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feat: camera ground-truth training pipeline (ADR-079, #362)
Add 4 scripts for camera-supervised WiFlow pose training: - collect-ground-truth.py: synchronized webcam + CSI capture via MediaPipe PoseLandmarker (17 COCO keypoints at 30fps) - align-ground-truth.js: time-align camera keypoints with CSI windows using binary search, confidence-weighted averaging - train-wiflow-supervised.js: 3-phase supervised training (contrastive pretrain → supervised keypoint regression → bone-constrained refinement) with curriculum learning and CSI augmentation - eval-wiflow.js: PCK@10/20/50, MPJPE, per-joint breakdown, baseline proxy mode for benchmarking Baseline benchmark (proxy poses, no camera supervision): PCK@10: 11.8% | PCK@20: 35.3% | PCK@50: 94.1% | MPJPE: 0.067 Camera pipeline validated over Tailscale to Mac Mini M4 Pro (1920x1080, 14/17 keypoints visible, MediaPipe confidence 0.94-1.0). Target after camera-supervised training: PCK@20 > 50% Closes #362 Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
ruv
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#!/usr/bin/env node
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/**
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* Ground-Truth Alignment — Camera Keypoints <-> CSI Recording
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*
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* Time-aligns camera keypoint data with CSI recording data to produce
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* paired training samples for WiFlow supervised training (ADR-079).
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*
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* Camera keypoints: data/ground-truth/gt-{timestamp}.jsonl
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* CSI recordings: data/recordings/*.csi.jsonl
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* Paired output: data/paired/*.paired.jsonl
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*
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* Usage:
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* node scripts/align-ground-truth.js \
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* --gt data/ground-truth/gt-1775300000.jsonl \
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* --csi data/recordings/overnight-1775217646.csi.jsonl \
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* --output data/paired/aligned.paired.jsonl
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*
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* # With clock offset correction (camera ahead by 50ms)
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* node scripts/align-ground-truth.js \
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* --gt data/ground-truth/gt-1775300000.jsonl \
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* --csi data/recordings/overnight-1775217646.csi.jsonl \
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* --clock-offset-ms -50
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*
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* ADR: docs/adr/ADR-079
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*/
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'use strict';
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const fs = require('fs');
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const path = require('path');
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const { parseArgs } = require('util');
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// ---------------------------------------------------------------------------
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// CLI argument parsing
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// ---------------------------------------------------------------------------
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const { values: args } = parseArgs({
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options: {
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gt: { type: 'string' },
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csi: { type: 'string' },
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output: { type: 'string', short: 'o' },
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'window-ms': { type: 'string', default: '200' },
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'window-frames': { type: 'string', default: '20' },
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'min-camera-frames': { type: 'string', default: '3' },
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'min-confidence': { type: 'string', default: '0.5' },
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'clock-offset-ms': { type: 'string', default: '0' },
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help: { type: 'boolean', short: 'h', default: false },
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},
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strict: true,
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});
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if (args.help || !args.gt || !args.csi) {
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console.log(`
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Usage: node scripts/align-ground-truth.js --gt <gt.jsonl> --csi <csi.jsonl> [options]
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Required:
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--gt <path> Camera ground-truth JSONL file
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--csi <path> CSI recording JSONL file
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Options:
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--output, -o <path> Output paired JSONL (default: data/paired/<basename>.paired.jsonl)
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--window-ms <ms> CSI window size in ms (default: 200)
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--window-frames <n> Frames per CSI window (default: 20)
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--min-camera-frames <n> Minimum camera frames per window (default: 3)
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--min-confidence <f> Minimum average confidence threshold (default: 0.5)
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--clock-offset-ms <ms> Manual clock offset: added to camera timestamps (default: 0)
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--help, -h Show this help
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`);
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process.exit(args.help ? 0 : 1);
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}
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const WINDOW_FRAMES = parseInt(args['window-frames'], 10);
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const WINDOW_MS = parseInt(args['window-ms'], 10);
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const MIN_CAMERA_FRAMES = parseInt(args['min-camera-frames'], 10);
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const MIN_CONFIDENCE = parseFloat(args['min-confidence']);
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const CLOCK_OFFSET_MS = parseFloat(args['clock-offset-ms']);
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const NUM_KEYPOINTS = 17; // COCO 17-keypoint format
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// ---------------------------------------------------------------------------
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// Timestamp conversion
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// ---------------------------------------------------------------------------
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/**
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* Convert camera nanosecond timestamp to milliseconds.
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* Applies clock offset correction.
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*/
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function cameraTsToMs(tsNs) {
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return tsNs / 1e6 + CLOCK_OFFSET_MS;
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}
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/**
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* Convert ISO 8601 timestamp string to milliseconds since epoch.
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*/
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function isoToMs(isoStr) {
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return new Date(isoStr).getTime();
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}
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// ---------------------------------------------------------------------------
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// IQ hex parsing (matches train-wiflow.js conventions)
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// ---------------------------------------------------------------------------
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/**
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* Parse IQ hex string into signed byte pairs [I0, Q0, I1, Q1, ...].
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*/
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function parseIqHex(hexStr) {
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const bytes = [];
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for (let i = 0; i < hexStr.length; i += 2) {
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let val = parseInt(hexStr.substr(i, 2), 16);
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if (val > 127) val -= 256; // signed byte
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bytes.push(val);
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}
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return bytes;
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}
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/**
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* Extract amplitude from IQ data for a given number of subcarriers.
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* Returns Float32Array of amplitudes [nSubcarriers].
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* Skips first I/Q pair (DC offset) per WiFlow paper recommendation.
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*/
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function extractAmplitude(iqBytes, nSubcarriers) {
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const amp = new Float32Array(nSubcarriers);
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const start = 2; // skip first IQ pair (DC offset)
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for (let sc = 0; sc < nSubcarriers; sc++) {
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const idx = start + sc * 2;
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if (idx + 1 < iqBytes.length) {
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const I = iqBytes[idx];
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const Q = iqBytes[idx + 1];
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amp[sc] = Math.sqrt(I * I + Q * Q);
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}
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}
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return amp;
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}
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// ---------------------------------------------------------------------------
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// File loading
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// ---------------------------------------------------------------------------
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/**
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* Load and parse a JSONL file, skipping blank/malformed lines.
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*/
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function loadJsonl(filePath) {
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const lines = fs.readFileSync(filePath, 'utf8').split('\n');
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const records = [];
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for (const line of lines) {
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const trimmed = line.trim();
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if (!trimmed) continue;
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try {
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records.push(JSON.parse(trimmed));
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} catch {
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// skip malformed lines
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}
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}
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return records;
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}
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/**
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* Load camera ground-truth file.
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* Returns array of { tsMs, keypoints, confidence, nVisible, nPersons }.
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*/
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function loadGroundTruth(filePath) {
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const raw = loadJsonl(filePath);
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const frames = [];
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for (const r of raw) {
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if (r.ts_ns == null || !r.keypoints) continue;
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frames.push({
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tsMs: cameraTsToMs(r.ts_ns),
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keypoints: r.keypoints,
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confidence: r.confidence ?? 0,
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nVisible: r.n_visible ?? 0,
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nPersons: r.n_persons ?? 1,
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});
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}
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// Sort by timestamp
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frames.sort((a, b) => a.tsMs - b.tsMs);
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return frames;
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}
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/**
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* Load CSI recording file.
