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| import numpy as np | |
| from scipy.misc import ascent | |
| from skimage.measure import compare_psnr, compare_mse, compare_ssim | |
| from .predict_utils import normalize_mi_ma | |
| def normalize(x, pmin=2, pmax=99.8, axis=None, clip=False, eps=1e-20, dtype=np.float32): | |
| """Percentile-based image normalization.""" | |
| mi = np.percentile(x,pmin,axis=axis,keepdims=True) | |
| ma = np.percentile(x,pmax,axis=axis,keepdims=True) | |
| return normalize_mi_ma(x, mi, ma, clip=clip, eps=eps, dtype=dtype) | |
| def norm_minmse(gt, x, normalize_gt=True): | |
| """ | |
| normalizes and affinely scales an image pair such that the MSE is minimized | |
| Parameters | |
| ---------- | |
| gt: ndarray | |
| the ground truth image | |
| x: ndarray | |
| the image that will be affinely scaled | |
| normalize_gt: bool | |
| set to True of gt image should be normalized (default) | |
| Returns | |
| ------- | |
| gt_scaled, x_scaled | |
| """ | |
| if normalize_gt: | |
| gt = normalize(gt, 0.1, 99.9, clip=False).astype(np.float32, copy = False) | |
| x = x.astype(np.float32, copy=False) - np.mean(x) | |
| gt = gt.astype(np.float32, copy=False) - np.mean(gt) | |
| scale = np.cov(x.flatten(), gt.flatten())[0, 1] / np.var(x.flatten()) | |
| return gt, scale * x | |
| def get_scores(gt, x, multichan=False): | |
| gt_, x_ = norm_minmse(gt, x) | |
| mse = compare_mse(gt_, x_) | |
| psnr = compare_psnr(gt_, x_, data_range = 1.) | |
| ssim = compare_ssim(gt_, x_, data_range = 1., multichannel=multichan) | |
| return np.sqrt(mse), psnr, ssim | |
| if __name__ == '__main__': | |
| # ground truth image | |
| y = ascent().astype(np.float32) | |
| # input image to compare to | |
| x1 = y + 30*np.random.normal(0,1,y.shape) | |
| # a scaled and shifted version of x1 | |
| x2 = 2*x1+100 | |
| # calulate mse, psnr, and ssim of the normalized/scaled images | |
| mse1 = compare_mse(*norm_minmse(y, x1)) | |
| mse2 = compare_mse(*norm_minmse(y, x2)) | |
| # should be the same | |
| print("MSE1 = %.6f\nMSE2 = %.6f"%(mse1, mse2)) | |
| psnr1 = compare_psnr(*norm_minmse(y, x1), data_range = 1.) | |
| psnr2 = compare_psnr(*norm_minmse(y, x2), data_range = 1.) | |
| # should be the same | |
| print("PSNR1 = %.6f\nPSNR2 = %.6f"%(psnr1,psnr2)) | |
| ssim1 = compare_ssim(*norm_minmse(y, x1), data_range = 1.) | |
| ssim2 = compare_ssim(*norm_minmse(y, x2), data_range = 1.) | |
| # should be the same | |
| print("SSIM1 = %.6f\nSSIM2 = %.6f"%(ssim1,ssim2)) |