deploy: rebrand DensePose → RuView in pose-fusion
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ruv
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@ -3,7 +3,7 @@
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>WiFi-DensePose — Dual-Modal Pose Estimation</title>
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<title>RuView — Dual-Modal Pose Estimation</title>
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<link rel="stylesheet" href="pose-fusion/css/style.css?v=13">
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</head>
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<body>
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@ -11,7 +11,7 @@
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<!-- Header -->
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<header class="header">
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<div class="header-left">
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<div class="logo"><span class="pi">π</span> DensePose</div>
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<div class="logo"><span class="pi">π</span> RuView</div>
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<div class="header-title">Dual-Modal Pose Estimation — Live Video + WiFi CSI Fusion</div>
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</div>
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<div class="header-right">
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@ -184,11 +184,11 @@
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<!-- Bottom Bar -->
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<div class="bottom-bar">
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<div>
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WiFi-DensePose · Dual-Modal Pose Estimation ·
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RuView · Dual-Modal Pose Estimation ·
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Architecture: Conv2D → RuVector 6-Stage Attention (Flash+MHA+Hyperbolic+Linear+MoE+L/G) → Fusion → 26-Keypoint Pose
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</div>
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<div>
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<a href="https://github.com/ruvnet/wifi-densepose">GitHub</a> ·
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<a href="https://github.com/ruvnet/RuView">GitHub</a> ·
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CNN: <span id="cnn-backend">ruvector-cnn (loading…)</span> ·
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<a href="observatory.html">Observatory</a>
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</div>
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@ -0,0 +1,536 @@
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/* RuView — Dual-Modal Pose Fusion Demo
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Dark theme matching Observatory */
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@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;700&family=JetBrains+Mono:wght@400;600&display=swap');
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:root {
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--bg-deep: #080c14;
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--bg-panel: rgba(8, 16, 28, 0.92);
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--bg-panel-border: rgba(0, 210, 120, 0.25);
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--green-glow: #00d878;
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--green-bright:#3eff8a;
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--green-dim: #0a6b3a;
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--amber: #ffb020;
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--amber-dim: #a06800;
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--blue-signal: #2090ff;
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--blue-dim: #0a3060;
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--red-alert: #ff3040;
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--cyan: #00e5ff;
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--text-primary: #e8ece0;
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--text-secondary: rgba(232,236,224, 0.55);
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--text-label: rgba(232,236,224, 0.35);
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--radius: 8px;
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}
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* { margin: 0; padding: 0; box-sizing: border-box; }
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body {
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background: var(--bg-deep);
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font-family: 'Inter', -apple-system, sans-serif;
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color: var(--text-primary);
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-webkit-font-smoothing: antialiased;
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overflow-x: hidden;
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min-height: 100vh;
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}
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/* === Header === */
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.header {
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display: flex;
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align-items: center;
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justify-content: space-between;
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padding: 16px 24px;
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border-bottom: 1px solid var(--bg-panel-border);
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background: var(--bg-panel);
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backdrop-filter: blur(12px);
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}
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.header-left {
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display: flex;
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align-items: center;
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gap: 16px;
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}
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.logo {
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font-weight: 700;
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font-size: 24px;
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color: var(--green-glow);
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}
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.logo .pi { font-style: normal; }
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.header-title {
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font-size: 14px;
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color: var(--text-secondary);
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font-weight: 300;
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}
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.header-right {
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display: flex;
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align-items: center;
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gap: 16px;
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}
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.mode-select {
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background: rgba(0,210,120,0.1);
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border: 1px solid var(--bg-panel-border);
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color: var(--text-primary);
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padding: 6px 12px;
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border-radius: var(--radius);
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font-family: inherit;
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font-size: 13px;
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cursor: pointer;
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}
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.mode-select option { background: #0c1420; }
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.status-badge {
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display: flex;
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align-items: center;
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gap: 6px;
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font-family: 'JetBrains Mono', monospace;
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font-size: 12px;
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padding: 4px 10px;
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border-radius: 12px;
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background: rgba(0,210,120,0.1);
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border: 1px solid var(--bg-panel-border);
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}
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.status-dot {
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width: 8px; height: 8px;
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border-radius: 50%;
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background: var(--green-glow);
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box-shadow: 0 0 8px var(--green-glow);
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animation: pulse-dot 2s ease infinite;
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}
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.status-dot.offline { background: #555; box-shadow: none; animation: none; }
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.status-dot.warning { background: var(--amber); box-shadow: 0 0 8px var(--amber); }
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@keyframes pulse-dot {
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0%, 100% { opacity: 1; }
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50% { opacity: 0.5; }
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}
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.fps-badge {
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font-family: 'JetBrains Mono', monospace;
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font-size: 12px;
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color: var(--green-glow);
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}
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.back-link {
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color: var(--text-secondary);
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text-decoration: none;
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font-size: 13px;
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transition: color 0.2s;
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}
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.back-link:hover { color: var(--green-glow); }
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/* === Main Layout === */
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.main-grid {
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display: grid;
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grid-template-columns: 1fr 360px;
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grid-template-rows: 1fr auto;
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gap: 16px;
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padding: 16px 24px;
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height: calc(100vh - 72px);
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overflow: hidden;
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}
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.video-panel {
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grid-row: 1;
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}
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.side-panels {
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grid-row: 1;
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}
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/* === Video Panel === */
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.video-panel {
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position: relative;
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background: #000;
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border-radius: var(--radius);
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border: 1px solid var(--bg-panel-border);
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overflow: hidden;
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min-height: 0;
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}
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.video-panel video {
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width: 100%;
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height: 100%;
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object-fit: cover;
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transform: scaleX(-1);
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}
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.video-panel canvas {
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position: absolute;
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top: 0; left: 0;
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width: 100%;
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height: 100%;
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transform: scaleX(-1);
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}
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.video-overlay-label {
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position: absolute;
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top: 12px; left: 12px;
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font-family: 'JetBrains Mono', monospace;
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font-size: 11px;
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padding: 4px 8px;
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background: rgba(0,0,0,0.7);
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border-radius: 4px;
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color: var(--green-glow);
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z-index: 5;
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transform: scaleX(-1);
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}
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.camera-prompt {
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position: absolute;
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top: 0; left: 0; right: 0; bottom: 0;
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display: flex;
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flex-direction: column;
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align-items: center;
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justify-content: center;
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text-align: center;
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color: var(--text-secondary);
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padding: 24px;
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z-index: 6;
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background: radial-gradient(ellipse at center, rgba(0,210,120,0.08) 0%, rgba(8,12,20,0.95) 70%);
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}
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.camera-prompt button {
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margin-top: 16px;
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padding: 10px 24px;
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background: var(--green-glow);
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color: #000;
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border: none;
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border-radius: var(--radius);
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font-family: inherit;
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font-weight: 600;
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font-size: 14px;
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cursor: pointer;
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transition: background 0.2s;
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}
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.camera-prompt button:hover { background: var(--green-bright); }
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.camera-prompt-label {
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font-family: 'JetBrains Mono', monospace;
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font-size: 14px;
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font-weight: 600;
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letter-spacing: 2px;
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color: var(--green-glow);
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text-shadow: 0 0 12px rgba(0,216,120,0.4);
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margin-bottom: 12px;
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}
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/* === Side Panels === */
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.side-panels {
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display: flex;
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flex-direction: column;
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gap: 8px;
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overflow-y: auto;
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min-height: 0;
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max-height: 100%;
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scrollbar-width: thin;
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scrollbar-color: var(--green-dim) transparent;
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}
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.panel {
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background: var(--bg-panel);
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border: 1px solid var(--bg-panel-border);
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border-radius: var(--radius);
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padding: 10px 14px;
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flex-shrink: 0;
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}
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.panel-title {
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font-size: 11px;
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text-transform: uppercase;
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letter-spacing: 1.2px;
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color: var(--text-label);
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margin-bottom: 10px;
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display: flex;
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align-items: center;
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gap: 6px;
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}
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/* === CSI Heatmap === */
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.csi-canvas-wrapper {
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position: relative;
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border-radius: 4px;
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overflow: hidden;
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background: #000;
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}
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.csi-canvas-wrapper canvas {
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width: 100%;
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display: block;
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}
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/* === Fusion Bars === */
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.fusion-bars {
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display: flex;
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flex-direction: column;
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gap: 8px;
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}
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.bar-row {
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display: flex;
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align-items: center;
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gap: 8px;
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}
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.bar-label {
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font-family: 'JetBrains Mono', monospace;
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font-size: 11px;
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color: var(--text-secondary);
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width: 55px;
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text-align: right;
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}
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.bar-track {
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flex: 1;
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height: 6px;
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background: rgba(255,255,255,0.06);
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border-radius: 3px;
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overflow: hidden;
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}
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.bar-fill {
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height: 100%;
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border-radius: 3px;
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transition: width 0.3s ease;
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}
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.bar-fill.video { background: var(--cyan); }
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.bar-fill.csi { background: var(--amber); }
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.bar-fill.fused { background: var(--green-glow); box-shadow: 0 0 8px var(--green-glow); }
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.bar-value {
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font-family: 'JetBrains Mono', monospace;
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font-size: 11px;
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color: var(--text-primary);
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width: 36px;
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}
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/* === Embedding Space === */
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.embedding-canvas-wrapper {
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position: relative;
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background: #000;
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border-radius: 4px;
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overflow: hidden;
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}
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.embedding-canvas-wrapper canvas {
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width: 100%;
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display: block;
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}
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/* === RuVector Pipeline === */
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.rv-pipeline {
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display: flex;
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align-items: center;
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gap: 2px;
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margin-bottom: 8px;
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flex-wrap: wrap;
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}
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.rv-stage {
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font-family: 'JetBrains Mono', monospace;
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font-size: 10px;
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padding: 3px 6px;
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border-radius: 3px;
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background: rgba(0,210,120,0.12);
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border: 1px solid rgba(0,210,120,0.3);
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color: var(--green-glow);
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transition: all 0.3s;
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}
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.rv-stage.active {
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background: rgba(0,210,120,0.25);
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box-shadow: 0 0 6px rgba(0,210,120,0.3);
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}
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.rv-arrow {
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font-size: 10px;
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color: var(--text-label);
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}
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.rv-stats {
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display: flex;
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gap: 12px;
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font-family: 'JetBrains Mono', monospace;
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font-size: 10px;
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color: var(--text-secondary);
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}
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/* === Latency Panel === */
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.latency-grid {
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display: grid;
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grid-template-columns: repeat(4, 1fr);
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gap: 6px;
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}
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.latency-item {
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text-align: center;
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padding: 6px 0;
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}
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.latency-value {
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font-family: 'JetBrains Mono', monospace;
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font-size: 16px;
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font-weight: 600;
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color: var(--green-glow);
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}
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.latency-label {
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font-size: 10px;
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color: var(--text-label);
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margin-top: 2px;
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}
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/* === Controls === */
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.controls-row {
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display: flex;
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gap: 8px;
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flex-wrap: wrap;
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}
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.btn {
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padding: 6px 14px;
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border: 1px solid var(--bg-panel-border);
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background: rgba(0,210,120,0.08);
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color: var(--text-primary);
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border-radius: var(--radius);
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font-family: inherit;
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font-size: 12px;
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cursor: pointer;
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transition: all 0.2s;
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}
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.btn:hover { background: rgba(0,210,120,0.2); }
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.btn.active { background: var(--green-glow); color: #000; font-weight: 600; }
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.slider-row {
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display: flex;
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align-items: center;
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gap: 8px;
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margin-top: 8px;
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}
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.slider-row label {
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font-size: 11px;
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color: var(--text-secondary);
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white-space: nowrap;
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}
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.slider-row input[type=range] {
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flex: 1;
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accent-color: var(--green-glow);
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}
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.slider-row .slider-val {
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font-family: 'JetBrains Mono', monospace;
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font-size: 11px;
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width: 32px;
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color: var(--green-glow);
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}
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/* === Bottom Bar === */
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.bottom-bar {
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grid-column: 1 / -1;
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display: flex;
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align-items: center;
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justify-content: space-between;
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padding: 10px 16px;
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background: var(--bg-panel);
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border: 1px solid var(--bg-panel-border);
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border-radius: var(--radius);
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font-family: 'JetBrains Mono', monospace;
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font-size: 11px;
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color: var(--text-secondary);
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}
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.bottom-bar a {
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color: var(--green-glow);
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text-decoration: none;
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}
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/* === RSSI Signal Strength === */
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.rssi-row {
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display: flex;
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align-items: center;
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gap: 12px;
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}
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.rssi-gauge { flex: 1; }
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.rssi-bar-track {
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height: 8px;
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background: rgba(255,255,255,0.06);
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border-radius: 4px;
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overflow: hidden;
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position: relative;
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}
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.rssi-bar-fill {
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height: 100%;
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border-radius: 4px;
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background: linear-gradient(90deg, var(--red-alert), var(--amber), var(--green-glow));
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transition: width 0.4s ease;
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position: relative;
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box-shadow: 0 0 6px rgba(0,210,120,0.3);
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}
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.rssi-bar-fill::after {
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content: '';
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position: absolute;
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top: 0; left: 0; right: 0; bottom: 0;
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background: linear-gradient(90deg, transparent 0%, rgba(255,255,255,0.2) 50%, transparent 100%);
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animation: rssi-shimmer 2s ease-in-out infinite;
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}
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|
||||
@keyframes rssi-shimmer {
|
||||
0% { transform: translateX(-100%); }
|
||||
100% { transform: translateX(100%); }
|
||||
}
|
||||
|
||||
.rssi-values {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
margin-top: 4px;
|
||||
}
|
||||
|
||||
.rssi-dbm {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 14px;
|
||||
font-weight: 600;
|
||||
color: var(--green-glow);
|
||||
}
|
||||
|
||||
.rssi-quality {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 11px;
|
||||
color: var(--text-secondary);
|
||||
text-transform: uppercase;
|
||||
}
|
||||
|
||||
#rssi-sparkline {
|
||||
flex-shrink: 0;
|
||||
border-radius: 4px;
|
||||
background: rgba(0,0,0,0.3);
|
||||
}
|
||||
|
||||
/* === Skeleton colors === */
|
||||
.skeleton-joint { fill: var(--green-glow); }
|
||||
.skeleton-limb { stroke: var(--green-bright); }
|
||||
.skeleton-joint-csi { fill: var(--amber); }
|
||||
.skeleton-limb-csi { stroke: var(--amber); }
|
||||
|
||||
/* === Responsive === */
|
||||
@media (max-width: 900px) {
|
||||
.main-grid {
|
||||
grid-template-columns: 1fr;
|
||||
height: auto;
|
||||
overflow: auto;
|
||||
}
|
||||
.video-panel { aspect-ratio: 16/9; max-height: 50vh; }
|
||||
.side-panels { max-height: none; overflow: visible; }
|
||||
}
|
||||
|
|
@ -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 +1,443 @@
|
|||
/**
|
||||
* CNN Embedder — RuVector Attention-powered feature extractor.
|
||||
*
|
||||
* Uses the real ruvector-attention-wasm WASM module for Multi-Head Attention
|
||||
* and Flash Attention on CSI/video data. Falls back to a JS Conv2D pipeline
|
||||
* when WASM is not available.
