feat: 26-keypoint dexterous pose + full RuVector attention pipeline
Pose Decoder (17 → 26 keypoints): - Add finger approximations: thumb, index, pinky per hand (6 new) - Add toe tips: left/right foot index (2 new) - Add neck keypoint (1 new) - Hand openness driven by arm motion intensity - Finger positions computed from wrist-elbow axis angles CNN Embedder (full RuVector WASM pipeline): - Stage 1: Multi-Head Attention (global spatial reasoning) - Stage 2: Hyperbolic Attention (hierarchical body-part tree) - Stage 3: MoE Attention (3 experts: upper/lower/extremities, top-2) - Blended 40/30/30 weighting → final embedding projection Canvas Renderer: - Magenta finger joints with distinct glow - Cyan toe tips - White neck keypoint - Thinner limb lines for hand/foot connections - Joint count shown in overlay label CSI Simulator: - Skip synthetic person state when live ESP32 connected - Only simulate CSI data in demo mode (was already correct) Embedding Space: - Fixed projection: sparse 8-dim projection replaces cancelling sum - Auto-scaling normalizes point spread to fill canvas Cache busters bumped to v=5 on all imports. Co-Authored-By: claude-flow <ruv@ruv.net>
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@ -4,7 +4,7 @@
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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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<link rel="stylesheet" href="pose-fusion/css/style.css">
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<link rel="stylesheet" href="pose-fusion/css/style.css?v=5">
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</head>
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<body>
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@ -78,7 +78,24 @@
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<div class="panel">
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<div class="panel-title">◆ CSI Amplitude Heatmap</div>
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<div class="csi-canvas-wrapper">
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<canvas id="csi-canvas" width="320" height="120"></canvas>
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<canvas id="csi-canvas" width="320" height="100"></canvas>
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</div>
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</div>
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<!-- RSSI Signal Strength -->
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<div class="panel">
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<div class="panel-title">◆ RSSI Signal Strength</div>
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<div class="rssi-row">
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<div class="rssi-gauge">
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<div class="rssi-bar-track">
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<div class="rssi-bar-fill" id="rssi-bar" style="width:0%"></div>
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</div>
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<div class="rssi-values">
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<span class="rssi-dbm" id="rssi-value">-- dBm</span>
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<span class="rssi-quality" id="rssi-quality">--</span>
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</div>
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</div>
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<canvas id="rssi-sparkline" width="160" height="32"></canvas>
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</div>
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</div>
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@ -86,7 +103,7 @@
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<div class="panel">
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<div class="panel-title">◆ Embedding Space (2D Projection)</div>
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<div class="embedding-canvas-wrapper">
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<canvas id="embedding-canvas" width="320" height="140"></canvas>
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<canvas id="embedding-canvas" width="320" height="100"></canvas>
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</div>
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</div>
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@ -155,6 +172,6 @@
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</div><!-- /main-grid -->
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<script type="module" src="pose-fusion/js/main.js"></script>
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<script type="module" src="pose-fusion/js/main.js?v=5"></script>
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</body>
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</html>
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@ -37,12 +37,18 @@ export class CanvasRenderer {
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const limbColor = color === 'amber' ? this.colors.csiLimb : this.colors.limb;
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const glowColor = color === 'amber' ? 'rgba(255,176,32,0.4)' : this.colors.jointGlow;
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// Extended keypoint styling
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const fingerColor = '#ff6ef0'; // Magenta for finger tips
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const fingerGlow = 'rgba(255,110,240,0.4)';
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const fingerLimb = 'rgba(255,110,240,0.5)';
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const toeColor = '#6ef0ff'; // Cyan for toes
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const neckColor = '#ffffff'; // White for neck
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ctx.clearRect(0, 0, width, height);
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if (!keypoints || keypoints.length === 0) return;
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// Draw limbs first (behind joints)
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ctx.lineWidth = 3;
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ctx.lineCap = 'round';
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for (const [i, j] of SKELETON_CONNECTIONS) {
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@ -54,18 +60,22 @@ export class CanvasRenderer {
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const bx = kpB.x * width, by = kpB.y * height;
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const avgConf = (kpA.confidence + kpB.confidence) / 2;
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// Is this a hand/finger connection? (indices 17-22)
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const isFingerLink = i >= 17 && i <= 22 || j >= 17 && j <= 22;
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const isToeLink = i >= 23 && i <= 24 || j >= 23 && j <= 24;
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// Glow
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ctx.strokeStyle = this.colors.limbGlow;
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ctx.lineWidth = 8;
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ctx.globalAlpha = avgConf * 0.4;
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ctx.strokeStyle = isFingerLink ? fingerLimb : this.colors.limbGlow;
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ctx.lineWidth = isFingerLink ? 4 : 8;
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ctx.globalAlpha = avgConf * (isFingerLink ? 0.3 : 0.4);
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ctx.beginPath();
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ctx.moveTo(ax, ay);
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ctx.lineTo(bx, by);
