ente/mobile/lib/face/model/face.dart
2024-05-20 14:18:34 +05:30

85 lines
2.2 KiB
Dart

import "package:photos/face/model/detection.dart";
import 'package:photos/services/machine_learning/face_ml/face_filtering/face_filtering_constants.dart';
import "package:photos/services/machine_learning/face_ml/face_ml_result.dart";
// FileInfo contains the image width and height of the image the face was detected in.
class FileInfo {
int? imageWidth;
int? imageHeight;
FileInfo({
this.imageWidth,
this.imageHeight,
});
}
class Face {
final String faceID;
final List<double> embedding;
Detection detection;
final double score;
final double blur;
///#region Local DB fields
// This is not stored on the server, using it for local DB row
FileInfo? fileInfo;
final int fileID;
///#endregion
bool get isBlurry => blur < kLaplacianHardThreshold;
bool get hasHighScore => score > kMinimumQualityFaceScore;
bool get isHighQuality => (!isBlurry) && hasHighScore;
int area({int? w, int? h}) {
return detection.getFaceArea(
fileInfo?.imageWidth ?? w ?? 0,
fileInfo?.imageHeight ?? h ?? 0,
);
}
Face(
this.faceID,
this.fileID,
this.embedding,
this.score,
this.detection,
this.blur, {
this.fileInfo,
});
factory Face.empty(int fileID, {bool error = false}) {
return Face(
"$fileID-0",
fileID,
<double>[],
error ? -1.0 : 0.0,
Detection.empty(),
0.0,
);
}
factory Face.fromJson(Map<String, dynamic> json) {
final String faceID = json['faceID'] as String;
final int fileID = getFileIdFromFaceId(faceID);
return Face(
faceID,
fileID,
List<double>.from((json['embedding'] ?? json['embeddings']) as List),
json['score'] as double,
Detection.fromJson(json['detection'] as Map<String, dynamic>),
// high value means t
(json['blur'] ?? kLapacianDefault) as double,
);
}
// Note: Keep the information in toJson minimum. Keep in sync with desktop.
// Derive fields like fileID from other values whenever possible
Map<String, dynamic> toJson() => {
'faceID': faceID,
'embedding': embedding,
'detection': detection.toJson(),
'score': score,
'blur': blur,
};
}