# scikit-image/scikit-image

Image processing in Python

Repository: https://github.com/scikit-image/scikit-image
Canonical: https://ross.abutalabs.com/products/scikit-image
Homepage: https://scikit-image.org
Language: Python
License: NOASSERTION
License Family: other
Topics: image-processing, python, computer-vision
Last push: 2026-08-26T21:40:54+00:00

## Health v2 (maintenance only)
Score: 82/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 50, longevity 100
- inputs: {"age_days": 5536, "days_push": 7, "days_rel": 256, "gap_med": 43, "n_releases_24m": 4}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6577, forks 2406 (observed 2026-08-28T04:09:45.341408+00:00)

## What it is
scikit-image is a Python library providing a collection of peer-reviewed image processing algorithms built on NumPy and SciPy. It offers routines for filtering, segmentation, feature detection, transformation, and measurement of images.

## Use cases
- filter and denoise images in python
- segment objects in microscopy images
- detect edges and features in images
- warp and transform images with python
- measure region properties of labeled images
- convert color spaces like rgb to grayscale
- analyze scientific images in a jupyter notebook

## When to choose
- you need well-tested, peer-reviewed image processing algorithms in Python
- you work within the NumPy/SciPy scientific Python ecosystem
- you need segmentation, morphology, or feature detection without deep learning
- you want a permissively licensed, actively maintained library with strong documentation

## When to avoid
- you need GPU-accelerated or deep-learning-based vision models (use PyTorch or OpenCV DNN)
- you need real-time video processing performance
- you work outside Python

## Facets
- artifact type: library
- maturity: stable
- function: image-processing, computer-vision, data-science
- domain: image-processing, computer-vision, data-science
- platform: python, windows, cross-platform
- tags: scipy-ecosystem, numpy, scientific-imaging, segmentation, morphology, feature-detection, linux, macos

## Member repositories
- scikit-image/scikit-image (main) score 82

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:45.341408+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:43:37.158873+00:00, confidence not recorded.
  - readme: https://github.com/scikit-image/scikit-image (fetched 2026-08-28T04:09:45.341408+00:00, sha 14a743232140)
  - homepage: https://scikit-image.org (fetched 2026-08-29T08:40:17.021745+00:00, sha c40146345231)
  - site_page: https://scikit-image.org/docs/stable/user_guide/install.html (fetched 2026-08-29T08:40:17.031172+00:00, sha fe8ca72434ee)
  - site_page: https://scikit-image.org/docs/stable/auto_examples (fetched 2026-08-29T08:40:17.034381+00:00, sha 80f3e5d13831)
  - site_page: https://scikit-image.org/docs/stable (fetched 2026-08-29T08:40:17.036634+00:00, sha a95aa605ace8)
  - site_page: https://scikit-image.org/docs/stable/development/contribute.html (fetched 2026-08-29T08:40:17.038587+00:00, sha d378c9abeb54)
  - site_page: https://scikit-image.org/docs/stable/release_notes/release_0.26.html (fetched 2026-08-29T08:40:17.042386+00:00, sha 320483757243)
  - site_page: https://scikit-image.org/docs/stable/release_notes/release_0.25.html (fetched 2026-08-29T08:40:17.044495+00:00, sha 23043bc773a0)
  - site_page: https://scikit-image.org/docs/stable/release_notes/release_0.24.html (fetched 2026-08-29T08:40:17.046593+00:00, sha 73946bc3e59c)
- Data as of 2026-08-30T08:39:29.467469+00:00.