def shift_cPSNR(sr, hr, hr_map, border_w=3):
"""
cPSNR score adjusted for registration errors. Computes the max cPSNR score across shifts of up to `border_w` pixels.
Args:
sr: np.ndarray (n, m), super-resolved image
hr: np.ndarray (n, m), high-res ground-truth image
hr_map: np.ndarray (n, m), high-res status map
border_w: int, width of the trimming border around `hr` and `hr_map`
Returns:
max_cPSNR: float, score of the super-resolved image
"""
size = sr.shape[1] - (2 * border_w) # patch size
sr = get_patch(img=sr, x=border_w, y=border_w, size=size)
pos = list(
itertools.product(range(2 * border_w + 1), range(2 * border_w + 1)))
iter_hr = patch_iterator(img=hr, positions=pos, size=size)
iter_hr_map = patch_iterator(img=hr_map, positions=pos, size=size)
site_cPSNR = np.array([
cPSNR(sr, hr, hr_map)
for hr, hr_map in tqdm(zip(iter_hr, iter_hr_map),
disable=(len(sr.shape) == 2))
])
max_cPSNR = np.max(site_cPSNR, axis=0)
return max_cPSNR
def patch_iterator(img, positions, size):
"""Iterator across square patches of `img` located in `positions`."""
for x, y in positions:
yield get_patch(img=img, x=x, y=y, size=size)