def threshold(image, *, sigma=0., radius=0, offset=0.,
method='sauvola', smooth_method='Gaussian'):
"""Use scikit-image filters to "intelligently" threshold an image.
Parameters
----------
image : array, shape (M, N, ...[, 3])
Input image, conformant with scikit-image data type
specification [1]_.
sigma : float, optional
If positive, use Gaussian filtering to smooth the image before
thresholding.
radius : int, optional
If given, use local median thresholding instead of global.
offset : float, optional
If given, reduce the threshold by this amount. Higher values
result in fewer pixels above the threshold.
method: {'sauvola', 'niblack', 'median'}
Which method to use for thresholding. Sauvola is 100x faster, but
median might be more accurate.
smooth_method: {'Gaussian', 'TV', 'NL'}
Which method to use for smoothing. Choose from Gaussian smoothing,
total variation denoising, and non-local means denoising.
Returns
-------
thresholded : image of bool, same shape as `image`
The thresholded image.
References
----------
.. [1] http://scikit-image.org/docs/dev/user_guide/data_types.html
"""
if sigma > 0:
if smooth_method.lower() == 'gaussian':
image = filters.gaussian(image, sigma=sigma)
elif smooth_method.lower() == 'tv':
image = restoration.denoise_tv_bregman(image, weight=sigma)
elif smooth_method.lower() == 'nl':
image = restoration.denoise_nl_means(image,
patch_size=round(2 * sigma))
if radius == 0:
t = filters.threshold_otsu(image) + offset
else:
if method == 'median':
footprint = hyperball(image.ndim, radius=radius)
t = ndi.median_filter(image, footprint=footprint) + offset
elif method == 'sauvola':
w = 2 * radius + 1
t = threshold_sauvola(image, window_size=w, k=offset)
elif method == 'niblack':
w = 2 * radius + 1
t = threshold_niblack(image, window_size=w, k=offset)
else:
raise ValueError('Unknown method %s. Valid methods are median,'
'niblack, and sauvola.' % method)
thresholded = image > t
return thresholded
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