Unclass indexer
This commit is contained in:
Manav Rathi
parent
3db91d2034
commit
054b4c7cfb
4 changed files with 271 additions and 276 deletions
260
web/apps/photos/src/services/face/f-index.ts
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260
web/apps/photos/src/services/face/f-index.ts
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@ -0,0 +1,260 @@
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import { openCache } from "@/next/blob-cache";
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import log from "@/next/log";
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import { faceAlignment } from "services/face/align";
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import mlIDbStorage from "services/face/db";
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import { detectFaces, getRelativeDetection } from "services/face/detect";
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import { faceEmbeddings, mobileFaceNetFaceSize } from "services/face/embed";
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import {
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DetectedFace,
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Face,
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MLSyncContext,
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MLSyncFileContext,
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type FaceAlignment,
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} from "services/face/types";
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import { imageBitmapToBlob, warpAffineFloat32List } from "utils/image";
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import { detectBlur } from "./blur";
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import { clusterFaces } from "./cluster";
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import { getFaceCrop } from "./crop";
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import {
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fetchImageBitmap,
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fetchImageBitmapForContext,
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getFaceId,
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getLocalFile,
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} from "./image";
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export const syncFileAnalyzeFaces = async (
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext,
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) => {
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const { newMlFile } = fileContext;
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const startTime = Date.now();
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await syncFileFaceDetections(syncContext, fileContext);
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if (newMlFile.faces && newMlFile.faces.length > 0) {
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await syncFileFaceCrops(syncContext, fileContext);
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const alignedFacesData = await syncFileFaceAlignments(
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syncContext,
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fileContext,
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);
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await syncFileFaceEmbeddings(
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syncContext,
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fileContext,
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alignedFacesData,
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);
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await syncFileFaceMakeRelativeDetections(syncContext, fileContext);
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}
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log.debug(
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() =>
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`Face detection for file ${fileContext.enteFile.id} took ${Math.round(Date.now() - startTime)} ms`,
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);
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};
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const syncFileFaceDetections = async (
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext,
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) => {
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const { newMlFile } = fileContext;
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newMlFile.faceDetectionMethod = {
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value: "YoloFace",
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version: 1,
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};
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fileContext.newDetection = true;
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const imageBitmap = await fetchImageBitmapForContext(fileContext);
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const faceDetections = await detectFaces(imageBitmap);
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// TODO: reenable faces filtering based on width
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const detectedFaces = faceDetections?.map((detection) => {
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return {
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fileId: fileContext.enteFile.id,
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detection,
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} as DetectedFace;
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});
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newMlFile.faces = detectedFaces?.map((detectedFace) => ({
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...detectedFace,
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id: getFaceId(detectedFace, newMlFile.imageDimensions),
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}));
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// ?.filter((f) =>
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// f.box.width > syncContext.config.faceDetection.minFaceSize
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// );
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log.info("[MLService] Detected Faces: ", newMlFile.faces?.length);
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};
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const syncFileFaceCrops = async (
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext,
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) => {
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const { newMlFile } = fileContext;
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const imageBitmap = await fetchImageBitmapForContext(fileContext);
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newMlFile.faceCropMethod = {
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value: "ArcFace",
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version: 1,
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};
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for (const face of newMlFile.faces) {
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await saveFaceCrop(imageBitmap, face);
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}
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};
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const syncFileFaceAlignments = async (
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext,
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): Promise<Float32Array> => {
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const { newMlFile } = fileContext;
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newMlFile.faceAlignmentMethod = {
