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import random
import numpy as np
from skimage.filters import gaussian
import torch
from PIL import Image, ImageFilter
class RandomVerticalFlip(object):
def __call__(self, img):
if random.random() < 0.5:
return img.transpose(Image.FLIP_TOP_BOTTOM)
return img
class DeNormalize(object):
def __init__(self, mean, std):
self.mean = mean
self.std = std
def __call__(self, tensor):
for t, m, s in zip(tensor, self.mean, self.std):
t.mul_(s).add_(m)
return tensor
class MaskToTensor(object):
def __call__(self, img):
return torch.from_numpy(np.array(img, dtype=np.int32)).long()
class FreeScale(object):
def __init__(self, size, interpolation=Image.BILINEAR):
self.size = tuple(reversed(size)) # size: (h, w)
self.interpolation = interpolation
def __call__(self, img):
return img.resize(self.size, self.interpolation)
class FlipChannels(object):
def __call__(self, img):
img = np.array(img)[:, :, ::-1]
return Image.fromarray(img.astype(np.uint8))
class RandomGaussianBlur(object):
def __call__(self, img):
sigma = 0.15 + random.random() * 1.15
blurred_img = gaussian(np.array(img), sigma=sigma, multichannel=True)
blurred_img *= 255
return Image.fromarray(blurred_img.astype(np.uint8))
# Lighting data augmentation take from here - https://github.com/eladhoffer/convNet.pytorch/blob/master/preprocess.py
class Lighting(object):
"""Lighting noise(AlexNet - style PCA - based noise)"""
def __init__(self, alphastd,
eigval=(0.2175, 0.0188, 0.0045),
eigvec=((-0.5675, 0.7192, 0.4009),
(-0.5808, -0.0045, -0.8140),
(-0.5836, -0.6948, 0.4203))):
self.alphastd = alphastd
self.eigval = torch.Tensor(eigval)
self.eigvec = torch.Tensor(eigvec)
def __call__(self, img):
if self.alphastd == 0:
return img
alpha = img.new().resize_(3).normal_(0, self.alphastd)
rgb = self.eigvec.type_as(img).clone()\
.mul(alpha.view(1, 3).expand(3, 3))\
.mul(self.eigval.view(1, 3).expand(3, 3))\
.sum(1).squeeze()
return img.add(rgb.view(3, 1, 1).expand_as(img))
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