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import imgaug.augmenters as iaa
from albumentations import to_tuple
from albumentations.imgaug.transforms import DualIAATransform
class IAAAffine2(DualIAATransform):
"""Place a regular grid of points on the input and randomly move the neighbourhood of these point around
via affine transformations.
Note: This class introduce interpolation artifacts to mask if it has values other than {0;1}
Args:
p (float): probability of applying the transform. Default: 0.5.
Targets:
image, mask
"""
def __init__(
self,
scale=(0.7, 1.3),
translate_percent=None,
translate_px=None,
rotate=0.0,
shear=(-0.1, 0.1),
order=1,
cval=0,
mode="reflect",
always_apply=False,
p=0.5,
):
super(IAAAffine2, self).__init__(always_apply, p)
self.scale = dict(x=scale, y=scale)
self.translate_percent = to_tuple(translate_percent, 0)
self.translate_px = to_tuple(translate_px, 0)
self.rotate = to_tuple(rotate)
self.shear = dict(x=shear, y=shear)
self.order = order
self.cval = cval
self.mode = mode
@property
def processor(self):
return iaa.Affine(
self.scale,
self.translate_percent,
self.translate_px,
self.rotate,
self.shear,
self.order,
self.cval,
self.mode,
)
def get_transform_init_args_names(self):
return (
"scale",
"translate_percent",
"translate_px",
"rotate",
"shear",
"order",
"cval",
"mode",
)
class IAAPerspective2(DualIAATransform):
"""Perform a random four point perspective transform of the input.
Note: This class introduce interpolation artifacts to mask if it has values other than {0;1}
Args:
scale ((float, float): standard deviation of the normal distributions. These are used to sample
the random distances of the subimage's corners from the full image's corners. Default: (0.05, 0.1).
p (float): probability of applying the transform. Default: 0.5.
Targets:
image, mask
"""
def __init__(
self,
scale=(0.05, 0.1),
keep_size=True,
always_apply=False,
p=0.5,
order=1,
cval=0,
mode="replicate",
):
super(IAAPerspective2, self).__init__(always_apply, p)
self.scale = to_tuple(scale, 1.0)
self.keep_size = keep_size
self.cval = cval
self.mode = mode
@property
def processor(self):
return iaa.PerspectiveTransform(
self.scale, keep_size=self.keep_size, mode=self.mode, cval=self.cval
)
def get_transform_init_args_names(self):
return ("scale", "keep_size")
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