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#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact [email protected]
#
import torch
import torch.nn.functional as F
def mse(img1, img2):
return (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True)
def psnr(img1, img2):
mse = (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True)
return 20 * torch.log10(1.0 / torch.sqrt(mse))
def dilate(bin_img, ksize=5):
pad = (ksize - 1) // 2
bin_img = F.pad(bin_img, pad=[pad, pad, pad, pad], mode='reflect')
out = F.max_pool2d(bin_img, kernel_size=ksize, stride=1, padding=0)
return out
def erode(bin_img, ksize=5):
out = 1 - dilate(1 - bin_img, ksize)
return out
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