Recognize Landmarks with ML Kit on Android

This page is about an old version of the Landmark Recognition API, which was part of ML Kit for Firebase. For the latest docs, seethe latest version in theFirebase ML section.

You can use ML Kit to recognize well-known landmarks in an image.

Use of ML Kit to access Cloud ML functionality is subject to theGoogle Cloud Platform LicenseAgreement andServiceSpecific Terms, and billed accordingly. For billing information, see theFirebasePricing 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 (usuallyapp/build.gradle):
    applyplugin:'com.android.application'applyplugin:'com.google.gms.google-services'dependencies{// ...implementation'com.google.firebase:firebase-ml-vision:24.0.3'}

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

    1. Open theML Kit APIs page of theFirebase console.

    2. If you have not already upgraded your project to a Blaze pricing plan, clickUpgrade 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, clickEnable Cloud-based APIs.
    Before you deploy to production an app that uses a Cloud API, you should takesome additional steps toprevent and mitigate theeffect of unauthorized API access.

Configure the landmark detector

By default, the Cloud detector uses theSTABLE version of themodel and returns up to 10 results. If you want to change either of thesesettings, specify them with aFirebaseVisionCloudDetectorOptionsobject.

For example, to change both of the default settings, build aFirebaseVisionCloudDetectorOptions object as in the followingexample:

Java

FirebaseVisionCloudDetectorOptionsoptions=newFirebaseVisionCloudDetectorOptions.Builder().setModelType(FirebaseVisionCloudDetectorOptions.LATEST_MODEL).setMaxResults(15).build();

Kotlin

valoptions=FirebaseVisionCloudDetectorOptions.Builder().setModelType(FirebaseVisionCloudDetectorOptions.LATEST_MODEL).setMaxResults(15).build()

To use the default settings, you can useFirebaseVisionCloudDetectorOptions.DEFAULT in the next step.

Run the landmark detector

To recognize landmarks in an image, create aFirebaseVisionImage objectfrom either aBitmap,media.Image,ByteBuffer, byte array, or a file onthe device. Then, pass theFirebaseVisionImage object to theFirebaseVisionCloudLandmarkDetector'sdetectInImage method.

  1. Create aFirebaseVisionImage object from your image.

    • To create aFirebaseVisionImage object from amedia.Image object, such as when capturing an image from a device's camera, pass themedia.Image object and the image's rotation toFirebaseVisionImage.fromMediaImage().

      If you use the CameraX library, theOnImageCapturedListener andImageAnalysis.Analyzer classes calculate the rotation value for you, so you just need to convert the rotation to one of ML Kit'sROTATION_ constants before callingFirebaseVisionImage.fromMediaImage():

      Java

      privateclassYourAnalyzerimplementsImageAnalysis.Analyzer{privateintdegreesToFirebaseRotation(intdegrees){switch(degrees){case0:returnFirebaseVisionImageMetadata.ROTATION_0;case90:returnFirebaseVisionImageMetadata.ROTATION_90;case180:returnFirebaseVisionImageMetadata.ROTATION_180;case270:returnFirebaseVisionImageMetadata.ROTATION_270;default:thrownewIllegalArgumentException("Rotation must be 0, 90, 180, or 270.");}}@Overridepublicvoidanalyze(ImageProxyimageProxy,intdegrees){if(imageProxy==null||imageProxy.getImage()==null){return;}ImagemediaImage=imageProxy.getImage();introtation=degreesToFirebaseRotation(degrees);FirebaseVisionImageimage=FirebaseVisionImage.fromMediaImage(mediaImage,rotation);// Pass image to an ML Kit Vision API// ...}}

      Kotlin

      privateclassYourImageAnalyzer:ImageAnalysis.Analyzer{privatefundegreesToFirebaseRotation(degrees:Int):Int=when(degrees){0->FirebaseVisionImageMetadata.ROTATION_090->FirebaseVisionImageMetadata.ROTATION_90180->FirebaseVisionImageMetadata.ROTATION_180270->FirebaseVisionImageMetadata.ROTATION_270else->throwException("Rotation must be 0, 90, 180, or 270.")}overridefunanalyze(imageProxy:ImageProxy?,degrees:Int){valmediaImage=imageProxy?.imagevalimageRotation=degreesToFirebaseRotation(degrees)if(mediaImage!=null){valimage=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

      privatestaticfinalSparseIntArrayORIENTATIONS=newSparseIntArray();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)privateintgetRotationCompensation(StringcameraId,Activityactivity,Contextcontext)throwsCameraAccessException{// 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.intdeviceRotation=activity.getWindowManager().getDefaultDisplay().getRotation();introtationCompensation=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.CameraManagercameraManager=(CameraManager)context.getSystemService(CAMERA_SERVICE);intsensorOrientation=cameraManager.getCameraCharacteristics(cameraId).get(CameraCharacteristics.SENSOR_ORIENTATION);rotationCompensation=(rotationCompensation+sensorOrientation+270)%360;// Return the corresponding FirebaseVisionImageMetadata rotation value.intresult;switch(rotationCompensation){case0:result=FirebaseVisionImageMetadata.ROTATION_0;break;case90:result=FirebaseVisionImageMetadata.ROTATION_90;break;case180:result=FirebaseVisionImageMetadata.ROTATION_180;break;case270:result=FirebaseVisionImageMetadata.ROTATION_270;break;default:result=FirebaseVisionImageMetadata.ROTATION_0;Log.e(TAG,"Bad rotation value: "+rotationCompensation);}returnresult;}

