hessian_matrix_eigvals skimage.feature.hessian_matrix_eigvals(Hxx, Hxy, Hyy) [source] Compute Eigen values of Hessian matrix. Parameters: Hxx : ndarray Element of the Hessian matrix for each pixel in the input image. Hxy : ndarray Element of the Hessian matrix for each pixel in the input image. Hyy : ndarray Element of the Hessian matrix for each pixel in the input image. Returns: l1 : ndarray Larger eigen value for each input matrix. l2 : ndarray Smaller eigen value for each inp

hessian_matrix_det skimage.feature.hessian_matrix_det(image, sigma) [source] Computes the approximate Hessian Determinant over an image. This method uses box filters over integral images to compute the approximate Hessian Determinant as described in [R151]. Parameters: image : array The image over which to compute Hessian Determinant. sigma : float Standard deviation used for the Gaussian kernel, used for the Hessian matrix. Returns: out : array The array of the Determinant of Hessia

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

hed2rgb skimage.color.hed2rgb(hed) [source] Haematoxylin-Eosin-DAB (HED) to RGB color space conversion. Parameters: hed : array_like The image in the HED color space, in a 3-D array of shape (.., .., 3). Returns: out : ndarray The image in RGB, in a 3-D array of shape (.., .., 3). Raises: ValueError If hed is not a 3-D array of shape (.., .., 3). References [R42] A. C. Ruifrok and D. A. Johnston, “Quantification of histochemical staining by color deconvolution.,” Analytical and q

guess_spatial_dimensions skimage.color.guess_spatial_dimensions(image) [source] Make an educated guess about whether an image has a channels dimension. Parameters: image : ndarray The input image. Returns: spatial_dims : int or None The number of spatial dimensions of image. If ambiguous, the value is None. Raises: ValueError If the image array has less than two or more than four dimensions.

grid_points_in_poly skimage.measure.grid_points_in_poly() Test whether points on a specified grid are inside a polygon. For each (r, c) coordinate on a grid, i.e. (0, 0), (0, 1) etc., test whether that point lies inside a polygon. Parameters: shape : tuple (M, N) Shape of the grid. verts : (V, 2) array Specify the V vertices of the polygon, sorted either clockwise or anti-clockwise. The first point may (but does not need to be) duplicated. Returns: mask : (M, N) ndarray of bool True

greycoprops skimage.feature.greycoprops(P, prop='contrast') [source] Calculate texture properties of a GLCM. Compute a feature of a grey level co-occurrence matrix to serve as a compact summary of the matrix. The properties are computed as follows: ‘contrast’: ‘dissimilarity’: ‘homogeneity’: ‘ASM’: ‘energy’: ‘correlation’: Parameters: P : ndarray Input array. P is the grey-level co-occurrence histogram for which to compute the specified property. The value P[i,j,d,theta] is the n

greycomatrix skimage.feature.greycomatrix(image, distances, angles, levels=256, symmetric=False, normed=False) [source] Calculate the grey-level co-occurrence matrix. A grey level co-occurrence matrix is a histogram of co-occurring greyscale values at a given offset over an image. Parameters: image : array_like of uint8 Integer typed input image. The image will be cast to uint8, so the maximum value must be less than 256. distances : array_like List of pixel pair distance offsets. angle

gray2rgb skimage.color.gray2rgb(image, alpha=None) [source] Create an RGB representation of a gray-level image. Parameters: image : array_like Input image of shape (M, N [, P]). alpha : bool, optional Ensure that the output image has an alpha layer. If None, alpha layers are passed through but not created. Returns: rgb : ndarray RGB image of shape (M, N, [, P], 3). Raises: ValueError If the input is not a 2- or 3-dimensional image.

gradient_percentile skimage.filters.rank.gradient_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Return local gradient of an image (i.e. local maximum - local minimum). 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