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5 Commits
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| Author | SHA1 | Date |
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ruv
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ruv
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ruv
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ruv
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ruv
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@ -1,13 +0,0 @@
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{"type":"edit","file":"unknown","timestamp":1772725155061,"sessionId":null}
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{"type":"edit","file":"unknown","timestamp":1772725341920,"sessionId":null}
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{"type":"edit","file":"unknown","timestamp":1772725344759,"sessionId":null}
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{"type":"edit","file":"unknown","timestamp":1772725350123,"sessionId":null}
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{"type":"edit","file":"unknown","timestamp":1772725549376,"sessionId":null}
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{"type":"edit","file":"unknown","timestamp":1772725716975,"sessionId":null}
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{"type":"edit","file":"unknown","timestamp":1772725889463,"sessionId":null}
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{"type":"edit","file":"unknown","timestamp":1772725893374,"sessionId":null}
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{"type":"edit","file":"unknown","timestamp":1772726006058,"sessionId":null}
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{"type":"edit","file":"unknown","timestamp":1772726169252,"sessionId":null}
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{"type":"edit","file":"unknown","timestamp":1772726170029,"sessionId":null}
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{"type":"edit","file":"unknown","timestamp":1772726177792,"sessionId":null}
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{"type":"edit","file":"unknown","timestamp":1772726186165,"sessionId":null}
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@ -1,12 +0,0 @@
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{
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||||
"id": "session-1772726138295",
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"startedAt": "2026-03-05T15:55:38.296Z",
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"cwd": "C:\\Users\\ruv\\Projects\\wifi-densepose",
|
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"context": {},
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||||
"metrics": {
|
||||
"edits": 4,
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"commands": 0,
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"tasks": 0,
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"errors": 0
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}
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}
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@ -1 +0,0 @@
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Subproject commit 79d412ea5fcf92f0efe658d52827a0e0a96ff442
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|
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@ -44,6 +44,7 @@
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|||
</div>
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||||
<div id="scenario-description"></div>
|
||||
<div id="fps-counter" style="display:none">60 FPS</div>
|
||||
<a href="pose-fusion.html" title="Dual-Modal Pose Fusion" style="pointer-events:auto;color:#00e5ff;text-decoration:none;font-size:13px;font-weight:600;padding:4px 10px;border:1px solid rgba(0,229,255,0.3);border-radius:6px;background:rgba(0,229,255,0.08);transition:background 0.2s" onmouseover="this.style.background='rgba(0,229,255,0.2)'" onmouseout="this.style.background='rgba(0,229,255,0.08)'">Pose Fusion</a>
|
||||
<button id="settings-btn" title="Settings">⚙</button>
|
||||
</div>
|
||||
|
||||
|
|
|
|||
|
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@ -0,0 +1,160 @@
|
|||
<!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">
|
||||
</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>
|
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</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>
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||||
|
||||
<div id="camera-prompt" class="camera-prompt">
|
||||
<p>Enable your webcam for live video pose estimation.<br>
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||||
Or switch to <strong>CSI Only</strong> mode for WiFi-based sensing.</p>
|
||||
<button id="start-camera-btn">Enable Camera</button>
|
||||
</div>
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||||
</div>
|
||||
|
||||
<!-- Side Panels -->
|
||||
<div class="side-panels">
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||||
|
||||
<!-- 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">
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||||
<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="120"></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="140"></canvas>
|
||||
</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: MobileNet-V3 × 2 → Attention Fusion → 17-Keypoint COCO
|
||||
</div>
|
||||
<div>
|
||||
<a href="https://github.com/ruvnet/wifi-densepose">GitHub</a> ·
|
||||
CNN: ruvector-cnn (JS fallback) ·
|
||||
<a href="observatory.html">Observatory</a>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</div><!-- /main-grid -->
|
||||
|
||||
<script type="module" src="pose-fusion/js/main.js"></script>
|
||||
</body>
|
||||
</html>
|
||||
|
|
@ -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"
|
||||
|
|
@ -0,0 +1,405 @@
|
|||
/* 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 === */
|
||||
.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: 50%; left: 50%;
|
||||
transform: translate(-50%, -50%);
|
||||
text-align: center;
|
||||
color: var(--text-secondary);
|
||||
}
|
||||
|
||||
.camera-prompt button {
|
||||
margin-top: 12px;
|
||||
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); }
|
||||
|
||||
/* === Side Panels === */
|
||||
.side-panels {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 12px;
|
||||
overflow-y: auto;
|
||||
min-height: 0;
|
||||
}
|
||||
|
||||
.panel {
|
||||
background: var(--bg-panel);
|
||||
border: 1px solid var(--bg-panel-border);
|
||||
border-radius: var(--radius);
|
||||
padding: 14px;
|
||||
}
|
||||
|
||||
.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;
|
||||
}
|
||||
|
||||
/* === 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;
|
||||
}
|
||||
|
||||
/* === 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; }
|
||||
}
|
||||
|
|
@ -0,0 +1,247 @@
|
|||
/**
|
||||
* 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;
|
||||
|
||||
ctx.clearRect(0, 0, width, height);
|
||||
|
||||
if (!keypoints || keypoints.length === 0) return;
|
||||
|
||||
// Draw limbs first (behind joints)
|
||||
ctx.lineWidth = 3;
|
||||
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;
|
||||
|
||||
// Glow
|
||||
ctx.strokeStyle = this.colors.limbGlow;
|
||||
ctx.lineWidth = 8;
|
||||
ctx.globalAlpha = avgConf * 0.4;
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(ax, ay);
|
||||
ctx.lineTo(bx, by);
|
||||
ctx.stroke();
|
||||
|
||||
// Main line
|
||||
ctx.strokeStyle = limbColor;
|
||||
ctx.lineWidth = 2.5;
|
||||
ctx.globalAlpha = avgConf;
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(ax, ay);
|
||||
ctx.lineTo(bx, by);
|
||||
ctx.stroke();
|
||||
}
|
||||
|
||||
// Draw joints
|
||||
ctx.globalAlpha = 1;
|
||||
for (const kp of keypoints) {
|
||||
if (!kp || kp.confidence < minConf) continue;
|
||||
|
||||
const x = kp.x * width;
|
||||
const y = kp.y * height;
|
||||
const r = 3 + kp.confidence * 3;
|
||||
|
||||
// Glow
|
||||
ctx.beginPath();
|
||||
ctx.arc(x, y, r + 4, 0, Math.PI * 2);
|
||||
ctx.fillStyle = glowColor;
|
||||
ctx.globalAlpha = kp.confidence * 0.6;
|
||||
ctx.fill();
|
||||
|
||||
// Joint dot
|
||||
ctx.beginPath();
|
||||
ctx.arc(x, y, r, 0, Math.PI * 2);
|
||||
ctx.fillStyle = jointColor;
|
||||
ctx.globalAlpha = kp.confidence;
|
||||
ctx.fill();
|
||||
|
||||
// White center
|
||||
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
|
||||
if (opts.label) {
|
||||
ctx.font = '11px "JetBrains Mono", monospace';
|
||||
ctx.fillStyle = jointColor;
|
||||
ctx.globalAlpha = 0.8;
|
||||
ctx.fillText(opts.label, 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();
|
||||
|
||||
const drawPoints = (pts, color, size) => {
|
||||
if (!pts || pts.length === 0) return;
|
||||
const len = pts.length;
|
||||
for (let i = 0; i < len; i++) {
|
||||
const p = pts[i];
|
||||
if (!p) continue;
|
||||
const age = 1 - (i / len) * 0.7; // Fade older points
|
||||
const px = w / 2 + p[0] * w * 0.35;
|
||||
const py = h / 2 + p[1] * h * 0.35;
|
||||
|
||||
if (px < 0 || px > w || py < 0 || py > h) continue;
|
||||
|
||||
ctx.beginPath();
|
||||
ctx.arc(px, py, size, 0, Math.PI * 2);
|
||||
ctx.fillStyle = color;
|
||||
ctx.globalAlpha = age * 0.7;
|
||||
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)})`;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,226 @@
|
|||
/**
|
||||
* CNN Embedder — Lightweight MobileNet-V3-style feature extractor.
|
||||
*
|
||||
* Architecture mirrors ruvector-cnn: Conv2D → BatchNorm → ReLU → Pool → Project → L2 Normalize
|
||||
* Uses pre-seeded random weights (deterministic). When ruvector-cnn-wasm is available,
|
||||
* transparently delegates to the WASM implementation.
