Hyunji

transforms

import numpy as np
from imgaug import augmenters as iaa
from scipy.ndimage import interpolation, rotate
import torch
class ImgAugTranslation(object):
"""Translation
Arg:
Pixels: number of pixels to apply translation to the image"""
def __init__(self, pixels):
n_pixels = int(pixels)
self.aug = iaa.Affine(translate_px=(-n_pixels, n_pixels))
def __call__(self, img):
img = np.array(img)
return self.aug.augment_image(img)
class ImgAugRotation(object):
"""Rotation
Arg:
Degrees: number of degrees to rotate the image"""
def __init__(self, degrees):
n_degrees = float(degrees)
self.aug = iaa.Affine(rotate=(-n_degrees, n_degrees), mode='symmetric')
def __call__(self, img):
img = np.array(img)
return self.aug.augment_image(img)
class Translation(object):
"""Translation"""
def __init__(self, offset, order=0, isseg=False, mode='nearest'):
self.order = order if isseg else 5
self.offset = offset
self.mode = 'nearest' if isseg else 'mirror'
def __call__(self, img):
return interpolation.shift(img, self.offset , order=self.order, mode=self.mode)
class Rotation(object):
"""Rotation"""
def __init__(self, theta, order=0, isseg=False, mode='nearest'):
self.order = order if isseg else 5
self.theta = float(theta)
self.mode = 'nearest' if isseg else 'mirror'
def __call__(self, img):
return rotate(img, self.theta, reshape=False, order=self.order, mode=self.mode)
class ToTensor(object):
"""Convert ndarrays in sample to Tensors."""
def __call__(self, sample):
return torch.from_numpy(np.asarray(sample).astype(np.float32))
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