mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-04 19:49:32 +08:00
Tony S Yu
106 lines
2.8 KiB
Python
106 lines
2.8 KiB
Python
import numpy as np
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__all__ = ['otsu_threshold', 'binarize']
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def otsu_threshold(image, bins=256):
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"""Return threshold value based on Otsu's method.
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Parameters
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----------
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image : array
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Input image.
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bins : int
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Number of bins used to calculate histogram. This value is ignored for
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integer arrays.
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Returns
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-------
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threshold : numeric
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Threshold value. int or float depending on input image.
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References
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----------
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.. [1] Wikipedia, http://en.wikipedia.org/wiki/Otsu's_Method
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"""
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hist, bin_centers = histogram(image, bins)
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hist = hist.astype(float)
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# class probabilities for all possible thresholds
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weight1 = np.cumsum(hist)
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weight2 = np.cumsum(hist[::-1])[::-1]
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# class means for all possible thresholds
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mean1 = np.cumsum(hist * bin_centers) / weight1
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mean2 = (np.cumsum((hist * bin_centers)[::-1]) / weight2[::-1])[::-1]
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# Clip ends to align class 1 and class 2 variables:
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# The last value of `weight1`/`mean1` should pair with zero values in
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# `weight2`/`mean2`, which do not exist.
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variance12 = weight1[:-1] * weight2[1:] * (mean1[:-1] - mean2[1:])**2
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idx = np.argmax(variance12)
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threshold = bin_centers[:-1][idx]
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return threshold
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_threshold_funcs = {'otsu': otsu_threshold}
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def binarize(image, method='otsu'):
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"""Return binary image using an automatic thresholding method.
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Parameters
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----------
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image : array
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Input array.
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method : {'otsu'}
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Method used to calculate threshold value. Currently, only Otsu's method
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is implemented.
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Returns
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-------
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out : array
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Thresholded image.
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"""
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get_threshold = _threshold_funcs[method]
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threshold = get_threshold(image)
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return image > threshold
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def histogram(image, bins):
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"""Return histogram of image.
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Unlike `numpy.histogram`, this function returns the centers of bins and
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does not rebin integer arrays.
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Parameters
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----------
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image : array
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Input image.
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bins : int
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Number of bins used to calculate histogram. This value is ignored for
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integer arrays.
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Returns
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-------
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hist : array
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The values of the histogram.
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bin_centers : array
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The values at the center of the bins.
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"""
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if np.issubdtype(image.dtype, np.integer):
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if np.min(image) < 0:
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msg = "Images with negative values not allowed"
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raise NotImplementedError(msg)
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hist = np.bincount(image.flat)
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bin_centers = np.arange(len(hist))
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# clip histogram to return only non-zero bins
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idx = np.nonzero(hist)[0][0]
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return hist[idx:], bin_centers[idx:]
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else:
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hist, bin_edges = np.histogram(image, bins=bins)
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bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2.
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return hist, bin_centers
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