refactor ml service to move faceDetection logic to seperate service
This commit is contained in:
Abhinav
parent
8a3b08f4a7
commit
ed3b3313b4
192
src/services/machineLearning/faceDetectionService.ts
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192
src/services/machineLearning/faceDetectionService.ts
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@ -0,0 +1,192 @@
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import {
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MLSyncContext,
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MLSyncFileContext,
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DetectedFace,
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Face,
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} from 'types/machineLearning';
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import {
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isDifferentOrOld,
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getFaceId,
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areFaceIdsSame,
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extractFaceImages,
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} from 'utils/machineLearning';
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import { storeFaceCrop } from 'utils/machineLearning/faceCrop';
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import ReaderService from './readerService';
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class FaceDetectionService {
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async syncFileFaceDetections(
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext
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) {
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const { oldMlFile, newMlFile } = fileContext;
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if (
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!isDifferentOrOld(
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oldMlFile?.faceDetectionMethod,
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syncContext.faceDetectionService.method
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) &&
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oldMlFile?.imageSource === syncContext.config.imageSource
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) {
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newMlFile.faces = oldMlFile?.faces?.map((existingFace) => ({
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id: existingFace.id,
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fileId: existingFace.fileId,
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detection: existingFace.detection,
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}));
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newMlFile.imageSource = oldMlFile.imageSource;
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newMlFile.imageDimentions = oldMlFile.imageDimentions;
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newMlFile.faceDetectionMethod = oldMlFile.faceDetectionMethod;
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return;
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}
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newMlFile.faceDetectionMethod = syncContext.faceDetectionService.method;
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fileContext.newDetection = true;
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const imageBitmap = await ReaderService.getImageBitmap(
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syncContext,
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fileContext
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);
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const faceDetections =
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await syncContext.faceDetectionService.detectFaces(imageBitmap);
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// console.log('3 TF Memory stats: ', tf.memory());
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// TODO: reenable faces filtering based on width
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const detectedFaces = faceDetections?.map((detection) => {
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return {
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fileId: fileContext.enteFile.id,
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detection,
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} as DetectedFace;
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});
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newMlFile.faces = detectedFaces?.map((detectedFace) => ({
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...detectedFace,
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id: getFaceId(detectedFace, newMlFile.imageDimentions),
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}));
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// ?.filter((f) =>
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// f.box.width > syncContext.config.faceDetection.minFaceSize
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// );
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console.log('[MLService] Detected Faces: ', newMlFile.faces?.length);
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}
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async syncFileFaceCrops(
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext
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) {
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const { oldMlFile, newMlFile } = fileContext;
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if (
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// !syncContext.config.faceCrop.enabled ||
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!fileContext.newDetection &&
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!isDifferentOrOld(
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oldMlFile?.faceCropMethod,
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syncContext.faceCropService.method
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) &&
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areFaceIdsSame(newMlFile.faces, oldMlFile?.faces)
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) {
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for (const [index, face] of newMlFile.faces.entries()) {
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face.crop = oldMlFile.faces[index].crop;
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}
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newMlFile.faceCropMethod = oldMlFile.faceCropMethod;
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return;
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}
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const imageBitmap = await ReaderService.getImageBitmap(
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syncContext,
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fileContext
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);
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newMlFile.faceCropMethod = syncContext.faceCropService.method;
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for (const face of newMlFile.faces) {
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await this.saveFaceCrop(imageBitmap, face, syncContext);
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}
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}
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async syncFileFaceAlignments(
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext
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) {
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const { oldMlFile, newMlFile } = fileContext;
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if (
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!fileContext.newDetection &&
