mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-04 20:09:02 +08:00
Johannes Schönberger
90 lines
2.7 KiB
Python
90 lines
2.7 KiB
Python
import numpy as np
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from skimage.exposure import histogram
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from ._thresholding import _threshold_adaptive
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__all__ = ['threshold_otsu', 'threshold_adaptive']
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def threshold_adaptive(image, block_size, offset, method='gaussian'):
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"""Applies an adaptive threshold to an array.
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Also known as local or dynamic thresholding where the threshold value is the
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weighted mean for the local neighborhood of a pixel subtracted by a
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constant.
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Parameters
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----------
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image : NxM ndarray
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Input image.
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block_size : int
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uneven size of pixel neighborhood which is used to calculate the
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threshold value (e.g. 3, 5, 7, ..., 21, ...)
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offset : float
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constant subtracted from weighted mean of neighborhood to calculate
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the local threshold value
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method : string, optional
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thresholding type which must be one of 'gaussian', 'mean' or 'median'.
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By default the 'gaussian' method is used.
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Returns
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-------
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threshold : NxM ndarray
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thresholded binary image
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References
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----------
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http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations
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.html?highlight=threshold#adaptivethreshold
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"""
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# not using img_as_float because offset parameter wouldn't work
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image = image.astype('double')
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return _threshold_adaptive(image, block_size, offset, method)
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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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