116 lines
3.5 KiB
Dart
116 lines
3.5 KiB
Dart
// import 'dart:math';
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// 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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// /// Classifier
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// class CocoSSDClassifier extends Classifier {
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// static final _logger = Logger("CocoSSDClassifier");
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// static const double threshold = 0.4;
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// @override
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// String get modelPath => "models/cocossd/model.tflite";
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// @override
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// String get labelPath => "assets/models/cocossd/labels.txt";
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// @override
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// int get inputSize => 300;
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// @override
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// Logger get logger => _logger;
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// static const int numResults = 10;
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// CocoSSDClassifier({
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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 outputLocations = TensorBufferFloat(outputShapes[0]);
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// final outputClasses = TensorBufferFloat(outputShapes[1]);
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// final outputScores = TensorBufferFloat(outputShapes[2]);
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// final numLocations = TensorBufferFloat(outputShapes[3]);
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// // Inputs object for runForMultipleInputs
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// // Use [TensorImage.buffer] or [TensorBuffer.buffer] to pass by reference
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// final inputs = [inputImage.buffer];
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// // Outputs map
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// final outputs = {
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// 0: outputLocations.buffer,
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// 1: outputClasses.buffer,
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// 2: outputScores.buffer,
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// 3: numLocations.buffer,
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// };
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// final inferenceTimeStart = DateTime.now().millisecondsSinceEpoch;
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// // run inference
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// interpreter.runForMultipleInputs(inputs, outputs);
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// final inferenceTimeElapsed =
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// DateTime.now().millisecondsSinceEpoch - inferenceTimeStart;
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// // Maximum number of results to show
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// final resultsCount = min(numResults, numLocations.getIntValue(0));
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// // Using labelOffset = 1 as ??? at index 0
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// const labelOffset = 1;
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// final recognitions = <Recognition>[];
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// for (int i = 0; i < resultsCount; i++) {
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// // Prediction score
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// final score = outputScores.getDoubleValue(i);
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// // Label string
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// final labelIndex = outputClasses.getIntValue(i) + labelOffset;
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// final label = labels.elementAt(labelIndex);
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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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