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!isDifferentOrOld(
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oldMlFile?.faceAlignmentMethod,
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syncContext.faceAlignmentService.method
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) &&
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areFaceIdsSame(newMlFile.faces, oldMlFile?.faces)
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) {
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for (const [index, face] of newMlFile.faces.entries()) {
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face.alignment = oldMlFile.faces[index].alignment;
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}
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newMlFile.faceAlignmentMethod = oldMlFile.faceAlignmentMethod;
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return;
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}
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newMlFile.faceAlignmentMethod = syncContext.faceAlignmentService.method;
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fileContext.newAlignment = true;
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for (const face of newMlFile.faces) {
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face.alignment = syncContext.faceAlignmentService.getFaceAlignment(
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face.detection
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);
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}
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console.log('[MLService] alignedFaces: ', newMlFile.faces?.length);
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// console.log('4 TF Memory stats: ', tf.memory());
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}
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async syncFileFaceEmbeddings(
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext
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) {
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const { oldMlFile, newMlFile } = fileContext;
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if (
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!fileContext.newAlignment &&
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!isDifferentOrOld(
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oldMlFile?.faceEmbeddingMethod,
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syncContext.faceEmbeddingService.method
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) &&
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areFaceIdsSame(newMlFile.faces, oldMlFile?.faces)
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) {
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for (const [index, face] of newMlFile.faces.entries()) {
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face.embedding = oldMlFile.faces[index].embedding;
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}
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newMlFile.faceEmbeddingMethod = oldMlFile.faceEmbeddingMethod;
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return;
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}
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newMlFile.faceEmbeddingMethod = syncContext.faceEmbeddingService.method;
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// TODO: when not storing face crops, image will be needed to extract faces
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// fileContext.imageBitmap ||
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// (await this.getImageBitmap(syncContext, fileContext));
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const faceImages = await extractFaceImages(
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newMlFile.faces,
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syncContext.faceEmbeddingService.faceSize
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);
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const embeddings =
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await syncContext.faceEmbeddingService.getFaceEmbeddings(
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faceImages
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);
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faceImages.forEach((faceImage) => faceImage.close());
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newMlFile.faces.forEach((f, i) => (f.embedding = embeddings[i]));
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console.log(
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'[MLService] facesWithEmbeddings: ',
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newMlFile.faces.length
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);
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// console.log('5 TF Memory stats: ', tf.memory());
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}
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private async saveFaceCrop(
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imageBitmap: ImageBitmap,
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face: Face,
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syncContext: MLSyncContext
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) {
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const faceCrop = await syncContext.faceCropService.getFaceCrop(
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imageBitmap,
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face.detection,
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syncContext.config.faceCrop
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);
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face.crop = await storeFaceCrop(
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face.id,
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faceCrop,
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syncContext.config.faceCrop.blobOptions
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);
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faceCrop.image.close();
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}
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}
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export default new FaceDetectionService();
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@ -1,6 +1,5 @@
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import { getLocalFiles } from 'services/fileService';
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import { EnteFile } from 'types/file';
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import { FILE_TYPE } from 'constants/file';
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import * as tf from '@tensorflow/tfjs-core';
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import '@tensorflow/tfjs-backend-webgl';
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@ -9,7 +8,6 @@ import '@tensorflow/tfjs-backend-webgl';
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// import '@tensorflow/tfjs-backend-cpu';
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import {
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DetectedFace,
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Face,
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MlFileData,
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MLSyncContext,
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@ -24,23 +22,18 @@ import { toTSNE } from 'utils/machineLearning/visualization';
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// mlFilesStore
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// } from 'utils/storage/mlStorage';
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import {
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areFaceIdsSame,
