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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This code is based on https://github.com/DeepVoltaire/AutoAugment/blob/master/autoaugment.py
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from PIL import Image, ImageEnhance, ImageOps
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import numpy as np
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import random
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class ImageNetPolicy(object):
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""" Randomly choose one of the best 24 Sub-policies on ImageNet.
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Example:
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>>> policy = ImageNetPolicy()
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>>> transformed = policy(image)
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Example as a PyTorch Transform:
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>>> transform=transforms.Compose([
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>>> transforms.Resize(256),
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>>> ImageNetPolicy(),
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>>> transforms.ToTensor()])
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"""
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def __init__(self, fillcolor=(128, 128, 128)):
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self.policies = [
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SubPolicy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor),
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SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
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SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor),
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SubPolicy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor),
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SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
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SubPolicy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor),
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SubPolicy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor),
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SubPolicy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor),
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SubPolicy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor),
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SubPolicy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor),
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SubPolicy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor),
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SubPolicy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor),
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SubPolicy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor),
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SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
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SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
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SubPolicy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor),
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SubPolicy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor),
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SubPolicy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor),
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SubPolicy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor),
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SubPolicy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor),
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SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
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SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
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SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
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SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
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SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor)
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]
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def __call__(self, img, policy_idx=None):
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if policy_idx is None or not isinstance(policy_idx, int):
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policy_idx = random.randint(0, len(self.policies) - 1)
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else:
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policy_idx = policy_idx % len(self.policies)
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return self.policies[policy_idx](img)
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def __repr__(self):
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return "AutoAugment ImageNet Policy"
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class CIFAR10Policy(object):
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""" Randomly choose one of the best 25 Sub-policies on CIFAR10.
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Example:
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>>> policy = CIFAR10Policy()
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>>> transformed = policy(image)
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Example as a PyTorch Transform:
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>>> transform=transforms.Compose([
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>>> transforms.Resize(256),
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>>> CIFAR10Policy(),
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>>> transforms.ToTensor()])
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"""
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def __init__(self, fillcolor=(128, 128, 128)):
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self.policies = [
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SubPolicy(0.1, "invert", 7, 0.2, "contrast", 6, fillcolor),
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SubPolicy(0.7, "rotate", 2, 0.3, "translateX", 9, fillcolor),
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SubPolicy(0.8, "sharpness", 1, 0.9, "sharpness", 3, fillcolor),
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SubPolicy(0.5, "shearY", 8, 0.7, "translateY", 9, fillcolor),
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SubPolicy(0.5, "autocontrast", 8, 0.9, "equalize", 2, fillcolor),
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SubPolicy(0.2, "shearY", 7, 0.3, "posterize", 7, fillcolor),
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SubPolicy(0.4, "color", 3, 0.6, "brightness", 7, fillcolor),
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SubPolicy(0.3, "sharpness", 9, 0.7, "brightness", 9, fillcolor),
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SubPolicy(0.6, "equalize", 5, 0.5, "equalize", 1, fillcolor),
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SubPolicy(0.6, "contrast", 7, 0.6, "sharpness", 5, fillcolor),
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SubPolicy(0.7, "color", 7, 0.5, "translateX", 8, fillcolor),
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SubPolicy(0.3, "equalize", 7, 0.4, "autocontrast", 8, fillcolor),
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SubPolicy(0.4, "translateY", 3, 0.2, "sharpness", 6, fillcolor),
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SubPolicy(0.9, "brightness", 6, 0.2, "color", 8, fillcolor),
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SubPolicy(0.5, "solarize", 2, 0.0, "invert", 3, fillcolor),
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SubPolicy(0.2, "equalize", 0, 0.6, "autocontrast", 0, fillcolor),
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SubPolicy(0.2, "equalize", 8, 0.8, "equalize", 4, fillcolor),
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SubPolicy(0.9, "color", 9, 0.6, "equalize", 6, fillcolor),
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SubPolicy(0.8, "autocontrast", 4, 0.2, "solarize", 8, fillcolor),
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SubPolicy(0.1, "brightness", 3, 0.7, "color", 0, fillcolor),
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SubPolicy(0.4, "solarize", 5, 0.9, "autocontrast", 3, fillcolor),
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SubPolicy(0.9, "translateY", 9, 0.7, "translateY", 9, fillcolor),
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SubPolicy(0.9, "autocontrast", 2, 0.8, "solarize", 3, fillcolor),
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SubPolicy(0.8, "equalize", 8, 0.1, "invert", 3, fillcolor),
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SubPolicy(0.7, "translateY", 9, 0.9, "autocontrast", 1, fillcolor)
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]
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def __call__(self, img, policy_idx=None):
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if policy_idx is None or not isinstance(policy_idx, int):
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policy_idx = random.randint(0, len(self.policies) - 1)
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else:
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policy_idx = policy_idx % len(self.policies)
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return self.policies[policy_idx](img)
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def __repr__(self):
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return "AutoAugment CIFAR10 Policy"
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class SVHNPolicy(object):
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""" Randomly choose one of the best 25 Sub-policies on SVHN.
