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