Merge remote-tracking branch 'origin/mobile_face' into mobile_face

.gitignore

@@ -1,8 +1,6 @@
-# Let folks use their custom .vscode settings
+# Let folks use their custom editor settings
 .vscode
+.idea
 
 # macOS
 .DS_Store
-.idea
-.ente.authenticator.db
-.ente.offline_authenticator.db

.gitmodules

@@ -2,6 +2,10 @@
 	path = auth/thirdparty/sentry-dart
 	url = https://github.com/ente-io/sentry-dart.git
 	branch = sentry_flutter_ente
+[submodule "auth/flutter"]
+	path = auth/flutter
+	url = https://github.com/flutter/flutter.git
+	branch = stable
 [submodule "auth/assets/simple-icons"]
 	path = auth/assets/simple-icons
 	url = https://github.com/simple-icons/simple-icons.git

desktop/package.json

@@ -14,7 +14,7 @@
         "build:ci": "yarn build-renderer && tsc",
         "build:quick": "yarn build-renderer && yarn build-main:quick",
         "dev": "concurrently --kill-others --success first --names 'main,rndr' \"yarn dev-main\" \"yarn dev-renderer\"",
-        "dev-main": "tsc && electron app/main.js",
+        "dev-main": "tsc && electron .",
         "dev-renderer": "cd ../web && yarn install && yarn dev:photos",
         "postinstall": "electron-builder install-app-deps",
         "lint": "yarn prettier --check --log-level warn . && eslint --ext .ts src && yarn tsc",

desktop/src/main.ts

@@ -315,32 +315,18 @@ const setupTrayItem = (mainWindow: BrowserWindow) => {
 
 /**
  * Older versions of our app used to maintain a cache dir using the main
- * process. This has been removed in favor of cache on the web layer.
+ * process. This has been removed in favor of cache on the web layer. Delete the
+ * old cache dir if it exists.
  *
- * Delete the old cache dir if it exists.
+ * Added May 2024, v1.7.0. This migration code can be removed after some time
- *
+ * once most people have upgraded to newer versions.
- * This will happen in two phases. The cache had three subdirectories:
- *
- * - Two of them, "thumbs" and "files", will be removed now (v1.7.0, May 2024).
- *
- * - The third one, "face-crops" will be removed once we finish the face search
- *   changes. See: [Note: Legacy face crops].
- *
- * This migration code can be removed after some time once most people have
- * upgraded to newer versions.
  */
 const deleteLegacyDiskCacheDirIfExists = async () => {
-    const removeIfExists = async (dirPath: string) => {
-        if (existsSync(dirPath)) {
-            log.info(`Removing legacy disk cache from ${dirPath}`);
-            await fs.rm(dirPath, { recursive: true });
-        }
-    };
     // [Note: Getting the cache path]
     //
     // The existing code was passing "cache" as a parameter to getPath.
     //
-    // However, "cache" is not a valid parameter to getPath. It works! (for
+    // However, "cache" is not a valid parameter to getPath. It works (for
     // example, on macOS I get `~/Library/Caches`), but it is intentionally not
     // documented as part of the public API:
     //
@@ -353,8 +339,8 @@ const deleteLegacyDiskCacheDirIfExists = async () => {
     // @ts-expect-error "cache" works but is not part of the public API.
     const cacheDir = path.join(app.getPath("cache"), "ente");
     if (existsSync(cacheDir)) {
-        await removeIfExists(path.join(cacheDir, "thumbs"));
+        log.info(`Removing legacy disk cache from ${cacheDir}`);
-        await removeIfExists(path.join(cacheDir, "files"));
+        await fs.rm(cacheDir, { recursive: true });
     }
 };
 

desktop/src/main/ipc.ts

@@ -24,7 +24,6 @@ import {
     updateOnNextRestart,
 } from "./services/app-update";
 import {
-    legacyFaceCrop,
     openDirectory,
     openLogDirectory,
     selectDirectory,
@@ -43,10 +42,10 @@ import {
 import { convertToJPEG, generateImageThumbnail } from "./services/image";
 import { logout } from "./services/logout";
 import {
-    clipImageEmbedding,
+    computeCLIPImageEmbedding,
-    clipTextEmbeddingIfAvailable,
+    computeCLIPTextEmbeddingIfAvailable,
 } from "./services/ml-clip";
-import { detectFaces, faceEmbeddings } from "./services/ml-face";
+import { computeFaceEmbeddings, detectFaces } from "./services/ml-face";
 import { encryptionKey, saveEncryptionKey } from "./services/store";
 import {
     clearPendingUploads,
@@ -170,24 +169,22 @@ export const attachIPCHandlers = () => {
 
     // - ML
 
-    ipcMain.handle("clipImageEmbedding", (_, jpegImageData: Uint8Array) =>
+    ipcMain.handle(
-        clipImageEmbedding(jpegImageData),
+        "computeCLIPImageEmbedding",
+        (_, jpegImageData: Uint8Array) =>
+            computeCLIPImageEmbedding(jpegImageData),
     );
 
-    ipcMain.handle("clipTextEmbeddingIfAvailable", (_, text: string) =>
+    ipcMain.handle("computeCLIPTextEmbeddingIfAvailable", (_, text: string) =>
-        clipTextEmbeddingIfAvailable(text),
+        computeCLIPTextEmbeddingIfAvailable(text),
     );
 
     ipcMain.handle("detectFaces", (_, input: Float32Array) =>
         detectFaces(input),
     );
 
-    ipcMain.handle("faceEmbeddings", (_, input: Float32Array) =>
+    ipcMain.handle("computeFaceEmbeddings", (_, input: Float32Array) =>
-        faceEmbeddings(input),
+        computeFaceEmbeddings(input),
-    );
-
-    ipcMain.handle("legacyFaceCrop", (_, faceID: string) =>
-        legacyFaceCrop(faceID),
     );
 
     // - Upload

desktop/src/main/services/app-update.ts

@@ -163,7 +163,7 @@ const checkForUpdatesAndNotify = async (mainWindow: BrowserWindow) => {
 };
 
 /**
- * Return the version of the desktop app
+ * Return the version of the desktop app.
  *
  * The return value is of the form `v1.2.3`.
  */

desktop/src/main/services/dir.ts

@@ -1,7 +1,5 @@
 import { shell } from "electron/common";
 import { app, dialog } from "electron/main";
-import { existsSync } from "fs";
-import fs from "node:fs/promises";
 import path from "node:path";
 import { posixPath } from "../utils/electron";
 
@@ -78,16 +76,3 @@ export const openLogDirectory = () => openDirectory(logDirectoryPath());
  * - Windows: %USERPROFILE%\AppData\Roaming\ente\logs\ente.log
  */
 const logDirectoryPath = () => app.getPath("logs");
-
-/**
- * See: [Note: Legacy face crops]
- */
-export const legacyFaceCrop = async (
-    faceID: string,
-): Promise<Uint8Array | undefined> => {
-    // See: [Note: Getting the cache path]
-    // @ts-expect-error "cache" works but is not part of the public API.
-    const cacheDir = path.join(app.getPath("cache"), "ente");
-    const filePath = path.join(cacheDir, "face-crops", faceID);
-    return existsSync(filePath) ? await fs.readFile(filePath) : undefined;
-};

desktop/src/main/services/ml-clip.ts

@@ -11,7 +11,7 @@ import * as ort from "onnxruntime-node";
 import Tokenizer from "../../thirdparty/clip-bpe-ts/mod";
 import log from "../log";
 import { writeStream } from "../stream";
-import { ensure } from "../utils/common";
+import { ensure, wait } from "../utils/common";
 import { deleteTempFile, makeTempFilePath } from "../utils/temp";
 import { makeCachedInferenceSession } from "./ml";
 
@@ -20,7 +20,7 @@ const cachedCLIPImageSession = makeCachedInferenceSession(
     351468764 /* 335.2 MB */,
 );
 
-export const clipImageEmbedding = async (jpegImageData: Uint8Array) => {
+export const computeCLIPImageEmbedding = async (jpegImageData: Uint8Array) => {
     const tempFilePath = await makeTempFilePath();
     const imageStream = new Response(jpegImageData.buffer).body;
     await writeStream(tempFilePath, ensure(imageStream));
@@ -42,7 +42,7 @@ const clipImageEmbedding_ = async (jpegFilePath: string) => {
     const results = await session.run(feeds);
     log.debug(
         () =>
-            `onnx/clip image embedding took ${Date.now() - t1} ms (prep: ${t2 - t1} ms, inference: ${Date.now() - t2} ms)`,
+            `ONNX/CLIP image embedding took ${Date.now() - t1} ms (prep: ${t2 - t1} ms, inference: ${Date.now() - t2} ms)`,
     );
     /* Need these model specific casts to type the result */
     const imageEmbedding = ensure(results.output).data as Float32Array;
@@ -140,21 +140,23 @@ const getTokenizer = () => {
     return _tokenizer;
 };
 
-export const clipTextEmbeddingIfAvailable = async (text: string) => {
+export const computeCLIPTextEmbeddingIfAvailable = async (text: string) => {
-    const sessionOrStatus = await Promise.race([
+    const sessionOrSkip = await Promise.race([
         cachedCLIPTextSession(),
-        "downloading-model",
+        // Wait for a tick to get the session promise to resolved the first time
+        // this code runs on each app start (and the model has been downloaded).
+        wait(0).then(() => 1),
     ]);
 
-    // Don't wait for the download to complete
+    // Don't wait for the download to complete.
-    if (typeof sessionOrStatus == "string") {
+    if (typeof sessionOrSkip == "number") {
         log.info(
             "Ignoring CLIP text embedding request because model download is pending",
         );
         return undefined;
     }
 
-    const session = sessionOrStatus;
+    const session = sessionOrSkip;
     const t1 = Date.now();
     const tokenizer = getTokenizer();
     const tokenizedText = Int32Array.from(tokenizer.encodeForCLIP(text));
@@ -165,7 +167,7 @@ export const clipTextEmbeddingIfAvailable = async (text: string) => {
     const results = await session.run(feeds);
     log.debug(
         () =>
-            `onnx/clip text embedding took ${Date.now() - t1} ms (prep: ${t2 - t1} ms, inference: ${Date.now() - t2} ms)`,
+            `ONNX/CLIP text embedding took ${Date.now() - t1} ms (prep: ${t2 - t1} ms, inference: ${Date.now() - t2} ms)`,
     );
     const textEmbedding = ensure(results.output).data as Float32Array;
     return normalizeEmbedding(textEmbedding);

desktop/src/main/services/ml-face.ts

@@ -23,7 +23,7 @@ export const detectFaces = async (input: Float32Array) => {
         input: new ort.Tensor("float32", input, [1, 3, 640, 640]),
     };
     const results = await session.run(feeds);
-    log.debug(() => `onnx/yolo face detection took ${Date.now() - t} ms`);
+    log.debug(() => `ONNX/YOLO face detection took ${Date.now() - t} ms`);
     return ensure(results.output).data;
 };
 
@@ -32,7 +32,7 @@ const cachedFaceEmbeddingSession = makeCachedInferenceSession(
     5286998 /* 5 MB */,
 );
 
-export const faceEmbeddings = async (input: Float32Array) => {
+export const computeFaceEmbeddings = async (input: Float32Array) => {
     // Dimension of each face (alias)
     const mobileFaceNetFaceSize = 112;
     // Smaller alias
@@ -45,7 +45,7 @@ export const faceEmbeddings = async (input: Float32Array) => {
     const t = Date.now();
     const feeds = { img_inputs: inputTensor };
     const results = await session.run(feeds);
-    log.debug(() => `onnx/yolo face embedding took ${Date.now() - t} ms`);
+    log.debug(() => `ONNX/MFNT face embedding took ${Date.now() - t} ms`);
     /* Need these model specific casts to extract and type the result */
     return (results.embeddings as unknown as Record<string, unknown>)
         .cpuData as Float32Array;

desktop/src/main/utils/common.ts

@@ -13,3 +13,12 @@ export const ensure = <T>(v: T | null | undefined): T => {
     if (v === undefined) throw new Error("Required value was not found");
     return v;
 };
+
+/**
+ * Wait for {@link ms} milliseconds
+ *
+ * This function is a promisified `setTimeout`. It returns a promise that
+ * resolves after {@link ms} milliseconds.
+ */
+export const wait = (ms: number) =>
+    new Promise((resolve) => setTimeout(resolve, ms));

desktop/src/main/utils/electron.ts

@@ -55,9 +55,7 @@ export const execAsync = async (command: string | string[]) => {
         : command;
     const startTime = Date.now();
     const result = await execAsync_(escapedCommand);
-    log.debug(
+    log.debug(() => `${escapedCommand} (${Date.now() - startTime} ms)`);
-        () => `${escapedCommand} (${Math.round(Date.now() - startTime)} ms)`,
-    );
     return result;
 };
 

desktop/src/preload.ts

@@ -153,20 +153,17 @@ const ffmpegExec = (
 
 // - ML
 
-const clipImageEmbedding = (jpegImageData: Uint8Array) =>
+const computeCLIPImageEmbedding = (jpegImageData: Uint8Array) =>
-    ipcRenderer.invoke("clipImageEmbedding", jpegImageData);
+    ipcRenderer.invoke("computeCLIPImageEmbedding", jpegImageData);
 
-const clipTextEmbeddingIfAvailable = (text: string) =>
+const computeCLIPTextEmbeddingIfAvailable = (text: string) =>
-    ipcRenderer.invoke("clipTextEmbeddingIfAvailable", text);
+    ipcRenderer.invoke("computeCLIPTextEmbeddingIfAvailable", text);
 
 const detectFaces = (input: Float32Array) =>
     ipcRenderer.invoke("detectFaces", input);
 
-const faceEmbeddings = (input: Float32Array) =>
+const computeFaceEmbeddings = (input: Float32Array) =>
-    ipcRenderer.invoke("faceEmbeddings", input);
+    ipcRenderer.invoke("computeFaceEmbeddings", input);
-
-const legacyFaceCrop = (faceID: string) =>
-    ipcRenderer.invoke("legacyFaceCrop", faceID);
 
 // - Watch
 
@@ -340,11 +337,10 @@ contextBridge.exposeInMainWorld("electron", {
 
     // - ML
 
-    clipImageEmbedding,
+    computeCLIPImageEmbedding,
-    clipTextEmbeddingIfAvailable,
+    computeCLIPTextEmbeddingIfAvailable,
     detectFaces,
-    faceEmbeddings,
+    computeFaceEmbeddings,
-    legacyFaceCrop,
 
     // - Watch
 

server/pkg/controller/embedding/delete.go

@@ -71,37 +71,21 @@ func (c *Controller) deleteEmbedding(qItem repo.QueueItem) {
 		ctxLogger.WithError(err).Error("Failed to fetch datacenters")
 		return
 	}
-	// Ensure that the object are deleted from active derived storage dc. Ideally, this section should never be executed
+	ctxLogger.Infof("Deleting from all datacenters %v", datacenters)
-	// unless there's a bug in storing the DC or the service restarts before removing the rows from the table
-	// todo:(neeraj): remove this section after a few weeks of deployment
-	if len(datacenters) == 0 {
-		ctxLogger.Warn("No datacenters found for file, ensuring deletion from derived storage  and hot DC")
-		err = c.ObjectCleanupController.DeleteAllObjectsWithPrefix(prefix, c.S3Config.GetDerivedStorageDataCenter())
-		if err != nil {
-			ctxLogger.WithError(err).Error("Failed to delete all objects")
-			return
-		}
-		// if Derived DC is different from hot DC, delete from hot DC as well
-		if c.derivedStorageDataCenter != c.S3Config.GetHotDataCenter() {
-			err = c.ObjectCleanupController.DeleteAllObjectsWithPrefix(prefix, c.S3Config.GetHotDataCenter())
-			if err != nil {
-				ctxLogger.WithError(err).Error("Failed to delete all objects from hot DC")
-				return
-			}
-		}
-	} else {
-		ctxLogger.Infof("Deleting from all datacenters %v", datacenters)
-	}
-
 	for i := range datacenters {
-		err = c.ObjectCleanupController.DeleteAllObjectsWithPrefix(prefix, datacenters[i])
+		dc := datacenters[i]
+		err = c.ObjectCleanupController.DeleteAllObjectsWithPrefix(prefix, dc)
 		if err != nil {
-			ctxLogger.WithError(err).Errorf("Failed to delete all objects from %s", datacenters[i])
+			ctxLogger.WithError(err).
+				WithField("dc", dc).
+				Errorf("Failed to delete all objects from %s", datacenters[i])
 			return
 		} else {
 			removeErr := c.Repo.RemoveDatacenter(context.Background(), fileID, datacenters[i])
 			if removeErr != nil {
-				ctxLogger.WithError(removeErr).Error("Failed to remove datacenter from db")
+				ctxLogger.WithError(removeErr).
+					WithField("dc", dc).
+					Error("Failed to remove datacenter from db")
 				return
 			}
 		}

server/pkg/controller/object_cleanup.go

@@ -260,7 +260,10 @@ func (c *ObjectCleanupController) DeleteAllObjectsWithPrefix(prefix string, dc s
 		Prefix: &prefix,
 	})
 	if err != nil {
-		log.Error(err)
+		log.WithFields(log.Fields{
+			"prefix": prefix,
+			"dc":     dc,
+		}).WithError(err).Error("Failed to list objects")
 		return stacktrace.Propagate(err, "")
 	}
 	var keys []string
@@ -270,7 +273,10 @@ func (c *ObjectCleanupController) DeleteAllObjectsWithPrefix(prefix string, dc s
 	for _, key := range keys {
 		err = c.DeleteObjectFromDataCenter(key, dc)
 		if err != nil {
-			log.Error(err)
+			log.WithFields(log.Fields{
+				"object_key": key,
+				"dc":         dc,
+			}).WithError(err).Error("Failed to delete object")
 			return stacktrace.Propagate(err, "")
 		}
 	}

web/apps/photos/src/components/Search/SearchBar/searchInput/index.tsx

@@ -9,7 +9,7 @@ import { useCallback, useContext, useEffect, useRef, useState } from "react";
 import { components } from "react-select";
 import AsyncSelect from "react-select/async";
 import { InputActionMeta } from "react-select/src/types";
-import { Person } from "services/face/types";
+import type { Person } from "services/face/people";
 import { City } from "services/locationSearchService";
 import {
     getAutoCompleteSuggestions,

web/apps/photos/src/components/ml/MLSearchSettings.tsx

@@ -270,14 +270,7 @@ function EnableMLSearch({ onClose, enableMlSearch, onRootClose }) {
                     {" "}
                     <Typography color="text.muted">
                         {/* <Trans i18nKey={"ENABLE_ML_SEARCH_DESCRIPTION"} /> */}
-                        <p>
+                        We're putting finishing touches, coming back soon!
-                            We're putting finishing touches, coming back soon!
-                        </p>
-                        <p>
-                            <small>
-                                Existing indexed faces will continue to show.
-                            </small>
-                        </p>
                     </Typography>
                 </Box>
                 {isInternalUserForML() && (

web/apps/photos/src/components/ml/PeopleList.tsx

@@ -1,10 +1,11 @@
+import { blobCache } from "@/next/blob-cache";
 import log from "@/next/log";
 import { Skeleton, styled } from "@mui/material";
 import { Legend } from "components/PhotoViewer/styledComponents/Legend";
 import { t } from "i18next";
 import React, { useEffect, useState } from "react";
 import mlIDbStorage from "services/face/db";
-import { Face, Person, type MlFileData } from "services/face/types";
+import type { Person } from "services/face/people";
 import { EnteFile } from "types/file";
 
 const FaceChipContainer = styled("div")`
@@ -57,10 +58,7 @@ export const PeopleList = React.memo((props: PeopleListProps) => {
                         props.onSelect && props.onSelect(person, index)
                     }
                 >
-                    <FaceCropImageView
+                    <FaceCropImageView faceID={person.displayFaceId} />
-                        faceID={person.displayFaceId}
-                        cacheKey={person.faceCropCacheKey}
-                    />
                 </FaceChip>
             ))}
         </FaceChipContainer>
@@ -108,7 +106,7 @@ export function UnidentifiedFaces(props: {
     file: EnteFile;
     updateMLDataIndex: number;
 }) {
-    const [faces, setFaces] = useState<Array<Face>>([]);
+    const [faces, setFaces] = useState<{ id: string }[]>([]);
 
     useEffect(() => {
         let didCancel = false;
@@ -136,10 +134,7 @@ export function UnidentifiedFaces(props: {
                 {faces &&
                     faces.map((face, index) => (
                         <FaceChip key={index}>
-                            <FaceCropImageView
+                            <FaceCropImageView faceID={face.id} />
-                                faceID={face.id}
-                                cacheKey={face.crop?.cacheKey}
-                            />
                         </FaceChip>
                     ))}
             </FaceChipContainer>
@@ -149,29 +144,22 @@ export function UnidentifiedFaces(props: {
 
 interface FaceCropImageViewProps {
     faceID: string;
-    cacheKey?: string;
 }
 
-const FaceCropImageView: React.FC<FaceCropImageViewProps> = ({
+const FaceCropImageView: React.FC<FaceCropImageViewProps> = ({ faceID }) => {
-    faceID,
-    cacheKey,
-}) => {
     const [objectURL, setObjectURL] = useState<string | undefined>();
 
     useEffect(() => {
         let didCancel = false;
-        const electron = globalThis.electron;
+        if (faceID) {
-
+            blobCache("face-crops")
-        if (faceID && electron) {
+                .then((cache) => cache.get(faceID))
-            electron
-                .legacyFaceCrop(faceID)
-                /*
-            cachedOrNew("face-crops", cacheKey, async () => {
-                return machineLearningService.regenerateFaceCrop(
-                    faceId,
-                );
-            })*/
                 .then((data) => {
+                    /*
+                    TODO(MR): regen if needed and get this to work on web too.
+                    cachedOrNew("face-crops", cacheKey, async () => {
+                        return regenerateFaceCrop(faceId);
+                    })*/
                     if (data) {
                         const blob = new Blob([data]);
                         if (!didCancel) setObjectURL(URL.createObjectURL(blob));
@@ -183,7 +171,7 @@ const FaceCropImageView: React.FC<FaceCropImageViewProps> = ({
             didCancel = true;
             if (objectURL) URL.revokeObjectURL(objectURL);
         };
-    }, [faceID, cacheKey]);
+    }, [faceID]);
 
     return objectURL ? (
         <img src={objectURL} />
@@ -192,9 +180,9 @@ const FaceCropImageView: React.FC<FaceCropImageViewProps> = ({
     );
 };
 
