[mob][photos] Move listeners inside init
This commit is contained in:
laurenspriem
parent
678efd1e8b
commit
ffc9eecbd1
2 changed files with 156 additions and 155 deletions
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@ -242,8 +242,6 @@ Future<void> _init(bool isBackground, {String via = ''}) async {
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// unawaited(ObjectDetectionService.instance.init());
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if (flagService.faceSearchEnabled) {
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unawaited(FaceMlService.instance.init());
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FaceMlService.instance.listenIndexOnDiffSync();
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FaceMlService.instance.listenOnPeopleChangedSync();
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} else {
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if (LocalSettings.instance.isFaceIndexingEnabled) {
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unawaited(LocalSettings.instance.toggleFaceIndexing());
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@ -158,6 +158,9 @@ class FaceMlService {
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} else {
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unawaited(indexAndClusterAll());
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}
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_listenIndexOnDiffSync();
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_listenOnPeopleChangedSync();
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});
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}
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@ -165,13 +168,13 @@ class FaceMlService {
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OrtEnv.instance.init();
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}
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void listenIndexOnDiffSync() {
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void _listenIndexOnDiffSync() {
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Bus.instance.on<DiffSyncCompleteEvent>().listen((event) async {
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unawaited(sync());
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});
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}
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void listenOnPeopleChangedSync() {
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void _listenOnPeopleChangedSync() {
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Bus.instance.on<PeopleChangedEvent>().listen((event) {
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_shouldSyncPeople = true;
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});
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@ -372,157 +375,6 @@ class FaceMlService {
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}
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}
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Future<void> clusterAllImages({
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double minFaceScore = kMinimumQualityFaceScore,
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bool clusterInBuckets = true,
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}) async {
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if (_cannotRunMLFunction()) return;
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_logger.info("`clusterAllImages()` called");
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_isIndexingOrClusteringRunning = true;
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final clusterAllImagesTime = DateTime.now();
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try {
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// Get a sense of the total number of faces in the database
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final int totalFaces = await FaceMLDataDB.instance
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.getTotalFaceCount(minFaceScore: minFaceScore);
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final fileIDToCreationTime =
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await FilesDB.instance.getFileIDToCreationTime();
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final startEmbeddingFetch = DateTime.now();
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// read all embeddings
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final result = await FaceMLDataDB.instance.getFaceInfoForClustering(
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minScore: minFaceScore,
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maxFaces: totalFaces,
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);
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final Set<int> missingFileIDs = {};
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final allFaceInfoForClustering = <FaceInfoForClustering>[];
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for (final faceInfo in result) {
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if (!fileIDToCreationTime.containsKey(faceInfo.fileID)) {
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missingFileIDs.add(faceInfo.fileID);
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} else {
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allFaceInfoForClustering.add(faceInfo);
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}
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}
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// sort the embeddings based on file creation time, oldest first
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allFaceInfoForClustering.sort((a, b) {
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return fileIDToCreationTime[a.fileID]!
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.compareTo(fileIDToCreationTime[b.fileID]!);
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});
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_logger.info(
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'Getting and sorting embeddings took ${DateTime.now().difference(startEmbeddingFetch).inMilliseconds} ms for ${allFaceInfoForClustering.length} embeddings'
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'and ${missingFileIDs.length} missing fileIDs',
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);
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// Get the current cluster statistics
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final Map<int, (Uint8List, int)> oldClusterSummaries =
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await FaceMLDataDB.instance.getAllClusterSummary();
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if (clusterInBuckets) {
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const int bucketSize = 20000;
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const int offsetIncrement = 7500;
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int offset = 0;
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int bucket = 1;
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while (true) {
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if (_shouldPauseIndexingAndClustering) {
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_logger.info(
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"MLController does not allow running ML, stopping before clustering bucket $bucket",
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);
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break;
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}
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if (offset > allFaceInfoForClustering.length - 1) {
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_logger.warning(
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'faceIdToEmbeddingBucket is empty, this should ideally not happen as it should have stopped earlier. offset: $offset, totalFaces: $totalFaces',
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);
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break;
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}
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if (offset > totalFaces) {
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_logger.warning(
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'offset > totalFaces, this should ideally not happen. offset: $offset, totalFaces: $totalFaces',
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);
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break;
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}
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final bucketStartTime = DateTime.now();
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final faceInfoForClustering = allFaceInfoForClustering.sublist(
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offset,
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min(offset + bucketSize, allFaceInfoForClustering.length),
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);
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final clusteringResult =
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await FaceClusteringService.instance.predictLinear(
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faceInfoForClustering.toSet(),
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fileIDToCreationTime: fileIDToCreationTime,
