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import numpy as np
import math
def blend_rgba(img):
img = img[..., :3] * img[..., -1:] + (1. - img[..., -1:]) # blend A to RGB
return img
class PSNR():
@classmethod
def psnr(cls, ref, pred, max=1.0):
# if inputs of type int, then make sure max is 255
mse = ((ref - pred) ** 2).mean()
return cls.psnr_from_mse(mse, max)
@staticmethod
def psnr_from_mse(mse, max=1.0):
psnr = 20 * math.log10(max) - 10 * math.log10(mse)
return psnr
@staticmethod
def psnr_to_rms(psnr_diff):
"""rms error improvement _ratio_ from psnr _diff_"""
ratio = 10 ** (-psnr_diff / 20)
return ratio
class Scrambler():
def __init__(self, N):
self.perm = np.random.permutation(N)
def apply(self, *items):
return [elem[self.perm] for elem in items]
def unscramble(self, *items):
ret = []
for elem in items:
clean = np.zeros_like(elem)
clean[self.perm] = elem
ret.append(clean)
return ret
def trailing_window_view(xs, window_size):
assert (window_size % 2) == 1, "window size should be odd"
view = np.lib.stride_tricks.sliding_window_view(
np.pad(xs, (window_size - 1, 0), mode="edge"), window_size
)
return view
def to_step(pbar, percent):
step = int(pbar.total * percent / 100)
return step
def every(pbar, *, percent=None, step=None):
if step is None:
step = to_step(pbar, percent)
return (pbar.n + 1) % step == 0
def at(pbar, *, percent=None, step=None):
if step is None:
step = to_step(pbar, percent)
return (pbar.n + 1) == step
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