Files
tensordex-mobile/lib/widgets/poke_finder.dart
T
2023-01-29 00:39:03 -05:00

289 lines
9.8 KiB
Dart

import 'dart:convert';
import 'dart:io';
import 'dart:isolate';
import 'dart:math';
import 'package:camera/camera.dart';
import 'package:flutter/material.dart';
import 'package:flutter/services.dart';
import 'package:get_it/get_it.dart';
import 'package:image/image.dart';
import 'package:path_provider/path_provider.dart';
import 'package:tensordex_mobile/tflite/ml_isolate.dart';
import 'package:tensordex_mobile/tflite/model/configuration.dart';
import 'package:tensordex_mobile/tflite/model/outputs/stats.dart';
import 'package:tflite_flutter/tflite_flutter.dart';
import '../main.dart';
import '../tflite/classifier.dart';
import '../tflite/model/outputs/recognition.dart';
import '../utils/logger.dart';
class PokeFinder extends StatefulWidget {
/// Callback to pass results after inference to [HomeView]
final Function(List<Recognition> recognitions) resultsCallback;
/// Callback to inference stats to [HomeView]
final Function(Stats stats) statsCallback;
/// Constructor
const PokeFinder(
{Key? key, required this.resultsCallback, required this.statsCallback})
: super(key: key);
@override
State<PokeFinder> createState() => _PokeFinderState();
}
class _PokeFinderState extends State<PokeFinder> with WidgetsBindingObserver {
/// true when inference is ongoing
///
final double _zoomSliderLogFactor = 1000.0;
bool predicting = false;
bool _cameraReady = false;
bool _classifierInitialized = false;
bool _saveClassifierImage = false;
int _cameraIndex = 0;
double _minZoom = 1.0;
double _maxZoom = 1.0;
double _currentZoom = 1.0;
GetIt getIt = GetIt.instance;
late CameraController cameraController;
//ml variables
late Interpreter interpreter;
late Classifier classifier;
late MLIsolate _mlIsolate;
late List<ModelConfiguration> modelConfigurations;
@override
void initState() {
initStateAsync();
super.initState();
}
void initStateAsync() async {
WidgetsBinding.instance.addObserver(this);
_mlIsolate = MLIsolate();
await _mlIsolate.start();
swapToCamera(cameras[0]);
for (CameraDescription cam in cameras) {
logger.i(cam);
}
initializeModel();
predicting = false;
}
Future<List<String>> getModelFiles() async {
final manifestContent = await rootBundle.loadString('AssetManifest.json');
final Map<String, dynamic> manifestMap = json.decode(manifestContent);
return manifestMap.keys
.where((String key) => key.contains('.tflite'))
.map((String key) => key.substring(7))
.toList();
}
void initializeModel() async {
var modelFiles = await getModelFiles();
var modelConfigurations =
modelFiles.map((e) => ModelConfiguration(e)).toList();
var currentConfig = modelConfigurations[0];
logger.i(modelFiles);
interpreter = await createInterpreter(currentConfig);
classifier = Classifier(interpreter);
_classifierInitialized = true;
}
Future<Interpreter> createInterpreter(ModelConfiguration config) async {
return await Interpreter.fromAsset(config.name,
options: config.interpreters[0]);
}
void swapCamera() async {
logger.i(cameras);
logger.i(_cameraIndex);
_cameraIndex += 1;
if (cameras.length <= _cameraIndex) {
_cameraIndex = 0;
}
swapToCamera(cameras[_cameraIndex]);
}
void swapToCamera(CameraDescription cameraDescription) async {
setState(() {
_cameraReady = false;
});
_refreshIndicatorKey.currentState?.show();
cameraController = CameraController(cameraDescription, 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
// await cameraController.startImageStream(onLatestImageAvailable);
_maxZoom = await cameraController.getMaxZoomLevel();
