Add MobileNetv1

lib/services/object_detection/object_detection_service.dart

@@ -4,18 +4,18 @@ import "dart:typed_data";
 import "package:logging/logging.dart";
 import "package:photos/services/object_detection/models/predictions.dart";
 import 'package:photos/services/object_detection/models/recognition.dart';
-import "package:photos/services/object_detection/tflite/classifier.dart";
+import 'package:photos/services/object_detection/tflite/cocossd_classifier.dart';
+import "package:photos/services/object_detection/tflite/mobilenet_classifier.dart";
 import "package:photos/services/object_detection/utils/isolate_utils.dart";
 
 class ObjectDetectionService {
-  static const scoreThreshold = 0.6;
+  static const scoreThreshold = 0.5;
 
   final _logger = Logger("ObjectDetectionService");
 
-  /// Instance of [ObjectClassifier]
-  late ObjectClassifier _classifier;
+  late CocoSSDClassifier _objectClassifier;
+  late MobileNetClassifier _mobileNetClassifier;
 
-  /// Instance of [IsolateUtils]
   late IsolateUtils _isolateUtils;
 
   ObjectDetectionService._privateConstructor();
@@ -23,7 +23,8 @@ class ObjectDetectionService {
   Future<void> init() async {
     _isolateUtils = IsolateUtils();
     await _isolateUtils.start();
-    _classifier = ObjectClassifier();
+    _objectClassifier = CocoSSDClassifier();
+    _mobileNetClassifier = MobileNetClassifier();
   }
 
   static ObjectDetectionService instance =
@@ -31,18 +32,11 @@ class ObjectDetectionService {
 
   Future<List<String>> predict(Uint8List bytes) async {
     try {
-      final isolateData = IsolateData(
-        bytes,
-        _classifier.interpreter.address,
-        _classifier.labels,
-      );
-      final predictions = await _inference(isolateData);
-      final Set<String> results = {};
-      for (final Recognition result in predictions.recognitions) {
-        if (result.score > scoreThreshold) {
-          results.add(result.label);
-        }
-      }
+      final results = <String>{};
+      final objectResults = await _getObjects(bytes);
+      results.addAll(objectResults);
+      final mobileNetResults = await _getMobileNetResults(bytes);
+      results.addAll(mobileNetResults);
       return results.toList();
     } catch (e, s) {
       _logger.severe(e, s);
@@ -50,6 +44,40 @@ class ObjectDetectionService {
     }
   }
 
+  Future<List<String>> _getObjects(Uint8List bytes) async {
+    final isolateData = IsolateData(
+      bytes,
+      _objectClassifier.interpreter.address,
+      _objectClassifier.labels,
+      ClassifierType.cocossd,
+    );
+    final predictions = await _inference(isolateData);
+    final Set<String> results = {};
+    for (final Recognition result in predictions.recognitions) {
+      if (result.score > scoreThreshold) {
+        results.add(result.label);
+      }
+    }
+    return results.toList();
+  }
+
+  Future<List<String>> _getMobileNetResults(Uint8List bytes) async {
+    final isolateData = IsolateData(
+      bytes,
+      _mobileNetClassifier.interpreter.address,
+      _mobileNetClassifier.labels,
+      ClassifierType.mobilenet,
+    );
+    final predictions = await _inference(isolateData);
+    final Set<String> results = {};
+    for (final Recognition result in predictions.recognitions) {
+      if (result.score > scoreThreshold) {
+        results.add(result.label);
+      }
+    }
+    return results.toList();
+  }
+
   /// Runs inference in another isolate
   Future<Predictions> _inference(IsolateData isolateData) async {
     final responsePort = ReceivePort();

lib/services/object_detection/tflite/classifier.dart

@@ -1,179 +1,6 @@
-import 'dart:math';
-
 import 'package:image/image.dart' as imageLib;
-import "package:logging/logging.dart";
-import 'package:photos/services/object_detection/models/predictions.dart';
-import 'package:photos/services/object_detection/models/recognition.dart';
-import "package:photos/services/object_detection/models/stats.dart";
-import "package:tflite_flutter/tflite_flutter.dart";
-import "package:tflite_flutter_helper/tflite_flutter_helper.dart";
+import "package:photos/services/object_detection/models/predictions.dart";
 
