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Merge remote-tracking branch 'origin/main' into thumbnail_duration

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This commit is contained in:
laurenspriem
2024-05-30 23:08:02 +05:30
parent 6635f4c9a4 62e5950429
commit e99da59699
30 changed files with 752 additions and 1091 deletions
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26
auth/assets/custom-icons/_data/custom-icons.json
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@@ -32,7 +32,10 @@
},
{
"title": "Bloom Host",
"slug": "bloom_host"
"slug": "bloom_host",
"altNames": [
"Bloom Host Billing"
]
},
{
"title": "BorgBase",
@@ -190,6 +193,15 @@
{
"title": "Letterboxd"
},
{
"title": "Local",
"slug": "local_wp",
"altNames": [
"LocalWP",
"Local WP",
"Local Wordpress"
]
},
{
"title": "Mastodon",
"altNames": [
@@ -203,7 +215,12 @@
},
{
"title": "Mercado Livre",
"slug": "mercado_livre"
"slug": "mercado_livre",
"altNames": [
"Mercado Libre",
"MercadoLibre",
"MercadoLivre"
]
},
{
"title": "Murena",
@@ -345,7 +362,10 @@
"hex": "FFFFFF"
},
{
"title": "Techlore"
"title": "Techlore",
"altNames": [
"Techlore Courses"
]
},
{
"title": "Termius",

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21
desktop/.github/workflows/desktop-release.yml vendored
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@@ -5,12 +5,19 @@ name: "Release"
# For more details, see `docs/release.md` in ente-io/ente.
on:
# Trigger manually or `gh workflow run desktop-release.yml`.
# Trigger manually or `gh workflow run desktop-release.yml --source=foo`.
workflow_dispatch:
inputs:
source:
description: "Branch (ente-io/ente) to build"
type: string
schedule:
# Run everyday at ~8:00 AM IST (except Sundays).
# See: [Note: Run workflow every 24 hours]
#
- cron: "45 2 * * 1-6"
push:
# Run when a tag matching the pattern "v*"" is pushed.
#
# See: [Note: Testing release workflows that are triggered by tags].
tags:
- "v*"
@@ -30,9 +37,13 @@ jobs:
- name: Checkout code
uses: actions/checkout@v4
with:
# Checkout the desktop/rc branch from the source repository.
# If triggered by a tag, checkout photosd-$tag from the source
# repository. Otherwise checkout $source (default: "main").
repository: ente-io/ente
ref: desktop/rc
ref:
"${{ startsWith(github.ref, 'refs/tags/v') &&
format('photosd-{0}', github.ref_name) || ( inputs.source
|| 'main' ) }}"
submodules: recursive
- name: Setup node

56
desktop/docs/release.md
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@@ -5,7 +5,7 @@ creates a draft release with artifacts built. When ready, we publish that
release. The download links on our website, and existing apps already check the
latest GitHub release and update accordingly.
The complication comes by the fact that electron-builder's auto updaterr (the
The complication comes by the fact that electron-builder's auto updater (the
mechanism that we use for auto updates) doesn't work with monorepos. So we need
to keep a separate (non-mono) repository just for doing releases.
@@ -16,48 +16,45 @@ to keep a separate (non-mono) repository just for doing releases.
## Workflow - Release Candidates
Leading up to the release, we can make one or more draft releases that are not
intended to be published, but serve as test release candidates.
Nightly RC builds of `main` are published by a scheduled workflow automatically.
If needed, these builds can also be manually triggered, including specifying the
source repository branch to build:
The workflow for making such "rc" builds is:
```sh
gh workflow run desktop-release.yml --source=<branch>
```
1. Update `package.json` in the source repo to use version `1.x.x-rc`. Create a
new draft release in the release repo with title `1.x.x-rc`. In the tag
input enter `v1.x.x-rc` and select the option to "create a new tag on
publish".
2. Push code to the `desktop/rc` branch in the source repo.
3. Trigger the GitHub action in the release repo
```sh
gh workflow run desktop-release.yml
```
We can do steps 2 and 3 multiple times: each time it'll just update the
artifacts attached to the same draft.
Each such workflow run will update the artifacts attached to the same
(pre-existing) pre-release.
## Workflow - Release
1. Update source repo to set version `1.x.x` in `package.json` and finalize the
CHANGELOG.
2. Push code to the `desktop/rc` branch in the source repo.
2. Merge PR then tag the merge commit on `main` in the source repo:
3. In the release repo
```sh
git tag photosd-v1.x.x
git push origin photosd-v1.x.x
```
3. In the release repo:
```sh
./.github/trigger-release.sh v1.x.x
```
4. If the build is successful, tag `desktop/rc` in the source repo.
This'll trigger the workflow and create a new draft release, which you can
publish after adding the release notes.
```sh
# Assuming we're on desktop/rc that just got built
The release is done at this point, and we can now start a new RC train for
subsequent nightly builds.
git tag photosd-v1.x.x
git push origin photosd-v1.x.x
```
1. Update `package.json` in the source repo to use version `1.x.x-rc`. Create a
new draft release in the release repo with title `1.x.x-rc`. In the tag
input enter `v1.x.x-rc` and select the option to "create a new tag on
publish".
## Post build
@@ -87,8 +84,3 @@ everything is automated:
now their maintainers automatically bump the SHA, version number and the
(derived from the version) URL in the formula when their tools notice a new
release on our GitHub.
We can also publish the draft releases by checking the "pre-release" option.
Such releases don't cause any of the channels (our website, or the desktop app
auto updater, or brew) to be notified, instead these are useful for giving links
to pre-release builds to customers.

2
mobile/lib/l10n/intl_en.arb
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@@ -409,7 +409,7 @@
"manageDeviceStorage": "Manage device storage",
"machineLearning": "Machine learning",
"magicSearch": "Magic search",
"mlIndexingDescription": "Please note that ML indexing will result in a higher bandwidth and battery usage until all items are indexed.",
"mlIndexingDescription": "Please note that machine learning will result in a higher bandwidth and battery usage until all items are indexed.",
"loadingModel": "Downloading models...",
"waitingForWifi": "Waiting for WiFi...",
"status": "Status",

14
web/apps/photos/src/components/PhotoViewer/FileInfo/index.tsx
View File

@@ -11,7 +11,7 @@ import { Box, DialogProps, Link, Stack, styled } from "@mui/material";
import { Chip } from "components/Chip";
import { EnteDrawer } from "components/EnteDrawer";
import Titlebar from "components/Titlebar";
import { PhotoPeopleList, UnidentifiedFaces } from "components/ml/PeopleList";
import { UnidentifiedFaces } from "components/ml/PeopleList";
import LinkButton from "components/pages/gallery/LinkButton";
import { t } from "i18next";
import { AppContext } from "pages/_app";
@@ -96,8 +96,6 @@ export function FileInfo({
const [parsedExifData, setParsedExifData] = useState<Record<string, any>>();
const [showExif, setShowExif] = useState(false);
// eslint-disable-next-line @typescript-eslint/no-unused-vars
const [updateMLDataIndex, setUpdateMLDataIndex] = useState(0);
const openExif = () => setShowExif(true);
const closeExif = () => setShowExif(false);
@@ -332,14 +330,8 @@ export function FileInfo({
{appContext.mlSearchEnabled && (
<>
<PhotoPeopleList
file={file}
updateMLDataIndex={updateMLDataIndex}
/>
<UnidentifiedFaces
file={file}
updateMLDataIndex={updateMLDataIndex}
/>
{/* <PhotoPeopleList file={file} /> */}
<UnidentifiedFaces file={file} />
</>
)}
</Stack>

3
web/apps/photos/src/components/Search/SearchBar/searchInput/MenuWithPeople.tsx
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@@ -5,7 +5,6 @@ import { t } from "i18next";
import { AppContext } from "pages/_app";
import { useContext } from "react";
import { components } from "react-select";
import { IndexStatus } from "services/face/db-old";
import { Suggestion, SuggestionType } from "types/search";
const { Menu } = components;
@@ -35,7 +34,7 @@ const MenuWithPeople = (props) => {
(o) => o.type === SuggestionType.INDEX_STATUS,
)[0] as Suggestion;
const indexStatus = indexStatusSuggestion?.value as IndexStatus;
const indexStatus = indexStatusSuggestion?.value;
return (
<Menu {...props}>
<Box my={1}>

