nicetoolbox.detectors.data.SequenceData¶

class nicetoolbox.detectors.data.SequenceData(sequence_context: SequenceRuntimeConfig, io: SequenceIO)[source]¶

Bases: object

Facade for NICE Toolbox input data preparation.

Determines which modalities to prepare based on ‘input_data_type’ fields in the selected detector/algorithm configs from detectors_config.toml.

Supported input_data_type values: - “video” : video files / frame sequences (always prepared by default) - “audio” : audio tracks extracted from video or standalone files - “frames” : (future) non-temporal image datasets

Algorithms may declare a single string or a list for cross-modal use:: input_data_type = “video” input_data_type = [“video”, “audio”]

Feature detectors (those with input_detector_names instead of input_data_type) do not trigger raw data preparation directly.

Initialize data facade and orchestrate data handling.

Methods

`get_audio_input_recipe`	Get audio input recipe for audio data loaders.
`get_input_recipes`	Get composed InputRecipes containing all available modality recipes.
`get_video_input_recipe`	Get video/frame input recipe for video data loaders.
`has_audio`	Check if audio data is available.

Attributes

`calibration`	Camera calibration data (video-specific).
`video_start_frame_index`
`video_length_frames`
`fps`

property calibration: Dict[str, Any] | None¶: Camera calibration data (video-specific).

get_audio_input_recipe() → AudioInputRecipe | None[source]¶: Get audio input recipe for audio data loaders.

get_input_recipes() → InputRecipes[source]¶

Get composed InputRecipes containing all available modality recipes.

This is the primary method used by BaseMethod to build the runtime config for subprocesses. Each recipe is validated via Pydantic.

Returns:: InputRecipes with video and/or audio recipes set.

get_video_input_recipe() → VideoInputRecipe[source]¶: Get video/frame input recipe for video data loaders.

has_audio() → bool[source]¶: Check if audio data is available.