|
||||
*
|
||||
* 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;
|
||||
|
||||
// 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
|
||||
this.projWeights = new Float32Array(16 * this.embeddingDim);
|
||||
for (let i = 0; i < this.projWeights.length; i++) {
|
||||
this.projWeights[i] = randRange(-0.1, 0.1);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Try to load WASM embedder from ruvector-cnn-wasm package
|
||||
* @param {string} wasmPath - Path to the WASM package directory
|
||||
*/
|
||||
async tryLoadWasm(wasmPath) {
|
||||
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 embedder loaded successfully');
|
||||
return true;
|
||||
} catch (e) {
|
||||
console.log('[CNN] WASM 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 → 16-dim
|
||||
const outH = sz - 2, outW = sz - 2;
|
||||
const pooled = new Float32Array(16);
|
||||
const spatial = outH * outW;
|
||||
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;
|
||||
|
||||
// 7. 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;
|
||||
}
|
||||
|
||||
// 8. 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;
|
||||
}
|
||||
|
||||
_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 between two embeddings */
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,267 @@
|
|||
/**
|
||||
* 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 {
|
||||
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;
|
||||
}
|
||||
|
||||
// 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) {
|
||||
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();
|
||||
}
|
||||
|
||||
// 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];
|
||||
|
||||
// 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) {
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
||||
_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;
|
||||
};
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,166 @@
|
|||
/**
|
||||
* 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
|
||||
*/
|
||||
constructor(embeddingDim = 128) {
|
||||
this.embeddingDim = embeddingDim;
|
||||
|
||||
// Learnable attention weights (initialized to balanced 0.5)
|
||||
// In production, these would be loaded from trained JSON
|
||||
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;
|
||||
}
|
||||
|
||||
/**
|
||||
* 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
|
||||
let norm = 0;
|
||||
for (let i = 0; i < dim; i++) norm += fused[i] * fused[i];
|
||||
norm = Math.sqrt(norm);
|
||||
if (norm > 1e-8) {
|
||||
for (let i = 0; i < dim; i++) fused[i] /= norm;
|
||||
}
|
||||
|
||||
this._recordEmbedding(videoEmb, csiEmb, fused);
|
||||
return fused;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get embedding pairs for 2D visualization (PCA projection)
|
||||
* @returns {{ video: Array, csi: Array, fused: Array }}
|
||||
*/
|
||||
getEmbeddingPoints() {
|
||||
// Simple 2D projection using first two principal components (approximated)
|
||||
const project = (emb) => {
|
||||
if (!emb || emb.length < 4) return null;
|
||||
// Use pairs of dimensions as crude 2D projection
|
||||
let x = 0, y = 0;
|
||||
for (let i = 0; i < emb.length; i += 2) {
|
||||
x += emb[i] * (i % 4 < 2 ? 1 : -1);
|
||||
if (i + 1 < emb.length) {
|
||||
y += emb[i + 1] * (i % 4 < 2 ? 1 : -1);
|
||||
}
|
||||
}
|
||||
return [x * 2, y * 2]; // Scale for visibility
|
||||
};
|
||||
|
||||
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;
|
||||
|
||||
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;
|
||||
}
|
||||
|
||||
_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();
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,303 @@
|
|||
/**
|
||||
* WiFi-DensePose — Dual-Modal Pose Estimation Demo
|
||||
*
|
||||
* Main orchestration: video capture → CNN embedding → CSI processing → fusion → rendering
|
||||
*/
|
||||
|
||||
import { VideoCapture } from './video-capture.js';
|
||||
import { CsiSimulator } from './csi-simulator.js';
|
||||
import { CnnEmbedder } from './cnn-embedder.js';
|
||||
import { FusionEngine } from './fusion-engine.js';
|
||||
import { PoseDecoder } from './pose-decoder.js';
|
||||
import { CanvasRenderer } from './canvas-renderer.js';
|
||||
|
||||
// === 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');
|
||||
|
||||
// === Initialize ===
|
||||
function init() {
|
||||
resizeCanvases();
|
||||
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 WASM embedders (non-blocking)
|
||||
// Resolve relative to this JS module file (in pose-fusion/js/) → ../pkg/
|
||||
const wasmBase = new URL('../pkg/ruvector_cnn_wasm', import.meta.url).href;
|
||||
visualCnn.tryLoadWasm(wasmBase);
|
||||
csiCnn.tryLoadWasm(wasmBase);
|
||||
|
||||
// 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';
|
||||
const needsCsi = mode !== 'video';
|
||||
|
||||
// Show/hide camera prompt
|
||||
if (needsVideo && !videoCapture.isActive) {
|
||||
cameraPrompt.style.display = 'flex';
|
||||
} else {
|
||||
cameraPrompt.style.display = 'none';
|
||||
}
|
||||
}
|
||||
|
||||
function resizeCanvases() {
|
||||
const videoPanel = document.querySelector('.video-panel');
|
||||
if (videoPanel) {
|
||||
const rect = videoPanel.getBoundingClientRect();
|
||||
skeletonCanvas.width = rect.width;
|
||||
skeletonCanvas.height = rect.height;
|
||||
}
|
||||
|
||||
// CSI canvas
|
||||
csiCanvas.width = csiCanvas.parentElement.clientWidth;
|
||||
csiCanvas.height = 120;
|
||||
|
||||
// Embedding canvas
|
||||
embeddingCanvas.width = embeddingCanvas.parentElement.clientWidth;
|
||||
embeddingCanvas.height = 140;
|
||||
}
|
||||
|
||||
// === Main Loop ===
|
||||
function mainLoop(timestamp) {
|
||||
if (!isRunning) return;
|
||||
requestAnimationFrame(mainLoop);
|
||||
|
||||
if (isPaused) return;
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
// Boot
|
||||
document.addEventListener('DOMContentLoaded', init);
|
||||
|
|
@ -0,0 +1,373 @@
|
|||
/**
|
||||
* 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).
|
||||
*/
|
||||
|
||||
// COCO keypoint definitions
|
||||
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'
|
||||
];
|
||||
|
||||
// Skeleton connections (pairs of keypoint indices)
|
||||
export const SKELETON_CONNECTIONS = [
|
||||
[0, 1], [0, 2], [1, 3], [2, 4], // Head
|
||||
[5, 6], // 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
|
||||
];
|
||||
|
||||
// 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,
|
||||
};
|
||||
|
||||
export class PoseDecoder {
|
||||
constructor(embeddingDim = 128) {
|
||||
this.embeddingDim = embeddingDim;
|
||||
this.smoothedKeypoints = null;
|
||||
this.smoothingFactor = 0.45; // Lower = more responsive to movement
|
||||
this._time = 0;
|
||||
|
||||
// Through-wall tracking state
|
||||
this._lastBodyState = null;
|
||||
this._ghostState = null;
|
||||
this._ghostConfidence = 0;
|
||||
this._ghostVelocity = { x: 0, y: 0 };
|
||||
|
||||
// Arm tracking history (smoothed positions)
|
||||
this._leftArmY = 0.5;
|
||||
this._rightArmY = 0.5;
|
||||
this._leftArmX = 0;
|
||||
this._rightArmX = 0;
|
||||
this._headOffsetX = 0;
|
||||
}
|
||||
|
||||
/**
|
||||
* 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.
|
||||
* The grid tells us WHERE motion is happening → we map that to joint positions.
|
||||
*/
|
||||
_trackFromMotionGrid(region, embedding, elapsed) {
|
||||
const grid = region.motionGrid;
|
||||
const cols = region.gridCols || 10;
|
||||
const rows = region.gridRows || 8;
|
||||
|
||||
// Body bounding box
|
||||
const cx = region.x + region.w / 2;
|
||||
const cy = region.y + region.h / 2;
|
||||
const bodyH = Math.max(region.h, 0.3);
|
||||
const bodyW = Math.max(region.w, 0.15);
|
||||
|
||||
// Analyze the motion grid to find arm positions
|
||||
// Divide body into zones: head (top 20%), arms (top 60% sides), torso (center), legs (bottom 40%)
|
||||
if (grid) {
|
||||
const armAnalysis = this._analyzeArmMotion(grid, cols, rows, region);
|
||||
// Smooth arm tracking
|
||||
this._leftArmY = 0.6 * this._leftArmY + 0.4 * armAnalysis.leftArmHeight;
|
||||
this._rightArmY = 0.6 * this._rightArmY + 0.4 * armAnalysis.rightArmHeight;
|
||||
this._leftArmX = 0.6 * this._leftArmX + 0.4 * armAnalysis.leftArmSpread;
|
||||
this._rightArmX = 0.6 * this._rightArmX + 0.4 * armAnalysis.rightArmSpread;
|
||||
this._headOffsetX = 0.7 * this._headOffsetX + 0.3 * armAnalysis.headOffsetX;
|
||||
}
|
||||
|
||||
const P = PROPORTIONS;
|
||||
const halfW = P.shoulderWidth * bodyH / 2;
|
||||
const hipHalfW = P.hipWidth * bodyH / 2;
|
||||
|
||||
// Breathing (subtle)
|
||||
const breathe = Math.sin(elapsed * 1.5) * 0.002;
|
||||
|
||||
// Core body positions from detection center
|
||||
const hipY = cy + bodyH * 0.15;
|
||||
const shoulderY = hipY - P.shoulderToHip * bodyH + breathe;
|
||||
const headY = shoulderY - P.headToShoulder * bodyH;
|
||||
const kneeY = hipY + P.hipToKnee * bodyH;
|
||||
const ankleY = kneeY + P.kneeToAnkle * bodyH;
|
||||
|
||||
// HEAD follows motion centroid
|
||||
const headX = cx + this._headOffsetX * bodyW * 0.3;
|
||||
|
||||
// ARM POSITIONS driven by motion grid analysis
|
||||
// leftArmY: 0 = arm down at side, 1 = arm fully raised
|
||||
// leftArmSpread: how far out the arm extends
|
||||
const leftArmRaise = this._leftArmY; // 0-1
|
||||
const rightArmRaise = this._rightArmY;
|
||||
const leftSpread = 0.02 + this._leftArmX * 0.12;
|
||||
const rightSpread = 0.02 + this._rightArmX * 0.12;
|
||||
|
||||
// Elbow: interpolate between "at side" and "raised"
|
||||
const lElbowY = shoulderY + P.shoulderToElbow * bodyH * (1 - leftArmRaise * 0.9);
|
||||
const rElbowY = shoulderY + P.shoulderToElbow * bodyH * (1 - rightArmRaise * 0.9);
|
||||
const lElbowX = cx - halfW - leftSpread;
|
||||
const rElbowX = cx + halfW + rightSpread;
|
||||
|
||||
// Wrist: extends further when raised
|
||||
const lWristY = lElbowY + P.elbowToWrist * bodyH * (1 - leftArmRaise * 1.1);
|
||||
const rWristY = rElbowY + P.elbowToWrist * bodyH * (1 - rightArmRaise * 1.1);
|
||||
const lWristX = lElbowX - leftSpread * 0.6;
|
||||
const rWristX = rElbowX + rightSpread * 0.6;
|
||||
|
||||
// Leg motion from lower grid cells
|
||||
const legMotion = grid ? this._analyzeLegMotion(grid, cols, rows) : { left: 0, right: 0 };
|
||||
const legSwing = 0.015;
|
||||
|
||||
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: cx - halfW, y: shoulderY, confidence: 0.94 },
|
||||
// 6: right_shoulder
|
||||
{ x: cx + halfW, y: shoulderY, 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: cx - hipHalfW, y: hipY, confidence: 0.91 },
|
||||
// 12: right_hip
|
||||
{ x: cx + hipHalfW, y: hipY, confidence: 0.91 },
|
||||
// 13: left_knee
|
||||
{ x: cx - hipHalfW + legMotion.left * legSwing, y: kneeY, confidence: 0.88 },
|
||||
// 14: right_knee
|
||||
{ x: cx + hipHalfW + legMotion.right * legSwing, y: kneeY, confidence: 0.88 },
|
||||
// 15: left_ankle
|
||||
{ x: cx - hipHalfW + legMotion.left * legSwing * 1.3, y: ankleY, confidence: 0.83 },
|
||||
// 16: right_ankle
|
||||
{ x: cx + hipHalfW + legMotion.right * legSwing * 1.3, y: ankleY, confidence: 0.83 },
|
||||
];
|
||||
|
||||
for (let i = 0; i < keypoints.length; i++) {
|
||||
keypoints[i].name = KEYPOINT_NAMES[i];
|
||||
}
|
||||
|
||||
return keypoints;
|
||||
}
|
||||
|
||||
/**
|
||||
* Analyze the motion grid to determine arm positions.
