beginning work on ui

lib/tflite/classifier.dart

@@ -1,7 +1,9 @@
+import 'dart:io';
 import 'dart:math';
 
 import 'package:collection/collection.dart';
 import 'package:image/image.dart' as image_lib;
+import 'package:path_provider/path_provider.dart';
 import 'package:tflite_flutter/tflite_flutter.dart';
 import 'package:tflite_flutter_helper/tflite_flutter_helper.dart';
 
@@ -31,6 +33,7 @@ class Classifier {
   /// Labels file loaded as list
   late List<String> _labels;
   int classifierCreationStart = -1;
+  bool _shouldReturnFrame = false;
 
   Classifier(
     Interpreter interpreter, {
@@ -67,6 +70,10 @@ class Classifier {
     }
   }
 
+  void setReturnFrame(bool returnFrame) {
+    _shouldReturnFrame = returnFrame;
+  }
+
   /// Pre-process the image
   TensorImage? getProcessedImage(TensorImage? inputImage) {
     int cropSize = min(_inputImage.height, _inputImage.width);
@@ -83,12 +90,14 @@ class Classifier {
   }
 
   /// Runs object detection on the input image
-  Map<String, dynamic>? predict(image_lib.Image image) {
+  Future<Map<String, dynamic>?> predict(image_lib.Image image) async {
     var preProcStart = DateTime.now().millisecondsSinceEpoch;
-    _inputImage.loadImage(image);
+    _inputImage.loadImage(image_lib.copyRotate(image, 90));
     _inputImage = getProcessedImage(_inputImage)!;
+
+
     var inferenceStart = DateTime.now().millisecondsSinceEpoch;
-    _interpreter.run(_inputImage.buffer, _outputBuffer.getBuffer());
+        _interpreter.run(_inputImage.buffer, _outputBuffer.getBuffer());
     var postProcStart = DateTime.now().millisecondsSinceEpoch;
     Map<String, double> labeledProb =
         TensorLabel.fromList(labels, _outputProcessor.process(_outputBuffer))
@@ -106,6 +115,7 @@ class Classifier {
         inferenceTime: postProcStart - inferenceStart,
         postProcessingTime: endTime - postProcStart,
       ),
+      'image': _shouldReturnFrame? _inputImage.image : null,
     };
   }
 

lib/tflite/ml_isolate.dart

@@ -5,6 +5,7 @@ import 'package:tensordex_mobile/tflite/classifier.dart';
 import 'package:tflite_flutter/tflite_flutter.dart';
 
 import '../utils/image_utils.dart';
+import '../utils/logger.dart';
 
 class IsolateBase {
   final ReceivePort _receivePort = ReceivePort();
@@ -25,6 +26,7 @@ class MLIsolate extends IsolateBase {
     _sendPort = await _receivePort.first;
   }
 
+
   static void entryPoint(SendPort sendPort) async {
     final port = ReceivePort();
     sendPort.send(port.sendPort);
@@ -33,10 +35,15 @@ class MLIsolate extends IsolateBase {
       var cameraImage = mlIsolateData.cameraImage;
       var converted = ImageUtils.convertCameraImage(cameraImage);
       if (converted != null) {
-        Classifier classifier = Classifier(
+        var classifier = Classifier(
             Interpreter.fromAddress(mlIsolateData.interpreterAddress),
             labels: mlIsolateData.labels);
-        var result = classifier.predict(converted);
+        if (classifier.interpreter.address !=
+            mlIsolateData.interpreterAddress) {
+          logger.e('INTERPRETER ADDRESS MISMATCH!');
+        }
+        classifier.setReturnFrame(mlIsolateData.shouldSaveFrame);
+        var result = await classifier.predict(converted);
         mlIsolateData.responsePort?.send(result);
       } else {
         mlIsolateData.responsePort?.send({'response': 'not working yet'});
@@ -49,6 +56,7 @@ class MLIsolate extends IsolateBase {
 class MLIsolateData {
   CameraImage cameraImage;
   int interpreterAddress;
+  bool shouldSaveFrame;
   List<String> labels;
   SendPort? responsePort;
 
@@ -56,5 +64,6 @@ class MLIsolateData {
     this.cameraImage,
     this.interpreterAddress,
     this.labels,
+    this.shouldSaveFrame,
   );
 }