subtract_mean skimage.filters.rank.subtract_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return image subtracted from its local mean. 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 : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, th

corner_kitchen_rosenfeld skimage.feature.corner_kitchen_rosenfeld(image, mode='constant', cval=0) [source] Compute Kitchen and Rosenfeld corner measure response image. The corner measure is calculated as follows: (imxx * imy**2 + imyy * imx**2 - 2 * imxy * imx * imy) / (imx**2 + imy**2) Where imx and imy are the first and imxx, imxy, imyy the second derivatives. Parameters: image : ndarray Input image. mode : {‘constant’, ‘reflect’, ‘wrap’, ‘nearest’, ‘mirror’}, optional How to hand

shortest_path skimage.graph.shortest_path(arr, reach=1, axis=-1, output_indexlist=False) [source] Find the shortest path through an n-d array from one side to another. Parameters: arr : ndarray of float64 reach : int, optional By default (reach = 1), the shortest path can only move one row up or down for every step it moves forward (i.e., the path gradient is limited to 1). reach defines the number of elements that can be skipped along each non-axis dimension at each step. axis : int, opt

rgb2xyz skimage.color.rgb2xyz(rgb) [source] RGB to XYZ color space conversion. Parameters: rgb : array_like The image in RGB format, in a 3- or 4-D array of shape (.., ..,[ ..,] 3). Returns: out : ndarray The image in XYZ format, in a 3- or 4-D array of shape (.., ..,[ ..,] 3). Raises: ValueError If rgb is not a 3- or 4-D array of shape (.., ..,[ ..,] 3). Notes The CIE XYZ color space is derived from the CIE RGB color space. Note however that this function converts from sRGB. Ref

ComboBox class skimage.viewer.widgets.ComboBox(name, items, ptype='kwarg', callback=None) [source] Bases: skimage.viewer.widgets.core.BaseWidget ComboBox widget for selecting among a list of choices. Parameters: name : str Name of ComboBox parameter. If this parameter is passed as a keyword argument, it must match the name of that keyword argument (spaces are replaced with underscores). In addition, this name is displayed as the name of the ComboBox. items: list of str Allowed parameter

Module: measure skimage.measure.approximate_polygon(coords, ...) Approximate a polygonal chain with the specified tolerance. skimage.measure.block_reduce(image, block_size) Down-sample image by applying function to local blocks. skimage.measure.compare_mse(im1, im2) Compute the mean-squared error between two images. skimage.measure.compare_nrmse(im_true, im_test) Compute the normalized root mean-squared error (NRMSE) between two images. skimage.measure.compare_psnr(im_true, im_test) Comput

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

noise_filter skimage.filters.rank.noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Noise feature. 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 : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (defaul

Module: morphology skimage.morphology.ball(radius[, dtype]) Generates a ball-shaped structuring element. skimage.morphology.binary_closing(image[, selem]) Return fast binary morphological closing of an image. skimage.morphology.binary_dilation(image[, ...]) Return fast binary morphological dilation of an image. skimage.morphology.binary_erosion(image[, selem]) Return fast binary morphological erosion of an image. skimage.morphology.binary_opening(image[, selem]) Return fast binary morpholo

guess_spatial_dimensions skimage.color.guess_spatial_dimensions(image) [source] Make an educated guess about whether an image has a channels dimension. Parameters: image : ndarray The input image. Returns: spatial_dims : int or None The number of spatial dimensions of image. If ambiguous, the value is None. Raises: ValueError If the image array has less than two or more than four dimensions.