mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-30 21:11:39 +08:00
Tony S Yu
756dfd5020
This change distinguishes it from the `bins` argument in numpy.histogram, which can accept both the number of bins or a sequence bin edges. Also, this name matches other function parameters in the scikit (e.g. `histograms` in io/_plugins/util.py).
90 lines
2.4 KiB
Python
90 lines
2.4 KiB
Python
import numpy as np
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__all__ = ['threshold_otsu']
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def threshold_otsu(image, nbins=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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nbins : 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 : float
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Threshold value.
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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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Examples
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--------
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>>> from skimage.data import camera
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>>> image = camera()
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>>> thresh = threshold_otsu(image)
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>>> binary = image > thresh
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"""
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hist, bin_centers = histogram(image, nbins)
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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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def histogram(image, nbins):
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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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nbins : 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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offset = 0
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if np.min(image) < 0:
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offset = np.min(image)
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hist = np.bincount(image.ravel() - offset)
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bin_centers = np.arange(len(hist)) + offset
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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=nbins)
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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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