transform.py: código de preprocesamiento de datos, puede personalizar el aumento de datos
from __future__ import absolute_import
from torchvision.transforms import *
from PIL import Image
import random
import numpy as np
class Random2DTranslation(object):
"""
With a probability, first increase image size to (1 + 1/8), and then perform random crop.
Args:
height (int): target height.
width (int): target width.
p (float): probability of performing this transformation. Default: 0.5.
"""
def __init__(self, height, width, p=0.5, interpolation=Image.BILINEAR):
self.height = height
self.width = width
self.p = p
self.interpolation = interpolation
def __call__(self, img):
"""
Args:
img (PIL Image): Image to be cropped.
Returns:
PIL Image: Cropped image.
"""
if random.random() < self.p:
return img.resize((self.width, self.height), self.interpolation)
new_width, new_height = int(round(self.width * 1.125)), int(round(self.height * 1.125))
resized_img = img.resize((new_width, new_height), self.interpolation)
x_maxrange = new_width - self.width
y_maxrange = new_height - self.height
x1 = int(round(random.uniform(0, x_maxrange)))
y1 = int(round(random.uniform(0, y_maxrange)))
croped_img = resized_img.crop((x1, y1, x1 + self.width, y1 + self.height))
return croped_img
if __name__ == '__main__':
pass
