| import torch |
| import torch.nn.functional as F |
| from math import exp |
| import numpy as np |
| |
| 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=1): |
| _1D_window = gaussian(window_size, 1.5).unsqueeze(1) |
| _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) |
| window = _2D_window.expand(channel, 1, window_size, window_size).contiguous() |
| return window |
|
|
| def SSIM(img1, img2, window_size=11, window=None, size_average=True, full=False): |
| img1 = (img1 * 0.5 + 0.5) * 255 |
| img2 = (img2 * 0.5 + 0.5) * 255 |
| min_val = 0 |
| max_val = 255 |
| L = max_val - min_val |
| img2 = torch.clamp(img2, 0.0, 255.0) |
| |
| padd = 0 |
| (_, channel, height, width) = img1.size() |
| if window is None: |
| real_size = min(window_size, height, width) |
| window = create_window(real_size, channel=channel).to(img1.device) |
| |
| mu1 = F.conv2d(img1, window, padding=padd, groups=channel) |
| mu2 = F.conv2d(img2, window, padding=padd, groups=channel) |
| |
| mu1_sq = mu1.pow(2) |
| mu2_sq = mu2.pow(2) |
| mu1_mu2 = mu1 * mu2 |
| |
| sigma1_sq = F.conv2d(img1 * img1, window, padding=padd, groups=channel) - mu1_sq |
| sigma2_sq = F.conv2d(img2 * img2, window, padding=padd, groups=channel) - mu2_sq |
| sigma12 = F.conv2d(img1 * img2, window, padding=padd, groups=channel) - mu1_mu2 |
| |
| C1 = (0.01 * L) ** 2 |
| C2 = (0.03 * L) ** 2 |
| |
| v1 = 2.0 * sigma12 + C2 |
| v2 = sigma1_sq + sigma2_sq + C2 |
| cs = torch.mean(v1 / v2) |
| |
| ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2) |
| |
| if size_average: |
| ret = ssim_map.mean() |
| else: |
| ret = ssim_map.mean(1).mean(1).mean(1) |
| |
| if full: |
| return ret, cs |
| return ret |
| |
| def tf_log10(x): |
| numerator = torch.log(x) |
| denominator = torch.log(torch.tensor(10.0)) |
| return numerator / denominator |
|
|
| def PSNR(img1, img2): |
| img1 = (img1 * 0.5 + 0.5) * 255 |
| img2 = (img2 * 0.5 + 0.5) * 255 |
| max_pixel = 255.0 |
| img2 = torch.clamp(img2, 0.0, 255.0) |
| return 10.0 * tf_log10((max_pixel ** 2) / (torch.mean(torch.pow(img2 - img1, 2)))) |
|
|