-/// Classifier
-class ObjectClassifier {
-  final _logger = Logger("Classifier");
-
-  /// Instance of Interpreter
-  late Interpreter _interpreter;
-
-  /// Labels file loaded as list
-  late List<String> _labels;
-
-  /// Input size of image (height = width = 300)
-  static const int inputSize = 300;
-
-  /// Result score threshold
-  static const double threshold = 0.5;
-
-  static const String modelFileName = "detect.tflite";
-  static const String labelFileName = "labelmap.txt";
-
-  /// [ImageProcessor] used to pre-process the image
-  ImageProcessor? imageProcessor;
-
-  /// Padding the image to transform into square
-  late int padSize;
-
-  /// Shapes of output tensors
-  late List<List<int>> _outputShapes;
-
-  /// Types of output tensors
-  late List<TfLiteType> _outputTypes;
-
-  /// Number of results to show
-  static const int numResults = 10;
-
-  ObjectClassifier({
-    Interpreter? interpreter,
-    List<String>? labels,
-  }) {
-    loadModel(interpreter);
-    loadLabels(labels);
-  }
-
-  /// Loads interpreter from asset
-  void loadModel(Interpreter? interpreter) async {
-    try {
-      _interpreter = interpreter ??
-          await Interpreter.fromAsset(
-            "models/" + modelFileName,
-            options: InterpreterOptions()..threads = 4,
-          );
-      final outputTensors = _interpreter.getOutputTensors();
-      _outputShapes = [];
-      _outputTypes = [];
-      outputTensors.forEach((tensor) {
-        _outputShapes.add(tensor.shape);
-        _outputTypes.add(tensor.type);
-      });
-      _logger.info("Interpreter initialized");
-    } catch (e, s) {
-      _logger.severe("Error while creating interpreter", e, s);
-    }
-  }
-
-  /// Loads labels from assets
-  void loadLabels(List<String>? labels) async {
-    try {
-      _labels =
-          labels ?? await FileUtil.loadLabels("assets/models/" + labelFileName);
-      _logger.info("Labels initialized");
-    } catch (e, s) {
-      _logger.severe("Error while loading labels", e, s);
-    }
-  }
-
-  /// Pre-process the image
-  TensorImage _getProcessedImage(TensorImage inputImage) {
-    padSize = max(inputImage.height, inputImage.width);
-    imageProcessor ??= ImageProcessorBuilder()
-        .add(ResizeWithCropOrPadOp(padSize, padSize))
-        .add(ResizeOp(inputSize, inputSize, ResizeMethod.BILINEAR))
-        .build();
-    inputImage = imageProcessor!.process(inputImage);
-    return inputImage;
-  }
-
-  /// Runs object detection on the input image
-  Predictions? predict(imageLib.Image image) {
-    final predictStartTime = DateTime.now().millisecondsSinceEpoch;
-
-    final preProcessStart = DateTime.now().millisecondsSinceEpoch;
-
-    // Create TensorImage from image
-    TensorImage inputImage = TensorImage.fromImage(image);
-
-    // Pre-process TensorImage
-    inputImage = _getProcessedImage(inputImage);
-
-    final preProcessElapsedTime =
-        DateTime.now().millisecondsSinceEpoch - preProcessStart;
-
-    // TensorBuffers for output tensors
-    final outputLocations = TensorBufferFloat(_outputShapes[0]);
-    final outputClasses = TensorBufferFloat(_outputShapes[1]);
-    final outputScores = TensorBufferFloat(_outputShapes[2]);
-    final numLocations = TensorBufferFloat(_outputShapes[3]);
-
-    // Inputs object for runForMultipleInputs
-    // Use [TensorImage.buffer] or [TensorBuffer.buffer] to pass by reference
-    final inputs = [inputImage.buffer];
-
-    // Outputs map
-    final outputs = {
-      0: outputLocations.buffer,
-      1: outputClasses.buffer,
-      2: outputScores.buffer,
-      3: numLocations.buffer,
-    };
-
-    final inferenceTimeStart = DateTime.now().millisecondsSinceEpoch;
-
-    // run inference
-    _interpreter.runForMultipleInputs(inputs, outputs);
-
-    final inferenceTimeElapsed =
-        DateTime.now().millisecondsSinceEpoch - inferenceTimeStart;
-
-    // Maximum number of results to show
-    final resultsCount = min(numResults, numLocations.getIntValue(0));
-
-    // Using labelOffset = 1 as ??? at index 0
-    const labelOffset = 1;
-
-    final recognitions = <Recognition>[];
-
-    for (int i = 0; i < resultsCount; i++) {
-      // Prediction score
-      final score = outputScores.getDoubleValue(i);
-
-      // Label string
-      final labelIndex = outputClasses.getIntValue(i) + labelOffset;
-      final label = _labels.elementAt(labelIndex);
-
-      if (score > threshold) {
-        recognitions.add(
-          Recognition(i, label, score),
-        );
-      }
-    }
-
-    final predictElapsedTime =
-        DateTime.now().millisecondsSinceEpoch - predictStartTime;
-    _logger.info(recognitions);
-    return Predictions(
-      recognitions,
-      Stats(
-        predictElapsedTime,
-        predictElapsedTime,
-        inferenceTimeElapsed,
-        preProcessElapsedTime,
-      ),
-    );
-  }
-
-  /// Gets the interpreter instance
-  Interpreter get interpreter => _interpreter;
-
-  /// Gets the loaded labels
-  List<String> get labels => _labels;
+abstract class Classifier {
+  Predictions? predict(imageLib.Image image);
 }