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extractFaceImages,
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findFirstIfSorted,
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getAllFacesFromMap,
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getFaceId,
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getLocalFile,
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getLocalFileImageBitmap,
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getOriginalImageBitmap,
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getThumbnailImageBitmap,
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isDifferentOrOld,
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} from 'utils/machineLearning';
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import { MLFactory } from './machineLearningFactory';
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import mlIDbStorage from 'utils/storage/mlIDbStorage';
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import { storeFaceCrop } from 'utils/machineLearning/faceCrop';
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import { getMLSyncConfig } from 'utils/machineLearning/config';
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import { CustomError, parseServerError } from 'utils/error';
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import { MAX_ML_SYNC_ERROR_COUNT } from 'constants/machineLearning/config';
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import FaceDetectionService from './faceDetectionService';
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class MachineLearningService {
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private initialized = false;
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@ -409,14 +402,26 @@ class MachineLearningService {
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newMlFile.mlVersion = fileContext.oldMlFile.mlVersion;
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}
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await this.syncFileFaceDetections(syncContext, fileContext);
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await FaceDetectionService.syncFileFaceDetections(
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syncContext,
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fileContext
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);
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if (newMlFile.faces && newMlFile.faces.length > 0) {
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await this.syncFileFaceCrops(syncContext, fileContext);
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await FaceDetectionService.syncFileFaceCrops(
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syncContext,
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fileContext
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);
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await this.syncFileFaceAlignments(syncContext, fileContext);
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await FaceDetectionService.syncFileFaceAlignments(
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syncContext,
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fileContext
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);
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await this.syncFileFaceEmbeddings(syncContext, fileContext);
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await FaceDetectionService.syncFileFaceEmbeddings(
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syncContext,
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fileContext
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);
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}
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fileContext.tfImage && fileContext.tfImage.dispose();
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@ -436,217 +441,6 @@ class MachineLearningService {
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return newMlFile;
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}
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private async getImageBitmap(
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext
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) {
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if (fileContext.imageBitmap) {
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return fileContext.imageBitmap;
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}
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// console.log('1 TF Memory stats: ', tf.memory());
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if (fileContext.localFile) {
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if (fileContext.enteFile.metadata.fileType !== FILE_TYPE.IMAGE) {
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throw new Error('Local file of only image type is supported');
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}
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fileContext.imageBitmap = await getLocalFileImageBitmap(
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fileContext.enteFile,
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fileContext.localFile,
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() => syncContext.getEnteWorker(fileContext.enteFile.id)
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);
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} else if (
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syncContext.config.imageSource === 'Original' &&
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[FILE_TYPE.IMAGE, FILE_TYPE.LIVE_PHOTO].includes(
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fileContext.enteFile.metadata.fileType
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)
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) {
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fileContext.imageBitmap = await getOriginalImageBitmap(
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fileContext.enteFile,
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syncContext.token,
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await syncContext.getEnteWorker(fileContext.enteFile.id)
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);
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} else {
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fileContext.imageBitmap = await getThumbnailImageBitmap(
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fileContext.enteFile,
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syncContext.token
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);
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}
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fileContext.newMlFile.imageSource = syncContext.config.imageSource;
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const { width, height } = fileContext.imageBitmap;
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fileContext.newMlFile.imageDimentions = { width, height };
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// console.log('2 TF Memory stats: ', tf.memory());
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return fileContext.imageBitmap;
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}
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private async syncFileFaceDetections(
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext
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) {
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const { oldMlFile, newMlFile } = fileContext;
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if (
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!isDifferentOrOld(
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oldMlFile?.faceDetectionMethod,
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syncContext.faceDetectionService.method