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Example:
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>>> policy = SVHNPolicy()
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>>> transformed = policy(image)
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Example as a PyTorch Transform:
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>>> transform=transforms.Compose([
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>>> transforms.Resize(256),
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>>> SVHNPolicy(),
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>>> transforms.ToTensor()])
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"""
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def __init__(self, fillcolor=(128, 128, 128)):
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self.policies = [
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SubPolicy(0.9, "shearX", 4, 0.2, "invert", 3, fillcolor),
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SubPolicy(0.9, "shearY", 8, 0.7, "invert", 5, fillcolor),
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SubPolicy(0.6, "equalize", 5, 0.6, "solarize", 6, fillcolor),
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SubPolicy(0.9, "invert", 3, 0.6, "equalize", 3, fillcolor),
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SubPolicy(0.6, "equalize", 1, 0.9, "rotate", 3, fillcolor),
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SubPolicy(0.9, "shearX", 4, 0.8, "autocontrast", 3, fillcolor),
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SubPolicy(0.9, "shearY", 8, 0.4, "invert", 5, fillcolor),
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SubPolicy(0.9, "shearY", 5, 0.2, "solarize", 6, fillcolor),
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SubPolicy(0.9, "invert", 6, 0.8, "autocontrast", 1, fillcolor),
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SubPolicy(0.6, "equalize", 3, 0.9, "rotate", 3, fillcolor),
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SubPolicy(0.9, "shearX", 4, 0.3, "solarize", 3, fillcolor),
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SubPolicy(0.8, "shearY", 8, 0.7, "invert", 4, fillcolor),
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SubPolicy(0.9, "equalize", 5, 0.6, "translateY", 6, fillcolor),
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SubPolicy(0.9, "invert", 4, 0.6, "equalize", 7, fillcolor),
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SubPolicy(0.3, "contrast", 3, 0.8, "rotate", 4, fillcolor),
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SubPolicy(0.8, "invert", 5, 0.0, "translateY", 2, fillcolor),
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SubPolicy(0.7, "shearY", 6, 0.4, "solarize", 8, fillcolor),
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SubPolicy(0.6, "invert", 4, 0.8, "rotate", 4, fillcolor), SubPolicy(
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0.3, "shearY", 7, 0.9, "translateX", 3, fillcolor), SubPolicy(
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0.1, "shearX", 6, 0.6, "invert", 5, fillcolor), SubPolicy(
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0.7, "solarize", 2, 0.6, "translateY", 7,
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fillcolor), SubPolicy(0.8, "shearY", 4, 0.8, "invert",
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8, fillcolor), SubPolicy(
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0.7, "shearX", 9, 0.8,
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"translateY", 3,
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fillcolor), SubPolicy(
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0.8, "shearY", 5, 0.7,
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"autocontrast", 3,
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fillcolor),
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SubPolicy(0.7, "shearX", 2, 0.1, "invert", 5, fillcolor)
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]
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def __call__(self, img, policy_idx=None):
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if policy_idx is None or not isinstance(policy_idx, int):
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policy_idx = random.randint(0, len(self.policies) - 1)
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else:
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policy_idx = policy_idx % len(self.policies)
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return self.policies[policy_idx](img)
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def __repr__(self):
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return "AutoAugment SVHN Policy"
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class SubPolicy(object):
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def __init__(self,
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p1,
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operation1,
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magnitude_idx1,
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p2,
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operation2,
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magnitude_idx2,
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fillcolor=(128, 128, 128)):
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ranges = {
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"shearX": np.linspace(0, 0.3, 10),
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"shearY": np.linspace(0, 0.3, 10),
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"translateX": np.linspace(0, 150 / 331, 10),
