MCP class skimage.graph.MCP(costs, offsets=None, fully_connected=True, sampling=None) Bases: object A class for finding the minimum cost path through a given n-d costs array. Given an n-d costs array, this class can be used to find the minimum-cost path through that array from any set of points to any other set of points. Basic usage is to initialize the class and call find_costs() with a one or more starting indices (and an optional list of end indices). After that, call traceback() one or

load_surf skimage.io.load_surf(f) [source] Read SIFT or SURF features from a file. Parameters: f : string or open file Input file generated by the feature detectors from http://people.cs.ubc.ca/~lowe/keypoints/ or http://www.vision.ee.ethz.ch/~surf/ Returns: data : record array with fields row: int row position of feature column: int column position of feature scale: float feature scale orientation: float feature orientation data: array feature values

plugin_info skimage.io.plugin_info(plugin) [source] Return plugin meta-data. Parameters: plugin : str Name of plugin. Returns: m : dict Meta data as specified in plugin .ini.

ClearColormap class skimage.viewer.utils.ClearColormap(rgb, max_alpha=1, name='clear_color') [source] Bases: skimage.viewer.utils.core.LinearColormap Color map that varies linearly from alpha = 0 to 1 __init__(rgb, max_alpha=1, name='clear_color') [source]

draw_multiblock_lbp skimage.feature.draw_multiblock_lbp(img, r, c, width, height, lbp_code=0, color_greater_block=[1, 1, 1], color_less_block=[0, 0.69, 0.96], alpha=0.5) [source] Multi-block local binary pattern visualization. Blocks with higher sums are colored with alpha-blended white rectangles, whereas blocks with lower sums are colored alpha-blended cyan. Colors and the alpha parameter can be changed. Parameters: img : ndarray of float or uint Image on which to visualize the pattern.

pyramid_gaussian skimage.transform.pyramid_gaussian(image, max_layer=-1, downscale=2, sigma=None, order=1, mode='reflect', cval=0) [source] Yield images of the Gaussian pyramid formed by the input image. Recursively applies the pyramid_reduce function to the image, and yields the downscaled images. Note that the first image of the pyramid will be the original, unscaled image. The total number of images is max_layer + 1. In case all layers are computed, the last image is either a one-pixel im

hed2rgb skimage.color.hed2rgb(hed) [source] Haematoxylin-Eosin-DAB (HED) to RGB color space conversion. Parameters: hed : array_like The image in the HED color space, in a 3-D array of shape (.., .., 3). Returns: out : ndarray The image in RGB, in a 3-D array of shape (.., .., 3). Raises: ValueError If hed is not a 3-D array of shape (.., .., 3). References [R42] A. C. Ruifrok and D. A. Johnston, “Quantification of histochemical staining by color deconvolution.,” Analytical and q

push skimage.io.push(img) [source] Push an image onto the shared image stack. Parameters: img : ndarray Image to push.

sobel_h skimage.filters.sobel_h(image, mask=None) [source] Find the horizontal edges of an image using the Sobel transform. Parameters: image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns: output : 2-D array The Sobel edge map. Notes We use the following kernel: 1 2 1 0 0 0 -1 -2

circle skimage.draw.circle(r, c, radius, shape=None) [source] Generate coordinates of pixels within circle. Parameters: r, c : double Centre coordinate of circle. radius: double Radius of circle. shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for circles which exceed the image size. By default the full extent of the circle are used. Returns: rr, cc : ndarray of int Pixel coordinates of circle. May be use