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* Separates raw_csi frames and feature frames.
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*/
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function loadCsi(filePath) {
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const raw = loadJsonl(filePath);
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const rawCsi = [];
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const features = [];
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for (const r of raw) {
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if (!r.timestamp) continue;
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const tsMs = isoToMs(r.timestamp);
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if (isNaN(tsMs)) continue;
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if (r.type === 'raw_csi') {
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rawCsi.push({
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tsMs,
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nodeId: r.node_id,
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subcarriers: r.subcarriers ?? 128,
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iqHex: r.iq_hex,
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rssi: r.rssi,
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seq: r.seq,
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});
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} else if (r.type === 'feature') {
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features.push({
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tsMs,
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nodeId: r.node_id,
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features: r.features,
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rssi: r.rssi,
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seq: r.seq,
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});
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}
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}
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// Sort by timestamp
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rawCsi.sort((a, b) => a.tsMs - b.tsMs);
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features.sort((a, b) => a.tsMs - b.tsMs);
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return { rawCsi, features };
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}
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// ---------------------------------------------------------------------------
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// Windowing
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// ---------------------------------------------------------------------------
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/**
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* Group frames into non-overlapping windows of `windowSize` consecutive frames.
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*/
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function groupIntoWindows(frames, windowSize) {
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const windows = [];
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for (let i = 0; i + windowSize <= frames.length; i += windowSize) {
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windows.push(frames.slice(i, i + windowSize));
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}
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return windows;
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}
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// ---------------------------------------------------------------------------
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// Camera frame matching (binary search)
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// ---------------------------------------------------------------------------
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/**
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* Find all camera frames within [tStart, tEnd] using binary search.
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*/
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function findCameraFramesInRange(cameraFrames, tStartMs, tEndMs) {
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// Binary search for first frame >= tStartMs
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let lo = 0;
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let hi = cameraFrames.length;
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while (lo < hi) {
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const mid = (lo + hi) >>> 1;
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if (cameraFrames[mid].tsMs < tStartMs) lo = mid + 1;
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else hi = mid;
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}
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const matched = [];
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for (let i = lo; i < cameraFrames.length; i++) {
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if (cameraFrames[i].tsMs > tEndMs) break;
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matched.push(cameraFrames[i]);
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}
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return matched;
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}
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// ---------------------------------------------------------------------------
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// Keypoint averaging (confidence-weighted)
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// ---------------------------------------------------------------------------
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/**
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* Average keypoints weighted by per-frame confidence.
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* Returns { keypoints: [[x,y],...], avgConfidence }.
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*/
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function averageKeypoints(cameraFrames) {
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let totalWeight = 0;
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const sumKp = new Array(NUM_KEYPOINTS).fill(null).map(() => [0, 0]);
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for (const f of cameraFrames) {
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const w = f.confidence || 1e-6;
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totalWeight += w;
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for (let k = 0; k < NUM_KEYPOINTS && k < f.keypoints.length; k++) {
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sumKp[k][0] += f.keypoints[k][0] * w;
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sumKp[k][1] += f.keypoints[k][1] * w;
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}
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}
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if (totalWeight === 0) totalWeight = 1;
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const keypoints = sumKp.map(([x, y]) => [x / totalWeight, y / totalWeight]);
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const avgConfidence = cameraFrames.reduce((s, f) => s + (f.confidence || 0), 0) / cameraFrames.length;
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return { keypoints, avgConfidence };
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}
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// ---------------------------------------------------------------------------
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// CSI matrix extraction
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// ---------------------------------------------------------------------------
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/**
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* Extract CSI amplitude matrix from raw_csi window.
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* Returns { data: flat Float32Array, shape: [subcarriers, windowFrames] }.
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*/
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function extractCsiMatrix(window) {
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const nFrames = window.length;
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const nSc = window[0].subcarriers || 128;
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const matrix = new Float32Array(nSc * nFrames);
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for (let f = 0; f < nFrames; f++) {
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const frame = window[f];
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if (frame.iqHex) {
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const iq = parseIqHex(frame.iqHex);
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const amp = extractAmplitude(iq, nSc);
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matrix.set(amp, f * nSc);
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}
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}
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return { data: Array.from(matrix), shape: [nSc, nFrames] };
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}
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/**
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* Extract feature matrix from feature-type window.
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* Returns { data: flat array, shape: [featureDim, windowFrames] }.
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*/
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function extractFeatureMatrix(window) {
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const nFrames = window.length;
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const dim = window[0].features ? window[0].features.length : 8;
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const matrix = new Float32Array(dim * nFrames);
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for (let f = 0; f < nFrames; f++) {
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const feats = window[f].features || new Array(dim).fill(0);
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for (let d = 0; d < dim; d++) {
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matrix[f * dim + d] = feats[d] || 0;
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}
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}
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return { data: Array.from(matrix), shape: [dim, nFrames] };
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}
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// ---------------------------------------------------------------------------
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// Main alignment
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// ---------------------------------------------------------------------------
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function align() {
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const gtPath = path.resolve(args.gt);
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const csiPath = path.resolve(args.csi);
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// Determine output path
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let outputPath;
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if (args.output) {
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outputPath = path.resolve(args.output);
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} else {
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const baseName = path.basename(csiPath, '.csi.jsonl');
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outputPath = path.resolve('data', 'paired', `${baseName}.paired.jsonl`);