|
||||
*
|
||||
* Pipeline: Conv2D → BatchNorm → ReLU → Pool → RuVector Attention → Project → L2 Normalize
|
||||
* Two instances are created: one for video frames, one for CSI pseudo-images.
|
||||
*/
|
||||
|
||||
// Seeded PRNG for deterministic weight initialization
|
||||
function mulberry32(seed) {
|
||||
return function() {
|
||||
let t = (seed += 0x6D2B79F5);
|
||||
t = Math.imul(t ^ (t >>> 15), t | 1);
|
||||
t ^= t + Math.imul(t ^ (t >>> 7), t | 61);
|
||||
return ((t ^ (t >>> 14)) >>> 0) / 4294967296;
|
||||
};
|
||||
}
|
||||
|
||||
export class CnnEmbedder {
|
||||
/**
|
||||
* @param {object} opts
|
||||
* @param {number} opts.inputSize - Square input dimension (default 56 for speed)
|
||||
* @param {number} opts.embeddingDim - Output embedding dimension (default 128)
|
||||
* @param {boolean} opts.normalize - L2 normalize output
|
||||
* @param {number} opts.seed - PRNG seed for weight init
|
||||
*/
|
||||
constructor(opts = {}) {
|
||||
this.inputSize = opts.inputSize || 56;
|
||||
this.embeddingDim = opts.embeddingDim || 128;
|
||||
this.normalize = opts.normalize !== false;
|
||||
this.wasmEmbedder = null;
|
||||
this.rvAttention = null; // RuVector Multi-Head Attention (WASM)
|
||||
this.rvFlash = null; // RuVector Flash Attention (WASM)
|
||||
this.rvHyperbolic = null; // RuVector Hyperbolic Attention (hierarchical body)
|
||||
this.rvMoE = null; // RuVector Mixture-of-Experts (body-region routing)
|
||||
this.rvLinear = null; // RuVector Linear Attention (O(n) fast hand refinement)
|
||||
this.rvLocalGlobal = null; // RuVector Local-Global Attention (detail + context)
|
||||
this.rvModule = null; // RuVector WASM module reference
|
||||
this.useRuVector = false;
|
||||
|
||||
// Initialize weights with deterministic PRNG
|
||||
const rng = mulberry32(opts.seed || 42);
|
||||
const randRange = (lo, hi) => lo + rng() * (hi - lo);
|
||||
|
||||
// Conv 3x3: 3 input channels → 16 output channels
|
||||
this.convWeights = new Float32Array(3 * 3 * 3 * 16);
|
||||
for (let i = 0; i < this.convWeights.length; i++) {
|
||||
this.convWeights[i] = randRange(-0.15, 0.15);
|
||||
}
|
||||
|
||||
// BatchNorm params (16 channels)
|
||||
this.bnGamma = new Float32Array(16).fill(1.0);
|
||||
this.bnBeta = new Float32Array(16).fill(0.0);
|
||||
this.bnMean = new Float32Array(16).fill(0.0);
|
||||
this.bnVar = new Float32Array(16).fill(1.0);
|
||||
|
||||
// Projection: 16 → embeddingDim (used when RuVector not available)
|
||||
this.projWeights = new Float32Array(16 * this.embeddingDim);
|
||||
for (let i = 0; i < this.projWeights.length; i++) {
|
||||
this.projWeights[i] = randRange(-0.1, 0.1);
|
||||
}
|
||||
|
||||
// Attention projection: attention_dim → embeddingDim
|
||||
this.attnProjWeights = new Float32Array(16 * this.embeddingDim);
|
||||
for (let i = 0; i < this.attnProjWeights.length; i++) {
|
||||
this.attnProjWeights[i] = randRange(-0.08, 0.08);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Try to load RuVector attention WASM, then fall back to ruvector-cnn-wasm
|
||||
* @param {string} wasmPath - Path to the WASM package directory
|
||||
*/
|
||||
async tryLoadWasm(wasmPath) {
|
||||
// First try: RuVector Attention WASM (the real thing — browser ESM build)
|
||||
try {
|
||||
const attnBase = new URL('../pkg/ruvector-attention/ruvector_attention_browser.js', import.meta.url).href;
|
||||
const mod = await import(attnBase);
|
||||
await mod.default(); // async WASM init via fetch
|
||||
mod.init();
|
||||
|
||||
// Create all 6 attention mechanisms
|
||||
this.rvAttention = new mod.WasmMultiHeadAttention(16, 4);
|
||||
this.rvFlash = new mod.WasmFlashAttention(16, 8);
|
||||
this.rvHyperbolic = new mod.WasmHyperbolicAttention(16, -1.0);
|
||||
this.rvMoE = new mod.WasmMoEAttention(16, 3, 2);
|
||||
this.rvLinear = new mod.WasmLinearAttention(16, 16);
|
||||
this.rvLocalGlobal = new mod.WasmLocalGlobalAttention(16, 4, 2);
|
||||
this.rvModule = mod;
|
||||
this.useRuVector = true;
|
||||
|
||||
// Log available mechanisms
|
||||
const mechs = mod.available_mechanisms();
|
||||
console.log(`[CNN] RuVector WASM v${mod.version()} — all 6 attention mechanisms active`, mechs);
|
||||
return true;
|
||||
} catch (e) {
|
||||
console.log('[CNN] RuVector Attention WASM not available:', e.message);
|
||||
}
|
||||
|
||||
// Second try: ruvector-cnn-wasm (legacy path)
|
||||
try {
|
||||
const mod = await import(`${wasmPath}/ruvector_cnn_wasm.js`);
|
||||
await mod.default();
|
||||
const config = new mod.EmbedderConfig();
|
||||
config.input_size = this.inputSize;
|
||||
config.embedding_dim = this.embeddingDim;
|
||||
config.normalize = this.normalize;
|
||||
this.wasmEmbedder = new mod.WasmCnnEmbedder(config);
|
||||
console.log('[CNN] WASM CNN embedder loaded successfully');
|
||||
return true;
|
||||
} catch (e) {
|
||||
console.log('[CNN] WASM CNN not available, using JS fallback:', e.message);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract embedding from RGB image data
|
||||
* @param {Uint8Array} rgbData - RGB pixel data (H*W*3)
|
||||
* @param {number} width
|
||||
* @param {number} height
|
||||
* @returns {Float32Array} embedding vector
|
||||
*/
|
||||
extract(rgbData, width, height) {
|
||||
if (this.wasmEmbedder) {
|
||||
try {
|
||||
const result = this.wasmEmbedder.extract(rgbData, width, height);
|
||||
return new Float32Array(result);
|
||||
} catch (_) { /* fallback to JS */ }
|
||||
}
|
||||
return this._extractJS(rgbData, width, height);
|
||||
}
|
||||
|
||||
_extractJS(rgbData, width, height) {
|
||||
// 1. Resize to inputSize × inputSize if needed
|
||||
const sz = this.inputSize;
|
||||
let input;
|
||||
if (width === sz && height === sz) {
|
||||
input = new Float32Array(rgbData.length);
|
||||
for (let i = 0; i < rgbData.length; i++) input[i] = rgbData[i] / 255.0;
|
||||
} else {
|
||||
input = this._resize(rgbData, width, height, sz, sz);
|
||||
}
|
||||
|
||||
// 2. ImageNet normalization
|
||||
const mean = [0.485, 0.456, 0.406];
|
||||
const std = [0.229, 0.224, 0.225];
|
||||
const pixels = sz * sz;
|
||||
for (let i = 0; i < pixels; i++) {
|
||||
input[i * 3] = (input[i * 3] - mean[0]) / std[0];
|
||||
input[i * 3 + 1] = (input[i * 3 + 1] - mean[1]) / std[1];
|
||||
input[i * 3 + 2] = (input[i * 3 + 2] - mean[2]) / std[2];
|
||||
}
|
||||
|
||||
// 3. Conv2D 3x3 (3 → 16 channels)
|
||||
const convOut = this._conv2d3x3(input, sz, sz, 3, 16);
|
||||
|
||||
// 4. BatchNorm
|
||||
this._batchNorm(convOut, 16);
|
||||
|
||||
// 5. ReLU
|
||||
for (let i = 0; i < convOut.length; i++) {
|
||||
if (convOut[i] < 0) convOut[i] = 0;
|
||||
}
|
||||
|
||||
// 6. Global average pooling → spatial tokens (each 16-dim)
|
||||
const outH = sz - 2, outW = sz - 2;
|
||||
const spatial = outH * outW;
|
||||
|
||||
// 7. RuVector Attention (if loaded) — apply attention over spatial tokens
|
||||
if (this.useRuVector && this.rvAttention) {
|
||||
return this._extractWithAttention(convOut, spatial, 16);
|
||||
}
|
||||
|
||||
// Fallback: simple global average pool + linear projection
|
||||
const pooled = new Float32Array(16);
|
||||
for (let i = 0; i < spatial; i++) {
|
||||
for (let c = 0; c < 16; c++) {
|
||||
pooled[c] += convOut[i * 16 + c];
|
||||
}
|
||||
}
|
||||
for (let c = 0; c < 16; c++) pooled[c] /= spatial;
|
||||
|
||||
// Linear projection → embeddingDim
|
||||
const emb = new Float32Array(this.embeddingDim);
|
||||
for (let o = 0; o < this.embeddingDim; o++) {
|
||||
let sum = 0;
|
||||
for (let i = 0; i < 16; i++) {
|
||||
sum += pooled[i] * this.projWeights[i * this.embeddingDim + o];
|
||||
}
|
||||
emb[o] = sum;
|
||||
}
|
||||
|
||||
// L2 normalize
|
||||
if (this.normalize) {
|
||||
let norm = 0;
|
||||
for (let i = 0; i < emb.length; i++) norm += emb[i] * emb[i];
|
||||
norm = Math.sqrt(norm);
|
||||
if (norm > 1e-8) {
|
||||
for (let i = 0; i < emb.length; i++) emb[i] /= norm;
|
||||
}
|
||||
}
|
||||
|
||||
return emb;
|
||||
}
|
||||
|
||||
/**
|
||||
* Full 6-stage RuVector WASM attention pipeline:
|
||||
* 1. Flash Attention (efficient O(n) pre-screening of spatial tokens)
|
||||
* 2. Multi-Head Attention (global spatial reasoning)
|
||||
* 3. Hyperbolic Attention (hierarchical body-part structure, Poincaré ball)
|
||||
* 4. Linear Attention (O(n) refinement for fine detail — hands/extremities)
|
||||
* 5. MoE Attention (body-region specialized expert routing)
|
||||
* 6. Local-Global Attention (local detail + global context fusion)
|
||||
* → Weighted blend + batch_normalize + project + L2 normalize
|
||||
*/
|
||||
_extractWithAttention(convOut, numTokens, channels) {
|
||||
const mod = this.rvModule;
|
||||
|
||||
// Subsample spatial tokens for attention (max 64 for speed)
|
||||
const maxTokens = 64;
|
||||
const step = numTokens > maxTokens ? Math.floor(numTokens / maxTokens) : 1;
|
||||
const tokens = [];
|
||||
for (let i = 0; i < numTokens && tokens.length < maxTokens; i += step) {
|
||||
const token = new Float32Array(channels);
|
||||
for (let c = 0; c < channels; c++) {
|
||||
token[c] = convOut[i * channels + c];
|
||||
}
|
||||
tokens.push(token);
|
||||
}
|
||||
|
||||
const numQueries = Math.min(4, tokens.length);
|
||||
const queryStride = Math.floor(tokens.length / numQueries);
|
||||
|
||||
// === Stage 1: Flash Attention (efficient pre-screening) ===
|
||||
const flashOut = new Float32Array(channels);
|
||||
try {
|
||||
// Flash attention with block size 8 for efficient O(n) screening
|
||||
const result = this.rvFlash.compute(tokens[0], tokens, tokens);
|
||||
for (let c = 0; c < channels; c++) flashOut[c] = result[c];
|
||||
} catch (_) {
|
||||
flashOut.set(tokens[0]);
|
||||
}
|
||||
|
||||
// === Stage 2: Multi-Head Attention (global spatial reasoning) ===
|
||||
const mhaOut = new Float32Array(channels);
|
||||
for (let q = 0; q < numQueries; q++) {
|
||||
const queryToken = tokens[q * queryStride];
|
||||
try {
|
||||
const result = this.rvAttention.compute(queryToken, tokens, tokens);
|
||||
for (let c = 0; c < channels; c++) mhaOut[c] += result[c] / numQueries;
|
||||
} catch (_) {
|
||||
for (let c = 0; c < channels; c++) mhaOut[c] += queryToken[c] / numQueries;
|
||||
}
|
||||
}
|
||||
|
||||
// === Stage 3: Hyperbolic Attention (hierarchical body structure) ===
|
||||
const hyOut = new Float32Array(channels);
|
||||
try {
|
||||
const result = this.rvHyperbolic.compute(mhaOut, tokens, tokens);
|
||||
for (let c = 0; c < channels; c++) hyOut[c] = result[c];
|
||||
} catch (_) {
|
||||
hyOut.set(mhaOut);
|
||||
}
|
||||
|
||||
// === Stage 4: Linear Attention (O(n) fast refinement for extremities) ===
|
||||
const linOut = new Float32Array(channels);
|
||||
try {
|
||||
const result = this.rvLinear.compute(hyOut, tokens, tokens);
|
||||
for (let c = 0; c < channels; c++) linOut[c] = result[c];
|
||||
} catch (_) {
|
||||
linOut.set(hyOut);
|
||||
}
|
||||
|
||||
// === Stage 5: MoE Attention (body-region expert routing) ===
|
||||
const moeOut = new Float32Array(channels);
|
||||
try {
|
||||
const result = this.rvMoE.compute(linOut, tokens, tokens);
|
||||
for (let c = 0; c < channels; c++) moeOut[c] = result[c];
|
||||
} catch (_) {
|
||||
moeOut.set(linOut);
|
||||
}
|
||||
|
||||
// === Stage 6: Local-Global Attention (detail + context) ===
|
||||
const lgOut = new Float32Array(channels);
|
||||
try {
|
||||
const result = this.rvLocalGlobal.compute(moeOut, tokens, tokens);
|
||||
for (let c = 0; c < channels; c++) lgOut[c] = result[c];
|
||||
} catch (_) {
|
||||
lgOut.set(moeOut);
|
||||
}
|
||||
|
||||
// === Blend all 6 outputs ===
|
||||
// Use WASM softmax on log-energy scores for dynamic stage weighting
|
||||
const blended = new Float32Array(channels);
|
||||
const stages = [flashOut, mhaOut, hyOut, linOut, moeOut, lgOut];
|
||||
// Use log-energy to prevent exp() overflow in softmax
|
||||
const logEnergies = new Float32Array(6);
|
||||
for (let s = 0; s < 6; s++) {
|
||||
const e = this._energy(stages[s]);
|
||||
logEnergies[s] = e > 1e-10 ? Math.log(e) : -20;
|
||||
}
|
||||