lib/services/object_detection/tflite/cocossd_classifier.dart

@@ -0,0 +1,180 @@
+import 'dart:math';
+
+import 'package:image/image.dart' as imageLib;
+import "package:logging/logging.dart";
+import 'package:photos/services/object_detection/models/predictions.dart';
+import 'package:photos/services/object_detection/models/recognition.dart';
+import "package:photos/services/object_detection/models/stats.dart";
+import "package:photos/services/object_detection/tflite/classifier.dart";
+import "package:tflite_flutter/tflite_flutter.dart";
+import "package:tflite_flutter_helper/tflite_flutter_helper.dart";
+
+/// Classifier
+class CocoSSDClassifier extends Classifier {
+  final _logger = Logger("Classifier");
+
+  /// Instance of Interpreter
+  late Interpreter _interpreter;
+
+  /// Labels file loaded as list
+  late List<String> _labels;
+
+  /// Input size of image (height = width = 300)
+  static const int inputSize = 300;
+
+  /// Result score threshold
+  static const double threshold = 0.5;
+
+  static const String modelFileName = "model.tflite";
+  static const String labelFileName = "labels.txt";
+
+  /// [ImageProcessor] used to pre-process the image
+  ImageProcessor? imageProcessor;
+
+  /// Padding the image to transform into square
+  late int padSize;
+
+  /// Shapes of output tensors
+  late List<List<int>> _outputShapes;
+
+  /// Types of output tensors
+  late List<TfLiteType> _outputTypes;
+
+  /// Number of results to show
+  static const int numResults = 10;
+
+  CocoSSDClassifier({
+    Interpreter? interpreter,
+    List<String>? labels,
+  }) {
+    loadModel(interpreter);
+    loadLabels(labels);
+  }
+
+  /// Loads interpreter from asset
+  void loadModel(Interpreter? interpreter) async {
+    try {
+      _interpreter = interpreter ??
+          await Interpreter.fromAsset(
+            "models/cocossd/" + modelFileName,
+            options: InterpreterOptions()..threads = 4,
+          );
+      final outputTensors = _interpreter.getOutputTensors();
+      _outputShapes = [];
+      _outputTypes = [];
+      outputTensors.forEach((tensor) {
+        _outputShapes.add(tensor.shape);
+        _outputTypes.add(tensor.type);
+      });
+      _logger.info("Interpreter initialized");
+    } catch (e, s) {
+      _logger.severe("Error while creating interpreter", e, s);
+    }
+  }
+
+  /// Loads labels from assets
+  void loadLabels(List<String>? labels) async {
+    try {
+      _labels = labels ??
+          await FileUtil.loadLabels("assets/models/cocossd/" + labelFileName);
+      _logger.info("Labels initialized");
+    } catch (e, s) {
+      _logger.severe("Error while loading labels", e, s);
+    }
+  }
+
+  /// Pre-process the image
+  TensorImage _getProcessedImage(TensorImage inputImage) {
+    padSize = max(inputImage.height, inputImage.width);
