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892天前'自动数据增强方法AutoAugment' |
""" |
基于扩充样本操作的流程：比如先找来500张车辆的图片，并标注得xml文件，再转化得txt文件。 |
然后用此autoaugment.py做扩充操作—>比如每张原图扩充为10张，那么这10张对应的新txt文件里的坐标还是同一个！ |
这样的好处是扩充后只是加了各种噪声、锐化、光暗之类的，不需要再对新生成的图做xml坐标标注了！ |
故：为了不需再对新生成的图做xml坐标标注，我把这个autoaugment.py的类ImageNetPolicy(object)里 |
所有引用到了类SubPolicy(object)里的旋转操作rotate的代码全屏蔽掉了。 |
""" |
from PIL import Image, ImageEnhance, ImageOps |
import numpy as np |
import random |
|
class ImageNetPolicy(object): |
""" Randomly choose one of the best 24 Sub-policies on ImageNet. |
Example: |
# >>> policy = ImageNetPolicy() |
# >>> transformed = policy(image) |
Example as a PyTorch Transform: |
# >>> transform=transforms.Compose([ |
# >>> transforms.Resize(256), |
# >>> ImageNetPolicy(), |
# >>> transforms.ToTensor()]) |
""" |
def __init__(self, fillcolor=(128, 128, 128)): |
self.policies = [ |
# SubPolicy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor), |
SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor), |
SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor), |
SubPolicy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor), |
SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor), |
|
# SubPolicy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor), |
SubPolicy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor), |
SubPolicy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor), |
# SubPolicy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor), |
SubPolicy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor), |
|
# SubPolicy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor), |
# SubPolicy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor), |
SubPolicy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor), |
SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor), |
SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor), |
|
# SubPolicy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor), |
SubPolicy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor), |
SubPolicy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor), |
# SubPolicy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor), |
SubPolicy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor), |
|
SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor), |
SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor), |
SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor), |
SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor) |
] |
|
def __call__(self, img): |
policy_idx = random.randint(0, len(self.policies) - 1) |
print(policy_idx) |
return self.policies[policy_idx](img) |
|
def __repr__(self): |
return "AutoAugment ImageNet Policy" |
|
class CIFAR10Policy(object): |
""" Randomly choose one of the best 25 Sub-policies on CIFAR10. |
Example: |
# >>> policy = CIFAR10Policy() |
# >>> transformed = policy(image) |
# |
Example as a PyTorch Transform: |
# >>> transform=transforms.Compose([ |
# >>> transforms.Resize(256), |
# >>> CIFAR10Policy(), |
# >>> transforms.ToTensor()]) |
""" |
def __init__(self, fillcolor=(128, 128, 128)): |
self.policies = [ |
SubPolicy(0.1, "invert", 7, 0.2, "contrast", 6, fillcolor), |
SubPolicy(0.7, "rotate", 2, 0.3, "translateX", 9, fillcolor), |
SubPolicy(0.8, "sharpness", 1, 0.9, "sharpness", 3, fillcolor), |
SubPolicy(0.5, "shearY", 8, 0.7, "translateY", 9, fillcolor), |
SubPolicy(0.5, "autocontrast", 8, 0.9, "equalize", 2, fillcolor), |
|
