skimage2.filters.rank.entropy#

skimage2.filters.rank.entropy(image, footprint, out=None, mask=None, shift_x=0, shift_y=0, shift_z=0)[source]#

Local entropy.

The entropy is computed using base 2 logarithm i.e. the filter returns the minimum number of bits needed to encode the local gray level distribution.

Parameters:

imagendarray of shape ([P,] M, N) and dtype (uint8 or uint16): Input image.
footprintndarray: The neighborhood expressed as an ndarray of 1’s and 0’s.
outndarray of shape ([P,] M, N), same dtype as input image: If None, a new array is allocated.
maskndarray of dtype (int or float), optional: Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
shift_x, shift_y, shift_zint: Offset added to the footprint center point. Shift is bounded to the footprint sizes (center must be inside the given footprint).

Returns:

outndarray of shape ([P,] M, N) and dtype float: Output image.

References

[1]

https://en.wikipedia.org/wiki/Entropy_(information_theory)

Examples

>>> from _skimage2 import data
>>> from _skimage2.filters.rank import entropy
>>> from _skimage2.morphology import disk, ball
>>> import numpy as np
>>> img = data.camera()
>>> rng = np.random.default_rng()
>>> volume = rng.integers(0, 255, size=(10,10,10), dtype=np.uint8)
>>> ent = entropy(img, disk(5))
>>> ent_vol = entropy(volume, ball(5))