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offset: offset,
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oldClusterSummaries: oldClusterSummaries,
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);
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if (clusteringResult == null) {
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_logger.warning("faceIdToCluster is null");
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return;
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}
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await FaceMLDataDB.instance
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.updateFaceIdToClusterId(clusteringResult.newFaceIdToCluster);
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await FaceMLDataDB.instance
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.clusterSummaryUpdate(clusteringResult.newClusterSummaries!);
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for (final faceInfo in faceInfoForClustering) {
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faceInfo.clusterId ??=
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clusteringResult.newFaceIdToCluster[faceInfo.faceID];
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}
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for (final clusterUpdate
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in clusteringResult.newClusterSummaries!.entries) {
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oldClusterSummaries[clusterUpdate.key] = clusterUpdate.value;
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}
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_logger.info(
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'Done with clustering ${offset + faceInfoForClustering.length} embeddings (${(100 * (offset + faceInfoForClustering.length) / totalFaces).toStringAsFixed(0)}%) in bucket $bucket, offset: $offset, in ${DateTime.now().difference(bucketStartTime).inSeconds} seconds',
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);
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if (offset + bucketSize >= totalFaces) {
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_logger.info('All faces clustered');
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break;
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}
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offset += offsetIncrement;
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bucket++;
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}
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} else {
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final clusterStartTime = DateTime.now();
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// Cluster the embeddings using the linear clustering algorithm, returning a map from faceID to clusterID
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final clusteringResult =
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await FaceClusteringService.instance.predictLinear(
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allFaceInfoForClustering.toSet(),
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fileIDToCreationTime: fileIDToCreationTime,
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oldClusterSummaries: oldClusterSummaries,
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);
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if (clusteringResult == null) {
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_logger.warning("faceIdToCluster is null");
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return;
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}
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final clusterDoneTime = DateTime.now();
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_logger.info(
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'done with clustering ${allFaceInfoForClustering.length} in ${clusterDoneTime.difference(clusterStartTime).inSeconds} seconds ',
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);
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// Store the updated clusterIDs in the database
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_logger.info(
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'Updating ${clusteringResult.newFaceIdToCluster.length} FaceIDs with clusterIDs in the DB',
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);
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await FaceMLDataDB.instance
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.updateFaceIdToClusterId(clusteringResult.newFaceIdToCluster);
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await FaceMLDataDB.instance
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.clusterSummaryUpdate(clusteringResult.newClusterSummaries!);
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_logger.info('Done updating FaceIDs with clusterIDs in the DB, in '
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'${DateTime.now().difference(clusterDoneTime).inSeconds} seconds');
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}
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Bus.instance.fire(PeopleChangedEvent());
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_logger.info('clusterAllImages() finished, in '
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'${DateTime.now().difference(clusterAllImagesTime).inSeconds} seconds');
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} catch (e, s) {
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_logger.severe("`clusterAllImages` failed", e, s);
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} finally {
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_isIndexingOrClusteringRunning = false;
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_shouldPauseIndexingAndClustering = false;
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}
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}
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/// Analyzes all the images in the database with the latest ml version and stores the results in the database.
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///
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/// This function first checks if the image has already been analyzed with the lastest faceMlVersion and stored in the database. If so, it skips the image.
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@ -699,6 +551,157 @@ class FaceMlService {
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}
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}
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Future<void> clusterAllImages({
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double minFaceScore = kMinimumQualityFaceScore,
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bool clusterInBuckets = true,
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}) async {
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if (_cannotRunMLFunction()) return;
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_logger.info("`clusterAllImages()` called");
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_isIndexingOrClusteringRunning = true;
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final clusterAllImagesTime = DateTime.now();
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try {
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// Get a sense of the total number of faces in the database
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final int totalFaces = await FaceMLDataDB.instance
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.getTotalFaceCount(minFaceScore: minFaceScore);
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final fileIDToCreationTime =
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await FilesDB.instance.getFileIDToCreationTime();
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final startEmbeddingFetch = DateTime.now();
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// read all embeddings
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final result = await FaceMLDataDB.instance.getFaceInfoForClustering(
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minScore: minFaceScore,
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maxFaces: totalFaces,
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);
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final Set<int> missingFileIDs = {};
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final allFaceInfoForClustering = <FaceInfoForClustering>[];
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for (final faceInfo in result) {
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if (!fileIDToCreationTime.containsKey(faceInfo.fileID)) {
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missingFileIDs.add(faceInfo.fileID);
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} else {
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allFaceInfoForClustering.add(faceInfo);
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}
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}
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// sort the embeddings based on file creation time, oldest first
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allFaceInfoForClustering.sort((a, b) {
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return fileIDToCreationTime[a.fileID]!
