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[web] ML cleanup - Part 6/x (#1766)

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Manav Rathi 2024-05-18 20:57:24 +05:30 committed by GitHub
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486
web/apps/photos/src/services/face/f-index.ts
View file

@ -4,15 +4,13 @@ import log from "@/next/log";
import { workerBridge } from "@/next/worker/worker-bridge";
import { euclidean } from "hdbscan";
import { Matrix } from "ml-matrix";
import { Box, Dimensions, Point, enlargeBox, newBox } from "services/face/geom";
import { Box, Dimensions, Point, enlargeBox } from "services/face/geom";
import {
DetectedFace,
Face,
FaceAlignment,
FaceCrop,
FaceDetection,
FaceEmbedding,
MLSyncFileContext,
type MlFileData,
} from "services/face/types";
import { defaultMLVersion } from "services/machineLearning/machineLearningService";
@ -23,7 +21,6 @@ import {
createGrayscaleIntMatrixFromNormalized2List,
fetchImageBitmap,
getLocalFileImageBitmap,
getThumbnailImageBitmap,
pixelRGBBilinear,
warpAffineFloat32List,
} from "./image";
@ -38,110 +35,100 @@ import { transformFaceDetections } from "./transform-box";
* 1. Downloading the original if needed.
* 2. Detect faces using ONNX/YOLO
* 3. Align the face rectangles, compute blur.
* 4. Compute embbeddings for the detected face (crops).
* 4. Compute embeddings for the detected face (crops).
*
* Once all of it is done, it returns the face rectangles and embeddings to the
* higher layer (which saves them to 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).
* 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?: globalThis.File,
) => {
log.debug(() => ({ a: "Indexing faces in file", enteFile }));
const fileContext: MLSyncFileContext = { enteFile, localFile };
const newMlFile = (fileContext.newMlFile = {
fileId: enteFile.id,
mlVersion: defaultMLVersion,
errorCount: 0,
} as MlFileData);
try {
await fetchImageBitmapForContext(fileContext);
await syncFileAnalyzeFaces(fileContext);
newMlFile.errorCount = 0;
} finally {
fileContext.imageBitmap && fileContext.imageBitmap.close();
}
return newMlFile;
};
const fetchImageBitmapForContext = async (fileContext: MLSyncFileContext) => {
if (fileContext.imageBitmap) {
return fileContext.imageBitmap;
}
if (fileContext.localFile) {
if (fileContext.enteFile.metadata.fileType !== FILE_TYPE.IMAGE) {
throw new Error("Local file of only image type is supported");
}
fileContext.imageBitmap = await getLocalFileImageBitmap(
fileContext.enteFile,
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,
);
}
const { width, height } = fileContext.imageBitmap;
fileContext.newMlFile.imageDimensions = { width, height };
return fileContext.imageBitmap;
};
const syncFileAnalyzeFaces = async (fileContext: MLSyncFileContext) => {
const { newMlFile } = fileContext;
export const indexFaces = async (enteFile: EnteFile, localFile?: File) => {
const startTime = Date.now();
await syncFileFaceDetections(fileContext);
if (newMlFile.faces && newMlFile.faces.length > 0) {
await syncFileFaceCrops(fileContext);
const alignedFacesData = await syncFileFaceAlignments(fileContext);
await syncFileFaceEmbeddings(fileContext, alignedFacesData);
await syncFileFaceMakeRelativeDetections(fileContext);
const imageBitmap = await fetchOrCreateImageBitmap(enteFile, localFile);
let mlFile: MlFileData;
try {
mlFile = await indexFaces_(enteFile, imageBitmap);
} finally {
imageBitmap.close();
}
log.debug(
() =>
`Face detection for file ${fileContext.enteFile.id} took ${Math.round(Date.now() - startTime)} ms`,
);
log.debug(() => {
const ms = Math.round(Date.now() - startTime);
const nf = mlFile.faces?.length ?? 0;
return `Indexed ${nf} faces in file ${enteFile.id} (${ms} ms)`;
});
return mlFile;
};
const syncFileFaceDetections = async (fileContext: MLSyncFileContext) => {
const { newMlFile } = fileContext;
fileContext.newDetection = true;
const imageBitmap = await fetchImageBitmapForContext(fileContext);
/**
* 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 mlFile: MlFileData = {
fileId: fileID,
mlVersion: defaultMLVersion,
imageDimensions: { width, height },
errorCount: 0,
};
const faceDetections = await detectFaces(imageBitmap);
// TODO-ML(MR): reenable faces filtering based on width
const detectedFaces = faceDetections?.map((detection) => {
return {
fileId: fileContext.enteFile.id,
detection,
} as DetectedFace;
});
newMlFile.faces = detectedFaces?.map((detectedFace) => ({
...detectedFace,
id: makeFaceID(detectedFace, newMlFile.imageDimensions),
const detectedFaces = faceDetections.map((detection) => ({
id: makeFaceID(fileID, detection, mlFile.imageDimensions),
