiSimulator({ subcarriers: 52, timeWindow: 56 });
|
||||
const visualCnn = new CnnEmbedder({ inputSize: 56, embeddingDim: 128, seed: 42 });
|
||||
const csiCnn = new CnnEmbedder({ inputSize: 56, embeddingDim: 128, seed: 137 });
|
||||
const fusionEngine = new FusionEngine(128);
|
||||
const poseDecoder = new PoseDecoder(128);
|
||||
const renderer = new CanvasRenderer();
|
||||
|
||||
// === Canvas Elements ===
|
||||
const skeletonCanvas = document.getElementById('skeleton-canvas');
|
||||
const skeletonCtx = skeletonCanvas.getContext('2d');
|
||||
const csiCanvas = document.getElementById('csi-canvas');
|
||||
const csiCtx = csiCanvas.getContext('2d');
|
||||
const embeddingCanvas = document.getElementById('embedding-canvas');
|
||||
const embeddingCtx = embeddingCanvas.getContext('2d');
|
||||
|
||||
// === UI Elements ===
|
||||
const modeSelect = document.getElementById('mode-select');
|
||||
const statusDot = document.getElementById('status-dot');
|
||||
const statusLabel = document.getElementById('status-label');
|
||||
const fpsDisplay = document.getElementById('fps-display');
|
||||
const cameraPrompt = document.getElementById('camera-prompt');
|
||||
const startCameraBtn = document.getElementById('start-camera-btn');
|
||||
const pauseBtn = document.getElementById('pause-btn');
|
||||
const confSlider = document.getElementById('confidence-slider');
|
||||
const confValue = document.getElementById('confidence-value');
|
||||
const wsUrlInput = document.getElementById('ws-url');
|
||||
const connectWsBtn = document.getElementById('connect-ws-btn');
|
||||
|
||||
// Fusion bar elements
|
||||
const videoBar = document.getElementById('video-bar');
|
||||
const csiBar = document.getElementById('csi-bar');
|
||||
const fusedBar = document.getElementById('fused-bar');
|
||||
const videoBarVal = document.getElementById('video-bar-val');
|
||||
const csiBarVal = document.getElementById('csi-bar-val');
|
||||
const fusedBarVal = document.getElementById('fused-bar-val');
|
||||
|
||||
// Latency elements
|
||||
const latVideoEl = document.getElementById('lat-video');
|
||||
const latCsiEl = document.getElementById('lat-csi');
|
||||
const latFusionEl = document.getElementById('lat-fusion');
|
||||
const latTotalEl = document.getElementById('lat-total');
|
||||
|
||||
// Cross-modal similarity
|
||||
const crossModalEl = document.getElementById('cross-modal-sim');
|
||||
|
||||
// RSSI elements
|
||||
const rssiBarEl = document.getElementById('rssi-bar');
|
||||
const rssiValueEl = document.getElementById('rssi-value');
|
||||
const rssiQualityEl = document.getElementById('rssi-quality');
|
||||
const rssiSparkCanvas = document.getElementById('rssi-sparkline');
|
||||
const rssiSparkCtx = rssiSparkCanvas ? rssiSparkCanvas.getContext('2d') : null;
|
||||
const rssiHistory = [];
|
||||
const RSSI_HISTORY_MAX = 80;
|
||||
|
||||
// === Initialize ===
|
||||
function init() {
|
||||
console.log(`[PoseFusion] init() v4 — CsiSimulator=${CsiSimulator.VERSION || 'OLD'}, starting...`);
|
||||
resizeCanvases();
|
||||
console.log(`[PoseFusion] canvases: skeleton=${skeletonCanvas.width}x${skeletonCanvas.height}, csi=${csiCanvas.width}x${csiCanvas.height}, emb=${embeddingCanvas.width}x${embeddingCanvas.height}`);
|
||||
window.addEventListener('resize', resizeCanvases);
|
||||
|
||||
// Mode change
|
||||
modeSelect.addEventListener('change', (e) => {
|
||||
mode = e.target.value;
|
||||
updateModeUI();
|
||||
});
|
||||
|
||||
// Camera start
|
||||
startCameraBtn.addEventListener('click', startCamera);
|
||||
|
||||
// Pause
|
||||
pauseBtn.addEventListener('click', () => {
|
||||
isPaused = !isPaused;
|
||||
pauseBtn.textContent = isPaused ? '▶ Resume' : '⏸ Pause';
|
||||
pauseBtn.classList.toggle('active', isPaused);
|
||||
});
|
||||
|
||||
// Confidence slider
|
||||
confSlider.addEventListener('input', (e) => {
|
||||
confidenceThreshold = parseFloat(e.target.value);
|
||||
confValue.textContent = confidenceThreshold.toFixed(2);
|
||||
});
|
||||
|
||||
// WebSocket connect
|
||||
connectWsBtn.addEventListener('click', async () => {
|
||||
const url = wsUrlInput.value.trim();
|
||||
if (!url) return;
|
||||
connectWsBtn.textContent = 'Connecting...';
|
||||
const ok = await csiSimulator.connectLive(url);
|
||||
connectWsBtn.textContent = ok ? '✓ Connected' : 'Connect';
|
||||
if (ok) {
|
||||
connectWsBtn.classList.add('active');
|
||||
}
|
||||
});
|
||||
|
||||
// Try to load RuVector Attention WASM embedders (non-blocking)
|
||||
const wasmBase = new URL('../pkg/ruvector-attention', import.meta.url).href;
|
||||
visualCnn.tryLoadWasm(wasmBase).then((ok) => {
|
||||
// Share the WASM module with FusionEngine for cosine_similarity, normalize, etc.
|
||||
if (visualCnn.rvModule) fusionEngine.setWasmModule(visualCnn.rvModule);
|
||||
// Update footer backend label
|
||||
const backendEl = document.getElementById('cnn-backend');
|
||||
if (backendEl) {
|
||||
backendEl.textContent = ok && visualCnn.useRuVector
|
||||
? `RuVector WASM v${visualCnn.rvModule.version()} — 6 attention mechanisms`
|
||||
: 'ruvector-cnn (JS fallback)';
|
||||
}
|
||||
});
|
||||
csiCnn.tryLoadWasm(wasmBase);
|
||||
|
||||
// Auto-connect to local sensing server WebSocket if available
|
||||
const defaultWsUrl = 'ws://localhost:8765/ws/sensing';
|
||||
if (wsUrlInput) wsUrlInput.value = defaultWsUrl;
|
||||
csiSimulator.connectLive(defaultWsUrl).then(ok => {
|
||||
if (ok && connectWsBtn) {
|
||||
connectWsBtn.textContent = '✓ Live ESP32';
|
||||
connectWsBtn.classList.add('active');
|
||||
statusLabel.textContent = 'LIVE CSI';
|
||||
statusDot.classList.remove('offline');
|
||||
}
|
||||
});
|
||||
|
||||
// Auto-start camera for video/dual modes
|
||||
updateModeUI();
|
||||
startTime = performance.now() / 1000;
|
||||
isRunning = true;
|
||||
requestAnimationFrame(mainLoop);
|
||||
}
|
||||
|
||||
async function startCamera() {
|
||||
cameraPrompt.style.display = 'none';
|
||||
const ok = await videoCapture.start();
|
||||
if (ok) {
|
||||
statusDot.classList.remove('offline');
|
||||
statusLabel.textContent = 'LIVE';
|
||||
resizeCanvases();
|
||||
} else {
|
||||
cameraPrompt.style.display = 'flex';
|
||||
cameraPrompt.querySelector('p').textContent = 'Camera access denied. Try CSI-only mode.';
|
||||
}
|
||||
}
|
||||
|
||||
function updateModeUI() {
|
||||
const needsVideo = mode !== 'csi';
|
||||
|
||||
// Show/hide camera prompt
|
||||
if (needsVideo && !videoCapture.isActive) {
|
||||
cameraPrompt.style.display = 'flex';
|
||||
} else {
|
||||
cameraPrompt.style.display = 'none';
|
||||
}
|
||||
|
||||
// Update mode label in both the overlay and the camera prompt
|
||||
const labelMap = { dual: 'DUAL FUSION', video: 'VIDEO ONLY', csi: 'CSI ONLY' };
|
||||
const modeLabel = document.getElementById('mode-label');
|
||||
const promptLabel = document.getElementById('prompt-mode-label');
|
||||
if (modeLabel) modeLabel.textContent = labelMap[mode] || mode;
|
||||
if (promptLabel) promptLabel.textContent = labelMap[mode] || mode;
|
||||
}
|
||||
|
||||
function resizeCanvases() {
|
||||
const videoPanel = document.querySelector('.video-panel');
|
||||
if (videoPanel) {
|
||||
const rect = videoPanel.getBoundingClientRect();
|
||||
skeletonCanvas.width = rect.width;
|
||||
skeletonCanvas.height = rect.height;
|
||||
}
|
||||
|
||||
// CSI canvas (min 200px width)
|
||||
csiCanvas.width = Math.max(200, csiCanvas.parentElement.clientWidth);
|
||||
csiCanvas.height = 120;
|
||||
|
||||
// Embedding canvas (min 200px width)
|
||||
embeddingCanvas.width = Math.max(200, embeddingCanvas.parentElement.clientWidth);
|
||||
embeddingCanvas.height = 140;
|
||||
}
|
||||
|
||||
// === Main Loop ===
|
||||
let _loopErrorShown = false;
|
||||
let _diagDone = false;
|
||||
function mainLoop(timestamp) {
|
||||
if (!isRunning) return;
|
||||
requestAnimationFrame(mainLoop);
|
||||
|
||||
if (isPaused) return;
|
||||
|
||||
try {
|
||||
const elapsed = performance.now() / 1000 - startTime;
|
||||
const totalStart = performance.now();
|
||||
|
||||
// --- Video Pipeline ---
|
||||
let videoEmb = null;
|
||||
let motionRegion = null;
|
||||
if (mode !== 'csi' && videoCapture.isActive) {
|
||||
const t0 = performance.now();
|
||||
const frame = videoCapture.captureFrame(56, 56);
|
||||
if (frame) {
|
||||
videoEmb = visualCnn.extract(frame.rgb, frame.width, frame.height);
|
||||
motionRegion = videoCapture.detectMotionRegion(56, 56);
|
||||
|
||||
// Feed motion to CSI simulator for correlated demo data
|
||||
// When detected=false, CSI simulator handles through-wall persistence
|
||||
csiSimulator.updatePersonState(
|
||||
motionRegion.detected ? 1.0 : 0,
|
||||
motionRegion.detected ? motionRegion.x + motionRegion.w / 2 : 0.5,
|
||||
motionRegion.detected ? motionRegion.y + motionRegion.h / 2 : 0.5,
|
||||
frame.motion
|
||||
);
|
||||
|
||||
fusionEngine.updateConfidence(
|
||||
frame.brightness, frame.motion,
|
||||
0, csiSimulator.isLive || mode === 'dual'
|
||||
);
|
||||
}
|
||||
latency.video = performance.now() - t0;
|
||||
}
|
||||
|
||||
// --- CSI Pipeline ---
|
||||
let csiEmb = null;
|
||||
if (mode !== 'video') {
|
||||
const t0 = performance.now();
|
||||
const csiFrame = csiSimulator.nextFrame(elapsed);
|
||||
const pseudoImage = csiSimulator.buildPseudoImage(56);
|
||||
csiEmb = csiCnn.extract(pseudoImage, 56, 56);
|
||||
|
||||
fusionEngine.updateConfidence(
|
||||
videoCapture.brightnessScore,
|
||||
videoCapture.motionScore,
|
||||
csiFrame.snr,
|
||||
true
|
||||
);
|
||||
|
||||
// Draw CSI heatmap
|
||||
const heatmap = csiSimulator.getHeatmapData();
|
||||
renderer.drawCsiHeatmap(csiCtx, heatmap, csiCanvas.width, csiCanvas.height);
|
||||
|
||||
latency.csi = performance.now() - t0;
|
||||
}
|
||||
|
||||
// --- Fusion ---
|
||||
const t0f = performance.now();
|
||||
const fusedEmb = fusionEngine.fuse(videoEmb, csiEmb, mode);
|
||||
latency.fusion = performance.now() - t0f;
|
||||
|
||||
// --- Pose Decode ---
|
||||
// For CSI-only mode, generate a synthetic motion region from CSI energy
|
||||
if (mode === 'csi' && (!motionRegion || !motionRegion.detected)) {
|
||||
const csiPresence = csiSimulator.personPresence;
|
||||
if (csiPresence > 0.1) {
|
||||
motionRegion = {
|
||||
detected: true,
|
||||
x: 0.25, y: 0.15, w: 0.5, h: 0.7,
|
||||
coverage: csiPresence,
|
||||
motionGrid: null,
|
||||
gridCols: 10,
|
||||
gridRows: 8
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
// CSI state for through-wall tracking
|
||||
const csiState = {
|
||||
csiPresence: csiSimulator.personPresence,
|
||||
isLive: csiSimulator.isLive
|
||||
};
|
||||
|
||||
const keypoints = poseDecoder.decode(fusedEmb, motionRegion, elapsed, csiState);
|
||||
|
||||
// --- Render Skeleton ---
|
||||
const labelMap = { dual: 'DUAL FUSION', video: 'VIDEO ONLY', csi: 'CSI ONLY' };
|
||||
renderer.drawSkeleton(skeletonCtx, keypoints, skeletonCanvas.width, skeletonCanvas.height, {
|
||||
minConfidence: confidenceThreshold,
|
||||
color: mode === 'csi' ? 'amber' : 'green',
|
||||
label: labelMap[mode]
|
||||
});
|
||||
|
||||
// --- Render Embedding Space ---
|
||||
const embPoints = fusionEngine.getEmbeddingPoints();
|
||||
renderer.drawEmbeddingSpace(embeddingCtx, embPoints, embeddingCanvas.width, embeddingCanvas.height);
|
||||
|
||||
// --- Update UI ---
|
||||
latency.total = performance.now() - totalStart;
|
||||
|
||||
// FPS
|
||||
frameCount++;
|
||||
if (timestamp - lastFpsTime > 500) {
|
||||
fps = Math.round(frameCount * 1000 / (timestamp - lastFpsTime));
|
||||
lastFpsTime = timestamp;
|
||||
frameCount = 0;
|
||||
fpsDisplay.textContent = `${fps} FPS`;
|
||||
}
|
||||
|
||||
// Fusion bars
|
||||
const vc = fusionEngine.videoConfidence;
|
||||
const cc = fusionEngine.csiConfidence;
|
||||
const fc = fusionEngine.fusedConfidence;
|
||||
videoBar.style.width = `${vc * 100}%`;
|
||||
csiBar.style.width = `${cc * 100}%`;
|
||||
fusedBar.style.width = `${fc * 100}%`;
|
||||
videoBarVal.textContent = `${Math.round(vc * 100)}%`;
|
||||
csiBarVal.textContent = `${Math.round(cc * 100)}%`;
|
||||
fusedBarVal.textContent = `${Math.round(fc * 100)}%`;
|
||||
|
||||
// Latency
|
||||
latVideoEl.textContent = `${latency.video.toFixed(1)}ms`;
|
||||
latCsiEl.textContent = `${latency.csi.toFixed(1)}ms`;
|
||||
latFusionEl.textContent = `${latency.fusion.toFixed(1)}ms`;
|
||||
latTotalEl.textContent = `${latency.total.toFixed(1)}ms`;
|
||||
|
||||
// Cross-modal similarity
|
||||
const sim = fusionEngine.getCrossModalSimilarity();
|
||||
crossModalEl.textContent = sim.toFixed(3);
|
||||
|
||||
// RuVector attention pipeline stats
|
||||
const rvStats = poseDecoder.attentionStats;
|
||||
const rvEnergyEl = document.getElementById('rv-energy');
|
||||
const rvRefineEl = document.getElementById('rv-refine');
|
||||
const rvImpactEl = document.getElementById('rv-impact');
|
||||
if (rvEnergyEl) rvEnergyEl.textContent = rvStats.energy.toFixed(2);
|
||||
if (rvRefineEl) rvRefineEl.textContent = (rvStats.refinementMag * 1000).toFixed(1) + 'px';
|
||||
if (rvImpactEl) {
|
||||
const impact = Math.min(100, rvStats.refinementMag * 5000);
|
||||
rvImpactEl.textContent = impact.toFixed(0) + '%';
|
||||
}
|
||||
// Pulse the pipeline stages when active
|
||||
if (visualCnn.useRuVector && rvStats.energy > 0.1) {
|
||||
document.querySelectorAll('.rv-stage').forEach(el => el.classList.add('active'));
|
||||
}
|
||||
|
||||
// RSSI update
|
||||
updateRssi(csiSimulator.rssiDbm);
|
||||
|
||||
// One-time diagnostic
|
||||
if (!_diagDone) {
|
||||
_diagDone = true;
|
||||
console.log(`[PoseFusion] frame 1 OK — mode=${mode}, csi.bufLen=${csiSimulator.amplitudeBuffer.length}, embPts=${embPoints.fused.length}, rssi=${csiSimulator.rssiDbm.toFixed(1)}`);
|
||||
}
|
||||
|
||||
} catch (err) {
|
||||
if (!_loopErrorShown) {
|
||||
_loopErrorShown = true;
|
||||
console.error('[MainLoop]', err);
|
||||
// Show error visually on page
|
||||
const errDiv = document.createElement('div');
|
||||
errDiv.style.cssText = 'position:fixed;bottom:60px;left:24px;right:24px;background:rgba(255,48,64,0.95);color:#fff;padding:12px 16px;border-radius:8px;font:12px/1.4 "JetBrains Mono",monospace;z-index:9999;max-height:120px;overflow:auto';
|
||||
errDiv.textContent = `[MainLoop Error] ${err.message}\n${err.stack?.split('\n').slice(0,3).join('\n')}`;
|
||||
document.body.appendChild(errDiv);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// === RSSI Visualization ===
|
||||
function updateRssi(dbm) {
|
||||
if (!rssiBarEl) return;
|
||||
|
||||
// Clamp to typical WiFi range: -100 (worst) to -30 (best)
|
||||
const clamped = Math.max(-100, Math.min(-30, dbm));
|
||||
const pct = ((clamped + 100) / 70) * 100; // 0-100%
|
||||
|
||||
rssiBarEl.style.width = `${pct}%`;
|
||||
rssiValueEl.textContent = `${Math.round(clamped)} dBm`;
|
||||
|
||||
// Quality label
|
||||
let quality;
|
||||
if (clamped > -50) quality = 'Excellent';
|
||||
else if (clamped > -60) quality = 'Good';
|
||||
else if (clamped > -70) quality = 'Fair';
|
||||
else if (clamped > -80) quality = 'Weak';
|
||||
else quality = 'Poor';
|
||||
rssiQualityEl.textContent = quality;
|
||||
|
||||
// Color the dBm value based on quality
|
||||
if (clamped > -60) rssiValueEl.style.color = 'var(--green-glow)';
|
||||
else if (clamped > -75) rssiValueEl.style.color = 'var(--amber)';
|
||||
else rssiValueEl.style.color = 'var(--red-alert)';
|
||||
|
||||
// Sparkline history
|
||||
rssiHistory.push(clamped);
|
||||
if (rssiHistory.length > RSSI_HISTORY_MAX) rssiHistory.shift();
|
||||
drawRssiSparkline();
|
||||
}
|
||||
|
||||
function drawRssiSparkline() {
|
||||
if (!rssiSparkCtx || rssiHistory.length < 2) return;
|
||||
const w = rssiSparkCanvas.width;
|
||||
const h = rssiSparkCanvas.height;
|
||||
const ctx = rssiSparkCtx;
|
||||
|
||||
ctx.clearRect(0, 0, w, h);
|
||||
|
||||
// Draw signal strength line
|
||||
const len = rssiHistory.length;
|
||||
const step = w / (RSSI_HISTORY_MAX - 1);
|
||||
|
||||
// Gradient fill under line
|
||||
const grad = ctx.createLinearGradient(0, 0, 0, h);
|
||||
grad.addColorStop(0, 'rgba(0,210,120,0.3)');
|
||||
grad.addColorStop(1, 'rgba(0,210,120,0)');
|
||||
|
||||
ctx.beginPath();
|
||||
for (let i = 0; i < len; i++) {
|
||||
const x = (RSSI_HISTORY_MAX - len + i) * step;
|
||||
const y = h - ((rssiHistory[i] + 100) / 70) * h;
|
||||
if (i === 0) ctx.moveTo(x, y);
|
||||
else ctx.lineTo(x, y);
|
||||
}
|
||||
// Fill area
|
||||
const lastX = (RSSI_HISTORY_MAX - 1) * step;
|
||||
const firstX = (RSSI_HISTORY_MAX - len) * step;
|
||||
ctx.lineTo(lastX, h);
|
||||
ctx.lineTo(firstX, h);
|
||||
ctx.closePath();
|
||||
ctx.fillStyle = grad;
|
||||
ctx.fill();
|
||||
|
||||
// Draw line on top
|
||||
ctx.beginPath();
|
||||
for (let i = 0; i < len; i++) {
|
||||
const x = (RSSI_HISTORY_MAX - len + i) * step;
|
||||
const y = h - ((rssiHistory[i] + 100) / 70) * h;
|
||||
if (i === 0) ctx.moveTo(x, y);
|
||||
else ctx.lineTo(x, y);
|
||||
}
|
||||
ctx.strokeStyle = '#00d878';
|
||||
ctx.lineWidth = 1.5;
|
||||
ctx.stroke();
|
||||
|
||||
// Pulsing dot at latest value
|
||||
const latestX = lastX;
|
||||
const latestY = h - ((rssiHistory[len - 1] + 100) / 70) * h;
|
||||
const pulse = 0.5 + 0.5 * Math.sin(performance.now() / 300);
|
||||
ctx.beginPath();
|
||||
ctx.arc(latestX, latestY, 2 + pulse, 0, Math.PI * 2);
|
||||
ctx.fillStyle = '#00d878';
|
||||
ctx.fill();
|
||||
ctx.beginPath();
|
||||
ctx.arc(latestX, latestY, 4 + pulse * 2, 0, Math.PI * 2);
|
||||
ctx.strokeStyle = `rgba(0,216,120,${0.3 + pulse * 0.3})`;
|
||||
ctx.lineWidth = 1;
|
||||
ctx.stroke();
|
||||
}
|
||||
|
||||
// Boot
|
||||
document.addEventListener('DOMContentLoaded', init);
|
||||
553
ui/pose-fusion/js/pose-decoder.js
Normal file
553
ui/pose-fusion/js/pose-decoder.js
Normal file
|
|
@ -0,0 +1,553 @@
|
|||
/**
|
||||
* PoseDecoder — Maps motion detection grid → 17 COCO keypoints.
