mlkit

Description:

mlkit

mlkit #

A Flutter plugin to use the Firebase ML Kit.
⭐Only your star motivate me!⭐
this is not official package #
The flutter team now has the firebase_ml_vision 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:

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:

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());
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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();
}
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