nicetoolbox.detectors.method_detectors.mmpose.mmpose_framework_3d.MotionBERT

class nicetoolbox.detectors.method_detectors.mmpose.mmpose_framework_3d.MotionBERT(io: SequenceIO, data: SequenceData, sequence_context: SequenceRuntimeConfig, algorithm_instance: str)

Bases: MMPose3D

Handler for MotionBERT models trained on Human3.6M (17 Keypoints).

Initialize base method detector with references.

Methods

compute_output_folders	Compute extra output folders for all components.
compute_result_folders	Compute result folders for all components.
compute_viz_folder	Return None so base __init__ does not mkdir the visualization folder.
compute_viz_folders	Compute visualization folders for all components.
get_per_component_keypoint_mapping	This method extracts the keypoint indices and descriptions for each pose estimation component.
post_inference	Post-inference processing for 3D pose estimation.
run	Execute method detector: run subprocess inference + post_inference.
visualization	Generates a visualization video for each camera from the processed image frames.

Attributes

algorithm_type
components
inference_package_name
predictions_mapping	Access predictions mapping from runtime config.
runtime
os_type
conda_path
venv
env_name
script_path
visualize
requires_out_folder
out_folders
result_folders
viz_folders
config_paths
data
io
sequence_context
detector_config
algorithm_instance
inference_config

compute_output_folders(requires_out_folder: bool) → Dict[str, str]

Compute extra output folders for all components.

compute_result_folders() → Dict[str, str]

Compute result folders for all components.

compute_viz_folder(_visualize: bool) → str | None

Return None so base __init__ does not mkdir the visualization folder.

The subprocess creates the per-camera frame folders itself; the main process assembles the mp4s in visualization() after inference.

compute_viz_folders(visualize: bool) → Dict[str, str]

Compute visualization folders for all components.

get_per_component_keypoint_mapping(keypoints_indices)

This method extracts the keypoint indices and descriptions for each pose estimation component.

It has to be implemented by the derived classes associated to the available pose estimation algorithms. (See HRNetw48 and Vitpose classes below)

Available algorithms are: hrnetw48, vitpose

Parameters:

keypoints_indices (_type_) – _description_

post_inference()

Post-inference processing for 3D pose estimation.

property predictions_mapping

Access predictions mapping from runtime config.

run() → None

Execute method detector: run subprocess inference + post_inference.

Returns None - visualization uses external data.

visualization(_)

Generates a visualization video for each camera from the processed image frames.

This method takes the processed image frames for each camera and compiles them into a video file. It uses the frames_to_video() function from utils.video.py. The success of the video creation is tracked, and the method logs the outcome of the visualization process for each camera. :param data: An instance of a class that stores all data related

information, including the frame rate (fps) for the video and the starting frame number (video_start).

Note

• The method assumes that the processed image frames are named in a

specific format (%09d.jpg), where each frame’s name is a zero-padded five-digit number representing its sequence in the video.