|
||||
*
|
||||
* Uses per-cell motion intensity to track actual body part positions:
|
||||
* - Head: top-center motion cluster
|
||||
* - Shoulders/Elbows/Wrists: lateral motion in upper body zone
|
||||
* - Hips/Knees/Ankles: lower body motion distribution
|
||||
*
|
||||
* When person exits frame, CSI data continues tracking (through-wall mode).
|
||||
*/
|
||||
|
||||
// Extended keypoint definitions: 17 COCO + 9 hand/fingertip approximations = 26 total
|
||||
export const KEYPOINT_NAMES = [
|
||||
'nose', 'left_eye', 'right_eye', 'left_ear', 'right_ear',
|
||||
'left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow',
|
||||
'left_wrist', 'right_wrist', 'left_hip', 'right_hip',
|
||||
'left_knee', 'right_knee', 'left_ankle', 'right_ankle',
|
||||
// Extended: hand keypoints (17-25)
|
||||
'left_thumb', 'left_index', 'left_pinky', // 17, 18, 19
|
||||
'right_thumb', 'right_index', 'right_pinky', // 20, 21, 22
|
||||
'left_foot_index', 'right_foot_index', // 23, 24 (toe tips)
|
||||
'neck', // 25 (mid-shoulder)
|
||||
];
|
||||
|
||||
// Skeleton connections (pairs of keypoint indices)
|
||||
export const SKELETON_CONNECTIONS = [
|
||||
[0, 1], [0, 2], [1, 3], [2, 4], // Head
|
||||
[0, 25], // Nose → neck
|
||||
[25, 5], [25, 6], // Neck → shoulders
|
||||
[5, 7], [7, 9], // Left arm
|
||||
[6, 8], [8, 10], // Right arm
|
||||
[5, 11], [6, 12], // Torso
|
||||
[11, 12], // Hips
|
||||
[11, 13], [13, 15], // Left leg
|
||||
[12, 14], [14, 16], // Right leg
|
||||
// Hand connections
|
||||
[9, 17], [9, 18], [9, 19], // Left wrist → fingers
|
||||
[10, 20], [10, 21], [10, 22], // Right wrist → fingers
|
||||
// Foot connections
|
||||
[15, 23], [16, 24], // Ankles → toes
|
||||
];
|
||||
|
||||
// Standard body proportions (relative to body height)
|
||||
const PROPORTIONS = {
|
||||
headToShoulder: 0.15,
|
||||
shoulderWidth: 0.25,
|
||||
shoulderToElbow: 0.18,
|
||||
elbowToWrist: 0.16,
|
||||
shoulderToHip: 0.30,
|
||||
hipWidth: 0.18,
|
||||
hipToKnee: 0.24,
|
||||
kneeToAnkle: 0.24,
|
||||
eyeSpacing: 0.04,
|
||||
earSpacing: 0.07,
|
||||
// Hand proportions
|
||||
wristToFinger: 0.09,
|
||||
fingerSpread: 0.04,
|
||||
thumbAngle: 0.6, // radians from wrist-elbow axis
|
||||
// Foot proportions
|
||||
ankleToToe: 0.06,
|
||||
};
|
||||
|
||||
export class PoseDecoder {
|
||||
constructor(embeddingDim = 128) {
|
||||
this.embeddingDim = embeddingDim;
|
||||
this.smoothedKeypoints = null;
|
||||
this.smoothingFactor = 0.25; // Low = responsive to real movement
|
||||
this._time = 0;
|
||||
|
||||
// Through-wall tracking state
|
||||
this._lastBodyState = null;
|
||||
this._ghostState = null;
|
||||
this._ghostConfidence = 0;
|
||||
this._ghostVelocity = { x: 0, y: 0 };
|
||||
|
||||
// Zone centroid tracking (normalized 0-1 positions)
|
||||
this._headCx = 0.5;
|
||||
this._headCy = 0.15;
|
||||
this._leftArmCx = 0.3;
|
||||
this._leftArmCy = 0.35;
|
||||
this._rightArmCx = 0.7;
|
||||
this._rightArmCy = 0.35;
|
||||
this._leftLegCx = 0.4;
|
||||
this._leftLegCy = 0.8;
|
||||
this._rightLegCx = 0.6;
|
||||
this._rightLegCy = 0.8;
|
||||
this._torsoCx = 0.5;
|
||||
this._torsoCy = 0.45;
|
||||
|
||||
// RuVector embedding → joint mapping
|
||||
// Each joint gets 2 consecutive embedding dimensions (dx, dy offset)
|
||||
// and 1 dimension for confidence modulation. 26 joints × 3 = 78 dims used from 128.
|
||||
// Remaining 50 dims encode global pose features (body scale, rotation, lean).
|
||||
this._jointEmbMap = this._buildJointEmbeddingMap(embeddingDim);
|
||||
|
||||
// Attention contribution tracking (for UI overlay)
|
||||
this.attentionStats = { energy: 0, maxDim: 0, refinementMag: 0 };
|
||||
}
|
||||
|
||||
/**
|
||||
* Build the mapping from embedding dimensions to joint refinement signals.
|
||||
* This maps the RuVector attention output to anatomically meaningful joint offsets.
|
||||
*/
|
||||
_buildJointEmbeddingMap(dim) {
|
||||
const map = [];
|
||||
// 26 joints × 3 dims each (dx, dy, confidence_mod) = 78 dims
|
||||
for (let j = 0; j < 26; j++) {
|
||||
const base = j * 3;
|
||||
if (base + 2 < dim) {
|
||||
map.push({ dxDim: base, dyDim: base + 1, confDim: base + 2 });
|
||||
} else {
|
||||
map.push({ dxDim: j % dim, dyDim: (j + 1) % dim, confDim: (j + 2) % dim });
|
||||
}
|
||||
}
|
||||
// Global pose features from dims 78-127
|
||||
return {
|
||||
joints: map,
|
||||
scaleDim: Math.min(78, dim - 1), // body scale factor
|
||||
rotDim: Math.min(79, dim - 1), // body rotation
|
||||
leanXDim: Math.min(80, dim - 1), // lateral lean
|
||||
leanYDim: Math.min(81, dim - 1), // forward/back lean
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Decode motion data into 17 keypoints
|
||||
* @param {Float32Array} embedding - Fused embedding vector
|
||||
* @param {{ detected, x, y, w, h, motionGrid, gridCols, gridRows, motionCx, motionCy, exitDirection }} motionRegion
|
||||
* @param {number} elapsed - Time in seconds
|
||||
* @param {{ csiPresence: number }} csiState - CSI sensing state for through-wall
|
||||
* @returns {Array<{x: number, y: number, confidence: number, name: string}>}
|
||||
*/
|
||||
decode(embedding, motionRegion, elapsed, csiState = {}) {
|
||||
this._time = elapsed;
|
||||
|
||||
const hasMotion = motionRegion && motionRegion.detected;
|
||||
const hasCsi = csiState && csiState.csiPresence > 0.1;
|
||||
|
||||
if (hasMotion) {
|
||||
// Active tracking from video motion grid
|
||||
this._ghostConfidence = 0;
|
||||
const rawKeypoints = this._trackFromMotionGrid(motionRegion, embedding, elapsed);
|
||||
this._lastBodyState = { keypoints: rawKeypoints.map(kp => ({...kp})), time: elapsed };
|
||||
|
||||
// Track exit velocity
|
||||
if (motionRegion.exitDirection) {
|
||||
const speed = 0.008;
|
||||
this._ghostVelocity = {
|
||||
x: motionRegion.exitDirection === 'left' ? -speed : motionRegion.exitDirection === 'right' ? speed : 0,
|
||||
y: motionRegion.exitDirection === 'up' ? -speed : motionRegion.exitDirection === 'down' ? speed : 0
|
||||
};
|
||||
}
|
||||
|
||||
// Apply temporal smoothing
|
||||
if (this.smoothedKeypoints && this.smoothedKeypoints.length === rawKeypoints.length) {
|
||||
const alpha = this.smoothingFactor;
|
||||
for (let i = 0; i < rawKeypoints.length; i++) {
|
||||
rawKeypoints[i].x = alpha * this.smoothedKeypoints[i].x + (1 - alpha) * rawKeypoints[i].x;
|
||||
rawKeypoints[i].y = alpha * this.smoothedKeypoints[i].y + (1 - alpha) * rawKeypoints[i].y;
|
||||
}
|
||||
}
|
||||
|
||||
this.smoothedKeypoints = rawKeypoints;
|
||||
return rawKeypoints;
|
||||
|
||||
} else if (this._lastBodyState && (hasCsi || this._ghostConfidence > 0.05)) {
|
||||
// Through-wall mode: person left frame but CSI still senses them
|
||||
return this._trackThroughWall(elapsed, csiState);
|
||||
|
||||
} else if (this.smoothedKeypoints) {
|
||||
// Fade out
|
||||
const faded = this.smoothedKeypoints.map(kp => ({
|
||||
...kp,
|
||||
confidence: kp.confidence * 0.88
|
||||
})).filter(kp => kp.confidence > 0.05);
|
||||
if (faded.length === 0) this.smoothedKeypoints = null;
|
||||
else this.smoothedKeypoints = faded;
|
||||
return faded;
|
||||
}
|
||||
|
||||
return [];
|
||||
}
|
||||
|
||||
/**
|
||||
* Track body parts from the motion grid.
|
||||
* Finds the centroid of motion in each body zone and positions joints there.
|
||||
*/
|
||||
_trackFromMotionGrid(region, embedding, elapsed) {
|
||||
const grid = region.motionGrid;
|
||||
const cols = region.gridCols || 10;
|
||||
const rows = region.gridRows || 8;
|
||||
|
||||
// Body bounding box (in normalized 0-1 coords)
|
||||
const bx = region.x, by = region.y, bw = region.w, bh = region.h;
|
||||
const cx = bx + bw / 2;
|
||||
const cy = by + bh / 2;
|
||||
const bodyH = Math.max(bh, 0.3);
|
||||
const bodyW = Math.max(bw, 0.15);
|
||||
|
||||
// Find motion centroids per body zone from the grid
|
||||
if (grid) {
|
||||
const zones = this._findZoneCentroids(grid, cols, rows, bx, by, bw, bh);
|
||||
// Smooth with low alpha for responsiveness
|
||||
const a = 0.3; // 30% old, 70% new → responsive
|
||||
this._headCx = a * this._headCx + (1 - a) * zones.head.x;
|
||||
this._headCy = a * this._headCy + (1 - a) * zones.head.y;
|
||||
this._leftArmCx = a * this._leftArmCx + (1 - a) * zones.leftArm.x;
|
||||
this._leftArmCy = a * this._leftArmCy + (1 - a) * zones.leftArm.y;
|
||||
this._rightArmCx= a * this._rightArmCx+ (1 - a) * zones.rightArm.x;
|
||||
this._rightArmCy= a * this._rightArmCy+ (1 - a) * zones.rightArm.y;
|
||||
this._leftLegCx = a * this._leftLegCx + (1 - a) * zones.leftLeg.x;
|
||||
this._leftLegCy = a * this._leftLegCy + (1 - a) * zones.leftLeg.y;
|
||||
this._rightLegCx= a * this._rightLegCx+ (1 - a) * zones.rightLeg.x;
|
||||
this._rightLegCy= a * this._rightLegCy+ (1 - a) * zones.rightLeg.y;
|
||||
this._torsoCx = a * this._torsoCx + (1 - a) * zones.torso.x;
|
||||
this._torsoCy = a * this._torsoCy + (1 - a) * zones.torso.y;
|
||||
}
|
||||
|
||||
const P = PROPORTIONS;
|
||||
|
||||
// Breathing (subtle)
|
||||
const breathe = Math.sin(elapsed * 1.5) * 0.002;
|
||||
|
||||
// === Position joints using tracked centroids ===
|
||||
|
||||
// HEAD: tracked centroid (top zone)
|
||||
const headX = this._headCx;
|
||||
const headY = this._headCy;
|
||||
|
||||
// TORSO center drives shoulder/hip
|
||||
const torsoX = this._torsoCx;
|
||||
const shoulderY = this._torsoCy - bodyH * 0.08 + breathe;
|
||||
const halfW = P.shoulderWidth * bodyH / 2;
|
||||
const hipHalfW = P.hipWidth * bodyH / 2;
|
||||
const hipY = shoulderY + P.shoulderToHip * bodyH;
|
||||
|
||||
// ARMS: elbow + wrist driven toward arm zone centroids
|
||||
// Left arm: shoulder is fixed, elbow/wrist pulled toward left arm centroid
|
||||
const lShX = torsoX - halfW;
|
||||
const lShY = shoulderY;
|
||||
// Vector from shoulder toward arm centroid
|
||||
const lArmDx = this._leftArmCx - lShX;
|
||||
const lArmDy = this._leftArmCy - lShY;
|
||||
const lArmDist = Math.sqrt(lArmDx * lArmDx + lArmDy * lArmDy) || 0.01;
|
||||
const lArmNx = lArmDx / lArmDist;
|
||||
const lArmNy = lArmDy / lArmDist;
|
||||
// Elbow at shoulderToElbow distance along that direction
|
||||
const elbowLen = P.shoulderToElbow * bodyH;
|
||||
const lElbowX = lShX + lArmNx * elbowLen;
|
||||
const lElbowY = lShY + lArmNy * elbowLen;
|
||||
// Wrist continues further
|
||||
const wristLen = P.elbowToWrist * bodyH;
|
||||
const lWristX = lElbowX + lArmNx * wristLen;
|
||||
const lWristY = lElbowY + lArmNy * wristLen;
|
||||
|
||||
// Right arm: same approach
|
||||
const rShX = torsoX + halfW;
|
||||
const rShY = shoulderY;
|
||||
const rArmDx = this._rightArmCx - rShX;
|
||||
const rArmDy = this._rightArmCy - rShY;
|
||||
const rArmDist = Math.sqrt(rArmDx * rArmDx + rArmDy * rArmDy) || 0.01;
|
||||
const rArmNx = rArmDx / rArmDist;
|
||||
const rArmNy = rArmDy / rArmDist;
|
||||
const rElbowX = rShX + rArmNx * elbowLen;
|
||||
const rElbowY = rShY + rArmNy * elbowLen;
|
||||
const rWristX = rElbowX + rArmNx * wristLen;
|
||||
const rWristY = rElbowY + rArmNy * wristLen;
|
||||
|
||||
// LEGS: knees/ankles pulled toward leg zone centroids
|
||||
const lHipX = torsoX - hipHalfW;
|
||||
const rHipX = torsoX + hipHalfW;
|
||||
const lLegDx = this._leftLegCx - lHipX;
|
||||
const lLegDy = Math.max(0.05, this._leftLegCy - hipY); // always downward
|
||||
const lLegDist = Math.sqrt(lLegDx * lLegDx + lLegDy * lLegDy) || 0.01;
|
||||
const lLegNx = lLegDx / lLegDist;
|
||||
const lLegNy = lLegDy / lLegDist;
|
||||
const kneeLen = P.hipToKnee * bodyH;
|
||||
const ankleLen = P.kneeToAnkle * bodyH;
|
||||
const lKneeX = lHipX + lLegNx * kneeLen;
|
||||
const lKneeY = hipY + lLegNy * kneeLen;
|
||||
const lAnkleX = lKneeX + lLegNx * ankleLen;
|
||||
const lAnkleY = lKneeY + lLegNy * ankleLen;
|
||||
|
||||
const rLegDx = this._rightLegCx - rHipX;
|
||||
const rLegDy = Math.max(0.05, this._rightLegCy - hipY);
|
||||
const rLegDist = Math.sqrt(rLegDx * rLegDx + rLegDy * rLegDy) || 0.01;
|
||||
const rLegNx = rLegDx / rLegDist;
|
||||
const rLegNy = rLegDy / rLegDist;
|
||||
const rKneeX = rHipX + rLegNx * kneeLen;
|
||||
const rKneeY = hipY + rLegNy * kneeLen;
|
||||
const rAnkleX = rKneeX + rLegNx * ankleLen;
|
||||
const rAnkleY = rKneeY + rLegNy * ankleLen;
|
||||
|
||||
// Arm raise amount (for hand openness)
|
||||
const leftArmRaise = Math.max(0, Math.min(1, (shoulderY - this._leftArmCy) / (bodyH * 0.3)));
|
||||
const rightArmRaise = Math.max(0, Math.min(1, (shoulderY - this._rightArmCy) / (bodyH * 0.3)));
|
||||
|
||||
// Compute hand finger positions from wrist-elbow axis
|
||||
const lHandAngle = Math.atan2(lWristY - lElbowY, lWristX - lElbowX);
|
||||
const rHandAngle = Math.atan2(rWristY - rElbowY, rWristX - rElbowX);
|
||||
const fingerLen = P.wristToFinger * bodyH;
|
||||
const fingerSpr = P.fingerSpread * bodyH;
|
||||
|
||||
// Hand openness driven by arm raise + arm lateral spread
|
||||
const lArmSpread = Math.abs(this._leftArmCx - (bx + bw * 0.3)) / (bw * 0.3);
|
||||
const rArmSpread = Math.abs(this._rightArmCx - (bx + bw * 0.7)) / (bw * 0.3);
|
||||
const lHandOpen = Math.min(1, leftArmRaise * 0.5 + lArmSpread * 0.5);
|
||||
const rHandOpen = Math.min(1, rightArmRaise * 0.5 + rArmSpread * 0.5);
|
||||
|
||||
const keypoints = [
|
||||
// 0: nose
|
||||
{ x: headX, y: headY + 0.01, confidence: 0.92 },
|
||||
// 1: left_eye
|
||||
{ x: headX - P.eyeSpacing * bodyH, y: headY - 0.005, confidence: 0.88 },
|
||||
// 2: right_eye
|
||||
{ x: headX + P.eyeSpacing * bodyH, y: headY - 0.005, confidence: 0.88 },
|
||||
// 3: left_ear
|
||||
{ x: headX - P.earSpacing * bodyH, y: headY + 0.005, confidence: 0.72 },
|
||||
// 4: right_ear
|
||||
{ x: headX + P.earSpacing * bodyH, y: headY + 0.005, confidence: 0.72 },
|
||||
// 5: left_shoulder
|
||||
{ x: lShX, y: lShY, confidence: 0.94 },
|
||||
// 6: right_shoulder
|
||||
{ x: rShX, y: rShY, confidence: 0.94 },
|
||||
// 7: left_elbow
|
||||
{ x: lElbowX, y: lElbowY, confidence: 0.87 },
|
||||
// 8: right_elbow
|
||||
{ x: rElbowX, y: rElbowY, confidence: 0.87 },
|
||||
// 9: left_wrist
|
||||