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value: "ArcFace",
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version: 1,
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};
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fileContext.newAlignment = true;
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const imageBitmap =
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fileContext.imageBitmap ||
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(await fetchImageBitmapForContext(fileContext));
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// Execute the face alignment calculations
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for (const face of newMlFile.faces) {
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face.alignment = faceAlignment(face.detection);
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}
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// Extract face images and convert to Float32Array
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const faceAlignments = newMlFile.faces.map((f) => f.alignment);
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const faceImages = await extractFaceImagesToFloat32(
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faceAlignments,
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mobileFaceNetFaceSize,
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imageBitmap,
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);
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const blurValues = detectBlur(faceImages, newMlFile.faces);
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newMlFile.faces.forEach((f, i) => (f.blurValue = blurValues[i]));
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imageBitmap.close();
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log.info("[MLService] alignedFaces: ", newMlFile.faces?.length);
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return faceImages;
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};
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const syncFileFaceEmbeddings = async (
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext,
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alignedFacesInput: Float32Array,
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) => {
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const { newMlFile } = fileContext;
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newMlFile.faceEmbeddingMethod = {
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value: "MobileFaceNet",
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version: 2,
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};
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// TODO: when not storing face crops, image will be needed to extract faces
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// fileContext.imageBitmap ||
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// (await this.getImageBitmap(fileContext));
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const embeddings = await faceEmbeddings(alignedFacesInput);
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newMlFile.faces.forEach((f, i) => (f.embedding = embeddings[i]));
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log.info("[MLService] facesWithEmbeddings: ", newMlFile.faces.length);
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};
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const syncFileFaceMakeRelativeDetections = async (
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext,
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) => {
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const { newMlFile } = fileContext;
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for (let i = 0; i < newMlFile.faces.length; i++) {
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const face = newMlFile.faces[i];
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if (face.detection.box.x + face.detection.box.width < 2) continue; // Skip if somehow already relative
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face.detection = getRelativeDetection(
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face.detection,
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newMlFile.imageDimensions,
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);
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}
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};
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export const saveFaceCrop = async (imageBitmap: ImageBitmap, face: Face) => {
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const faceCrop = getFaceCrop(imageBitmap, face.detection);
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const blob = await imageBitmapToBlob(faceCrop.image);
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const cache = await openCache("face-crops");
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await cache.put(face.id, blob);
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faceCrop.image.close();
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return blob;
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};
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export const getAllSyncedFacesMap = async (syncContext: MLSyncContext) => {
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if (syncContext.allSyncedFacesMap) {
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return syncContext.allSyncedFacesMap;
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}
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syncContext.allSyncedFacesMap = await mlIDbStorage.getAllFacesMap();
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return syncContext.allSyncedFacesMap;
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};
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export const runFaceClustering = async (
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syncContext: MLSyncContext,
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allFaces: Array<Face>,
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) => {
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// await this.init();
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if (!allFaces || allFaces.length < 50) {
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log.info(
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`Skipping clustering since number of faces (${allFaces.length}) is less than the clustering threshold (50)`,
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);
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return;
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}
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log.info("Running clustering allFaces: ", allFaces.length);
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syncContext.mlLibraryData.faceClusteringResults = await clusterFaces(
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allFaces.map((f) => Array.from(f.embedding)),
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);
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syncContext.mlLibraryData.faceClusteringMethod = {
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value: "Hdbscan",
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version: 1,
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};
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log.info(
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"[MLService] Got face clustering results: ",
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JSON.stringify(syncContext.mlLibraryData.faceClusteringResults),
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);
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// syncContext.faceClustersWithNoise = {
