combine_stains skimage.color.combine_stains(stains, conv_matrix) [source] Stain to RGB color space conversion. Parameters: stains : array_like The image in stain color space, in a 3-D array of shape (.., .., 3). conv_matrix: ndarray The stain separation matrix as described by G. Landini [R31]. Returns: out : ndarray The image in RGB format, in a 3-D array of shape (.., .., 3). Raises: ValueError If stains is not a 3-D array of shape (.., .., 3). Notes Stain combination matrices

Button class skimage.viewer.widgets.Button(name, callback) [source] Bases: skimage.viewer.widgets.core.BaseWidget Button which calls callback upon click. Parameters: name : str Name of button. callback : callable f() Function to call when button is clicked. __init__(name, callback) [source]

corner_orientations skimage.feature.corner_orientations() Compute the orientation of corners. The orientation of corners is computed using the first order central moment i.e. the center of mass approach. The corner orientation is the angle of the vector from the corner coordinate to the intensity centroid in the local neighborhood around the corner calculated using first order central moment. Parameters: image : 2D array Input grayscale image. corners : (N, 2) array Corner coordinates as

Module: viewer.canvastools skimage.viewer.canvastools.LineTool(manager) Widget for line selection in a plot. skimage.viewer.canvastools.PaintTool(...[, ...]) Widget for painting on top of a plot. skimage.viewer.canvastools.RectangleTool(manager) Widget for selecting a rectangular region in a plot. skimage.viewer.canvastools.ThickLineTool(manager) Widget for line selection in a plot.

adjust_sigmoid skimage.exposure.adjust_sigmoid(image, cutoff=0.5, gain=10, inv=False) [source] Performs Sigmoid Correction on the input image. Also known as Contrast Adjustment. This function transforms the input image pixelwise according to the equation O = 1/(1 + exp*(gain*(cutoff - I))) after scaling each pixel to the range 0 to 1. Parameters: image : ndarray Input image. cutoff : float Cutoff of the sigmoid function that shifts the characteristic curve in horizontal direction. Defaul

resize skimage.transform.resize(image, output_shape, order=1, mode='constant', cval=0, clip=True, preserve_range=False) [source] Resize image to match a certain size. Performs interpolation to up-size or down-size images. For down-sampling N-dimensional images by applying the arithmetic sum or mean, see skimage.measure.local_sum and skimage.transform.downscale_local_mean, respectively. Parameters: image : ndarray Input image. output_shape : tuple or ndarray Size of the generated output i

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

mark_boundaries skimage.segmentation.mark_boundaries(image, label_img, color=(1, 1, 0), outline_color=None, mode='outer', background_label=0) [source] Return image with boundaries between labeled regions highlighted. Parameters: image : (M, N[, 3]) array Grayscale or RGB image. label_img : (M, N) array of int Label array where regions are marked by different integer values. color : length-3 sequence, optional RGB color of boundaries in the output image. outline_color : length-3 sequen

pop_percentile skimage.filters.rank.pop_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Return the local number (population) of pixels. The number of pixels is defined as the number of pixels which are included in the structuring element and the mask. 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 arr

entropy skimage.filters.rank.entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Local entropy. The entropy is computed using base 2 logarithm i.e. the filter returns the minimum number of bits needed to encode the local greylevel distribution. 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 :