diff --git a/lib/tflite/classifier.dart b/lib/tflite/classifier.dart index fc65b16..bc3bf4c 100644 --- a/lib/tflite/classifier.dart +++ b/lib/tflite/classifier.dart @@ -1,42 +1,35 @@ -import 'dart:math'; -import 'dart:ui'; - import 'package:collection/collection.dart'; import 'package:image/image.dart' as image_lib; import 'package:tflite_flutter/tflite_flutter.dart'; import 'package:tflite_flutter_helper/tflite_flutter_helper.dart'; import '../utils/logger.dart'; -import '../utils/recognition.dart'; -import '../utils/stats.dart'; +import 'data/recognition.dart'; +import 'data/stats.dart'; /// Classifier class Classifier { - static const String MODEL_FILE_NAME = "detect.tflite"; - static const String LABEL_FILE_NAME = "labelmap.txt"; - - /// Input size of image (height = width = 300) - static const int INPUT_SIZE = 224; - - /// Result score threshold - static const double THRESHOLD = 0.5; + static const String modelFileName = "efficientnet_v2s.tflite"; + static const int inputSize = 224; /// [ImageProcessor] used to pre-process the image ImageProcessor? imageProcessor; - /// Padding the image to transform into square - // int padSize = 0; + ///Tensor image to move image data into + late TensorImage _inputImage; + /// Instance of Interpreter late Interpreter _interpreter; - late TensorBuffer _outputBuffer; - late var _probabilityProcessor; + late TfLiteType _inputType; + late TfLiteType _outputType; + + late SequentialProcessor _outputProcessor; /// Labels file loaded as list late List _labels; + int classifierCreationStart = -1; - /// Number of results to show - static const int NUM_RESULTS = 10; Classifier({ Interpreter? interpreter, @@ -51,19 +44,18 @@ class Classifier { try { _interpreter = interpreter ?? await Interpreter.fromAsset( - MODEL_FILE_NAME, - options: InterpreterOptions()..threads = 4, + modelFileName, + options: InterpreterOptions()..threads = 8, ); var outputTensor = _interpreter.getOutputTensor(0); var outputShape = outputTensor.shape; - var outputType = outputTensor.type; - + _outputType = outputTensor.type; var inputTensor = _interpreter.getInputTensor(0); - var intputShape = inputTensor.shape; - var intputType = inputTensor.type; - - _outputBuffer = TensorBuffer.createFixedSize(outputShape, outputType); - _probabilityProcessor = + // var intputShape = inputTensor.shape; + _inputType = inputTensor.type; + _inputImage = TensorImage(_inputType); + _outputBuffer = TensorBuffer.createFixedSize(outputShape, _outputType); + _outputProcessor = TensorProcessorBuilder().add(NormalizeOp(0, 1)).build(); } catch (e) { logger.e("Error while creating interpreter: ", e); @@ -80,61 +72,45 @@ class Classifier { } /// Pre-process the image - TensorImage? getProcessedImage(TensorImage inputImage) { + TensorImage? getProcessedImage(TensorImage? inputImage) { // padSize = max(inputImage.height, inputImage.width); - imageProcessor ??= ImageProcessorBuilder() - // .add(ResizeWithCropOrPadOp(padSize, padSize)) - .add(ResizeOp(INPUT_SIZE, INPUT_SIZE, ResizeMethod.BILINEAR)) - .add(NormalizeOp(127.5, 127.5)) - .build(); - return imageProcessor?.process(inputImage); + if (inputImage != null) { + imageProcessor ??= ImageProcessorBuilder() + .add(ResizeWithCropOrPadOp(224, 224)) + .add(ResizeOp(inputSize, inputSize, ResizeMethod.BILINEAR)) + // .add(NormalizeOp(127.5, 127.5)) + .build(); + return imageProcessor?.process(inputImage); + } + return null; } /// Runs object detection on the input image Map? predict(image_lib.Image image) { - logger.i(labels); - var predictStartTime = DateTime.now().millisecondsSinceEpoch; - if (_interpreter == null) { - logger.e("Interpreter not initialized"); - return null; - } - var preProcessStart = DateTime.now().millisecondsSinceEpoch; - // Create