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"translateY": np.linspace(0, 150 / 331, 10),
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"rotate": np.linspace(0, 30, 10),
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"color": np.linspace(0.0, 0.9, 10),
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"posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
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"solarize": np.linspace(256, 0, 10),
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"contrast": np.linspace(0.0, 0.9, 10),
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"sharpness": np.linspace(0.0, 0.9, 10),
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"brightness": np.linspace(0.0, 0.9, 10),
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"autocontrast": [0] * 10,
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"equalize": [0] * 10,
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"invert": [0] * 10
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}
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# from https://stackoverflow.com/questions/5252170/specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
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def rotate_with_fill(img, magnitude):
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rot = img.convert("RGBA").rotate(magnitude)
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return Image.composite(rot,
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Image.new("RGBA", rot.size, (128, ) * 4),
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rot).convert(img.mode)
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func = {
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"shearX": lambda img, magnitude: img.transform(
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img.size, Image.AFFINE, (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0),
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Image.BICUBIC, fillcolor=fillcolor),
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"shearY": lambda img, magnitude: img.transform(
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img.size, Image.AFFINE, (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0),
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Image.BICUBIC, fillcolor=fillcolor),
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"translateX": lambda img, magnitude: img.transform(
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img.size, Image.AFFINE, (1, 0, magnitude * img.size[0] * random.choice([-1, 1]), 0, 1, 0),
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fillcolor=fillcolor),
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"translateY": lambda img, magnitude: img.transform(
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img.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude * img.size[1] * random.choice([-1, 1])),
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fillcolor=fillcolor),
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"rotate": lambda img, magnitude: rotate_with_fill(img, magnitude),
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# "rotate": lambda img, magnitude: img.rotate(magnitude * random.choice([-1, 1])),
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"color": lambda img, magnitude: ImageEnhance.Color(img).enhance(1 + magnitude * random.choice([-1, 1])),
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"posterize": lambda img, magnitude: ImageOps.posterize(img, magnitude),
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"solarize": lambda img, magnitude: ImageOps.solarize(img, magnitude),
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"contrast": lambda img, magnitude: ImageEnhance.Contrast(img).enhance(
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1 + magnitude * random.choice([-1, 1])),
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"sharpness": lambda img, magnitude: ImageEnhance.Sharpness(img).enhance(
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1 + magnitude * random.choice([-1, 1])),
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"brightness": lambda img, magnitude: ImageEnhance.Brightness(img).enhance(
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1 + magnitude * random.choice([-1, 1])),
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"autocontrast": lambda img, magnitude: ImageOps.autocontrast(img),
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"equalize": lambda img, magnitude: ImageOps.equalize(img),
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"invert": lambda img, magnitude: ImageOps.invert(img)
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}
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self.p1 = p1
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self.operation1 = func[operation1]
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self.magnitude1 = ranges[operation1][magnitude_idx1]
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self.p2 = p2
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self.operation2 = func[operation2]
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self.magnitude2 = ranges[operation2][magnitude_idx2]
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def __call__(self, img):
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if random.random() < self.p1:
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img = self.operation1(img, self.magnitude1)
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if random.random() < self.p2:
|
|
|
|
|
img = self.operation2(img, self.magnitude2)
|
|
|
|
|
return img
|
