"""
Augmenters that blend two images with each other.
List of augmenters:
* :class:`BlendAlpha`
* :class:`BlendAlphaMask`
* :class:`BlendAlphaElementwise`
* :class:`BlendAlphaSimplexNoise`
* :class:`BlendAlphaFrequencyNoise`
* :class:`BlendAlphaSomeColors`
* :class:`BlendAlphaHorizontalLinearGradient`
* :class:`BlendAlphaVerticalLinearGradient`
* :class:`BlendAlphaSegMapClassIds`
* :class:`BlendAlphaBoundingBoxes`
* :class:`BlendAlphaRegularGrid`
* :class:`BlendAlphaCheckerboard`
"""
from __future__ import print_function, division, absolute_import
from abc import ABCMeta, abstractmethod
import numpy as np
import six
import cv2
import imgaug as ia
from imgaug.imgaug import _normalize_cv2_input_arr_
from . import meta
from .. import parameters as iap
from .. import dtypes as iadt
from .. import random as iarandom
from ..augmentables import utils as augm_utils
def _split_1d_array_to_list(arr, sizes):
result = []
i = 0
for size in sizes:
result.append(arr[i:i+size])
i += size
return result
[docs]def blend_alpha(image_fg, image_bg, alpha, eps=1e-2):
"""
Blend two images using an alpha blending.
In alpha blending, the two images are naively mixed using a multiplier.
Let ``A`` be the foreground image and ``B`` the background image and
``a`` is the alpha value. Each pixel intensity is then computed as
``a * A_ij + (1-a) * B_ij``.
**Supported dtypes**:
* ``uint8``: yes; fully tested
* ``uint16``: yes; fully tested
* ``uint32``: yes; fully tested
* ``uint64``: yes; fully tested (1)
* ``int8``: yes; fully tested
* ``int16``: yes; fully tested
* ``int32``: yes; fully tested
* ``int64``: yes; fully tested (1)
* ``float16``: yes; fully tested
* ``float32``: yes; fully tested
* ``float64``: yes; fully tested (1)
* ``float128``: no (2)
* ``bool``: yes; fully tested (2)
- (1) Tests show that these dtypes work, but a conversion to
``float128`` happens, which only has 96 bits of size instead of
true 128 bits and hence not twice as much resolution. It is
possible that these dtypes result in inaccuracies, though the
tests did not indicate that.
- (2) Not available due to the input dtype having to be increased to
an equivalent float dtype with two times the input resolution.
- (3) Mapped internally to ``float16``.
Parameters
----------
image_fg : (H,W,[C]) ndarray
Foreground image. Shape and dtype kind must match the one of the
background image.
image_bg : (H,W,[C]) ndarray
Background image. Shape and dtype kind must match the one of the
foreground image.
alpha : number or iterable of number or ndarray
The blending factor, between ``0.0`` and ``1.0``. Can be interpreted
as the opacity of the foreground image. Values around ``1.0`` result
in only the foreground image being visible. Values around ``0.0``
result in only the background image being visible. Multiple alphas
may be provided. In these cases, there must be exactly one alpha per
channel in the foreground/background image. Alternatively, for
``(H,W,C)`` images, either one ``(H,W)`` array or an ``(H,W,C)``
array of alphas may be provided, denoting the elementwise alpha value.
eps : number, optional
Controls when an alpha is to be interpreted as exactly ``1.0`` or
exactly ``0.0``, resulting in only the foreground/background being
visible and skipping the actual computation.
Returns
-------
image_blend : (H,W,C) ndarray
Blend of foreground and background image.
"""
assert image_fg.shape == image_bg.shape, (
"Expected foreground and background images to have the same shape. "
"Got %s and %s." % (image_fg.shape, image_bg.shape))
assert image_fg.dtype.kind == image_bg.dtype.kind, (
"Expected foreground and background images to have the same dtype "
"kind. Got %s and %s." % (image_fg.dtype.kind, image_bg.dtype.kind))
# TODO switch to gate_dtypes()
assert image_fg.dtype.name not in ["float128"], (
"Foreground image was float128, but blend_alpha() cannot handle that "
"dtype.")
assert image_bg.dtype.name not in ["float128"], (
"Background image was float128, but blend_alpha() cannot handle that "
"dtype.")
input_was_2d = (image_fg.ndim == 2)
if input_was_2d:
image_fg = image_fg[..., np.newaxis]
image_bg = image_bg[..., np.newaxis]
input_was_bool = False
if image_fg.dtype.kind == "b":
input_was_bool = True
# use float32 instead of float16 here because it seems to be faster
image_fg = image_fg.astype(np.float32)
image_bg = image_bg.astype(np.float32)
alpha = np.array(alpha, dtype=np.float64)
if alpha.size == 1:
pass
else:
if alpha.ndim == 2:
assert alpha.shape == image_fg.shape[0:2], (
"'alpha' given as an array must match the height and width "
"of the foreground and background image. Got shape %s vs "
"foreground/background shape %s." % (
alpha.shape, image_fg.shape))
alpha = alpha.reshape((alpha.shape[0], alpha.shape[1], 1))
elif alpha.ndim == 3:
assert (
alpha.shape == image_fg.shape
or alpha.shape == image_fg.shape[0:2] + (1,)), (
"'alpha' given as an array must match the height and "
"width of the foreground and background image. Got "
"shape %s vs foreground/background shape %s." % (
alpha.shape, image_fg.shape))
else:
alpha = alpha.reshape((1, 1, -1))
if alpha.shape[2] != image_fg.shape[2]:
alpha = np.tile(alpha, (1, 1, image_fg.shape[2]))
if not input_was_bool:
if np.all(alpha >= 1.0 - eps):
if input_was_2d:
image_fg = image_fg[..., 0]
return np.copy(image_fg)
if np.all(alpha <= eps):
if input_was_2d:
image_bg = image_bg[..., 0]
return np.copy(image_bg)
# for efficiency reaons, only test one value of alpha here, even if alpha
# is much larger
if alpha.size > 0:
assert 0 <= alpha.item(0) <= 1.0, (
"Expected 'alpha' value(s) to be in the interval [0.0, 1.0]. "
"Got min %.4f and max %.4f." % (np.min(alpha), np.max(alpha)))
dt_images = iadt.get_minimal_dtype([image_fg, image_bg])
# doing the below itemsize increase only for non-float images led to
# inaccuracies for large float values
# we also use a minimum of 4 bytes (=float32), as float32 tends to be
# faster than float16
isize = dt_images.itemsize * 2
isize = max(isize, 4)
dt_blend = np.dtype("f%d" % (isize,))
if alpha.dtype.name != dt_blend.name:
alpha = alpha.astype(dt_blend)
if image_fg.dtype.name != dt_blend.name:
image_fg = image_fg.astype(dt_blend)
if image_bg.dtype.name != dt_blend.name:
image_bg = image_bg.astype(dt_blend)
# the following is equivalent to
# image_blend = alpha * image_fg + (1 - alpha) * image_bg
# but supposedly faster
image_blend = image_bg + alpha * (image_fg - image_bg)
if input_was_bool:
image_blend = image_blend > 0.5
else:
# skip clip, because alpha is expected to be in range [0.0, 1.0] and
# both images must have same dtype dont skip round, because otherwise
# it is very unlikely to hit the image's max possible value
image_blend = iadt.restore_dtypes_(
image_blend, dt_images, clip=False, round=True)
if input_was_2d:
return image_blend[:, :, 0]
return image_blend
# Added in 0.4.0.
def _generate_branch_outputs(augmenter, batch, hooks, parents):
parents_extended = parents + [augmenter]
# Note here that the propagation hook removes columns in the batch
# and re-adds them afterwards. So the batch should not be copied
# after the `with` statement.
outputs_fg = batch
if augmenter.foreground is not None:
outputs_fg = outputs_fg.deepcopy()
with outputs_fg.propagation_hooks_ctx(augmenter, hooks, parents):
if augmenter.foreground is not None:
outputs_fg = augmenter.foreground.augment_batch_(
outputs_fg,
parents=parents_extended,
hooks=hooks
)
outputs_bg = batch
if augmenter.background is not None:
outputs_bg = outputs_bg.deepcopy()
with outputs_bg.propagation_hooks_ctx(augmenter, hooks, parents):
outputs_bg = augmenter.background.augment_batch_(
outputs_bg,
parents=parents_extended,
hooks=hooks
)
return outputs_fg, outputs_bg
# Added in 0.4.0.
def _to_deterministic(augmenter):
aug = augmenter.copy()
aug.foreground = (
aug.foreground.to_deterministic()
if aug.foreground is not None
else None)
aug.background = (
aug.background.to_deterministic()
if aug.background is not None
else None)
aug.deterministic = True
aug.random_state = augmenter.random_state.derive_rng_()
return aug
[docs]class BlendAlpha(meta.Augmenter):
"""
Alpha-blend two image sources using an alpha/opacity value.