{ x: lWristX, y: lWristY, confidence: 0.82 },
|
||||
// 10: right_wrist
|
||||
{ x: rWristX, y: rWristY, confidence: 0.82 },
|
||||
// 11: left_hip
|
||||
{ x: lHipX, y: hipY, confidence: 0.91 },
|
||||
// 12: right_hip
|
||||
{ x: rHipX, y: hipY, confidence: 0.91 },
|
||||
// 13: left_knee
|
||||
{ x: lKneeX, y: lKneeY, confidence: 0.88 },
|
||||
// 14: right_knee
|
||||
{ x: rKneeX, y: rKneeY, confidence: 0.88 },
|
||||
// 15: left_ankle
|
||||
{ x: lAnkleX, y: lAnkleY, confidence: 0.83 },
|
||||
// 16: right_ankle
|
||||
{ x: rAnkleX, y: rAnkleY, confidence: 0.83 },
|
||||
|
||||
// === Extended keypoints (17-25) ===
|
||||
|
||||
// 17: left_thumb — offset at thumb angle from wrist-elbow axis
|
||||
{ x: lWristX + fingerLen * Math.cos(lHandAngle + P.thumbAngle) * (0.6 + lHandOpen * 0.4),
|
||||
y: lWristY + fingerLen * Math.sin(lHandAngle + P.thumbAngle) * (0.6 + lHandOpen * 0.4),
|
||||
confidence: 0.68 * (0.5 + lHandOpen * 0.5) },
|
||||
// 18: left_index — extends along wrist-elbow axis
|
||||
{ x: lWristX + fingerLen * Math.cos(lHandAngle) + fingerSpr * lHandOpen * Math.cos(lHandAngle + 0.3),
|
||||
y: lWristY + fingerLen * Math.sin(lHandAngle) + fingerSpr * lHandOpen * Math.sin(lHandAngle + 0.3),
|
||||
confidence: 0.72 * (0.5 + lHandOpen * 0.5) },
|
||||
// 19: left_pinky — offset opposite thumb
|
||||
{ x: lWristX + fingerLen * 0.85 * Math.cos(lHandAngle - P.thumbAngle * 0.7),
|
||||
y: lWristY + fingerLen * 0.85 * Math.sin(lHandAngle - P.thumbAngle * 0.7),
|
||||
confidence: 0.60 * (0.5 + lHandOpen * 0.5) },
|
||||
|
||||
// 20: right_thumb
|
||||
{ x: rWristX + fingerLen * Math.cos(rHandAngle - P.thumbAngle) * (0.6 + rHandOpen * 0.4),
|
||||
y: rWristY + fingerLen * Math.sin(rHandAngle - P.thumbAngle) * (0.6 + rHandOpen * 0.4),
|
||||
confidence: 0.68 * (0.5 + rHandOpen * 0.5) },
|
||||
// 21: right_index
|
||||
{ x: rWristX + fingerLen * Math.cos(rHandAngle) + fingerSpr * rHandOpen * Math.cos(rHandAngle - 0.3),
|
||||
y: rWristY + fingerLen * Math.sin(rHandAngle) + fingerSpr * rHandOpen * Math.sin(rHandAngle - 0.3),
|
||||
confidence: 0.72 * (0.5 + rHandOpen * 0.5) },
|
||||
// 22: right_pinky
|
||||
{ x: rWristX + fingerLen * 0.85 * Math.cos(rHandAngle + P.thumbAngle * 0.7),
|
||||
y: rWristY + fingerLen * 0.85 * Math.sin(rHandAngle + P.thumbAngle * 0.7),
|
||||
confidence: 0.60 * (0.5 + rHandOpen * 0.5) },
|
||||
|
||||
// 23: left_foot_index (toe tip) — extends forward from ankle
|
||||
{ x: lAnkleX + P.ankleToToe * bodyH * 0.5,
|
||||
y: lAnkleY + P.ankleToToe * bodyH * 0.3,
|
||||
confidence: 0.65 },
|
||||
// 24: right_foot_index
|
||||
{ x: rAnkleX + P.ankleToToe * bodyH * 0.5,
|
||||
y: rAnkleY + P.ankleToToe * bodyH * 0.3,
|
||||
confidence: 0.65 },
|
||||
|
||||
// 25: neck (midpoint between shoulders, slightly above)
|
||||
{ x: (lShX + rShX) / 2, y: shoulderY - P.headToShoulder * bodyH * 0.35, confidence: 0.93 },
|
||||
];
|
||||
|
||||
for (let i = 0; i < keypoints.length; i++) {
|
||||
keypoints[i].name = KEYPOINT_NAMES[i];
|
||||
}
|
||||
|
||||
// === RuVector Attention Embedding Refinement ===
|
||||
// Compute attention stats for the UI pipeline display, but only apply
|
||||
// positional refinement when a trained model is loaded (random-weight
|
||||
// embeddings carry no meaningful spatial signal and distort the skeleton).
|
||||
if (embedding && embedding.length >= 26 * 3) {
|
||||
this._computeEmbeddingStats(keypoints, embedding, bodyH);
|
||||
}
|
||||
|
||||
return keypoints;
|
||||
}
|
||||
|
||||
/**
|
||||
* Apply RuVector attention embedding to refine joint positions and confidence.
|
||||
*
|
||||
* The 128-dim fused embedding is decoded as:
|
||||
* - Dims 0-77: Per-joint (dx, dy, confidence_mod) × 26 joints
|
||||
* - Dims 78-81: Global pose parameters (scale, rotation, lean)
|
||||
* - Dims 82-127: Reserved for cross-modal fusion features
|
||||
*
|
||||
* The attention mechanism determines HOW MUCH each spatial region contributes
|
||||
* to each joint's refinement. Multi-Head captures global relationships,
|
||||
* Hyperbolic captures hierarchical (torso→limb→hand) dependencies,
|
||||
* MoE routes different body regions to specialized experts,
|
||||
* Linear provides fast extremity refinement, Local-Global balances detail/context.
|
||||
*/
|
||||
/**
|
||||
* Compute embedding statistics for UI display without modifying joint positions.
|
||||
* The 6-stage attention pipeline stats are shown in the RuVector panel.
|
||||
* Position refinement is disabled until a trained model replaces random weights.
|
||||
*/
|
||||
_computeEmbeddingStats(keypoints, emb, bodyH) {
|
||||
const map = this._jointEmbMap;
|
||||
const tc = (v) => Math.tanh(Number(v) || 0);
|
||||
|
||||
// Embedding energy (L2 norm of the used dims)
|
||||
let energy = 0;
|
||||
for (let i = 0; i < Math.min(emb.length, 82); i++) {
|
||||
energy += emb[i] * emb[i];
|
||||
}
|
||||
energy = Math.sqrt(energy);
|
||||
|
||||
// Simulated per-joint refinement magnitude (what WOULD be applied)
|
||||
const scale = bodyH * 0.015;
|
||||
let totalRefinement = 0;
|
||||
let maxDimVal = 0;
|
||||
|
||||
for (let j = 0; j < Math.min(keypoints.length, 26); j++) {
|
||||
const jmap = map.joints[j];
|
||||
if (!jmap) continue;
|
||||
const dx = tc(emb[jmap.dxDim]) * scale;
|
||||
const dy = tc(emb[jmap.dyDim]) * scale;
|
||||
totalRefinement += Math.sqrt(dx * dx + dy * dy);
|
||||
maxDimVal = Math.max(maxDimVal, Math.abs(tc(emb[jmap.dxDim])), Math.abs(tc(emb[jmap.dyDim])));
|
||||
}
|
||||
|
||||
this.attentionStats.energy = energy;
|
||||
this.attentionStats.maxDim = maxDimVal;
|
||||
this.attentionStats.refinementMag = totalRefinement / 26;
|
||||
}
|
||||
|
||||
/**
|
||||
* Find weighted motion centroids for each body zone.
|
||||
* Divides the bounding box into 6 zones: head, left arm, right arm, torso, left leg, right leg.
|
||||
* Returns the (x,y) centroid of motion intensity for each zone.
|
||||
*/
|
||||
_findZoneCentroids(grid, cols, rows, bx, by, bw, bh) {
|
||||
// Zone definitions (in grid-relative fractions)
|
||||
const zones = {
|
||||
head: { rMin: 0, rMax: 0.2, cMin: 0.25, cMax: 0.75, wx: 0, wy: 0, wt: 0 },
|
||||
leftArm: { rMin: 0.1, rMax: 0.6, cMin: 0, cMax: 0.35, wx: 0, wy: 0, wt: 0 },
|
||||
rightArm: { rMin: 0.1, rMax: 0.6, cMin: 0.65, cMax: 1.0, wx: 0, wy: 0, wt: 0 },
|
||||
torso: { rMin: 0.15, rMax: 0.55, cMin: 0.3, cMax: 0.7, wx: 0, wy: 0, wt: 0 },
|
||||
leftLeg: { rMin: 0.5, rMax: 1.0, cMin: 0.1, cMax: 0.5, wx: 0, wy: 0, wt: 0 },
|
||||
rightLeg: { rMin: 0.5, rMax: 1.0, cMin: 0.5, cMax: 0.9, wx: 0, wy: 0, wt: 0 },
|
||||
};
|
||||
|
||||
// Accumulate weighted centroids per zone
|
||||
for (let r = 0; r < rows; r++) {
|
||||
const ry = r / rows; // 0-1 within grid
|
||||
for (let c = 0; c < cols; c++) {
|
||||
const cx_g = c / cols; // 0-1 within grid
|
||||
const val = grid[r][c];
|
||||
if (val < 0.005) continue; // skip near-zero motion
|
||||
|
||||
// Map grid position to body-space coordinates (0-1)
|
||||
const worldX = bx + cx_g * bw;
|
||||
const worldY = by + ry * bh;
|
||||
|
||||
// Assign to matching zones (a cell can contribute to multiple overlapping zones)
|
||||
for (const z of Object.values(zones)) {
|
||||
if (ry >= z.rMin && ry < z.rMax && cx_g >= z.cMin && cx_g < z.cMax) {
|
||||
z.wx += worldX * val;
|
||||
z.wy += worldY * val;
|
||||
z.wt += val;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Compute centroids with fallback defaults
|
||||
const centroid = (z, defX, defY) => ({
|
||||
x: z.wt > 0.01 ? z.wx / z.wt : defX,
|
||||
y: z.wt > 0.01 ? z.wy / z.wt : defY,
|
||||
weight: z.wt
|
||||
});
|
||||
|
||||
const midX = bx + bw / 2;
|
||||
const midY = by + bh / 2;
|
||||
|
||||
return {
|
||||
head: centroid(zones.head, midX, by + bh * 0.1),
|
||||
leftArm: centroid(zones.leftArm, bx + bw * 0.2, midY - bh * 0.05),
|
||||
rightArm: centroid(zones.rightArm, bx + bw * 0.8, midY - bh * 0.05),
|
||||
torso: centroid(zones.torso, midX, midY),
|
||||
leftLeg: centroid(zones.leftLeg, bx + bw * 0.35,by + bh * 0.75),
|
||||
rightLeg: centroid(zones.rightLeg, bx + bw * 0.65,by + bh * 0.75),
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Through-wall tracking: continue showing pose via CSI when person left video frame.
|
||||
* The skeleton drifts in the exit direction with decreasing confidence.
|
||||
*/
|
||||
_trackThroughWall(elapsed, csiState) {
|
||||
if (!this._lastBodyState) return [];
|
||||
|
||||
const dt = elapsed - this._lastBodyState.time;
|
||||
const csiPresence = csiState.csiPresence || 0;
|
||||
|
||||
// Initialize ghost on first call
|
||||
if (this._ghostConfidence <= 0.05) {
|
||||
this._ghostConfidence = 0.8;
|
||||
this._ghostState = this._lastBodyState.keypoints.map(kp => ({...kp}));
|
||||
}
|
||||
|
||||
// Ghost confidence decays, but CSI presence sustains it
|
||||
const csiBoost = Math.min(0.7, csiPresence * 0.8);
|
||||
this._ghostConfidence = Math.max(0.05, this._ghostConfidence * 0.995 - 0.001 + csiBoost * 0.002);
|
||||
|
||||
// Drift the ghost in exit direction
|
||||
const vx = this._ghostVelocity.x;
|
||||
const vy = this._ghostVelocity.y;
|
||||
|
||||
// Breathing continues via CSI
|
||||
const breathe = Math.sin(elapsed * 1.5) * 0.003 * csiPresence;
|
||||
|
||||
const keypoints = this._ghostState.map((kp, i) => {
|
||||
return {
|
||||
x: kp.x + vx * dt * 0.3,
|
||||
y: kp.y + vy * dt * 0.3 + (i >= 5 && i <= 6 ? breathe : 0),
|
||||
confidence: kp.confidence * this._ghostConfidence * (0.5 + csiPresence * 0.5),
|
||||
name: kp.name
|
||||
};
|
||||
});
|
||||
|
||||
// Slow down drift over time
|
||||
this._ghostVelocity.x *= 0.998;
|
||||
this._ghostVelocity.y *= 0.998;
|
||||
|
||||
this.smoothedKeypoints = keypoints;
|
||||
return keypoints;
|
||||
}
|
||||
}
|
||||
235
ui/pose-fusion/js/video-capture.js
Normal file
235
ui/pose-fusion/js/video-capture.js
Normal file
|
|
@ -0,0 +1,235 @@
|
|||
/**
|
||||
* VideoCapture — getUserMedia webcam capture with frame extraction.
|
||||
* Provides quality metrics (brightness, motion) for fusion confidence gating.
|
||||
*/
|
||||
|
||||
export class VideoCapture {
|
||||
constructor(videoElement) {
|
||||
this.video = videoElement;
|
||||
this.stream = null;
|
||||
this.offscreen = document.createElement('canvas');
|
||||
this.offCtx = this.offscreen.getContext('2d', { willReadFrequently: true });
|
||||
this.prevFrame = null;
|
||||
this.motionScore = 0;
|
||||
this.brightnessScore = 0;
|
||||
}
|
||||
|
||||
async start(constraints = {}) {
|
||||
const defaultConstraints = {
|
||||
video: {
|
||||
width: { ideal: 640 },
|
||||
height: { ideal: 480 },
|
||||
facingMode: 'user',
|
||||
frameRate: { ideal: 30 }
|
||||
},
|
||||
audio: false
|
||||
};
|
||||
|
||||
try {
|
||||
this.stream = await navigator.mediaDevices.getUserMedia(
|
||||
Object.keys(constraints).length ? constraints : defaultConstraints
|
||||
);
|
||||
this.video.srcObject = this.stream;
|
||||
await this.video.play();
|
||||
|
||||
this.offscreen.width = this.video.videoWidth;
|
||||
this.offscreen.height = this.video.videoHeight;
|
||||
|
||||
return true;
|
||||
} catch (err) {
|
||||
console.error('[Video] Camera access failed:', err.message);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
stop() {
|
||||
if (this.stream) {
|
||||
this.stream.getTracks().forEach(t => t.stop());
|
||||
this.stream = null;
|
||||
}
|
||||
this.video.srcObject = null;
|
||||
}
|
||||
|
||||
get isActive() {
|
||||
return this.stream !== null && this.video.readyState >= 2;
|
||||
}
|
||||
|
||||
get width() { return this.video.videoWidth || 640; }
|
||||
get height() { return this.video.videoHeight || 480; }
|
||||
|
||||
/**
|
||||
* Capture current frame as RGB Uint8Array + compute quality metrics.
|
||||
* @param {number} targetW - Target width for CNN input
|
||||
* @param {number} targetH - Target height for CNN input
|
||||
* @returns {{ rgb: Uint8Array, width: number, height: number, motion: number, brightness: number }}
|
||||
*/
|
||||
captureFrame(targetW = 56, targetH = 56) {
|
||||
if (!this.isActive) return null;
|
||||
|
||||
// Draw to offscreen at target resolution
|
||||
this.offscreen.width = targetW;
|
||||
this.offscreen.height = targetH;
|
||||
this.offCtx.drawImage(this.video, 0, 0, targetW, targetH);
|
||||
const imageData = this.offCtx.getImageData(0, 0, targetW, targetH);
|
||||
const rgba = imageData.data;
|
||||
|
||||
// Convert RGBA → RGB
|
||||
const pixels = targetW * targetH;
|
||||
const rgb = new Uint8Array(pixels * 3);
|
||||
let brightnessSum = 0;
|
||||
let motionSum = 0;
|
||||
|
||||
for (let i = 0; i < pixels; i++) {
|
||||
const r = rgba[i * 4];
|
||||
const g = rgba[i * 4 + 1];
|
||||
const b = rgba[i * 4 + 2];
|
||||
rgb[i * 3] = r;
|
||||
rgb[i * 3 + 1] = g;
|
||||
rgb[i * 3 + 2] = b;
|
||||
|
||||
// Luminance for brightness
|
||||
const lum = 0.299 * r + 0.587 * g + 0.114 * b;
|
||||
brightnessSum += lum;
|
||||
|
||||
// Motion: diff from previous frame
|
||||
if (this.prevFrame) {
|
||||
const pr = this.prevFrame[i * 3];
|
||||
const pg = this.prevFrame[i * 3 + 1];
|
||||
const pb = this.prevFrame[i * 3 + 2];
|
||||
motionSum += Math.abs(r - pr) + Math.abs(g - pg) + Math.abs(b - pb);
|
||||
}
|
||||
}
|
||||
|
||||
this.brightnessScore = brightnessSum / (pixels * 255);
|
||||
this.motionScore = this.prevFrame ? Math.min(1, motionSum / (pixels * 100)) : 0;
|
||||
this.prevFrame = new Uint8Array(rgb);
|
||||
|
||||
return {
|
||||
rgb,
|
||||
width: targetW,
|
||||
height: targetH,
|
||||
motion: this.motionScore,
|
||||
brightness: this.brightnessScore
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Capture full-resolution RGBA for overlay rendering
|
||||
* @returns {ImageData|null}
|
||||
*/
|
||||
captureFullFrame() {
|
||||
if (!this.isActive) return null;
|
||||
this.offscreen.width = this.width;
|
||||
this.offscreen.height = this.height;
|
||||
this.offCtx.drawImage(this.video, 0, 0);
|
||||
return this.offCtx.getImageData(0, 0, this.width, this.height);
|
||||
}
|
||||
|
||||
/**
|
||||
* Detect motion region + detailed motion grid for body-part tracking.