TensorImage from image - // Pre-process TensorImage - var procImage = getProcessedImage(TensorImage.fromImage(image)); - - var preProcessElapsedTime = - DateTime.now().millisecondsSinceEpoch - preProcessStart; - if (procImage != null) { - var inferenceTimeStart = DateTime.now().millisecondsSinceEpoch; - // run inference - var inferenceTimeElapsed = - DateTime.now().millisecondsSinceEpoch - inferenceTimeStart; - - logger.i("Sending image to ML"); - - logger.i(procImage.buffer.asFloat32List()); - logger.i(procImage.width); - logger.i(procImage.height); - logger.i(procImage.tensorBuffer.shape); - logger.i(procImage.tensorBuffer.isDynamic); - _interpreter.run(procImage.buffer, _outputBuffer.getBuffer()); - - Map labeledProb = TensorLabel.fromList( - labels, _probabilityProcessor.process(_outputBuffer)) - .getMapWithFloatValue(); - final pred = getTopProbability(labeledProb); - Recognition rec = Recognition(1, pred.key, pred.value); - var predictElapsedTime = DateTime.now().millisecondsSinceEpoch - predictStartTime; - return { - "recognitions": rec, - "stats": Stats(predictElapsedTime, predictElapsedTime, predictElapsedTime, predictElapsedTime), - }; - } else { - return null; - } + var preProcStart = DateTime.now().millisecondsSinceEpoch; + _inputImage.loadImage(image); + _inputImage = getProcessedImage(_inputImage)!; + var inferenceStart = DateTime.now().millisecondsSinceEpoch; + _interpreter.run(_inputImage.buffer, _outputBuffer.getBuffer()); + var postProcStart = DateTime.now().millisecondsSinceEpoch; + Map labeledProb = TensorLabel.fromList( + labels, _outputProcessor.process(_outputBuffer)) + .getMapWithFloatValue(); + final predictions = getTopProbabilities(labeledProb, number: 5) + .mapIndexed( + (index, element) => Recognition(index, element.key, element.value)) + .toList(); + var endTime = DateTime.now().millisecondsSinceEpoch; + return { + "recognitions": predictions, + "stats": Stats( + totalTime: endTime - preProcStart, + preProcessingTime: inferenceStart - preProcStart, + inferenceTime: postProcStart - inferenceStart, + postProcessingTime: endTime - postProcStart, + ), + }; } - /// Gets the interpreter instance Interpreter get interpreter => _interpreter; @@ -142,11 +118,12 @@ class Classifier { List get labels => _labels; } -MapEntry getTopProbability(Map labeledProb) { +List> getTopProbabilities( + Map labeledProb, + {int number = 3}) { var pq = PriorityQueue>(compare); pq.addAll(labeledProb.entries); - - return pq.first; + return [for (var i = 0; i < number; i += 1) pq.removeFirst()]; } int compare(MapEntry e1, MapEntry e2) { diff --git a/lib/utils/recognition.dart b/lib/tflite/data/recognition.dart similarity index 100% rename from lib/utils/recognition.dart rename to lib/tflite/data/recognition.dart diff --git a/lib/tflite/data/stats.dart b/lib/tflite/data/stats.dart new file mode 100644 index 0000000..4f7d91e --- /dev/null +++ b/lib/tflite/data/stats.dart @@ -0,0 +1,18 @@ + +class Stats { + int totalTime; + int preProcessingTime; + int inferenceTime; + int postProcessingTime; + + Stats( + {this.totalTime = -1, + this.preProcessingTime = -1, + this.inferenceTime = -1, + this.postProcessingTime = -1}); + + @override + String toString() { + return 'Stats{totalPredictTime: $totalTime, preProcessingTime: $preProcessingTime, inferenceTime: $inferenceTime, postProcessingTime: $postProcessingTime}'; + } +} diff --git a/lib/tflite/ml_isolate.dart b/lib/tflite/ml_isolate.dart new file mode 100644 index 0000000..4ea45a2 --- /dev/null +++ b/lib/tflite/ml_isolate.dart @@ -0,0 +1,62 @@ +import 'dart:isolate'; + +import 'package:camera/camera.dart'; +import 'package:tensordex_mobile/tflite/classifier.dart'; +import 'package:tflite_flutter/tflite_flutter.dart'; + +import '../utils/image_utils.dart'; +import '../utils/logger.dart'; + +class IsolateBase { + final ReceivePort _receivePort = ReceivePort(); +} + +class MLIsolate extends IsolateBase { + static const String debugIsolate = "MLIsolate"; + late SendPort _sendPort; + + SendPort get sendPort => _sendPort; + + Future start() async { + await Isolate.spawn( + entryPoint, + _receivePort.sendPort, + debugName: debugIsolate, + ); + _sendPort = await _receivePort.first; + } + + static void entryPoint(SendPort sendPort) async { + final port = ReceivePort(); + sendPort.send(port.sendPort); + + await for (final MLIsolateData mlIsolateData in port) { + var cameraImage = mlIsolateData.cameraImage; + var converted = ImageUtils.convertCameraImage(cameraImage); + if (converted != null) { + Classifier classifier = Classifier( + interpreter: + Interpreter.fromAddress(mlIsolateData.interpreterAddress), + labels: mlIsolateData.labels); + var result = classifier.predict(converted); + mlIsolateData.responsePort?.send(result); + } else { + mlIsolateData.responsePort?.send({"response": "not working yet"}); + } + } + } +} + +/// Bundles data to pass between Isolate +class MLIsolateData { + CameraImage cameraImage; + int interpreterAddress; + List labels; + SendPort? responsePort; + + MLIsolateData( + this.cameraImage, + this.interpreterAddress, + this.labels, + ); +} diff --git a/lib/ui/poke_view.dart b/lib/ui/poke_view.dart index 72d6845..b3848c6 100644 --- a/lib/ui/poke_view.dart +++ b/lib/ui/poke_view.dart @@ -2,16 +2,16 @@ import 'dart:isolate'; import 'package:camera/camera.dart'; import 'package:flutter/material.dart'; -import 'package:tensordex_mobile/tflite/classifier.dart'; +import 'package:tensordex_mobile/tflite/ml_isolate.dart'; import 'package:tflite_flutter/tflite_flutter.dart'; -import 'package:tensordex_mobile/utils/image_utils.dart'; +import '../tflite/classifier.dart'; import '../utils/logger.dart'; -import '../utils/recognition.dart'; -import '../utils/stats.dart'; +import '../tflite/data/recognition.dart'; +import '../tflite/data/stats.dart'; -/// [CameraView] sends each frame for inference -class CameraView extends StatefulWidget { +/// [PokedexView] sends each frame for inference +class PokedexView extends StatefulWidget { /// Callback to pass results after inference to [HomeView] final Function(List recognitions) resultsCallback; @@ -19,32 +19,26 @@ class CameraView extends StatefulWidget { final Function(Stats stats) statsCallback; /// Constructor - const CameraView( + const PokedexView( {Key? key, required this.resultsCallback, required this.statsCallback}) : super(key: key); @override - State createState() => _CameraViewState(); + State createState() => _PokedexViewState(); } -class _CameraViewState extends State with WidgetsBindingObserver { - /// List of available cameras +class _PokedexViewState extends State with WidgetsBindingObserver { late List cameras; - - /// Controller late CameraController cameraController; - Interpreter? interp; + late MLIsolate _mlIsolate; /// true when inference is ongoing bool predicting = false; + bool _cameraInitialized = false; + bool _classifierInitialized = false; - late Classifier classy; - - // /// Instance of [Classifier] - // Classifier classifier; - // - // /// Instance of [IsolateUtils] - // IsolateUtils isolateUtils; + late Interpreter interpreter; + late Classifier classifier; @override void initState() { @@ -54,40 +48,21 @@ class _CameraViewState extends State with WidgetsBindingObserver { void initStateAsync() async { WidgetsBinding.instance.addObserver(this); - - // Spawn a new isolate - // isolateUtils = IsolateUtils(); - // await isolateUtils.start(); - - // Camera initialization + _mlIsolate = MLIsolate(); + await _mlIsolate.start(); initializeCamera(); - - // final gpuDelegateV2 = GpuDelegateV2( - // options: GpuDelegateOptionsV2( - // isPrecisionLossAllowed: false, - // inferencePreference: TfLiteGpuInferenceUsage.fastSingleAnswer, - // inferencePriority1: TfLiteGpuInferencePriority.minLatency, - // inferencePriority2: TfLiteGpuInferencePriority.auto, - // inferencePriority3: TfLiteGpuInferencePriority.auto, - // )); - - - logger.e("CREATING THE INTERPRETOR"); - var interpreterOptions = InterpreterOptions();//..addDelegate(gpuDelegateV2); - interp = await Interpreter.fromAsset('efficientnet_v2s.tflite', - options: interpreterOptions); - logger.e("CREATING THE INTERPRETOR"); - - classy = Classifier(interpreter: interp); - logger.i(interp?.getOutputTensors()); - // Create an instance of classifier to load model and labels - // classifier = Classifier(); - - - // Initially predicting = false + initializeModel(); predicting = false; } + void initializeModel() async { + var interpreterOptions = InterpreterOptions()..threads = 8; + interpreter = await Interpreter.fromAsset('efficientnet_v2s.tflite', + options: interpreterOptions); + classifier = Classifier(interpreter: interpreter); + _classifierInitialized = true; + } + /// Initializes the camera by setting [cameraController] void initializeCamera() async { cameras = await availableCameras(); @@ -97,101 +72,62 @@ class _CameraViewState extends State with WidgetsBindingObserver { CameraController(cameras[0], ResolutionPreset.low, enableAudio: false); cameraController.initialize().then((_) async { + /// previewSize is size of each image frame captured by controller + /// 352x288 on iOS, 240p (320x240) on Android with ResolutionPreset.low // Stream of image passed to [onLatestImageAvailable] callback await cameraController.startImageStream(onLatestImageAvailable); - - /// previewSize is size of each image frame captured by controller - /// - /// 352x288 on iOS, 240p (320x240) on Android with ResolutionPreset.low - // Size previewSize = cameraController.value.previewSize; - // - // /// previewSize is size of raw input image to the model - // CameraViewSingleton.inputImageSize = previewSize; - // - // // the display width of image on screen is - // // same as screenWidth while maintaining the aspectRatio - // Size screenSize = MediaQuery.of(context).size; - // CameraViewSingleton.screenSize = screenSize; - // CameraViewSingleton.ratio = screenSize.width / previewSize.height; + setState(() { + _cameraInitialized = true; + }); }); } + /// Callback to receive each frame [CameraImage] perform inference on it + onLatestImageAvailable(CameraImage cameraImage) async { + if (_classifierInitialized) { + if (predicting) { + return; + } + setState(() { + predicting = true; + }); + var results = await inference(MLIsolateData( + cameraImage, classifier.interpreter.address, classifier.labels)); + + if (results.containsKey("recognitions")) { + widget.resultsCallback(results["recognitions"]); + } + if (results.containsKey("stats")) { + widget.statsCallback(results["stats"]); + } + logger.i(results); + + setState(() { + predicting = false; + }); + } + } + @override Widget build(BuildContext context) { // Return empty container while the camera is not initialized - if (!cameraController.value.isInitialized) { + if (!