diff --git a/connectors/photo-exif/.env.example b/connectors/photo-exif/.env.example index 81dee23..4baf511 100644 --- a/connectors/photo-exif/.env.example +++ b/connectors/photo-exif/.env.example @@ -35,13 +35,19 @@ PHOTO_ROOT=/path/to/your/photos # PHOTO_EXIF_CAPTION_STATE_PATH=/home/you/.life-context/photo-exif-captions.json # --- face-worker.js (#53): local, offline face detection -> `pictured` entity hints --- -# Directory holding the face-api model weights (ssdMobilenetv1 + faceLandmark68Net + -# faceRecognitionNet). One-time download, fully offline after. Required to run `node face-worker.js` -# (scan). See README "Face detection" for the download step. +# Directory holding the ONNX face models (#268 — SCRFD detection + ArcFace recognition, buffalo_l +# set: det_10g.onnx + w600k_r50.onnx). One-time download, fully offline after. Required to run +# `node face-worker.js` (scan). See README "Face detection" for the download step. # FACE_MODELS_PATH=/home/you/.life-context/face-models +# Model filenames within FACE_MODELS_PATH, if you use different ones than buffalo_l's defaults. +# FACE_DET_MODEL=det_10g.onnx +# FACE_REC_MODEL=w600k_r50.onnx # Euclidean distance below which two face descriptors are treated as the same person (cluster). -# FACE_MATCH_THRESHOLD=0.6 +# Descriptors are L2-normalized 512-d ArcFace embeddings (#268): for unit vectors, +# euclidean_d = sqrt(2*(1-cosine)), so raise this to merge more aggressively, lower to split more. +# NOT comparable to the pre-#268 face-api value (0.6 there was a 128-d, non-normalized space). +# FACE_MATCH_THRESHOLD=1.0 # Confidence on emitted `pictured` hints (core caps name-type aliases at 0.9 regardless). # FACE_HINT_CONFIDENCE=0.6 # Delay between photos during a scan pass (ms); 0 = no throttle. @@ -53,9 +59,10 @@ PHOTO_ROOT=/path/to/your/photos # `node face-worker.js suggest-labels` (#84): distance threshold for matching a contact's own # preserved photo (core's CONTACTS_RAW_DIR, from vCard PHOTO import) against an unlabeled # cluster centroid. Separate from FACE_MATCH_THRESHOLD — a posed contact photo and a candid -# camera-roll photo differ enough that cross-source matching may need a different threshold. -# Defaults to FACE_MATCH_THRESHOLD if unset. Only ever PRINTS suggestions — never writes a -# label. Requires this connector's process to be able to read the raw_path LifeContext returns -# (same machine or a shared/synced volume as core's CONTACTS_RAW_DIR — there is no endpoint to -# fetch the bytes over HTTP). See README "Face detection" for the suggest-labels workflow. -# FACE_SEED_THRESHOLD=0.6 +# camera-roll photo differ enough that cross-source matching may need a different (looser) +# threshold; defaults to 1.15 (~= cosine 0.34) rather than FACE_MATCH_THRESHOLD. Only ever PRINTS +# suggestions — never writes a label. Requires this connector's process to be able to read the +# raw_path LifeContext returns (same machine or a shared/synced volume as core's +# CONTACTS_RAW_DIR — there is no endpoint to fetch the bytes over HTTP). See README "Face +# detection" for the suggest-labels workflow. +# FACE_SEED_THRESHOLD=1.15 diff --git a/connectors/photo-exif/README.md b/connectors/photo-exif/README.md index 9fdc081..308d8d6 100644 --- a/connectors/photo-exif/README.md +++ b/connectors/photo-exif/README.md @@ -40,8 +40,8 @@ The roadmap flags this as an open question ("worker lives with core or alongside A third, local-first enrichment pass that links photos to *who is in them* via the contract's `pictured` `entity_hints` role (doc 04 §4). Detection runs entirely on-device — no cloud face API (Prime Directive: local-first). -1. Detects faces in each photo (local `@vladmandic/face-api` + `@tensorflow/tfjs-node`, models loaded from `FACE_MODELS_PATH`). -2. Clusters the 128-d face descriptors into anonymous groups by nearest-centroid (`FACE_MATCH_THRESHOLD`). **Descriptors never leave the machine** — they live only in the local clusters file; doc 04 §11 forbids connectors sending embeddings. +1. Detects faces in each photo (local `onnxruntime-node` + `sharp` — SCRFD detection + ArcFace recognition, buffalo_l ONNX models loaded from `FACE_MODELS_PATH`; #268 swapped this off `@tensorflow/tfjs-node`/`@vladmandic/face-api`/`canvas`, which have no prebuilt Windows binary for current Node and need a VS C++ workload to build from source). +2. Clusters the 512-d, L2-normalized ArcFace face descriptors into anonymous groups by nearest-centroid (`FACE_MATCH_THRESHOLD`). **Descriptors never leave the machine** — they live only in the local clusters file; doc 04 §11 forbids connectors sending embeddings. 