The two image sources can be imagined as branches.
If a source is not given, it is automatically the same as the input.
Let ``FG`` be the foreground branch and ``BG`` be the background branch.
Then the result images are defined as ``factor * FG + (1-factor) * BG``,
where ``factor`` is an overlay factor.
.. note::
It is not recommended to use ``BlendAlpha`` with augmenters
that change the geometry of images (e.g. horizontal flips, affine
transformations) if you *also* want to augment coordinates (e.g.
keypoints, polygons, ...), as it is unclear which of the two
coordinate results (foreground or background branch) should be used
as the coordinates after augmentation.
Currently, if ``factor >= 0.5`` (per image), the results of the
foreground branch are used as the new coordinates, otherwise the
results of the background branch.
Added in 0.4.0. (Before that named `Alpha`.)
**Supported dtypes**:
See :func:`~imgaug.augmenters.blend.blend_alpha`.
Parameters
----------
factor : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
Opacity of the results of the foreground branch. Values close to
``0.0`` mean that the results from the background branch (see
parameter `background`) make up most of the final image.
* If float, then that value will be used for all images.
* If tuple ``(a, b)``, then a random value from the interval
``[a, b]`` will be sampled per image.
* If a list, then a random value will be picked from that list per
image.
* If ``StochasticParameter``, then that parameter will be used to
sample a value per image.
foreground : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the foreground branch.
High alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the foreground branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
background : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the background branch.
Low alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the background branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
per_channel : bool or float or imgaug.parameters.StochasticParameter, optional
Whether to use the same factor for all channels (``False``)
or to sample a new value for each channel (``True``).
If this value is a float ``p``, then for ``p`` percent of all images
`per_channel` will be treated as True, otherwise as False.
seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
name : None or str, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
Old name for parameter `seed`.
Its usage will not yet cause a deprecation warning,
but it is still recommended to use `seed` now.
Outdated since 0.4.0.
deterministic : bool, optional
Deprecated since 0.4.0.
See method ``to_deterministic()`` for an alternative and for
details about what the "deterministic mode" actually does.
Examples
--------
>>> import imgaug.augmenters as iaa
>>> aug = iaa.BlendAlpha(0.5, iaa.Grayscale(1.0))
Convert each image to pure grayscale and alpha-blend the result with the
original image using an alpha of ``50%``, thereby removing about ``50%`` of
all color. This is equivalent to ``iaa.Grayscale(0.5)``.
>>> aug = iaa.BlendAlpha((0.0, 1.0), iaa.Grayscale(1.0))
Same as in the previous example, but the alpha factor is sampled uniformly
from the interval ``[0.0, 1.0]`` once per image, thereby removing a random
fraction of all colors. This is equivalent to
``iaa.Grayscale((0.0, 1.0))``.
>>> aug = iaa.BlendAlpha(
>>> (0.0, 1.0),
>>> iaa.Affine(rotate=(-20, 20)),
>>> per_channel=0.5)
First, rotate each image by a random degree sampled uniformly from the
interval ``[-20, 20]``. Then, alpha-blend that new image with the original
one using a random factor sampled uniformly from the interval
``[0.0, 1.0]``. For ``50%`` of all images, the blending happens
channel-wise and the factor is sampled independently per channel
(``per_channel=0.5``). As a result, e.g. the red channel may look visibly
rotated (factor near ``1.0``), while the green and blue channels may not
look rotated (factors near ``0.0``).
>>> aug = iaa.BlendAlpha(
>>> (0.0, 1.0),
>>> foreground=iaa.Add(100),
>>> background=iaa.Multiply(0.2))
Apply two branches of augmenters -- ``A`` and ``B`` -- *independently*
to input images and alpha-blend the results of these branches using a
factor ``f``. Branch ``A`` increases image pixel intensities by ``100``
and ``B`` multiplies the pixel intensities by ``0.2``. ``f`` is sampled
uniformly from the interval ``[0.0, 1.0]`` per image. The resulting images
contain a bit of ``A`` and a bit of ``B``.
>>> aug = iaa.BlendAlpha([0.25, 0.75], iaa.MedianBlur(13))
Apply median blur to each image and alpha-blend the result with the
original image using an alpha factor of either exactly ``0.25`` or
exactly ``0.75`` (sampled once per image).
"""
# Added in 0.4.0.
def __init__(self, factor=(0.0, 1.0), foreground=None, background=None,
per_channel=False,
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
super(BlendAlpha, self).__init__(
seed=seed, name=name,
random_state=random_state, deterministic=deterministic)
self.factor = iap.handle_continuous_param(
factor, "factor", value_range=(0, 1.0), tuple_to_uniform=True,
list_to_choice=True)
assert foreground is not None or background is not None, (
"Expected 'foreground' and/or 'background' to not be None (i.e. "
"at least one Augmenter), but got two None values.")
self.foreground = meta.handle_children_list(
foreground, self.name, "foreground", default=None)
self.background = meta.handle_children_list(
background, self.name, "background", default=None)
self.per_channel = iap.handle_probability_param(per_channel,
"per_channel")
self.epsilon = 1e-2
# Added in 0.4.0.
def _augment_batch_(self, batch, random_state, parents, hooks):
batch_fg, batch_bg = _generate_branch_outputs(
self, batch, hooks, parents)
columns = batch.columns
shapes = batch.get_rowwise_shapes()
nb_images = len(shapes)
nb_channels_max = max([shape[2] if len(shape) > 2 else 1
for shape in shapes])
rngs = random_state.duplicate(2)
per_channel = self.per_channel.draw_samples(nb_images,
random_state=rngs[0])
alphas = self.factor.draw_samples((nb_images, nb_channels_max),
random_state=rngs[1])
for i, shape in enumerate(shapes):
if per_channel[i] > 0.5:
nb_channels = shape[2] if len(shape) > 2 else 1
alphas_i = alphas[i, 0:nb_channels]
else:
# We catch here the case of alphas[i] being empty, which can
# happen if all images have 0 channels.
# In that case the alpha value doesn't matter as the image
# contains zero values anyways.
alphas_i = alphas[i, 0] if alphas[i].size > 0 else 0
# compute alpha for non-image data -- average() also works with
# scalars
alphas_i_avg = np.average(alphas_i)
use_fg_branch = alphas_i_avg >= 0.5
# blend images
if batch.images is not None:
batch.images[i] = blend_alpha(batch_fg.images[i],
batch_bg.images[i],
alphas_i, eps=self.epsilon)
# blend non-images
# TODO Use gradual blending for heatmaps here (as for images)?
# Heatmaps are probably the only augmentable where this makes
# sense.
for column in columns:
if column.name != "images":
batch_use = (batch_fg if use_fg_branch
else batch_bg)
column.value[i] = getattr(batch_use, column.attr_name)[i]
return batch
# Added in 0.4.0.
def _to_deterministic(self):
return _to_deterministic(self)
# Added in 0.4.0.
[docs] def get_parameters(self):
"""See :func:`~imgaug.augmenters.meta.Augmenter.get_parameters`."""
return [self.factor, self.per_channel]
# Added in 0.4.0.
[docs] def get_children_lists(self):
"""See :func:`~imgaug.augmenters.meta.Augmenter.get_children_lists`."""
return [lst for lst in [self.foreground, self.background]
if lst is not None]
# Added in 0.4.0.
def __str__(self):
pattern = (
"%s("
"factor=%s, per_channel=%s, name=%s, "
"foreground=%s, background=%s, "
"deterministic=%s"
")"
)
return pattern % (
self.__class__.__name__, self.factor, self.per_channel, self.name,
self.foreground, self.background, self.deterministic)
# tested indirectly via BlendAlphaElementwise for historic reasons
[docs]class BlendAlphaMask(meta.Augmenter):
"""
Alpha-blend two image sources using non-binary masks generated per image.