114
web/apps/photos/src/components/ml/PeopleList.tsx
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@@ -1,10 +1,9 @@
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-old";
import { unidentifiedFaceIDs } from "services/face/indexer";
import type { Person } from "services/face/people";
import { EnteFile } from "types/file";
@@ -67,63 +66,29 @@ export const PeopleList = React.memo((props: PeopleListProps) => {
export interface PhotoPeopleListProps extends PeopleListPropsBase {
file: EnteFile;
updateMLDataIndex: number;
}
export function PhotoPeopleList(props: PhotoPeopleListProps) {
const [people, setPeople] = useState<Array<Person>>([]);
export function PhotoPeopleList() {
return <></>;
}
export function UnidentifiedFaces({ file }: { file: EnteFile }) {
const [faceIDs, setFaceIDs] = useState<string[]>([]);
useEffect(() => {
let didCancel = false;
async function updateFaceImages() {
log.info("calling getPeopleList");
const startTime = Date.now();
const people = await getPeopleList(props.file);
log.info(`getPeopleList ${Date.now() - startTime} ms`);
log.info(`getPeopleList done, didCancel: ${didCancel}`);
!didCancel && setPeople(people);
}
updateFaceImages();
(async () => {
const faceIDs = await unidentifiedFaceIDs(file);
!didCancel && setFaceIDs(faceIDs);
})();
return () => {
didCancel = true;
};
}, [props.file, props.updateMLDataIndex]);
}, [file]);
if (people.length === 0) return <></>;
return (
<div>
<Legend>{t("PEOPLE")}</Legend>
<PeopleList people={people} onSelect={props.onSelect}></PeopleList>
</div>
);
}
export function UnidentifiedFaces(props: {
file: EnteFile;
updateMLDataIndex: number;
}) {
const [faces, setFaces] = useState<{ id: string }[]>([]);
useEffect(() => {
let didCancel = false;
async function updateFaceImages() {
const faces = await getUnidentifiedFaces(props.file);
!didCancel && setFaces(faces);
}
updateFaceImages();
return () => {
didCancel = true;
};
}, [props.file, props.updateMLDataIndex]);
if (!faces || faces.length === 0) return <></>;
if (faceIDs.length == 0) return <></>;
return (
<>
@@ -131,12 +96,11 @@ export function UnidentifiedFaces(props: {
<Legend>{t("UNIDENTIFIED_FACES")}</Legend>
</div>
<FaceChipContainer>
{faces &&
faces.map((face, index) => (
<FaceChip key={index}>
<FaceCropImageView faceID={face.id} />
</FaceChip>
))}
{faceIDs.map((faceID) => (
<FaceChip key={faceID}>
<FaceCropImageView {...{ faceID }} />
</FaceChip>
))}
</FaceChipContainer>
</>
);
@@ -179,45 +143,3 @@ const FaceCropImageView: React.FC<FaceCropImageViewProps> = ({ faceID }) => {
<Skeleton variant="circular" height={120} width={120} />
);
};
async function getPeopleList(file: EnteFile): Promise<Person[]> {
let startTime = Date.now();
const mlFileData = await mlIDbStorage.getFile(file.id);
log.info(
"getPeopleList:mlFilesStore:getItem",
Date.now() - startTime,
"ms",
);
if (!mlFileData?.faces || mlFileData.faces.length < 1) {
return [];
}
const peopleIds = mlFileData.faces
.filter((f) => f.personId !== null && f.personId !== undefined)
.map((f) => f.personId);
if (!peopleIds || peopleIds.length < 1) {
return [];
}
// log.info("peopleIds: ", peopleIds);
startTime = Date.now();
const peoplePromises = peopleIds.map(
(p) => mlIDbStorage.getPerson(p) as Promise<Person>,
);
const peopleList = await Promise.all(peoplePromises);
log.info(
"getPeopleList:mlPeopleStore:getItems",
Date.now() - startTime,
"ms",
);
// log.info("peopleList: ", peopleList);
return peopleList;
}
async function getUnidentifiedFaces(file: EnteFile): Promise<{ id: string }[]> {
const mlFileData = await mlIDbStorage.getFile(file.id);
return mlFileData?.faces?.filter(
(f) => f.personId === null || f.personId === undefined,
);
}

18
web/apps/photos/src/pages/_app.tsx
View File

@@ -50,11 +50,11 @@ import { createContext, useContext, useEffect, useRef, useState } from "react";
import LoadingBar from "react-top-loading-bar";
import DownloadManager from "services/download";
import { resumeExportsIfNeeded } from "services/export";
import { photosLogout } from "services/logout";
import {
getMLSearchConfig,
updateMLSearchConfig,
} from "services/machineLearning/machineLearningService";
isFaceIndexingEnabled,
setIsFaceIndexingEnabled,
} from "services/face/indexer";
import { photosLogout } from "services/logout";
import mlWorkManager from "services/machineLearning/mlWorkManager";
import {
getFamilyPortalRedirectURL,
@@ -186,9 +186,9 @@ export default function App({ Component, pageProps }: AppProps) {
}
const loadMlSearchState = async () => {
try {
const mlSearchConfig = await getMLSearchConfig();
setMlSearchEnabled(mlSearchConfig.enabled);
mlWorkManager.setMlSearchEnabled(mlSearchConfig.enabled);
const enabled = await isFaceIndexingEnabled();
setMlSearchEnabled(enabled);
mlWorkManager.setMlSearchEnabled(enabled);
} catch (e) {
log.error("Error while loading mlSearchEnabled", e);
}
@@ -286,9 +286,7 @@ export default function App({ Component, pageProps }: AppProps) {
const showNavBar = (show: boolean) => setShowNavBar(show);
const updateMlSearchEnabled = async (enabled: boolean) => {
try {
const mlSearchConfig = await getMLSearchConfig();
mlSearchConfig.enabled = enabled;
await updateMLSearchConfig(mlSearchConfig);
await setIsFaceIndexingEnabled(enabled);
setMlSearchEnabled(enabled);
mlWorkManager.setMlSearchEnabled(enabled);
} catch (e) {

10
web/apps/photos/src/pages/gallery/index.tsx
View File

@@ -85,10 +85,7 @@ import {
getSectionSummaries,
} from "services/collectionService";
import downloadManager from "services/download";
import {
syncCLIPEmbeddings,
syncFaceEmbeddings,
} from "services/embeddingService";
import { syncCLIPEmbeddings } from "services/embeddingService";
import { syncEntities } from "services/entityService";
import { getLocalFiles, syncFiles } from "services/fileService";
import locationSearchService from "services/locationSearchService";
@@ -130,7 +127,6 @@ import {
} from "utils/file";
import { isArchivedFile } from "utils/magicMetadata";
import { getSessionExpiredMessage } from "utils/ui";
import { isInternalUserForML } from "utils/user";
import { getLocalFamilyData } from "utils/user/family";
export const DeadCenter = styled("div")`
@@ -720,7 +716,9 @@ export default function Gallery() {
const electron = globalThis.electron;
if (electron) {
await syncCLIPEmbeddings();
if (isInternalUserForML()) await syncFaceEmbeddings();
// TODO-ML(MR): Disable fetch until we start storing it in the
// same place as the local ones.
// if (isInternalUserForML()) await syncFaceEmbeddings();
}
if (clipService.isPlatformSupported()) {
void clipService.scheduleImageEmbeddingExtraction();

5
web/apps/photos/src/services/embeddingService.ts
View File

@@ -7,7 +7,6 @@ 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/face/remote";
import type {
Embedding,
EmbeddingModel,
@@ -17,9 +16,13 @@ import type {
} from "types/embedding";
import { EnteFile } from "types/file";
import { getLocalCollections } from "./collectionService";
import type { FaceIndex } from "./face/types";
import { getAllLocalFiles } from "./fileService";
import { getLocalTrashedFiles } from "./trashService";
type FileML = FaceIndex & {
updatedAt: number;
};
const DIFF_LIMIT = 500;
/** Local storage key suffix for embedding sync times */

12
web/apps/photos/src/services/face/crop.ts
View File

@@ -1,14 +1,18 @@
import { blobCache } from "@/next/blob-cache";
import type { FaceAlignment } from "./f-index";
import type { Box } from "./types";
import type { Face, FaceAlignment } from "./types-old";
export const saveFaceCrop = async (imageBitmap: ImageBitmap, face: Face) => {
const faceCrop = extractFaceCrop(imageBitmap, face.alignment);
export const saveFaceCrop = async (
imageBitmap: ImageBitmap,
faceID: string,
alignment: FaceAlignment,
) => {
const faceCrop = extractFaceCrop(imageBitmap, alignment);
const blob = await imageBitmapToBlob(faceCrop);
faceCrop.close();
const cache = await blobCache("face-crops");
await cache.put(face.id, blob);
await cache.put(faceID, blob);
return blob;
};