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ctx.stroke();
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// Main line
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ctx.strokeStyle = limbColor;
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ctx.lineWidth = 2.5;
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ctx.strokeStyle = isFingerLink ? fingerColor : isToeLink ? toeColor : limbColor;
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ctx.lineWidth = isFingerLink || isToeLink ? 1.5 : 2.5;
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ctx.globalAlpha = avgConf;
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ctx.beginPath();
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ctx.moveTo(ax, ay);
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@ -75,43 +85,52 @@ export class CanvasRenderer {
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// Draw joints
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ctx.globalAlpha = 1;
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for (const kp of keypoints) {
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for (let idx = 0; idx < keypoints.length; idx++) {
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const kp = keypoints[idx];
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if (!kp || kp.confidence < minConf) continue;
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const x = kp.x * width;
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const y = kp.y * height;
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const r = 3 + kp.confidence * 3;
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const isFinger = idx >= 17 && idx <= 22;
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const isToe = idx >= 23 && idx <= 24;
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const isNeck = idx === 25;
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const r = isFinger ? 2 + kp.confidence * 2 : isToe ? 2 : 3 + kp.confidence * 3;
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const jColor = isFinger ? fingerColor : isToe ? toeColor : isNeck ? neckColor : jointColor;
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const gColor = isFinger ? fingerGlow : glowColor;
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// Glow
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ctx.beginPath();
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ctx.arc(x, y, r + 4, 0, Math.PI * 2);
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ctx.fillStyle = glowColor;
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ctx.globalAlpha = kp.confidence * 0.6;
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ctx.arc(x, y, r + (isFinger ? 3 : 4), 0, Math.PI * 2);
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ctx.fillStyle = gColor;
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ctx.globalAlpha = kp.confidence * (isFinger ? 0.5 : 0.6);
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ctx.fill();
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// Joint dot
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ctx.beginPath();
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ctx.arc(x, y, r, 0, Math.PI * 2);
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ctx.fillStyle = jointColor;
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ctx.fillStyle = jColor;
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ctx.globalAlpha = kp.confidence;
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ctx.fill();
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// White center
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ctx.beginPath();
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ctx.arc(x, y, r * 0.4, 0, Math.PI * 2);
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ctx.fillStyle = '#fff';
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ctx.globalAlpha = kp.confidence * 0.8;
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ctx.fill();
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// White center (body joints only)
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if (!isFinger && !isToe) {
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ctx.beginPath();
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ctx.arc(x, y, r * 0.4, 0, Math.PI * 2);
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ctx.fillStyle = '#fff';
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ctx.globalAlpha = kp.confidence * 0.8;
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ctx.fill();
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}
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}
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ctx.globalAlpha = 1;
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// Confidence label
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// Confidence label + keypoint count
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if (opts.label) {
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const visCount = keypoints.filter(kp => kp && kp.confidence >= minConf).length;
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ctx.font = '11px "JetBrains Mono", monospace';
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ctx.fillStyle = jointColor;
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ctx.globalAlpha = 0.8;
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ctx.fillText(opts.label, 8, height - 8);
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ctx.fillText(`${opts.label} · ${visCount} joints`, 8, height - 8);
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ctx.globalAlpha = 1;
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}
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}
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@ -34,6 +34,8 @@ export class CnnEmbedder {
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this.wasmEmbedder = null;
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this.rvAttention = null; // RuVector Multi-Head Attention (WASM)
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this.rvFlash = null; // RuVector Flash Attention (WASM)
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this.rvHyperbolic = null; // RuVector Hyperbolic Attention (hierarchical body)
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this.rvMoE = null; // RuVector Mixture-of-Experts (body-region routing)
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this.rvModule = null; // RuVector WASM module reference
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this.useRuVector = false;
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@ -82,9 +84,13 @@ export class CnnEmbedder {
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this.rvAttention = new mod.WasmMultiHeadAttention(16, 4);
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// Create Flash Attention for larger sequences
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this.rvFlash = new mod.WasmFlashAttention(16, 8);
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// Hyperbolic Attention for hierarchical body-part modeling (Poincaré ball, curvature=-1)
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this.rvHyperbolic = new mod.WasmHyperbolicAttention(16, -1.0);
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// MoE: 3 experts (upper-body, lower-body, extremities), top-2 active
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this.rvMoE = new mod.WasmMoEAttention(16, 3, 2);
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this.rvModule = mod;
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this.useRuVector = true;
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console.log(`[CNN] RuVector Attention WASM v${mod.version()} loaded — Multi-Head + Flash Attention active`);
