firebase_livestream_ml_vision 1.0.1

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ML Kit Vision for Firebase with AutoML Vision Edge and Camera Live Streaming Support #


A Flutter plugin to use the ML Kit Vision for Firebase API.

For Flutter plugins for other Firebase products, see

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

Usage #

To use this plugin, add firebase_livestream_ml_vision as a dependency in your pubspec.yaml file. You must also configure Firebase for each platform project: Android and iOS (see the example folder or for step by step details).

AutoML Vision Edge #

If you plan to use AutoML Vision Edge to detect labels using a custom model, either download or host the trained model by following these instructions.

If you downloaded the file, follow the instructions below to enable the plugin.

Unzip the file downloaded, and rename the folder to reflect the model name.

Create a assets folder and place the previous folder within it. In pubspec.yaml add the appropriate paths:

   - assets/<foldername>/dict.txt
   - assets/<foldername>/manifest.json
   - assets/<foldername>/model.tflite

Android #

Change the minimum Android sdk version to 21 (or higher) in your android/app/build.gradle file.

minSdkVersion 21

If you're using local AutoML VisionEdge Models, include this in your app-level build.gradle file.

aaptOptions {
    noCompress "tflite"

If you're using the on-device ImageLabeler, include the latest matching ML Kit: Image Labeling dependency in your app-level build.gradle file.

android {
    dependencies {
        // ...

        api ''

If you receive compilation errors, try an earlier version of ML Kit: Image Labeling.

Optional but recommended: If you use the on-device API, configure your app to automatically download the ML model to the device after your app is installed from the Play Store. To do so, add the following declaration to your app's AndroidManifest.xml file:

<application ...>
    android:value="ocr" />
  <!-- To use multiple models: android:value="ocr,label,barcode,face" -->

iOS #

Versions 0.7.0+ use the latest ML Kit for Firebase version which requires a minimum deployment target of 9.0. You can add the line platform :ios, '9.0' in your iOS project Podfile.

If you're using one of the on-device APIs, include the corresponding ML Kit library model in your Podfile. Then run pod update in a terminal within the same directory as your Podfile.

pod 'Firebase/MLVisionBarcodeModel'
pod 'Firebase/MLVisionFaceModel'
pod 'Firebase/MLVisionLabelModel'
pod 'Firebase/MLVisionTextModel'

Add one row in ios/Runner/Info.plist:

The key Privacy - Camera Usage Description and a usage description.

In text format:

<string>Can I use the camera please?</string>

Using an ML Vision Detector #

1. Create a Camera View #

import 'package:firebase_livestream_ml_vision/firebase_livestream_ml_vision.dart';
import 'package:flutter/material.dart';

void main() => runApp(MaterialApp(home: _MyHomePage()));

class _MyHomePage extends StatefulWidget {
  _MyHomePageState createState() => _MyHomePageState();

class _MyHomePageState extends State<_MyHomePage> {
  FirebaseVision _vision;
  dynamic _scanResults;

  void initState() {

  void _initializeCamera() async {
    List<FirebaseCameraDescription> cameras = await camerasAvailable();
    _vision = FirebaseVision(cameras[0], ResolutionSetting.high);
    _vision.initialize().then((_) {
      if (!mounted) {
      setState(() {});
Widget _buildImage() {
    return Container(
      constraints: const BoxConstraints.expand(),
      child: _vision == null
              fit: StackFit.expand,
              children: <Widget>[
  Widget build(BuildContext context) {
    return Scaffold(
    body: _buildImage(),

 void dispose() {
    _vision.dispose().then((_) {
  // close all detectors


See the example app to learn more on incorporating detectors in the camera app, check it out here.

2. Using detectors #

Special Instructions for using VisionEdgeImageLabeler #

Get an object of ModelManager, and setup the local or remote model(optional, results in faster first-use)

FirebaseVision.modelManager().setupModel('<foldername(modelname)>', modelLocation);

Calling a Labeler/Detector #

a. Image Labeler

        onValue.listen((onData) => // do something with data

b. Cloud Image Labeler

        onValue.listen((onData) => // do something with data

c. Barcode Detector

          onValue.listen((onData) => // do something with data

d. Face Detector

          onValue.listen((onData) => // do something with data

e. Text Recognizer

          onValue.listen((onData) => // do something with data

f. Vision Edge Image Labeler

_vision.addVisionEdgeImageLabeler('<foldername(modelname)>', modelLocation).then((onValue){
          onValue.listen((onData) => // do something with data

