docs: add quixzoom-auth-core product to AAMOS
- Product documentation in docs/products/ - Updated MEMORY.md with product info - quiXzoom Auth Core as AAMOS Identity product
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/**
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* POST /v1/track — Spårning över tid
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* Sparar tracking-data och analyserar förändringar över tid
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*/
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import { Router } from 'express';
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import * as ort from 'onnxruntime-node';
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import sharp from 'sharp';
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import { fetchImage, hashInput, saveResult, genReqId, requireAuth, dbPool } from './utils.mjs';
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const router = Router();
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const FACE_DET_MODEL = '/opt/amos/data/kyc-service/models/face_detection_yunet_2023mar.onnx';
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const FACE_REC_MODEL = '/opt/amos/data/kyc-service/models/face_recognition_sface_2021dec.onnx';
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let detSession = null, recSession = null;
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async function getDetSession() {
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if (!detSession) detSession = await ort.InferenceSession.create(FACE_DET_MODEL);
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return detSession;
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}
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async function getRecSession() {
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if (!recSession) recSession = await ort.InferenceSession.create(FACE_REC_MODEL);
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return recSession;
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}
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async function getFaceEmbedding(buffer) {
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// Detect face
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const raw = await sharp(buffer).resize(640, 640).raw().toBuffer({ resolveWithObject: true });
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const { data, info } = raw;
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const h = info.height, w = info.width;
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const floatData = new Float32Array(1 * 3 * h * w);
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for (let y = 0; y < h; y++) {
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for (let x = 0; x < w; x++) {
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const idx = (y * w + x) * 3;
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floatData[0 * h * w + y * w + x] = data[idx] / 255.0;
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floatData[1 * h * w + y * w + x] = data[idx + 1] / 255.0;
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floatData[2 * h * w + y * w + x] = data[idx + 2] / 255.0;
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}
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}
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const tensor = new ort.Tensor('float32', floatData, [1, 3, h, w]);
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const detSess = await getDetSession();
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const feeds = {}; feeds[detSess.inputNames[0]] = tensor;
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const out = await detSess.run(feeds);
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const outTensor = out[detSess.outputNames[0]];
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const outData = outTensor.data;
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const dims = outTensor.dims;
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const stride = dims[dims.length - 1];
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let bestScore = 0, bestBox = null;
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for (let i = 0; i < dims[0]; i++) {
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const row = Array.from(outData.slice(i * stride, (i + 1) * stride));
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if (row[2] > bestScore) { bestScore = row[2]; bestBox = row; }
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}
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if (!bestBox || bestScore < 0.5) return null;
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// Get embedding
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const orig = await sharp(buffer).raw().toBuffer({ resolveWithObject: true });
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const ow = orig.info.width, oh = orig.info.height;
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const x1 = Math.max(0, Math.round(bestBox[3] * ow));
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const y1 = Math.max(0, Math.round(bestBox[4] * oh));
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const x2 = Math.min(ow, Math.round(bestBox[5] * ow));
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const y2 = Math.min(oh, Math.round(bestBox[6] * oh));
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const faceBuf = await sharp(buffer)
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.extract({ left: x1, top: y1, width: x2 - x1, height: y2 - y1 })
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.resize(112, 112)
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.raw()
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.toBuffer();
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const recFloat = new Float32Array(1 * 3 * 112 * 112);
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for (let i = 0; i < 112 * 112; i++) {
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recFloat[0 * 12544 + i] = faceBuf[i * 3] / 255.0;
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recFloat[1 * 12544 + i] = faceBuf[i * 3 + 1] / 255.0;
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recFloat[2 * 12544 + i] = faceBuf[i * 3 + 2] / 255.0;
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}
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const recTensor = new ort.Tensor('float32', recFloat, [1, 3, 112, 112]);
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const recSess = await getRecSession();
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const recFeeds = {}; recFeeds[recSess.inputNames[0]] = recTensor;
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const recOut = await recSess.run(recFeeds);
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return {
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embedding: Array.from(recOut[recSess.outputNames[0]].data),
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face_confidence: parseFloat(bestScore.toFixed(4)),
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bbox: { x: x1, y: y1, width: x2 - x1, height: y2 - y1 }
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};
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}
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function cosineSimilarity(a, b) {
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let dot = 0, na = 0, nb = 0;
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for (let i = 0; i < a.length; i++) {
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dot += a[i] * b[i];
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na += a[i] * a[i];
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nb += b[i] * b[i];
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}
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return dot / (Math.sqrt(na) * Math.sqrt(nb) + 1e-6);
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}
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router.post('/', requireAuth, async (req, res) => {
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const requestId = genReqId();
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const start = Date.now();
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try {
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const { image_url, image_base64, track_id, track_type = 'face' } = req.body || {};
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if (!track_id) return res.status(400).json({ ok: false, error: 'track_id required' });
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const img = await fetchImage({ image_url, image_base64 });
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const inputHash = hashInput(img.buffer);
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// Get face embedding for tracking
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const faceData = await getFaceEmbedding(img.buffer);
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if (!faceData) {
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return res.status(400).json({ ok: false, error: 'No face detected for tracking', track_id });
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}
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// Save tracking event
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await dbPool.query(
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`CREATE TABLE IF NOT EXISTS aamos_tracking (
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id SERIAL PRIMARY KEY,
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track_id VARCHAR(128) NOT NULL,
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track_type VARCHAR(32) NOT NULL,
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request_id VARCHAR(64) NOT NULL,
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embedding VECTOR(512),
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face_confidence NUMERIC(5,4),
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bbox JSONB,
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input_hash VARCHAR(64),
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created_at TIMESTAMPTZ DEFAULT NOW()
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)`
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).catch(() => {}); // Ignore if exists or pgvector not available
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// Try to insert without vector type first
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try {
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await dbPool.query(
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`INSERT INTO aamos_tracking (track_id, track_type, request_id, embedding, face_confidence, bbox, input_hash)
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VALUES ($1,$2,$3,$4,$5,$6,$7)`,
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[track_id, track_type, requestId, JSON.stringify(faceData.embedding), faceData.face_confidence, JSON.stringify(faceData.bbox), inputHash]
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);
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} catch (dbErr) {
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// Fallback: create simple table without vector
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await dbPool.query(
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`CREATE TABLE IF NOT EXISTS aamos_tracking_simple (
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id SERIAL PRIMARY KEY,
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track_id VARCHAR(128) NOT NULL,
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track_type VARCHAR(32) NOT NULL,
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request_id VARCHAR(64) NOT NULL,
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face_confidence NUMERIC(5,4),
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bbox JSONB,
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input_hash VARCHAR(64),
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created_at TIMESTAMPTZ DEFAULT NOW()
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)`
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);
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await dbPool.query(
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`INSERT INTO aamos_tracking_simple (track_id, track_type, request_id, face_confidence, bbox, input_hash)
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VALUES ($1,$2,$3,$4,$5,$6)`,
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[track_id, track_type, requestId, faceData.face_confidence, JSON.stringify(faceData.bbox), inputHash]
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);
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}
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// Find previous tracking events for this track_id
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let previousEvents = [];
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try {
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const { rows } = await dbPool.query(
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`SELECT request_id, face_confidence, bbox, created_at
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FROM aamos_tracking_simple
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WHERE track_id=$1 AND request_id!=$2
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ORDER BY created_at DESC LIMIT 5`,
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[track_id, requestId]
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);
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previousEvents = rows;
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} catch {}
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// Calculate similarity with previous if available
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let similarity = null;
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if (previousEvents.length > 0 && previousEvents[0].embedding) {
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try {
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const prevEmb = JSON.parse(previousEvents[0].embedding);
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similarity = parseFloat(cosineSimilarity(faceData.embedding, prevEmb).toFixed(4));
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} catch {}
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}
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const confidence = faceData.face_confidence;
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const result = {
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ok: true,
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endpoint: 'track',
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request_id: requestId,
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track_id,
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track_type,
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face_detected: true,
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face_confidence: faceData.face_confidence,
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bbox: faceData.bbox,
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previous_sightings: previousEvents.length,
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similarity_to_previous: similarity,
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tracking_status: similarity !== null ? (similarity > 0.7 ? 'confirmed_match' : 'possible_match') : 'new_tracking',
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inference_time_ms: Date.now() - start,
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};
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await saveResult('track', requestId, inputHash, result, confidence, { track_id, track_type, source: img.source });
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res.json(result);
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} catch (e) {
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console.error('[track]', e);
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res.status(500).json({ ok: false, error: e.message, request_id: requestId });
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}
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});
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export default router;
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