OKCancelButtons class skimage.viewer.widgets.OKCancelButtons(button_width=80) [source] Bases: skimage.viewer.widgets.core.BaseWidget Buttons that close the parent plugin. OK will replace the original image with the current (filtered) image. Cancel will just close the plugin. __init__(button_width=80) [source] close_plugin() [source] update_original_image() [source]

octagon skimage.morphology.octagon(m, n, dtype=) [source] Generates an octagon shaped structuring element. For a given size of (m) horizontal and vertical sides and a given (n) height or width of slanted sides octagon is generated. The slanted sides are 45 or 135 degrees to the horizontal axis and hence the widths and heights are equal. Parameters: m : int The size of the horizontal and vertical sides. n : int The height or width of the slanted sides. Returns: selem : ndarray The str

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

nl_means_denoising skimage.restoration.nl_means_denoising(*args, **kwargs) [source] Deprecated function. Use skimage.restoration.denoise_nl_means instead. Perform non-local means denoising on 2-D or 3-D grayscale images, and 2-D RGB images. Parameters: image : 2D or 3D ndarray Input image to be denoised, which can be 2D or 3D, and grayscale or RGB (for 2D images only, see multichannel parameter). patch_size : int, optional Size of patches used for denoising. patch_distance : int, option

new_plot skimage.viewer.utils.new_plot(parent=None, subplot_kw=None, **fig_kw) [source] Return new figure and axes. Parameters: parent : QtWidget Qt widget that displays the plot objects. If None, you must manually call canvas.setParent and pass the parent widget. subplot_kw : dict Keyword arguments passed matplotlib.figure.Figure.add_subplot. fig_kw : dict Keyword arguments passed matplotlib.figure.Figure.

ncut skimage.future.graph.ncut(labels, rag, thresh=0.001, num_cuts=10, in_place=True, max_edge=1.0) [source] Perform Normalized Graph cut on the Region Adjacency Graph. Given an image’s labels and its similarity RAG, recursively perform a 2-way normalized cut on it. All nodes belonging to a subgraph that cannot be cut further are assigned a unique label in the output. Parameters: labels : ndarray The array of labels. rag : RAG The region adjacency graph. thresh : float The threshold. A

MultiImage class skimage.io.MultiImage(filename, conserve_memory=True, dtype=None, **imread_kwargs) [source] Bases: skimage.io.collection.ImageCollection A class containing a single multi-frame image. Parameters: filename : str The complete path to the image file. conserve_memory : bool, optional Whether to conserve memory by only caching a single frame. Default is True. Notes If conserve_memory=True the memory footprint can be reduced, however the performance can be affected because f

multiblock_lbp skimage.feature.multiblock_lbp(int_image, r, c, width, height) [source] Multi-block local binary pattern (MB-LBP). The features are calculated similarly to local binary patterns (LBPs), (See local_binary_pattern()) except that summed blocks are used instead of individual pixel values. MB-LBP is an extension of LBP that can be computed on multiple scales in constant time using the integral image. Nine equally-sized rectangles are used to compute a feature. For each rectangle, t

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

moon skimage.data.moon() [source] Surface of the moon. This low-contrast image of the surface of the moon is useful for illustrating histogram equalization and contrast stretching.