Pose landmark detection guide for iOS

The Pose Landmarker task lets you detect landmarks of human bodies in an image or video. You can use this task to identify key body locations, analyze posture, and categorize movements. This task uses machine learning (ML) models that work with single images or video. The task outputs body pose landmarks in image coordinates and in 3-dimensional world coordinates.

These instructions show you how to use the Pose Landmarker with iOS apps. The code sample described in these instructions is available on GitHub.

You can see this task in action by viewing this Web demo. For more information about the capabilities, models, and configuration options of this task, see the Overview.

Code example

The MediaPipe Tasks example code is a basic implementation of a Pose Landmarker app for iOS. The example uses the camera on a physical iOS device to detect detect poses in a continuous video stream. The app can also detect poses in images and videos from the device gallery.

You can use the app as a starting point for your own iOS app, or refer to it when modifying an existing app. The Pose Landmarker example code is hosted on GitHub.

Download the code

The following instructions show you how to create a local copy of the example code using the git command line tool.

To download the example code:

1. Clone the git repository using the following command:

   git clone https://github.com/google-ai-edge/mediapipe-samples
   

2. Optionally, configure your git instance to use sparse checkout, so you have only the files for the Pose Landmarker example app:

   cd mediapipe
   git sparse-checkout init --cone
   git sparse-checkout set examples/pose_landmarker/ios/
   

After creating a local version of the example code, you can install the MediaPipe task library, open the project using Xcode and run the app. For instructions, see the Setup Guide for iOS.

Key components

The following files contain the crucial code for the Pose Landmarker example application:

• PoseLandmarkerService.swift: Initializes the landmarker, handles the model selection and runs inference on the input data.
• CameraViewController: Implements the UI for the live camera feed input mode and visualizes the landmarks.
• MediaLibraryViewController.swift: Implements the UI for the still image and video file input mode and visualizes the landmarks.

Setup

This section describes key steps for setting up your development environment and code projects to use Pose Landmarker. For general information on setting up your development environment for using MediaPipe tasks, including platform version requirements, see the Setup guide for iOS.

Dependencies

Pose Landmarker uses the MediaPipeTasksVision library, which must be installed using CocoaPods. The library is compatible with both Swift and Objective-C apps and does not require any additional language-specific setup.

For instructions to install CocoaPods on macOS, refer to the CocoaPods installation guide. For instructions on how to create a Podfile with the necessary pods for your app, refer to Using CocoaPods.

Add the MediaPipeTasksVision pod in the Podfile using the following code:

target 'MyPoseLandmarkerApp' do
  use_frameworks!
  pod 'MediaPipeTasksVision'
end

If your app includes unit test targets, refer to the Set Up Guide for iOS for additional information on setting up your Podfile.

Model

The MediaPipe Pose Landmarker task requires a trained bundle that is compatible with this task. For more information on available trained models for Pose Landmarker, see the task overview Models section.

Use the download_models.sh script to download the models and add it to your project directory using Xcode. For instructions on how to add files to your Xcode project, refer to Managing files and folders in your Xcode project.

Use the BaseOptions.modelAssetPath property to specify the path to the model in your app bundle. For a code example, see the next section.

Create the task

You can create the Pose Landmarker task by calling one of its initializers. The PoseLandmarker(options:) initializer accepts values for the configuration options.

If you don't need a Pose Landmarker initialized with customized configuration options, you can use the PoseLandmarker(modelPath:) initializer to create an Pose Landmarker with the default options. For more information about configuration options, see Configuration Overview.

The Pose Landmarker task supports 3 input data types: still images, video files and live video streams. By default, PoseLandmarker(modelPath:) initializes a task for still images. If you want your task to be initialized to process video files or live video streams, use PoseLandmarker(options:) to specify the video or livestream running mode. The livestream mode also requires the additional poseLandmarkerLiveStreamDelegate configuration option, which enables the Pose Landmarker to deliver the pose landmark detection results to the delegate asynchronously.

Choose the tab corresponding to your running mode to see how to create the task and run inference.

