Label Images with ML Kit on Android

This page describes an old version of the Image Labeling API, which was part of ML Kit for Firebase. The functionality of this API has been split into two new APIs (learn more):

• On-device image labeling is part of the new standalone ML Kit SDK, which you can use with or without Firebase.
• Cloud image labeling is part of Firebase ML, which includes all of Firebase's cloud-based ML features.

You can use ML Kit to label objects recognized in an image, using either an on-device model or a cloud model. See the overview to learn about the benefits of each approach.

Use of ML Kit to access Cloud ML functionality is subject to the Google Cloud Platform License Agreement and Service Specific Terms, and billed accordingly. For billing information, see the Firebase Pricing page.

Before you begin

1. If you haven't already, add Firebase to your Android project.
2. Add the dependencies for the ML Kit Android libraries to your module (app-level) Gradle file (usually app/build.gradle):

   apply plugin: 'com.android.application'
   apply plugin: 'com.google.gms.google-services'
   
   dependencies {
     // ...
   
     implementation 'com.google.firebase:firebase-ml-vision:24.0.3'
     implementation 'com.google.firebase:firebase-ml-vision-image-label-model:20.0.1'
   }

3. 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="label" />
     <!-- To use multiple models: android:value="label,model2,model3" -->
   </application>

   If you do not enable install-time model downloads, the model will be downloaded the first time you run the on-device detector. Requests you make before the download has completed will produce no results.

4. If you want to use the Cloud-based model, and you have not already enabled the Cloud-based APIs for your project, do so now:

   1. Open the ML Kit APIs page of the Firebase console.

   2. If you have not already upgraded your project to a Blaze pricing plan, click Upgrade to do so. (You will be prompted to upgrade only if your project isn't on the Blaze plan.)

      Only Blaze-level projects can use Cloud-based APIs.

   3. If Cloud-based APIs aren't already enabled, click Enable Cloud-based APIs.
   Before you deploy to production an app that uses a Cloud API, you should take some additional steps to prevent and mitigate the effect of unauthorized API access.

   If you want to use only the on-device model, you can skip this step.

Now you are ready to label images using either an on-device model or a cloud-based model.

1. Prepare the input image

Create a FirebaseVisionImage object from your image. The image labeler runs fastest when you use a Bitmap or, if you use the camera2 API, a JPEG-formatted media.Image, which are recommended when possible.

• To create a FirebaseVisionImage object from a media.Image object, such as when capturing an image from a device's camera, pass the media.Image object and the image's rotation to FirebaseVisionImage.fromMediaImage().

  If you use the CameraX library, the OnImageCapturedListener and ImageAnalysis.Analyzer classes calculate the rotation value for you, so you just need to convert the rotation to one of ML Kit's ROTATION_ constants before calling FirebaseVisionImage.fromMediaImage():

  Java

  private class YourAnalyzer implements ImageAnalysis.Analyzer {
  
      private int degreesToFirebaseRotation(int degrees) {
          switch (degrees) {
              case 0:
                  return FirebaseVisionImageMetadata.ROTATION_0;
              case 90:
                  return FirebaseVisionImageMetadata.ROTATION_90;
              case 180:
                  return FirebaseVisionImageMetadata.ROTATION_180;
              case 270:
                  return FirebaseVisionImageMetadata.ROTATION_270;
              default:
                  throw new IllegalArgumentException(
                          "Rotation must be 0, 90, 180, or 270.");
          }
      }
  
      @Override
      public void analyze(ImageProxy imageProxy, int degrees) {
          if (imageProxy == null || imageProxy.getImage() == null) {
              return;
          }
          Image mediaImage = imageProxy.getImage();
          int rotation = degreesToFirebaseRotation(degrees);
          FirebaseVisionImage image =
                  FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
          // Pass image to an ML Kit Vision API
          // ...
      }
  }

