Clusterface (#4626)

## Description

Fixed bug where we showed "Face not clustered yet, please come back
later" toast message even for faces which had a score too low to ever be
clustered automatically.

mobile/lib/db/ml/db.dart

@@ -604,8 +604,6 @@ class MLDataDB {
   }
 
   Future<List<FaceDbInfoForClustering>> getFaceInfoForClustering({
-    double minScore = kMinimumQualityFaceScore,
-    int minClarity = kLaplacianHardThreshold,
     int maxFaces = 20000,
     int offset = 0,
     int batchSize = 10000,
@@ -622,7 +620,7 @@ class MLDataDB {
         // Query a batch of rows
         final List<Map<String, dynamic>> maps = await db.getAll(
           'SELECT $faceIDColumn, $embeddingColumn, $faceScore, $faceBlur, $isSideways FROM $facesTable'
-          ' WHERE $faceScore > $minScore AND $faceBlur > $minClarity'
+          ' WHERE $faceScore > $kMinimumQualityFaceScore AND $faceBlur > $kLaplacianHardThreshold'
           ' ORDER BY $faceIDColumn'
           ' DESC LIMIT $batchSize OFFSET $offset',
         );
@@ -698,12 +696,10 @@ class MLDataDB {
     return result;
   }
 
-  Future<int> getTotalFaceCount({
+  Future<int> getTotalFaceCount() async {
-    double minFaceScore = kMinimumQualityFaceScore,
-  }) async {
     final db = await instance.asyncDB;
     final List<Map<String, dynamic>> maps = await db.getAll(
-      'SELECT COUNT(*) as count FROM $facesTable WHERE $faceScore > $minFaceScore AND $faceBlur > $kLaplacianHardThreshold',
+      'SELECT COUNT(*) as count FROM $facesTable WHERE $faceScore > $kMinimumQualityFaceScore AND $faceBlur > $kLaplacianHardThreshold',
     );
     return maps.first['count'] as int;
   }

mobile/lib/services/machine_learning/ml_service.dart

@@ -17,7 +17,6 @@ import "package:photos/services/filedata/filedata_service.dart";
 import "package:photos/services/filedata/model/file_data.dart";
 import 'package:photos/services/machine_learning/face_ml/face_clustering/face_clustering_service.dart';
 import "package:photos/services/machine_learning/face_ml/face_clustering/face_db_info_for_clustering.dart";
-import 'package:photos/services/machine_learning/face_ml/face_filtering/face_filtering_constants.dart';
 import "package:photos/services/machine_learning/face_ml/person/person_service.dart";
 import "package:photos/services/machine_learning/ml_indexing_isolate.dart";
 import 'package:photos/services/machine_learning/ml_result.dart';
@@ -238,10 +237,7 @@ class MLService {
     }
   }
 
-  Future<void> clusterAllImages({
+  Future<void> clusterAllImages({bool clusterInBuckets = true}) async {
-    double minFaceScore = kMinimumQualityFaceScore,
-    bool clusterInBuckets = true,
-  }) async {
     if (_cannotRunMLFunction()) return;
 
     _logger.info("`clusterAllImages()` called");
@@ -269,13 +265,12 @@ class MLService {
 
       // Get a sense of the total number of faces in the database
       final int totalFaces =
-          await MLDataDB.instance.getTotalFaceCount(minFaceScore: minFaceScore);
+          await MLDataDB.instance.getTotalFaceCount();
       final fileIDToCreationTime =
           await FilesDB.instance.getFileIDToCreationTime();
       final startEmbeddingFetch = DateTime.now();
       // read all embeddings
       final result = await MLDataDB.instance.getFaceInfoForClustering(
-        minScore: minFaceScore,
         maxFaces: totalFaces,
       );
       final Set<int> missingFileIDs = {};

mobile/lib/ui/viewer/file_details/face_widget.dart

@@ -5,12 +5,13 @@ import "package:flutter/cupertino.dart";
 import "package:flutter/foundation.dart" show kDebugMode;
 import "package:flutter/material.dart";
 import "package:photos/db/ml/db.dart";
-import "package:photos/extensions/stop_watch.dart";
 import "package:photos/generated/l10n.dart";
+import "package:photos/models/base/id.dart";
 import 'package:photos/models/file/file.dart';
 import "package:photos/models/ml/face/face.dart";
 import "package:photos/models/ml/face/person.dart";
 import "package:photos/services/machine_learning/face_ml/face_detection/detection.dart";
+import "package:photos/services/machine_learning/face_ml/face_filtering/face_filtering_constants.dart";
 import "package:photos/services/machine_learning/face_ml/feedback/cluster_feedback.dart";
 import "package:photos/services/search_service.dart";
 import "package:photos/theme/ente_theme.dart";
@@ -68,16 +69,13 @@ class _FaceWidgetState extends State<FaceWidget> {
               if (widget.editMode) return;
 
               log(
-                "FaceWidget is tapped, with person ${widget.person} and clusterID ${widget.clusterID}",
+                "FaceWidget is tapped, with person ${widget.person?.data.name} and clusterID ${widget.clusterID}",
                 name: "FaceWidget",
               );
               if (widget.person == null && widget.clusterID == null) {
-                // Get faceID and double check that it doesn't belong to an existing clusterID. If it does, push that cluster page
+                // Double check that it doesn't belong to an existing clusterID.
-                final w = (kDebugMode ? EnteWatch('FaceWidget') : null)
-                  ?..start();
                 final existingClusterID = await MLDataDB.instance
                     .getClusterIDForFaceID(widget.face.faceID);
-                w?.log('getting existing clusterID for faceID');
                 if (existingClusterID != null) {
                   final fileIdsToClusterIds =
                       await MLDataDB.instance.getFileIdToClusterIds();
@@ -99,6 +97,24 @@ class _FaceWidgetState extends State<FaceWidget> {
                       ),
                     ),
                   );
+                  return;
+                }
+                if (widget.face.score <= kMinimumQualityFaceScore) {
+                  // The face score is too low for automatic clustering,
+                  // assigning a manual new clusterID so that the user can cluster it manually
+                  final String clusterID = newClusterID();
+                  await MLDataDB.instance.updateFaceIdToClusterId(
+                    {widget.face.faceID: clusterID},
+                  );
+                  await Navigator.of(context).push(
+                    MaterialPageRoute(
+                      builder: (context) => ClusterPage(
+                        [widget.file],
+                        clusterID: clusterID,
+                      ),
+                    ),
+                  );
+                  return;
                 }
 
                 showShortToast(