/usr/share/pyshared/dipy/segment/threshold.py is in python-dipy 0.7.1-2.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | import numpy as np
def otsu(image, nbins=256):
"""
Return threshold value based on Otsu's method.
Copied from scikit-image to remove dependency.
Parameters
----------
image : array
Input image.
nbins : int
Number of bins used to calculate histogram. This value is ignored for
integer arrays.
Returns
-------
threshold : float
Threshold value.
"""
hist, bin_centers = np.histogram(image, nbins)
hist = hist.astype(np.float)
# class probabilities for all possible thresholds
weight1 = np.cumsum(hist)
weight2 = np.cumsum(hist[::-1])[::-1]
# class means for all possible thresholds
mean1 = np.cumsum(hist * bin_centers[1:]) / weight1
mean2 = (np.cumsum((hist * bin_centers[1:])[::-1]) / weight2[::-1])[::-1]
# Clip ends to align class 1 and class 2 variables:
# The last value of `weight1`/`mean1` should pair with zero values in
# `weight2`/`mean2`, which do not exist.
variance12 = weight1[:-1] * weight2[1:] * (mean1[:-1] - mean2[1:])**2
idx = np.argmax(variance12)
threshold = bin_centers[:-1][idx]
return threshold
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