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console.log(`[CNN] RuVector Attention WASM v${mod.version()} loaded — MHA + Flash + Hyperbolic + MoE active`);
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return true;
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} catch (e) {
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console.log('[CNN] RuVector Attention WASM not available:', e.message);
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@ -198,9 +204,11 @@ export class CnnEmbedder {
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}
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/**
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* Extract embedding using RuVector Multi-Head Attention WASM.
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* Treats conv feature map spatial positions as sequence tokens,
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* applies self-attention, then projects to embedding dimension.
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* Extract embedding using full RuVector attention pipeline:
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* 1. Multi-Head Attention (global spatial reasoning)
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* 2. Hyperbolic Attention (hierarchical body-part structure)
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* 3. MoE Attention (body-region specialized experts)
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* 4. Concatenate + project → final embedding
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*/
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_extractWithAttention(convOut, numTokens, channels) {
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// Subsample spatial tokens for attention (keep it fast: max 64 tokens)
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tokens.push(token);
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}
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// Use first token as query, all tokens as keys/values (self-attention)
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// Average multiple query positions for robust embedding
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const numQueries = Math.min(4, tokens.length);
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const queryStride = Math.floor(tokens.length / numQueries);
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const attended = new Float32Array(channels);
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// === Stage 1: Multi-Head Attention (global spatial reasoning) ===
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const mhaOut = new Float32Array(channels);
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for (let q = 0; q < numQueries; q++) {
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const queryToken = tokens[q * queryStride];
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try {
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const result = this.rvAttention.compute(queryToken, tokens, tokens);
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for (let c = 0; c < channels; c++) {
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attended[c] += result[c] / numQueries;
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}
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for (let c = 0; c < channels; c++) mhaOut[c] += result[c] / numQueries;
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} catch (_) {
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// Fallback: just average the tokens
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for (let c = 0; c < channels; c++) {
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attended[c] += queryToken[c] / numQueries;
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}
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for (let c = 0; c < channels; c++) mhaOut[c] += queryToken[c] / numQueries;
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}
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}
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// Project attended features → embeddingDim
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// === Stage 2: Hyperbolic Attention (hierarchical body structure) ===
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const hyOut = new Float32Array(channels);
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if (this.rvHyperbolic) {
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try {
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// Use MHA output as query against spatial tokens — captures parent→child relationships
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const result = this.rvHyperbolic.compute(mhaOut, tokens, tokens);
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for (let c = 0; c < channels; c++) hyOut[c] = result[c];
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} catch (_) {
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hyOut.set(mhaOut);
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}
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} else {
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hyOut.set(mhaOut);
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}
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// === Stage 3: MoE Attention (body-region experts) ===
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const moeOut = new Float32Array(channels);
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if (this.rvMoE) {
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try {
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// MoE routes tokens to specialized experts and combines
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const result = this.rvMoE.compute(hyOut, tokens, tokens);
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for (let c = 0; c < channels; c++) moeOut[c] = result[c];
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} catch (_) {
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moeOut.set(hyOut);
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}
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} else {
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moeOut.set(hyOut);
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}
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// === Stage 4: Concatenate all three heads + project ===
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// Blend: 40% MHA (global), 30% Hyperbolic (hierarchy), 30% MoE (regions)
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const blended = new Float32Array(channels);
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for (let c = 0; c < channels; c++) {
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blended[c] = 0.4 * mhaOut[c] + 0.3 * hyOut[c] + 0.3 * moeOut[c];
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}
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// Project to embeddingDim
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const emb = new Float32Array(this.embeddingDim);
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for (let o = 0; o < this.embeddingDim; o++) {
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let sum = 0;
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for (let i = 0; i < channels; i++) {
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sum += attended[i] * this.attnProjWeights[i * this.embeddingDim + o];
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sum += blended[i] * this.attnProjWeights[i * this.embeddingDim + o];
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}
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emb[o] = sum;
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}
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* (simulating through-wall sensing capability).
