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

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

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

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.

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

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

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

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