TheFirebase ML Vision SDK for recognizing well-known landmarks in an image is now deprecated(See the outdated docs here). This page describes how, as an alternative to the deprecated SDK, you can call Cloud Vision APIs using Firebase Auth and Firebase Functions to allow only authenticated users to access the API.

In order to call a Google Cloud API from your app, you need to create an intermediate REST API that handles authorization and protects secret values such as API keys. You then need to write code in your mobile app to authenticate to and communicate with this intermediate service.

One way to create this REST API is by using Firebase Authentication and Functions, which gives you a managed, serverless gateway to Google Cloud APIs that handles authentication and can be called from your mobile app with pre-built SDKs.

This guide demonstrates how to use this technique to call the Cloud Vision API from your app. This method will allow all authenticated users to access Cloud Vision billed services through your Cloud project, so consider whether this auth mechanism is sufficient for your use case before proceeding.

Before you begin

Configure your project

1. If you haven't already,add Firebase to your Android project.

2. If you haven't already enabled Cloud-based APIs for your project, do so now:

   1. Open theFirebase ML APIs page in theFirebase console.

   2. If you haven't already upgraded your project to thepay-as-you-go Blaze pricing plan, clickUpgrade to do so. (You'll be prompted to upgrade only if your project isn't on the Blaze pricing plan.)

      Only projects on the Blaze pricing plan can use Cloud-based APIs.

   3. If Cloud-based APIs aren't already enabled, clickEnable Cloud-based APIs.
3. Configure your existing Firebase API keys to disallow access to the Cloud Vision API:
   1. Open theCredentials page of the Cloud console.
   2. For each API key in the list, open the editing view, and in the Key Restrictions section, add all of the available APIsexcept the Cloud Vision API to the list.

Deploy the callable function

Next, deploy the Cloud Function you will use to bridge your app and the Cloud Vision API. Thefunctions-samples repository contains an example you can use.

By default, accessing the Cloud Vision API through this function will allow only authenticated users of your app access to the Cloud Vision API. You can modify the function for different requirements.

To deploy the function:

1. Clone or download thefunctions-samples repo and change to theNode-1st-gen/vision-annotate-image directory:

   git clone https://github.com/firebase/functions-samplescd Node-1st-gen/vision-annotate-image

2. Install dependencies:

   cd functionsnpm installcd ..

3. If you don't have the Firebase CLI,install it.
4. Initialize a Firebase project in thevision-annotate-image directory. When prompted, select your project in the list.

   firebase init

5. Deploy the function:

   firebase deploy --only functions:annotateImage

Add Firebase Auth to your app

The callable function deployed above will reject any request from non-authenticatedusers of your app. If you have not already done so, you will need toadd FirebaseAuth to your app.

Add necessary dependencies to your app

implementation("com.google.firebase:firebase-functions:22.1.0")implementation("com.google.code.gson:gson:2.8.6")

1. Prepare the input image

1. Get the image as aBitmap object:

   Kotlin

   varbitmap:Bitmap=MediaStore.Images.Media.getBitmap(contentResolver,uri)

   Java

   Bitmapbitmap=MediaStore.Images.Media.getBitmap(getContentResolver(),uri);

2. Optionally, scale down the image to save on bandwidth. See the Cloud Vision recommended image sizes.

   Kotlin

   privatefunscaleBitmapDown(bitmap:Bitmap,maxDimension:Int):Bitmap{valoriginalWidth=bitmap.widthvaloriginalHeight=bitmap.heightvarresizedWidth=maxDimensionvarresizedHeight=maxDimensionif(originalHeight >originalWidth){resizedHeight=maxDimensionresizedWidth=(resizedHeight*originalWidth.toFloat()/originalHeight.toFloat()).toInt()}elseif(originalWidth >originalHeight){resizedWidth=maxDimensionresizedHeight=(resizedWidth*originalHeight.toFloat()/originalWidth.toFloat()).toInt()}elseif(originalHeight==originalWidth){resizedHeight=maxDimensionresizedWidth=maxDimension}returnBitmap.createScaledBitmap(bitmap,resizedWidth,resizedHeight,false)}

   Java

   privateBitmapscaleBitmapDown(Bitmapbitmap,intmaxDimension){intoriginalWidth=bitmap.getWidth();intoriginalHeight=bitmap.getHeight();intresizedWidth=maxDimension;intresizedHeight=maxDimension;if(originalHeight >originalWidth){resizedHeight=maxDimension;resizedWidth=(int)(resizedHeight*(float)originalWidth/(float)originalHeight);}elseif(originalWidth >originalHeight){resizedWidth=maxDimension;resizedHeight=(int)(resizedWidth*(float)originalHeight/(float)originalWidth);}elseif(originalHeight==originalWidth){resizedHeight=maxDimension;resizedWidth=maxDimension;}returnBitmap.createScaledBitmap(bitmap,resizedWidth,resizedHeight,false);}

   Kotlin

   // Scale down bitmap sizebitmap=scaleBitmapDown(bitmap,640)

   Java

   // Scale down bitmap sizebitmap=scaleBitmapDown(bitmap,640);

