mp.tasks.vision.HolisticLandmarker

Class that performs holistic landmarks detection on images.

mp.tasks.vision.HolisticLandmarker(
    graph_config: mp.calculators.core.constant_side_packet_calculator_pb2.mediapipe_dot_framework_dot_calculator__pb2.CalculatorGraphConfig,
    running_mode: mp.tasks.vision.RunningMode,
    packet_callback: Optional[Callable[[Mapping[str, packet_module.Packet]], None]] = None
) -> None

graph_config The mediapipe vision task graph config proto.
running_mode The running mode of the mediapipe vision task.
packet_callback The optional packet callback for getting results asynchronously in the live stream mode.

ValueError The packet callback is not properly set based on the task's running mode.

Methods

close

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close() -> None

Shuts down the mediapipe vision task instance.

Raises
RuntimeError If the mediapipe vision task failed to close.

convert_to_normalized_rect

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convert_to_normalized_rect(
    options: mp.tasks.vision.holistic_landmarker.image_processing_options_module.ImageProcessingOptions,
    image: mp.Image,
    roi_allowed: bool = True
) -> mp.tasks.components.containers.NormalizedRect

Converts from ImageProcessingOptions to NormalizedRect, performing sanity checks on-the-fly.

If the input ImageProcessingOptions is not present, returns a default NormalizedRect covering the whole image with rotation set to 0. If 'roi_allowed' is false, an error will be returned if the input ImageProcessingOptions has its 'region_of_interest' field set.

Args
options Options for image processing.
image The image to process.
roi_allowed Indicates if the region_of_interest field is allowed to be set. By default, it's set to True.

Returns
A normalized rect proto that represents the image processing options.

create_from_model_path

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@classmethod
create_from_model_path(
    model_path: str
) -> 'HolisticLandmarker'

Creates an HolisticLandmarker object from a TensorFlow Lite model and the default HolisticLandmarkerOptions.

Note that the created HolisticLandmarker instance is in image mode, for detecting holistic landmarks on single image inputs.

Args
model_path Path to the model.

Returns
HolisticLandmarker object that's created from the model file and the default HolisticLandmarkerOptions.

Raises
ValueError If failed to create HolisticLandmarker object from the provided file such as invalid file path.
RuntimeError If other types of error occurred.

create_from_options

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@classmethod
create_from_options(
    options: mp.tasks.vision.HolisticLandmarkerOptions
) -> 'HolisticLandmarker'

Creates the HolisticLandmarker object from holistic landmarker options.

Args
options Options for the holistic landmarker task.

Returns
HolisticLandmarker object that's created from options.

Raises
ValueError If failed to create HolisticLandmarker object from HolisticLandmarkerOptions such as missing the model.
RuntimeError If other types of error occurred.

detect

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detect(
    image: mp.Image
) -> mp.tasks.vision.HolisticLandmarkerResult

Performs holistic landmarks detection on the given image.

Only use this method when the HolisticLandmarker is created with the image running mode.

The image can be of any size with format RGB or RGBA.

Args
image MediaPipe Image.

Returns
The holistic landmarks detection results.

Raises
ValueError If any of the input arguments is invalid.
RuntimeError If holistic landmarker detection failed to run.

detect_async

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detect_async(
    image: mp.Image,
    timestamp_ms: int
) -> None

Sends live image data to perform holistic landmarks detection.

The results will be available via the "result_callback" provided in the HolisticLandmarkerOptions. Only use this method when the HolisticLandmarker is created with the live stream running mode.

Only use this method when the HolisticLandmarker is created with the live stream running mode. The input timestamps should be monotonically increasing for adjacent calls of this method. This method will return immediately after the input image is accepted. The results will be available via the result_callback provided in the HolisticLandmarkerOptions. The detect_async method is designed to process live stream data such as camera input. To lower the overall latency, holistic landmarker may drop the input images if needed. In other words, it's not guaranteed to have output per input image.

The result_callback provides:

  • The holistic landmarks detection results.
  • The input image that the holistic landmarker runs on.
  • The input timestamp in milliseconds.

Args
image MediaPipe Image.
timestamp_ms The timestamp of the input image in milliseconds.

Raises
ValueError If the current input timestamp is smaller than what the holistic landmarker has already processed.

detect_for_video

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detect_for_video(
    image: mp.Image,
    timestamp_ms: int
) -> mp.tasks.vision.HolisticLandmarkerResult

Performs holistic landmarks detection on the provided video frame.

Only use this method when the HolisticLandmarker is created with the video running mode.

Only use this method when the HolisticLandmarker is created with the video running mode. It's required to provide the video frame's timestamp (in milliseconds) along with the video frame. The input timestamps should be monotonically increasing for adjacent calls of this method.

Args
image MediaPipe Image.
timestamp_ms The timestamp of the input video frame in milliseconds.

Returns
The holistic landmarks detection results.

Raises
ValueError If any of the input arguments is invalid.
RuntimeError If holistic landmarker detection failed to run.

get_graph_config

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get_graph_config() -> mp.calculators.core.constant_side_packet_calculator_pb2.mediapipe_dot_framework_dot_calculator__pb2.CalculatorGraphConfig

Returns the canonicalized CalculatorGraphConfig of the underlying graph.

__enter__

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__enter__()

Return self upon entering the runtime context.

__exit__

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__exit__(
    unused_exc_type, unused_exc_value, unused_traceback
)

Shuts down the mediapipe vision task instance on exit of the context manager.

Raises
RuntimeError If the mediapipe vision task failed to close.