SubPolicy(0.2, "shearY", 7, 0.3, "posterize", 7, fillcolor), |
SubPolicy(0.4, "color", 3, 0.6, "brightness", 7, fillcolor), |
SubPolicy(0.3, "sharpness", 9, 0.7, "brightness", 9, fillcolor), |
SubPolicy(0.6, "equalize", 5, 0.5, "equalize", 1, fillcolor), |
SubPolicy(0.6, "contrast", 7, 0.6, "sharpness", 5, fillcolor), |
|
SubPolicy(0.7, "color", 7, 0.5, "translateX", 8, fillcolor), |
SubPolicy(0.3, "equalize", 7, 0.4, "autocontrast", 8, fillcolor), |
SubPolicy(0.4, "translateY", 3, 0.2, "sharpness", 6, fillcolor), |
SubPolicy(0.9, "brightness", 6, 0.2, "color", 8, fillcolor), |
SubPolicy(0.5, "solarize", 2, 0.0, "invert", 3, fillcolor), |
|
SubPolicy(0.2, "equalize", 0, 0.6, "autocontrast", 0, fillcolor), |
SubPolicy(0.2, "equalize", 8, 0.8, "equalize", 4, fillcolor), |
SubPolicy(0.9, "color", 9, 0.6, "equalize", 6, fillcolor), |
SubPolicy(0.8, "autocontrast", 4, 0.2, "solarize", 8, fillcolor), |
SubPolicy(0.1, "brightness", 3, 0.7, "color", 0, fillcolor), |
|
SubPolicy(0.4, "solarize", 5, 0.9, "autocontrast", 3, fillcolor), |
SubPolicy(0.9, "translateY", 9, 0.7, "translateY", 9, fillcolor), |
SubPolicy(0.9, "autocontrast", 2, 0.8, "solarize", 3, fillcolor), |
SubPolicy(0.8, "equalize", 8, 0.1, "invert", 3, fillcolor), |
SubPolicy(0.7, "translateY", 9, 0.9, "autocontrast", 1, fillcolor) |
] |
|
def __call__(self, img): |
policy_idx = random.randint(0, len(self.policies) - 1) |
return self.policies[policy_idx](img) |
|
def __repr__(self): |
return "AutoAugment CIFAR10 Policy" |
|
|
class SVHNPolicy(object): |
""" Randomly choose one of the best 25 Sub-policies on SVHN. |
Example: |
# >>> policy = SVHNPolicy() |
# >>> transformed = policy(image) |
Example as a PyTorch Transform: |
# >>> transform=transforms.Compose([ |
# >>> transforms.Resize(256), |
# >>> SVHNPolicy(), |
# >>> transforms.ToTensor()]) |
""" |
def __init__(self, fillcolor=(128, 128, 128)): |
self.policies = [ |
SubPolicy(0.9, "shearX", 4, 0.2, "invert", 3, fillcolor), |
SubPolicy(0.9, "shearY", 8, 0.7, "invert", 5, fillcolor), |
SubPolicy(0.6, "equalize", 5, 0.6, "solarize", 6, fillcolor), |
SubPolicy(0.9, "invert", 3, 0.6, "equalize", 3, fillcolor), |
SubPolicy(0.6, "equalize", 1, 0.9, "rotate", 3, fillcolor), |
|
SubPolicy(0.9, "shearX", 4, 0.8, "autocontrast", 3, fillcolor), |
SubPolicy(0.9, "shearY", 8, 0.4, "invert", 5, fillcolor), |
SubPolicy(0.9, "shearY", 5, 0.2, "solarize", 6, fillcolor), |
SubPolicy(0.9, "invert", 6, 0.8, "autocontrast", 1, fillcolor), |
SubPolicy(0.6, "equalize", 3, 0.9, "rotate", 3, fillcolor), |
|
SubPolicy(0.9, "shearX", 4, 0.3, "solarize", 3, fillcolor), |
SubPolicy(0.8, "shearY", 8, 0.7, "invert", 4, fillcolor), |
SubPolicy(0.9, "equalize", 5, 0.6, "translateY", 6, fillcolor), |
SubPolicy(0.9, "invert", 4, 0.6, "equalize", 7, fillcolor), |
SubPolicy(0.3, "contrast", 3, 0.8, "rotate", 4, fillcolor), |
|
SubPolicy(0.8, "invert", 5, 0.0, "translateY", 2, fillcolor), |
SubPolicy(0.7, "shearY", 6, 0.4, "solarize", 8, fillcolor), |
SubPolicy(0.6, "invert", 4, 0.8, "rotate", 4, fillcolor), |
SubPolicy(0.3, "shearY", 7, 0.9, "translateX", 3, fillcolor), |
SubPolicy(0.1, "shearX", 6, 0.6, "invert", 5, fillcolor), |
|
SubPolicy(0.7, "solarize", 2, 0.6, "translateY", 7, fillcolor), |
SubPolicy(0.8, "shearY", 4, 0.8, "invert", 8, fillcolor), |
SubPolicy(0.7, "shearX", 9, 0.8, "translateY", 3, fillcolor), |
SubPolicy(0.8, "shearY", 5, 0.7, "autocontrast", 3, fillcolor), |
SubPolicy(0.7, "shearX", 2, 0.1, "invert", 5, fillcolor) |
] |
|
def __call__(self, img): |
policy_idx = random.randint(0, len(self.policies) - 1) |
return self.policies[policy_idx](img) |
|
def __repr__(self): |
return "AutoAugment SVHN Policy" |
|
|
class SubPolicy(object): |
def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx2, fillcolor=(128, 128, 128)): |
ranges = { |
"shearX": np.linspace(0, 0.3, 10), |
"shearY": np.linspace(0, 0.3, 10), |
"translateX": np.linspace(0, 150 / 331, 10), |
"translateY": np.linspace(0, 150 / 331, 10), |
"rotate": np.linspace(0, 30, 10), |
"color": np.linspace(0.0, 0.9, 10), |
"posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int), |
"solarize": np.linspace(256, 0, 10), |
"contrast": np.linspace(0.0, 0.9, 10), |
"sharpness": np.linspace(0.0, 0.9, 10), |
"brightness": np.linspace(0.0, 0.9, 10), |
"autocontrast": [0] * 10, |
"equalize": [0] * 10, |
"invert": [0] * 10 |
} |
|
# from https://stackoverflow.com/questions/5252170/specify-image-filling |
# -color-when-rotating-in-python-with-pil-and-setting-expand |
def rotate_with_fill(img, magnitude): |
rot = img.convert("RGBA").rotate(magnitude) |
return Image.composite(rot, Image.new("RGBA", rot.size, (128,) * 4), rot).convert(img.mode) |
|
func = { |
"shearX": lambda img, magnitude: img.transform( |
img.size, Image.AFFINE, (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0), |
Image.BICUBIC, fillcolor=fillcolor), |
"shearY": lambda img, magnitude: img.transform( |
img.size, Image.AFFINE, (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0), |
Image.BICUBIC, fillcolor=fillcolor), |
"translateX": lambda img, magnitude: img.transform( |
img.size, Image.AFFINE, (1, 0, magnitude * img.size[0] * random.choice([-1, 1]), 0, 1, 0), |
fillcolor=fillcolor), |
"translateY": lambda img, magnitude: img.transform( |
img.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude * img.size[1] * random.choice([-1, 1])), |
fillcolor=fillcolor), |
"rotate": lambda img, magnitude: rotate_with_fill(img, magnitude), |
# "rotate": lambda img, magnitude: img.rotate(magnitude * random.choice([-1, 1])), |
"color": lambda img, magnitude: ImageEnhance.Color(img).enhance(1 + magnitude * random.choice([-1, 1])), |
"posterize": lambda img, magnitude: ImageOps.posterize(img, magnitude), |
"solarize": lambda img, magnitude: ImageOps.solarize(img, magnitude), |
"contrast": lambda img, magnitude: ImageEnhance.Contrast(img).enhance( |
1 + magnitude * random.choice([-1, 1])), |
"sharpness": lambda img, magnitude: ImageEnhance.Sharpness(img).enhance( |
1 + magnitude * random.choice([-1, 1])), |
"brightness": lambda img, magnitude: ImageEnhance.Brightness(img).enhance( |
1 + magnitude * random.choice([-1, 1])), |
"autocontrast": lambda img, magnitude: ImageOps.autocontrast(img), |
"equalize": lambda img, magnitude: ImageOps.equalize(img), |
"invert": lambda img, magnitude: ImageOps.invert(img) |
} |
|
# self.name = "{}_{:.2f}_and_{}_{:.2f}".format( |
# operation1, ranges[operation1][magnitude_idx1], |
# operation2, ranges[operation2][magnitude_idx2]) |
self.p1 = p1 |
self.operation1 = func[operation1] |
self.magnitude1 = ranges[operation1][magnitude_idx1] |
self.p2 = p2 |
self.operation2 = func[operation2] |
self.magnitude2 = ranges[operation2][magnitude_idx2] |
|
def __call__(self, img): |
if random.random() < self.p1: |
img = self.operation1(img, self.magnitude1) |
if random.random() < self.p2: |
img = self.operation2(img, self.magnitude2) |
return img |
