camera skimage.data.camera() [source] Gray-level “camera” image. Often used for segmentation and denoising examples.

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

MCP_Geometric class skimage.graph.MCP_Geometric(costs, offsets=None, fully_connected=True) Bases: skimage.graph._mcp.MCP Find distance-weighted minimum cost paths through an n-d costs array. See the documentation for MCP for full details. This class differs from MCP in that the cost of a path is not simply the sum of the costs along that path. This class instead assumes that the costs array contains at each position the “cost” of a unit distance of travel through that position. For example,

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.

vsobel skimage.filters.vsobel(*args, **kwargs) [source] Deprecated function. Use skimage.filters.sobel_v instead. Find the vertical 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 absolute Sobel edge map. Note

join_segmentations skimage.segmentation.join_segmentations(s1, s2) [source] Return the join of the two input segmentations. The join J of S1 and S2 is defined as the segmentation in which two voxels are in the same segment if and only if they are in the same segment in both S1 and S2. Parameters: s1, s2 : numpy arrays s1 and s2 are label fields of the same shape. Returns: j : numpy array The join segmentation of s1 and s2. Examples >>> from skimage.segmentation import join_s

Module: draw skimage.draw.bezier_curve Generate Bezier curve coordinates. skimage.draw.circle(r, c, radius[, shape]) Generate coordinates of pixels within circle. skimage.draw.circle_perimeter Generate circle perimeter coordinates. skimage.draw.circle_perimeter_aa Generate anti-aliased circle perimeter coordinates. skimage.draw.ellipse(r, c, yradius, xradius) Generate coordinates of pixels within ellipse. skimage.draw.ellipse_perimeter Generate ellipse perimeter coordinates. skimage.draw.ell

octahedron skimage.morphology.octahedron(radius, dtype=) [source] Generates a octahedron-shaped structuring element. This is the 3D equivalent of a diamond. A pixel is part of the neighborhood (i.e. labeled 1) if the city block/Manhattan distance between it and the center of the neighborhood is no greater than radius. Parameters: radius : int The radius of the octahedron-shaped structuring element. Returns: selem : ndarray The structuring element where elements of the neighborhood are

pyramid_reduce skimage.transform.pyramid_reduce(image, downscale=2, sigma=None, order=1, mode='reflect', cval=0) [source] Smooth and then downsample image. Parameters: image : array Input image. downscale : float, optional Downscale factor. sigma : float, optional Sigma for Gaussian filter. Default is 2 * downscale / 6.0 which corresponds to a filter mask twice the size of the scale factor that covers more than 99% of the Gaussian distribution. order : int, optional Order of splines

plot_matches skimage.feature.plot_matches(ax, image1, image2, keypoints1, keypoints2, matches, keypoints_color='k', matches_color=None, only_matches=False) [source] Plot matched features. Parameters: ax : matplotlib.axes.Axes Matches and image are drawn in this ax. image1 : (N, M [, 3]) array First grayscale or color image. image2 : (N, M [, 3]) array Second grayscale or color image. keypoints1 : (K1, 2) array First keypoint coordinates as (row, col). keypoints2 : (K2, 2) array Sec