remove_small_holes skimage.morphology.remove_small_holes(ar, min_size=64, connectivity=1, in_place=False) [source] Remove continguous holes smaller than the specified size. Parameters: ar : ndarray (arbitrary shape, int or bool type) The array containing the connected components of interest. min_size : int, optional (default: 64) The hole component size. connectivity : int, {1, 2, ..., ar.ndim}, optional (default: 1) The connectivity defining the neighborhood of a pixel. in_place : bo

ComboBox class skimage.viewer.widgets.ComboBox(name, items, ptype='kwarg', callback=None) [source] Bases: skimage.viewer.widgets.core.BaseWidget ComboBox widget for selecting among a list of choices. Parameters: name : str Name of ComboBox parameter. If this parameter is passed as a keyword argument, it must match the name of that keyword argument (spaces are replaced with underscores). In addition, this name is displayed as the name of the ComboBox. items: list of str Allowed parameter

noise_filter skimage.filters.rank.noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Noise feature. 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

Module: viewer.utils skimage.viewer.utils.figimage(image[, ...]) Return figure and axes with figure tightly surrounding image. skimage.viewer.utils.init_qtapp() Initialize QAppliction. skimage.viewer.utils.new_plot([parent, ...]) Return new figure and axes. skimage.viewer.utils.start_qtapp([app]) Start Qt mainloop skimage.viewer.utils.update_axes_image(...) Update the image displayed by an image plot. skimage.viewer.utils.ClearColormap(rgb[, ...]) Color map that varies linearly from alpha

subtract_mean_percentile skimage.filters.rank.subtract_mean_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Return image subtracted from its local mean. 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 array of 1’s and 0’s. out : 2-D array (same dtype as input) If None, a new array is allocated. mask

gaussian_filter skimage.filters.gaussian_filter(*args, **kwargs) [source] Deprecated function. Use skimage.filters.gaussian instead. Multi-dimensional Gaussian filter Parameters: image : array-like input image (grayscale or color) to filter. sigma : scalar or sequence of scalars standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes. output : array, option

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.

deltaE_ciede2000 skimage.color.deltaE_ciede2000(lab1, lab2, kL=1, kC=1, kH=1) [source] Color difference as given by the CIEDE 2000 standard. CIEDE 2000 is a major revision of CIDE94. The perceptual calibration is largely based on experience with automotive paint on smooth surfaces. Parameters: lab1 : array_like reference color (Lab colorspace) lab2 : array_like comparison color (Lab colorspace) kL : float (range), optional lightness scale factor, 1 for “acceptably close”; 2 for “imperc

scharr skimage.filters.scharr(image, mask=None) [source] Find the edge magnitude using the Scharr transform. Parameters: image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns: output : 2-D array The Scharr edge map. See also sobel, prewitt, canny Notes Take the square root of the sum of the s

Module: data Standard test images. For more images, see http://sipi.usc.edu/database/database.php skimage.data.astronaut() Colour image of the astronaut Eileen Collins. skimage.data.binary_blobs([length, ...]) Generate synthetic binary image with several rounded blob-like objects. skimage.data.camera() Gray-level “camera” image. skimage.data.checkerboard() Checkerboard image. skimage.data.chelsea() Chelsea the cat. skimage.data.clock() Motion blurred clock. skimage.data.coffee() Coffee