lucasoskorep/tensordex-mobile
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investigating why the input to the model is incorrect resulting in error on initialization.

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This commit is contained in:
Lucas Oskorep
2022-06-21 23:41:42 -04:00
parent dc4bf39c14
commit ebfbfb503d
10 changed files with 340 additions and 17 deletions
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lib/classifier.dart
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lib/main.dart
+1 -1
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@@ -1,5 +1,5 @@
import 'package:flutter/material.dart';
import 'package:tensordex_mobile/ui/home.dart';
import 'package:tensordex_mobile/ui/tensordex_home.dart';
import 'package:tensordex_mobile/utils/logger.dart';
Future<void> main() async {
lib/tflite/classifier.dart
+160
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@@ -0,0 +1,160 @@
import 'dart:math';
import 'dart:ui';
import 'package:collection/collection.dart';
import 'package:image/image.dart' as image_lib;
import 'package:tflite_flutter/tflite_flutter.dart';
import 'package:tflite_flutter_helper/tflite_flutter_helper.dart';
import '../utils/logger.dart';
import '../utils/recognition.dart';
import '../utils/stats.dart';
/// Classifier
class Classifier {
static const String MODEL_FILE_NAME = "detect.tflite";
static const String LABEL_FILE_NAME = "labelmap.txt";
/// Input size of image (height = width = 300)
static const int INPUT_SIZE = 224;
/// Result score threshold
static const double THRESHOLD = 0.5;
/// [ImageProcessor] used to pre-process the image
ImageProcessor? imageProcessor;
/// Padding the image to transform into square
// int padSize = 0;
/// Instance of Interpreter
late Interpreter _interpreter;
late TensorBuffer _outputBuffer;
late var _probabilityProcessor;
/// Labels file loaded as list
late List<String> _labels;
/// Number of results to show
static const int NUM_RESULTS = 10;
Classifier({
Interpreter? interpreter,
List<String>? labels,
}) {
loadModel(interpreter: interpreter);
loadLabels(labels: labels);
}
/// Loads interpreter from asset
void loadModel({Interpreter? interpreter}) async {
try {
_interpreter = interpreter ??
await Interpreter.fromAsset(
MODEL_FILE_NAME,
options: InterpreterOptions()..threads = 4,
);
var outputTensor = _interpreter.getOutputTensor(0);
var outputShape = outputTensor.shape;
var outputType = outputTensor.type;
var inputTensor = _interpreter.getInputTensor(0);
var intputShape = inputTensor.shape;
var intputType = inputTensor.type;
_outputBuffer = TensorBuffer.createFixedSize(outputShape, outputType);
_probabilityProcessor =
TensorProcessorBuilder().add(NormalizeOp(0, 1)).build();
} catch (e) {
logger.e("Error while creating interpreter: ", e);
}
}
/// Loads labels from assets
void loadLabels({List<String>? labels}) async {
try {
_labels = labels ?? await FileUtil.loadLabels("assets/labels.txt");
} catch (e) {
logger.e("Error while loading labels: $e");
}
}
/// Pre-process the image
TensorImage? getProcessedImage(TensorImage inputImage) {
// padSize = max(inputImage.height, inputImage.width);
imageProcessor ??= ImageProcessorBuilder()
// .add(ResizeWithCropOrPadOp(padSize, padSize))
.add(ResizeOp(INPUT_SIZE, INPUT_SIZE, ResizeMethod.BILINEAR))
.add(NormalizeOp(127.5, 127.5))
.build();
return imageProcessor?.process(inputImage);
}
/// Runs object detection on the input image
Map<String, dynamic>? predict(image_lib.Image image) {
logger.i(labels);
var predictStartTime = DateTime.now().millisecondsSinceEpoch;
if (_interpreter == null) {
logger.e("Interpreter not initialized");
return null;
}
var preProcessStart = DateTime.now().millisecondsSinceEpoch;
// Create TensorImage from image
// Pre-process TensorImage
var procImage = getProcessedImage(TensorImage.fromImage(image));
var preProcessElapsedTime =
DateTime.now().millisecondsSinceEpoch - preProcessStart;
if (procImage != null) {
var inferenceTimeStart = DateTime.now().millisecondsSinceEpoch;
// run inference
var inferenceTimeElapsed =
DateTime.now().millisecondsSinceEpoch - inferenceTimeStart;
logger.i("Sending image to ML");
logger.i(procImage.buffer.asFloat32List());
logger.i(procImage.width);
logger.i(procImage.height);
logger.i(procImage.tensorBuffer.shape);
logger.i(procImage.tensorBuffer.isDynamic);
_interpreter.run(procImage.buffer, _outputBuffer.getBuffer());
Map<String, double> labeledProb = TensorLabel.fromList(
labels, _probabilityProcessor.process(_outputBuffer))
.getMapWithFloatValue();
final pred = getTopProbability(labeledProb);
Recognition rec = Recognition(1, pred.key, pred.value);
var predictElapsedTime = DateTime.now().millisecondsSinceEpoch - predictStartTime;
return {
"recognitions": rec,
"stats": Stats(predictElapsedTime, predictElapsedTime, predictElapsedTime, predictElapsedTime),
};
} else {
return null;
}
}
/// Gets the interpreter instance
Interpreter get interpreter => _interpreter;
/// Gets the loaded labels
List<String> get labels => _labels;
}
MapEntry<String, double> getTopProbability(Map<String, double> labeledProb) {