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}
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// Ensure output directory exists
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const outputDir = path.dirname(outputPath);
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if (!fs.existsSync(outputDir)) {
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fs.mkdirSync(outputDir, { recursive: true });
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}
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console.log('=== Ground-Truth Alignment (ADR-079) ===');
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console.log(` GT file: ${gtPath}`);
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console.log(` CSI file: ${csiPath}`);
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console.log(` Output: ${outputPath}`);
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console.log(` Window: ${WINDOW_FRAMES} frames / ${WINDOW_MS} ms`);
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console.log(` Min camera frames: ${MIN_CAMERA_FRAMES}`);
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console.log(` Min confidence: ${MIN_CONFIDENCE}`);
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console.log(` Clock offset: ${CLOCK_OFFSET_MS} ms`);
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console.log();
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// Load data
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console.log('Loading ground-truth...');
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const cameraFrames = loadGroundTruth(gtPath);
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console.log(` ${cameraFrames.length} camera frames loaded`);
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if (cameraFrames.length > 0) {
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console.log(` Time range: ${new Date(cameraFrames[0].tsMs).toISOString()} -> ${new Date(cameraFrames[cameraFrames.length - 1].tsMs).toISOString()}`);
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}
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console.log('Loading CSI data...');
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const { rawCsi, features } = loadCsi(csiPath);
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console.log(` ${rawCsi.length} raw_csi frames, ${features.length} feature frames`);
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// Decide which CSI source to use
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const useRawCsi = rawCsi.length >= WINDOW_FRAMES;
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const csiSource = useRawCsi ? rawCsi : features;
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const sourceLabel = useRawCsi ? 'raw_csi' : 'feature';
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if (csiSource.length < WINDOW_FRAMES) {
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console.error(`ERROR: Not enough CSI frames (${csiSource.length}) for even one window of ${WINDOW_FRAMES} frames.`);
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process.exit(1);
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}
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console.log(` Using ${sourceLabel} frames (${csiSource.length} total)`);
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if (csiSource.length > 0) {
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console.log(` CSI time range: ${new Date(csiSource[0].tsMs).toISOString()} -> ${new Date(csiSource[csiSource.length - 1].tsMs).toISOString()}`);
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}
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console.log();
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// Group CSI into windows
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const windows = groupIntoWindows(csiSource, WINDOW_FRAMES);
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console.log(`Grouped into ${windows.length} CSI windows`);
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// Align
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const paired = [];
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let totalConfidence = 0;
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for (const window of windows) {
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const tStartMs = window[0].tsMs;
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const tEndMs = window[window.length - 1].tsMs;
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// Expand window if actual time span is smaller than window-ms
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const halfWindow = WINDOW_MS / 2;
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const midpoint = (tStartMs + tEndMs) / 2;
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const searchStart = Math.min(tStartMs, midpoint - halfWindow);
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const searchEnd = Math.max(tEndMs, midpoint + halfWindow);
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// Find matching camera frames
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const matched = findCameraFramesInRange(cameraFrames, searchStart, searchEnd);
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if (matched.length < MIN_CAMERA_FRAMES) continue;
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// Check average confidence
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const avgConf = matched.reduce((s, f) => s + (f.confidence || 0), 0) / matched.length;
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if (avgConf < MIN_CONFIDENCE) continue;
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// Average keypoints weighted by confidence
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const { keypoints, avgConfidence } = averageKeypoints(matched);
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// Extract CSI matrix
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const csiMatrix = useRawCsi
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? extractCsiMatrix(window)
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: extractFeatureMatrix(window);
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paired.push({
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csi: csiMatrix.data,
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csi_shape: csiMatrix.shape,
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kp: keypoints,
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conf: Math.round(avgConfidence * 1000) / 1000,
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n_camera_frames: matched.length,
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ts_start: new Date(tStartMs).toISOString(),
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ts_end: new Date(tEndMs).toISOString(),
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});
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totalConfidence += avgConfidence;
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}
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// Write output
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const outputLines = paired.map(s => JSON.stringify(s));
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fs.writeFileSync(outputPath, outputLines.join('\n') + (outputLines.length > 0 ? '\n' : ''));
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// Print summary
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const alignmentRate = windows.length > 0 ? (paired.length / windows.length * 100) : 0;
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const avgPairedConf = paired.length > 0 ? (totalConfidence / paired.length) : 0;
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console.log();
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console.log('=== Alignment Summary ===');
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console.log(` Total CSI windows: ${windows.length}`);
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console.log(` Paired samples: ${paired.length}`);
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console.log(` Alignment rate: ${alignmentRate.toFixed(1)}%`);
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console.log(` Avg confidence (paired): ${avgPairedConf.toFixed(3)}`);
|
||||
console.log(` CSI source: ${sourceLabel} (${csiMatrix_shapeLabel(paired, useRawCsi)})`);
|
||||
if (paired.length > 0) {
|
||||
console.log(` Time range covered: ${paired[0].ts_start} -> ${paired[paired.length - 1].ts_end}`);
|
||||
}
|
||||
console.log(` Output written: ${outputPath}`);
|
||||
console.log();
|
||||
|
||||
if (paired.length === 0) {
|
||||
console.log('WARNING: No paired samples produced. Check that camera and CSI time ranges overlap.');
|
||||
console.log(' Hint: Use --clock-offset-ms to correct misaligned clocks.');
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Format CSI matrix shape label for summary.
|
||||
*/
|
||||
function csiMatrix_shapeLabel(paired, useRawCsi) {
|
||||
if (paired.length === 0) return useRawCsi ? `[128, ${WINDOW_FRAMES}]` : `[8, ${WINDOW_FRAMES}]`;
|
||||
const shape = paired[0].csi_shape;
|
||||
return `[${shape[0]}, ${shape[1]}]`;
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Entry point
|
||||
// ---------------------------------------------------------------------------
|
||||
align();
|
||||
@@ -0,0 +1,341 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Camera ground-truth collection for WiFi pose estimation training (ADR-079).
|
||||
|
||||
Captures webcam keypoints via MediaPipe PoseLandmarker (Tasks API) and
|
||||
synchronizes with ESP32 CSI recording from the sensing server.
|
||||
|
||||
Output: JSONL file in data/ground-truth/ with per-frame 17-keypoint COCO poses.
|
||||
|
||||
Usage:
|
||||
python scripts/collect-ground-truth.py --preview --duration 60
|
||||
python scripts/collect-ground-truth.py --server http://192.168.1.10:3000
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import signal
|
||||
import sys
|
||||
import time
|
||||
import urllib.request
|
||||
import urllib.error
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
import mediapipe as mp
|
||||
from mediapipe.tasks.python import BaseOptions
|
||||
from mediapipe.tasks.python.vision import (
|
||||
PoseLandmarker,
|
||||
PoseLandmarkerOptions,
|
||||
RunningMode,
|
||||
)
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# MediaPipe 33 landmarks -> 17 COCO keypoints
|
||||
# ---------------------------------------------------------------------------
|
||||
# COCO idx : MP idx : joint name
|
||||
# 0 : 0 : nose
|
||||
# 1 : 2 : left_eye
|
||||
# 2 : 5 : right_eye
|
||||
# 3 : 7 : left_ear
|
||||
# 4 : 8 : right_ear
|
||||
# 5 : 11 : left_shoulder
|
||||
# 6 : 12 : right_shoulder
|
||||
# 7 : 13 : left_elbow
|
||||
# 8 : 14 : right_elbow
|
||||
# 9 : 15 : left_wrist
|
||||
# 10 : 16 : right_wrist
|
||||
# 11 : 23 : left_hip
|
||||
# 12 : 24 : right_hip
|
||||
# 13 : 25 : left_knee
|
||||
# 14 : 26 : right_knee
|
||||
# 15 : 27 : left_ankle
|
||||
# 16 : 28 : right_ankle
|
||||
|
||||
MP_TO_COCO = [0, 2, 5, 7, 8, 11, 12, 13, 14, 15, 16, 23, 24, 25, 26, 27, 28]
|
||||
|
||||
COCO_BONES = [
|
||||
(5, 7), (7, 9), (6, 8), (8, 10), # arms
|
||||
(5, 6), # shoulders
|
||||
(11, 13), (13, 15), (12, 14), (14, 16), # legs
|
||||
(11, 12), # hips
|
||||
(5, 11), (6, 12), # torso
|
||||
(0, 1), (0, 2), (1, 3), (2, 4), # face
|
||||
]
|
||||
|
||||
MODEL_URL = (
|
||||
"https://storage.googleapis.com/mediapipe-models/"
|
||||
"pose_landmarker/pose_landmarker_lite/float16/latest/"
|
||||
"pose_landmarker_lite.task"
|
||||
)
|
||||
MODEL_FILENAME = "pose_landmarker_lite.task"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def ensure_model(cache_dir: Path) -> Path:
|
||||
"""Download the PoseLandmarker model if not already cached."""
|
||||
model_path = cache_dir / MODEL_FILENAME
|
||||
if model_path.exists():
|
||||
return model_path
|
||||
|
||||
cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
print(f"Downloading {MODEL_FILENAME} ...")
