ncolor 2.0.1

Label matrix 4-color graph coloring + Voronoi expansion (C++).

ncolor

Fast remapping of instance labels 1,2,3,...,M to a smaller set of repeating, disjoint labels 1,2,...,N. The four color theorem guarantees N ≤ 4 for any 2D segmentation. The picker will fall back to N = 5 if a 4-coloring cannot be found within the time budget. Also works for 3D labels (< 8 typically) and higher dimensions.

Install

pip install ncolor

Pulls a precompiled wheel (Linux x86_64 / aarch64, macOS arm64, Windows AMD64). Only runtime deps are numpy and platformdirs.

Usage

import ncolor
ncolor_masks = ncolor.label(masks)                    # 4-color
ncolor_masks, n = ncolor.label(masks, return_n=True)  # +color count
labels = ncolor.format_labels(masks)                  # compact to 1..N
labels = ncolor.format_labels(masks, clean=True)      # + split disjoint pieces, drop tiny components

Expand-labels is on by default (so that close-but-not-touching cells tend to be assigned distinct colors). Pass expand=False for 3D inputs where cells can over-expand. Thanks to Ryan Peters (@ryanirl) for the original suggestion.

New in v2

v2 is a complete C++ rewrite. Every stage of the pipeline including label expansion has been optimized, resulting in 7–12× speedups end-to-end. The new default expand removes 1-pixel bridges and spurs before the picker sees them, and an auto-soft constraint refines the hard 4-coloring via local search to differentiate near-adjacent cells. Together these break the K₅-shaped convergence clusters that forced the v1 numba pipeline up to N = 5. See CHANGELOG.md for the full list of changes and the migration table from v1.

The rewrite also brings drop-in C++ replacements for the scikit-image and scipy.ndimage / edt calls the old pipeline relied on, with no extra install:

ncolor	replaces	typical speedup
ncolor.connected_components	skimage.measure.label	1.5–3×
ncolor.regionprops	skimage.measure.regionprops (vectorized subset: area / bbox / centroid)	1.5–3×
ncolor.expand_labels	skimage.segmentation.expand_labels + scipy.ndimage.distance_transform_edt	ND-parallel L1 / L2 in-engine; no scipy or edt dependency
ncolor.delete_spurs	hand-rolled morphology / not in scikit-image	ND, parallel

For C++ engine internals, file-by-file architecture, and threadpool design, see ARCHITECTURE.md.