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import numpy as np |
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import cv2 |
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from scipy import ndimage as ndi |
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from skimage import filters |
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class XDoG: |
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def __init__(self, |
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gamma=0.98, |
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phi=200, |
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eps=-0.1, |
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sigma=0.8, |
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k=10, |
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binarize: bool = True): |
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""" |
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XDoG algorithm. |
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Args: |
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gamma: Control the size of the Gaussian filter |
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phi: Control changes in edge strength |
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eps: Threshold for controlling edge strength |
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sigma: The standard deviation of the Gaussian filter controls the degree of smoothness |
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k: Control the size ratio of Gaussian filter, (k=10 or k=1.6) |
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binarize(bool): Whether to binarize the output |
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""" |
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super(XDoG, self).__init__() |
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self.gamma = gamma |
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assert 0 <= self.gamma <= 1 |
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self.phi = phi |
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assert 0 <= self.phi <= 1500 |
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self.eps = eps |
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assert -1 <= self.eps <= 1 |
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self.sigma = sigma |
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assert 0.1 <= self.sigma <= 10 |
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self.k = k |
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assert 1 <= self.k <= 100 |
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self.binarize = binarize |
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def __call__(self, img): |
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if len(img.shape) == 3 and img.shape[2] == 3: |
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
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elif len(img.shape) == 3 and img.shape[2] == 4: |
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img = cv2.cvtColor(img, cv2.COLOR_BGRA2GRAY) |
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if np.isnan(img).any(): |
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img[np.isnan(img)] = np.mean(img[~np.isnan(img)]) |
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imf1 = ndi.gaussian_filter(img, self.sigma) |
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imf2 = ndi.gaussian_filter(img, self.sigma * self.k) |
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imdiff = imf1 - self.gamma * imf2 |
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imdiff = (imdiff < self.eps) * 1.0 + (imdiff >= self.eps) * (1.0 + np.tanh(self.phi * imdiff)) |
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imdiff -= imdiff.min() |
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imdiff /= imdiff.max() |
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if self.binarize: |
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th = filters.threshold_otsu(imdiff) |
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imdiff = (imdiff >= th).astype('float32') |
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return imdiff |
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