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import numpy as np
def denorm(img, max_value):
img = img * float(max_value)
return img
def preprocess_test_patch(input_image, target_image, gt_image):
input_patch_list = []
target_patch_list = []
gt_patch_list = []
H = input_image.shape[2]
W = input_image.shape[3]
for i in range(3):
for j in range(3):
input_patch = input_image[
:,
:,
int(i * H / 3) : int((i + 1) * H / 3),
int(j * W / 3) : int((j + 1) * W / 3),
]
target_patch = target_image[
:,
:,
int(i * H / 3) : int((i + 1) * H / 3),
int(j * W / 3) : int((j + 1) * W / 3),
]
gt_patch = gt_image[
:,
:,
int(i * H / 3) : int((i + 1) * H / 3),
int(j * W / 3) : int((j + 1) * W / 3),
]
input_patch_list.append(input_patch)
target_patch_list.append(target_patch)
gt_patch_list.append(gt_patch)
return input_patch_list, target_patch_list, gt_patch_list
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