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[mob][photos] Suggestions change parameters

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This commit is contained in:
laurenspriem 2024-04-23 13:37:53 +05:30
parent 6fe8dc7c66
commit 3786c9def9
1 changed files with 8 additions and 3 deletions
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11
mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
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@ -65,8 +65,13 @@ class ClusterFeedbackService {
try { try {
// Get the suggestions for the person using centroids and median // Get the suggestions for the person using centroids and median
final startTime = DateTime.now();
final List<(int, double, bool)> suggestClusterIds = final List<(int, double, bool)> suggestClusterIds =
await _getSuggestions(person); await _getSuggestions(person);
final findSuggestionsTime = DateTime.now();
_logger.info(
'`_getSuggestions`: Found ${suggestClusterIds.length} suggestions in ${findSuggestionsTime.difference(startTime).inMilliseconds} ms',
);
// Get the files for the suggestions // Get the files for the suggestions
final Map<int, Set<int>> fileIdToClusterID = final Map<int, Set<int>> fileIdToClusterID =
@ -437,10 +442,10 @@ class ClusterFeedbackService {
Future<List<(int, double, bool)>> _getSuggestions( Future<List<(int, double, bool)>> _getSuggestions(
PersonEntity p, { PersonEntity p, {
int sampleSize = 50, int sampleSize = 50,
double maxMedianDistance = 0.65, double maxMedianDistance = 0.62,
double goodMedianDistance = 0.55, double goodMedianDistance = 0.55,
double maxMeanDistance = 0.65, double maxMeanDistance = 0.65,
double goodMeanDistance = 0.5, double goodMeanDistance = 0.54,
}) async { }) async {
// Get all the cluster data // Get all the cluster data
final startTime = DateTime.now(); final startTime = DateTime.now();
@ -459,7 +464,7 @@ class ClusterFeedbackService {
final smallestPersonClusterSize = personClusters final smallestPersonClusterSize = personClusters
.map((clusterID) => allClusterIdsToCountMap[clusterID] ?? 0) .map((clusterID) => allClusterIdsToCountMap[clusterID] ?? 0)
.reduce((value, element) => min(value, element)); .reduce((value, element) => min(value, element));
final checkSizes = [kMinimumClusterSizeSearchResult, 20, 10, 5]; final checkSizes = [kMinimumClusterSizeSearchResult, 20, 10, 5, 1];
for (final minimumSize in checkSizes.toSet()) { for (final minimumSize in checkSizes.toSet()) {
if (smallestPersonClusterSize >= minimumSize) { if (smallestPersonClusterSize >= minimumSize) {
final Map<int, List<double>> clusterAvgBigClusters = final Map<int, List<double>> clusterAvgBigClusters =