|
||||
* Left side of grid = left side of body, etc.
|
||||
*/
|
||||
_analyzeArmMotion(grid, cols, rows, region) {
|
||||
// Body center column
|
||||
const centerCol = Math.floor(cols / 2);
|
||||
|
||||
// Upper body rows (top 60% of detected region)
|
||||
const upperEnd = Math.floor(rows * 0.6);
|
||||
|
||||
// Compute motion intensity for left vs right, at different heights
|
||||
let leftUpperMotion = 0, leftMidMotion = 0;
|
||||
let rightUpperMotion = 0, rightMidMotion = 0;
|
||||
let leftCount = 0, rightCount = 0;
|
||||
let headMotionX = 0, headMotionWeight = 0;
|
||||
|
||||
for (let r = 0; r < upperEnd; r++) {
|
||||
const heightWeight = 1.0 - (r / upperEnd) * 0.3; // Upper rows weighted more
|
||||
|
||||
// Head zone: top 25%, center 40% of width
|
||||
if (r < Math.floor(rows * 0.25)) {
|
||||
const headLeft = Math.floor(cols * 0.3);
|
||||
const headRight = Math.floor(cols * 0.7);
|
||||
for (let c = headLeft; c <= headRight; c++) {
|
||||
const val = grid[r][c];
|
||||
headMotionX += (c / cols - 0.5) * val;
|
||||
headMotionWeight += val;
|
||||
}
|
||||
}
|
||||
|
||||
// Left arm zone: left 40% of grid
|
||||
for (let c = 0; c < Math.floor(cols * 0.4); c++) {
|
||||
const val = grid[r][c];
|
||||
if (r < rows * 0.3) leftUpperMotion += val * heightWeight;
|
||||
else leftMidMotion += val * heightWeight;
|
||||
leftCount++;
|
||||
}
|
||||
|
||||
// Right arm zone: right 40% of grid
|
||||
for (let c = Math.floor(cols * 0.6); c < cols; c++) {
|
||||
const val = grid[r][c];
|
||||
if (r < rows * 0.3) rightUpperMotion += val * heightWeight;
|
||||
else rightMidMotion += val * heightWeight;
|
||||
rightCount++;
|
||||
}
|
||||
}
|
||||
|
||||
// Normalize
|
||||
const leftTotal = leftUpperMotion + leftMidMotion;
|
||||
const rightTotal = rightUpperMotion + rightMidMotion;
|
||||
const maxMotion = 0.15; // Calibration threshold
|
||||
|
||||
// Arm height: 0 = at side, 1 = raised
|
||||
// High motion in upper-left → left arm is raised
|
||||
const leftArmHeight = Math.min(1, (leftUpperMotion / maxMotion) * 2);
|
||||
const rightArmHeight = Math.min(1, (rightUpperMotion / maxMotion) * 2);
|
||||
|
||||
// Arm spread: how far out from body
|
||||
const leftArmSpread = Math.min(1, leftTotal / maxMotion);
|
||||
const rightArmSpread = Math.min(1, rightTotal / maxMotion);
|
||||
|
||||
// Head offset
|
||||
const headOffsetX = headMotionWeight > 0.01 ? headMotionX / headMotionWeight : 0;
|
||||
|
||||
return { leftArmHeight, rightArmHeight, leftArmSpread, rightArmSpread, headOffsetX };
|
||||
}
|
||||
|
||||
/**
|
||||
* Analyze lower grid for leg motion.
|
||||
*/
|
||||
_analyzeLegMotion(grid, cols, rows) {
|
||||
const lowerStart = Math.floor(rows * 0.6);
|
||||
let leftMotion = 0, rightMotion = 0;
|
||||
|
||||
for (let r = lowerStart; r < rows; r++) {
|
||||
for (let c = 0; c < Math.floor(cols / 2); c++) {
|
||||
leftMotion += grid[r][c];
|
||||
}
|
||||
for (let c = Math.floor(cols / 2); c < cols; c++) {
|
||||
rightMotion += grid[r][c];
|
||||
}
|
||||
}
|
||||
|
||||
// Return as -1 to 1 range (asymmetry indicates which leg is moving)
|
||||
const total = leftMotion + rightMotion + 0.001;
|
||||
return {
|
||||
left: (leftMotion - rightMotion) / total,
|
||||
right: (rightMotion - leftMotion) / total
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* 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;
|
||||
}
|
||||
}
|
||||
|
|
@ -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;
|
||||
}
|
||||
}
|
||||
|
|
@ -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"
|
||||
]
|
||||
}
|
||||
|
|
@ -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 };
|
||||
Binary file not shown.
Binary file not shown.
