{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"},{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/10"}],"enrichment":{"capability":"Computes Euclidean distance transforms for 1D, 2D, or 3D labeled images with support for multiple labels and anisotropic voxel dimensions in a single pass.","skillfed_tags":["image-processing","connectomics","volumetric-data"],"use_cases":["Compute distance transforms on densely labeled 3D connectomics volumes to support skeletonization algorithms like TEASAR.","Correct for anisotropic voxel spacing in microscopy data (e.g., different X/Y vs. Z resolution) during distance field computation.","Extract individual label components from a multi-label volume and process their distance transforms sequentially.","Generate signed distance fields for geometric analysis of labeled structures in volumetric medical or biological imaging.","Accelerate batch distance transform operations on binary images using specialized optimizations."],"what_it_does":"edt is a compiled Python library that computes Euclidean distance transforms on labeled volumetric data. It handles 1D, 2D, and 3D arrays and supports anisotropic voxel spacing (e.g., different resolutions along each axis), which is common in microscopy and connectomics datasets. The package processes multiple labels simultaneously in a single pass, avoiding the need to compute transforms separately for each label.\n\nThe library is built on a C++ core with a Python wrapper and depends only on numpy. It offers both standard Euclidean distance and squared Euclidean distance variants, signed distance fields, and optional multi-threaded computation. Special optimizations exist for binary images. The package is designed for scientific and research workflows, particularly in connectomics and image analysis, where large labeled volumes require fast distance computations.","worth_installing":"Yes. edt is actively maintained, production-stable, has no known vulnerabilities, and offers genuine performance advantages for multi-label and anisotropic distance transforms in scientific workflows. The LGPL-3.0-or-later license requires compliance review if used in proprietary software. Medium install friction is acceptable given prebuilt wheels for common platforms; source builds require a C++ compiler."},"id":"edt","links":{"html":"https://skillfed.io/packages/edt","md":"https://skillfed.io/packages/edt.md","pypi":"https://pypi.org/project/edt/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-02","license_spdx":null,"license_treatment":"copyleft","name":"edt","python_support":"supports_current","summary":"Multi-Label Anisotropic Euclidean Distance Transform 3D"},"popularity":{"monthly_downloads":101514,"position":12926,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.1.2"}