Recognize Landmarks with ML Kit on iOS

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

Before you begin

  1. If you have not already added Firebase to your app, do so by following the steps in the getting started guide.
  2. Include the ML Kit libraries in your Podfile: 
    pod 'Firebase/MLVision', '6.25.0'
    After you install or update your project's Pods, be sure to open your Xcode project using its .xcworkspace.
  3. In your app, import Firebase:

    Swift

    import Firebase

    Objective-C

    @import Firebase;

  4. If you have not already enabled 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.

Configure the landmark detector

By default, the Cloud detector uses the stable version of the model and returns up to 10 results. If you want to change either of these settings, specify them with a VisionCloudDetectorOptions object as in the following example:

Swift

let options = VisionCloudDetectorOptions()
options.modelType = .latest
options.maxResults = 20

Objective-C

  FIRVisionCloudDetectorOptions *options =
      [[FIRVisionCloudDetectorOptions alloc] init];
  options.modelType = FIRVisionCloudModelTypeLatest;
  options.maxResults = 20;

In the next step, pass the VisionCloudDetectorOptions object when you create the Cloud detector object.

Run the landmark detector

To recognize landmarks in an image, pass the image as a UIImage or a CMSampleBufferRef to the VisionCloudLandmarkDetector's detect(in:) method:

  1. Get an instance of VisionCloudLandmarkDetector:

    Swift

    lazy var vision = Vision.vision()

let cloudDetector = vision.cloudLandmarkDetector(options: options)
// Or, to use the default settings:
// let cloudDetector = vision.cloudLandmarkDetector()

    Objective-C

    FIRVision *vision = [FIRVision vision];
FIRVisionCloudLandmarkDetector *landmarkDetector = [vision cloudLandmarkDetector];
// Or, to change the default settings:
// FIRVisionCloudLandmarkDetector *landmarkDetector =
//     [vision cloudLandmarkDetectorWithOptions:options];

  2. Create a VisionImage object using a UIImage or a CMSampleBufferRef.

    To use a UIImage:

    1. If necessary, rotate the image so that its imageOrientation property is .up.
    2. Create a VisionImage object using the correctly-rotated UIImage. Do not specify any rotation metadata—the default value, .topLeft, must be used.

      Swift

      let image = VisionImage(image: uiImage)

      Objective-C

      FIRVisionImage *image = [[FIRVisionImage alloc] initWithImage:uiImage];

    To use a CMSampleBufferRef:

    1. Create a VisionImageMetadata object that specifies the orientation of the image data contained in the CMSampleBufferRef buffer.

      To get the image orientation:

      Swift

      func imageOrientation(
    deviceOrientation: UIDeviceOrientation,
    cameraPosition: AVCaptureDevice.Position
    ) -> VisionDetectorImageOrientation {
    switch deviceOrientation {
    case .portrait:
        return cameraPosition == .front ? .leftTop : .rightTop
    case .landscapeLeft:
        return cameraPosition == .front ? .bottomLeft : .topLeft
    case .portraitUpsideDown:
        return cameraPosition == .front ? .rightBottom : .leftBottom
    case .landscapeRight:
        return cameraPosition == .front ? .topRight : .bottomRight
    case .faceDown, .faceUp, .unknown:
        return .leftTop
    }
}

      Objective-C

      - (FIRVisionDetectorImageOrientation)
    imageOrientationFromDeviceOrientation:(UIDeviceOrientation)deviceOrientation
                           cameraPosition:(AVCaptureDevicePosition)cameraPosition {
  switch (deviceOrientation) {
    case UIDeviceOrientationPortrait:
      if (cameraPosition == AVCaptureDevicePositionFront) {
        return FIRVisionDetectorImageOrientationLeftTop;
      } else {
        return FIRVisionDetectorImageOrientationRightTop;
      }
    case UIDeviceOrientationLandscapeLeft:
      if (cameraPosition == AVCaptureDevicePositionFront) {
        return FIRVisionDetectorImageOrientationBottomLeft;
      } else {
        return FIRVisionDetectorImageOrientationTopLeft;
      }
    case UIDeviceOrientationPortraitUpsideDown:
      if (cameraPosition == AVCaptureDevicePositionFront) {
        return FIRVisionDetectorImageOrientationRightBottom;
      } else {
        return FIRVisionDetectorImageOrientationLeftBottom;
      }
    case UIDeviceOrientationLandscapeRight:
      if (cameraPosition == AVCaptureDevicePositionFront) {
        return FIRVisionDetectorImageOrientationTopRight;
      } else {
        return FIRVisionDetectorImageOrientationBottomRight;
      }
    default:
      return FIRVisionDetectorImageOrientationTopLeft;
  }
}

      Then, create the metadata object:

      Swift

      let cameraPosition = AVCaptureDevice.Position.back  // Set to the capture device you used.
let metadata = VisionImageMetadata()
metadata.orientation = imageOrientation(
    deviceOrientation: UIDevice.current.orientation,
    cameraPosition: cameraPosition
)

      Objective-C

      FIRVisionImageMetadata *metadata = [[FIRVisionImageMetadata alloc] init];
AVCaptureDevicePosition cameraPosition =
    AVCaptureDevicePositionBack;  // Set to the capture device you used.
metadata.orientation =
    [self imageOrientationFromDeviceOrientation:UIDevice.currentDevice.orientation
                                 cameraPosition:cameraPosition];
    2. Create a VisionImage object using the CMSampleBufferRef object and the rotation metadata:

      Swift

      let image = VisionImage(buffer: sampleBuffer)
image.metadata = metadata

      Objective-C

      FIRVisionImage *image = [[FIRVisionImage alloc] initWithBuffer:sampleBuffer];
image.metadata = metadata;
  3. Then, pass the image to the detect(in:) method:

    Swift

    cloudDetector.detect(in: visionImage) { landmarks, error in
  guard error == nil, let landmarks = landmarks, !landmarks.isEmpty else {
    // ...
    return
  }

  // Recognized landmarks
  // ...
}

    Objective-C

    [landmarkDetector detectInImage:image
                     completion:^(NSArray<FIRVisionCloudLandmark *> *landmarks,
                                  NSError *error) {
  if (error != nil) {
    return;
  } else if (landmarks != nil) {
    // Got landmarks
  }
}];

Get information about the recognized landmarks

If landmark recognition succeeds, an array of VisionCloudLandmark objects will be passed to the completion handler. From each object, you can get information about a landmark recognized in the image.

For example:

Swift

for landmark in landmarks {
  let landmarkDesc = landmark.landmark
  let boundingPoly = landmark.frame
  let entityId = landmark.entityId

  // A landmark can have multiple locations: for example, the location the image
  // was taken, and the location of the landmark depicted.
  for location in landmark.locations {
    let latitude = location.latitude
    let longitude = location.longitude
  }

  let confidence = landmark.confidence
}

Objective-C

for (FIRVisionCloudLandmark *landmark in landmarks) {
   NSString *landmarkDesc = landmark.landmark;
   CGRect frame = landmark.frame;
   NSString *entityId = landmark.entityId;

   // A landmark can have multiple locations: for example, the location the image
   // was taken, and the location of the landmark depicted.
   for (FIRVisionLatitudeLongitude *location in landmark.locations) {
     double latitude = [location.latitude doubleValue];
     double longitude = [location.longitude doubleValue];
   }

   float confidence = [landmark.confidence floatValue];
}

Next steps