hessian_matrix skimage.feature.hessian_matrix(image, sigma=1, mode='constant', cval=0) [source] Compute Hessian matrix. The Hessian matrix is defined as: H = [Hxx Hxy] [Hxy Hyy] which is computed by convolving the image with the second derivatives of the Gaussian kernel in the respective x- and y-directions. Parameters: image : ndarray Input image. sigma : float Standard deviation used for the Gaussian kernel, which is used as weighting function for the auto-correlation matrix. mod

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

pyramid_laplacian skimage.transform.pyramid_laplacian(image, max_layer=-1, downscale=2, sigma=None, order=1, mode='reflect', cval=0) [source] Yield images of the laplacian pyramid formed by the input image. Each layer contains the difference between the downsampled and the downsampled, smoothed image: layer = resize(prev_layer) - smooth(resize(prev_layer)) Note that the first image of the pyramid will be the difference between the original, unscaled image and its smoothed version. The total

Module: novice

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

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

histogram skimage.exposure.histogram(image, nbins=256) [source] Return histogram of image. Unlike numpy.histogram, this function returns the centers of bins and does not rebin integer arrays. For integer arrays, each integer value has its own bin, which improves speed and intensity-resolution. The histogram is computed on the flattened image: for color images, the function should be used separately on each channel to obtain a histogram for each color channel. Parameters: image : array Inpu

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

LPIFilter2D class skimage.filters.LPIFilter2D(impulse_response, **filter_params) [source] Bases: object Linear Position-Invariant Filter (2-dimensional) __init__(impulse_response, **filter_params) [source] Parameters: impulse_response : callable f(r, c, **filter_params) Function that yields the impulse response. r and c are 1-dimensional vectors that represent row and column positions, in other words coordinates are (r[0],c[0]),(r[0],c[1]) etc. **filter_params are passed through. In oth

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,