  Kotlin

  private class YourImageAnalyzer : ImageAnalysis.Analyzer {
      private fun degreesToFirebaseRotation(degrees: Int): Int = when(degrees) {
          0 -> FirebaseVisionImageMetadata.ROTATION_0
          90 -> FirebaseVisionImageMetadata.ROTATION_90
          180 -> FirebaseVisionImageMetadata.ROTATION_180
          270 -> FirebaseVisionImageMetadata.ROTATION_270
          else -> throw Exception("Rotation must be 0, 90, 180, or 270.")
      }
  
      override fun analyze(imageProxy: ImageProxy?, degrees: Int) {
          val mediaImage = imageProxy?.image
          val imageRotation = degreesToFirebaseRotation(degrees)
          if (mediaImage != null) {
              val image = FirebaseVisionImage.fromMediaImage(mediaImage, imageRotation)
              // Pass image to an ML Kit Vision API
              // ...
          }
      }
  }

  If you don't use a camera library that gives you the image's rotation, you can calculate it from the device's rotation and the orientation of camera sensor in the device:

  Java

  private static final SparseIntArray ORIENTATIONS = new SparseIntArray();
  static {
      ORIENTATIONS.append(Surface.ROTATION_0, 90);
      ORIENTATIONS.append(Surface.ROTATION_90, 0);
      ORIENTATIONS.append(Surface.ROTATION_180, 270);
      ORIENTATIONS.append(Surface.ROTATION_270, 180);
  }
  
  /**
   * Get the angle by which an image must be rotated given the device's current
   * orientation.
   */
  @RequiresApi(api = Build.VERSION_CODES.LOLLIPOP)
  private int getRotationCompensation(String cameraId, Activity activity, Context context)
          throws CameraAccessException {
      // Get the device's current rotation relative to its "native" orientation.
      // Then, from the ORIENTATIONS table, look up the angle the image must be
      // rotated to compensate for the device's rotation.
      int deviceRotation = activity.getWindowManager().getDefaultDisplay().getRotation();
      int rotationCompensation = ORIENTATIONS.get(deviceRotation);
  
      // On most devices, the sensor orientation is 90 degrees, but for some
      // devices it is 270 degrees. For devices with a sensor orientation of
      // 270, rotate the image an additional 180 ((270 + 270) % 360) degrees.
      CameraManager cameraManager = (CameraManager) context.getSystemService(CAMERA_SERVICE);
      int sensorOrientation = cameraManager
              .getCameraCharacteristics(cameraId)
              .get(CameraCharacteristics.SENSOR_ORIENTATION);
      rotationCompensation = (rotationCompensation + sensorOrientation + 270) % 360;
  
      // Return the corresponding FirebaseVisionImageMetadata rotation value.
      int result;
      switch (rotationCompensation) {
          case 0:
              result = FirebaseVisionImageMetadata.ROTATION_0;
              break;
          case 90:
              result = FirebaseVisionImageMetadata.ROTATION_90;
              break;
          case 180:
              result = FirebaseVisionImageMetadata.ROTATION_180;
              break;
          case 270:
              result = FirebaseVisionImageMetadata.ROTATION_270;
              break;
          default:
              result = FirebaseVisionImageMetadata.ROTATION_0;
              Log.e(TAG, "Bad rotation value: " + rotationCompensation);
      }
      return result;
  }
  VisionImage.java

  Kotlin

  private val ORIENTATIONS = SparseIntArray()
  
  init {
      ORIENTATIONS.append(Surface.ROTATION_0, 90)
      ORIENTATIONS.append(Surface.ROTATION_90, 0)
      ORIENTATIONS.append(Surface.ROTATION_180, 270)
      ORIENTATIONS.append(Surface.ROTATION_270, 180)
  }
  /**
   * Get the angle by which an image must be rotated given the device's current
   * orientation.
   */
  @RequiresApi(api = Build.VERSION_CODES.LOLLIPOP)
  @Throws(CameraAccessException::class)
  private fun getRotationCompensation(cameraId: String, activity: Activity, context: Context): Int {
      // Get the device's current rotation relative to its "native" orientation.
      // Then, from the ORIENTATIONS table, look up the angle the image must be
      // rotated to compensate for the device's rotation.
      val deviceRotation = activity.windowManager.defaultDisplay.rotation
      var rotationCompensation = ORIENTATIONS.get(deviceRotation)
  