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// clusters: syncContext.faceClusteringResults.clusters.map(
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// (faces) => ({
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// faces,
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// })
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// ),
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// noise: syncContext.faceClusteringResults.noise,
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// };
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};
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export const regenerateFaceCrop = async (faceID: string) => {
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const fileID = Number(faceID.split("-")[0]);
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const personFace = await mlIDbStorage.getFace(fileID, faceID);
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if (!personFace) {
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throw Error("Face not found");
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}
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const file = await getLocalFile(personFace.fileId);
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const imageBitmap = await fetchImageBitmap(file);
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return await saveFaceCrop(imageBitmap, personFace);
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};
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async function extractFaceImagesToFloat32(
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faceAlignments: Array<FaceAlignment>,
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faceSize: number,
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image: ImageBitmap,
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): Promise<Float32Array> {
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const faceData = new Float32Array(
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faceAlignments.length * faceSize * faceSize * 3,
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);
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for (let i = 0; i < faceAlignments.length; i++) {
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const alignedFace = faceAlignments[i];
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const faceDataOffset = i * faceSize * faceSize * 3;
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warpAffineFloat32List(
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image,
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alignedFace,
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faceSize,
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faceData,
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faceDataOffset,
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);
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}
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return faceData;
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}
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@ -1,6 +1,10 @@
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import mlIDbStorage from "services/face/db";
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import { Face, MLSyncContext, Person } from "services/face/types";
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import FaceService from "../machineLearning/faceService";
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import {
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getAllSyncedFacesMap,
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runFaceClustering,
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saveFaceCrop,
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} from "./f-index";
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import { fetchImageBitmap, getLocalFile } from "./image";
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export const syncPeopleIndex = async (syncContext: MLSyncContext) => {
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@ -12,10 +16,10 @@ export const syncPeopleIndex = async (syncContext: MLSyncContext) => {
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// TODO: have faces addresable through fileId + faceId
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// to avoid index based addressing, which is prone to wrong results
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// one way could be to match nearest face within threshold in the file
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const allFacesMap = await FaceService.getAllSyncedFacesMap(syncContext);
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const allFacesMap = await getAllSyncedFacesMap(syncContext);
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const allFaces = [...allFacesMap.values()].flat();
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await FaceService.runFaceClustering(syncContext, allFaces);
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await runFaceClustering(syncContext, allFaces);
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await syncPeopleFromClusters(syncContext, allFacesMap, allFaces);
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await mlIDbStorage.setIndexVersion("people", filesVersion);
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@ -48,7 +52,7 @@ const syncPeopleFromClusters = async (
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if (personFace && !personFace.crop?.cacheKey) {
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const file = await getLocalFile(personFace.fileId);
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const imageBitmap = await fetchImageBitmap(file);
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await FaceService.saveFaceCrop(imageBitmap, personFace);
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await saveFaceCrop(imageBitmap, personFace);
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}
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const person: Person = {
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@ -1,233 +0,0 @@
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import { openCache } from "@/next/blob-cache";
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import log from "@/next/log";
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import { faceAlignment } from "services/face/align";
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import mlIDbStorage from "services/face/db";
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import { detectFaces, getRelativeDetection } from "services/face/detect";
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import { faceEmbeddings, mobileFaceNetFaceSize } from "services/face/embed";
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import {
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DetectedFace,
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Face,
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MLSyncContext,
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MLSyncFileContext,
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type FaceAlignment,
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} from "services/face/types";
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import { imageBitmapToBlob, warpAffineFloat32List } from "utils/image";
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import { detectBlur } from "../face/blur";
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import { clusterFaces } from "../face/cluster";
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import { getFaceCrop } from "../face/crop";
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import {
