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[mob] Better organization of face detection code.

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This commit is contained in:
laurenspriem 2024-04-11 11:42:31 +05:30
parent aad0a5a1d4
commit c902f24e63
8 changed files with 38 additions and 87 deletions
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3
mobile/lib/services/machine_learning/face_ml/face_detection/face_detection_exceptions.dart Normal file
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@ -0,0 +1,3 @@
class YOLOFaceInterpreterInitializationException implements Exception {}
class YOLOFaceInterpreterRunException implements Exception {}

40
mobile/lib/services/machine_learning/face_ml/face_detection/yolov5face/onnx_face_detection.dart → mobile/lib/services/machine_learning/face_ml/face_detection/face_detection_service.dart
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@ -11,9 +11,9 @@ import 'package:logging/logging.dart';
import 'package:onnxruntime/onnxruntime.dart';
import "package:photos/face/model/dimension.dart";
import 'package:photos/services/machine_learning/face_ml/face_detection/detection.dart';
import 'package:photos/services/machine_learning/face_ml/face_detection/face_detection_exceptions.dart';
import 'package:photos/services/machine_learning/face_ml/face_detection/naive_non_max_suppression.dart';
import 'package:photos/services/machine_learning/face_ml/face_detection/yolov5face/yolo_face_detection_exceptions.dart';
import 'package:photos/services/machine_learning/face_ml/face_detection/yolov5face/yolo_filter_extract_detections.dart';
import 'package:photos/services/machine_learning/face_ml/face_detection/yolo_filter_extract_detections.dart';
import "package:photos/services/remote_assets_service.dart";
import "package:photos/utils/image_ml_isolate.dart";
import "package:photos/utils/image_ml_util.dart";
@ -21,22 +21,26 @@ import "package:synchronized/synchronized.dart";
enum FaceDetectionOperation { yoloInferenceAndPostProcessing }
class YoloOnnxFaceDetection {
/// This class is responsible for running the face detection model (YOLOv5Face) on ONNX runtime, and can be accessed through the singleton instance [FaceDetectionService.instance].
class FaceDetectionService {
static final _logger = Logger('YOLOFaceDetectionService');
final _computer = Computer.shared();
int sessionAddress = 0;
static const kModelBucketEndpoint = "https://models.ente.io/";
static const kRemoteBucketModelPath = "yolov5s_face_640_640_dynamic.onnx";
static const String kModelBucketEndpoint = "https://models.ente.io/";
static const String kRemoteBucketModelPath =
"yolov5s_face_640_640_dynamic.onnx";
// static const kRemoteBucketModelPath = "yolov5n_face_640_640.onnx";
static const modelRemotePath = kModelBucketEndpoint + kRemoteBucketModelPath;
static const String modelRemotePath =
kModelBucketEndpoint + kRemoteBucketModelPath;
static const kInputWidth = 640;
static const kInputHeight = 640;
static const kIouThreshold = 0.4;
static const kMinScoreSigmoidThreshold = 0.7;
static const int kInputWidth = 640;
static const int kInputHeight = 640;
static const double kIouThreshold = 0.4;
static const double kMinScoreSigmoidThreshold = 0.7;
static const int kNumKeypoints = 5;
bool isInitialized = false;
@ -55,7 +59,7 @@ class YoloOnnxFaceDetection {
bool isRunning = false;
// singleton pattern
YoloOnnxFaceDetection._privateConstructor();
FaceDetectionService._privateConstructor();
/// Use this instance to access the FaceDetection service. Make sure to call `init()` before using it.
/// e.g. `await FaceDetection.instance.init();`
@ -63,9 +67,9 @@ class YoloOnnxFaceDetection {
/// Then you can use `predict()` to get the bounding boxes of the faces, so `FaceDetection.instance.predict(imageData)`
///
/// config options: yoloV5FaceN //
static final instance = YoloOnnxFaceDetection._privateConstructor();
static final instance = FaceDetectionService._privateConstructor();
factory YoloOnnxFaceDetection() => instance;
factory FaceDetectionService() => instance;
/// Check if the interpreter is initialized, if not initialize it with `loadModel()`
Future<void> init() async {
@ -178,7 +182,7 @@ class YoloOnnxFaceDetection {
dev.log(
'[YOLOFaceDetectionService] Error while running inference: $e \n $s',
);
throw YOLOInterpreterRunException();
throw YOLOFaceInterpreterRunException();
}
stopwatchInterpreter.stop();
dev.log(
@ -297,7 +301,7 @@ class YoloOnnxFaceDetection {
// runOptions.release();
} catch (e, s) {
_logger.severe('Error while running inference: $e \n $s');
throw YOLOInterpreterRunException();
throw YOLOFaceInterpreterRunException();
}
stopwatchInterpreter.stop();
_logger.info(
@ -367,7 +371,7 @@ class YoloOnnxFaceDetection {
// runOptions.release();
} catch (e, s) {
_logger.severe('Error while running inference: $e \n $s');
throw YOLOInterpreterRunException();
throw YOLOFaceInterpreterRunException();
}
stopwatchInterpreter.stop();
_logger.info(
@ -585,7 +589,7 @@ class YoloOnnxFaceDetection {
runOptions.release();
} catch (e, s) {
_logger.severe('Error while running inference: $e \n $s');
throw YOLOInterpreterRunException();
throw YOLOFaceInterpreterRunException();
}
stopwatchInterpreter.stop();
_logger.info(
@ -770,7 +774,7 @@ class YoloOnnxFaceDetection {
dev.log(
'[YOLOFaceDetectionService] Error while running inference: $e \n $s',
);
throw YOLOInterpreterRunException();
throw YOLOFaceInterpreterRunException();
}
stopwatchInterpreter.stop();
dev.log(

