{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"}],"enrichment":{"capability":"Fills interior voids in 2D and 3D binary images using a fast flood-fill algorithm, designed as a higher-performance alternative to scipy.ndimage.binary_fill_holes.","skillfed_tags":["image-processing","morphology","high-performance"],"use_cases":["Cleaning up connectomics segmentation masks by filling small holes in labeled regions before downstream analysis.","Preprocessing binary medical images to remove noise artifacts before feature extraction or registration.","Morphological post-processing in computer vision pipelines where speed matters on large 3D volumetric data.","Filling voids in 2D binary masks for image segmentation refinement in scientific imaging workflows.","Real-time or batch processing of densely labeled 3D datasets where scipy's performance is a bottleneck."],"what_it_does":"fill-voids is a Python library that fills interior holes in binary images using a fast scan-line flood-fill algorithm. It works on both 2D and 3D arrays and is designed to be significantly faster and more memory-efficient than scipy's binary_fill_holes, which uses slower serial dilations. The library is written in C++ with Python bindings, and it exposes both a Python API and a C++ header for direct use.\n\nThe algorithm marks foreground pixels, scans from image boundaries to identify background regions connected to the exterior, then flood-fills those regions. Everything not visited by the flood fill is marked as foreground, effectively filling all interior voids. It uses performance tricks like libdivide for coordinate computation and directional scanning to exploit memory locality. The package is actively maintained, supports Python 3.10, 3.11, and 3.12 with prebuilt wheels, and has no known security vulnerabilities.","worth_installing":"Yes, if you work with binary image morphology and need better performance than scipy.ndimage.binary_fill_holes. The package is actively maintained, has no vulnerabilities, and offers prebuilt wheels for common platforms. The LGPL-3.0-or-later license is permissive for research and open-source use but requires derivative works to remain open; verify compatibility with your project's licensing model. Medium install friction is acceptable for the performance gain in image processing workflows."},"id":"fill-voids","links":{"html":"https://skillfed.io/packages/fill-voids","md":"https://skillfed.io/packages/fill-voids.md","pypi":"https://pypi.org/project/fill-voids/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-30","license_spdx":null,"license_treatment":"copyleft","name":"fill-voids","python_support":"unspecified","summary":"Fill voids in 3D binary images fast."},"popularity":{"monthly_downloads":113036,"position":12353,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.1.2"}