coins skimage.data.coins() [source] Greek coins from Pompeii. This image shows several coins outlined against a gray background. It is especially useful in, e.g. segmentation tests, where individual objects need to be identified against a background. The background shares enough grey levels with the coins that a simple segmentation is not sufficient. Notes This image was downloaded from the Brooklyn Museum Collection. No known copyright restrictions.

clock skimage.data.clock() [source] Motion blurred clock. This photograph of a wall clock was taken while moving the camera in an aproximately horizontal direction. It may be used to illustrate inverse filters and deconvolution. Released into the public domain by the photographer (Stefan van der Walt).

ClearColormap class skimage.viewer.utils.ClearColormap(rgb, max_alpha=1, name='clear_color') [source] Bases: skimage.viewer.utils.core.LinearColormap Color map that varies linearly from alpha = 0 to 1 __init__(rgb, max_alpha=1, name='clear_color') [source]

clear_border skimage.segmentation.clear_border(labels, buffer_size=0, bgval=0, in_place=False) [source] Clear objects connected to the label image border. The changes will be applied directly to the input. Parameters: labels : (N, M) array of int Label or binary image. buffer_size : int, optional The width of the border examined. By default, only objects that touch the outside of the image are removed. bgval : float or int, optional Cleared objects are set to this value. in_place : bo

circle_perimeter_aa skimage.draw.circle_perimeter_aa() Generate anti-aliased circle perimeter coordinates. Parameters: cy, cx : int Centre coordinate of circle. radius: int Radius of circle. shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for circles which exceed the image size. By default the full extent of the circle are used. Returns: rr, cc, val : (N,) ndarray (int, int, float) Indices of pixels (rr,

CircleModel class skimage.measure.CircleModel [source] Bases: skimage.measure.fit.BaseModel Total least squares estimator for 2D circles. The functional model of the circle is: r**2 = (x - xc)**2 + (y - yc)**2 This estimator minimizes the squared distances from all points to the circle: min{ sum((r - sqrt((x_i - xc)**2 + (y_i - yc)**2))**2) } A minimum number of 3 points is required to solve for the parameters. Attributes params (tuple) Circle model parameters in the following order xc, yc

circle_perimeter skimage.draw.circle_perimeter() Generate circle perimeter coordinates. Parameters: cy, cx : int Centre coordinate of circle. radius: int Radius of circle. method : {‘bresenham’, ‘andres’}, optional bresenham : Bresenham method (default) andres : Andres method shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for circles which exceed the image size. By default the full extent of the circle are

chelsea skimage.data.chelsea() [source] Chelsea the cat. An example with texture, prominent edges in horizontal and diagonal directions, as well as features of differing scales. Notes No copyright restrictions. CC0 by the photographer (Stefan van der Walt).

circle skimage.draw.circle(r, c, radius, shape=None) [source] Generate coordinates of pixels within circle. Parameters: r, c : double Centre coordinate of circle. radius: double Radius of circle. shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for circles which exceed the image size. By default the full extent of the circle are used. Returns: rr, cc : ndarray of int Pixel coordinates of circle. May be use

checkerboard skimage.data.checkerboard() [source] Checkerboard image. Checkerboards are often used in image calibration, since the corner-points are easy to locate. Because of the many parallel edges, they also visualise distortions particularly well.