Source code for skan.pre

import numpy as np
from scipy import spatial, ndimage as ndi
from skimage import filters, restoration
from skimage.filters import threshold_sauvola, threshold_niblack

        'Gaussian': filters.gaussian,
        'TV': restoration.denoise_tv_bregman,

[docs]def hyperball(ndim, radius): """Return a binary morphological filter containing pixels within `radius`. Parameters ---------- ndim : int The number of dimensions of the filter. radius : int The radius of the filter. Returns ------- ball : array of bool, shape [2 * radius + 1,] * ndim The required structural element """ size = 2*radius + 1 center = [(radius,) * ndim] coords = np.mgrid[[ slice(None, size), ] * ndim].reshape(ndim, -1).T distances = np.ravel(spatial.distance_matrix(coords, center)) selector = distances <= radius ball = np.zeros((size,) * ndim, dtype=bool) ball.ravel()[selector] = True return ball
[docs]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'} Which method to use for smoothing. Choose from Gaussian smoothing and total variation denoising. Returns ------- thresholded : image of bool, same shape as `image` The thresholded image. References ---------- .. [1] """ 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) 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