Getting help on using skimage

Besides the user guide, there exist other opportunities to get help on using skimage.

Examples gallery

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 scikit-image).

Search field

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.).

API Discovery

NumPy provides a lookfor function to search API functions. By default lookfor will search the NumPy API. NumPy lookfor example: `np.lookfor('eigenvector') `

But it can be used to search in modules, by passing in the module name as a string:

` np.lookfor('boundaries', 'skimage') `

or the module itself. ` > import skimage > np.lookfor('boundaries', skimage) `

Docstrings

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.

Mailing-list

The scikit-image mailing-list is scikit-image@python.org (users should join 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!