Module: viewer.canvastools skimage.viewer.canvastools.LineTool(manager) Widget for line selection in a plot. skimage.viewer.canvastools.PaintTool(...[, ...]) Widget for painting on top of a plot. skimage.viewer.canvastools.RectangleTool(manager) Widget for selecting a rectangular region in a plot. skimage.viewer.canvastools.ThickLineTool(manager) Widget for line selection in a plot.

img_as_bool skimage.util.img_as_bool(image, force_copy=False) [source] Convert an image to boolean format. Parameters: image : ndarray Input image. force_copy : bool Force a copy of the data, irrespective of its current dtype. Returns: out : ndarray of bool (bool_) Output image. Notes The upper half of the input dtype’s positive range is True, and the lower half is False. All negative values (if present) are False.

Module: data Standard test images. For more images, see http://sipi.usc.edu/database/database.php skimage.data.astronaut() Colour image of the astronaut Eileen Collins. skimage.data.binary_blobs([length, ...]) Generate synthetic binary image with several rounded blob-like objects. skimage.data.camera() Gray-level “camera” image. skimage.data.checkerboard() Checkerboard image. skimage.data.chelsea() Chelsea the cat. skimage.data.clock() Motion blurred clock. skimage.data.coffee() Coffee

Tutorials Image Segmentation How to parallelize loops

histogram skimage.exposure.histogram(image, nbins=256) [source] Return histogram of image. Unlike numpy.histogram, this function returns the centers of bins and does not rebin integer arrays. For integer arrays, each integer value has its own bin, which improves speed and intensity-resolution. The histogram is computed on the flattened image: for color images, the function should be used separately on each channel to obtain a histogram for each color channel. Parameters: image : array Inpu

autolevel skimage.filters.rank.autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Auto-level image using local histogram. This filter locally stretches the histogram of greyvalues to cover the entire range of values from “white” to “black”. Parameters: image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1’s and 0’s. out : 2-D array (same dtype as input) If None, a new array is allocated. mask :

imread_collection skimage.io.imread_collection(load_pattern, conserve_memory=True, plugin=None, **plugin_args) [source] Load a collection of images. Parameters: load_pattern : str or list List of objects to load. These are usually filenames, but may vary depending on the currently active plugin. See the docstring for ImageCollection for the default behaviour of this parameter. conserve_memory : bool, optional If True, never keep more than one in memory at a specific time. Otherwise, imag

warn skimage.viewer.warn(message, category=None, stacklevel=2) [source] A version of warnings.warn with a default stacklevel of 2.

ellipse_perimeter skimage.draw.ellipse_perimeter() Generate ellipse perimeter coordinates. Parameters: cy, cx : int Centre coordinate of ellipse. yradius, xradius : int Minor and major semi-axes. (x/xradius)**2 + (y/yradius)**2 = 1. orientation : double, optional (default 0) Major axis orientation in clockwise direction as radians. shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for ellipses which exceed th

felzenszwalb skimage.segmentation.felzenszwalb(image, scale=1, sigma=0.8, min_size=20) [source] Computes Felsenszwalb’s efficient graph based image segmentation. Produces an oversegmentation of a multichannel (i.e. RGB) image using a fast, minimum spanning tree based clustering on the image grid. The parameter scale sets an observation level. Higher scale means less and larger segments. sigma is the diameter of a Gaussian kernel, used for smoothing the image prior to segmentation. The number