387
web/apps/photos/src/services/face/db-old.ts
View File

@@ -1,387 +0,0 @@
import { haveWindow } from "@/next/env";
import log from "@/next/log";
import {
DBSchema,
IDBPDatabase,
IDBPTransaction,
StoreNames,
deleteDB,
openDB,
} from "idb";
import isElectron from "is-electron";
import type { Person } from "services/face/people";
import type { MlFileData } from "services/face/types-old";
import {
DEFAULT_ML_SEARCH_CONFIG,
MAX_ML_SYNC_ERROR_COUNT,
} from "services/machineLearning/machineLearningService";
export interface IndexStatus {
outOfSyncFilesExists: boolean;
nSyncedFiles: number;
nTotalFiles: number;
localFilesSynced: boolean;
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";
const MLDATA_DB_NAME = "mldata";
interface MLDb extends DBSchema {
files: {
key: number;
value: MinimalPersistedFileData;
indexes: { mlVersion: [number, number] };
};
people: {
key: number;
value: Person;
};
// Unused, we only retain this is the schema so that we can delete it during
// migration.
things: {
key: number;
value: unknown;
};
versions: {
key: string;
value: number;
};
library: {
key: string;
value: unknown;
};
configs: {
key: string;
value: Config;
};
}
class MLIDbStorage {
public _db: Promise<IDBPDatabase<MLDb>>;
constructor() {
if (!haveWindow() || !isElectron()) {
return;
}
this.db;
}
private openDB(): Promise<IDBPDatabase<MLDb>> {
return openDB<MLDb>(MLDATA_DB_NAME, 4, {
terminated: async () => {
log.error("ML Indexed DB terminated");
this._db = undefined;
// TODO: remove if there is chance of this going into recursion in some case
await this.db;
},
blocked() {
// TODO: make sure we dont allow multiple tabs of app
log.error("ML Indexed DB blocked");
},
blocking() {
// TODO: make sure we dont allow multiple tabs of app
log.error("ML Indexed DB blocking");
},
async upgrade(db, oldVersion, newVersion, tx) {
let wasMLSearchEnabled = false;
try {
const searchConfig: unknown = await tx
.objectStore("configs")
.get(ML_SEARCH_CONFIG_NAME);
if (
searchConfig &&
typeof searchConfig == "object" &&
"enabled" in searchConfig &&
typeof searchConfig.enabled == "boolean"
) {
wasMLSearchEnabled = searchConfig.enabled;
}
} catch (e) {
// The configs store might not exist (e.g. during logout).
// Ignore.
}
log.info(
`Previous ML database v${oldVersion} had ML search ${wasMLSearchEnabled ? "enabled" : "disabled"}`,
);
if (oldVersion < 1) {
const filesStore = db.createObjectStore("files", {
keyPath: "fileId",
});
filesStore.createIndex("mlVersion", [
"mlVersion",
"errorCount",
]);
db.createObjectStore("people", {
keyPath: "id",
});
db.createObjectStore("things", {
keyPath: "id",
});
db.createObjectStore("versions");
db.createObjectStore("library");
}
if (oldVersion < 2) {
// TODO: update configs if version is updated in defaults
db.createObjectStore("configs");
/*
await tx
.objectStore("configs")
.add(
DEFAULT_ML_SYNC_JOB_CONFIG,
"ml-sync-job",
);
await tx
.objectStore("configs")
.add(DEFAULT_ML_SYNC_CONFIG, ML_SYNC_CONFIG_NAME);
*/
}
if (oldVersion < 3) {
await tx
.objectStore("configs")
.add(DEFAULT_ML_SEARCH_CONFIG, ML_SEARCH_CONFIG_NAME);
}
/*
This'll go in version 5. Note that version 4 was never released,
but it was in main for a while, so we'll just skip it to avoid
breaking the upgrade path for people who ran the mainline.
*/
if (oldVersion < 4) {
/*
try {
await tx
.objectStore("configs")
.delete(ML_SEARCH_CONFIG_NAME);
await tx
.objectStore("configs")
.delete(""ml-sync"");
await tx
.objectStore("configs")
.delete("ml-sync-job");
await tx
.objectStore("configs")
.add(
{ enabled: wasMLSearchEnabled },
ML_SEARCH_CONFIG_NAME,
);
db.deleteObjectStore("library");
db.deleteObjectStore("things");
} catch {
// TODO: ignore for now as we finalize the new version
// the shipped implementation should have a more
// deterministic migration.
}
*/
}
log.info(
`ML DB upgraded from version ${oldVersion} to version ${newVersion}`,
);
},
});
}
public get db(): Promise<IDBPDatabase<MLDb>> {
if (!this._db) {
this._db = this.openDB();
log.info("Opening Ml DB");
}
return this._db;
}
public async clearMLDB() {
const db = await this.db;
db.close();
await deleteDB(MLDATA_DB_NAME);
log.info("Cleared Ml DB");
this._db = undefined;
await this.db;
}
public async getAllFileIdsForUpdate(
tx: IDBPTransaction<MLDb, ["files"], "readwrite">,
) {
return tx.store.getAllKeys();
}
public async getFileIds(
count: number,
limitMlVersion: number,
maxErrorCount: number,
) {
const db = await this.db;
const tx = db.transaction("files", "readonly");
const index = tx.store.index("mlVersion");
let cursor = await index.openKeyCursor(
IDBKeyRange.upperBound([limitMlVersion], true),
);
const fileIds: number[] = [];
while (cursor && fileIds.length < count) {
if (
cursor.key[0] < limitMlVersion &&
cursor.key[1] <= maxErrorCount
) {
fileIds.push(cursor.primaryKey);
}
cursor = await cursor.continue();
}
await tx.done;
return fileIds;
}
public async getFile(fileId: number): Promise<MinimalPersistedFileData> {
const db = await this.db;
return db.get("files", fileId);
}
public async putFile(mlFile: MlFileData) {
const db = await this.db;
return db.put("files", mlFile);
}
public async upsertFileInTx(
fileId: number,
upsert: (mlFile: MinimalPersistedFileData) => MinimalPersistedFileData,
) {
const db = await this.db;
const tx = db.transaction("files", "readwrite");
const existing = await tx.store.get(fileId);
const updated = upsert(existing);
await tx.store.put(updated);
await tx.done;
return updated;
}
public async putAllFiles(
mlFiles: MinimalPersistedFileData[],
tx: IDBPTransaction<MLDb, ["files"], "readwrite">,
) {
await Promise.all(mlFiles.map((mlFile) => tx.store.put(mlFile)));
}
public async removeAllFiles(
fileIds: Array<number>,
tx: IDBPTransaction<MLDb, ["files"], "readwrite">,
) {
await Promise.all(fileIds.map((fileId) => tx.store.delete(fileId)));
}
public async getPerson(id: number) {
const db = await this.db;
return db.get("people", id);
}
public async getAllPeople() {
const db = await this.db;
return db.getAll("people");
}
public async incrementIndexVersion(index: StoreNames<MLDb>) {
if (index === "versions") {
throw new Error("versions store can not be versioned");
}
const db = await this.db;
const tx = db.transaction(["versions", index], "readwrite");
let version = await tx.objectStore("versions").get(index);
version = (version || 0) + 1;
tx.objectStore("versions").put(version, index);
await tx.done;
return version;
}
public async getConfig<T extends Config>(name: string, def: T) {
const db = await this.db;
const tx = db.transaction("configs", "readwrite");
let config = (await tx.store.get(name)) as T;
if (!config) {
config = def;
await tx.store.put(def, name);
}
await tx.done;
return config;
}
public async putConfig(name: string, data: Config) {
const db = await this.db;
return db.put("configs", data, name);
}
public async getIndexStatus(latestMlVersion: number): Promise<IndexStatus> {
const db = await this.db;
const tx = db.transaction(["files", "versions"], "readonly");
const mlVersionIdx = tx.objectStore("files").index("mlVersion");
let outOfSyncCursor = await mlVersionIdx.openKeyCursor(
IDBKeyRange.upperBound([latestMlVersion], true),
);
let outOfSyncFilesExists = false;
while (outOfSyncCursor && !outOfSyncFilesExists) {
if (
outOfSyncCursor.key[0] < latestMlVersion &&
outOfSyncCursor.key[1] <= MAX_ML_SYNC_ERROR_COUNT
) {
outOfSyncFilesExists = true;
}
outOfSyncCursor = await outOfSyncCursor.continue();
}
const nSyncedFiles = await mlVersionIdx.count(
IDBKeyRange.lowerBound([latestMlVersion]),
);
const nTotalFiles = await mlVersionIdx.count();
const filesIndexVersion = await tx.objectStore("versions").get("files");
const peopleIndexVersion = await tx
.objectStore("versions")
.get("people");
const filesIndexVersionExists =
filesIndexVersion !== null && filesIndexVersion !== undefined;
const peopleIndexVersionExists =
peopleIndexVersion !== null && peopleIndexVersion !== undefined;
await tx.done;
return {
outOfSyncFilesExists,
nSyncedFiles,
nTotalFiles,
localFilesSynced: filesIndexVersionExists,
peopleIndexSynced:
peopleIndexVersionExists &&
peopleIndexVersion === filesIndexVersion,
};
}
}
export default new MLIDbStorage();