|
||||
* Returns bounding box + a grid showing WHERE motion is concentrated.
|
||||
* @returns {{ x, y, w, h, detected: boolean, motionGrid: number[][], gridCols: number, gridRows: number, exitDirection: string|null }}
|
||||
*/
|
||||
detectMotionRegion(targetW = 56, targetH = 56) {
|
||||
if (!this.isActive || !this.prevFrame) return { detected: false, motionGrid: null };
|
||||
|
||||
this.offscreen.width = targetW;
|
||||
this.offscreen.height = targetH;
|
||||
this.offCtx.drawImage(this.video, 0, 0, targetW, targetH);
|
||||
const rgba = this.offCtx.getImageData(0, 0, targetW, targetH).data;
|
||||
|
||||
let minX = targetW, minY = targetH, maxX = 0, maxY = 0;
|
||||
let motionPixels = 0;
|
||||
const threshold = 25;
|
||||
|
||||
// Motion grid: divide frame into cells and track motion intensity per cell
|
||||
const gridCols = 10;
|
||||
const gridRows = 8;
|
||||
const cellW = targetW / gridCols;
|
||||
const cellH = targetH / gridRows;
|
||||
const motionGrid = Array.from({ length: gridRows }, () => new Float32Array(gridCols));
|
||||
const cellPixels = cellW * cellH;
|
||||
|
||||
// Also track motion centroid weighted by intensity
|
||||
let motionCxSum = 0, motionCySum = 0, motionWeightSum = 0;
|
||||
|
||||
for (let y = 0; y < targetH; y++) {
|
||||
for (let x = 0; x < targetW; x++) {
|
||||
const i = y * targetW + x;
|
||||
const r = rgba[i * 4], g = rgba[i * 4 + 1], b = rgba[i * 4 + 2];
|
||||
const pr = this.prevFrame[i * 3], pg = this.prevFrame[i * 3 + 1], pb = this.prevFrame[i * 3 + 2];
|
||||
const diff = Math.abs(r - pr) + Math.abs(g - pg) + Math.abs(b - pb);
|
||||
|
||||
if (diff > threshold * 3) {
|
||||
motionPixels++;
|
||||
if (x < minX) minX = x;
|
||||
if (y < minY) minY = y;
|
||||
if (x > maxX) maxX = x;
|
||||
if (y > maxY) maxY = y;
|
||||
}
|
||||
|
||||
// Accumulate per-cell motion intensity
|
||||
const gc = Math.min(Math.floor(x / cellW), gridCols - 1);
|
||||
const gr = Math.min(Math.floor(y / cellH), gridRows - 1);
|
||||
const intensity = diff / (3 * 255); // Normalize 0-1
|
||||
motionGrid[gr][gc] += intensity / cellPixels;
|
||||
|
||||
// Weighted centroid
|
||||
if (diff > threshold) {
|
||||
motionCxSum += x * diff;
|
||||
motionCySum += y * diff;
|
||||
motionWeightSum += diff;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const detected = motionPixels > (targetW * targetH * 0.02);
|
||||
|
||||
// Motion centroid (normalized 0-1)
|
||||
const motionCx = motionWeightSum > 0 ? motionCxSum / (motionWeightSum * targetW) : 0.5;
|
||||
const motionCy = motionWeightSum > 0 ? motionCySum / (motionWeightSum * targetH) : 0.5;
|
||||
|
||||
// Detect exit direction: if centroid is near edges
|
||||
let exitDirection = null;
|
||||
if (detected && motionCx < 0.1) exitDirection = 'left';
|
||||
else if (detected && motionCx > 0.9) exitDirection = 'right';
|
||||
else if (detected && motionCy < 0.1) exitDirection = 'up';
|
||||
else if (detected && motionCy > 0.9) exitDirection = 'down';
|
||||
|
||||
// Track last known position for through-wall persistence
|
||||
if (detected) {
|
||||
this._lastDetected = {
|
||||
x: minX / targetW,
|
||||
y: minY / targetH,
|
||||
w: (maxX - minX) / targetW,
|
||||
h: (maxY - minY) / targetH,
|
||||
cx: motionCx,
|
||||
cy: motionCy,
|
||||
exitDirection,
|
||||
time: performance.now()
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
detected,
|
||||
x: minX / targetW,
|
||||
y: minY / targetH,
|
||||
w: (maxX - minX) / targetW,
|
||||
h: (maxY - minY) / targetH,
|
||||
coverage: motionPixels / (targetW * targetH),
|
||||
motionGrid,
|
||||
gridCols,
|
||||
gridRows,
|
||||
motionCx,
|
||||
motionCy,
|
||||
exitDirection
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Get the last known detection info (for through-wall persistence)
|
||||
*/
|
||||
get lastDetection() {
|
||||
return this._lastDetected || null;
|
||||
}
|
||||
}
|
||||
21
ui/pose-fusion/pkg/ruvector-attention/LICENSE
Normal file
21
ui/pose-fusion/pkg/ruvector-attention/LICENSE
Normal file
|
|
@ -0,0 +1,21 @@
|
|||
MIT License
|
||||
|
||||
Copyright (c) 2025 rUv
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
220
ui/pose-fusion/pkg/ruvector-attention/README.md
Normal file
220
ui/pose-fusion/pkg/ruvector-attention/README.md
Normal file
|
|
@ -0,0 +1,220 @@
|
|||
# ruvector-attention-wasm
|
||||
|
||||
WebAssembly bindings for the ruvector-attention package, providing high-performance attention mechanisms for browser and Node.js environments.
|
||||
|
||||
## Features
|
||||
|
||||
- **Multiple Attention Mechanisms**:
|
||||
- Scaled Dot-Product Attention
|
||||
- Multi-Head Attention
|
||||
- Hyperbolic Attention (for hierarchical data)
|
||||
- Linear Attention (Performer-style)
|
||||
- Flash Attention (memory-efficient)
|
||||
- Local-Global Attention
|
||||
- Mixture of Experts (MoE) Attention
|
||||
- **CGT Sheaf Attention** (coherence-gated via Prime-Radiant)
|
||||
|
||||
- **Training Utilities**:
|
||||
- InfoNCE contrastive loss
|
||||
- Adam optimizer
|
||||
- AdamW optimizer (with decoupled weight decay)
|
||||
- Learning rate scheduler (warmup + cosine decay)
|
||||
|
||||
- **TypeScript Support**: Full type definitions and modern API
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
npm install ruvector-attention-wasm
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
### TypeScript/JavaScript
|
||||
|
||||
```typescript
|
||||
import { initialize, MultiHeadAttention, utils } from 'ruvector-attention-wasm';
|
||||
|
||||
// Initialize WASM module
|
||||
await initialize();
|
||||
|
||||
// Create multi-head attention
|
||||
const attention = new MultiHeadAttention({ dim: 64, numHeads: 8 });
|
||||
|
||||
// Prepare inputs
|
||||
const query = new Float32Array(64);
|
||||
const keys = [new Float32Array(64), new Float32Array(64)];
|
||||
const values = [new Float32Array(64), new Float32Array(64)];
|
||||
|
||||
// Compute attention
|
||||
const output = attention.compute(query, keys, values);
|
||||
|
||||
// Use utilities
|
||||
const similarity = utils.cosineSimilarity(query, keys[0]);
|
||||
```
|
||||
|
||||
### Advanced Examples
|
||||
|
||||
#### Hyperbolic Attention
|
||||
|
||||
```typescript
|
||||
import { HyperbolicAttention } from 'ruvector-attention-wasm';
|
||||
|
||||
const hyperbolic = new HyperbolicAttention({
|
||||
dim: 128,
|
||||
curvature: 1.0
|
||||
});
|
||||
|
||||
const output = hyperbolic.compute(query, keys, values);
|
||||
```
|
||||
|
||||
#### MoE Attention with Expert Stats
|
||||
|
||||
```typescript
|
||||
import { MoEAttention } from 'ruvector-attention-wasm';
|
||||
|
||||
const moe = new MoEAttention({
|
||||
dim: 64,
|
||||
numExperts: 4,
|
||||
topK: 2
|
||||
});
|
||||
|
||||
const output = moe.compute(query, keys, values);
|
||||
|
||||
// Get expert utilization
|
||||
const stats = moe.getExpertStats();
|
||||
console.log('Load balance:', stats.loadBalance);
|
||||
```
|
||||
|
||||
#### Training with InfoNCE Loss
|
||||
|
||||
```typescript
|
||||
import { InfoNCELoss, Adam } from 'ruvector-attention-wasm';
|
||||
|
||||
const loss = new InfoNCELoss(0.07);
|
||||
const optimizer = new Adam(paramCount, {
|
||||
learningRate: 0.001,
|
||||
beta1: 0.9,
|
||||
beta2: 0.999,
|
||||
});
|
||||
|
||||
// Training loop
|
||||
const lossValue = loss.compute(anchor, positive, negatives);
|
||||
optimizer.step(params, gradients);
|
||||
```
|
||||
|
||||
#### Learning Rate Scheduling
|
||||
|
||||
```typescript
|
||||
import { LRScheduler, AdamW } from 'ruvector-attention-wasm';
|
||||
|
||||
const scheduler = new LRScheduler({
|
||||
initialLR: 0.001,
|
||||
warmupSteps: 1000,
|
||||
totalSteps: 10000,
|
||||
});
|
||||
|
||||
const optimizer = new AdamW(paramCount, {
|
||||
learningRate: scheduler.getLR(),
|
||||
weightDecay: 0.01,
|
||||
});
|
||||
|
||||
// Training loop
|
||||
for (let step = 0; step < 10000; step++) {
|
||||
optimizer.learningRate = scheduler.getLR();
|
||||
optimizer.step(params, gradients);
|
||||
scheduler.step();
|
||||
}
|
||||
```
|
||||
|
||||
## Building from Source
|
||||
|
||||
### Prerequisites
|
||||
|
||||
- Rust 1.70+
|
||||
- wasm-pack
|
||||
|
||||
### Build Commands
|
||||
|
||||
```bash
|
||||
# Build for web (ES modules)
|
||||
wasm-pack build --target web --out-dir pkg
|
||||
|
||||
# Build for Node.js
|
||||
wasm-pack build --target nodejs --out-dir pkg-node
|
||||
|
||||
# Build for bundlers (webpack, vite, etc.)
|
||||
wasm-pack build --target bundler --out-dir pkg-bundler
|
||||
|
||||
# Run tests
|
||||
wasm-pack test --headless --firefox
|
||||
```
|
||||
|
||||
## API Reference
|
||||
|
||||
### Attention Mechanisms
|
||||
|
||||
- `MultiHeadAttention` - Standard multi-head attention
|
||||
- `HyperbolicAttention` - Attention in hyperbolic space
|
||||
- `LinearAttention` - Linear complexity attention (Performer)
|
||||
- `FlashAttention` - Memory-efficient attention
|
||||
- `LocalGlobalAttention` - Combined local and global attention
|
||||
- `MoEAttention` - Mixture of Experts attention
|
||||
- `CGTSheafAttention` - Coherence-gated via Prime-Radiant energy
|
||||
- `scaledDotAttention()` - Functional API for basic attention
|
||||
|
||||
### CGT Sheaf Attention (Prime-Radiant Integration)
|
||||
|
||||
The CGT (Coherence-Gated Transformer) Sheaf Attention mechanism uses Prime-Radiant's sheaf Laplacian energy to gate attention based on mathematical consistency:
|
||||
|
||||
```typescript
|
||||
import { CGTSheafAttention } from 'ruvector-attention-wasm';
|
||||
|
||||
const cgtAttention = new CGTSheafAttention({
|
||||
dim: 128,
|
||||
numHeads: 8,
|
||||
coherenceThreshold: 0.3, // Block if energy > threshold
|
||||
});
|
||||
|
||||
// Attention is gated by coherence energy
|
||||
const result = cgtAttention.compute(query, keys, values);
|
||||
console.log('Coherence energy:', result.energy);
|
||||
console.log('Is coherent:', result.isCoherent);
|
||||
```
|
||||
|
||||
**Key features:**
|
||||
- Energy-weighted attention: Lower coherence energy → higher attention
|
||||
- Automatic hallucination detection via residual analysis
|
||||
- GPU-accelerated with wgpu WGSL shaders (vec4 optimized)
|
||||
- SIMD fallback (AVX-512/AVX2/NEON)
|
||||
|
||||
### Training
|
||||
|
||||
- `InfoNCELoss` - Contrastive loss function
|
||||
- `Adam` - Adam optimizer
|
||||
- `AdamW` - AdamW optimizer with weight decay
|
||||
- `LRScheduler` - Learning rate scheduler
|
||||
|
||||
### Utilities
|
||||
|
||||
- `utils.cosineSimilarity()` - Cosine similarity between vectors
|
||||
- `utils.l2Norm()` - L2 norm of a vector
|
||||
- `utils.normalize()` - Normalize vector to unit length
|
||||
- `utils.softmax()` - Apply softmax transformation
|
||||
- `utils.attentionWeights()` - Compute attention weights from scores
|
||||
- `utils.batchNormalize()` - Batch normalization
|
||||
- `utils.randomOrthogonalMatrix()` - Generate random orthogonal matrix
|
||||
- `utils.pairwiseDistances()` - Compute pairwise distances
|
||||
|
||||
## Performance
|
||||
|
||||
The WASM bindings provide near-native performance for attention computations:
|
||||
|
||||
- Optimized with `opt-level = "s"` and LTO
|
||||
- SIMD acceleration where available
|
||||
- Efficient memory management
|
||||
- Zero-copy data transfer where possible
|
||||
|
||||
## License
|
||||
|
||||
MIT OR Apache-2.0
|
||||
28
ui/pose-fusion/pkg/ruvector-attention/package.json
Normal file
28
ui/pose-fusion/pkg/ruvector-attention/package.json
Normal file
|
|
@ -0,0 +1,28 @@
|
|||
{
|
||||
"name": "ruvector-attention-wasm",
|
||||
"collaborators": [
|
||||
"Ruvector Team"
|
||||
],
|
||||
"description": "High-performance WebAssembly attention mechanisms: Multi-Head, Flash, Hyperbolic, MoE, CGT Sheaf Attention with GPU acceleration for transformers and LLMs",
|
||||
"version": "2.0.5",
|
||||
"license": "MIT",
|
||||
"repository": {
|
||||
"type": "git",
|
||||
"url": "https://github.com/ruvnet/ruvector"
|
||||
},
|
||||
"files": [
|
||||
"ruvector_attention_wasm_bg.wasm",
|
||||
"ruvector_attention_wasm.js",
|
||||
"ruvector_attention_wasm.d.ts"
|
||||
],
|
||||
"main": "ruvector_attention_wasm.js",
|
||||
"homepage": "https://ruv.io/ruvector",
|
||||
"types": "ruvector_attention_wasm.d.ts",
|
||||
"keywords": [
|
||||
"wasm",
|
||||
"attention",
|
||||
"transformer",
|
||||
"flash-attention",
|
||||
"llm"
|
||||
]
|
||||
}
|
||||
|
|
@ -0,0 +1,642 @@
|
|||
/**
|
||||
* Browser ESM wrapper for ruvector-attention-wasm v2.0.5
|
||||
*
|
||||
* The upstream pkg/ was built with wasm-pack --target nodejs (CJS + fs.readFileSync).
|
||||
* This wrapper loads the same WASM binary via fetch() for browser use.
|
||||
*
|
||||
* Usage:
|
||||
* import initWasm, { WasmMultiHeadAttention, ... } from './ruvector_attention_browser.js';
|
||||
* await initWasm();
|
||||
* const attn = new WasmMultiHeadAttention(dim, heads);
|
||||
*/
|
||||
|
||||
let _wasm;
|
||||
let _initialized = false;
|
||||
|
||||
// The entire CJS module runs inside this IIFE to avoid polluting global scope.
|
||||
// We capture all exports in _mod.