|
||||
try:
|
||||
urllib.request.urlretrieve(MODEL_URL, str(model_path))
|
||||
print(f" saved to {model_path}")
|
||||
except Exception as exc:
|
||||
print(f"ERROR: Failed to download model: {exc}", file=sys.stderr)
|
||||
print(
|
||||
"Download manually from:\n"
|
||||
f" {MODEL_URL}\n"
|
||||
f"and place at {model_path}",
|
||||
file=sys.stderr,
|
||||
)
|
||||
sys.exit(1)
|
||||
return model_path
|
||||
|
||||
|
||||
def post_json(url: str, payload: dict | None = None, timeout: float = 5.0) -> bool:
|
||||
"""POST JSON to a URL. Returns True on success, False on failure."""
|
||||
data = json.dumps(payload or {}).encode("utf-8")
|
||||
req = urllib.request.Request(
|
||||
url,
|
||||
data=data,
|
||||
headers={"Content-Type": "application/json"},
|
||||
method="POST",
|
||||
)
|
||||
try:
|
||||
with urllib.request.urlopen(req, timeout=timeout) as resp:
|
||||
return 200 <= resp.status < 300
|
||||
except Exception as exc:
|
||||
print(f"WARNING: POST {url} failed: {exc}", file=sys.stderr)
|
||||
return False
|
||||
|
||||
|
||||
def draw_skeleton(frame: np.ndarray, keypoints: list[list[float]], w: int, h: int):
|
||||
"""Draw COCO skeleton overlay on a BGR frame."""
|
||||
pts = []
|
||||
for x, y in keypoints:
|
||||
px, py = int(x * w), int(y * h)
|
||||
pts.append((px, py))
|
||||
cv2.circle(frame, (px, py), 4, (0, 255, 0), -1)
|
||||
|
||||
for i, j in COCO_BONES:
|
||||
if i < len(pts) and j < len(pts):
|
||||
cv2.line(frame, pts[i], pts[j], (0, 200, 255), 2)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Main collection loop
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Collect camera ground-truth keypoints for WiFi pose training (ADR-079)."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--server",
|
||||
default="http://localhost:3000",
|
||||
help="Sensing server URL (default: http://localhost:3000)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--preview",
|
||||
action="store_true",
|
||||
help="Show live skeleton overlay window",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--duration",
|
||||
type=int,
|
||||
default=300,
|
||||
help="Recording duration in seconds (default: 300)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--camera",
|
||||
type=int,
|
||||
default=0,
|
||||
help="Camera device index (default: 0)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output",
|
||||
default="data/ground-truth",
|
||||
help="Output directory (default: data/ground-truth)",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
# --- Resolve paths relative to repo root ---
|
||||
repo_root = Path(__file__).resolve().parent.parent
|
||||
output_dir = repo_root / args.output
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
cache_dir = repo_root / "data" / ".cache"
|
||||
|
||||
# --- Download / locate model ---
|
||||
model_path = ensure_model(cache_dir)
|
||||
|
||||
# --- Open camera ---
|
||||
cap = cv2.VideoCapture(args.camera)
|
||||
if not cap.isOpened():
|
||||
print(
|
||||
f"ERROR: Cannot open camera index {args.camera}. "
|
||||
"Check that a webcam is connected and not in use by another app.",
|
||||
file=sys.stderr,
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
frame_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
frame_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
print(f"Camera opened: {frame_w}x{frame_h}")
|
||||
|
||||
# --- Create PoseLandmarker ---
|
||||
options = PoseLandmarkerOptions(
|
||||
base_options=BaseOptions(model_asset_path=str(model_path)),
|
||||
running_mode=RunningMode.IMAGE,
|
||||
num_poses=1,
|
||||
min_pose_detection_confidence=0.5,
|
||||
min_pose_presence_confidence=0.5,
|
||||
min_tracking_confidence=0.5,
|
||||
)
|
||||
landmarker = PoseLandmarker.create_from_options(options)
|
||||
|
||||
# --- Output file ---
|
||||
timestamp_str = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
out_path = output_dir / f"keypoints_{timestamp_str}.jsonl"
|
||||
out_file = open(out_path, "w", encoding="utf-8")
|
||||
print(f"Output: {out_path}")
|
||||
|
||||
# --- Start CSI recording ---
|
||||
recording_url_start = f"{args.server}/api/v1/recording/start"
|
||||
recording_url_stop = f"{args.server}/api/v1/recording/stop"
|
||||
csi_started = post_json(recording_url_start)
|
||||
if csi_started:
|
||||
print("CSI recording started on sensing server.")
|
||||
else:
|
||||
print(
|
||||
"WARNING: Could not start CSI recording. "
|
||||
"Camera keypoints will still be captured.",
|
||||
file=sys.stderr,
|
||||
)
|
||||
|
||||
# --- Graceful shutdown ---
|
||||
shutdown_requested = False
|
||||
|
||||
def _handle_signal(signum, frame):
|
||||
nonlocal shutdown_requested
|
||||
shutdown_requested = True
|
||||
|
||||
signal.signal(signal.SIGINT, _handle_signal)
|
||||
signal.signal(signal.SIGTERM, _handle_signal)
|
||||
|
||||
# --- Collection loop ---
|
||||
start_time = time.monotonic()
|
||||
frame_count = 0
|
||||
total_confidence = 0.0
|
||||
total_visible = 0
|
||||
|
||||
print(f"Collecting for {args.duration}s ... (press 'q' in preview to stop)")
|
||||
|
||||
try:
|
||||
while not shutdown_requested:
|
||||
elapsed = time.monotonic() - start_time
|
||||
if elapsed >= args.duration:
|
||||
break
|
||||
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
print("WARNING: Failed to read frame, retrying ...", file=sys.stderr)
|
||||
time.sleep(0.01)
|
||||
continue
|
||||
|
||||
ts_ns = time.time_ns()
|
||||
|
||||
# Convert BGR -> RGB for MediaPipe
|
||||
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||||
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb)
|
||||
|
||||
result = landmarker.detect(mp_image)
|
||||
|
||||
n_persons = len(result.pose_landmarks)
|
||||
|
||||
if n_persons > 0:
|
||||
landmarks = result.pose_landmarks[0]
|
||||
keypoints = []
|
||||
visibilities = []
|
||||
for coco_idx in range(17):
|
||||
mp_idx = MP_TO_COCO[coco_idx]
|
||||
lm = landmarks[mp_idx]
|
||||
keypoints.append([round(lm.x, 5), round(lm.y, 5)])
|
||||
visibilities.append(lm.visibility if lm.visibility else 0.0)
|
||||
|
||||
confidence = float(np.mean(visibilities))
|
||||
n_visible = int(sum(1 for v in visibilities if v > 0.5))
|
||||
else:
|
||||
keypoints = []
|
||||
confidence = 0.0
|
||||
n_visible = 0
|
||||
|
||||
record = {
|
||||
"ts_ns": ts_ns,
|
||||
"keypoints": keypoints,
|
||||
"confidence": round(confidence, 4),
|
||||
"n_visible": n_visible,
|
||||
"n_persons": n_persons,
|
||||
}
|
||||
out_file.write(json.dumps(record) + "\n")
|
||||
frame_count += 1
|
||||
total_confidence += confidence
|
||||
total_visible += n_visible
|
||||
|
||||
# Preview overlay
|
||||
if args.preview and keypoints:
|
||||
draw_skeleton(frame, keypoints, frame_w, frame_h)
|
||||
|
||||
if args.preview:
|
||||
remaining = max(0, int(args.duration - elapsed))
|
||||
cv2.putText(
|
||||
frame,
|
||||
f"Frames: {frame_count} Visible: {n_visible}/17 Time: {remaining}s",
|
||||
(10, 30),
|
||||
cv2.FONT_HERSHEY_SIMPLEX,
|
||||
0.7,
|
||||
(255, 255, 255),
|
||||
2,
|
||||
)
|
||||
cv2.imshow("Ground Truth Collection (ADR-079)", frame)
|
||||
if cv2.waitKey(1) & 0xFF == ord("q"):
|
||||
break
|
||||
|
||||
finally:
|
||||
# --- Cleanup ---
|
||||
out_file.close()
|
||||
cap.release()
|
||||
if args.preview:
|
||||
cv2.destroyAllWindows()
|
||||
landmarker.close()
|
||||
|
||||
# Stop CSI recording
|
||||
if csi_started:
|
||||
if post_json(recording_url_stop):
|
||||
print("CSI recording stopped.")