-async function getPeopleList(file: EnteFile): Promise<Array<Person>> {
+async function getPeopleList(file: EnteFile): Promise<Person[]> {
     let startTime = Date.now();
-    const mlFileData: MlFileData = await mlIDbStorage.getFile(file.id);
+    const mlFileData = await mlIDbStorage.getFile(file.id);
     log.info(
         "getPeopleList:mlFilesStore:getItem",
         Date.now() - startTime,
@@ -226,8 +214,8 @@ async function getPeopleList(file: EnteFile): Promise<Array<Person>> {
     return peopleList;
 }
 
-async function getUnidentifiedFaces(file: EnteFile): Promise<Array<Face>> {
+async function getUnidentifiedFaces(file: EnteFile): Promise<{ id: string }[]> {
-    const mlFileData: MlFileData = await mlIDbStorage.getFile(file.id);
+    const mlFileData = await mlIDbStorage.getFile(file.id);
 
     return mlFileData?.faces?.filter(
         (f) => f.personId === null || f.personId === undefined,

web/apps/photos/src/services/clip-service.ts

@@ -184,7 +184,7 @@ class CLIPService {
     };
 
     getTextEmbeddingIfAvailable = async (text: string) => {
-        return ensureElectron().clipTextEmbeddingIfAvailable(text);
+        return ensureElectron().computeCLIPTextEmbeddingIfAvailable(text);
     };
 
     private runClipEmbeddingExtraction = async (canceller: AbortController) => {
@@ -294,7 +294,7 @@ class CLIPService {
         const file = await localFile
             .arrayBuffer()
             .then((buffer) => new Uint8Array(buffer));
-        return await ensureElectron().clipImageEmbedding(file);
+        return await ensureElectron().computeCLIPImageEmbedding(file);
     };
 
     private encryptAndUploadEmbedding = async (
@@ -328,7 +328,8 @@ class CLIPService {
 
     private extractFileClipImageEmbedding = async (file: EnteFile) => {
         const thumb = await downloadManager.getThumbnail(file);
-        const embedding = await ensureElectron().clipImageEmbedding(thumb);
+        const embedding =
+            await ensureElectron().computeCLIPImageEmbedding(thumb);
         return embedding;
     };
 

web/apps/photos/src/services/download/index.ts

@@ -1,6 +1,6 @@
 import { FILE_TYPE } from "@/media/file-type";
 import { decodeLivePhoto } from "@/media/live-photo";
-import { openCache, type BlobCache } from "@/next/blob-cache";
+import { blobCache, type BlobCache } from "@/next/blob-cache";
 import log from "@/next/log";
 import { APPS } from "@ente/shared/apps/constants";
 import ComlinkCryptoWorker from "@ente/shared/crypto";
@@ -91,7 +91,7 @@ class DownloadManagerImpl {
         }
         this.downloadClient = createDownloadClient(app, tokens);
         try {
-            this.thumbnailCache = await openCache("thumbs");
+            this.thumbnailCache = await blobCache("thumbs");
         } catch (e) {
             log.error(
                 "Failed to open thumbnail cache, will continue without it",
@@ -100,7 +100,7 @@ class DownloadManagerImpl {
         }
         // TODO (MR): Revisit full file caching cf disk space usage
         // try {
-        //     if (isElectron()) this.fileCache = await openCache("files");
+        //     if (isElectron()) this.fileCache = await cache("files");
         // } catch (e) {
         //     log.error("Failed to open file cache, will continue without it", e);
         // }

web/apps/photos/src/services/embeddingService.ts

@@ -7,7 +7,7 @@ import HTTPService from "@ente/shared/network/HTTPService";
 import { getEndpoint } from "@ente/shared/network/api";
 import localForage from "@ente/shared/storage/localForage";
 import { getToken } from "@ente/shared/storage/localStorage/helpers";
-import { FileML } from "services/machineLearning/machineLearningService";
+import { FileML } from "services/face/remote";
 import type {
     Embedding,
     EmbeddingModel,

web/apps/photos/src/services/face/align.ts

@@ -1,88 +0,0 @@
-import { Matrix } from "ml-matrix";
-import { Point } from "services/face/geom";
-import { FaceAlignment, FaceDetection } from "services/face/types";
-import { getSimilarityTransformation } from "similarity-transformation";
-
-const ARCFACE_LANDMARKS = [
-    [38.2946, 51.6963],
-    [73.5318, 51.5014],
-    [56.0252, 71.7366],
-    [56.1396, 92.2848],
-] as Array<[number, number]>;
-
-const ARCFACE_LANDMARKS_FACE_SIZE = 112;
-
-const ARC_FACE_5_LANDMARKS = [
-    [38.2946, 51.6963],
-    [73.5318, 51.5014],
-    [56.0252, 71.7366],
-    [41.5493, 92.3655],
-    [70.7299, 92.2041],
-] as Array<[number, number]>;
-
-/**
- * Compute and return an {@link FaceAlignment} for the given face detection.
- *
- * @param faceDetection A geometry indicating a face detected in an image.
- */
-export const faceAlignment = (faceDetection: FaceDetection): FaceAlignment => {
-    const landmarkCount = faceDetection.landmarks.length;
-    return getFaceAlignmentUsingSimilarityTransform(
-        faceDetection,
-        normalizeLandmarks(
-            landmarkCount === 5 ? ARC_FACE_5_LANDMARKS : ARCFACE_LANDMARKS,
-            ARCFACE_LANDMARKS_FACE_SIZE,
-        ),
-    );
-};
-
-function getFaceAlignmentUsingSimilarityTransform(
-    faceDetection: FaceDetection,
-    alignedLandmarks: Array<[number, number]>,
-): FaceAlignment {
-    const landmarksMat = new Matrix(
-        faceDetection.landmarks
-            .map((p) => [p.x, p.y])
-            .slice(0, alignedLandmarks.length),
-    ).transpose();
-    const alignedLandmarksMat = new Matrix(alignedLandmarks).transpose();
-
-    const simTransform = getSimilarityTransformation(
-        landmarksMat,
-        alignedLandmarksMat,
-    );
-
-    const RS = Matrix.mul(simTransform.rotation, simTransform.scale);
-    const TR = simTransform.translation;
-
-    const affineMatrix = [
-        [RS.get(0, 0), RS.get(0, 1), TR.get(0, 0)],
-        [RS.get(1, 0), RS.get(1, 1), TR.get(1, 0)],
-        [0, 0, 1],
-    ];
-
-    const size = 1 / simTransform.scale;
-    const meanTranslation = simTransform.toMean.sub(0.5).mul(size);
-    const centerMat = simTransform.fromMean.sub(meanTranslation);
-    const center = new Point(centerMat.get(0, 0), centerMat.get(1, 0));
-    const rotation = -Math.atan2(
-        simTransform.rotation.get(0, 1),
-        simTransform.rotation.get(0, 0),
-    );
-
-    return {
-        affineMatrix,
-        center,
-        size,
-        rotation,
-    };
-}
-
-function normalizeLandmarks(
-    landmarks: Array<[number, number]>,
-    faceSize: number,
-): Array<[number, number]> {
-    return landmarks.map((landmark) =>
-        landmark.map((p) => p / faceSize),
-    ) as Array<[number, number]>;
-}

web/apps/photos/src/services/face/blur.ts

@@ -1,187 +0,0 @@
-import { Face } from "services/face/types";
-import { createGrayscaleIntMatrixFromNormalized2List } from "utils/image";
-import { mobileFaceNetFaceSize } from "./embed";
-
-/**
- * Laplacian blur detection.
- */
-export const detectBlur = (
-    alignedFaces: Float32Array,
-    faces: Face[],
-): number[] => {
-    const numFaces = Math.round(
-        alignedFaces.length /
-            (mobileFaceNetFaceSize * mobileFaceNetFaceSize * 3),
-    );
-    const blurValues: number[] = [];
-    for (let i = 0; i < numFaces; i++) {
-        const face = faces[i];
-        const direction = faceDirection(face);
-        const faceImage = createGrayscaleIntMatrixFromNormalized2List(
-            alignedFaces,
-            i,
-        );
-        const laplacian = applyLaplacian(faceImage, direction);
-        blurValues.push(matrixVariance(laplacian));
-    }
-    return blurValues;
-};
-
-type FaceDirection = "left" | "right" | "straight";
-
-const faceDirection = (face: Face): FaceDirection => {
-    const landmarks = face.detection.landmarks;
-    const leftEye = landmarks[0];
-    const rightEye = landmarks[1];
-    const nose = landmarks[2];
-    const leftMouth = landmarks[3];
-    const rightMouth = landmarks[4];
-
-    const eyeDistanceX = Math.abs(rightEye.x - leftEye.x);
-    const eyeDistanceY = Math.abs(rightEye.y - leftEye.y);
-    const mouthDistanceY = Math.abs(rightMouth.y - leftMouth.y);
-
-    const faceIsUpright =
-        Math.max(leftEye.y, rightEye.y) + 0.5 * eyeDistanceY < nose.y &&
-        nose.y + 0.5 * mouthDistanceY < Math.min(leftMouth.y, rightMouth.y);
-
-    const noseStickingOutLeft =
-        nose.x < Math.min(leftEye.x, rightEye.x) &&
-        nose.x < Math.min(leftMouth.x, rightMouth.x);
-
-    const noseStickingOutRight =
-        nose.x > Math.max(leftEye.x, rightEye.x) &&
-        nose.x > Math.max(leftMouth.x, rightMouth.x);
-
-    const noseCloseToLeftEye =
-        Math.abs(nose.x - leftEye.x) < 0.2 * eyeDistanceX;
-    const noseCloseToRightEye =
-        Math.abs(nose.x - rightEye.x) < 0.2 * eyeDistanceX;
-
-    if (noseStickingOutLeft || (faceIsUpright && noseCloseToLeftEye)) {
-        return "left";
-    } else if (noseStickingOutRight || (faceIsUpright && noseCloseToRightEye)) {
-        return "right";
-    }
-
-    return "straight";
-};
-
-/**
- * Return a new image by applying a Laplacian blur kernel to each pixel.
- */
-const applyLaplacian = (
-    image: number[][],
-    direction: FaceDirection,
-): number[][] => {
-    const paddedImage: number[][] = padImage(image, direction);
-    const numRows = paddedImage.length - 2;
-    const numCols = paddedImage[0].length - 2;
-
-    // Create an output image initialized to 0.
-    const outputImage: number[][] = Array.from({ length: numRows }, () =>
-        new Array(numCols).fill(0),
-    );
-
-    // Define the Laplacian kernel.
-    const kernel: number[][] = [
-        [0, 1, 0],
-        [1, -4, 1],
-        [0, 1, 0],
-    ];
-
-    // Apply the kernel to each pixel
-    for (let i = 0; i < numRows; i++) {
-        for (let j = 0; j < numCols; j++) {
-            let sum = 0;
-            for (let ki = 0; ki < 3; ki++) {
-                for (let kj = 0; kj < 3; kj++) {
-                    sum += paddedImage[i + ki][j + kj] * kernel[ki][kj];
-                }
-            }
-            // Adjust the output value if necessary (e.g., clipping).
-            outputImage[i][j] = sum;
-        }
-    }
-
-    return outputImage;
-};
-
-const padImage = (image: number[][], direction: FaceDirection): number[][] => {
-    const removeSideColumns = 56; /* must be even */
-
-    const numRows = image.length;
-    const numCols = image[0].length;
-    const paddedNumCols = numCols + 2 - removeSideColumns;
-    const paddedNumRows = numRows + 2;
-
-    // Create a new matrix with extra padding.
-    const paddedImage: number[][] = Array.from({ length: paddedNumRows }, () =>
-        new Array(paddedNumCols).fill(0),
-    );
-
-    if (direction === "straight") {
-        // Copy original image into the center of the padded image.
-        for (let i = 0; i < numRows; i++) {
-            for (let j = 0; j < paddedNumCols - 2; j++) {
-                paddedImage[i + 1][j + 1] =
-                    image[i][j + Math.round(removeSideColumns / 2)];
-            }
-        }
-    } else if (direction === "left") {
-        // If the face is facing left, we only take the right side of the face image.
-        for (let i = 0; i < numRows; i++) {
-            for (let j = 0; j < paddedNumCols - 2; j++) {
-                paddedImage[i + 1][j + 1] = image[i][j + removeSideColumns];
-            }
-        }
-    } else if (direction === "right") {
-        // If the face is facing right, we only take the left side of the face image.
-        for (let i = 0; i < numRows; i++) {
-            for (let j = 0; j < paddedNumCols - 2; j++) {
-                paddedImage[i + 1][j + 1] = image[i][j];
-            }
-        }
-    }
-
-    // Reflect padding
-    // Top and bottom rows
-    for (let j = 1; j <= paddedNumCols - 2; j++) {
-        paddedImage[0][j] = paddedImage[2][j]; // Top row
-        paddedImage[numRows + 1][j] = paddedImage[numRows - 1][j]; // Bottom row
-    }
-    // Left and right columns
-    for (let i = 0; i < numRows + 2; i++) {
-        paddedImage[i][0] = paddedImage[i][2]; // Left column
-        paddedImage[i][paddedNumCols - 1] = paddedImage[i][paddedNumCols - 3]; // Right column
-    }
-
-    return paddedImage;
-};
-
-const matrixVariance = (matrix: number[][]): number => {
-    const numRows = matrix.length;
-    const numCols = matrix[0].length;
-    const totalElements = numRows * numCols;
-
-    // Calculate the mean.
-    let mean: number = 0;
-    matrix.forEach((row) => {
-        row.forEach((value) => {
-            mean += value;
-        });
-    });
-    mean /= totalElements;
-
-    // Calculate the variance.
-    let variance: number = 0;
-    matrix.forEach((row) => {
-        row.forEach((value) => {
-            const diff: number = value - mean;
-            variance += diff * diff;
-        });
-    });
-    variance /= totalElements;
-
-    return variance;
-};

web/apps/photos/src/services/face/cluster.ts

@@ -1,8 +1,9 @@
 import { Hdbscan, type DebugInfo } from "hdbscan";
-import { type Cluster } from "services/face/types";
+
+export type Cluster = number[];
 
 export interface ClusterFacesResult {
-    clusters: Array<Cluster>;
+    clusters: Cluster[];
     noise: Cluster;
     debugInfo?: DebugInfo;
 }

web/apps/photos/src/services/face/crop.ts

@@ -1,32 +0,0 @@
-import { Box, enlargeBox } from "services/face/geom";
-import { FaceCrop, FaceDetection } from "services/face/types";
-import { cropWithRotation } from "utils/image";
-import { faceAlignment } from "./align";
-
-export const getFaceCrop = (
-    imageBitmap: ImageBitmap,
-    faceDetection: FaceDetection,
-): FaceCrop => {
-    const alignment = faceAlignment(faceDetection);
-
-    const padding = 0.25;
-    const maxSize = 256;
-
-    const alignmentBox = new Box({
-        x: alignment.center.x - alignment.size / 2,
-        y: alignment.center.y - alignment.size / 2,
-        width: alignment.size,
-        height: alignment.size,
-    }).round();
-    const scaleForPadding = 1 + padding * 2;
-    const paddedBox = enlargeBox(alignmentBox, scaleForPadding).round();
-    const faceImageBitmap = cropWithRotation(imageBitmap, paddedBox, 0, {
-        width: maxSize,
-        height: maxSize,
-    });
-
-    return {
-        image: faceImageBitmap,
-        imageBox: paddedBox,
-    };
-};

web/apps/photos/src/services/face/db.ts

@@ -9,7 +9,8 @@ import {
     openDB,
 } from "idb";
 import isElectron from "is-electron";
-import { Face, MLLibraryData, MlFileData, Person } from "services/face/types";
+import type { Person } from "services/face/people";
+import type { MlFileData } from "services/face/types";
 import {
     DEFAULT_ML_SEARCH_CONFIG,
     MAX_ML_SYNC_ERROR_COUNT,
@@ -23,6 +24,18 @@ export interface IndexStatus {
     peopleIndexSynced: boolean;
 }
 
+/**
+ * TODO(MR): Transient type with an intersection of values that both existing
+ * and new types during the migration will have. Eventually we'll store the the
+ * server ML data shape here exactly.
+ */
+export interface MinimalPersistedFileData {
+    fileId: number;
+    mlVersion: number;
+    errorCount: number;
+    faces?: { personId?: number; id: string }[];
+}
+
 interface Config {}
 
 export const ML_SEARCH_CONFIG_NAME = "ml-search";
@@ -31,7 +44,7 @@ const MLDATA_DB_NAME = "mldata";
 interface MLDb extends DBSchema {
     files: {
         key: number;
-        value: MlFileData;
+        value: MinimalPersistedFileData;
         indexes: { mlVersion: [number, number] };
     };
     people: {
@@ -50,7 +63,7 @@ interface MLDb extends DBSchema {
     };
     library: {
         key: string;
-        value: MLLibraryData;
+        value: unknown;
     };
     configs: {
         key: string;
@@ -177,6 +190,7 @@ class MLIDbStorage {
                                 ML_SEARCH_CONFIG_NAME,
                             );
 
+                        db.deleteObjectStore("library");
                         db.deleteObjectStore("things");
                     } catch {
                         // TODO: ignore for now as we finalize the new version
@@ -210,38 +224,6 @@ class MLIDbStorage {
         await this.db;
     }
 
-    public async getAllFileIds() {
-        const db = await this.db;
-        return db.getAllKeys("files");
-    }
-
-    public async putAllFilesInTx(mlFiles: Array<MlFileData>) {
-        const db = await this.db;
-        const tx = db.transaction("files", "readwrite");
-        await Promise.all(mlFiles.map((mlFile) => tx.store.put(mlFile)));
-        await tx.done;
-    }
-
-    public async removeAllFilesInTx(fileIds: Array<number>) {
-        const db = await this.db;
-        const tx = db.transaction("files", "readwrite");
-
-        await Promise.all(fileIds.map((fileId) => tx.store.delete(fileId)));
-        await tx.done;
-    }
-
-    public async newTransaction<
-        Name extends StoreNames<MLDb>,
-        Mode extends IDBTransactionMode = "readonly",
-    >(storeNames: Name, mode?: Mode) {
-        const db = await this.db;
-        return db.transaction(storeNames, mode);
-    }
-
-    public async commit(tx: IDBPTransaction<MLDb>) {
-        return tx.done;
-    }
-
     public async getAllFileIdsForUpdate(
         tx: IDBPTransaction<MLDb, ["files"], "readwrite">,
     ) {
@@ -275,16 +257,11 @@ class MLIDbStorage {
         return fileIds;
     }
 
-    public async getFile(fileId: number) {
+    public async getFile(fileId: number): Promise<MinimalPersistedFileData> {
         const db = await this.db;
         return db.get("files", fileId);
     }
 
-    public async getAllFiles() {
-        const db = await this.db;
-        return db.getAll("files");
-    }
-
     public async putFile(mlFile: MlFileData) {
         const db = await this.db;
         return db.put("files", mlFile);
@@ -292,7 +269,7 @@ class MLIDbStorage {
 
     public async upsertFileInTx(
         fileId: number,
-        upsert: (mlFile: MlFileData) => MlFileData,
+        upsert: (mlFile: MinimalPersistedFileData) => MinimalPersistedFileData,
     ) {
         const db = await this.db;
         const tx = db.transaction("files", "readwrite");
@@ -305,7 +282,7 @@ class MLIDbStorage {
     }
 
     public async putAllFiles(
-        mlFiles: Array<MlFileData>,
+        mlFiles: MinimalPersistedFileData[],
         tx: IDBPTransaction<MLDb, ["files"], "readwrite">,
     ) {
         await Promise.all(mlFiles.map((mlFile) => tx.store.put(mlFile)));
@@ -318,44 +295,6 @@ class MLIDbStorage {
         await Promise.all(fileIds.map((fileId) => tx.store.delete(fileId)));
     }
 
-    public async getFace(fileID: number, faceId: string) {
-        const file = await this.getFile(fileID);
-        const face = file.faces.filter((f) => f.id === faceId);
-        return face[0];
-    }
-
-    public async getAllFacesMap() {
-        const startTime = Date.now();
-        const db = await this.db;
-        const allFiles = await db.getAll("files");
-        const allFacesMap = new Map<number, Array<Face>>();
-        allFiles.forEach(
-            (mlFileData) =>
-                mlFileData.faces &&
-                allFacesMap.set(mlFileData.fileId, mlFileData.faces),
-        );
-        log.info("getAllFacesMap", Date.now() - startTime, "ms");
-
-        return allFacesMap;
-    }
-
-    public async updateFaces(allFacesMap: Map<number, Face[]>) {
-        const startTime = Date.now();
-        const db = await this.db;
-        const tx = db.transaction("files", "readwrite");
-        let cursor = await tx.store.openCursor();
-        while (cursor) {
-            if (allFacesMap.has(cursor.key)) {
-                const mlFileData = { ...cursor.value };
-                mlFileData.faces = allFacesMap.get(cursor.key);
-                cursor.update(mlFileData);
-            }
-            cursor = await cursor.continue();
-        }
-        await tx.done;
-        log.info("updateFaces", Date.now() - startTime, "ms");
-    }
-
     public async getPerson(id: number) {
         const db = await this.db;
         return db.get("people", id);
@@ -366,21 +305,6 @@ class MLIDbStorage {
         return db.getAll("people");
     }
 
-    public async putPerson(person: Person) {
-        const db = await this.db;
-        return db.put("people", person);
-    }
-
-    public async clearAllPeople() {
-        const db = await this.db;
-        return db.clear("people");
-    }
-
-    public async getIndexVersion(index: string) {
-        const db = await this.db;
-        return db.get("versions", index);
-    }
-
     public async incrementIndexVersion(index: StoreNames<MLDb>) {
         if (index === "versions") {
             throw new Error("versions store can not be versioned");
@@ -395,21 +319,6 @@ class MLIDbStorage {
         return version;
     }
 