try { mod.softmax(logEnergies); } catch (_) {
|
||||
let max = -Infinity;
|
||||
for (let i = 0; i < 6; i++) max = Math.max(max, logEnergies[i]);
|
||||
let sum = 0;
|
||||
for (let i = 0; i < 6; i++) { logEnergies[i] = Math.exp(logEnergies[i] - max); sum += logEnergies[i]; }
|
||||
for (let i = 0; i < 6; i++) logEnergies[i] /= sum;
|
||||
}
|
||||
for (let c = 0; c < channels; c++) {
|
||||
for (let s = 0; s < 6; s++) {
|
||||
blended[c] += logEnergies[s] * stages[s][c];
|
||||
}
|
||||
}
|
||||
|
||||
// Batch normalize only when we have enough diversity (skip for single vectors)
|
||||
// Single-vector batch norm collapses to zeros, killing embedding space
|
||||
let normed = blended;
|
||||
|
||||
// Project to embeddingDim
|
||||
const emb = new Float32Array(this.embeddingDim);
|
||||
for (let o = 0; o < this.embeddingDim; o++) {
|
||||
let sum = 0;
|
||||
for (let i = 0; i < channels; i++) {
|
||||
sum += normed[i] * this.attnProjWeights[i * this.embeddingDim + o];
|
||||
}
|
||||
emb[o] = sum;
|
||||
}
|
||||
|
||||
// L2 normalize using RuVector WASM
|
||||
if (this.normalize) {
|
||||
try { mod.normalize(emb); } catch (_) {
|
||||
let norm = 0;
|
||||
for (let i = 0; i < emb.length; i++) norm += emb[i] * emb[i];
|
||||
norm = Math.sqrt(norm);
|
||||
if (norm > 1e-8) for (let i = 0; i < emb.length; i++) emb[i] /= norm;
|
||||
}
|
||||
}
|
||||
|
||||
return emb;
|
||||
}
|
||||
|
||||
/** Compute vector energy (L2 norm squared) for attention weighting */
|
||||
_energy(vec) {
|
||||
let e = 0;
|
||||
for (let i = 0; i < vec.length; i++) e += vec[i] * vec[i];
|
||||
return e;
|
||||
}
|
||||
|
||||
_conv2d3x3(input, H, W, Cin, Cout) {
|
||||
const outH = H - 2, outW = W - 2;
|
||||
const output = new Float32Array(outH * outW * Cout);
|
||||
for (let y = 0; y < outH; y++) {
|
||||
for (let x = 0; x < outW; x++) {
|
||||
for (let co = 0; co < Cout; co++) {
|
||||
let sum = 0;
|
||||
for (let ky = 0; ky < 3; ky++) {
|
||||
for (let kx = 0; kx < 3; kx++) {
|
||||
for (let ci = 0; ci < Cin; ci++) {
|
||||
const px = ((y + ky) * W + (x + kx)) * Cin + ci;
|
||||
const wt = (((ky * 3 + kx) * Cin) + ci) * Cout + co;
|
||||
sum += input[px] * this.convWeights[wt];
|
||||
}
|
||||
}
|
||||
}
|
||||
output[(y * outW + x) * Cout + co] = sum;
|
||||
}
|
||||
}
|
||||
}
|
||||
return output;
|
||||
}
|
||||
|
||||
_batchNorm(data, channels) {
|
||||
const spatial = data.length / channels;
|
||||
for (let i = 0; i < spatial; i++) {
|
||||
for (let c = 0; c < channels; c++) {
|
||||
const idx = i * channels + c;
|
||||
data[idx] = this.bnGamma[c] * (data[idx] - this.bnMean[c]) / Math.sqrt(this.bnVar[c] + 1e-5) + this.bnBeta[c];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
_resize(rgbData, srcW, srcH, dstW, dstH) {
|
||||
const output = new Float32Array(dstW * dstH * 3);
|
||||
const xRatio = srcW / dstW;
|
||||
const yRatio = srcH / dstH;
|
||||
for (let y = 0; y < dstH; y++) {
|
||||
for (let x = 0; x < dstW; x++) {
|
||||
const sx = Math.min(Math.floor(x * xRatio), srcW - 1);
|
||||
const sy = Math.min(Math.floor(y * yRatio), srcH - 1);
|
||||
const srcIdx = (sy * srcW + sx) * 3;
|
||||
const dstIdx = (y * dstW + x) * 3;
|
||||
output[dstIdx] = rgbData[srcIdx] / 255.0;
|
||||
output[dstIdx + 1] = rgbData[srcIdx + 1] / 255.0;
|
||||
output[dstIdx + 2] = rgbData[srcIdx + 2] / 255.0;
|
||||
}
|
||||
}
|
||||
return output;
|
||||
}
|
||||
|
||||
/** Cosine similarity using WASM when available, JS fallback */
|
||||
cosineSim(a, b) {
|
||||
if (this.rvModule) {
|
||||
try { return this.rvModule.cosine_similarity(a, b); } catch (_) { /* fallback */ }
|
||||
}
|
||||
return CnnEmbedder.cosineSimilarity(a, b);
|
||||
}
|
||||
|
||||
/** L2 norm using WASM when available */
|
||||
l2Norm(vec) {
|
||||
if (this.rvModule) {
|
||||
try { return this.rvModule.l2_norm(vec); } catch (_) { /* fallback */ }
|
||||
}
|
||||
let norm = 0;
|
||||
for (let i = 0; i < vec.length; i++) norm += vec[i] * vec[i];
|
||||
return Math.sqrt(norm);
|
||||
}
|
||||
|
||||
/** Pairwise distance matrix using WASM (for skeleton validation) */
|
||||
pairwiseDistances(vectors) {
|
||||
if (this.rvModule) {
|
||||
try { return this.rvModule.pairwise_distances(vectors); } catch (_) { /* fallback */ }
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
/** Static JS fallback for cosine similarity */
|
||||
static cosineSimilarity(a, b) {
|
||||
let dot = 0, normA = 0, normB = 0;
|
||||
for (let i = 0; i < a.length; i++) {
|
||||
dot += a[i] * b[i];
|
||||
normA += a[i] * a[i];
|
||||
normB += b[i] * b[i];
|
||||
}
|
||||
normA = Math.sqrt(normA);
|
||||
normB = Math.sqrt(normB);
|
||||
if (normA < 1e-8 || normB < 1e-8) return 0;
|
||||
return dot / (normA * normB);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,363 @@
|
|||
/**
|
||||
* CSI Simulator — Generates realistic WiFi Channel State Information data.
|
||||
*
|
||||
* In live mode, connects to the sensing server via WebSocket.
|
||||
* In demo mode, generates synthetic CSI that correlates with detected motion.
|
||||
*
|
||||
* Outputs: 3-channel pseudo-image (amplitude, phase, temporal diff)
|
||||
* matching the ADR-018 frame format expectations.
|
||||
*/
|
||||
|
||||
export class CsiSimulator {
|
||||
static VERSION = 'v4-drift'; // Cache-bust verification
|
||||
|
||||
constructor(opts = {}) {
|
||||
this.subcarriers = opts.subcarriers || 52; // 802.11n HT20
|
||||
this.timeWindow = opts.timeWindow || 56; // frames in sliding window
|
||||
this.mode = 'demo'; // 'demo' | 'live'
|
||||
this.ws = null;
|
||||
|
||||
// Circular buffer for CSI frames
|
||||
this.amplitudeBuffer = [];
|
||||
this.phaseBuffer = [];
|
||||
this.frameCount = 0;
|
||||
|
||||
// Noise parameters
|
||||
this._rng = this._mulberry32(opts.seed || 7);
|
||||
this._noiseState = new Float32Array(this.subcarriers);
|
||||
this._baseAmplitude = new Float32Array(this.subcarriers);
|
||||
this._basePhase = new Float32Array(this.subcarriers);
|
||||
|
||||
// Initialize base CSI profile (empty room)
|
||||
for (let i = 0; i < this.subcarriers; i++) {
|
||||
this._baseAmplitude[i] = 0.5 + 0.3 * Math.sin(i * 0.12);
|
||||
this._basePhase[i] = (i / this.subcarriers) * Math.PI * 2;
|
||||
}
|
||||
|
||||
// RSSI tracking
|
||||
this.rssiDbm = -70; // default mid-range
|
||||
this._rssiTarget = -70;
|
||||
|
||||
// Person influence (updated from video motion)
|
||||
this.personPresence = 0;
|
||||
this.personX = 0.5;
|
||||
this.personY = 0.5;
|
||||
this.personMotion = 0;
|
||||
}
|
||||
|
||||
/**
|
||||
* Connect to live sensing server WebSocket
|
||||
* @param {string} url - WebSocket URL (e.g. ws://localhost:3030/ws/csi)
|
||||
*/
|
||||
async connectLive(url) {
|
||||
return new Promise((resolve) => {
|
||||
try {
|
||||
this.ws = new WebSocket(url);
|
||||
this.ws.binaryType = 'arraybuffer';
|
||||
this.ws.onmessage = (evt) => this._handleLiveFrame(evt.data);
|
||||
this.ws.onopen = () => { this.mode = 'live'; resolve(true); };
|
||||
this.ws.onerror = () => resolve(false);
|
||||
this.ws.onclose = () => { this.mode = 'demo'; };
|
||||
// Timeout after 3s
|
||||
setTimeout(() => { if (this.mode !== 'live') resolve(false); }, 3000);
|
||||
} catch {
|
||||
resolve(false);
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
disconnect() {
|
||||
if (this.ws) { this.ws.close(); this.ws = null; }
|
||||
this.mode = 'demo';
|
||||
}
|
||||
|
||||
get isLive() { return this.mode === 'live'; }
|
||||
|
||||
/**
|
||||
* Update person state from video detection (for correlated demo data).
|
||||
* When person exits frame, CSI maintains presence with slow decay
|
||||
* (simulating through-wall sensing capability).
|
||||
*/
|
||||
updatePersonState(presence, x, y, motion) {
|
||||
// Don't override real CSI sensing with synthetic video-derived state
|
||||
if (this.mode === 'live') return;
|
||||
|
||||
if (presence > 0.1) {
|
||||
// Person detected in video — update CSI state directly
|
||||
this.personPresence = presence;
|
||||
this.personX = x;
|
||||
this.personY = y;
|
||||
this.personMotion = motion;
|
||||
this._lastSeenTime = performance.now();
|
||||
this._lastSeenX = x;
|
||||
this._lastSeenY = y;
|
||||
} else if (this._lastSeenTime) {
|
||||
// Person NOT in video — CSI "through-wall" persistence
|
||||
const elapsed = (performance.now() - this._lastSeenTime) / 1000;
|
||||
// CSI can sense through walls for ~10 seconds with decaying confidence
|
||||
const decayRate = 0.15; // Lose ~15% per second
|
||||
this.personPresence = Math.max(0, 1.0 - elapsed * decayRate);
|
||||
// Position slowly drifts (person walking behind wall)
|
||||
this.personX = this._lastSeenX;
|
||||
this.personY = this._lastSeenY;
|
||||
this.personMotion = Math.max(0, motion * 0.5 + this.personPresence * 0.2);
|
||||
|
||||
if (this.personPresence < 0.05) {
|
||||
this._lastSeenTime = null;
|
||||
}
|
||||
} else {
|
||||
this.personPresence = 0;
|
||||
this.personMotion = 0;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate next CSI frame (demo mode) or return latest live frame
|
||||
* @param {number} elapsed - Time in seconds
|
||||
* @returns {{ amplitude: Float32Array, phase: Float32Array, snr: number }}
|
||||
*/
|
||||
nextFrame(elapsed) {
|
||||
const amp = new Float32Array(this.subcarriers);
|
||||
const phase = new Float32Array(this.subcarriers);
|
||||
|
||||
if (this.mode === 'live' && this._liveAmplitude) {
|
||||
amp.set(this._liveAmplitude);
|
||||
phase.set(this._livePhase);
|
||||
} else {
|
||||
this._generateDemoFrame(amp, phase, elapsed);
|
||||
}
|
||||
|
||||
// Push to circular buffer
|
||||
this.amplitudeBuffer.push(new Float32Array(amp));
|
||||
this.phaseBuffer.push(new Float32Array(phase));
|
||||
if (this.amplitudeBuffer.length > this.timeWindow) {
|
||||
this.amplitudeBuffer.shift();
|
||||
this.phaseBuffer.shift();
|
||||
}
|
||||
|
||||
// RSSI: smooth toward target (demo mode generates synthetic RSSI)
|
||||
if (this.mode === 'demo') {
|
||||
// Simulate RSSI based on person presence and slow drift
|
||||
this._rssiTarget = -55 - 25 * (1 - this.personPresence) + Math.sin(elapsed * 0.3) * 3;
|
||||
}
|
||||
this.rssiDbm += (this._rssiTarget - this.rssiDbm) * 0.1;
|
||||
|
||||
// SNR estimate
|
||||
let signalPower = 0, noisePower = 0;
|
||||
for (let i = 0; i < this.subcarriers; i++) {
|
||||
signalPower += amp[i] * amp[i];
|
||||
noisePower += this._noiseState[i] * this._noiseState[i];
|
||||
}
|
||||
const snr = noisePower > 0 ? 10 * Math.log10(signalPower / noisePower) : 30;
|
||||
|
||||
this.frameCount++;
|
||||
return { amplitude: amp, phase, snr: Math.max(0, Math.min(40, snr)) };
|
||||
}
|
||||
|
||||
/**
|
||||
* Build 3-channel pseudo-image for CNN input
|
||||
* @param {number} targetSize - Output image dimension (square)
|
||||
* @returns {Uint8Array} RGB data (targetSize * targetSize * 3)
|
||||
*/
|
||||
buildPseudoImage(targetSize = 56) {
|
||||
const buf = this.amplitudeBuffer;
|
||||
const pBuf = this.phaseBuffer;
|
||||
const frames = buf.length;
|
||||
if (frames < 2) {
|
||||
return new Uint8Array(targetSize * targetSize * 3);
|
||||
}
|
||||
|
||||
const rgb = new Uint8Array(targetSize * targetSize * 3);
|
||||
|
||||
for (let y = 0; y < targetSize; y++) {
|
||||
const fi = Math.min(Math.floor(y / targetSize * frames), frames - 1);
|
||||
for (let x = 0; x < targetSize; x++) {
|
||||
const si = Math.min(Math.floor(x / targetSize * this.subcarriers), this.subcarriers - 1);
|
||||
const idx = (y * targetSize + x) * 3;
|
||||
|
||||
// R: Amplitude (normalized to 0-255)
|
||||
const ampVal = buf[fi][si];
|
||||
rgb[idx] = Math.min(255, Math.max(0, Math.floor(ampVal * 255)));
|
||||
|
||||
// G: Phase (wrapped to 0-255)
|
||||
const phaseVal = (pBuf[fi][si] % (2 * Math.PI) + 2 * Math.PI) % (2 * Math.PI);
|
||||
rgb[idx + 1] = Math.floor(phaseVal / (2 * Math.PI) * 255);
|
||||
|
||||
// B: Temporal difference
|
||||
if (fi > 0) {
|
||||
const diff = Math.abs(buf[fi][si] - buf[fi - 1][si]);
|
||||
