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import numpy as np
import cv2
def resize_pad(img, size = 256):
if len(img.shape) == 2:
img = np.expand_dims(img, 2)
if img.shape[2] == 1:
img = np.repeat(img, 3, 2)
if img.shape[2] == 4:
img = img[:, :, :3]
pad = None
if (img.shape[0] < img.shape[1]):
height = img.shape[0]
ratio = height / (size * 1.5)
width = int(np.ceil(img.shape[1] / ratio))
img = cv2.resize(img, (width, int(size * 1.5)), interpolation = cv2.INTER_AREA)
new_width = width + (32 - width % 32)
pad = (0, new_width - width)
img = np.pad(img, ((0, 0), (0, pad[1]), (0, 0)), 'maximum')
else:
width = img.shape[1]
ratio = width / size
height = int(np.ceil(img.shape[0] / ratio))
img = cv2.resize(img, (size, height), interpolation = cv2.INTER_AREA)
new_height = height + (32 - height % 32)
pad = (new_height - height, 0)
img = np.pad(img, ((0, pad[0]), (0, 0), (0, 0)), 'maximum')
if (img.dtype == 'float32'):
np.clip(img, 0, 1, out = img)
return img[:, :, :1], pad
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