-    public async setIndexVersion(index: string, version: number) {
-        const db = await this.db;
-        return db.put("versions", version, index);
-    }
-
-    public async getLibraryData() {
-        const db = await this.db;
-        return db.get("library", "data");
-    }
-
-    public async putLibraryData(data: MLLibraryData) {
-        const db = await this.db;
-        return db.put("library", data, "data");
-    }
-
     public async getConfig<T extends Config>(name: string, def: T) {
         const db = await this.db;
         const tx = db.transaction("configs", "readwrite");
@@ -473,66 +382,6 @@ class MLIDbStorage {
                 peopleIndexVersion === filesIndexVersion,
         };
     }
-
-    // for debug purpose
-    public async getAllMLData() {
-        const db = await this.db;
-        const tx = db.transaction(db.objectStoreNames, "readonly");
-        const allMLData: any = {};
-        for (const store of tx.objectStoreNames) {
-            const keys = await tx.objectStore(store).getAllKeys();
-            const data = await tx.objectStore(store).getAll();
-
-            allMLData[store] = {};
-            for (let i = 0; i < keys.length; i++) {
-                allMLData[store][keys[i]] = data[i];
-            }
-        }
-        await tx.done;
-
-        const files = allMLData["files"];
-        for (const fileId of Object.keys(files)) {
-            const fileData = files[fileId];
-            fileData.faces?.forEach(
-                (f) => (f.embedding = Array.from(f.embedding)),
-            );
-        }
-
-        return allMLData;
-    }
-
-    // for debug purpose, this will overwrite all data
-    public async putAllMLData(allMLData: Map<string, any>) {
-        const db = await this.db;
-        const tx = db.transaction(db.objectStoreNames, "readwrite");
-        for (const store of tx.objectStoreNames) {
-            const records = allMLData[store];
-            if (!records) {
-                continue;
-            }
-            const txStore = tx.objectStore(store);
-
-            if (store === "files") {
-                const files = records;
-                for (const fileId of Object.keys(files)) {
-                    const fileData = files[fileId];
-                    fileData.faces?.forEach(
-                        (f) => (f.embedding = Float32Array.from(f.embedding)),
-                    );
-                }
-            }
-
-            await txStore.clear();
-            for (const key of Object.keys(records)) {
-                if (txStore.keyPath) {
-                    txStore.put(records[key]);
-                } else {
-                    txStore.put(records[key], key);
-                }
-            }
-        }
-        await tx.done;
-    }
 }
 
 export default new MLIDbStorage();

web/apps/photos/src/services/face/detect.ts

@@ -1,316 +0,0 @@
-import { workerBridge } from "@/next/worker/worker-bridge";
-import { euclidean } from "hdbscan";
-import {
-    Box,
-    Dimensions,
-    Point,
-    boxFromBoundingBox,
-    newBox,
-} from "services/face/geom";
-import { FaceDetection } from "services/face/types";
-import {
-    Matrix,
-    applyToPoint,
-    compose,
-    scale,
-    translate,
-} from "transformation-matrix";
-import {
-    clamp,
-    getPixelBilinear,
-    normalizePixelBetween0And1,
-} from "utils/image";
-
-/**
- * Detect faces in the given {@link imageBitmap}.
- *
- * The model used is YOLO, running in an ONNX runtime.
- */
-export const detectFaces = async (
-    imageBitmap: ImageBitmap,
-): Promise<Array<FaceDetection>> => {
-    const maxFaceDistancePercent = Math.sqrt(2) / 100;
-    const maxFaceDistance = imageBitmap.width * maxFaceDistancePercent;
-    const preprocessResult = preprocessImageBitmapToFloat32ChannelsFirst(
-        imageBitmap,
-        640,
-        640,
-    );
-    const data = preprocessResult.data;
-    const resized = preprocessResult.newSize;
-    const outputData = await workerBridge.detectFaces(data);
-    const faces = getFacesFromYOLOOutput(outputData as Float32Array, 0.7);
-    const inBox = newBox(0, 0, resized.width, resized.height);
-    const toBox = newBox(0, 0, imageBitmap.width, imageBitmap.height);
-    const transform = computeTransformToBox(inBox, toBox);
-    const faceDetections: Array<FaceDetection> = faces?.map((f) => {
-        const box = transformBox(f.box, transform);
-        const normLandmarks = f.landmarks;
-        const landmarks = transformPoints(normLandmarks, transform);
-        return {
-            box,
-            landmarks,
-            probability: f.probability as number,
-        } as FaceDetection;
-    });
-    return removeDuplicateDetections(faceDetections, maxFaceDistance);
-};
-
-const preprocessImageBitmapToFloat32ChannelsFirst = (
-    imageBitmap: ImageBitmap,
-    requiredWidth: number,
-    requiredHeight: number,
-    maintainAspectRatio: boolean = true,
-    normFunction: (pixelValue: number) => number = normalizePixelBetween0And1,
-) => {
-    // Create an OffscreenCanvas and set its size.
-    const offscreenCanvas = new OffscreenCanvas(
-        imageBitmap.width,
-        imageBitmap.height,
-    );
-    const ctx = offscreenCanvas.getContext("2d");
-    ctx.drawImage(imageBitmap, 0, 0, imageBitmap.width, imageBitmap.height);
-    const imageData = ctx.getImageData(
-        0,
-        0,
-        imageBitmap.width,
-        imageBitmap.height,
-    );
-    const pixelData = imageData.data;
-
-    let scaleW = requiredWidth / imageBitmap.width;
-    let scaleH = requiredHeight / imageBitmap.height;
-    if (maintainAspectRatio) {
-        const scale = Math.min(
-            requiredWidth / imageBitmap.width,
-            requiredHeight / imageBitmap.height,
-        );
-        scaleW = scale;
-        scaleH = scale;
-    }
-    const scaledWidth = clamp(
-        Math.round(imageBitmap.width * scaleW),
-        0,
-        requiredWidth,
-    );
-    const scaledHeight = clamp(
-        Math.round(imageBitmap.height * scaleH),
-        0,
-        requiredHeight,
-    );
-
-    const processedImage = new Float32Array(
-        1 * 3 * requiredWidth * requiredHeight,
-    );
-
-    // Populate the Float32Array with normalized pixel values
-    let pixelIndex = 0;
-    const channelOffsetGreen = requiredHeight * requiredWidth;
-    const channelOffsetBlue = 2 * requiredHeight * requiredWidth;
-    for (let h = 0; h < requiredHeight; h++) {
-        for (let w = 0; w < requiredWidth; w++) {
-            let pixel: {
-                r: number;
-                g: number;
-                b: number;
-            };
-            if (w >= scaledWidth || h >= scaledHeight) {
-                pixel = { r: 114, g: 114, b: 114 };
-            } else {
-                pixel = getPixelBilinear(
-                    w / scaleW,
-                    h / scaleH,
-                    pixelData,
-                    imageBitmap.width,
-                    imageBitmap.height,
-                );
-            }
-            processedImage[pixelIndex] = normFunction(pixel.r);
-            processedImage[pixelIndex + channelOffsetGreen] = normFunction(
-                pixel.g,
-            );
-            processedImage[pixelIndex + channelOffsetBlue] = normFunction(
-                pixel.b,
-            );
-            pixelIndex++;
-        }
-    }
-
-    return {
-        data: processedImage,
-        originalSize: {
-            width: imageBitmap.width,
-            height: imageBitmap.height,
-        },
-        newSize: { width: scaledWidth, height: scaledHeight },
-    };
-};
-
-/**
- * @param rowOutput A Float32Array of shape [25200, 16], where each row
- * represents a bounding box.
- */
-const getFacesFromYOLOOutput = (
-    rowOutput: Float32Array,
-    minScore: number,
-): Array<FaceDetection> => {
-    const faces: Array<FaceDetection> = [];
-    // Iterate over each row.
-    for (let i = 0; i < rowOutput.length; i += 16) {
-        const score = rowOutput[i + 4];
-        if (score < minScore) {
-            continue;
-        }
-        // The first 4 values represent the bounding box's coordinates:
-        //
-        //     (x1, y1, x2, y2)
-        //
-        const xCenter = rowOutput[i];
-        const yCenter = rowOutput[i + 1];
-        const width = rowOutput[i + 2];
-        const height = rowOutput[i + 3];
-        const xMin = xCenter - width / 2.0; // topLeft
-        const yMin = yCenter - height / 2.0; // topLeft
-
-        const leftEyeX = rowOutput[i + 5];
-        const leftEyeY = rowOutput[i + 6];
-        const rightEyeX = rowOutput[i + 7];
-        const rightEyeY = rowOutput[i + 8];
-        const noseX = rowOutput[i + 9];
-        const noseY = rowOutput[i + 10];
-        const leftMouthX = rowOutput[i + 11];
-        const leftMouthY = rowOutput[i + 12];
-        const rightMouthX = rowOutput[i + 13];
-        const rightMouthY = rowOutput[i + 14];
-
-        const box = new Box({
-            x: xMin,
-            y: yMin,
-            width: width,
-            height: height,
-        });
-        const probability = score as number;
-        const landmarks = [
-            new Point(leftEyeX, leftEyeY),
-            new Point(rightEyeX, rightEyeY),
-            new Point(noseX, noseY),
-            new Point(leftMouthX, leftMouthY),
-            new Point(rightMouthX, rightMouthY),
-        ];
-        faces.push({ box, landmarks, probability });
-    }
-    return faces;
-};
-
-export const getRelativeDetection = (
-    faceDetection: FaceDetection,
-    dimensions: Dimensions,
-): FaceDetection => {
-    const oldBox: Box = faceDetection.box;
-    const box = new Box({
-        x: oldBox.x / dimensions.width,
-        y: oldBox.y / dimensions.height,
-        width: oldBox.width / dimensions.width,
-        height: oldBox.height / dimensions.height,
-    });
-    const oldLandmarks: Point[] = faceDetection.landmarks;
-    const landmarks = oldLandmarks.map((l) => {
-        return new Point(l.x / dimensions.width, l.y / dimensions.height);
-    });
-    const probability = faceDetection.probability;
-    return { box, landmarks, probability };
-};
-
-/**
- * Removes duplicate face detections from an array of detections.
- *
- * This function sorts the detections by their probability in descending order,
- * then iterates over them.
- *
- * For each detection, it calculates the Euclidean distance to all other
- * detections.
- *
- * If the distance is less than or equal to the specified threshold
- * (`withinDistance`), the other detection is considered a duplicate and is
- * removed.
- *
- * @param detections - An array of face detections to remove duplicates from.
- *
- * @param withinDistance - The maximum Euclidean distance between two detections
- * for them to be considered duplicates.
- *
- * @returns An array of face detections with duplicates removed.
- */
-const removeDuplicateDetections = (
-    detections: Array<FaceDetection>,
-    withinDistance: number,
-) => {
-    detections.sort((a, b) => b.probability - a.probability);
-    const isSelected = new Map<number, boolean>();
-    for (let i = 0; i < detections.length; i++) {
-        if (isSelected.get(i) === false) {
-            continue;
-        }
-        isSelected.set(i, true);
-        for (let j = i + 1; j < detections.length; j++) {
-            if (isSelected.get(j) === false) {
-                continue;
-            }
-            const centeri = getDetectionCenter(detections[i]);
-            const centerj = getDetectionCenter(detections[j]);
-            const dist = euclidean(
-                [centeri.x, centeri.y],
-                [centerj.x, centerj.y],
-            );
-            if (dist <= withinDistance) {
-                isSelected.set(j, false);
-            }
-        }
-    }
-
-    const uniques: Array<FaceDetection> = [];
-    for (let i = 0; i < detections.length; i++) {
-        isSelected.get(i) && uniques.push(detections[i]);
-    }
-    return uniques;
-};
-
-function getDetectionCenter(detection: FaceDetection) {
-    const center = new Point(0, 0);
-    // TODO: first 4 landmarks is applicable to blazeface only
-    // this needs to consider eyes, nose and mouth landmarks to take center
-    detection.landmarks?.slice(0, 4).forEach((p) => {
-        center.x += p.x;
-        center.y += p.y;
-    });
-
-    return new Point(center.x / 4, center.y / 4);
-}
-
-function computeTransformToBox(inBox: Box, toBox: Box): Matrix {
-    return compose(
-        translate(toBox.x, toBox.y),
-        scale(toBox.width / inBox.width, toBox.height / inBox.height),
-    );
-}
-
-function transformPoint(point: Point, transform: Matrix) {
-    const txdPoint = applyToPoint(transform, point);
-    return new Point(txdPoint.x, txdPoint.y);
-}
-
-function transformPoints(points: Point[], transform: Matrix) {
-    return points?.map((p) => transformPoint(p, transform));
-}
-
-function transformBox(box: Box, transform: Matrix) {
-    const topLeft = transformPoint(box.topLeft, transform);
-    const bottomRight = transformPoint(box.bottomRight, transform);
-
-    return boxFromBoundingBox({
-        left: topLeft.x,
-        top: topLeft.y,
-        right: bottomRight.x,
-        bottom: bottomRight.y,
-    });
-}

web/apps/photos/src/services/face/embed.ts

@@ -1,26 +0,0 @@
-import { workerBridge } from "@/next/worker/worker-bridge";
-import { FaceEmbedding } from "services/face/types";
-
-export const mobileFaceNetFaceSize = 112;
-
-/**
- * Compute embeddings for the given {@link faceData}.
- *
- * The model used is MobileFaceNet, running in an ONNX runtime.
- */
-export const faceEmbeddings = async (
-    faceData: Float32Array,
-): Promise<Array<FaceEmbedding>> => {
-    const outputData = await workerBridge.faceEmbeddings(faceData);
-
-    const embeddingSize = 192;
-    const embeddings = new Array<FaceEmbedding>(
-        outputData.length / embeddingSize,
-    );
-    for (let i = 0; i < embeddings.length; i++) {
-        embeddings[i] = new Float32Array(
-            outputData.slice(i * embeddingSize, (i + 1) * embeddingSize),
-        );
-    }
-    return embeddings;
-};

web/apps/photos/src/services/face/f-index.ts

@@ -1,194 +1,742 @@
-import { openCache } from "@/next/blob-cache";
+import { FILE_TYPE } from "@/media/file-type";
+import { blobCache } from "@/next/blob-cache";
 import log from "@/next/log";
-import { faceAlignment } from "services/face/align";
+import { workerBridge } from "@/next/worker/worker-bridge";
-import mlIDbStorage from "services/face/db";
+import { euclidean } from "hdbscan";
-import { detectFaces, getRelativeDetection } from "services/face/detect";
+import { Matrix } from "ml-matrix";
-import { faceEmbeddings, mobileFaceNetFaceSize } from "services/face/embed";
 import {
-    DetectedFace,
+    Box,
+    Dimensions,
+    Point,
+    enlargeBox,
+    roundBox,
+} from "services/face/geom";
+import type {
     Face,
-    MLSyncFileContext,
+    FaceAlignment,
-    type FaceAlignment,
+    FaceDetection,
+    MlFileData,
 } from "services/face/types";
-import { imageBitmapToBlob, warpAffineFloat32List } from "utils/image";
+import { defaultMLVersion } from "services/machineLearning/machineLearningService";
-import { detectBlur } from "./blur";
+import { getSimilarityTransformation } from "similarity-transformation";
-import { getFaceCrop } from "./crop";
+import type { EnteFile } from "types/file";
+import { fetchImageBitmap, getLocalFileImageBitmap } from "./file";
 import {
-    fetchImageBitmap,
+    clamp,
-    fetchImageBitmapForContext,
+    grayscaleIntMatrixFromNormalized2List,
-    getFaceId,
+    pixelRGBBilinear,
-    getLocalFile,
+    warpAffineFloat32List,
 } from "./image";
+import { transformFaceDetections } from "./transform-box";
 
-export const syncFileAnalyzeFaces = async (fileContext: MLSyncFileContext) => {
+/**
-    const { newMlFile } = fileContext;
+ * Index faces in the given file.
+ *
+ * This function is the entry point to the indexing pipeline. The file goes
+ * through various stages:
+ *
+ * 1. Downloading the original if needed.
+ * 2. Detect faces using ONNX/YOLO
+ * 3. Align the face rectangles, compute blur.
+ * 4. Compute embeddings for the detected face (crops).
+ *
+ * Once all of it is done, it returns the face rectangles and embeddings so that
+ * they can be saved locally for offline use, and encrypts and uploads them to
+ * the user's remote storage so that their other devices can download them
+ * instead of needing to reindex.
+ */
+export const indexFaces = async (enteFile: EnteFile, localFile?: File) => {
     const startTime = Date.now();
 
-    await syncFileFaceDetections(fileContext);
+    const imageBitmap = await fetchOrCreateImageBitmap(enteFile, localFile);
-
+    let mlFile: MlFileData;
-    if (newMlFile.faces && newMlFile.faces.length > 0) {
+    try {
-        await syncFileFaceCrops(fileContext);
+        mlFile = await indexFaces_(enteFile, imageBitmap);
-
+    } finally {
-        const alignedFacesData = await syncFileFaceAlignments(fileContext);
+        imageBitmap.close();
-
-        await syncFileFaceEmbeddings(fileContext, alignedFacesData);
-
-        await syncFileFaceMakeRelativeDetections(fileContext);
     }
-    log.debug(
-        () =>
-            `Face detection for file ${fileContext.enteFile.id} took ${Math.round(Date.now() - startTime)} ms`,
-    );
-};
 
-const syncFileFaceDetections = async (fileContext: MLSyncFileContext) => {
+    log.debug(() => {
-    const { newMlFile } = fileContext;
+        const nf = mlFile.faces?.length ?? 0;
-    newMlFile.faceDetectionMethod = {
+        const ms = Date.now() - startTime;
-        value: "YoloFace",
+        return `Indexed ${nf} faces in file ${enteFile.id} (${ms} ms)`;
-        version: 1,
-    };
-    fileContext.newDetection = true;
-    const imageBitmap = await fetchImageBitmapForContext(fileContext);
-    const faceDetections = await detectFaces(imageBitmap);
-    // TODO: reenable faces filtering based on width
-    const detectedFaces = faceDetections?.map((detection) => {
-        return {
-            fileId: fileContext.enteFile.id,
-            detection,
-        } as DetectedFace;
     });
-    newMlFile.faces = detectedFaces?.map((detectedFace) => ({
+    return mlFile;
-        ...detectedFace,
+};
-        id: getFaceId(detectedFace, newMlFile.imageDimensions),
+
+/**
+ * Return a {@link ImageBitmap}, using {@link localFile} if present otherwise
+ * downloading the source image corresponding to {@link enteFile} from remote.
+ */
+const fetchOrCreateImageBitmap = async (
+    enteFile: EnteFile,
+    localFile: File,
+) => {
+    const fileType = enteFile.metadata.fileType;
+    if (localFile) {
+        // TODO-ML(MR): Could also be image part of live photo?
+        if (fileType !== FILE_TYPE.IMAGE)
+            throw new Error("Local file of only image type is supported");
+
+        return await getLocalFileImageBitmap(enteFile, localFile);
+    } else if ([FILE_TYPE.IMAGE, FILE_TYPE.LIVE_PHOTO].includes(fileType)) {
+        return await fetchImageBitmap(enteFile);
+    } else {
+        throw new Error(`Cannot index unsupported file type ${fileType}`);
+    }
+};
+
+const indexFaces_ = async (enteFile: EnteFile, imageBitmap: ImageBitmap) => {
+    const fileID = enteFile.id;
+    const { width, height } = imageBitmap;
+    const imageDimensions = { width, height };
+    const mlFile: MlFileData = {
+        fileId: fileID,
+        mlVersion: defaultMLVersion,
+        imageDimensions,
+        errorCount: 0,
+    };
+
+    const faceDetections = await detectFaces(imageBitmap);
+    const detectedFaces = faceDetections.map((detection) => ({
+        id: makeFaceID(fileID, detection, imageDimensions),
+        fileId: fileID,
+        detection,
     }));
-    // ?.filter((f) =>
+    mlFile.faces = detectedFaces;
-    //     f.box.width > syncContext.config.faceDetection.minFaceSize
-    // );
-    log.info("[MLService] Detected Faces: ", newMlFile.faces?.length);
-};
 
-const syncFileFaceCrops = async (fileContext: MLSyncFileContext) => {
+    if (detectedFaces.length > 0) {
-    const { newMlFile } = fileContext;
+        const alignments: FaceAlignment[] = [];
-    const imageBitmap = await fetchImageBitmapForContext(fileContext);
-    newMlFile.faceCropMethod = {
-        value: "ArcFace",
-        version: 1,
-    };
 
-    for (const face of newMlFile.faces) {
+        for (const face of mlFile.faces) {
-        await saveFaceCrop(imageBitmap, face);
+            const alignment = faceAlignment(face.detection);
-    }
+            face.alignment = alignment;
-};
+            alignments.push(alignment);
 
-const syncFileFaceAlignments = async (
+            await saveFaceCrop(imageBitmap, face);
-    fileContext: MLSyncFileContext,
+        }
-): Promise<Float32Array> => {
-    const { newMlFile } = fileContext;
-    newMlFile.faceAlignmentMethod = {
-        value: "ArcFace",
-        version: 1,
-    };
-    fileContext.newAlignment = true;
-    const imageBitmap =
-        fileContext.imageBitmap ||
-        (await fetchImageBitmapForContext(fileContext));
 
-    // Execute the face alignment calculations
+        const alignedFacesData = convertToMobileFaceNetInput(
-    for (const face of newMlFile.faces) {
+            imageBitmap,
-        face.alignment = faceAlignment(face.detection);
+            alignments,
-    }
-    // Extract face images and convert to Float32Array
-    const faceAlignments = newMlFile.faces.map((f) => f.alignment);
-    const faceImages = await extractFaceImagesToFloat32(
-        faceAlignments,
-        mobileFaceNetFaceSize,
-        imageBitmap,
-    );
-    const blurValues = detectBlur(faceImages, newMlFile.faces);
-    newMlFile.faces.forEach((f, i) => (f.blurValue = blurValues[i]));
-
-    imageBitmap.close();
-    log.info("[MLService] alignedFaces: ", newMlFile.faces?.length);
-
-    return faceImages;
-};
-
-const syncFileFaceEmbeddings = async (
-    fileContext: MLSyncFileContext,
-    alignedFacesInput: Float32Array,
-) => {
-    const { newMlFile } = fileContext;
-    newMlFile.faceEmbeddingMethod = {
-        value: "MobileFaceNet",
-        version: 2,
-    };
-    // TODO: when not storing face crops, image will be needed to extract faces
-    // fileContext.imageBitmap ||
-    //     (await this.getImageBitmap(fileContext));
-
-    const embeddings = await faceEmbeddings(alignedFacesInput);
-    newMlFile.faces.forEach((f, i) => (f.embedding = embeddings[i]));
-
-    log.info("[MLService] facesWithEmbeddings: ", newMlFile.faces.length);
-};
-
-const syncFileFaceMakeRelativeDetections = async (
-    fileContext: MLSyncFileContext,
-) => {
-    const { newMlFile } = fileContext;
-    for (let i = 0; i < newMlFile.faces.length; i++) {
-        const face = newMlFile.faces[i];
-        if (face.detection.box.x + face.detection.box.width < 2) continue; // Skip if somehow already relative
-        face.detection = getRelativeDetection(
-            face.detection,
-            newMlFile.imageDimensions,
         );
-    }
-};
 
