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[mob][photos] Automatically reject overlapping suggestions

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This commit is contained in:
laurenspriem 2024-04-25 16:13:29 +05:30
parent 7370557b08
commit 43cbfbfa33
1 changed files with 29 additions and 6 deletions
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35
mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
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@ -98,10 +98,10 @@ class ClusterFeedbackService {
} }
} }
final List<ClusterSuggestion> clusterIdAndFiles = []; final List<ClusterSuggestion> finalSuggestions = [];
for (final clusterSuggestion in foundSuggestions) { for (final clusterSuggestion in foundSuggestions) {
if (clusterIDToFiles.containsKey(clusterSuggestion.$1)) { if (clusterIDToFiles.containsKey(clusterSuggestion.$1)) {
clusterIdAndFiles.add( finalSuggestions.add(
ClusterSuggestion( ClusterSuggestion(
clusterSuggestion.$1, clusterSuggestion.$1,
clusterSuggestion.$2, clusterSuggestion.$2,
@ -116,13 +116,13 @@ class ClusterFeedbackService {
final sortingStartTime = DateTime.now(); final sortingStartTime = DateTime.now();
if (extremeFilesFirst) { if (extremeFilesFirst) {
await _sortSuggestionsOnDistanceToPerson(person, clusterIdAndFiles); await _sortSuggestionsOnDistanceToPerson(person, finalSuggestions);
} }
_logger.info( _logger.info(
'getSuggestionForPerson post-processing suggestions took ${DateTime.now().difference(findSuggestionsTime).inMilliseconds} ms, of which sorting took ${DateTime.now().difference(sortingStartTime).inMilliseconds} ms and getting files took ${getFilesTime.difference(findSuggestionsTime).inMilliseconds} ms', 'getSuggestionForPerson post-processing suggestions took ${DateTime.now().difference(findSuggestionsTime).inMilliseconds} ms, of which sorting took ${DateTime.now().difference(sortingStartTime).inMilliseconds} ms and getting files took ${getFilesTime.difference(findSuggestionsTime).inMilliseconds} ms',
); );
return clusterIdAndFiles; return finalSuggestions;
} catch (e, s) { } catch (e, s) {
_logger.severe("Error in getClusterFilesForPersonID", e, s); _logger.severe("Error in getClusterFilesForPersonID", e, s);
rethrow; rethrow;
@ -463,9 +463,15 @@ class ClusterFeedbackService {
final allClusterIdsToCountMap = await faceMlDb.clusterIdToFaceCount(); final allClusterIdsToCountMap = await faceMlDb.clusterIdToFaceCount();
final ignoredClusters = await faceMlDb.getPersonIgnoredClusters(p.remoteID); final ignoredClusters = await faceMlDb.getPersonIgnoredClusters(p.remoteID);
final personClusters = await faceMlDb.getPersonClusterIDs(p.remoteID); final personClusters = await faceMlDb.getPersonClusterIDs(p.remoteID);
final personFaceIDs =
await FaceMLDataDB.instance.getFaceIDsForPerson(p.remoteID);
final personFileIDs = personFaceIDs.map(getFileIdFromFaceId).toSet();
w?.log( w?.log(
'${p.data.name} has ${personClusters.length} existing clusters, getting all database data done', '${p.data.name} has ${personClusters.length} existing clusters, getting all database data done',
); );
final allClusterIdToFaceIDs =
await FaceMLDataDB.instance.getAllClusterIdToFaceIDs();
w?.log('getAllClusterIdToFaceIDs done');
// First only do a simple check on the big clusters, if the person does not have small clusters yet // First only do a simple check on the big clusters, if the person does not have small clusters yet
final smallestPersonClusterSize = personClusters final smallestPersonClusterSize = personClusters
@ -473,6 +479,7 @@ class ClusterFeedbackService {
.reduce((value, element) => min(value, element)); .reduce((value, element) => min(value, element));
final checkSizes = [kMinimumClusterSizeSearchResult, 20, 10, 5, 1]; final checkSizes = [kMinimumClusterSizeSearchResult, 20, 10, 5, 1];
late Map<int, Vector> clusterAvgBigClusters; late Map<int, Vector> clusterAvgBigClusters;
final List<(int, double)> suggestionsMean = [];
for (final minimumSize in checkSizes.toSet()) { for (final minimumSize in checkSizes.toSet()) {
// if (smallestPersonClusterSize >= minimumSize) { // if (smallestPersonClusterSize >= minimumSize) {
clusterAvgBigClusters = await _getUpdateClusterAvg( clusterAvgBigClusters = await _getUpdateClusterAvg(
@ -493,8 +500,24 @@ class ClusterFeedbackService {
w?.log( w?.log(
'Calculate suggestions using mean for ${clusterAvgBigClusters.length} clusters of min size $minimumSize', 'Calculate suggestions using mean for ${clusterAvgBigClusters.length} clusters of min size $minimumSize',
); );
if (suggestionsMeanBigClusters.isNotEmpty) { for (final suggestion in suggestionsMeanBigClusters) {
return suggestionsMeanBigClusters // Skip suggestions that have a high overlap with the person's files
final suggestionSet = allClusterIdToFaceIDs[suggestion.$1]!
.map((faceID) => getFileIdFromFaceId(faceID))
.toSet();
final overlap = personFileIDs.intersection(suggestionSet);
if (overlap.isNotEmpty &&
((overlap.length / suggestionSet.length) > 0.5)) {
await FaceMLDataDB.instance.captureNotPersonFeedback(
personID: p.remoteID,
clusterID: suggestion.$1,
);
continue;
}
suggestionsMean.add(suggestion);
}
if (suggestionsMean.isNotEmpty) {
return suggestionsMean
.map((e) => (e.$1, e.$2, true)) .map((e) => (e.$1, e.$2, true))
.toList(growable: false); .toList(growable: false);
// } // }