This augmenter queries for each image a mask generator to generate
a ``(H,W)`` or ``(H,W,C)`` channelwise mask ``[0.0, 1.0]``, where
``H`` is the image height and ``W`` the width.
The mask will then be used to alpha-blend pixel- and possibly channel-wise
between a foreground branch of augmenters and a background branch.
(Both branches default to the identity operation if not provided.)
See also :class:`~imgaug.augmenters.blend.BlendAlpha`.
.. note::
It is not recommended to use ``BlendAlphaMask`` with augmenters
that change the geometry of images (e.g. horizontal flips, affine
transformations) if you *also* want to augment coordinates (e.g.
keypoints, polygons, ...), as it is unclear which of the two
coordinate results (foreground or background branch) should be used
as the final output coordinates after augmentation.
Currently, for keypoints the results of the
foreground and background branch will be mixed. That means that for
each coordinate the augmented result will be picked from the
foreground or background branch based on the average alpha mask value
at the corresponding spatial location.
For bounding boxes, line strings and polygons, either all objects
(on an image) of the foreground or all of the background branch will
be used, based on the average over the whole alpha mask.
Added in 0.4.0.
**Supported dtypes**:
See :func:`~imgaug.augmenters.blend.blend_alpha`.
Parameters
----------
mask_generator : IBatchwiseMaskGenerator
A generator that will be queried per image to generate a mask.
foreground : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the foreground branch.
High alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the foreground branch (i.e. identity function).
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
background : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the background branch.
Low alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the background branch (i.e. identity function).
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
name : None or str, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
Old name for parameter `seed`.
Its usage will not yet cause a deprecation warning,
but it is still recommended to use `seed` now.
Outdated since 0.4.0.
deterministic : bool, optional
Deprecated since 0.4.0.
See method ``to_deterministic()`` for an alternative and for
details about what the "deterministic mode" actually does.
Examples
--------
>>> import imgaug.augmenters as iaa
>>> aug = iaa.BlendAlphaMask(
>>> iaa.InvertMaskGen(0.5, iaa.VerticalLinearGradientMaskGen()),
>>> iaa.Sequential([
>>> iaa.Clouds(),
>>> iaa.WithChannels([1, 2], iaa.Multiply(0.5))
>>> ])
>>> )
Create an augmenter that sometimes adds clouds at the bottom and sometimes
at the top of the image.
"""
# Currently the mode is only used for keypoint augmentation.
# either or: use all keypoints from fg or all from bg branch (based
# on average of the whole mask).
# pointwise: decide for each point whether to use the fg or bg
# branch's keypoint (based on the average mask value at the point's
# xy-location).
_MODE_EITHER_OR = "either-or"
_MODE_POINTWISE = "pointwise"
_MODES = [_MODE_POINTWISE, _MODE_EITHER_OR]
# Added in 0.4.0.
def __init__(self, mask_generator,
foreground=None, background=None,
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
super(BlendAlphaMask, self).__init__(
seed=seed, name=name,
random_state=random_state, deterministic=deterministic)
self.mask_generator = mask_generator
assert foreground is not None or background is not None, (
"Expected 'foreground' and/or 'background' to not be None (i.e. "
"at least one Augmenter), but got two None values.")
self.foreground = meta.handle_children_list(
foreground, self.name, "foreground", default=None)
self.background = meta.handle_children_list(
background, self.name, "background", default=None)
# this controls how keypoints and polygons are augmented
# Non-keypoints currently uses an either-or approach.
# Using pointwise augmentation is problematic for polygons and line
# strings, because the order of the points may have changed (e.g.
# from clockwise to counter-clockwise). For polygons, it is also
# overall more likely that some child-augmenter added/deleted points
# and we would need a polygon recoverer.
# Overall it seems to be the better approach to use all polygons
# from one branch or the other, which guarantuees their validity.
# TODO decide the either-or not based on the whole average mask
# value but on the average mask value within the polygon's area?
self._coord_modes = {
"keypoints": self._MODE_POINTWISE,
"polygons": self._MODE_EITHER_OR,
"line_strings": self._MODE_EITHER_OR,
"bounding_boxes": self._MODE_EITHER_OR
}
self.epsilon = 1e-2
# Added in 0.4.0.
def _augment_batch_(self, batch, random_state, parents, hooks):
batch_fg, batch_bg = _generate_branch_outputs(
self, batch, hooks, parents)
masks = self.mask_generator.draw_masks(batch, random_state)
for i, mask in enumerate(masks):
if batch.images is not None:
batch.images[i] = blend_alpha(batch_fg.images[i],
batch_bg.images[i],
mask, eps=self.epsilon)
if batch.heatmaps is not None:
arr = batch.heatmaps[i].arr_0to1
arr_height, arr_width = arr.shape[0:2]
mask_binarized = self._binarize_mask(mask,
arr_height, arr_width)
batch.heatmaps[i].arr_0to1 = blend_alpha(
batch_fg.heatmaps[i].arr_0to1,
batch_bg.heatmaps[i].arr_0to1,
mask_binarized, eps=self.epsilon)
if batch.segmentation_maps is not None:
arr = batch.segmentation_maps[i].arr
arr_height, arr_width = arr.shape[0:2]
mask_binarized = self._binarize_mask(mask,
arr_height, arr_width)
batch.segmentation_maps[i].arr = blend_alpha(
batch_fg.segmentation_maps[i].arr,
batch_bg.segmentation_maps[i].arr,
mask_binarized, eps=self.epsilon)
for augm_attr_name in ["keypoints", "bounding_boxes", "polygons",
"line_strings"]:
augm_value = getattr(batch, augm_attr_name)
if augm_value is not None:
augm_value[i] = self._blend_coordinates(
augm_value[i],
getattr(batch_fg, augm_attr_name)[i],
getattr(batch_bg, augm_attr_name)[i],
mask,
self._coord_modes[augm_attr_name]
)
return batch
# Added in 0.4.0.
@classmethod
def _binarize_mask(cls, mask, arr_height, arr_width):
# Average over channels, resize to heatmap/segmap array size
# (+clip for cubic interpolation). We can use none-NN interpolation
# for segmaps here as this is just the mask and not the segmap
# array.
mask_3d = np.atleast_3d(mask)
# masks with zero-sized axes crash in np.average() and cannot be
# upscaled in imresize_single_image()
if mask.size == 0:
mask_rs = np.zeros((arr_height, arr_width),
dtype=np.float32)
else:
mask_avg = (
np.average(mask_3d, axis=2) if mask_3d.shape[2] > 0 else 1.0)
mask_rs = ia.imresize_single_image(mask_avg,
(arr_height, arr_width))
mask_arr = iadt.clip_(mask_rs, 0, 1.0)
mask_arr_binarized = (mask_arr >= 0.5)
return mask_arr_binarized
# Added in 0.4.0.
@classmethod
def _blend_coordinates(cls, cbaoi, cbaoi_fg, cbaoi_bg, mask_image,
mode):
coords = augm_utils.convert_cbaois_to_kpsois(cbaoi)
coords_fg = augm_utils.convert_cbaois_to_kpsois(cbaoi_fg)
coords_bg = augm_utils.convert_cbaois_to_kpsois(cbaoi_bg)
coords = coords.to_xy_array()
coords_fg = coords_fg.to_xy_array()
coords_bg = coords_bg.to_xy_array()
assert coords.shape == coords_fg.shape == coords_bg.shape, (
"Expected number of coordinates to not be changed by foreground "
"or background branch in BlendAlphaMask. But input coordinates "
"of shape %s were changed to %s (foreground) and %s "
"(background). Make sure to not use any augmenters that affect "
"the existence of coordinates." % (
coords.shape, coords_fg.shape, coords_bg.shape))
h_img, w_img = mask_image.shape[0:2]
if mode == cls._MODE_POINTWISE:
# Augment pointwise, i.e. check for each point and its
# xy-location the average mask value and pick based on that
# either the point from the foreground or background branch.
assert len(coords_fg) == len(coords_bg), (
"Got different numbers of coordinates before/after "
"augmentation in BlendAlphaMask. The number of "
"coordinates is currently not allowed to change for this "
"augmenter. Input contained %d coordinates, foreground "
"branch %d, backround branch %d." % (
len(coords), len(coords_fg), len(coords_bg)))
coords_aug = []
subgen = zip(coords, coords_fg, coords_bg)
for coord, coord_fg, coord_bg in subgen:
x_int = int(np.round(coord[0]))
y_int = int(np.round(coord[1]))
if 0 <= y_int < h_img and 0 <= x_int < w_img:
alphas_i = mask_image[y_int, x_int, ...]