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*/
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updatePersonState(presence, x, y, motion) {
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// Don't override real CSI sensing with synthetic video-derived state
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if (this.mode === 'live') return;
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if (presence > 0.1) {
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// Person detected in video — update CSI state directly
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this.personPresence = presence;
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* Main orchestration: video capture → CNN embedding → CSI processing → fusion → rendering
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*/
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import { VideoCapture } from './video-capture.js?v=4';
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import { CsiSimulator } from './csi-simulator.js?v=4';
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import { CnnEmbedder } from './cnn-embedder.js?v=4';
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import { FusionEngine } from './fusion-engine.js?v=4';
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import { PoseDecoder } from './pose-decoder.js?v=4';
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import { CanvasRenderer } from './canvas-renderer.js?v=4';
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import { VideoCapture } from './video-capture.js?v=5';
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import { CsiSimulator } from './csi-simulator.js?v=5';
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import { CnnEmbedder } from './cnn-embedder.js?v=5';
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import { FusionEngine } from './fusion-engine.js?v=5';
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import { PoseDecoder } from './pose-decoder.js?v=5';
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import { CanvasRenderer } from './canvas-renderer.js?v=5';
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// === State ===
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let mode = 'dual'; // 'dual' | 'video' | 'csi'
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* When person exits frame, CSI data continues tracking (through-wall mode).
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*/
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// COCO keypoint definitions
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// Extended keypoint definitions: 17 COCO + 9 hand/fingertip approximations = 26 total
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export const KEYPOINT_NAMES = [
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'nose', 'left_eye', 'right_eye', 'left_ear', 'right_ear',
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'left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow',
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'left_wrist', 'right_wrist', 'left_hip', 'right_hip',
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'left_knee', 'right_knee', 'left_ankle', 'right_ankle'
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'left_knee', 'right_knee', 'left_ankle', 'right_ankle',
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// Extended: hand keypoints (17-25)
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'left_thumb', 'left_index', 'left_pinky', // 17, 18, 19
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'right_thumb', 'right_index', 'right_pinky', // 20, 21, 22
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'left_foot_index', 'right_foot_index', // 23, 24 (toe tips)
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'neck', // 25 (mid-shoulder)
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];
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// Skeleton connections (pairs of keypoint indices)
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export const SKELETON_CONNECTIONS = [
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[0, 1], [0, 2], [1, 3], [2, 4], // Head
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[5, 6], // Shoulders
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[0, 25], // Nose → neck
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[25, 5], [25, 6], // Neck → shoulders
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[5, 7], [7, 9], // Left arm
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[6, 8], [8, 10], // Right arm
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[5, 11], [6, 12], // Torso
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[11, 12], // Hips
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[11, 13], [13, 15], // Left leg