|
||||
else:
|
||||
print("WARNING: Failed to stop CSI recording.", file=sys.stderr)
|
||||
|
||||
# --- Summary ---
|
||||
avg_conf = total_confidence / frame_count if frame_count > 0 else 0.0
|
||||
avg_vis = total_visible / frame_count if frame_count > 0 else 0.0
|
||||
print()
|
||||
print("=== Collection Summary ===")
|
||||
print(f" Total frames: {frame_count}")
|
||||
print(f" Avg confidence: {avg_conf:.3f}")
|
||||
print(f" Avg visible joints: {avg_vis:.1f} / 17")
|
||||
print(f" Output: {out_path}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,625 @@
|
||||
#!/usr/bin/env node
|
||||
/**
|
||||
* WiFlow PCK Evaluation Script (ADR-079)
|
||||
*
|
||||
* Measures accuracy of WiFi-based pose estimation against ground-truth
|
||||
* camera keypoints using PCK (Percentage of Correct Keypoints) and MPJPE
|
||||
* (Mean Per-Joint Position Error) metrics.
|
||||
*
|
||||
* Usage:
|
||||
* node scripts/eval-wiflow.js --model models/wiflow-supervised/wiflow-v1.json --data data/paired/aligned.paired.jsonl
|
||||
* node scripts/eval-wiflow.js --baseline --data data/paired/aligned.paired.jsonl
|
||||
* node scripts/eval-wiflow.js --model models/wiflow-supervised/wiflow-v1.json --data data/paired/aligned.paired.jsonl --verbose
|
||||
*
|
||||
* ADR: docs/adr/ADR-079
|
||||
*/
|
||||
|
||||
'use strict';
|
||||
|
||||
const fs = require('fs');
|
||||
const path = require('path');
|
||||
const { parseArgs } = require('util');
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Resolve WiFlow model dependencies
|
||||
// ---------------------------------------------------------------------------
|
||||
const {
|
||||
WiFlowModel,
|
||||
COCO_KEYPOINTS,
|
||||
createRng,
|
||||
} = require(path.join(__dirname, 'wiflow-model.js'));
|
||||
|
||||
const RUVLLM_PATH = path.resolve(__dirname, '..', 'vendor', 'ruvector', 'npm', 'packages', 'ruvllm', 'src');
|
||||
const { SafeTensorsReader } = require(path.join(RUVLLM_PATH, 'export.js'));
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Constants
|
||||
// ---------------------------------------------------------------------------
|
||||
const NUM_KEYPOINTS = 17;
|
||||
const DEFAULT_TORSO_LENGTH = 0.3; // normalized coords fallback
|
||||
|
||||
// Joint name aliases for display (short form)
|
||||
const JOINT_NAMES = [
|
||||
'nose', 'l_eye', 'r_eye', 'l_ear', 'r_ear',
|
||||
'l_shoulder', 'r_shoulder', 'l_elbow', 'r_elbow',
|
||||
'l_wrist', 'r_wrist', 'l_hip', 'r_hip',
|
||||
'l_knee', 'r_knee', 'l_ankle', 'r_ankle',
|
||||
];
|
||||
|
||||
// Shoulder indices: l_shoulder=5, r_shoulder=6
|
||||
// Hip indices: l_hip=11, r_hip=12
|
||||
const L_SHOULDER = 5;
|
||||
const R_SHOULDER = 6;
|
||||
const L_HIP = 11;
|
||||
const R_HIP = 12;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// CLI argument parsing
|
||||
// ---------------------------------------------------------------------------
|
||||
const { values: args } = parseArgs({
|
||||
options: {
|
||||
model: { type: 'string', short: 'm' },
|
||||
data: { type: 'string', short: 'd' },
|
||||
baseline: { type: 'boolean', default: false },
|
||||
output: { type: 'string', short: 'o' },
|
||||
verbose: { type: 'boolean', short: 'v', default: false },
|
||||
},
|
||||
strict: true,
|
||||
});
|
||||
|
||||
if (!args.data) {
|
||||
console.error('Usage: node scripts/eval-wiflow.js --data <paired-jsonl> [--model <path>] [--baseline] [--output <path>]');
|
||||
console.error('');
|
||||
console.error('Required:');
|
||||
console.error(' --data, -d <path> Paired CSI + keypoint JSONL (from align-ground-truth.js)');
|
||||
console.error('');
|
||||
console.error('Options:');
|
||||
console.error(' --model, -m <path> Path to trained model directory or JSON');
|
||||
console.error(' --baseline Evaluate proxy-based baseline (no model)');
|
||||
console.error(' --output, -o <path> Output eval report JSON');
|
||||
console.error(' --verbose, -v Verbose output');
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
if (!args.model && !args.baseline) {
|
||||
console.error('Error: Must specify either --model <path> or --baseline');
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Data loading
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/**
|
||||
* Load paired JSONL samples.
|
||||
* Each line: { csi: [...], csi_shape: [S, T], kp: [[x,y],...], conf: 0.xx, ... }
|
||||
*/
|
||||
function loadPairedData(filePath) {
|
||||
const content = fs.readFileSync(filePath, 'utf-8');
|
||||
const samples = [];
|
||||
for (const line of content.split('\n')) {
|
||||
if (!line.trim()) continue;
|
||||
try {
|
||||
const s = JSON.parse(line);
|
||||
if (!s.kp || !Array.isArray(s.kp)) continue;
|
||||
if (!s.csi && !s.csi_shape) continue;
|
||||
samples.push(s);
|
||||
} catch (e) {
|
||||
// skip malformed lines
|
||||
}
|
||||
}
|
||||
return samples;
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Model loading
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/**
|
||||
* Load WiFlow model from a directory or JSON file.
|
||||
* Tries: model.safetensors, then config.json for architecture config.
|
||||
* Returns { model, name }.
