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def empty_aug1():
return [
HorizontalFlip(p=0.001),
# IAAPiecewiseAffine(p=1.0),
OneOf(
[
# OpticalDistortion(p=0.1),
# GridDistortion(p=0.1),
# IAAPerspective(p=1.0),
# IAAAffine(p=1.0),
IAAPiecewiseAffine(p=1.0)
],
p=0.0,
),
def light_augmentations():
return A.Compose([
A.HorizontalFlip(),
A.RandomBrightnessContrast(),
A.OneOf([
A.ShiftScaleRotate(scale_limit=0.05, rotate_limit=15, border_mode=cv2.BORDER_CONSTANT),
A.IAAAffine(),
A.IAAPerspective(),
A.NoOp()
]),
A.HueSaturationValue(),
A.Normalize()
])
),
height=train_parameters["height_crop_size"],
width=train_parameters["width_crop_size"],
w2h_ratio=1.0,
p=1,
),
albu.ShiftScaleRotate(
border_mode=cv2.BORDER_CONSTANT, rotate_limit=10, scale_limit=0, p=0.5, mask_value=ignore_index
),
albu.RandomBrightnessContrast(p=0.5),
albu.RandomGamma(p=0.5),
albu.ImageCompression(quality_lower=20, quality_upper=100, p=0.5),
albu.GaussNoise(p=0.5),
albu.Blur(p=0.5),
albu.CoarseDropout(p=0.5, max_height=26, max_width=16),
albu.OneOf([albu.HueSaturationValue(p=0.5), albu.RGBShift(p=0.5)], p=0.5),
normalization,
],
p=1,
)
val_augmentations = albu.Compose(
[
albu.PadIfNeeded(
min_height=1024, min_width=2048, border_mode=cv2.BORDER_CONSTANT, mask_value=ignore_index, p=1
),
normalization,
],
p=1,
)
test_augmentations = albu.Compose([normalization], p=1)
value=0),
A.NoOp()
]),
A.OneOf([
A.RandomSizedCrop(min_max_height=(int(image_size[0] * 0.75), image_size[0]),
height=image_size[0],
width=image_size[1], p=0.3),
A.NoOp()
]),
A.ISONoise(p=0.5),
# Brightness/contrast augmentations
A.OneOf([
A.RandomBrightnessContrast(brightness_limit=0.5,
contrast_limit=0.4),
IndependentRandomBrightnessContrast(brightness_limit=0.25,
contrast_limit=0.24),
A.RandomGamma(gamma_limit=(50, 150)),
A.NoOp()
]),
A.OneOf([
A.RGBShift(r_shift_limit=40, b_shift_limit=30, g_shift_limit=30),
A.HueSaturationValue(hue_shift_limit=10,
sat_shift_limit=10),
A.ToGray(p=0.2),
A.NoOp()
]),
def get_augumentation(phase, width=512, height=512, min_area=0., min_visibility=0.):
list_transforms = []
if phase == 'train':
list_transforms.extend([
albu.OneOf([
albu.ShiftScaleRotate(shift_limit=0.05, scale_limit=0.1,
rotate_limit=15,
border_mode=cv2.BORDER_CONSTANT, value=0),
albu.NoOp()
]),
albu.augmentations.transforms.RandomResizedCrop(
height=height,
width=width, p=1.0),
albu.OneOf([
albu.RandomBrightnessContrast(brightness_limit=0.5,
contrast_limit=0.4),
albu.RandomGamma(gamma_limit=(50, 150)),
albu.NoOp()
]),
albu.OneOf([
albu.RGBShift(r_shift_limit=20, b_shift_limit=15,
),
height=train_parameters["height_crop_size"],
width=train_parameters["width_crop_size"],
w2h_ratio=1.0,
p=1,
),
albu.ShiftScaleRotate(
border_mode=cv2.BORDER_CONSTANT, rotate_limit=10, scale_limit=0, p=0.5, mask_value=ignore_index
),
albu.RandomBrightnessContrast(p=0.5),
albu.RandomGamma(p=0.5),
albu.ImageCompression(quality_lower=20, quality_upper=100, p=0.5),
albu.GaussNoise(p=0.5),
albu.Blur(p=0.5),
albu.CoarseDropout(p=0.5, max_height=26, max_width=16),
albu.OneOf([albu.HueSaturationValue(p=0.5), albu.RGBShift(p=0.5)], p=0.5),
normalization,
],
p=1,
)
val_augmentations = albu.Compose(
[
albu.PadIfNeeded(
min_height=1024, min_width=2048, border_mode=cv2.BORDER_CONSTANT, mask_value=ignore_index, p=1
),
normalization,
],
p=1,
)
test_augmentations = albu.Compose([normalization], p=1)