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- /fotos/encontre + POST /api/fotos/selfie: só o vetor 128-d, sem imagem, sem score e sem nomes de terceiros; fail-closed pela flag photoAlbum.selfieSearchEnabled (default false) e pelos Consents por chave estável (busca-selfie-fotos/busca-selfie-indice, não seedados — a superfície nasce fechada até o aval jurídico/DPIA). - Índice A/C do gate (PR #1370): só quem aderiu é indexado; descriptors do acervo são transitórios. Collection faceSubject (admin-only, sem vínculo com Contact), vínculos matchedPhotos e marker faces.checkedKey no archivePhoto (modelo + enrolledAt). - Engine @vladmandic/face-api nos dois lados: browser lazy com modelos same-origin (copy-face-vision-assets no prebuild/predev/Docker) e CLI node-wasm sem tfjs-node. CLIs ops-only faces:enroll / faces:index --apply|--verify com guardas C231/C232. - Opt-out self-service por reconhecimento (leave-index) + canal de remoção do C233; rate limit próprio (namespace selfie:<hash>) e bounded body extraído do beacon. - UI portada classe-a-classe do design estendido (paridade certificada, tier primário); e2e frontendFotosSelfie (manifest+curado+projeto, engine stubada) e canário do engine real; runbook §C234, contrato em AGENTS-public.md e changelog. - Migration aditiva add_face_subject (SQL revisado); hard-stops aprovados na sessão.
84 lines
3.1 KiB
JavaScript
84 lines
3.1 KiB
JavaScript
/**
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* C234 — the Node face engine of the maintenance CLIs: the SAME face-api nets
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* the visitor's browser loads (`tinyFaceDetector` + `faceLandmark68Net` +
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* `faceRecognitionNet`), with the same options, so a descriptor computed by the
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* enrollment/index CLI is comparable with the one computed on the device. The
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* wasm backend is the package's own Node pairing (`face-api.node-wasm.js` +
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* `@tensorflow/tfjs-backend-wasm`) — no native `tfjs-node` build, no headless
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* browser, no network.
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*
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* The engine only turns prepared pixels into descriptors; the caller owns the
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* image preparation (`prepareFaceImage`) and the distance math
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* (`src/lib/faceSearch.ts`). It is a manual smoke dependency, never in the
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* test suite (the suites inject a stub analyzer).
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*/
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import { createRequire } from 'node:module'
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import path from 'node:path'
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const require = createRequire(import.meta.url)
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/**
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* Loads the nets once per process. Throws a named error when the wasm assets or
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* the models are missing — a broken install must fail before any DB write.
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*
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* @param {string} repoRoot
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*/
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export const createNodeFaceEngine = async (repoRoot) => {
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const { FACE_DETECTOR_INPUT_SIZE, FACE_DETECTOR_SCORE_THRESHOLD } =
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await import('../../src/lib/faceSearch.ts')
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let faceapi
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try {
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// The tfjs runtime is required explicitly first: it is the exact instance
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// the node-wasm build and the wasm backend register against (same module
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// cache), and it keeps the dependency honest for the static checks.
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require('@tensorflow/tfjs')
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faceapi = require('@vladmandic/face-api/dist/face-api.node-wasm.js')
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const { setWasmPaths } = require('@tensorflow/tfjs-backend-wasm')
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setWasmPaths(path.join(repoRoot, 'node_modules/@tensorflow/tfjs-backend-wasm/dist/'))
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} catch (error) {
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throw new Error(
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`engine indisponível (instale @vladmandic/face-api e @tensorflow/tfjs-backend-wasm): ${
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error instanceof Error ? error.message : error
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}`,
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)
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}
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const tf = faceapi.tf
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await tf.setBackend('wasm')
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await tf.ready()
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const modelDir = path.join(repoRoot, 'node_modules/@vladmandic/face-api/model')
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await faceapi.nets.tinyFaceDetector.loadFromDisk(modelDir)
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await faceapi.nets.faceLandmark68Net.loadFromDisk(modelDir)
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await faceapi.nets.faceRecognitionNet.loadFromDisk(modelDir)
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const options = new faceapi.TinyFaceDetectorOptions({
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inputSize: FACE_DETECTOR_INPUT_SIZE,
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scoreThreshold: FACE_DETECTOR_SCORE_THRESHOLD,
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})
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return {
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/**
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* Descriptors of every detected face, in detection order.
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* @param {{ pixels: Uint8Array, width: number, height: number }} image
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* @returns {Promise<number[][]>}
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*/
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detectPixels: async ({ pixels, width, height }) => {
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const tensor = tf.tensor3d(
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new Uint8Array(pixels.buffer, pixels.byteOffset, pixels.byteLength),
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[height, width, 3],
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'int32',
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)
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try {
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const results = await faceapi
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.detectAllFaces(tensor, options)
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.withFaceLandmarks()
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.withFaceDescriptors()
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return results.map((result) => Array.from(result.descriptor))
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} finally {
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tensor.dispose()
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}
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},
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}
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}
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