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

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

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

cube skimage.morphology.cube(width, dtype=) [source] Generates a cube-shaped structuring element. This is the 3D equivalent of a square. Every pixel along the perimeter has a chessboard distance no greater than radius (radius=floor(width/2)) pixels. Parameters: width : int The width, height and depth of the cube. Returns: selem : ndarray A structuring element consisting only of ones, i.e. every pixel belongs to the neighborhood. Other Parameters: dtype : data-type The data type of

crop skimage.util.crop(ar, crop_width, copy=False, order='K') [source] Crop array ar by crop_width along each dimension. Parameters: ar : array-like of rank N Input array. crop_width : {sequence, int} Number of values to remove from the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) specifies unique crop widths at the start and end of each axis. ((before, after),) specifies a fixed start and end crop for every axis. (n,) or n for integer n is a shortcut for before = a

correct_mesh_orientation skimage.measure.correct_mesh_orientation(volume, verts, faces, spacing=(1.0, 1.0, 1.0), gradient_direction='descent') [source] Correct orientations of mesh faces. Parameters: volume : (M, N, P) array of doubles Input data volume to find isosurfaces. Will be cast to np.float64. verts : (V, 3) array of floats Array containing (x, y, z) coordinates for V unique mesh vertices. faces : (F, 3) array of ints List of length-3 lists of integers, referencing vertex coord

corner_subpix skimage.feature.corner_subpix(image, corners, window_size=11, alpha=0.99) [source] Determine subpixel position of corners. A statistical test decides whether the corner is defined as the intersection of two edges or a single peak. Depending on the classification result, the subpixel corner location is determined based on the local covariance of the grey-values. If the significance level for either statistical test is not sufficient, the corner cannot be classified, and the outp

corner_shi_tomasi skimage.feature.corner_shi_tomasi(image, sigma=1) [source] Compute Shi-Tomasi (Kanade-Tomasi) corner measure response image. This corner detector uses information from the auto-correlation matrix A: A = [(imx**2) (imx*imy)] = [Axx Axy] [(imx*imy) (imy**2)] [Axy Ayy] Where imx and imy are first derivatives, averaged with a gaussian filter. The corner measure is then defined as the smaller eigenvalue of A: ((Axx + Ayy) - sqrt((Axx - Ayy)**2 + 4 * Axy**2)) / 2 Para

corner_peaks skimage.feature.corner_peaks(image, min_distance=1, threshold_abs=None, threshold_rel=0.1, exclude_border=True, indices=True, num_peaks=inf, footprint=None, labels=None) [source] Find corners in corner measure response image. This differs from skimage.feature.peak_local_max in that it suppresses multiple connected peaks with the same accumulator value. Parameters: * : * See skimage.feature.peak_local_max(). Examples >>> from skimage.feature import peak_local_max >

corner_orientations skimage.feature.corner_orientations() Compute the orientation of corners. The orientation of corners is computed using the first order central moment i.e. the center of mass approach. The corner orientation is the angle of the vector from the corner coordinate to the intensity centroid in the local neighborhood around the corner calculated using first order central moment. Parameters: image : 2D array Input grayscale image. corners : (N, 2) array Corner coordinates as