"""MIT License Copyright (c) 2019 Jungdae Kim, Qing Yu Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ # 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) 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, 16) 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, 16) return img