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detectors

Shape detectors: interrogate a cached frame to decide which pipeline it needs.

Selection is by shape — the wide columns (the keys of each packed inferences_json dict) and their value structure — not by matching on the producing model's ref/version. A mock model that emits the same output shape as the real model it stands in for therefore resolves to the same pipeline with no special-casing. Each detector pairs with a pipeline in bitfount.preprocessing.pipelines.

Module​

Functions​

matches_fovea_v7_output​

def matches_fovea_v7_output(    df: pd.DataFrame, inferences_column: str = 'inferences_json',) ‑> bool:

Whether df holds raw fovea v7 landmark output.

Fingerprint: an integer-"0" wide column whose value is a dict carrying the v7 landmark fields (fovea_pit_coordinates).

matches_pathology_output​

def matches_pathology_output(df: pd.DataFrame) ‑> bool:

Whether df holds pathology model output (segmentation-per-B-scan).

Fingerprint: a wide column matching Pixel_Data_\d+_prediction whose element dict carries a classes key OR a cnv_probability key. Both are absent from retinal-layers output, which shares the wide-column shape — so this value-structure test is what disambiguates the two (see matches_retinal_layers_output).

matches_retinal_layers_output​

def matches_retinal_layers_output(df: pd.DataFrame) ‑> bool:

Whether df holds retinal-layers model output (layer segmentation).

Fingerprint: a wide column matching Pixel_Data_\d+_prediction whose element dict carries instances — either at the top level (element['instances'], Heidelberg) or nested under mask (element['mask']['instances'], Altris) — AND carries NEITHER classes NOR cnv_probability. The classes/cnv exclusion is the primary disambiguator from pathology output (see matches_pathology_output).

wide_columns​

def wide_columns(    df: pd.DataFrame, inferences_column: str = 'inferences_json',) ‑> set[str]:

Return the wide column names encoded in the packed inference frame.

Each row's inferences_json is a dict whose keys are the wide prediction columns (see apply_preprocessing). Returns the keys of the first non-null dict, or an empty set if the frame carries no inference dicts.