cut_threshold skimage.future.graph.cut_threshold(labels, rag, thresh, in_place=True) [source] Combine regions separated by weight less than threshold. Given an image’s labels and its RAG, output new labels by combining regions whose nodes are separated by a weight less than the given threshold. Parameters: labels : ndarray The array of labels. rag : RAG The region adjacency graph. thresh : float The threshold. Regions connected by edges with smaller weights are combined. in_place : bo

rgb2grey skimage.color.rgb2grey(rgb) [source] Compute luminance of an RGB image. Parameters: rgb : array_like The image in RGB format, in a 3-D array of shape (.., .., 3), or in RGBA format with shape (.., .., 4). Returns: out : ndarray The luminance image, a 2-D array. Raises: ValueError If rgb2gray is not a 3-D array of shape (.., .., 3) or (.., .., 4). Notes The weights used in this conversion are calibrated for contemporary CRT phosphors: Y = 0.2125 R + 0.7154 G + 0.0721 B I

warp_coords skimage.transform.warp_coords(coord_map, shape, dtype=) [source] Build the source coordinates for the output of a 2-D image warp. Parameters: coord_map : callable like GeometricTransform.inverse Return input coordinates for given output coordinates. Coordinates are in the shape (P, 2), where P is the number of coordinates and each element is a (row, col) pair. shape : tuple Shape of output image (rows, cols[, bands]). dtype : np.dtype or string dtype for return value (sane

ellipsoid_stats skimage.draw.ellipsoid_stats(a, b, c) [source] Calculates analytical surface area and volume for ellipsoid with semimajor axes aligned with grid dimensions of specified spacing. Parameters: a : float Length of semimajor axis aligned with x-axis. b : float Length of semimajor axis aligned with y-axis. c : float Length of semimajor axis aligned with z-axis. Returns: vol : float Calculated volume of ellipsoid. surf : float Calculated surface area of ellipsoid.

skeletonize_3d skimage.morphology.skeletonize_3d(img) [source] Compute the skeleton of a binary image. Thinning is used to reduce each connected component in a binary image to a single-pixel wide skeleton. Parameters: img : ndarray, 2D or 3D A binary image containing the objects to be skeletonized. Zeros represent background, nonzero values are foreground. Returns: skeleton : ndarray The thinned image. See also skeletonize, medial_axis Notes The method of [Lee94] uses an octree dat

cut_normalized skimage.future.graph.cut_normalized(labels, rag, thresh=0.001, num_cuts=10, in_place=True, max_edge=1.0) [source] Perform Normalized Graph cut on the Region Adjacency Graph. Given an image’s labels and its similarity RAG, recursively perform a 2-way normalized cut on it. All nodes belonging to a subgraph that cannot be cut further are assigned a unique label in the output. Parameters: labels : ndarray The array of labels. rag : RAG The region adjacency graph. thresh : flo

LineModelND class skimage.measure.LineModelND [source] Bases: skimage.measure.fit.BaseModel Total least squares estimator for N-dimensional lines. Lines are defined by a point (origin) and a unit vector (direction) according to the following vector equation: X = origin + lambda * direction Attributes params (tuple) Line model parameters in the following order origin, direction. __init__() [source] estimate(data) [source] Estimate line model from data. Parameters: data : (N, dim) arra

imshow skimage.external.tifffile.imshow(data, title=None, vmin=0, vmax=None, cmap=None, bitspersample=None, photometric='rgb', interpolation='nearest', dpi=96, figure=None, subplot=111, maxdim=8192, **kwargs) [source] Plot n-dimensional images using matplotlib.pyplot. Return figure, subplot and plot axis. Requires pyplot already imported from matplotlib import pyplot. Parameters: bitspersample : int or None Number of bits per channel in integer RGB images. photometric : {‘miniswhite’, ‘mi

cumulative_distribution skimage.exposure.cumulative_distribution(image, nbins=256) [source] Return cumulative distribution function (cdf) for the given image. Parameters: image : array Image array. nbins : int Number of bins for image histogram. Returns: img_cdf : array Values of cumulative distribution function. bin_centers : array Centers of bins. See also histogram References [R85] http://en.wikipedia.org/wiki/Cumulative_distribution_function Examples >>> from skima

rgb2hsv skimage.color.rgb2hsv(rgb) [source] RGB to HSV color space conversion. Parameters: rgb : array_like The image in RGB format, in a 3-D array of shape (.., .., 3). Returns: out : ndarray The image in HSV format, in a 3-D array of shape (.., .., 3). Raises: ValueError If rgb is not a 3-D array of shape (.., .., 3). Notes The conversion assumes an input data range of [0, 1] for all color components. Conversion between RGB and HSV color spaces results in some loss of precision