Module: filters.rank skimage.filters.rank.autolevel(image, selem) Auto-level image using local histogram. skimage.filters.rank.autolevel_percentile(...) Return greyscale local autolevel of an image. skimage.filters.rank.bottomhat(image, selem) Local bottom-hat of an image. skimage.filters.rank.enhance_contrast(image, ...) Enhance contrast of an image. skimage.filters.rank.enhance_contrast_percentile(...) Enhance contrast of an image. skimage.filters.rank.entropy(image, selem[, ...]) Local

PolynomialTransform class skimage.transform.PolynomialTransform(params=None) [source] Bases: skimage.transform._geometric.GeometricTransform 2D transformation of the form: ..:math: X = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i )) Y = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i )) Parameters: params : (2, N) array, optional Polynomial coefficients where N * 2 = (order + 1) * (order + 2). So, a_ji is defined in params[0, :] and b_ji in params[1, :]. Attributes params (

line skimage.draw.line() Generate line pixel coordinates. Parameters: y0, x0 : int Starting position (row, column). y1, x1 : int End position (row, column). Returns: rr, cc : (N,) ndarray of int Indices of pixels that belong to the line. May be used to directly index into an array, e.g. img[rr, cc] = 1. See also line_aa Anti-aliased line generator Examples >>> from skimage.draw import line >>> img = np.zeros((10, 10), dtype=np.uint8) >>> rr, cc = lin

rgb2grey skimage.color.rgb2grey(rgb) [source] Compute luminance of an RGB image. Parameters: rgb : array_like The image in RGB format, in a 3-D array of shape (.., .., 3), or in RGBA format with shape (.., .., 4). Returns: out : ndarray The luminance image, a 2-D array. Raises: ValueError If rgb2gray is not a 3-D array of shape (.., .., 3) or (.., .., 4). Notes The weights used in this conversion are calibrated for contemporary CRT phosphors: Y = 0.2125 R + 0.7154 G + 0.0721 B I

erosion skimage.morphology.erosion(image, selem=None, *args, **kwargs) [source] Return greyscale morphological erosion of an image. Morphological erosion sets a pixel at (i,j) to the minimum over all pixels in the neighborhood centered at (i,j). Erosion shrinks bright regions and enlarges dark regions. Parameters: image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as an array of 1’s and 0’s. If None, use cross-shaped structuring element (connectivity=1). o

moments_central skimage.measure.moments_central(image, cr, cc, order=3) [source] Calculate all central image moments up to a certain order. The center coordinates (cr, cc) can be calculated from the raw moments as: {m[0, 1] / m[0, 0], m[1, 0] / m[0, 0]}. Note that central moments are translation invariant but not scale and rotation invariant. Parameters: image : 2D double or uint8 array Rasterized shape as image. cr : double Center row coordinate. cc : double Center column coordinate.

Module: viewer.viewers skimage.viewer.viewers.CollectionViewer(...) Viewer for displaying image collections. skimage.viewer.viewers.ImageViewer(image[, ...]) Viewer for displaying images.

LinearColormap class skimage.viewer.utils.LinearColormap(name, segmented_data, **kwargs) [source] Bases: matplotlib.colors.LinearSegmentedColormap LinearSegmentedColormap in which color varies smoothly. This class is a simplification of LinearSegmentedColormap, which doesn’t support jumps in color intensities. Parameters: name : str Name of colormap. segmented_data : dict Dictionary of ‘red’, ‘green’, ‘blue’, and (optionally) ‘alpha’ values. Each color key contains a list of x, y tuples.

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.

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