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28e6f98 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | import numpy as np
import torch
def bright(x, a,b):
# input datatype np.uint8
x = np.array(x, dtype='float')
x = x/(b-a) - 255*a/(b-a)
x[x>255.0] = 255.0
x[x<0.0] = 0.0
x = x.astype(np.uint8)
return x
def trunc(x):
# input datatype float
x[x>255.0] = 255.0
x[x<0.0] = 0.0
return x
def cc(img1, img2):
eps = torch.finfo(torch.float32).eps
"""Correlation coefficient for (N, C, H, W) image; torch.float32 [0.,1.]."""
N, C, _, _ = img1.shape
img1 = img1.reshape(N, C, -1)
img2 = img2.reshape(N, C, -1)
img1 = img1 - img1.mean(dim=-1, keepdim=True)
img2 = img2 - img2.mean(dim=-1, keepdim=True)
cc = torch.sum(img1 * img2, dim=-1) / (eps + torch.sqrt(torch.sum(
img1 **2, dim=-1)) * torch.sqrt(torch.sum(img2**2, dim=-1)))
cc = torch.clamp(cc, -1., 1.)
return cc.mean() |