|
|
@ -1,812 +0,0 @@
|
|||
/* ============================================================
|
||||
RuView Observatory — Foundation Color Scheme
|
||||
Warm dark background, electric green wireframe, amber data
|
||||
============================================================ */
|
||||
|
||||
@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, 0.85);
|
||||
--bg-panel-border: rgba(0, 210, 120, 0.2);
|
||||
--green-glow: #00d878;
|
||||
--green-bright:#3eff8a;
|
||||
--green-dim: #0a6b3a;
|
||||
--amber: #ffb020;
|
||||
--amber-dim: #a06800;
|
||||
--blue-signal: #2090ff;
|
||||
--blue-dim: #0a3060;
|
||||
--red-alert: #ff3040;
|
||||
--red-heart: #ff4060;
|
||||
--text-primary: #e8ece0;
|
||||
--text-secondary: rgba(232,236,224, 0.55);
|
||||
--text-label: rgba(232,236,224, 0.4);
|
||||
}
|
||||
|
||||
* { margin: 0; padding: 0; box-sizing: border-box; }
|
||||
|
||||
body {
|
||||
background: var(--bg-deep);
|
||||
overflow: hidden;
|
||||
font-family: 'Inter', -apple-system, sans-serif;
|
||||
color: var(--text-primary);
|
||||
-webkit-font-smoothing: antialiased;
|
||||
}
|
||||
|
||||
#observatory-canvas {
|
||||
position: fixed;
|
||||
top: 0; left: 0;
|
||||
width: 100vw; height: 100vh;
|
||||
touch-action: none;
|
||||
}
|
||||
|
||||
/* ---- HUD Overlay ---- */
|
||||
#hud {
|
||||
position: fixed;
|
||||
top: 0; left: 0;
|
||||
width: 100%; height: 100%;
|
||||
pointer-events: none;
|
||||
z-index: 10;
|
||||
}
|
||||
|
||||
/* ---- Brand ---- */
|
||||
#brand {
|
||||
position: absolute;
|
||||
top: 24px; left: 28px;
|
||||
}
|
||||
|
||||
#brand-logo {
|
||||
font-family: 'Inter', sans-serif;
|
||||
font-weight: 700;
|
||||
font-size: 32px;
|
||||
color: var(--text-primary);
|
||||
letter-spacing: -0.5px;
|
||||
text-shadow: 0 0 30px rgba(0, 216, 120, 0.3);
|
||||
}
|
||||
|
||||
.pi {
|
||||
color: var(--green-glow);
|
||||
font-style: italic;
|
||||
margin-right: 2px;
|
||||
}
|
||||
|
||||
#brand-tagline {
|
||||
font-size: 11px;
|
||||
color: var(--text-secondary);
|
||||
letter-spacing: 1.5px;
|
||||
text-transform: uppercase;
|
||||
margin-top: 2px;
|
||||
}
|
||||
|
||||
/* ---- Status bar (top right) ---- */
|
||||
#status-bar {
|
||||
position: absolute;
|
||||
top: 24px; right: 28px;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 12px;
|
||||
}
|
||||
|
||||
#data-source-badge {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
padding: 5px 12px;
|
||||
border-radius: 20px;
|
||||
background: rgba(0, 216, 120, 0.1);
|
||||
border: 1px solid rgba(0, 216, 120, 0.25);
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 11px;
|
||||
letter-spacing: 1px;
|
||||
color: var(--green-glow);
|
||||
}
|
||||
|
||||
.dot {
|
||||
width: 7px; height: 7px;
|
||||
border-radius: 50%;
|
||||
display: inline-block;
|
||||
}
|
||||
.dot--demo { background: var(--amber); box-shadow: 0 0 6px var(--amber); }
|
||||
.dot--live { background: var(--green-glow); box-shadow: 0 0 6px var(--green-glow); animation: pulse-dot 2s infinite; }
|
||||
|
||||
@keyframes pulse-dot {
|
||||
0%, 100% { opacity: 1; }
|
||||
50% { opacity: 0.4; }
|
||||
}
|
||||
|
||||
#scenario-area {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
padding: 5px 14px;
|
||||
border-radius: 20px;
|
||||
background: rgba(255, 176, 32, 0.1);
|
||||
border: 1px solid rgba(255, 176, 32, 0.25);
|
||||
pointer-events: auto;
|
||||
}
|
||||
#autoplay-icon {
|
||||
font-size: 10px;
|
||||
color: var(--green-glow);
|
||||
animation: pulse-dot 2s infinite;
|
||||
}
|
||||
#autoplay-icon.hidden { display: none; }
|
||||
#scenario-quick-select {
|
||||
background: none;
|
||||
border: none;
|
||||
padding: 0;
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 11px;
|
||||
letter-spacing: 0.5px;
|
||||
color: var(--amber);
|
||||
cursor: pointer;
|
||||
outline: none;
|
||||
}
|
||||
#scenario-quick-select:hover,
|
||||
#scenario-quick-select:focus { color: var(--green-glow); }
|
||||
#scenario-quick-select option {
|
||||
background: #0c1420;
|
||||
color: var(--text-primary);
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 12px;
|
||||
padding: 4px 8px;
|
||||
}
|
||||
|
||||
#fps-counter {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 11px;
|
||||
color: var(--text-secondary);
|
||||
}
|
||||
|
||||
/* ---- Data Panels ---- */
|
||||
.data-panel {
|
||||
position: absolute;
|
||||
width: 220px;
|
||||
background: var(--bg-panel);
|
||||
border: 1px solid var(--bg-panel-border);
|
||||
border-radius: 12px;
|
||||
padding: 16px;
|
||||
backdrop-filter: blur(12px);
|
||||
-webkit-backdrop-filter: blur(12px);
|
||||
pointer-events: auto;
|
||||
}
|
||||
|
||||
.panel-header {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 10px;
|
||||
font-weight: 600;
|
||||
letter-spacing: 2px;
|
||||
text-transform: uppercase;
|
||||
color: var(--text-label);
|
||||
margin-bottom: 14px;
|
||||
padding-bottom: 8px;
|
||||
border-bottom: 1px solid rgba(255,255,255,0.06);
|
||||
}
|
||||
|
||||
/* ---- Vitals Panel (left) ---- */
|
||||
#panel-vitals {
|
||||
left: 28px;
|
||||
top: 50%;
|
||||
transform: translateY(-50%);
|
||||
}
|
||||
|
||||
.vital-row {
|
||||
display: flex;
|
||||
align-items: flex-start;
|
||||
gap: 12px;
|
||||
margin-bottom: 18px;
|
||||
}
|
||||
.vital-row:last-child { margin-bottom: 0; }
|
||||
|
||||
.vital-icon {
|
||||
font-size: 20px;
|
||||
line-height: 1;
|
||||
margin-top: 2px;
|
||||
width: 24px;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.vital-row:nth-child(2) .vital-icon { color: var(--red-heart); }
|
||||
.vital-row:nth-child(3) .vital-icon { color: var(--green-glow); }
|
||||
.vital-row:nth-child(4) .vital-icon { color: var(--amber); }
|
||||
|
||||
.vital-data { flex: 1; }
|
||||
|
||||
.vital-label {
|
||||
font-size: 10px;
|
||||
color: var(--text-label);
|
||||
letter-spacing: 1px;
|
||||
text-transform: uppercase;
|
||||
margin-bottom: 3px;
|
||||
}
|
||||
|
||||
.vital-value {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 26px;
|
||||
font-weight: 600;
|
||||
line-height: 1.1;
|
||||
}
|
||||
|
||||
.vital-unit {
|
||||
font-size: 12px;
|
||||
font-weight: 400;
|
||||
color: var(--text-secondary);
|
||||
}
|
||||
|
||||
.vital-bar {
|
||||
height: 3px;
|
||||
background: rgba(255,255,255,0.06);
|
||||
border-radius: 2px;
|
||||
margin-top: 6px;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.vital-bar-fill {
|
||||
height: 100%;
|
||||
border-radius: 2px;
|
||||
transition: width 0.5s ease;
|
||||
}
|
||||
|
||||
.vital-bar--hr { background: var(--red-heart); width: 0%; }
|
||||
.vital-bar--br { background: var(--green-glow); width: 0%; }
|
||||
.vital-bar--conf { background: var(--amber); width: 0%; }
|
||||
|
||||
/* ---- Signal Panel (right) ---- */
|
||||
#panel-signal {
|
||||
right: 28px;
|
||||
top: 50%;
|
||||
transform: translateY(-50%);
|
||||
}
|
||||
|
||||
.signal-row {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
margin-bottom: 8px;
|
||||
}
|
||||
|
||||
.signal-label {
|
||||
font-size: 11px;
|
||||
color: var(--text-label);
|
||||
letter-spacing: 0.5px;
|
||||
}
|
||||
|
||||
.signal-value {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 13px;
|
||||
font-weight: 600;
|
||||
color: var(--blue-signal);
|
||||
}
|
||||
|
||||
#rssi-sparkline {
|
||||
width: 100%;
|
||||
height: 48px;
|
||||
margin-top: 8px;
|
||||
border-radius: 6px;
|
||||
background: rgba(0,0,0,0.3);
|
||||
}
|
||||
|
||||
/* Presence */
|
||||
.presence-state {
|
||||
text-align: center;
|
||||
padding: 8px;
|
||||
border-radius: 8px;
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 14px;
|
||||
font-weight: 600;
|
||||
letter-spacing: 2px;
|
||||
transition: all 0.5s ease;
|
||||
}
|
||||
|
||||
.presence--absent {
|
||||
background: rgba(255,255,255,0.03);
|
||||
color: var(--text-label);
|
||||
border: 1px solid rgba(255,255,255,0.05);
|
||||
}
|
||||
|
||||
.presence--present {
|
||||
background: rgba(0, 216, 120, 0.1);
|
||||
color: var(--green-glow);
|
||||
border: 1px solid rgba(0, 216, 120, 0.3);
|
||||
box-shadow: 0 0 20px rgba(0, 216, 120, 0.1);
|
||||
}
|
||||
|
||||
.presence--active {
|
||||
background: rgba(255, 176, 32, 0.1);
|
||||
color: var(--amber);
|
||||
border: 1px solid rgba(255, 176, 32, 0.3);
|
||||
box-shadow: 0 0 20px rgba(255, 176, 32, 0.1);
|
||||
}
|
||||
|
||||
.fall-alert {
|
||||
margin-top: 10px;
|
||||
text-align: center;
|
||||
padding: 8px;
|
||||
border-radius: 8px;
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 12px;
|
||||
font-weight: 700;
|
||||
letter-spacing: 2px;
|
||||