alpha = (
np.average(alphas_i) if alphas_i.size > 0 else 1.0)
if alpha > 0.5:
coords_aug.append(coord_fg)
else:
coords_aug.append(coord_bg)
else:
coords_aug.append((x_int, y_int))
else:
# Augment with an either-or approach over all points, i.e.
# based on the average of the whole mask, either all points
# from the foreground or all points from the background branch
# are used.
# Note that we ensured above that _keypoint_mode must be
# _MODE_EITHER_OR if it wasn't _MODE_POINTWISE.
mask_image_avg = (
np.average(mask_image) if mask_image.size > 0 else 1.0)
if mask_image_avg > 0.5:
coords_aug = coords_fg
else:
coords_aug = coords_bg
kpsoi_aug = ia.KeypointsOnImage.from_xy_array(
coords_aug, shape=cbaoi.shape)
return augm_utils.invert_convert_cbaois_to_kpsois_(cbaoi, kpsoi_aug)
# Added in 0.4.0.
def _to_deterministic(self):
return _to_deterministic(self)
# Added in 0.4.0.
[docs] def get_parameters(self):
"""See :func:`~imgaug.augmenters.meta.Augmenter.get_parameters`."""
return [self.mask_generator]
# Added in 0.4.0.
[docs] def get_children_lists(self):
"""See :func:`~imgaug.augmenters.meta.Augmenter.get_children_lists`."""
return [lst for lst in [self.foreground, self.background]
if lst is not None]
# Added in 0.4.0.
def __str__(self):
pattern = (
"%s("
"mask_generator=%s, name=%s, foreground=%s, background=%s, "
"deterministic=%s"
")"
)
return pattern % (
self.__class__.__name__, self.mask_generator, self.name,
self.foreground, self.background, self.deterministic)
# FIXME the output of the third example makes it look like per_channel isn't
# working
[docs]class BlendAlphaElementwise(BlendAlphaMask):
"""
Alpha-blend two image sources using alpha/opacity values sampled per pixel.
This is the same as :class:`BlendAlpha`, except that the opacity factor is
sampled once per *pixel* instead of once per *image* (or a few times per
image, if ``BlendAlpha.per_channel`` is set to ``True``).
See :class:`BlendAlpha` for more details.
This class is a wrapper around
:class:`~imgaug.augmenters.blend.BlendAlphaMask`.
.. note::
Avoid using augmenters as children that affect pixel locations (e.g.
horizontal flips). See
:class:`~imgaug.augmenters.blend.BlendAlphaMask` for details.
Added in 0.4.0. (Before that named `AlphaElementwise`.)
**Supported dtypes**:
See :class:`~imgaug.augmenters.blend.BlendAlphaMask`.
Parameters
----------
factor : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
Opacity of the results of the foreground branch. Values close to
``0.0`` mean that the results from the background branch (see
parameter `background`) make up most of the final image.
* If float, then that value will be used for all images.
* If tuple ``(a, b)``, then a random value from the interval
``[a, b]`` will be sampled per image.
* If a list, then a random value will be picked from that list per
image.
* If ``StochasticParameter``, then that parameter will be used to
sample a value per image.
foreground : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the foreground branch.
High alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the foreground branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
background : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the background branch.
Low alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the background branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
per_channel : bool or float, optional
Whether to use the same factor for all channels (``False``)
or to sample a new value for each channel (``True``).
If this value is a float ``p``, then for ``p`` percent of all images
`per_channel` will be treated as True, otherwise as False.
seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
name : None or str, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
Old name for parameter `seed`.
Its usage will not yet cause a deprecation warning,
but it is still recommended to use `seed` now.
Outdated since 0.4.0.
deterministic : bool, optional
Deprecated since 0.4.0.
See method ``to_deterministic()`` for an alternative and for
details about what the "deterministic mode" actually does.
Examples
--------
>>> import imgaug.augmenters as iaa
>>> aug = iaa.BlendAlphaElementwise(0.5, iaa.Grayscale(1.0))
Convert each image to pure grayscale and alpha-blend the result with the
original image using an alpha of ``50%`` for all pixels, thereby removing
about ``50%`` of all color. This is equivalent to ``iaa.Grayscale(0.5)``.
This is also equivalent to ``iaa.BlendAlpha(0.5, iaa.Grayscale(1.0))``, as
the opacity has a fixed value of ``0.5`` and is hence identical for all
pixels.
>>> aug = iaa.BlendAlphaElementwise((0, 1.0), iaa.AddToHue(100))
Same as in the previous example, but here with hue-shift instead
of grayscaling and additionally the alpha factor is sampled uniformly
from the interval ``[0.0, 1.0]`` once per pixel, thereby shifting the
hue by a random fraction for each pixel.
>>> aug = iaa.BlendAlphaElementwise(
>>> (0.0, 1.0),
>>> iaa.Affine(rotate=(-20, 20)),
>>> per_channel=0.5)
First, rotate each image by a random degree sampled uniformly from the
interval ``[-20, 20]``. Then, alpha-blend that new image with the original
one using a random factor sampled uniformly from the interval
``[0.0, 1.0]`` per pixel. For ``50%`` of all images, the blending happens
channel-wise and the factor is sampled independently per pixel *and*
channel (``per_channel=0.5``). As a result, e.g. the red channel may look
visibly rotated (factor near ``1.0``), while the green and blue channels
may not look rotated (factors near ``0.0``).
>>> aug = iaa.BlendAlphaElementwise(
>>> (0.0, 1.0),
>>> foreground=iaa.Add(100),
>>> background=iaa.Multiply(0.2))
Apply two branches of augmenters -- ``A`` and ``B`` -- *independently*
to input images and alpha-blend the results of these branches using a
factor ``f``. Branch ``A`` increases image pixel intensities by ``100``
and ``B`` multiplies the pixel intensities by ``0.2``. ``f`` is sampled
uniformly from the interval ``[0.0, 1.0]`` per pixel. The resulting images
contain a bit of ``A`` and a bit of ``B``.
>>> aug = iaa.BlendAlphaElementwise([0.25, 0.75], iaa.MedianBlur(13))
Apply median blur to each image and alpha-blend the result with the
original image using an alpha factor of either exactly ``0.25`` or
exactly ``0.75`` (sampled once per pixel).
"""
# Added in 0.4.0.
def __init__(self, factor=(0.0, 1.0), foreground=None, background=None,
per_channel=False,
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
factor = iap.handle_continuous_param(
factor, "factor", value_range=(0, 1.0), tuple_to_uniform=True,
list_to_choice=True)
mask_gen = StochasticParameterMaskGen(factor, per_channel)
super(BlendAlphaElementwise, self).__init__(
mask_gen, foreground, background,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic)
# Added in 0.4.0.
@property
def factor(self):
return self.mask_generator.parameter
[docs]class BlendAlphaSimplexNoise(BlendAlphaElementwise):
"""Alpha-blend two image sources using simplex noise alpha masks.
The alpha masks are sampled using a simplex noise method, roughly creating
connected blobs of 1s surrounded by 0s. If nearest neighbour
upsampling is used, these blobs can be rectangular with sharp edges.
Added in 0.4.0. (Before that named `SimplexNoiseAlpha`.)
**Supported dtypes**:
See :class:`~imgaug.augmenters.blend.BlendAlphaElementwise`.
Parameters
----------
foreground : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the foreground branch.
High alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the foreground branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
background : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the background branch.
Low alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the background branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
per_channel : bool or float, optional
Whether to use the same factor for all channels (``False``)
or to sample a new value for each channel (``True``).
If this value is a float ``p``, then for ``p`` percent of all images
`per_channel` will be treated as ``True``, otherwise as ``False``.
size_px_max : int or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional
The simplex noise is always generated in a low resolution environment.
This parameter defines the maximum size of that environment (in
pixels). The environment is initialized at the same size as the input
image and then downscaled, so that no side exceeds `size_px_max`
(aspect ratio is kept).
* If int, then that number will be used as the size for all
iterations.
* If tuple of two ``int`` s ``(a, b)``, then a value will be
sampled per iteration from the discrete interval ``[a..b]``.
* If a list of ``int`` s, then a value will be picked per iteration
at random from that list.
