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import torch |
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import torch.nn.functional as F |
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from math import exp |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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def gaussian(window_size, sigma): |
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gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)]) |
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return gauss/gauss.sum() |
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def create_window(window_size, channel=1): |
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_1D_window = gaussian(window_size, 1.5).unsqueeze(1) |
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_2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0).to(device) |
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window = _2D_window.expand(channel, 1, window_size, window_size).contiguous() |
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return window |
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def create_window_3d(window_size, channel=1): |
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_1D_window = gaussian(window_size, 1.5).unsqueeze(1) |
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_2D_window = _1D_window.mm(_1D_window.t()) |
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_3D_window = _2D_window.unsqueeze(2) @ (_1D_window.t()) |
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window = _3D_window.expand(1, channel, window_size, window_size, window_size).contiguous().to(device) |
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return window |
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def ssim(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None): |
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if val_range is None: |
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if torch.max(img1) > 128: |
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max_val = 255 |
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else: |
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max_val = 1 |
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if torch.min(img1) < -0.5: |
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min_val = -1 |
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else: |
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min_val = 0 |
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L = max_val - min_val |
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else: |
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L = val_range |
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padd = 0 |
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(_, channel, height, width) = img1.size() |
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if window is None: |
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real_size = min(window_size, height, width) |
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window = create_window(real_size, channel=channel).to(img1.device) |
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mu1 = F.conv2d(F.pad(img1, (5, 5, 5, 5), mode='replicate'), window, padding=padd, groups=channel) |
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mu2 = F.conv2d(F.pad(img2, (5, 5, 5, 5), mode='replicate'), window, padding=padd, groups=channel) |
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mu1_sq = mu1.pow(2) |
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mu2_sq = mu2.pow(2) |
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mu1_mu2 = mu1 * mu2 |
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sigma1_sq = F.conv2d(F.pad(img1 * img1, (5, 5, 5, 5), 'replicate'), window, padding=padd, groups=channel) - mu1_sq |
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sigma2_sq = F.conv2d(F.pad(img2 * img2, (5, 5, 5, 5), 'replicate'), window, padding=padd, groups=channel) - mu2_sq |
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sigma12 = F.conv2d(F.pad(img1 * img2, (5, 5, 5, 5), 'replicate'), window, padding=padd, groups=channel) - mu1_mu2 |
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C1 = (0.01 * L) ** 2 |
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C2 = (0.03 * L) ** 2 |
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v1 = 2.0 * sigma12 + C2 |
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v2 = sigma1_sq + sigma2_sq + C2 |
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cs = torch.mean(v1 / v2) |
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ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2) |
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if size_average: |
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ret = ssim_map.mean() |
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else: |
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ret = ssim_map.mean(1).mean(1).mean(1) |
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if full: |
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return ret, cs |
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return ret |
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def calculate_ssim(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None): |
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if val_range is None: |
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if torch.max(img1) > 128: |
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max_val = 255 |
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else: |
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max_val = 1 |
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if torch.min(img1) < -0.5: |
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min_val = -1 |
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else: |
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min_val = 0 |
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L = max_val - min_val |
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else: |
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L = val_range |
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padd = 0 |
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(_, _, height, width) = img1.size() |
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if window is None: |
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real_size = min(window_size, height, width) |
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window = create_window_3d(real_size, channel=1).to(img1.device) |
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img1 = img1.unsqueeze(1) |
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img2 = img2.unsqueeze(1) |
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mu1 = F.conv3d(F.pad(img1, (5, 5, 5, 5, 5, 5), mode='replicate'), window, padding=padd, groups=1) |
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mu2 = F.conv3d(F.pad(img2, (5, 5, 5, 5, 5, 5), mode='replicate'), window, padding=padd, groups=1) |
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mu1_sq = mu1.pow(2) |
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mu2_sq = mu2.pow(2) |
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mu1_mu2 = mu1 * mu2 |
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sigma1_sq = F.conv3d(F.pad(img1 * img1, (5, 5, 5, 5, 5, 5), 'replicate'), window, padding=padd, groups=1) - mu1_sq |
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sigma2_sq = F.conv3d(F.pad(img2 * img2, (5, 5, 5, 5, 5, 5), 'replicate'), window, padding=padd, groups=1) - mu2_sq |
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sigma12 = F.conv3d(F.pad(img1 * img2, (5, 5, 5, 5, 5, 5), 'replicate'), window, padding=padd, groups=1) - mu1_mu2 |
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C1 = (0.01 * L) ** 2 |
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C2 = (0.03 * L) ** 2 |
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v1 = 2.0 * sigma12 + C2 |
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v2 = sigma1_sq + sigma2_sq + C2 |
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cs = torch.mean(v1 / v2) |
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ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2) |
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if size_average: |
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ret = ssim_map.mean() |
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else: |
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ret = ssim_map.mean(1).mean(1).mean(1) |
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if full: |
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return ret, cs |
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return ret.detach().cpu().numpy() |
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def calculate_psnr(img1, img2): |
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psnr = -10 * torch.log10(((img1 - img2) * (img1 - img2)).mean()) |
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return psnr.detach().cpu().numpy() |
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def calculate_ie(img1, img2): |
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ie = torch.abs(torch.round(img1 * 255.0) - torch.round(img2 * 255.0)).mean() |
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return ie.detach().cpu().numpy() |
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