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fetchImageBitmap,
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fetchImageBitmapForContext,
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getFaceId,
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getLocalFile,
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} from "../face/image";
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class FaceService {
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async syncFileFaceDetections(
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext,
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) {
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const { newMlFile } = fileContext;
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newMlFile.faceDetectionMethod = {
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value: "YoloFace",
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version: 1,
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};
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fileContext.newDetection = true;
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const imageBitmap = await fetchImageBitmapForContext(fileContext);
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const faceDetections = await detectFaces(imageBitmap);
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// TODO: reenable faces filtering based on width
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const detectedFaces = faceDetections?.map((detection) => {
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return {
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fileId: fileContext.enteFile.id,
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detection,
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} as DetectedFace;
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});
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newMlFile.faces = detectedFaces?.map((detectedFace) => ({
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...detectedFace,
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id: getFaceId(detectedFace, newMlFile.imageDimensions),
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}));
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// ?.filter((f) =>
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// f.box.width > syncContext.config.faceDetection.minFaceSize
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// );
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log.info("[MLService] Detected Faces: ", newMlFile.faces?.length);
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}
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async syncFileFaceCrops(
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext,
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) {
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const { newMlFile } = fileContext;
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const imageBitmap = await fetchImageBitmapForContext(fileContext);
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newMlFile.faceCropMethod = {
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value: "ArcFace",
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version: 1,
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};
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for (const face of newMlFile.faces) {
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await this.saveFaceCrop(imageBitmap, face);
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}
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}
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async syncFileFaceAlignments(
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext,
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): Promise<Float32Array> {
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const { newMlFile } = fileContext;
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newMlFile.faceAlignmentMethod = {
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value: "ArcFace",
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version: 1,
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};
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fileContext.newAlignment = true;
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const imageBitmap =
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fileContext.imageBitmap ||
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(await fetchImageBitmapForContext(fileContext));
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// Execute the face alignment calculations
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for (const face of newMlFile.faces) {
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face.alignment = faceAlignment(face.detection);
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}
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// Extract face images and convert to Float32Array
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const faceAlignments = newMlFile.faces.map((f) => f.alignment);
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const faceImages = await extractFaceImagesToFloat32(
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faceAlignments,
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mobileFaceNetFaceSize,
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imageBitmap,
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);
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const blurValues = detectBlur(faceImages, newMlFile.faces);
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newMlFile.faces.forEach((f, i) => (f.blurValue = blurValues[i]));
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imageBitmap.close();
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log.info("[MLService] alignedFaces: ", newMlFile.faces?.length);
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return faceImages;
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}
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async syncFileFaceEmbeddings(
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext,
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alignedFacesInput: Float32Array,
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) {
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const { newMlFile } = fileContext;
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newMlFile.faceEmbeddingMethod = {
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value: "MobileFaceNet",
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version: 2,
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};
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// TODO: when not storing face crops, image will be needed to extract faces
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// fileContext.imageBitmap ||
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// (await this.getImageBitmap(fileContext));
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const embeddings = await faceEmbeddings(alignedFacesInput);
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newMlFile.faces.forEach((f, i) => (f.embedding = embeddings[i]));
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log.info("[MLService] facesWithEmbeddings: ", newMlFile.faces.length);
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}
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async syncFileFaceMakeRelativeDetections(
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext,
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) {
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const { newMlFile } = fileContext;
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for (let i = 0; i < newMlFile.faces.length; i++) {
