entropy

entropy

skimage.filters.rank.entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [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 greylevel distribution.

Parameters:

image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1’s and 0’s. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray 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 : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns:

out : ndarray (double) Output image.

References

[R215]

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

Examples

>>> from skimage import data
>>> from skimage.filters.rank import entropy
>>> from skimage.morphology import disk
>>> img = data.camera()
>>> ent = entropy(img, disk(5))