Label Images with Firebase ML on Android

This page describes an old version of labeling objects recognized in an image using the deprecated Firebase ML Vision SDK. As an alternative, you may call Cloud Vision APIs using Firebase Auth and Callable Functions to allow only users logged into your app to access the API.

You can use Firebase ML to label objects recognized in an image. See the overview for information about this API's features.

Use of the Cloud Vision APIs is subject to the Google Cloud Platform License Agreement and Service Specific Terms, and billed accordingly. For billing information, see the Pricing page.

Looking for on-device image labeling? Try the standalone ML Kit library.

Before you begin

1. If you haven't already, add Firebase to your Android project.
2. In your module (app-level) Gradle file (usually <project>/<app-module>/build.gradle.kts or <project>/<app-module>/build.gradle), add the dependency for the Firebase ML Vision library for Android. We recommend using the Firebase Android BoM to control library versioning.

   dependencies {
       // Import the BoM for the Firebase platform
       implementation(platform("com.google.firebase:firebase-bom:33.5.1"))
   
       // Add the dependency for the Firebase ML Vision library
       // When using the BoM, you don't specify versions in Firebase library dependencies
       implementation 'com.google.firebase:firebase-ml-vision'
   }

   By using the Firebase Android BoM, your app will always use compatible versions of Firebase Android libraries.

   (Alternative)  Add Firebase library dependencies without using the BoM

   If you choose not to use the Firebase BoM, you must specify each Firebase library version in its dependency line.

   Note that if you use multiple Firebase libraries in your app, we strongly recommend using the BoM to manage library versions, which ensures that all versions are compatible.

   dependencies {
       // Add the dependency for the Firebase ML Vision library
       // When NOT using the BoM, you must specify versions in Firebase library dependencies
       implementation 'com.google.firebase:firebase-ml-vision:24.1.0'
   }

   Looking for a Kotlin-specific library module? Starting in October 2023 (Firebase BoM 32.5.0), both Kotlin and Java developers can depend on the main library module (for details, see the FAQ about this initiative).

3. If you have not already enabled Cloud-based APIs for your project, do so now:

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

   2. If you have not already upgraded your project to the 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.

Now you are ready to label images.

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 Firebase ML's ROTATION_ constants before calling FirebaseVisionImage.fromMediaImage():

  Kotlin+KTX

  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 Vision API
              // ...
          }
      }
  }

  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 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:

  Kotlin+KTX

  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

  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

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

  Kotlin+KTX

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

  Java

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

• 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.

  Kotlin+KTX

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

  Java

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

• 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:

  Kotlin+KTX

  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

  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

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

  Kotlin+KTX

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

  Java

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

• To create a FirebaseVisionImage object from a Bitmap object:

  Kotlin+KTX

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

  Java

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

  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.

   Kotlin+KTX

   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)
   

   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);
   

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

   Kotlin+KTX

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

   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
           // ...
         }
       });
   

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:

Kotlin+KTX

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

Java

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

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.