Skip to main content
`Ctrl`+`K`
scipy.ndimage.

# grey_opening

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

  Multidimensional grayscale opening.

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

  Parameters:
    input array_like

      Array over which the grayscale opening is to be computed.

    size tuple of ints

      Shape of a flat and full structuring element used for the grayscale opening. 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 opening.

    structure array of ints, optional

      Structuring element used for the grayscale opening. 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 opening 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_opening ndarray

      Result of the grayscale opening of input with structure.

  See also

  `binary_opening`, `grey_dilation`, `grey_erosion`, `grey_closing`
  `generate_binary_structure`

  Notes

  The action of a grayscale opening with a flat structuring element amounts to smoothen high local maxima, whereas binary opening erases small objects.

  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] = 50
      >>> a
      array([[ 0,  1,  2,  3,  4,  5],
             [ 6,  7,  8,  9, 10, 11],
             [12, 13, 14, 15, 16, 17],
             [18, 19, 20, 50, 22, 23],
             [24, 25, 26, 27, 28, 29],
             [30, 31, 32, 33, 34, 35]])
      >>> ndimage.grey_opening(a, size=(3,3))
      array([[ 0,  1,  2,  3,  4,  4],
             [ 6,  7,  8,  9, 10, 10],
             [12, 13, 14, 15, 16, 16],
             [18, 19, 20, 22, 22, 22],
             [24, 25, 26, 27, 28, 28],
             [24, 25, 26, 27, 28, 28]])
      >>> # Note that the local maximum a[3,3] has disappeared