fast-regionprops

A faster (vectorized) version of regionprops that supports a subset of region-property features, yielding a typical ~10-80× speedup (especially for many regions). Can be used as a drop-in replacement for skimage.measure.regionprops_table for the following set of properties:

label, area, centroid, bbox, equivalent_diameter_area,
intensity_mean, intensity_min, intensity_max, intensity_std,
inertia_tensor, border_dist
Dim Image size #Regions skimage (ms) fast (ms) Speedup
2D 512×512 100 10.5 0.4 26.8×
2D 512×512 400 40.8 0.5 77.8×
2D 1024×1024 1600 163.1 2.0 82.8×
2D 2048×2048 3600 368.7 6.5 57.0×
3D 128×128×128 512 76.5 3.3 23.5×
3D 256×256×256 1728 229.6 19.6 11.7×

Install

pip install fast-regionprops
# dev / tests
pip install -e ".[test]"
# dev / tests / pre-commit
pip install -e ".[dev]"
pre-commit install

Usage

import numpy as np
from skimage.data import binary_blobs
from skimage.measure import label

from fast_regionprops import regionprops_table_fast

# example data: blobs as mask, random image as intensity
mask = label(binary_blobs(length=512, blob_size_fraction=0.05, volume_fraction=0.1))
intensity = np.random.default_rng(0).random(mask.shape).astype("float32")

# drop-in for skimage.measure.regionprops_table (same column names)
table = regionprops_table_fast(
    mask,
    intensity,
    properties=("label", "area", "centroid", "intensity_mean", "inertia_tensor"),
)
table["centroid-0"], table["inertia_tensor-0-1"]   # 1D arrays, one row per label

Output is a dict[str, np.ndarray] (same as for skimage.measure.regionprops_table)

Property notes

  • Calling regionprops_table_fast(label_image) uses the same default properties as skimage.measure.regionprops_table: label and bbox.

  • label_image must be an integer 2D or 3D label image. Boolean and floating point inputs are rejected as ambiguous; labels <= 0 are ignored.

  • intensity_image may have the same shape as label_image, or one extra trailing channel axis. Multichannel intensity columns follow skimage naming, for example intensity_mean-0, intensity_mean-1.

  • border_dist is an extra property (not in skimage): the minimum pixel distance from each label to any image border. The per-axis distances are returned as border_dist-0, border_dist-1, and, for 3D images, border_dist-2.

  • properties needing a convex hull / perimeter / topology (solidity, perimeter, euler_number, etc.) are not yet supported.

License

BSD-3-Clause.

Project links

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Credits

Author: Martin Weigert

GitHub Statistics

Data verified by PyPI on Jul 18, 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-3-Clause

Requires

Python >=3.10

Provides Extra

dev test

Tags

image-analysis microscopy numpy regionprops scikit-image

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