Swift

import MediaPipeTasksVision

let modelPath = Bundle.main.path(forResource: "pose_landmarker",
                                      ofType: "task")

let options = PoseLandmarkerOptions()
options.baseOptions.modelAssetPath = modelPath
options.runningMode = .image
options.minPoseDetectionConfidence = minPoseDetectionConfidence
options.minPosePresenceConfidence = minPosePresenceConfidence
options.minTrackingConfidence = minTrackingConfidence
options.numPoses = numPoses

let poseLandmarker = try PoseLandmarker(options: options)
    

import MediaPipeTasksVision

let modelPath = Bundle.main.path(forResource: "pose_landmarker",
                                      ofType: "task")

let options = PoseLandmarkerOptions()
options.baseOptions.modelAssetPath = modelPath
options.runningMode = .video
options.minPoseDetectionConfidence = minPoseDetectionConfidence
options.minPosePresenceConfidence = minPosePresenceConfidence
options.minTrackingConfidence = minTrackingConfidence
options.numPoses = numPoses

let poseLandmarker = try PoseLandmarker(options: options)
    

import MediaPipeTasksVision

// Class that conforms to the `PoseLandmarkerLiveStreamDelegate` protocol and
// implements the method that the pose landmarker calls once it finishes
// performing pose landmark detection in each input frame.
class PoseLandmarkerResultProcessor: NSObject, PoseLandmarkerLiveStreamDelegate {

  func poseLandmarker(
    _ poseLandmarker: PoseLandmarker,
    didFinishDetection result: PoseLandmarkerResult?,
    timestampInMilliseconds: Int,
    error: Error?) {

    // Process the pose landmarker result or errors here.

  }
}

let modelPath = Bundle.main.path(forResource: "pose_landmarker",
                                      ofType: "task")

let options = PoseLandmarkerOptions()
options.baseOptions.modelAssetPath = modelPath
options.runningMode = .liveStream
options.minPoseDetectionConfidence = minPoseDetectionConfidence
options.minPosePresenceConfidence = minPosePresenceConfidence
options.minTrackingConfidence = minTrackingConfidence
options.numPoses = numPoses

// Assign an object of the class to the `poseLandmarkerLiveStreamDelegate`
// property.
let processor = PoseLandmarkerResultProcessor()
options.poseLandmarkerLiveStreamDelegate = processor

let poseLandmarker = try PoseLandmarker(options: options)
    

Objective-C

@import MediaPipeTasksVision;

NSString *modelPath = [[NSBundle mainBundle] pathForResource:@"pose_landmarker"
                                                      ofType:@"task"];

MPPPoseLandmarkerOptions *options = [[MPPPoseLandmarkerOptions alloc] init];
options.baseOptions.modelAssetPath = modelPath;
options.runningMode = MPPRunningModeImage;
options.minPoseDetectionConfidence = minPoseDetectionConfidence;
options.minPosePresenceConfidence = minPosePresenceConfidence;
options.minTrackingConfidence = minTrackingConfidence;
options.numPoses = numPoses;

MPPPoseLandmarker *poseLandmarker =
  [[MPPPoseLandmarker alloc] initWithOptions:options error:nil];
    

@import MediaPipeTasksVision;

NSString *modelPath = [[NSBundle mainBundle] pathForResource:@"pose_landmarker"
                                                      ofType:@"task"];

MPPPoseLandmarkerOptions *options = [[MPPPoseLandmarkerOptions alloc] init];
options.baseOptions.modelAssetPath = modelPath;
options.runningMode = MPPRunningModeVideo;
options.minPoseDetectionConfidence = minPoseDetectionConfidence;
options.minPosePresenceConfidence = minPosePresenceConfidence;
options.minTrackingConfidence = minTrackingConfidence;
options.numPoses = numPoses;

MPPPoseLandmarker *poseLandmarker =
  [[MPPPoseLandmarker alloc] initWithOptions:options error:nil];
    

@import MediaPipeTasksVision;

// Class that conforms to the `MPPPoseLandmarkerLiveStreamDelegate` protocol
// and implements the method that the pose landmarker calls once it finishes
// performing pose landmarks= detection in each input frame.

@interface APPPoseLandmarkerResultProcessor : NSObject 

@end

@implementation APPPoseLandmarkerResultProcessor

-   (void)poseLandmarker:(MPPPoseLandmarker *)poseLandmarker
    didFinishDetectionWithResult:(MPPPoseLandmarkerResult *)poseLandmarkerResult
         timestampInMilliseconds:(NSInteger)timestampInMilliseconds
                           error:(NSError *)error {

    // Process the pose landmarker result or errors here.