-export const saveFaceCrop = async (imageBitmap: ImageBitmap, face: Face) => {
+        const blurValues = detectBlur(alignedFacesData, mlFile.faces);
-    const faceCrop = getFaceCrop(imageBitmap, face.detection);
+        mlFile.faces.forEach((f, i) => (f.blurValue = blurValues[i]));
 
-    const blob = await imageBitmapToBlob(faceCrop.image);
+        const embeddings = await computeEmbeddings(alignedFacesData);
+        mlFile.faces.forEach((f, i) => (f.embedding = embeddings[i]));
 
-    const cache = await openCache("face-crops");
+        mlFile.faces.forEach((face) => {
-    await cache.put(face.id, blob);
+            face.detection = relativeDetection(face.detection, imageDimensions);
-
+        });
-    faceCrop.image.close();
-
-    return blob;
-};
-
-export const regenerateFaceCrop = async (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);
+    return mlFile;
-    const imageBitmap = await fetchImageBitmap(file);
-    return await saveFaceCrop(imageBitmap, personFace);
 };
 
-async function extractFaceImagesToFloat32(
+/**
-    faceAlignments: Array<FaceAlignment>,
+ * Detect faces in the given {@link imageBitmap}.
+ *
+ * The model used is YOLO, running in an ONNX runtime.
+ */
+const detectFaces = async (
+    imageBitmap: ImageBitmap,
+): Promise<FaceDetection[]> => {
+    const rect = ({ width, height }: Dimensions) =>
+        new Box({ x: 0, y: 0, width, height });
+
+    const { yoloInput, yoloSize } =
+        convertToYOLOInputFloat32ChannelsFirst(imageBitmap);
+    const yoloOutput = await workerBridge.detectFaces(yoloInput);
+    const faces = faceDetectionsFromYOLOOutput(yoloOutput);
+    const faceDetections = transformFaceDetections(
+        faces,
+        rect(yoloSize),
+        rect(imageBitmap),
+    );
+
+    const maxFaceDistancePercent = Math.sqrt(2) / 100;
+    const maxFaceDistance = imageBitmap.width * maxFaceDistancePercent;
+    return removeDuplicateDetections(faceDetections, maxFaceDistance);
+};
+
+/**
+ * Convert {@link imageBitmap} into the format that the YOLO face detection
+ * model expects.
+ */
+const convertToYOLOInputFloat32ChannelsFirst = (imageBitmap: ImageBitmap) => {
+    const requiredWidth = 640;
+    const requiredHeight = 640;
+
+    const { width, height } = imageBitmap;
+
+    // Create an OffscreenCanvas and set its size.
+    const offscreenCanvas = new OffscreenCanvas(width, height);
+    const ctx = offscreenCanvas.getContext("2d");
+    ctx.drawImage(imageBitmap, 0, 0, width, height);
+    const imageData = ctx.getImageData(0, 0, width, height);
+    const pixelData = imageData.data;
+
+    // Maintain aspect ratio.
+    const scale = Math.min(requiredWidth / width, requiredHeight / height);
+
+    const scaledWidth = clamp(Math.round(width * scale), 0, requiredWidth);
+    const scaledHeight = clamp(Math.round(height * scale), 0, requiredHeight);
+
+    const yoloInput = new Float32Array(1 * 3 * requiredWidth * requiredHeight);
+    const yoloSize = { width: scaledWidth, height: scaledHeight };
+
+    // Populate the Float32Array with normalized pixel values.
+    let pi = 0;
+    const channelOffsetGreen = requiredHeight * requiredWidth;
+    const channelOffsetBlue = 2 * requiredHeight * requiredWidth;
+    for (let h = 0; h < requiredHeight; h++) {
+        for (let w = 0; w < requiredWidth; w++) {
+            const { r, g, b } =
+                w >= scaledWidth || h >= scaledHeight
+                    ? { r: 114, g: 114, b: 114 }
+                    : pixelRGBBilinear(
+                          w / scale,
+                          h / scale,
+                          pixelData,
+                          width,
+                          height,
+                      );
+            yoloInput[pi] = r / 255.0;
+            yoloInput[pi + channelOffsetGreen] = g / 255.0;
+            yoloInput[pi + channelOffsetBlue] = b / 255.0;
+            pi++;
+        }
+    }
+
+    return { yoloInput, yoloSize };
+};
+
+/**
+ * Extract detected faces from the YOLO's output.
+ *
+ * Only detections that exceed a minimum score are returned.
+ *
+ * @param rows A Float32Array of shape [25200, 16], where each row
+ * represents a bounding box.
+ */
+const faceDetectionsFromYOLOOutput = (rows: Float32Array): FaceDetection[] => {
+    const faces: FaceDetection[] = [];
+    // Iterate over each row.
+    for (let i = 0; i < rows.length; i += 16) {
+        const score = rows[i + 4];
+        if (score < 0.7) continue;
+
+        const xCenter = rows[i];
+        const yCenter = rows[i + 1];
+        const width = rows[i + 2];
+        const height = rows[i + 3];
+        const xMin = xCenter - width / 2.0; // topLeft
+        const yMin = yCenter - height / 2.0; // topLeft
+
+        const leftEyeX = rows[i + 5];
+        const leftEyeY = rows[i + 6];
+        const rightEyeX = rows[i + 7];
+        const rightEyeY = rows[i + 8];
+        const noseX = rows[i + 9];
+        const noseY = rows[i + 10];
+        const leftMouthX = rows[i + 11];
+        const leftMouthY = rows[i + 12];
+        const rightMouthX = rows[i + 13];
+        const rightMouthY = rows[i + 14];
+
+        const box = new Box({
+            x: xMin,
+            y: yMin,
+            width: width,
+            height: height,
+        });
+        const probability = score as number;
+        const landmarks = [
+            new Point(leftEyeX, leftEyeY),
+            new Point(rightEyeX, rightEyeY),
+            new Point(noseX, noseY),
+            new Point(leftMouthX, leftMouthY),
+            new Point(rightMouthX, rightMouthY),
+        ];
+        faces.push({ box, landmarks, probability });
+    }
+    return faces;
+};
+
+/**
+ * Removes duplicate face detections from an array of detections.
+ *
+ * This function sorts the detections by their probability in descending order,
+ * then iterates over them.
+ *
+ * For each detection, it calculates the Euclidean distance to all other
+ * detections.
+ *
+ * If the distance is less than or equal to the specified threshold
+ * (`withinDistance`), the other detection is considered a duplicate and is
+ * removed.
+ *
+ * @param detections - An array of face detections to remove duplicates from.
+ *
+ * @param withinDistance - The maximum Euclidean distance between two detections
+ * for them to be considered duplicates.
+ *
+ * @returns An array of face detections with duplicates removed.
+ */
+const removeDuplicateDetections = (
+    detections: FaceDetection[],
+    withinDistance: number,
+) => {
+    detections.sort((a, b) => b.probability - a.probability);
+
+    const dupIndices = new Set<number>();
+    for (let i = 0; i < detections.length; i++) {
+        if (dupIndices.has(i)) continue;
+
+        for (let j = i + 1; j < detections.length; j++) {
+            if (dupIndices.has(j)) continue;
+
+            const centeri = faceDetectionCenter(detections[i]);
+            const centerj = faceDetectionCenter(detections[j]);
+            const dist = euclidean(
+                [centeri.x, centeri.y],
+                [centerj.x, centerj.y],
+            );
+
+            if (dist <= withinDistance) dupIndices.add(j);
+        }
+    }
+
+    return detections.filter((_, i) => !dupIndices.has(i));
+};
+
+const faceDetectionCenter = (detection: FaceDetection) => {
+    const center = new Point(0, 0);
+    // TODO-ML(LAURENS): first 4 landmarks is applicable to blazeface only this
+    // needs to consider eyes, nose and mouth landmarks to take center
+    detection.landmarks?.slice(0, 4).forEach((p) => {
+        center.x += p.x;
+        center.y += p.y;
+    });
+    return new Point(center.x / 4, center.y / 4);
+};
+
+const makeFaceID = (
+    fileID: number,
+    detection: FaceDetection,
+    imageDims: Dimensions,
+) => {
+    const part = (v: number) => clamp(v, 0.0, 0.999999).toFixed(5).substring(2);
+    const xMin = part(detection.box.x / imageDims.width);
+    const yMin = part(detection.box.y / imageDims.height);
+    const xMax = part(
+        (detection.box.x + detection.box.width) / imageDims.width,
+    );
+    const yMax = part(
+        (detection.box.y + detection.box.height) / imageDims.height,
+    );
+    return [`${fileID}`, xMin, yMin, xMax, yMax].join("_");
+};
+
+/**
+ * Compute and return an {@link FaceAlignment} for the given face detection.
+ *
+ * @param faceDetection A geometry indicating a face detected in an image.
+ */
+const faceAlignment = (faceDetection: FaceDetection): FaceAlignment =>
+    faceAlignmentUsingSimilarityTransform(
+        faceDetection,
+        normalizeLandmarks(idealMobileFaceNetLandmarks, mobileFaceNetFaceSize),
+    );
+
+/**
+ * The ideal location of the landmarks (eye etc) that the MobileFaceNet
+ * embedding model expects.
+ */
+const idealMobileFaceNetLandmarks: [number, number][] = [
+    [38.2946, 51.6963],
+    [73.5318, 51.5014],
+    [56.0252, 71.7366],
+    [41.5493, 92.3655],
+    [70.7299, 92.2041],
+];
+
+const normalizeLandmarks = (
+    landmarks: [number, number][],
     faceSize: number,
-    image: ImageBitmap,
+): [number, number][] =>
-): Promise<Float32Array> {
+    landmarks.map(([x, y]) => [x / faceSize, y / faceSize]);
+
+const faceAlignmentUsingSimilarityTransform = (
+    faceDetection: FaceDetection,
+    alignedLandmarks: [number, number][],
+): FaceAlignment => {
+    const landmarksMat = new Matrix(
+        faceDetection.landmarks
+            .map((p) => [p.x, p.y])
+            .slice(0, alignedLandmarks.length),
+    ).transpose();
+    const alignedLandmarksMat = new Matrix(alignedLandmarks).transpose();
+
+    const simTransform = getSimilarityTransformation(
+        landmarksMat,
+        alignedLandmarksMat,
+    );
+
+    const RS = Matrix.mul(simTransform.rotation, simTransform.scale);
+    const TR = simTransform.translation;
+
+    const affineMatrix = [
+        [RS.get(0, 0), RS.get(0, 1), TR.get(0, 0)],
+        [RS.get(1, 0), RS.get(1, 1), TR.get(1, 0)],
+        [0, 0, 1],
+    ];
+
+    const size = 1 / simTransform.scale;
+    const meanTranslation = simTransform.toMean.sub(0.5).mul(size);
+    const centerMat = simTransform.fromMean.sub(meanTranslation);
+    const center = new Point(centerMat.get(0, 0), centerMat.get(1, 0));
+    const rotation = -Math.atan2(
+        simTransform.rotation.get(0, 1),
+        simTransform.rotation.get(0, 0),
+    );
+
+    return { affineMatrix, center, size, rotation };
+};
+
+const convertToMobileFaceNetInput = (
+    imageBitmap: ImageBitmap,
+    faceAlignments: FaceAlignment[],
+): Float32Array => {
+    const faceSize = mobileFaceNetFaceSize;
     const faceData = new Float32Array(
         faceAlignments.length * faceSize * faceSize * 3,
     );
     for (let i = 0; i < faceAlignments.length; i++) {
-        const alignedFace = faceAlignments[i];
+        const { affineMatrix } = faceAlignments[i];
         const faceDataOffset = i * faceSize * faceSize * 3;
         warpAffineFloat32List(
-            image,
+            imageBitmap,
-            alignedFace,
+            affineMatrix,
             faceSize,
             faceData,
             faceDataOffset,
         );
     }
     return faceData;
-}
+};
+
+/**
+ * Laplacian blur detection.
+ *
+ * Return an array of detected blur values, one for each face in {@link faces}.
+ * The face data is taken from the slice of {@link alignedFacesData}
+ * corresponding to each face of {@link faces}.
+ */
+const detectBlur = (alignedFacesData: Float32Array, faces: Face[]): number[] =>
+    faces.map((face, i) => {
+        const faceImage = grayscaleIntMatrixFromNormalized2List(
+            alignedFacesData,
+            i,
+            mobileFaceNetFaceSize,
+            mobileFaceNetFaceSize,
+        );
+        return matrixVariance(applyLaplacian(faceImage, faceDirection(face)));
+    });
+
+type FaceDirection = "left" | "right" | "straight";
+
+const faceDirection = (face: Face): FaceDirection => {
+    const landmarks = face.detection.landmarks;
+    const leftEye = landmarks[0];
+    const rightEye = landmarks[1];
+    const nose = landmarks[2];
+    const leftMouth = landmarks[3];
+    const rightMouth = landmarks[4];
+
+    const eyeDistanceX = Math.abs(rightEye.x - leftEye.x);
+    const eyeDistanceY = Math.abs(rightEye.y - leftEye.y);
+    const mouthDistanceY = Math.abs(rightMouth.y - leftMouth.y);
+
+    const faceIsUpright =
+        Math.max(leftEye.y, rightEye.y) + 0.5 * eyeDistanceY < nose.y &&
+        nose.y + 0.5 * mouthDistanceY < Math.min(leftMouth.y, rightMouth.y);
+
+    const noseStickingOutLeft =
+        nose.x < Math.min(leftEye.x, rightEye.x) &&
+        nose.x < Math.min(leftMouth.x, rightMouth.x);
+
+    const noseStickingOutRight =
+        nose.x > Math.max(leftEye.x, rightEye.x) &&
+        nose.x > Math.max(leftMouth.x, rightMouth.x);
+
+    const noseCloseToLeftEye =
+        Math.abs(nose.x - leftEye.x) < 0.2 * eyeDistanceX;
+    const noseCloseToRightEye =
+        Math.abs(nose.x - rightEye.x) < 0.2 * eyeDistanceX;
+
+    if (noseStickingOutLeft || (faceIsUpright && noseCloseToLeftEye)) {
+        return "left";
+    } else if (noseStickingOutRight || (faceIsUpright && noseCloseToRightEye)) {
+        return "right";
+    }
+
+    return "straight";
+};
+
+/**
+ * Return a new image by applying a Laplacian blur kernel to each pixel.
+ */
+const applyLaplacian = (
+    image: number[][],
+    direction: FaceDirection,
+): number[][] => {
+    const paddedImage = padImage(image, direction);
+    const numRows = paddedImage.length - 2;
+    const numCols = paddedImage[0].length - 2;
+
+    // Create an output image initialized to 0.
+    const outputImage: number[][] = Array.from({ length: numRows }, () =>
+        new Array(numCols).fill(0),
+    );
+
+    // Define the Laplacian kernel.
+    const kernel = [
+        [0, 1, 0],
+        [1, -4, 1],
+        [0, 1, 0],
+    ];
+
+    // Apply the kernel to each pixel
+    for (let i = 0; i < numRows; i++) {
+        for (let j = 0; j < numCols; j++) {
+            let sum = 0;
+            for (let ki = 0; ki < 3; ki++) {
+                for (let kj = 0; kj < 3; kj++) {
+                    sum += paddedImage[i + ki][j + kj] * kernel[ki][kj];
+                }
+            }
+            // Adjust the output value if necessary (e.g., clipping).
+            outputImage[i][j] = sum;
+        }
+    }
+
+    return outputImage;
+};
+
+const padImage = (image: number[][], direction: FaceDirection): number[][] => {
+    const removeSideColumns = 56; /* must be even */
+
+    const numRows = image.length;
+    const numCols = image[0].length;
+    const paddedNumCols = numCols + 2 - removeSideColumns;
+    const paddedNumRows = numRows + 2;
+
+    // Create a new matrix with extra padding.
+    const paddedImage: number[][] = Array.from({ length: paddedNumRows }, () =>
+        new Array(paddedNumCols).fill(0),
+    );
+
+    if (direction === "straight") {
+        // Copy original image into the center of the padded image.
+        for (let i = 0; i < numRows; i++) {
+            for (let j = 0; j < paddedNumCols - 2; j++) {
+                paddedImage[i + 1][j + 1] =
+                    image[i][j + Math.round(removeSideColumns / 2)];
+            }
+        }
+    } else if (direction === "left") {
+        // If the face is facing left, we only take the right side of the face
+        // image.
+        for (let i = 0; i < numRows; i++) {
+            for (let j = 0; j < paddedNumCols - 2; j++) {
+                paddedImage[i + 1][j + 1] = image[i][j + removeSideColumns];
+            }
+        }
+    } else if (direction === "right") {
+        // If the face is facing right, we only take the left side of the face
+        // image.
+        for (let i = 0; i < numRows; i++) {
+            for (let j = 0; j < paddedNumCols - 2; j++) {
+                paddedImage[i + 1][j + 1] = image[i][j];
+            }
+        }
+    }
+
+    // Reflect padding
+    // - Top and bottom rows
+    for (let j = 1; j <= paddedNumCols - 2; j++) {
+        // Top row
+        paddedImage[0][j] = paddedImage[2][j];
+        // Bottom row
+        paddedImage[numRows + 1][j] = paddedImage[numRows - 1][j];
+    }
+    // - Left and right columns
+    for (let i = 0; i < numRows + 2; i++) {
+        // Left column
+        paddedImage[i][0] = paddedImage[i][2];
+        // Right column
+        paddedImage[i][paddedNumCols - 1] = paddedImage[i][paddedNumCols - 3];
+    }
+
+    return paddedImage;
+};
+
+const matrixVariance = (matrix: number[][]): number => {
+    const numRows = matrix.length;
+    const numCols = matrix[0].length;
+    const totalElements = numRows * numCols;
+
+    // Calculate the mean.
+    let mean: number = 0;
+    matrix.forEach((row) => {
+        row.forEach((value) => {
+            mean += value;
+        });
+    });
+    mean /= totalElements;
+
+    // Calculate the variance.
+    let variance: number = 0;
+    matrix.forEach((row) => {
+        row.forEach((value) => {
+            const diff: number = value - mean;
+            variance += diff * diff;
+        });
+    });
+    variance /= totalElements;
+
+    return variance;
+};
+
+const mobileFaceNetFaceSize = 112;
+const mobileFaceNetEmbeddingSize = 192;
+
+/**
+ * Compute embeddings for the given {@link faceData}.
+ *
+ * The model used is MobileFaceNet, running in an ONNX runtime.
+ */
+const computeEmbeddings = async (
+    faceData: Float32Array,
+): Promise<Float32Array[]> => {
+    const outputData = await workerBridge.computeFaceEmbeddings(faceData);
+
+    const embeddingSize = mobileFaceNetEmbeddingSize;
+    const embeddings = new Array<Float32Array>(
+        outputData.length / embeddingSize,
+    );
+    for (let i = 0; i < embeddings.length; i++) {
+        embeddings[i] = new Float32Array(
+            outputData.slice(i * embeddingSize, (i + 1) * embeddingSize),
+        );
+    }
+    return embeddings;
+};
+
+/**
+ * Convert the coordinates to between 0-1, normalized by the image's dimensions.
+ */
+const relativeDetection = (
+    faceDetection: FaceDetection,
+    { width, height }: Dimensions,
+): FaceDetection => {
+    const oldBox: Box = faceDetection.box;
+    const box = new Box({
+        x: oldBox.x / width,
+        y: oldBox.y / height,
+        width: oldBox.width / width,
+        height: oldBox.height / height,
+    });
+    const landmarks = faceDetection.landmarks.map((l) => {
+        return new Point(l.x / width, l.y / height);
+    });
+    const probability = faceDetection.probability;
+    return { box, landmarks, probability };
+};
+
+export const saveFaceCrop = async (imageBitmap: ImageBitmap, face: Face) => {
+    const faceCrop = extractFaceCrop(imageBitmap, face.alignment);
+    const blob = await imageBitmapToBlob(faceCrop);
+    faceCrop.close();
+
+    const cache = await blobCache("face-crops");
+    await cache.put(face.id, blob);
+
+    return blob;
+};
+
+const imageBitmapToBlob = (imageBitmap: ImageBitmap) => {
+    const canvas = new OffscreenCanvas(imageBitmap.width, imageBitmap.height);
+    canvas.getContext("2d").drawImage(imageBitmap, 0, 0);
+    return canvas.convertToBlob({ type: "image/jpeg", quality: 0.8 });
+};
+
+const extractFaceCrop = (
+    imageBitmap: ImageBitmap,
+    alignment: FaceAlignment,
+): ImageBitmap => {
+    const alignmentBox = new Box({
+        x: alignment.center.x - alignment.size / 2,
+        y: alignment.center.y - alignment.size / 2,
+        width: alignment.size,
+        height: alignment.size,
+    });
+
+    const padding = 0.25;
+    const scaleForPadding = 1 + padding * 2;
+    const paddedBox = roundBox(enlargeBox(alignmentBox, scaleForPadding));
+
+    // TODO-ML(LAURENS): The rotation doesn't seem to be used? it's set to 0.
+    return cropWithRotation(imageBitmap, paddedBox, 0, 256);
+};
+
+const cropWithRotation = (
+    imageBitmap: ImageBitmap,
+    cropBox: Box,
+    rotation: number,
+    maxDimension: number,
+) => {
+    const box = roundBox(cropBox);
+
+    const outputSize = { width: box.width, height: box.height };
+
+    const scale = Math.min(maxDimension / box.width, maxDimension / box.height);
+    if (scale < 1) {
+        outputSize.width = Math.round(scale * box.width);
+        outputSize.height = Math.round(scale * box.height);
+    }
+
+    const offscreen = new OffscreenCanvas(outputSize.width, outputSize.height);
+    const offscreenCtx = offscreen.getContext("2d");
+    offscreenCtx.imageSmoothingQuality = "high";
+
+    offscreenCtx.translate(outputSize.width / 2, outputSize.height / 2);
+    rotation && offscreenCtx.rotate(rotation);
+
+    const outputBox = new Box({
+        x: -outputSize.width / 2,
+        y: -outputSize.height / 2,
+        width: outputSize.width,
+        height: outputSize.height,
+    });
+
+    const enlargedBox = enlargeBox(box, 1.5);
+    const enlargedOutputBox = enlargeBox(outputBox, 1.5);
+
+    offscreenCtx.drawImage(
+        imageBitmap,
+        enlargedBox.x,
+        enlargedBox.y,
+        enlargedBox.width,
+        enlargedBox.height,
+        enlargedOutputBox.x,
+        enlargedOutputBox.y,
+        enlargedOutputBox.width,
+        enlargedOutputBox.height,
+    );
+
+    return offscreen.transferToImageBitmap();
+};

web/apps/photos/src/services/face/face.worker.ts

@@ -12,20 +12,16 @@ export class DedicatedMLWorker {
     public async syncLocalFile(
         token: string,
         userID: number,
+        userAgent: string,
         enteFile: EnteFile,
         localFile: globalThis.File,
     ) {
-        mlService.syncLocalFile(token, userID, enteFile, localFile);
+        mlService.syncLocalFile(token, userID, userAgent, enteFile, localFile);
     }
 