84
web/apps/photos/src/services/face/db.ts
View File

@@ -82,6 +82,8 @@ interface FileStatus {
let _faceDB: ReturnType<typeof openFaceDB> | undefined;
const openFaceDB = async () => {
deleteLegacyDB();
const db = await openDB<FaceDBSchema>("face", 1, {
upgrade(db, oldVersion, newVersion) {
log.info(`Upgrading face DB ${oldVersion} => ${newVersion}`);
@@ -112,6 +114,13 @@ const openFaceDB = async () => {
return db;
};
const deleteLegacyDB = () => {
// Delete the legacy face DB.
// This code was added June 2024 (v1.7.1-rc) and can be removed once clients
// have migrated over.
void deleteDB("mldata");
};
/**
* @returns a lazily created, cached connection to the face DB.
*/
@@ -138,6 +147,7 @@ export const closeFaceDBConnectionsIfNeeded = async () => {
* Meant to be called during logout.
*/
export const clearFaceData = async () => {
deleteLegacyDB();
await closeFaceDBConnectionsIfNeeded();
return deleteDB("face", {
blocked() {
@@ -173,6 +183,14 @@ export const saveFaceIndex = async (faceIndex: FaceIndex) => {
]);
};
/**
* Return the {@link FaceIndex}, if any, for {@link fileID}.
*/
export const faceIndex = async (fileID: number) => {
const db = await faceDB();
return db.get("face-index", fileID);
};
/**
* Record the existence of a file so that entities in the face indexing universe
* know about it (e.g. can index it if it is new and it needs indexing).
@@ -197,6 +215,66 @@ export const addFileEntry = async (fileID: number) => {
return tx.done;
};
/**
* Sync entries in the face DB to align with the given list of local indexable
* file IDs.
*
* @param localFileIDs The IDs of all the files that the client is aware of,
* filtered to only keep the files that the user owns and the formats that can
* be indexed by our current face indexing pipeline.
*
* This function syncs the state of file entries in face DB to the state of file
* entries stored otherwise by the local client.
*
* - Files (identified by their ID) that are present locally but are not yet in
* face DB get a fresh entry in face DB (and are marked as indexable).
*
* - Files that are not present locally but still exist in face DB are removed
* from face DB (including its face index, if any).
*/
export const syncWithLocalIndexableFileIDs = async (localFileIDs: number[]) => {
const db = await faceDB();
const tx = db.transaction(["face-index", "file-status"], "readwrite");
const fdbFileIDs = await tx.objectStore("file-status").getAllKeys();
const local = new Set(localFileIDs);
const fdb = new Set(fdbFileIDs);
const newFileIDs = localFileIDs.filter((id) => !fdb.has(id));
const removedFileIDs = fdbFileIDs.filter((id) => !local.has(id));
return Promise.all(
[
newFileIDs.map((id) =>
tx.objectStore("file-status").put({
fileID: id,
isIndexable: 1,
failureCount: 0,
}),
),
removedFileIDs.map((id) =>
tx.objectStore("file-status").delete(id),
),
removedFileIDs.map((id) => tx.objectStore("face-index").delete(id)),
tx.done,
].flat(),
);
};
/**
* Return the count of files that can be, and that have been, indexed.
*/
export const indexedAndIndexableCounts = async () => {
const db = await faceDB();
const tx = db.transaction(["face-index", "file-status"], "readwrite");
const indexedCount = await tx.objectStore("face-index").count();
const indexableCount = await tx
.objectStore("file-status")
.index("isIndexable")
.count(IDBKeyRange.only(1));
return { indexedCount, indexableCount };
};
/**
* Return a list of fileIDs that need to be indexed.
*
@@ -204,11 +282,13 @@ export const addFileEntry = async (fileID: number) => {
* (can use {@link addFileEntry} to inform it about new files). From this
* universe, we filter out fileIDs the files corresponding to which have already
* been indexed, or for which we attempted indexing but failed.
*
* @param count Limit the result to up to {@link count} items.
*/
export const unindexedFileIDs = async () => {
export const unindexedFileIDs = async (count?: number) => {
const db = await faceDB();
const tx = db.transaction("file-status", "readonly");
return tx.store.index("isIndexable").getAllKeys(IDBKeyRange.only(1));
return tx.store.index("isIndexable").getAllKeys(IDBKeyRange.only(1), count);
};
/**