0
mobile/lib/services/machine_learning/face_ml/face_detection/yolov5face/yolo_filter_extract_detections.dart → mobile/lib/services/machine_learning/face_ml/face_detection/yolo_filter_extract_detections.dart
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3
mobile/lib/services/machine_learning/face_ml/face_detection/yolov5face/yolo_face_detection_exceptions.dart
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@ -1,3 +0,0 @@
class YOLOInterpreterInitializationException implements Exception {}
class YOLOInterpreterRunException implements Exception {}

31
mobile/lib/services/machine_learning/face_ml/face_detection/yolov5face/yolo_face_detection_options.dart
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@ -1,31 +0,0 @@
import 'dart:math' as math show log;
class FaceDetectionOptionsYOLO {
final double minScoreSigmoidThreshold;
final double iouThreshold;
final int inputWidth;
final int inputHeight;
final int numCoords;
final int numKeypoints;
final int numValuesPerKeypoint;
final int maxNumFaces;
final double scoreClippingThresh;
final double inverseSigmoidMinScoreThreshold;
final bool useSigmoidScore;
final bool flipVertically;
FaceDetectionOptionsYOLO({
required this.minScoreSigmoidThreshold,
required this.iouThreshold,
required this.inputWidth,
required this.inputHeight,
this.numCoords = 14,
this.numKeypoints = 5,
this.numValuesPerKeypoint = 2,
this.maxNumFaces = 100,
this.scoreClippingThresh = 100.0,
this.useSigmoidScore = true,
this.flipVertically = false,
}) : inverseSigmoidMinScoreThreshold =
math.log(minScoreSigmoidThreshold / (1 - minScoreSigmoidThreshold));
}

22
mobile/lib/services/machine_learning/face_ml/face_detection/yolov5face/yolo_model_config.dart
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@ -1,22 +0,0 @@
import 'package:photos/services/machine_learning/face_ml/face_detection/yolov5face/yolo_face_detection_options.dart';
import 'package:photos/services/machine_learning/face_ml/model_file.dart';
class YOLOModelConfig {
final String modelPath;
final FaceDetectionOptionsYOLO faceOptions;
YOLOModelConfig({
required this.modelPath,
required this.faceOptions,
});
}
final YOLOModelConfig yoloV5FaceS640x640DynamicBatchonnx = YOLOModelConfig(
modelPath: ModelFile.yoloV5FaceS640x640DynamicBatchonnx,
faceOptions: FaceDetectionOptionsYOLO(
minScoreSigmoidThreshold: 0.8,
iouThreshold: 0.4,
inputWidth: 640,
inputHeight: 640,
),
);