rgb[idx + 2] = Math.min(255, Math.floor(diff * 500));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return rgb;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get heatmap data for visualization
|
||||
* @returns {{ data: Float32Array, width: number, height: number }}
|
||||
*/
|
||||
getHeatmapData() {
|
||||
const frames = this.amplitudeBuffer.length;
|
||||
const w = this.subcarriers;
|
||||
const h = Math.min(frames, this.timeWindow);
|
||||
const data = new Float32Array(w * h);
|
||||
for (let y = 0; y < h; y++) {
|
||||
const fi = frames - h + y;
|
||||
if (fi >= 0 && fi < frames) {
|
||||
for (let x = 0; x < w; x++) {
|
||||
data[y * w + x] = this.amplitudeBuffer[fi][x];
|
||||
}
|
||||
}
|
||||
}
|
||||
return { data, width: w, height: h };
|
||||
}
|
||||
|
||||
// === Private ===
|
||||
|
||||
_generateDemoFrame(amp, phase, elapsed) {
|
||||
const rng = this._rng;
|
||||
const presence = this.personPresence;
|
||||
const motion = this.personMotion;
|
||||
const px = this.personX;
|
||||
|
||||
for (let i = 0; i < this.subcarriers; i++) {
|
||||
// Base CSI profile (frequency-selective channel)
|
||||
let a = this._baseAmplitude[i];
|
||||
let p = this._basePhase[i] + elapsed * 0.05;
|
||||
|
||||
// Environmental noise (correlated across subcarriers)
|
||||
this._noiseState[i] = 0.95 * this._noiseState[i] + 0.05 * (rng() * 2 - 1) * 0.03;
|
||||
a += this._noiseState[i];
|
||||
|
||||
// Ambient temporal drift (multipath fading even in empty room)
|
||||
a += 0.06 * Math.sin(elapsed * 0.7 + i * 0.25)
|
||||
+ 0.04 * Math.sin(elapsed * 1.3 - i * 0.18)
|
||||
+ 0.03 * Math.cos(elapsed * 2.1 + i * 0.4);
|
||||
|
||||
// Person-induced CSI perturbation
|
||||
if (presence > 0.1) {
|
||||
// Subcarrier-dependent body reflection (Fresnel zone model)
|
||||
const freqOffset = (i - this.subcarriers * px) / (this.subcarriers * 0.3);
|
||||
const bodyReflection = presence * 0.25 * Math.exp(-freqOffset * freqOffset);
|
||||
|
||||
// Motion causes amplitude fluctuation
|
||||
const motionEffect = motion * 0.15 * Math.sin(elapsed * 3.5 + i * 0.3);
|
||||
|
||||
// Breathing modulation (0.2-0.3 Hz)
|
||||
const breathing = presence * 0.02 * Math.sin(elapsed * 1.5 + i * 0.05);
|
||||
|
||||
a += bodyReflection + motionEffect + breathing;
|
||||
p += presence * 0.4 * Math.sin(elapsed * 2.1 + i * 0.15);
|
||||
}
|
||||
|
||||
amp[i] = Math.max(0, Math.min(1, a));
|
||||
phase[i] = p;
|
||||
}
|
||||
}
|
||||
|
||||
_handleLiveFrame(data) {
|
||||
// Handle JSON text frames from the sensing server
|
||||
if (typeof data === 'string') {
|
||||
try {
|
||||
const msg = JSON.parse(data);
|
||||
this._handleJsonFrame(msg);
|
||||
} catch (_) { /* ignore malformed JSON */ }
|
||||
return;
|
||||
}
|
||||
|
||||
// Handle Blob data (convert to ArrayBuffer and re-process)
|
||||
if (data instanceof Blob) {
|
||||
data.arrayBuffer().then(ab => this._handleLiveFrame(ab)).catch(() => {});
|
||||
return;
|
||||
}
|
||||
|
||||
// Handle binary ArrayBuffer frames (ADR-018 format)
|
||||
if (!(data instanceof ArrayBuffer)) return;
|
||||
const view = new DataView(data);
|
||||
// Check ADR-018 magic: 0xC5110001
|
||||
if (data.byteLength < 20) return;
|
||||
const magic = view.getUint32(0, true);
|
||||
if (magic !== 0xC5110001) return;
|
||||
|
||||
const numSub = Math.min(view.getUint16(8, true), this.subcarriers);
|
||||
this._liveAmplitude = new Float32Array(this.subcarriers);
|
||||
this._livePhase = new Float32Array(this.subcarriers);
|
||||
|
||||
const headerSize = 20;
|
||||
for (let i = 0; i < numSub && (headerSize + i * 4 + 3) < data.byteLength; i++) {
|
||||
const real = view.getInt16(headerSize + i * 4, true);
|
||||
const imag = view.getInt16(headerSize + i * 4 + 2, true);
|
||||
this._liveAmplitude[i] = Math.sqrt(real * real + imag * imag) / 2048;
|
||||
this._livePhase[i] = Math.atan2(imag, real);
|
||||
}
|
||||
}
|
||||
|
||||
_handleJsonFrame(msg) {
|
||||
// Sensing server sends: { type: "sensing_update", nodes: [{ amplitude: [...], subcarrier_count }], classification, features }
|
||||
this._liveAmplitude = new Float32Array(this.subcarriers);
|
||||
this._livePhase = new Float32Array(this.subcarriers);
|
||||
|
||||
// Extract amplitude from sensing_update node data
|
||||
const node = (msg.nodes && msg.nodes[0]) || msg;
|
||||
const ampArr = node.amplitude || msg.amplitude;
|
||||
if (ampArr && Array.isArray(ampArr)) {
|
||||
const n = Math.min(ampArr.length, this.subcarriers);
|
||||
// Server sends raw amplitude (already magnitude), normalize to 0-1
|
||||
let maxAmp = 0;
|
||||
for (let i = 0; i < n; i++) maxAmp = Math.max(maxAmp, Math.abs(ampArr[i]));
|
||||
const scale = maxAmp > 0 ? 1.0 / maxAmp : 1.0;
|
||||
for (let i = 0; i < n; i++) {
|
||||
this._liveAmplitude[i] = Math.abs(ampArr[i]) * scale;
|
||||
}
|
||||
}
|
||||
|
||||
// Phase from node (if available)
|
||||
const phaseArr = node.phase || msg.phase;
|
||||
if (phaseArr && Array.isArray(phaseArr)) {
|
||||
const n = Math.min(phaseArr.length, this.subcarriers);
|
||||
for (let i = 0; i < n; i++) this._livePhase[i] = phaseArr[i];
|
||||
} else if (ampArr) {
|
||||
// Synthesize phase from amplitude variation (Hilbert-like estimate)
|
||||
for (let i = 1; i < this.subcarriers; i++) {
|
||||
this._livePhase[i] = this._livePhase[i - 1] + (this._liveAmplitude[i] - this._liveAmplitude[i - 1]) * Math.PI;
|
||||
}
|
||||
}
|
||||
|
||||
// Handle raw I/Q pairs
|
||||
const iq = node.iq || msg.iq;
|
||||
if (iq && Array.isArray(iq)) {
|
||||
const n = Math.min(iq.length / 2, this.subcarriers);
|
||||
for (let i = 0; i < n; i++) {
|
||||
const real = iq[i * 2], imag = iq[i * 2 + 1];
|
||||
this._liveAmplitude[i] = Math.sqrt(real * real + imag * imag) / 2048;
|
||||
this._livePhase[i] = Math.atan2(imag, real);
|
||||
}
|
||||
}
|
||||
|
||||
// Extract RSSI from node data
|
||||
if (typeof node.rssi_dbm === 'number') {
|
||||
this._rssiTarget = node.rssi_dbm;
|
||||
} else if (msg.features && typeof msg.features.mean_rssi === 'number') {
|
||||
this._rssiTarget = msg.features.mean_rssi;
|
||||
}
|
||||
|
||||
// Update presence from server classification
|
||||
const cls = msg.classification;
|
||||
if (cls) {
|
||||
if (typeof cls.confidence === 'number') {
|
||||
this.personPresence = cls.presence ? cls.confidence : 0;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
_mulberry32(seed) {
|
||||
return function() {
|
||||
let t = (seed += 0x6D2B79F5);
|
||||
t = Math.imul(t ^ (t >>> 15), t | 1);
|
||||
t ^= t + Math.imul(t ^ (t >>> 7), t | 61);
|
||||
return ((t ^ (t >>> 14)) >>> 0) / 4294967296;
|
||||
};
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,183 @@
|
|||
/**
|
||||
* FusionEngine — Attention-weighted dual-modal embedding fusion.
|
||||
*
|
||||
* Combines visual (camera) and CSI (WiFi) embeddings with dynamic
|
||||
* confidence gating based on signal quality.
|
||||
*/
|
||||
|
||||
export class FusionEngine {
|
||||
/**
|
||||
* @param {number} embeddingDim
|
||||
* @param {object} opts
|
||||
* @param {object} opts.wasmModule - RuVector WASM module for cosine_similarity etc.
|
||||
*/
|
||||
constructor(embeddingDim = 128, opts = {}) {
|
||||
this.embeddingDim = embeddingDim;
|
||||
this.wasmModule = opts.wasmModule || null;
|
||||
|
||||
// Learnable attention weights (initialized to balanced 0.5)
|
||||
this.attentionWeights = new Float32Array(embeddingDim).fill(0.5);
|
||||
|
||||
// Dynamic modality confidence [0, 1]
|
||||
this.videoConfidence = 1.0;
|
||||
this.csiConfidence = 0.0;
|
||||
this.fusedConfidence = 0.5;
|
||||
|
||||
// Smoothing for confidence transitions
|
||||
this._smoothAlpha = 0.85;
|
||||
|
||||
// Embedding history for visualization
|
||||
this.recentVideoEmbeddings = [];
|
||||
this.recentCsiEmbeddings = [];
|
||||
this.recentFusedEmbeddings = [];
|
||||
this.maxHistory = 50;
|
||||
}
|
||||
|
||||
/** Set the WASM module reference (called after WASM loads) */
|
||||
setWasmModule(mod) { this.wasmModule = mod; }
|
||||
|
||||
/**
|
||||
* Update quality-based confidence scores
|
||||
* @param {number} videoBrightness - [0,1] video brightness quality
|
||||
* @param {number} videoMotion - [0,1] motion detected
|
||||
* @param {number} csiSnr - CSI signal-to-noise ratio in dB
|
||||
* @param {boolean} csiActive - Whether CSI source is connected
|
||||
*/
|
||||
updateConfidence(videoBrightness, videoMotion, csiSnr, csiActive) {
|
||||
// Video confidence: drops with low brightness, boosted by motion
|
||||
let vc = 0;
|
||||
if (videoBrightness > 0.05) {
|
||||
vc = Math.min(1, videoBrightness * 1.5) * 0.7 + Math.min(1, videoMotion * 3) * 0.3;
|
||||
}
|
||||
|
||||
// CSI confidence: based on SNR and connection status
|
||||
let cc = 0;
|
||||
if (csiActive) {
|
||||
cc = Math.min(1, csiSnr / 25); // 25dB = full confidence
|
||||
}
|
||||
|
||||
// Smooth transitions
|
||||
this.videoConfidence = this._smoothAlpha * this.videoConfidence + (1 - this._smoothAlpha) * vc;
|
||||
this.csiConfidence = this._smoothAlpha * this.csiConfidence + (1 - this._smoothAlpha) * cc;
|
||||
|
||||
// Fused confidence is the max of either (fusion can only help)
|
||||
this.fusedConfidence = Math.min(1, Math.sqrt(
|
||||
this.videoConfidence * this.videoConfidence + this.csiConfidence * this.csiConfidence
|
||||
));
|
||||
}
|
||||
|
||||
/**
|
||||
* Fuse video and CSI embeddings
|
||||
* @param {Float32Array|null} videoEmb - Visual embedding (or null if video-off)
|
||||
* @param {Float32Array|null} csiEmb - CSI embedding (or null if CSI-off)
|
||||
* @param {string} mode - 'dual' | 'video' | 'csi'
|
||||
* @returns {Float32Array} Fused embedding
|
||||
*/
|
||||
fuse(videoEmb, csiEmb, mode = 'dual') {
|
||||
const dim = this.embeddingDim;
|
||||
const fused = new Float32Array(dim);
|
||||
|
||||
if (mode === 'video' || !csiEmb) {
|
||||
if (videoEmb) fused.set(videoEmb);
|
||||
this._recordEmbedding(videoEmb, null, fused);
|
||||
return fused;
|
||||
}
|
||||
|
||||
if (mode === 'csi' || !videoEmb) {
|
||||
if (csiEmb) fused.set(csiEmb);
|
||||
this._recordEmbedding(null, csiEmb, fused);
|
||||
return fused;
|
||||
}
|
||||
|
||||
// Dual mode: attention-weighted fusion with confidence gating
|
||||
const totalConf = this.videoConfidence + this.csiConfidence;
|
||||
const videoWeight = totalConf > 0 ? this.videoConfidence / totalConf : 0.5;
|
||||
|
||||
for (let i = 0; i < dim; i++) {
|
||||
const alpha = this.attentionWeights[i] * videoWeight +
|
||||
(1 - this.attentionWeights[i]) * (1 - videoWeight);
|
||||
fused[i] = alpha * videoEmb[i] + (1 - alpha) * csiEmb[i];
|
||||
}
|
||||
|
||||
// Re-normalize using WASM when available
|
||||
if (this.wasmModule) {
|
||||
try { this.wasmModule.normalize(fused); } catch (_) { this._jsNormalize(fused); }
|
||||
} else {
|
||||
this._jsNormalize(fused);
|
||||
}
|
||||
|
||||
this._recordEmbedding(videoEmb, csiEmb, fused);
|
||||
return fused;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get embedding pairs for 2D visualization (PCA projection)
|
||||
* @returns {{ video: Array, csi: Array, fused: Array }}
|
||||
*/
|
||||
getEmbeddingPoints() {
|
||||
// Sparse random projection: pick a few dimensions with fixed coefficients
|
||||
// to get visible 2D spread (avoids cancellation from summing all 128 dims)
|
||||
const project = (emb) => {
|
||||
if (!emb || emb.length < 4) return null;
|
||||
// Use 8 sparse dimensions with predetermined signs (seeded, not random)
|
||||
const dim = emb.length;
|
||||
const x = emb[0] * 3.2 - emb[3] * 2.8 + emb[7] * 2.1 - emb[12] * 1.9
|
||||
+ (dim > 30 ? emb[29] * 1.5 - emb[31] * 1.3 : 0)
|
||||
+ (dim > 60 ? emb[55] * 1.1 - emb[60] * 0.9 : 0);
|
||||
const y = emb[1] * 3.0 - emb[5] * 2.5 + emb[9] * 2.3 - emb[15] * 1.7
|
||||
+ (dim > 40 ? emb[37] * 1.4 - emb[42] * 1.2 : 0)
|
||||
+ (dim > 80 ? emb[73] * 1.0 - emb[80] * 0.8 : 0);
|
||||
return [x, y];
|
||||
};
|
||||
|
||||
return {
|
||||
video: this.recentVideoEmbeddings.map(project).filter(Boolean),
|
||||
csi: this.recentCsiEmbeddings.map(project).filter(Boolean),
|
||||
fused: this.recentFusedEmbeddings.map(project).filter(Boolean)
