ThickLineTool class skimage.viewer.canvastools.ThickLineTool(manager, on_move=None, on_enter=None, on_release=None, on_change=None, maxdist=10, line_props=None, handle_props=None) [source] Bases: skimage.viewer.canvastools.linetool.LineTool Widget for line selection in a plot. The thickness of the line can be varied using the mouse scroll wheel, or with the ‘+’ and ‘-‘ keys. Parameters: manager : Viewer or PlotPlugin. Skimage viewer or plot plugin object. on_move : function Function call

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

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

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

Module: morphology skimage.morphology.ball(radius[, dtype]) Generates a ball-shaped structuring element. skimage.morphology.binary_closing(image[, selem]) Return fast binary morphological closing of an image. skimage.morphology.binary_dilation(image[, ...]) Return fast binary morphological dilation of an image. skimage.morphology.binary_erosion(image[, selem]) Return fast binary morphological erosion of an image. skimage.morphology.binary_opening(image[, selem]) Return fast binary morpholo

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

skimage.novice A special Python image submodule for beginners. Description skimage.novice provides a simple image manipulation interface for beginners. It allows for easy loading, manipulating, and saving of image files. This module is primarily intended for teaching and differs significantly from the normal, array-oriented image functions used by scikit-image. Note This module uses the Cartesian coordinate system, where the origin is at the lower-left corner instead of the upper-right and the

Module: future.graph skimage.future.graph.cut_normalized(labels, rag) Perform Normalized Graph cut on the Region Adjacency Graph. skimage.future.graph.cut_threshold(labels, ...) Combine regions separated by weight less than threshold. skimage.future.graph.draw_rag(labels, rag, img) Draw a Region Adjacency Graph on an image. skimage.future.graph.merge_hierarchical(...) Perform hierarchical merging of a RAG. skimage.future.graph.ncut(labels, rag[, ...]) Perform Normalized Graph cut on the Re

luv2rgb skimage.color.luv2rgb(luv) [source] Luv to RGB color space conversion. Parameters: luv : (M, N, [P,] 3) array_like The 3 or 4 dimensional image in CIE Luv format. Final dimension denotes channels. Returns: out : (M, N, [P,] 3) ndarray The image in RGB format. Same dimensions as input. Raises: ValueError If luv is not a 3-D or 4-D array of shape (M, N, [P,] 3). Notes This function uses luv2xyz and xyz2rgb.

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