264
web/apps/photos/src/services/face/f-index.ts
View File

@@ -1,8 +1,9 @@
import { FILE_TYPE } from "@/media/file-type";
import { decodeLivePhoto } from "@/media/live-photo";
import log from "@/next/log";
import { workerBridge } from "@/next/worker/worker-bridge";
import { Matrix } from "ml-matrix";
import { defaultMLVersion } from "services/machineLearning/machineLearningService";
import DownloadManager from "services/download";
import { getSimilarityTransformation } from "similarity-transformation";
import {
Matrix as TransformationMatrix,
@@ -12,21 +13,15 @@ import {
translate,
} from "transformation-matrix";
import type { EnteFile } from "types/file";
import { getRenderableImage } from "utils/file";
import { saveFaceCrop } from "./crop";
import { fetchImageBitmap, getLocalFileImageBitmap } from "./file";
import {
clamp,
grayscaleIntMatrixFromNormalized2List,
pixelRGBBilinear,
warpAffineFloat32List,
} from "./image";
import type { Box, Dimensions } from "./types";
import type {
Face,
FaceAlignment,
FaceDetection,
MlFileData,
} from "./types-old";
import type { Box, Dimensions, Face, Point } from "./types";
/**
* Index faces in the given file.
@@ -43,95 +38,120 @@ import type {
* 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.
*
* @param enteFile The {@link EnteFile} to index.
*
* @param file The contents of {@link enteFile} as a web {@link File}, if
* available. These are used when they are provided, otherwise the file is
* downloaded and decrypted from remote.
*
* @param userAgent The UA of the current client (the client that is generating
* the embedding).
*/
export const indexFaces = async (enteFile: EnteFile, localFile?: File) => {
const startTime = Date.now();
export const indexFaces = async (
enteFile: EnteFile,
file: File | undefined,
userAgent: string,
) => {
const imageBitmap = await renderableImageBlob(enteFile, file).then(
createImageBitmap,
);
const { width, height } = imageBitmap;
const fileID = enteFile.id;
const imageBitmap = await fetchOrCreateImageBitmap(enteFile, localFile);
let mlFile: MlFileData;
try {
mlFile = await indexFaces_(enteFile, imageBitmap);
return {
fileID,
width,
height,
faceEmbedding: {
version: 1,
client: userAgent,
faces: await indexFacesInBitmap(fileID, imageBitmap),
},
};
} finally {
imageBitmap.close();
}
log.debug(() => {
const nf = mlFile.faces?.length ?? 0;
const ms = Date.now() - startTime;
return `Indexed ${nf} faces in file ${enteFile.id} (${ms} ms)`;
});
return mlFile;
};
/**
* Return a {@link ImageBitmap}, using {@link localFile} if present otherwise
* Return a "renderable" image blob, using {@link file} 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");
const renderableImageBlob = async (enteFile: EnteFile, file: File) =>
file
? getRenderableImage(enteFile.metadata.title, file)
: fetchRenderableBlob(enteFile);
return await getLocalFileImageBitmap(enteFile, localFile);
} else if ([FILE_TYPE.IMAGE, FILE_TYPE.LIVE_PHOTO].includes(fileType)) {
return await fetchImageBitmap(enteFile);
const fetchRenderableBlob = async (enteFile: EnteFile) => {
const fileStream = await DownloadManager.getFile(enteFile);
const fileBlob = await new Response(fileStream).blob();
const fileType = enteFile.metadata.fileType;
if (fileType == FILE_TYPE.IMAGE) {
return getRenderableImage(enteFile.metadata.title, fileBlob);
} else if (fileType == FILE_TYPE.LIVE_PHOTO) {
const { imageFileName, imageData } = await decodeLivePhoto(
enteFile.metadata.title,
fileBlob,
);
return getRenderableImage(imageFileName, new Blob([imageData]));
} else {
// A layer above us should've already filtered these out.
throw new Error(`Cannot index unsupported file type ${fileType}`);
}
};
const indexFaces_ = async (enteFile: EnteFile, imageBitmap: ImageBitmap) => {
const fileID = enteFile.id;
const indexFacesInBitmap = async (
fileID: number,
imageBitmap: ImageBitmap,
): Promise<Face[]> => {
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,
}));
mlFile.faces = detectedFaces;
const yoloFaceDetections = await detectFaces(imageBitmap);
const partialResult = yoloFaceDetections.map(
({ box, landmarks, score }) => {
const faceID = makeFaceID(fileID, box, imageDimensions);
const detection = { box, landmarks };
return { faceID, detection, score };
},
);
if (detectedFaces.length > 0) {
const alignments: FaceAlignment[] = [];
const alignments: FaceAlignment[] = [];
for (const face of mlFile.faces) {
const alignment = faceAlignment(face.detection);
face.alignment = alignment;
alignments.push(alignment);
for (const { faceID, detection } of partialResult) {
const alignment = computeFaceAlignment(detection);
alignments.push(alignment);
await saveFaceCrop(imageBitmap, face);
// This step is not part of the indexing pipeline, we just do it here
// since we have already computed the face alignment. Ignore errors that
// happen during this since it does not impact the generated face index.
try {
await saveFaceCrop(imageBitmap, faceID, alignment);
} catch (e) {
log.error(`Failed to save face crop for faceID ${faceID}`, e);
}
const alignedFacesData = convertToMobileFaceNetInput(
imageBitmap,
alignments,
);
const blurValues = detectBlur(alignedFacesData, mlFile.faces);
mlFile.faces.forEach((f, i) => (f.blurValue = blurValues[i]));
const embeddings = await computeEmbeddings(alignedFacesData);
mlFile.faces.forEach((f, i) => (f.embedding = embeddings[i]));
mlFile.faces.forEach((face) => {
face.detection = relativeDetection(face.detection, imageDimensions);
});
}
return mlFile;
const alignedFacesData = convertToMobileFaceNetInput(
imageBitmap,
alignments,
);
const embeddings = await computeEmbeddings(alignedFacesData);
const blurs = detectBlur(
alignedFacesData,
partialResult.map((f) => f.detection),
);
return partialResult.map(({ faceID, detection, score }, i) => ({
faceID,
detection: normalizeToImageDimensions(detection, imageDimensions),
score,
blur: blurs[i],
embedding: Array.from(embeddings[i]),
}));
};
/**
@@ -141,14 +161,14 @@ const indexFaces_ = async (enteFile: EnteFile, imageBitmap: ImageBitmap) => {
*/
const detectFaces = async (
imageBitmap: ImageBitmap,
): Promise<FaceDetection[]> => {
): Promise<YOLOFaceDetection[]> => {
const rect = ({ width, height }) => ({ x: 0, y: 0, width, height });
const { yoloInput, yoloSize } =
convertToYOLOInputFloat32ChannelsFirst(imageBitmap);
const yoloOutput = await workerBridge.detectFaces(yoloInput);
const faces = filterExtractDetectionsFromYOLOOutput(yoloOutput);
const faceDetections = transformFaceDetections(
const faceDetections = transformYOLOFaceDetections(
faces,
rect(yoloSize),
rect(imageBitmap),
@@ -209,6 +229,12 @@ const convertToYOLOInputFloat32ChannelsFirst = (imageBitmap: ImageBitmap) => {
return { yoloInput, yoloSize };
};
export interface YOLOFaceDetection {
box: Box;
landmarks: Point[];
score: number;
}
/**
* Extract detected faces from the YOLOv5Face's output.
*
@@ -227,8 +253,8 @@ const convertToYOLOInputFloat32ChannelsFirst = (imageBitmap: ImageBitmap) => {
*/
const filterExtractDetectionsFromYOLOOutput = (
rows: Float32Array,
): FaceDetection[] => {
const faces: FaceDetection[] = [];
): YOLOFaceDetection[] => {
const faces: YOLOFaceDetection[] = [];
// Iterate over each row.
for (let i = 0; i < rows.length; i += 16) {
const score = rows[i + 4];
@@ -253,7 +279,6 @@ const filterExtractDetectionsFromYOLOOutput = (
const rightMouthY = rows[i + 14];
const box = { x, y, width, height };
const probability = score as number;
const landmarks = [
{ x: leftEyeX, y: leftEyeY },
{ x: rightEyeX, y: rightEyeY },
@@ -261,26 +286,26 @@ const filterExtractDetectionsFromYOLOOutput = (
{ x: leftMouthX, y: leftMouthY },
{ x: rightMouthX, y: rightMouthY },
];
faces.push({ box, landmarks, probability });
faces.push({ box, landmarks, score });
}
return faces;
};
/**
* Transform the given {@link faceDetections} from their coordinate system in
* Transform the given {@link yoloFaceDetections} from their coordinate system in
* which they were detected ({@link inBox}) back to the coordinate system of the
* original image ({@link toBox}).
*/
const transformFaceDetections = (
faceDetections: FaceDetection[],
const transformYOLOFaceDetections = (
yoloFaceDetections: YOLOFaceDetection[],
inBox: Box,
toBox: Box,
): FaceDetection[] => {
): YOLOFaceDetection[] => {
const transform = boxTransformationMatrix(inBox, toBox);
return faceDetections.map((f) => ({
return yoloFaceDetections.map((f) => ({
box: transformBox(f.box, transform),
landmarks: f.landmarks.map((p) => applyToPoint(transform, p)),
probability: f.probability,
score: f.score,
}));
};
@@ -312,8 +337,8 @@ const transformBox = (box: Box, transform: TransformationMatrix): Box => {
* Remove overlapping faces from an array of face detections through non-maximum
* suppression algorithm.
*
* This function sorts the detections by their probability in descending order,
* then iterates over them.
* This function sorts the detections by their score in descending order, then
* iterates over them.
*
* For each detection, it calculates the Intersection over Union (IoU) with all
* other detections.
@@ -322,8 +347,8 @@ const transformBox = (box: Box, transform: TransformationMatrix): Box => {
* (`iouThreshold`), the other detection is considered overlapping and is
* removed.
*
* @param detections - An array of face detections to remove overlapping faces
* from.
* @param detections - An array of YOLO face detections to remove overlapping
* faces from.
*
* @param iouThreshold - The minimum IoU between two detections for them to be
* considered overlapping.
@@ -331,11 +356,11 @@ const transformBox = (box: Box, transform: TransformationMatrix): Box => {
* @returns An array of face detections with overlapping faces removed
*/
const naiveNonMaxSuppression = (
detections: FaceDetection[],
detections: YOLOFaceDetection[],
iouThreshold: number,
): FaceDetection[] => {
): YOLOFaceDetection[] => {
// Sort the detections by score, the highest first.
detections.sort((a, b) => b.probability - a.probability);
detections.sort((a, b) => b.score - a.score);
// Loop through the detections and calculate the IOU.
for (let i = 0; i < detections.length - 1; i++) {
@@ -379,11 +404,7 @@ const intersectionOverUnion = (a: FaceDetection, b: FaceDetection): number => {
return intersectionArea / unionArea;
};
const makeFaceID = (
fileID: number,
{ box }: FaceDetection,
image: Dimensions,
) => {
const makeFaceID = (fileID: number, box: Box, image: Dimensions) => {
const part = (v: number) => clamp(v, 0.0, 0.999999).toFixed(5).substring(2);
const xMin = part(box.x / image.width);
const yMin = part(box.y / image.height);
@@ -392,13 +413,30 @@ const makeFaceID = (
return [`${fileID}`, xMin, yMin, xMax, yMax].join("_");
};
export interface FaceAlignment {
/**
* An affine transformation matrix (rotation, translation, scaling) to align
* the face extracted from the image.
*/
affineMatrix: number[][];
/**
* The bounding box of the transformed box.
*
* The affine transformation shifts the original detection box a new,
* transformed, box (possibily rotated). This property is the bounding box
* of that transformed box. It is in the coordinate system of the original,
* full, image on which the detection occurred.
*/
boundingBox: Box;
}
/**
* 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(
const computeFaceAlignment = (faceDetection: FaceDetection): FaceAlignment =>
computeFaceAlignmentUsingSimilarityTransform(
faceDetection,
normalizeLandmarks(idealMobileFaceNetLandmarks, mobileFaceNetFaceSize),
);
@@ -421,7 +459,7 @@ const normalizeLandmarks = (
): [number, number][] =>
landmarks.map(([x, y]) => [x / faceSize, y / faceSize]);
const faceAlignmentUsingSimilarityTransform = (
const computeFaceAlignmentUsingSimilarityTransform = (
faceDetection: FaceDetection,
alignedLandmarks: [number, number][],
): FaceAlignment => {
@@ -483,28 +521,35 @@ const convertToMobileFaceNetInput = (
return faceData;
};
interface FaceDetection {
box: Box;
landmarks: Point[];
}
/**
* 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}.
* Return an array of detected blur values, one for each face detection in
* {@link faceDetections}. The face data is taken from the slice of
* {@link alignedFacesData} corresponding to the face of {@link faceDetections}.
*/
const detectBlur = (alignedFacesData: Float32Array, faces: Face[]): number[] =>
faces.map((face, i) => {
const detectBlur = (
alignedFacesData: Float32Array,
faceDetections: FaceDetection[],
): number[] =>
faceDetections.map((d, i) => {
const faceImage = grayscaleIntMatrixFromNormalized2List(
alignedFacesData,
i,
mobileFaceNetFaceSize,
mobileFaceNetFaceSize,
);
return matrixVariance(applyLaplacian(faceImage, faceDirection(face)));
return matrixVariance(applyLaplacian(faceImage, faceDirection(d)));
});
type FaceDirection = "left" | "right" | "straight";
const faceDirection = (face: Face): FaceDirection => {
const landmarks = face.detection.landmarks;
const faceDirection = ({ landmarks }: FaceDetection): FaceDirection => {
const leftEye = landmarks[0];
const rightEye = landmarks[1];
const nose = landmarks[2];
@@ -694,7 +739,7 @@ const computeEmbeddings = async (
/**
* Convert the coordinates to between 0-1, normalized by the image's dimensions.
*/
const relativeDetection = (
const normalizeToImageDimensions = (
faceDetection: FaceDetection,
{ width, height }: Dimensions,
): FaceDetection => {
@@ -709,6 +754,5 @@ const relativeDetection = (
x: l.x / width,
y: l.y / height,
}));
const probability = faceDetection.probability;
return { box, landmarks, probability };
return { box, landmarks };
};