_cameraInitialized) { return Container(); } - return AspectRatio( - aspectRatio: 1/cameraController.value.aspectRatio, + aspectRatio: 1 / cameraController.value.aspectRatio, child: CameraPreview(cameraController)); } - /// Callback to receive each frame [CameraImage] perform inference on it - onLatestImageAvailable(CameraImage cameraImage) async { - // if (classifier.interpreter != null && classifier.labels != null) { - // // If previous inference has not completed then return - if (predicting) { - return; - } - setState(() { - predicting = true; - }); - logger.i("RECIEVED IMAGE"); - logger.i(cameraImage.format.group); - logger.i(cameraImage); - var converted = ImageUtils.convertCameraImage(cameraImage); - if (converted != null){ - - var result = classy.predict(converted); - - logger.e("PREDICTED IMAGE"); - logger.i(result); - } - // logger.i(cameraImage); - // logger.i(cameraImage.height); - // logger.i(cameraImage.width); - // logger.i(cameraImage.planes[0]); - // - // var uiThreadTimeStart = DateTime.now().millisecondsSinceEpoch; - // - // // Data to be passed to inference isolate - // var isolateData = IsolateData( - // cameraImage, classifier.interpreter.address, classifier.labels); - // - // // We could have simply used the compute method as well however - // // it would be as in-efficient as we need to continuously passing data - // // to another isolate. - // - // /// perform inference in separate isolate - // Map inferenceResults = await inference(isolateData); - // - // var uiThreadInferenceElapsedTime = - // DateTime.now().millisecondsSinceEpoch - uiThreadTimeStart; - // - // // pass results to HomeView - // widget.resultsCallback(inferenceResults["recognitions"]); - // - // // pass stats to HomeView - // widget.statsCallback((inferenceResults["stats"] as Stats) - // ..totalElapsedTime = uiThreadInferenceElapsedTime); - - // set predicting to false to allow new frames - setState(() { - predicting = false; - }); + /// Runs inference in another isolate + Future> inference(MLIsolateData mlIsolateData) async { + ReceivePort responsePort = ReceivePort(); + _mlIsolate.sendPort + .send(mlIsolateData..responsePort = responsePort.sendPort); + var results = await responsePort.first; + return results; } -// /// Runs inference in another isolate -// Future> inference(IsolateData isolateData) async { -// ReceivePort responsePort = ReceivePort(); -// isolateUtils.sendPort -// .send(isolateData..responsePort = responsePort.sendPort); -// var results = await responsePort.first; -// return results; -// } - @override void didChangeAppLifecycleState(AppLifecycleState state) async { switch (state) { diff --git a/lib/ui/results_view.dart b/lib/ui/results_view.dart index 4c0af40..38f899e 100644 --- a/lib/ui/results_view.dart +++ b/lib/ui/results_view.dart @@ -1,26 +1,21 @@ import 'package:flutter/material.dart'; import 'package:tensordex_mobile/ui/poke_view.dart'; -import 'package:tensordex_mobile/utils/recognition.dart'; +import 'package:tensordex_mobile/tflite/data/recognition.dart'; +import 'package:tensordex_mobile/tflite/data/stats.dart'; -import '../utils/logger.dart'; -/// [CameraView] sends each frame for inference +/// [PokedexView] sends each frame for inference class ResultsView extends StatefulWidget { - + final List recognitions; + final Stats stats; /// Constructor - const ResultsView({Key? key}) : super(key: key); - - - void setResults(Recognition results){ - logger.i("RESULTS IN THE RESULT VIEW"); - } + const ResultsView(this.recognitions, this.stats, {Key? key}) : super(key: key); @override State createState() => _ResultsViewState(); } class _ResultsViewState extends State { - @override void initState() { super.initState(); @@ -28,6 +23,6 @@ class _ResultsViewState extends State { @override Widget build(BuildContext context) { - return Text("data"); + return Text(widget.recognitions.toString()); } -} \ No newline at end of file +} diff --git a/lib/ui/tensordex_home.dart b/lib/ui/tensordex_home.dart index 27a0096..8e5e722 100644 --- a/lib/ui/tensordex_home.dart +++ b/lib/ui/tensordex_home.dart @@ -3,16 +3,12 @@ import 'package:tensordex_mobile/ui/poke_view.dart'; import 'package:tensordex_mobile/ui/results_view.dart'; import '../utils/logger.dart'; -import '../utils/recognition.dart'; -import '../utils/stats.dart'; +import '../tflite/data/recognition.dart'; +import '../tflite/data/stats.dart'; class TensordexHome extends StatefulWidget { const TensordexHome({Key? key, required this.title}) : super(key: key); - // This widget is the home page of your application. It is stateful, meaning - // that it has a State object (defined below) that contains fields that affect - // how it looks. - // This class is the configuration for the state. It holds the values (in this // case the title) provided by the parent (in this case the App widget) and // used by the build method of the State. Fields in a Widget subclass are @@ -25,12 +21,9 @@ class TensordexHome extends StatefulWidget { } class _TensordexHomeState extends State { - - /// Results to draw bounding boxes - List? results; - - /// Realtime stats - Stats? stats; + /// Results from the image classifier + List results = [Recognition(1, "NOTHING DETECTED", .5)]; + Stats stats = Stats(); /// Scaffold Key GlobalKey scaffoldKey = GlobalKey(); @@ -38,106 +31,27 @@ class _TensordexHomeState extends State { void _incrementCounter() { setState(() { logger.d("Counter Incremented!"); - logger.w("Counter Incremented!"); - logger.e("Counter Incremented!"); }); } - // void onNewCameraSelected(CameraDescription cameraDescription) async { - // final previousCameraController = controller; - // // Instantiating the camera controller - // final CameraController cameraController = CameraController( - // cameraDescription, - // ResolutionPreset.high, - // imageFormatGroup: ImageFormatGroup.jpeg, - // ); - // - // // Dispose the previous controller - // await previousCameraController.dispose(); - // - // // Replace with the new controller - // if (mounted) { - // setState(() { - // controller = cameraController; - // }); - // } - // - // // Update UI if controller updated - // cameraController.addListener(() { - // if (mounted) setState(() {}); - // }); - // - // // Initialize controller - // try { - // await cameraController.initialize(); - // } on CameraException catch (e) { - // logger.e('Error initializing camera:', e); - // } - // - // // Update the Boolean - // if (mounted) { - // setState(() { - // _isCameraInitialized = controller.value.isInitialized; - // }); - // } - // } - - // @override - // void initState() { - // super.initState(); - // WidgetsBinding.instance.addObserver(this); - - // controller = CameraController(_cameras[0], ResolutionPreset.max); - // controller.initialize().then((_) { - // if (!mounted) { - // return; - // } - // - // setState(() {onNewCameraSelected(_cameras[0]);}); - // }).catchError((Object e) { - // if (e is CameraException) { - // switch (e.code) { - // case 'CameraAccessDenied': - // logger.w('User denied camera access.'); - // controller.initialize().then((_) { - // if (!mounted) { - // return; - // } - // setState(() {}); - // }).catchError((Object e) { - // if (e is CameraException) { - // switch (e.code) { - // case 'CameraAccessDenied': - // logger.i('User denied camera access.'); - // break; - // default: - // logger.i('Handle other errors.'); - // break; - // } - // } - // }); - // break; - // default: - // logger.i('Handle other errors.'); - // break; - // } - // } - // }); - // } + @override + void initState() { + super.initState(); + } @override void dispose() { super.dispose(); } - /// Callback to get inference results from [CameraView] + /// Callback to get inference results from [PokedexView] void resultsCallback(List results) { setState(() { this.results = results; }); } - /// Callback to get inference stats from [CameraView] + /// Callback to get inference stats from [PokedexView] void statsCallback(Stats stats) { setState(() { this.stats = stats; @@ -152,12 +66,12 @@ class _TensordexHomeState extends State { ), body: Center( child: Column( - mainAxisAlignment: MainAxisAlignment.center, + mainAxisAlignment: MainAxisAlignment.start, children: [ - CameraView( + PokedexView( resultsCallback: resultsCallback, statsCallback: statsCallback), - const ResultsView(), + ResultsView(results, stats), ], ), ), diff --git a/lib/utils/camera_view_singleton.dart b/lib/utils/camera_view_singleton.dart deleted file mode 100644 index 562fd87..0000000 --- a/lib/utils/camera_view_singleton.dart +++ /dev/null @@ -1,10 +0,0 @@ -import 'dart:ui'; - -class CameraViewSingleton { - static double ratio = 0.0; - static Size screenSize = const Size(0, 0); - static Size inputImageSize = const Size(0, 0); - - static Size get actualPreviewSize => - Size(screenSize.width, screenSize.width * ratio); -} diff --git a/lib/utils/stats.dart b/lib/utils/stats.dart deleted file mode 100644 index f1582f6..0000000 --- a/lib/utils/stats.dart +++ /dev/null @@ -1,23 +0,0 @@ -/// Bundles different elapsed times -class Stats { - /// Total time taken in the isolate where the inference runs - int totalPredictTime; - - /// [totalPredictTime] + communication overhead time - /// between main isolate and another isolate - int totalElapsedTime; - - /// Time for which inference runs - int inferenceTime; - - /// Time taken to pre-process the image - int preProcessingTime; - - Stats(this.totalPredictTime, this.totalElapsedTime, this.inferenceTime, - this.preProcessingTime); - - @override - String toString() { - return 'Stats{totalPredictTime: $totalPredictTime, totalElapsedTime: $totalElapsedTime, inferenceTime: $inferenceTime, preProcessingTime: $preProcessingTime}'; - } -} diff --git a/oldCameraCode.dart b/oldCameraCode.dart new file mode 100644 index 0000000..4fb25ca --- /dev/null +++ b/oldCameraCode.dart @@ -0,0 +1,81 @@ + +// void onNewCameraSelected(CameraDescription cameraDescription) async { +// final previousCameraController = controller; +// // Instantiating the camera controller +// final CameraController cameraController = CameraController( +// cameraDescription, +// ResolutionPreset.high, +// imageFormatGroup: ImageFormatGroup.jpeg, +// ); +// +// // Dispose the previous controller +// await previousCameraController.dispose(); +// +// // Replace with the new controller +// if (mounted) { +// setState(() { +// controller = cameraController; +// }); +// } +// +// // Update UI if controller updated +// cameraController.addListener(() { +// if (mounted) setState(() {}); +// }); +// +// // Initialize controller +// try { +// await cameraController.initialize(); +// } on CameraException catch (e) { +// logger.e('Error initializing camera:', e); +// } +// +// // Update the Boolean +// if (mounted) { +// setState(() { +// _isCameraInitialized = controller.value.isInitialized; +// }); +// } +// } + + + +// WidgetsBinding.instance.addObserver(this); + +// controller = CameraController(_cameras[0], ResolutionPreset.max); +// controller.initialize().then((_) { +// if (!mounted) { +// return; +// } +// +// setState(() {onNewCameraSelected(_cameras[0]);}); +// }).catchError((Object e) { +// if (e is CameraException) { +// switch (e.code) { +// case 'CameraAccessDenied': +// logger.w('User denied camera access.'); +// controller.initialize().then((_) { +// if (!mounted) { +// return; +// } +// setState(() {}); +// }).catchError((Object e) { +// if (e is CameraException) { +// switch (e.code) { +// case 'CameraAccessDenied': +// logger.i('User denied camera access.'); +// break; +// default: +// logger.i('Handle other errors.'); +// break; +// } +// } +// }); +// break; +// default: +// logger.i('Handle other errors.'); +// break; +// } +// } +// }); +// }