threshold_percentile skimage.filters.rank.threshold_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0) [source] Local threshold of an image. The resulting binary mask is True if the greyvalue of the center pixel is greater than the 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 :

threshold_otsu skimage.filters.threshold_otsu(image, nbins=256) [source] Return threshold value based on Otsu’s method. Parameters: image : array Grayscale input image. nbins : int, optional Number of bins used to calculate histogram. This value is ignored for integer arrays. Returns: threshold : float Upper threshold value. All pixels intensities that less or equal of this value assumed as foreground. Notes The input image must be grayscale. References [R206] Wikipedia, http://en.

threshold_li skimage.filters.threshold_li(image) [source] Return threshold value based on adaptation of Li’s Minimum Cross Entropy method. Parameters: image : array Input image. Returns: threshold : float Upper threshold value. All pixels intensities more than this value are assumed to be foreground. References [R202] Li C.H. and Lee C.K. (1993) “Minimum Cross Entropy Thresholding” Pattern Recognition, 26(4): 617-625 [R203] Li C.H. and Tam P.K.S. (1998) “An Iterative Algorithm for Mi

threshold_isodata skimage.filters.threshold_isodata(image, nbins=256, return_all=False) [source] Return threshold value(s) based on ISODATA method. Histogram-based threshold, known as Ridler-Calvard method or inter-means. Threshold values returned satisfy the following equality: threshold = (image[image <= threshold].mean() + image[image > threshold].mean()) / 2.0 That is, returned thresholds are intensities that separate the image into two groups of pixels, where the threshold inten

threshold_adaptive skimage.filters.threshold_adaptive(image, block_size, method='gaussian', offset=0, mode='reflect', param=None) [source] Applies an adaptive threshold to an array. Also known as local or dynamic thresholding where the threshold value is the weighted mean for the local neighborhood of a pixel subtracted by a constant. Alternatively the threshold can be determined dynamically by a a given function using the ‘generic’ method. Parameters: image : (N, M) ndarray Input image.

threshold skimage.filters.rank.threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Local threshold of an image. The resulting binary mask is True if the greyvalue of the center pixel is greater than the local mean. 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 de

ThickLineTool class skimage.viewer.canvastools.ThickLineTool(manager, on_move=None, on_enter=None, on_release=None, on_change=None, maxdist=10, line_props=None, handle_props=None) [source] Bases: skimage.viewer.canvastools.linetool.LineTool Widget for line selection in a plot. The thickness of the line can be varied using the mouse scroll wheel, or with the ‘+’ and ‘-‘ keys. Parameters: manager : Viewer or PlotPlugin. Skimage viewer or plot plugin object. on_move : function Function call

Text class skimage.viewer.widgets.Text(name=None, text='') [source] Bases: skimage.viewer.widgets.core.BaseWidget __init__(name=None, text='') [source] text

text skimage.data.text() [source] Gray-level “text” image used for corner detection. Notes This image was downloaded from Wikipedia <http://en.wikipedia.org/wiki/File:Corner.png>`__. No known copyright restrictions, released into the public domain.

test skimage.test(doctest=False, verbose=False) [source] Run all unit tests.