var pq = PriorityQueue<MapEntry<String, double>>(compare);
pq.addAll(labeledProb.entries);
return pq.first;
}
int compare(MapEntry<String, double> e1, MapEntry<String, double> e2) {
if (e1.value > e2.value) {
return -1;
} else if (e1.value == e2.value) {
return 0;
} else {
return 1;
}
}
lib/ui/poke_view.dart
+36 -1
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@@ -2,6 +2,9 @@ import 'dart:isolate';
import 'package:camera/camera.dart';
import 'package:flutter/material.dart';
import 'package:tensordex_mobile/tflite/classifier.dart';
import 'package:tflite_flutter/tflite_flutter.dart';
import 'package:tensordex_mobile/utils/image_utils.dart';
import '../utils/logger.dart';
import '../utils/recognition.dart';
@@ -30,10 +33,13 @@ class _CameraViewState extends State<CameraView> with WidgetsBindingObserver {
/// Controller
late CameraController cameraController;
Interpreter? interp;
/// true when inference is ongoing
bool predicting = false;
late Classifier classy;
// /// Instance of [Classifier]
// Classifier classifier;
//
@@ -56,9 +62,28 @@ class _CameraViewState extends State<CameraView> with WidgetsBindingObserver {
// Camera initialization
initializeCamera();
// final gpuDelegateV2 = GpuDelegateV2(
// options: GpuDelegateOptionsV2(
// isPrecisionLossAllowed: false,
// inferencePreference: TfLiteGpuInferenceUsage.fastSingleAnswer,
// inferencePriority1: TfLiteGpuInferencePriority.minLatency,
// inferencePriority2: TfLiteGpuInferencePriority.auto,
// inferencePriority3: TfLiteGpuInferencePriority.auto,
// ));
logger.e("CREATING THE INTERPRETOR");
var interpreterOptions = InterpreterOptions();//..addDelegate(gpuDelegateV2);
interp = await Interpreter.fromAsset('efficientnet_v2s.tflite',
options: interpreterOptions);
logger.e("CREATING THE INTERPRETOR");
classy = Classifier(interpreter: interp);
logger.i(interp?.getOutputTensors());
// Create an instance of classifier to load model and labels
// classifier = Classifier();
// Initially predicting = false
predicting = false;
}
@@ -94,7 +119,7 @@ class _CameraViewState extends State<CameraView> with WidgetsBindingObserver {
@override
Widget build(BuildContext context) {
// Return empty container while the camera is not initialized
if (!cameraController.value.isInitialized || cameraController == null) {
if (!cameraController.value.isInitialized) {
return Container();
}
@@ -114,6 +139,16 @@ class _CameraViewState extends State<CameraView> with WidgetsBindingObserver {
predicting = true;
});
logger.i("RECIEVED IMAGE");
logger.i(cameraImage.format.group);
logger.i(cameraImage);
var converted = ImageUtils.convertCameraImage(cameraImage);
if (converted != null){
var result = classy.predict(converted);
logger.e("PREDICTED IMAGE");
logger.i(result);
}
// logger.i(cameraImage);
// logger.i(cameraImage.height);
// logger.i(cameraImage.width);
lib/ui/results_view.dart
+33
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@@ -0,0 +1,33 @@
import 'package:flutter/material.dart';
import 'package:tensordex_mobile/ui/poke_view.dart';
import 'package:tensordex_mobile/utils/recognition.dart';
import '../utils/logger.dart';
/// [CameraView] sends each frame for inference
class ResultsView extends StatefulWidget {
/// Constructor
const ResultsView({Key? key}) : super(key: key);
void setResults(Recognition results){
logger.i("RESULTS IN THE RESULT VIEW");
}
@override
State<ResultsView> createState() => _ResultsViewState();
}
class _ResultsViewState extends State<ResultsView> {
@override
void initState() {
super.initState();
}
@override
Widget build(BuildContext context) {
return Text("data");
}
}
lib/ui/home.dart → lib/ui/tensordex_home.dart
+3 -14
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@@ -1,6 +1,6 @@
import 'package:flutter/material.dart';
import 'package:camera/camera.dart';
import 'package:tensordex_mobile/ui/poke_view.dart';
import 'package:tensordex_mobile/ui/results_view.dart';
import '../utils/logger.dart';
import '../utils/recognition.dart';
@@ -25,7 +25,6 @@ class TensordexHome extends StatefulWidget {
}
class _TensordexHomeState extends State<TensordexHome> {
int _counter = 0;
/// Results to draw bounding boxes
List<Recognition>? results;
@@ -38,7 +37,6 @@ class _TensordexHomeState extends State<TensordexHome> {
void _incrementCounter() {
setState(() {
_counter++;
logger.d("Counter Incremented!");
logger.w("Counter Incremented!");
logger.e("Counter Incremented!");
@@ -129,8 +127,6 @@ class _TensordexHomeState extends State<TensordexHome> {
@override
void dispose() {
// controller.dispose();
// WidgetsBinding.instance.removeObserver(this);
super.dispose();
}
@@ -158,17 +154,10 @@ class _TensordexHomeState extends State<TensordexHome> {
child: Column(
mainAxisAlignment: MainAxisAlignment.center,
children: <Widget>[
const Text(
'You have pushed the button this many times:',
),
Text(
'$_counter',
style: Theme.of(context).textTheme.headline4,
),
CameraView(
resultsCallback: resultsCallback,
statsCallback: statsCallback
),
statsCallback: statsCallback),
const ResultsView(),
],
),
),