fileId: fileID,
detection,
}));
// ?.filter((f) =>
// f.box.width > syncContext.config.faceDetection.minFaceSize
// );
log.info("[MLService] Detected Faces: ", newMlFile.faces?.length);
mlFile.faces = detectedFaces;
if (detectedFaces.length > 0) {
await Promise.all(
detectedFaces.map((face) => saveFaceCrop(imageBitmap, face)),
);
// Execute the face alignment calculations
for (const face of mlFile.faces) {
face.alignment = faceAlignment(face.detection);
}
// Extract face images and convert to Float32Array
const faceAlignments = mlFile.faces.map((f) => f.alignment);
const alignedFacesData = await extractFaceImagesToFloat32(
faceAlignments,
mobileFaceNetFaceSize,
imageBitmap,
);
const blurValues = detectBlur(alignedFacesData, mlFile.faces);
mlFile.faces.forEach((f, i) => (f.blurValue = blurValues[i]));
const embeddings = await faceEmbeddings(alignedFacesData);
mlFile.faces.forEach((f, i) => (f.embedding = embeddings[i]));
convertFaceDetectionsToRelative(mlFile);
}
return mlFile;
};
/**
@ -151,14 +138,24 @@ const syncFileFaceDetections = async (fileContext: MLSyncFileContext) => {
*/
const detectFaces = async (
imageBitmap: ImageBitmap,
): Promise<Array<FaceDetection>> => {
): 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 inBox = newBox(0, 0, yoloSize.width, yoloSize.height);
const toBox = newBox(0, 0, imageBitmap.width, imageBitmap.height);
const faceDetections = transformFaceDetections(faces, inBox, toBox);
const faceDetections = transformFaceDetections(
faces,
rect(yoloSize),
rect(imageBitmap),
);
// TODO-ML: reenable faces filtering based on width ?? else remove me
// ?.filter((f) =>
// f.box.width > syncContext.config.faceDetection.minFaceSize
// );
const maxFaceDistancePercent = Math.sqrt(2) / 100;
const maxFaceDistance = imageBitmap.width * maxFaceDistancePercent;
@ -270,25 +267,6 @@ const faceDetectionsFromYOLOOutput = (rows: Float32Array): FaceDetection[] => {
return faces;
};
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.
*
@ -310,126 +288,90 @@ const getRelativeDetection = (
* @returns An array of face detections with duplicates removed.
*/
const removeDuplicateDetections = (
detections: Array<FaceDetection>,
detections: FaceDetection[],
withinDistance: number,
) => {
detections.sort((a, b) => b.probability - a.probability);
const isSelected = new Map<number, boolean>();
const dupIndices = new Set<number>();
for (let i = 0; i < detections.length; i++) {
if (isSelected.get(i) === false) {
continue;
}
isSelected.set(i, true);
if (dupIndices.has(i)) continue;
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]);
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) {
isSelected.set(j, false);
}
if (dist <= withinDistance) dupIndices.add(j);
}
}
const uniques: Array<FaceDetection> = [];
for (let i = 0; i < detections.length; i++) {
isSelected.get(i) && uniques.push(detections[i]);
}
return uniques;
return detections.filter((_, i) => !dupIndices.has(i));
};
function getDetectionCenter(detection: FaceDetection) {
const faceDetectionCenter = (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
// TODO-ML: 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 syncFileFaceCrops = async (fileContext: MLSyncFileContext) => {
const { newMlFile } = fileContext;
const imageBitmap = await fetchImageBitmapForContext(fileContext);
for (const face of newMlFile.faces) {
await saveFaceCrop(imageBitmap, face);
}
};
const syncFileFaceAlignments = async (
fileContext: MLSyncFileContext,
): Promise<Float32Array> => {
const { newMlFile } = fileContext;
fileContext.newAlignment = true;
const imageBitmap =
fileContext.imageBitmap ||
(await fetchImageBitmapForContext(fileContext));
// Execute the face alignment calculations
for (const face of newMlFile.faces) {
face.alignment = faceAlignment(face.detection);
}
// Extract face images and convert to Float32Array
const faceAlignments = newMlFile.faces.map((f) => f.alignment);
const faceImages = await extractFaceImagesToFloat32(
faceAlignments,
mobileFaceNetFaceSize,
imageBitmap,
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 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 yMax = part(
(detection.box.y + detection.box.height) / imageDims.height,
);
return [`${fileID}`, xMin, yMin, xMax, yMax].join("_");
};
// TODO-ML(MR): When is this used or is it as Blazeface leftover?
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.
*/
const faceAlignment = (faceDetection: FaceDetection): FaceAlignment => {
const landmarkCount = faceDetection.landmarks.length;
return getFaceAlignmentUsingSimilarityTransform(
const faceAlignment = (faceDetection: FaceDetection): FaceAlignment =>
