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# code in this file is adpated from
# https://github.com/ildoonet/pytorch-randaugment/blob/master/RandAugment/augmentations.py
# https://github.com/google-research/fixmatch/blob/master/third_party/auto_augment/augmentations.py
# https://github.com/google-research/fixmatch/blob/master/libml/ctaugment.py
import logging
import random
import numpy as np
import PIL
import PIL.ImageOps
import PIL.ImageEnhance
import PIL.ImageDraw
from PIL import Image
logger = logging.getLogger(__name__)
PARAMETER_MAX = 10
def AutoContrast(img, **kwarg):
return PIL.ImageOps.autocontrast(img)
def Brightness(img, v, max_v, bias=0):
v = _float_parameter(v, max_v) + bias
return PIL.ImageEnhance.Brightness(img).enhance(v)
def Color(img, v, max_v, bias=0):
v = _float_parameter(v, max_v) + bias
return PIL.ImageEnhance.Color(img).enhance(v)
def Contrast(img, v, max_v, bias=0):
v = _float_parameter(v, max_v) + bias
return PIL.ImageEnhance.Contrast(img).enhance(v)
def Cutout(img, v, max_v, bias=0):
if v == 0:
return img
v = _float_parameter(v, max_v) + bias
v = int(v * min(img.size))
return CutoutAbs(img, v)
def CutoutAbs(img, v, **kwarg):
w, h = img.size
x0 = np.random.uniform(0, w)
y0 = np.random.uniform(0, h)
x0 = int(max(0, x0 - v / 2.))
y0 = int(max(0, y0 - v / 2.))
x1 = int(min(w, x0 + v))
y1 = int(min(h, y0 + v))
xy = (x0, y0, x1, y1)
# gray
# color = (127, 127, 127)
# black
color = (0, 0, 0)
img = img.copy()
PIL.ImageDraw.Draw(img).rectangle(xy, color)
return img
def Equalize(img, **kwarg):
return PIL.ImageOps.equalize(img)
def Identity(img, **kwarg):
return img
def Invert(img, **kwarg):
return PIL.ImageOps.invert(img)
def Posterize(img, v, max_v, bias=0):
v = _int_parameter(v, max_v) + bias
return PIL.ImageOps.posterize(img, v)
def Rotate(img, v, max_v, bias=0):
v = _int_parameter(v, max_v) + bias
if random.random() < 0.5:
v = -v
return img.rotate(v)
def Sharpness(img, v, max_v, bias=0):
v = _float_parameter(v, max_v) + bias
return PIL.ImageEnhance.Sharpness(img).enhance(v)
def ShearX(img, v, max_v, bias=0):
v = _float_parameter(v, max_v) + bias
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, max_v, bias=0):
v = _float_parameter(v, max_v) + bias
if random.random() < 0.5:
v = -v
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, v, 1, 0))
def Solarize(img, v, max_v, bias=0):
v = _int_parameter(v, max_v) + bias
return PIL.ImageOps.solarize(img, 256 - v)
def SolarizeAdd(img, v, max_v, bias=0, threshold=128):
v = _int_parameter(v, max_v) + bias
if random.random() < 0.5:
v = -v
img_np = np.array(img).astype(np.int)
img_np = img_np + v
img_np = np.clip(img_np, 0, 255)
img_np = img_np.astype(np.uint8)
img = Image.fromarray(img_np)
return PIL.ImageOps.solarize(img, threshold)
def TranslateX(img, v, max_v, bias=0):
v = _float_parameter(v, max_v) + bias
if random.random() < 0.5:
v = -v
v = int(v * img.size[0])
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, v, 0, 1, 0))
def TranslateY(img, v, max_v, bias=0):
v = _float_parameter(v, max_v) + bias
if random.random() < 0.5:
v = -v
v = int(v * img.size[1])
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, 0, 1, v))
def _float_parameter(v, max_v):
return float(v) * max_v / PARAMETER_MAX
def _int_parameter(v, max_v):
return int(v * max_v / PARAMETER_MAX)
def fixmatch_augment_pool():
# FixMatch paper
augs = [(AutoContrast, None, None),
(Brightness, 0.9, 0.05),
(Color, 0.9, 0.05),
(Contrast, 0.9, 0.05),
(Equalize, None, None),
(Identity, None, None),
(Posterize, 4, 4),
(Rotate, 30, 0),
(Sharpness, 0.9, 0.05),
(ShearX, 0.3, 0),
(ShearY, 0.3, 0),
(Solarize, 256, 0),
(TranslateX, 0.3, 0),
(TranslateY, 0.3, 0)]
return augs
def my_augment_pool():
# Test
augs = [(AutoContrast, None, None),
(Brightness, 1.8, 0.1),
(Color, 1.8, 0.1),
(Contrast, 1.8, 0.1),
(Cutout, 0.2, 0),
(Equalize, None, None),
(Invert, None, None),
(Posterize, 4, 4),
(Rotate, 30, 0),
(Sharpness, 1.8, 0.1),
(ShearX, 0.3, 0),
(ShearY, 0.3, 0),
(Solarize, 256, 0),
(SolarizeAdd, 110, 0),
(TranslateX, 0.45, 0),
(TranslateY, 0.45, 0)]
return augs
class RandAugmentPC(object):
def __init__(self, n, m):
assert n >= 1
assert 1 <= m <= 10
self.n = n
self.m = m
self.augment_pool = my_augment_pool()
def __call__(self, img):
ops = random.choices(self.augment_pool, k=self.n)
for op, max_v, bias in ops:
prob = np.random.uniform(0.2, 0.8)
if random.random() + prob >= 1:
img = op(img, v=self.m, max_v=max_v, bias=bias)
img = CutoutAbs(img, int(32*0.5))
return img
class RandAugmentMC(object):
def __init__(self, n, m):
assert n >= 1
assert 1 <= m <= 10
self.n = n
self.m = m
self.augment_pool = fixmatch_augment_pool()
def __call__(self, img):
ops = random.choices(self.augment_pool, k=self.n)
for op, max_v, bias in ops:
v = np.random.randint(1, self.m)
if random.random() < 0.5:
img = op(img, v=v, max_v=max_v, bias=bias)
img = CutoutAbs(img, int(32*0.5))
return img
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