background: rgba(255, 48, 64, 0.15);
|
||||
color: var(--red-alert);
|
||||
border: 1px solid rgba(255, 48, 64, 0.4);
|
||||
animation: pulse-alert 0.8s infinite;
|
||||
}
|
||||
|
||||
@keyframes pulse-alert {
|
||||
0%, 100% { opacity: 1; }
|
||||
50% { opacity: 0.5; }
|
||||
}
|
||||
|
||||
/* ---- Capabilities Bar (bottom center) ---- */
|
||||
#capabilities-bar {
|
||||
position: absolute;
|
||||
bottom: 20px;
|
||||
left: 50%;
|
||||
transform: translateX(-50%);
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 0;
|
||||
background: var(--bg-panel);
|
||||
border: 1px solid var(--bg-panel-border);
|
||||
border-radius: 30px;
|
||||
padding: 8px 24px;
|
||||
backdrop-filter: blur(12px);
|
||||
}
|
||||
|
||||
.cap-item {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
font-size: 12px;
|
||||
font-weight: 500;
|
||||
color: var(--text-secondary);
|
||||
padding: 0 16px;
|
||||
}
|
||||
|
||||
.cap-icon {
|
||||
font-size: 16px;
|
||||
color: var(--green-glow);
|
||||
}
|
||||
|
||||
.cap-item:nth-child(3) .cap-icon { color: var(--red-heart); }
|
||||
.cap-item:nth-child(5) .cap-icon { color: var(--blue-signal); }
|
||||
|
||||
.cap-divider {
|
||||
width: 1px;
|
||||
height: 20px;
|
||||
background: rgba(255,255,255,0.1);
|
||||
}
|
||||
|
||||
/* ---- Key hints ---- */
|
||||
#key-hints {
|
||||
position: absolute;
|
||||
bottom: 24px;
|
||||
right: 28px;
|
||||
display: flex;
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
.key-hint {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 10px;
|
||||
color: rgba(255,255,255,0.2);
|
||||
letter-spacing: 0.5px;
|
||||
padding: 3px 8px;
|
||||
border-radius: 4px;
|
||||
background: rgba(255,255,255,0.03);
|
||||
border: 1px solid rgba(255,255,255,0.05);
|
||||
}
|
||||
|
||||
/* ---- Settings button ---- */
|
||||
#settings-btn {
|
||||
pointer-events: auto;
|
||||
background: rgba(255,255,255,0.06);
|
||||
border: 1px solid rgba(255,255,255,0.1);
|
||||
color: var(--text-secondary);
|
||||
font-size: 18px;
|
||||
width: 34px; height: 34px;
|
||||
border-radius: 50%;
|
||||
cursor: pointer;
|
||||
transition: all 0.2s;
|
||||
display: flex; align-items: center; justify-content: center;
|
||||
padding: 0;
|
||||
}
|
||||
#settings-btn:hover {
|
||||
background: rgba(0, 216, 120, 0.15);
|
||||
border-color: var(--green-glow);
|
||||
color: var(--green-glow);
|
||||
}
|
||||
|
||||
/* ---- Settings Dialog ---- */
|
||||
.settings-overlay {
|
||||
position: fixed;
|
||||
top: 0; left: 0;
|
||||
width: 100%; height: 100%;
|
||||
z-index: 100;
|
||||
background: rgba(0,0,0,0.5);
|
||||
backdrop-filter: blur(4px);
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
pointer-events: auto;
|
||||
}
|
||||
|
||||
.settings-dialog {
|
||||
background: rgba(10, 16, 28, 0.96);
|
||||
border: 1px solid rgba(0, 216, 120, 0.2);
|
||||
border-radius: 16px;
|
||||
width: 440px;
|
||||
max-height: 80vh;
|
||||
overflow-y: auto;
|
||||
padding: 0;
|
||||
box-shadow: 0 20px 60px rgba(0,0,0,0.6), 0 0 40px rgba(0,216,120,0.05);
|
||||
}
|
||||
|
||||
.settings-header {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
padding: 16px 20px;
|
||||
border-bottom: 1px solid rgba(255,255,255,0.06);
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 13px;
|
||||
font-weight: 600;
|
||||
letter-spacing: 1px;
|
||||
text-transform: uppercase;
|
||||
color: var(--text-primary);
|
||||
}
|
||||
|
||||
.settings-header button {
|
||||
background: none;
|
||||
border: none;
|
||||
color: var(--text-secondary);
|
||||
font-size: 22px;
|
||||
cursor: pointer;
|
||||
padding: 0 4px;
|
||||
line-height: 1;
|
||||
}
|
||||
.settings-header button:hover { color: var(--red-alert); }
|
||||
|
||||
.settings-tabs {
|
||||
display: flex;
|
||||
border-bottom: 1px solid rgba(255,255,255,0.06);
|
||||
padding: 0 12px;
|
||||
}
|
||||
|
||||
.stab {
|
||||
background: none;
|
||||
border: none;
|
||||
color: var(--text-label);
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 10px;
|
||||
letter-spacing: 1px;
|
||||
text-transform: uppercase;
|
||||
padding: 10px 14px;
|
||||
cursor: pointer;
|
||||
border-bottom: 2px solid transparent;
|
||||
transition: all 0.2s;
|
||||
}
|
||||
.stab:hover { color: var(--text-secondary); }
|
||||
.stab.active {
|
||||
color: var(--green-glow);
|
||||
border-bottom-color: var(--green-glow);
|
||||
}
|
||||
|
||||
.stab-content {
|
||||
display: none;
|
||||
padding: 16px 20px;
|
||||
}
|
||||
.stab-content.active { display: block; }
|
||||
|
||||
.setting-row {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
gap: 12px;
|
||||
margin-bottom: 14px;
|
||||
font-size: 12px;
|
||||
color: var(--text-secondary);
|
||||
}
|
||||
|
||||
.setting-row span:first-child {
|
||||
min-width: 120px;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.setting-row input[type="range"] {
|
||||
flex: 1;
|
||||
height: 4px;
|
||||
-webkit-appearance: none;
|
||||
appearance: none;
|
||||
background: rgba(255,255,255,0.08);
|
||||
border-radius: 2px;
|
||||
outline: none;
|
||||
}
|
||||
.setting-row input[type="range"]::-webkit-slider-thumb {
|
||||
-webkit-appearance: none;
|
||||
width: 14px; height: 14px;
|
||||
border-radius: 50%;
|
||||
background: var(--green-glow);
|
||||
cursor: pointer;
|
||||
box-shadow: 0 0 6px rgba(0,216,120,0.4);
|
||||
}
|
||||
|
||||
.setting-row input[type="color"] {
|
||||
-webkit-appearance: none;
|
||||
width: 36px; height: 24px;
|
||||
border: 1px solid rgba(255,255,255,0.15);
|
||||
border-radius: 4px;
|
||||
background: none;
|
||||
cursor: pointer;
|
||||
padding: 0;
|
||||
}
|
||||
.setting-row input[type="color"]::-webkit-color-swatch-wrapper { padding: 2px; }
|
||||
.setting-row input[type="color"]::-webkit-color-swatch { border-radius: 2px; border: none; }
|
||||
|
||||
.setting-row select,
|
||||
.setting-row input[type="text"] {
|
||||
flex: 1;
|
||||
background: #0c1420;
|
||||
border: 1px solid rgba(255,255,255,0.1);
|
||||
color: var(--text-primary);
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 11px;
|
||||
padding: 6px 10px;
|
||||
border-radius: 6px;
|
||||
outline: none;
|
||||
}
|
||||
.setting-row select:focus,
|
||||
.setting-row input[type="text"]:focus {
|
||||
border-color: var(--green-glow);
|
||||
}
|
||||
.setting-row select option {
|
||||
background: #0c1420;
|
||||
color: var(--text-primary);
|
||||
padding: 6px 10px;
|
||||
}
|
||||
.setting-row select optgroup {
|
||||
background: #0a1018;
|
||||
color: var(--green-glow);
|
||||
font-style: normal;
|
||||
font-weight: 600;
|
||||
padding: 4px 0;
|
||||
}
|
||||
|
||||
.setting-row input[type="checkbox"] {
|
||||
width: 18px; height: 18px;
|
||||
accent-color: var(--green-glow);
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.check-row {
|
||||
flex-direction: row;
|
||||
}
|
||||
|
||||
.range-val {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 10px;
|
||||
color: var(--green-glow);
|
||||
min-width: 44px;
|
||||
text-align: right;
|
||||
}
|
||||
|
||||
.settings-btn {
|
||||
width: 100%;
|
||||
padding: 8px;
|
||||
margin-top: 6px;
|
||||
background: rgba(0, 216, 120, 0.08);
|
||||
border: 1px solid rgba(0, 216, 120, 0.2);
|
||||
color: var(--green-glow);
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 11px;
|
||||
letter-spacing: 1px;
|
||||
border-radius: 6px;
|
||||
cursor: pointer;
|
||||
transition: all 0.2s;
|
||||
}
|
||||
.settings-btn:hover {
|
||||
background: rgba(0, 216, 120, 0.15);
|
||||
border-color: var(--green-glow);
|
||||
}
|
||||
|
||||
/* ---- Scenario Description ---- */
|
||||
#scenario-description {
|
||||
position: absolute;
|
||||
top: 60px;
|
||||
right: 28px;
|
||||
max-width: 340px;
|
||||
font-size: 11px;
|
||||
color: var(--text-secondary);
|
||||
font-style: italic;
|
||||
letter-spacing: 0.3px;
|
||||
line-height: 1.4;
|
||||
pointer-events: none;
|
||||
opacity: 0.7;
|
||||
transition: opacity 0.5s ease;
|
||||
}
|
||||
|
||||
/* ---- Edge Module Badges ---- */
|
||||
#edge-modules-bar {
|
||||
position: absolute;
|
||||
bottom: 58px;
|
||||
left: 50%;
|
||||
transform: translateX(-50%);
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
pointer-events: none;
|
||||
}
|
||||
|
||||
.edge-badge {
|
||||
display: inline-block;
|
||||
padding: 2px 8px;
|
||||
border-radius: 10px;
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 9px;
|
||||
font-weight: 600;
|
||||
letter-spacing: 1px;
|
||||
color: var(--badge-color, var(--text-secondary));
|
||||
background: rgba(255,255,255,0.04);
|
||||
border: 1px solid var(--badge-color, rgba(255,255,255,0.1));
|