You can also configure all detectors, except TextRecognizer, with desired options.

final ImageLabeler labeler = FirebaseVision.instance.addImageLabler(
  ImageLabelerOptions(confidenceThreshold: 0.75),

3. Extract data #

a. Extract barcodes.

for (Barcode barcode in barcodes) {
  final Rectangle<int> boundingBox = barcode.boundingBox;
  final List<Point<int>> cornerPoints = barcode.cornerPoints;

  final String rawValue = barcode.rawValue;

  final BarcodeValueType valueType = barcode.valueType;

  // See API reference for complete list of supported types
  switch (valueType) {
    case BarcodeValueType.wifi:
      final String ssid = barcode.wifi.ssid;
      final String password = barcode.wifi.password;
      final BarcodeWiFiEncryptionType type = barcode.wifi.encryptionType;
    case BarcodeValueType.url:
      final String title = barcode.url.title;
      final String url = barcode.url.url;

b. Extract faces.

for (Face face in faces) {
  final Rectangle<int> boundingBox = face.boundingBox;

  final double rotY = face.headEulerAngleY; // Head is rotated to the right rotY degrees
  final double rotZ = face.headEulerAngleZ; // Head is tilted sideways rotZ degrees

  // If landmark detection was enabled with FaceDetectorOptions (mouth, ears,
  // eyes, cheeks, and nose available):
  final FaceLandmark leftEar = face.getLandmark(FaceLandmarkType.leftEar);
  if (leftEar != null) {
    final Point<double> leftEarPos = leftEar.position;

  // If classification was enabled with FaceDetectorOptions:
  if (face.smilingProbability != null) {
    final double smileProb = face.smilingProbability;

  // If face tracking was enabled with FaceDetectorOptions:
  if (face.trackingId != null) {
    final int id = face.trackingId;

c. Extract labels.

for (ImageLabel label in labels) {
  final String text = label.text;
  final String entityId = label.entityId;
  final double confidence = label.confidence;

c. Extract Cloud Vision Edge labels.

for (VisionEdgeImageLabel label in labels) {
  final String text = label.text;
  final double confidence = label.confidence;

d. Extract text.

String text = visionText.text;
for (TextBlock block in visionText.blocks) {
  final Rect boundingBox = block.boundingBox;
  final List<Offset> cornerPoints = block.cornerPoints;
  final String text = block.text;
  final List<RecognizedLanguage> languages = block.recognizedLanguages;

  for (TextLine line in block.lines) {
    // Same getters as TextBlock
    for (TextElement element in line.elements) {
      // Same getters as TextBlock

Getting Started #

See the example directory for a complete sample app using ML Kit Vision for Firebase with Camera Streaming.

1.0.1 #

  • Ensure FirebaseVision is initialized before adding detectors.

1.0.0 #

  • Initial Release


firebase_livestream_ml_vision_example #

Demonstrates how to use the firebase_livestream_ml_vision plugin.

Getting Started #

This project is a starting point for a Flutter application.

A few resources to get you started if this is your first Flutter project:

For help getting started with Flutter, view our online documentation, which offers tutorials, samples, guidance on mobile development, and a full API reference.

Use this package as a library

1. Depend on it

Add this to your package's pubspec.yaml file:

  firebase_livestream_ml_vision: ^1.0.1

2. Install it

You can install packages from the command line:

with Flutter:

$ flutter pub get

Alternatively, your editor might support flutter pub get. Check the docs for your editor to learn more.

3. Import it

Now in your Dart code, you can use:

import 'package:firebase_livestream_ml_vision/firebase_livestream_ml_vision.dart';
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Learn more about scoring.

We analyzed this package on Oct 22, 2019, and provided a score, details, and suggestions below. Analysis was completed with status completed using:

  • Dart: 2.5.1
  • pana: 0.12.21
  • Flutter: 1.9.1+hotfix.4


Detected platforms: Flutter

References Flutter, and has no conflicting libraries.

Maintenance suggestions

The package description is too short. (-7 points)

Add more detail to the description field of pubspec.yaml. Use 60 to 180 characters to describe the package, what it does, and its target use case.


Package Constraint Resolved Available
Direct dependencies
Dart SDK >=2.1.0 <3.0.0
flutter 0.0.0
Transitive dependencies
collection 1.14.11 1.14.12
meta 1.1.7
sky_engine 0.0.99
typed_data 1.1.6
vector_math 2.0.8
Dev dependencies
camera ^0.4.2
firebase_core ^0.4.0
test any