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Merge branch 'mobile_face' of https://github.com/ente-io/auth into mobile_face

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This commit is contained in:
Neeraj Gupta 2024-04-02 14:07:18 +05:30
commit e2ed836b16
1 changed files with 71 additions and 12 deletions
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83
mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
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@ -11,6 +11,7 @@ import "package:photos/face/model/person.dart";
import "package:photos/generated/protos/ente/common/vector.pb.dart";
import "package:photos/models/file/file.dart";
import 'package:photos/services/machine_learning/face_ml/face_clustering/cosine_distance.dart';
import "package:photos/services/machine_learning/face_ml/face_ml_result.dart";
import "package:photos/services/search_service.dart";
class ClusterFeedbackService {
@ -241,22 +242,13 @@ class ClusterFeedbackService {
/// 3. bool: whether the suggestion was found using the mean (true) or the median (false)
/// 4. List<EnteFile>: the files in the cluster
Future<List<(int, double, bool, List<EnteFile>)>> getClusterFilesForPersonID(
Person person,
) async {
Person person, {
bool extremeFilesFirst = true,
}) async {
_logger.info(
'getClusterFilesForPersonID ${kDebugMode ? person.attr.name : person.remoteID}',
);
// Get the suggestions for the person using only centroids
// final Map<int, List<(int, double)>> suggestions =
// await getSuggestionsUsingMean(person);
// final Set<int> suggestClusterIds = {};
// for (final List<(int, double)> suggestion in suggestions.values) {
// for (final clusterNeighbors in suggestion) {
// suggestClusterIds.add(clusterNeighbors.$1);
// }
// }
try {
// Get the suggestions for the person using centroids and median
final List<(int, double, bool)> suggestClusterIds =
@ -297,6 +289,10 @@ class ClusterFeedbackService {
}
}
if (extremeFilesFirst) {
await _sortSuggestionsOnDistanceToPerson(person, clusterIdAndFiles);
}
return clusterIdAndFiles;
} catch (e, s) {
_logger.severe("Error in getClusterFilesForPersonID", e, s);
@ -505,4 +501,67 @@ class ClusterFeedbackService {
return sampledEmbeddings;
}
Future<void> _sortSuggestionsOnDistanceToPerson(
Person person,
List<(int, double, bool, List<EnteFile>)> suggestions,
) async {
if (suggestions.isEmpty) {
debugPrint('No suggestions to sort');
return;
}
final startTime = DateTime.now();
final faceMlDb = FaceMLDataDB.instance;
// Get the cluster averages for the person's clusters and the suggestions' clusters
final Map<int, (Uint8List, int)> clusterToSummary =
await faceMlDb.clusterSummaryAll();
// Calculate the avg embedding of the person
final personClusters = await faceMlDb.getPersonClusterIDs(person.remoteID);
final personEmbeddingsCount = personClusters
.map((e) => clusterToSummary[e]!.$2)
.reduce((a, b) => a + b);
final List<double> personAvg = List.filled(192, 0);
for (final personClusterID in personClusters) {
final personClusterBlob = clusterToSummary[personClusterID]!.$1;
final personClusterAvg = EVector.fromBuffer(personClusterBlob).values;
final clusterWeight =
clusterToSummary[personClusterID]!.$2 / personEmbeddingsCount;
for (int i = 0; i < personClusterAvg.length; i++) {
personAvg[i] += personClusterAvg[i] *
clusterWeight; // Weighted sum of the cluster averages
}
}
// Sort the suggestions based on the distance to the person
for (final suggestion in suggestions) {
final clusterID = suggestion.$1;
final faceIdToEmbeddingMap = await faceMlDb.getFaceEmbeddingMapForFile(
suggestion.$4.map((e) => e.uploadedFileID!).toList(),
);
final fileIdToDistanceMap = {};
for (final entry in faceIdToEmbeddingMap.entries) {
fileIdToDistanceMap[getFileIdFromFaceId(entry.key)] =
cosineDistForNormVectors(
personAvg,
EVector.fromBuffer(entry.value).values,
);
}
suggestion.$4.sort((b, a) {
final double distanceA = fileIdToDistanceMap[a.uploadedFileID!];
final double distanceB = fileIdToDistanceMap[b.uploadedFileID!];
return distanceA.compareTo(distanceB);
});
debugPrint(
"[${_logger.name}] Sorted suggestions for cluster $clusterID based on distance to person: ${suggestion.$4.map((e) => fileIdToDistanceMap[e.uploadedFileID]).toList()}",
);
}
final endTime = DateTime.now();
_logger.info(
"Sorting suggestions based on distance to person took ${endTime.difference(startTime).inMilliseconds} ms for ${suggestions.length} suggestions",
);
}
}