-    public async sync(token: string, userID: number) {
+    public async sync(token: string, userID: number, userAgent: string) {
         await downloadManager.init(APPS.PHOTOS, { token });
-        return mlService.sync(token, userID);
+        return mlService.sync(token, userID, userAgent);
-    }
-
-    public async regenerateFaceCrop(token: string, faceID: string) {
-        await downloadManager.init(APPS.PHOTOS, { token });
-        return mlService.regenerateFaceCrop(faceID);
     }
 }
 

web/apps/photos/src/services/face/file.ts

@@ -0,0 +1,37 @@
+import { FILE_TYPE } from "@/media/file-type";
+import { decodeLivePhoto } from "@/media/live-photo";
+import DownloadManager from "services/download";
+import { getLocalFiles } from "services/fileService";
+import { EnteFile } from "types/file";
+import { getRenderableImage } from "utils/file";
+
+export async function getLocalFile(fileId: number) {
+    const localFiles = await getLocalFiles();
+    return localFiles.find((f) => f.id === fileId);
+}
+
+export const fetchImageBitmap = async (file: EnteFile) =>
+    fetchRenderableBlob(file).then(createImageBitmap);
+
+async function fetchRenderableBlob(file: EnteFile) {
+    const fileStream = await DownloadManager.getFile(file);
+    const fileBlob = await new Response(fileStream).blob();
+    if (file.metadata.fileType === FILE_TYPE.IMAGE) {
+        return await getRenderableImage(file.metadata.title, fileBlob);
+    } else {
+        const { imageFileName, imageData } = await decodeLivePhoto(
+            file.metadata.title,
+            fileBlob,
+        );
+        return await getRenderableImage(imageFileName, new Blob([imageData]));
+    }
+}
+
+export async function getLocalFileImageBitmap(
+    enteFile: EnteFile,
+    localFile: globalThis.File,
+) {
+    let fileBlob = localFile as Blob;
+    fileBlob = await getRenderableImage(enteFile.metadata.title, fileBlob);
+    return createImageBitmap(fileBlob);
+}

web/apps/photos/src/services/face/geom.ts

@@ -13,13 +13,6 @@ export interface Dimensions {
     height: number;
 }
 
-export interface IBoundingBox {
-    left: number;
-    top: number;
-    right: number;
-    bottom: number;
-}
-
 export interface IRect {
     x: number;
     y: number;
@@ -27,24 +20,6 @@ export interface IRect {
     height: number;
 }
 
-export function newBox(x: number, y: number, width: number, height: number) {
-    return new Box({ x, y, width, height });
-}
-
-export const boxFromBoundingBox = ({
-    left,
-    top,
-    right,
-    bottom,
-}: IBoundingBox) => {
-    return new Box({
-        x: left,
-        y: top,
-        width: right - left,
-        height: bottom - top,
-    });
-};
-
 export class Box implements IRect {
     public x: number;
     public y: number;
@@ -57,36 +32,26 @@ export class Box implements IRect {
         this.width = width;
         this.height = height;
     }
-
-    public get topLeft(): Point {
-        return new Point(this.x, this.y);
-    }
-
-    public get bottomRight(): Point {
-        return new Point(this.x + this.width, this.y + this.height);
-    }
-
-    public round(): Box {
-        const [x, y, width, height] = [
-            this.x,
-            this.y,
-            this.width,
-            this.height,
-        ].map((val) => Math.round(val));
-        return new Box({ x, y, width, height });
-    }
 }
 
-export function enlargeBox(box: Box, factor: number = 1.5) {
+/** Round all the components of the box. */
+export const roundBox = (box: Box): Box => {
+    const [x, y, width, height] = [box.x, box.y, box.width, box.height].map(
+        (val) => Math.round(val),
+    );
+    return new Box({ x, y, width, height });
+};
+
+/** Increase the size of the given {@link box} by {@link factor}. */
+export const enlargeBox = (box: Box, factor: number) => {
     const center = new Point(box.x + box.width / 2, box.y + box.height / 2);
+    const newWidth = factor * box.width;
+    const newHeight = factor * box.height;
 
-    const size = new Point(box.width, box.height);
+    return new Box({
-    const newHalfSize = new Point((factor * size.x) / 2, (factor * size.y) / 2);
+        x: center.x - newWidth / 2,
-
+        y: center.y - newHeight / 2,
-    return boxFromBoundingBox({
+        width: newWidth,
-        left: center.x - newHalfSize.x,
+        height: newHeight,
-        top: center.y - newHalfSize.y,
-        right: center.x + newHalfSize.x,
-        bottom: center.y + newHalfSize.y,
     });
-}
+};

web/apps/photos/src/services/face/image.ts

@@ -1,121 +1,295 @@
-import { FILE_TYPE } from "@/media/file-type";
+import { Matrix, inverse } from "ml-matrix";
-import { decodeLivePhoto } from "@/media/live-photo";
-import log from "@/next/log";
-import DownloadManager from "services/download";
-import { Dimensions } from "services/face/geom";
-import { DetectedFace, MLSyncFileContext } from "services/face/types";
-import { getLocalFiles } from "services/fileService";
-import { EnteFile } from "types/file";
-import { getRenderableImage } from "utils/file";
-import { clamp } from "utils/image";
 
-export const fetchImageBitmapForContext = async (
+/**
-    fileContext: MLSyncFileContext,
+ * Clamp {@link value} to between {@link min} and {@link max}, inclusive.
+ */
+export const clamp = (value: number, min: number, max: number) =>
+    Math.min(max, Math.max(min, value));
+
+/**
+ * Returns the pixel value (RGB) at the given coordinates ({@link fx},
+ * {@link fy}) using bilinear interpolation.
+ */
+export function pixelRGBBilinear(
+    fx: number,
+    fy: number,
+    imageData: Uint8ClampedArray,
+    imageWidth: number,
+    imageHeight: number,
+) {
+    // Clamp to image boundaries.
+    fx = clamp(fx, 0, imageWidth - 1);
+    fy = clamp(fy, 0, imageHeight - 1);
+
+    // Get the surrounding coordinates and their weights.
+    const x0 = Math.floor(fx);
+    const x1 = Math.ceil(fx);
+    const y0 = Math.floor(fy);
+    const y1 = Math.ceil(fy);
+    const dx = fx - x0;
+    const dy = fy - y0;
+    const dx1 = 1.0 - dx;
+    const dy1 = 1.0 - dy;
+
+    // Get the original pixels.
+    const pixel1 = pixelRGBA(imageData, imageWidth, imageHeight, x0, y0);
+    const pixel2 = pixelRGBA(imageData, imageWidth, imageHeight, x1, y0);
+    const pixel3 = pixelRGBA(imageData, imageWidth, imageHeight, x0, y1);
+    const pixel4 = pixelRGBA(imageData, imageWidth, imageHeight, x1, y1);
+
+    const bilinear = (val1: number, val2: number, val3: number, val4: number) =>
+        Math.round(
+            val1 * dx1 * dy1 +
+                val2 * dx * dy1 +
+                val3 * dx1 * dy +
+                val4 * dx * dy,
+        );
+
+    // Return interpolated pixel colors.
+    return {
+        r: bilinear(pixel1.r, pixel2.r, pixel3.r, pixel4.r),
+        g: bilinear(pixel1.g, pixel2.g, pixel3.g, pixel4.g),
+        b: bilinear(pixel1.b, pixel2.b, pixel3.b, pixel4.b),
+    };
+}
+
+const pixelRGBA = (
+    imageData: Uint8ClampedArray,
+    width: number,
+    height: number,
+    x: number,
+    y: number,
 ) => {
-    if (fileContext.imageBitmap) {
+    if (x < 0 || x >= width || y < 0 || y >= height) {
-        return fileContext.imageBitmap;
+        return { r: 0, g: 0, b: 0, a: 0 };
     }
-    if (fileContext.localFile) {
+    const index = (y * width + x) * 4;
-        if (fileContext.enteFile.metadata.fileType !== FILE_TYPE.IMAGE) {
+    return {
-            throw new Error("Local file of only image type is supported");
+        r: imageData[index],
-        }
+        g: imageData[index + 1],
-        fileContext.imageBitmap = await getLocalFileImageBitmap(
+        b: imageData[index + 2],
-            fileContext.enteFile,
+        a: imageData[index + 3],
-            fileContext.localFile,
+    };
-        );
-    } else if (
-        [FILE_TYPE.IMAGE, FILE_TYPE.LIVE_PHOTO].includes(
-            fileContext.enteFile.metadata.fileType,
-        )
-    ) {
-        fileContext.imageBitmap = await fetchImageBitmap(fileContext.enteFile);
-    } else {
-        // TODO-ML(MR): We don't do it on videos, when will we ever come
-        // here?
-        fileContext.imageBitmap = await getThumbnailImageBitmap(
-            fileContext.enteFile,
-        );
-    }
-
-    fileContext.newMlFile.imageSource = "Original";
-    const { width, height } = fileContext.imageBitmap;
-    fileContext.newMlFile.imageDimensions = { width, height };
-
-    return fileContext.imageBitmap;
 };
 
-export async function getLocalFile(fileId: number) {
+/**
-    const localFiles = await getLocalFiles();
+ * Returns the pixel value (RGB) at the given coordinates ({@link fx},
-    return localFiles.find((f) => f.id === fileId);
+ * {@link fy}) using bicubic interpolation.
-}
+ */
+const pixelRGBBicubic = (
+    fx: number,
+    fy: number,
+    imageData: Uint8ClampedArray,
+    imageWidth: number,
+    imageHeight: number,
+) => {
+    // Clamp to image boundaries.
+    fx = clamp(fx, 0, imageWidth - 1);
+    fy = clamp(fy, 0, imageHeight - 1);
 
-export function getFaceId(detectedFace: DetectedFace, imageDims: Dimensions) {
+    const x = Math.trunc(fx) - (fx >= 0.0 ? 0 : 1);
-    const xMin = clamp(
+    const px = x - 1;
-        detectedFace.detection.box.x / imageDims.width,
+    const nx = x + 1;
-        0.0,
+    const ax = x + 2;
-        0.999999,
+    const y = Math.trunc(fy) - (fy >= 0.0 ? 0 : 1);
-    )
+    const py = y - 1;
-        .toFixed(5)
+    const ny = y + 1;
-        .substring(2);
+    const ay = y + 2;
-    const yMin = clamp(
+    const dx = fx - x;
-        detectedFace.detection.box.y / imageDims.height,
+    const dy = fy - y;
-        0.0,
-        0.999999,
-    )
-        .toFixed(5)
-        .substring(2);
-    const xMax = clamp(
-        (detectedFace.detection.box.x + detectedFace.detection.box.width) /
-            imageDims.width,
-        0.0,
-        0.999999,
-    )
-        .toFixed(5)
-        .substring(2);
-    const yMax = clamp(
-        (detectedFace.detection.box.y + detectedFace.detection.box.height) /
-            imageDims.height,
-        0.0,
-        0.999999,
-    )
-        .toFixed(5)
-        .substring(2);
 
-    const rawFaceID = `${xMin}_${yMin}_${xMax}_${yMax}`;
+    const cubic = (
-    const faceID = `${detectedFace.fileId}_${rawFaceID}`;
+        dx: number,
+        ipp: number,
+        icp: number,
+        inp: number,
+        iap: number,
+    ) =>
+        icp +
+        0.5 *
+            (dx * (-ipp + inp) +
+                dx * dx * (2 * ipp - 5 * icp + 4 * inp - iap) +
+                dx * dx * dx * (-ipp + 3 * icp - 3 * inp + iap));
 
-    return faceID;
+    const icc = pixelRGBA(imageData, imageWidth, imageHeight, x, y);
-}
 
-export const fetchImageBitmap = async (file: EnteFile) =>
+    const ipp =
-    fetchRenderableBlob(file).then(createImageBitmap);
+        px < 0 || py < 0
+            ? icc
+            : pixelRGBA(imageData, imageWidth, imageHeight, px, py);
+    const icp =
+        px < 0 ? icc : pixelRGBA(imageData, imageWidth, imageHeight, x, py);
+    const inp =
+        py < 0 || nx >= imageWidth
+            ? icc
+            : pixelRGBA(imageData, imageWidth, imageHeight, nx, py);
+    const iap =
+        ax >= imageWidth || py < 0
+            ? icc
+            : pixelRGBA(imageData, imageWidth, imageHeight, ax, py);
 
-async function fetchRenderableBlob(file: EnteFile) {
+    const ip0 = cubic(dx, ipp.r, icp.r, inp.r, iap.r);
-    const fileStream = await DownloadManager.getFile(file);
+    const ip1 = cubic(dx, ipp.g, icp.g, inp.g, iap.g);
-    const fileBlob = await new Response(fileStream).blob();
+    const ip2 = cubic(dx, ipp.b, icp.b, inp.b, iap.b);
-    if (file.metadata.fileType === FILE_TYPE.IMAGE) {
+    // const ip3 = cubic(dx, ipp.a, icp.a, inp.a, iap.a);
-        return await getRenderableImage(file.metadata.title, fileBlob);
+
-    } else {
+    const ipc =
-        const { imageFileName, imageData } = await decodeLivePhoto(
+        px < 0 ? icc : pixelRGBA(imageData, imageWidth, imageHeight, px, y);
-            file.metadata.title,
+    const inc =
-            fileBlob,
+        nx >= imageWidth
-        );
+            ? icc
-        return await getRenderableImage(imageFileName, new Blob([imageData]));
+            : pixelRGBA(imageData, imageWidth, imageHeight, nx, y);
+    const iac =
+        ax >= imageWidth
+            ? icc
+            : pixelRGBA(imageData, imageWidth, imageHeight, ax, y);
+
+    const ic0 = cubic(dx, ipc.r, icc.r, inc.r, iac.r);
+    const ic1 = cubic(dx, ipc.g, icc.g, inc.g, iac.g);
+    const ic2 = cubic(dx, ipc.b, icc.b, inc.b, iac.b);
+    // const ic3 = cubic(dx, ipc.a, icc.a, inc.a, iac.a);
+
+    const ipn =
+        px < 0 || ny >= imageHeight
+            ? icc
+            : pixelRGBA(imageData, imageWidth, imageHeight, px, ny);
+    const icn =
+        ny >= imageHeight
+            ? icc
+            : pixelRGBA(imageData, imageWidth, imageHeight, x, ny);
+    const inn =
+        nx >= imageWidth || ny >= imageHeight
+            ? icc
+            : pixelRGBA(imageData, imageWidth, imageHeight, nx, ny);
+    const ian =
+        ax >= imageWidth || ny >= imageHeight
+            ? icc
+            : pixelRGBA(imageData, imageWidth, imageHeight, ax, ny);
+
+    const in0 = cubic(dx, ipn.r, icn.r, inn.r, ian.r);
+    const in1 = cubic(dx, ipn.g, icn.g, inn.g, ian.g);
+    const in2 = cubic(dx, ipn.b, icn.b, inn.b, ian.b);
+    // const in3 = cubic(dx, ipn.a, icn.a, inn.a, ian.a);
+
+    const ipa =
+        px < 0 || ay >= imageHeight
+            ? icc
+            : pixelRGBA(imageData, imageWidth, imageHeight, px, ay);
+    const ica =
+        ay >= imageHeight
+            ? icc
+            : pixelRGBA(imageData, imageWidth, imageHeight, x, ay);
+    const ina =
+        nx >= imageWidth || ay >= imageHeight
+            ? icc
+            : pixelRGBA(imageData, imageWidth, imageHeight, nx, ay);
+    const iaa =
+        ax >= imageWidth || ay >= imageHeight
+            ? icc
+            : pixelRGBA(imageData, imageWidth, imageHeight, ax, ay);
+
+    const ia0 = cubic(dx, ipa.r, ica.r, ina.r, iaa.r);
+    const ia1 = cubic(dx, ipa.g, ica.g, ina.g, iaa.g);
+    const ia2 = cubic(dx, ipa.b, ica.b, ina.b, iaa.b);
+    // const ia3 = cubic(dx, ipa.a, ica.a, ina.a, iaa.a);
+
+    const c0 = Math.trunc(clamp(cubic(dy, ip0, ic0, in0, ia0), 0, 255));
+    const c1 = Math.trunc(clamp(cubic(dy, ip1, ic1, in1, ia1), 0, 255));
+    const c2 = Math.trunc(clamp(cubic(dy, ip2, ic2, in2, ia2), 0, 255));
+    // const c3 = cubic(dy, ip3, ic3, in3, ia3);
+
+    return { r: c0, g: c1, b: c2 };
+};
+
+/**
+ * Transform {@link inputData} starting at {@link inputStartIndex}.
+ */
+export const warpAffineFloat32List = (
+    imageBitmap: ImageBitmap,
+    faceAlignmentAffineMatrix: number[][],
+    faceSize: number,
+    inputData: Float32Array,
+    inputStartIndex: number,
+): void => {
+    const { width, height } = imageBitmap;
+
+    // Get the pixel data.
+    const offscreenCanvas = new OffscreenCanvas(width, height);
+    const ctx = offscreenCanvas.getContext("2d");
+    ctx.drawImage(imageBitmap, 0, 0, width, height);
+    const imageData = ctx.getImageData(0, 0, width, height);
+    const pixelData = imageData.data;
+
+    const transformationMatrix = faceAlignmentAffineMatrix.map((row) =>
+        row.map((val) => (val != 1.0 ? val * faceSize : 1.0)),
+    ); // 3x3
+
+    const A: Matrix = new Matrix([
+        [transformationMatrix[0][0], transformationMatrix[0][1]],
+        [transformationMatrix[1][0], transformationMatrix[1][1]],
+    ]);
+    const Ainverse = inverse(A);
+
+    const b00 = transformationMatrix[0][2];
+    const b10 = transformationMatrix[1][2];
+    const a00Prime = Ainverse.get(0, 0);
+    const a01Prime = Ainverse.get(0, 1);
+    const a10Prime = Ainverse.get(1, 0);
+    const a11Prime = Ainverse.get(1, 1);
+
+    for (let yTrans = 0; yTrans < faceSize; ++yTrans) {
+        for (let xTrans = 0; xTrans < faceSize; ++xTrans) {
+            // Perform inverse affine transformation.
+            const xOrigin =
+                a00Prime * (xTrans - b00) + a01Prime * (yTrans - b10);
+            const yOrigin =
+                a10Prime * (xTrans - b00) + a11Prime * (yTrans - b10);
+
+            // Get the pixel RGB using bicubic interpolation.
+            const { r, g, b } = pixelRGBBicubic(
+                xOrigin,
+                yOrigin,
+                pixelData,
+                width,
+                height,
+            );
+
+            // Set the pixel in the input data.
+            const index = (yTrans * faceSize + xTrans) * 3;
+            inputData[inputStartIndex + index] = rgbToBipolarFloat(r);
+            inputData[inputStartIndex + index + 1] = rgbToBipolarFloat(g);
+            inputData[inputStartIndex + index + 2] = rgbToBipolarFloat(b);
+        }
     }
-}
+};
 
-export async function getThumbnailImageBitmap(file: EnteFile) {
+/** Convert a RGB component 0-255 to a floating point value between -1 and 1. */
-    const thumb = await DownloadManager.getThumbnail(file);
+const rgbToBipolarFloat = (pixelValue: number) => pixelValue / 127.5 - 1.0;
-    log.info("[MLService] Got thumbnail: ", file.id.toString());
 
-    return createImageBitmap(new Blob([thumb]));
+/** Convert a floating point value between -1 and 1 to a RGB component 0-255. */
-}
+const bipolarFloatToRGB = (pixelValue: number) =>
+    clamp(Math.round((pixelValue + 1.0) * 127.5), 0, 255);
 
-export async function getLocalFileImageBitmap(
+export const grayscaleIntMatrixFromNormalized2List = (
-    enteFile: EnteFile,
+    imageList: Float32Array,
-    localFile: globalThis.File,
+    faceNumber: number,
-) {
+    width: number,
-    let fileBlob = localFile as Blob;
+    height: number,
-    fileBlob = await getRenderableImage(enteFile.metadata.title, fileBlob);
+): number[][] => {
-    return createImageBitmap(fileBlob);
+    const startIndex = faceNumber * width * height * 3;
-}
+    return Array.from({ length: height }, (_, y) =>
+        Array.from({ length: width }, (_, x) => {
+            // 0.299 ∙ Red + 0.587 ∙ Green + 0.114 ∙ Blue
+            const pixelIndex = startIndex + 3 * (y * width + x);
+            return clamp(
+                Math.round(
+                    0.299 * bipolarFloatToRGB(imageList[pixelIndex]) +
+                        0.587 * bipolarFloatToRGB(imageList[pixelIndex + 1]) +
+                        0.114 * bipolarFloatToRGB(imageList[pixelIndex + 2]),
+                ),
+                0,
+                255,
+            );
+        }),
+    );
+};

web/apps/photos/src/services/face/people.ts

@@ -1,37 +1,54 @@
-import log from "@/next/log";
+export interface Person {
-import mlIDbStorage from "services/face/db";
+    id: number;
-import { Face, Person } from "services/face/types";
+    name?: string;
-import { type MLSyncContext } from "services/machineLearning/machineLearningService";
+    files: Array<number>;
-import { clusterFaces } from "./cluster";
+    displayFaceId?: string;
-import { saveFaceCrop } from "./f-index";
+}
-import { fetchImageBitmap, getLocalFile } from "./image";
+
+// TODO-ML(MR): Forced disable clustering. It doesn't currently work,
+// need to finalize it before we move out of beta.
+//
+// > Error: Failed to execute 'transferToImageBitmap' on
+// > 'OffscreenCanvas': ImageBitmap construction failed
+
+/*
+export const syncPeopleIndex = async () => {
+
+        if (
+            syncContext.outOfSyncFiles.length <= 0 ||
+            (syncContext.nSyncedFiles === batchSize && Math.random() < 0)
+        ) {
+            await this.syncIndex(syncContext);
+        }
+
+        public async syncIndex(syncContext: MLSyncContext) {
+            await this.getMLLibraryData(syncContext);
+
+            await syncPeopleIndex(syncContext);
+
+            await this.persistMLLibraryData(syncContext);
+        }
 