3. Records `extra.faces_detected` on every scanned photo. A photo whose faces are all in *unlabeled* clusters gets **no** `pictured` hint (its `text_repr` is re-sent as base + caption, reconstructed from the caption cache, so captioning is preserved, not extended). 4. Once you name a cluster (`label`), every photo containing it upserts `entity_hints: [{alias, alias_type:"name", role:"pictured", confidence}]` and a `text_repr` with a "Pictured: …" sentence appended to the base + caption text. @@ -65,15 +65,15 @@ node face-worker.js suggest-labels # photo-exif: suggest-labels — checked 18 contact photo(s), 1 cluster(s) suggested ``` -**Requires this connector's process to be able to read the file path LifeContext core returns** (`raw_path`, under core's `CONTACTS_RAW_DIR`) — i.e., this connector and core must share a filesystem (same machine, or a mounted/synced volume). There is no endpoint to fetch the raw bytes over HTTP; a `raw_path` this process can't read is skipped and logged, not fatal to the run. If `CONTACTS_RAW_DIR` is a relative path in core's `.env`, set it to an **absolute** path there for reliable resolution — a relative path resolves against whatever directory core's own process happened to start from, which this connector has no way to know. Already-labeled clusters are never re-suggested, and a reference photo with zero or multiple detected faces is skipped as ambiguous. Tune the match distance with `FACE_SEED_THRESHOLD` (defaults to `FACE_MATCH_THRESHOLD`) — see `.env.example`. +**Requires this connector's process to be able to read the file path LifeContext core returns** (`raw_path`, under core's `CONTACTS_RAW_DIR`) — i.e., this connector and core must share a filesystem (same machine, or a mounted/synced volume). There is no endpoint to fetch the raw bytes over HTTP; a `raw_path` this process can't read is skipped and logged, not fatal to the run. If `CONTACTS_RAW_DIR` is a relative path in core's `.env`, set it to an **absolute** path there for reliable resolution — a relative path resolves against whatever directory core's own process happened to start from, which this connector has no way to know. Already-labeled clusters are never re-suggested, and a reference photo with zero or multiple detected faces is skipped as ambiguous. Tune the match distance with `FACE_SEED_THRESHOLD` (defaults to `1.15`, looser than `FACE_MATCH_THRESHOLD`) — see `.env.example`. ## Setup 1. `cp .env.example .env` and fill in `LIFECONTEXT_URL` / `LIFECONTEXT_API_KEY` / `PHOTO_ROOT`. -2. `npm install` (real dependency: `exifr`; the face worker additionally needs `@vladmandic/face-api`, `@tensorflow/tfjs-node`, and `canvas` — native modules, only required if you run `face-worker.js`). +2. `npm install` (real dependency: `exifr`; the face worker additionally needs `onnxruntime-node` and `sharp` — both ship prebuilt binaries, no native compile step, only required if you run `face-worker.js`). 3. Backfill: `node scan.js`. 4. Optionally, once you have a vision model pulled in Ollama (`ollama pull llava`): `node caption-worker.js`. -5. Optionally, for face → contact linking: download the face-api model weights into `FACE_MODELS_PATH` (the `ssdMobilenetv1`, `faceLandmark68Net`, and `faceRecognitionNet` weight files from the `@vladmandic/face-api` model repo — one-time, offline after), then `node face-worker.js`, browse clusters with `export-thumbnails`, and `label` the ones you recognize. +5. Optionally, for face → contact linking: download the buffalo_l ONNX model pair into `FACE_MODELS_PATH` — `det_10g.onnx` (SCRFD detection) and `w600k_r50.onnx` (ArcFace recognition), from the InsightFace model zoo, one-time and fully offline after — then `node face-worker.js`, browse clusters with `export-thumbnails`, and `label` the ones you recognize. ### Prep a Takeout download (Windows) — `prep-takeout.ps1` @@ -141,8 +141,8 @@ On Windows, use Task Scheduler with a "Daily, 1:00 AM" trigger running `node cap - **Clustering is approximate** — nearest-centroid with a fixed Euclidean threshold, not a trained recognizer. Expect occasional split clusters (same person, two buckets) or, rarely, a merged one; `export-thumbnails` + re-`label` is the correction path. - **`export-thumbnails` writes the sample *image*, not a tight face crop** — a real crop would pull in the native image-processing stack at export time. Per-face bounding boxes aren't persisted today (the clusters file stores only centroid/count/label/sample), so a future cropped version would need to re-detect the sample image or start persisting boxes. -- **The ML stack is unverified in this repo's CI** — `test.mjs` covers the full clustering/label/ingest pipeline with an injected fixture detector (no models), so the wire behavior is tested, but real `face-api` detection quality/latency is a manual, on-device concern (same posture as the VLM caption worker). -- **Native