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| import cv2 | |
| import numpy as np | |
| import torch | |
| from torch.nn import functional as F | |
| def filter2D(img, kernel): | |
| """PyTorch version of cv2.filter2D | |
| Args: | |
| img (Tensor): (b, c, h, w) | |
| kernel (Tensor): (b, k, k) | |
| """ | |
| k = kernel.size(-1) | |
| b, c, h, w = img.size() | |
| if k % 2 == 1: | |
| img = F.pad(img, (k // 2, k // 2, k // 2, k // 2), mode='reflect') | |
| else: | |
| raise ValueError('Wrong kernel size') | |
| ph, pw = img.size()[-2:] | |
| if kernel.size(0) == 1: | |
| # apply the same kernel to all batch images | |
| img = img.view(b * c, 1, ph, pw) | |
| kernel = kernel.view(1, 1, k, k) | |
| return F.conv2d(img, kernel, padding=0).view(b, c, h, w) | |
| else: | |
| img = img.view(1, b * c, ph, pw) | |
| kernel = kernel.view(b, 1, k, k).repeat(1, c, 1, 1).view(b * c, 1, k, k) | |
| return F.conv2d(img, kernel, groups=b * c).view(b, c, h, w) | |
| def usm_sharp(img, weight=0.5, radius=50, threshold=10): | |
| """USM sharpening. | |
| Input image: I; Blurry image: B. | |
| 1. sharp = I + weight * (I - B) | |
| 2. Mask = 1 if abs(I - B) > threshold, else: 0 | |
| 3. Blur mask: | |
| 4. Out = Mask * sharp + (1 - Mask) * I | |
| Args: | |
| img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. | |
| weight (float): Sharp weight. Default: 1. | |
| radius (float): Kernel size of Gaussian blur. Default: 50. | |
| threshold (int): | |
| """ | |
| if radius % 2 == 0: | |
| radius += 1 | |
| blur = cv2.GaussianBlur(img, (radius, radius), 0) | |
| residual = img - blur | |
| mask = np.abs(residual) * 255 > threshold | |
| mask = mask.astype('float32') | |
| soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) | |
| sharp = img + weight * residual | |
| sharp = np.clip(sharp, 0, 1) | |
| return soft_mask * sharp + (1 - soft_mask) * img | |
| 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 | |