scikit-image
Image processing in Python
Decision gist · record as of 2026-08-14
Yes. scikit-image is actively maintained with no known vulnerabilities, permissive BSD licensing, and broad platform support. It is the standard choice for image processing in Python when you need algorithmic depth beyond basic I/O. Medium install friction is justified by comprehensive algorithm coverage and tight integration with the scientific Python stack.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Medium install friction due to 8 runtime dependencies including numpy, scipy, and imageio.
- Pre-built wheels available across multiple Python versions and platforms reduce compilation overhead.
License · maintenance · safety
permissive license (permissive) — BSD-2-Clause and BSD-3-Clause permissive licenses with some MIT components. You can use, modify, and distribute freely in commercial and open-source projects, provided you retain copyright notices.
last release 2025-12-20 (237 days) · last repo commit 2026-08-04 · 6,573 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 35,835,544 downloads/mo, #741 on PyPI
Alternatives
Verify before relying
pip install scikit-image
from skimage import io, filters
image = io.imread('image.png')
filtered = filters.gaussian(image, sigma=1.0)- Specific image formats supported beyond common types like PNG and JPEG
- Performance characteristics for large-scale batch processing workflows
- Memory requirements for typical image processing operations
What it is and what it does
scikit-image is a Python library implementing standard algorithms for image processing: filtering, morphological operations, segmentation, feature detection, and geometric transformations. It uses numpy arrays as its core data structure, integrating naturally with the scientific Python ecosystem through dependencies on numpy, scipy, pillow, imageio, networkx, and tifffile.
The library serves tasks from basic image enhancement and noise reduction to advanced operations like watershed segmentation and edge detection. It is actively maintained with recent development activity and broad platform support across macOS, Windows, and Linux.
Use it for
- Apply filters and morphological operations to binary or grayscale images
- Detect edges and corners in images using standard computer vision algorithms
- Segment images into regions using watershed or other segmentation methods
- Extract and analyze features from images for computer vision pipelines
- Transform and warp images geometrically including rotation and scaling
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
scikit-image is actively maintained with no known vulnerabilities, permissive BSD licensing, and broad platform support. It is the standard choice for image processing in Python when you need algorithmic depth beyond basic I/O. Medium install friction is justified by comprehensive algorithm coverage and tight integration with the scientific Python stack.
Install
scikit-image on PyPI
Before you install
Medium install friction due to 8 runtime dependencies including numpy, scipy, and imageio. Pre-built wheels available across multiple Python versions and platforms reduce compilation overhead.
License in practice
BSD-2-Clause and BSD-3-Clause permissive licenses with some MIT components. You can use, modify, and distribute freely in commercial and open-source projects, provided you retain copyright notices.
Quickstart
pip install scikit-image
from skimage import io, filters
image = io.imread('image.png')
filtered = filters.gaussian(image, sigma=1.0)Verify before relying
- Specific image formats supported beyond common types like PNG and JPEG
- Performance characteristics for large-scale batch processing workflows
- Memory requirements for typical image processing operations
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 8 packagesnumpyscipynetworkxpillowimageiotifffilepackaginglazy-loader |
| Maintenance | Actively maintained 237 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 35,835,544 / month, #741 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaEnvironment :: ConsoleIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: CProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Software Development :: Libraries |
Evidence: scikit_image-0.26.0-cp311-cp311-macosx_10_9_x86_64.whl; scikit_image-0.26.0-cp311-cp311-macosx_11_0_arm64.whl; scikit_image-0.26.0-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; scikit_image-0.26.0-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; scikit_image-0.26.0-cp311-cp311-musllinux_1_2_aarch64.whl; scikit_image-0.26.0-cp311-cp311-musllinux_1_2_x86_64.whl; scikit_image-0.26.0-cp311-cp311-win_amd64.whl; scikit_image-0.26.0-cp311-cp311-win_arm64.whl; scikit_image-0.26.0-cp312-cp312-macosx_10_13_x86_64.whl; scikit_image-0.26.0-cp312-cp312-macosx_11_0_arm64.whl; scikit_image-0.26.0-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; scikit_image-0.26.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; scikit_image-0.26.0-cp312-cp312-musllinux_1_2_aarch64.whl; scikit_image-0.26.0-cp312-cp312-musllinux_1_2_x86_64.whl; scikit_image-0.26.0-cp312-cp312-win_amd64.whl; scikit_image-0.26.0-cp312-cp312-win_arm64.whl; scikit_image-0.26.0-cp313-cp313-macosx_10_13_x86_64.whl; scikit_image-0.26.0-cp313-cp313-macosx_11_0_arm64.whl; scikit_image-0.26.0-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; scikit_image-0.26.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
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