37
web/apps/photos/src/services/face/file.ts
View File

@@ -1,37 +0,0 @@
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);
}

248
web/apps/photos/src/services/face/indexer.ts Normal file
View File

@@ -0,0 +1,248 @@
import { FILE_TYPE } from "@/media/file-type";
import { ComlinkWorker } from "@/next/worker/comlink-worker";
import { ensure } from "@/utils/ensure";
import { wait } from "@/utils/promise";
import { type Remote } from "comlink";
import { getLocalFiles } from "services/fileService";
import machineLearningService from "services/machineLearning/machineLearningService";
import mlWorkManager from "services/machineLearning/mlWorkManager";
import type { EnteFile } from "types/file";
import { isInternalUserForML } from "utils/user";
import {
faceIndex,
indexedAndIndexableCounts,
syncWithLocalIndexableFileIDs,
unindexedFileIDs,
} from "./db";
import { FaceIndexerWorker } from "./indexer.worker";
/**
* Face indexing orchestrator.
*
* This module exposes a singleton instance of this class which drives the face
* indexing process on the user's library.
*
* The indexer operates in two modes - live indexing and backfill.
*
* When live indexing, any files that are being uploaded from the current client
* are provided to the indexer, which puts them in a queue and indexes them one
* by one. This is more efficient since we already have the file's content at
* hand and do not have to download and decrypt it.
*
* When backfilling, the indexer figures out if any of the user's files
* (irrespective of where they were uploaded from) still need to be indexed, and
* if so, downloads, decrypts and indexes them.
*
* Live indexing has higher priority, backfilling runs otherwise. If nothing
* remains to be indexed, the indexer goes to sleep for a while.
*/
class FaceIndexer {
/** Live indexing queue. */
private liveItems: { enteFile: EnteFile; file: File | undefined }[];
/** Timeout for when the next time we will wake up. */
private wakeTimeout: ReturnType<typeof setTimeout> | undefined;
/**
* Add a file to the live indexing queue.
*
* @param enteFile An {@link EnteFile} that should be indexed.
*
* @param file The contents of {@link enteFile} as a web {@link File}
* object, if available.
*/
enqueueFile(enteFile: EnteFile, file: File | undefined) {
// If face indexing is not enabled, don't enqueue anything. Later on if
// the user turns on face indexing these files will get indexed as part
// of the backfilling anyway, the live indexing is just an optimization.
if (!mlWorkManager.isMlSearchEnabled) return;
this.liveItems.push({ enteFile, file });
this.wakeUpIfNeeded();
}
private wakeUpIfNeeded() {
// Already awake.
if (!this.wakeTimeout) return;
// Cancel the alarm, wake up now.
clearTimeout(this.wakeTimeout);
this.wakeTimeout = undefined;
// Get to work.
this.tick();
}
/**
* A promise for the lazily created singleton {@link FaceIndexerWorker} remote
* exposed by this module.
*/
_faceIndexer: Promise<Remote<FaceIndexerWorker>>;
/**
* Main thread interface to the face indexer.
*
* This function provides a promise that resolves to a lazily created singleton
* remote with a {@link FaceIndexerWorker} at the other end.
*/
faceIndexer = (): Promise<Remote<FaceIndexerWorker>> =>
(this._faceIndexer ??= createFaceIndexerComlinkWorker().remote);
private async tick() {
console.log("tick");
const item = this.liveItems.pop();
if (!item) {
// TODO-ML: backfill instead if needed here.
this.wakeTimeout = setTimeout(() => {
this.wakeTimeout = undefined;
this.wakeUpIfNeeded();
}, 30 * 1000);
return;
}
/*
const fileID = item.enteFile.id;
try {
const faceIndex = await indexFaces(item.enteFile, item.file, userAgent);
log.info(`faces in file ${fileID}`, faceIndex);
} catch (e) {
log.error(`Failed to index faces in file ${fileID}`, e);
markIndexingFailed(item.enteFile.id);
}
*/
// Let the runloop drain.
await wait(0);
// Run again.
// TODO
// this.tick();
}
/**
* Add a newly uploaded file to the face indexing queue.
*
* @param enteFile The {@link EnteFile} that was uploaded.
* @param file
*/
/*
indexFacesInFile = (enteFile: EnteFile, file: File) => {
if (!mlWorkManager.isMlSearchEnabled) return;
faceIndexer().then((indexer) => {
indexer.enqueueFile(file, enteFile);
});
};
*/
}
/** The singleton instance of {@link FaceIndexer}. */
export default new FaceIndexer();
const createFaceIndexerComlinkWorker = () =>
new ComlinkWorker<typeof FaceIndexerWorker>(
"face-indexer",
new Worker(new URL("indexer.worker.ts", import.meta.url)),
);
export interface FaceIndexingStatus {
/**
* Which phase we are in within the indexing pipeline when viewed across the
* user's entire library:
*
* - "scheduled": There are files we know of that have not been indexed.
*
* - "indexing": The face indexer is currently running.
*
* - "clustering": All files we know of have been indexed, and we are now
* clustering the faces that were found.
*
* - "done": Face indexing and clustering is complete for the user's
* library.
*/
phase: "scheduled" | "indexing" | "clustering" | "done";
/** The number of files that have already been indexed. */
nSyncedFiles: number;
/** The total number of files that are eligible for indexing. */
nTotalFiles: number;
}
export const faceIndexingStatus = async (): Promise<FaceIndexingStatus> => {
const isSyncing = machineLearningService.isSyncing;
const { indexedCount, indexableCount } = await indexedAndIndexableCounts();
let phase: FaceIndexingStatus["phase"];
if (indexedCount < indexableCount) {
if (!isSyncing) {
phase = "scheduled";
} else {
phase = "indexing";
}
} else {
phase = "done";
}
return {
phase,
nSyncedFiles: indexedCount,
nTotalFiles: indexableCount,
};
};
/**
* Return the IDs of all the faces in the given {@link enteFile} that are not
* associated with a person cluster.
*/
export const unidentifiedFaceIDs = async (
enteFile: EnteFile,
): Promise<string[]> => {
const index = await faceIndex(enteFile.id);
return index?.faceEmbedding.faces.map((f) => f.faceID) ?? [];
};
/**
* Return true if the user has enabled face indexing in the app's settings.
*
* This setting is persisted locally (in local storage) and is not synced with
* remote. There is a separate setting, "faceSearchEnabled" that is synced with
* remote, but that tracks whether or not the user has enabled face search once
* on any client. This {@link isFaceIndexingEnabled} property, on the other
* hand, denotes whether or not indexing is enabled on the current client.
*/
export const isFaceIndexingEnabled = async () => {
if (isInternalUserForML()) {
return localStorage.getItem("faceIndexingEnabled") == "1";
}
// Force disabled for everyone else while we finalize it to avoid redundant
// reindexing for users.
return false;
};
/**
* Update the (locally stored) value of {@link isFaceIndexingEnabled}.
*/
export const setIsFaceIndexingEnabled = async (enabled: boolean) => {
if (enabled) localStorage.setItem("faceIndexingEnabled", "1");
else localStorage.removeItem("faceIndexingEnabled");
};
/**
* Sync face DB with the local indexable files that we know about. Then return
* the next {@link count} files that still need to be indexed.
*
* For more specifics of what a "sync" entails, see
* {@link syncWithLocalIndexableFileIDs}.
*
* @param userID Limit indexing to files owned by a {@link userID}.
*
* @param count Limit the resulting list of files to {@link count}.
*/
export const getFilesToIndex = async (userID: number, count: number) => {
const localFiles = await getLocalFiles();
const indexableTypes = [FILE_TYPE.IMAGE, FILE_TYPE.LIVE_PHOTO];
const indexableFiles = localFiles.filter(
(f) =>
f.ownerID == userID && indexableTypes.includes(f.metadata.fileType),
);
const filesByID = new Map(indexableFiles.map((f) => [f.id, f]));
await syncWithLocalIndexableFileIDs([...filesByID.keys()]);
const fileIDsToIndex = await unindexedFileIDs(count);
return fileIDsToIndex.map((id) => ensure(filesByID.get(id)));
};

