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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) # contrast sensitivity
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))))
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