4
mobile/lib/services/machine_learning/face_ml/face_filtering/face_filtering_constants.dart
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@ -1,4 +1,4 @@
import 'package:photos/services/machine_learning/face_ml/face_detection/yolov5face/onnx_face_detection.dart';
import 'package:photos/services/machine_learning/face_ml/face_detection/face_detection_service.dart';
/// Blur detection threshold
const kLaplacianThreshold = 15;
@ -10,4 +10,4 @@ const kLapacianDefault = 10000.0;
const kMinHighQualityFaceScore = 0.80;
/// The minimum score for a face to be detected, regardless of quality. Use [kMinHighQualityFaceScore] for high quality faces.
const kMinFaceDetectionScore = YoloOnnxFaceDetection.kMinScoreSigmoidThreshold;
const kMinFaceDetectionScore = FaceDetectionService.kMinScoreSigmoidThreshold;

22
mobile/lib/services/machine_learning/face_ml/face_ml_service.dart
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@ -28,8 +28,8 @@ import "package:photos/models/file/file_type.dart";
import "package:photos/models/ml/ml_versions.dart";
import 'package:photos/services/machine_learning/face_ml/face_clustering/linear_clustering_service.dart';
import 'package:photos/services/machine_learning/face_ml/face_detection/detection.dart';
import 'package:photos/services/machine_learning/face_ml/face_detection/yolov5face/onnx_face_detection.dart';
import 'package:photos/services/machine_learning/face_ml/face_detection/yolov5face/yolo_face_detection_exceptions.dart';
import 'package:photos/services/machine_learning/face_ml/face_detection/face_detection_exceptions.dart';
import 'package:photos/services/machine_learning/face_ml/face_detection/face_detection_service.dart';
import 'package:photos/services/machine_learning/face_ml/face_embedding/face_embedding_exceptions.dart';
import 'package:photos/services/machine_learning/face_ml/face_embedding/face_embedding_service.dart';
import 'package:photos/services/machine_learning/face_ml/face_filtering/face_filtering_constants.dart';
@ -90,7 +90,7 @@ class FaceMlService {
_logger.info("init called");
await _computer.compute(initOrtEnv);
try {
await YoloOnnxFaceDetection.instance.init();
await FaceDetectionService.instance.init();
} catch (e, s) {
_logger.severe("Could not initialize yolo onnx", e, s);
}
@ -142,7 +142,7 @@ class FaceMlService {
return;
}
try {
await YoloOnnxFaceDetection.instance.release();
await FaceDetectionService.instance.release();
} catch (e, s) {
_logger.severe("Could not dispose yolo onnx", e, s);
}
@ -894,7 +894,7 @@ class FaceMlService {
"enteFileID": enteFile.uploadedFileID ?? -1,
"filePath": filePath,
"faceDetectionAddress":
YoloOnnxFaceDetection.instance.sessionAddress,
FaceDetectionService.instance.sessionAddress,
"faceEmbeddingAddress":
FaceEmbeddingService.instance.sessionAddress,
}
@ -1043,7 +1043,7 @@ class FaceMlService {
try {
// Get the bounding boxes of the faces
final (List<FaceDetectionRelative> faces, dataSize) =
await YoloOnnxFaceDetection.instance.predictInComputer(imagePath);
await FaceDetectionService.instance.predictInComputer(imagePath);
// Add detected faces to the resultBuilder
if (resultBuilder != null) {
@ -1051,9 +1051,9 @@ class FaceMlService {
}
return faces;
} on YOLOInterpreterInitializationException {
} on YOLOFaceInterpreterInitializationException {
throw CouldNotInitializeFaceDetector();
} on YOLOInterpreterRunException {
} on YOLOFaceInterpreterRunException {
throw CouldNotRunFaceDetector();
} catch (e) {
_logger.severe('Face detection failed: $e');
@ -1077,7 +1077,7 @@ class FaceMlService {
try {
// Get the bounding boxes of the faces
final (List<FaceDetectionRelative> faces, dataSize) =
await YoloOnnxFaceDetection.predictSync(
await FaceDetectionService.predictSync(
image,
imageByteData,
interpreterAddress,
@ -1089,9 +1089,9 @@ class FaceMlService {
}
return faces;
} on YOLOInterpreterInitializationException {
} on YOLOFaceInterpreterInitializationException {
throw CouldNotInitializeFaceDetector();
} on YOLOInterpreterRunException {
} on YOLOFaceInterpreterRunException {
throw CouldNotRunFaceDetector();
} catch (e) {
dev.log('[SEVERE] Face detection failed: $e');