skimage2.morphology.black_tophat#

skimage2.morphology.black_tophat(image, footprint=None, *, out=None, mode='ignore', cval=0.0)[source]#

Return black top hat of an image.

The black top hat of an image is defined as its morphological closing minus the original image. This operation returns the dark spots of the image that are smaller than the footprint. Note that dark spots in the original image are bright spots after the black top hat.

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’. See skimage2.morphology.closing(). 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 black top hat.

See also

skimage2.morphology.white_tophat

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().

References

Examples

>>> # Change dark peak to bright peak and subtract background
>>> import numpy as np
>>> from _skimage2.morphology import footprint_rectangle
>>> dark_on_gray = np.array([[7, 6, 6, 6, 7],
...                          [6, 5, 4, 5, 6],
...                          [6, 4, 0, 4, 6],
...                          [6, 5, 4, 5, 6],
...                          [7, 6, 6, 6, 7]], dtype=np.uint8)
>>> black_tophat(dark_on_gray, footprint_rectangle((3, 3)))
array([[0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0],
       [0, 1, 5, 1, 0],
       [0, 0, 1, 0, 0],
       [0, 0, 0, 0, 0]], dtype=uint8)