scikit-image: Image processing in Python — scikit-image
Scikit-image is an open-source image processing library for the Python programming language. It provides a collection of algorithms for image processing tasks such as segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
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Website Key Features
Segmentation
Tools for partitioning an image into multiple segments or regions.
Geometric Transformations
Functions for scaling, rotation, and other geometric transformations of images.
Color Space Manipulation
Utilities for converting images between different color spaces and manipulating color properties.
Filtering
A wide range of filters for smoothing, sharpening, and edge detection.
Morphology
Operations based on the shape of features in an image, such as erosion and dilation.
Feature Detection
Algorithms for detecting features such as edges, corners, and blobs.
Analysis
Tools for measuring properties of image regions, such as area, perimeter, and centroid.
I/O
Functions for reading and writing images in various formats.
Visualization
Utilities for displaying images and results of image processing operations.
Integration with NumPy
Seamless integration with NumPy arrays for efficient numerical computations.
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Additional information
License
Scikit-image is released under the BSD license, making it free for both academic and commercial use.
Community
The project has a vibrant community of contributors and users, with active development and support forums.
Documentation
Comprehensive documentation is available, including tutorials, API reference, and example galleries.
Dependencies
Scikit-image depends on NumPy, SciPy, and Matplotlib for numerical operations and visualization.
Performance
Optimized for performance, with many algorithms implemented in Cython for speed.
Extensibility
Designed to be easily extensible, allowing users to add new algorithms and functionality.
Cross-platform
Scikit-image is cross-platform and runs on Windows, macOS, and Linux.
Version Control
The project uses Git for version control, with the repository hosted on GitHub.
Contribution
Contributions are welcome, with guidelines provided for submitting bug reports, feature requests, and code contributions.
HTTP headers
Security headers report is a very important part of user data protection. Learn more about http headers for scikit-image.org