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) &&
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oldMlFile?.imageSource === syncContext.config.imageSource
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) {
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newMlFile.faces = oldMlFile?.faces?.map((existingFace) => ({
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id: existingFace.id,
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fileId: existingFace.fileId,
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detection: existingFace.detection,
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}));
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newMlFile.imageSource = oldMlFile.imageSource;
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newMlFile.imageDimentions = oldMlFile.imageDimentions;
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newMlFile.faceDetectionMethod = oldMlFile.faceDetectionMethod;
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return;
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}
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newMlFile.faceDetectionMethod = syncContext.faceDetectionService.method;
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fileContext.newDetection = true;
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const imageBitmap = await this.getImageBitmap(syncContext, fileContext);
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const faceDetections =
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await syncContext.faceDetectionService.detectFaces(imageBitmap);
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// console.log('3 TF Memory stats: ', tf.memory());
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// TODO: reenable faces filtering based on width
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const detectedFaces = faceDetections?.map((detection) => {
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return {
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fileId: fileContext.enteFile.id,
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detection,
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} as DetectedFace;
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});
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newMlFile.faces = detectedFaces?.map((detectedFace) => ({
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...detectedFace,
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id: getFaceId(detectedFace, newMlFile.imageDimentions),
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}));
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// ?.filter((f) =>
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// f.box.width > syncContext.config.faceDetection.minFaceSize
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// );
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console.log('[MLService] Detected Faces: ', newMlFile.faces?.length);
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}
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private async saveFaceCrop(
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imageBitmap: ImageBitmap,
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face: Face,
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syncContext: MLSyncContext
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) {
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const faceCrop = await syncContext.faceCropService.getFaceCrop(
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imageBitmap,
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face.detection,
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syncContext.config.faceCrop
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);
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face.crop = await storeFaceCrop(
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face.id,
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faceCrop,
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syncContext.config.faceCrop.blobOptions
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);
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faceCrop.image.close();
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}
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private async syncFileFaceCrops(
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext
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) {
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const { oldMlFile, newMlFile } = fileContext;
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if (
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// !syncContext.config.faceCrop.enabled ||
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!fileContext.newDetection &&
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!isDifferentOrOld(
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oldMlFile?.faceCropMethod,
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syncContext.faceCropService.method
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) &&
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areFaceIdsSame(newMlFile.faces, oldMlFile?.faces)
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) {
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for (const [index, face] of newMlFile.faces.entries()) {
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face.crop = oldMlFile.faces[index].crop;
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}
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newMlFile.faceCropMethod = oldMlFile.faceCropMethod;
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return;
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}
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const imageBitmap = await this.getImageBitmap(syncContext, fileContext);
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newMlFile.faceCropMethod = syncContext.faceCropService.method;
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for (const face of newMlFile.faces) {
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await this.saveFaceCrop(imageBitmap, face, syncContext);
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}
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}
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private async syncFileFaceAlignments(
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syncContext: MLSyncContext,
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fileContext: MLSyncFileContext
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) {
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const { oldMlFile, newMlFile } = fileContext;
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if (
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!fileContext.newDetection &&
|
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!isDifferentOrOld(
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oldMlFile?.faceAlignmentMethod,
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syncContext.faceAlignmentService.method
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) &&
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areFaceIdsSame(newMlFile.faces, oldMlFile?.faces)
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) {
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for (const [index, face] of newMlFile.faces.entries()) {
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face.alignment = oldMlFile.faces[index].alignment;
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}
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newMlFile.faceAlignmentMethod = oldMlFile.faceAlignmentMethod;
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return;
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}
