adding image picker camera screen to examples

example/lib/main.dart

@@ -1,6 +1,7 @@
 import 'package:flutter/material.dart';
 import 'package:pytorch_lite_example/run_model_by_camera_demo.dart';
 import 'package:pytorch_lite_example/run_model_by_image_demo.dart';
+import 'package:pytorch_lite_example/run_model_by_image_picker_camera_demo.dart';
 
 Future<void> main() async {
   runApp(const ChooseDemo());
@@ -60,7 +61,27 @@ class _ChooseDemoState extends State<ChooseDemo> {
                       color: Colors.white,
                     ),
                   ),
-                )
+                ),
+                TextButton(
+                  onPressed: () => {
+                    Navigator.push(
+                      context,
+                      MaterialPageRoute(
+                        builder: (context) =>
+                            const RunModelByImagePickerCameraDemo(),
+                      ),
+                    )
+                  },
+                  style: TextButton.styleFrom(
+                    backgroundColor: Colors.blue,
+                  ),
+                  child: const Text(
+                    "Run Model with ImagePicker Camera",
+                    style: TextStyle(
+                      color: Colors.white,
+                    ),
+                  ),
+                ),
               ],
             ),
           );
@@ -69,3 +90,5 @@ class _ChooseDemoState extends State<ChooseDemo> {
     );
   }
 }
+
+

example/lib/run_model_by_image_picker_camera_demo.dart

@@ -0,0 +1,149 @@
+import 'dart:io';
+import 'dart:typed_data';
+import 'package:flutter/material.dart';
+import 'package:image_picker/image_picker.dart';
+import 'package:pytorch_lite/pytorch_lite.dart';
+import 'package:pytorch_lite_example/ui/box_widget.dart';
+
+class RunModelByImagePickerCameraDemo extends StatefulWidget {
+  const RunModelByImagePickerCameraDemo({Key? key}) : super(key: key);
+
+  @override
+  _RunModelByImagePickerCameraDemoState createState() =>
+      _RunModelByImagePickerCameraDemoState();
+}
+
+class _RunModelByImagePickerCameraDemoState
+    extends State<RunModelByImagePickerCameraDemo> {
+  List<ResultObjectDetection>? objectDetectionResults;
+  String? classificationResult;
+  Duration? objectDetectionInferenceTime;
+  Duration? classificationInferenceTime;
+  File? _image;
+  ModelObjectDetection? _objectModel;
+  ClassificationModel? _imageModel;
+  bool _isLoading = false; // Add loading state
+
+  @override
+  void initState() {
+    super.initState();
+    loadModel();
+  }
+
+  Future loadModel() async {
+    String pathImageModel = "assets/models/model_classification.pt";
+    String pathObjectDetectionModel = "assets/models/yolov5s.torchscript";
+    try {
+      _imageModel = await PytorchLite.loadClassificationModel(
+        pathImageModel, 224, 224, 1000, // Adjust as needed
+        labelPath: "assets/labels/label_classification_imageNet.txt",
+      );
+      _objectModel = await PytorchLite.loadObjectDetectionModel(
+        pathObjectDetectionModel,
+        80,
+        640,
+        640,
+        labelPath: "assets/labels/labels_objectDetection_Coco.txt",
+      );
+    } catch (e) {
+      print("Error loading model: $e");
+    }
+  }
+
+  Future runModels() async {
+    setState(() => _isLoading = true);
+
+    final ImagePicker picker = ImagePicker();
+    final XFile? pickedImage =
+        await picker.pickImage(source: ImageSource.camera);
+    if (pickedImage == null) {
+      setState(() => _isLoading = false);
+      return;
+    }
+
+    File image = File(pickedImage.path);
+    Uint8List imageBytes = await image.readAsBytes(); // Read bytes once
+
+    // Run both models concurrently
+    final results = await Future.wait([
+      () async {
+        Stopwatch stopwatch = Stopwatch()..start();
+        try {
+          return await _imageModel?.getImagePrediction(imageBytes);
+        } catch (e) {
+          print("Error during classification: $e");
+          return null; // or handle the error as needed
+        } finally {
+          classificationInferenceTime = stopwatch.elapsed;
+        }
+      }(),
+      () async {
+        Stopwatch stopwatch = Stopwatch()..start();
+        try {
+          return await _objectModel?.getImagePrediction(
+            imageBytes,
+            minimumScore: 0.1,
+            iOUThreshold: 0.3,
+          );
+        } catch (e) {
+          print("Error during object detection: $e");
+          return null; // or handle the error as needed
+        } finally {
+          objectDetectionInferenceTime = stopwatch.elapsed;
+        }
+      }(),
+    ]);
+
+    classificationResult = results[0] as String?;
+    objectDetectionResults = results[1] as List<ResultObjectDetection>?;
+
+    setState(() {
+      _image = image;
+      _isLoading = false;
+    });
+  }
+
+  @override
+  Widget build(BuildContext context) {
+    return Scaffold(
+      appBar: AppBar(title: const Text('Run Models')),
+      body: Center(
+        child: _isLoading
+            ? const CircularProgressIndicator() // Show loading indicator
+            : Column(
+                mainAxisAlignment: MainAxisAlignment.center,
+                children: [
+                  if (_image != null) ...[
+                    SizedBox(
+                      height: MediaQuery.sizeOf(context).height * 0.5,
+                      child: Padding(
+                        padding: const EdgeInsets.all(20),
+                        child: _objectModel!.renderBoxesOnImage(
+                            _image!, objectDetectionResults ?? []),
+                      ),
+                    ),
+                    const SizedBox(height: 20),
+                    Text(
+                      "Classification Result: ${classificationResult ?? "N/A"}",
+                      style: const TextStyle(fontSize: 16),
+                    ),
+                    Text(
+                      "Classification Time: ${classificationInferenceTime?.inMilliseconds ?? "N/A"} ms",
+                      style: const TextStyle(fontSize: 16),
+                    ),
+                    Text(
+                      "Object Detection Time: ${objectDetectionInferenceTime?.inMilliseconds ?? "N/A"} ms",
+                      style: const TextStyle(fontSize: 16),
+                    ),
+                    const SizedBox(height: 20),
+                  ],
+                  ElevatedButton(
+                    onPressed: runModels,
+                    child: const Text('Take Photo & Run Models'),
+                  ),
+                ],
+              ),
+      ),
+    );
+  }
+}

example/pubspec.lock

@@ -469,7 +469,7 @@ packages:
       path: ".."
       relative: true
     source: path
-    version: "4.2.7"
+    version: "4.3.0"
   sky_engine:
     dependency: transitive
     description: flutter