[mob][photos] Remove ClusterResult old deprecated code
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laurenspriem
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1 changed files with 0 additions and 266 deletions
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@ -7,278 +7,12 @@ import "package:photos/models/file/file.dart";
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import 'package:photos/models/ml/ml_typedefs.dart';
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import "package:photos/models/ml/ml_versions.dart";
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import 'package:photos/services/machine_learning/face_ml/face_alignment/alignment_result.dart';
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import 'package:photos/services/machine_learning/face_ml/face_clustering/cosine_distance.dart';
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import 'package:photos/services/machine_learning/face_ml/face_detection/detection.dart';
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import 'package:photos/services/machine_learning/face_ml/face_filtering/face_filtering_constants.dart';
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import 'package:photos/services/machine_learning/face_ml/face_ml_methods.dart';
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final _logger = Logger('ClusterResult_FaceMlResult');
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// TODO: should I add [faceMlVersion] and [clusterMlVersion] to the [ClusterResult] class?
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@Deprecated('We are now just storing the cluster results directly in DB')
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class ClusterResult {
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final int personId;
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String? userDefinedName;
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bool get hasUserDefinedName => userDefinedName != null;
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String _thumbnailFaceId;
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bool thumbnailFaceIdIsUserDefined;
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final List<int> _fileIds;
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final List<String> _faceIds;
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final Embedding medoid;
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double medoidDistanceThreshold;
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List<int> get uniqueFileIds => _fileIds.toSet().toList();
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List<int> get fileIDsIncludingPotentialDuplicates => _fileIds;
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List<String> get faceIDs => _faceIds;
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String get thumbnailFaceId => _thumbnailFaceId;
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int get thumbnailFileId => getFileIdFromFaceId(_thumbnailFaceId);
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/// Sets the thumbnail faceId to the given faceId.
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/// Throws an exception if the faceId is not in the list of faceIds.
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set setThumbnailFaceId(String faceId) {
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if (!_faceIds.contains(faceId)) {
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throw Exception(
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"The faceId $faceId is not in the list of faceIds: $faceId",
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);
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}
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_thumbnailFaceId = faceId;
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thumbnailFaceIdIsUserDefined = true;
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}
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/// Sets the [userDefinedName] to the given [customName]
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set setUserDefinedName(String customName) {
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userDefinedName = customName;
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}
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int get clusterSize => _fileIds.toSet().length;
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ClusterResult({
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required this.personId,
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required String thumbnailFaceId,
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required List<int> fileIds,
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required List<String> faceIds,
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required this.medoid,
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required this.medoidDistanceThreshold,
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this.userDefinedName,
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this.thumbnailFaceIdIsUserDefined = false,
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}) : _thumbnailFaceId = thumbnailFaceId,
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_faceIds = faceIds,
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_fileIds = fileIds;
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void addFileIDsAndFaceIDs(List<int> fileIDs, List<String> faceIDs) {
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assert(fileIDs.length == faceIDs.length);
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_fileIds.addAll(fileIDs);
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_faceIds.addAll(faceIDs);
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}
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// TODO: Consider if we should recalculated the medoid and threshold when deleting or adding a file from the cluster
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int removeFileId(int fileId) {
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assert(_fileIds.length == _faceIds.length);
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if (!_fileIds.contains(fileId)) {
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throw Exception(
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"The fileId $fileId is not in the list of fileIds: $fileId, so it's not in the cluster and cannot be removed.",
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);
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}
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int removedCount = 0;
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for (var i = 0; i < _fileIds.length; i++) {
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if (_fileIds[i] == fileId) {
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assert(getFileIdFromFaceId(_faceIds[i]) == fileId);
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_fileIds.removeAt(i);
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_faceIds.removeAt(i);
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debugPrint(
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"Removed fileId $fileId from cluster $personId at index ${i + removedCount}}",
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);
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i--; // Adjust index due to removal
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removedCount++;
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}
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}
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_ensureClusterSizeIsAboveMinimum();
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return removedCount;
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}
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int addFileID(int fileID) {
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assert(_fileIds.length == _faceIds.length);
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if (_fileIds.contains(fileID)) {
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return 0;
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}
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_fileIds.add(fileID);
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_faceIds.add(FaceDetectionRelative.toFaceIDEmpty(fileID: fileID));
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return 1;
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}
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void ensureThumbnailFaceIdIsInCluster() {
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if (!_faceIds.contains(_thumbnailFaceId)) {
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_thumbnailFaceId = _faceIds[0];
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}
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}
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void _ensureClusterSizeIsAboveMinimum() {
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if (clusterSize < minimumClusterSize) {
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throw Exception(
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"Cluster size is below minimum cluster size of $minimumClusterSize",
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);
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}
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}
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Map<String, dynamic> _toJson() => {
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'personId': personId,
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'thumbnailFaceId': _thumbnailFaceId,
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'fileIds': _fileIds,
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'faceIds': _faceIds,
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'medoid': medoid,
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'medoidDistanceThreshold': medoidDistanceThreshold,
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if (userDefinedName != null) 'userDefinedName': userDefinedName,
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'thumbnailFaceIdIsUserDefined': thumbnailFaceIdIsUserDefined,
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};
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String toJsonString() => jsonEncode(_toJson());
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static ClusterResult _fromJson(Map<String, dynamic> json) {
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return ClusterResult(
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personId: json['personId'] ?? -1,
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thumbnailFaceId: json['thumbnailFaceId'] ?? '',
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fileIds:
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(json['fileIds'] as List?)?.map((item) => item as int).toList() ?? [],
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faceIds:
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(json['faceIds'] as List?)?.map((item) => item as String).toList() ??