+    imageProcessor ??= ImageProcessorBuilder()
+        .add(ResizeWithCropOrPadOp(padSize, padSize))
+        .add(ResizeOp(inputSize, inputSize, ResizeMethod.BILINEAR))
+        .build();
+    inputImage = imageProcessor!.process(inputImage);
+    return inputImage;
+  }
+
+  /// Runs object detection on the input image
+  Predictions? predict(imageLib.Image image) {
+    final predictStartTime = DateTime.now().millisecondsSinceEpoch;
+
+    final preProcessStart = DateTime.now().millisecondsSinceEpoch;
+
+    // Create TensorImage from image
+    TensorImage inputImage = TensorImage.fromImage(image);
+
+    // Pre-process TensorImage
+    inputImage = _getProcessedImage(inputImage);
+
+    final preProcessElapsedTime =
+        DateTime.now().millisecondsSinceEpoch - preProcessStart;
+
+    // TensorBuffers for output tensors
+    final outputLocations = TensorBufferFloat(_outputShapes[0]);
+    final outputClasses = TensorBufferFloat(_outputShapes[1]);
+    final outputScores = TensorBufferFloat(_outputShapes[2]);
+    final numLocations = TensorBufferFloat(_outputShapes[3]);
+
+    // Inputs object for runForMultipleInputs
+    // Use [TensorImage.buffer] or [TensorBuffer.buffer] to pass by reference
+    final inputs = [inputImage.buffer];
+
+    // Outputs map
+    final outputs = {
+      0: outputLocations.buffer,
+      1: outputClasses.buffer,
+      2: outputScores.buffer,
+      3: numLocations.buffer,
+    };
+
+    final inferenceTimeStart = DateTime.now().millisecondsSinceEpoch;
+
+    // run inference
+    _interpreter.runForMultipleInputs(inputs, outputs);
+
+    final inferenceTimeElapsed =
+        DateTime.now().millisecondsSinceEpoch - inferenceTimeStart;
+
+    // Maximum number of results to show
+    final resultsCount = min(numResults, numLocations.getIntValue(0));
+
+    // Using labelOffset = 1 as ??? at index 0
+    const labelOffset = 1;
+
+    final recognitions = <Recognition>[];
+
+    for (int i = 0; i < resultsCount; i++) {
+      // Prediction score
+      final score = outputScores.getDoubleValue(i);
+
+      // Label string
+      final labelIndex = outputClasses.getIntValue(i) + labelOffset;
+      final label = _labels.elementAt(labelIndex);
+
+      if (score > threshold) {
+        recognitions.add(
+          Recognition(i, label, score),
+        );
+      }
+    }
+
+    final predictElapsedTime =
+        DateTime.now().millisecondsSinceEpoch - predictStartTime;
+    _logger.info(recognitions);
+    return Predictions(
+      recognitions,
+      Stats(
+        predictElapsedTime,
+        predictElapsedTime,
+        inferenceTimeElapsed,
+        preProcessElapsedTime,
+      ),
+    );
+  }
+
+  /// Gets the interpreter instance
+  Interpreter get interpreter => _interpreter;
+
+  /// Gets the loaded labels
+  List<String> get labels => _labels;
+}

