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