* If a ``StochasticParameter``, then a value will be sampled from
that parameter per iteration.
upscale_method : None or imgaug.ALL or str or list of str or imgaug.parameters.StochasticParameter, optional
After generating the noise maps in low resolution environments, they
have to be upscaled to the input image size. This parameter controls
the upscaling method.
* If ``None``, then either ``nearest`` or ``linear`` or ``cubic``
is picked. Most weight is put on ``linear``, followed by
``cubic``.
* If ``imgaug.ALL``, then either ``nearest`` or ``linear`` or
``area`` or ``cubic`` is picked per iteration (all same
probability).
* If a string, then that value will be used as the method (must be
``nearest`` or ``linear`` or ``area`` or ``cubic``).
* If list of string, then a random value will be picked from that
list per iteration.
* If ``StochasticParameter``, then a random value will be sampled
from that parameter per iteration.
iterations : int or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional
How often to repeat the simplex noise generation process per image.
* If ``int``, then that number will be used as the iterations for
all images.
* If tuple of two ``int`` s ``(a, b)``, then a value will be
sampled per image from the discrete interval ``[a..b]``.
* If a list of ``int`` s, then a value will be picked per image at
random from that list.
* If a ``StochasticParameter``, then a value will be sampled from
that parameter per image.
aggregation_method : imgaug.ALL or str or list of str or imgaug.parameters.StochasticParameter, optional
The noise maps (from each iteration) are combined to one noise map
using an aggregation process. This parameter defines the method used
for that process. Valid methods are ``min``, ``max`` or ``avg``,
where ``min`` combines the noise maps by taking the (elementwise)
minimum over all iteration's results, ``max`` the (elementwise)
maximum and ``avg`` the (elementwise) average.
* If ``imgaug.ALL``, then a random value will be picked per image
from the valid ones.
* If a string, then that value will always be used as the method.
* If a list of string, then a random value will be picked from
that list per image.
* If a ``StochasticParameter``, then a random value will be
sampled from that paramter per image.
sigmoid : bool or number, optional
Whether to apply a sigmoid function to the final noise maps, resulting
in maps that have more extreme values (close to 0.0 or 1.0).
* If ``bool``, then a sigmoid will always (``True``) or never
(``False``) be applied.
* If a number ``p`` with ``0<=p<=1``, then a sigmoid will be
applied to ``p`` percent of all final noise maps.
sigmoid_thresh : None or number or tuple of number or imgaug.parameters.StochasticParameter, optional
Threshold of the sigmoid, when applied. Thresholds above zero
(e.g. ``5.0``) will move the saddle point towards the right, leading
to more values close to 0.0.
* If ``None``, then ``Normal(0, 5.0)`` will be used.
* If number, then that threshold will be used for all images.
* If tuple of two numbers ``(a, b)``, then a random value will
be sampled per image from the interval ``[a, b]``.
* If ``StochasticParameter``, then a random value will be sampled
from that parameter per image.
seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
name : None or str, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
Old name for parameter `seed`.
Its usage will not yet cause a deprecation warning,
but it is still recommended to use `seed` now.
Outdated since 0.4.0.
deterministic : bool, optional
Deprecated since 0.4.0.
See method ``to_deterministic()`` for an alternative and for
details about what the "deterministic mode" actually does.
Examples
--------
>>> import imgaug.augmenters as iaa
>>> aug = iaa.BlendAlphaSimplexNoise(iaa.EdgeDetect(1.0))
Detect per image all edges, mark them in a black and white image and
then alpha-blend the result with the original image using simplex noise
masks.
>>> aug = iaa.BlendAlphaSimplexNoise(
>>> iaa.EdgeDetect(1.0),
>>> upscale_method="nearest")
Same as in the previous example, but using only nearest neighbour
upscaling to scale the simplex noise masks to the final image sizes, i.e.
no nearest linear upsampling is used. This leads to rectangles with sharp
edges.
>>> aug = iaa.BlendAlphaSimplexNoise(
>>> iaa.EdgeDetect(1.0),
>>> upscale_method="linear")
Same as in the previous example, but using only linear upscaling to
scale the simplex noise masks to the final image sizes, i.e. no nearest
neighbour upsampling is used. This leads to rectangles with smooth edges.
>>> aug = iaa.BlendAlphaSimplexNoise(
>>> iaa.EdgeDetect(1.0),
>>> sigmoid_thresh=iap.Normal(10.0, 5.0))
Same as in the first example, but using a threshold for the sigmoid
function that is further to the right. This is more conservative, i.e.
the generated noise masks will be mostly black (values around ``0.0``),
which means that most of the original images (parameter/branch
`background`) will be kept, rather than using the results of the
augmentation (parameter/branch `foreground`).
"""
# Added in 0.4.0.
def __init__(self, foreground=None, background=None, per_channel=False,
size_px_max=(2, 16), upscale_method=None,
iterations=(1, 3), aggregation_method="max",
sigmoid=True, sigmoid_thresh=None,
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
upscale_method_default = iap.Choice(["nearest", "linear", "cubic"],
p=[0.05, 0.6, 0.35])
sigmoid_thresh_default = iap.Normal(0.0, 5.0)
noise = iap.SimplexNoise(
size_px_max=size_px_max,
upscale_method=(upscale_method
if upscale_method is not None
else upscale_method_default)
)
if iterations != 1:
noise = iap.IterativeNoiseAggregator(
noise,
iterations=iterations,
aggregation_method=aggregation_method
)
use_sigmoid = (
sigmoid is True
or (ia.is_single_number(sigmoid) and sigmoid >= 0.01))
if use_sigmoid:
noise = iap.Sigmoid.create_for_noise(
noise,
threshold=(sigmoid_thresh
if sigmoid_thresh is not None
else sigmoid_thresh_default),
activated=sigmoid
)
super(BlendAlphaSimplexNoise, self).__init__(
factor=noise, foreground=foreground, background=background,
per_channel=per_channel,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic)
[docs]class BlendAlphaFrequencyNoise(BlendAlphaElementwise):
"""Alpha-blend two image sources using frequency noise masks.
The alpha masks are sampled using frequency noise of varying scales,
which can sometimes create large connected blobs of ``1`` s surrounded
by ``0`` s and other times results in smaller patterns. If nearest
neighbour upsampling is used, these blobs can be rectangular with sharp
edges.
Added in 0.4.0. (Before that named `FrequencyNoiseAlpha`.)
**Supported dtypes**:
See :class:`~imgaug.augmenters.blend.BlendAlphaElementwise`.
Parameters
----------
exponent : number or tuple of number of list of number or imgaug.parameters.StochasticParameter, optional
Exponent to use when scaling in the frequency domain.
Sane values are in the range ``-4`` (large blobs) to ``4`` (small
patterns). To generate cloud-like structures, use roughly ``-2``.
* If number, then that number will be used as the exponent for all
iterations.
* If tuple of two numbers ``(a, b)``, then a value will be sampled
per iteration from the interval ``[a, b]``.
* If a list of numbers, then a value will be picked per iteration
at random from that list.
* If a ``StochasticParameter``, then a value will be sampled from
that parameter per iteration.
foreground : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the foreground branch.
High alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the foreground branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
background : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the background branch.
Low alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the background branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
per_channel : bool or float, optional
Whether to use the same factor for all channels (``False``)
or to sample a new value for each channel (``True``).
If this value is a float ``p``, then for ``p`` percent of all images
`per_channel` will be treated as ``True``, otherwise as ``False``.
size_px_max : int or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional
The noise is generated in a low resolution environment.
This parameter defines the maximum size of that environment (in
pixels). The environment is initialized at the same size as the input
image and then downscaled, so that no side exceeds `size_px_max`
(aspect ratio is kept).
* If ``int``, then that number will be used as the size for all
iterations.
* If tuple of two ``int`` s ``(a, b)``, then a value will be
sampled per iteration from the discrete interval ``[a..b]``.
* If a list of ``int`` s, then a value will be picked per
iteration at random from that list.
* If a ``StochasticParameter``, then a value will be sampled from
that parameter per iteration.
upscale_method : None or imgaug.ALL or str or list of str or imgaug.parameters.StochasticParameter, optional
After generating the noise maps in low resolution environments, they
have to be upscaled to the input image size. This parameter controls
the upscaling method.
* If ``None``, then either ``nearest`` or ``linear`` or ``cubic``
is picked. Most weight is put on ``linear``, followed by
``cubic``.