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const face = newMlFile.faces[i];
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if (face.detection.box.x + face.detection.box.width < 2) continue; // Skip if somehow already relative
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face.detection = getRelativeDetection(
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face.detection,
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newMlFile.imageDimensions,
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);
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}
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}
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async saveFaceCrop(imageBitmap: ImageBitmap, face: Face) {
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const faceCrop = getFaceCrop(imageBitmap, face.detection);
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const blob = await imageBitmapToBlob(faceCrop.image);
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const cache = await openCache("face-crops");
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await cache.put(face.id, blob);
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faceCrop.image.close();
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return blob;
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}
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async getAllSyncedFacesMap(syncContext: MLSyncContext) {
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if (syncContext.allSyncedFacesMap) {
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return syncContext.allSyncedFacesMap;
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}
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syncContext.allSyncedFacesMap = await mlIDbStorage.getAllFacesMap();
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return syncContext.allSyncedFacesMap;
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}
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public async runFaceClustering(
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syncContext: MLSyncContext,
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allFaces: Array<Face>,
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) {
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// await this.init();
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if (!allFaces || allFaces.length < 50) {
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log.info(
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`Skipping clustering since number of faces (${allFaces.length}) is less than the clustering threshold (50)`,
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);
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return;
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}
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log.info("Running clustering allFaces: ", allFaces.length);
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syncContext.mlLibraryData.faceClusteringResults = await clusterFaces(
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allFaces.map((f) => Array.from(f.embedding)),
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);
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syncContext.mlLibraryData.faceClusteringMethod = {
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value: "Hdbscan",
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version: 1,
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};
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log.info(
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"[MLService] Got face clustering results: ",
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JSON.stringify(syncContext.mlLibraryData.faceClusteringResults),
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);
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||||
// syncContext.faceClustersWithNoise = {
|
||||
// clusters: syncContext.faceClusteringResults.clusters.map(
|
||||
// (faces) => ({
|
||||
// faces,
|
||||
// })
|
||||
// ),
|
||||
// noise: syncContext.faceClusteringResults.noise,
|
||||
// };
|
||||
}
|
||||
|
||||
public async regenerateFaceCrop(faceID: string) {
|
||||
const fileID = Number(faceID.split("-")[0]);
|
||||
const personFace = await mlIDbStorage.getFace(fileID, faceID);
|
||||
if (!personFace) {
|
||||
throw Error("Face not found");
|
||||
}
|
||||
|
||||
const file = await getLocalFile(personFace.fileId);
|
||||
const imageBitmap = await fetchImageBitmap(file);
|
||||
return await this.saveFaceCrop(imageBitmap, personFace);
|
||||
}
|
||||
}
|
||||
|
||||
export default new FaceService();
|
||||
|
||||
async function extractFaceImagesToFloat32(
|
||||
faceAlignments: Array<FaceAlignment>,
|
||||
faceSize: number,
|
||||
image: ImageBitmap,
|
||||
): Promise<Float32Array> {
|
||||
const faceData = new Float32Array(
|
||||
faceAlignments.length * faceSize * faceSize * 3,
|
||||
);
|
||||
for (let i = 0; i < faceAlignments.length; i++) {
|
||||
const alignedFace = faceAlignments[i];
|
||||
const faceDataOffset = i * faceSize * faceSize * 3;
|
||||
warpAffineFloat32List(
|
||||
image,
|
||||
alignedFace,
|
||||
faceSize,
|
||||
faceData,
|
||||
faceDataOffset,
|
||||
);
|
||||
}
|
||||
return faceData;
|
||||
}
|
||||
|
|
@ -26,9 +26,9 @@ import {
|
|||
import { getLocalFiles } from "services/fileService";
|
||||
import { EnteFile } from "types/file";
|
||||
import { isInternalUserForML } from "utils/user";
|
||||
import { regenerateFaceCrop, syncFileAnalyzeFaces } from "../face/f-index";
|
||||
import { fetchImageBitmapForContext } from "../face/image";
|
||||
import { syncPeopleIndex } from "../face/people";
|
||||
import FaceService from "./faceService";
|
||||
|
||||
/**
|
||||
* TODO-ML(MR): What and why.
|
||||
|
|
@ -222,7 +222,7 @@ class MachineLearningService {
|
|||
faceID: string,
|
||||
) {
|
||||
await downloadManager.init(APPS.PHOTOS, { token });
|
||||
return FaceService.regenerateFaceCrop(faceID);
|
||||
return regenerateFaceCrop(faceID);
|
||||
}
|
||||
|
||||
private newMlData(fileId: number) {
|
||||
|
|
@ -467,9 +467,7 @@ class MachineLearningService {
|
|||
|
||||
try {
|
||||
await fetchImageBitmapForContext(fileContext);
|
||||
await Promise.all([
|
||||
this.syncFileAnalyzeFaces(syncContext, fileContext),
|
||||
]);
|
||||
await Promise.all([syncFileAnalyzeFaces(syncContext, fileContext)]);
|
||||
newMlFile.errorCount = 0;
|
||||
newMlFile.lastErrorMessage = undefined;
|
||||
await this.persistOnServer(newMlFile, enteFile);
|
||||
|
|
@ -548,40 +546,6 @@ class MachineLearningService {
|
|||
|
||||
await this.persistMLLibraryData(syncContext);
|
||||
}
|
||||
|
||||
private async syncFileAnalyzeFaces(
|
||||
syncContext: MLSyncContext,
|
||||
fileContext: MLSyncFileContext,
|
||||
) {
|
||||
const { newMlFile } = fileContext;
|
||||
const startTime = Date.now();
|
||||
await FaceService.syncFileFaceDetections(syncContext, fileContext);
|
||||
|
||||
if (newMlFile.faces && newMlFile.faces.length > 0) {
|
||||
await FaceService.syncFileFaceCrops(syncContext, fileContext);
|
||||
|
||||
const alignedFacesData = await FaceService.syncFileFaceAlignments(
|
||||
syncContext,
|
||||
fileContext,
|
||||
);
|
||||
|
||||
await FaceService.syncFileFaceEmbeddings(
|
||||
syncContext,
|
||||
fileContext,
|
||||
alignedFacesData,
|
||||
);
|
||||
|
||||
await FaceService.syncFileFaceMakeRelativeDetections(
|
||||
syncContext,
|
||||
fileContext,
|
||||
);
|
||||
}
|
||||
log.info(
|
||||
`face detection time taken ${fileContext.enteFile.id}`,
|
||||
Date.now() - startTime,
|
||||
"ms",
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
export default new MachineLearningService();
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue