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import math |
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import numpy as np |
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import torch |
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import skimage.measure |
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import skimage.color |
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def calc_psnr(sr, hr, scale, rgb_range, benchmark=False): |
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if sr.size(-2) > hr.size(-2) or sr.size(-1) > hr.size(-1): |
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print("the dimention of sr image is not equal to hr's! ") |
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sr = sr[:,:,:hr.size(-2),:hr.size(-1)] |
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diff = (sr - hr).data.div(rgb_range) |
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if benchmark: |
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shave = scale |
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if diff.size(1) > 1: |
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convert = diff.new(1, 3, 1, 1) |
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convert[0, 0, 0, 0] = 65.738 |
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convert[0, 1, 0, 0] = 129.057 |
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convert[0, 2, 0, 0] = 25.064 |
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diff.mul_(convert).div_(256) |
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diff = diff.sum(dim=1, keepdim=True) |
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else: |
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shave = scale + 6 |
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valid = diff[:, :, shave:-shave, shave:-shave] |
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mse = valid.pow(2).mean() |
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return -10 * math.log10(mse) |
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import numpy as np |
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from scipy import signal |
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def matlab_style_gauss2D(shape=(3,3),sigma=0.5): |
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""" |
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2D gaussian mask - should give the same result as MATLAB's fspecial('gaussian',[shape],[sigma]) |
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Acknowledgement : https://stackoverflow.com/questions/17190649/how-to-obtain-a-gaussian-filter-in-python (Author@ali_m) |
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""" |
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m,n = [(ss-1.)/2. for ss in shape] |
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y,x = np.ogrid[-m:m+1,-n:n+1] |
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h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) ) |
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h[ h < np.finfo(h.dtype).eps*h.max() ] = 0 |
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sumh = h.sum() |
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if sumh != 0: |
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h /= sumh |
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return h |
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def calc_ssim(X, Y, scale, rgb_range, dataset=None, sigma=1.5, K1=0.01, K2=0.03, R=255): |
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''' |
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X : y channel (i.e., luminance) of transformed YCbCr space of X |
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Y : y channel (i.e., luminance) of transformed YCbCr space of Y |
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Please follow the setting of psnr_ssim.m in EDSR (Enhanced Deep Residual Networks for Single Image Super-Resolution CVPRW2017). |
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Official Link : https://github.com/LimBee/NTIRE2017/tree/db34606c2844e89317aac8728a2de562ef1f8aba |
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The authors of EDSR use MATLAB's ssim as the evaluation tool, |
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thus this function is the same as ssim.m in MATLAB with C(3) == C(2)/2. |
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''' |
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gaussian_filter = matlab_style_gauss2D((11, 11), sigma) |
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if True: |
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shave = scale |
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if X.size(1) > 1: |
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gray_coeffs = [65.738, 129.057, 25.064] |
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convert = X.new_tensor(gray_coeffs).view(1, 3, 1, 1) / 256 |
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X = X.mul(convert).sum(dim=1) |
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Y = Y.mul(convert).sum(dim=1) |
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else: |
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shave = scale + 6 |
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X = X[..., shave:-shave, shave:-shave].squeeze().cpu().numpy().astype(np.float64) |
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Y = Y[..., shave:-shave, shave:-shave].squeeze().cpu().numpy().astype(np.float64) |
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window = gaussian_filter |
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ux = signal.convolve2d(X, window, mode='same', boundary='symm') |
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uy = signal.convolve2d(Y, window, mode='same', boundary='symm') |
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uxx = signal.convolve2d(X*X, window, mode='same', boundary='symm') |
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uyy = signal.convolve2d(Y*Y, window, mode='same', boundary='symm') |
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uxy = signal.convolve2d(X*Y, window, mode='same', boundary='symm') |
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vx = uxx - ux * ux |
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vy = uyy - uy * uy |
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vxy = uxy - ux * uy |
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C1 = (K1 * R) ** 2 |
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C2 = (K2 * R) ** 2 |
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A1, A2, B1, B2 = ((2 * ux * uy + C1, 2 * vxy + C2, ux ** 2 + uy ** 2 + C1, vx + vy + C2)) |
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D = B1 * B2 |
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S = (A1 * A2) / D |
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mssim = S.mean() |
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return mssim |
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