pytorch_lite 1.0.6 pytorch_lite: ^1.0.6 copied to clipboard
Flutter package to help run pytorch lite models classification and yolov5
import 'package:flutter/material.dart';
import 'dart:async';
import 'package:flutter/services.dart';
import 'dart:io';
import 'package:image_picker/image_picker.dart';
import 'package:pytorch_lite/pigeon.dart';
import 'package:pytorch_lite/pytorch_lite.dart';
void main() => runApp(MyApp());
class MyApp extends StatefulWidget {
@override
_MyAppState createState() => _MyAppState();
}
class _MyAppState extends State<MyApp> {
ClassificationModel? _imageModel;
//CustomModel? _customModel;
late ModelObjectDetection _objectModel;
String? _imagePrediction;
List? _prediction;
File? _image;
ImagePicker _picker = ImagePicker();
bool objectDetection = false;
List<ResultObjectDetection?> objDetect = [];
@override
void initState() {
super.initState();
loadModel();
}
//load your model
Future loadModel() async {
String pathImageModel = "assets/models/model_classification.pt";
//String pathCustomModel = "assets/models/custom_model.ptl";
String pathObjectDetectionModel = "assets/models/yolov5s.torchscript";
try {
_imageModel = await PytorchLite.loadClassificationModel(
pathImageModel, 224, 224,
labelPath: "assets/labels/label_classification_imageNet.txt");
//_customModel = await PytorchLite.loadCustomModel(pathCustomModel);
_objectModel = await PytorchLite.loadObjectDetectionModel(
pathObjectDetectionModel, 80, 640, 640,
labelPath: "assets/labels/labels_objectDetection_Coco.txt");
} catch (e) {
if (e is PlatformException) {
print("only supported for android, Error is $e");
} else {
print("Error is $e");
}
}
}
//run an image model
Future runObjectDetectionWithoutLabels() async {
//pick a random image
final XFile? image = await _picker.pickImage(source: ImageSource.gallery);
objDetect = await _objectModel
.getImagePredictionList(await File(image!.path).readAsBytes());
objDetect.forEach((element) {
print({
"score": element?.score,
"className": element?.className,
"class": element?.classIndex,
"rect": {
"left": element?.rect.left,
"top": element?.rect.top,
"width": element?.rect.width,
"height": element?.rect.height,
"right": element?.rect.right,
"bottom": element?.rect.bottom,
},
});
});
setState(() {
//this.objDetect = objDetect;
_image = File(image.path);
});
}
Future runObjectDetection() async {
//pick a random image
final XFile? image = await _picker.pickImage(source: ImageSource.gallery);
objDetect = await _objectModel.getImagePrediction(
await File(image!.path).readAsBytes(),
minimumScore: 0.1,
IOUThershold: 0.3);
objDetect.forEach((element) {
print({
"score": element?.score,
"className": element?.className,
"class": element?.classIndex,
"rect": {
"left": element?.rect.left,
"top": element?.rect.top,
"width": element?.rect.width,
"height": element?.rect.height,
"right": element?.rect.right,
"bottom": element?.rect.bottom,
},
});
});
setState(() {
//this.objDetect = objDetect;
_image = File(image.path);
});
}
Future runClassification() async {
objDetect = [];
//pick a random image
final XFile? image = await _picker.pickImage(source: ImageSource.gallery);
//get prediction
//labels are 1000 random english words for show purposes
print(image!.path);
_imagePrediction = await _imageModel!
.getImagePrediction(await File(image!.path).readAsBytes());
List<double?>? predictionList = await _imageModel!.getImagePredictionList(
await File(image.path).readAsBytes(),
);
print(predictionList);
List<double?>? predictionListProbabilites =
await _imageModel!.getImagePredictionListProbabilities(
await File(image.path).readAsBytes(),
);
//Gettting the highest Probability
double maxScoreProbability = double.negativeInfinity;
double sumOfProbabilites = 0;
int index = 0;
for (int i = 0; i < predictionListProbabilites!.length; i++) {
if (predictionListProbabilites[i]! > maxScoreProbability) {
maxScoreProbability = predictionListProbabilites[i]!;
sumOfProbabilites = sumOfProbabilites + predictionListProbabilites[i]!;
index = i;
}
}
print(predictionListProbabilites);
print(index);
print(sumOfProbabilites);
print(maxScoreProbability);
setState(() {
//this.objDetect = objDetect;
_image = File(image.path);
});
}
/*
//run a custom model with number inputs
Future runCustomModel() async {
_prediction = await _customModel!
.getPrediction([1, 2, 3, 4], [1, 2, 2], DType.float32);
setState(() {});
}
*/
@override
Widget build(BuildContext context) {
return MaterialApp(
home: Scaffold(
appBar: AppBar(
title: const Text('Pytorch Mobile Example'),
),
body: Column(
mainAxisAlignment: MainAxisAlignment.center,
children: <Widget>[
Expanded(
child: objDetect.isNotEmpty
? _image == null
? Text('No image selected.')
: _objectModel.renderBoxesOnImage(_image!, objDetect)
: _image == null
? Text('No image selected.')
: Image.file(_image!),
),
Center(
child: Visibility(
visible: _imagePrediction != null,
child: Text("$_imagePrediction"),
),
),
/*
Center(
child: TextButton(
onPressed: runImageModel,
child: Row(
children: [
Icon(
Icons.add_a_photo,
color: Colors.grey,
),
],
),
),
),
*/
TextButton(
onPressed: runClassification,
style: TextButton.styleFrom(
backgroundColor: Colors.blue,
),
child: const Text(
"Run Classification",
style: TextStyle(
color: Colors.white,
),
),
),
TextButton(
onPressed: runObjectDetection,
style: TextButton.styleFrom(
backgroundColor: Colors.blue,
),
child: Text(
"Run object detection with labels",
style: TextStyle(
color: Colors.white,
),
),
),
TextButton(
onPressed: runObjectDetectionWithoutLabels,
style: TextButton.styleFrom(
backgroundColor: Colors.blue,
),
child: Text(
"Run object detection without labels",
style: TextStyle(
color: Colors.white,
),
),
),
Center(
child: Visibility(
visible: _prediction != null,
child: Text(_prediction != null ? "${_prediction![0]}" : ""),
),
)
],
),
),
);
}
}