* If ``imgaug.ALL``, then either ``nearest`` or ``linear`` or
``area`` or ``cubic`` is picked per iteration (all same
probability).
* If string, then that value will be used as the method (must be
``nearest`` or ``linear`` or ``area`` or ``cubic``).
* If list of string, then a random value will be picked from that
list per iteration.
* If ``StochasticParameter``, then a random value will be sampled
from that parameter per iteration.
iterations : int or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional
How often to repeat the simplex noise generation process per
image.
* If ``int``, then that number will be used as the iterations for
all images.
* If tuple of two ``int`` s ``(a, b)``, then a value will be
sampled per image from the discrete interval ``[a..b]``.
* If a list of ``int`` s, then a value will be picked per image at
random from that list.
* If a ``StochasticParameter``, then a value will be sampled from
that parameter per image.
aggregation_method : imgaug.ALL or str or list of str or imgaug.parameters.StochasticParameter, optional
The noise maps (from each iteration) are combined to one noise map
using an aggregation process. This parameter defines the method used
for that process. Valid methods are ``min``, ``max`` or ``avg``,
where 'min' combines the noise maps by taking the (elementwise) minimum
over all iteration's results, ``max`` the (elementwise) maximum and
``avg`` the (elementwise) average.
* If ``imgaug.ALL``, then a random value will be picked per image
from the valid ones.
* If a string, then that value will always be used as the method.
* If a list of string, then a random value will be picked from
that list per image.
* If a ``StochasticParameter``, then a random value will be sampled
from that parameter per image.
sigmoid : bool or number, optional
Whether to apply a sigmoid function to the final noise maps, resulting
in maps that have more extreme values (close to ``0.0`` or ``1.0``).
* If ``bool``, then a sigmoid will always (``True``) or never
(``False``) be applied.
* If a number ``p`` with ``0<=p<=1``, then a sigmoid will be applied to
``p`` percent of all final noise maps.
sigmoid_thresh : None or number or tuple of number or imgaug.parameters.StochasticParameter, optional
Threshold of the sigmoid, when applied. Thresholds above zero
(e.g. ``5.0``) will move the saddle point towards the right, leading to
more values close to ``0.0``.
* If ``None``, then ``Normal(0, 5.0)`` will be used.
* If number, then that threshold will be used for all images.
* If tuple of two numbers ``(a, b)``, then a random value will
be sampled per image from the range ``[a, b]``.
* If ``StochasticParameter``, then a random value will be sampled
from that parameter per image.
seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
name : None or str, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
Old name for parameter `seed`.
Its usage will not yet cause a deprecation warning,
but it is still recommended to use `seed` now.
Outdated since 0.4.0.
deterministic : bool, optional
Deprecated since 0.4.0.
See method ``to_deterministic()`` for an alternative and for
details about what the "deterministic mode" actually does.
Examples
--------
>>> import imgaug.augmenters as iaa
>>> aug = iaa.BlendAlphaFrequencyNoise(foreground=iaa.EdgeDetect(1.0))
Detect per image all edges, mark them in a black and white image and
then alpha-blend the result with the original image using frequency noise
masks.
>>> aug = iaa.BlendAlphaFrequencyNoise(
>>> foreground=iaa.EdgeDetect(1.0),
>>> upscale_method="nearest")
Same as the first example, but using only linear upscaling to
scale the frequency noise masks to the final image sizes, i.e. no nearest
neighbour upsampling is used. This results in smooth edges.
>>> aug = iaa.BlendAlphaFrequencyNoise(
>>> foreground=iaa.EdgeDetect(1.0),
>>> upscale_method="linear")
Same as the first example, but using only linear upscaling to
scale the frequency noise masks to the final image sizes, i.e. no nearest
neighbour upsampling is used. This results in smooth edges.
>>> aug = iaa.BlendAlphaFrequencyNoise(
>>> foreground=iaa.EdgeDetect(1.0),
>>> upscale_method="linear",
>>> exponent=-2,
>>> sigmoid=False)
Same as in the previous example, but with the exponent set to a constant
``-2`` and the sigmoid deactivated, resulting in cloud-like patterns
without sharp edges.
>>> aug = iaa.BlendAlphaFrequencyNoise(
>>> foreground=iaa.EdgeDetect(1.0),
>>> sigmoid_thresh=iap.Normal(10.0, 5.0))
Same as the first example, but using a threshold for the sigmoid function
that is further to the right. This is more conservative, i.e. the generated
noise masks will be mostly black (values around ``0.0``), which means that
most of the original images (parameter/branch `background`) will be kept,
rather than using the results of the augmentation (parameter/branch
`foreground`).
"""
# Added in 0.4.0.
def __init__(self, exponent=(-4, 4), foreground=None, background=None,
per_channel=False, size_px_max=(4, 16), upscale_method=None,
iterations=(1, 3), aggregation_method=["avg", "max"],
sigmoid=0.5, sigmoid_thresh=None,
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
# pylint: disable=dangerous-default-value
upscale_method_default = iap.Choice(["nearest", "linear", "cubic"],
p=[0.05, 0.6, 0.35])
sigmoid_thresh_default = iap.Normal(0.0, 5.0)
noise = iap.FrequencyNoise(
exponent=exponent,
size_px_max=size_px_max,
upscale_method=(upscale_method
if upscale_method is not None
else upscale_method_default)
)
if iterations != 1:
noise = iap.IterativeNoiseAggregator(
noise,
iterations=iterations,
aggregation_method=aggregation_method
)
use_sigmoid = (
sigmoid is True
or (ia.is_single_number(sigmoid) and sigmoid >= 0.01))
if use_sigmoid:
noise = iap.Sigmoid.create_for_noise(
noise,
threshold=(sigmoid_thresh
if sigmoid_thresh is not None
else sigmoid_thresh_default),
activated=sigmoid
)
super(BlendAlphaFrequencyNoise, self).__init__(
factor=noise, foreground=foreground, background=background,
per_channel=per_channel,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic)
[docs]class BlendAlphaSomeColors(BlendAlphaMask):
"""Blend images from two branches using colorwise masks.
This class generates masks that "mark" a few colors and replace the
pixels within these colors with the results of the foreground branch.
The remaining pixels are replaced with the results of the background
branch (usually the identity function). That allows to e.g. selectively
grayscale a few colors, while keeping other colors unchanged.
This class is a thin wrapper around
:class:`~imgaug.augmenters.blend.BlendAlphaMask` together with
:class:`~imgaug.augmenters.blend.SomeColorsMaskGen`.
.. note::
The underlying mask generator will produce an ``AssertionError`` for
batches that contain no images.
.. note::
Avoid using augmenters as children that affect pixel locations (e.g.
horizontal flips). See
:class:`~imgaug.augmenters.blend.BlendAlphaMask` for details.
Added in 0.4.0.
**Supported dtypes**:
See :func:`~imgaug.augmenters.color.change_colorspaces_`.
Parameters
----------
foreground : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the foreground branch.
High alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the foreground branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
background : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the background branch.
Low alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the background branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
nb_bins : int or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional
See :class:`~imgaug.augmenters.blend.SomeColorsMaskGen`.
smoothness : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
See :class:`~imgaug.augmenters.blend.SomeColorsMaskGen`.
alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
See :class:`~imgaug.augmenters.blend.SomeColorsMaskGen`.
rotation_deg : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
See :class:`~imgaug.augmenters.blend.SomeColorsMaskGen`.
from_colorspace : str, optional
See :class:`~imgaug.augmenters.blend.SomeColorsMaskGen`.
seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
name : None or str, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
Old name for parameter `seed`.
Its usage will not yet cause a deprecation warning,
but it is still recommended to use `seed` now.
Outdated since 0.4.0.
deterministic : bool, optional
Deprecated since 0.4.0.
See method ``to_deterministic()`` for an alternative and for
details about what the "deterministic mode" actually does.
Examples
--------
>>> import imgaug.augmenters as iaa
>>> aug = iaa.BlendAlphaSomeColors(iaa.Grayscale(1.0))
Create an augmenter that turns randomly removes some colors in images by
grayscaling them.
>>> aug = iaa.BlendAlphaSomeColors(iaa.TotalDropout(1.0))
Create an augmenter that removes some colors in images by replacing them
with black pixels.
