skimage2.morphology.closing

skimage2.morphology.closing(image, footprint=None, *, out=None, mode='ignore', cval=0.0)

Return grayscale morphological closing of an image.

The morphological closing of an image is defined as a dilation followed by an erosion. Closing can remove small dark spots (i.e., “pepper”) and connect small bright cracks. This tends to “close” up (dark) gaps between (bright) features.

Parameters:

imagendarray

Input image.

footprintndarray or tuple, optional

The neighborhood expressed as a 2-D array of 1’s and 0’s. If None, use a cross-shaped footprint (so-called 1-connectivity). The footprint can also be provided as a sequence of smaller footprints as described in the notes below. See _Notes_ for more.

outndarray, optional

The array to store the result of the morphology. If None, a new array is allocated.

modestr, optional

The mode parameter determines how the array borders are handled. Valid modes are: ‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’, ‘max’, ‘min’, or ‘ignore’. If ‘ignore’, pixels outside the image domain are assumed to be the maximum for the image’s dtype in the erosion, and minimum in the dilation, which causes them to not influence the result. Default is ‘ignore’.

cvalscalar, optional

Value to fill past edges of input if mode is ‘constant’. Default is 0.0.

Returns:

outndarray, same shape and dtype as image

The result of the morphological closing.

Notes

The footprint can also be a provided as a sequence of 2-tuples where the first element of each 2-tuple is a footprint ndarray and the second element is an integer describing the number of times it should be iterated. For example footprint=[(np.ones((9, 1)), 1), (np.ones((1, 9)), 1)] would apply a 9x1 footprint followed by a 1x9 footprint resulting in a net effect that is the same as footprint=np.ones((9, 9)), but with lower computational cost. Most of the built-in footprints such as skimage2.morphology.disk() provide an option to automatically generate a footprint sequence of this type. Refer to the example Decompose flat footprints (structuring elements) for more insights.

If footprint contains even-sized dimensions, they are padded with zeros to an odd size at the front (at index 0) with pad_footprint().

Examples

>>> # Close a gap between two bright lines
>>> import numpy as np
>>> from _skimage2.morphology import footprint_rectangle
>>> broken_line = np.array([[0, 0, 0, 0, 0],
...                         [0, 0, 0, 0, 0],
...                         [1, 1, 0, 1, 1],
...                         [0, 0, 0, 0, 0],
...                         [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> closing(broken_line, footprint_rectangle((3, 3)))
array([[0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [1, 1, 1, 1, 1],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0]], dtype=uint8)