merge_hierarchical skimage.future.graph.merge_hierarchical(labels, rag, thresh, rag_copy, in_place_merge, merge_func, weight_func) [source] Perform hierarchical merging of a RAG. Greedily merges the most similar pair of nodes until no edges lower than thresh remain. Parameters: labels : ndarray The array of labels. rag : RAG The Region Adjacency Graph. thresh : float Regions connected by an edge with weight smaller than thresh are merged. rag_copy : bool If set, the RAG copied before

median skimage.filters.median(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return local median of an image. 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 (defau

median skimage.filters.rank.median(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return local median of an image. 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 (

medial_axis skimage.morphology.medial_axis(image, mask=None, return_distance=False) [source] Compute the medial axis transform of a binary image Parameters: image : binary ndarray, shape (M, N) The image of the shape to be skeletonized. mask : binary ndarray, shape (M, N), optional If a mask is given, only those elements in image with a true value in mask are used for computing the medial axis. return_distance : bool, optional If true, the distance transform is returned as well as the

mean_percentile skimage.filters.rank.mean_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Return local mean of an image. Only greyvalues between percentiles [p0, p1] are considered in the filter. 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 defi

mean skimage.filters.rank.mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return local mean of an image. 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

mean_bilateral skimage.filters.rank.mean_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10) [source] Apply a flat kernel bilateral filter. This is an edge-preserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity. Spatial closeness is measured by considering only the local pixel neighborhood given by a structuring element. Radiometric similarity is defined by the greylevel interval [g-s

MCP_Flexible class skimage.graph.MCP_Flexible(costs, offsets=None, fully_connected=True) Bases: skimage.graph._mcp.MCP Find minimum cost paths through an N-d costs array. See the documentation for MCP for full details. This class differs from MCP in that several methods can be overloaded (from pure Python) to modify the behavior of the algorithm and/or create custom algorithms based on MCP. Note that goal_reached can also be overloaded in the MCP class. __init__(costs, offsets=None, fully_c

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,

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