57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
from __future__ import absolute_import
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from torchvision.transforms import *
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from PIL import Image
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import random
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import numpy as np
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class Random2DTranslation(object):
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"""
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With a probability, first increase image size to (1 + 1/8), and then perform random crop.
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Args:
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height (int): target height.
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width (int): target width.
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p (float): probability of performing this transformation. Default: 0.5.
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"""
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def __init__(self, height, width, p=0.5, interpolation=Image.BILINEAR):
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self.height = height
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self.width = width
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self.p = p
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self.interpolation = interpolation
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def __call__(self, img):
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"""
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Args:
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img (PIL Image): Image to be cropped.
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Returns:
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PIL Image: Cropped image.
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"""
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if random.random() < self.p:
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return img.resize((self.width, self.height), self.interpolation)
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new_width, new_height = int(round(self.width * 1.125)), int(round(self.height * 1.125))
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resized_img = img.resize((new_width, new_height), self.interpolation)
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x_maxrange = new_width - self.width
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y_maxrange = new_height - self.height
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x1 = int(round(random.uniform(0, x_maxrange)))
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y1 = int(round(random.uniform(0, y_maxrange)))
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croped_img = resized_img.crop((x1, y1, x1 + self.width, y1 + self.height))
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return croped_img
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if __name__ == '__main__':
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"""import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('-impath', type=str)
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parser.add_argument('-nlevel', type=float, default=0.1)
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args = parser.parse_args()
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RC = RandomOcclusion(nlevel=args.nlevel, p=1)
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im = Image.open(args.impath)
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transformed_im = RC(im)
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basename = osp.basename(args.impath)
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save_name = osp.splitext(basename)[0] + '_nlevel_' + str(args.nlevel) + osp.splitext(basename)[1]
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transformed_im.save(save_name)"""
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pass
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