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