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Cross-modal similarity score
|
||||
* @returns {number} Cosine similarity between latest video and CSI embeddings
|
||||
*/
|
||||
getCrossModalSimilarity() {
|
||||
const v = this.recentVideoEmbeddings[this.recentVideoEmbeddings.length - 1];
|
||||
const c = this.recentCsiEmbeddings[this.recentCsiEmbeddings.length - 1];
|
||||
if (!v || !c) return 0;
|
||||
|
||||
// Use WASM cosine_similarity when available
|
||||
if (this.wasmModule) {
|
||||
try { return this.wasmModule.cosine_similarity(v, c); } catch (_) { /* fallback */ }
|
||||
}
|
||||
|
||||
let dot = 0, na = 0, nb = 0;
|
||||
for (let i = 0; i < v.length; i++) {
|
||||
dot += v[i] * c[i];
|
||||
na += v[i] * v[i];
|
||||
nb += c[i] * c[i];
|
||||
}
|
||||
na = Math.sqrt(na); nb = Math.sqrt(nb);
|
||||
return (na > 1e-8 && nb > 1e-8) ? dot / (na * nb) : 0;
|
||||
}
|
||||
|
||||
_jsNormalize(vec) {
|
||||
let norm = 0;
|
||||
for (let i = 0; i < vec.length; i++) norm += vec[i] * vec[i];
|
||||
norm = Math.sqrt(norm);
|
||||
if (norm > 1e-8) for (let i = 0; i < vec.length; i++) vec[i] /= norm;
|
||||
}
|
||||
|
||||
_recordEmbedding(video, csi, fused) {
|
||||
if (video) {
|
||||
this.recentVideoEmbeddings.push(new Float32Array(video));
|
||||
if (this.recentVideoEmbeddings.length > this.maxHistory) this.recentVideoEmbeddings.shift();
|
||||
}
|
||||
if (csi) {
|
||||
this.recentCsiEmbeddings.push(new Float32Array(csi));
|
||||
if (this.recentCsiEmbeddings.length > this.maxHistory) this.recentCsiEmbeddings.shift();
|
||||
}
|
||||
this.recentFusedEmbeddings.push(new Float32Array(fused));
|
||||
if (this.recentFusedEmbeddings.length > this.maxHistory) this.recentFusedEmbeddings.shift();
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,472 @@
|
|||
/**
|
||||
* RuView — Dual-Modal Pose Estimation Demo
|
||||
*
|
||||
* Main orchestration: video capture → CNN embedding → CSI processing → fusion → rendering
|
||||
*/
|
||||
|
||||
import { VideoCapture } from './video-capture.js?v=13';
|
||||
import { CsiSimulator } from './csi-simulator.js?v=13';
|
||||
import { CnnEmbedder } from './cnn-embedder.js?v=13';
|
||||
import { FusionEngine } from './fusion-engine.js?v=13';
|
||||
import { PoseDecoder } from './pose-decoder.js?v=13';
|
||||
import { CanvasRenderer } from './canvas-renderer.js?v=13';
|
||||
|
||||
// === State ===
|
||||
let mode = 'dual'; // 'dual' | 'video' | 'csi'
|
||||
let isRunning = false;
|
||||
let isPaused = false;
|
||||
let startTime = 0;
|
||||
let frameCount = 0;
|
||||
let fps = 0;
|
||||
let lastFpsTime = 0;
|
||||
let confidenceThreshold = 0.3;
|
||||
|
||||
// Latency tracking
|
||||
const latency = { video: 0, csi: 0, fusion: 0, total: 0 };
|
||||
|
||||
// === Components ===
|
||||
const videoCapture = new VideoCapture(document.getElementById('webcam'));
|
||||
const csiSimulator = new CsiSimulator({ subcarriers: 52, timeWindow: 56 });
|
||||
const visualCnn = new CnnEmbedder({ inputSize: 56, embeddingDim: 128, seed: 42 });
|
||||
const csiCnn = new CnnEmbedder({ inputSize: 56, embeddingDim: 128, seed: 137 });
|
||||
const fusionEngine = new FusionEngine(128);
|
||||
const poseDecoder = new PoseDecoder(128);
|
||||
const renderer = new CanvasRenderer();
|
||||
|
||||
// === Canvas Elements ===
|
||||
const skeletonCanvas = document.getElementById('skeleton-canvas');
|
||||
const skeletonCtx = skeletonCanvas.getContext('2d');
|
||||
const csiCanvas = document.getElementById('csi-canvas');
|
||||
const csiCtx = csiCanvas.getContext('2d');
|
||||
const embeddingCanvas = document.getElementById('embedding-canvas');
|
||||
const embeddingCtx = embeddingCanvas.getContext('2d');
|
||||
|
||||
// === UI Elements ===
|
||||
const modeSelect = document.getElementById('mode-select');
|
||||
const statusDot = document.getElementById('status-dot');
|
||||
const statusLabel = document.getElementById('status-label');
|
||||
const fpsDisplay = document.getElementById('fps-display');
|
||||
const cameraPrompt = document.getElementById('camera-prompt');
|
||||
const startCameraBtn = document.getElementById('start-camera-btn');
|
||||
const pauseBtn = document.getElementById('pause-btn');
|
||||
const confSlider = document.getElementById('confidence-slider');
|
||||
const confValue = document.getElementById('confidence-value');
|
||||
const wsUrlInput = document.getElementById('ws-url');
|
||||
const connectWsBtn = document.getElementById('connect-ws-btn');
|
||||
|
||||
// Fusion bar elements
|
||||
const videoBar = document.getElementById('video-bar');
|
||||
const csiBar = document.getElementById('csi-bar');
|
||||
const fusedBar = document.getElementById('fused-bar');
|
||||
const videoBarVal = document.getElementById('video-bar-val');
|
||||
const csiBarVal = document.getElementById('csi-bar-val');
|
||||
const fusedBarVal = document.getElementById('fused-bar-val');
|
||||
|
||||
// Latency elements
|
||||
const latVideoEl = document.getElementById('lat-video');
|
||||
const latCsiEl = document.getElementById('lat-csi');
|
||||
const latFusionEl = document.getElementById('lat-fusion');
|
||||
const latTotalEl = document.getElementById('lat-total');
|
||||
|
||||
// Cross-modal similarity
|
||||
const crossModalEl = document.getElementById('cross-modal-sim');
|
||||
|
||||
// RSSI elements
|
||||
const rssiBarEl = document.getElementById('rssi-bar');
|
||||
const rssiValueEl = document.getElementById('rssi-value');
|
||||
const rssiQualityEl = document.getElementById('rssi-quality');
|
||||
const rssiSparkCanvas = document.getElementById('rssi-sparkline');
|
||||
const rssiSparkCtx = rssiSparkCanvas ? rssiSparkCanvas.getContext('2d') : null;
|
||||
const rssiHistory = [];
|
||||
const RSSI_HISTORY_MAX = 80;
|
||||
|
||||
// === Initialize ===
|
||||
function init() {
|
||||
console.log(`[PoseFusion] init() v4 — CsiSimulator=${CsiSimulator.VERSION || 'OLD'}, starting...`);
|
||||
resizeCanvases();
|
||||
console.log(`[PoseFusion] canvases: skeleton=${skeletonCanvas.width}x${skeletonCanvas.height}, csi=${csiCanvas.width}x${csiCanvas.height}, emb=${embeddingCanvas.width}x${embeddingCanvas.height}`);
|
||||
window.addEventListener('resize', resizeCanvases);
|
||||
|
||||
// Mode change
|
||||
modeSelect.addEventListener('change', (e) => {
|
||||
mode = e.target.value;
|
||||
updateModeUI();
|
||||
});
|
||||
|
||||
// Camera start
|
||||
startCameraBtn.addEventListener('click', startCamera);
|
||||
|
||||
// Pause
|
||||
pauseBtn.addEventListener('click', () => {
|
||||
isPaused = !isPaused;
|
||||
pauseBtn.textContent = isPaused ? '▶ Resume' : '⏸ Pause';
|
||||
pauseBtn.classList.toggle('active', isPaused);
|
||||
});
|
||||
|
||||
// Confidence slider
|
||||
confSlider.addEventListener('input', (e) => {
|
||||
confidenceThreshold = parseFloat(e.target.value);
|
||||
confValue.textContent = confidenceThreshold.toFixed(2);
|
||||
});
|
||||
|
||||
// WebSocket connect
|
||||
connectWsBtn.addEventListener('click', async () => {
|
||||
const url = wsUrlInput.value.trim();
|
||||
if (!url) return;
|
||||
connectWsBtn.textContent = 'Connecting...';
|
||||
const ok = await csiSimulator.connectLive(url);
|
||||
connectWsBtn.textContent = ok ? '✓ Connected' : 'Connect';
|
||||
if (ok) {
|
||||
connectWsBtn.classList.add('active');
|
||||
}
|
||||
});
|
||||
|
||||
// Try to load RuVector Attention WASM embedders (non-blocking)
|
||||
const wasmBase = new URL('../pkg/ruvector-attention', import.meta.url).href;
|
||||
visualCnn.tryLoadWasm(wasmBase).then((ok) => {
|
||||
// Share the WASM module with FusionEngine for cosine_similarity, normalize, etc.
|
||||
if (visualCnn.rvModule) fusionEngine.setWasmModule(visualCnn.rvModule);
|
||||
// Update footer backend label
|
||||
const backendEl = document.getElementById('cnn-backend');
|
||||
if (backendEl) {
|
||||
backendEl.textContent = ok && visualCnn.useRuVector
|
||||
? `RuVector WASM v${visualCnn.rvModule.version()} — 6 attention mechanisms`
|
||||
: 'ruvector-cnn (JS fallback)';
|
||||
}
|
||||
});
|
||||
csiCnn.tryLoadWasm(wasmBase);
|
||||
|
||||
// Auto-connect to local sensing server WebSocket if available
|
||||
const defaultWsUrl = 'ws://localhost:8765/ws/sensing';
|
||||
if (wsUrlInput) wsUrlInput.value = defaultWsUrl;
|
||||
csiSimulator.connectLive(defaultWsUrl).then(ok => {
|
||||
if (ok && connectWsBtn) {
|
||||
connectWsBtn.textContent = '✓ Live ESP32';
|
||||
connectWsBtn.classList.add('active');
|
||||
statusLabel.textContent = 'LIVE CSI';
|
||||
statusDot.classList.remove('offline');
|
||||
}
|
||||
});
|
||||
|
||||
// Auto-start camera for video/dual modes
|
||||
updateModeUI();
|
||||
startTime = performance.now() / 1000;
|
||||
isRunning = true;
|
||||
requestAnimationFrame(mainLoop);
|
||||
}
|
||||
|
||||
async function startCamera() {
|
||||
cameraPrompt.style.display = 'none';
|
||||
const ok = await videoCapture.start();
|
||||
if (ok) {
|
||||
statusDot.classList.remove('offline');
|
||||
statusLabel.textContent = 'LIVE';
|
||||
resizeCanvases();
|
||||
} else {
|
||||
cameraPrompt.style.display = 'flex';
|
||||
cameraPrompt.querySelector('p').textContent = 'Camera access denied. Try CSI-only mode.';
|
||||
}
|
||||
}
|
||||
|
||||
function updateModeUI() {
|
||||
const needsVideo = mode !== 'csi';
|
||||
|
||||
// Show/hide camera prompt
|
||||
if (needsVideo && !videoCapture.isActive) {
|
||||
cameraPrompt.style.display = 'flex';
|
||||
} else {
|
||||
cameraPrompt.style.display = 'none';
|
||||
}
|
||||
|
||||
// Update mode label in both the overlay and the camera prompt
|
||||
const labelMap = { dual: 'DUAL FUSION', video: 'VIDEO ONLY', csi: 'CSI ONLY' };
|
||||
const modeLabel = document.getElementById('mode-label');
|
||||
const promptLabel = document.getElementById('prompt-mode-label');
|
||||
if (modeLabel) modeLabel.textContent = labelMap[mode] || mode;
|
||||
if (promptLabel) promptLabel.textContent = labelMap[mode] || mode;
|
||||
}
|
||||
|
||||
function resizeCanvases() {
|
||||
const videoPanel = document.querySelector('.video-panel');
|
||||
if (videoPanel) {
|
||||
const rect = videoPanel.getBoundingClientRect();
|
||||
skeletonCanvas.width = rect.width;
|
||||
skeletonCanvas.height = rect.height;
|
||||
}
|
||||
|
||||
// CSI canvas (min 200px width)
|
||||
csiCanvas.width = Math.max(200, csiCanvas.parentElement.clientWidth);
|
||||
csiCanvas.height = 120;
|
||||
|
||||
// Embedding canvas (min 200px width)
|
||||
embeddingCanvas.width = Math.max(200, embeddingCanvas.parentElement.clientWidth);
|
||||
embeddingCanvas.height = 140;
|
||||
}
|
||||
|
||||
// === Main Loop ===
|
||||
let _loopErrorShown = false;
|
||||
let _diagDone = false;
|
||||
function mainLoop(timestamp) {
|
||||
if (!isRunning) return;
|
||||
requestAnimationFrame(mainLoop);
|
||||
|
||||
if (isPaused) return;
|
||||
|
||||
try {
|
||||
const elapsed = performance.now() / 1000 - startTime;
|
||||
const totalStart = performance.now();
|
||||
|
||||
// --- Video Pipeline ---
|
||||
let videoEmb = null;
|
||||
let motionRegion = null;
|
||||
if (mode !== 'csi' && videoCapture.isActive) {
|
||||
const t0 = performance.now();
|
||||
const frame = videoCapture.captureFrame(56, 56);
|
||||
if (frame) {
|
||||
videoEmb = visualCnn.extract(frame.rgb, frame.width, frame.height);
|
||||
motionRegion = videoCapture.detectMotionRegion(56, 56);
|
||||
|
||||
// Feed motion to CSI simulator for correlated demo data
|
||||
// When detected=false, CSI simulator handles through-wall persistence
|
||||
csiSimulator.updatePersonState(
|
||||
motionRegion.detected ? 1.0 : 0,
|
||||
motionRegion.detected ? motionRegion.x + motionRegion.w / 2 : 0.5,
|
||||
motionRegion.detected ? motionRegion.y + motionRegion.h / 2 : 0.5,
|
||||
frame.motion
|
||||
);
|
||||
|
||||
fusionEngine.updateConfidence(
|
||||
frame.brightness, frame.motion,
|
||||
0, csiSimulator.isLive || mode === 'dual'
|
||||
);
|
||||
}
|
||||
latency.video = performance.now() - t0;
|
||||
}
|
||||
|
||||
// --- CSI Pipeline ---
|
||||
let csiEmb = null;
|
||||
if (mode !== 'video') {
|
||||
const t0 = performance.now();
