scipy.ndimage.

binary_opening#

scipy.ndimage.binary_opening(input, structure=None, iterations=1, output=None, origin=0, mask=None, border_value=0, brute_force=False, *, axes=None)[source]#

Multidimensional binary opening with the given structuring element.

The opening of an input image by a structuring element is the dilation of the erosion of the image by the structuring element.

Parameters:
inputarray_like

Binary array_like to be opened. Non-zero (True) elements form the subset to be opened.

structurearray_like, optional

Structuring element used for the opening. Non-zero elements are considered True. If no structuring element is provided an element is generated with a square connectivity equal to one (i.e., only nearest neighbors are connected to the center, diagonally-connected elements are not considered neighbors).

iterationsint, optional

The erosion step of the opening, then the dilation step are each repeated iterations times (one, by default). If iterations is less than 1, each operation is repeated until the result does not change anymore. Only an integer of iterations is accepted.

outputndarray, optional

Array of the same shape as input, into which the output is placed. By default, a new array is created.

originint or tuple of ints, optional

Placement of the filter, by default 0.

maskarray_like, optional

If a mask is given, only those elements with a True value at the corresponding mask element are modified at each iteration.

Added in version 1.1.0.

border_valueint (cast to 0 or 1), optional

Value at the border in the output array.

Added in version 1.1.0.

brute_forceboolean, optional

Memory condition: if False, only the pixels whose value was changed in the last iteration are tracked as candidates to be updated in the current iteration; if true all pixels are considered as candidates for update, regardless of what happened in the previous iteration. False by default.

Added in version 1.1.0.

axestuple of int or None

The axes over which to apply the filter. If None, input is filtered along all axes. If an origin tuple is provided, its length must match the number of axes.

Returns:
binary_openingndarray of bools

Opening of the input by the structuring element.

See also

grey_opening, binary_closing, binary_erosion, binary_dilation
generate_binary_structure

Notes

Opening [1] is a mathematical morphology operation [2] that consists in the succession of an erosion and a dilation of the input with the same structuring element. Opening, therefore, removes objects smaller than the structuring element.

Together with closing (binary_closing), opening can be used for noise removal.

References

Examples

>>> from scipy import ndimage
>>> import numpy as np
>>> a = np.zeros((5,5), dtype=int)
>>> a[1:4, 1:4] = 1; a[4, 4] = 1
>>> a
array([[0, 0, 0, 0, 0],
       [0, 1, 1, 1, 0],
       [0, 1, 1, 1, 0],
       [0, 1, 1, 1, 0],
       [0, 0, 0, 0, 1]])
>>> # Opening removes small objects
>>> ndimage.binary_opening(a, structure=np.ones((3,3))).astype(int)
array([[0, 0, 0, 0, 0],
       [0, 1, 1, 1, 0],
       [0, 1, 1, 1, 0],
       [0, 1, 1, 1, 0],
       [0, 0, 0, 0, 0]])
>>> # Opening can also smooth corners
>>> ndimage.binary_opening(a).astype(int)
array([[0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0],
       [0, 1, 1, 1, 0],
       [0, 0, 1, 0, 0],
       [0, 0, 0, 0, 0]])
>>> # Opening is the dilation of the erosion of the input
>>> ndimage.binary_erosion(a).astype(int)
array([[0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0]])
>>> ndimage.binary_dilation(ndimage.binary_erosion(a)).astype(int)
array([[0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0],
       [0, 1, 1, 1, 0],
       [0, 0, 1, 0, 0],
       [0, 0, 0, 0, 0]])
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