swirl skimage.transform.swirl(image, center=None, strength=1, radius=100, rotation=0, output_shape=None, order=1, mode='constant', cval=0, clip=True, preserve_range=False) [source] Perform a swirl transformation. Parameters: image : ndarray Input image. center : (row, column) tuple or (2,) ndarray, optional Center coordinate of transformation. strength : float, optional The amount of swirling applied. radius : float, optional The extent of the swirl in pixels. The effect dies out rap

sum_percentile skimage.filters.rank.sum_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Return the local sum of pixels. Only greyvalues between percentiles [p0, p1] are considered in the filter. Note that the sum may overflow depending on the data type of the input array. 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 inp

sum_bilateral skimage.filters.rank.sum_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 (selem). Radiometric similarity is defined by the greylevel interva

sum skimage.filters.rank.sum(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return the local sum of pixels. Note that the sum may overflow depending on the data type of the input array. 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 in

subtract_mean_percentile skimage.filters.rank.subtract_mean_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Return image subtracted from its local mean. 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

subtract_mean skimage.filters.rank.subtract_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return image subtracted from its local mean. 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, th

Subpackages color Color space conversion. data Test images and example data. draw Drawing primitives (lines, text, etc.) that operate on NumPy arrays. exposure Image intensity adjustment, e.g., histogram equalization, etc. feature Feature detection and extraction, e.g., texture analysis corners, etc. filters Sharpening, edge finding, rank filters, thresholding, etc. graph Graph-theoretic operations, e.g., shortest paths. io Reading, saving, and displaying images and video. measure Measurement o

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

structure_tensor_eigvals skimage.feature.structure_tensor_eigvals(Axx, Axy, Ayy) [source] Compute Eigen values of structure tensor. Parameters: Axx : ndarray Element of the structure tensor for each pixel in the input image. Axy : ndarray Element of the structure tensor for each pixel in the input image. Ayy : ndarray Element of the structure tensor for each pixel in the input image. Returns: l1 : ndarray Larger eigen value for each input matrix. l2 : ndarray Smaller eigen value

structure_tensor skimage.feature.structure_tensor(image, sigma=1, mode='constant', cval=0) [source] Compute structure tensor using sum of squared differences. The structure tensor A is defined as: A = [Axx Axy] [Axy Ayy] which is approximated by the weighted sum of squared differences in a local window around each pixel in the image. Parameters: image : ndarray Input image. sigma : float Standard deviation used for the Gaussian kernel, which is used as a weighting function for the l