|
||||
const _mod = {};
|
||||
|
||||
(function(exports, wasm_getter) {
|
||||
|
||||
// ── wasm-bindgen heap management ──────────────────────────────────
|
||||
const heap = new Array(128).fill(undefined);
|
||||
heap.push(undefined, null, true, false);
|
||||
let heap_next = heap.length;
|
||||
|
||||
function addHeapObject(obj) {
|
||||
if (heap_next === heap.length) heap.push(heap.length + 1);
|
||||
const idx = heap_next;
|
||||
heap_next = heap[idx];
|
||||
heap[idx] = obj;
|
||||
return idx;
|
||||
}
|
||||
function getObject(idx) { return heap[idx]; }
|
||||
function dropObject(idx) {
|
||||
if (idx < 132) return;
|
||||
heap[idx] = heap_next;
|
||||
heap_next = idx;
|
||||
}
|
||||
function takeObject(idx) {
|
||||
const ret = getObject(idx);
|
||||
dropObject(idx);
|
||||
return ret;
|
||||
}
|
||||
function isLikeNone(x) { return x === undefined || x === null; }
|
||||
|
||||
// ── Memory views ──────────────────────────────────────────────────
|
||||
let cachedDataViewMemory0 = null;
|
||||
let cachedUint8ArrayMemory0 = null;
|
||||
let cachedFloat32ArrayMemory0 = null;
|
||||
|
||||
function wasm() { return wasm_getter(); }
|
||||
|
||||
function getDataViewMemory0() {
|
||||
if (cachedDataViewMemory0 === null || cachedDataViewMemory0.buffer !== wasm().memory.buffer)
|
||||
cachedDataViewMemory0 = new DataView(wasm().memory.buffer);
|
||||
return cachedDataViewMemory0;
|
||||
}
|
||||
function getUint8ArrayMemory0() {
|
||||
if (cachedUint8ArrayMemory0 === null || cachedUint8ArrayMemory0.buffer !== wasm().memory.buffer)
|
||||
cachedUint8ArrayMemory0 = new Uint8Array(wasm().memory.buffer);
|
||||
return cachedUint8ArrayMemory0;
|
||||
}
|
||||
function getFloat32ArrayMemory0() {
|
||||
if (cachedFloat32ArrayMemory0 === null || cachedFloat32ArrayMemory0.buffer !== wasm().memory.buffer)
|
||||
cachedFloat32ArrayMemory0 = new Float32Array(wasm().memory.buffer);
|
||||
return cachedFloat32ArrayMemory0;
|
||||
}
|
||||
function getArrayF32FromWasm0(ptr, len) {
|
||||
ptr = ptr >>> 0;
|
||||
return getFloat32ArrayMemory0().subarray(ptr / 4, ptr / 4 + len);
|
||||
}
|
||||
function getArrayU8FromWasm0(ptr, len) {
|
||||
ptr = ptr >>> 0;
|
||||
return getUint8ArrayMemory0().subarray(ptr, ptr + len);
|
||||
}
|
||||
|
||||
let WASM_VECTOR_LEN = 0;
|
||||
|
||||
function passArrayF32ToWasm0(arg, malloc) {
|
||||
const ptr = malloc(arg.length * 4, 4) >>> 0;
|
||||
getFloat32ArrayMemory0().set(arg, ptr / 4);
|
||||
WASM_VECTOR_LEN = arg.length;
|
||||
return ptr;
|
||||
}
|
||||
|
||||
const cachedTextEncoder = new TextEncoder();
|
||||
const cachedTextDecoder = new TextDecoder('utf-8', { ignoreBOM: true, fatal: true });
|
||||
cachedTextDecoder.decode();
|
||||
|
||||
function getStringFromWasm0(ptr, len) {
|
||||
ptr = ptr >>> 0;
|
||||
return cachedTextDecoder.decode(getUint8ArrayMemory0().subarray(ptr, ptr + len));
|
||||
}
|
||||
|
||||
function passStringToWasm0(arg, malloc, realloc) {
|
||||
const buf = cachedTextEncoder.encode(arg);
|
||||
const ptr = malloc(buf.length, 1) >>> 0;
|
||||
getUint8ArrayMemory0().subarray(ptr, ptr + buf.length).set(buf);
|
||||
WASM_VECTOR_LEN = buf.length;
|
||||
return ptr;
|
||||
}
|
||||
|
||||
function debugString(val) {
|
||||
const type = typeof val;
|
||||
if (type == 'number' || type == 'boolean' || val == null) return `${val}`;
|
||||
if (type == 'string') return `"${val}"`;
|
||||
if (type == 'symbol') return val.description ? `Symbol(${val.description})` : 'Symbol';
|
||||
if (type == 'function') return 'Function';
|
||||
if (Array.isArray(val)) return `[${val.map(debugString).join(', ')}]`;
|
||||
try {
|
||||
const keys = Object.keys(val);
|
||||
return `{${keys.map(k => `${k}: ${debugString(val[k])}`).join(', ')}}`;
|
||||
} catch (_) { return Object.prototype.toString.call(val); }
|
||||
}
|
||||
|
||||
function handleError(f, args) {
|
||||
try { return f.apply(this, args); }
|
||||
catch (e) { wasm().__wbindgen_export3(addHeapObject(e)); }
|
||||
}
|
||||
|
||||
// ── FinalizationRegistry ──────────────────────────────────────────
|
||||
const FR = typeof FinalizationRegistry !== 'undefined'
|
||||
? FinalizationRegistry
|
||||
: class { register() {} unregister() {} };
|
||||
|
||||
const WasmMultiHeadAttentionFinalization = new FR(ptr => wasm().__wbg_wasmmultiheadattention_free(ptr >>> 0, 1));
|
||||
const WasmFlashAttentionFinalization = new FR(ptr => wasm().__wbg_wasmflashattention_free(ptr >>> 0, 1));
|
||||
const WasmHyperbolicAttentionFinalization = new FR(ptr => wasm().__wbg_wasmhyperbolicattention_free(ptr >>> 0, 1));
|
||||
const WasmMoEAttentionFinalization = new FR(ptr => wasm().__wbg_wasmmoeattention_free(ptr >>> 0, 1));
|
||||
const WasmLinearAttentionFinalization = new FR(ptr => wasm().__wbg_wasmlinearattention_free(ptr >>> 0, 1));
|
||||
const WasmLocalGlobalAttentionFinalization = new FR(ptr => wasm().__wbg_wasmlocalglobalattention_free(ptr >>> 0, 1));
|
||||
|
||||
// ── Classes ───────────────────────────────────────────────────────
|
||||
|
||||
class WasmMultiHeadAttention {
|
||||
constructor(dim, num_heads) {
|
||||
const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
|
||||
try {
|
||||
wasm().wasmmultiheadattention_new(retptr, dim, num_heads);
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
|
||||
if (r2) throw takeObject(r1);
|
||||
this.__wbg_ptr = r0 >>> 0;
|
||||
WasmMultiHeadAttentionFinalization.register(this, this.__wbg_ptr, this);
|
||||
} finally {
|
||||
wasm().__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
free() {
|
||||
const ptr = this.__wbg_ptr; this.__wbg_ptr = 0;
|
||||
WasmMultiHeadAttentionFinalization.unregister(this);
|
||||
wasm().__wbg_wasmmultiheadattention_free(ptr, 0);
|
||||
}
|
||||
get dim() { return wasm().wasmmultiheadattention_dim(this.__wbg_ptr); }
|
||||
get num_heads() { return wasm().wasmmultiheadattention_num_heads(this.__wbg_ptr); }
|
||||
compute(query, keys, values) {
|
||||
const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
|
||||
try {
|
||||
const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm().wasmmultiheadattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values));
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
|
||||
var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
|
||||
if (r3) throw takeObject(r2);
|
||||
var v1 = getArrayF32FromWasm0(r0, r1).slice();
|
||||
wasm().__wbindgen_export4(r0, r1 * 4, 4);
|
||||
return v1;
|
||||
} finally {
|
||||
wasm().__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
class WasmFlashAttention {
|
||||
constructor(dim, block_size) {
|
||||
const ret = wasm().wasmflashattention_new(dim, block_size);
|
||||
this.__wbg_ptr = ret >>> 0;
|
||||
WasmFlashAttentionFinalization.register(this, this.__wbg_ptr, this);
|
||||
}
|
||||
free() {
|
||||
const ptr = this.__wbg_ptr; this.__wbg_ptr = 0;
|
||||
WasmFlashAttentionFinalization.unregister(this);
|
||||
wasm().__wbg_wasmflashattention_free(ptr, 0);
|
||||
}
|
||||
compute(query, keys, values) {
|
||||
const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
|
||||
try {
|
||||
const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm().wasmflashattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values));
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
|
||||
var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
|
||||
if (r3) throw takeObject(r2);
|
||||
var v1 = getArrayF32FromWasm0(r0, r1).slice();
|
||||
wasm().__wbindgen_export4(r0, r1 * 4, 4);
|
||||
return v1;
|
||||
} finally {
|
||||
wasm().__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
class WasmHyperbolicAttention {
|
||||
constructor(dim, curvature) {
|
||||
const ret = wasm().wasmhyperbolicattention_new(dim, curvature);
|
||||
this.__wbg_ptr = ret >>> 0;
|
||||
WasmHyperbolicAttentionFinalization.register(this, this.__wbg_ptr, this);
|
||||
}
|
||||
free() {
|
||||
const ptr = this.__wbg_ptr; this.__wbg_ptr = 0;
|
||||
WasmHyperbolicAttentionFinalization.unregister(this);
|
||||
wasm().__wbg_wasmhyperbolicattention_free(ptr, 0);
|
||||
}
|
||||
get curvature() { return wasm().wasmhyperbolicattention_curvature(this.__wbg_ptr); }
|
||||
compute(query, keys, values) {
|
||||
const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
|
||||
try {
|
||||
const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm().wasmhyperbolicattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values));
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
|
||||
var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
|
||||
if (r3) throw takeObject(r2);
|
||||
var v1 = getArrayF32FromWasm0(r0, r1).slice();
|
||||
wasm().__wbindgen_export4(r0, r1 * 4, 4);
|
||||
return v1;
|
||||
} finally {
|
||||
wasm().__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
class WasmMoEAttention {
|
||||
constructor(dim, num_experts, top_k) {
|
||||
const ret = wasm().wasmmoeattention_new(dim, num_experts, top_k);
|
||||
this.__wbg_ptr = ret >>> 0;
|
||||
WasmMoEAttentionFinalization.register(this, this.__wbg_ptr, this);
|
||||
}
|
||||
free() {
|
||||
const ptr = this.__wbg_ptr; this.__wbg_ptr = 0;
|
||||
WasmMoEAttentionFinalization.unregister(this);
|
||||
wasm().__wbg_wasmmoeattention_free(ptr, 0);
|
||||
}
|
||||
compute(query, keys, values) {
|
||||
const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
|
||||
try {
|
||||
const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm().wasmmoeattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values));
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
|
||||
var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
|
||||
if (r3) throw takeObject(r2);
|
||||
var v1 = getArrayF32FromWasm0(r0, r1).slice();
|
||||
wasm().__wbindgen_export4(r0, r1 * 4, 4);
|
||||
return v1;
|
||||
} finally {
|
||||
wasm().__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
class WasmLinearAttention {
|
||||
constructor(dim, num_features) {
|
||||
const ret = wasm().wasmlinearattention_new(dim, num_features || dim);
|
||||
this.__wbg_ptr = ret >>> 0;
|
||||
WasmLinearAttentionFinalization.register(this, this.__wbg_ptr, this);
|
||||
}
|
||||
free() {
|
||||
const ptr = this.__wbg_ptr; this.__wbg_ptr = 0;
|
||||
WasmLinearAttentionFinalization.unregister(this);
|
||||
wasm().__wbg_wasmlinearattention_free(ptr, 0);
|
||||
}
|
||||
compute(query, keys, values) {
|
||||
const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
|
||||
try {
|
||||
const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm().wasmlinearattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values));
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
|
||||
var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
|
||||
if (r3) throw takeObject(r2);
|
||||
var v1 = getArrayF32FromWasm0(r0, r1).slice();
|
||||
wasm().__wbindgen_export4(r0, r1 * 4, 4);
|
||||
return v1;
|
||||
} finally {
|
||||
wasm().__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
class WasmLocalGlobalAttention {
|
||||
constructor(dim, local_window, global_tokens) {
|
||||
const ret = wasm().wasmlocalglobalattention_new(dim, local_window || 4, global_tokens || 2);
|
||||
this.__wbg_ptr = ret >>> 0;
|
||||
WasmLocalGlobalAttentionFinalization.register(this, this.__wbg_ptr, this);
|
||||
}
|
||||
free() {
|
||||
const ptr = this.__wbg_ptr; this.__wbg_ptr = 0;
|
||||
WasmLocalGlobalAttentionFinalization.unregister(this);
|
||||
wasm().__wbg_wasmlocalglobalattention_free(ptr, 0);
|
||||
}
|
||||
compute(query, keys, values) {
|
||||
const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
|
||||
try {
|
||||
const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm().wasmlocalglobalattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values));
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
|
||||
var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
|
||||
if (r3) throw takeObject(r2);
|
||||
var v1 = getArrayF32FromWasm0(r0, r1).slice();
|
||||
wasm().__wbindgen_export4(r0, r1 * 4, 4);
|
||||
return v1;
|
||||
} finally {
|
||||
wasm().__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ── Standalone functions ──────────────────────────────────────────
|
||||
|
||||
function cosine_similarity(a, b) {
|
||||
const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
|
||||
try {
|
||||
const ptr0 = passArrayF32ToWasm0(a, wasm().__wbindgen_export);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
const ptr1 = passArrayF32ToWasm0(b, wasm().__wbindgen_export);
|
||||
const len1 = WASM_VECTOR_LEN;
|
||||
wasm().cosine_similarity(retptr, ptr0, len0, ptr1, len1);
|
||||
var r0 = getDataViewMemory0().getFloat64(retptr + 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 8, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 12, true);
|
||||
if (r2) throw takeObject(r1);
|
||||
return r0;
|
||||
} finally {
|
||||
wasm().__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
|
||||
function normalize(vec) {
|
||||
const ptr0 = passArrayF32ToWasm0(vec, wasm().__wbindgen_export);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm().normalize(ptr0, len0, addHeapObject(vec));
|
||||
}
|
||||
|
||||
function l2_norm(vec) {
|
||||
const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
|
||||
try {
|
||||
const ptr0 = passArrayF32ToWasm0(vec, wasm().__wbindgen_export);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm().l2_norm(retptr, ptr0, len0);
|
||||
var r0 = getDataViewMemory0().getFloat64(retptr + 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 8, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 12, true);
|
||||
if (r2) throw takeObject(r1);
|
||||
return r0;
|
||||
} finally {
|
||||
wasm().__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
|
||||
function softmax(vec) {
|
||||
const ptr0 = passArrayF32ToWasm0(vec, wasm().__wbindgen_export);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm().softmax(ptr0, len0, addHeapObject(vec));
|
||||
}
|
||||
|
||||
function batch_normalize(vectors, epsilon) {
|
||||
const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
|
||||
try {
|
||||
wasm().batch_normalize(retptr, addHeapObject(vectors), isLikeNone(epsilon) ? 0x100000001 : Math.fround(epsilon));
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
|
||||
var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
|
||||
if (r3) throw takeObject(r2);
|
||||
var v1 = getArrayF32FromWasm0(r0, r1).slice();
|
||||
wasm().__wbindgen_export4(r0, r1 * 4, 4);
|
||||
return v1;
|
||||
} finally {
|
||||
wasm().__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
|
||||
function pairwise_distances(vectors) {
|
||||
const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
|
||||
try {
|
||||
wasm().pairwise_distances(retptr, addHeapObject(vectors));
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
|
||||
var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
|
||||
if (r3) throw takeObject(r2);
|
||||
var v1 = getArrayF32FromWasm0(r0, r1).slice();
|
||||
wasm().__wbindgen_export4(r0, r1 * 4, 4);
|
||||
return v1;
|
||||
} finally {
|
||||
wasm().__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
|
||||
function scaled_dot_attention(query, keys, values, scale) {
|
||||
const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
|
||||
try {
|
||||
const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm().scaled_dot_attention(retptr, ptr0, len0, addHeapObject(keys), addHeapObject(values), isLikeNone(scale) ? 0x100000001 : Math.fround(scale));
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
|
||||
var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
|
||||
if (r3) throw takeObject(r2);
|
||||
var v1 = getArrayF32FromWasm0(r0, r1).slice();
|
||||
wasm().__wbindgen_export4(r0, r1 * 4, 4);
|
||||
return v1;
|
||||
} finally {
|
||||
wasm().__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
|
||||
function attention_weights(scores, temperature) {
|
||||
const ptr0 = passArrayF32ToWasm0(scores, wasm().__wbindgen_export);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm().attention_weights(ptr0, len0, addHeapObject(scores), isLikeNone(temperature) ? 0x100000001 : Math.fround(temperature));
|
||||
}
|
||||
|
||||
function available_mechanisms() {
|
||||
const ret = wasm().available_mechanisms();
|
||||
return takeObject(ret);
|
||||
}
|
||||
|
||||
function random_orthogonal_matrix(dim) {
|
||||
const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
|
||||
try {
|
||||
wasm().random_orthogonal_matrix(retptr, dim);
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
|
||||
var v1 = getArrayF32FromWasm0(r0, r1).slice();
|
||||
wasm().__wbindgen_export4(r0, r1 * 4, 4);
|
||||
return v1;
|
||||
} finally {
|
||||
wasm().__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
|
||||
function rv_init() { wasm().init(); }
|
||||
|
||||
function rv_version() {
|
||||
let d0, d1;
|
||||
const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
|
||||
try {
|
||||
wasm().version(retptr);
|
||||
d0 = getDataViewMemory0().getInt32(retptr + 0, true);
|
||||
d1 = getDataViewMemory0().getInt32(retptr + 4, true);
|
||||
return getStringFromWasm0(d0, d1);
|
||||
} finally {
|
||||
wasm().__wbindgen_add_to_stack_pointer(16);
|
||||
if (d0 !== undefined) wasm().__wbindgen_export4(d0, d1, 1);
|
||||
}
|
||||
}
|
||||
|
||||
// ── Collect exports ───────────────────────────────────────────────
|
||||
exports.WasmMultiHeadAttention = WasmMultiHeadAttention;
|
||||
exports.WasmFlashAttention = WasmFlashAttention;
|
||||
exports.WasmHyperbolicAttention = WasmHyperbolicAttention;
|
||||
exports.WasmMoEAttention = WasmMoEAttention;
|
||||
exports.WasmLinearAttention = WasmLinearAttention;
|
||||
exports.WasmLocalGlobalAttention = WasmLocalGlobalAttention;
|
||||
exports.cosine_similarity = cosine_similarity;
|
||||
exports.normalize = normalize;
|
||||
exports.l2_norm = l2_norm;
|
||||
exports.softmax = softmax;
|
||||
exports.batch_normalize = batch_normalize;
|
||||
exports.pairwise_distances = pairwise_distances;
|
||||
exports.scaled_dot_attention = scaled_dot_attention;
|
||||
exports.attention_weights = attention_weights;
|
||||
exports.available_mechanisms = available_mechanisms;
|
||||
exports.random_orthogonal_matrix = random_orthogonal_matrix;
|
||||
exports.init = rv_init;
|
||||
exports.version = rv_version;
|
||||
|
||||
// ── Build WASM import object ──────────────────────────────────────
|
||||
exports.__wbg_get_imports = function() {