75
web/apps/photos/src/services/face/indexer.worker.ts Normal file
View File

@@ -0,0 +1,75 @@
import log from "@/next/log";
import type { EnteFile } from "types/file";
import { fileLogID } from "utils/file";
import {
closeFaceDBConnectionsIfNeeded,
markIndexingFailed,
saveFaceIndex,
} from "./db";
import { indexFaces } from "./f-index";
import { putFaceIndex } from "./remote";
import type { FaceIndex } from "./types";
/**
* Index faces in a file, save the persist the results locally, and put them on
* remote.
*
* This class is instantiated in a Web Worker so as to not get in the way of the
* main thread. It could've been a bunch of free standing functions too, it is
* just a class for convenience of compatibility with how the rest of our
* comlink workers are structured.
*/
export class FaceIndexerWorker {
/*
* Index faces in a file, save the persist the results locally, and put them
* on remote.
*
* @param enteFile The {@link EnteFile} to index.
*
* @param file If the file is one which is being uploaded from the current
* client, then we will also have access to the file's content. In such
* cases, pass a web {@link File} object to use that its data directly for
* face indexing. If this is not provided, then the file's contents will be
* downloaded and decrypted from remote.
*/
async index(enteFile: EnteFile, file: File | undefined, userAgent: string) {
const f = fileLogID(enteFile);
const startTime = Date.now();
let faceIndex: FaceIndex;
try {
faceIndex = await indexFaces(enteFile, file, userAgent);
} catch (e) {
// Mark indexing as having failed only if the indexing itself
// failed, not if there were subsequent failures (like when trying
// to put the result to remote or save it to the local face DB).
log.error(`Failed to index faces in ${f}`, e);
markIndexingFailed(enteFile.id);
throw e;
}
try {
await putFaceIndex(enteFile, faceIndex);
await saveFaceIndex(faceIndex);
} catch (e) {
log.error(`Failed to put/save face index for ${f}`, e);
throw e;
}
log.debug(() => {
const nf = faceIndex.faceEmbedding.faces.length;
const ms = Date.now() - startTime;
return `Indexed ${nf} faces in ${f} (${ms} ms)`;
});
return faceIndex;
}
/**
* Calls {@link closeFaceDBConnectionsIfNeeded} to close any open
* connections to the face DB from the web worker's context.
*/
closeFaceDB() {
closeFaceDBConnectionsIfNeeded();
}
}

4
web/apps/photos/src/services/face/people.ts
View File

@@ -84,6 +84,10 @@ export const syncPeopleIndex = async () => {
: best,
);
export async function getLocalFile(fileId: number) {
const localFiles = await getLocalFiles();
return localFiles.find((f) => f.id === fileId);
}
if (personFace && !personFace.crop?.cacheKey) {
const file = await getLocalFile(personFace.fileId);

136
web/apps/photos/src/services/face/remote.ts
View File

@@ -2,24 +2,20 @@ 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 "./types";
import type { Face, FaceDetection, MlFileData } from "./types-old";
import type { FaceIndex } from "./types";
export const putFaceEmbedding = async (
export const putFaceIndex = async (
enteFile: EnteFile,
mlFileData: MlFileData,
userAgent: string,
faceIndex: FaceIndex,
) => {
const serverMl = LocalFileMlDataToServerFileMl(mlFileData, userAgent);
log.debug(() => ({ t: "Local ML file data", mlFileData }));
log.debug(() => ({
t: "Uploaded ML file data",
d: JSON.stringify(serverMl),
t: "Uploading faceEmbedding",
d: JSON.stringify(faceIndex),
}));
const comlinkCryptoWorker = await ComlinkCryptoWorker.getInstance();
const { file: encryptedEmbeddingData } =
await comlinkCryptoWorker.encryptMetadata(serverMl, enteFile.key);
await comlinkCryptoWorker.encryptMetadata(faceIndex, enteFile.key);
await putEmbedding({
fileID: enteFile.id,
encryptedEmbedding: encryptedEmbeddingData.encryptedData,
@@ -27,123 +23,3 @@ export const putFaceEmbedding = async (
model: "file-ml-clip-face",
});
};
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 x: number;
public y: number;
public width: number;
public height: number;
public constructor(x: number, y: number, width: number, height: number) {
this.x = x;
this.y = y;
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,
);
}

46
web/apps/photos/src/services/face/types-old.ts
View File

@@ -1,46 +0,0 @@
import type { Box, Dimensions, Point } from "./types";
export interface FaceDetection {
// box and landmarks is relative to image dimentions stored at mlFileData
box: Box;
landmarks?: Point[];
probability?: number;
}
export interface FaceAlignment {
/**
* An affine transformation matrix (rotation, translation, scaling) to align
* the face extracted from the image.
*/
affineMatrix: number[][];
/**
* The bounding box of the transformed box.
*
* The affine transformation shifts the original detection box a new,
* transformed, box (possibily rotated). This property is the bounding box
* of that transformed box. It is in the coordinate system of the original,
* full, image on which the detection occurred.
*/
boundingBox: Box;
}
export interface Face {
fileId: number;
detection: FaceDetection;
id: string;
alignment?: FaceAlignment;
blurValue?: number;
embedding?: Float32Array;
personId?: number;
}
export interface MlFileData {
fileId: number;
faces?: Face[];
imageDimensions?: Dimensions;
mlVersion: number;
errorCount: number;
}

186
web/apps/photos/src/services/machineLearning/machineLearningService.ts
View File