      Kotlin

      privatevalORIENTATIONS=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)privatefungetRotationCompensation(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.valdeviceRotation=activity.windowManager.defaultDisplay.rotationvarrotationCompensation=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.valcameraManager=context.getSystemService(CAMERA_SERVICE)asCameraManagervalsensorOrientation=cameraManager.getCameraCharacteristics(cameraId).get(CameraCharacteristics.SENSOR_ORIENTATION)!!rotationCompensation=(rotationCompensation+sensorOrientation+270)%360// Return the corresponding FirebaseVisionImageMetadata rotation value.valresult:Intwhen(rotationCompensation){0->result=FirebaseVisionImageMetadata.ROTATION_090->result=FirebaseVisionImageMetadata.ROTATION_90180->result=FirebaseVisionImageMetadata.ROTATION_180270->result=FirebaseVisionImageMetadata.ROTATION_270else->{result=FirebaseVisionImageMetadata.ROTATION_0Log.e(TAG,"Bad rotation value:$rotationCompensation")}}returnresult}

      Then, pass themedia.Image object and the rotation value toFirebaseVisionImage.fromMediaImage():

      Java

      FirebaseVisionImageimage=FirebaseVisionImage.fromMediaImage(mediaImage,rotation);

      Kotlin

      valimage=FirebaseVisionImage.fromMediaImage(mediaImage,rotation)
    • To create aFirebaseVisionImage object from a file URI, pass the app context and file URI toFirebaseVisionImage.fromFilePath(). This is useful when you use anACTION_GET_CONTENT intent to prompt the user to select an image from their gallery app.

      Java

      FirebaseVisionImageimage;try{image=FirebaseVisionImage.fromFilePath(context,uri);}catch(IOExceptione){e.printStackTrace();}

      Kotlin

      valimage:FirebaseVisionImagetry{image=FirebaseVisionImage.fromFilePath(context,uri)}catch(e:IOException){e.printStackTrace()}
    • To create aFirebaseVisionImage object from aByteBuffer or a byte array, first calculate the image rotation as described above formedia.Image input.

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

      Java

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

      Kotlin

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

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

      Java

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

      Kotlin

      valimage=FirebaseVisionImage.fromByteBuffer(buffer,metadata)// Or: val image = FirebaseVisionImage.fromByteArray(byteArray, metadata)
    • To create aFirebaseVisionImage object from aBitmap object:

      Java

      FirebaseVisionImageimage=FirebaseVisionImage.fromBitmap(bitmap);

      Kotlin

      valimage=FirebaseVisionImage.fromBitmap(bitmap)
      The image represented by theBitmap object must be upright, with no additional rotation required.

  2. Get an instance ofFirebaseVisionCloudLandmarkDetector:

    Java

    FirebaseVisionCloudLandmarkDetectordetector=FirebaseVision.getInstance().getVisionCloudLandmarkDetector();// Or, to change the default settings:// FirebaseVisionCloudLandmarkDetector detector = FirebaseVision.getInstance()//         .getVisionCloudLandmarkDetector(options);

    Kotlin

    valdetector=FirebaseVision.getInstance().visionCloudLandmarkDetector// Or, to change the default settings:// val detector = FirebaseVision.getInstance()//         .getVisionCloudLandmarkDetector(options)

  3. Finally, pass the image to thedetectInImage method:

    Java

    Task<List<FirebaseVisionCloudLandmark>>result=detector.detectInImage(image).addOnSuccessListener(newOnSuccessListener<List<FirebaseVisionCloudLandmark>>(){@OverridepublicvoidonSuccess(List<FirebaseVisionCloudLandmark>firebaseVisionCloudLandmarks){// Task completed successfully// ...}}).addOnFailureListener(newOnFailureListener(){@OverridepublicvoidonFailure(@NonNullExceptione){// Task failed with an exception// ...}});

    Kotlin

    valresult=detector.detectInImage(image).addOnSuccessListener{firebaseVisionCloudLandmarks->// Task completed successfully// ...}.addOnFailureListener{e->// Task failed with an exception// ...}

Get information about the recognized landmarks

If the landmark recognition operation succeeds, a list ofFirebaseVisionCloudLandmark objects will be passed to the success listener. EachFirebaseVisionCloudLandmark object represents a landmark that was recognized in theimage. For each landmark, you can get its bounding coordinates in the input image,the landmark's name, its latitude and longitude, itsKnowledge Graph entity ID(if available), and the confidence score of the match. For example:

Java

for(FirebaseVisionCloudLandmarklandmark:firebaseVisionCloudLandmarks){Rectbounds=landmark.getBoundingBox();StringlandmarkName=landmark.getLandmark();StringentityId=landmark.getEntityId();floatconfidence=landmark.getConfidence();// Multiple locations are possible, e.g., the location of the depicted// landmark and the location the picture was taken.for(FirebaseVisionLatLngloc:landmark.getLocations()){doublelatitude=loc.getLatitude();doublelongitude=loc.getLongitude();}}

Kotlin

for(landmarkinfirebaseVisionCloudLandmarks){valbounds=landmark.boundingBoxvallandmarkName=landmark.landmarkvalentityId=landmark.entityIdvalconfidence=landmark.confidence// Multiple locations are possible, e.g., the location of the depicted// landmark and the location the picture was taken.for(locinlandmark.locations){vallatitude=loc.latitudevallongitude=loc.longitude}}

Next steps

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Last updated 2026-02-18 UTC.