scipy.ndimage.grey_erosion¶
- scipy.ndimage.grey_erosion(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0)[source]¶
Calculate a greyscale erosion, using either a structuring element, or a footprint corresponding to a flat structuring element.
Grayscale erosion is a mathematical morphology operation. For the simple case of a full and flat structuring element, it can be viewed as a minimum filter over a sliding window.
- Parameters
- inputarray_like
Array over which the grayscale erosion is to be computed.
- sizetuple of ints
Shape of a flat and full structuring element used for the grayscale erosion. 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 erosion. Non-zero values give the set of neighbors of the center over which the minimum is chosen.
- structurearray of ints, optional
Structuring element used for the grayscale erosion. structure may be a non-flat structuring element.
- outputarray, optional
An array used for storing the output of the erosion 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
- outputndarray
Grayscale erosion of input.
See also
Notes
The grayscale erosion of an image input by a structuring element s defined over a domain E is given by:
(input+s)(x) = min {input(y) - s(x-y), for y in E}
In particular, for structuring elements defined as s(y) = 0 for y in E, the grayscale erosion computes the minimum of the input image inside a sliding window defined by E.
Grayscale erosion [1] is a mathematical morphology operation [2].
References
Examples
>>> from scipy import ndimage >>> a = np.zeros((7,7), dtype=int) >>> a[1:6, 1:6] = 3 >>> a[4,4] = 2; a[2,3] = 1 >>> a array([[0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 3, 3, 3, 0], [0, 3, 3, 1, 3, 3, 0], [0, 3, 3, 3, 3, 3, 0], [0, 3, 3, 3, 2, 3, 0], [0, 3, 3, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> ndimage.grey_erosion(a, size=(3,3)) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 3, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> footprint = ndimage.generate_binary_structure(2, 1) >>> footprint array([[False, True, False], [ True, True, True], [False, True, False]], dtype=bool) >>> # Diagonally-connected elements are not considered neighbors >>> ndimage.grey_erosion(a, size=(3,3), footprint=footprint) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 3, 1, 2, 0, 0], [0, 0, 3, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]])