cube skimage.morphology.cube(width, dtype=) [source] Generates a cube-shaped structuring element. This is the 3D equivalent of a square. Every pixel along the perimeter has a chessboard distance no greater than radius (radius=floor(width/2)) pixels. Parameters: width : int The width, height and depth of the cube. Returns: selem : ndarray A structuring element consisting only of ones, i.e. every pixel belongs to the neighborhood. Other Parameters: dtype : data-type The data type of

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.

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.

label2rgb skimage.color.label2rgb(label, image=None, colors=None, alpha=0.3, bg_label=-1, bg_color=(0, 0, 0), image_alpha=1, kind='overlay') [source] Return an RGB image where color-coded labels are painted over the image. Parameters: label : array, shape (M, N) Integer array of labels with the same shape as image. image : array, shape (M, N, 3), optional Image used as underlay for labels. If the input is an RGB image, it’s converted to grayscale before coloring. colors : list, optional

Module: viewer skimage.viewer.warn(message[, category, ...]) A version of warnings.warn with a default stacklevel of 2. skimage.viewer.CollectionViewer(image_collection) Viewer for displaying image collections. skimage.viewer.ImageViewer(image[, useblit]) Viewer for displaying images.

star skimage.morphology.star(a, dtype=) [source] Generates a star shaped structuring element. Start has 8 vertices and is an overlap of square of size 2*a + 1 with its 45 degree rotated version. The slanted sides are 45 or 135 degrees to the horizontal axis. Parameters: a : int Parameter deciding the size of the star structural element. The side of the square array returned is 2*a + 1 + 2*floor(a / 2). Returns: selem : ndarray The structuring element where elements of the neighborhood

img_as_uint skimage.util.img_as_uint(image, force_copy=False) [source] Convert an image to 16-bit unsigned integer format. Parameters: image : ndarray Input image. force_copy : bool Force a copy of the data, irrespective of its current dtype. Returns: out : ndarray of uint16 Output image. Notes Negative input values will be clipped. Positive values are scaled between 0 and 65535.

skeletonize_3d skimage.morphology.skeletonize_3d(img) [source] Compute the skeleton of a binary image. Thinning is used to reduce each connected component in a binary image to a single-pixel wide skeleton. Parameters: img : ndarray, 2D or 3D A binary image containing the objects to be skeletonized. Zeros represent background, nonzero values are foreground. Returns: skeleton : ndarray The thinned image. See also skeletonize, medial_axis Notes The method of [Lee94] uses an octree dat

xyz2rgb skimage.color.xyz2rgb(xyz) [source] XYZ to RGB color space conversion. Parameters: xyz : array_like The image in XYZ format, in a 3-D array of shape (.., .., 3). Returns: out : ndarray The image in RGB format, in a 3-D array of shape (.., .., 3). Raises: ValueError If xyz is not a 3-D array of shape (.., .., 3). Notes The CIE XYZ color space is derived from the CIE RGB color space. Note however that this function converts to sRGB. References [R60] http://en.wikipedia.org/

equalize_adapthist skimage.exposure.equalize_adapthist(image, *args, **kwargs) [source] Contrast Limited Adaptive Histogram Equalization (CLAHE). An algorithm for local contrast enhancement, that uses histograms computed over different tile regions of the image. Local details can therefore be enhanced even in regions that are darker or lighter than most of the image. Parameters: image : array-like Input image. kernel_size: integer or 2-tuple Defines the shape of contextual regions used i