forked from 170010011/fr
42 lines
1.1 KiB
Python
42 lines
1.1 KiB
Python
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from numpy import unique
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from scipy.stats import entropy as scipy_entropy
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def shannon_entropy(image, base=2):
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"""Calculate the Shannon entropy of an image.
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The Shannon entropy is defined as S = -sum(pk * log(pk)),
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where pk are frequency/probability of pixels of value k.
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Parameters
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----------
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image : (N, M) ndarray
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Grayscale input image.
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base : float, optional
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The logarithmic base to use.
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Returns
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-------
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entropy : float
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Notes
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-----
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The returned value is measured in bits or shannon (Sh) for base=2, natural
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unit (nat) for base=np.e and hartley (Hart) for base=10.
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References
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----------
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.. [1] `https://en.wikipedia.org/wiki/Entropy_(information_theory) <https://en.wikipedia.org/wiki/Entropy_(information_theory)>`_
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.. [2] https://en.wiktionary.org/wiki/Shannon_entropy
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Examples
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--------
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>>> from skimage import data
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>>> from skimage.measure import shannon_entropy
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>>> shannon_entropy(data.camera())
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7.231695011055706
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"""
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_, counts = unique(image, return_counts=True)
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return scipy_entropy(counts, base=base)
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