||||
box-shadow: 0 0 6px color-mix(in srgb, var(--badge-color, transparent) 30%, transparent);
|
||||
}
|
||||
|
||||
/* ---- Person Count Dots ---- */
|
||||
.persons-dots {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
gap: 3px;
|
||||
margin-left: 6px;
|
||||
vertical-align: middle;
|
||||
}
|
||||
|
||||
.person-dot {
|
||||
width: 6px;
|
||||
height: 6px;
|
||||
border-radius: 50%;
|
||||
display: inline-block;
|
||||
background: rgba(255,255,255,0.08);
|
||||
border: 1px solid rgba(255,255,255,0.1);
|
||||
transition: background 0.4s ease, border-color 0.4s ease, box-shadow 0.4s ease;
|
||||
}
|
||||
|
||||
.person-dot--active {
|
||||
background: var(--green-glow);
|
||||
border-color: var(--green-glow);
|
||||
box-shadow: 0 0 4px rgba(0, 216, 120, 0.4);
|
||||
}
|
||||
|
||||
/* ---- Vital Value Color Transitions ---- */
|
||||
.vital-value span:first-child {
|
||||
transition: color 0.6s ease;
|
||||
}
|
||||
|
||||
.vital-bar-fill {
|
||||
transition: width 0.5s ease, background 0.6s ease;
|
||||
}
|
||||
|
||||
/* ---- Responsive ---- */
|
||||
@media (max-width: 1200px) {
|
||||
.data-panel { width: 190px; padding: 12px; }
|
||||
.vital-value { font-size: 22px; }
|
||||
#capabilities-bar { display: none; }
|
||||
}
|
||||
|
||||
@media (max-width: 800px) {
|
||||
/* Brand — smaller, top-left */
|
||||
#brand { top: 12px; left: 14px; }
|
||||
#brand-logo { font-size: 22px; }
|
||||
#brand-tagline { font-size: 9px; letter-spacing: 1px; }
|
||||
|
||||
/* Status bar — compact, below brand */
|
||||
#status-bar {
|
||||
top: 12px; right: 14px;
|
||||
gap: 6px;
|
||||
}
|
||||
#data-source-badge { padding: 3px 8px; font-size: 9px; }
|
||||
#scenario-area { padding: 3px 10px; }
|
||||
#scenario-quick-select { font-size: 10px; }
|
||||
#settings-btn { width: 30px; height: 30px; font-size: 15px; }
|
||||
|
||||
/* Scenario description — under status bar */
|
||||
#scenario-description {
|
||||
top: 46px; right: 14px;
|
||||
max-width: 200px;
|
||||
font-size: 10px;
|
||||
}
|
||||
|
||||
/* Data panels — horizontal strip at bottom instead of side panels */
|
||||
.data-panel {
|
||||
position: fixed;
|
||||
width: auto;
|
||||
left: 8px;
|
||||
right: 8px;
|
||||
top: auto;
|
||||
transform: none;
|
||||
border-radius: 10px;
|
||||
padding: 10px 14px;
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
gap: 4px 16px;
|
||||
align-items: center;
|
||||
}
|
||||
|
||||
#panel-vitals {
|
||||
bottom: 100px;
|
||||
left: 8px;
|
||||
right: 8px;
|
||||
transform: none;
|
||||
}
|
||||
#panel-vitals .panel-header { display: none; }
|
||||
#panel-vitals .vital-row {
|
||||
margin-bottom: 0;
|
||||
flex: 1;
|
||||
min-width: 90px;
|
||||
}
|
||||
#panel-vitals .vital-icon { font-size: 16px; }
|
||||
#panel-vitals .vital-value { font-size: 18px; }
|
||||
#panel-vitals .vital-bar { display: none; }
|
||||
#panel-vitals .vital-label { font-size: 8px; }
|
||||
#panel-vitals .vital-unit { font-size: 10px; }
|
||||
|
||||
#panel-signal {
|
||||
bottom: 8px;
|
||||
left: 8px;
|
||||
right: 8px;
|
||||
transform: none;
|
||||
}
|
||||
#panel-signal .panel-header { display: none; }
|
||||
#panel-signal .signal-row { margin-bottom: 2px; }
|
||||
#panel-signal .signal-label { font-size: 10px; }
|
||||
#panel-signal .signal-value { font-size: 11px; }
|
||||
#panel-signal #rssi-sparkline { display: none; }
|
||||
#panel-signal .presence-state {
|
||||
display: inline-block;
|
||||
padding: 4px 12px;
|
||||
font-size: 11px;
|
||||
}
|
||||
#panel-signal .fall-alert { padding: 4px 8px; font-size: 10px; }
|
||||
|
||||
/* Key hints — hidden on mobile (no keyboard) */
|
||||
#key-hints { display: none; }
|
||||
|
||||
/* Capabilities bar — hidden */
|
||||
#capabilities-bar { display: none; }
|
||||
|
||||
/* Edge modules — smaller */
|
||||
#edge-modules-bar { bottom: 196px; }
|
||||
.edge-badge { font-size: 8px; padding: 1px 6px; }
|
||||
|
||||
/* Settings dialog — full width on mobile */
|
||||
.settings-dialog {
|
||||
width: 96vw;
|
||||
max-height: 85vh;
|
||||
border-radius: 12px;
|
||||
}
|
||||
.settings-header { padding: 12px 16px; font-size: 12px; }
|
||||
.stab { padding: 8px 10px; font-size: 9px; }
|
||||
.stab-content { padding: 12px 16px; }
|
||||
.setting-row { font-size: 11px; gap: 8px; margin-bottom: 10px; }
|
||||
.setting-row span:first-child { min-width: 90px; }
|
||||
}
|
||||
|
||||
/* Extra small screens (phones in portrait) */
|
||||
@media (max-width: 480px) {
|
||||
#brand-tagline { display: none; }
|
||||
#scenario-area { display: none; }
|
||||
#scenario-description { display: none; }
|
||||
|
||||
#panel-vitals {
|
||||
bottom: 70px;
|
||||
}
|
||||
#panel-vitals .vital-row { min-width: 70px; }
|
||||
#panel-vitals .vital-value { font-size: 16px; }
|
||||
|
||||
#panel-signal {
|
||||
flex-wrap: nowrap;
|
||||
overflow-x: auto;
|
||||
gap: 2px 12px;
|
||||
}
|
||||
|
||||
#edge-modules-bar { display: none; }
|
||||
}
|
||||
|
|
@ -0,0 +1,405 @@
|
|||
/* 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 === */
|
||||
.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: 50%; left: 50%;
|
||||
transform: translate(-50%, -50%);
|
||||
text-align: center;
|
||||
color: var(--text-secondary);
|
||||
}
|
||||
|
||||
.camera-prompt button {
|
||||
margin-top: 12px;
|
||||
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); }
|
||||
|
||||
/* === Side Panels === */
|
||||
.side-panels {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 12px;
|
||||
overflow-y: auto;
|
||||
min-height: 0;
|
||||
}
|
||||
|
||||
.panel {
|
||||
background: var(--bg-panel);
|
||||
border: 1px solid var(--bg-panel-border);
|
||||
border-radius: var(--radius);
|
||||
padding: 14px;
|
||||
}
|
||||
|
||||
.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;
|
||||
}
|
||||
|
||||
/* === 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;
|
||||
}
|
||||
|
||||
/* === 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; }
|
||||
}
|
||||
|
|
@ -0,0 +1,247 @@
|
|||
/**
|
||||
* 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;
|
||||
|
||||
ctx.clearRect(0, 0, width, height);
|
||||
|
||||
if (!keypoints || keypoints.length === 0) return;
|
||||
|
||||
// Draw limbs first (behind joints)
|
||||
ctx.lineWidth = 3;
|
||||
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;
|
||||
|
||||
// Glow
|
||||
ctx.strokeStyle = this.colors.limbGlow;
|
||||
ctx.lineWidth = 8;
|
||||
ctx.globalAlpha = avgConf * 0.4;
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(ax, ay);
|
||||
ctx.lineTo(bx, by);
|
||||
ctx.stroke();
|
||||
|
||||
// Main line
|
||||
ctx.strokeStyle = limbColor;
|
||||
ctx.lineWidth = 2.5;
|
||||
ctx.globalAlpha = avgConf;
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(ax, ay);
|
||||
ctx.lineTo(bx, by);
|
||||
ctx.stroke();
|
||||
}
|
||||
|
||||
// Draw joints
|
||||
ctx.globalAlpha = 1;
|
||||
for (const kp of keypoints) {
|
||||
if (!kp || kp.confidence < minConf) continue;
|
||||
|
||||
const x = kp.x * width;
|
||||
const y = kp.y * height;
|
||||
const r = 3 + kp.confidence * 3;
|
||||
|
||||
// Glow
|
||||
ctx.beginPath();
|
||||
ctx.arc(x, y, r + 4, 0, Math.PI * 2);
|
||||
ctx.fillStyle = glowColor;
|
||||
ctx.globalAlpha = kp.confidence * 0.6;
|
||||
ctx.fill();
|
||||
|
||||
// Joint dot
|
||||
ctx.beginPath();
|
||||
ctx.arc(x, y, r, 0, Math.PI * 2);
|
||||
ctx.fillStyle = jointColor;
|
||||
ctx.globalAlpha = kp.confidence;
|
||||
ctx.fill();
|
||||
|
||||
// White center
|
||||
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
|
||||
if (opts.label) {
|
||||
ctx.font = '11px "JetBrains Mono", monospace';
|
||||
ctx.fillStyle = jointColor;
|
||||
ctx.globalAlpha = 0.8;
|
||||
ctx.fillText(opts.label, 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();
|
||||
|
||||
const drawPoints = (pts, color, size) => {
|
||||
if (!pts || pts.length === 0) return;
|
||||
const len = pts.length;
|
||||
for (let i = 0; i < len; i++) {
|
||||
const p = pts[i];
|
||||
if (!p) continue;
|
||||
const age = 1 - (i / len) * 0.7; // Fade older points
|
||||
const px = w / 2 + p[0] * w * 0.35;
|
||||
const py = h / 2 + p[1] * h * 0.35;
|
||||
|
||||
if (px < 0 || px > w || py < 0 || py > h) continue;
|
||||
|
||||
ctx.beginPath();
|
||||
ctx.arc(px, py, size, 0, Math.PI * 2);
|
||||
ctx.fillStyle = color;
|
||||
ctx.globalAlpha = age * 0.7;
|
||||
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)})`;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,226 @@
|
|||
/**
|
||||
* CNN Embedder — Lightweight MobileNet-V3-style feature extractor.