      // On most devices, the sensor orientation is 90 degrees, but for some
      // devices it is 270 degrees. For devices with a sensor orientation of
      // 270, rotate the image an additional 180 ((270 + 270) % 360) degrees.
      val cameraManager = context.getSystemService(CAMERA_SERVICE) as CameraManager
      val sensorOrientation = cameraManager
              .getCameraCharacteristics(cameraId)
              .get(CameraCharacteristics.SENSOR_ORIENTATION)!!
      rotationCompensation = (rotationCompensation + sensorOrientation + 270) % 360
  
      // Return the corresponding FirebaseVisionImageMetadata rotation value.
      val result: Int
      when (rotationCompensation) {
          0 -> result = FirebaseVisionImageMetadata.ROTATION_0
          90 -> result = FirebaseVisionImageMetadata.ROTATION_90
          180 -> result = FirebaseVisionImageMetadata.ROTATION_180
          270 -> result = FirebaseVisionImageMetadata.ROTATION_270
          else -> {
              result = FirebaseVisionImageMetadata.ROTATION_0
              Log.e(TAG, "Bad rotation value: $rotationCompensation")
          }
      }
      return result
  }
  VisionImage.kt

  Then, pass the media.Image object and the rotation value to FirebaseVisionImage.fromMediaImage():

  Java

  FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
  VisionImage.java

  Kotlin

  val image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation)
  VisionImage.kt

• To create a FirebaseVisionImage object from a file URI, pass the app context and file URI to FirebaseVisionImage.fromFilePath(). This is useful when you use an ACTION_GET_CONTENT intent to prompt the user to select an image from their gallery app.

  Java

  FirebaseVisionImage image;
  try {
      image = FirebaseVisionImage.fromFilePath(context, uri);
  } catch (IOException e) {
      e.printStackTrace();
  }
  VisionImage.java

  Kotlin

  val image: FirebaseVisionImage
  try {
      image = FirebaseVisionImage.fromFilePath(context, uri)
  } catch (e: IOException) {
      e.printStackTrace()
  }
  VisionImage.kt

• To create a FirebaseVisionImage object from a ByteBuffer or a byte array, first calculate the image rotation as described above for media.Image input.

  Then, create a FirebaseVisionImageMetadata object that contains the image's height, width, color encoding format, and rotation:

  Java

  FirebaseVisionImageMetadata metadata = new FirebaseVisionImageMetadata.Builder()
          .setWidth(480)   // 480x360 is typically sufficient for
          .setHeight(360)  // image recognition
          .setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21)
          .setRotation(rotation)
          .build();
  VisionImage.java

  Kotlin

  val metadata = FirebaseVisionImageMetadata.Builder()
          .setWidth(480) // 480x360 is typically sufficient for
          .setHeight(360) // image recognition
          .setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21)
          .setRotation(rotation)
          .build()
  VisionImage.kt

  Use the buffer or array, and the metadata object, to create a FirebaseVisionImage object:

  Java

  FirebaseVisionImage image = FirebaseVisionImage.fromByteBuffer(buffer, metadata);
  // Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
  VisionImage.java

  Kotlin

  val image = FirebaseVisionImage.fromByteBuffer(buffer, metadata)
  // Or: val image = FirebaseVisionImage.fromByteArray(byteArray, metadata)
  VisionImage.kt

• To create a FirebaseVisionImage object from a Bitmap object:

  Java

  FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
  VisionImage.java

  Kotlin

  val image = FirebaseVisionImage.fromBitmap(bitmap)
  VisionImage.kt

  The image represented by the Bitmap object must be upright, with no additional rotation required.

2. Configure and run the image labeler

To label objects in an image, pass the FirebaseVisionImage object to the FirebaseVisionImageLabeler's processImage method.