faceAlignmentUsingSimilarityTransform(
faceDetection,
normalizeLandmarks(
landmarkCount === 5 ? ARC_FACE_5_LANDMARKS : ARCFACE_LANDMARKS,
ARCFACE_LANDMARKS_FACE_SIZE,
),
normalizeLandmarks(arcFaceLandmarks, mobileFaceNetFaceSize),
);
};
function getFaceAlignmentUsingSimilarityTransform(
// TODO-ML: Rename?
const arcFaceLandmarks: [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,
): [number, number][] =>
landmarks.map(([x, y]) => [x / faceSize, y / faceSize]);
const faceAlignmentUsingSimilarityTransform = (
faceDetection: FaceDetection,
alignedLandmarks: Array<[number, number]>,
): FaceAlignment {
alignedLandmarks: [number, number][],
): FaceAlignment => {
const landmarksMat = new Matrix(
faceDetection.landmarks
.map((p) => [p.x, p.y])
@ -460,22 +402,8 @@ function getFaceAlignmentUsingSimilarityTransform(
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]>;
}
return { affineMatrix, center, size, rotation };
};
async function extractFaceImagesToFloat32(
faceAlignments: Array<FaceAlignment>,
@ -499,44 +427,21 @@ async function extractFaceImagesToFloat32(
return faceData;
}
const makeFaceID = (detectedFace: DetectedFace, imageDims: Dimensions) => {
const part = (v: number) => clamp(v, 0.0, 0.999999).toFixed(5).substring(2);
const xMin = part(detectedFace.detection.box.x / imageDims.width);
const yMin = part(detectedFace.detection.box.y / imageDims.height);
const xMax = part(
(detectedFace.detection.box.x + detectedFace.detection.box.width) /
imageDims.width,
);
const yMax = part(
(detectedFace.detection.box.y + detectedFace.detection.box.height) /
imageDims.height,
);
return [detectedFace.fileId, xMin, yMin, xMax, yMax].join("_");
};
/**
* Laplacian blur detection.
*
* Return an array of detected blur values, one for each face in {@link faces}.
*/
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 detectBlur = (alignedFaces: Float32Array, faces: Face[]): number[] =>
faces.map((face, i) => {
const faceImage = createGrayscaleIntMatrixFromNormalized2List(
alignedFaces,
i,
mobileFaceNetFaceSize,
mobileFaceNetFaceSize,
);
const laplacian = applyLaplacian(faceImage, direction);
blurValues.push(matrixVariance(laplacian));
}
return blurValues;
};
return matrixVariance(applyLaplacian(faceImage, faceDirection(face)));
});
type FaceDirection = "left" | "right" | "straight";
@ -585,7 +490,7 @@ const applyLaplacian = (
image: number[][],
direction: FaceDirection,
): number[][] => {
const paddedImage: number[][] = padImage(image, direction);
const paddedImage = padImage(image, direction);
const numRows = paddedImage.length - 2;
const numCols = paddedImage[0].length - 2;
@ -595,7 +500,7 @@ const applyLaplacian = (
);
// Define the Laplacian kernel.
const kernel: number[][] = [
const kernel = [
[0, 1, 0],
[1, -4, 1],
[0, 1, 0],
@ -640,14 +545,16 @@ const padImage = (image: number[][], direction: FaceDirection): number[][] => {
}
}
} else if (direction === "left") {
// If the face is facing left, we only take the right side of the face image.
// 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.
// 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];
@ -656,15 +563,19 @@ const padImage = (image: number[][], direction: FaceDirection): number[][] => {
}
// Reflect padding
// Top and bottom rows
// - 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
// Top row
paddedImage[0][j] = paddedImage[2][j];
// Bottom row
paddedImage[numRows + 1][j] = paddedImage[numRows - 1][j];
}
// Left and right columns
// - 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
// Left column
paddedImage[i][0] = paddedImage[i][2];
// Right column
paddedImage[i][paddedNumCols - 1] = paddedImage[i][paddedNumCols - 3];
}
return paddedImage;
@ -697,21 +608,6 @@ const matrixVariance = (matrix: number[][]): number => {
return variance;
};
const syncFileFaceEmbeddings = async (
fileContext: MLSyncFileContext,
alignedFacesInput: Float32Array,
) => {
const { newMlFile } = fileContext;
// 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 mobileFaceNetFaceSize = 112;
/**
@ -736,20 +632,36 @@ const faceEmbeddings = async (
return embeddings;
};
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(
const convertFaceDetectionsToRelative = (mlFile: MlFileData) => {
for (let i = 0; i < mlFile.faces.length; i++) {
const face = mlFile.faces[i];
// Skip if somehow already relative.
if (face.detection.box.x + face.detection.box.width < 2) continue;
face.detection = relativeDetection(
face.detection,
newMlFile.imageDimensions,
mlFile.imageDimensions,
);
}
};
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 = getFaceCrop(imageBitmap, face.detection);

