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from typing import Union
import albumentations as A
import cv2
import numpy as np
import torch
from albumentations.pytorch.transforms import ToTensorV2
from omegaconf import OmegaConf
class IdentityTransform(A.ImageOnlyTransform):
def apply(self, img, **params):
return img
def get_transform_init_args_names(self):
return ()
class RandomAdditiveShade(A.ImageOnlyTransform):
def __init__(
self,
nb_ellipses=10,
transparency_limit=[-0.5, 0.8],
kernel_size_limit=[150, 350],
always_apply=False,
p=0.5,
):
super().__init__(always_apply, p)
self.nb_ellipses = nb_ellipses
self.transparency_limit = transparency_limit
self.kernel_size_limit = kernel_size_limit
def apply(self, img, **params):
if img.dtype == np.float32:
shaded = self._py_additive_shade(img * 255.0)
shaded /= 255.0
elif img.dtype == np.uint8:
shaded = self._py_additive_shade(img.astype(np.float32))
shaded = shaded.astype(np.uint8)
else:
raise NotImplementedError(
f"Data augmentation not available for type: {img.dtype}"
)
return shaded
def _py_additive_shade(self, img):
grayscale = len(img.shape) == 2
if grayscale:
img = img[None]
min_dim = min(img.shape[:2]) / 4
mask = np.zeros(img.shape[:2], img.dtype)
for i in range(self.nb_ellipses):
ax = int(max(np.random.rand() * min_dim, min_dim / 5))
ay = int(max(np.random.rand() * min_dim, min_dim / 5))
max_rad = max(ax, ay)
x = np.random.randint(max_rad, img.shape[1] - max_rad) # center
y = np.random.randint(max_rad, img.shape[0] - max_rad)
angle = np.random.rand() * 90
cv2.ellipse(mask, (x, y), (ax, ay), angle, 0, 360, 255, -1)
transparency = np.random.uniform(*self.transparency_limit)
ks = np.random.randint(*self.kernel_size_limit)
if (ks % 2) == 0: # kernel_size has to be odd
ks += 1
mask = cv2.GaussianBlur(mask.astype(np.float32), (ks, ks), 0)
shaded = img * (1 - transparency * mask[..., np.newaxis] / 255.0)
out = np.clip(shaded, 0, 255)
if grayscale:
out = out.squeeze(0)
return out
def get_transform_init_args_names(self):
return "transparency_limit", "kernel_size_limit", "nb_ellipses"
def kw(entry: Union[float, dict], n=None, **default):
if not isinstance(entry, dict):
entry = {"p": entry}
entry = OmegaConf.create(entry)
if n is not None:
entry = default.get(n, entry)
return OmegaConf.merge(default, entry)
def kwi(entry: Union[float, dict], n=None, **default):
conf = kw(entry, n=n, **default)
return {k: conf[k] for k in set(default.keys()).union(set(["p"]))}
def replay_str(transforms, s="Replay:\n", log_inactive=True):
for t in transforms:
if "transforms" in t.keys():
s = replay_str(t["transforms"], s=s)
elif t["applied"] or log_inactive:
s += t["__class_fullname__"] + " " + str(t["applied"]) + "\n"
return s
class BaseAugmentation(object):
base_default_conf = {
"name": "???",
"shuffle": False,
"p": 1.0,
"verbose": False,
"dtype": "uint8", # (byte, float)
}
default_conf = {}
def __init__(self, conf={}):
"""Perform some logic and call the _init method of the child model."""
default_conf = OmegaConf.merge(
OmegaConf.create(self.base_default_conf),
OmegaConf.create(self.default_conf),
)
OmegaConf.set_struct(default_conf, True)
if isinstance(conf, dict):
conf = OmegaConf.create(conf)
self.conf = OmegaConf.merge(default_conf, conf)
OmegaConf.set_readonly(self.conf, True)
self._init(self.conf)
self.conf = OmegaConf.merge(self.conf, conf)
if self.conf.verbose:
self.compose = A.ReplayCompose
else:
self.compose = A.Compose
if self.conf.dtype == "uint8":
self.dtype = np.uint8
self.preprocess = A.FromFloat(always_apply=True, dtype="uint8")
self.postprocess = A.ToFloat(always_apply=True)
elif self.conf.dtype == "float32":
self.dtype = np.float32
self.preprocess = A.ToFloat(always_apply=True)
self.postprocess = IdentityTransform()
else:
raise ValueError(f"Unsupported dtype {self.conf.dtype}")
self.to_tensor = ToTensorV2()
def _init(self, conf):
"""Child class overwrites this, setting up a list of transforms"""
self.transforms = []
def __call__(self, image, return_tensor=False):
"""image as HW or HWC"""
if isinstance(image, torch.Tensor):
image = image.cpu().detach().numpy()
data = {"image": image}
if image.dtype != self.dtype:
data = self.preprocess(**data)
transforms = self.transforms
if self.conf.shuffle:
order = [i for i, _ in enumerate(transforms)]
np.random.shuffle(order)
transforms = [transforms[i] for i in order]
transformed = self.compose(transforms, p=self.conf.p)(**data)
if self.conf.verbose:
print(replay_str(transformed["replay"]["transforms"]))
transformed = self.postprocess(**transformed)
if return_tensor:
return self.to_tensor(**transformed)["image"]
else:
return transformed["image"]
class IdentityAugmentation(BaseAugmentation):
default_conf = {}
def _init(self, conf):
self.transforms = [IdentityTransform(p=1.0)]
class DarkAugmentation(BaseAugmentation):
default_conf = {"p": 0.75}
def _init(self, conf):
bright_contr = 0.5
blur = 0.1
random_gamma = 0.1
hue = 0.1
self.transforms = [
A.RandomRain(p=0.2),
A.RandomBrightnessContrast(
**kw(
bright_contr,
brightness_limit=(-0.4, 0.0),
contrast_limit=(-0.3, 0.0),
)
),
A.OneOf(
[
A.Blur(**kwi(blur, p=0.1, blur_limit=(3, 9), n="blur")),
A.MotionBlur(
**kwi(blur, p=0.2, blur_limit=(3, 25), n="motion_blur")
),
A.ISONoise(),
A.ImageCompression(),
],
**kwi(blur, p=0.1),
),
A.RandomGamma(**kw(random_gamma, gamma_limit=(15, 65))),
A.OneOf(
[
A.Equalize(),
A.CLAHE(p=0.2),
A.ToGray(),
A.ToSepia(p=0.1),
A.HueSaturationValue(**kw(hue, val_shift_limit=(-100, -40))),
],
p=0.5,
),
]
class LGAugmentation(BaseAugmentation):
default_conf = {"p": 0.95}
def _init(self, conf):
self.transforms = [
A.RandomGamma(p=0.1, gamma_limit=(15, 65)),
A.HueSaturationValue(p=0.1, val_shift_limit=(-100, -40)),
A.OneOf(
[
A.Blur(blur_limit=(3, 9)),
A.MotionBlur(blur_limit=(3, 25)),
A.ISONoise(),
A.ImageCompression(),
],
p=0.1,
),
A.Blur(p=0.1, blur_limit=(3, 9)),
A.MotionBlur(p=0.1, blur_limit=(3, 25)),
A.RandomBrightnessContrast(
p=0.5, brightness_limit=(-0.4, 0.0), contrast_limit=(-0.3, 0.0)
),
A.CLAHE(p=0.2),
]
augmentations = {
"dark": DarkAugmentation,
"lg": LGAugmentation,
"identity": IdentityAugmentation,
}
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