transforms.py
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import numpy as np
import torch.nn as nn
import torchvision.transforms as transforms
from abc import ABC, abstractmethod
from PIL import Image, ImageOps, ImageEnhance
class BaseTransform(ABC):
def __init__(self, prob, mag):
self.prob = prob
self.mag = mag
def __call__(self, img):
return transforms.RandomApply([self.transform], self.prob)(img) ##
def __repr__(self):
return '%s(prob=%.2f, magnitude=%.2f)' % \
(self.__class__.__name__, self.prob, self.mag)
@abstractmethod
def transform(self, img):
pass
class ShearXY(BaseTransform):
def transform(self, img):
degrees = self.mag * 360
t = transforms.RandomAffine(0, shear=degrees, resample=Image.BILINEAR)
return t(img)
class TranslateXY(BaseTransform):
def transform(self, img):
translate = (self.mag, self.mag)
t = transforms.RandomAffine(0, translate=translate, resample=Image.BILINEAR)
return t(img)
class Rotate(BaseTransform):
def transform(self, img):
degrees = self.mag * 360
t = transforms.RandomRotation(degrees, Image.BILINEAR)
return t(img)
class AutoContrast(BaseTransform):
def transform(self, img):
cutoff = int(self.mag * 49)
return ImageOps.autocontrast(img, cutoff=cutoff)
class Invert(BaseTransform):
def transform(self, img):
return ImageOps.invert(img)
class Equalize(BaseTransform):
def transform(self, img):
return ImageOps.equalize(img)
class Solarize(BaseTransform):
def transform(self, img):
threshold = (1-self.mag) * 255
return ImageOps.solarize(img, threshold)
class Posterize(BaseTransform):
def transform(self, img):
bits = int((1-self.mag) * 8)
return ImageOps.posterize(img, bits=bits)
class Contrast(BaseTransform):
def transform(self, img):
factor = self.mag * 10
return ImageEnhance.Contrast(img).enhance(factor)
class Color(BaseTransform):
def transform(self, img):
factor = self.mag * 10
return ImageEnhance.Color(img).enhance(factor)
class Brightness(BaseTransform):
def transform(self, img):
factor = self.mag * 10
return ImageEnhance.Brightness(img).enhance(factor)
class Sharpness(BaseTransform):
def transform(self, img):
factor = self.mag * 10
return ImageEnhance.Sharpness(img).enhance(factor)
class Cutout(BaseTransform):
def transform(self, img):
n_holes = 1
length = 24 * self.mag
cutout_op = CutoutOp(n_holes=n_holes, length=length)
return cutout_op(img)
class CutoutOp(object):
"""
https://github.com/uoguelph-mlrg/Cutout
Randomly mask out one or more patches from an image.
Args:
n_holes (int): Number of patches to cut out of each image.
length (int): The length (in pixels) of each square patch.
"""
def __init__(self, n_holes, length):
self.n_holes = n_holes
self.length = length
def __call__(self, img):
"""
Args:
img (Tensor): Tensor image of size (C, H, W).
Returns:
Tensor: Image with n_holes of dimension length x length cut out of it.
"""
w, h = img.size
mask = np.ones((h, w, 1), np.uint8)
for n in range(self.n_holes):
y = np.random.randint(h)
x = np.random.randint(w)
y1 = np.clip(y - self.length // 2, 0, h).astype(int)
y2 = np.clip(y + self.length // 2, 0, h).astype(int)
x1 = np.clip(x - self.length // 2, 0, w).astype(int)
x2 = np.clip(x + self.length // 2, 0, w).astype(int)
mask[y1: y2, x1: x2, :] = 0.
# img = mask*np.asarray(img).astype(np.uint8)
# print("\nmask max:\n", np.amax(mask), mask.shape) #(32, 32, 1)
# print("\nnp.asarray(img) max: \n", np.amax(np.asarray(img)), np.asarray(img).shape) #(32, 32, 32)
# img = Image.fromarray(mask*np.asarray(img)) #(32, 32, 32)
mask = np.reshape(mask, (32,32))
#print("\n(img) max: \n", np.amax(np.asarray(img)), np.asarray(img).shape) #[0, 255] (32, 32)
# print("\nmask: ", mask.shape) #(32, 32)
# print("\img: ", np.asarray(img).shape) #(32, 32)
# print("mask*img:\n", (mask*np.asarray(img).astype(np.uint8)).shape)
#ㄴ[0, 255], (32, 32)
img = Image.fromarray(mask*np.asarray(img).astype(np.uint8)) #
return img