Label Images Securely with Cloud Vision using Firebase Auth and Functions on Apple platforms

The Firebase ML Vision SDK for labeling objects in an image is now deprecated (See the outdated docs here).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.

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

Configure your project

If you have not already added Firebase to your app, do so by following the steps in the getting started guide.

Use Swift Package Manager to install and manage Firebase dependencies.

Visit our installation guide to learn about the different ways you can add Firebase SDKs to your Apple project, including importing frameworks directly and using CocoaPods.

1. In Xcode, with your app project open, navigate to File > Add Packages.
2. When prompted, add the Firebase Apple platforms SDK repository:

  https://github.com/firebase/firebase-ios-sdk.git

Note: New projects should use the default (latest) SDK version, but you can choose an older version if needed.
3. Choose the Firebase ML library.
4. Add the -ObjC flag to the Other Linker Flags section of your target's build settings.
5. When finished, Xcode will automatically begin resolving and downloading your dependencies in the background.

Next, perform some in-app setup:

1. In your app, import Firebase:

   Swift

   import FirebaseMLModelDownloader

   Objective-C

   @import FirebaseMLModelDownloader;

A few more configuration steps, and we're ready to go:

1. 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.
2. Configure your existing Firebase API keys to disallow access to the Cloud Vision API:
   1. Open the Credentials 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 APIs except 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. The functions-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 the functions-samples repo and change to the Node-1st-gen/vision-annotate-image directory:

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

2. Install dependencies:

   cd functions
   npm install
   cd ..

3. If you don't have the Firebase CLI, install it.
4. Initialize a Firebase project in the vision-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-authenticated users of your app. If you have not already done so, you will need to add Firebase Auth to your app.

Add necessary dependencies to your app

Use Swift Package Manager to install the Cloud Functions for Firebase library.

Now you are ready to label images.

1. Prepare the input image

In order to call Cloud Vision, the image must be formatted as a base64-encoded string. To process a UIImage:

Swift

guard let imageData = uiImage.jpegData(compressionQuality: 1.0) else { return }
let base64encodedImage = imageData.base64EncodedString()

Objective-C

NSData *imageData = UIImageJPEGRepresentation(uiImage, 1.0f);
NSString *base64encodedImage =
  [imageData base64EncodedStringWithOptions:NSDataBase64Encoding76CharacterLineLength];

2. Invoke the callable function to label the image

To label objects in an image, invoke the callable function passing a JSON Cloud Vision request.

1. First, initialize an instance of Cloud Functions:

   Swift

   lazy var functions = Functions.functions()
   

   Objective-C

   @property(strong, nonatomic) FIRFunctions *functions;
   

2. Create a request with Type set to LABEL_DETECTION:

   Swift

   let requestData = [
     "image": ["content": base64encodedImage],
     "features": ["maxResults": 5, "type": "LABEL_DETECTION"]
   ]
   

   Objective-C

   NSDictionary *requestData = @{
     @"image": @{@"content": base64encodedImage},
     @"features": @{@"maxResults": @5, @"type": @"LABEL_DETECTION"}
   };
   

3. Finally, invoke the function:

   Swift

   do {
     let result = try await functions.httpsCallable("annotateImage").call(requestData)
     print(result)
   } catch {
     if let error = error as NSError? {
       if error.domain == FunctionsErrorDomain {
         let code = FunctionsErrorCode(rawValue: error.code)
         let message = error.localizedDescription
         let details = error.userInfo[FunctionsErrorDetailsKey]
       }
       // ...
     }
   }
   

   Objective-C

   [[_functions HTTPSCallableWithName:@"annotateImage"]
                             callWithObject:requestData
                                 completion:^(FIRHTTPSCallableResult * _Nullable result, NSError * _Nullable error) {
           if (error) {
             if ([error.domain isEqualToString:@"com.firebase.functions"]) {
               FIRFunctionsErrorCode code = error.code;
               NSString *message = error.localizedDescription;
               NSObject *details = error.userInfo[@"details"];
             }
             // ...
           }
           // Function completed succesfully
           // Get information about labeled objects
   
         }];
   

3. Get information about labeled objects

If the image labeling operation succeeds, a JSON response of BatchAnnotateImagesResponse will be returned in the task's result. Each object in the labelAnnotations array 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:

Swift

if let labelArray = (result?.data as? [String: Any])?["labelAnnotations"] as? [[String:Any]] {
  for labelObj in labelArray {
    let text = labelObj["description"]
    let entityId = labelObj["mid"]
    let confidence = labelObj["score"]
  }
}

Objective-C

NSArray *labelArray = result.data[@"labelAnnotations"];
for (NSDictionary *labelObj in labelArray) {
  NSString *text = labelObj[@"description"];
  NSString *entityId = labelObj[@"mid"];
  NSNumber *confidence = labelObj[@"score"];
}