mean skimage.filters.rank.mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return local mean of an image. 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 : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (defaul

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.

rgb2lab skimage.color.rgb2lab(rgb) [source] RGB to lab color space conversion. Parameters: rgb : array_like The image in RGB format, in a 3- or 4-D array of shape (.., ..,[ ..,] 3). Returns: out : ndarray The image in Lab format, in a 3- or 4-D array of shape (.., ..,[ ..,] 3). Raises: ValueError If rgb is not a 3- or 4-D array of shape (.., ..,[ ..,] 3). Notes This function uses rgb2xyz and xyz2lab.

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]

dilation skimage.morphology.dilation(image, selem=None, *args, **kwargs) [source] Return greyscale morphological dilation of an image. Morphological dilation sets a pixel at (i,j) to the maximum over all pixels in the neighborhood centered at (i,j). Dilation enlarges bright regions and shrinks dark regions. Parameters: image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as a 2-D array of 1’s and 0’s. If None, use cross-shaped structuring element (connectivit

ellipse_perimeter skimage.draw.ellipse_perimeter() Generate ellipse perimeter coordinates. Parameters: cy, cx : int Centre coordinate of ellipse. yradius, xradius : int Minor and major semi-axes. (x/xradius)**2 + (y/yradius)**2 = 1. orientation : double, optional (default 0) Major axis orientation in clockwise direction as radians. shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for ellipses which exceed th

img_as_uint skimage.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.

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.

moments_normalized skimage.measure.moments_normalized(mu, order=3) [source] Calculate all normalized central image moments up to a certain order. Note that normalized central moments are translation and scale invariant but not rotation invariant. Parameters: mu : (M, M) array Central image moments, where M must be > order. order : int, optional Maximum order of moments. Default is 3. Returns: nu : (order + 1, order + 1) array Normalized central image moments. References [R278] W

AffineTransform class skimage.transform.AffineTransform(matrix=None, scale=None, rotation=None, shear=None, translation=None) [source] Bases: skimage.transform._geometric.ProjectiveTransform 2D affine transformation of the form: ..:math: X = a0*x + a1*y + a2 = = sx*x*cos(rotation) - sy*y*sin(rotation + shear) + a2 Y = b0*x + b1*y + b2 = = sx*x*sin(rotation) + sy*y*cos(rotation + shear) + b2 where sx and sy are zoom factors in the x and y directions, and the homogeneous transformation matrix