lib/services/object_detection/tflite/mobilenet_classifier.dart

@@ -0,0 +1,151 @@
+import 'dart:math';
+
+import 'package:image/image.dart' as imageLib;
+import "package:logging/logging.dart";
+import 'package:photos/services/object_detection/models/predictions.dart';
+import 'package:photos/services/object_detection/models/recognition.dart';
+import "package:photos/services/object_detection/models/stats.dart";
+import "package:photos/services/object_detection/tflite/classifier.dart";
+import "package:tflite_flutter/tflite_flutter.dart";
+import "package:tflite_flutter_helper/tflite_flutter_helper.dart";
+
+class MobileNetClassifier extends Classifier {
+  final _logger = Logger("MobileNetClassifier");
+
+  /// Instance of Interpreter
+  late Interpreter _interpreter;
+
+  /// Labels file loaded as list
+  late List<String> _labels;
+
+  /// Input size of image (height = width = 300)
+  static const int inputSize = 224;
+
+  /// Result score threshold
+  static const double threshold = 0.5;
+
+  static const String modelFileName = "mobilenet_v1_1.0_224_quant.tflite";
+  static const String labelFileName = "labels_mobilenet_quant_v1_224.txt";
+
+  /// [ImageProcessor] used to pre-process the image
+  ImageProcessor? imageProcessor;
+
+  /// Padding the image to transform into square
+  late int padSize;
+
+  /// Shapes of output tensors
+  late List<List<int>> _outputShapes;
+
+  /// Types of output tensors
+  late List<TfLiteType> _outputTypes;
+
+  /// Number of results to show
+  static const int numResults = 10;
+
+  MobileNetClassifier({
+    Interpreter? interpreter,
+    List<String>? labels,
+  }) {
+    loadModel(interpreter);
+    loadLabels(labels);
+  }
+
+  /// Loads interpreter from asset
+  void loadModel(Interpreter? interpreter) async {
+    try {
+      _interpreter = interpreter ??
+          await Interpreter.fromAsset(
+            "models/mobilenet/" + modelFileName,
+            options: InterpreterOptions()..threads = 4,
+          );
+      final outputTensors = _interpreter.getOutputTensors();
+      _outputShapes = [];
+      _outputTypes = [];
+      outputTensors.forEach((tensor) {
+        _outputShapes.add(tensor.shape);
+        _outputTypes.add(tensor.type);
+      });
+      _logger.info("Interpreter initialized");
+    } catch (e, s) {
+      _logger.severe("Error while creating interpreter", e, s);
+    }
+  }
+
+  /// Loads labels from assets
+  void loadLabels(List<String>? labels) async {
+    try {
+      _labels = labels ??
+          await FileUtil.loadLabels("assets/models/mobilenet/" + labelFileName);
+      _logger.info("Labels initialized");
+    } catch (e, s) {
+      _logger.severe("Error while loading labels", e, s);
+    }
+  }
+
+  /// Pre-process the image
+  TensorImage _getProcessedImage(TensorImage inputImage) {
+    padSize = max(inputImage.height, inputImage.width);
+    imageProcessor ??= ImageProcessorBuilder()
+        // .add(ResizeWithCropOrPadOp(padSize, padSize))
+        .add(ResizeOp(inputSize, inputSize, ResizeMethod.BILINEAR))
+        .build();
+    inputImage = imageProcessor!.process(inputImage);
+    return inputImage;
+  }
+
+  /// Runs object detection on the input image
+  Predictions? predict(imageLib.Image image) {
+    final predictStartTime = DateTime.now().millisecondsSinceEpoch;
+
+    final preProcessStart = DateTime.now().millisecondsSinceEpoch;
+
+    // Create TensorImage from image
+    TensorImage inputImage = TensorImage.fromImage(image);
+
+    // Pre-process TensorImage
+    inputImage = _getProcessedImage(inputImage);
+
+    final preProcessElapsedTime =
+        DateTime.now().millisecondsSinceEpoch - preProcessStart;
+
+    // TensorBuffers for output tensors
+    final output = TensorBufferUint8(_outputShapes[0]);
+    final inferenceTimeStart = DateTime.now().millisecondsSinceEpoch;
+    // run inference
+    _interpreter.run(inputImage.buffer, output.buffer);
+
+    final inferenceTimeElapsed =
+        DateTime.now().millisecondsSinceEpoch - inferenceTimeStart;
+
+    final recognitions = <Recognition>[];
+    for (int i = 0; i < 1001; i++) {
+      final score = output.getDoubleValue(i) / 255;
+      if (score >= threshold) {
+        final label = _labels.elementAt(i);
+
+        recognitions.add(
+          Recognition(i, "#" + label, score),
+        );
+      }
+    }
+
+    final predictElapsedTime =
+        DateTime.now().millisecondsSinceEpoch - predictStartTime;
+    _logger.info(recognitions);
+    return Predictions(
+      recognitions,
+      Stats(
+        predictElapsedTime,
+        predictElapsedTime,
+        inferenceTimeElapsed,
+        preProcessElapsedTime,
+      ),
+    );
+  }
+
+  /// Gets the interpreter instance
+  Interpreter get interpreter => _interpreter;
+
+  /// Gets the loaded labels
+  List<String> get labels => _labels;
+}

