The Pose Landmarker uses a series of models to predict pose landmarks. Sets the result listener to receive the landmarker resultsĪsynchronously when Pose Landmarker is in the live stream mode.Ĭan only be used when running mode is set to LIVE_STREAM Whether Pose Landmarker outputs a segmentation mask for the detected The minimum confidence score for the pose tracking The minimum confidence score of pose presence

The minimum confidence score for the pose detection to be The maximum number of poses that can be detected by the In this mode, you mustĬall the result_callback listener to receive the Live stream of input data, such as from camera.

LIVE_STREAM: The mode for recognizing pose landmarks on a.

VIDEO: The mode for recognizing pose landmarks on the.

IMAGE: The mode for recognizing pose landmarks on.

This task has the following configuration options: Option Name

Optional: a segmentation mask for the pose.

Pose landmarks in normalized image coordinates.

The Pose Landmarker outputs the following results: The Pose Landmarker accepts an input of one of the following data types:

Score threshold - Filter results based on prediction scores.

Input image processing - Processing includes image rotation, resizing, normalization, and color space conversion.

This section describes the capabilities, inputs, outputs, and configuration Implementation of this task, including a recommended model, and code example These platform-specific guides walk you through a basic Start using this task by following the implementation guide for your The task outputs body pose landmarks in imageĬoordinates and in 3-dimensional world coordinates. This task uses machine learning (ML) models that You can use this task to identify key body locations, analyze posture,Īnd categorize movements. The MediaPipe Pose Landmarker task lets you detect landmarks of human bodies in an image or

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