84 lines
2.7 KiB
Dart
84 lines
2.7 KiB
Dart
// import 'package:image/image.dart' as image_lib;
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// import "package:logging/logging.dart";
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// import 'package:photos/services/object_detection/models/predictions.dart';
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// import 'package:photos/services/object_detection/models/recognition.dart';
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// import "package:photos/services/object_detection/models/stats.dart";
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// import "package:photos/services/object_detection/tflite/classifier.dart";
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// import "package:tflite_flutter/tflite_flutter.dart";
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// import "package:tflite_flutter_helper/tflite_flutter_helper.dart";
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// // Source: https://tfhub.dev/tensorflow/lite-model/mobilenet_v1_1.0_224/1/default/1
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// class MobileNetClassifier extends Classifier {
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// static final _logger = Logger("MobileNetClassifier");
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// static const double threshold = 0.4;
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// @override
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// String get modelPath => "models/mobilenet/mobilenet_v1_1.0_224_quant.tflite";
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// @override
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// String get labelPath =>
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// "assets/models/mobilenet/labels_mobilenet_quant_v1_224.txt";
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// @override
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// int get inputSize => 224;
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// @override
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// Logger get logger => _logger;
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// MobileNetClassifier({
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// Interpreter? interpreter,
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// List<String>? labels,
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// }) {
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// loadModel(interpreter);
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// loadLabels(labels);
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// }
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// @override
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// Predictions? predict(image_lib.Image image) {
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// final predictStartTime = DateTime.now().millisecondsSinceEpoch;
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// final preProcessStart = DateTime.now().millisecondsSinceEpoch;
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// // Create TensorImage from image
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// TensorImage inputImage = TensorImage.fromImage(image);
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// // Pre-process TensorImage
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// inputImage = getProcessedImage(inputImage);
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// final preProcessElapsedTime =
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// DateTime.now().millisecondsSinceEpoch - preProcessStart;
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// // TensorBuffers for output tensors
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// final output = TensorBufferUint8(outputShapes[0]);
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// final inferenceTimeStart = DateTime.now().millisecondsSinceEpoch;
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// // run inference
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// interpreter.run(inputImage.buffer, output.buffer);
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// final inferenceTimeElapsed =
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// DateTime.now().millisecondsSinceEpoch - inferenceTimeStart;
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// final recognitions = <Recognition>[];
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// for (int i = 0; i < labels.length; i++) {
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// final score = output.getDoubleValue(i) / 255;
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// final label = labels.elementAt(i);
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// if (score >= threshold) {
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// recognitions.add(
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// Recognition(i, label, score),
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// );
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// }
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// }
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// final predictElapsedTime =
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// DateTime.now().millisecondsSinceEpoch - predictStartTime;
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// return Predictions(
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// recognitions,
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// Stats(
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// predictElapsedTime,
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// predictElapsedTime,
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// inferenceTimeElapsed,
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// preProcessElapsedTime,
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// ),
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// );
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// }
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// }
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