nicetoolbox.detectors.method_detectors.spiga.spiga_detector.Spiga

class nicetoolbox.detectors.method_detectors.spiga.spiga_detector.Spiga(io: SequenceIO, data: SequenceData, sequence_context: SequenceRuntimeConfig, algorithm_instance: str)[source]

Bases: BaseMethod

SPIGA is a method detector that computes the head_orientation component.

Component: head_orientation

components

A list containing the name of the component: head_orientation

Type:

list

algorithm

Algorithm name used to compute the head_orientation component.

Type:

str

camera_names

List of camera names used to capture original input data.

Type:

list

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_folders

Compute visualization folders for all components.

post_inference

Calculate head orientation in 2D image after SPIGA inference.

run

Execute method detector: run subprocess inference + post_inference.

visualization

Visualize detector output.

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_folders(visualize: bool) Dict[str, str]

Compute visualization folders for all components.

post_inference()[source]

Calculate head orientation in 2D image after SPIGA inference.

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(data)[source]

Visualize detector output.

Parameters:

data – Output from run() or external data source