Detect poses with ML Kit on iOS

ML Kit provides two optimized SDKs for pose detection.

Try it out

• Play around with the sample app to see an example usage of this API.

Before you begin

1. Include the following ML Kit pods in your Podfile:

   # If you want to use the base implementation:
   pod 'GoogleMLKit/PoseDetection', '3.2.0'
   
   # If you want to use the accurate implementation:
   pod 'GoogleMLKit/PoseDetectionAccurate', '3.2.0'
   

2. After you install or update your project’s pods, open your Xcode project using its xcworkspace. ML Kit is supported in Xcode version 13.2.1 or higher.

1. Create an instance of PoseDetector

To detect a pose in an image, first create an instance of PoseDetector and optionally specify the detector settings.

PoseDetector options

Detection Mode

The PoseDetector operates in two detection modes. Be sure you choose the one that matches your use case.

stream (default)

The pose detector will first detect the most prominent person in the image and then run pose detection. In subsequent frames, the person-detection step will not be conducted unless the person becomes obscured or is no longer detected with high confidence. The pose detector will attempt to track the most-prominent person and return their pose in each inference. This reduces latency and smooths detection. Use this mode when you want to detect pose in a video stream.

singleImage

The pose detector will detect a person and then run pose detection. The person-detection step will run for every image, so latency will be higher, and there is no person-tracking. Use this mode when using pose detection on static images or where tracking is not desired.

Specify the pose detector options:

// Base pose detector with streaming, when depending on the PoseDetection SDK
let options = PoseDetectorOptions()
options.detectorMode = .stream

// Accurate pose detector on static images, when depending on the
// PoseDetectionAccurate SDK
let options = AccuratePoseDetectorOptions()
options.detectorMode = .singleImage

// Base pose detector with streaming, when depending on the PoseDetection SDK
MLKPoseDetectorOptions *options = [[MLKPoseDetectorOptions alloc] init];
options.detectorMode = MLKPoseDetectorModeStream;

// Accurate pose detector on static images, when depending on the
// PoseDetectionAccurate SDK
MLKAccuratePoseDetectorOptions *options =
    [[MLKAccuratePoseDetectorOptions alloc] init];
options.detectorMode = MLKPoseDetectorModeSingleImage;

Finally, get an instance of PoseDetector. Pass the options you specified:

let poseDetector = PoseDetector.poseDetector(options: options)

MLKPoseDetector *poseDetector =
    [MLKPoseDetector poseDetectorWithOptions:options];

2. Prepare the input image

To detect poses, do the following for each image or frame of video. If you enabled stream mode, you must create VisionImage objects from CMSampleBuffers.

Create a VisionImage object using a UIImage or a CMSampleBuffer.

If you use a UIImage, follow these steps:

• Create a VisionImage object with the UIImage. Make sure to specify the correct .orientation.

  Swift

  let image = VisionImage(image: UIImage)
  visionImage.orientation = image.imageOrientation

  Objective-C

  MLKVisionImage *visionImage = [[MLKVisionImage alloc] initWithImage:image];
  visionImage.orientation = image.imageOrientation;

If you use a CMSampleBuffer, follow these steps:

• Specify the orientation of the image data contained in the CMSampleBuffer.

  To get the image orientation:

  Swift

  func imageOrientation(
    deviceOrientation: UIDeviceOrientation,
    cameraPosition: AVCaptureDevice.Position
  ) -> UIImage.Orientation {
    switch deviceOrientation {
    case .portrait:
      return cameraPosition == .front ? .leftMirrored : .right
    case .landscapeLeft:
      return cameraPosition == .front ? .downMirrored : .up
    case .portraitUpsideDown:
      return cameraPosition == .front ? .rightMirrored : .left
    case .landscapeRight:
      return cameraPosition == .front ? .upMirrored : .down
    case .faceDown, .faceUp, .unknown:
      return .up
    }
  }
        

  Objective-C

  - (UIImageOrientation)
    imageOrientationFromDeviceOrientation:(UIDeviceOrientation)deviceOrientation
                           cameraPosition:(AVCaptureDevicePosition)cameraPosition {
    switch (deviceOrientation) {
      case UIDeviceOrientationPortrait:
        return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationLeftMirrored
                                                              : UIImageOrientationRight;
  
      case UIDeviceOrientationLandscapeLeft:
        return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationDownMirrored
                                                              : UIImageOrientationUp;
      case UIDeviceOrientationPortraitUpsideDown:
        return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationRightMirrored
                                                              : UIImageOrientationLeft;
      case UIDeviceOrientationLandscapeRight:
        return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationUpMirrored
                                                              : UIImageOrientationDown;
      case UIDeviceOrientationUnknown:
      case UIDeviceOrientationFaceUp:
      case UIDeviceOrientationFaceDown:
        return UIImageOrientationUp;
    }
  }
        

