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52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
import torch
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from math import exp
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import torch.nn.functional as F
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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)
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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 ssim(img1, img2, val_range, window_size=11, window=None, size_average=True, full=False):
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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(img1, window, padding=padd, groups=channel)
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mu2 = F.conv2d(img2, 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(img1 * img1, window, padding=padd, groups=channel) - mu1_sq
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sigma2_sq = F.conv2d(img2 * img2, window, padding=padd, groups=channel) - mu2_sq
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sigma12 = F.conv2d(img1 * img2, 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) # contrast sensitivity
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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 |