fixed classifier and added in a preliminary results view that shows what pokemon are currently being looked at.
This commit is contained in:
+65
-129
@@ -2,16 +2,16 @@ import 'dart:isolate';
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import 'package:camera/camera.dart';
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import 'package:flutter/material.dart';
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import 'package:tensordex_mobile/tflite/classifier.dart';
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import 'package:tensordex_mobile/tflite/ml_isolate.dart';
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import 'package:tflite_flutter/tflite_flutter.dart';
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import 'package:tensordex_mobile/utils/image_utils.dart';
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import '../tflite/classifier.dart';
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import '../utils/logger.dart';
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import '../utils/recognition.dart';
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import '../utils/stats.dart';
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import '../tflite/data/recognition.dart';
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import '../tflite/data/stats.dart';
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/// [CameraView] sends each frame for inference
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class CameraView extends StatefulWidget {
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/// [PokedexView] sends each frame for inference
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class PokedexView extends StatefulWidget {
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/// Callback to pass results after inference to [HomeView]
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final Function(List<Recognition> recognitions) resultsCallback;
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@@ -19,32 +19,26 @@ class CameraView extends StatefulWidget {
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final Function(Stats stats) statsCallback;
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/// Constructor
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const CameraView(
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const PokedexView(
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{Key? key, required this.resultsCallback, required this.statsCallback})
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: super(key: key);
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@override
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State<CameraView> createState() => _CameraViewState();
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State<PokedexView> createState() => _PokedexViewState();
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}
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class _CameraViewState extends State<CameraView> with WidgetsBindingObserver {
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/// List of available cameras
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class _PokedexViewState extends State<PokedexView> with WidgetsBindingObserver {
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late List<CameraDescription> cameras;
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/// Controller
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late CameraController cameraController;
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Interpreter? interp;
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late MLIsolate _mlIsolate;
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/// true when inference is ongoing
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bool predicting = false;
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bool _cameraInitialized = false;
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bool _classifierInitialized = false;
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late Classifier classy;
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// /// Instance of [Classifier]
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// Classifier classifier;
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//
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// /// Instance of [IsolateUtils]
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// IsolateUtils isolateUtils;
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late Interpreter interpreter;
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late Classifier classifier;
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@override
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void initState() {
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@@ -54,40 +48,21 @@ class _CameraViewState extends State<CameraView> with WidgetsBindingObserver {
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void initStateAsync() async {
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WidgetsBinding.instance.addObserver(this);
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// Spawn a new isolate
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// isolateUtils = IsolateUtils();
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// await isolateUtils.start();
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// Camera initialization
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_mlIsolate = MLIsolate();
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await _mlIsolate.start();
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initializeCamera();
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// final gpuDelegateV2 = GpuDelegateV2(
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// options: GpuDelegateOptionsV2(
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// isPrecisionLossAllowed: false,
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// inferencePreference: TfLiteGpuInferenceUsage.fastSingleAnswer,
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// inferencePriority1: TfLiteGpuInferencePriority.minLatency,
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// inferencePriority2: TfLiteGpuInferencePriority.auto,
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// inferencePriority3: TfLiteGpuInferencePriority.auto,
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// ));
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logger.e("CREATING THE INTERPRETOR");
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var interpreterOptions = InterpreterOptions();//..addDelegate(gpuDelegateV2);
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interp = await Interpreter.fromAsset('efficientnet_v2s.tflite',
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options: interpreterOptions);
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logger.e("CREATING THE INTERPRETOR");
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classy = Classifier(interpreter: interp);
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logger.i(interp?.getOutputTensors());
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// Create an instance of classifier to load model and labels
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// classifier = Classifier();
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// Initially predicting = false
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initializeModel();
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predicting = false;
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}
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void initializeModel() async {
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var interpreterOptions = InterpreterOptions()..threads = 8;
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interpreter = await Interpreter.fromAsset('efficientnet_v2s.tflite',
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options: interpreterOptions);
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classifier = Classifier(interpreter: interpreter);
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_classifierInitialized = true;
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}
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/// Initializes the camera by setting [cameraController]
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void initializeCamera() async {
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cameras = await availableCameras();
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@@ -97,101 +72,62 @@ class _CameraViewState extends State<CameraView> with WidgetsBindingObserver {
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CameraController(cameras[0], ResolutionPreset.low, enableAudio: false);
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cameraController.initialize().then((_) async {
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/// previewSize is size of each image frame captured by controller
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/// 352x288 on iOS, 240p (320x240) on Android with ResolutionPreset.low
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// Stream of image passed to [onLatestImageAvailable] callback
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await cameraController.startImageStream(onLatestImageAvailable);
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/// previewSize is size of each image frame captured by controller
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///
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/// 352x288 on iOS, 240p (320x240) on Android with ResolutionPreset.low
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// Size previewSize = cameraController.value.previewSize;
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//
