A Flutter plugin to use the Firebase ML Kit

mlkit

A Flutter plugin to use the Firebase ML Kit.

this is not official package

The flutter team now has the firebase_ml_vision or firebase_ml_custom package for Firebase ML Kit. Please consider trying to use firebase_ml_vision.

Note: This plugin is still under development, and some APIs might not be available yet. Feedback and Pull Requests are most welcome!

Features

Feature	Android	iOS
Recognize text(on device)	✅	✅
Recognize text(cloud)	yet	yet
Detect faces(on device)	✅	✅
Scan barcodes(on device)	✅	✅
Label Images(on device)	✅	✅
Label Images(cloud)	yet	yet
Object detection & tracking	yet	yet
Recognize landmarks(cloud)	yet	yet
Language identification	✅	✅
Translation	yet	yet
Smart Reply	yet	yet
AutoML model inference	yet	yet
Custom model(on device)	✅	✅
Custom model(cloud)	✅	✅

What features are available on device or in the cloud?

Usage

To use this plugin, add mlkit as a dependency in your pubspec.yaml file.

Getting Started

Check out the example directory for a sample app using Firebase Cloud Messaging.

Android Integration

To integrate your plugin into the Android part of your app, follow these steps:

1. Using the Firebase Console add an Android app to your project: Follow the assistant, download the generated google-services.json file and place it inside android/app. Next, modify the android/build.gradle file and the android/app/build.gradle file to add the Google services plugin as described by the Firebase assistant.

iOS Integration

To integrate your plugin into the iOS part of your app, follow these steps:

1. Using the Firebase Console add an iOS app to your project: Follow the assistant, download the generated GoogleService-Info.plist file, open ios/Runner.xcworkspace with Xcode, and within Xcode place the file inside ios/Runner. Don't follow the steps named "Add Firebase SDK" and "Add initialization code" in the Firebase assistant.

Dart/Flutter Integration

From your Dart code, you need to import the plugin and instantiate it:

import 'package:mlkit/mlkit.dart';

FirebaseVisionTextDetector detector = FirebaseVisionTextDetector.instance;

// Detect form file/image by path
var currentLabels = await detector.detectFromPath(_file?.path);

// Detect from binary data of a file/image
var currentLabels = await detector.detectFromBinary(_file?.readAsBytesSync());

custom model interpreter

native sample code

import 'package:mlkit/mlkit.dart';
import 'package:image/image.dart' as img;

FirebaseModelInterpreter interpreter = FirebaseModelInterpreter.instance;
FirebaseModelManager manager = FirebaseModelManager.instance;

//Register Cloud Model
manager.registerRemoteModelSource(
        FirebaseRemoteModelSource(modelName: "mobilenet_v1_224_quant"));

//Register Local Backup
manager.registerLocalModelSource(FirebaseLocalModelSource(modelName: 'mobilenet_v1_224_quant',  assetFilePath: 'ml/mobilenet_v1_224_quant.tflite');


var imageBytes = (await rootBundle.load("assets/mountain.jpg")).buffer;
img.Image image = img.decodeJpg(imageBytes.asUint8List());
image = img.copyResize(image, 224, 224);

//The app will download the remote model. While the remote model is being downloaded, it will use the local model.
var results = await interpreter.run(
        remoteModelName: "mobilenet_v1_224_quant",
        localModelName: "mobilenet_v1_224_quant",
        inputOutputOptions: FirebaseModelInputOutputOptions([
          FirebaseModelIOOption(FirebaseModelDataType.FLOAT32, [1, 224, 224, 3])
        ], [
          FirebaseModelIOOption(FirebaseModelDataType.FLOAT32, [1, 1001])
        ]),
        inputBytes: imageToByteList(image));

// int model
Uint8List imageToByteList(img.Image image) {
    var _inputSize = 224;
    var convertedBytes = new Uint8List(1 * _inputSize * _inputSize * 3);
    var buffer = new ByteData.view(convertedBytes.buffer);
    int pixelIndex = 0;
    for (var i = 0; i < _inputSize; i++) {
      for (var j = 0; j < _inputSize; j++) {
        var pixel = image.getPixel(i, j);
        buffer.setUint8(pixelIndex, (pixel >> 16) & 0xFF);
        pixelIndex++;
        buffer.setUint8(pixelIndex, (pixel >> 8) & 0xFF);
        pixelIndex++;
        buffer.setUint8(pixelIndex, (pixel) & 0xFF);
        pixelIndex++;
      }
    }
    return convertedBytes;
  }

// float model
Uint8List imageToByteList(img.Image image) {
  var _inputSize = 224;
  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(i, j);
      buffer[pixelIndex] = ((pixel >> 16) & 0xFF) / 255;
      pixelIndex += 1;
      buffer[pixelIndex] = ((pixel >> 8) & 0xFF) / 255;
      pixelIndex += 1;
      buffer[pixelIndex] = ((pixel) & 0xFF) / 255;
      pixelIndex += 1;
    }
  }
  return convertedBytes.buffer.asUint8List();
}

GitHub

https://github.com/azihsoyn/flutter_mlkit