|
||||
*
|
||||
* Architecture mirrors ruvector-cnn: Conv2D → BatchNorm → ReLU → Pool → Project → L2 Normalize
|
||||
* Uses pre-seeded random weights (deterministic). When ruvector-cnn-wasm is available,
|
||||
* transparently delegates to the WASM implementation.
|
||||
*
|
||||
* 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;
|
||||
|
||||
// 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
|
||||
this.projWeights = new Float32Array(16 * this.embeddingDim);
|
||||
for (let i = 0; i < this.projWeights.length; i++) {
|
||||
this.projWeights[i] = randRange(-0.1, 0.1);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Try to load WASM embedder from ruvector-cnn-wasm package
|
||||
* @param {string} wasmPath - Path to the WASM package directory
|
||||
*/
|
||||
async tryLoadWasm(wasmPath) {
|
||||
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 embedder loaded successfully');
|
||||
return true;
|
||||
} catch (e) {
|
||||
console.log('[CNN] WASM 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 → 16-dim
|
||||
const outH = sz - 2, outW = sz - 2;
|
||||
const pooled = new Float32Array(16);
|
||||
const spatial = outH * outW;
|
||||
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;
|
||||
|
||||
// 7. 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;
|
||||
}
|
||||
|
||||
// 8. 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;
|
||||
}
|
||||
|
||||
_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 between two embeddings */
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,267 @@
|
|||
/**
|
||||
* 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 {
|
||||
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;
|
||||
}
|
||||
|
||||
// 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) {
|
||||
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();
|
||||
}
|
||||
|
||||
// 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];
|
||||
|
||||
// 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) {
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
||||
_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;
|
||||
};
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,166 @@
|
|||
/**
|
||||
* 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
|
||||
*/
|
||||
constructor(embeddingDim = 128) {
|
||||
this.embeddingDim = embeddingDim;
|
||||
|
||||
// Learnable attention weights (initialized to balanced 0.5)
|
||||
// In production, these would be loaded from trained JSON
|
||||
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;
|
||||
}
|
||||
|
||||
/**
|
||||
* 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
|
||||
let norm = 0;
|
||||
for (let i = 0; i < dim; i++) norm += fused[i] * fused[i];
|
||||
norm = Math.sqrt(norm);
|
||||
if (norm > 1e-8) {
|
||||
for (let i = 0; i < dim; i++) fused[i] /= norm;
|
||||
}
|
||||
|
||||
this._recordEmbedding(videoEmb, csiEmb, fused);
|
||||
return fused;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get embedding pairs for 2D visualization (PCA projection)
|
||||
* @returns {{ video: Array, csi: Array, fused: Array }}
|
||||
*/
|
||||
getEmbeddingPoints() {
|
||||
// Simple 2D projection using first two principal components (approximated)
|
||||
const project = (emb) => {
|
||||
if (!emb || emb.length < 4) return null;
|
||||
// Use pairs of dimensions as crude 2D projection
|
||||
let x = 0, y = 0;
|
||||
for (let i = 0; i < emb.length; i += 2) {
|
||||
x += emb[i] * (i % 4 < 2 ? 1 : -1);
|
||||
if (i + 1 < emb.length) {
|
||||
y += emb[i + 1] * (i % 4 < 2 ? 1 : -1);
|
||||
}
|
||||
}
|
||||
return [x * 2, y * 2]; // Scale for visibility
|
||||
};
|
||||
|
||||
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;
|
||||
|
||||
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;
|
||||
}
|
||||
|
||||
_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();
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,303 @@
|
|||
/**
|
||||
* WiFi-DensePose — Dual-Modal Pose Estimation Demo
|
||||
*
|
||||
* Main orchestration: video capture → CNN embedding → CSI processing → fusion → rendering
|
||||
*/
|
||||
|
||||
import { VideoCapture } from './video-capture.js';
|
||||
import { CsiSimulator } from './csi-simulator.js';
|
||||
import { CnnEmbedder } from './cnn-embedder.js';
|
||||
import { FusionEngine } from './fusion-engine.js';
|
||||
import { PoseDecoder } from './pose-decoder.js';
|
||||
import { CanvasRenderer } from './canvas-renderer.js';
|
||||
|
||||
// === 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');
|
||||
|
||||
// === Initialize ===
|
||||
function init() {
|
||||
resizeCanvases();
|
||||
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 WASM embedders (non-blocking)
|
||||
// Resolve relative to this JS module file (in pose-fusion/js/) → ../pkg/
|
||||
const wasmBase = new URL('../pkg/ruvector_cnn_wasm', import.meta.url).href;
|
||||
visualCnn.tryLoadWasm(wasmBase);
|
||||
csiCnn.tryLoadWasm(wasmBase);
|
||||
|
||||
// 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';
|
||||
const needsCsi = mode !== 'video';
|
||||
|
||||
// Show/hide camera prompt
|
||||
if (needsVideo && !videoCapture.isActive) {
|
||||
cameraPrompt.style.display = 'flex';
|
||||
} else {
|
||||
cameraPrompt.style.display = 'none';
|
||||
}
|
||||
}
|
||||
|
||||
function resizeCanvases() {
|
||||
const videoPanel = document.querySelector('.video-panel');
|
||||
if (videoPanel) {
|
||||
const rect = videoPanel.getBoundingClientRect();
|
||||
skeletonCanvas.width = rect.width;
|
||||
skeletonCanvas.height = rect.height;
|
||||
}
|
||||
|
||||
// CSI canvas
|
||||
csiCanvas.width = csiCanvas.parentElement.clientWidth;
|
||||
csiCanvas.height = 120;
|
||||
|
||||
// Embedding canvas
|
||||
embeddingCanvas.width = embeddingCanvas.parentElement.clientWidth;
|
||||
embeddingCanvas.height = 140;
|
||||
}
|
||||
|
||||
// === Main Loop ===
|
||||
function mainLoop(timestamp) {
|
||||
if (!isRunning) return;
|
||||
requestAnimationFrame(mainLoop);
|
||||
|
||||
if (isPaused) return;
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
// Boot
|
||||
document.addEventListener('DOMContentLoaded', init);
|
||||
|
|
@ -0,0 +1,373 @@
|
|||
/**
|
||||
* 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).
|
||||
*/
|
||||
|
||||
// COCO keypoint definitions
|
||||
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'
|
||||
];
|
||||
|
||||
// Skeleton connections (pairs of keypoint indices)
|
||||
export const SKELETON_CONNECTIONS = [
|
||||
[0, 1], [0, 2], [1, 3], [2, 4], // Head
|
||||
[5, 6], // 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
|
||||
];
|
||||
|
||||
// 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,
|
||||
};
|
||||
|
||||
export class PoseDecoder {
|
||||
constructor(embeddingDim = 128) {
|
||||
this.embeddingDim = embeddingDim;
|
||||
this.smoothedKeypoints = null;
|
||||
this.smoothingFactor = 0.45; // Lower = more responsive to movement
|
||||
this._time = 0;
|
||||
|
||||
// Through-wall tracking state
|
||||
this._lastBodyState = null;
|
||||
this._ghostState = null;
|
||||
this._ghostConfidence = 0;
|
||||
this._ghostVelocity = { x: 0, y: 0 };
|
||||
|
||||
// Arm tracking history (smoothed positions)
|
||||
this._leftArmY = 0.5;
|
||||
this._rightArmY = 0.5;
|
||||
this._leftArmX = 0;
|
||||
this._rightArmX = 0;
|
||||
this._headOffsetX = 0;
|
||||
}
|
||||
|
||||
/**
|
||||
* 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.
|
||||
* The grid tells us WHERE motion is happening → we map that to joint positions.