4
web/apps/photos/src/services/face/geom.ts
View file

@ -27,10 +27,6 @@ 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,

8
web/apps/photos/src/services/face/image.ts
View file

@ -1,6 +1,5 @@
import { FILE_TYPE } from "@/media/file-type";
import { decodeLivePhoto } from "@/media/live-photo";
import log from "@/next/log";
import { Matrix, inverse } from "ml-matrix";
import DownloadManager from "services/download";
import { FaceAlignment } from "services/face/types";
@ -36,13 +35,6 @@ async function fetchRenderableBlob(file: EnteFile) {
}
}
export async function getThumbnailImageBitmap(file: EnteFile) {
const thumb = await DownloadManager.getThumbnail(file);
log.info("[MLService] Got thumbnail: ", file.id.toString());
return createImageBitmap(new Blob([thumb]));
}
export async function getLocalFileImageBitmap(
enteFile: EnteFile,
localFile: globalThis.File,

10
web/apps/photos/src/services/face/transform-box.ts
View file

@ -13,17 +13,17 @@ import {
} from "transformation-matrix";
/**
* Detect faces in the given {@link imageBitmap}.
*
* The model used is YOLO, running in an ONNX runtime.
* 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 = (
faces: FaceDetection[],
faceDetections: FaceDetection[],
inBox: Box,
toBox: Box,
): FaceDetection[] => {
const transform = computeTransformToBox(inBox, toBox);
return faces.map((f) => {
return faceDetections.map((f) => {
const box = transformBox(f.box, transform);
const normLandmarks = f.landmarks;
const landmarks = transformPoints(normLandmarks, transform);

20
web/apps/photos/src/services/face/types.ts
View file

@ -1,5 +1,4 @@
import { Box, Dimensions, Point } from "services/face/geom";
import { EnteFile } from "types/file";
export declare type Cluster = Array<number>;
@ -76,22 +75,3 @@ export interface MlFileData {
mlVersion: number;
errorCount: number;
}
export interface MLSearchConfig {
enabled: boolean;
}
export interface MLSyncFileContext {
enteFile: EnteFile;
localFile?: globalThis.File;
oldMlFile?: MlFileData;
newMlFile?: MlFileData;
imageBitmap?: ImageBitmap;
newDetection?: boolean;
newAlignment?: boolean;
}
export declare type MLIndex = "files" | "people";

12
web/apps/photos/src/services/machineLearning/machineLearningService.ts
View file

@ -10,13 +10,7 @@ import PQueue from "p-queue";
import { putEmbedding } from "services/embeddingService";
import mlIDbStorage, { ML_SEARCH_CONFIG_NAME } from "services/face/db";
import { fetchImageBitmap, getLocalFile } from "services/face/image";
import {
Face,
FaceDetection,
Landmark,
MLSearchConfig,
MlFileData,
} from "services/face/types";
import { Face, FaceDetection, Landmark, MlFileData } from "services/face/types";
import { getLocalFiles } from "services/fileService";
import { EnteFile } from "types/file";
import { isInternalUserForML } from "utils/user";
@ -32,6 +26,10 @@ 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,
};