tflite 1.0.1

A Flutter plugin for accessing TensorFlow Lite. Supports both iOS and Android.

example/lib/main.dart

import 'dart:async';
import 'dart:io';
import 'dart:typed_data';
import 'package:flutter/material.dart';
import 'package:flutter/services.dart';
import 'package:image/image.dart' as img;

import 'package:tflite/tflite.dart';
import 'package:image_picker/image_picker.dart';

void main() => runApp(new App());

const String mobile = "MobileNet";
const String ssd = "SSD MobileNet";
const String yolo = "Tiny YOLOv2";

class App extends StatelessWidget {
  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      home: MyApp(),
    );
  }
}

class MyApp extends StatefulWidget {
  @override
  _MyAppState createState() => new _MyAppState();
}

class _MyAppState extends State<MyApp> {
  File _image;
  List _recognitions;
  String _model = "";
  double _imageHeight;
  double _imageWidth;

  Future getImage() async {
    var image = await ImagePicker.pickImage(source: ImageSource.gallery);

    switch (_model) {
      case yolo:
        yolov2Tiny(image);
        break;
      case ssd:
        ssdMobileNet(image);
        break;
      default:
        recognizeImage(image);
      // recognizeImageBinary(image);
    }

    new FileImage(image)
        .resolve(new ImageConfiguration())
        .addListener((ImageInfo info, bool _) {
      setState(() {
        _imageHeight = info.image.height.toDouble();
        _imageWidth = info.image.width.toDouble();
      });
    });

    setState(() {
      _image = image;
    });
  }

  @override
  void initState() {
    super.initState();
  }

  Future loadModel() async {
    try {
      String res;
      switch (_model) {
        case yolo:
          res = await Tflite.loadModel(
            model: "assets/yolov2_tiny.tflite",
            labels: "assets/yolov2_tiny.txt",
          );
          break;
        case ssd:
          res = await Tflite.loadModel(
              model: "assets/ssd_mobilenet.tflite",
              labels: "assets/ssd_mobilenet.txt");
          break;
        default:
          res = await Tflite.loadModel(
            model: "assets/mobilenet_v1_1.0_224.tflite",
            labels: "assets/mobilenet_v1_1.0_224.txt",
          );
      }
      print(res);
    } on PlatformException {
      print('Failed to load model.');
    }
  }

  Uint8List imageToByteListFloat32(
      img.Image image, int inputSize, double mean, double std) {
    var convertedBytes = Float32List(1 * inputSize * inputSize * 3);
    var buffer = Float32List.view(convertedBytes.buffer);
    int pixelIndex = 0;
    for (var i = 0; i < inputSize; i++) {
      for (var j = 0; j < inputSize; j++) {
        var pixel = image.getPixel(j, i);
        buffer[pixelIndex++] = (img.getRed(pixel) - mean) / std;
        buffer[pixelIndex++] = (img.getGreen(pixel) - mean) / std;
        buffer[pixelIndex++] = (img.getBlue(pixel) - mean) / std;
      }
    }
    return convertedBytes.buffer.asUint8List();
  }

  Uint8List imageToByteListUint8(img.Image image, int inputSize) {
    var convertedBytes = Uint8List(1 * inputSize * inputSize * 3);
    var buffer = Uint8List.view(convertedBytes.buffer);
    int pixelIndex = 0;
    for (var i = 0; i < inputSize; i++) {
      for (var j = 0; j < inputSize; j++) {
        var pixel = image.getPixel(j, i);
        buffer[pixelIndex++] = img.getRed(pixel);
        buffer[pixelIndex++] = img.getGreen(pixel);
        buffer[pixelIndex++] = img.getBlue(pixel);
      }
    }
    return convertedBytes.buffer.asUint8List();
  }

  Future recognizeImage(File image) async {
    var recognitions = await Tflite.runModelOnImage(
      path: image.path,
      numResults: 6,
      threshold: 0.05,
      imageMean: 127.5,
      imageStd: 127.5,
    );
    setState(() {
      _recognitions = recognitions;
    });
  }

  Future recognizeImageBinary(File image) async {
    var imageBytes = (await rootBundle.load(image.path)).buffer;
    img.Image oriImage = img.decodeJpg(imageBytes.asUint8List());
    img.Image resizedImage = img.copyResize(oriImage, 224, 224);
    var recognitions = await Tflite.runModelOnBinary(
      binary: imageToByteListFloat32(resizedImage, 224, 127.5, 127.5),
      numResults: 6,
      threshold: 0.05,
    );
    setState(() {
      _recognitions = recognitions;
    });
  }