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[],
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medoid:
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(json['medoid'] as List?)?.map((item) => item as double).toList() ??
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[],
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medoidDistanceThreshold: json['medoidDistanceThreshold'] ?? 0,
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userDefinedName: json['userDefinedName'],
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thumbnailFaceIdIsUserDefined:
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json['thumbnailFaceIdIsUserDefined'] as bool,
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);
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}
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static ClusterResult fromJsonString(String jsonString) {
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return _fromJson(jsonDecode(jsonString));
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}
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}
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class ClusterResultBuilder {
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int personId = -1;
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String? userDefinedName;
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String thumbnailFaceId = '';
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bool thumbnailFaceIdIsUserDefined = false;
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List<int> fileIds = <int>[];
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List<String> faceIds = <String>[];
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List<Embedding> embeddings = <Embedding>[];
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Embedding medoid = <double>[];
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double medoidDistanceThreshold = 0;
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bool medoidAndThresholdCalculated = false;
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final int k = 5;
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ClusterResultBuilder.createFromIndices({
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required List<int> clusterIndices,
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required List<int> labels,
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required List<Embedding> allEmbeddings,
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required List<int> allFileIds,
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required List<String> allFaceIds,
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}) {
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final clusteredFileIds =
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clusterIndices.map((fileIndex) => allFileIds[fileIndex]).toList();
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final clusteredFaceIds =
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clusterIndices.map((fileIndex) => allFaceIds[fileIndex]).toList();
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final clusteredEmbeddings =
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clusterIndices.map((fileIndex) => allEmbeddings[fileIndex]).toList();
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personId = labels[clusterIndices[0]];
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fileIds = clusteredFileIds;
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faceIds = clusteredFaceIds;
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thumbnailFaceId = faceIds[0];
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embeddings = clusteredEmbeddings;
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}
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void calculateAndSetMedoidAndThreshold() {
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if (embeddings.isEmpty) {
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throw Exception("Cannot calculate medoid and threshold for empty list");
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}
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// Calculate the medoid and threshold
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final (tempMedoid, distanceThreshold) =
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_calculateMedoidAndDistanceTreshold(embeddings);
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// Update the medoid
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medoid = List.from(tempMedoid);
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// Update the medoidDistanceThreshold as the distance of the medoid to its k-th nearest neighbor
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medoidDistanceThreshold = distanceThreshold;
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medoidAndThresholdCalculated = true;
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}
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(List<double>, double) _calculateMedoidAndDistanceTreshold(
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List<List<double>> embeddings,
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) {
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double minDistance = double.infinity;
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List<double>? medoid;
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// Calculate the distance between all pairs
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for (int i = 0; i < embeddings.length; ++i) {
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double totalDistance = 0;
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for (int j = 0; j < embeddings.length; ++j) {
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if (i != j) {
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totalDistance += cosineDistance(embeddings[i], embeddings[j]);
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// Break early if we already exceed minDistance
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if (totalDistance > minDistance) {
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break;
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}
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}
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}
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// Find the minimum total distance
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if (totalDistance < minDistance) {
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minDistance = totalDistance;
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medoid = embeddings[i];
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}
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}
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// Now, calculate k-th nearest neighbor for the medoid
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final List<double> distancesToMedoid = [];
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for (List<double> embedding in embeddings) {
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if (embedding != medoid) {
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distancesToMedoid.add(cosineDistance(medoid!, embedding));
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}
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}
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distancesToMedoid.sort();
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// TODO: empirically find the best k. Probably it should be dynamic in some way, so for instance larger for larger clusters and smaller for smaller clusters, especially since there are a lot of really small clusters and a few really large ones.
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final double kthDistance = distancesToMedoid[
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distancesToMedoid.length >= k ? k - 1 : distancesToMedoid.length - 1];
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return (medoid!, kthDistance);
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}
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void changeThumbnailFaceId(String faceId) {
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if (!faceIds.contains(faceId)) {
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throw Exception(
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"The faceId $faceId is not in the list of faceIds: $faceIds",
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);
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}
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thumbnailFaceId = faceId;
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}
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void addFileIDsAndFaceIDs(List<int> addedFileIDs, List<String> addedFaceIDs) {
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assert(addedFileIDs.length == addedFaceIDs.length);
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fileIds.addAll(addedFileIDs);
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faceIds.addAll(addedFaceIDs);
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}
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}
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@immutable
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class FaceMlResult {
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final int fileId;
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