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