1. First, get an instance of FirebaseVisionImageLabeler.

   If you want to use the on-device image labeler:

   Java

   FirebaseVisionImageLabeler labeler = FirebaseVision.getInstance()
       .getOnDeviceImageLabeler();
   
   // Or, to set the minimum confidence required:
   // FirebaseVisionOnDeviceImageLabelerOptions options =
   //     new FirebaseVisionOnDeviceImageLabelerOptions.Builder()
   //         .setConfidenceThreshold(0.7f)
   //         .build();
   // FirebaseVisionImageLabeler labeler = FirebaseVision.getInstance()
   //     .getOnDeviceImageLabeler(options);
   

   Kotlin

   val labeler = FirebaseVision.getInstance().getOnDeviceImageLabeler()
   
   // Or, to set the minimum confidence required:
   // val options = FirebaseVisionOnDeviceImageLabelerOptions.Builder()
   //     .setConfidenceThreshold(0.7f)
   //     .build()
   // val labeler = FirebaseVision.getInstance().getOnDeviceImageLabeler(options)
   

   If you want to use the cloud image labeler:

   Java

   FirebaseVisionImageLabeler labeler = FirebaseVision.getInstance()
       .getCloudImageLabeler();
   
   // Or, to set the minimum confidence required:
   // FirebaseVisionCloudImageLabelerOptions options =
   //     new FirebaseVisionCloudImageLabelerOptions.Builder()
   //         .setConfidenceThreshold(0.7f)
   //         .build();
   // FirebaseVisionImageLabeler labeler = FirebaseVision.getInstance()
   //     .getCloudImageLabeler(options);
   

   Kotlin

   val labeler = FirebaseVision.getInstance().getCloudImageLabeler()
   
   // Or, to set the minimum confidence required:
   // val options = FirebaseVisionCloudImageLabelerOptions.Builder()
   //     .setConfidenceThreshold(0.7f)
   //     .build()
   // val labeler = FirebaseVision.getInstance().getCloudImageLabeler(options)
   

2. Then, pass the image to the processImage() method:

   Java

   labeler.processImage(image)
       .addOnSuccessListener(new OnSuccessListener<List<FirebaseVisionImageLabel>>() {
         @Override
         public void onSuccess(List<FirebaseVisionImageLabel> labels) {
           // Task completed successfully
           // ...
         }
       })
       .addOnFailureListener(new OnFailureListener() {
         @Override
         public void onFailure(@NonNull Exception e) {
           // Task failed with an exception
           // ...
         }
       });
   

   Kotlin

   labeler.processImage(image)
       .addOnSuccessListener { labels ->
         // Task completed successfully
         // ...
       }
       .addOnFailureListener { e ->
         // Task failed with an exception
         // ...
       }
   

3. Get information about labeled objects

If the image labeling operation succeeds, a list of FirebaseVisionImageLabel objects will be passed to the success listener. Each FirebaseVisionImageLabel object represents something that was labeled in the image. For each label, you can get the label's text description, its Knowledge Graph entity ID (if available), and the confidence score of the match. For example:

Java

for (FirebaseVisionImageLabel label: labels) {
  String text = label.getText();
  String entityId = label.getEntityId();
  float confidence = label.getConfidence();
}

Kotlin

for (label in labels) {
  val text = label.text
  val entityId = label.entityId
  val confidence = label.confidence
}

Tips to improve real-time performance

If you want to label images in a real-time application, follow these guidelines to achieve the best framerates:

• Throttle calls to the image labeler. If a new video frame becomes available while the image labeler is running, drop the frame.
• If you are using the output of the image labeler to overlay graphics on the input image, first get the result from ML Kit, then render the image and overlay in a single step. By doing so, you render to the display surface only once for each input frame.

• If you use the Camera2 API, capture images in ImageFormat.YUV_420_888 format.

  If you use the older Camera API, capture images in ImageFormat.NV21 format.

Next steps

• Before you deploy to production an app that uses a Cloud API, you should take some additional steps to prevent and mitigate the effect of unauthorized API access.