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