|
||||
const csiFrame = csiSimulator.nextFrame(elapsed);
|
||||
const pseudoImage = csiSimulator.buildPseudoImage(56);
|
||||
csiEmb = csiCnn.extract(pseudoImage, 56, 56);
|
||||
|
||||
fusionEngine.updateConfidence(
|
||||
videoCapture.brightnessScore,
|
||||
videoCapture.motionScore,
|
||||
csiFrame.snr,
|
||||
true
|
||||
);
|
||||
|
||||
// Draw CSI heatmap
|
||||
const heatmap = csiSimulator.getHeatmapData();
|
||||
renderer.drawCsiHeatmap(csiCtx, heatmap, csiCanvas.width, csiCanvas.height);
|
||||
|
||||
latency.csi = performance.now() - t0;
|
||||
}
|
||||
|
||||
// --- Fusion ---
|
||||
const t0f = performance.now();
|
||||
const fusedEmb = fusionEngine.fuse(videoEmb, csiEmb, mode);
|
||||
latency.fusion = performance.now() - t0f;
|
||||
|
||||
// --- Pose Decode ---
|
||||
// For CSI-only mode, generate a synthetic motion region from CSI energy
|
||||
if (mode === 'csi' && (!motionRegion || !motionRegion.detected)) {
|
||||
const csiPresence = csiSimulator.personPresence;
|
||||
if (csiPresence > 0.1) {
|
||||
motionRegion = {
|
||||
detected: true,
|
||||
x: 0.25, y: 0.15, w: 0.5, h: 0.7,
|
||||
coverage: csiPresence,
|
||||
motionGrid: null,
|
||||
gridCols: 10,
|
||||
gridRows: 8
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
// CSI state for through-wall tracking
|
||||
const csiState = {
|
||||
csiPresence: csiSimulator.personPresence,
|
||||
isLive: csiSimulator.isLive
|
||||
};
|
||||
|
||||
const keypoints = poseDecoder.decode(fusedEmb, motionRegion, elapsed, csiState);
|
||||
|
||||
// --- Render Skeleton ---
|
||||
const labelMap = { dual: 'DUAL FUSION', video: 'VIDEO ONLY', csi: 'CSI ONLY' };
|
||||
renderer.drawSkeleton(skeletonCtx, keypoints, skeletonCanvas.width, skeletonCanvas.height, {
|
||||
minConfidence: confidenceThreshold,
|
||||
color: mode === 'csi' ? 'amber' : 'green',
|
||||
label: labelMap[mode]
|
||||
});
|
||||
|
||||
// --- Render Embedding Space ---
|
||||
const embPoints = fusionEngine.getEmbeddingPoints();
|
||||
renderer.drawEmbeddingSpace(embeddingCtx, embPoints, embeddingCanvas.width, embeddingCanvas.height);
|
||||
|
||||
// --- Update UI ---
|
||||
latency.total = performance.now() - totalStart;
|
||||
|
||||
// FPS
|
||||
frameCount++;
|
||||
if (timestamp - lastFpsTime > 500) {
|
||||
fps = Math.round(frameCount * 1000 / (timestamp - lastFpsTime));
|
||||
lastFpsTime = timestamp;
|
||||
frameCount = 0;
|
||||
fpsDisplay.textContent = `${fps} FPS`;
|
||||
}
|
||||
|
||||
// Fusion bars
|
||||
const vc = fusionEngine.videoConfidence;
|
||||
const cc = fusionEngine.csiConfidence;
|
||||
const fc = fusionEngine.fusedConfidence;
|
||||
videoBar.style.width = `${vc * 100}%`;
|
||||
csiBar.style.width = `${cc * 100}%`;
|
||||
fusedBar.style.width = `${fc * 100}%`;
|
||||
videoBarVal.textContent = `${Math.round(vc * 100)}%`;
|
||||
csiBarVal.textContent = `${Math.round(cc * 100)}%`;
|
||||
fusedBarVal.textContent = `${Math.round(fc * 100)}%`;
|
||||
|
||||
// Latency
|
||||
latVideoEl.textContent = `${latency.video.toFixed(1)}ms`;
|
||||
latCsiEl.textContent = `${latency.csi.toFixed(1)}ms`;
|
||||
latFusionEl.textContent = `${latency.fusion.toFixed(1)}ms`;
|
||||
latTotalEl.textContent = `${latency.total.toFixed(1)}ms`;
|
||||
|
||||
// Cross-modal similarity
|
||||
const sim = fusionEngine.getCrossModalSimilarity();
|
||||
crossModalEl.textContent = sim.toFixed(3);
|
||||
|
||||
// RuVector attention pipeline stats
|
||||
const rvStats = poseDecoder.attentionStats;
|
||||
const rvEnergyEl = document.getElementById('rv-energy');
|
||||
const rvRefineEl = document.getElementById('rv-refine');
|
||||
const rvImpactEl = document.getElementById('rv-impact');
|
||||
if (rvEnergyEl) rvEnergyEl.textContent = (rvStats.energy || 0).toFixed(2);
|
||||
if (rvRefineEl) rvRefineEl.textContent = ((rvStats.refinementMag || 0) * 1000).toFixed(1) + 'px';
|
||||
if (rvImpactEl) {
|
||||
const impact = Math.min(100, (rvStats.refinementMag || 0) * 5000);
|
||||
rvImpactEl.textContent = impact.toFixed(0) + '%';
|
||||
}
|
||||
// Pulse the pipeline stages when active
|
||||
if (visualCnn.useRuVector && rvStats.energy > 0.1) {
|
||||
document.querySelectorAll('.rv-stage').forEach(el => el.classList.add('active'));
|
||||
}
|
||||
|
||||
// RSSI update
|
||||
updateRssi(csiSimulator.rssiDbm);
|
||||
|
||||
// One-time diagnostic
|
||||
if (!_diagDone) {
|
||||
_diagDone = true;
|
||||
console.log(`[PoseFusion] frame 1 OK — mode=${mode}, csi.bufLen=${csiSimulator.amplitudeBuffer.length}, embPts=${embPoints?.fused?.length ?? 0}, rssi=${(csiSimulator.rssiDbm ?? -99).toFixed(1)}`);
|
||||
}
|
||||
|
||||
} catch (err) {
|
||||
if (!_loopErrorShown) {
|
||||
_loopErrorShown = true;
|
||||
console.error('[MainLoop]', err);
|
||||
// Show error visually on page
|
||||
const errDiv = document.createElement('div');
|
||||
errDiv.style.cssText = 'position:fixed;bottom:60px;left:24px;right:24px;background:rgba(255,48,64,0.95);color:#fff;padding:12px 16px;border-radius:8px;font:12px/1.4 "JetBrains Mono",monospace;z-index:9999;max-height:120px;overflow:auto';
|
||||
errDiv.textContent = `[MainLoop Error] ${err.message}\n${err.stack?.split('\n').slice(0,3).join('\n')}`;
|
||||
document.body.appendChild(errDiv);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// === RSSI Visualization ===
|
||||
function updateRssi(dbm) {
|
||||
if (!rssiBarEl) return;
|
||||
|
||||
// Clamp to typical WiFi range: -100 (worst) to -30 (best)
|
||||
const clamped = Math.max(-100, Math.min(-30, dbm));
|
||||
const pct = ((clamped + 100) / 70) * 100; // 0-100%
|
||||
|
||||
rssiBarEl.style.width = `${pct}%`;
|
||||
rssiValueEl.textContent = `${Math.round(clamped)} dBm`;
|
||||
|
||||
// Quality label
|
||||
let quality;
|
||||
if (clamped > -50) quality = 'Excellent';
|
||||
else if (clamped > -60) quality = 'Good';
|
||||
else if (clamped > -70) quality = 'Fair';
|
||||
else if (clamped > -80) quality = 'Weak';
|
||||
else quality = 'Poor';
|
||||
rssiQualityEl.textContent = quality;
|
||||
|
||||
// Color the dBm value based on quality
|
||||
if (clamped > -60) rssiValueEl.style.color = 'var(--green-glow)';
|
||||
else if (clamped > -75) rssiValueEl.style.color = 'var(--amber)';
|
||||
else rssiValueEl.style.color = 'var(--red-alert)';
|
||||
|
||||
// Sparkline history
|
||||
rssiHistory.push(clamped);
|
||||
if (rssiHistory.length > RSSI_HISTORY_MAX) rssiHistory.shift();
|
||||
drawRssiSparkline();
|
||||
}
|
||||
|
||||
function drawRssiSparkline() {
|
||||
if (!rssiSparkCtx || rssiHistory.length < 2) return;
|
||||
const w = rssiSparkCanvas.width;
|
||||
const h = rssiSparkCanvas.height;
|
||||
const ctx = rssiSparkCtx;
|
||||
|
||||
ctx.clearRect(0, 0, w, h);
|
||||
|
||||
// Draw signal strength line
|
||||
const len = rssiHistory.length;
|
||||
const step = w / (RSSI_HISTORY_MAX - 1);
|
||||
|
||||
// Gradient fill under line
|
||||
const grad = ctx.createLinearGradient(0, 0, 0, h);
|
||||
grad.addColorStop(0, 'rgba(0,210,120,0.3)');
|
||||
grad.addColorStop(1, 'rgba(0,210,120,0)');
|
||||
|
||||
ctx.beginPath();
|
||||
for (let i = 0; i < len; i++) {
|
||||
const x = (RSSI_HISTORY_MAX - len + i) * step;
|
||||
const y = h - ((rssiHistory[i] + 100) / 70) * h;
|
||||
if (i === 0) ctx.moveTo(x, y);
|
||||
else ctx.lineTo(x, y);
|
||||
}
|
||||
// Fill area
|
||||
const lastX = (RSSI_HISTORY_MAX - 1) * step;
|
||||
const firstX = (RSSI_HISTORY_MAX - len) * step;
|
||||
ctx.lineTo(lastX, h);
|
||||
ctx.lineTo(firstX, h);
|
||||
ctx.closePath();
|
||||
ctx.fillStyle = grad;
|
||||
ctx.fill();
|
||||
|
||||
// Draw line on top
|
||||
ctx.beginPath();
|
||||
for (let i = 0; i < len; i++) {
|
||||
const x = (RSSI_HISTORY_MAX - len + i) * step;
|
||||
const y = h - ((rssiHistory[i] + 100) / 70) * h;
|
||||
if (i === 0) ctx.moveTo(x, y);
|
||||
else ctx.lineTo(x, y);
|
||||
}
|
||||
ctx.strokeStyle = '#00d878';
|
||||
ctx.lineWidth = 1.5;
|
||||
ctx.stroke();
|
||||
|
||||
// Pulsing dot at latest value
|
||||
const latestX = lastX;
|
||||
const latestY = h - ((rssiHistory[len - 1] + 100) / 70) * h;
|
||||
const pulse = 0.5 + 0.5 * Math.sin(performance.now() / 300);
|
||||
ctx.beginPath();
|
||||
ctx.arc(latestX, latestY, 2 + pulse, 0, Math.PI * 2);
|
||||
ctx.fillStyle = '#00d878';
|
||||
ctx.fill();
|
||||
ctx.beginPath();
|
||||
ctx.arc(latestX, latestY, 4 + pulse * 2, 0, Math.PI * 2);
|
||||
ctx.strokeStyle = `rgba(0,216,120,${0.3 + pulse * 0.3})`;
|
||||
ctx.lineWidth = 1;
|
||||
ctx.stroke();
|
||||
}
|
||||
|
||||
// Boot
|
||||
document.addEventListener('DOMContentLoaded', init);
|
||||
|
|
@ -0,0 +1,553 @@
|
|||
/**
|
||||
* PoseDecoder — Maps motion detection grid → 17 COCO keypoints.
|
||||
*
|
||||
* Uses per-cell motion intensity to track actual body part positions:
|
||||
* - Head: top-center motion cluster
|
||||
* - Shoulders/Elbows/Wrists: lateral motion in upper body zone
|
||||
* - Hips/Knees/Ankles: lower body motion distribution
|
||||
*
|
||||
* When person exits frame, CSI data continues tracking (through-wall mode).
|
||||
*/
|
||||
|
||||
// Extended keypoint definitions: 17 COCO + 9 hand/fingertip approximations = 26 total
|
||||
export const KEYPOINT_NAMES = [
|
||||
'nose', 'left_eye', 'right_eye', 'left_ear', 'right_ear',
|
||||
'left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow',
|
||||
'left_wrist', 'right_wrist', 'left_hip', 'right_hip',
|
||||
'left_knee', 'right_knee', 'left_ankle', 'right_ankle',
|
||||
// Extended: hand keypoints (17-25)
|
||||
'left_thumb', 'left_index', 'left_pinky', // 17, 18, 19
|
||||
'right_thumb', 'right_index', 'right_pinky', // 20, 21, 22
|
||||
'left_foot_index', 'right_foot_index', // 23, 24 (toe tips)
|
||||
'neck', // 25 (mid-shoulder)
|
||||
];
|
||||
|
||||
// Skeleton connections (pairs of keypoint indices)
|
||||
export const SKELETON_CONNECTIONS = [
|
||||
[0, 1], [0, 2], [1, 3], [2, 4], // Head
|
||||
[0, 25], // Nose → neck
|
||||
[25, 5], [25, 6], // Neck → shoulders
|
||||
[5, 7], [7, 9], // Left arm
|
||||
[6, 8], [8, 10], // Right arm
|
||||
[5, 11], [6, 12], // Torso
|
||||
[11, 12], // Hips
|
||||
[11, 13], [13, 15], // Left leg
|
||||
[12, 14], [14, 16], // Right leg
|
||||
// Hand connections
|
||||
[9, 17], [9, 18], [9, 19], // Left wrist → fingers
|
||||
[10, 20], [10, 21], [10, 22], // Right wrist → fingers
|
||||
// Foot connections
|
||||
[15, 23], [16, 24], // Ankles → toes
|
||||
];
|
||||
|
||||
// Standard body proportions (relative to body height)
|
||||
const PROPORTIONS = {
|
||||
headToShoulder: 0.15,
|
||||
shoulderWidth: 0.25,
|
||||
shoulderToElbow: 0.18,
|
||||
elbowToWrist: 0.16,
|
||||
shoulderToHip: 0.30,
|
||||
hipWidth: 0.18,
|
||||
hipToKnee: 0.24,
|
||||
kneeToAnkle: 0.24,
|
||||
eyeSpacing: 0.04,
|
||||
earSpacing: 0.07,
|
||||
// Hand proportions
|
||||
wristToFinger: 0.09,
|
||||
fingerSpread: 0.04,
|
||||
thumbAngle: 0.6, // radians from wrist-elbow axis
|
||||
// Foot proportions
|
||||
ankleToToe: 0.06,
|
||||
};
|
||||
|
||||
export class PoseDecoder {
|
||||
constructor(embeddingDim = 128) {
|
||||
this.embeddingDim = embeddingDim;
|
||||
this.smoothedKeypoints = null;
|
||||
this.smoothingFactor = 0.25; // Low = responsive to real movement
|
||||
this._time = 0;
|
||||
|
||||
// Through-wall tracking state
|
||||
this._lastBodyState = null;
|
||||
this._ghostState = null;
|
||||
this._ghostConfidence = 0;
|
||||
this._ghostVelocity = { x: 0, y: 0 };
|
||||
|
||||
// Zone centroid tracking (normalized 0-1 positions)
|
||||
this._headCx = 0.5;
|
||||
this._headCy = 0.15;
|
||||
this._leftArmCx = 0.3;
|
||||
this._leftArmCy = 0.35;
|
||||
this._rightArmCx = 0.7;
|
||||
this._rightArmCy = 0.35;
|
||||
this._leftLegCx = 0.4;
|
||||
this._leftLegCy = 0.8;
|
||||
this._rightLegCx = 0.6;
|
||||
this._rightLegCy = 0.8;
|
||||
this._torsoCx = 0.5;
|
||||
this._torsoCy = 0.45;
|
||||
|
||||
// RuVector embedding → joint mapping
|
||||
// Each joint gets 2 consecutive embedding dimensions (dx, dy offset)
|
||||
// and 1 dimension for confidence modulation. 26 joints × 3 = 78 dims used from 128.
