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evaluate.py
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evaluate.py
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# 2020.04.02
# @yifan
import numpy as np
from skimage.metrics import mean_squared_error
from core.util.bd_rate import BD_PSNR, BD_RATE
from core.util.ssim import structural_similarity, MultiScaleSSIM
def MSE(ref, X):
if len(X.shape) == 4:
mse = []
for i in range(X.shape[0]):
mse.append(mean_squared_error(ref[i], X[i]))
return np.mean(mse)
else:
return mean_squared_error(ref, X)
def PSNR(ref, X, max_val=255):
if len(X.shape) == 4:
psnr = []
for i in range(X.shape[0]):
psnr.append(20*np.log10(max_val/np.sqrt(mean_squared_error(ref[i], X[i]))))
return np.mean(psnr)
else:
return 20*np.log10(max_val/np.sqrt(mean_squared_error(XX, X)))
def BD(ref_R1, ref_PSNR1, R2, PSNR2):
print('BD-PSNR: ', BD_PSNR(ref_R1, ref_PSNR1, R2, PSNR2))
print('BD-RATE: ', BD_RATE(ref_R1, ref_PSNR1, R2, PSNR2))
def SSIM(ref, X, multichannel=True):
if len(X.shape) == 4:
ssim = []
for i in range(X.shape[0]):
ssim.append(structural_similarity(ref[i], X[i], multichannel=multichannel))
return np.mean(ssim)
return structural_similarity(ref, X, multichannel=multichannel)
def MS_SSIM(ref, X, max_val=255):
return MultiScaleSSIM(ref, X, max_val=max_val)