draw_rag skimage.future.graph.draw_rag(labels, rag, img, border_color=None, node_color='#ffff00', edge_color='#00ff00', colormap=None, thresh=inf, desaturate=False, in_place=True) [source] Draw a Region Adjacency Graph on an image. Given a labelled image and its corresponding RAG, draw the nodes and edges of the RAG on the image with the specified colors. Nodes are marked by the centroids of the corresponding regions. Parameters: labels : ndarray, shape (M, N) The labelled image. rag : RA

Module: segmentation skimage.segmentation.active_contour(image, snake) Active contour model. skimage.segmentation.clear_border(labels[, ...]) Clear objects connected to the label image border. skimage.segmentation.felzenszwalb(image[, ...]) Computes Felsenszwalb’s efficient graph based image segmentation. skimage.segmentation.find_boundaries(label_img) Return bool array where boundaries between labeled regions are True. skimage.segmentation.join_segmentations(s1, s2) Return the join of the

white_tophat skimage.morphology.white_tophat(image, selem=None, *args, **kwargs) [source] Return white top hat of an image. The white top hat of an image is defined as the image minus its morphological opening. This operation returns the bright spots of the image that are smaller than the structuring element. Parameters: image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as an array of 1’s and 0’s. If None, use cross-shaped structuring element (connectivity

points_in_poly skimage.measure.points_in_poly() Test whether points lie inside a polygon. Parameters: points : (N, 2) array Input points, (x, y). verts : (M, 2) array Vertices of the polygon, sorted either clockwise or anti-clockwise. The first point may (but does not need to be) duplicated. Returns: mask : (N,) array of bool True if corresponding point is inside the polygon. See also grid_points_in_poly

pop_percentile skimage.filters.rank.pop_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Return the local number (population) of pixels. The number of pixels is defined as the number of pixels which are included in the structuring element and the mask. 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 arr

subdivide_polygon skimage.measure.subdivide_polygon(coords, degree=2, preserve_ends=False) [source] Subdivision of polygonal curves using B-Splines. Note that the resulting curve is always within the convex hull of the original polygon. Circular polygons stay closed after subdivision. Parameters: coords : (N, 2) array Coordinate array. degree : {1, 2, 3, 4, 5, 6, 7}, optional Degree of B-Spline. Default is 2. preserve_ends : bool, optional Preserve first and last coordinate of non-circ

otsu skimage.filters.rank.otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Local Otsu’s threshold value for each pixel. Parameters: image : ndarray Image array (uint8 array). selem : 2-D array The neighborhood expressed as a 2-D array of 1’s and 0’s. out : ndarray If None, a new array will be allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shif

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,

pop_bilateral skimage.filters.rank.pop_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10) [source] Return the local number (population) of pixels. The number of pixels is defined as the number of pixels which are included in the structuring element and the mask. Additionally pixels must have a greylevel inside the interval [g-s0, g+s1] where g is the greyvalue of the center pixel. Parameters: image : 2-D array (uint8, uint16) Input image. selem : 2-D

vprewitt skimage.filters.vprewitt(*args, **kwargs) [source] Deprecated function. Use skimage.filters.prewitt_v instead. Find the vertical edges of an image using the Prewitt 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 Prewitt edge