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try:
import imgaug as ia
except ImportError as e:
raise ImportError(
"You are trying to import an augmentation that depends on the imgaug library, but imgaug is not installed. To "
"install a version of Albumentations that contains imgaug please run 'pip install -U albumentations[imgaug]'"
) from e
try:
from imgaug import augmenters as iaa
except ImportError:
import imgaug.imgaug.augmenters as iaa
import warnings
from custom_albumentations.core.bbox_utils import (
convert_bboxes_from_albumentations,
convert_bboxes_to_albumentations,
)
from custom_albumentations.core.keypoints_utils import (
convert_keypoints_from_albumentations,
convert_keypoints_to_albumentations,
)
from ..augmentations import Perspective
from ..core.transforms_interface import (
BasicTransform,
DualTransform,
ImageOnlyTransform,
to_tuple,
)
__all__ = [
"BasicIAATransform",
"DualIAATransform",
"ImageOnlyIAATransform",
"IAAEmboss",
"IAASuperpixels",
"IAASharpen",
"IAAAdditiveGaussianNoise",
"IAACropAndPad",
"IAAFliplr",
"IAAFlipud",
"IAAAffine",
"IAAPiecewiseAffine",
"IAAPerspective",
]
class BasicIAATransform(BasicTransform):
def __init__(self, always_apply=False, p=0.5):
super(BasicIAATransform, self).__init__(always_apply, p)
@property
def processor(self):
return iaa.Noop()
def update_params(self, params, **kwargs):
params = super(BasicIAATransform, self).update_params(params, **kwargs)
params["deterministic_processor"] = self.processor.to_deterministic()
return params
def apply(self, img, deterministic_processor=None, **params):
return deterministic_processor.augment_image(img)
class DualIAATransform(DualTransform, BasicIAATransform):
def apply_to_bboxes(self, bboxes, deterministic_processor=None, rows=0, cols=0, **params):
if len(bboxes) > 0:
bboxes = convert_bboxes_from_albumentations(bboxes, "pascal_voc", rows=rows, cols=cols)
bboxes_t = ia.BoundingBoxesOnImage([ia.BoundingBox(*bbox[:4]) for bbox in bboxes], (rows, cols))
bboxes_t = deterministic_processor.augment_bounding_boxes([bboxes_t])[0].bounding_boxes
bboxes_t = [
[bbox.x1, bbox.y1, bbox.x2, bbox.y2] + list(bbox_orig[4:])
for (bbox, bbox_orig) in zip(bboxes_t, bboxes)
]
bboxes = convert_bboxes_to_albumentations(bboxes_t, "pascal_voc", rows=rows, cols=cols)
return bboxes
"""Applies transformation to keypoints.
Notes:
Since IAA supports only xy keypoints, scale and orientation will remain unchanged.
TODO:
Emit a warning message if child classes of DualIAATransform are instantiated
inside Compose with keypoints format other than 'xy'.
"""
def apply_to_keypoints(self, keypoints, deterministic_processor=None, rows=0, cols=0, **params):
if len(keypoints) > 0:
keypoints = convert_keypoints_from_albumentations(keypoints, "xy", rows=rows, cols=cols)
keypoints_t = ia.KeypointsOnImage([ia.Keypoint(*kp[:2]) for kp in keypoints], (rows, cols))
keypoints_t = deterministic_processor.augment_keypoints([keypoints_t])[0].keypoints
bboxes_t = [[kp.x, kp.y] + list(kp_orig[2:]) for (kp, kp_orig) in zip(keypoints_t, keypoints)]
keypoints = convert_keypoints_to_albumentations(bboxes_t, "xy", rows=rows, cols=cols)
return keypoints
class ImageOnlyIAATransform(ImageOnlyTransform, BasicIAATransform):
pass
class IAACropAndPad(DualIAATransform):
"""This augmentation is deprecated. Please use CropAndPad instead."""