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// /// previewSize is size of raw input image to the model
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// CameraViewSingleton.inputImageSize = previewSize;
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//
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// // the display width of image on screen is
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// // same as screenWidth while maintaining the aspectRatio
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// Size screenSize = MediaQuery.of(context).size;
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// CameraViewSingleton.screenSize = screenSize;
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// CameraViewSingleton.ratio = screenSize.width / previewSize.height;
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setState(() {
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_cameraInitialized = true;
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});
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});
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}
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/// Callback to receive each frame [CameraImage] perform inference on it
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onLatestImageAvailable(CameraImage cameraImage) async {
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if (_classifierInitialized) {
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if (predicting) {
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return;
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}
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setState(() {
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predicting = true;
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});
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var results = await inference(MLIsolateData(
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cameraImage, classifier.interpreter.address, classifier.labels));
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if (results.containsKey("recognitions")) {
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widget.resultsCallback(results["recognitions"]);
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}
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if (results.containsKey("stats")) {
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widget.statsCallback(results["stats"]);
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}
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logger.i(results);
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setState(() {
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predicting = false;
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});
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}
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}
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@override
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Widget build(BuildContext context) {
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// Return empty container while the camera is not initialized
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if (!cameraController.value.isInitialized) {
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if (!_cameraInitialized) {
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return Container();
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}
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return AspectRatio(
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aspectRatio: 1/cameraController.value.aspectRatio,
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aspectRatio: 1 / cameraController.value.aspectRatio,
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child: CameraPreview(cameraController));
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}
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/// Callback to receive each frame [CameraImage] perform inference on it
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onLatestImageAvailable(CameraImage cameraImage) async {
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// if (classifier.interpreter != null && classifier.labels != null) {
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// // If previous inference has not completed then return
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if (predicting) {
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return;
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}
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setState(() {
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predicting = true;
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});
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logger.i("RECIEVED IMAGE");
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logger.i(cameraImage.format.group);
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logger.i(cameraImage);
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var converted = ImageUtils.convertCameraImage(cameraImage);
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if (converted != null){
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var result = classy.predict(converted);
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logger.e("PREDICTED IMAGE");
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logger.i(result);
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}
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// logger.i(cameraImage);
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// logger.i(cameraImage.height);
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// logger.i(cameraImage.width);
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// logger.i(cameraImage.planes[0]);
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//
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// var uiThreadTimeStart = DateTime.now().millisecondsSinceEpoch;
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//
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// // Data to be passed to inference isolate
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// var isolateData = IsolateData(
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// cameraImage, classifier.interpreter.address, classifier.labels);
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//
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// // We could have simply used the compute method as well however
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// // it would be as in-efficient as we need to continuously passing data
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// // to another isolate.
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//
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// /// perform inference in separate isolate
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// Map<String, dynamic> inferenceResults = await inference(isolateData);
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//
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// var uiThreadInferenceElapsedTime =
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// DateTime.now().millisecondsSinceEpoch - uiThreadTimeStart;
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//
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// // pass results to HomeView
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// widget.resultsCallback(inferenceResults["recognitions"]);
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//
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// // pass stats to HomeView
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// widget.statsCallback((inferenceResults["stats"] as Stats)
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// ..totalElapsedTime = uiThreadInferenceElapsedTime);
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// set predicting to false to allow new frames
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setState(() {
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predicting = false;
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});
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/// Runs inference in another isolate
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Future<Map<String, dynamic>> inference(MLIsolateData mlIsolateData) async {
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ReceivePort responsePort = ReceivePort();
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_mlIsolate.sendPort
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.send(mlIsolateData..responsePort = responsePort.sendPort);
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var results = await responsePort.first;
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return results;
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}
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// /// Runs inference in another isolate
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// Future<Map<String, dynamic>> inference(IsolateData isolateData) async {
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// ReceivePort responsePort = ReceivePort();
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// isolateUtils.sendPort
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// .send(isolateData..responsePort = responsePort.sendPort);
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// var results = await responsePort.first;
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// return results;
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// }
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@override
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void didChangeAppLifecycleState(AppLifecycleState state) async {
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switch (state) {
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