|
||||
*/
|
||||
_trackFromMotionGrid(region, embedding, elapsed) {
|
||||
const grid = region.motionGrid;
|
||||
const cols = region.gridCols || 10;
|
||||
const rows = region.gridRows || 8;
|
||||
|
||||
// Body bounding box
|
||||
const cx = region.x + region.w / 2;
|
||||
const cy = region.y + region.h / 2;
|
||||
const bodyH = Math.max(region.h, 0.3);
|
||||
const bodyW = Math.max(region.w, 0.15);
|
||||
|
||||
// Analyze the motion grid to find arm positions
|
||||
// Divide body into zones: head (top 20%), arms (top 60% sides), torso (center), legs (bottom 40%)
|
||||
if (grid) {
|
||||
const armAnalysis = this._analyzeArmMotion(grid, cols, rows, region);
|
||||
// Smooth arm tracking
|
||||
this._leftArmY = 0.6 * this._leftArmY + 0.4 * armAnalysis.leftArmHeight;
|
||||
this._rightArmY = 0.6 * this._rightArmY + 0.4 * armAnalysis.rightArmHeight;
|
||||
this._leftArmX = 0.6 * this._leftArmX + 0.4 * armAnalysis.leftArmSpread;
|
||||
this._rightArmX = 0.6 * this._rightArmX + 0.4 * armAnalysis.rightArmSpread;
|
||||
this._headOffsetX = 0.7 * this._headOffsetX + 0.3 * armAnalysis.headOffsetX;
|
||||
}
|
||||
|
||||
const P = PROPORTIONS;
|
||||
const halfW = P.shoulderWidth * bodyH / 2;
|
||||
const hipHalfW = P.hipWidth * bodyH / 2;
|
||||
|
||||
// Breathing (subtle)
|
||||
const breathe = Math.sin(elapsed * 1.5) * 0.002;
|
||||
|
||||
// Core body positions from detection center
|
||||
const hipY = cy + bodyH * 0.15;
|
||||
const shoulderY = hipY - P.shoulderToHip * bodyH + breathe;
|
||||
const headY = shoulderY - P.headToShoulder * bodyH;
|
||||
const kneeY = hipY + P.hipToKnee * bodyH;
|
||||
const ankleY = kneeY + P.kneeToAnkle * bodyH;
|
||||
|
||||
// HEAD follows motion centroid
|
||||
const headX = cx + this._headOffsetX * bodyW * 0.3;
|
||||
|
||||
// ARM POSITIONS driven by motion grid analysis
|
||||
// leftArmY: 0 = arm down at side, 1 = arm fully raised
|
||||
// leftArmSpread: how far out the arm extends
|
||||
const leftArmRaise = this._leftArmY; // 0-1
|
||||
const rightArmRaise = this._rightArmY;
|
||||
const leftSpread = 0.02 + this._leftArmX * 0.12;
|
||||
const rightSpread = 0.02 + this._rightArmX * 0.12;
|
||||
|
||||
// Elbow: interpolate between "at side" and "raised"
|
||||
const lElbowY = shoulderY + P.shoulderToElbow * bodyH * (1 - leftArmRaise * 0.9);
|
||||
const rElbowY = shoulderY + P.shoulderToElbow * bodyH * (1 - rightArmRaise * 0.9);
|
||||
const lElbowX = cx - halfW - leftSpread;
|
||||
const rElbowX = cx + halfW + rightSpread;
|
||||
|
||||
// Wrist: extends further when raised
|
||||
const lWristY = lElbowY + P.elbowToWrist * bodyH * (1 - leftArmRaise * 1.1);
|
||||
const rWristY = rElbowY + P.elbowToWrist * bodyH * (1 - rightArmRaise * 1.1);
|
||||
const lWristX = lElbowX - leftSpread * 0.6;
|
||||
const rWristX = rElbowX + rightSpread * 0.6;
|
||||
|
||||
// Leg motion from lower grid cells
|
||||
const legMotion = grid ? this._analyzeLegMotion(grid, cols, rows) : { left: 0, right: 0 };
|
||||
const legSwing = 0.015;
|
||||
|
||||
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: cx - halfW, y: shoulderY, confidence: 0.94 },
|
||||
// 6: right_shoulder
|
||||
{ x: cx + halfW, y: shoulderY, 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: cx - hipHalfW, y: hipY, confidence: 0.91 },
|
||||
// 12: right_hip
|
||||
{ x: cx + hipHalfW, y: hipY, confidence: 0.91 },
|
||||
// 13: left_knee
|
||||
{ x: cx - hipHalfW + legMotion.left * legSwing, y: kneeY, confidence: 0.88 },
|
||||
// 14: right_knee
|
||||
{ x: cx + hipHalfW + legMotion.right * legSwing, y: kneeY, confidence: 0.88 },
|
||||
// 15: left_ankle
|
||||
{ x: cx - hipHalfW + legMotion.left * legSwing * 1.3, y: ankleY, confidence: 0.83 },
|
||||
// 16: right_ankle
|
||||
{ x: cx + hipHalfW + legMotion.right * legSwing * 1.3, y: ankleY, confidence: 0.83 },
|
||||
];
|
||||
|
||||
for (let i = 0; i < keypoints.length; i++) {
|
||||
keypoints[i].name = KEYPOINT_NAMES[i];
|
||||
}
|
||||
|
||||
return keypoints;
|
||||
}
|
||||
|
||||
/**
|
||||
* Analyze the motion grid to determine arm positions.
|
||||
* Left side of grid = left side of body, etc.
|
||||
*/
|
||||
_analyzeArmMotion(grid, cols, rows, region) {
|
||||
// Body center column
|
||||
const centerCol = Math.floor(cols / 2);
|
||||
|
||||
// Upper body rows (top 60% of detected region)
|
||||
const upperEnd = Math.floor(rows * 0.6);
|
||||
|
||||
// Compute motion intensity for left vs right, at different heights
|
||||
let leftUpperMotion = 0, leftMidMotion = 0;
|
||||
let rightUpperMotion = 0, rightMidMotion = 0;
|
||||
let leftCount = 0, rightCount = 0;
|
||||
let headMotionX = 0, headMotionWeight = 0;
|
||||
|
||||
for (let r = 0; r < upperEnd; r++) {
|
||||
const heightWeight = 1.0 - (r / upperEnd) * 0.3; // Upper rows weighted more
|
||||
|
||||
// Head zone: top 25%, center 40% of width
|
||||
if (r < Math.floor(rows * 0.25)) {
|
||||
const headLeft = Math.floor(cols * 0.3);
|
||||
const headRight = Math.floor(cols * 0.7);
|
||||
for (let c = headLeft; c <= headRight; c++) {
|
||||
const val = grid[r][c];
|
||||
headMotionX += (c / cols - 0.5) * val;
|
||||
headMotionWeight += val;
|
||||
}
|
||||
}
|
||||
|
||||
// Left arm zone: left 40% of grid
|
||||
for (let c = 0; c < Math.floor(cols * 0.4); c++) {
|
||||
const val = grid[r][c];
|
||||
if (r < rows * 0.3) leftUpperMotion += val * heightWeight;
|
||||
else leftMidMotion += val * heightWeight;
|
||||
leftCount++;
|
||||
}
|
||||
|
||||
// Right arm zone: right 40% of grid
|
||||
for (let c = Math.floor(cols * 0.6); c < cols; c++) {
|
||||
const val = grid[r][c];
|
||||
if (r < rows * 0.3) rightUpperMotion += val * heightWeight;
|
||||
else rightMidMotion += val * heightWeight;
|
||||
rightCount++;
|
||||
}
|
||||
}
|
||||
|
||||
// Normalize
|
||||
const leftTotal = leftUpperMotion + leftMidMotion;
|
||||
const rightTotal = rightUpperMotion + rightMidMotion;
|
||||
const maxMotion = 0.15; // Calibration threshold
|
||||
|
||||
// Arm height: 0 = at side, 1 = raised
|
||||
// High motion in upper-left → left arm is raised
|
||||
const leftArmHeight = Math.min(1, (leftUpperMotion / maxMotion) * 2);
|
||||
const rightArmHeight = Math.min(1, (rightUpperMotion / maxMotion) * 2);
|
||||
|
||||
// Arm spread: how far out from body
|
||||
const leftArmSpread = Math.min(1, leftTotal / maxMotion);
|
||||
const rightArmSpread = Math.min(1, rightTotal / maxMotion);
|
||||
|
||||
// Head offset
|
||||
const headOffsetX = headMotionWeight > 0.01 ? headMotionX / headMotionWeight : 0;
|
||||
|
||||
return { leftArmHeight, rightArmHeight, leftArmSpread, rightArmSpread, headOffsetX };
|
||||
}
|
||||
|
||||
/**
|
||||
* Analyze lower grid for leg motion.
|
||||
*/
|
||||
_analyzeLegMotion(grid, cols, rows) {
|
||||
const lowerStart = Math.floor(rows * 0.6);
|
||||
let leftMotion = 0, rightMotion = 0;
|
||||
|
||||
for (let r = lowerStart; r < rows; r++) {
|
||||
for (let c = 0; c < Math.floor(cols / 2); c++) {
|
||||
leftMotion += grid[r][c];
|
||||
}
|
||||
for (let c = Math.floor(cols / 2); c < cols; c++) {
|
||||
rightMotion += grid[r][c];
|
||||
}
|
||||
}
|
||||
|
||||
// Return as -1 to 1 range (asymmetry indicates which leg is moving)
|
||||
const total = leftMotion + rightMotion + 0.001;
|
||||
return {
|
||||
left: (leftMotion - rightMotion) / total,
|
||||
right: (rightMotion - leftMotion) / total
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* 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;
|
||||
}
|
||||
}
|
||||
|
|
@ -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;
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue