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import cv2
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import numpy as np
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import math
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import numbers
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import torch
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from torch import nn
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from torch.nn import functional as F
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class GaussianSmoothing(nn.Module):
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"""
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Apply gaussian smoothing on a
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1d, 2d or 3d tensor. Filtering is performed seperately for each channel
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in the input using a depthwise convolution.
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Arguments:
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channels (int, sequence): Number of channels of the input tensors. Output will
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have this number of channels as well.
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kernel_size (int, sequence): Size of the gaussian kernel.
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sigma (float, sequence): Standard deviation of the gaussian kernel.
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dim (int, optional): The number of dimensions of the data.
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Default value is 2 (spatial).
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"""
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def __init__(self, channels, kernel_size, sigma=0.1, dim=2):
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super(GaussianSmoothing, self).__init__()
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self.kernel_size = kernel_size
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if isinstance(kernel_size, numbers.Number):
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kernel_size = [kernel_size] * dim
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if isinstance(sigma, numbers.Number):
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sigma = [sigma] * dim
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kernel = 1
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meshgrids = torch.meshgrid(
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[
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torch.arange(size, dtype=torch.float32)
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for size in kernel_size
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]
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)
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for size, std, mgrid in zip(kernel_size, sigma, meshgrids):
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mean = (size - 1) / 2
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kernel *= 1 / (std * math.sqrt(2 * math.pi)) * \
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torch.exp(-((mgrid - mean) / std) ** 2 / 2)
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kernel = kernel / torch.sum(kernel)
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kernel = kernel.view(1, 1, *kernel.size())
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kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1))
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self.register_buffer('weight', kernel)
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self.groups = channels
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if dim == 1:
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self.conv = F.conv1d
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elif dim == 2:
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self.conv = F.conv2d
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elif dim == 3:
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self.conv = F.conv3d
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else:
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raise RuntimeError(
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'Only 1, 2 and 3 dimensions are supported. Received {}.'.format(
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dim)
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)
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def forward(self, input):
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"""
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Apply gaussian filter to input.
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Arguments:
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input (torch.Tensor): Input to apply gaussian filter on.
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Returns:
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filtered (torch.Tensor): Filtered output.
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"""
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if self.training:
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return self.conv(input, weight=self.weight, groups=self.groups, padding=self.kernel_size//2)
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else:
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return input
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class GaussianNoise(nn.Module):
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def __init__(self, mean=0, std=0.1, clip=1):
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super(GaussianNoise, self).__init__()
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self.mean = mean
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self.std = std
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self.clip = clip
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def forward(self, x):
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if self.training:
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noise = x.data.new(x.size()).normal_(self.mean, self.std)
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return torch.clamp(x + noise, -self.clip, self.clip)
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else:
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return x
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if __name__ == "__main__":
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im = cv2.imread('E:\SRM\component\FF-F2F_0.png')
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im_ten = im/255*2-1
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im_ten = torch.from_numpy(im_ten).unsqueeze(0).permute(0, 3, 1, 2).float()
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blur = GaussianSmoothing(channels=3, kernel_size=7, sigma=0.8)
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noise = GaussianNoise()
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noise_im = torch.clamp(noise(im_ten), -1, 1)
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blur_im = blur(im_ten)
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print(blur_im.size())
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def t2im(t):
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t = (t+1)/2*255
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im = t.squeeze().cpu().numpy().transpose(1, 2, 0).astype(np.uint8)
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return im
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cv2.imshow('ori', im)
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cv2.imshow('blur', t2im(blur_im))
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cv2.imshow('noise', t2im(noise_im))
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cv2.waitKey()
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