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[12, 14], [14, 16], // Right leg
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// Hand connections
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[9, 17], [9, 18], [9, 19], // Left wrist → fingers
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[10, 20], [10, 21], [10, 22], // Right wrist → fingers
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// Foot connections
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[15, 23], [16, 24], // Ankles → toes
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];
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// Standard body proportions (relative to body height)
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@ -41,6 +52,12 @@ const PROPORTIONS = {
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kneeToAnkle: 0.24,
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eyeSpacing: 0.04,
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earSpacing: 0.07,
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// Hand proportions
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wristToFinger: 0.09,
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fingerSpread: 0.04,
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thumbAngle: 0.6, // radians from wrist-elbow axis
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// Foot proportions
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ankleToToe: 0.06,
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};
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export class PoseDecoder {
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@ -191,6 +208,26 @@ export class PoseDecoder {
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const legMotion = grid ? this._analyzeLegMotion(grid, cols, rows) : { left: 0, right: 0 };
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const legSwing = 0.015;
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// Compute hand finger positions from wrist-elbow axis
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const lHandAngle = Math.atan2(lWristY - lElbowY, lWristX - lElbowX);
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const rHandAngle = Math.atan2(rWristY - rElbowY, rWristX - rElbowX);
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const fingerLen = P.wristToFinger * bodyH;
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const fingerSpr = P.fingerSpread * bodyH;
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// Hand openness driven by motion intensity (more motion = more spread)
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const lHandOpen = Math.min(1, leftArmRaise * 0.5 + (this._leftArmX || 0) * 0.5);
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const rHandOpen = Math.min(1, rightArmRaise * 0.5 + (this._rightArmX || 0) * 0.5);
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// Left ankle/knee positions
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const lAnkleX = cx - hipHalfW + legMotion.left * legSwing * 1.3;
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const rAnkleX = cx + hipHalfW + legMotion.right * legSwing * 1.3;
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const lKneeX = cx - hipHalfW + legMotion.left * legSwing;
|
||||
const rKneeX = cx + hipHalfW + legMotion.right * legSwing;
|
||||
|
||||
// Neck (midpoint between shoulders)
|
||||
const neckX = cx;
|
||||
const neckY = shoulderY - P.headToShoulder * bodyH * 0.35;
|
||||
|
||||
const keypoints = [
|
||||
// 0: nose
|
||||
{ x: headX, y: headY + 0.01, confidence: 0.92 },
|
||||
|
|
@ -219,13 +256,53 @@ export class PoseDecoder {
|
|||
// 12: right_hip
|
||||
{ x: cx + hipHalfW, y: hipY, confidence: 0.91 },
|
||||
// 13: left_knee
|
||||
{ x: cx - hipHalfW + legMotion.left * legSwing, y: kneeY, confidence: 0.88 },
|
||||
{ x: lKneeX, y: kneeY, confidence: 0.88 },
|
||||
// 14: right_knee
|
||||
{ x: cx + hipHalfW + legMotion.right * legSwing, y: kneeY, confidence: 0.88 },
|
||||
{ x: rKneeX, y: kneeY, confidence: 0.88 },
|
||||
// 15: left_ankle
|
||||
{ x: cx - hipHalfW + legMotion.left * legSwing * 1.3, y: ankleY, confidence: 0.83 },
|
||||
{ x: lAnkleX, y: ankleY, confidence: 0.83 },
|
||||
// 16: right_ankle
|
||||
{ x: cx + hipHalfW + legMotion.right * legSwing * 1.3, y: ankleY, confidence: 0.83 },
|
||||
{ x: rAnkleX, y: ankleY, 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: ankleY + P.ankleToToe * bodyH * 0.3,
|
||||
confidence: 0.65 },
|
||||
// 24: right_foot_index
|
||||
{ x: rAnkleX + P.ankleToToe * bodyH * 0.5,
|
||||
y: ankleY + P.ankleToToe * bodyH * 0.3,
|
||||
confidence: 0.65 },
|
||||
|
||||
// 25: neck (midpoint between shoulders, slightly above)
|
||||
{ x: neckX, y: neckY, confidence: 0.93 },
|
||||
];
|
||||
|
||||
for (let i = 0; i < keypoints.length; i++) {
|
||||
|
|
|
|||
Loading…
Reference in New Issue