entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False)

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 : 2-D array (integer or float)

Input image.

selem : 2-D array (integer or float)

The neighborhood expressed as a 2-D array of 1's and 0's.

out : 2-D array (integer or float), optional

If None, a new array is allocated.

mask : ndarray (integer 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 : int, optional

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 (float)

Output image.

Local entropy.

Examples

This example is valid syntax, but we were not able to check execution

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

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

skimage.filters.rank.generic.entropy

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