def __init__(self, px=None, percent=None, pad_mode="constant", pad_cval=0, keep_size=True, always_apply=False, p=1):
super(IAACropAndPad, self).__init__(always_apply, p)
self.px = px
self.percent = percent
self.pad_mode = pad_mode
self.pad_cval = pad_cval
self.keep_size = keep_size
warnings.warn("IAACropAndPad is deprecated. Please use CropAndPad instead", FutureWarning)
@property
def processor(self):
return iaa.CropAndPad(self.px, self.percent, self.pad_mode, self.pad_cval, self.keep_size)
def get_transform_init_args_names(self):
return ("px", "percent", "pad_mode", "pad_cval", "keep_size")
class IAAFliplr(DualIAATransform):
"""This augmentation is deprecated. Please use HorizontalFlip instead."""
def __init__(self, always_apply=False, p=0.5):
super().__init__(always_apply, p)
warnings.warn("IAAFliplr is deprecated. Please use HorizontalFlip instead.", FutureWarning)
@property
def processor(self):
return iaa.Fliplr(1)
def get_transform_init_args_names(self):
return ()
class IAAFlipud(DualIAATransform):
"""This augmentation is deprecated. Please use VerticalFlip instead."""
def __init__(self, always_apply=False, p=0.5):
super().__init__(always_apply, p)
warnings.warn("IAAFlipud is deprecated. Please use VerticalFlip instead.", FutureWarning)
@property
def processor(self):
return iaa.Flipud(1)
def get_transform_init_args_names(self):
return ()
class IAAEmboss(ImageOnlyIAATransform):
"""Emboss the input image and overlays the result with the original image.
This augmentation is deprecated. Please use Emboss instead.
Args:
alpha ((float, float)): range to choose the visibility of the embossed image. At 0, only the original image is
visible,at 1.0 only its embossed version is visible. Default: (0.2, 0.5).
strength ((float, float)): strength range of the embossing. Default: (0.2, 0.7).
p (float): probability of applying the transform. Default: 0.5.
Targets:
image
"""
def __init__(self, alpha=(0.2, 0.5), strength=(0.2, 0.7), always_apply=False, p=0.5):
super(IAAEmboss, self).__init__(always_apply, p)
self.alpha = to_tuple(alpha, 0.0)
self.strength = to_tuple(strength, 0.0)
warnings.warn("This augmentation is deprecated. Please use Emboss instead", FutureWarning)
@property
def processor(self):
return iaa.Emboss(self.alpha, self.strength)
def get_transform_init_args_names(self):
return ("alpha", "strength")
class IAASuperpixels(ImageOnlyIAATransform):
"""Completely or partially transform the input image to its superpixel representation. Uses skimage's version
of the SLIC algorithm. May be slow.
This augmentation is deprecated. Please use Superpixels instead.
Args:
p_replace (float): defines the probability of any superpixel area being replaced by the superpixel, i.e. by
the average pixel color within its area. Default: 0.1.
n_segments (int): target number of superpixels to generate. Default: 100.
p (float): probability of applying the transform. Default: 0.5.
Targets:
image
"""
def __init__(self, p_replace=0.1, n_segments=100, always_apply=False, p=0.5):
super(IAASuperpixels, self).__init__(always_apply, p)
self.p_replace = p_replace
self.n_segments = n_segments
warnings.warn("IAASuperpixels is deprecated. Please use Superpixels instead.", FutureWarning)
@property
def processor(self):
return iaa.Superpixels(p_replace=self.p_replace, n_segments=self.n_segments)
def get_transform_init_args_names(self):
return ("p_replace", "n_segments")
class IAASharpen(ImageOnlyIAATransform):
"""Sharpen the input image and overlays the result with the original image.
This augmentation is deprecated. Please use Sharpen instead
Args:
alpha ((float, float)): range to choose the visibility of the sharpened image. At 0, only the original image is
visible, at 1.0 only its sharpened version is visible. Default: (0.2, 0.5).
lightness ((float, float)): range to choose the lightness of the sharpened image. Default: (0.5, 1.0).
p (float): probability of applying the transform. Default: 0.5.
Targets:
image
"""
def __init__(self, alpha=(0.2, 0.5), lightness=(0.5, 1.0), always_apply=False, p=0.5):
super(IAASharpen, self).__init__(always_apply, p)
self.alpha = to_tuple(alpha, 0)
self.lightness = to_tuple(lightness, 0)
warnings.warn("IAASharpen is deprecated. Please use Sharpen instead", FutureWarning)
@property
def processor(self):
return iaa.Sharpen(self.alpha, self.lightness)
def get_transform_init_args_names(self):
return ("alpha", "lightness")
class IAAAdditiveGaussianNoise(ImageOnlyIAATransform):
"""Add gaussian noise to the input image.