|
||||
*/
|
||||
function loadModel(modelPath) {
|
||||
const stat = fs.statSync(modelPath);
|
||||
let modelDir;
|
||||
|
||||
if (stat.isDirectory()) {
|
||||
modelDir = modelPath;
|
||||
} else {
|
||||
// Assume JSON file in a model directory
|
||||
modelDir = path.dirname(modelPath);
|
||||
}
|
||||
|
||||
// Load architecture config if available
|
||||
let config = {};
|
||||
const configPath = path.join(modelDir, 'config.json');
|
||||
if (fs.existsSync(configPath)) {
|
||||
try {
|
||||
const raw = JSON.parse(fs.readFileSync(configPath, 'utf-8'));
|
||||
if (raw.custom) {
|
||||
config.inputChannels = raw.custom.inputChannels || 128;
|
||||
config.timeSteps = raw.custom.timeSteps || 20;
|
||||
config.numKeypoints = raw.custom.numKeypoints || 17;
|
||||
config.numHeads = raw.custom.numHeads || 8;
|
||||
config.seed = raw.custom.seed || 42;
|
||||
}
|
||||
} catch (e) {
|
||||
// use defaults
|
||||
}
|
||||
}
|
||||
|
||||
// Load training-metrics.json for additional config
|
||||
const metricsPath = path.join(modelDir, 'training-metrics.json');
|
||||
if (fs.existsSync(metricsPath)) {
|
||||
try {
|
||||
const metrics = JSON.parse(fs.readFileSync(metricsPath, 'utf-8'));
|
||||
if (metrics.model && metrics.model.architecture === 'wiflow') {
|
||||
// metrics available for report
|
||||
}
|
||||
} catch (e) {
|
||||
// ignore
|
||||
}
|
||||
}
|
||||
|
||||
// Create model with config
|
||||
const model = new WiFlowModel(config);
|
||||
model.setTraining(false); // eval mode
|
||||
|
||||
// Load weights from SafeTensors
|
||||
const safetensorsPath = path.join(modelDir, 'model.safetensors');
|
||||
if (fs.existsSync(safetensorsPath)) {
|
||||
const buffer = new Uint8Array(fs.readFileSync(safetensorsPath));
|
||||
const reader = new SafeTensorsReader(buffer);
|
||||
const tensorNames = reader.getTensorNames();
|
||||
|
||||
// Build tensor map for fromTensorMap
|
||||
const tensorMap = new Map();
|
||||
for (const name of tensorNames) {
|
||||
const tensor = reader.getTensor(name);
|
||||
if (tensor) {
|
||||
tensorMap.set(name, tensor.data);
|
||||
}
|
||||
}
|
||||
|
||||
model.fromTensorMap(tensorMap);
|
||||
if (args.verbose) {
|
||||
console.log(`Loaded ${tensorNames.length} tensors from ${safetensorsPath}`);
|
||||
console.log(`Model params: ${model.numParams().toLocaleString()}`);
|
||||
}
|
||||
} else {
|
||||
console.warn(`WARN: No model.safetensors found in ${modelDir}, using random weights`);
|
||||
}
|
||||
|
||||
// Derive model name
|
||||
const name = path.basename(modelDir);
|
||||
return { model, name };
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Baseline proxy pose generation (ADR-072 Phase 2 heuristic)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/**
|
||||
* Generate a proxy standing skeleton from CSI features.
|
||||
* If presence detected (amplitude energy > threshold), place a standing
|
||||
* person at center with standard COCO proportions, perturbed by motion energy.
|
||||
*/
|
||||
function generateBaselinePose(sample) {
|
||||
const rng = createRng(42);
|
||||
|
||||
// Estimate presence from CSI amplitude energy
|
||||
const csi = sample.csi;
|
||||
let energy = 0;
|
||||
if (Array.isArray(csi)) {
|
||||
for (let i = 0; i < csi.length; i++) {
|
||||
energy += csi[i] * csi[i];
|
||||
}
|
||||
energy = Math.sqrt(energy / csi.length);
|
||||
}
|
||||
|
||||
// Estimate motion energy (variance across subcarriers)
|
||||
let motionEnergy = 0;
|
||||
if (Array.isArray(csi) && sample.csi_shape) {
|
||||
const [S, T] = sample.csi_shape;
|
||||
if (T > 1) {
|
||||
for (let s = 0; s < S; s++) {
|
||||
let sum = 0;
|
||||
let sumSq = 0;
|
||||
for (let t = 0; t < T; t++) {
|
||||
const v = csi[s * T + t] || 0;
|
||||
sum += v;
|
||||
sumSq += v * v;
|
||||
}
|
||||
const mean = sum / T;
|
||||
motionEnergy += (sumSq / T) - (mean * mean);
|
||||
}
|
||||
motionEnergy = Math.sqrt(Math.max(0, motionEnergy / S));
|
||||
}
|
||||
}
|
||||
|
||||
// Normalized presence heuristic
|
||||
const presence = Math.min(1, energy / 10);
|
||||
|
||||
if (presence < 0.3) {
|
||||
// No person detected: return zero pose
|
||||
return new Float32Array(NUM_KEYPOINTS * 2);
|
||||
}
|
||||
|
||||
// Standing skeleton at center (0.5, 0.5) with standard proportions
|
||||
// Coordinates are [x, y] in normalized [0, 1] space
|
||||
// y=0 is top, y=1 is bottom (image convention)
|
||||
const cx = 0.5;
|
||||
const headY = 0.2;
|
||||
const shoulderY = 0.32;
|
||||
const elbowY = 0.45;
|
||||
const wristY = 0.55;
|
||||
const hipY = 0.55;
|
||||
const kneeY = 0.72;
|
||||
const ankleY = 0.88;
|
||||
const shoulderW = 0.08;
|
||||
const hipW = 0.06;
|
||||
const armSpread = 0.12;
|
||||
|
||||
// Standard standing pose keypoints [x, y]
|
||||
const skeleton = [
|
||||
[cx, headY], // 0: nose
|
||||
[cx - 0.02, headY - 0.02], // 1: l_eye
|
||||
[cx + 0.02, headY - 0.02], // 2: r_eye
|
||||
[cx - 0.04, headY], // 3: l_ear
|
||||
[cx + 0.04, headY], // 4: r_ear
|
||||
[cx - shoulderW, shoulderY], // 5: l_shoulder
|
||||
[cx + shoulderW, shoulderY], // 6: r_shoulder
|
||||
[cx - armSpread, elbowY], // 7: l_elbow
|
||||
[cx + armSpread, elbowY], // 8: r_elbow
|
||||
[cx - armSpread - 0.02, wristY], // 9: l_wrist
|
||||
[cx + armSpread + 0.02, wristY], // 10: r_wrist
|
||||
[cx - hipW, hipY], // 11: l_hip
|
||||
[cx + hipW, hipY], // 12: r_hip
|
||||
[cx - hipW, kneeY], // 13: l_knee
|
||||
[cx + hipW, kneeY], // 14: r_knee
|
||||
[cx - hipW, ankleY], // 15: l_ankle
|
||||
[cx + hipW, ankleY], // 16: r_ankle
|
||||
];
|
||||
|
||||
// Perturb limbs by motion energy
|
||||
const perturbScale = Math.min(motionEnergy * 0.1, 0.05);
|
||||
const result = new Float32Array(NUM_KEYPOINTS * 2);
|
||||
for (let k = 0; k < NUM_KEYPOINTS; k++) {
|
||||
const px = (rng() - 0.5) * 2 * perturbScale;
|
||||
const py = (rng() - 0.5) * 2 * perturbScale;
|
||||
result[k * 2] = Math.max(0, Math.min(1, skeleton[k][0] + px));
|
||||
result[k * 2 + 1] = Math.max(0, Math.min(1, skeleton[k][1] + py));
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Metric computation
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/** Euclidean distance between two 2D points */
|
||||
function dist2d(x1, y1, x2, y2) {
|
||||
const dx = x1 - x2;
|
||||
const dy = y1 - y2;
|
||||
return Math.sqrt(dx * dx + dy * dy);
|
||||
}
|
||||
|
||||
/**
|
||||
* Compute torso length from ground-truth keypoints.
