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scipy.ndimage.

# grey_closing

scipy.ndimage.grey_closing(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0, *, axes=None)

  Multidimensional grayscale closing.

  A grayscale closing consists in the succession of a grayscale dilation, and a grayscale erosion.

  Parameters:
    input array_like

      Array over which the grayscale closing is to be computed.

    size tuple of ints

      Shape of a flat and full structuring element used for the grayscale closing. Optional if footprint or structure is provided.

    footprint array of ints, optional

      Positions of non-infinite elements of a flat structuring element used for the grayscale closing.

    structure array of ints, optional

      Structuring element used for the grayscale closing. structure may be a non-flat structuring element. The structure array applies offsets to the pixels in a neighborhood (the offset is additive during dilation and subtractive during erosion)

    output array, optional

      An array used for storing the output of the closing may be provided.

    mode{‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional

      The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’

    cval scalar, optional

      Value to fill past edges of input if mode is ‘constant’. Default is 0.0.

    origin scalar, optional

      The origin parameter controls the placement of the filter. Default 0

    axes tuple of int or None

      The axes over which to apply the filter. If None, input is filtered along all axes. If an origin tuple is provided, its length must match the number of axes.

  Returns:
    grey_closing ndarray

      Result of the grayscale closing of input with structure.

  See also

  `binary_closing`, `grey_dilation`, `grey_erosion`, `grey_opening`
  `generate_binary_structure`

  Notes

  The action of a grayscale closing with a flat structuring element amounts to smoothen deep local minima, whereas binary closing fills small holes.

  References

  [1]

  https://en.wikipedia.org/wiki/Mathematical_morphology

  Examples

      >>> from scipy import ndimage
      >>> import numpy as np
      >>> a = np.arange(36).reshape((6,6))
      >>> a[3,3] = 0
      >>> a
      array([[ 0,  1,  2,  3,  4,  5],
             [ 6,  7,  8,  9, 10, 11],
             [12, 13, 14, 15, 16, 17],
             [18, 19, 20,  0, 22, 23],
             [24, 25, 26, 27, 28, 29],
             [30, 31, 32, 33, 34, 35]])
      >>> ndimage.grey_closing(a, size=(3,3))
      array([[ 7,  7,  8,  9, 10, 11],
             [ 7,  7,  8,  9, 10, 11],
             [13, 13, 14, 15, 16, 17],
             [19, 19, 20, 20, 22, 23],
             [25, 25, 26, 27, 28, 29],
             [31, 31, 32, 33, 34, 35]])
      >>> # Note that the local minimum a[3,3] has disappeared