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| import numpy as np
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| import os
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| import cv2
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| import math
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| from skimage import metrics
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| from sklearn.metrics import mean_absolute_error
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| def MAE(img1, img2):
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| mae_0=mean_absolute_error(img1[:,:,0], img2[:,:,0],
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| multioutput='uniform_average')
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| mae_1=mean_absolute_error(img1[:,:,1], img2[:,:,1],
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| multioutput='uniform_average')
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| mae_2=mean_absolute_error(img1[:,:,2], img2[:,:,2],
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| multioutput='uniform_average')
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| return np.mean([mae_0,mae_1,mae_2])
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| def PSNR(img1, img2):
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| mse_ = np.mean( (img1 - img2) ** 2 )
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| if mse_ == 0:
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| return 100
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| return 10 * math.log10(1 / mse_)
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| def SSIM(img1, img2):
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| return metrics.structural_similarity(img1, img2, data_range=1, multichannel=True)
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| def load_img(filepath):
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| return cv2.cvtColor(cv2.imread(filepath), cv2.COLOR_BGR2RGB)
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| def load_img16(filepath):
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| return cv2.cvtColor(cv2.imread(filepath, -1), cv2.COLOR_BGR2RGB)
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| def save_img(filepath, img):
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| cv2.imwrite(filepath,cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
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