mirror of https://github.com/JDAI-CV/fast-reid.git
81 lines
2.3 KiB
Python
81 lines
2.3 KiB
Python
# encoding: utf-8
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"""
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@author: liaoxingyu
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@contact: sherlockliao01@gmail.com
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import random
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from PIL import Image
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from torchvision import transforms as T
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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(
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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(
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(x1, y1, x1 + self.width, y1 + self.height))
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return croped_img
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class TrainTransform(object):
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def __init__(self, h, w):
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self.h = h
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self.w = w
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def __call__(self, x):
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x = Random2DTranslation(self.h, self.w)(x)
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x = T.RandomHorizontalFlip()(x)
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x = T.ToTensor()(x)
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x = T.Normalize(mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225])(x)
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return x
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class TestTransform(object):
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def __init__(self, h, w):
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self.h = h
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self.w = w
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def __call__(self, x=None):
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x = T.Resize((self.h, self.w))(x)
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x = T.ToTensor()(x)
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x = T.Normalize(mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225])(x)
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return x
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