ML Kit Vision for Firebase with AutoML Vision Edge Support

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

For Flutter plugins for other Firebase products, see FlutterFire.md.

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

Note: AutoML Vision Edge: iOS Only

Usage

To use this plugin, add firebase_mlvision 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 https://codelabs.developers.google.com/codelabs/flutter-firebase/#4 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:
   - assets/<foldername>/dict.txt
   - assets/<foldername>/manifest.json
   - assets/<foldername>/model.tflite

Android

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 'com.google.firebase:firebase-ml-vision-image-label-model:17.0.2'
    }
}

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 ...>
  ...
  <meta-data
    android:name="com.google.firebase.ml.vision.DEPENDENCIES"
    android:value="ocr" />
  <!-- To use multiple models: android:value="ocr,label,barcode,face" -->
</application>

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'

Using an ML Vision Detector

1. Create a FirebaseVisionImage.

Create a FirebaseVisionImage object from your image. To create a FirebaseVisionImage from an image File object:

final File imageFile = getImageFile();
final FirebaseVisionImage visionImage = FirebaseVisionImage.fromFile(imageFile);

Special Instructions for VisionEdgeImageLabeler.

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

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

2. Create an instance of a detector.

Get an instance of a FirebaseVisionDetector.

final BarcodeDetector barcodeDetector = FirebaseVision.instance.barcodeDetector();
final ImageLabeler cloudLabeler = FirebaseVision.instance.cloudImageLabeler();
final FaceDetector faceDetector = FirebaseVision.instance.faceDetector();
final ImageLabeler labeler = FirebaseVision.instance.imageLabeler();
final TextRecognizer textRecognizer = FirebaseVision.instance.textRecognizer();
final VisionEdgeImageLabeler visionEdgeLabeler = FirebaseVision.instance.visionEdgeImageLabeler('<foldername(modelname)>', modelLocation);

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

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

3. Call detectInImage() with visionImage.

final List<Barcode> barcodes = await barcodeDetector.detectInImage(visionImage);
final List<ImageLabel> cloudLabels = await cloudLabeler.processImage(visionImage);
final List<Face> faces = await faceDetector.processImage(visionImage);
final List<ImageLabel> labels = await labeler.processImage(visionImage);
final VisionText visionText = await textRecognizer.processImage(visionImage);
final List<VisionEdgeImageLabel> visionEdgeLabels = await visionEdgeLabeler.processImage(visionImage);

4. 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;
      break;
    case BarcodeValueType.url:
      final String title = barcode.url.title;
      final String url = barcode.url.url;
      break;
  }
}

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.