>>> aug = iaa.BlendAlphaSomeColors(
>>> iaa.MultiplySaturation(0.5), iaa.MultiplySaturation(1.5))
Create an augmenter that desaturates some colors and increases the
saturation of the remaining ones.
>>> aug = iaa.BlendAlphaSomeColors(
>>> iaa.AveragePooling(7), alpha=[0.0, 1.0], smoothness=0.0)
Create an augmenter that applies average pooling to some colors.
Each color tune is either selected (alpha of ``1.0``) or not
selected (``0.0``). There is no gradual change between similar colors.
>>> aug = iaa.BlendAlphaSomeColors(
>>> iaa.AveragePooling(7), nb_bins=2, smoothness=0.0)
Create an augmenter that applies average pooling to some colors.
Choose on average half of all colors in images for the blending operation.
>>> aug = iaa.BlendAlphaSomeColors(
>>> iaa.AveragePooling(7), from_colorspace="BGR")
Create an augmenter that applies average pooling to some colors with
input images being in BGR colorspace.
"""
# Added in 0.4.0.
def __init__(self, foreground=None, background=None,
nb_bins=(5, 15), smoothness=(0.1, 0.3),
alpha=[0.0, 1.0], rotation_deg=(0, 360),
from_colorspace="RGB",
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
# pylint: disable=dangerous-default-value
super(BlendAlphaSomeColors, self).__init__(
SomeColorsMaskGen(
nb_bins=nb_bins,
smoothness=smoothness,
alpha=alpha,
rotation_deg=rotation_deg,
from_colorspace=from_colorspace
),
foreground=foreground,
background=background,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic)
[docs]class BlendAlphaHorizontalLinearGradient(BlendAlphaMask):
"""Blend images from two branches along a horizontal linear gradient.
This class generates a horizontal linear gradient mask (i.e. usually a
mask with low values on the left and high values on the right) and
alphas-blends between foreground and background branch using that
mask.
This class is a thin wrapper around
:class:`~imgaug.augmenters.blend.BlendAlphaMask` together with
:class:`~imgaug.augmenters.blend.HorizontalLinearGradientMaskGen`.
.. note::
Avoid using augmenters as children that affect pixel locations (e.g.
horizontal flips). See
:class:`~imgaug.augmenters.blend.BlendAlphaMask` for details.
Added in 0.4.0.
**Supported dtypes**:
See :class:`~imgaug.augmenters.blend.BlendAlphaMask`.
Parameters
----------
foreground : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the foreground branch.
High alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the foreground branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
background : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the background branch.
Low alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the background branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
min_value : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
See :class:`~imgaug.augmenters.blend.HorizontalLinearGradientMaskGen`.
max_value : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
See :class:`~imgaug.augmenters.blend.HorizontalLinearGradientMaskGen`.
start_at : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
See :class:`~imgaug.augmenters.blend.HorizontalLinearGradientMaskGen`.
end_at : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
See :class:`~imgaug.augmenters.blend.HorizontalLinearGradientMaskGen`.
seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
name : None or str, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
Old name for parameter `seed`.
Its usage will not yet cause a deprecation warning,
but it is still recommended to use `seed` now.
Outdated since 0.4.0.
deterministic : bool, optional
Deprecated since 0.4.0.
See method ``to_deterministic()`` for an alternative and for
details about what the "deterministic mode" actually does.
Examples
--------
>>> import imgaug.augmenters as iaa
>>> aug = iaa.BlendAlphaHorizontalLinearGradient(iaa.AddToHue((-100, 100)))
Create an augmenter that randomizes the hue towards the right of the
image.
>>> aug = iaa.BlendAlphaHorizontalLinearGradient(
>>> iaa.TotalDropout(1.0),
>>> min_value=0.2, max_value=0.8)
Create an augmenter that replaces pixels towards the right with darker
and darker values. However it always keeps at least
20% (``1.0 - max_value``) of the original pixel value on the far right
and always replaces at least 20% on the far left (``min_value=0.2``).
>>> aug = iaa.BlendAlphaHorizontalLinearGradient(
>>> iaa.AveragePooling(11),
>>> start_at=(0.0, 1.0), end_at=(0.0, 1.0))
Create an augmenter that blends with an average-pooled image according
to a horizontal gradient that starts at a random x-coordinate and reaches
its maximum at another random x-coordinate. Due to that randomness,
the gradient may increase towards the left or right.
"""
# Added in 0.4.0.
def __init__(self, foreground=None, background=None,
min_value=(0.0, 0.2), max_value=(0.8, 1.0),
start_at=(0.0, 0.2), end_at=(0.8, 1.0),
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
super(BlendAlphaHorizontalLinearGradient, self).__init__(
HorizontalLinearGradientMaskGen(
min_value=min_value,
max_value=max_value,
start_at=start_at,
end_at=end_at
),
foreground=foreground,
background=background,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic)
[docs]class BlendAlphaVerticalLinearGradient(BlendAlphaMask):
"""Blend images from two branches along a vertical linear gradient.
This class generates a vertical linear gradient mask (i.e. usually a
mask with low values on the left and high values on the right) and
alphas-blends between foreground and background branch using that
mask.
This class is a thin wrapper around
:class:`~imgaug.augmenters.blend.BlendAlphaMask` together with
:class:`~imgaug.augmenters.blend.VerticalLinearGradientMaskGen`.
.. note::
Avoid using augmenters as children that affect pixel locations (e.g.
horizontal flips). See
:class:`~imgaug.augmenters.blend.BlendAlphaMask` for details.
Added in 0.4.0.
**Supported dtypes**:
See :class:`~imgaug.augmenters.blend.BlendAlphaMask`.
Parameters
----------
foreground : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the foreground branch.
High alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the foreground branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
background : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the background branch.
Low alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the background branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
min_value : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
See :class:`~imgaug.augmenters.blend.VerticalLinearGradientMaskGen`.
max_value : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
See :class:`~imgaug.augmenters.blend.VerticalLinearGradientMaskGen`.
start_at : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
See :class:`~imgaug.augmenters.blend.VerticalLinearGradientMaskGen`.
end_at : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
See :class:`~imgaug.augmenters.blend.VerticalLinearGradientMaskGen`.
seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
name : None or str, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
Old name for parameter `seed`.
Its usage will not yet cause a deprecation warning,
but it is still recommended to use `seed` now.
Outdated since 0.4.0.
deterministic : bool, optional
Deprecated since 0.4.0.
See method ``to_deterministic()`` for an alternative and for
details about what the "deterministic mode" actually does.
Examples
--------
>>> import imgaug.augmenters as iaa
>>> aug = iaa.BlendAlphaVerticalLinearGradient(iaa.AddToHue((-100, 100)))
Create an augmenter that randomizes the hue towards the bottom of the
image.
>>> aug = iaa.BlendAlphaVerticalLinearGradient(
>>> iaa.TotalDropout(1.0),
>>> min_value=0.2, max_value=0.8)
Create an augmenter that replaces pixels towards the bottom with darker
and darker values. However it always keeps at least
20% (``1.0 - max_value``) of the original pixel value on the far bottom
and always replaces at least 20% on the far top (``min_value=0.2``).
>>> aug = iaa.BlendAlphaVerticalLinearGradient(
>>> iaa.AveragePooling(11),
>>> start_at=(0.0, 1.0), end_at=(0.0, 1.0))
Create an augmenter that blends with an average-pooled image according
to a vertical gradient that starts at a random y-coordinate and reaches
its maximum at another random y-coordinate. Due to that randomness,
the gradient may increase towards the bottom or top.
>>> aug = iaa.BlendAlphaVerticalLinearGradient(
>>> iaa.Clouds(),
>>> start_at=(0.15, 0.35), end_at=0.0)
Create an augmenter that draws clouds in roughly the top quarter of the
image.
"""
# Added in 0.4.0.
def __init__(self, foreground=None, background=None,
min_value=(0.0, 0.2), max_value=(0.8, 1.0),
start_at=(0.0, 0.2), end_at=(0.8, 1.0),
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
super(BlendAlphaVerticalLinearGradient, self).__init__(
VerticalLinearGradientMaskGen(
min_value=min_value,
max_value=max_value,
start_at=start_at,
end_at=end_at
),
foreground=foreground,
background=background,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic)
[docs]class BlendAlphaRegularGrid(BlendAlphaMask):
"""Blend images from two branches according to a regular grid.