|
||||
// Remaining 50 dims encode global pose features (body scale, rotation, lean).
|
||||
this._jointEmbMap = this._buildJointEmbeddingMap(embeddingDim);
|
||||
|
||||
// Attention contribution tracking (for UI overlay)
|
||||
this.attentionStats = { energy: 0, maxDim: 0, refinementMag: 0 };
|
||||
}
|
||||
|
||||
/**
|
||||
* Build the mapping from embedding dimensions to joint refinement signals.
|
||||
* This maps the RuVector attention output to anatomically meaningful joint offsets.
|
||||
*/
|
||||
_buildJointEmbeddingMap(dim) {
|
||||
const map = [];
|
||||
// 26 joints × 3 dims each (dx, dy, confidence_mod) = 78 dims
|
||||
for (let j = 0; j < 26; j++) {
|
||||
const base = j * 3;
|
||||
if (base + 2 < dim) {
|
||||
map.push({ dxDim: base, dyDim: base + 1, confDim: base + 2 });
|
||||
} else {
|
||||
map.push({ dxDim: j % dim, dyDim: (j + 1) % dim, confDim: (j + 2) % dim });
|
||||
}
|
||||
}
|
||||
// Global pose features from dims 78-127
|
||||
return {
|
||||
joints: map,
|
||||
scaleDim: Math.min(78, dim - 1), // body scale factor
|
||||
rotDim: Math.min(79, dim - 1), // body rotation
|
||||
leanXDim: Math.min(80, dim - 1), // lateral lean
|
||||
leanYDim: Math.min(81, dim - 1), // forward/back lean
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Decode motion data into 17 keypoints
|
||||
* @param {Float32Array} embedding - Fused embedding vector
|
||||
* @param {{ detected, x, y, w, h, motionGrid, gridCols, gridRows, motionCx, motionCy, exitDirection }} motionRegion
|
||||
* @param {number} elapsed - Time in seconds
|
||||
* @param {{ csiPresence: number }} csiState - CSI sensing state for through-wall
|
||||
* @returns {Array<{x: number, y: number, confidence: number, name: string}>}
|
||||
*/
|
||||
decode(embedding, motionRegion, elapsed, csiState = {}) {
|
||||
this._time = elapsed;
|
||||
|
||||
const hasMotion = motionRegion && motionRegion.detected;
|
||||
const hasCsi = csiState && csiState.csiPresence > 0.1;
|
||||
|
||||
if (hasMotion) {
|
||||
// Active tracking from video motion grid
|
||||
this._ghostConfidence = 0;
|
||||
const rawKeypoints = this._trackFromMotionGrid(motionRegion, embedding, elapsed);
|
||||
this._lastBodyState = { keypoints: rawKeypoints.map(kp => ({...kp})), time: elapsed };
|
||||
|
||||
// Track exit velocity
|
||||
if (motionRegion.exitDirection) {
|
||||
const speed = 0.008;
|
||||
this._ghostVelocity = {
|
||||
x: motionRegion.exitDirection === 'left' ? -speed : motionRegion.exitDirection === 'right' ? speed : 0,
|
||||
y: motionRegion.exitDirection === 'up' ? -speed : motionRegion.exitDirection === 'down' ? speed : 0
|
||||
};
|
||||
}
|
||||
|
||||
// Apply temporal smoothing
|
||||
if (this.smoothedKeypoints && this.smoothedKeypoints.length === rawKeypoints.length) {
|
||||
const alpha = this.smoothingFactor;
|
||||
for (let i = 0; i < rawKeypoints.length; i++) {
|
||||
rawKeypoints[i].x = alpha * this.smoothedKeypoints[i].x + (1 - alpha) * rawKeypoints[i].x;
|
||||
rawKeypoints[i].y = alpha * this.smoothedKeypoints[i].y + (1 - alpha) * rawKeypoints[i].y;
|
||||
}
|
||||
}
|
||||
|
||||
this.smoothedKeypoints = rawKeypoints;
|
||||
return rawKeypoints;
|
||||
|
||||
} else if (this._lastBodyState && (hasCsi || this._ghostConfidence > 0.05)) {
|
||||
// Through-wall mode: person left frame but CSI still senses them
|
||||
return this._trackThroughWall(elapsed, csiState);
|
||||
|
||||
} else if (this.smoothedKeypoints) {
|
||||
// Fade out
|
||||
const faded = this.smoothedKeypoints.map(kp => ({
|
||||
...kp,
|
||||
confidence: kp.confidence * 0.88
|
||||
})).filter(kp => kp.confidence > 0.05);
|
||||
if (faded.length === 0) this.smoothedKeypoints = null;
|
||||
else this.smoothedKeypoints = faded;
|
||||
return faded;
|
||||
}
|
||||
|
||||
return [];
|
||||
}
|
||||
|
||||
/**
|
||||
* Track body parts from the motion grid.
|
||||
* Finds the centroid of motion in each body zone and positions joints there.
|
||||
*/
|
||||
_trackFromMotionGrid(region, embedding, elapsed) {
|
||||
const grid = region.motionGrid;
|
||||
const cols = region.gridCols || 10;
|
||||
const rows = region.gridRows || 8;
|
||||
|
||||
// Body bounding box (in normalized 0-1 coords)
|
||||
const bx = region.x, by = region.y, bw = region.w, bh = region.h;
|
||||
const cx = bx + bw / 2;
|
||||
const cy = by + bh / 2;
|
||||
const bodyH = Math.max(bh, 0.3);
|
||||
const bodyW = Math.max(bw, 0.15);
|
||||
|
||||
// Find motion centroids per body zone from the grid
|
||||
if (grid) {
|
||||
const zones = this._findZoneCentroids(grid, cols, rows, bx, by, bw, bh);
|
||||
// Smooth with low alpha for responsiveness
|
||||
const a = 0.3; // 30% old, 70% new → responsive
|
||||
this._headCx = a * this._headCx + (1 - a) * zones.head.x;
|
||||
this._headCy = a * this._headCy + (1 - a) * zones.head.y;
|
||||
this._leftArmCx = a * this._leftArmCx + (1 - a) * zones.leftArm.x;
|
||||
this._leftArmCy = a * this._leftArmCy + (1 - a) * zones.leftArm.y;
|
||||
this._rightArmCx= a * this._rightArmCx+ (1 - a) * zones.rightArm.x;
|
||||
this._rightArmCy= a * this._rightArmCy+ (1 - a) * zones.rightArm.y;
|
||||
this._leftLegCx = a * this._leftLegCx + (1 - a) * zones.leftLeg.x;
|
||||
this._leftLegCy = a * this._leftLegCy + (1 - a) * zones.leftLeg.y;
|
||||
this._rightLegCx= a * this._rightLegCx+ (1 - a) * zones.rightLeg.x;
|
||||
this._rightLegCy= a * this._rightLegCy+ (1 - a) * zones.rightLeg.y;
|
||||
this._torsoCx = a * this._torsoCx + (1 - a) * zones.torso.x;
|
||||
this._torsoCy = a * this._torsoCy + (1 - a) * zones.torso.y;
|
||||
}
|
||||
|
||||
const P = PROPORTIONS;
|
||||
|
||||
// Breathing (subtle)
|
||||
const breathe = Math.sin(elapsed * 1.5) * 0.002;
|
||||
|
||||
// === Position joints using tracked centroids ===
|
||||
|
||||
// HEAD: tracked centroid (top zone)
|
||||
const headX = this._headCx;
|
||||
const headY = this._headCy;
|
||||
|
||||
// TORSO center drives shoulder/hip
|
||||
const torsoX = this._torsoCx;
|
||||
const shoulderY = this._torsoCy - bodyH * 0.08 + breathe;
|
||||
const halfW = P.shoulderWidth * bodyH / 2;
|
||||
const hipHalfW = P.hipWidth * bodyH / 2;
|
||||
const hipY = shoulderY + P.shoulderToHip * bodyH;
|
||||
|
||||
// ARMS: elbow + wrist driven toward arm zone centroids
|
||||
// Left arm: shoulder is fixed, elbow/wrist pulled toward left arm centroid
|
||||
const lShX = torsoX - halfW;
|
||||
const lShY = shoulderY;
|
||||
// Vector from shoulder toward arm centroid
|
||||
const lArmDx = this._leftArmCx - lShX;
|
||||
const lArmDy = this._leftArmCy - lShY;
|
||||
const lArmDist = Math.sqrt(lArmDx * lArmDx + lArmDy * lArmDy) || 0.01;
|
||||
const lArmNx = lArmDx / lArmDist;
|
||||
const lArmNy = lArmDy / lArmDist;
|
||||
// Elbow at shoulderToElbow distance along that direction
|
||||
const elbowLen = P.shoulderToElbow * bodyH;
|
||||
const lElbowX = lShX + lArmNx * elbowLen;
|
||||
const lElbowY = lShY + lArmNy * elbowLen;
|
||||
// Wrist continues further
|
||||
const wristLen = P.elbowToWrist * bodyH;
|
||||
const lWristX = lElbowX + lArmNx * wristLen;
|
||||
const lWristY = lElbowY + lArmNy * wristLen;
|
||||
|
||||
// Right arm: same approach
|
||||
const rShX = torsoX + halfW;
|
||||
const rShY = shoulderY;
|
||||
const rArmDx = this._rightArmCx - rShX;
|
||||
const rArmDy = this._rightArmCy - rShY;
|
||||
const rArmDist = Math.sqrt(rArmDx * rArmDx + rArmDy * rArmDy) || 0.01;
|
||||
const rArmNx = rArmDx / rArmDist;
|
||||
const rArmNy = rArmDy / rArmDist;
|
||||
const rElbowX = rShX + rArmNx * elbowLen;
|
||||
const rElbowY = rShY + rArmNy * elbowLen;
|
||||
const rWristX = rElbowX + rArmNx * wristLen;
|
||||
const rWristY = rElbowY + rArmNy * wristLen;
|
||||
|
||||
// LEGS: knees/ankles pulled toward leg zone centroids
|
||||
const lHipX = torsoX - hipHalfW;
|
||||
const rHipX = torsoX + hipHalfW;
|
||||
const lLegDx = this._leftLegCx - lHipX;
|
||||
const lLegDy = Math.max(0.05, this._leftLegCy - hipY); // always downward
|
||||
const lLegDist = Math.sqrt(lLegDx * lLegDx + lLegDy * lLegDy) || 0.01;
|
||||
const lLegNx = lLegDx / lLegDist;
|
||||
const lLegNy = lLegDy / lLegDist;
|
||||
const kneeLen = P.hipToKnee * bodyH;
|
||||
const ankleLen = P.kneeToAnkle * bodyH;
|
||||
const lKneeX = lHipX + lLegNx * kneeLen;
|
||||
const lKneeY = hipY + lLegNy * kneeLen;
|
||||
const lAnkleX = lKneeX + lLegNx * ankleLen;
|
||||
const lAnkleY = lKneeY + lLegNy * ankleLen;
|
||||
|
||||
const rLegDx = this._rightLegCx - rHipX;
|
||||
const rLegDy = Math.max(0.05, this._rightLegCy - hipY);
|
||||
const rLegDist = Math.sqrt(rLegDx * rLegDx + rLegDy * rLegDy) || 0.01;
|
||||
const rLegNx = rLegDx / rLegDist;
|
||||
const rLegNy = rLegDy / rLegDist;
|
||||
const rKneeX = rHipX + rLegNx * kneeLen;
|
||||
const rKneeY = hipY + rLegNy * kneeLen;
|
||||
const rAnkleX = rKneeX + rLegNx * ankleLen;
|
||||
const rAnkleY = rKneeY + rLegNy * ankleLen;
|
||||
|
||||
// Arm raise amount (for hand openness)
|
||||
const leftArmRaise = Math.max(0, Math.min(1, (shoulderY - this._leftArmCy) / (bodyH * 0.3)));
|
||||
const rightArmRaise = Math.max(0, Math.min(1, (shoulderY - this._rightArmCy) / (bodyH * 0.3)));
|
||||
|
||||
// Compute hand finger positions from wrist-elbow axis
|
||||
const lHandAngle = Math.atan2(lWristY - lElbowY, lWristX - lElbowX);
|
||||
const rHandAngle = Math.atan2(rWristY - rElbowY, rWristX - rElbowX);
|
||||
const fingerLen = P.wristToFinger * bodyH;
|
||||
const fingerSpr = P.fingerSpread * bodyH;
|
||||
|
||||
// Hand openness driven by arm raise + arm lateral spread
|
||||
const lArmSpread = Math.abs(this._leftArmCx - (bx + bw * 0.3)) / (bw * 0.3);
|
||||
const rArmSpread = Math.abs(this._rightArmCx - (bx + bw * 0.7)) / (bw * 0.3);
|
||||
const lHandOpen = Math.min(1, leftArmRaise * 0.5 + lArmSpread * 0.5);
|
||||
const rHandOpen = Math.min(1, rightArmRaise * 0.5 + rArmSpread * 0.5);
|
||||
|
||||
const keypoints = [
|
||||
// 0: nose
|
||||
{ x: headX, y: headY + 0.01, confidence: 0.92 },
|
||||
// 1: left_eye
|
||||
{ x: headX - P.eyeSpacing * bodyH, y: headY - 0.005, confidence: 0.88 },
|
||||
// 2: right_eye
|
||||
{ x: headX + P.eyeSpacing * bodyH, y: headY - 0.005, confidence: 0.88 },
|
||||
// 3: left_ear
|
||||
{ x: headX - P.earSpacing * bodyH, y: headY + 0.005, confidence: 0.72 },
|
||||
// 4: right_ear
|
||||
{ x: headX + P.earSpacing * bodyH, y: headY + 0.005, confidence: 0.72 },
|
||||
// 5: left_shoulder
|
||||
{ x: lShX, y: lShY, confidence: 0.94 },
|
||||
// 6: right_shoulder
|
||||
{ x: rShX, y: rShY, confidence: 0.94 },
|
||||
// 7: left_elbow
|
||||
{ x: lElbowX, y: lElbowY, confidence: 0.87 },
|
||||
// 8: right_elbow
|
||||
{ x: rElbowX, y: rElbowY, confidence: 0.87 },
|
||||
// 9: left_wrist
|
||||
{ x: lWristX, y: lWristY, confidence: 0.82 },
|
||||
// 10: right_wrist
|
||||
{ x: rWristX, y: rWristY, confidence: 0.82 },
|
||||
// 11: left_hip
|
||||
{ x: lHipX, y: hipY, confidence: 0.91 },
|
||||
// 12: right_hip
|
||||
{ x: rHipX, y: hipY, confidence: 0.91 },
|
||||
// 13: left_knee
|
||||
{ x: lKneeX, y: lKneeY, confidence: 0.88 },
|
||||
// 14: right_knee
|
||||
{ x: rKneeX, y: rKneeY, confidence: 0.88 },
|
||||
// 15: left_ankle
|
||||
