BaseWidget class skimage.viewer.widgets.BaseWidget(name, ptype=None, callback=None) [source] Bases: PyQt4.QtGui.QWidget __init__(name, ptype=None, callback=None) [source] plugin = 'Widget is not attached to a Plugin.' val

autolevel_percentile skimage.filters.rank.autolevel_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Return greyscale local autolevel of an image. This filter locally stretches the histogram of greyvalues to cover the entire range of values from “white” to “black”. Only greyvalues between percentiles [p0, p1] are considered in the filter. Parameters: image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed

ball skimage.morphology.ball(radius, dtype=) [source] Generates a ball-shaped structuring element. This is the 3D equivalent of a disk. A pixel is within the neighborhood if the euclidean distance between it and the origin is no greater than radius. Parameters: radius : int The radius of the ball-shaped structuring element. Returns: selem : ndarray The structuring element where elements of the neighborhood are 1 and 0 otherwise. Other Parameters: dtype : data-type The data type of

astronaut skimage.data.astronaut() [source] Colour image of the astronaut Eileen Collins. Photograph of Eileen Collins, an American astronaut. She was selected as an astronaut in 1992 and first piloted the space shuttle STS-63 in 1995. She retired in 2006 after spending a total of 38 days, 8 hours and 10 minutes in outer space. This image was downloaded from the NASA Great Images database <http://grin.hq.nasa.gov/ABSTRACTS/GPN-2000-001177.html>`__. No known copyright restrictions, rele

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 :

apply_parallel skimage.util.apply_parallel(function, array, chunks=None, depth=0, mode=None, extra_arguments=(), extra_keywords={}) [source] Map a function in parallel across an array. Split an array into possibly overlapping chunks of a given depth and boundary type, call the given function in parallel on the chunks, combine the chunks and return the resulting array. Parameters: function : function Function to be mapped which takes an array as an argument. array : numpy array Array whic

API Reference skimageSubpackages Utility Functions dtype_limits img_as_bool img_as_float img_as_int img_as_ubyte img_as_uint test Module: colorcombine_stains convert_colorspace deltaE_cie76 deltaE_ciede2000 deltaE_ciede94 deltaE_cmc gray2rgb guess_spatial_dimensions hed2rgb hsv2rgb lab2lch lab2rgb lab2xyz label2rgb lch2lab luv2rgb luv2xyz rgb2gray rgb2grey rgb2hed rgb2hsv rgb2lab rgb2luv rgb2rgbcie rgb2xyz rgbcie2rgb separate_stains xyz2lab xyz2luv xyz2rgb Module: dataastronaut binary_blob

approximate_polygon skimage.measure.approximate_polygon(coords, tolerance) [source] Approximate a polygonal chain with the specified tolerance. It is based on the Douglas-Peucker algorithm. Note that the approximated polygon is always within the convex hull of the original polygon. Parameters: coords : (N, 2) array Coordinate array. tolerance : float Maximum distance from original points of polygon to approximated polygonal chain. If tolerance is 0, the original coordinate array is retur

AffineTransform class skimage.transform.AffineTransform(matrix=None, scale=None, rotation=None, shear=None, translation=None) [source] Bases: skimage.transform._geometric.ProjectiveTransform 2D affine transformation of the form: ..:math: X = a0*x + a1*y + a2 = = sx*x*cos(rotation) - sy*y*sin(rotation + shear) + a2 Y = b0*x + b1*y + b2 = = sx*x*sin(rotation) + sy*y*cos(rotation + shear) + b2 where sx and sy are zoom factors in the x and y directions, and the homogeneous transformation matrix

adjust_sigmoid skimage.exposure.adjust_sigmoid(image, cutoff=0.5, gain=10, inv=False) [source] Performs Sigmoid Correction on the input image. Also known as Contrast Adjustment. This function transforms the input image pixelwise according to the equation O = 1/(1 + exp*(gain*(cutoff - I))) after scaling each pixel to the range 0 to 1. Parameters: image : ndarray Input image. cutoff : float Cutoff of the sigmoid function that shifts the characteristic curve in horizontal direction. Defaul