|
||||
const import0 = {
|
||||
__proto__: null,
|
||||
__wbg_Error_4577686b3a6d9b3a: (arg0, arg1) => addHeapObject(Error(getStringFromWasm0(arg0, arg1))),
|
||||
__wbg_String_8564e559799eccda: (arg0, arg1) => {
|
||||
const ret = String(getObject(arg1));
|
||||
const ptr1 = passStringToWasm0(ret, wasm().__wbindgen_export, wasm().__wbindgen_export2);
|
||||
const len1 = WASM_VECTOR_LEN;
|
||||
getDataViewMemory0().setInt32(arg0 + 4, len1, true);
|
||||
getDataViewMemory0().setInt32(arg0, ptr1, true);
|
||||
},
|
||||
__wbg___wbindgen_boolean_get_18c4ed9422296fff: (arg0) => {
|
||||
const v = getObject(arg0);
|
||||
const ret = typeof v === 'boolean' ? v : undefined;
|
||||
return isLikeNone(ret) ? 0xFFFFFF : ret ? 1 : 0;
|
||||
},
|
||||
__wbg___wbindgen_copy_to_typed_array_5294f8e46aecc086: (arg0, arg1, arg2) => {
|
||||
new Uint8Array(getObject(arg2).buffer, getObject(arg2).byteOffset, getObject(arg2).byteLength).set(getArrayU8FromWasm0(arg0, arg1));
|
||||
},
|
||||
__wbg___wbindgen_debug_string_ddde1867f49c2442: (arg0, arg1) => {
|
||||
const ret = debugString(getObject(arg1));
|
||||
const ptr1 = passStringToWasm0(ret, wasm().__wbindgen_export, wasm().__wbindgen_export2);
|
||||
const len1 = WASM_VECTOR_LEN;
|
||||
getDataViewMemory0().setInt32(arg0 + 4, len1, true);
|
||||
getDataViewMemory0().setInt32(arg0, ptr1, true);
|
||||
},
|
||||
__wbg___wbindgen_is_function_d633e708baf0d146: (arg0) => typeof getObject(arg0) === 'function',
|
||||
__wbg___wbindgen_is_object_4b3de556756ee8a8: (arg0) => {
|
||||
const val = getObject(arg0);
|
||||
return typeof val === 'object' && val !== null;
|
||||
},
|
||||
__wbg___wbindgen_jsval_loose_eq_1562ceb9af84e990: (arg0, arg1) => getObject(arg0) == getObject(arg1),
|
||||
__wbg___wbindgen_number_get_5854912275df1894: (arg0, arg1) => {
|
||||
const obj = getObject(arg1);
|
||||
const ret = typeof obj === 'number' ? obj : undefined;
|
||||
getDataViewMemory0().setFloat64(arg0 + 8, isLikeNone(ret) ? 0 : ret, true);
|
||||
getDataViewMemory0().setInt32(arg0, !isLikeNone(ret), true);
|
||||
},
|
||||
__wbg___wbindgen_string_get_3e5751597f39a112: (arg0, arg1) => {
|
||||
const obj = getObject(arg1);
|
||||
const ret = typeof obj === 'string' ? obj : undefined;
|
||||
var ptr1 = isLikeNone(ret) ? 0 : passStringToWasm0(ret, wasm().__wbindgen_export, wasm().__wbindgen_export2);
|
||||
var len1 = WASM_VECTOR_LEN;
|
||||
getDataViewMemory0().setInt32(arg0 + 4, len1, true);
|
||||
getDataViewMemory0().setInt32(arg0, ptr1, true);
|
||||
},
|
||||
__wbg___wbindgen_throw_39bc967c0e5a9b58: (arg0, arg1) => { throw new Error(getStringFromWasm0(arg0, arg1)); },
|
||||
__wbg_call_73af281463ec8b58: function() { return handleError(function(arg0, arg1) {
|
||||
return addHeapObject(getObject(arg0).call(getObject(arg1)));
|
||||
}, arguments); },
|
||||
__wbg_done_5aad55ec6b1954b1: (arg0) => getObject(arg0).done,
|
||||
__wbg_error_a6fa202b58aa1cd3: (arg0, arg1) => {
|
||||
try { console.error(getStringFromWasm0(arg0, arg1)); }
|
||||
finally { wasm().__wbindgen_export4(arg0, arg1, 1); }
|
||||
},
|
||||
__wbg_error_ad28debb48b5c6bb: (arg0) => console.error(getObject(arg0)),
|
||||
__wbg_get_4920fefd3451364b: function() { return handleError(function(arg0, arg1) {
|
||||
return addHeapObject(Reflect.get(getObject(arg0), getObject(arg1)));
|
||||
}, arguments); },
|
||||
__wbg_get_unchecked_3d0f4b91c8eca4f0: (arg0, arg1) => addHeapObject(getObject(arg0)[arg1 >>> 0]),
|
||||
__wbg_instanceof_ArrayBuffer_15859862b80b732d: (arg0) => {
|
||||
try { return getObject(arg0) instanceof ArrayBuffer; } catch (_) { return false; }
|
||||
},
|
||||
__wbg_instanceof_Uint8Array_2240b7046ac16f05: (arg0) => {
|
||||
try { return getObject(arg0) instanceof Uint8Array; } catch (_) { return false; }
|
||||
},
|
||||
__wbg_isArray_fad08a0d12828686: (arg0) => Array.isArray(getObject(arg0)),
|
||||
__wbg_iterator_fc7ad8d33bab9e26: () => addHeapObject(Symbol.iterator),
|
||||
__wbg_length_5855c1f289dfffc1: (arg0) => getObject(arg0).length,
|
||||
__wbg_length_a31e05262e09b7f8: (arg0) => getObject(arg0).length,
|
||||
__wbg_log_3c5e4b64af29e724: (arg0) => console.log(getObject(arg0)),
|
||||
__wbg_new_09959f7b4c92c246: (arg0) => addHeapObject(new Uint8Array(getObject(arg0))),
|
||||
__wbg_new_227d7c05414eb861: () => addHeapObject(new Error()),
|
||||
__wbg_new_cbee8c0d5c479eac: () => addHeapObject(new Array()),
|
||||
__wbg_next_a5fe6f328f7affc2: (arg0) => addHeapObject(getObject(arg0).next),
|
||||
__wbg_next_e592122bb4ed4c67: function() { return handleError(function(arg0) {
|
||||
return addHeapObject(getObject(arg0).next());
|
||||
}, arguments); },
|
||||
__wbg_prototypesetcall_f034d444741426c3: (arg0, arg1, arg2) => {
|
||||
Uint8Array.prototype.set.call(getArrayU8FromWasm0(arg0, arg1), getObject(arg2));
|
||||
},
|
||||
__wbg_random_2b7bed8995d680fb: () => Math.random(),
|
||||
__wbg_set_4c81cfb5dc3a333c: (arg0, arg1, arg2) => { getObject(arg0)[arg1 >>> 0] = takeObject(arg2); },
|
||||
__wbg_stack_3b0d974bbf31e44f: (arg0, arg1) => {
|
||||
const ret = getObject(arg1).stack;
|
||||
const ptr1 = passStringToWasm0(ret, wasm().__wbindgen_export, wasm().__wbindgen_export2);
|
||||
const len1 = WASM_VECTOR_LEN;
|
||||
getDataViewMemory0().setInt32(arg0 + 4, len1, true);
|
||||
getDataViewMemory0().setInt32(arg0, ptr1, true);
|
||||
},
|
||||
__wbg_value_667dcb90597486a6: (arg0) => addHeapObject(getObject(arg0).value),
|
||||
__wbindgen_cast_0000000000000001: (arg0, arg1) => addHeapObject(getStringFromWasm0(arg0, arg1)),
|
||||
__wbindgen_object_drop_ref: (arg0) => takeObject(arg0),
|
||||
};
|
||||
return { __proto__: null, "./ruvector_attention_wasm_bg.js": import0 };
|
||||
};
|
||||
|
||||
})(_mod, () => _wasm);
|
||||
|
||||
|
||||
// ── Async WASM init (fetch-based for browsers) ───────────────────
|
||||
|
||||
export default async function initWasm() {
|
||||
if (_initialized) return;
|
||||
const wasmUrl = new URL('ruvector_attention_wasm_bg.wasm', import.meta.url);
|
||||
const imports = _mod.__wbg_get_imports();
|
||||
let result;
|
||||
if (typeof WebAssembly.instantiateStreaming === 'function') {
|
||||
try {
|
||||
result = await WebAssembly.instantiateStreaming(fetch(wasmUrl), imports);
|
||||
} catch (e) {
|
||||
// Fallback if streaming fails (e.g. wrong MIME type)
|
||||
const bytes = await (await fetch(wasmUrl)).arrayBuffer();
|
||||
result = await WebAssembly.instantiate(bytes, imports);
|
||||
}
|
||||
} else {
|
||||
const bytes = await (await fetch(wasmUrl)).arrayBuffer();
|
||||
result = await WebAssembly.instantiate(bytes, imports);
|
||||
}
|
||||
_wasm = result.instance.exports;
|
||||
_wasm.__wbindgen_start();
|
||||
_initialized = true;
|
||||
}
|
||||
|
||||
// ── ESM re-exports ────────────────────────────────────────────────
|
||||
// Attention mechanism classes
|
||||
export const WasmMultiHeadAttention = _mod.WasmMultiHeadAttention;
|
||||
export const WasmFlashAttention = _mod.WasmFlashAttention;
|
||||
export const WasmHyperbolicAttention = _mod.WasmHyperbolicAttention;
|
||||
export const WasmMoEAttention = _mod.WasmMoEAttention;
|
||||
export const WasmLinearAttention = _mod.WasmLinearAttention;
|
||||
export const WasmLocalGlobalAttention = _mod.WasmLocalGlobalAttention;
|
||||
// Utility functions
|
||||
export const cosine_similarity = _mod.cosine_similarity;
|
||||
export const normalize = _mod.normalize;
|
||||
export const l2_norm = _mod.l2_norm;
|
||||
export const softmax = _mod.softmax;
|
||||
export const batch_normalize = _mod.batch_normalize;
|
||||
export const pairwise_distances = _mod.pairwise_distances;
|
||||
export const scaled_dot_attention = _mod.scaled_dot_attention;
|
||||
export const attention_weights = _mod.attention_weights;
|
||||
export const random_orthogonal_matrix = _mod.random_orthogonal_matrix;
|
||||
export const available_mechanisms = _mod.available_mechanisms;
|
||||
// Lifecycle
|
||||
export const init = _mod.init;
|
||||
export const version = _mod.version;
|
||||
359
ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm.d.ts
vendored
Normal file
359
ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm.d.ts
vendored
Normal file
|
|
@ -0,0 +1,359 @@
|
|||
/* tslint:disable */
|
||||
/* eslint-disable */
|
||||
|
||||
/**
|
||||
* Adam optimizer
|
||||
*/
|
||||
export class WasmAdam {
|
||||
free(): void;
|
||||
[Symbol.dispose](): void;
|
||||
/**
|
||||
* Create a new Adam optimizer
|
||||
*
|
||||
* # Arguments
|
||||
* * `param_count` - Number of parameters
|
||||
* * `learning_rate` - Learning rate
|
||||
*/
|
||||
constructor(param_count: number, learning_rate: number);
|
||||
/**
|
||||
* Reset optimizer state
|
||||
*/
|
||||
reset(): void;
|
||||
/**
|
||||
* Perform optimization step
|
||||
*
|
||||
* # Arguments
|
||||
* * `params` - Current parameter values (will be updated in-place)
|
||||
* * `gradients` - Gradient values
|
||||
*/
|
||||
step(params: Float32Array, gradients: Float32Array): void;
|
||||
/**
|
||||
* Get current learning rate
|
||||
*/
|
||||
learning_rate: number;
|
||||
}
|
||||
|
||||
/**
|
||||
* AdamW optimizer (Adam with decoupled weight decay)
|
||||
*/
|
||||
export class WasmAdamW {
|
||||
free(): void;
|
||||
[Symbol.dispose](): void;
|
||||
/**
|
||||
* Create a new AdamW optimizer
|
||||
*
|
||||
* # Arguments
|
||||
* * `param_count` - Number of parameters
|
||||
* * `learning_rate` - Learning rate
|
||||
* * `weight_decay` - Weight decay coefficient
|
||||
*/
|
||||
constructor(param_count: number, learning_rate: number, weight_decay: number);
|
||||
/**
|
||||
* Reset optimizer state
|
||||
*/
|
||||
reset(): void;
|
||||
/**
|
||||
* Perform optimization step with weight decay
|
||||
*/
|
||||
step(params: Float32Array, gradients: Float32Array): void;
|
||||
/**
|
||||
* Get current learning rate
|
||||
*/
|
||||
learning_rate: number;
|
||||
/**
|
||||
* Get weight decay
|
||||
*/
|
||||
readonly weight_decay: number;
|
||||
}
|
||||
|
||||
/**
|
||||
* Flash attention mechanism
|
||||
*/
|
||||
export class WasmFlashAttention {
|
||||
free(): void;
|
||||
[Symbol.dispose](): void;
|
||||
/**
|
||||
* Compute flash attention
|
||||
*/
|
||||
compute(query: Float32Array, keys: any, values: any): Float32Array;
|
||||
/**
|
||||
* Create a new flash attention instance
|
||||
*
|
||||
* # Arguments
|
||||
* * `dim` - Embedding dimension
|
||||
* * `block_size` - Block size for tiling
|
||||
*/
|
||||
constructor(dim: number, block_size: number);
|
||||
}
|
||||
|
||||
/**
|
||||
* Hyperbolic attention mechanism
|
||||
*/
|
||||
export class WasmHyperbolicAttention {
|
||||
free(): void;
|
||||
[Symbol.dispose](): void;
|
||||
/**
|
||||
* Compute hyperbolic attention
|
||||
*/
|
||||
compute(query: Float32Array, keys: any, values: any): Float32Array;
|
||||
/**
|
||||
* Create a new hyperbolic attention instance
|
||||
*
|
||||
* # Arguments
|
||||
* * `dim` - Embedding dimension
|
||||
* * `curvature` - Hyperbolic curvature parameter
|
||||
*/
|
||||
constructor(dim: number, curvature: number);
|
||||
/**
|
||||
* Get the curvature
|
||||
*/
|
||||
readonly curvature: number;
|
||||
}
|
||||
|
||||
/**
|
||||
* InfoNCE contrastive loss for training
|
||||
*/
|
||||
export class WasmInfoNCELoss {
|
||||
free(): void;
|
||||
[Symbol.dispose](): void;
|
||||
/**
|
||||
* Compute InfoNCE loss
|
||||
*
|
||||
* # Arguments
|
||||
* * `anchor` - Anchor embedding
|
||||
* * `positive` - Positive example embedding
|
||||
* * `negatives` - Array of negative example embeddings
|
||||
*/
|
||||
compute(anchor: Float32Array, positive: Float32Array, negatives: any): number;
|
||||
/**
|
||||
* Create a new InfoNCE loss instance
|
||||
*
|
||||
* # Arguments
|
||||
* * `temperature` - Temperature parameter for softmax
|
||||
*/
|
||||
constructor(temperature: number);
|
||||
}
|
||||
|
||||
/**
|
||||
* Learning rate scheduler
|
||||
*/
|
||||
export class WasmLRScheduler {
|
||||
free(): void;
|
||||
[Symbol.dispose](): void;
|
||||
/**
|
||||
* Get learning rate for current step
|
||||
*/
|
||||
get_lr(): number;
|
||||
/**
|
||||
* Create a new learning rate scheduler with warmup and cosine decay
|
||||
*
|
||||
* # Arguments
|
||||
* * `initial_lr` - Initial learning rate
|
||||
* * `warmup_steps` - Number of warmup steps
|
||||
* * `total_steps` - Total training steps
|
||||
*/
|
||||
constructor(initial_lr: number, warmup_steps: number, total_steps: number);
|
||||
/**
|
||||
* Reset scheduler
|
||||
*/
|
||||
reset(): void;
|
||||
/**
|
||||
* Advance to next step
|
||||
*/
|
||||
step(): void;
|
||||
}
|
||||
|
||||
/**
|
||||
* Linear attention (Performer-style)
|
||||
*/
|
||||
export class WasmLinearAttention {
|
||||
free(): void;
|
||||
[Symbol.dispose](): void;
|
||||
/**
|
||||
* Compute linear attention
|
||||
*/
|
||||
compute(query: Float32Array, keys: any, values: any): Float32Array;
|
||||
/**
|
||||
* Create a new linear attention instance
|
||||
*
|
||||
* # Arguments
|
||||
* * `dim` - Embedding dimension
|
||||
* * `num_features` - Number of random features
|
||||
*/
|
||||
constructor(dim: number, num_features: number);
|
||||
}
|
||||
|
||||
/**
|
||||
* Local-global attention mechanism
|
||||
*/
|
||||
export class WasmLocalGlobalAttention {
|
||||
free(): void;
|
||||
[Symbol.dispose](): void;
|
||||
/**
|
||||
* Compute local-global attention
|
||||
*/
|
||||
compute(query: Float32Array, keys: any, values: any): Float32Array;
|
||||
/**
|
||||
* Create a new local-global attention instance
|
||||
*
|
||||
* # Arguments
|
||||
* * `dim` - Embedding dimension
|
||||
* * `local_window` - Size of local attention window
|
||||
* * `global_tokens` - Number of global attention tokens
|
||||
*/
|
||||
constructor(dim: number, local_window: number, global_tokens: number);
|
||||
}
|
||||
|
||||
/**
|
||||
* Mixture of Experts (MoE) attention
|
||||
*/
|
||||
export class WasmMoEAttention {
|
||||
free(): void;
|
||||
[Symbol.dispose](): void;
|
||||
/**
|
||||
* Compute MoE attention
|
||||
*/
|
||||
compute(query: Float32Array, keys: any, values: any): Float32Array;
|
||||
/**
|
||||
* Create a new MoE attention instance
|
||||
*
|
||||
* # Arguments
|
||||
* * `dim` - Embedding dimension
|
||||
* * `num_experts` - Number of expert attention mechanisms
|
||||
* * `top_k` - Number of experts to use per query
|
||||
*/
|
||||
constructor(dim: number, num_experts: number, top_k: number);
|
||||
}
|
||||
|
||||
/**
|
||||
* Multi-head attention mechanism
|
||||
*/
|
||||
export class WasmMultiHeadAttention {
|
||||
free(): void;
|
||||
[Symbol.dispose](): void;
|
||||
/**
|
||||
* Compute multi-head attention
|
||||
*/
|
||||
compute(query: Float32Array, keys: any, values: any): Float32Array;
|
||||
/**
|
||||
* Create a new multi-head attention instance
|
||||
*
|
||||
* # Arguments
|
||||
* * `dim` - Embedding dimension
|
||||
* * `num_heads` - Number of attention heads
|
||||
*/
|
||||
constructor(dim: number, num_heads: number);
|
||||
/**
|
||||
* Get the dimension
|
||||
*/
|
||||
readonly dim: number;
|
||||
/**
|
||||
* Get the number of heads
|
||||
*/
|
||||
readonly num_heads: number;
|
||||
}
|
||||
|
||||
/**
|
||||
* SGD optimizer with momentum
|
||||
*/
|
||||
export class WasmSGD {
|
||||
free(): void;
|
||||
[Symbol.dispose](): void;
|
||||
/**
|
||||
* Create a new SGD optimizer
|
||||
*
|
||||
* # Arguments
|
||||
* * `param_count` - Number of parameters
|
||||
* * `learning_rate` - Learning rate
|
||||
* * `momentum` - Momentum coefficient (default: 0)
|
||||
*/
|
||||
constructor(param_count: number, learning_rate: number, momentum?: number | null);
|
||||
/**
|
||||
* Reset optimizer state
|
||||
*/
|
||||
reset(): void;
|
||||
/**
|
||||
* Perform optimization step
|
||||
*/
|
||||
step(params: Float32Array, gradients: Float32Array): void;
|
||||
/**
|
||||
* Get current learning rate
|
||||
*/
|
||||
learning_rate: number;
|
||||
}
|
||||
|
||||
/**
|
||||
* Compute attention weights from scores
|
||||
*/
|
||||
export function attention_weights(scores: Float32Array, temperature?: number | null): void;
|
||||
|
||||
/**
|
||||
* Get information about available attention mechanisms
|
||||
*/
|
||||
export function available_mechanisms(): any;
|
||||
|
||||
/**
|
||||
* Batch normalize vectors
|
||||
*/
|
||||
export function batch_normalize(vectors: any, epsilon?: number | null): Float32Array;
|
||||
|
||||
/**
|
||||
* Compute cosine similarity between two vectors
|
||||
*/
|
||||
export function cosine_similarity(a: Float32Array, b: Float32Array): number;
|
||||
|
||||
/**
|
||||
* Initialize the WASM module with panic hook
|
||||
*/
|
||||
export function init(): void;
|
||||
|
||||
/**
|
||||
* Compute L2 norm of a vector
|
||||
*/
|
||||
export function l2_norm(vec: Float32Array): number;
|
||||
|
||||
/**
|
||||
* Log a message to the browser console
|
||||
*/
|
||||
export function log(message: string): void;
|
||||
|
||||
/**
|
||||
* Log an error to the browser console
|
||||
*/
|
||||
export function log_error(message: string): void;
|
||||
|
||||
/**
|
||||
* Normalize a vector to unit length
|
||||
*/
|
||||
export function normalize(vec: Float32Array): void;
|
||||
|
||||
/**
|
||||
* Compute pairwise distances between vectors
|
||||
*/
|
||||
export function pairwise_distances(vectors: any): Float32Array;
|
||||
|
||||
/**
|
||||
* Generate random orthogonal matrix (for initialization)
|
||||
*/
|
||||
export function random_orthogonal_matrix(dim: number): Float32Array;
|
||||
|
||||
/**
|
||||
* Compute scaled dot-product attention
|
||||
*
|
||||
* # Arguments
|
||||
* * `query` - Query vector as Float32Array
|
||||
* * `keys` - Array of key vectors
|
||||
* * `values` - Array of value vectors
|
||||
* * `scale` - Optional scaling factor (defaults to 1/sqrt(dim))
|
||||
*/
|
||||
export function scaled_dot_attention(query: Float32Array, keys: any, values: any, scale?: number | null): Float32Array;
|
||||
|
||||
/**
|
||||
* Compute softmax of a vector
|
||||
*/
|
||||
export function softmax(vec: Float32Array): void;
|
||||
|
||||
/**
|
||||
* Get the version of the ruvector-attention-wasm crate
|
||||
*/
|
||||
export function version(): string;
|
||||
1417
ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm.js
Normal file
1417
ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm.js
Normal file
File diff suppressed because it is too large
Load diff
Binary file not shown.
