blob_distance — BLOB `split` op

• Data kinds: mask → image2d

• Call: import fullseye as fs; fs.ledger.blob_distance(region: 'Any', spacing: 'float' = 1.0) -> 'np.ndarray' (to call the implementation directly, import blob2d; blob2d.blob_distance(region: 'Any', spacing: 'float' = 1.0) -> 'np.ndarray'; from the registry, opsblob.get("blob_distance"))

Usage

Distance from each foreground pixel to the nearest background. The unit is multiples of spacing.

Exactly scipy.ndimage.distance_transform_edt. The reason for making a dedicated entry point is that the evolved op distance_transform returns its result normalised by the maximum (measured in poc_cell_counting: the output maximum is always 1.0), so distances in pixel units cannot be obtained. Building watershed seeds needs absolute values.

Examples

>>> import numpy as np

>>> m = np.zeros((21, 21), bool); m[5:16, 5:16] = True

>>> float(blob_distance(m).max()) # to the centre of the 11x11 square

6.0

Detailed usage guide

• blob_analysis family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• blob_split_tour — py -3.11 examples/blob_split_tour.py

• poc_bone_trabecular_thickness — py -3.11 examples/poc_bone_trabecular_thickness.py

• poc_segmentation_gauntlet — py -3.11 examples/poc_segmentation_gauntlet.py

Ops the type connects to (they accept `image2d` as input)

blob_seeds · blob_split · blob_overlay

Same category (`split`)

blob_seeds · blob_split

*Provenance: blob2d.py — BLOB operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

blob_distance — BLOB split op