minimum skimage.filters.rank.minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return local minimum 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 use

shortest_path skimage.graph.shortest_path(arr, reach=1, axis=-1, output_indexlist=False) [source] Find the shortest path through an n-d array from one side to another. Parameters: arr : ndarray of float64 reach : int, optional By default (reach = 1), the shortest path can only move one row up or down for every step it moves forward (i.e., the path gradient is limited to 1). reach defines the number of elements that can be skipped along each non-axis dimension at each step. axis : int, opt

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.

estimate_transform skimage.transform.estimate_transform(ttype, src, dst, **kwargs) [source] Estimate 2D geometric transformation parameters. You can determine the over-, well- and under-determined parameters with the total least-squares method. Number of source and destination coordinates must match. Parameters: ttype : {‘similarity’, ‘affine’, ‘piecewise-affine’, ‘projective’, ‘polynomial’} Type of transform. kwargs : array or int Function parameters (src, dst, n, angle): NAME / TTYPE

pop skimage.filters.rank.pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return the local number (population) of pixels. The number of pixels is defined as the number of pixels which are included in the structuring element and the mask. 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

deprecated class skimage.data.deprecated(alt_func=None, behavior='warn') [source] Bases: object Decorator to mark deprecated functions with warning. Adapted from <http://wiki.python.org/moin/PythonDecoratorLibrary>. Parameters: alt_func : str If given, tell user what function to use instead. behavior : {‘warn’, ‘raise’} Behavior during call to deprecated function: ‘warn’ = warn user that function is deprecated; ‘raise’ = raise error. __init__(alt_func=None, behavior='warn') [sou

disk skimage.morphology.disk(radius, dtype=) [source] Generates a flat, disk-shaped structuring element. A pixel is within the neighborhood if the euclidean distance between it and the origin is no greater than radius. Parameters: radius : int The radius of the disk-shaped structuring element. Returns: selem : ndarray The structuring element where elements of the neighborhood are 1 and 0 otherwise. Other Parameters: dtype : data-type The data type of the structuring element.

profile_line skimage.measure.profile_line(img, src, dst, linewidth=1, order=1, mode='constant', cval=0.0) [source] Return the intensity profile of an image measured along a scan line. Parameters: img : numeric array, shape (M, N[, C]) The image, either grayscale (2D array) or multichannel (3D array, where the final axis contains the channel information). src : 2-tuple of numeric scalar (float or int) The start point of the scan line. dst : 2-tuple of numeric scalar (float or int) The e

binary_dilation skimage.morphology.binary_dilation(image, selem=None, *args, **kwargs) [source] Return fast binary morphological dilation of an image. This function returns the same result as greyscale dilation but performs faster for binary images. 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 Binary input image. selem : ndarray, opti

integral_image skimage.transform.integral_image(img) [source] Integral image / summed area table. The integral image contains the sum of all elements above and to the left of it, i.e.: Parameters: img : ndarray Input image. Returns: S : ndarray Integral image/summed area table of same shape as input image. References [R369] F.C. Crow, “Summed-area tables for texture mapping,” ACM SIGGRAPH Computer Graphics, vol. 18, 1984, pp. 207-212.