nicetoolbox.detectors.method_detectors.py_feat.py_feat.PyFeat¶

class nicetoolbox.detectors.method_detectors.py_feat.py_feat.PyFeat(io: SequenceIO, data: SequenceData, sequence_context: SequenceRuntimeConfig, algorithm_instance: str)[source]¶

The Python - Facial Expression Analysis Toolbox (Py-feat) is a method detector that computes emotion_individual component.

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`	Post processing after inference.
`run`	Execute method detector: run subprocess inference + post_inference.
`visualization`	Visualizes the processed frames of the pyfeat algorithm as a video.

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.

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(_)[source]¶: Visualizes the processed frames of the pyfeat algorithm as a video.