split op• Data kinds: labels2d × labels2d × image2d → labels2d
• Call: import fullseye as fs; fs.ledger.blob_split(labels: 'Any', seeds: 'Any', distance: 'Any') -> 'np.ndarray' (to call the implementation directly, import blob2d; blob2d.blob_split(labels: 'Any', seeds: 'Any', distance: 'Any') -> 'np.ndarray'; from the registry, opsblob.get("blob_split"))
Grows regions from the seeds in order from the highest points and splits merged clumps (watershed).
Starting from the pixels with the highest distance, each absorbs an adjacent known label (immersion-type watershed run in descending order of distance). Clumps without a seed are kept as they are -- deleting them when there is nothing to split them with would be a lie.
segmentation.watersheds_marker is not used because in an environment without skimage it silently falls back to a different algorithm (nearest neighbour from the markers). That fallback never looks at the image at all, so the same function name returns something different. This one is closed under numpy and scipy alone.
labels : array_like
What to split. Output of blob_label (only pixels > 0 are split).
seeds : array_like
Output of blob_seeds. Seeds outside labels are ignored.
distance : array_like
Priority. Usually the output of blob_distance.
numpy.ndarray
int32 label image. Numbers are reassigned from 1.
• 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.
• blob_split_tour — py -3.11 examples/blob_split_tour.py
labels2d as input)blob_features · blob_select · blob_select_largest · blob_region · blob_boundaries · blob_overlay
split)*Provenance: blob2d.py — BLOB operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.