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# copyright: https://github.com/ildoonet/pytorch-randaugment
# code in this file is adpated from rpmcruz/autoaugment
# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
# This code is modified version of one of ildoonet, for randaugmentation of fixmatch.
import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
def AutoContrast(img, _):
return PIL.ImageOps.autocontrast(img)
def Brightness(img, v):
assert v >= 0.0
return PIL.ImageEnhance.Brightness(img).enhance(v)
def Color(img, v):
assert v >= 0.0
return PIL.ImageEnhance.Color(img).enhance(v)
def Contrast(img, v):
assert v >= 0.0
return PIL.ImageEnhance.Contrast(img).enhance(v)
def Equalize(img, _):
return PIL.ImageOps.equalize(img)
def Invert(img, _):
return PIL.ImageOps.invert(img)
def Identity(img, v):
return img
def Posterize(img, v): # [4, 8]
v = int(v)
v = max(1, v)
return PIL.ImageOps.posterize(img, v)
def Rotate(img, v): # [-30, 30]
#assert -30 <= v <= 30
#if random.random() > 0.5:
# v = -v
return img.rotate(v)
def Sharpness(img, v): # [0.1,1.9]
assert v >= 0.0
return PIL.ImageEnhance.Sharpness(img).enhance(v)
def ShearX(img, v): # [-0.3, 0.3]
#assert -0.3 <= v <= 0.3
#if random.random() > 0.5:
# v = -v
return img.transform(img.size, PIL.Image.AFFINE, (1, v, 0, 0, 1, 0))
def ShearY(img, v): # [-0.3, 0.3]
#assert -0.3 <= v <= 0.3
#if random.random() > 0.5:
# v = -v
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, v, 1, 0))
def TranslateX(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
#assert -0.3 <= v <= 0.3
#if random.random() > 0.5:
# v = -v
v = v * img.size[0]
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, v, 0, 1, 0))
def TranslateXabs(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
#assert v >= 0.0
#if random.random() > 0.5:
# v = -v
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, v, 0, 1, 0))
def TranslateY(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
#assert -0.3 <= v <= 0.3
#if random.random() > 0.5:
# v = -v
v = v * img.size[1]
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, 0, 1, v))
def TranslateYabs(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
#assert 0 <= v
#if random.random() > 0.5:
# v = -v
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, 0, 1, v))
def Solarize(img, v): # [0, 256]
assert 0 <= v <= 256
return PIL.ImageOps.solarize(img, v)
def Cutout(img, v): #[0, 60] => percentage: [0, 0.2] => change to [0, 0.5]
assert 0.0 <= v <= 0.5
if v <= 0.:
return img
v = v * img.size[0]
return CutoutAbs(img, v)
def CutoutAbs(img, v): # [0, 60] => percentage: [0, 0.2]
# assert 0 <= v <= 20
if v < 0:
return img
w, h = img.size
x0 = np.random.uniform(w)
y0 = np.random.uniform(h)
x0 = int(max(0, x0 - v / 2.))
y0 = int(max(0, y0 - v / 2.))
x1 = min(w, x0 + v)
y1 = min(h, y0 + v)
xy = (x0, y0, x1, y1)
color = (125, 123, 114)
# color = (0, 0, 0)
img = img.copy()
PIL.ImageDraw.Draw(img).rectangle(xy, color)
return img
def augment_list():
l = [
(AutoContrast, 0, 1),
(Brightness, 0.05, 0.95),
(Color, 0.05, 0.95),
(Contrast, 0.05, 0.95),
(Equalize, 0, 1),
(Identity, 0, 1),
(Posterize, 4, 8),
# (Rotate, -30, 30),
(Sharpness, 0.05, 0.95),
# (ShearX, -0.3, 0.3),
# (ShearY, -0.3, 0.3),
(Solarize, 0, 256),
# (TranslateX, -0.3, 0.3),
# (TranslateY, -0.3, 0.3)
]
return l
class RandAugment:
def __init__(self, n, m):
self.n = n
self.m = m # [0, 30] in fixmatch, deprecated.
self.augment_list = augment_list()
def __call__(self, img, cutout=True):
ops = random.choices(self.augment_list, k=self.n)
for op, min_val, max_val in ops:
val = min_val + float(max_val - min_val)*random.random()
img = op(img, val)
if cutout:
cutout_val = random.random() * 0.5
img = Cutout(img, cutout_val) #for fixmatch
return img
if __name__ == '__main__':
# randaug = RandAugment(3,5)
# print(randaug)
# for item in randaug.augment_list:
# print(item)
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
img = PIL.Image.open('./u.jpg')
randaug = RandAugment(3,6)
img = randaug(img)
import matplotlib
from matplotlib import pyplot as plt
plt.imshow(img)
plt.show() |