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