}

@end

NSString *modelPath = [[NSBundle mainBundle] pathForResource:@"pose_landmarker"
                                                      ofType:@"task"];

MPPPoseLandmarkerOptions *options = [[MPPPoseLandmarkerOptions alloc] init];
options.baseOptions.modelAssetPath = modelPath;
options.runningMode = MPPRunningModeLiveStream;
options.minPoseDetectionConfidence = minPoseDetectionConfidence;
options.minPosePresenceConfidence = minPosePresenceConfidence;
options.minTrackingConfidence = minTrackingConfidence;
options.numPoses = numPoses;

// Assign an object of the class to the `poseLandmarkerLiveStreamDelegate`
// property.
APPPoseLandmarkerResultProcessor *processor =
  [APPPoseLandmarkerResultProcessor new];
options.poseLandmarkerLiveStreamDelegate = processor;

MPPPoseLandmarker *poseLandmarker =
  [[MPPPoseLandmarker alloc] initWithOptions:options error:nil];
    

Note: If you use the video mode or livestream mode, Pose Landmarker uses tracking to avoid triggering palm detection model on every frame, which helps reduce latency.

Configuration options

This task has the following configuration options for iOS apps:

Livestream configuration

When the running mode is set to livestream, the Pose Landmarker requires the additional poseLandmarkerLiveStreamDelegate configuration option, which enables the Pose Landmarker to deliver pose landmark detection results asynchronously. The delegate must implement the poseLandmarker(_:didFinishDetection:timestampInMilliseconds:error:) method, which the Pose Landmarker calls after processing the results of performing pose landmark detection on each frame.

Prepare data

You need to convert the input image or frame to an MPImage object before passing it to the Pose Landmarker. MPImage supports different types of iOS image formats, and can use them in any running mode for inference. For more information about MPImage, refer to the MPImage API.

Choose an iOS image format based on your use case and the running mode your application requires.MPImage accepts the UIImage, CVPixelBuffer, and CMSampleBuffer iOS image formats.

UIImage

The UIImage format is well-suited for the following running modes:

• Images: images from an app bundle, user gallery, or file system formatted as UIImage images can be converted to an MPImage object.

• Videos: use AVAssetImageGenerator to extract video frames to the CGImage format, then convert them to UIImage images.

// Load an image on the user's device as an iOS `UIImage` object.

// Convert the `UIImage` object to a MediaPipe's Image object having the default
// orientation `UIImage.Orientation.up`.
let image = try MPImage(uiImage: image)
    

// Load an image on the user's device as an iOS `UIImage` object.

// Convert the `UIImage` object to a MediaPipe's Image object having the default
// orientation `UIImageOrientationUp`.
MPImage *image = [[MPPImage alloc] initWithUIImage:image error:nil];
    

The example initializes an MPImage with the default UIImage.Orientation.Up orientation. You can initialize an MPImage with any of the supported UIImage.Orientation values. Pose Landmarker does not support mirrored orientations like .upMirrored, .downMirrored, .leftMirrored, .rightMirrored.

For more information about UIImage, refer to the UIImage Apple Developer Documentation.

CVPixelBuffer

The CVPixelBuffer format is well-suited for applications that generate frames and use the iOS CoreImage framework for processing.

The CVPixelBuffer format is well-suited for the following running modes:

• Images: apps that generate CVPixelBuffer images after some processing using iOS's CoreImage framework can be sent to the Pose Landmarker in the image running mode.

• Videos: video frames can be converted to the CVPixelBuffer format for processing, and then sent to the Pose Landmarker in video mode.

• livestream: apps using an iOS camera to generate frames may be converted into the CVPixelBuffer format for processing before being sent to the Pose Landmarker in livestream mode.

// Obtain a CVPixelBuffer.

// Convert the `CVPixelBuffer` object to a MediaPipe's Image object having the default
// orientation `UIImage.Orientation.up`.
let image = try MPImage(pixelBuffer: pixelBuffer)
    

// Obtain a CVPixelBuffer.

// Convert the `CVPixelBuffer` object to a MediaPipe's Image object having the
// default orientation `UIImageOrientationUp`.
MPImage *image = [[MPPImage alloc] initWithUIImage:image error:nil];
    

For more information about CVPixelBuffer, refer to the CVPixelBuffer Apple Developer Documentation.

CMSampleBuffer

The CMSampleBuffer format stores media samples of a uniform media type, and is well-suited for the livestream running mode. Live frames from iOS cameras are asynchronously delivered in the CMSampleBuffer format by iOS AVCaptureVideoDataOutput.

// Obtain a CMSampleBuffer.

// Convert the `CMSampleBuffer` object to a MediaPipe's Image object having the default
// orientation `UIImage.Orientation.up`.
let image = try MPImage(sampleBuffer: sampleBuffer)
    

// Obtain a `CMSampleBuffer`.

