scipy.ndimage.grey_closing¶

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

Multidimensional grayscale closing.

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

Parameters
inputarray_like

Array over which the grayscale closing is to be computed.

sizetuple of ints

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

footprintarray of ints, optional

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

structurearray of ints, optional

Structuring element used for the grayscale closing. structure may be a non-flat structuring element.

outputarray, 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’

cvalscalar, optional

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

originscalar, optional

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

Returns
grey_closingndarray

Result of the grayscale closing of input with 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
>>> 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


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