-export const syncPeopleIndex = async (syncContext: MLSyncContext) => {
     const filesVersion = await mlIDbStorage.getIndexVersion("files");
     if (filesVersion <= (await mlIDbStorage.getIndexVersion("people"))) {
         return;
     }
 
+
     // TODO: have faces addresable through fileId + faceId
     // to avoid index based addressing, which is prone to wrong results
     // one way could be to match nearest face within threshold in the file
+
     const allFacesMap =
         syncContext.allSyncedFacesMap ??
         (syncContext.allSyncedFacesMap = await mlIDbStorage.getAllFacesMap());
-    const allFaces = [...allFacesMap.values()].flat();
 
-    await runFaceClustering(syncContext, allFaces);
-    await syncPeopleFromClusters(syncContext, allFacesMap, allFaces);
 
-    await mlIDbStorage.setIndexVersion("people", filesVersion);
-};
-
-const runFaceClustering = async (
-    syncContext: MLSyncContext,
-    allFaces: Array<Face>,
-) => {
     // await this.init();
 
+    const allFacesMap = await mlIDbStorage.getAllFacesMap();
+    const allFaces = [...allFacesMap.values()].flat();
+
     if (!allFaces || allFaces.length < 50) {
         log.info(
             `Skipping clustering since number of faces (${allFaces.length}) is less than the clustering threshold (50)`,
@@ -40,34 +57,15 @@ const runFaceClustering = async (
     }
 
     log.info("Running clustering allFaces: ", allFaces.length);
-    syncContext.mlLibraryData.faceClusteringResults = await clusterFaces(
+    const faceClusteringResults = await clusterFaces(
         allFaces.map((f) => Array.from(f.embedding)),
     );
-    syncContext.mlLibraryData.faceClusteringMethod = {
-        value: "Hdbscan",
-        version: 1,
-    };
     log.info(
         "[MLService] Got face clustering results: ",
-        JSON.stringify(syncContext.mlLibraryData.faceClusteringResults),
+        JSON.stringify(faceClusteringResults),
     );
 
-    // syncContext.faceClustersWithNoise = {
+    const clusters = faceClusteringResults?.clusters;
-    //     clusters: syncContext.faceClusteringResults.clusters.map(
-    //         (faces) => ({
-    //             faces,
-    //         })
-    //     ),
-    //     noise: syncContext.faceClusteringResults.noise,
-    // };
-};
-
-const syncPeopleFromClusters = async (
-    syncContext: MLSyncContext,
-    allFacesMap: Map<number, Array<Face>>,
-    allFaces: Array<Face>,
-) => {
-    const clusters = syncContext.mlLibraryData.faceClusteringResults?.clusters;
     if (!clusters || clusters.length < 1) {
         return;
     }
@@ -86,17 +84,18 @@ const syncPeopleFromClusters = async (
                 : best,
         );
 
+
         if (personFace && !personFace.crop?.cacheKey) {
             const file = await getLocalFile(personFace.fileId);
             const imageBitmap = await fetchImageBitmap(file);
             await saveFaceCrop(imageBitmap, personFace);
         }
 
+
         const person: Person = {
             id: index,
             files: faces.map((f) => f.fileId),
             displayFaceId: personFace?.id,
-            faceCropCacheKey: personFace?.crop?.cacheKey,
         };
 
         await mlIDbStorage.putPerson(person);
@@ -108,4 +107,24 @@ const syncPeopleFromClusters = async (
     }
 
     await mlIDbStorage.updateFaces(allFacesMap);
+
+    // await mlIDbStorage.setIndexVersion("people", filesVersion);
 };
+
+    public async regenerateFaceCrop(token: string, faceID: string) {
+        await downloadManager.init(APPS.PHOTOS, { token });
+        return mlService.regenerateFaceCrop(faceID);
+    }
+
+export const regenerateFaceCrop = async (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 saveFaceCrop(imageBitmap, personFace);
+};
+*/

web/apps/photos/src/services/face/remote.ts

@@ -0,0 +1,158 @@
+import log from "@/next/log";
+import ComlinkCryptoWorker from "@ente/shared/crypto";
+import { putEmbedding } from "services/embeddingService";
+import type { EnteFile } from "types/file";
+import type { Point } from "./geom";
+import type { Face, FaceDetection, MlFileData } from "./types";
+
+export const putFaceEmbedding = async (
+    enteFile: EnteFile,
+    mlFileData: MlFileData,
+    userAgent: string,
+) => {
+    const serverMl = LocalFileMlDataToServerFileMl(mlFileData, userAgent);
+    log.debug(() => ({ t: "Local ML file data", mlFileData }));
+    log.debug(() => ({
+        t: "Uploaded ML file data",
+        d: JSON.stringify(serverMl),
+    }));
+
+    const comlinkCryptoWorker = await ComlinkCryptoWorker.getInstance();
+    const { file: encryptedEmbeddingData } =
+        await comlinkCryptoWorker.encryptMetadata(serverMl, enteFile.key);
+    log.info(
+        `putEmbedding embedding to server for file: ${enteFile.metadata.title} fileID: ${enteFile.id}`,
+    );
+    const res = await putEmbedding({
+        fileID: enteFile.id,
+        encryptedEmbedding: encryptedEmbeddingData.encryptedData,
+        decryptionHeader: encryptedEmbeddingData.decryptionHeader,
+        model: "file-ml-clip-face",
+    });
+    log.info("putEmbedding response: ", res);
+};
+
+export interface FileML extends ServerFileMl {
+    updatedAt: number;
+}
+
+class ServerFileMl {
+    public fileID: number;
+    public height?: number;
+    public width?: number;
+    public faceEmbedding: ServerFaceEmbeddings;
+
+    public constructor(
+        fileID: number,
+        faceEmbedding: ServerFaceEmbeddings,
+        height?: number,
+        width?: number,
+    ) {
+        this.fileID = fileID;
+        this.height = height;
+        this.width = width;
+        this.faceEmbedding = faceEmbedding;
+    }
+}
+
+class ServerFaceEmbeddings {
+    public faces: ServerFace[];
+    public version: number;
+    public client: string;
+
+    public constructor(faces: ServerFace[], client: string, version: number) {
+        this.faces = faces;
+        this.client = client;
+        this.version = version;
+    }
+}
+
+class ServerFace {
+    public faceID: string;
+    public embedding: number[];
+    public detection: ServerDetection;
+    public score: number;
+    public blur: number;
+
+    public constructor(
+        faceID: string,
+        embedding: number[],
+        detection: ServerDetection,
+        score: number,
+        blur: number,
+    ) {
+        this.faceID = faceID;
+        this.embedding = embedding;
+        this.detection = detection;
+        this.score = score;
+        this.blur = blur;
+    }
+}
+
+class ServerDetection {
+    public box: ServerFaceBox;
+    public landmarks: Point[];
+
+    public constructor(box: ServerFaceBox, landmarks: Point[]) {
+        this.box = box;
+        this.landmarks = landmarks;
+    }
+}
+
+class ServerFaceBox {
+    public xMin: number;
+    public yMin: number;
+    public width: number;
+    public height: number;
+
+    public constructor(
+        xMin: number,
+        yMin: number,
+        width: number,
+        height: number,
+    ) {
+        this.xMin = xMin;
+        this.yMin = yMin;
+        this.width = width;
+        this.height = height;
+    }
+}
+
+function LocalFileMlDataToServerFileMl(
+    localFileMlData: MlFileData,
+    userAgent: string,
+): ServerFileMl {
+    if (localFileMlData.errorCount > 0) {
+        return null;
+    }
+    const imageDimensions = localFileMlData.imageDimensions;
+
+    const faces: ServerFace[] = [];
+    for (let i = 0; i < localFileMlData.faces.length; i++) {
+        const face: Face = localFileMlData.faces[i];
+        const faceID = face.id;
+        const embedding = face.embedding;
+        const score = face.detection.probability;
+        const blur = face.blurValue;
+        const detection: FaceDetection = face.detection;
+        const box = detection.box;
+        const landmarks = detection.landmarks;
+        const newBox = new ServerFaceBox(box.x, box.y, box.width, box.height);
+
+        const newFaceObject = new ServerFace(
+            faceID,
+            Array.from(embedding),
+            new ServerDetection(newBox, landmarks),
+            score,
+            blur,
+        );
+        faces.push(newFaceObject);
+    }
+    const faceEmbeddings = new ServerFaceEmbeddings(faces, userAgent, 1);
+    return new ServerFileMl(
+        localFileMlData.fileId,
+        faceEmbeddings,
+        imageDimensions.height,
+        imageDimensions.width,
+    );
+}

web/apps/photos/src/services/face/transform-box.ts

@@ -0,0 +1,57 @@
+import { Box, Point } from "services/face/geom";
+import type { FaceDetection } from "services/face/types";
+// TODO-ML(LAURENS): Do we need two separate Matrix libraries?
+//
+// Keeping this in a separate file so that we can audit this. If these can be
+// expressed using ml-matrix, then we can move this code to f-index.ts
+import {
+    Matrix,
+    applyToPoint,
+    compose,
+    scale,
+    translate,
+} from "transformation-matrix";
+
+/**
+ * Transform the given {@link faceDetections} from their coordinate system in
+ * which they were detected ({@link inBox}) back to the coordinate system of the
+ * original image ({@link toBox}).
+ */
+export const transformFaceDetections = (
+    faceDetections: FaceDetection[],
+    inBox: Box,
+    toBox: Box,
+): FaceDetection[] => {
+    const transform = boxTransformationMatrix(inBox, toBox);
+    return faceDetections.map((f) => ({
+        box: transformBox(f.box, transform),
+        landmarks: f.landmarks.map((p) => transformPoint(p, transform)),
+        probability: f.probability,
+    }));
+};
+
+const boxTransformationMatrix = (inBox: Box, toBox: Box): Matrix =>
+    compose(
+        translate(toBox.x, toBox.y),
+        scale(toBox.width / inBox.width, toBox.height / inBox.height),
+    );
+
+const transformPoint = (point: Point, transform: Matrix) => {
+    const txdPoint = applyToPoint(transform, point);
+    return new Point(txdPoint.x, txdPoint.y);
+};
+
+const transformBox = (box: Box, transform: Matrix) => {
+    const topLeft = transformPoint(new Point(box.x, box.y), transform);
+    const bottomRight = transformPoint(
+        new Point(box.x + box.width, box.y + box.height),
+        transform,
+    );
+
+    return new Box({
+        x: topLeft.x,
+        y: topLeft.y,
+        width: bottomRight.x - topLeft.x,
+        height: bottomRight.y - topLeft.y,
+    });
+};

web/apps/photos/src/services/face/types.ts

@@ -1,161 +1,39 @@
-import type { ClusterFacesResult } from "services/face/cluster";
+import { Box, Dimensions, Point } from "services/face/geom";
-import { Dimensions } from "services/face/geom";
-import { EnteFile } from "types/file";
-import { Box, Point } from "./geom";
-
-export interface MLSyncResult {
-    nOutOfSyncFiles: number;
-    nSyncedFiles: number;
-    nSyncedFaces: number;
-    nFaceClusters: number;
-    nFaceNoise: number;
-    error?: Error;
-}
-
-export declare type FaceDescriptor = Float32Array;
-
-export declare type Cluster = Array<number>;
-
-export interface FacesCluster {
-    faces: Cluster;
-    summary?: FaceDescriptor;
-}
-
-export interface FacesClustersWithNoise {
-    clusters: Array<FacesCluster>;
-    noise: Cluster;
-}
-
-export interface NearestCluster {
-    cluster: FacesCluster;
-    distance: number;
-}
-
-export declare type Landmark = Point;
-
-export declare type ImageType = "Original" | "Preview";
-
-export declare type FaceDetectionMethod = "YoloFace";
-
-export declare type FaceCropMethod = "ArcFace";
-
-export declare type FaceAlignmentMethod = "ArcFace";
-
-export declare type FaceEmbeddingMethod = "MobileFaceNet";
-
-export declare type BlurDetectionMethod = "Laplacian";
-
-export declare type ClusteringMethod = "Hdbscan" | "Dbscan";
-
-export class AlignedBox {
-    box: Box;
-    rotation: number;
-}
-
-export interface Versioned<T> {
-    value: T;
-    version: number;
-}
 
 export interface FaceDetection {
     // box and landmarks is relative to image dimentions stored at mlFileData
     box: Box;
-    landmarks?: Array<Landmark>;
+    landmarks?: Point[];
     probability?: number;
 }
 
-export interface DetectedFace {
-    fileId: number;
-    detection: FaceDetection;
-}
-
-export interface DetectedFaceWithId extends DetectedFace {
-    id: string;
-}
-
-export interface FaceCrop {
-    image: ImageBitmap;
-    // imageBox is relative to image dimentions stored at mlFileData
-    imageBox: Box;
-}
-
-export interface StoredFaceCrop {
-    cacheKey: string;
-    imageBox: Box;
-}
-
-export interface CroppedFace extends DetectedFaceWithId {
-    crop?: StoredFaceCrop;
-}
-
 export interface FaceAlignment {
-    // TODO: remove affine matrix as rotation, size and center
+    // TODO-ML(MR): remove affine matrix as rotation, size and center
     // are simple to store and use, affine matrix adds complexity while getting crop
-    affineMatrix: Array<Array<number>>;
+    affineMatrix: number[][];
     rotation: number;
     // size and center is relative to image dimentions stored at mlFileData
     size: number;
     center: Point;
 }
 
-export interface AlignedFace extends CroppedFace {
+export interface Face {
+    fileId: number;
+    detection: FaceDetection;
+    id: string;
+
     alignment?: FaceAlignment;
     blurValue?: number;
-}
 
-export declare type FaceEmbedding = Float32Array;
+    embedding?: Float32Array;
 
-export interface FaceWithEmbedding extends AlignedFace {
-    embedding?: FaceEmbedding;
-}
-
-export interface Face extends FaceWithEmbedding {
     personId?: number;
 }
 
-export interface Person {
-    id: number;
-    name?: string;
-    files: Array<number>;
-    displayFaceId?: string;
-    faceCropCacheKey?: string;
-}
-
 export interface MlFileData {
     fileId: number;
     faces?: Face[];
-    imageSource?: ImageType;
     imageDimensions?: Dimensions;
-    faceDetectionMethod?: Versioned<FaceDetectionMethod>;
-    faceCropMethod?: Versioned<FaceCropMethod>;
-    faceAlignmentMethod?: Versioned<FaceAlignmentMethod>;
-    faceEmbeddingMethod?: Versioned<FaceEmbeddingMethod>;
     mlVersion: number;
     errorCount: number;
-    lastErrorMessage?: string;
 }
-
-export interface MLSearchConfig {
-    enabled: boolean;
-}
-
-export interface MLSyncFileContext {
-    enteFile: EnteFile;
-    localFile?: globalThis.File;
-
-    oldMlFile?: MlFileData;
-    newMlFile?: MlFileData;
-
-    imageBitmap?: ImageBitmap;
-
-    newDetection?: boolean;
-    newAlignment?: boolean;
-}
-
-export interface MLLibraryData {
-    faceClusteringMethod?: Versioned<ClusteringMethod>;
-    faceClusteringResults?: ClusterFacesResult;
-    faceClustersWithNoise?: FacesClustersWithNoise;
-}
-
-export declare type MLIndex = "files" | "people";

web/apps/photos/src/services/heic-convert.ts

@@ -51,9 +51,7 @@ class HEICConverter {
                                     const startTime = Date.now();
                                     const convertedHEIC =
                                         await worker.heicToJPEG(fileBlob);
-                                    const ms = Math.round(
+                                    const ms = Date.now() - startTime;
-                                        Date.now() - startTime,
-                                    );
                                     log.debug(() => `heic => jpeg (${ms} ms)`);
                                     clearTimeout(timeout);
                                     resolve(convertedHEIC);

web/apps/photos/src/services/machineLearning/machineLearningService.ts

@@ -1,41 +1,26 @@
-import { haveWindow } from "@/next/env";
 import log from "@/next/log";
-import { ComlinkWorker } from "@/next/worker/comlink-worker";
-import ComlinkCryptoWorker, {
-    getDedicatedCryptoWorker,
-} from "@ente/shared/crypto";
-import { DedicatedCryptoWorker } from "@ente/shared/crypto/internal/crypto.worker";
 import { CustomError, parseUploadErrorCodes } from "@ente/shared/error";
 import PQueue from "p-queue";
-import { putEmbedding } from "services/embeddingService";
+import mlIDbStorage, {
-import mlIDbStorage, { ML_SEARCH_CONFIG_NAME } from "services/face/db";
+    ML_SEARCH_CONFIG_NAME,
-import {
+    type MinimalPersistedFileData,
-    Face,
+} from "services/face/db";
-    FaceDetection,
+import { putFaceEmbedding } from "services/face/remote";
-    Landmark,
-    MLLibraryData,
-    MLSearchConfig,
-    MLSyncFileContext,
-    MLSyncResult,
-    MlFileData,
-} from "services/face/types";
 import { getLocalFiles } from "services/fileService";
 import { EnteFile } from "types/file";
 import { isInternalUserForML } from "utils/user";
-import { regenerateFaceCrop, syncFileAnalyzeFaces } from "../face/f-index";
+import { indexFaces } from "../face/f-index";
-import { fetchImageBitmapForContext } from "../face/image";
-import { syncPeopleIndex } from "../face/people";
 
-/**
+export const defaultMLVersion = 1;
- * TODO-ML(MR): What and why.
- * Also, needs to be 1 (in sync with mobile) when we move out of beta.
- */
-export const defaultMLVersion = 3;
 
 const batchSize = 200;
 
 export const MAX_ML_SYNC_ERROR_COUNT = 1;
 
+export interface MLSearchConfig {
+    enabled: boolean;
+}
+
 export const DEFAULT_ML_SEARCH_CONFIG: MLSearchConfig = {
     enabled: false,
 };
@@ -56,107 +41,54 @@ export async function updateMLSearchConfig(newConfig: MLSearchConfig) {
     return mlIDbStorage.putConfig(ML_SEARCH_CONFIG_NAME, newConfig);
 }
 
-export interface MLSyncContext {
+class MLSyncContext {
-    token: string;
-    userID: number;
-
-    localFilesMap: Map<number, EnteFile>;
-    outOfSyncFiles: EnteFile[];
-    nSyncedFiles: number;
-    nSyncedFaces: number;
-    allSyncedFacesMap?: Map<number, Array<Face>>;
-
-    error?: Error;
-
-    // oldMLLibraryData: MLLibraryData;
-    mlLibraryData: MLLibraryData;
-
-    syncQueue: PQueue;
-
-    getEnteWorker(id: number): Promise<any>;
-    dispose(): Promise<void>;
-}
-
-export class LocalMLSyncContext implements MLSyncContext {
     public token: string;
     public userID: number;
+    public userAgent: string;
 
     public localFilesMap: Map<number, EnteFile>;
     public outOfSyncFiles: EnteFile[];
     public nSyncedFiles: number;
-    public nSyncedFaces: number;
-    public allSyncedFacesMap?: Map<number, Array<Face>>;
-
     public error?: Error;
 
-    public mlLibraryData: MLLibraryData;
-
     public syncQueue: PQueue;
-    // TODO: wheather to limit concurrent downloads
-    // private downloadQueue: PQueue;
 
-    private concurrency: number;
+    constructor(token: string, userID: number, userAgent: string) {
-    private comlinkCryptoWorker: Array<
-        ComlinkWorker<typeof DedicatedCryptoWorker>
-    >;
-    private enteWorkers: Array<any>;
-
-    constructor(token: string, userID: number, concurrency?: number) {
         this.token = token;
         this.userID = userID;
+        this.userAgent = userAgent;
 
         this.outOfSyncFiles = [];
         this.nSyncedFiles = 0;
-        this.nSyncedFaces = 0;
 
-        this.concurrency = concurrency ?? getConcurrency();
+        const concurrency = getConcurrency();
-
+        this.syncQueue = new PQueue({ concurrency });
-        log.info("Using concurrency: ", this.concurrency);
-        // timeout is added on downloads
-        // timeout on queue will keep the operation open till worker is terminated
-        this.syncQueue = new PQueue({ concurrency: this.concurrency });
-        logQueueStats(this.syncQueue, "sync");
-        // this.downloadQueue = new PQueue({ concurrency: 1 });
-        // logQueueStats(this.downloadQueue, 'download');
-
-        this.comlinkCryptoWorker = new Array(this.concurrency);
-        this.enteWorkers = new Array(this.concurrency);
-    }
-
-    public async getEnteWorker(id: number): Promise<any> {
-        const wid = id % this.enteWorkers.length;
-        console.log("getEnteWorker: ", id, wid);
-        if (!this.enteWorkers[wid]) {
-            this.comlinkCryptoWorker[wid] = getDedicatedCryptoWorker();
-            this.enteWorkers[wid] = await this.comlinkCryptoWorker[wid].remote;
-        }
-
-        return this.enteWorkers[wid];
     }
 
     public async dispose() {
         this.localFilesMap = undefined;
         await this.syncQueue.onIdle();
         this.syncQueue.removeAllListeners();
-        for (const enteComlinkWorker of this.comlinkCryptoWorker) {
-            enteComlinkWorker?.terminate();
-        }
     }
 }
 
-export const getConcurrency = () =>
+const getConcurrency = () =>
-    haveWindow() && Math.max(2, Math.ceil(navigator.hardwareConcurrency / 2));
+    Math.max(2, Math.ceil(navigator.hardwareConcurrency / 2));
 
 class MachineLearningService {
     private localSyncContext: Promise<MLSyncContext>;
     private syncContext: Promise<MLSyncContext>;
 