dependencies** — `@tensorflow/tfjs-node` and `canvas` are native modules; they're only loaded (via dynamic import) when you actually run a scan, so the other two scripts and the test suite need none of them. +- **The ML stack is unverified in this repo's CI** — `test.mjs` covers the full clustering/label/ingest pipeline with an injected fixture detector (no models), so the wire behavior is tested, but real ONNX detection/recognition quality/latency is a manual, on-device concern (same posture as the VLM caption worker). The pure decode/alignment math (`lib/face-align.js`) — SCRFD box/keypoint decode, NMS, and the Umeyama similarity + bilinear warp used to align each face crop — **is** unit-tested without any model. +- **Native dependencies** — `onnxruntime-node` and `sharp` ship prebuilt binaries for current Node/Windows (no VS C++ workload, unlike the tfjs-node stack this replaced, #268); they're only loaded (via dynamic import) when you actually run a scan, so the other two scripts and the test suite need none of them. - **`suggest-labels` requires a shared filesystem with core (#84)** — it reads `raw_path` values LifeContext core returns; there's no HTTP endpoint to fetch those bytes, so this connector must be able to read the same disk (or a mounted/synced volume) core wrote contact photos to. Cross-machine setups (this connector on a different host than core, e.g. the Mac Mini/Windows-server iMessage topology) aren't supported for this command specifically — everything else in this connector works unchanged in that topology. ## Files @@ -155,6 +155,7 @@ On Windows, use Task Scheduler with a "Daily, 1:00 AM" trigger running `node cap - `lib/describe.js` — shared EXIF + Takeout-sidecar description logic (`describePhoto`/`readSidecar`/`sidecarPathFor`), used by every script so they can never drift - `lib/caption-cache.js` — caption state (relPath→text map) + `currentTextRepr`, shared by caption + face workers - `lib/face-cluster.js` — pure, IO-free descriptor clustering (euclidean, nearest-centroid, (de)serialization) +- `lib/face-align.js` — pure, IO-free SCRFD decode (distance2bbox/kps, anchor centers, NMS) + Umeyama similarity/bilinear warp used to align each 112x112 ArcFace input crop (#268) - `lib/face-detect.js` — lazy ML-model detector + the test fixture detector - `test.mjs` — `node --test` suite - `.env.example` — copy to `.env` diff --git a/connectors/photo-exif/face-worker.js b/connectors/photo-exif/face-worker.js index 0dca27a..2b344cd 100644 --- a/connectors/photo-exif/face-worker.js +++ b/connectors/photo-exif/face-worker.js @@ -38,8 +38,11 @@ const FACE_STATE_PATH = process.env.PHOTO_EXIF_FACE_STATE_PATH || HOME_STATE('ph const CLUSTERS_PATH = process.env.PHOTO_EXIF_FACE_CLUSTERS_PATH || HOME_STATE('photo-exif-face-clusters.json'); const CAPTION_STATE_PATH = process.env.PHOTO_EXIF_CAPTION_STATE_PATH || HOME_STATE('photo-exif-captions.json'); // Number()-with-isFinite so an explicit 0 isn't overridden by a `|| default` (0 is falsy). +// Euclidean thresholds for L2-normalized 512-d ArcFace descriptors (#268) — NOT comparable to the +// old 128-d face-api value (0.6). For unit vectors, euclidean d = sqrt(2*(1-cos)), so raising the +// threshold merges more aggressively, lowering it splits more aggressively. const matchRaw = Number(process.env.FACE_MATCH_THRESHOLD); -const FACE_MATCH_THRESHOLD = Number.isFinite(matchRaw) ? matchRaw : 0.6; +const FACE_MATCH_THRESHOLD = Number.isFinite(matchRaw) ? matchRaw : 1.0; // ~= cosine 0.50 const confRaw = Number(process.env.FACE_HINT_CONFIDENCE); const FACE_HINT_CONFIDENCE = Number.isFinite(confRaw) ? confRaw : 0.6; const throttleRaw = Number(process.env.FACE_THROTTLE_MS); @@ -50,7 +53,7 @@ const FACE_THROTTLE_MS = Number.isFinite(throttleRaw) ? throttleRaw : 0; // cross-source matching may warrant a different threshold. Defaults to the same value so // behavior is a pure addition until tuned. const seedRaw = Number(process.env.FACE_SEED_THRESHOLD); -const FACE_SEED_THRESHOLD = Number.isFinite(seedRaw) ? seedRaw : FACE_MATCH_THRESHOLD; +const FACE_SEED_THRESHOLD = Number.isFinite(seedRaw) ? seedRaw : 1.15; // ~= cosine 0.34, looser than match const sleep = (ms) => new Promise((resolve) => setTimeout(resolve, ms)); const requireApiKey = () => { diff --git a/connectors/photo-exif/lib/face-align.js b/connectors/photo-exif/lib/face-align.js new file mode 100644 index 0000000..5deefaf --- /dev/null +++ b/connectors/photo-exif/lib/face-align.js @@ -0,0 +1,214 @@ +// Pure, IO-free math for ONNX face detection/alignment (SCRFD decode + Umeyama similarity + +// bilinear warp). No ML runtime, no disk, no network — unit-testable without any model file. +// Mirrors the public InsightFace reference algorithms (scrfd.py / face_align.py) closely enough +// that model outputs decode the same way; only the language changed. + +// Standard 112x112 ArcFace 5-point reference template (insightface `arcface_dst`), in +// (leye, reye, nose, lmouth, rmouth) order — the same order SCRFD's keypoint head emits. +export const DST_112 = [ + [38.2946, 51.6963], + [73.5318, 51.5014], + [56.0252, 71.7366], + [41.5493, 92.3655], + [70.7299, 92.2041], +]; + +// --- SCRFD box/keypoint decode ----------------------------------------------------------------- + +// anchor center point + 4 edge distances (l,t,r,b) -> [x1,y1,x2,y2], both in feature-map pixels. +export function distance2bbox(point, distance) { + const [px, py] = point; + return [px - distance[0], py - distance[1], px + distance[2], py + distance[3]]; +} + +// anchor center point + 10 keypoint offsets (dx0,dy0,...,dx4,dy4) -> 5 [x,y] keypoints. +export function distance2kps(point, distance) { + const [px, py] = point; + const kps = []; + for (let i = 0; i < distance.length; i += 2) { + kps.push([px + distance[i], py + distance[i + 1]]); + } + return kps; +} + +// SCRFD anchor centers for one FPN stride level: a (width/stride * height/stride) grid, x fastest, +// each cell repeated `numAnchors` times consecutively (buffalo_l det_10g uses 2 anchors/cell) — +// matches scrfd.py's center_cache layout so the flat model output indexes align. +export function generateAnchorCenters(height, width, stride, numAnchors = 2) { + const gh = Math.floor(height / stride); + const gw = Math.floor(width / stride); + const centers = []; + for (let y = 0; y < gh; y++) { + for (let x = 0; x < gw; x++) { + for (let a = 0; a < numAnchors; a++) centers.push([x * stride, y * stride]); + } + } + return centers; +} + +// Greedy NMS over {box:[x1,y1,x2,y2], score} candidates (already-decoded, original-image px). +// Pixel-inclusive area (+1), matching the reference SCRFD implementation's convention. +export function nms(boxes, iouThreshold) { + const order = boxes.map((_, i) => i).sort((a, b) => boxes[b].score - boxes[a].score); + const area = (b) => Math.max(0, b.box[2] - b.box[0] + 1) * Math.max(0, b.box[3] - b.box[1] + 1); + const areas = boxes.map(area); + const keep = []; + const suppressed = new Set(); + for (const i of order) { + if (suppressed.has(i)) continue; + keep.push(i); + for (const j of order) { + if (j === i || suppressed.has(j)) continue; + const [ax1, ay1, ax2, ay2] = boxes[i].box; + const [bx1, by1, bx2, by2] = boxes[j].box; + const xx1 = Math.max(ax1, bx1); + const yy1 = Math.max(ay1, by1); + const xx2 = Math.min(ax2, bx2); + const yy2 = Math.min(ay2, by2); + const w = Math.max(0, xx2 - xx1 + 1); + const h = Math.max(0, yy2 - yy1 + 1); + const inter = w * h; + const iou = inter / (areas[i] + areas[j] - inter); + if (iou > iouThreshold) suppressed.add(j); + } + } + return keep; +} + +// --- 2x2 SVD (inline, no BLAS/dep) -------------------------------------------------------------- + +// Eigen-decomposition of a symmetric 2x2 matrix [[m11,m12],[m12,m22]]: closed-form via the +// quadratic formula, robust to the axis-aligned case (m12===0) by picking whichever of the two +// candidate eigenvector formulas has the larger norm. +function eigSym2x2(m11, m12, m22) { + const tr = m11 + m22; + const det = m11 * m22 - m12 * m12; + const disc = Math.sqrt(Math.max(0, (tr * tr) / 4 - det)); + const l1 = tr / 2 + disc; + const l2 = tr / 2 - disc; + const pick = (l) => { + const a = [m12, l - m11]; + const b = [l - m22, m12]; + const na = Math.hypot(a[0], a[1]); + const nb = Math.hypot(b[0], b[1]); + if (na < 1e-12 && nb < 1e-12) return [1, 0]; // fully degenerate (scalar) matrix + const v = na >= nb ? a : b; + const n = Math.hypot(v[0], v[1]); + return [v[0] / n, v[1] / n]; + }; + const v1 = pick(l1); + const v2 = [-v1[1], v1[0]]; // orthogonal complement + return { values: [l1, l2], vectors: [v1, v2] }; +} + +// SVD of a general 2x2 matrix A (row-major [[a,b],[c,d]]) via eigendecomposition of A^T A. +// Returns { u: [[..],[..]], s: [s1,s2], v: [[..],[..]] } (columns of u/v) such that A = U*diag(S)*V^T. +function svd2x2([[a, b], [c, d]]) { + const m11 = a * a + c * c; + const m12 = a * b + c * d; + const m22 = b * b + d * d; + const { values, vectors } = eigSym2x2(m11, m12, m22); + const s = values.map((l) => Math.sqrt(Math.max(0, l))); + const uCols = vectors.map((v, i) => { + const av = [a * v[0] + b * v[1], c * v[0] + d * v[1]]; + if (s[i] > 1e-12) return [av[0] / s[i], av[1] / s[i]]; + return i === 0 ? [1, 0] : null; // filled below once u0 is known + }); + if (uCols[1] === null) uCols[1] = [-uCols[0][1], uCols[0][0]]; + // v/u as column-major 2x2 matrices [[v0x,v1x],[v0y,v1y]] + const V = [[vectors[0][0], vectors[1][0]], [vectors[0][1], vectors[1][1]]]; + const U = [[uCols[0][0], uCols[1][0]], [uCols[0][1], uCols[1][1]]]; + return { u: U, s, v: V }; +} + +const det2x2 = ([[a, b], [c, d]]) => a * d - b * c; + +// --- Umeyama similarity transform --------------------------------------------------------------- + +// Least-squares similarity transform (scale + rotation + translation, no reflection) mapping +// `src` points onto `dst` points (both arrays of [x,y], same length, dim=2). Returns a 2x3 affine +// matrix [[a,b,tx],[c,d,ty]] such that dst ≈ M @ [srcX, srcY, 1]. Reference: Umeyama (1991), the +// same algorithm scikit-image's SimilarityTransform / insightface's face_align.py use. +export function umeyama(src, dst) { + const n = src.length; + const mean = (pts) => [pts.reduce((s, p) => s + p[0], 0) / n, pts.reduce((s, p) => s + p[1], 0) / n]; + const srcMean = mean(src); + const dstMean = mean(dst); + const srcDemean = src.map((p) => [p[0] - srcMean[0], p[1] - srcMean[1]]); + const dstDemean = dst.map((p) => [p[0] - dstMean[0], p[1] - dstMean[1]]); + + let varSrc = 0; + for (const [x, y] of srcDemean) varSrc += x * x + y * y; + varSrc /= n; + + let a00 = 0, a01 = 0, a10 = 0, a11 = 0; + for (let i = 0; i < n; i++) { + const [sx, sy] = srcDemean[i]; + const [dx, dy] = dstDemean[i]; + a00 += dx * sx; a01 += dx * sy; + a10 += dy * sx; a11 += dy * sy; + } + const A = [[a00 / n, a01 / n], [a10 / n, a11 / n]]; + + const { u, s, v } = svd2x2(A); + const d = [1, 1]; + if (det2x2(A) < 0) d[1] = -1; + // R = U @ diag(d) @ V^T + const Vt = [[v[0][0], v[1][0]], [v[0][1], v[1][1]]]; + const UD = [[u[0][0] * d[0], u[0][1] * d[1]], [u[1][0] * d[0], u[1][1] * d[1]]]; + const R = [ + [UD[0][0] * Vt[0][0] + UD[0][1] * Vt[1][0], UD[0][0] * Vt[0][1] + UD[0][1] * Vt[1][1]], + [UD[1][0] * Vt[0][0] + UD[1][1] * Vt[1][0], UD[1][0] * Vt[0][1] + UD[1][1] * Vt[1][1]], + ]; + + const scale = varSrc > 1e-12 ? (s[0] * d[0] + s[1] * d[1]) / varSrc : 1; + const Rs = [[R[0][0] * scale, R[0][1] * scale], [R[1][0] * scale, R[1][1] * scale]]; + const tx = dstMean[0] - (Rs[0][0] * srcMean[0] + Rs[0][1] * srcMean[1]); + const ty = dstMean[1] - (Rs[1][0] * srcMean[0] + Rs[1][1] * srcMean[1]); + return [[Rs[0][0], Rs[0][1], tx], [Rs[1][0], Rs[1][1], ty]]; +} + +// Invert a 2x3 affine matrix [[a,b,c],[d,e,f]] (maps [x,y,1] -> [x',y']) so it maps back. +export function invertAffine([[a, b, c], [d, e, f]]) { + const det = a * e - b * d; + if (Math.abs(det) < 1e-12) throw new Error('face-align: affine matrix is not invertible'); + return [ + [e / det, -b / det, (b * f - e * c) / det], + [-d / det, a / det, (d * c - a * f) / det], + ]; +} + +export function applyAffine([[a, b, c], [d, e, f]], x, y) { + return [a * x + b * y + c, d * x + e * y + f]; +} + +// Warp the aligned 112x112 ArcFace input crop out of a decoded RGB image (Uint8Array/Array, HWC, +// 3 channels) via the inverse of the src->DST_112 similarity transform, bilinear-sampled, and +// return it pre-normalized to NCHW float32 [1,3,112,112] with (px-127.5)/127.5. +export function warpTo112(rgb, width, height, landmarks) { + const M = umeyama(landmarks, DST_112); + const Minv = invertAffine(M); + const SIZE = 112; + const out = new Float32Array(3 * SIZE * SIZE); + const plane = SIZE * SIZE; + const sample = (x, y, ch) => { + if (x < 0 || y < 0 || x > width - 1 || y > height - 1) return 0; + const x0 = Math.floor(x), y0 = Math.floor(y); + const x1 = Math.min(x0 + 1, width - 1), y1 = Math.min(y0 + 1, height - 1); + const fx = x - x0, fy = y - y0; + const at = (xx, yy) => rgb[(yy * width + xx) * 3 + ch]; + const top = at(x0, y0) * (1 - fx) + at(x1, y0) * fx; + const bot = at(x0, y1) * (1 - fx) + at(x1, y1) * fx; + return top * (1 - fy) + bot * fy; + }; + for (let y = 0; y < SIZE; y++) { + for (let x = 0; x < SIZE; x++) { + const [sx, sy] = applyAffine(Minv, x + 0.5, y + 0.5); + const idx = y * SIZE + x; + for (let ch = 0; ch < 3; ch++) { + out[ch * plane + idx] = (sample(sx, sy, ch) - 127.5) / 127.5; + } + } + } + return out; +} diff --git a/connectors/photo-exif/lib/face-cluster.js b/connectors/photo-exif/lib/face-cluster.js index 403ed0e..c9c02d4 100644 --- a/connectors/photo-exif/lib/face-cluster.js +++ b/connectors/photo-exif/lib/face-cluster.js @@ -3,6 +3,10 @@ // testable directly and the face worker owns all IO. Descriptors never leave the connector // (doc 04 §11: core rejects connector-supplied embeddings); only human-assigned names ever go // on the wire, as `pictured` hints. +// +// Descriptors are L2-normalized 512-d ArcFace embeddings (face-detect.js), so Euclidean distance +// on them is monotonic with cosine distance (d = sqrt(2*(1-cos))) — nearest-centroid clustering +// below is unaffected by the model swap; only the threshold VALUE changes (see face-worker.js). export function euclideanDistance(a, b) { let sum = 0; diff --git a/connectors/photo-exif/lib/face-detect.js b/connectors/photo-exif/lib/face-detect.js index 786aba0..68a23a3 100644 --- a/connectors/photo-exif/lib/face-detect.js +++ b/connectors/photo-exif/lib/face-detect.js @@ -1,9 +1,11 @@ -// Local, offline face detection. The heavy ML stack (@vladmandic/face-api + @tensorflow/tfjs-node -// + canvas) is loaded via DYNAMIC import only when a real detection actually runs, so importing -// this module (or the face worker) costs nothing and needs none of those packages present unless -// you actually scan — and the test suite injects a fixture detector instead (below), never -// touching ML. Models load once from FACE_MODELS_PATH (one-time download; fully offline after). +// Local, offline face detection. The heavy ML stack (onnxruntime-node + sharp) is loaded via +// DYNAMIC import only when a real detection actually runs, so importing this module (or the face +// worker) costs nothing and needs neither package present unless you actually scan — and the test +// suite injects a fixture detector instead (below), never touching ML. Models load once from +// FACE_MODELS_PATH (one-time download; fully offline after). import { readFileSync } from 'node:fs'; +import path from 'node:path'; +import { distance2bbox, distance2kps, generateAnchorCenters, nms, warpTo112 } from './face-align.js'; // Test seam: PHOTO_EXIF_FACE_FIXTURE points at a JSON file mapping relPath -> an array of faces, // each face either a bare descriptor array or { box, descriptor }. When set, detection returns @@ -19,26 +21,130 @@ export function fixtureDetector(fixturePath) { }; } -// Real detector: lazy-load the ML stack, load models once, return detectFaces(absPath). Unverified -// in this repo's CI (no models/GPU here) — same posture as the VLM caption path; behavior is -// covered by the fixture detector, model quality/latency is a manual, on-device concern. +// SCRFD (buffalo_l det_10g) detection constants — the model's fixed export shape, not tunable. +const DET_INPUT_SIZE = 640; +const DET_SCORE_THRESHOLD = 0.5; +const DET_NMS_IOU = 0.4; +const DET_STRIDES = [8, 16, 32]; +const DET_NUM_ANCHORS = 2; +const DET_OUTPUT_COUNT = DET_STRIDES.length * 3; // scores + bboxes + kps per stride + +// One decode pipeline, cloned for two outputs (full-res RGB for alignment + a letterboxed 640x640 +// blob for detection) so a photo is only read/decoded off disk once, not twice. +function orientedRgbPipeline(sharp, absPath) { + return sharp(absPath).rotate().toColourspace('srgb').removeAlpha(); +} + +// Full-res 3-channel sRGB raw pixels (HWC, uint8), EXIF-oriented — the source `warpTo112` aligns +// crops out of. +async function decodeRgb(pipeline) { + const { data, info } = await pipeline.clone().raw().toBuffer({ resolveWithObject: true }); + return { data, width: info.width, height: info.height }; +} + +// Letterbox: isotropic resize to fit DET_INPUT_SIZE, zero-padded at the bottom/right (top-left +// aligned, so mapping detections back to original px is a pure division by `scale`, no offset). +// The pad value is normalized like every other pixel (cv2.dnn.blobFromImage's convention: the +// whole canvas — including padding — runs through (px-127.5)/128), not left as a raw 0. +async function letterbox(pipeline, width, height) { + const scale = Math.min(DET_INPUT_SIZE / width, DET_INPUT_SIZE / height); + const rw = Math.max(1, Math.round(width * scale)); + const rh = Math.max(1, Math.round(height * scale)); + const { data } = await pipeline.clone().resize(rw, rh, { fit: 'fill' }).raw().toBuffer({ resolveWithObject: true }); + const blob = new Float32Array(3 * DET_INPUT_SIZE * DET_INPUT_SIZE); + blob.fill((0 - 127.5) / 128); // pad value in normalized space, not raw 0 + const plane = DET_INPUT_SIZE * DET_INPUT_SIZE; + for (let y = 0; y < rh; y++) { + for (let x = 0; x < rw; x++) { + const srcIdx = (y * rw + x) * 3; + const dstIdx = y * DET_INPUT_SIZE + x; + for (let ch = 0; ch < 3; ch++) { + blob[ch * plane + dstIdx] = (data[srcIdx + ch] - 127.5) / 128; + } + } + } + return { blob, scale }; +} + +// Run SCRFD on the letterboxed blob, decode all 3 stride heads, NMS, then scale boxes/kps back to +// original-image px (a pure division — the letterbox padding never offsets the top-left corner). +async function detectAndDecode(ort, detSession, blob, scale) { + const inputName = detSession.inputNames[0]; + const feeds = { [inputName]: new ort.Tensor('float32', blob, [1, 3, DET_INPUT_SIZE, DET_INPUT_SIZE]) }; + const results = await detSession.run(feeds); + const outputs = detSession.outputNames.map((name) => results[name].data); + + const candidates = []; + DET_STRIDES.forEach((stride, idx) => { + const scores = outputs[idx]; + const bboxPreds = outputs[idx + DET_STRIDES.length]; + const kpsPreds = outputs[idx + DET_STRIDES.length * 2]; + const anchors = generateAnchorCenters(DET_INPUT_SIZE, DET_INPUT_SIZE, stride, DET_NUM_ANCHORS); + for (let i = 0; i < anchors.length; i++) { + const score = scores[i]; + if (score < DET_SCORE_THRESHOLD) continue; + const bd = [bboxPreds[i * 4] * stride, bboxPreds[i * 4 + 1] * stride, bboxPreds[i * 4 + 2] * stride, bboxPreds[i * 4 + 3] * stride]; + const kd = []; + for (let k = 0; k < 10; k++) kd.push(kpsPreds[i * 10 + k] * stride); + candidates.push({ box: distance2bbox(anchors[i], bd), kps: distance2kps(anchors[i], kd), score }); + } + }); + + const keep = nms(candidates, DET_NMS_IOU); + return keep.map((i) => { + const c = candidates[i]; + return { + box: c.box.map((v) => v / scale), + kps: c.kps.map(([x, y]) => [x / scale, y / scale]), + }; + }); +} + +export function l2Normalize(vec) { + let sumSq = 0; + for (const v of vec) sumSq += v * v; + const norm = Math.sqrt(sumSq); + if (!Number.isFinite(norm) || norm < 1e-12) return null; + return Array.from(vec, (v) => v / norm); +} + +// Real detector: lazy-load onnxruntime-node + sharp, load the SCRFD (detection) + ArcFace +// (recognition) ONNX sessions once, return detectFaces(absPath). Unverified in this repo's CI (no +// models here) — same posture as the VLM caption path; behavior is covered by the fixture +// detector, model quality/latency is a manual, on-device concern. export async function loadModelDetector(modelsPath) { if (!modelsPath) throw new Error('FACE_MODELS_PATH not set (required for face detection)'); - // These are CJS packages; a dynamic import wraps their module.exports under `.default`, so fall - // back to the namespace only if there's no default (keeps working whichever way Node resolves it). - const faceapiMod = await import('@vladmandic/face-api'); - const faceapi = faceapiMod.default ?? faceapiMod; - const canvasMod = await import('canvas'); - const { Canvas, Image, ImageData, loadImage } = canvasMod.default ?? canvasMod; - await import('@tensorflow/tfjs-node'); // registers the native backend as a side effect - faceapi.env.monkeyPatch({ Canvas, Image, ImageData }); - await faceapi.nets.ssdMobilenetv1.loadFromDisk(modelsPath); - await faceapi.nets.faceLandmark68Net.loadFromDisk(modelsPath); - await faceapi.nets.faceRecognitionNet.loadFromDisk(modelsPath); + const ortMod = await import('onnxruntime-node'); + const ort = ortMod.default ?? ortMod; + const sharpMod = await import('sharp'); + const sharp = sharpMod.default ?? sharpMod; + + const detModelFile = process.env.FACE_DET_MODEL || 'det_10g.onnx'; + const recModelFile = process.env.FACE_REC_MODEL || 'w600k_r50.onnx'; + const detSession = await ort.InferenceSession.create(path.join(modelsPath, detModelFile), { executionProviders: ['cpu'] }); + const recSession = await ort.InferenceSession.create(path.join(modelsPath, recModelFile), { executionProviders: ['cpu'] }); + if (detSession.outputNames.length !== DET_OUTPUT_COUNT) { + throw new Error(`face-detect: ${detModelFile} has ${detSession.outputNames.length} outputs, expected ${DET_OUTPUT_COUNT} (3 strides x scores/bboxes/kps)`); + } + return async function detectFaces(absPath) { - const img = await loadImage(absPath); - const results = await faceapi.detectAllFaces(img).withFaceLandmarks().withFaceDescriptors(); - return results.map((r) => ({ box: r.detection.box, descriptor: Array.from(r.descriptor) })); + const pipeline = orientedRgbPipeline(sharp, absPath); + const { data: rgb, width, height } = await decodeRgb(pipeline); + const { blob, scale } = await letterbox(pipeline, width, height); + const faces = await detectAndDecode(ort, detSession, blob, scale); + + const out = []; + for (const face of faces) { + const aligned = warpTo112(rgb, width, height, face.kps); + const recInputName = recSession.inputNames[0]; + const recResults = await recSession.run({ [recInputName]: new ort.Tensor('float32', aligned, [1, 3, 112, 112]) }); + const embedding = recResults[recSession.outputNames[0]].data; + const descriptor = l2Normalize(embedding); + if (!descriptor) continue; // zero-norm/NaN embedding: skip this face, not the image + const [x1, y1, x2, y2] = face.box; + out.push({ box: { x: x1, y: y1, width: x2 - x1, height: y2 - y1 }, descriptor }); + } + return out; }; } diff --git a/connectors/photo-exif/package-lock.json b/connectors/photo-exif/package-lock.json index d8e861f..58f76cd 100644 --- a/connectors/photo-exif/package-lock.json +++ b/connectors/photo-exif/package-lock.json @@ -8,1752 +8,865 @@ "name": "life-context-connector-photo-exif", "version": "0.1.0", "dependencies": { - 