|
||||
* Torso = distance(mid_shoulder, mid_hip).
|
||||
* Returns DEFAULT_TORSO_LENGTH if shoulders or hips not visible.
|
||||
*/
|
||||
function computeTorsoLength(kp) {
|
||||
if (!kp || kp.length < 13) return DEFAULT_TORSO_LENGTH;
|
||||
|
||||
const lsX = kp[L_SHOULDER][0];
|
||||
const lsY = kp[L_SHOULDER][1];
|
||||
const rsX = kp[R_SHOULDER][0];
|
||||
const rsY = kp[R_SHOULDER][1];
|
||||
const lhX = kp[L_HIP][0];
|
||||
const lhY = kp[L_HIP][1];
|
||||
const rhX = kp[R_HIP][0];
|
||||
const rhY = kp[R_HIP][1];
|
||||
|
||||
// Check if joints are at origin (not visible)
|
||||
const shoulderVisible = (lsX !== 0 || lsY !== 0) && (rsX !== 0 || rsY !== 0);
|
||||
const hipVisible = (lhX !== 0 || lhY !== 0) && (rhX !== 0 || rhY !== 0);
|
||||
|
||||
if (!shoulderVisible || !hipVisible) return DEFAULT_TORSO_LENGTH;
|
||||
|
||||
const midShoulderX = (lsX + rsX) / 2;
|
||||
const midShoulderY = (lsY + rsY) / 2;
|
||||
const midHipX = (lhX + rhX) / 2;
|
||||
const midHipY = (lhY + rhY) / 2;
|
||||
|
||||
const torso = dist2d(midShoulderX, midShoulderY, midHipX, midHipY);
|
||||
return torso > 0.01 ? torso : DEFAULT_TORSO_LENGTH;
|
||||
}
|
||||
|
||||
/**
|
||||
* Evaluate predictions against ground truth.
|
||||
*
|
||||
* @param {Array<{pred: Float32Array, gt: number[][], conf: number}>} results
|
||||
* @returns {object} Evaluation report
|
||||
*/
|
||||
function computeMetrics(results) {
|
||||
const n = results.length;
|
||||
if (n === 0) {
|
||||
return {
|
||||
n_samples: 0,
|
||||
pck_10: 0, pck_20: 0, pck_50: 0,
|
||||
mpjpe: 0,
|
||||
per_joint_pck20: {},
|
||||
per_joint_mpjpe: {},
|
||||
conf_weighted_pck20: 0,
|
||||
conf_weighted_mpjpe: 0,
|
||||
};
|
||||
}
|
||||
|
||||
// Accumulators
|
||||
const pckCounts = { 10: 0, 20: 0, 50: 0 };
|
||||
let totalJoints = 0;
|
||||
let totalMPJPE = 0;
|
||||
|
||||
const perJointPck20 = new Float64Array(NUM_KEYPOINTS);
|
||||
const perJointMPJPE = new Float64Array(NUM_KEYPOINTS);
|
||||
const perJointCount = new Float64Array(NUM_KEYPOINTS);
|
||||
|
||||
// Confidence-weighted accumulators
|
||||
let confWeightedPck20Num = 0;
|
||||
let confWeightedPck20Den = 0;
|
||||
let confWeightedMpjpeNum = 0;
|
||||
let confWeightedMpjpeDen = 0;
|
||||
|
||||
for (const { pred, gt, conf } of results) {
|
||||
const torso = computeTorsoLength(gt);
|
||||
const w = Math.max(conf, 1e-6);
|
||||
|
||||
for (let k = 0; k < NUM_KEYPOINTS; k++) {
|
||||
if (k >= gt.length) continue;
|
||||
|
||||
const gtX = gt[k][0];
|
||||
const gtY = gt[k][1];
|
||||
const predX = pred[k * 2];
|
||||
const predY = pred[k * 2 + 1];
|
||||
|
||||
const d = dist2d(predX, predY, gtX, gtY);
|
||||
|
||||
totalJoints++;
|
||||
totalMPJPE += d;
|
||||
|
||||
perJointMPJPE[k] += d;
|
||||
perJointCount[k] += 1;
|
||||
|
||||
// PCK at different thresholds
|
||||
if (d < 0.10 * torso) pckCounts[10]++;
|
||||
if (d < 0.20 * torso) {
|
||||
pckCounts[20]++;
|
||||
perJointPck20[k]++;
|
||||
confWeightedPck20Num += w;
|
||||
}
|
||||
if (d < 0.50 * torso) pckCounts[50]++;
|
||||
|
||||
confWeightedPck20Den += w;
|
||||
confWeightedMpjpeNum += d * w;
|
||||
confWeightedMpjpeDen += w;
|
||||
}
|
||||
}
|
||||
|
||||
// Aggregate metrics
|
||||
const pck10 = totalJoints > 0 ? pckCounts[10] / totalJoints : 0;
|
||||
const pck20 = totalJoints > 0 ? pckCounts[20] / totalJoints : 0;
|
||||
const pck50 = totalJoints > 0 ? pckCounts[50] / totalJoints : 0;
|
||||
const mpjpe = totalJoints > 0 ? totalMPJPE / totalJoints : 0;
|
||||
|
||||
// Per-joint breakdown
|
||||
const perJointPck20Map = {};
|
||||
const perJointMpjpeMap = {};
|
||||
for (let k = 0; k < NUM_KEYPOINTS; k++) {
|
||||
const name = JOINT_NAMES[k];
|
||||
perJointPck20Map[name] = perJointCount[k] > 0 ? perJointPck20[k] / perJointCount[k] : 0;
|
||||
perJointMpjpeMap[name] = perJointCount[k] > 0 ? perJointMPJPE[k] / perJointCount[k] : 0;
|
||||
}
|
||||
|
||||
// Confidence-weighted
|
||||
const confPck20 = confWeightedPck20Den > 0 ? confWeightedPck20Num / confWeightedPck20Den : 0;
|
||||
const confMpjpe = confWeightedMpjpeDen > 0 ? confWeightedMpjpeNum / confWeightedMpjpeDen : 0;
|
||||
|
||||
return {
|
||||
n_samples: n,
|
||||
pck_10: pck10,
|
||||
pck_20: pck20,
|
||||
pck_50: pck50,
|
||||
mpjpe,
|
||||
per_joint_pck20: perJointPck20Map,
|
||||
per_joint_mpjpe: perJointMpjpeMap,
|
||||
conf_weighted_pck20: confPck20,
|
||||
conf_weighted_mpjpe: confMpjpe,
|
||||
};
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Inference
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/**
|
||||
* Run model inference on a single paired sample.