{ x: lAnkleX, y: lAnkleY, confidence: 0.83 },
|
||||
// 16: right_ankle
|
||||
{ x: rAnkleX, y: rAnkleY, confidence: 0.83 },
|
||||
|
||||
// === Extended keypoints (17-25) ===
|
||||
|
||||
// 17: left_thumb — offset at thumb angle from wrist-elbow axis
|
||||
{ x: lWristX + fingerLen * Math.cos(lHandAngle + P.thumbAngle) * (0.6 + lHandOpen * 0.4),
|
||||
y: lWristY + fingerLen * Math.sin(lHandAngle + P.thumbAngle) * (0.6 + lHandOpen * 0.4),
|
||||
confidence: 0.68 * (0.5 + lHandOpen * 0.5) },
|
||||
// 18: left_index — extends along wrist-elbow axis
|
||||
{ x: lWristX + fingerLen * Math.cos(lHandAngle) + fingerSpr * lHandOpen * Math.cos(lHandAngle + 0.3),
|
||||
y: lWristY + fingerLen * Math.sin(lHandAngle) + fingerSpr * lHandOpen * Math.sin(lHandAngle + 0.3),
|
||||
confidence: 0.72 * (0.5 + lHandOpen * 0.5) },
|
||||
// 19: left_pinky — offset opposite thumb
|
||||
{ x: lWristX + fingerLen * 0.85 * Math.cos(lHandAngle - P.thumbAngle * 0.7),
|
||||
y: lWristY + fingerLen * 0.85 * Math.sin(lHandAngle - P.thumbAngle * 0.7),
|
||||
confidence: 0.60 * (0.5 + lHandOpen * 0.5) },
|
||||
|
||||
// 20: right_thumb
|
||||
{ x: rWristX + fingerLen * Math.cos(rHandAngle - P.thumbAngle) * (0.6 + rHandOpen * 0.4),
|
||||
y: rWristY + fingerLen * Math.sin(rHandAngle - P.thumbAngle) * (0.6 + rHandOpen * 0.4),
|
||||
confidence: 0.68 * (0.5 + rHandOpen * 0.5) },
|
||||
// 21: right_index
|
||||
{ x: rWristX + fingerLen * Math.cos(rHandAngle) + fingerSpr * rHandOpen * Math.cos(rHandAngle - 0.3),
|
||||
y: rWristY + fingerLen * Math.sin(rHandAngle) + fingerSpr * rHandOpen * Math.sin(rHandAngle - 0.3),
|
||||
confidence: 0.72 * (0.5 + rHandOpen * 0.5) },
|
||||
// 22: right_pinky
|
||||
{ x: rWristX + fingerLen * 0.85 * Math.cos(rHandAngle + P.thumbAngle * 0.7),
|
||||
y: rWristY + fingerLen * 0.85 * Math.sin(rHandAngle + P.thumbAngle * 0.7),
|
||||
confidence: 0.60 * (0.5 + rHandOpen * 0.5) },
|
||||
|
||||
// 23: left_foot_index (toe tip) — extends forward from ankle
|
||||
{ x: lAnkleX + P.ankleToToe * bodyH * 0.5,
|
||||
y: lAnkleY + P.ankleToToe * bodyH * 0.3,
|
||||
confidence: 0.65 },
|
||||
// 24: right_foot_index
|
||||
{ x: rAnkleX + P.ankleToToe * bodyH * 0.5,
|
||||
y: rAnkleY + P.ankleToToe * bodyH * 0.3,
|
||||
confidence: 0.65 },
|
||||
|
||||
// 25: neck (midpoint between shoulders, slightly above)
|
||||
{ x: (lShX + rShX) / 2, y: shoulderY - P.headToShoulder * bodyH * 0.35, confidence: 0.93 },
|
||||
];
|
||||
|
||||
for (let i = 0; i < keypoints.length; i++) {
|
||||
keypoints[i].name = KEYPOINT_NAMES[i];
|
||||
}
|
||||
|
||||
// === RuVector Attention Embedding Refinement ===
|
||||
// Compute attention stats for the UI pipeline display, but only apply
|
||||
// positional refinement when a trained model is loaded (random-weight
|
||||
// embeddings carry no meaningful spatial signal and distort the skeleton).
|
||||
if (embedding && embedding.length >= 26 * 3) {
|
||||
this._computeEmbeddingStats(keypoints, embedding, bodyH);
|
||||
}
|
||||
|
||||
return keypoints;
|
||||
}
|
||||
|
||||
/**
|
||||
* Apply RuVector attention embedding to refine joint positions and confidence.
|
||||
*
|
||||
* The 128-dim fused embedding is decoded as:
|
||||
* - Dims 0-77: Per-joint (dx, dy, confidence_mod) × 26 joints
|
||||
* - Dims 78-81: Global pose parameters (scale, rotation, lean)
|
||||
* - Dims 82-127: Reserved for cross-modal fusion features
|
||||
*
|
||||
* The attention mechanism determines HOW MUCH each spatial region contributes
|
||||
* to each joint's refinement. Multi-Head captures global relationships,
|
||||
* Hyperbolic captures hierarchical (torso→limb→hand) dependencies,
|
||||
* MoE routes different body regions to specialized experts,
|
||||
* Linear provides fast extremity refinement, Local-Global balances detail/context.
|
||||
*/
|
||||
/**
|
||||
* Compute embedding statistics for UI display without modifying joint positions.
|
||||
* The 6-stage attention pipeline stats are shown in the RuVector panel.
|
||||
* Position refinement is disabled until a trained model replaces random weights.
|
||||
*/
|
||||
_computeEmbeddingStats(keypoints, emb, bodyH) {
|
||||
const map = this._jointEmbMap;
|
||||
const tc = (v) => Math.tanh(Number(v) || 0);
|
||||
|
||||
// Embedding energy (L2 norm of the used dims)
|
||||
let energy = 0;
|
||||
for (let i = 0; i < Math.min(emb.length, 82); i++) {
|
||||
energy += emb[i] * emb[i];
|
||||
}
|
||||
energy = Math.sqrt(energy);
|
||||
|
||||
// Simulated per-joint refinement magnitude (what WOULD be applied)
|
||||
const scale = bodyH * 0.015;
|
||||
let totalRefinement = 0;
|
||||
let maxDimVal = 0;
|
||||
|
||||
for (let j = 0; j < Math.min(keypoints.length, 26); j++) {
|
||||
const jmap = map.joints[j];
|
||||
if (!jmap) continue;
|
||||
const dx = tc(emb[jmap.dxDim]) * scale;
|
||||
const dy = tc(emb[jmap.dyDim]) * scale;
|
||||
totalRefinement += Math.sqrt(dx * dx + dy * dy);
|
||||
maxDimVal = Math.max(maxDimVal, Math.abs(tc(emb[jmap.dxDim])), Math.abs(tc(emb[jmap.dyDim])));
|
||||
}
|
||||
|
||||
this.attentionStats.energy = energy;
|
||||
this.attentionStats.maxDim = maxDimVal;
|
||||
this.attentionStats.refinementMag = totalRefinement / 26;
|
||||
}
|
||||
|
||||
/**
|
||||
* Find weighted motion centroids for each body zone.
|
||||
* Divides the bounding box into 6 zones: head, left arm, right arm, torso, left leg, right leg.
|
||||
* Returns the (x,y) centroid of motion intensity for each zone.
|
||||
*/
|
||||
_findZoneCentroids(grid, cols, rows, bx, by, bw, bh) {
|
||||
// Zone definitions (in grid-relative fractions)
|
||||
const zones = {
|
||||
head: { rMin: 0, rMax: 0.2, cMin: 0.25, cMax: 0.75, wx: 0, wy: 0, wt: 0 },
|
||||
leftArm: { rMin: 0.1, rMax: 0.6, cMin: 0, cMax: 0.35, wx: 0, wy: 0, wt: 0 },
|
||||
rightArm: { rMin: 0.1, rMax: 0.6, cMin: 0.65, cMax: 1.0, wx: 0, wy: 0, wt: 0 },
|
||||
torso: { rMin: 0.15, rMax: 0.55, cMin: 0.3, cMax: 0.7, wx: 0, wy: 0, wt: 0 },
|
||||
leftLeg: { rMin: 0.5, rMax: 1.0, cMin: 0.1, cMax: 0.5, wx: 0, wy: 0, wt: 0 },
|
||||
rightLeg: { rMin: 0.5, rMax: 1.0, cMin: 0.5, cMax: 0.9, wx: 0, wy: 0, wt: 0 },
|
||||
};
|
||||
|
||||
// Accumulate weighted centroids per zone
|
||||
for (let r = 0; r < rows; r++) {
|
||||
const ry = r / rows; // 0-1 within grid
|
||||
for (let c = 0; c < cols; c++) {
|
||||
const cx_g = c / cols; // 0-1 within grid
|
||||
const val = grid[r][c];
|
||||
if (val < 0.005) continue; // skip near-zero motion
|
||||
|
||||
// Map grid position to body-space coordinates (0-1)
|
||||
const worldX = bx + cx_g * bw;
|
||||
const worldY = by + ry * bh;
|
||||
|
||||
// Assign to matching zones (a cell can contribute to multiple overlapping zones)
|
||||
for (const z of Object.values(zones)) {
|
||||
if (ry >= z.rMin && ry < z.rMax && cx_g >= z.cMin && cx_g < z.cMax) {
|
||||
z.wx += worldX * val;
|
||||
z.wy += worldY * val;
|
||||
z.wt += val;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Compute centroids with fallback defaults
|
||||
const centroid = (z, defX, defY) => ({
|
||||
x: z.wt > 0.01 ? z.wx / z.wt : defX,
|
||||
y: z.wt > 0.01 ? z.wy / z.wt : defY,
|
||||
weight: z.wt
|
||||
});
|
||||
|
||||
const midX = bx + bw / 2;
|
||||
const midY = by + bh / 2;
|
||||
|
||||
return {
|
||||
head: centroid(zones.head, midX, by + bh * 0.1),
|
||||
leftArm: centroid(zones.leftArm, bx + bw * 0.2, midY - bh * 0.05),
|
||||
rightArm: centroid(zones.rightArm, bx + bw * 0.8, midY - bh * 0.05),
|
||||
torso: centroid(zones.torso, midX, midY),
|
||||
leftLeg: centroid(zones.leftLeg, bx + bw * 0.35,by + bh * 0.75),
|
||||
rightLeg: centroid(zones.rightLeg, bx + bw * 0.65,by + bh * 0.75),
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Through-wall tracking: continue showing pose via CSI when person left video frame.
|
||||
* The skeleton drifts in the exit direction with decreasing confidence.
|
||||
*/
|
||||
_trackThroughWall(elapsed, csiState) {
|
||||
if (!this._lastBodyState) return [];
|
||||
|
||||
const dt = elapsed - this._lastBodyState.time;
|
||||
const csiPresence = csiState.csiPresence || 0;
|
||||
|
||||
// Initialize ghost on first call
|
||||
if (this._ghostConfidence <= 0.05) {
|
||||
this._ghostConfidence = 0.8;
|
||||
this._ghostState = this._lastBodyState.keypoints.map(kp => ({...kp}));
|
||||
}
|
||||
|
||||
// Ghost confidence decays, but CSI presence sustains it
|
||||
const csiBoost = Math.min(0.7, csiPresence * 0.8);
|
||||
this._ghostConfidence = Math.max(0.05, this._ghostConfidence * 0.995 - 0.001 + csiBoost * 0.002);
|
||||
|
||||
// Drift the ghost in exit direction
|
||||
const vx = this._ghostVelocity.x;
|
||||
const vy = this._ghostVelocity.y;
|
||||
|
||||
// Breathing continues via CSI
|
||||
const breathe = Math.sin(elapsed * 1.5) * 0.003 * csiPresence;
|
||||
|
||||
const keypoints = this._ghostState.map((kp, i) => {
|
||||
return {
|
||||
x: kp.x + vx * dt * 0.3,
|
||||
y: kp.y + vy * dt * 0.3 + (i >= 5 && i <= 6 ? breathe : 0),
|
||||
confidence: kp.confidence * this._ghostConfidence * (0.5 + csiPresence * 0.5),
|
||||
name: kp.name
|
||||
};
|
||||
});
|
||||
|
||||
// Slow down drift over time
|
||||
this._ghostVelocity.x *= 0.998;
|
||||
this._ghostVelocity.y *= 0.998;
|
||||
|
||||
this.smoothedKeypoints = keypoints;
|
||||
return keypoints;
|
||||
}
|
||||
}
|
||||
|
|
@ -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;
|
||||
}
|
||||
}
|
||||
|
|
@ -3,7 +3,7 @@
|
|||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>WiFi-DensePose — Dual-Modal Pose Estimation</title>
|
||||
<title>RuView — Dual-Modal Pose Estimation</title>
|
||||
<link rel="stylesheet" href="pose-fusion/css/style.css?v=13">
|
||||
</head>
|
||||
<body>
|
||||
|
|
@ -11,7 +11,7 @@
|
|||
<!-- Header -->
|
||||
<header class="header">
|
||||
<div class="header-left">
|
||||
<div class="logo"><span class="pi">π</span> DensePose</div>
|
||||
<div class="logo"><span class="pi">π</span> RuView</div>
|
||||
<div class="header-title">Dual-Modal Pose Estimation — Live Video + WiFi CSI Fusion</div>
|
||||
</div>
|
||||
<div class="header-right">
|
||||
|
|
@ -184,11 +184,11 @@
|
|||
<!-- Bottom Bar -->
|
||||
<div class="bottom-bar">
|
||||
<div>
|
||||
WiFi-DensePose · Dual-Modal Pose Estimation ·
|
||||
RuView · Dual-Modal Pose Estimation ·
|
||||
Architecture: Conv2D → RuVector 6-Stage Attention (Flash+MHA+Hyperbolic+Linear+MoE+L/G) → Fusion → 26-Keypoint Pose
|
||||
</div>
|
||||
<div>
|
||||
<a href="https://github.com/ruvnet/wifi-densepose">GitHub</a> ·
|
||||
<a href="https://github.com/ruvnet/RuView">GitHub</a> ·
|
||||
CNN: <span id="cnn-backend">ruvector-cnn (loading…)</span> ·
|
||||
<a href="observatory.html">Observatory</a>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
/* WiFi-DensePose — Dual-Modal Pose Fusion Demo
|
||||
/* RuView — 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');
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
/**
|
||||
* WiFi-DensePose — Dual-Modal Pose Estimation Demo
|
||||
* RuView — Dual-Modal Pose Estimation Demo
|
||||
*
|
||||
* Main orchestration: video capture → CNN embedding → CSI processing → fusion → rendering
|
||||
*/
|
||||
|
|
|
|||
Loading…
Reference in New Issue