[;j$ddlmZddlmZddZy))unique)entropyc<t|d\}}t||S)aCalculate the Shannon entropy of an image. The Shannon entropy is defined as S = -sum(pk * log(pk)), where pk are frequency/probability of pixels of value k. Parameters ---------- image : (M, N) ndarray Grayscale input image. base : float, optional The logarithmic base to use. Returns ------- entropy : float Notes ----- The returned value is measured in bits or shannon (Sh) for base=2, natural unit (nat) for base=np.e and hartley (Hart) for base=10. References ---------- .. [1] `https://en.wikipedia.org/wiki/Entropy_(information_theory) `_ .. [2] https://en.wiktionary.org/wiki/Shannon_entropy Examples -------- >>> from skimage import data >>> from skimage.measure import shannon_entropy >>> shannon_entropy(data.camera()) 7.231695011055706 T) return_counts)base)r scipy_entropy)imager_countss `G:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\skimage/measure/entropy.pyshannon_entropyr s"FuD1IAv d ++N))numpyr scipy.statsrrr rr rs0$,r