58 lines
1.6 KiB
Dart
58 lines
1.6 KiB
Dart
import "dart:convert";
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import 'package:photos/face/db_fields.dart';
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import "package:photos/face/model/detection.dart";
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import "package:photos/face/model/face.dart";
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import "package:photos/generated/protos/ente/common/vector.pb.dart";
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import "package:photos/models/ml/ml_versions.dart";
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int boolToSQLInt(bool? value, {bool defaultValue = false}) {
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final bool v = value ?? defaultValue;
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if (v == false) {
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return 0;
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} else {
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return 1;
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}
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}
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bool sqlIntToBool(int? value, {bool defaultValue = false}) {
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final int v = value ?? (defaultValue ? 1 : 0);
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if (v == 0) {
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return false;
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} else {
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return true;
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}
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}
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Map<String, dynamic> mapRemoteToFaceDB(Face face) {
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return {
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faceIDColumn: face.faceID,
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fileIDColumn: face.fileID,
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faceDetectionColumn: json.encode(face.detection.toJson()),
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faceEmbeddingBlob: EVector(
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values: face.embedding,
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).writeToBuffer(),
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faceScore: face.score,
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faceBlur: face.blur,
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isSideways: face.detection.faceIsSideways() ? 1 : 0,
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mlVersionColumn: faceMlVersion,
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imageWidth: face.fileInfo?.imageWidth ?? 0,
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imageHeight: face.fileInfo?.imageHeight ?? 0,
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};
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}
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Face mapRowToFace(Map<String, dynamic> row) {
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return Face(
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row[faceIDColumn] as String,
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row[fileIDColumn] as int,
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EVector.fromBuffer(row[faceEmbeddingBlob] as List<int>).values,
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row[faceScore] as double,
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Detection.fromJson(json.decode(row[faceDetectionColumn] as String)),
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row[faceBlur] as double,
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fileInfo: FileInfo(
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imageWidth: row[imageWidth] as int,
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imageHeight: row[imageHeight] as int,
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),
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);
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}
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