scipy.ndimage.morphological_gradient¶
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scipy.ndimage.
morphological_gradient
(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0)[source]¶ Multi-dimensional morphological gradient.
The morphological gradient is calculated as the difference between a dilation and an erosion of the input with a given structuring element.
Parameters: input : array_like
Array over which to compute the morphlogical gradient.
size : tuple of ints
Shape of a flat and full structuring element used for the mathematical morphology operations. Optional if footprint or structure is provided. A larger size yields a more blurred gradient.
footprint : array of ints, optional
Positions of non-infinite elements of a flat structuring element used for the morphology operations. Larger footprints give a more blurred morphological gradient.
structure : array of ints, optional
Structuring element used for the morphology operations. structure may be a non-flat structuring element.
output : array, optional
An array used for storing the ouput of the morphological gradient 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: morphological_gradient : ndarray
Morphological gradient of input.
See also
grey_dilation
,grey_erosion
,ndimage.gaussian_gradient_magnitude
Notes
For a flat structuring element, the morphological gradient computed at a given point corresponds to the maximal difference between elements of the input among the elements covered by the structuring element centered on the point.
References
[R185] http://en.wikipedia.org/wiki/Mathematical_morphology Examples
>>> from scipy import ndimage >>> a = np.zeros((7,7), dtype=int) >>> a[2:5, 2:5] = 1 >>> ndimage.morphological_gradient(a, size=(3,3)) array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> # The morphological gradient is computed as the difference >>> # between a dilation and an erosion >>> ndimage.grey_dilation(a, size=(3,3)) -\ ... ndimage.grey_erosion(a, size=(3,3)) array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> a = np.zeros((7,7), dtype=int) >>> a[2:5, 2:5] = 1 >>> a[4,4] = 2; a[2,3] = 3 >>> a array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 3, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> ndimage.morphological_gradient(a, size=(3,3)) array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 3, 3, 3, 1, 0], [0, 1, 3, 3, 3, 1, 0], [0, 1, 3, 2, 3, 2, 0], [0, 1, 1, 2, 2, 2, 0], [0, 1, 1, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0]])