Sourceskimage¶Besides the user guide, there exist other opportunities to get help on
using skimage.
The General examples gallery provides graphical examples of typical image processing tasks. By a quick glance at the different thumbnails, the user may find an example close to a typical use case of interest. Each graphical example page displays an introductory paragraph, a figure, and the source code that generated the figure. Downloading the Python source code enables one to modify quickly the example into a case closer to one’s image processing applications.
Users are warmly encouraged to report on their use of skimage on the
Mailing-list, in order to propose more examples in the future.
Contributing examples to the gallery can be done on github (see
How to contribute to skimage).
The quick search field located in the navigation bar of the html
documentation can be used to search for specific keywords (segmentation,
rescaling, denoising, etc.).
Docstrings of skimage functions are formatted using Numpy’s
documentation standard,
starting with a Parameters section for the arguments and a
Returns section for the objects returned by the function. Also, most
functions include one or more examples.
The scikit-image mailing-list is scikit-image@googlegroups.com (users
should join the Google Group before posting). This
mailing-list is shared by users and developers, and it is the right
place to ask any question about skimage, or in general, image
processing using Python. Posting snippets of code with minimal examples
ensures to get more relevant and focused answers.
We would love to hear from how you use skimage for your work on the
mailing-list!