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.compareTo(fileIDToCreationTime[b.fileID]!);
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});
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_logger.info(
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'Getting and sorting embeddings took ${DateTime.now().difference(startEmbeddingFetch).inMilliseconds} ms for ${allFaceInfoForClustering.length} embeddings'
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'and ${missingFileIDs.length} missing fileIDs',
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);
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// Get the current cluster statistics
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final Map<int, (Uint8List, int)> oldClusterSummaries =
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await FaceMLDataDB.instance.getAllClusterSummary();
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if (clusterInBuckets) {
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const int bucketSize = 20000;
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const int offsetIncrement = 7500;
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int offset = 0;
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int bucket = 1;
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while (true) {
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if (_shouldPauseIndexingAndClustering) {
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_logger.info(
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"MLController does not allow running ML, stopping before clustering bucket $bucket",
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);
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break;
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}
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if (offset > allFaceInfoForClustering.length - 1) {
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_logger.warning(
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'faceIdToEmbeddingBucket is empty, this should ideally not happen as it should have stopped earlier. offset: $offset, totalFaces: $totalFaces',
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);
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break;
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}
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if (offset > totalFaces) {
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_logger.warning(
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'offset > totalFaces, this should ideally not happen. offset: $offset, totalFaces: $totalFaces',
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);
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break;
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}
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final bucketStartTime = DateTime.now();
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final faceInfoForClustering = allFaceInfoForClustering.sublist(
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offset,
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min(offset + bucketSize, allFaceInfoForClustering.length),
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);
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final clusteringResult =
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await FaceClusteringService.instance.predictLinear(
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faceInfoForClustering.toSet(),
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fileIDToCreationTime: fileIDToCreationTime,
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offset: offset,
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oldClusterSummaries: oldClusterSummaries,
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);
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if (clusteringResult == null) {
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_logger.warning("faceIdToCluster is null");
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return;
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}
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await FaceMLDataDB.instance
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.updateFaceIdToClusterId(clusteringResult.newFaceIdToCluster);
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await FaceMLDataDB.instance
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.clusterSummaryUpdate(clusteringResult.newClusterSummaries!);
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for (final faceInfo in faceInfoForClustering) {
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faceInfo.clusterId ??=
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clusteringResult.newFaceIdToCluster[faceInfo.faceID];
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}
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for (final clusterUpdate
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in clusteringResult.newClusterSummaries!.entries) {
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oldClusterSummaries[clusterUpdate.key] = clusterUpdate.value;
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}
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_logger.info(
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'Done with clustering ${offset + faceInfoForClustering.length} embeddings (${(100 * (offset + faceInfoForClustering.length) / totalFaces).toStringAsFixed(0)}%) in bucket $bucket, offset: $offset, in ${DateTime.now().difference(bucketStartTime).inSeconds} seconds',
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);
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if (offset + bucketSize >= totalFaces) {
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_logger.info('All faces clustered');
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break;
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}
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offset += offsetIncrement;
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bucket++;
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}
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} else {
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final clusterStartTime = DateTime.now();
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// Cluster the embeddings using the linear clustering algorithm, returning a map from faceID to clusterID
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final clusteringResult =
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await FaceClusteringService.instance.predictLinear(
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allFaceInfoForClustering.toSet(),
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fileIDToCreationTime: fileIDToCreationTime,
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oldClusterSummaries: oldClusterSummaries,
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);
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if (clusteringResult == null) {
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_logger.warning("faceIdToCluster is null");
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return;
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}
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final clusterDoneTime = DateTime.now();
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_logger.info(
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'done with clustering ${allFaceInfoForClustering.length} in ${clusterDoneTime.difference(clusterStartTime).inSeconds} seconds ',
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);
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// Store the updated clusterIDs in the database
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_logger.info(
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'Updating ${clusteringResult.newFaceIdToCluster.length} FaceIDs with clusterIDs in the DB',
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);
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await FaceMLDataDB.instance
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.updateFaceIdToClusterId(clusteringResult.newFaceIdToCluster);
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await FaceMLDataDB.instance
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.clusterSummaryUpdate(clusteringResult.newClusterSummaries!);
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_logger.info('Done updating FaceIDs with clusterIDs in the DB, in '
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'${DateTime.now().difference(clusterDoneTime).inSeconds} seconds');
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}
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Bus.instance.fire(PeopleChangedEvent());
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_logger.info('clusterAllImages() finished, in '
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'${DateTime.now().difference(clusterAllImagesTime).inSeconds} seconds');
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} catch (e, s) {
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_logger.severe("`clusterAllImages` failed", e, s);
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} finally {
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_isIndexingOrClusteringRunning = false;
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_shouldPauseIndexingAndClustering = false;
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}
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}
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bool _shouldDiscardRemoteEmbedding(FileMl fileMl) {
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if (fileMl.faceEmbedding.version < faceMlVersion) {
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debugPrint("Discarding remote embedding for fileID ${fileMl.fileID} "
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