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 greyscale opening.
A greyscale opening consists in the succession of a greyscale erosion, and a greyscale 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 noninfinite 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 nonflat structuring element.
 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
Returns:  grey_opening : ndarray
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] http://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