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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 |