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class ARGameView extends StatefulWidget {
ARGameView({
Key? key,
required this.title,
required this.onDetectedObject,
}) : super(key: key);
final String title;
final Function(DetectedObject) onDetectedObject;
@override
State<ARGameView> createState() => _ARGameViewState();
}
The _ARGameViewState class manages the state of the ARGameView widget. It initializes the object detector and other necessary variables in the initState method.
class _ARGameViewState extends State<ARGameView> {
ObjectDetector? _objectDetector;
DetectionMode _mode = DetectionMode.stream;
bool _canProcess = false;
bool _isBusy = false;
CustomPaint? _customPaint;
String? _text;
var _cameraLensDirection = CameraLensDirection.back;
int _option = 0;
final _options = {
'default': '',
'object_custom': 'object_labeler.tflite',
};
@override
void initState() {
super.initState();
_initializeDetector();
}
void _initializeDetector() async {
_objectDetector?.close();
_objectDetector = null;
if (_option == 0) {
final options = ObjectDetectorOptions(
mode: _mode,
classifyObjects: true,
multipleObjects: true,
);
_objectDetector = GoogleMlKit.vision.objectDetector(options);
} else if (_option > 0 && _option <= _options.length) {
final option = _options[_options.keys.toList()[_option]] ?? '';
final modelPath = await getAssetPath('assets/ml/$option');
final options = LocalObjectDetectorOptions(
mode: _mode,
modelPath: modelPath,
classifyObjects: true,
multipleObjects: true,
);
_objectDetector = GoogleMlKit.vision.objectDetector(options);
}
_canProcess = true;
}
The _processImage method employs the image recognition technique to analyze the captured image and detect objects using the initialized detector. Once objects are detected, the UI is updated accordingly using the _updateUI method.
Future<void> _processImage(InputImage inputImage) async {
if (_objectDetector == null) return;
if (!_canProcess) return;
if (_isBusy) return;
_isBusy = true;
setState(() {
_text = '';
});
final objects = await _objectDetector!.processImage(inputImage);
_updateUI(objects);
_isBusy = false;
if (mounted) {
setState(() {});
}
}
The _updateUI method updates the UI with the detected objects. If objects are detected, it displays the number of objects detected along with a visual representation of the objects using the CustomPaint widget. Otherwise, it displays a message indicating that no objects were detected.
void _updateUI(List<DetectedObject> objects) {
if (objects.isNotEmpty) {
setState(() {
_text = 'Objects Detected: ${objects.length}';
_customPaint = CustomPaint(
painter: ObjectDetectPainter(objects),
);
});
} else {
setState(() {
_text = 'No Objects Detected';
_customPaint = null;
});
}
}
Here's how the final code looks like,
class ARGameView extends StatefulWidget {ARGameView({
Key? key,
required this.title,
required this.onDetectedObject,
}) : super(key: key);
final String title;
final Function(DetectedObject) onDetectedObject;
@override
State<ARGameView> createState() => _ARGameViewState();
}
class _ARGameViewState extends State<ARGameView> {
ObjectDetector? _objectDetector;
DetectionMode _mode = DetectionMode.stream;
bool _canProcess = false;
bool _isBusy = false;
CustomPaint? _customPaint;
String? _text;
var _cameraLensDirection = CameraLensDirection.back;
int _option = 0;
final _options = {
'default': '',
'object_custom': 'object_labeler.tflite',
};
@override
void initState() {
super.initState();
_initializeDetector();
}
@override
Widget build(BuildContext context) {
return Scaffold(
appBar: AppBar(
title: Text(widget.title),
),
body: Stack(
children: [
DetectorView(
title: 'AR Game Detector',
customPaint: _customPaint,
text: _text,
onImage: _processImage,
initialCameraLensDirection: _cameraLensDirection,
onCameraLensDirectionChanged: (value) =>
_cameraLensDirection = value,
onCameraFeedReady: _initializeDetector,
initialDetectionMode: DetectorViewMode.values[_mode.index],
onDetectorViewModeChanged: _onScreenModeChanged,
),
Positioned(
top: 30,
left: 100,
right: 100,
child: Row(
children: [
Spacer(),
Container(
decoration: BoxDecoration(
color: Colors.black54,
borderRadius: BorderRadius.circular(10.0),
),
child: Padding(
padding: const EdgeInsets.all(4.0),
child: _buildDropdown(),
),
),
Spacer(),
],
),
),
],
),
);
}
Widget _buildDropdown() => DropdownButton<int>(
value: _option,
icon: const Icon(Icons.arrow_downward),
elevation: 16,
style: const TextStyle(color: Colors.blue),
underline: Container(
height: 2,
color: Colors.blue,
),
onChanged: (int? option) {
if (option != null) {
setState(() {
_option = option;
_initializeDetector();
});
}
},
items: List<int>.generate(_options.length, (i) => i)
.map<DropdownMenuItem<int>>((option) {
return DropdownMenuItem<int>(
value: option,
child: Text(_options.keys.toList()[option]),
);
}).toList(),
);
void _onScreenModeChanged(DetectorViewMode mode) {
switch (mode) {
case DetectorViewMode.gallery:
_mode = DetectionMode.single;
_initializeDetector();
return;
case DetectorViewMode.liveFeed:
_mode = DetectionMode.stream;
_initializeDetector();
return;
}
}
void _initializeDetector() async {
_objectDetector?.close();
_objectDetector = null;
if (_option == 0) {
final options = ObjectDetectorOptions(
mode: _mode,
classifyObjects: true,
multipleObjects: true,
);
_objectDetector = GoogleMlKit.vision.objectDetector(options);
} else if (_option > 0 && _option <= _options.length) {
final option = _options[_options.keys.toList()[_option]] ?? '';
final modelPath = await getAssetPath('assets/ml/$option');
final options = LocalObjectDetectorOptions(
mode: _mode,
modelPath: modelPath,
classifyObjects: true,
multipleObjects: true,
);
_objectDetector = GoogleMlKit.vision.objectDetector(options);
}
_canProcess = true;
}
Future<void> _processImage(InputImage inputImage) async {
if (_objectDetector == null) return;
if (!_canProcess) return;
if (_isBusy) return;
_isBusy = true;
setState(() {
_text = '';
});
final objects = await _objectDetector!.processImage(inputImage);
_updateUI(objects);
_isBusy = false;
if (mounted) {
setState(() {});
}
}
void _updateUI(List<DetectedObject> objects) {
if (objects.isNotEmpty) {
// Update UI with detected objects
setState(() {
_text = 'Objects Detected: ${objects.length}';
_customPaint = CustomPaint(
painter: ObjectDetectPainter(objects),
);
});
} else {
setState(() {
_text = 'No Objects Detected';
_customPaint = null;
});
}
}
}
Integrating object detection in Mobile App Development for game development opens up a plethora of use cases and gameplay possibilities, leveraging Machine learning and Google ML Kit:
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