This augmentation is deprecated. Please use GaussNoise instead.
Args:
loc (int): mean of the normal distribution that generates the noise. Default: 0.
scale ((float, float)): standard deviation of the normal distribution that generates the noise.
Default: (0.01 * 255, 0.05 * 255).
p (float): probability of applying the transform. Default: 0.5.
Targets:
image
"""
def __init__(self, loc=0, scale=(0.01 * 255, 0.05 * 255), per_channel=False, always_apply=False, p=0.5):
super(IAAAdditiveGaussianNoise, self).__init__(always_apply, p)
self.loc = loc
self.scale = to_tuple(scale, 0.0)
self.per_channel = per_channel
warnings.warn("IAAAdditiveGaussianNoise is deprecated. Please use GaussNoise instead", FutureWarning)
@property
def processor(self):
return iaa.AdditiveGaussianNoise(self.loc, self.scale, self.per_channel)
def get_transform_init_args_names(self):
return ("loc", "scale", "per_channel")
class IAAPiecewiseAffine(DualIAATransform):
"""Place a regular grid of points on the input and randomly move the neighbourhood of these point around
via affine transformations.
This augmentation is deprecated. Please use PiecewiseAffine instead.
Note: This class introduce interpolation artifacts to mask if it has values other than {0;1}
Args:
scale ((float, float): factor range that determines how far each point is moved. Default: (0.03, 0.05).
nb_rows (int): number of rows of points that the regular grid should have. Default: 4.
nb_cols (int): number of columns of points that the regular grid should have. Default: 4.
p (float): probability of applying the transform. Default: 0.5.
Targets:
image, mask
"""
def __init__(
self, scale=(0.03, 0.05), nb_rows=4, nb_cols=4, order=1, cval=0, mode="constant", always_apply=False, p=0.5
):
super(IAAPiecewiseAffine, self).__init__(always_apply, p)
self.scale = to_tuple(scale, 0.0)
self.nb_rows = nb_rows
self.nb_cols = nb_cols
self.order = order
self.cval = cval
self.mode = mode
warnings.warn("This IAAPiecewiseAffine is deprecated. Please use PiecewiseAffine instead", FutureWarning)
@property
def processor(self):
return iaa.PiecewiseAffine(self.scale, self.nb_rows, self.nb_cols, self.order, self.cval, self.mode)
def get_transform_init_args_names(self):
return ("scale", "nb_rows", "nb_cols", "order", "cval", "mode")
class IAAAffine(DualIAATransform):
"""Place a regular grid of points on the input and randomly move the neighbourhood of these point around
via affine transformations.
This augmentation is deprecated. Please use Affine instead.
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=1.0,
translate_percent=None,
translate_px=None,
rotate=0.0,
shear=0.0,
order=1,
cval=0,
mode="reflect",
always_apply=False,
p=0.5,
):
super(IAAAffine, self).__init__(always_apply, p)
self.scale = to_tuple(scale, 1.0)
self.translate_percent = to_tuple(translate_percent, 0)
self.translate_px = to_tuple(translate_px, 0)
self.rotate = to_tuple(rotate)
self.shear = to_tuple(shear)
self.order = order
self.cval = cval
self.mode = mode
warnings.warn("This IAAAffine is deprecated. Please use Affine instead", FutureWarning)
@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 IAAPerspective(Perspective):
"""Perform a random four point perspective transform of the input.
This augmentation is deprecated. Please use Perspective instead.
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):
super(IAAPerspective, self).__init__(always_apply, p)
self.scale = to_tuple(scale, 1.0)
self.keep_size = keep_size
warnings.warn("This IAAPerspective is deprecated. Please use Perspective instead", FutureWarning)
@property
def processor(self):
return iaa.PerspectiveTransform(self.scale, keep_size=self.keep_size)
def get_transform_init_args_names(self):
return ("scale", "keep_size")
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