refactoring code to use proper views in the UI and turned the camera instance into its own view with callbacks.

Also added in the model for initial use, and working to create the inferences off of TFLite using that view.

lib/utils/camera_view_singleton.dart

@@ -0,0 +1,10 @@
+import 'dart:ui';
+
+class CameraViewSingleton {
+  static double ratio = 0.0;
+  static Size screenSize = const Size(0, 0);
+  static Size inputImageSize = const Size(0, 0);
+
+  static Size get actualPreviewSize =>
+      Size(screenSize.width, screenSize.width * ratio);
+}

lib/utils/image_utils.dart

@@ -0,0 +1,86 @@
+import 'dart:io';
+
+import 'package:camera/camera.dart';
+import 'package:image/image.dart' as image_lib;
+import 'package:logger/logger.dart';
+import 'package:path_provider/path_provider.dart';
+
+import 'logger.dart';
+
+/// ImageUtils
+class ImageUtils {
+  /// Converts a [CameraImage] in YUV420 format to [image_lib.Image] in RGB format
+  static image_lib.Image? convertCameraImage(CameraImage cameraImage) {
+    if (cameraImage.format.group == ImageFormatGroup.yuv420) {
+      return convertYUV420ToImage(cameraImage);
+    } else if (cameraImage.format.group == ImageFormatGroup.bgra8888) {
+      return convertBGRA8888ToImage(cameraImage);
+    } else {
+      return null;
+    }
+  }
+
+  /// Converts a [CameraImage] in BGRA888 format to [image_lib.Image] in RGB format
+  static image_lib.Image convertBGRA8888ToImage(CameraImage cameraImage) {
+    image_lib.Image img = image_lib.Image.fromBytes(
+        cameraImage.planes[0].width ?? 0,
+        cameraImage.planes[0].height ?? 0,
+        cameraImage.planes[0].bytes,
+        format: image_lib.Format.bgra
+    );
+    return img;
+  }
+
+  /// Converts a [CameraImage] in YUV420 format to [image_lib.Image] in RGB format
+  static image_lib.Image convertYUV420ToImage(CameraImage cameraImage) {
+    final int width = cameraImage.width;
+    final int height = cameraImage.height;
+
+    final int uvRowStride = cameraImage.planes[1].bytesPerRow;
+    final int uvPixelStride = cameraImage.planes[1].bytesPerPixel?? 0;
+
+    final image = image_lib.Image(width, height);
+
+    for (int w = 0; w < width; w++) {
+      for (int h = 0; h < height; h++) {
+        final int uvIndex =
+            uvPixelStride * (w / 2).floor() + uvRowStride * (h / 2).floor();
+        final int index = h * width + w;
+
+        final y = cameraImage.planes[0].bytes[index];
+        final u = cameraImage.planes[1].bytes[uvIndex];
+        final v = cameraImage.planes[2].bytes[uvIndex];
+
+        image.data[index] = ImageUtils.yuv2rgb(y, u, v);
+      }
+    }
+    return image;
+  }
+
+  /// Convert a single YUV pixel to RGB
+  static int yuv2rgb(int y, int u, int v) {
+    // Convert yuv pixel to rgb
+    int r = (y + v * 1436 / 1024 - 179).round();
+    int g = (y - u * 46549 / 131072 + 44 - v * 93604 / 131072 + 91).round();
+    int b = (y + u * 1814 / 1024 - 227).round();
+
+    // Clipping RGB values to be inside boundaries [ 0 , 255 ]
+    r = r.clamp(0, 255);
+    g = g.clamp(0, 255);
+    b = b.clamp(0, 255);
+
+    return 0xff000000 |
+        ((b << 16) & 0xff0000) |
+        ((g << 8) & 0xff00) |
+        (r & 0xff);
+  }
+
+  static void saveImage(image_lib.Image image, [int i = 0]) async {
+    List<int> jpeg = image_lib.JpegEncoder().encodeImage(image);
+    final appDir = await getTemporaryDirectory();
+    final appPath = appDir.path;
+    final fileOnDevice = File('$appPath/out$i.jpg');
+    await fileOnDevice.writeAsBytes(jpeg, flush: true);
+    logger.i('Saved $appPath/out$i.jpg');
+  }
+}

lib/utils/logger.dart

@@ -0,0 +1,5 @@
+import 'package:logger/logger.dart';
+
+var logger = Logger(
+  printer: PrettyPrinter(),
+);

lib/utils/recognition.dart

@@ -0,0 +1,21 @@
+
+/// Represents the recognition output from the model
+class Recognition {
+  /// Index of the result
+  final int _id;
+  /// Label of the result
+  final String _label;
+  /// Confidence [0.0, 1.0]
+  final double _score;
+
+  Recognition(this._id, this._label, this._score);
+
+  int get id => _id;
+  String get label => _label;
+  double get score => _score;
+
+  @override
+  String toString() {
+    return 'Recognition(id: $id, label: $label, score: $score)';
+  }
+}

lib/utils/stats.dart

@@ -0,0 +1,23 @@
+/// Bundles different elapsed times
+class Stats {
+  /// Total time taken in the isolate where the inference runs
+  int totalPredictTime;
+
+  /// [totalPredictTime] + communication overhead time
+  /// between main isolate and another isolate
+  int totalElapsedTime;
+
+  /// Time for which inference runs
+  int inferenceTime;
+
+  /// Time taken to pre-process the image
+  int preProcessingTime;
+
+  Stats(this.totalPredictTime, this.totalElapsedTime, this.inferenceTime,
+      this.preProcessingTime);
+
+  @override
+  String toString() {
+    return 'Stats{totalPredictTime: $totalPredictTime, totalElapsedTime: $totalElapsedTime, inferenceTime: $inferenceTime, preProcessingTime: $preProcessingTime}';
+  }
+}