`skimage.filters.rank.generic:entropy`

source: /dev/scikit-image/src/skimage/filters/rank/generic.py :1483

Signature

   def     entropy (    image    ,    footprint    ,    out    =  None   ,    mask    =  None   ,    shift_x    =  0   ,    shift_y    =  0   ,    shift_z    =  0     )

Summary

Local entropy.

Extended Summary

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

image : ndarray of shape ([P,] M, N) and dtype (uint8 or uint16): Input image.
footprint : ndarray: The neighborhood expressed as an ndarray of 1's and 0's.
out : ndarray of shape ([P,] M, N), same dtype as input `image`: If None, a new array is allocated.
mask : ndarray 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_z : int: Offset added to the footprint center point. Shift is bounded to the footprint sizes (center must be inside the given footprint).

Returns

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

Examples

from skimage import data
from skimage.filters.rank import entropy
from skimage.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))

✓

Aliases

skimage.filters.rank.entropy