71
ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm_bg.wasm.d.ts
vendored
Normal file
71
ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm_bg.wasm.d.ts
vendored
Normal file
|
|
@ -0,0 +1,71 @@
|
|||
/* tslint:disable */
|
||||
/* eslint-disable */
|
||||
export const memory: WebAssembly.Memory;
|
||||
export const __wbg_wasmadam_free: (a: number, b: number) => void;
|
||||
export const __wbg_wasmadamw_free: (a: number, b: number) => void;
|
||||
export const __wbg_wasmflashattention_free: (a: number, b: number) => void;
|
||||
export const __wbg_wasmhyperbolicattention_free: (a: number, b: number) => void;
|
||||
export const __wbg_wasminfonceloss_free: (a: number, b: number) => void;
|
||||
export const __wbg_wasmlinearattention_free: (a: number, b: number) => void;
|
||||
export const __wbg_wasmmoeattention_free: (a: number, b: number) => void;
|
||||
export const __wbg_wasmmultiheadattention_free: (a: number, b: number) => void;
|
||||
export const __wbg_wasmsgd_free: (a: number, b: number) => void;
|
||||
export const attention_weights: (a: number, b: number, c: number, d: number) => void;
|
||||
export const available_mechanisms: () => number;
|
||||
export const batch_normalize: (a: number, b: number, c: number) => void;
|
||||
export const cosine_similarity: (a: number, b: number, c: number, d: number, e: number) => void;
|
||||
export const l2_norm: (a: number, b: number) => number;
|
||||
export const log: (a: number, b: number) => void;
|
||||
export const log_error: (a: number, b: number) => void;
|
||||
export const normalize: (a: number, b: number, c: number, d: number) => void;
|
||||
export const pairwise_distances: (a: number, b: number) => void;
|
||||
export const random_orthogonal_matrix: (a: number, b: number) => void;
|
||||
export const scaled_dot_attention: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
|
||||
export const softmax: (a: number, b: number, c: number) => void;
|
||||
export const version: (a: number) => void;
|
||||
export const wasmadam_learning_rate: (a: number) => number;
|
||||
export const wasmadam_new: (a: number, b: number) => number;
|
||||
export const wasmadam_reset: (a: number) => void;
|
||||
export const wasmadam_set_learning_rate: (a: number, b: number) => void;
|
||||
export const wasmadam_step: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
|
||||
export const wasmadamw_new: (a: number, b: number, c: number) => number;
|
||||
export const wasmadamw_reset: (a: number) => void;
|
||||
export const wasmadamw_step: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
|
||||
export const wasmadamw_weight_decay: (a: number) => number;
|
||||
export const wasmflashattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
|
||||
export const wasmflashattention_new: (a: number, b: number) => number;
|
||||
export const wasmhyperbolicattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
|
||||
export const wasmhyperbolicattention_curvature: (a: number) => number;
|
||||
export const wasmhyperbolicattention_new: (a: number, b: number) => number;
|
||||
export const wasminfonceloss_compute: (a: number, b: number, c: number, d: number, e: number, f: number, g: number) => void;
|
||||
export const wasminfonceloss_new: (a: number) => number;
|
||||
export const wasmlinearattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
|
||||
export const wasmlinearattention_new: (a: number, b: number) => number;
|
||||
export const wasmlocalglobalattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
|
||||
export const wasmlocalglobalattention_new: (a: number, b: number, c: number) => number;
|
||||
export const wasmlrscheduler_get_lr: (a: number) => number;
|
||||
export const wasmlrscheduler_new: (a: number, b: number, c: number) => number;
|
||||
export const wasmlrscheduler_reset: (a: number) => void;
|
||||
export const wasmlrscheduler_step: (a: number) => void;
|
||||
export const wasmmoeattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
|
||||
export const wasmmoeattention_new: (a: number, b: number, c: number) => number;
|
||||
export const wasmmultiheadattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
|
||||
export const wasmmultiheadattention_dim: (a: number) => number;
|
||||
export const wasmmultiheadattention_new: (a: number, b: number, c: number) => void;
|
||||
export const wasmmultiheadattention_num_heads: (a: number) => number;
|
||||
export const wasmsgd_learning_rate: (a: number) => number;
|
||||
export const wasmsgd_new: (a: number, b: number, c: number) => number;
|
||||
export const wasmsgd_reset: (a: number) => void;
|
||||
export const wasmsgd_set_learning_rate: (a: number, b: number) => void;
|
||||
export const wasmsgd_step: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
|
||||
export const init: () => void;
|
||||
export const wasmadamw_set_learning_rate: (a: number, b: number) => void;
|
||||
export const wasmadamw_learning_rate: (a: number) => number;
|
||||
export const __wbg_wasmlocalglobalattention_free: (a: number, b: number) => void;
|
||||
export const __wbg_wasmlrscheduler_free: (a: number, b: number) => void;
|
||||
export const __wbindgen_export: (a: number, b: number) => number;
|
||||
export const __wbindgen_export2: (a: number, b: number, c: number, d: number) => number;
|
||||
export const __wbindgen_export3: (a: number) => void;
|
||||
export const __wbindgen_export4: (a: number, b: number, c: number) => void;
|
||||
export const __wbindgen_add_to_stack_pointer: (a: number) => number;
|
||||
export const __wbindgen_start: () => void;
|
||||
26
ui/pose-fusion/pkg/ruvector_cnn_wasm/package.json
Normal file
26
ui/pose-fusion/pkg/ruvector_cnn_wasm/package.json
Normal file
|
|
@ -0,0 +1,26 @@
|
|||
{
|
||||
"name": "ruvector-cnn-wasm",
|
||||
"type": "module",
|
||||
"description": "WASM bindings for ruvector-cnn - CNN feature extraction for image embeddings",
|
||||
"version": "0.1.0",
|
||||
"license": "MIT OR Apache-2.0",
|
||||
"repository": {
|
||||
"type": "git",
|
||||
"url": "https://github.com/ruvnet/ruvector"
|
||||
},
|
||||
"files": [
|
||||
"ruvector_cnn_wasm_bg.wasm",
|
||||
"ruvector_cnn_wasm.js"
|
||||
],
|
||||
"main": "ruvector_cnn_wasm.js",
|
||||
"sideEffects": [
|
||||
"./snippets/*"
|
||||
],
|
||||
"keywords": [
|
||||
"cnn",
|
||||
"embeddings",
|
||||
"wasm",
|
||||
"simd",
|
||||
"machine-learning"
|
||||
]
|
||||
}
|
||||
802
ui/pose-fusion/pkg/ruvector_cnn_wasm/ruvector_cnn_wasm.js
Normal file
802
ui/pose-fusion/pkg/ruvector_cnn_wasm/ruvector_cnn_wasm.js
Normal file
|
|
@ -0,0 +1,802 @@
|
|||
/**
|
||||
* Configuration for CNN embedder
|
||||
*/
|
||||
export class EmbedderConfig {
|
||||
__destroy_into_raw() {
|
||||
const ptr = this.__wbg_ptr;
|
||||
this.__wbg_ptr = 0;
|
||||
EmbedderConfigFinalization.unregister(this);
|
||||
return ptr;
|
||||
}
|
||||
free() {
|
||||
const ptr = this.__destroy_into_raw();
|
||||
wasm.__wbg_embedderconfig_free(ptr, 0);
|
||||
}
|
||||
constructor() {
|
||||
const ret = wasm.embedderconfig_new();
|
||||
this.__wbg_ptr = ret >>> 0;
|
||||
EmbedderConfigFinalization.register(this, this.__wbg_ptr, this);
|
||||
return this;
|
||||
}
|
||||
/**
|
||||
* Output embedding dimension
|
||||
* @returns {number}
|
||||
*/
|
||||
get embedding_dim() {
|
||||
const ret = wasm.__wbg_get_embedderconfig_embedding_dim(this.__wbg_ptr);
|
||||
return ret >>> 0;
|
||||
}
|
||||
/**
|
||||
* Input image size (square)
|
||||
* @returns {number}
|
||||
*/
|
||||
get input_size() {
|
||||
const ret = wasm.__wbg_get_embedderconfig_input_size(this.__wbg_ptr);
|
||||
return ret >>> 0;
|
||||
}
|
||||
/**
|
||||
* Whether to L2 normalize embeddings
|
||||
* @returns {boolean}
|
||||
*/
|
||||
get normalize() {
|
||||
const ret = wasm.__wbg_get_embedderconfig_normalize(this.__wbg_ptr);
|
||||
return ret !== 0;
|
||||
}
|
||||
/**
|
||||
* Output embedding dimension
|
||||
* @param {number} arg0
|
||||
*/
|
||||
set embedding_dim(arg0) {
|
||||
wasm.__wbg_set_embedderconfig_embedding_dim(this.__wbg_ptr, arg0);
|
||||
}
|
||||
/**
|
||||
* Input image size (square)
|
||||
* @param {number} arg0
|
||||
*/
|
||||
set input_size(arg0) {
|
||||
wasm.__wbg_set_embedderconfig_input_size(this.__wbg_ptr, arg0);
|
||||
}
|
||||
/**
|
||||
* Whether to L2 normalize embeddings
|
||||
* @param {boolean} arg0
|
||||
*/
|
||||
set normalize(arg0) {
|
||||
wasm.__wbg_set_embedderconfig_normalize(this.__wbg_ptr, arg0);
|
||||
}
|
||||
}
|
||||
if (Symbol.dispose) EmbedderConfig.prototype[Symbol.dispose] = EmbedderConfig.prototype.free;
|
||||
|
||||
/**
|
||||
* Layer operations for building custom networks
|
||||
*/
|
||||
export class LayerOps {
|
||||
__destroy_into_raw() {
|
||||
const ptr = this.__wbg_ptr;
|
||||
this.__wbg_ptr = 0;
|
||||
LayerOpsFinalization.unregister(this);
|
||||
return ptr;
|
||||
}
|
||||
free() {
|
||||
const ptr = this.__destroy_into_raw();
|
||||
wasm.__wbg_layerops_free(ptr, 0);
|
||||
}
|
||||
/**
|
||||
* Apply batch normalization (returns new array)
|
||||
* @param {Float32Array} input
|
||||
* @param {Float32Array} gamma
|
||||
* @param {Float32Array} beta
|
||||
* @param {Float32Array} mean
|
||||
* @param {Float32Array} _var
|
||||
* @param {number} epsilon
|
||||
* @returns {Float32Array}
|
||||
*/
|
||||
static batch_norm(input, gamma, beta, mean, _var, epsilon) {
|
||||
try {
|
||||
const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
|
||||
const ptr0 = passArrayF32ToWasm0(input, wasm.__wbindgen_export2);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
const ptr1 = passArrayF32ToWasm0(gamma, wasm.__wbindgen_export2);
|
||||
const len1 = WASM_VECTOR_LEN;
|
||||
const ptr2 = passArrayF32ToWasm0(beta, wasm.__wbindgen_export2);
|
||||
const len2 = WASM_VECTOR_LEN;
|
||||
const ptr3 = passArrayF32ToWasm0(mean, wasm.__wbindgen_export2);
|
||||
const len3 = WASM_VECTOR_LEN;
|
||||
const ptr4 = passArrayF32ToWasm0(_var, wasm.__wbindgen_export2);
|
||||
const len4 = WASM_VECTOR_LEN;
|
||||
wasm.layerops_batch_norm(retptr, ptr0, len0, ptr1, len1, ptr2, len2, ptr3, len3, ptr4, len4, epsilon);
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
|
||||
var v6 = getArrayF32FromWasm0(r0, r1).slice();
|
||||
wasm.__wbindgen_export(r0, r1 * 4, 4);
|
||||
return v6;
|
||||
} finally {
|
||||
wasm.__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
/**
|
||||
* Apply global average pooling
|
||||
* Returns one value per channel
|
||||
* @param {Float32Array} input
|
||||
* @param {number} height
|
||||
* @param {number} width
|
||||
* @param {number} channels
|
||||
* @returns {Float32Array}
|
||||
*/
|
||||
static global_avg_pool(input, height, width, channels) {
|
||||
try {
|
||||
const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
|
||||
const ptr0 = passArrayF32ToWasm0(input, wasm.__wbindgen_export2);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm.layerops_global_avg_pool(retptr, ptr0, len0, height, width, channels);
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
|
||||
var v2 = getArrayF32FromWasm0(r0, r1).slice();
|
||||
wasm.__wbindgen_export(r0, r1 * 4, 4);
|
||||
return v2;
|
||||
} finally {
|
||||
wasm.__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
}
|
||||
if (Symbol.dispose) LayerOps.prototype[Symbol.dispose] = LayerOps.prototype.free;
|
||||
|
||||
/**
|
||||
* SIMD-optimized operations
|
||||
*/
|
||||
export class SimdOps {
|
||||
__destroy_into_raw() {
|
||||
const ptr = this.__wbg_ptr;
|
||||
this.__wbg_ptr = 0;
|
||||
SimdOpsFinalization.unregister(this);
|
||||
return ptr;
|
||||
}
|
||||
free() {
|
||||
const ptr = this.__destroy_into_raw();
|
||||
wasm.__wbg_simdops_free(ptr, 0);
|
||||
}
|
||||
/**
|
||||
* Dot product of two vectors
|
||||
* @param {Float32Array} a
|
||||
* @param {Float32Array} b
|
||||
* @returns {number}
|
||||
*/
|
||||
static dot_product(a, b) {
|
||||
const ptr0 = passArrayF32ToWasm0(a, wasm.__wbindgen_export2);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
const ptr1 = passArrayF32ToWasm0(b, wasm.__wbindgen_export2);
|
||||
const len1 = WASM_VECTOR_LEN;
|
||||
const ret = wasm.simdops_dot_product(ptr0, len0, ptr1, len1);
|
||||
return ret;
|
||||
}
|
||||
/**
|
||||
* L2 normalize a vector (returns new array)
|
||||
* @param {Float32Array} data
|
||||
* @returns {Float32Array}
|
||||
*/
|
||||
static l2_normalize(data) {
|
||||
try {
|
||||
const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
|
||||
const ptr0 = passArrayF32ToWasm0(data, wasm.__wbindgen_export2);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm.simdops_l2_normalize(retptr, ptr0, len0);
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
|
||||
var v2 = getArrayF32FromWasm0(r0, r1).slice();
|
||||
wasm.__wbindgen_export(r0, r1 * 4, 4);
|
||||
return v2;
|
||||
} finally {
|
||||
wasm.__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
/**
|
||||
* ReLU activation (returns new array)
|
||||
* @param {Float32Array} data
|
||||
* @returns {Float32Array}
|
||||
*/
|
||||
static relu(data) {
|
||||
try {
|
||||
const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
|
||||
const ptr0 = passArrayF32ToWasm0(data, wasm.__wbindgen_export2);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm.simdops_relu(retptr, ptr0, len0);
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
|
||||
var v2 = getArrayF32FromWasm0(r0, r1).slice();
|
||||
wasm.__wbindgen_export(r0, r1 * 4, 4);
|
||||
return v2;
|
||||
} finally {
|
||||
wasm.__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
/**
|
||||
* ReLU6 activation (returns new array)
|
||||
* @param {Float32Array} data
|
||||
* @returns {Float32Array}
|
||||
*/
|
||||
static relu6(data) {
|
||||
try {
|
||||
const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
|
||||
const ptr0 = passArrayF32ToWasm0(data, wasm.__wbindgen_export2);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm.simdops_relu6(retptr, ptr0, len0);
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
|
||||
var v2 = getArrayF32FromWasm0(r0, r1).slice();
|
||||
wasm.__wbindgen_export(r0, r1 * 4, 4);
|
||||
return v2;
|
||||
} finally {
|
||||
wasm.__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
}
|
||||
if (Symbol.dispose) SimdOps.prototype[Symbol.dispose] = SimdOps.prototype.free;
|
||||
|
||||
/**
|
||||
* WASM CNN Embedder for image feature extraction
|
||||
*/
|
||||
export class WasmCnnEmbedder {
|
||||
__destroy_into_raw() {
|
||||
const ptr = this.__wbg_ptr;
|
||||
this.__wbg_ptr = 0;
|
||||
WasmCnnEmbedderFinalization.unregister(this);
|
||||
return ptr;
|
||||
}
|
||||
free() {
|
||||
const ptr = this.__destroy_into_raw();
|
||||
wasm.__wbg_wasmcnnembedder_free(ptr, 0);
|
||||
}
|
||||
/**
|
||||
* Compute cosine similarity between two embeddings
|
||||
* @param {Float32Array} a
|
||||
* @param {Float32Array} b
|
||||
* @returns {number}
|
||||
*/
|
||||
cosine_similarity(a, b) {
|
||||
try {
|
||||
const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
|
||||
const ptr0 = passArrayF32ToWasm0(a, wasm.__wbindgen_export2);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
const ptr1 = passArrayF32ToWasm0(b, wasm.__wbindgen_export2);
|
||||
const len1 = WASM_VECTOR_LEN;
|
||||
wasm.wasmcnnembedder_cosine_similarity(retptr, this.__wbg_ptr, ptr0, len0, ptr1, len1);
|
||||
var r0 = getDataViewMemory0().getFloat32(retptr + 4 * 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true);
|
||||
if (r2) {
|
||||
throw takeObject(r1);
|
||||
}
|
||||
return r0;
|
||||
} finally {
|
||||
wasm.__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
/**
|
||||
* Get the embedding dimension
|
||||
* @returns {number}
|
||||
*/
|
||||
get embedding_dim() {
|
||||
const ret = wasm.wasmcnnembedder_embedding_dim(this.__wbg_ptr);
|
||||
return ret >>> 0;
|
||||
}
|
||||
/**
|
||||
* Extract embedding from image data (RGB format, row-major)
|
||||
* @param {Uint8Array} image_data
|
||||
* @param {number} width
|
||||
* @param {number} height
|
||||
* @returns {Float32Array}
|
||||
*/
|
||||
extract(image_data, width, height) {
|
||||
try {
|
||||
const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
|
||||
const ptr0 = passArray8ToWasm0(image_data, wasm.__wbindgen_export2);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm.wasmcnnembedder_extract(retptr, this.__wbg_ptr, ptr0, len0, width, height);
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true);
|
||||
var r3 = getDataViewMemory0().getInt32(retptr + 4 * 3, true);
|
||||
if (r3) {
|
||||
throw takeObject(r2);
|
||||
}
|
||||
var v2 = getArrayF32FromWasm0(r0, r1).slice();
|
||||
wasm.__wbindgen_export(r0, r1 * 4, 4);
|
||||
return v2;
|
||||
} finally {
|
||||
wasm.__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
/**
|
||||
* Create a new CNN embedder
|
||||
* @param {EmbedderConfig | null} [config]
|
||||
*/
|
||||
constructor(config) {
|
||||
try {
|
||||
const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
|
||||
let ptr0 = 0;
|
||||
if (!isLikeNone(config)) {
|
||||
_assertClass(config, EmbedderConfig);
|
||||
ptr0 = config.__destroy_into_raw();
|
||||
}
|
||||
wasm.wasmcnnembedder_new(retptr, ptr0);
|
||||
