import numpy as np import torch from torchvision import transforms import torch.nn.functional as F from torch.autograd.variable import Variable NORMALIZE_IMAGENET = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") image_mean = torch.Tensor(NORMALIZE_IMAGENET.mean).view(-1, 1, 1).to(device) image_std = torch.Tensor(NORMALIZE_IMAGENET.std).view(-1, 1, 1).to(device) def normalize_img(x): return (x.to(device) - image_mean) / image_std def unnormalize_img(x): return (x.to(device) * image_std) + image_mean def round_pixel(x): x_pixel = 255 * unnormalize_img(x) y = torch.round(x_pixel).clamp(0, 255) y = normalize_img(y/255.0) return y def project_linf(x, y, radius): """ Clamp x-y so that Linf(x,y)<=radius """ delta = x - y delta = 255 * (delta * image_std) delta = torch.clamp(delta, -radius, radius) delta = (delta / 255.0) / image_std return y + delta def psnr_clip(x, y, target_psnr): """ Clip x-y so that PSNR(x,y)=target_psnr """ delta = x - y delta = 255 * (delta * image_std) psnr = 20*np.log10(255) - 10*torch.log10(torch.mean(delta**2)) if psnr<target_psnr: delta = (torch.sqrt(10**((psnr-target_psnr)/10))) * delta psnr = 20*np.log10(255) - 10*torch.log10(torch.mean(delta**2)) delta = (delta / 255.0) / image_std return y + delta def ssim_heatmap(img1, img2, window_size): """ Compute the SSIM heatmap between 2 images """ _1D_window = torch.Tensor( [np.exp(-(x - window_size//2)**2/float(2*1.5**2)) for x in range(window_size)] ).to(device, non_blocking=True) _1D_window = (_1D_window/_1D_window.sum()).unsqueeze(1) _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) window = Variable(_2D_window.expand(3, 1, window_size, window_size).contiguous()) mu1 = F.conv2d(img1, window, padding = window_size//2, groups = 3) mu2 = F.conv2d(img2, window, padding = window_size//2, groups = 3) 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 = 3) - mu1_sq sigma2_sq = F.conv2d(img2*img2, window, padding = window_size//2, groups = 3) - mu2_sq sigma12 = F.conv2d(img1*img2, window, padding = window_size//2, groups = 3) - 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)) return ssim_map def ssim_attenuation(x, y): """ attenuate x-y using SSIM heatmap """ delta = x - y ssim_map = ssim_heatmap(x, y, window_size=17) # 1xCxHxW ssim_map = torch.sum(ssim_map, dim=1, keepdim=True) ssim_map = torch.clamp_min(ssim_map,0) # min_v = torch.min(ssim_map) # range_v = torch.max(ssim_map) - min_v # if range_v < 1e-10: # return y + delta # ssim_map = (ssim_map - min_v) / range_v delta = delta*ssim_map return y + delta