mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 19:33:19 +08:00
Tony S Yu
137 lines
4.8 KiB
Python
137 lines
4.8 KiB
Python
__all__ = ['threshold_otsu', 'threshold_adaptive']
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import numpy as np
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import scipy.ndimage
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from skimage.exposure import histogram
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def threshold_adaptive(image, block_size, method='gaussian', offset=0,
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mode='reflect', param=None):
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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. Alternatively the threshold can be determined dynamically by a
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a given function using the 'generic' method.
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Parameters
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----------
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image : (N, M) 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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method : {'generic', 'gaussian', 'mean', 'median'}, optional
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Method used to determine adaptive threshold for local neighbourhood in
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weighted mean image.
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* 'generic': use custom function (see `param` parameter)
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* 'gaussian': apply gaussian filter (see `param` parameter for custom
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sigma value)
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* 'mean': apply arithmetic mean filter
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* 'median' apply median rank filter
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By default the 'gaussian' method is used.
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offset : float, optional
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Constant subtracted from weighted mean of neighborhood to calculate
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the local threshold value. Default offset is 0.
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mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
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The mode parameter determines how the array borders are handled, where
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cval is the value when mode is equal to 'constant'.
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Default is 'reflect'.
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param : {int, function}, optional
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Either specify sigma for 'gaussian' method or function object for
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'generic' method. This functions takes the flat array of local
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neighbourhood as a single argument and returns the calculated threshold
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for the centre pixel.
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Returns
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-------
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threshold : (N, M) 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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Examples
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--------
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>>> from skimage.data import camera
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>>> image = camera()
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>>> binary_image1 = threshold_adaptive(image, 15, 'mean')
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>>> func = lambda arr: arr.mean()
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>>> binary_image2 = threshold_adaptive(image, 15, 'generic', param=func)
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"""
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thresh_image = np.zeros(image.shape, 'double')
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if method == 'generic':
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scipy.ndimage.generic_filter(image, param, block_size,
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output=thresh_image, mode=mode)
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elif method == 'gaussian':
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if param is None:
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# automatically determine sigma which covers > 99% of distribution
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sigma = (block_size - 1) / 6.0
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else:
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sigma = param
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scipy.ndimage.gaussian_filter(image, sigma, output=thresh_image,
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mode=mode)
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elif method == 'mean':
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mask = 1. / block_size * np.ones((block_size,))
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# separation of filters to speedup convolution
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scipy.ndimage.convolve1d(image, mask, axis=0, output=thresh_image,
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mode=mode)
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scipy.ndimage.convolve1d(thresh_image, mask, axis=1,
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output=thresh_image, mode=mode)
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elif method == 'median':
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scipy.ndimage.median_filter(image, block_size, output=thresh_image,
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mode=mode)
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return image > (thresh_image - offset)
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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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