@@ -1,46 +1,12 @@
import log from "@/next/log";
import { CustomError, parseUploadErrorCodes } from "@ente/shared/error";
import PQueue from "p-queue";
import mlIDbStorage, {
ML_SEARCH_CONFIG_NAME,
type MinimalPersistedFileData,
} from "services/face/db-old";
import { putFaceEmbedding } from "services/face/remote";
import { getLocalFiles } from "services/fileService";
import { getFilesToIndex } from "services/face/indexer";
import { FaceIndexerWorker } from "services/face/indexer.worker";
import { EnteFile } from "types/file";
import { isInternalUserForML } from "utils/user";
import { indexFaces } from "../face/f-index";
export const defaultMLVersion = 1;
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,
};
export async function getMLSearchConfig() {
if (isInternalUserForML()) {
return mlIDbStorage.getConfig(
ML_SEARCH_CONFIG_NAME,
DEFAULT_ML_SEARCH_CONFIG,
);
}
// Force disabled for everyone else while we finalize it to avoid redundant
// reindexing for users.
return DEFAULT_ML_SEARCH_CONFIG;
}
export async function updateMLSearchConfig(newConfig: MLSearchConfig) {
return mlIDbStorage.putConfig(ML_SEARCH_CONFIG_NAME, newConfig);
}
class MLSyncContext {
public token: string;
public userID: number;
@@ -79,6 +45,8 @@ class MachineLearningService {
private localSyncContext: Promise<MLSyncContext>;
private syncContext: Promise<MLSyncContext>;
public isSyncing = false;
public async sync(
token: string,
userID: number,
@@ -90,9 +58,7 @@ class MachineLearningService {
const syncContext = await this.getSyncContext(token, userID, userAgent);
await this.syncLocalFiles(syncContext);
await this.getOutOfSyncFiles(syncContext);
syncContext.outOfSyncFiles = await getFilesToIndex(userID, batchSize);
if (syncContext.outOfSyncFiles.length > 0) {
await this.syncFiles(syncContext);
@@ -103,96 +69,8 @@ class MachineLearningService {
return !error && nOutOfSyncFiles > 0;
}
private newMlData(fileId: number) {
return {
fileId,
mlVersion: 0,
errorCount: 0,
} as MinimalPersistedFileData;
}
private async getLocalFilesMap(syncContext: MLSyncContext) {
if (!syncContext.localFilesMap) {
const localFiles = await getLocalFiles();
const personalFiles = localFiles.filter(
(f) => f.ownerID === syncContext.userID,
);
syncContext.localFilesMap = new Map<number, EnteFile>();
personalFiles.forEach((f) =>
syncContext.localFilesMap.set(f.id, f),
);
}
return syncContext.localFilesMap;
}
private async syncLocalFiles(syncContext: MLSyncContext) {
const startTime = Date.now();
const localFilesMap = await this.getLocalFilesMap(syncContext);
const db = await mlIDbStorage.db;
const tx = db.transaction("files", "readwrite");
const mlFileIdsArr = await mlIDbStorage.getAllFileIdsForUpdate(tx);
const mlFileIds = new Set<number>();
mlFileIdsArr.forEach((mlFileId) => mlFileIds.add(mlFileId));
const newFileIds: Array<number> = [];
for (const localFileId of localFilesMap.keys()) {
if (!mlFileIds.has(localFileId)) {
newFileIds.push(localFileId);
}
}
let updated = false;
if (newFileIds.length > 0) {
log.info("newFiles: ", newFileIds.length);
const newFiles = newFileIds.map((fileId) => this.newMlData(fileId));
await mlIDbStorage.putAllFiles(newFiles, tx);
updated = true;
}
const removedFileIds: Array<number> = [];
for (const mlFileId of mlFileIds) {
if (!localFilesMap.has(mlFileId)) {
removedFileIds.push(mlFileId);
}
}
if (removedFileIds.length > 0) {
log.info("removedFiles: ", removedFileIds.length);
await mlIDbStorage.removeAllFiles(removedFileIds, tx);
updated = true;
}
await tx.done;
if (updated) {
// TODO: should do in same transaction
await mlIDbStorage.incrementIndexVersion("files");
}
log.info("syncLocalFiles", Date.now() - startTime, "ms");
}
private async getOutOfSyncFiles(syncContext: MLSyncContext) {
const startTime = Date.now();
const fileIds = await mlIDbStorage.getFileIds(
batchSize,
defaultMLVersion,
MAX_ML_SYNC_ERROR_COUNT,
);
log.info("fileIds: ", JSON.stringify(fileIds));
const localFilesMap = await this.getLocalFilesMap(syncContext);
syncContext.outOfSyncFiles = fileIds.map((fileId) =>
localFilesMap.get(fileId),
);
log.info("getOutOfSyncFiles", Date.now() - startTime, "ms");
}
private async syncFiles(syncContext: MLSyncContext) {
this.isSyncing = true;
try {
const functions = syncContext.outOfSyncFiles.map(
(outOfSyncfile) => async () => {
@@ -212,12 +90,7 @@ class MachineLearningService {
syncContext.error = error;
}
await syncContext.syncQueue.onIdle();
// TODO: In case syncJob has to use multiple ml workers
// do in same transaction with each file update
// or keep in files store itself
await mlIDbStorage.incrementIndexVersion("files");
// await this.disposeMLModels();
this.isSyncing = false;
}
private async getSyncContext(
@@ -300,23 +173,10 @@ class MachineLearningService {
localFile?: globalThis.File,
) {
try {
const mlFileData = await this.syncFile(
enteFile,
localFile,
syncContext.userAgent,
);
await this.syncFile(enteFile, localFile, syncContext.userAgent);
syncContext.nSyncedFiles += 1;
return mlFileData;
} catch (e) {
log.error("ML syncFile failed", e);
let error = e;
console.error(
"Error in ml sync, fileId: ",
enteFile.id,
"name: ",
enteFile.metadata.title,
error,
);
if ("status" in error) {
const parsedMessage = parseUploadErrorCodes(error);
error = parsedMessage;
@@ -331,42 +191,18 @@ class MachineLearningService {
throw error;
}
await this.persistMLFileSyncError(enteFile, error);
syncContext.nSyncedFiles += 1;
}
}
private async syncFile(
enteFile: EnteFile,
localFile: globalThis.File | undefined,
file: File | undefined,
userAgent: string,
) {
const oldMlFile = await mlIDbStorage.getFile(enteFile.id);
if (oldMlFile && oldMlFile.mlVersion) {
return oldMlFile;
}
const worker = new FaceIndexerWorker();
const newMlFile = await indexFaces(enteFile, localFile);
await putFaceEmbedding(enteFile, newMlFile, userAgent);
await mlIDbStorage.putFile(newMlFile);
return newMlFile;
}
private async persistMLFileSyncError(enteFile: EnteFile, e: Error) {
try {
await mlIDbStorage.upsertFileInTx(enteFile.id, (mlFileData) => {
if (!mlFileData) {
mlFileData = this.newMlData(enteFile.id);
}
mlFileData.errorCount = (mlFileData.errorCount || 0) + 1;
console.error(`lastError for ${enteFile.id}`, e);
return mlFileData;
});
} catch (e) {
// TODO: logError or stop sync job after most of the requests are failed
console.error("Error while storing ml sync error", e);
}
await worker.index(enteFile, file, userAgent);
}
}

11
web/apps/photos/src/services/machineLearning/mlWorkManager.ts
View File

@@ -8,7 +8,6 @@ import { getToken, getUserID } from "@ente/shared/storage/localStorage/helpers";
import debounce from "debounce";
import PQueue from "p-queue";
import { createFaceComlinkWorker } from "services/face";
import mlIDbStorage from "services/face/db-old";
import type { DedicatedMLWorker } from "services/face/face.worker";
import { EnteFile } from "types/file";
@@ -117,6 +116,10 @@ class MLWorkManager {
);
}
public isMlSearchEnabled() {
return this.mlSearchEnabled;
}
public async setMlSearchEnabled(enabled: boolean) {
if (!this.mlSearchEnabled && enabled) {
log.info("Enabling MLWorkManager");
@@ -163,7 +166,6 @@ class MLWorkManager {
this.stopSyncJob();
this.mlSyncJob = undefined;
await this.terminateLiveSyncWorker();
await mlIDbStorage.clearMLDB();
}
private async fileUploadedHandler(arg: {
@@ -224,7 +226,11 @@ class MLWorkManager {
this.mlSearchEnabled && this.startSyncJob();
}
// eslint-disable-next-line @typescript-eslint/no-unused-vars
public async syncLocalFile(enteFile: EnteFile, localFile: globalThis.File) {
return;
/*
TODO-ML(MR): Disable live sync for now
await this.liveSyncQueue.add(async () => {
this.stopSyncJob();
const token = getToken();
@@ -239,6 +245,7 @@ class MLWorkManager {
localFile,
);
});
*/
}
// Sync Job

34
web/apps/photos/src/services/searchService.ts
View File

@@ -2,9 +2,7 @@ import { FILE_TYPE } from "@/media/file-type";
import log from "@/next/log";
import * as chrono from "chrono-node";
import { t } from "i18next";
import mlIDbStorage from "services/face/db-old";
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";
import { EnteFile } from "types/file";
@@ -22,6 +20,7 @@ import { getFormattedDate } from "utils/search";
import { clipService, computeClipMatchScore } from "./clip-service";
import { localCLIPEmbeddings } from "./embeddingService";
import { getLatestEntities } from "./entityService";
import { faceIndexingStatus } from "./face/indexer";
import locationSearchService, { City } from "./locationSearchService";
const DIGITS = new Set(["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]);
@@ -175,19 +174,24 @@ export async function getAllPeopleSuggestion(): Promise<Array<Suggestion>> {
export async function getIndexStatusSuggestion(): Promise<Suggestion> {
try {
const indexStatus = await mlIDbStorage.getIndexStatus(defaultMLVersion);
const indexStatus = await faceIndexingStatus();
let label;
if (!indexStatus.localFilesSynced) {
label = t("INDEXING_SCHEDULED");
} else if (indexStatus.outOfSyncFilesExists) {
label = t("ANALYZING_PHOTOS", {
indexStatus,
});
} else if (!indexStatus.peopleIndexSynced) {
label = t("INDEXING_PEOPLE", { indexStatus });
} else {
label = t("INDEXING_DONE", { indexStatus });
let label: string;
switch (indexStatus.phase) {
case "scheduled":
label = t("INDEXING_SCHEDULED");
break;
case "indexing":
label = t("ANALYZING_PHOTOS", {
indexStatus,
});
break;
case "clustering":
label = t("INDEXING_PEOPLE", { indexStatus });
break;
case "done":
label = t("INDEXING_DONE", { indexStatus });
break;
}
return {
@@ -430,7 +434,7 @@ function convertSuggestionToSearchQuery(option: Suggestion): Search {
}
async function getAllPeople(limit: number = undefined) {
let people: Array<Person> = await mlIDbStorage.getAllPeople();
let people: Array<Person> = []; // await mlIDbStorage.getAllPeople();
// await mlPeopleStore.iterate<Person, void>((person) => {
// people.push(person);
// });

4
web/apps/photos/src/types/search/index.ts
View File

@@ -1,5 +1,5 @@
import { FILE_TYPE } from "@/media/file-type";
import { IndexStatus } from "services/face/db-old";
import type { FaceIndexingStatus } from "services/face/indexer";
import type { Person } from "services/face/people";
import { City } from "services/locationSearchService";
import { LocationTagData } from "types/entity";
@@ -31,7 +31,7 @@ export interface Suggestion {
| DateValue
| number[]
| Person
| IndexStatus
| FaceIndexingStatus
| LocationTagData
| City
| FILE_TYPE

8
web/apps/photos/src/utils/file/index.ts
View File

@@ -81,6 +81,14 @@ class ModuleState {
const moduleState = new ModuleState();
/**
* @returns a string to use as an identifier when logging information about the
* given {@link enteFile}. The returned string contains the file name (for ease
* of debugging) and the file ID (for exactness).
*/
export const fileLogID = (enteFile: EnteFile) =>
`file ${enteFile.metadata.title ?? "-"} (${enteFile.id})`;
export async function getUpdatedEXIFFileForDownload(
fileReader: FileReader,
file: EnteFile,