Linkedin Instagram Facebook X-twitter Introduction: Augmented reality (AR) has transformed the gaming industry, offering players immersive experiences that blend the virtual…
Augmented reality (AR) has transformed the gaming industry, offering players immersive experiences that blend the virtual and real worlds seamlessly. One of the key technologies driving AR gaming is object detection, which allows games to recognize and interact with real-world objects captured by a device’s camera. In this blog post, we’ll explore how object detection is used in game development, diving into a codebase that demonstrates its implementation.
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();
}
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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