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newMlFile.faceAlignmentMethod = syncContext.faceAlignmentService.method;
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fileContext.newAlignment = true;
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for (const face of newMlFile.faces) {
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face.alignment = syncContext.faceAlignmentService.getFaceAlignment(
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face.detection
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);
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}
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console.log('[MLService] alignedFaces: ', newMlFile.faces?.length);
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// console.log('4 TF Memory stats: ', tf.memory());
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}
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private async syncFileFaceEmbeddings(
|
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syncContext: MLSyncContext,
|
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fileContext: MLSyncFileContext
|
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) {
|
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const { oldMlFile, newMlFile } = fileContext;
|
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if (
|
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!fileContext.newAlignment &&
|
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!isDifferentOrOld(
|
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oldMlFile?.faceEmbeddingMethod,
|
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syncContext.faceEmbeddingService.method
|
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) &&
|
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areFaceIdsSame(newMlFile.faces, oldMlFile?.faces)
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) {
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for (const [index, face] of newMlFile.faces.entries()) {
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face.embedding = oldMlFile.faces[index].embedding;
|
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}
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newMlFile.faceEmbeddingMethod = oldMlFile.faceEmbeddingMethod;
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return;
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}
|
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|
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newMlFile.faceEmbeddingMethod = syncContext.faceEmbeddingService.method;
|
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// TODO: when not storing face crops, image will be needed to extract faces
|
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// fileContext.imageBitmap ||
|
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// (await this.getImageBitmap(syncContext, fileContext));
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const faceImages = await extractFaceImages(
|
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newMlFile.faces,
|
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syncContext.faceEmbeddingService.faceSize
|
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);
|
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|
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const embeddings =
|
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await syncContext.faceEmbeddingService.getFaceEmbeddings(
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faceImages
|
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);
|
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faceImages.forEach((faceImage) => faceImage.close());
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newMlFile.faces.forEach((f, i) => (f.embedding = embeddings[i]));
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|
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console.log(
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'[MLService] facesWithEmbeddings: ',
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newMlFile.faces.length
|
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);
|
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// console.log('5 TF Memory stats: ', tf.memory());
|
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}
|
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public async init() {
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if (this.initialized) {
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return;
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|
|
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53
src/services/machineLearning/readerService.ts
Normal file
53
src/services/machineLearning/readerService.ts
Normal file
|
|
@ -0,0 +1,53 @@
|
|||
import { FILE_TYPE } from 'constants/file';
|
||||
import { MLSyncContext, MLSyncFileContext } from 'types/machineLearning';
|
||||
import {
|
||||
getLocalFileImageBitmap,
|
||||
getOriginalImageBitmap,
|
||||
getThumbnailImageBitmap,
|
||||
} from 'utils/machineLearning';
|
||||
|
||||
class ReaderService {
|
||||
async getImageBitmap(
|
||||
syncContext: MLSyncContext,
|
||||
fileContext: MLSyncFileContext
|
||||
) {
|
||||
if (fileContext.imageBitmap) {
|
||||
return fileContext.imageBitmap;
|
||||
}
|
||||
// console.log('1 TF Memory stats: ', tf.memory());
|
||||
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,
|
||||
() => syncContext.getEnteWorker(fileContext.enteFile.id)
|
||||
);
|
||||
} else if (
|
||||
syncContext.config.imageSource === 'Original' &&
|
||||
[FILE_TYPE.IMAGE, FILE_TYPE.LIVE_PHOTO].includes(
|
||||
fileContext.enteFile.metadata.fileType
|
||||
)
|
||||
) {
|
||||
fileContext.imageBitmap = await getOriginalImageBitmap(
|
||||
fileContext.enteFile,
|
||||
syncContext.token,
|
||||
await syncContext.getEnteWorker(fileContext.enteFile.id)
|
||||
);
|
||||
} else {
|
||||
fileContext.imageBitmap = await getThumbnailImageBitmap(
|
||||
fileContext.enteFile,
|
||||
syncContext.token
|
||||
);
|
||||
}
|
||||
|
||||
fileContext.newMlFile.imageSource = syncContext.config.imageSource;
|
||||
const { width, height } = fileContext.imageBitmap;
|
||||
fileContext.newMlFile.imageDimentions = { width, height };
|
||||
// console.log('2 TF Memory stats: ', tf.memory());
|
||||
|
||||
return fileContext.imageBitmap;
|
||||
}
|
||||
}
|
||||
export default new ReaderService();
|
||||
|
|
@ -278,7 +278,7 @@ export async function getTFImage(blob): Promise<tf.Tensor3D> {
|
|||
return tfImage;
|
||||
}
|
||||
|
||||
export async function getImageBitmap(blob: Blob): Promise<ImageBitmap> {
|
||||
export async function getImageBlobBitmap(blob: Blob): Promise<ImageBitmap> {
|
||||
return await createImageBitmap(blob);
|
||||
}
|
||||
|
||||
|
|
@ -352,7 +352,7 @@ export async function getOriginalImageBitmap(
|
|||
}
|
||||
console.log('[MLService] Got file: ', file.id.toString());
|
||||
|
||||
return getImageBitmap(fileBlob);
|
||||
return getImageBlobBitmap(fileBlob);
|
||||
}
|
||||
|
||||
export async function getThumbnailImageBitmap(file: EnteFile, token: string) {
|
||||
|
|
@ -365,7 +365,7 @@ export async function getThumbnailImageBitmap(file: EnteFile, token: string) {
|
|||
|
||||
const thumbFile = await fetch(fileUrl);
|
||||
|
||||
return getImageBitmap(await thumbFile.blob());
|
||||
return getImageBlobBitmap(await thumbFile.blob());
|
||||
}
|
||||
|
||||
export async function getLocalFileImageBitmap(
|
||||
|
|
@ -378,7 +378,7 @@ export async function getLocalFileImageBitmap(
|
|||
const enteWorker = await enteWorkerProvider();
|
||||
fileBlob = await convertForPreview(enteFile, fileBlob, enteWorker);
|
||||
}
|
||||
return getImageBitmap(fileBlob);
|
||||
return getImageBlobBitmap(fileBlob);
|
||||
}
|
||||
|
||||
export async function getPeopleList(file: EnteFile): Promise<Array<Person>> {
|
||||
|
|
|
|||
Loading…
Reference in a new issue