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import cv2 |
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import numpy as np |
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import torch |
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from .common import filter2D |
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class USMSharp(torch.nn.Module): |
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def __init__(self, radius=50, sigma=0): |
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super(USMSharp, self).__init__() |
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if radius % 2 == 0: |
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radius += 1 |
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self.radius = radius |
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kernel = cv2.getGaussianKernel(radius, sigma) |
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kernel = torch.FloatTensor(np.dot(kernel, kernel.transpose())).unsqueeze_(0) |
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self.register_buffer('kernel', kernel) |
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def forward(self, img, weight=0.5, threshold=10): |
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blur = filter2D(img, self.kernel) |
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residual = img - blur |
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mask = torch.abs(residual) * 255 > threshold |
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mask = mask.float() |
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soft_mask = filter2D(mask, self.kernel) |
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sharp = img + weight * residual |
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sharp = torch.clip(sharp, 0, 1) |
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return soft_mask * sharp + (1 - soft_mask) * img |
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