3. Convert the bitmap object to a base64 encoded string:

   Kotlin

   // Convert bitmap to base64 encoded stringvalbyteArrayOutputStream=ByteArrayOutputStream()bitmap.compress(Bitmap.CompressFormat.JPEG,100,byteArrayOutputStream)valimageBytes:ByteArray=byteArrayOutputStream.toByteArray()valbase64encoded=Base64.encodeToString(imageBytes,Base64.NO_WRAP)

   Java

   // Convert bitmap to base64 encoded stringByteArrayOutputStreambyteArrayOutputStream=newByteArrayOutputStream();bitmap.compress(Bitmap.CompressFormat.JPEG,100,byteArrayOutputStream);byte[]imageBytes=byteArrayOutputStream.toByteArray();Stringbase64encoded=Base64.encodeToString(imageBytes,Base64.NO_WRAP);

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

2. Invoke the callable function to recognize landmarks

1. First, initialize an instance of Cloud Functions:

   Kotlin

   privatelateinitvarfunctions:FirebaseFunctions// ...functions=Firebase.functions

   Java

   privateFirebaseFunctionsmFunctions;// ...mFunctions=FirebaseFunctions.getInstance();

2. Define a method for invoking the function:

   Kotlin

   privatefunannotateImage(requestJson:String):Task<JsonElement>{returnfunctions.getHttpsCallable("annotateImage").call(requestJson).continueWith{task->// This continuation runs on either success or failure, but if the task// has failed then result will throw an Exception which will be// propagated down.valresult=task.result?.dataJsonParser.parseString(Gson().toJson(result))}}

   Java

   privateTask<JsonElement>annotateImage(StringrequestJson){returnmFunctions.getHttpsCallable("annotateImage").call(requestJson).continueWith(newContinuation<HttpsCallableResult,JsonElement>(){@OverridepublicJsonElementthen(@NonNullTask<HttpsCallableResult>task){// This continuation runs on either success or failure, but if the task// has failed then getResult() will throw an Exception which will be// propagated down.returnJsonParser.parseString(newGson().toJson(task.getResult().getData()));}});}

3. Create a JSON request withTypeLANDMARK_DETECTION:

   Kotlin

   // Create json request to cloud visionvalrequest=JsonObject()// Add image to requestvalimage=JsonObject()image.add("content",JsonPrimitive(base64encoded))request.add("image",image)// Add features to the requestvalfeature=JsonObject()feature.add("maxResults",JsonPrimitive(5))feature.add("type",JsonPrimitive("LANDMARK_DETECTION"))valfeatures=JsonArray()features.add(feature)request.add("features",features)

   Java

   // Create json request to cloud visionJsonObjectrequest=newJsonObject();// Add image to requestJsonObjectimage=newJsonObject();image.add("content",newJsonPrimitive(base64encoded));request.add("image",image);//Add features to the requestJsonObjectfeature=newJsonObject();feature.add("maxResults",newJsonPrimitive(5));feature.add("type",newJsonPrimitive("LANDMARK_DETECTION"));JsonArrayfeatures=newJsonArray();features.add(feature);request.add("features",features);

4. Finally, invoke the function:

   Kotlin

   annotateImage(request.toString()).addOnCompleteListener{task->if(!task.isSuccessful){// Task failed with an exception// ...}else{// Task completed successfully// ...}}

   Java

   annotateImage(request.toString()).addOnCompleteListener(newOnCompleteListener<JsonElement>(){@OverridepublicvoidonComplete(@NonNullTask<JsonElement>task){if(!task.isSuccessful()){// Task failed with an exception// ...}else{// Task completed successfully// ...}}});

3. Get information about the recognized landmarks

for(labelintask.result!!.asJsonArray[0].asJsonObject["landmarkAnnotations"].asJsonArray){vallabelObj=label.asJsonObjectvallandmarkName=labelObj["description"]valentityId=labelObj["mid"]valscore=labelObj["score"]valbounds=labelObj["boundingPoly"]// Multiple locations are possible, e.g., the location of the depicted// landmark and the location the picture was taken.for(locinlabelObj["locations"].asJsonArray){vallatitude=loc.asJsonObject["latLng"].asJsonObject["latitude"]vallongitude=loc.asJsonObject["latLng"].asJsonObject["longitude"]}}

for(JsonElementlabel:task.getResult().getAsJsonArray().get(0).getAsJsonObject().get("landmarkAnnotations").getAsJsonArray()){JsonObjectlabelObj=label.getAsJsonObject();StringlandmarkName=labelObj.get("description").getAsString();StringentityId=labelObj.get("mid").getAsString();floatscore=labelObj.get("score").getAsFloat();JsonObjectbounds=labelObj.get("boundingPoly").getAsJsonObject();// Multiple locations are possible, e.g., the location of the depicted// landmark and the location the picture was taken.for(JsonElementloc:labelObj.get("locations").getAsJsonArray()){JsonObjectlatLng=loc.getAsJsonObject().get("latLng").getAsJsonObject();doublelatitude=latLng.get("latitude").getAsDouble();doublelongitude=latLng.get("longitude").getAsDouble();}}