skimage2.morphology.binary_closing

skimage2.morphology.binary_closing(image, footprint=None, out=None, *, mode='ignore')

Return fast binary morphological closing of an image.

Deprecated since version 0.26: binary_closing is deprecated since version 0.26 and will be removed in version 0.28. Use skimage.morphology.closing instead.

This function returns the same result as grayscale closing but performs faster for binary images.

The morphological closing on 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

Binary 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 (connectivity=1). The footprint can also be provided as a sequence of smaller footprints as described in the notes below.

outndarray of bool, optional

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

modestr, optional

The mode parameter determines how the array borders are handled. Valid modes are: ‘max’, ‘min’, ‘ignore’. If ‘ignore’, pixels outside the image domain are assumed to be True for the erosion and False for the dilation, which causes them to not influence the result. Default is ‘ignore’.

Added in version 0.23: mode was added in 0.23.

Returns:

closingndarray of bool

The result of the morphological closing.

See also

skimage.morphology.isotropic_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 builtin footprints such as skimage.morphology.disk() provide an option to automatically generate a footprint sequence of this type.