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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 george.drettakis@inria.fr
#
from math import exp
import torch
import torch.nn.functional as F
from torch.autograd import Variable
def l1_loss(network_output, gt):
return torch.abs((network_output - gt)).mean()
def l2_loss(network_output, gt):
return ((network_output - gt) ** 2).mean()
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
return gauss / gauss.sum()
def create_window(window_size, channel):
_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
_2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
return window
def ssim(img1, img2, window_size=11, size_average=True):
channel = img1.size(-3)
window = create_window(window_size, channel)
if img1.is_cuda:
window = window.cuda(img1.get_device())
window = window.type_as(img1)
return _ssim(img1, img2, window, window_size, channel, size_average)
def _ssim(img1, img2, window, window_size, channel, size_average=True):
mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
mu1_sq = mu1.pow(2)
mu2_sq = mu2.pow(2)
mu1_mu2 = mu1 * mu2
sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
C1 = 0.01 ** 2
C2 = 0.03 ** 2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
if size_average:
return ssim_map.mean()
else:
return ssim_map.mean(1).mean(1).mean(1)
import numpy as np
import cv2
def image2canny(image, thres1, thres2, isEdge1=True):
""" image: (H, W, 3)"""
canny_mask = torch.from_numpy(cv2.Canny((image.detach().cpu().numpy()*255.).astype(np.uint8), thres1, thres2)/255.)
if not isEdge1:
canny_mask = 1. - canny_mask
return canny_mask.float()
with torch.no_grad():
kernelsize=3
conv = torch.nn.Conv2d(1, 1, kernel_size=kernelsize, padding=(kernelsize//2))
kernel = torch.tensor([[0.,1.,0.],[1.,0.,1.],[0.,1.,0.]]).reshape(1,1,kernelsize,kernelsize)
conv.weight.data = kernel #torch.ones((1,1,kernelsize,kernelsize))
conv.bias.data = torch.tensor([0.])
conv.requires_grad_(False)
conv = conv.cuda()
def nearMean_map(array, mask, kernelsize=3):
""" array: (H,W) / mask: (H,W) """
cnt_map = torch.ones_like(array)
nearMean_map = conv((array * mask)[None,None])
cnt_map = conv((cnt_map * mask)[None,None])
nearMean_map = (nearMean_map / (cnt_map+1e-8)).squeeze()
return nearMean_map |