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ncolor bacteria

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 / x86_64, Windows AMD64 / ARM64) for CPython 3.11 to 3.15. 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
ncolor.release_buffers()                              # free the scratch kept between calls (optional)

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.

Threading needs nothing special. A lone call uses every core; calls that overlap are handed narrower engines whose threads add up to about one machine, so several images color at once:

import concurrent.futures as cf, ncolor

with cf.ThreadPoolExecutor(4) as pool:
    colored = list(pool.map(ncolor.label, images))   # 1.3-2x on 4 threads

Nothing is measured or configured for this. Whether spreading calls out beats taking turns was swept over four machines and eight image sizes, and it depends on the image at least as much as on the machine, so there is no per-machine answer worth caching: on an 8-core i9 the same arrangement ran 1.3x faster at 512 by 512, 0.8x at 1024, and 1.1x at 4096. Averaged over sizes it pays on every machine tested, from 1.07x on that i9 to 1.9x on a 64-core Threadripper, with a worst single case of 0.76x. NCOLOR_MAX_ENGINES=1 turns it off and calls take turns on one pool as they always have. Engines are built only when calls actually overlap, so a single-threaded program holds one, and once the parallel phase is over calls go back to full width. At most NCOLOR_MAX_ENGINES (4) are created; each keeps the working set of the largest image it has seen, roughly 20 bytes per pixel. ncolor.Engine does the same by hand for callers who would rather size the threads themselves.

Any integer, bool or float label array is accepted; the cast to the engine's int32 runs in parallel inside the call. Labels beyond the int32 range are compacted automatically by label and format_labels. The engine keeps the scratch memory of the largest image it has processed until release_buffers() is called.

Vector geometry (GeoDataFrames, GeoJSON)

Data that starts out as polygons does not have to be rasterized to be colored. ncolor.geo.label reads the adjacency straight off the geometry, so nothing is lost on the way in:

pip install "ncolor[geo]"      # adds shapely; geopandas is optional

import geopandas as gpd
from ncolor import geo

gdf = gpd.read_file("cells.geojson")
gdf["color"] = geo.label(gdf)                         # 4-color, one per feature
gdf.plot(column="color", cmap="viridis")

It accepts a GeoDataFrame, a GeoSeries, a GeoJSON file / string / dict, a list of Shapely geometries, or anything with a __geo_interface__, and returns a uint8 array of colors in 1..n in input order (0 for missing or empty geometries). Pass return_frame=True for a GeoDataFrame with the colors in a column instead.

Two knobs mirror the raster ones:

kwarg meaning raster analogue
min_shared_length contact must be a shared border, not a point. The default 0.0 drops corner-only touches (the "Four Corners" rule), which is what keeps the graph planar and 4-colorable. None counts them, and then n = 5 is likely. conn=1 vs conn=2
tolerance treat features within this distance as touching, for polygons whose neighbors are separated by a hairline gap after vectorization or reprojection. In CRS units. connect_radius

With a tolerance, a positive min_shared_length is the vector form of the raster min_contact filter: dilating polygons to close gaps also creates one-point leaks between features that were never really neighbors, and those are what push a coloring from 4 up to 5. On a 5000-cell Voronoi tessellation with hairline gaps, tolerance=0.2 alone needs 5 colors; adding min_shared_length=1.0 brings it back to 4.

Pairs the filters reject become soft constraints: they still get different colors where that is free, without constraining the coloring. ncolor.geo.connect returns the adjacency pairs alone (0-indexed by row), and ncolor.color_graph colors any edge list you build yourself:

colors = ncolor.color_graph(edges, n_vertices=len(nodes))   # 0-indexed pairs

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.

Project links

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Newer release available (2.2.0)

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Avatar for kevinjohncutler from gravatar.com kevinjohncutler

Credits

Author: Kevin Cutler

GitHub Statistics

Data verified by PyPI on Sep 8, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

BSD License (BSD)

Requires

Python >=3.11

Provides Extra

geo

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2.2.0

31 files

This release

2.1.1 This release

31 files

2.1.0

31 files

2.0.2

21 files

2.0.1

21 files

2.0.0

21 files

1.5.3

2 files

1.5.2

2 files

1.5.1

2 files

1.5.0

2 files

1.4.5

2 files

1.4.4

2 files

1.4.3

2 files

1.4.2

2 files

1.4.1

2 files

1.3.0

2 files

1.2.1

2 files

1.2.0

2 files

1.1.8

2 files

Yanked

1.1.6

1 file

1.1.5

1 file

1.1.2

2 files

1.1.1

2 files

1.1.0

2 files

1.0.1

2 files

1.0.0

2 files

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