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"@tensorflow/tfjs-node": "^4.22.0", - "@vladmandic/face-api": "^1.7.15", - "canvas": "^3.1.0", - "exifr": "^7.1.3" + "exifr": "^7.1.3", + "onnxruntime-node": "^1.27.0", + "sharp": "^0.35.3" }, "devDependencies": { "piexifjs": "^1.0.6" }, "engines": { - "node": ">=18.12.0" + "node": ">=20.9.0" } } diff --git a/connectors/photo-exif/test.mjs b/connectors/photo-exif/test.mjs index 00a19f4..ff3cc7f 100644 --- a/connectors/photo-exif/test.mjs +++ b/connectors/photo-exif/test.mjs @@ -12,8 +12,10 @@ import http from 'node:http'; import piexif from 'piexifjs'; import { euclideanDistance, assignCluster, parseClustersFile, serializeClustersFile } from './lib/face-cluster.js'; import { readCaptionCache, writeCaptionCache, currentTextRepr } from './lib/caption-cache.js'; +import { umeyama, invertAffine, applyAffine, distance2bbox, distance2kps, generateAnchorCenters, nms, warpTo112, DST_112 } from './lib/face-align.js'; +import { l2Normalize } from './lib/face-detect.js'; -const __dirname = path.dirname(fileURLToPath(import.meta.url)); // import.meta.dirname needs Node 20.11+; this connector declares >=18 +const __dirname = path.dirname(fileURLToPath(import.meta.url)); // import.meta.dirname needs Node 20.11+; this connector declares >=20.9.0 (sharp's floor, #268) // source_id is now the content hash (keyForMedia): generic photos → source='photo-exif', // source_id=; Google-origin (the scan ROOT is a Takeout export — isTakeoutRoot, #176, NOT @@ -605,6 +607,67 @@ test('face-cluster: euclidean + nearest-centroid grouping and new-cluster creati assert.deepEqual(parseClustersFile('not json'), { version: 0, clusters: [] }); }); +test('face-align: umeyama recovers a known scale/rotation/translation and invertAffine round-trips', () => { + const angle = (25 * Math.PI) / 180; + const scale = 1.8; + const cos = Math.cos(angle), sin = Math.sin(angle); + const t = [15, -7]; + const src = [[0, 0], [10, 0], [5, 10], [2, 7], [8, 3]]; + const dst = src.map(([x, y]) => [scale * (cos * x - sin * y) + t[0], scale * (sin * x + cos * y) + t[1]]); + + const M = umeyama(src, dst); + for (let i = 0; i < src.length; i++) { + const [px, py] = applyAffine(M, src[i][0], src[i][1]); + assert.ok(Math.abs(px - dst[i][0]) < 1e-6 && Math.abs(py - dst[i][1]) < 1e-6, `point ${i} maps to dst`); + } + + const Minv = invertAffine(M); + const [rx, ry] = applyAffine(Minv, dst[0][0], dst[0][1]); + assert.ok(Math.abs(rx - src[0][0]) < 1e-6 && Math.abs(ry - src[0][1]) < 1e-6, 'invertAffine round-trips'); + + // umeyama against the real DST_112 template (5-point face alignment) also succeeds and is finite. + const M2 = umeyama(src.slice(0, 5), DST_112); + for (const row of M2) for (const v of row) assert.ok(Number.isFinite(v)); +}); + +test('face-align: SCRFD decode helpers (distance2bbox/kps, anchor grid, NMS)', () => { + assert.deepEqual(distance2bbox([10, 10], [2, 3, 4, 5]), [8, 7, 14, 15]); + assert.deepEqual(distance2kps([10, 10], [1, 1, 2, 2, 3, 3, 4, 4, 5, 5]), [[11, 11], [12, 12], [13, 13], [14, 14], [15, 15]]); + + const anchors = generateAnchorCenters(16, 16, 8, 2); + assert.equal(anchors.length, 8); // (16/8)^2 cells * 2 anchors/cell + assert.deepEqual(anchors.slice(0, 2), [[0, 0], [0, 0]]); // first cell, both anchors at the same center + + const boxes = [ + { box: [0, 0, 10, 10], score: 0.9 }, + { box: [1, 1, 11, 11], score: 0.8 }, // heavy overlap with the first -> suppressed + { box: [50, 50, 60, 60], score: 0.7 }, // disjoint -> kept + ]; + assert.deepEqual(nms(boxes, 0.3), [0, 2]); +}); + +test('face-align: warpTo112 produces a normalized 112x112 NCHW float32 crop', () => { + const width = 200, height = 150; + const rgb = new Uint8Array(width * height * 3).fill(200); + // 5 plausible face keypoints within the image (order doesn't matter for a shape/range check). + const kps = [[70, 60], [130, 60], [100, 90], [80, 120], [120, 120]]; + const out = warpTo112(rgb, width, height, kps); + assert.equal(out.length, 3 * 112 * 112); + for (const v of out) assert.ok(v >= -1 - 1e-6 && v <= 1 + 1e-6, 'normalized to [-1,1]'); + // uniform 200-value input warps to a uniform (200-127.5)/127.5 output, modulo edge zero-padding. + const expected = (200 - 127.5) / 127.5; + assert.ok(Math.abs(out[112 * 56 + 56] - expected) < 1e-6, 'center pixel matches the uniform fill'); +}); + +test('face-detect: l2Normalize produces a unit vector, and rejects a zero/degenerate vector', () => { + const v = l2Normalize([3, 4, 0]); + assert.equal(v.length, 3); + const norm = Math.sqrt(v.reduce((s, x) => s + x * x, 0)); + assert.ok(Math.abs(norm - 1) < 1e-9); + assert.equal(l2Normalize([0, 0, 0]), null); + assert.equal(l2Normalize([NaN, 0, 0]), null); +}); + test('caption-cache: legacy array read, map round-trip, currentTextRepr', () => { const tmp = mkdtempSync(path.join(tmpdir(), 'caption-cache-')); const p = path.join(tmp, 'captions.json');