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"""
Adapted from https://github.com/nv-nguyen/template-pose/blob/main/src/utils/augmentation.py
"""
from torchvision import transforms
from PIL import ImageEnhance, ImageFilter, Image
import numpy as np
import random
import logging
from torchvision.transforms import RandomResizedCrop, ToTensor
class PillowRGBAugmentation:
def __init__(self, pillow_fn, p, factor_interval):
self._pillow_fn = pillow_fn
self.p = p
self.factor_interval = factor_interval
def __call__(self, PIL_image):
if random.random() <= self.p:
factor = random.uniform(*self.factor_interval)
if PIL_image.mode != "RGB":
logging.warning(
f"Error when apply data aug, image mode: {PIL_image.mode}"
)
imgs = imgs.convert("RGB")
logging.warning(f"Success to change to {PIL_image.mode}")
PIL_image = (self._pillow_fn(PIL_image).enhance(factor=factor)).convert(
"RGB"
)
return PIL_image
class PillowSharpness(PillowRGBAugmentation):
def __init__(
self,
p=0.3,
factor_interval=(0, 40.0),
):
super().__init__(
pillow_fn=ImageEnhance.Sharpness,
p=p,
factor_interval=factor_interval,
)
class PillowContrast(PillowRGBAugmentation):
def __init__(
self,
p=0.3,
factor_interval=(0.5, 1.6),
):
super().__init__(
pillow_fn=ImageEnhance.Contrast,
p=p,
factor_interval=factor_interval,
)
class PillowBrightness(PillowRGBAugmentation):
def __init__(
self,
p=0.5,
factor_interval=(0.5, 2.0),
):
super().__init__(
pillow_fn=ImageEnhance.Brightness,
p=p,
factor_interval=factor_interval,
)
class PillowColor(PillowRGBAugmentation):
def __init__(
self,
p=1,
factor_interval=(0.0, 20.0),
):
super().__init__(
pillow_fn=ImageEnhance.Color,
p=p,
factor_interval=factor_interval,
)
class PillowBlur:
def __init__(self, p=0.4, factor_interval=(1, 3)):
self.p = p
self.k = random.randint(*factor_interval)
def __call__(self, PIL_image):
if random.random() <= self.p:
PIL_image = PIL_image.filter(ImageFilter.GaussianBlur(self.k))
return PIL_image
class NumpyGaussianNoise:
def __init__(self, p, factor_interval=(0.01, 0.3)):
self.noise_ratio = random.uniform(*factor_interval)
self.p = p
def __call__(self, img):
if random.random() <= self.p:
img = np.copy(img)
noisesigma = random.uniform(0, self.noise_ratio)
gauss = np.random.normal(0, noisesigma, img.shape) * 255
img = img + gauss
img[img > 255] = 255
img[img < 0] = 0
return Image.fromarray(np.uint8(img))
class StandardAugmentation:
def __init__(
self, names, brightness, contrast, sharpness, color, blur, gaussian_noise
):
self.brightness = brightness
self.contrast = contrast
self.sharpness = sharpness
self.color = color
self.blur = blur
self.gaussian_noise = gaussian_noise
# define a dictionary of augmentation functions to be applied
self.names = names.split(",")
self.augmentations = {
"brightness": self.brightness,
"contrast": self.contrast,
"sharpness": self.sharpness,
"color": self.color,
"blur": self.blur,
"gaussian_noise": self.gaussian_noise,
}
def __call__(self, img):
for name in self.names:
img = self.augmentations[name](img)
return img
class GeometricAugmentation:
def __init__(
self,
names,
random_resized_crop,
random_horizontal_flip,
random_vertical_flip,
random_rotation,
):
self.random_resized_crop = random_resized_crop
self.random_horizontal_flip = random_horizontal_flip
self.random_vertical_flip = random_vertical_flip
self.random_rotation = random_rotation
self.names = names.split(",")
self.augmentations = {
"random_resized_crop": self.random_resized_crop,
"random_horizontal_flip": self.random_horizontal_flip,
"random_vertical_flip": self.random_vertical_flip,
"random_rotation": self.random_rotation,
}
def __call__(self, img):
for name in self.names:
img = self.augmentations[name](img)
return img
class ImageAugmentation:
def __init__(
self, names, clip_transform, standard_augmentation, geometric_augmentation
):
self.clip_transform = clip_transform
self.standard_augmentation = standard_augmentation
self.geometric_augmentation = geometric_augmentation
self.names = names.split(",")
self.transforms = {
"clip_transform": self.clip_transform,
"standard_augmentation": self.standard_augmentation,
"geometric_augmentation": self.geometric_augmentation,
}
print(f"Image augmentation: {self.names}")
def __call__(self, img):
for name in self.names:
img = self.transforms[name](img)
return img
if __name__ == "__main__":
# sanity check
import glob
import torchvision.transforms as transforms
from torchvision.utils import save_image
from omegaconf import DictConfig, OmegaConf
from hydra.utils import instantiate
import torch
from PIL import Image
augmentation_config = OmegaConf.load(
"./configs/dataset/train_transform/augmentation.yaml"
)
augmentation_config.names = "standard_augmentation,geometric_augmentation"
augmentation_transform = instantiate(augmentation_config)
img_paths = glob.glob("./datasets/osv5m/test/images/*.jpg")
num_try = 20
num_try_per_image = 8
num_imgs = 8
for idx in range(num_try):
imgs = []
for idx_img in range(num_imgs):
img = Image.open(img_paths[idx_img])
for idx_try in range(num_try_per_image):
if idx_try == 0:
imgs.append(ToTensor()(img.resize((224, 224))))
img_aug = augmentation_transform(img.copy())
img_aug = ToTensor()(img_aug)
imgs.append(img_aug)
imgs = torch.stack(imgs)
save_image(imgs, f"augmentation_{idx:03d}.png", nrow=9)
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