• Create a VisionImage object using the CMSampleBuffer object and orientation:

  Swift

  let image = VisionImage(buffer: sampleBuffer)
  image.orientation = imageOrientation(
    deviceOrientation: UIDevice.current.orientation,
    cameraPosition: cameraPosition)

  Objective-C

   MLKVisionImage *image = [[MLKVisionImage alloc] initWithBuffer:sampleBuffer];
   image.orientation =
     [self imageOrientationFromDeviceOrientation:UIDevice.currentDevice.orientation
                                  cameraPosition:cameraPosition];

3. Process the image

Pass the VisionImage to one of the pose detector's image processing methods. You can either use the asynchronous process(image:) method or the synchronous results() method.

To detect objects synchronously:

var results: [Pose]
do {
  results = try poseDetector.results(in: image)
} catch let error {
  print("Failed to detect pose with error: \(error.localizedDescription).")
  return
}
guard let detectedPoses = results, !detectedPoses.isEmpty else {
  print("Pose detector returned no results.")
  return
}

// Success. Get pose landmarks here.

NSError *error;
NSArray *poses = [poseDetector resultsInImage:image error:&error];
if (error != nil) {
  // Error.
  return;
}
if (poses.count == 0) {
  // No pose detected.
  return;
}

// Success. Get pose landmarks here.

To detect objects asynchronously:

poseDetector.process(image) { detectedPoses, error in
  guard error == nil else {
    // Error.
    return
  }
  guard !detectedPoses.isEmpty else {
    // No pose detected.
    return
  }

  // Success. Get pose landmarks here.
}

[poseDetector processImage:image
                completion:^(NSArray * _Nullable poses,
                             NSError * _Nullable error) {
                    if (error != nil) {
                      // Error.
                      return;
                    }
                    if (poses.count == 0) {
                      // No pose detected.
                      return;
                    }

                    // Success. Get pose landmarks here.
                  }];

4. Get information about the detected pose

If a person is detected in the image, the pose detection API either passes an array of Pose objects to the completion handler or returns the array, depending on whether you called the asynchronous or synchronous method.

If the person was not completely inside the image, the model assigns the missing landmarks coordinates outside the frame and gives them low InFrameConfidence values.

If no person was detected the array is empty.

for pose in detectedPoses {
  let leftAnkleLandmark = pose.landmark(ofType: .leftAnkle)
  if leftAnkleLandmark.inFrameLikelihood > 0.5 {
    let position = leftAnkleLandmark.position
  }
}

for (MLKPose *pose in detectedPoses) {
  MLKPoseLandmark *leftAnkleLandmark =
      [pose landmarkOfType:MLKPoseLandmarkTypeLeftAnkle];
  if (leftAnkleLandmark.inFrameLikelihood > 0.5) {
    MLKVision3DPoint *position = leftAnkleLandmark.position;
  }
}

Tips to improve performance

The quality of your results depends on the quality of the input image:

• For ML Kit to accurately detect pose, the person in the image should be represented by sufficient pixel data; for best performance, the subject should be at least 256x256 pixels.
• If you detect pose in a real-time application, you might also want to consider the overall dimensions of the input images. Smaller images can be processed faster, so to reduce latency, capture images at lower resolutions, but keep in mind the above resolution requirements and ensure that the subject occupies as much of the image as possible.
• Poor image focus can also impact accuracy. If you don't get acceptable results, ask the user to recapture the image.

If you want to use pose detection in a real-time application, follow these guidelines to achieve the best framerates:

• Use the base PoseDetection SDK and stream detection mode.
• Consider capturing images at a lower resolution. However, also keep in mind this API's image dimension requirements.
• For processing video frames, use the results(in:) synchronous API of the detector. Call this method from the AVCaptureVideoDataOutputSampleBufferDelegate's captureOutput(_, didOutput:from:) function to synchronously get results from the given video frame. Keep AVCaptureVideoDataOutput's alwaysDiscardsLateVideoFrames as true to throttle calls to the detector. If a new video frame becomes available while the detector is running, it will be dropped.
• If you use the output of the detector to overlay graphics on the input image, first get the result from ML Kit, then render the image and overlay in a single step. By doing so, you render to the display surface only once for each processed input frame. See the previewOverlayView and MLKDetectionOverlayView classes in the showcase sample app for an example.

Next steps

• To learn how to use pose landmarks to classify poses, see Pose Classification Tips.
• See the ML Kit quickstart sample on GitHub for an example of this API in use.