_minZoom = await cameraController.getMinZoomLevel();
setState(() {
_cameraReady = true;
_currentZoom = 1.0;
});
});
}
void saveMLImage() async {
logger.i('setting save classifier to true');
_saveClassifierImage = true;
}
/// Callback to receive each frame [CameraImage] perform inference on it
onLatestImageAvailable(CameraImage cameraImage) async {
if (_classifierInitialized) {
if (predicting) {
return;
}
setState(() {
predicting = true;
});
logger.i(_saveClassifierImage);
var results = await inference(MLIsolateData(
cameraImage,
classifier.interpreter.address,
classifier.labels,
_saveClassifierImage));
if (results.containsKey('recognitions')) {
widget.resultsCallback(results['recognitions']);
}
if (results.containsKey('stats')) {
widget.statsCallback(results['stats']);
}
if (results.containsKey('image')) {
var image = results['image'];
if (image != null) {
Directory tempDir = await getTemporaryDirectory();
String tempPath = tempDir.path;
logger.i(tempPath);
logger.i('SAVING IMAGE!');
await File('$tempPath/${DateTime.now().millisecondsSinceEpoch}.png')
.writeAsBytes(encodePng(image));
_saveClassifierImage = false;
}
}
setState(() {
predicting = false;
});
}
}
final GlobalKey<RefreshIndicatorState> _refreshIndicatorKey =
GlobalKey<RefreshIndicatorState>();
@override
Widget build(BuildContext context) {
return Column(
children: [
RefreshIndicator(
key: _refreshIndicatorKey,
onRefresh: () async {
// Replace this delay with the code to be executed during refresh
// and return a Future when code finishes execution.
while (!_cameraReady) {
await Future.delayed(const Duration(milliseconds: 100));
}
return;
},
child:
!_cameraReady ?
Container(
width: 100,
height: 100,
)
: Stack(
clipBehavior: Clip.none,
alignment: Alignment.center,
children: [
AspectRatio(
aspectRatio: 1 / cameraController.value.aspectRatio,
child: CameraPreview(cameraController)),
Positioned(
top: -10,
child: TextButton(
onPressed: swapCamera,
child: const Text('Change Camera!')),
),
Positioned(
bottom: -10,
child: Row(
children: [
SizedBox(
width:
MediaQuery.of(context).size.width - 100,
child: Slider(
min: pow(_minZoom,
1 / _zoomSliderLogFactor)
.toDouble(),
max: pow(_maxZoom,
1 / _zoomSliderLogFactor)
.toDouble(),
divisions: 100,
value: _currentZoom,
onChanged: (double value) {
var newValue = pow(
_currentZoom,
_zoomSliderLogFactor)
.toDouble();
logger.i('Zoom updated $value $newValue');
_currentZoom = value;
cameraController.setZoomLevel(pow(
_currentZoom,
_zoomSliderLogFactor)
.toDouble());
})),
Text(
pow(_currentZoom, _zoomSliderLogFactor)
.toStringAsFixed(2),
style: const TextStyle(
color: Colors.lightBlue))
// style: const TextStyle(color: Colors.lightBlue))
],
))
])),
TextButton(
onPressed: saveMLImage, child: const Text('Save Model Image')),
],
);
}
/// Runs inference in another isolate
Future<Map<String, dynamic>> inference(MLIsolateData mlIsolateData) async {
ReceivePort responsePort = ReceivePort();
_mlIsolate.sendPort
.send(mlIsolateData..responsePort = responsePort.sendPort);
var results = await responsePort.first;
return results;
}
@override
void didChangeAppLifecycleState(AppLifecycleState state) async {
switch (state) {
case AppLifecycleState.paused:
cameraController.stopImageStream();
break;
case AppLifecycleState.resumed:
if (!cameraController.value.isStreamingImages) {
swapToCamera(cameras[_cameraIndex]);
await cameraController.startImageStream(onLatestImageAvailable);
}
break;
default:
}
}
@override
void dispose() {
WidgetsBinding.instance.removeObserver(this);
cameraController.dispose();
super.dispose();
}
}