clock skimage.data.clock() [source] Motion blurred clock. This photograph of a wall clock was taken while moving the camera in an aproximately horizontal direction. It may be used to illustrate inverse filters and deconvolution. Released into the public domain by the photographer (Stefan van der Walt).

moon skimage.data.moon() [source] Surface of the moon. This low-contrast image of the surface of the moon is useful for illustrating histogram equalization and contrast stretching.

pop skimage.io.pop() [source] Pop an image from the shared image stack. Returns: img : ndarray Image popped from the stack.

rag_mean_color skimage.future.graph.rag_mean_color(image, labels, connectivity=2, mode='distance', sigma=255.0) [source] Compute the Region Adjacency Graph using mean colors. Given an image and its initial segmentation, this method constructs the corresponding Region Adjacency Graph (RAG). Each node in the RAG represents a set of pixels within image with the same label in labels. The weight between two adjacent regions represents how similar or dissimilar two regions are depending on the mod

frt2 skimage.transform.frt2(a) [source] Compute the 2-dimensional finite radon transform (FRT) for an n x n integer array. Parameters: a : array_like A 2-D square n x n integer array. Returns: FRT : 2-D ndarray Finite Radon Transform array of (n+1) x n integer coefficients. See also ifrt2 The two-dimensional inverse FRT. Notes The FRT has a unique inverse if and only if n is prime. [FRT] The idea for this algorithm is due to Vlad Negnevitski. References [FRT] A. Kingston and I.

deltaE_ciede2000 skimage.color.deltaE_ciede2000(lab1, lab2, kL=1, kC=1, kH=1) [source] Color difference as given by the CIEDE 2000 standard. CIEDE 2000 is a major revision of CIDE94. The perceptual calibration is largely based on experience with automotive paint on smooth surfaces. Parameters: lab1 : array_like reference color (Lab colorspace) lab2 : array_like comparison color (Lab colorspace) kL : float (range), optional lightness scale factor, 1 for “acceptably close”; 2 for “imperc

I/O Plugin Infrastructure A plugin consists of two files, the source and the descriptor .ini. Let’s say we’d like to provide a plugin for imshow using matplotlib. We’ll call our plugin mpl: skimage/io/_plugins/mpl.py skimage/io/_plugins/mpl.ini The name of the .py and .ini files must correspond. Inside the .ini file, we give the plugin meta-data: [mpl] <-- name of the plugin, may be anything description = Matplotlib image I/O plugin provides = imshow <-- a comma-separated list, one or mo

MultiImage class skimage.io.MultiImage(filename, conserve_memory=True, dtype=None, **imread_kwargs) [source] Bases: skimage.io.collection.ImageCollection A class containing a single multi-frame image. Parameters: filename : str The complete path to the image file. conserve_memory : bool, optional Whether to conserve memory by only caching a single frame. Default is True. Notes If conserve_memory=True the memory footprint can be reduced, however the performance can be affected because f

hessian_matrix_det skimage.feature.hessian_matrix_det(image, sigma) [source] Computes the approximate Hessian Determinant over an image. This method uses box filters over integral images to compute the approximate Hessian Determinant as described in [R151]. Parameters: image : array The image over which to compute Hessian Determinant. sigma : float Standard deviation used for the Gaussian kernel, used for the Hessian matrix. Returns: out : array The array of the Determinant of Hessia

gaussian_filter skimage.filters.gaussian_filter(*args, **kwargs) [source] Deprecated function. Use skimage.filters.gaussian instead. Multi-dimensional Gaussian filter Parameters: image : array-like input image (grayscale or color) to filter. sigma : scalar or sequence of scalars standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes. output : array, option