lib/services/object_detection/utils/isolate_utils.dart

@@ -2,7 +2,8 @@ import 'dart:isolate';
 import "dart:typed_data";
 
 import 'package:image/image.dart' as imgLib;
-import "package:photos/services/object_detection/tflite/classifier.dart";
+import 'package:photos/services/object_detection/tflite/cocossd_classifier.dart';
+import "package:photos/services/object_detection/tflite/mobilenet_classifier.dart";
 import 'package:tflite_flutter/tflite_flutter.dart';
 
 /// Manages separate Isolate instance for inference
@@ -29,10 +30,17 @@ class IsolateUtils {
     sendPort.send(port.sendPort);
 
     await for (final IsolateData isolateData in port) {
-      final classifier = ObjectClassifier(
-        interpreter: Interpreter.fromAddress(isolateData.interpreterAddress),
-        labels: isolateData.labels,
-      );
+      final classifier = isolateData.type == ClassifierType.cocossd
+          ? CocoSSDClassifier(
+              interpreter:
+                  Interpreter.fromAddress(isolateData.interpreterAddress),
+              labels: isolateData.labels,
+            )
+          : MobileNetClassifier(
+              interpreter:
+                  Interpreter.fromAddress(isolateData.interpreterAddress),
+              labels: isolateData.labels,
+            );
       final image = imgLib.decodeImage(isolateData.input);
       final results = classifier.predict(image!);
       isolateData.responsePort.send(results);
@@ -45,11 +53,18 @@ class IsolateData {
   Uint8List input;
   int interpreterAddress;
   List<String> labels;
+  ClassifierType type;
   late SendPort responsePort;
 
   IsolateData(
     this.input,
     this.interpreterAddress,
     this.labels,
+    this.type,
   );
 }
+
+enum ClassifierType {
+  cocossd,
+  mobilenet,
+}

pubspec.yaml

@@ -165,7 +165,8 @@ flutter_native_splash:
 flutter:
   assets:
     - assets/
-    - assets/models/
+    - assets/models/cocossd/
+    - assets/models/mobilenet/
   fonts:
   - family: Inter
     fonts: