Improve code documentation and readability

Author: am15h

android/app/build.gradle

@@ -34,6 +34,7 @@ android {
 
     lintOptions {
         disable 'InvalidPackage'
+        checkReleaseBuilds false // Adding this line to avoid release build failure
     }
 
     defaultConfig {

lib/tflite/classifier.dart

@@ -9,11 +9,12 @@ import 'package:tflite_flutter_helper/tflite_flutter_helper.dart';
 
 import 'stats.dart';
 
+/// Classifier
 class Classifier {
   /// Instance of Interpreter
   Interpreter _interpreter;
 
-  /// Instance of loaded labels
+  /// Labels file loaded as list
   List<String> _labels;
 
   static const String MODEL_FILE_NAME = "detect.tflite";
@@ -79,6 +80,7 @@ class Classifier {
     }
   }
 
+  /// Pre-process the image
   TensorImage getProcessedImage(TensorImage inputImage) {
     padSize = max(inputImage.height, inputImage.width);
     if (imageProcessor == null) {
@@ -91,7 +93,8 @@ class Classifier {
     return inputImage;
   }
 
-  List predict(imageLib.Image image) {
+  /// Runs object detection on the input image
+  Map<String, dynamic> predict(imageLib.Image image) {
     var predictStartTime = DateTime.now().millisecondsSinceEpoch;
 
     if (_interpreter == null) {
@@ -110,16 +113,16 @@ class Classifier {
     var preProcessElapsedTime =
         DateTime.now().millisecondsSinceEpoch - preProcessStart;
 
-    // Inputs object for runForMultipleInputs
-    // Use [TensorImage.buffer] or [TensorBuffer.buffer] to pass by reference
-    List<Object> inputs = [inputImage.buffer];
-
     // TensorBuffers for output tensors
     TensorBuffer outputLocations = TensorBufferFloat(_outputShapes[0]);
     TensorBuffer outputClasses = TensorBufferFloat(_outputShapes[1]);
     TensorBuffer outputScores = TensorBufferFloat(_outputShapes[2]);
     TensorBuffer numLocations = TensorBufferFloat(_outputShapes[3]);
 
+    // Inputs object for runForMultipleInputs
+    // Use [TensorImage.buffer] or [TensorBuffer.buffer] to pass by reference
+    List<Object> inputs = [inputImage.buffer];
+
     // Outputs map
     Map<int, Object> outputs = {
       0: outputLocations.buffer,
@@ -129,6 +132,8 @@ class Classifier {
     };
 
     var inferenceTimeStart = DateTime.now().millisecondsSinceEpoch;
+
+    // run inference
     _interpreter.runForMultipleInputs(inputs, outputs);
 
     var inferenceTimeElapsed =
@@ -137,8 +142,6 @@ class Classifier {
     // Maximum number of results to show
     int resultsCount = min(NUM_RESULTS, numLocations.getIntValue(0));
 
-    List<Recognition> recognitions = [];
-
     // Using labelOffset = 1 as ??? at index 0
     int labelOffset = 1;
 
@@ -153,15 +156,23 @@ class Classifier {
       width: INPUT_SIZE,
     );
 
+    List<Recognition> recognitions = [];
+
     for (int i = 0; i < resultsCount; i++) {
-      var label = _labels.elementAt(outputClasses.getIntValue(i) + labelOffset);
+      // Prediction score
       var score = outputScores.getDoubleValue(i);
 
-      // inverse of rect
-      Rect transformedRect = imageProcessor.inverseTransformRect(
-          locations[i], image.height, image.width);
+      // Label string
+      var labelIndex = outputClasses.getIntValue(i) + labelOffset;
+      var label = _labels.elementAt(labelIndex);
 
       if (score > THRESHOLD) {
+        // inverse of rect
+        // [locations] corresponds to the image size 300 X 300
+        // inverseTransformRect transforms it our [inputImage]
+        Rect transformedRect = imageProcessor.inverseTransformRect(
+            locations[i], image.height, image.width);
+
         recognitions.add(
           Recognition(i, label, score, transformedRect),
         );
@@ -171,16 +182,13 @@ class Classifier {
     var predictElapsedTime =
         DateTime.now().millisecondsSinceEpoch - predictStartTime;
 
-//    print(
-//        'Classifier.predict | Pre-process: $preProcessElapsedTime ms | Inference: $inferenceTimeElapsed ms | Total: $predictElapsedTime');
-
-    return [
-      recognitions,
-      Stats(
+    return {
+      "recognitions": recognitions,
+      "stats": Stats(
           totalPredictTime: predictElapsedTime,
           inferenceTime: inferenceTimeElapsed,
           preProcessingTime: preProcessElapsedTime)
-    ];
+    };
   }
 
   /// Gets the interpreter instance

lib/tflite/recognition.dart

@@ -3,10 +3,21 @@ import 'dart:math';
 import 'package:flutter/cupertino.dart';
 import 'package:object_detection/ui/camera_view_singleton.dart';
 
+/// Represents the recognition output from the model
 class Recognition {
+  /// Index of the result
   int _id;
+
+  /// Label of the result
   String _label;
+
+  /// Confidence [0.0, 1.0]
   double _score;
+
+  /// Location of bounding box rect
+  ///
+  /// The rectangle corresponds to the raw input image
+  /// passed for inference
   Rect _location;
 
   Recognition(this._id, this._label, this._score, [this._location]);
@@ -19,6 +30,11 @@ class Recognition {
 
   Rect get location => _location;
 
+  /// Returns bounding box rectangle corresponding to the
+  /// displayed image on screen
+  ///
+  /// This is the actual location where rectangle is rendered on
+  /// the screen
   Rect get renderLocation {
     // ratioX = screenWidth / imageInputWidth
     // ratioY = ratioX if image fits screenWidth with aspectRatio = constant

lib/tflite/stats.dart

@@ -1,14 +1,26 @@
+/// Bundles different elapsed times
 class Stats {
-  int totalElapsedTime;
+  /// 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.totalElapsedTime, this.totalPredictTime, this.inferenceTime,
+  Stats(
+      {this.totalPredictTime,
+      this.totalElapsedTime,
+      this.inferenceTime,
       this.preProcessingTime});
 
   @override
   String toString() {
-    return 'Stats{totalElapsedTime: $totalElapsedTime, inferenceTime: $inferenceTime, preProcessingTime: $preProcessingTime}';
+    return 'Stats{totalPredictTime: $totalPredictTime, totalElapsedTime: $totalElapsedTime, inferenceTime: $inferenceTime, preProcessingTime: $preProcessingTime}';
   }
 }

lib/ui/box_widget.dart

@@ -8,7 +8,7 @@ class BoxWidget extends StatelessWidget {
   const BoxWidget({Key key, this.result}) : super(key: key);
   @override
   Widget build(BuildContext context) {
-
+    // Color for bounding box
     Color color = Colors.primaries[
         (result.label.length + result.label.codeUnitAt(0) + result.id) %
             Colors.primaries.length];
@@ -22,9 +22,8 @@ class BoxWidget extends StatelessWidget {
         width: result.renderLocation.width,
         height: result.renderLocation.height,
         decoration: BoxDecoration(
-          border: Border.all(color: color, width: 3),
-          borderRadius: BorderRadius.all(Radius.circular(2))
-        ),
+            border: Border.all(color: color, width: 3),
+            borderRadius: BorderRadius.all(Radius.circular(2))),
         child: Align(
           alignment: Alignment.topLeft,
           child: FittedBox(