|
||||
* @param {WiFlowModel} model
|
||||
* @param {object} sample - { csi, csi_shape, kp, conf }
|
||||
* @returns {Float32Array} - [17*2] predicted keypoints
|
||||
*/
|
||||
function runModelInference(model, sample) {
|
||||
const csi = sample.csi;
|
||||
const shape = sample.csi_shape;
|
||||
const S = shape ? shape[0] : 128;
|
||||
const T = shape ? shape[1] : 20;
|
||||
|
||||
// Prepare input as Float32Array [S, T]
|
||||
let input;
|
||||
if (csi instanceof Float32Array) {
|
||||
input = csi;
|
||||
} else if (Array.isArray(csi)) {
|
||||
input = new Float32Array(csi);
|
||||
} else {
|
||||
input = new Float32Array(S * T);
|
||||
}
|
||||
|
||||
// Ensure correct size (pad or truncate)
|
||||
const expectedLen = model.inputChannels * model.timeSteps;
|
||||
if (input.length !== expectedLen) {
|
||||
const resized = new Float32Array(expectedLen);
|
||||
const copyLen = Math.min(input.length, expectedLen);
|
||||
resized.set(input.subarray(0, copyLen));
|
||||
input = resized;
|
||||
}
|
||||
|
||||
return model.forward(input);
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Formatted output
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
function formatPercent(v) {
|
||||
return (v * 100).toFixed(1) + '%';
|
||||
}
|
||||
|
||||
function formatFloat(v, decimals) {
|
||||
decimals = decimals || 4;
|
||||
return v.toFixed(decimals);
|
||||
}
|
||||
|
||||
function printReport(report) {
|
||||
console.log('');
|
||||
console.log('WiFlow Evaluation Report (ADR-079)');
|
||||
console.log('===================================');
|
||||
console.log(`Model: ${report.model}`);
|
||||
console.log(`Samples: ${report.n_samples.toLocaleString()}`);
|
||||
console.log(`PCK@10: ${formatPercent(report.pck_10)}`);
|
||||
console.log(`PCK@20: ${formatPercent(report.pck_20)}`);
|
||||
console.log(`PCK@50: ${formatPercent(report.pck_50)}`);
|
||||
console.log(`MPJPE: ${formatFloat(report.mpjpe)}`);
|
||||
console.log('');
|
||||
console.log('Per-Joint PCK@20:');
|
||||
|
||||
const maxNameLen = Math.max(...JOINT_NAMES.map(n => n.length));
|
||||
for (const name of JOINT_NAMES) {
|
||||
const pck = report.per_joint_pck20[name] || 0;
|
||||
const pad = ' '.repeat(maxNameLen - name.length + 2);
|
||||
console.log(` ${name}${pad}${formatPercent(pck)}`);
|
||||
}
|
||||
|
||||
console.log('');
|
||||
console.log('Per-Joint MPJPE:');
|
||||
for (const name of JOINT_NAMES) {
|
||||
const mpjpe = report.per_joint_mpjpe[name] || 0;
|
||||
const pad = ' '.repeat(maxNameLen - name.length + 2);
|
||||
console.log(` ${name}${pad}${formatFloat(mpjpe)}`);
|
||||
}
|
||||
|
||||
console.log('');
|
||||
console.log('Confidence-Weighted:');
|
||||
console.log(` PCK@20: ${formatPercent(report.conf_weighted_pck20)}`);
|
||||
console.log(` MPJPE: ${formatFloat(report.conf_weighted_mpjpe)}`);
|
||||
console.log('');
|
||||
console.log(`Inference: ${report.inference_latency_ms.toFixed(2)}ms/sample`);
|
||||
console.log('');
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Main
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
function main() {
|
||||
// Load paired data
|
||||
if (args.verbose) console.log(`Loading paired data from ${args.data}...`);
|
||||
const samples = loadPairedData(args.data);
|
||||
if (samples.length === 0) {
|
||||
console.error('Error: No valid paired samples found in', args.data);
|
||||
process.exit(1);
|
||||
}
|
||||
if (args.verbose) console.log(`Loaded ${samples.length} paired samples`);
|
||||
|
||||
let modelName;
|
||||
let model = null;
|
||||
|
||||
if (args.baseline) {
|
||||
modelName = 'baseline-proxy';
|
||||
if (args.verbose) console.log('Running baseline proxy evaluation (ADR-072 Phase 2 heuristic)');
|
||||
} else {
|
||||
const loaded = loadModel(args.model);
|
||||
model = loaded.model;
|
||||
modelName = loaded.name;
|
||||
if (args.verbose) console.log(`Running model evaluation: ${modelName}`);
|
||||
}
|
||||
|
||||
// Run inference and collect results
|
||||
const results = [];
|
||||
const startTime = process.hrtime.bigint();
|
||||
|
||||
for (const sample of samples) {
|
||||
let pred;
|
||||
if (args.baseline) {
|
||||
pred = generateBaselinePose(sample);
|
||||
} else {
|
||||
pred = runModelInference(model, sample);
|
||||
}
|
||||
|
||||
results.push({
|
||||
pred,
|
||||
gt: sample.kp,
|
||||
conf: sample.conf || 0,
|
||||
});
|
||||
}
|
||||
|
||||
const endTime = process.hrtime.bigint();
|
||||
const totalMs = Number(endTime - startTime) / 1e6;
|
||||
const latencyMs = totalMs / samples.length;
|
||||
|
||||
// Compute metrics
|
||||
const metrics = computeMetrics(results);
|
||||
|
||||
// Build report
|
||||
const report = {
|
||||
model: modelName,
|
||||
n_samples: metrics.n_samples,
|
||||
pck_10: Math.round(metrics.pck_10 * 10000) / 10000,
|
||||
pck_20: Math.round(metrics.pck_20 * 10000) / 10000,
|
||||
pck_50: Math.round(metrics.pck_50 * 10000) / 10000,
|
||||
mpjpe: Math.round(metrics.mpjpe * 100000) / 100000,
|
||||
per_joint_pck20: {},
|
||||
per_joint_mpjpe: {},
|
||||
conf_weighted_pck20: Math.round(metrics.conf_weighted_pck20 * 10000) / 10000,
|
||||
conf_weighted_mpjpe: Math.round(metrics.conf_weighted_mpjpe * 100000) / 100000,
|
||||
inference_latency_ms: Math.round(latencyMs * 100) / 100,
|
||||
timestamp: new Date().toISOString(),
|
||||
};
|
||||
|
||||
// Round per-joint metrics
|
||||
for (const name of JOINT_NAMES) {
|
||||
report.per_joint_pck20[name] = Math.round((metrics.per_joint_pck20[name] || 0) * 10000) / 10000;
|
||||
report.per_joint_mpjpe[name] = Math.round((metrics.per_joint_mpjpe[name] || 0) * 100000) / 100000;
|
||||
}
|
||||
|
||||
// Print formatted report
|
||||
printReport(report);
|
||||
|
||||
// Write output JSON
|
||||
const outputPath = args.output ||
|
||||
(args.model
|
||||
? path.join(path.dirname(
|
||||
fs.statSync(args.model).isDirectory() ? path.join(args.model, '.') : args.model
|
||||
), 'eval-report.json')
|
||||
: 'models/wiflow-supervised/eval-report.json');
|
||||
|
||||
const outputDir = path.dirname(outputPath);
|
||||
if (!fs.existsSync(outputDir)) {
|
||||
fs.mkdirSync(outputDir, { recursive: true });
|
||||
}
|
||||
|
||||
fs.writeFileSync(outputPath, JSON.stringify(report, null, 2) + '\n');
|
||||
console.log(`Report saved to ${outputPath}`);
|
||||
}
|
||||
|
||||
main();
|
||||
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user