# scipy.ndimage.grey_opening¶

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

Multidimensional grayscale opening.

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

Parameters
inputarray_like

Array over which the grayscale opening is to be computed.

sizetuple of ints

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

structurearray of ints, optional

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

outputarray, 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’

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_openingndarray

Result of the grayscale opening of input with 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
>>> 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


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