var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true);
|
||||
if (r2) {
|
||||
throw takeObject(r1);
|
||||
}
|
||||
this.__wbg_ptr = r0 >>> 0;
|
||||
WasmCnnEmbedderFinalization.register(this, this.__wbg_ptr, this);
|
||||
return this;
|
||||
} finally {
|
||||
wasm.__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
}
|
||||
if (Symbol.dispose) WasmCnnEmbedder.prototype[Symbol.dispose] = WasmCnnEmbedder.prototype.free;
|
||||
|
||||
/**
|
||||
* InfoNCE loss for contrastive learning (SimCLR style)
|
||||
*/
|
||||
export class WasmInfoNCELoss {
|
||||
__destroy_into_raw() {
|
||||
const ptr = this.__wbg_ptr;
|
||||
this.__wbg_ptr = 0;
|
||||
WasmInfoNCELossFinalization.unregister(this);
|
||||
return ptr;
|
||||
}
|
||||
free() {
|
||||
const ptr = this.__destroy_into_raw();
|
||||
wasm.__wbg_wasminfonceloss_free(ptr, 0);
|
||||
}
|
||||
/**
|
||||
* Compute loss for a batch of embedding pairs
|
||||
* embeddings: [2N, D] flattened where (i, i+N) are positive pairs
|
||||
* @param {Float32Array} embeddings
|
||||
* @param {number} batch_size
|
||||
* @param {number} dim
|
||||
* @returns {number}
|
||||
*/
|
||||
forward(embeddings, batch_size, dim) {
|
||||
try {
|
||||
const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
|
||||
const ptr0 = passArrayF32ToWasm0(embeddings, wasm.__wbindgen_export2);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
wasm.wasminfonceloss_forward(retptr, this.__wbg_ptr, ptr0, len0, batch_size, dim);
|
||||
var r0 = getDataViewMemory0().getFloat32(retptr + 4 * 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true);
|
||||
if (r2) {
|
||||
throw takeObject(r1);
|
||||
}
|
||||
return r0;
|
||||
} finally {
|
||||
wasm.__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
/**
|
||||
* Create new InfoNCE loss with temperature parameter
|
||||
* @param {number} temperature
|
||||
*/
|
||||
constructor(temperature) {
|
||||
const ret = wasm.wasminfonceloss_new(temperature);
|
||||
this.__wbg_ptr = ret >>> 0;
|
||||
WasmInfoNCELossFinalization.register(this, this.__wbg_ptr, this);
|
||||
return this;
|
||||
}
|
||||
/**
|
||||
* Get the temperature parameter
|
||||
* @returns {number}
|
||||
*/
|
||||
get temperature() {
|
||||
const ret = wasm.wasminfonceloss_temperature(this.__wbg_ptr);
|
||||
return ret;
|
||||
}
|
||||
}
|
||||
if (Symbol.dispose) WasmInfoNCELoss.prototype[Symbol.dispose] = WasmInfoNCELoss.prototype.free;
|
||||
|
||||
/**
|
||||
* Triplet loss for metric learning
|
||||
*/
|
||||
export class WasmTripletLoss {
|
||||
__destroy_into_raw() {
|
||||
const ptr = this.__wbg_ptr;
|
||||
this.__wbg_ptr = 0;
|
||||
WasmTripletLossFinalization.unregister(this);
|
||||
return ptr;
|
||||
}
|
||||
free() {
|
||||
const ptr = this.__destroy_into_raw();
|
||||
wasm.__wbg_wasmtripletloss_free(ptr, 0);
|
||||
}
|
||||
/**
|
||||
* Compute loss for a batch of triplets
|
||||
* @param {Float32Array} anchors
|
||||
* @param {Float32Array} positives
|
||||
* @param {Float32Array} negatives
|
||||
* @param {number} dim
|
||||
* @returns {number}
|
||||
*/
|
||||
forward(anchors, positives, negatives, dim) {
|
||||
try {
|
||||
const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
|
||||
const ptr0 = passArrayF32ToWasm0(anchors, wasm.__wbindgen_export2);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
const ptr1 = passArrayF32ToWasm0(positives, wasm.__wbindgen_export2);
|
||||
const len1 = WASM_VECTOR_LEN;
|
||||
const ptr2 = passArrayF32ToWasm0(negatives, wasm.__wbindgen_export2);
|
||||
const len2 = WASM_VECTOR_LEN;
|
||||
wasm.wasmtripletloss_forward(retptr, this.__wbg_ptr, ptr0, len0, ptr1, len1, ptr2, len2, dim);
|
||||
var r0 = getDataViewMemory0().getFloat32(retptr + 4 * 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true);
|
||||
if (r2) {
|
||||
throw takeObject(r1);
|
||||
}
|
||||
return r0;
|
||||
} finally {
|
||||
wasm.__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
/**
|
||||
* Compute loss for a single triplet
|
||||
* @param {Float32Array} anchor
|
||||
* @param {Float32Array} positive
|
||||
* @param {Float32Array} negative
|
||||
* @returns {number}
|
||||
*/
|
||||
forward_single(anchor, positive, negative) {
|
||||
try {
|
||||
const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
|
||||
const ptr0 = passArrayF32ToWasm0(anchor, wasm.__wbindgen_export2);
|
||||
const len0 = WASM_VECTOR_LEN;
|
||||
const ptr1 = passArrayF32ToWasm0(positive, wasm.__wbindgen_export2);
|
||||
const len1 = WASM_VECTOR_LEN;
|
||||
const ptr2 = passArrayF32ToWasm0(negative, wasm.__wbindgen_export2);
|
||||
const len2 = WASM_VECTOR_LEN;
|
||||
wasm.wasmtripletloss_forward_single(retptr, this.__wbg_ptr, ptr0, len0, ptr1, len1, ptr2, len2);
|
||||
var r0 = getDataViewMemory0().getFloat32(retptr + 4 * 0, true);
|
||||
var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
|
||||
var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true);
|
||||
if (r2) {
|
||||
throw takeObject(r1);
|
||||
}
|
||||
return r0;
|
||||
} finally {
|
||||
wasm.__wbindgen_add_to_stack_pointer(16);
|
||||
}
|
||||
}
|
||||
/**
|
||||
* Get the margin parameter
|
||||
* @returns {number}
|
||||
*/
|
||||
get margin() {
|
||||
const ret = wasm.wasmtripletloss_margin(this.__wbg_ptr);
|
||||
return ret;
|
||||
}
|
||||
/**
|
||||
* Create new triplet loss with margin
|
||||
* @param {number} margin
|
||||
*/
|
||||
constructor(margin) {
|
||||
const ret = wasm.wasmtripletloss_new(margin);
|
||||
this.__wbg_ptr = ret >>> 0;
|
||||
WasmTripletLossFinalization.register(this, this.__wbg_ptr, this);
|
||||
return this;
|
||||
}
|
||||
}
|
||||
if (Symbol.dispose) WasmTripletLoss.prototype[Symbol.dispose] = WasmTripletLoss.prototype.free;
|
||||
|
||||
/**
|
||||
* Initialize panic hook for better error messages
|
||||
*/
|
||||
export function init() {
|
||||
wasm.init();
|
||||
}
|
||||
|
||||
function __wbg_get_imports() {
|
||||
const import0 = {
|
||||
__proto__: null,
|
||||
__wbg___wbindgen_throw_39bc967c0e5a9b58: function(arg0, arg1) {
|
||||
throw new Error(getStringFromWasm0(arg0, arg1));
|
||||
},
|
||||
__wbg_error_a6fa202b58aa1cd3: function(arg0, arg1) {
|
||||
let deferred0_0;
|
||||
let deferred0_1;
|
||||
try {
|
||||
deferred0_0 = arg0;
|
||||
deferred0_1 = arg1;
|
||||
console.error(getStringFromWasm0(arg0, arg1));
|
||||
} finally {
|
||||
wasm.__wbindgen_export(deferred0_0, deferred0_1, 1);
|
||||
}
|
||||
},
|
||||
__wbg_new_227d7c05414eb861: function() {
|
||||
const ret = new Error();
|
||||
return addHeapObject(ret);
|
||||
},
|
||||
__wbg_stack_3b0d974bbf31e44f: function(arg0, arg1) {
|
||||
const ret = getObject(arg1).stack;
|
||||
const ptr1 = passStringToWasm0(ret, wasm.__wbindgen_export2, wasm.__wbindgen_export3);
|
||||
const len1 = WASM_VECTOR_LEN;
|
||||
getDataViewMemory0().setInt32(arg0 + 4 * 1, len1, true);
|
||||
getDataViewMemory0().setInt32(arg0 + 4 * 0, ptr1, true);
|
||||
},
|
||||
__wbindgen_cast_0000000000000001: function(arg0, arg1) {
|
||||
// Cast intrinsic for `Ref(String) -> Externref`.
|
||||
const ret = getStringFromWasm0(arg0, arg1);
|
||||
return addHeapObject(ret);
|
||||
},
|
||||
__wbindgen_object_drop_ref: function(arg0) {
|
||||
takeObject(arg0);
|
||||
},
|
||||
};
|
||||
return {
|
||||
__proto__: null,
|
||||
"./ruvector_cnn_wasm_bg.js": import0,
|
||||
};
|
||||
}
|
||||
|
||||
const EmbedderConfigFinalization = (typeof FinalizationRegistry === 'undefined')
|
||||
? { register: () => {}, unregister: () => {} }
|
||||
: new FinalizationRegistry(ptr => wasm.__wbg_embedderconfig_free(ptr >>> 0, 1));
|
||||
const LayerOpsFinalization = (typeof FinalizationRegistry === 'undefined')
|
||||
? { register: () => {}, unregister: () => {} }
|
||||
: new FinalizationRegistry(ptr => wasm.__wbg_layerops_free(ptr >>> 0, 1));
|
||||
const SimdOpsFinalization = (typeof FinalizationRegistry === 'undefined')
|
||||
? { register: () => {}, unregister: () => {} }
|
||||
: new FinalizationRegistry(ptr => wasm.__wbg_simdops_free(ptr >>> 0, 1));
|
||||
const WasmCnnEmbedderFinalization = (typeof FinalizationRegistry === 'undefined')
|
||||
? { register: () => {}, unregister: () => {} }
|
||||
: new FinalizationRegistry(ptr => wasm.__wbg_wasmcnnembedder_free(ptr >>> 0, 1));
|
||||
const WasmInfoNCELossFinalization = (typeof FinalizationRegistry === 'undefined')
|
||||
? { register: () => {}, unregister: () => {} }
|
||||
: new FinalizationRegistry(ptr => wasm.__wbg_wasminfonceloss_free(ptr >>> 0, 1));
|
||||
const WasmTripletLossFinalization = (typeof FinalizationRegistry === 'undefined')
|
||||
? { register: () => {}, unregister: () => {} }
|
||||
: new FinalizationRegistry(ptr => wasm.__wbg_wasmtripletloss_free(ptr >>> 0, 1));
|
||||
|
||||
function addHeapObject(obj) {
|
||||
if (heap_next === heap.length) heap.push(heap.length + 1);
|
||||
const idx = heap_next;
|
||||
heap_next = heap[idx];
|
||||
|
||||
heap[idx] = obj;
|
||||
return idx;
|
||||
}
|
||||
|
||||
function _assertClass(instance, klass) {
|
||||
if (!(instance instanceof klass)) {
|
||||
throw new Error(`expected instance of ${klass.name}`);
|
||||
}
|
||||
}
|
||||
|
||||
function dropObject(idx) {
|
||||
if (idx < 1028) return;
|
||||
heap[idx] = heap_next;
|
||||
heap_next = idx;
|
||||
}
|
||||
|
||||
function getArrayF32FromWasm0(ptr, len) {
|
||||
ptr = ptr >>> 0;
|
||||
return getFloat32ArrayMemory0().subarray(ptr / 4, ptr / 4 + len);
|
||||
}
|
||||
|
||||
let cachedDataViewMemory0 = null;
|
||||
function getDataViewMemory0() {
|
||||
if (cachedDataViewMemory0 === null || cachedDataViewMemory0.buffer.detached === true || (cachedDataViewMemory0.buffer.detached === undefined && cachedDataViewMemory0.buffer !== wasm.memory.buffer)) {
|
||||
cachedDataViewMemory0 = new DataView(wasm.memory.buffer);
|
||||
}
|
||||
return cachedDataViewMemory0;
|
||||
}
|
||||
|
||||
let cachedFloat32ArrayMemory0 = null;
|
||||
function getFloat32ArrayMemory0() {
|
||||
if (cachedFloat32ArrayMemory0 === null || cachedFloat32ArrayMemory0.byteLength === 0) {
|
||||
cachedFloat32ArrayMemory0 = new Float32Array(wasm.memory.buffer);
|
||||
}
|
||||
return cachedFloat32ArrayMemory0;
|
||||
}
|
||||
|
||||
function getStringFromWasm0(ptr, len) {
|
||||
ptr = ptr >>> 0;
|
||||
return decodeText(ptr, len);
|
||||
}
|
||||
|
||||
let cachedUint8ArrayMemory0 = null;
|
||||
function getUint8ArrayMemory0() {
|
||||
if (cachedUint8ArrayMemory0 === null || cachedUint8ArrayMemory0.byteLength === 0) {
|
||||
cachedUint8ArrayMemory0 = new Uint8Array(wasm.memory.buffer);
|
||||
}
|
||||
return cachedUint8ArrayMemory0;
|
||||
}
|
||||
|
||||
function getObject(idx) { return heap[idx]; }
|
||||
|
||||
let heap = new Array(1024).fill(undefined);
|
||||
heap.push(undefined, null, true, false);
|
||||
|
||||
let heap_next = heap.length;
|
||||
|
||||
function isLikeNone(x) {
|
||||
return x === undefined || x === null;
|
||||
}
|
||||
|
||||
function passArray8ToWasm0(arg, malloc) {
|
||||
const ptr = malloc(arg.length * 1, 1) >>> 0;
|
||||
getUint8ArrayMemory0().set(arg, ptr / 1);
|
||||
WASM_VECTOR_LEN = arg.length;
|
||||
return ptr;
|
||||
}
|
||||
|
||||
function passArrayF32ToWasm0(arg, malloc) {
|
||||
const ptr = malloc(arg.length * 4, 4) >>> 0;
|
||||
getFloat32ArrayMemory0().set(arg, ptr / 4);
|
||||
WASM_VECTOR_LEN = arg.length;
|
||||
return ptr;
|
||||
}
|
||||
|
||||
function passStringToWasm0(arg, malloc, realloc) {
|
||||
if (realloc === undefined) {
|
||||
const buf = cachedTextEncoder.encode(arg);
|
||||
const ptr = malloc(buf.length, 1) >>> 0;
|
||||
getUint8ArrayMemory0().subarray(ptr, ptr + buf.length).set(buf);
|
||||
WASM_VECTOR_LEN = buf.length;
|
||||
return ptr;
|
||||
}
|
||||
|
||||
let len = arg.length;
|
||||
let ptr = malloc(len, 1) >>> 0;
|
||||
|
||||
const mem = getUint8ArrayMemory0();
|
||||
|
||||
let offset = 0;
|
||||
|
||||
for (; offset < len; offset++) {
|
||||
const code = arg.charCodeAt(offset);
|
||||
if (code > 0x7F) break;
|
||||
mem[ptr + offset] = code;
|
||||
}
|
||||
if (offset !== len) {
|
||||
if (offset !== 0) {
|
||||
arg = arg.slice(offset);
|
||||
}
|
||||
ptr = realloc(ptr, len, len = offset + arg.length * 3, 1) >>> 0;
|
||||
const view = getUint8ArrayMemory0().subarray(ptr + offset, ptr + len);
|
||||
const ret = cachedTextEncoder.encodeInto(arg, view);
|
||||
|
||||
offset += ret.written;
|
||||
ptr = realloc(ptr, len, offset, 1) >>> 0;
|
||||
}
|
||||
|
||||
WASM_VECTOR_LEN = offset;
|
||||
return ptr;
|
||||
}
|
||||
|
||||
function takeObject(idx) {
|
||||
const ret = getObject(idx);
|
||||
dropObject(idx);
|
||||
return ret;
|
||||
}
|
||||
|
||||
let cachedTextDecoder = new TextDecoder('utf-8', { ignoreBOM: true, fatal: true });
|
||||
cachedTextDecoder.decode();
|
||||
const MAX_SAFARI_DECODE_BYTES = 2146435072;
|
||||
let numBytesDecoded = 0;
|
||||
function decodeText(ptr, len) {
|
||||
numBytesDecoded += len;
|
||||
if (numBytesDecoded >= MAX_SAFARI_DECODE_BYTES) {
|
||||
cachedTextDecoder = new TextDecoder('utf-8', { ignoreBOM: true, fatal: true });
|
||||
cachedTextDecoder.decode();
|
||||
numBytesDecoded = len;
|
||||
}
|
||||
return cachedTextDecoder.decode(getUint8ArrayMemory0().subarray(ptr, ptr + len));
|
||||
}
|
||||
|
||||
const cachedTextEncoder = new TextEncoder();
|
||||
|
||||
if (!('encodeInto' in cachedTextEncoder)) {
|
||||
cachedTextEncoder.encodeInto = function (arg, view) {
|
||||
const buf = cachedTextEncoder.encode(arg);
|
||||
view.set(buf);
|
||||
return {
|
||||
read: arg.length,
|
||||
written: buf.length
|
||||
};
|
||||
};
|
||||
}
|
||||
|
||||
let WASM_VECTOR_LEN = 0;
|
||||
|
||||
let wasmModule, wasm;
|
||||
function __wbg_finalize_init(instance, module) {
|
||||
wasm = instance.exports;
|
||||
wasmModule = module;
|
||||
cachedDataViewMemory0 = null;
|
||||
cachedFloat32ArrayMemory0 = null;
|
||||
cachedUint8ArrayMemory0 = null;
|
||||
wasm.__wbindgen_start();
|
||||
return wasm;
|
||||
}
|
||||
|
||||
async function __wbg_load(module, imports) {
|
||||
if (typeof Response === 'function' && module instanceof Response) {
|
||||
if (typeof WebAssembly.instantiateStreaming === 'function') {
|
||||
try {
|
||||
return await WebAssembly.instantiateStreaming(module, imports);
|
||||
} catch (e) {
|
||||
const validResponse = module.ok && expectedResponseType(module.type);
|
||||
|
||||
if (validResponse && module.headers.get('Content-Type') !== 'application/wasm') {
|
||||
console.warn("`WebAssembly.instantiateStreaming` failed because your server does not serve Wasm with `application/wasm` MIME type. Falling back to `WebAssembly.instantiate` which is slower. Original error:\n", e);
|
||||
|
||||
} else { throw e; }
|
||||
}
|
||||
}
|
||||
|
||||
const bytes = await module.arrayBuffer();
|
||||
return await WebAssembly.instantiate(bytes, imports);
|
||||
} else {
|
||||
const instance = await WebAssembly.instantiate(module, imports);
|
||||
|
||||
if (instance instanceof WebAssembly.Instance) {
|
||||
return { instance, module };
|
||||
} else {
|
||||
return instance;
|
||||
}
|
||||
}
|
||||
|
||||
function expectedResponseType(type) {
|
||||
switch (type) {
|
||||
case 'basic': case 'cors': case 'default': return true;
|
||||
}
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
function initSync(module) {
|
||||
if (wasm !== undefined) return wasm;
|
||||
|
||||
|
||||
if (module !== undefined) {
|
||||
if (Object.getPrototypeOf(module) === Object.prototype) {
|
||||
({module} = module)
|
||||
} else {
|
||||
console.warn('using deprecated parameters for `initSync()`; pass a single object instead')
|
||||
}
|
||||
}
|
||||
|
||||
const imports = __wbg_get_imports();
|
||||
if (!(module instanceof WebAssembly.Module)) {
|
||||
module = new WebAssembly.Module(module);
|
||||
}
|
||||
const instance = new WebAssembly.Instance(module, imports);
|
||||
return __wbg_finalize_init(instance, module);
|
||||
}
|
||||
|
||||
async function __wbg_init(module_or_path) {
|
||||
if (wasm !== undefined) return wasm;
|
||||
|
||||
|
||||
if (module_or_path !== undefined) {
|
||||
if (Object.getPrototypeOf(module_or_path) === Object.prototype) {
|
||||
({module_or_path} = module_or_path)
|
||||
} else {
|
||||
console.warn('using deprecated parameters for the initialization function; pass a single object instead')
|
||||
}
|
||||
}
|
||||
|
||||
if (module_or_path === undefined) {
|
||||
module_or_path = new URL('ruvector_cnn_wasm_bg.wasm', import.meta.url);
|
||||
}
|
||||
const imports = __wbg_get_imports();
|
||||
|
||||
if (typeof module_or_path === 'string' || (typeof Request === 'function' && module_or_path instanceof Request) || (typeof URL === 'function' && module_or_path instanceof URL)) {
|
||||
module_or_path = fetch(module_or_path);
|
||||
}
|
||||
|
||||
const { instance, module } = await __wbg_load(await module_or_path, imports);
|
||||
|
||||
return __wbg_finalize_init(instance, module);
|
||||
}
|
||||
|
||||
export { initSync, __wbg_init as default };
|
||||
BIN
ui/pose-fusion/pkg/ruvector_cnn_wasm/ruvector_cnn_wasm_bg.wasm
Normal file
BIN
ui/pose-fusion/pkg/ruvector_cnn_wasm/ruvector_cnn_wasm_bg.wasm
Normal file
Binary file not shown.
Loading…
Add table
Add a link
Reference in a new issue