// Convert the `CMSampleBuffer` object to a MediaPipe's Image object having the
// default orientation `UIImageOrientationUp`.
MPImage *image = [[MPPImage alloc] initWithSampleBuffer:sampleBuffer error:nil];
    

For more information about CMSampleBuffer, refer to the CMSampleBuffer Apple Developer Documentation.

Run the task

To run the Pose Landmarker, use the detect() method specific to the assigned running mode:

• Still image: detect(image:)
• Video: detect(videoFrame:timestampInMilliseconds:)
• Livestream: detectAsync(image:timestampInMilliseconds:)

The following code samples show simple examples of how to run Pose Landmarker in these different running modes:

Swift

let result = try poseLandmarker.detect(image: image)
    

let result = try poseLandmarker.detect(
  videoFrame: image,
  timestampInMilliseconds: timestamp)
    

try poseLandmarker.detectAsync(
  image: image,
  timestampInMilliseconds: timestamp)
    

Objective-C

MPPPoseLandmarkerResult *result =
  [poseLandmarker detectImage:image error:nil];
    

MPPPoseLandmarkerResult *result =
  [poseLandmarker detectVideoFrame:image
           timestampInMilliseconds:timestamp
                             error:nil];
    

BOOL success =
  [poseLandmarker detectAsyncImage:image
           timestampInMilliseconds:timestamp
                             error:nil];
    

The Pose Landmarker code example shows the implementations of each of these modes in more detail detect(image:), detect(videoFrame:timestampInMilliseconds:), and detectAsync(image:timestampInMilliseconds:). The example code allows the user to switch between processing modes which may not be required for your use case.

Note the following:

• When running in video mode or livestream mode, you must also provide the timestamp of the input frame to the Pose Landmarker task.

• When running in image or video mode, the Pose Landmarker task blocks the current thread until it finishes processing the input image or frame. To avoid blocking the current thread, execute the processing in a background thread using iOS Dispatch or NSOperation frameworks.

• When running in livestream mode, the Pose Landmarker task returns immediately and doesn't block the current thread. It invokes the poseLandmarker(_:didFinishDetection:timestampInMilliseconds:error:) method with the pose landmarker result after processing each input frame. The Pose Landmarker invokes this method asynchronously on a dedicated serial dispatch queue. For displaying results on the user interface, dispatch the results to the main queue after processing the results. If the detectAsync function is called when the Pose Landmarker task is busy processing another frame, the Pose Landmarker ignores the new input frame.

Handle and display results

Upon running inference, the Pose Landmarker task returns a PoseLandmarkerResult which contains the coordinates for each pose landmark.

The following shows an example of the output data from this task:

PoseLandmarkerResult:
  Landmarks:
    Landmark #0:
      x            : 0.638852
      y            : 0.671197
      z            : 0.129959
      visibility   : 0.9999997615814209
      presence     : 0.9999984502792358
    Landmark #1:
      x            : 0.634599
      y            : 0.536441
      z            : -0.06984
      visibility   : 0.999909
      presence     : 0.999958
    ... (33 landmarks per pose)
  WorldLandmarks:
    Landmark #0:
      x            : 0.067485
      y            : 0.031084
      z            : 0.055223
      visibility   : 0.9999997615814209
      presence     : 0.9999984502792358
    Landmark #1:
      x            : 0.063209
      y            : -0.00382
      z            : 0.020920
      visibility   : 0.999976
      presence     : 0.999998
    ... (33 world landmarks per pose)
  SegmentationMasks:
    ... (pictured below)

The output contains both normalized coordinates (Landmarks) and world coordinates (WorldLandmarks) for each landmark.

The output contains the following normalized coordinates (Landmarks):

• x and y: Landmark coordinates normalized between 0.0 and 1.0 by the image width (x) and height (y).

• z: The landmark depth, with the depth at the midpoint of the hips as the origin. The smaller the value, the closer the landmark is to the camera. The magnitude of z uses roughly the same scale as x.

• visibility: The likelihood of the landmark being visible within the image.

The output contains the following world coordinates (WorldLandmarks):

• x, y, and z: Real-world 3-dimensional coordinates in meters, with the midpoint of the hips as the origin.

• visibility: The likelihood of the landmark being visible within the image.

The following image shows a visualization of the task output:

The optional segmentation mask represents the likelihood of each pixel belonging to a detected person. The following image is a segmentation mask of the task output:

The Pose Landmarker example code demonstrates how to display the Pose Landmarker results.