84 lines
2.5 KiB
Dart
84 lines
2.5 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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