-    public async sync(token: string, userID: number): Promise<MLSyncResult> {
+    public async sync(
+        token: string,
+        userID: number,
+        userAgent: string,
+    ): Promise<boolean> {
         if (!token) {
             throw Error("Token needed by ml service to sync file");
         }
 
-        const syncContext = await this.getSyncContext(token, userID);
+        const syncContext = await this.getSyncContext(token, userID, userAgent);
 
         await this.syncLocalFiles(syncContext);
 
@@ -166,38 +98,9 @@ class MachineLearningService {
             await this.syncFiles(syncContext);
         }
 
-        // TODO-ML(MR): Forced disable clustering. It doesn't currently work,
+        const error = syncContext.error;
-        // need to finalize it before we move out of beta.
+        const nOutOfSyncFiles = syncContext.outOfSyncFiles.length;
-        //
+        return !error && nOutOfSyncFiles > 0;
-        // > Error: Failed to execute 'transferToImageBitmap' on
-        // > 'OffscreenCanvas': ImageBitmap construction failed
-        /*
-        if (
-            syncContext.outOfSyncFiles.length <= 0 ||
-            (syncContext.nSyncedFiles === batchSize && Math.random() < 0)
-        ) {
-            await this.syncIndex(syncContext);
-        }
-        */
-
-        const mlSyncResult: MLSyncResult = {
-            nOutOfSyncFiles: syncContext.outOfSyncFiles.length,
-            nSyncedFiles: syncContext.nSyncedFiles,
-            nSyncedFaces: syncContext.nSyncedFaces,
-            nFaceClusters:
-                syncContext.mlLibraryData?.faceClusteringResults?.clusters
-                    .length,
-            nFaceNoise:
-                syncContext.mlLibraryData?.faceClusteringResults?.noise.length,
-            error: syncContext.error,
-        };
-        // log.info('[MLService] sync results: ', mlSyncResult);
-
-        return mlSyncResult;
-    }
-
-    public async regenerateFaceCrop(faceID: string) {
-        return regenerateFaceCrop(faceID);
     }
 
     private newMlData(fileId: number) {
@@ -205,7 +108,7 @@ class MachineLearningService {
             fileId,
             mlVersion: 0,
             errorCount: 0,
-        } as MlFileData;
+        } as MinimalPersistedFileData;
     }
 
     private async getLocalFilesMap(syncContext: MLSyncContext) {
@@ -309,7 +212,6 @@ class MachineLearningService {
             syncContext.error = error;
         }
         await syncContext.syncQueue.onIdle();
-        log.info("allFaces: ", syncContext.nSyncedFaces);
 
         // TODO: In case syncJob has to use multiple ml workers
         // do in same transaction with each file update
@@ -318,13 +220,17 @@ class MachineLearningService {
         // await this.disposeMLModels();
     }
 
-    private async getSyncContext(token: string, userID: number) {
+    private async getSyncContext(
+        token: string,
+        userID: number,
+        userAgent: string,
+    ) {
         if (!this.syncContext) {
             log.info("Creating syncContext");
 
             // TODO-ML(MR): Keep as promise for now.
             this.syncContext = new Promise((resolve) => {
-                resolve(new LocalMLSyncContext(token, userID));
+                resolve(new MLSyncContext(token, userID, userAgent));
             });
         } else {
             log.info("reusing existing syncContext");
@@ -332,13 +238,17 @@ class MachineLearningService {
         return this.syncContext;
     }
 
-    private async getLocalSyncContext(token: string, userID: number) {
+    private async getLocalSyncContext(
+        token: string,
+        userID: number,
+        userAgent: string,
+    ) {
         // TODO-ML(MR): This is updating the file ML version. verify.
         if (!this.localSyncContext) {
             log.info("Creating localSyncContext");
             // TODO-ML(MR):
             this.localSyncContext = new Promise((resolve) => {
-                resolve(new LocalMLSyncContext(token, userID));
+                resolve(new MLSyncContext(token, userID, userAgent));
             });
         } else {
             log.info("reusing existing localSyncContext");
@@ -358,10 +268,15 @@ class MachineLearningService {
     public async syncLocalFile(
         token: string,
         userID: number,
+        userAgent: string,
         enteFile: EnteFile,
         localFile?: globalThis.File,
     ) {
-        const syncContext = await this.getLocalSyncContext(token, userID);
+        const syncContext = await this.getLocalSyncContext(
+            token,
+            userID,
+            userAgent,
+        );
 
         try {
             await this.syncFileWithErrorHandler(
@@ -385,11 +300,11 @@ class MachineLearningService {
         localFile?: globalThis.File,
     ) {
         try {
-            console.log(
+            const mlFileData = await this.syncFile(
-                `Indexing ${enteFile.title ?? "<untitled>"} ${enteFile.id}`,
+                enteFile,
+                localFile,
+                syncContext.userAgent,
             );
-            const mlFileData = await this.syncFile(enteFile, localFile);
-            syncContext.nSyncedFaces += mlFileData.faces?.length || 0;
             syncContext.nSyncedFiles += 1;
             return mlFileData;
         } catch (e) {
@@ -421,62 +336,22 @@ class MachineLearningService {
         }
     }
 
-    private async syncFile(enteFile: EnteFile, localFile?: globalThis.File) {
+    private async syncFile(
-        log.debug(() => ({ a: "Syncing file", enteFile }));
+        enteFile: EnteFile,
-        const fileContext: MLSyncFileContext = { enteFile, localFile };
+        localFile: globalThis.File | undefined,
-        const oldMlFile = await this.getMLFileData(enteFile.id);
+        userAgent: string,
+    ) {
+        const oldMlFile = await mlIDbStorage.getFile(enteFile.id);
         if (oldMlFile && oldMlFile.mlVersion) {
             return oldMlFile;
         }
 
-        const newMlFile = (fileContext.newMlFile = this.newMlData(enteFile.id));
+        const newMlFile = await indexFaces(enteFile, localFile);
-        newMlFile.mlVersion = defaultMLVersion;
+        await putFaceEmbedding(enteFile, newMlFile, userAgent);
-
+        await mlIDbStorage.putFile(newMlFile);
-        try {
-            await fetchImageBitmapForContext(fileContext);
-            await syncFileAnalyzeFaces(fileContext);
-            newMlFile.errorCount = 0;
-            newMlFile.lastErrorMessage = undefined;
-            await this.persistOnServer(newMlFile, enteFile);
-            await mlIDbStorage.putFile(newMlFile);
-        } catch (e) {
-            log.error("ml detection failed", e);
-            newMlFile.mlVersion = oldMlFile.mlVersion;
-            throw e;
-        } finally {
-            fileContext.imageBitmap && fileContext.imageBitmap.close();
-        }
-
         return newMlFile;
     }
 
-    private async persistOnServer(mlFileData: MlFileData, enteFile: EnteFile) {
-        const serverMl = LocalFileMlDataToServerFileMl(mlFileData);
-        log.debug(() => ({ t: "Local ML file data", mlFileData }));
-        log.debug(() => ({
-            t: "Uploaded ML file data",
-            d: JSON.stringify(serverMl),
-        }));
-
-        const comlinkCryptoWorker = await ComlinkCryptoWorker.getInstance();
-        const { file: encryptedEmbeddingData } =
-            await comlinkCryptoWorker.encryptMetadata(serverMl, enteFile.key);
-        log.info(
-            `putEmbedding embedding to server for file: ${enteFile.metadata.title} fileID: ${enteFile.id}`,
-        );
-        const res = await putEmbedding({
-            fileID: enteFile.id,
-            encryptedEmbedding: encryptedEmbeddingData.encryptedData,
-            decryptionHeader: encryptedEmbeddingData.decryptionHeader,
-            model: "file-ml-clip-face",
-        });
-        log.info("putEmbedding response: ", res);
-    }
-
-    private async getMLFileData(fileId: number) {
-        return mlIDbStorage.getFile(fileId);
-    }
-
     private async persistMLFileSyncError(enteFile: EnteFile, e: Error) {
         try {
             await mlIDbStorage.upsertFileInTx(enteFile.id, (mlFileData) => {
@@ -484,7 +359,7 @@ class MachineLearningService {
                     mlFileData = this.newMlData(enteFile.id);
                 }
                 mlFileData.errorCount = (mlFileData.errorCount || 0) + 1;
-                mlFileData.lastErrorMessage = e.message;
+                console.error(`lastError for ${enteFile.id}`, e);
 
                 return mlFileData;
             });
@@ -493,183 +368,6 @@ class MachineLearningService {
             console.error("Error while storing ml sync error", e);
         }
     }
-
-    private async getMLLibraryData(syncContext: MLSyncContext) {
-        syncContext.mlLibraryData = await mlIDbStorage.getLibraryData();
-        if (!syncContext.mlLibraryData) {
-            syncContext.mlLibraryData = {};
-        }
-    }
-
-    private async persistMLLibraryData(syncContext: MLSyncContext) {
-        return mlIDbStorage.putLibraryData(syncContext.mlLibraryData);
-    }
-
-    public async syncIndex(syncContext: MLSyncContext) {
-        await this.getMLLibraryData(syncContext);
-
-        // TODO-ML(MR): Ensure this doesn't run until fixed.
-        await syncPeopleIndex(syncContext);
-
-        await this.persistMLLibraryData(syncContext);
-    }
 }
 
 export default new MachineLearningService();
-
-export interface FileML extends ServerFileMl {
-    updatedAt: number;
-}
-
-class ServerFileMl {
-    public fileID: number;
-    public height?: number;
-    public width?: number;
-    public faceEmbedding: ServerFaceEmbeddings;
-
-    public constructor(
-        fileID: number,
-        faceEmbedding: ServerFaceEmbeddings,
-        height?: number,
-        width?: number,
-    ) {
-        this.fileID = fileID;
-        this.height = height;
-        this.width = width;
-        this.faceEmbedding = faceEmbedding;
-    }
-}
-
-class ServerFaceEmbeddings {
-    public faces: ServerFace[];
-    public version: number;
-    public client?: string;
-    public error?: boolean;
-
-    public constructor(
-        faces: ServerFace[],
-        version: number,
-        client?: string,
-        error?: boolean,
-    ) {
-        this.faces = faces;
-        this.version = version;
-        this.client = client;
-        this.error = error;
-    }
-}
-
-class ServerFace {
-    public faceID: string;
-    public embeddings: number[];
-    public detection: ServerDetection;
-    public score: number;
-    public blur: number;
-
-    public constructor(
-        faceID: string,
-        embeddings: number[],
-        detection: ServerDetection,
-        score: number,
-        blur: number,
-    ) {
-        this.faceID = faceID;
-        this.embeddings = embeddings;
-        this.detection = detection;
-        this.score = score;
-        this.blur = blur;
-    }
-}
-
-class ServerDetection {
-    public box: ServerFaceBox;
-    public landmarks: Landmark[];
-
-    public constructor(box: ServerFaceBox, landmarks: Landmark[]) {
-        this.box = box;
-        this.landmarks = landmarks;
-    }
-}
-
-class ServerFaceBox {
-    public xMin: number;
-    public yMin: number;
-    public width: number;
-    public height: number;
-
-    public constructor(
-        xMin: number,
-        yMin: number,
-        width: number,
-        height: number,
-    ) {
-        this.xMin = xMin;
-        this.yMin = yMin;
-        this.width = width;
-        this.height = height;
-    }
-}
-
-function LocalFileMlDataToServerFileMl(
-    localFileMlData: MlFileData,
-): ServerFileMl {
-    if (
-        localFileMlData.errorCount > 0 &&
-        localFileMlData.lastErrorMessage !== undefined
-    ) {
-        return null;
-    }
-    const imageDimensions = localFileMlData.imageDimensions;
-
-    const faces: ServerFace[] = [];
-    for (let i = 0; i < localFileMlData.faces.length; i++) {
-        const face: Face = localFileMlData.faces[i];
-        const faceID = face.id;
-        const embedding = face.embedding;
-        const score = face.detection.probability;
-        const blur = face.blurValue;
-        const detection: FaceDetection = face.detection;
-        const box = detection.box;
-        const landmarks = detection.landmarks;
-        const newBox = new ServerFaceBox(box.x, box.y, box.width, box.height);
-        const newLandmarks: Landmark[] = [];
-        for (let j = 0; j < landmarks.length; j++) {
-            newLandmarks.push({
-                x: landmarks[j].x,
-                y: landmarks[j].y,
-            } as Landmark);
-        }
-
-        const newFaceObject = new ServerFace(
-            faceID,
-            Array.from(embedding),
-            new ServerDetection(newBox, newLandmarks),
-            score,
-            blur,
-        );
-        faces.push(newFaceObject);
-    }
-    const faceEmbeddings = new ServerFaceEmbeddings(
-        faces,
-        1,
-        localFileMlData.lastErrorMessage,
-    );
-    return new ServerFileMl(
-        localFileMlData.fileId,
-        faceEmbeddings,
-        imageDimensions.height,
-        imageDimensions.width,
-    );
-}
-
-export function logQueueStats(queue: PQueue, name: string) {
-    queue.on("active", () =>
-        log.info(
-            `queuestats: ${name}: Active, Size: ${queue.size} Pending: ${queue.pending}`,
-        ),
-    );
-    queue.on("idle", () => log.info(`queuestats: ${name}: Idle`));
-    queue.on("error", (error) =>
-        console.error(`queuestats: ${name}: Error, `, error),
-    );
-}

web/apps/photos/src/services/machineLearning/mlWorkManager.ts

@@ -1,6 +1,8 @@
 import { FILE_TYPE } from "@/media/file-type";
+import { ensureElectron } from "@/next/electron";
 import log from "@/next/log";
 import { ComlinkWorker } from "@/next/worker/comlink-worker";
+import { clientPackageNamePhotosDesktop } from "@ente/shared/apps/constants";
 import { eventBus, Events } from "@ente/shared/events";
 import { getToken, getUserID } from "@ente/shared/storage/localStorage/helpers";
 import debounce from "debounce";
@@ -8,25 +10,18 @@ import PQueue from "p-queue";
 import { createFaceComlinkWorker } from "services/face";
 import mlIDbStorage from "services/face/db";
 import type { DedicatedMLWorker } from "services/face/face.worker";
-import { MLSyncResult } from "services/face/types";
 import { EnteFile } from "types/file";
-import { logQueueStats } from "./machineLearningService";
 
 export type JobState = "Scheduled" | "Running" | "NotScheduled";
 
-export interface MLSyncJobResult {
-    shouldBackoff: boolean;
-    mlSyncResult: MLSyncResult;
-}
-
 export class MLSyncJob {
-    private runCallback: () => Promise<MLSyncJobResult>;
+    private runCallback: () => Promise<boolean>;
     private state: JobState;
     private stopped: boolean;
     private intervalSec: number;
     private nextTimeoutId: ReturnType<typeof setTimeout>;
 
-    constructor(runCallback: () => Promise<MLSyncJobResult>) {
+    constructor(runCallback: () => Promise<boolean>) {
         this.runCallback = runCallback;
         this.state = "NotScheduled";
         this.stopped = true;
@@ -65,13 +60,11 @@ export class MLSyncJob {
         this.state = "Running";
 
         try {
-            const jobResult = await this.runCallback();
+            if (await this.runCallback()) {
-            if (jobResult && jobResult.shouldBackoff) {
-                this.intervalSec = Math.min(960, this.intervalSec * 2);
-            } else {
                 this.resetInterval();
+            } else {
+                this.intervalSec = Math.min(960, this.intervalSec * 2);
             }
-            log.info("Job completed");
         } catch (e) {
             console.error("Error while running Job: ", e);
         } finally {
@@ -236,8 +229,15 @@ class MLWorkManager {
             this.stopSyncJob();
             const token = getToken();
             const userID = getUserID();
+            const userAgent = await getUserAgent();
             const mlWorker = await this.getLiveSyncWorker();
-            return mlWorker.syncLocalFile(token, userID, enteFile, localFile);
+            return mlWorker.syncLocalFile(
+                token,
+                userID,
+                userAgent,
+                enteFile,
+                localFile,
+            );
         });
     }
 
@@ -255,7 +255,14 @@ class MLWorkManager {
         this.syncJobWorker = undefined;
     }
 
-    private async runMLSyncJob(): Promise<MLSyncJobResult> {
+    /**
+     * Returns `false` to indicate that either an error occurred, or there are
+     * not more files to process, or that we cannot currently process files.
+     *
+     * Which means that when it returns true, all is well and there are more
+     * things pending to process, so we should chug along at full speed.
+     */
+    private async runMLSyncJob(): Promise<boolean> {
         try {
             // TODO: skipping is not required if we are caching chunks through service worker
             // currently worker chunk itself is not loaded when network is not there
@@ -263,29 +270,17 @@ class MLWorkManager {
                 log.info(
                     "Skipping ml-sync job run as not connected to internet.",
                 );
-                return {
+                return false;
-                    shouldBackoff: true,
-                    mlSyncResult: undefined,
-                };
             }
 
             const token = getToken();
             const userID = getUserID();
+            const userAgent = await getUserAgent();
             const jobWorkerProxy = await this.getSyncJobWorker();
 
-            const mlSyncResult = await jobWorkerProxy.sync(token, userID);
+            return await jobWorkerProxy.sync(token, userID, userAgent);
-
             // this.terminateSyncJobWorker();
-            const jobResult: MLSyncJobResult = {
-                shouldBackoff:
-                    !!mlSyncResult.error || mlSyncResult.nOutOfSyncFiles < 1,
-                mlSyncResult,
-            };
-            log.info("ML Sync Job result: ", JSON.stringify(jobResult));
-
             // TODO: redirect/refresh to gallery in case of session_expired, stop ml sync job
-
-            return jobResult;
         } catch (e) {
             log.error("Failed to run MLSync Job", e);
         }
@@ -323,3 +318,22 @@ class MLWorkManager {
 }
 
 export default new MLWorkManager();
+
+export function logQueueStats(queue: PQueue, name: string) {
+    queue.on("active", () =>
+        log.info(
+            `queuestats: ${name}: Active, Size: ${queue.size} Pending: ${queue.pending}`,
+        ),
+    );
+    queue.on("idle", () => log.info(`queuestats: ${name}: Idle`));
+    queue.on("error", (error) =>
+        console.error(`queuestats: ${name}: Error, `, error),
+    );
+}
+
+const getUserAgent = async () => {
+    const electron = ensureElectron();
+    const name = clientPackageNamePhotosDesktop;
+    const version = await electron.appVersion();
+    return `${name}/${version}`;
+};

web/apps/photos/src/services/searchService.ts

@@ -3,7 +3,7 @@ import log from "@/next/log";
 import * as chrono from "chrono-node";
 import { t } from "i18next";
 import mlIDbStorage from "services/face/db";
-import { Person } from "services/face/types";
+import type { Person } from "services/face/people";
 import { defaultMLVersion } from "services/machineLearning/machineLearningService";
 import { Collection } from "types/collection";
 import { EntityType, LocationTag, LocationTagData } from "types/entity";

web/apps/photos/src/types/search/index.ts

@@ -1,6 +1,6 @@
 import { FILE_TYPE } from "@/media/file-type";
 import { IndexStatus } from "services/face/db";
-import { Person } from "services/face/types";
+import type { Person } from "services/face/people";
 import { City } from "services/locationSearchService";
 import { LocationTagData } from "types/entity";
 import { EnteFile } from "types/file";

web/apps/photos/src/utils/comlink/ComlinkSearchWorker.ts

@@ -5,11 +5,13 @@ import { type DedicatedSearchWorker } from "worker/search.worker";
 
 class ComlinkSearchWorker {
     private comlinkWorkerInstance: Remote<DedicatedSearchWorker>;
+    private comlinkWorker: ComlinkWorker<typeof DedicatedSearchWorker>;
 
     async getInstance() {
         if (!this.comlinkWorkerInstance) {
-            this.comlinkWorkerInstance =
+            if (!this.comlinkWorker)
-                await getDedicatedSearchWorker().remote;
+                this.comlinkWorker = getDedicatedSearchWorker();
+            this.comlinkWorkerInstance = await this.comlinkWorker.remote;
         }
         return this.comlinkWorkerInstance;
     }

web/apps/photos/src/utils/image/index.ts

@@ -1,468 +0,0 @@
-// these utils only work in env where OffscreenCanvas is available
-
-import { Matrix, inverse } from "ml-matrix";
-import { Box, Dimensions, enlargeBox } from "services/face/geom";
-import { FaceAlignment } from "services/face/types";
-
-export function normalizePixelBetween0And1(pixelValue: number) {
-    return pixelValue / 255.0;
-}
-
-export function normalizePixelBetweenMinus1And1(pixelValue: number) {
-    return pixelValue / 127.5 - 1.0;
-}
-
-export function unnormalizePixelFromBetweenMinus1And1(pixelValue: number) {
-    return clamp(Math.round((pixelValue + 1.0) * 127.5), 0, 255);
-}
-
-export function readPixelColor(
-    imageData: Uint8ClampedArray,
-    width: number,
-    height: number,
-    x: number,
-    y: number,
-) {
-    if (x < 0 || x >= width || y < 0 || y >= height) {
-        return { r: 0, g: 0, b: 0, a: 0 };
-    }
-    const index = (y * width + x) * 4;
-    return {
-        r: imageData[index],
-        g: imageData[index + 1],
-        b: imageData[index + 2],
-        a: imageData[index + 3],
-    };
-}
-
-export function clamp(value: number, min: number, max: number) {
-    return Math.min(max, Math.max(min, value));
-}
-
-export function getPixelBicubic(
-    fx: number,
-    fy: number,
-    imageData: Uint8ClampedArray,
-    imageWidth: number,
-    imageHeight: number,
-) {
-    // Clamp to image boundaries
-    fx = clamp(fx, 0, imageWidth - 1);
-    fy = clamp(fy, 0, imageHeight - 1);
-
-    const x = Math.trunc(fx) - (fx >= 0.0 ? 0 : 1);
-    const px = x - 1;
-    const nx = x + 1;
-    const ax = x + 2;
-    const y = Math.trunc(fy) - (fy >= 0.0 ? 0 : 1);
-    const py = y - 1;
-    const ny = y + 1;
-    const ay = y + 2;
-    const dx = fx - x;
-    const dy = fy - y;
-
-    function cubic(
-        dx: number,
-        ipp: number,
-        icp: number,
-        inp: number,
-        iap: number,
-    ) {
-        return (
-            icp +
-            0.5 *
-                (dx * (-ipp + inp) +
-                    dx * dx * (2 * ipp - 5 * icp + 4 * inp - iap) +
-                    dx * dx * dx * (-ipp + 3 * icp - 3 * inp + iap))
-        );
-    }
-
-    const icc = readPixelColor(imageData, imageWidth, imageHeight, x, y);
-
-    const ipp =
-        px < 0 || py < 0
-            ? icc
-            : readPixelColor(imageData, imageWidth, imageHeight, px, py);
-    const icp =
-        px < 0
-            ? icc
-            : readPixelColor(imageData, imageWidth, imageHeight, x, py);
-    const inp =
-        py < 0 || nx >= imageWidth
-            ? icc
-            : readPixelColor(imageData, imageWidth, imageHeight, nx, py);
-    const iap =
-        ax >= imageWidth || py < 0
-            ? icc
-            : readPixelColor(imageData, imageWidth, imageHeight, ax, py);
-
-    const ip0 = cubic(dx, ipp.r, icp.r, inp.r, iap.r);
-    const ip1 = cubic(dx, ipp.g, icp.g, inp.g, iap.g);
-    const ip2 = cubic(dx, ipp.b, icp.b, inp.b, iap.b);
-    // const ip3 = cubic(dx, ipp.a, icp.a, inp.a, iap.a);
-
-    const ipc =
-        px < 0
-            ? icc
-            : readPixelColor(imageData, imageWidth, imageHeight, px, y);
-    const inc =
-        nx >= imageWidth
-            ? icc
-            : readPixelColor(imageData, imageWidth, imageHeight, nx, y);
-    const iac =
-        ax >= imageWidth
-            ? icc
-            : readPixelColor(imageData, imageWidth, imageHeight, ax, y);
-
-    const ic0 = cubic(dx, ipc.r, icc.r, inc.r, iac.r);
-    const ic1 = cubic(dx, ipc.g, icc.g, inc.g, iac.g);
-    const ic2 = cubic(dx, ipc.b, icc.b, inc.b, iac.b);
-    // const ic3 = cubic(dx, ipc.a, icc.a, inc.a, iac.a);
-
-    const ipn =
-        px < 0 || ny >= imageHeight
-            ? icc
-            : readPixelColor(imageData, imageWidth, imageHeight, px, ny);
-    const icn =
-        ny >= imageHeight
-            ? icc
-            : readPixelColor(imageData, imageWidth, imageHeight, x, ny);
-    const inn =
-        nx >= imageWidth || ny >= imageHeight
-            ? icc
-            : readPixelColor(imageData, imageWidth, imageHeight, nx, ny);
-    const ian =
-        ax >= imageWidth || ny >= imageHeight
-            ? icc
-            : readPixelColor(imageData, imageWidth, imageHeight, ax, ny);
-
-    const in0 = cubic(dx, ipn.r, icn.r, inn.r, ian.r);
-    const in1 = cubic(dx, ipn.g, icn.g, inn.g, ian.g);
-    const in2 = cubic(dx, ipn.b, icn.b, inn.b, ian.b);
-    // const in3 = cubic(dx, ipn.a, icn.a, inn.a, ian.a);
-
-    const ipa =
-        px < 0 || ay >= imageHeight
-            ? icc
-            : readPixelColor(imageData, imageWidth, imageHeight, px, ay);
-    const ica =
-        ay >= imageHeight
-            ? icc
-            : readPixelColor(imageData, imageWidth, imageHeight, x, ay);
-    const ina =
-        nx >= imageWidth || ay >= imageHeight
-            ? icc
-            : readPixelColor(imageData, imageWidth, imageHeight, nx, ay);
-    const iaa =
-        ax >= imageWidth || ay >= imageHeight
-            ? icc
-            : readPixelColor(imageData, imageWidth, imageHeight, ax, ay);
-
-    const ia0 = cubic(dx, ipa.r, ica.r, ina.r, iaa.r);
-    const ia1 = cubic(dx, ipa.g, ica.g, ina.g, iaa.g);
-    const ia2 = cubic(dx, ipa.b, ica.b, ina.b, iaa.b);
-    // const ia3 = cubic(dx, ipa.a, ica.a, ina.a, iaa.a);
-
-    const c0 = Math.trunc(clamp(cubic(dy, ip0, ic0, in0, ia0), 0, 255));
-    const c1 = Math.trunc(clamp(cubic(dy, ip1, ic1, in1, ia1), 0, 255));
-    const c2 = Math.trunc(clamp(cubic(dy, ip2, ic2, in2, ia2), 0, 255));
-    // const c3 = cubic(dy, ip3, ic3, in3, ia3);
-
-    return { r: c0, g: c1, b: c2 };
-}
-
-/// Returns the pixel value (RGB) at the given coordinates using bilinear interpolation.
-export function getPixelBilinear(
-    fx: number,
-    fy: number,
-    imageData: Uint8ClampedArray,
-    imageWidth: number,
-    imageHeight: number,
-) {
-    // Clamp to image boundaries
-    fx = clamp(fx, 0, imageWidth - 1);
-    fy = clamp(fy, 0, imageHeight - 1);
-
-    // Get the surrounding coordinates and their weights
-    const x0 = Math.floor(fx);
-    const x1 = Math.ceil(fx);
-    const y0 = Math.floor(fy);
-    const y1 = Math.ceil(fy);
-    const dx = fx - x0;
-    const dy = fy - y0;
-    const dx1 = 1.0 - dx;
-    const dy1 = 1.0 - dy;
-
-    // Get the original pixels
-    const pixel1 = readPixelColor(imageData, imageWidth, imageHeight, x0, y0);
-    const pixel2 = readPixelColor(imageData, imageWidth, imageHeight, x1, y0);
-    const pixel3 = readPixelColor(imageData, imageWidth, imageHeight, x0, y1);
-    const pixel4 = readPixelColor(imageData, imageWidth, imageHeight, x1, y1);
-
-    function bilinear(val1: number, val2: number, val3: number, val4: number) {
-        return Math.round(
-            val1 * dx1 * dy1 +
-                val2 * dx * dy1 +
-                val3 * dx1 * dy +
-                val4 * dx * dy,
-        );
-    }
-
-    // Interpolate the pixel values
-    const red = bilinear(pixel1.r, pixel2.r, pixel3.r, pixel4.r);
-    const green = bilinear(pixel1.g, pixel2.g, pixel3.g, pixel4.g);
-    const blue = bilinear(pixel1.b, pixel2.b, pixel3.b, pixel4.b);
-
-    return { r: red, g: green, b: blue };
-}
-
-export function warpAffineFloat32List(
-    imageBitmap: ImageBitmap,
-    faceAlignment: FaceAlignment,
-    faceSize: number,
-    inputData: Float32Array,
-    inputStartIndex: number,
-): void {
-    // Get the pixel data
-    const offscreenCanvas = new OffscreenCanvas(
-        imageBitmap.width,
-        imageBitmap.height,
-    );
-    const ctx = offscreenCanvas.getContext("2d");
-    ctx.drawImage(imageBitmap, 0, 0, imageBitmap.width, imageBitmap.height);
-    const imageData = ctx.getImageData(
-        0,
-        0,
-        imageBitmap.width,
-        imageBitmap.height,
-    );
-    const pixelData = imageData.data;
-
-    const transformationMatrix = faceAlignment.affineMatrix.map((row) =>
-        row.map((val) => (val != 1.0 ? val * faceSize : 1.0)),
-    ); // 3x3
-
-    const A: Matrix = new Matrix([
-        [transformationMatrix[0][0], transformationMatrix[0][1]],
-        [transformationMatrix[1][0], transformationMatrix[1][1]],
-    ]);
-    const Ainverse = inverse(A);
-
-    const b00 = transformationMatrix[0][2];
-    const b10 = transformationMatrix[1][2];
-    const a00Prime = Ainverse.get(0, 0);
-    const a01Prime = Ainverse.get(0, 1);
-    const a10Prime = Ainverse.get(1, 0);
-    const a11Prime = Ainverse.get(1, 1);
-
-    for (let yTrans = 0; yTrans < faceSize; ++yTrans) {
-        for (let xTrans = 0; xTrans < faceSize; ++xTrans) {
-            // Perform inverse affine transformation
-            const xOrigin =
-                a00Prime * (xTrans - b00) + a01Prime * (yTrans - b10);
-            const yOrigin =
-                a10Prime * (xTrans - b00) + a11Prime * (yTrans - b10);
-
-            // Get the pixel from interpolation
-            const pixel = getPixelBicubic(
-                xOrigin,
-                yOrigin,
-                pixelData,
-                imageBitmap.width,
-                imageBitmap.height,
-            );
-
-            // Set the pixel in the input data
-            const index = (yTrans * faceSize + xTrans) * 3;
-            inputData[inputStartIndex + index] =
-                normalizePixelBetweenMinus1And1(pixel.r);
-            inputData[inputStartIndex + index + 1] =
-                normalizePixelBetweenMinus1And1(pixel.g);
-            inputData[inputStartIndex + index + 2] =
-                normalizePixelBetweenMinus1And1(pixel.b);
-        }
-    }
-}
-
-export function createGrayscaleIntMatrixFromNormalized2List(
-    imageList: Float32Array,
-    faceNumber: number,
-    width: number = 112,
-    height: number = 112,
-): number[][] {
-    const startIndex = faceNumber * width * height * 3;
-    return Array.from({ length: height }, (_, y) =>
-        Array.from({ length: width }, (_, x) => {
-            // 0.299 ∙ Red + 0.587 ∙ Green + 0.114 ∙ Blue
-            const pixelIndex = startIndex + 3 * (y * width + x);
-            return clamp(
-                Math.round(
-                    0.299 *
-                        unnormalizePixelFromBetweenMinus1And1(
-                            imageList[pixelIndex],
-                        ) +
-                        0.587 *
-                            unnormalizePixelFromBetweenMinus1And1(
-                                imageList[pixelIndex + 1],
-                            ) +
-                        0.114 *
-                            unnormalizePixelFromBetweenMinus1And1(
-                                imageList[pixelIndex + 2],
-                            ),
-                ),
-                0,
-                255,
-            );
-        }),
-    );
-}
-
-export function resizeToSquare(img: ImageBitmap, size: number) {
-    const scale = size / Math.max(img.height, img.width);
-    const width = scale * img.width;
-    const height = scale * img.height;
-    const offscreen = new OffscreenCanvas(size, size);
-    const ctx = offscreen.getContext("2d");
-    ctx.imageSmoothingQuality = "high";
-    ctx.drawImage(img, 0, 0, width, height);
-    const resizedImage = offscreen.transferToImageBitmap();
-    return { image: resizedImage, width, height };
-}
-
-export function transform(
-    imageBitmap: ImageBitmap,
-    affineMat: number[][],
-    outputWidth: number,
-    outputHeight: number,
-) {
-    const offscreen = new OffscreenCanvas(outputWidth, outputHeight);
-    const context = offscreen.getContext("2d");
-    context.imageSmoothingQuality = "high";
-
-    context.transform(
-        affineMat[0][0],
-        affineMat[1][0],
-        affineMat[0][1],
-        affineMat[1][1],
-        affineMat[0][2],
-        affineMat[1][2],
-    );
-
-    context.drawImage(imageBitmap, 0, 0);
-    return offscreen.transferToImageBitmap();
-}
-
-export function crop(imageBitmap: ImageBitmap, cropBox: Box, size: number) {
-    const dimensions: Dimensions = {
-        width: size,
-        height: size,
-    };
-
-    return cropWithRotation(imageBitmap, cropBox, 0, dimensions, dimensions);
-}
-
-export function cropWithRotation(
-    imageBitmap: ImageBitmap,
-    cropBox: Box,
-    rotation?: number,
-    maxSize?: Dimensions,
-    minSize?: Dimensions,
-) {
-    const box = cropBox.round();
-
-    const outputSize = { width: box.width, height: box.height };
-    if (maxSize) {
-        const minScale = Math.min(
-            maxSize.width / box.width,
-            maxSize.height / box.height,
-        );
-        if (minScale < 1) {
-            outputSize.width = Math.round(minScale * box.width);
-            outputSize.height = Math.round(minScale * box.height);
-        }
-    }
-
-    if (minSize) {
-        const maxScale = Math.max(
-            minSize.width / box.width,
-            minSize.height / box.height,
-        );
-        if (maxScale > 1) {
-            outputSize.width = Math.round(maxScale * box.width);
-            outputSize.height = Math.round(maxScale * box.height);
-        }
-    }
-
-    // log.info({ imageBitmap, box, outputSize });
-
-    const offscreen = new OffscreenCanvas(outputSize.width, outputSize.height);
-    const offscreenCtx = offscreen.getContext("2d");
-    offscreenCtx.imageSmoothingQuality = "high";
-
-    offscreenCtx.translate(outputSize.width / 2, outputSize.height / 2);
-    rotation && offscreenCtx.rotate(rotation);
-
-    const outputBox = new Box({
-        x: -outputSize.width / 2,
-        y: -outputSize.height / 2,
-        width: outputSize.width,
-        height: outputSize.height,
-    });
-
-    const enlargedBox = enlargeBox(box, 1.5);
-    const enlargedOutputBox = enlargeBox(outputBox, 1.5);
-
-    offscreenCtx.drawImage(
-        imageBitmap,
-        enlargedBox.x,
-        enlargedBox.y,
-        enlargedBox.width,
-        enlargedBox.height,
-        enlargedOutputBox.x,
-        enlargedOutputBox.y,
-        enlargedOutputBox.width,
-        enlargedOutputBox.height,
-    );
-
-    return offscreen.transferToImageBitmap();
-}
-
-export function addPadding(image: ImageBitmap, padding: number) {
-    const scale = 1 + padding * 2;
-    const width = scale * image.width;
-    const height = scale * image.height;
-    const offscreen = new OffscreenCanvas(width, height);
-    const ctx = offscreen.getContext("2d");
-    ctx.imageSmoothingEnabled = false;
-    ctx.drawImage(
-        image,
-        width / 2 - image.width / 2,
-        height / 2 - image.height / 2,
-        image.width,
-        image.height,
-    );
-
-    return offscreen.transferToImageBitmap();
-}
-
-export interface BlobOptions {
-    type?: string;
-    quality?: number;
-}
-
-export async function imageBitmapToBlob(imageBitmap: ImageBitmap) {
-    const offscreen = new OffscreenCanvas(
-        imageBitmap.width,
-        imageBitmap.height,
-    );
-    offscreen.getContext("2d").drawImage(imageBitmap, 0, 0);
-
-    return offscreen.convertToBlob({
-        type: "image/jpeg",
-        quality: 0.8,
-    });
-}
-
-export async function imageBitmapFromBlob(blob: Blob) {
-    return createImageBitmap(blob);
-}

web/apps/photos/src/worker/ffmpeg.worker.ts

@@ -82,7 +82,7 @@ const ffmpegExec = async (
 
         const result = ffmpeg.FS("readFile", outputPath);
 
-        const ms = Math.round(Date.now() - startTime);
+        const ms = Date.now() - startTime;
         log.debug(() => `[wasm] ffmpeg ${cmd.join(" ")} (${ms} ms)`);
         return result;
     } finally {

web/packages/accounts/services/logout.ts

@@ -1,4 +1,4 @@
-import { clearCaches } from "@/next/blob-cache";
+import { clearBlobCaches } from "@/next/blob-cache";
 import log from "@/next/log";
 import InMemoryStore from "@ente/shared/storage/InMemoryStore";
 import localForage from "@ente/shared/storage/localForage";
@@ -43,7 +43,7 @@ export const accountLogout = async () => {
         log.error("Ignoring error during logout (local forage)", e);
     }
     try {
-        await clearCaches();
+        await clearBlobCaches();
     } catch (e) {
         log.error("Ignoring error during logout (cache)", e);
     }

web/packages/next/blob-cache.ts

@@ -20,8 +20,8 @@ export type BlobCacheNamespace = (typeof blobCacheNames)[number];
  *
  * This cache is suitable for storing large amounts of data (entire files).
  *
- * To obtain a cache for a given namespace, use {@link openCache}. To clear all
+ * To obtain a cache for a given namespace, use {@link openBlobCache}. To clear all
- * cached data (e.g. during logout), use {@link clearCaches}.
+ * cached data (e.g. during logout), use {@link clearBlobCaches}.
  *
  * [Note: Caching files]
  *
@@ -69,14 +69,31 @@ export interface BlobCache {
     delete: (key: string) => Promise<boolean>;
 }
 
+const cachedCaches = new Map<BlobCacheNamespace, BlobCache>();
+
 /**
  * Return the {@link BlobCache} corresponding to the given {@link name}.
  *
+ * This is a wrapper over {@link openBlobCache} that caches (pun intended) the
+ * cache and returns the same one each time it is called with the same name.
+ * It'll open the cache lazily the first time it is invoked.
+ */
+export const blobCache = async (
+    name: BlobCacheNamespace,
+): Promise<BlobCache> => {
+    let c = cachedCaches.get(name);
+    if (!c) cachedCaches.set(name, (c = await openBlobCache(name)));
+    return c;
+};
+
+/**
+ * Create a new {@link BlobCache} corresponding to the given {@link name}.
+ *
  * @param name One of the arbitrary but predefined namespaces of type
  * {@link BlobCacheNamespace} which group related data and allow us to use the
  * same key across namespaces.
  */
-export const openCache = async (
+export const openBlobCache = async (
     name: BlobCacheNamespace,
 ): Promise<BlobCache> =>
     isElectron() ? openOPFSCacheWeb(name) : openWebCache(name);
@@ -194,7 +211,7 @@ export const cachedOrNew = async (
     key: string,
     get: () => Promise<Blob>,
 ): Promise<Blob> => {
-    const cache = await openCache(cacheName);
+    const cache = await openBlobCache(cacheName);
     const cachedBlob = await cache.get(key);
     if (cachedBlob) return cachedBlob;
 
@@ -204,15 +221,17 @@ export const cachedOrNew = async (
 };
 
 /**
- * Delete all cached data.
+ * Delete all cached data, including cached caches.
  *
  * Meant for use during logout, to reset the state of the user's account.
  */
-export const clearCaches = async () =>
+export const clearBlobCaches = async () => {
-    isElectron() ? clearOPFSCaches() : clearWebCaches();
+    cachedCaches.clear();
+    return isElectron() ? clearOPFSCaches() : clearWebCaches();
+};
 
 const clearWebCaches = async () => {
-    await Promise.all(blobCacheNames.map((name) => caches.delete(name)));
+    await Promise.allSettled(blobCacheNames.map((name) => caches.delete(name)));
 };
 
 const clearOPFSCaches = async () => {

web/packages/next/types/ipc.ts

@@ -297,7 +297,9 @@ export interface Electron {
      *
      * @returns A CLIP embedding.
      */
-    clipImageEmbedding: (jpegImageData: Uint8Array) => Promise<Float32Array>;
+    computeCLIPImageEmbedding: (
+        jpegImageData: Uint8Array,
+    ) => Promise<Float32Array>;
 
     /**
      * Return a CLIP embedding of the given image if we already have the model
@@ -319,7 +321,7 @@ export interface Electron {
      *
      * @returns A CLIP embedding.
      */
-    clipTextEmbeddingIfAvailable: (
+    computeCLIPTextEmbeddingIfAvailable: (
         text: string,
     ) => Promise<Float32Array | undefined>;
 
@@ -337,29 +339,7 @@ export interface Electron {
      * Both the input and output are opaque binary data whose internal structure
      * is specific to our implementation and the model (MobileFaceNet) we use.
      */
-    faceEmbeddings: (input: Float32Array) => Promise<Float32Array>;
+    computeFaceEmbeddings: (input: Float32Array) => Promise<Float32Array>;
-
-    /**
-     * Return a face crop stored by a previous version of ML.
-     *
-     * [Note: Legacy face crops]
-     *
-     * Older versions of ML generated and stored face crops in a "face-crops"
-     * cache directory on the Electron side. For the time being, we have
-     * disabled the face search whilst we put finishing touches to it. However,
-     * it'll be nice to still show the existing faces that have been clustered
-     * for people who opted in to the older beta.
-     *
-     * So we retain the older "face-crops" disk cache, and use this method to
-     * serve faces from it when needed.
-     *
-     * @param faceID An identifier corresponding to which the face crop had been
-     * stored by the older version of our app.
-     *
-     * @returns the JPEG data of the face crop if a file is found for the given
-     * {@link faceID}, otherwise undefined.
-     */
-    legacyFaceCrop: (faceID: string) => Promise<Uint8Array | undefined>;
 
     // - Watch
 

web/packages/next/worker/comlink-worker.ts

@@ -47,8 +47,8 @@ const workerBridge = {
     convertToJPEG: (imageData: Uint8Array) =>
         ensureElectron().convertToJPEG(imageData),
     detectFaces: (input: Float32Array) => ensureElectron().detectFaces(input),
-    faceEmbeddings: (input: Float32Array) =>
+    computeFaceEmbeddings: (input: Float32Array) =>
-        ensureElectron().faceEmbeddings(input),
+        ensureElectron().computeFaceEmbeddings(input),
 };
 
 export type WorkerBridge = typeof workerBridge;

web/packages/shared/apps/constants.ts

@@ -14,6 +14,8 @@ export const CLIENT_PACKAGE_NAMES = new Map([
     [APPS.ACCOUNTS, "io.ente.accounts.web"],
 ]);
 
+export const clientPackageNamePhotosDesktop = "io.ente.photos.desktop";
+
 export const APP_TITLES = new Map([
     [APPS.ALBUMS, "Ente Albums"],
     [APPS.PHOTOS, "Ente Photos"],

web/packages/shared/network/HTTPService.ts

@@ -28,8 +28,8 @@ class HTTPService {
                         const responseData = response.data;
                         log.error(
                             `HTTP Service Error - ${JSON.stringify({
-                                url: config.url,
+                                url: config?.url,
-                                method: config.method,
+                                method: config?.method,
                                 xRequestId: response.headers["x-request-id"],
                                 httpStatus: response.status,
                                 errMessage: responseData.message,

web/packages/utils/promise.ts

@@ -10,6 +10,10 @@ export const wait = (ms: number) =>
 /**
  * Await the given {@link promise} for {@link timeoutMS} milliseconds. If it
  * does not resolve within {@link timeoutMS}, then reject with a timeout error.
+ *
+ * Note that this does not abort {@link promise} itself - it will still get
+ * resolved to completion, just its result will be ignored if it gets resolved
+ * after we've already timed out.
  */
 export const withTimeout = async <T>(promise: Promise<T>, ms: number) => {
     let timeoutId: ReturnType<typeof setTimeout>;