  Future yolov2Tiny(File image) async {
    var recognitions = await Tflite.detectObjectOnImage(
      path: image.path,
      model: "YOLO",
      threshold: 0.3,
      imageMean: 0.0,
      imageStd: 255.0,
      numResultsPerClass: 1,
    );
    // var imageBytes = (await rootBundle.load(image.path)).buffer;
    // img.Image oriImage = img.decodeJpg(imageBytes.asUint8List());
    // img.Image resizedImage = img.copyResize(oriImage, 416, 416);
    // var recognitions = await Tflite.detectObjectOnBinary(
    //   binary: imageToByteListFloat32(resizedImage, 416, 0.0, 255.0),
    //   model: "YOLO",
    //   threshold: 0.3,
    //   numResultsPerClass: 1,
    // );
    setState(() {
      _recognitions = recognitions;
    });
  }

  Future ssdMobileNet(File image) async {
    var recognitions = await Tflite.detectObjectOnImage(
      path: image.path,
      numResultsPerClass: 1,
    );
    // var imageBytes = (await rootBundle.load(image.path)).buffer;
    // img.Image oriImage = img.decodeJpg(imageBytes.asUint8List());
    // img.Image resizedImage = img.copyResize(oriImage, 300, 300);
    // var recognitions = await Tflite.detectObjectOnBinary(
    //   binary: imageToByteListUint8(resizedImage, 300),
    //   numResultsPerClass: 1,
    // );
    setState(() {
      _recognitions = recognitions;
    });
  }

  onSelect(model) {
    setState(() {
      _model = model;
    });
    loadModel();
  }

  List<Widget> renderBoxes(Size screen) {
    if (_recognitions == null) return [];
    double factorX = screen.width;
    double factorY = _imageHeight / _imageWidth * screen.width;
    Color blue = Color.fromRGBO(37, 213, 253, 1.0);
    return _recognitions.map((re) {
      return Positioned(
        left: re["rect"]["x"] * factorX,
        top: re["rect"]["y"] * factorY,
        width: re["rect"]["w"] * factorX,
        height: re["rect"]["h"] * factorY,
        child: Container(
          decoration: BoxDecoration(
            border: Border.all(
              color: blue,
              width: 2,
            ),
          ),
          child: Text(
            "${re["detectedClass"]} ${(re["confidenceInClass"] * 100).toStringAsFixed(0)}%",
            style: TextStyle(
              background: Paint()..color = blue,
              color: Colors.white,
              fontSize: 12.0,
            ),
          ),
        ),
      );
    }).toList();
  }

  @override
  Widget build(BuildContext context) {
    Size size = MediaQuery.of(context).size;
    return Scaffold(
      appBar: AppBar(
        title: const Text('tflite example app'),
      ),
      body: _model == ""
          ? Center(
              child: Column(
                children: <Widget>[
                  RaisedButton(
                    child: const Text(mobile),
                    onPressed: () => onSelect(mobile),
                  ),
                  RaisedButton(
                    child: const Text(ssd),
                    onPressed: () => onSelect(ssd),
                  ),
                  RaisedButton(
                    child: const Text(yolo),
                    onPressed: () => onSelect(yolo),
                  ),
                ],
              ),
            )
          : Stack(
              children: <Widget>[
                Container(
                  child: _image == null
                      ? Text('No image selected.')
                      : Image.file(_image),
                ),
                _model == mobile
                    ? Center(
                        child: Column(
                          children: _recognitions != null
                              ? _recognitions.map((res) {
                                  return Text(
                                    "${res["index"]} - ${res["label"]}: ${res["confidence"].toString()}",
                                    style: TextStyle(
                                      color: Colors.black,
                                      fontSize: 20.0,
                                      background: Paint()..color = Colors.white,
                                    ),
                                  );
                                }).toList()
                              : [],
                        ),
                      )
                    : Stack(children: renderBoxes(size)),
              ],
            ),
      floatingActionButton: FloatingActionButton(
        onPressed: getImage,
        tooltip: 'Pick Image',
        child: Icon(Icons.image),
      ),
    );
  }
}