This class generates for each image a mask that splits the image into a
grid-like pattern of ``H`` rows and ``W`` columns. Each cell is then
filled with an alpha value, sampled randomly per cell.
The difference to :class:`AlphaBlendCheckerboard` is that this class
samples random alpha values per grid cell, while in the checkerboard the
alpha values follow a fixed pattern.
This class is a thin wrapper around
:class:`~imgaug.augmenters.blend.BlendAlphaMask` together with
:class:`~imgaug.augmenters.blend.RegularGridMaskGen`.
.. note::
Avoid using augmenters as children that affect pixel locations (e.g.
horizontal flips). See
:class:`~imgaug.augmenters.blend.BlendAlphaMask` for details.
Added in 0.4.0.
**Supported dtypes**:
See :class:`~imgaug.augmenters.blend.BlendAlphaMask`.
Parameters
----------
nb_rows : int or tuple of int or list of int or imgaug.parameters.StochasticParameter
Number of rows of the checkerboard.
See :class:`~imgaug.augmenters.blend.CheckerboardMaskGen` for details.
nb_cols : int or tuple of int or list of int or imgaug.parameters.StochasticParameter
Number of columns of the checkerboard. Analogous to `nb_rows`.
See :class:`~imgaug.augmenters.blend.CheckerboardMaskGen` for details.
foreground : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the foreground branch.
High alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the foreground branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
background : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the background branch.
Low alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the background branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
Alpha value of each cell.
* If ``number``: Exactly that value will be used for all images.
* If ``tuple`` ``(a, b)``: A random value will be uniformly sampled
per image from the interval ``[a, b]``.
* If ``list``: A random value will be picked per image from that list.
* If ``StochasticParameter``: That parameter will be queried once
per batch for ``(N,)`` values -- one per image.
seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
name : None or str, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
Old name for parameter `seed`.
Its usage will not yet cause a deprecation warning,
but it is still recommended to use `seed` now.
Outdated since 0.4.0.
deterministic : bool, optional
Deprecated since 0.4.0.
See method ``to_deterministic()`` for an alternative and for
details about what the "deterministic mode" actually does.
Examples
--------
>>> import imgaug.augmenters as iaa
>>> aug = iaa.BlendAlphaRegularGrid(nb_rows=(4, 6), nb_cols=(1, 4),
>>> foreground=iaa.Multiply(0.0))
Create an augmenter that places a ``HxW`` grid on each image, where
``H`` (rows) is randomly and uniformly sampled from the interval ``[4, 6]``
and ``W`` is analogously sampled from the interval ``[1, 4]``. Roughly
half of the cells in the grid are filled with ``0.0``, the remaining ones
are unaltered. Which cells exactly are "dropped" is randomly decided
per image. The resulting effect is similar to
:class:`~imgaug.augmenters.arithmetic.CoarseDropout`.
>>> aug = iaa.BlendAlphaRegularGrid(nb_rows=2, nb_cols=2,
>>> foreground=iaa.Multiply(0.0),
>>> background=iaa.AveragePooling(8),
>>> alpha=[0.0, 0.0, 1.0])
Create an augmenter that always placed ``2x2`` cells on each image
and sets about ``1/3`` of them to zero (foreground branch) and
the remaining ``2/3`` to a pixelated version (background branch).
"""
# Added in 0.4.0.
def __init__(self, nb_rows, nb_cols,
foreground=None, background=None,
alpha=[0.0, 1.0],
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
# pylint: disable=dangerous-default-value
super(BlendAlphaRegularGrid, self).__init__(
RegularGridMaskGen(
nb_rows=nb_rows,
nb_cols=nb_cols,
alpha=alpha
),
foreground=foreground,
background=background,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic)
[docs]class BlendAlphaCheckerboard(BlendAlphaMask):
"""Blend images from two branches according to a checkerboard pattern.
This class generates for each image a mask following a checkboard layout of
``H`` rows and ``W`` columns. Each cell is then filled with either
``1.0`` or ``0.0``. The cell at the top-left is always ``1.0``. Its right
and bottom neighbour cells are ``0.0``. The 4-neighbours of any cell always
have a value opposite to the cell's value (``0.0`` vs. ``1.0``).
This class is a thin wrapper around
:class:`~imgaug.augmenters.blend.BlendAlphaMask` together with
:class:`~imgaug.augmenters.blend.CheckerboardMaskGen`.
.. note::
Avoid using augmenters as children that affect pixel locations (e.g.
horizontal flips). See
:class:`~imgaug.augmenters.blend.BlendAlphaMask` for details.
Added in 0.4.0.
**Supported dtypes**:
See :class:`~imgaug.augmenters.blend.BlendAlphaMask`.
Parameters
----------
nb_rows : int or tuple of int or list of int or imgaug.parameters.StochasticParameter
Number of rows of the checkerboard.
See :class:`~imgaug.augmenters.blend.CheckerboardMaskGen` for details.
nb_cols : int or tuple of int or list of int or imgaug.parameters.StochasticParameter
Number of columns of the checkerboard. Analogous to `nb_rows`.
See :class:`~imgaug.augmenters.blend.CheckerboardMaskGen` for details.
foreground : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the foreground branch.
High alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the foreground branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
background : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the background branch.
Low alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the background branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
name : None or str, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
Old name for parameter `seed`.
Its usage will not yet cause a deprecation warning,
but it is still recommended to use `seed` now.
Outdated since 0.4.0.
deterministic : bool, optional
Deprecated since 0.4.0.
See method ``to_deterministic()`` for an alternative and for
details about what the "deterministic mode" actually does.
Examples
--------
>>> import imgaug.augmenters as iaa
>>> aug = iaa.BlendAlphaCheckerboard(nb_rows=2, nb_cols=(1, 4),
>>> foreground=iaa.AddToHue((-100, 100)))
Create an augmenter that places a ``HxW`` grid on each image, where
``H`` (rows) is always ``2`` and ``W`` is randomly and uniformly sampled
from the interval ``[1, 4]``. For half of the cells in the grid the hue
is randomly modified, the other half of the cells is unaltered.
"""
# Added in 0.4.0.
def __init__(self, nb_rows, nb_cols,
foreground=None, background=None,
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
super(BlendAlphaCheckerboard, self).__init__(
CheckerboardMaskGen(
nb_rows=nb_rows,
nb_cols=nb_cols
),
foreground=foreground,
background=background,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic)
[docs]class BlendAlphaSegMapClassIds(BlendAlphaMask):
"""Blend images from two branches based on segmentation map ids.
This class generates masks that are ``1.0`` at pixel locations covered
by specific classes in segmentation maps.
This class is a thin wrapper around
:class:`~imgaug.augmenters.blend.BlendAlphaMask` together with
:class:`~imgaug.augmenters.blend.SegMapClassIdsMaskGen`.
.. note::
Avoid using augmenters as children that affect pixel locations (e.g.
horizontal flips). See
:class:`~imgaug.augmenters.blend.BlendAlphaMask` for details.
.. note::
Segmentation maps can have multiple channels. If that is the case
then for each position ``(x, y)`` it is sufficient that any class id
in any channel matches one of the desired class ids.
.. note::
This class will produce an ``AssertionError`` if there are no
segmentation maps in a batch.
Added in 0.4.0.
**Supported dtypes**:
See :class:`~imgaug.augmenters.blend.BlendAlphaMask`.
Parameters
----------
class_ids : int or tuple of int or list of int or imgaug.parameters.StochasticParameter
See :class:`~imgaug.augmenters.blend.SegMapClassIdsMaskGen`.
foreground : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the foreground branch.
High alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the foreground branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
background : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the background branch.
Low alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the background branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
nb_sample_classes : None or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional
See :class:`~imgaug.augmenters.blend.SegMapClassIdsMaskGen`.
seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
name : None or str, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
Old name for parameter `seed`.
Its usage will not yet cause a deprecation warning,
but it is still recommended to use `seed` now.
Outdated since 0.4.0.
deterministic : bool, optional
Deprecated since 0.4.0.
See method ``to_deterministic()`` for an alternative and for
details about what the "deterministic mode" actually does.
Examples
--------
>>> import imgaug.augmenters as iaa
>>> aug = iaa.BlendAlphaSegMapClassIds(
>>> [1, 3],
>>> foreground=iaa.AddToHue((-100, 100)))
Create an augmenter that randomizes the hue wherever the segmentation maps
contain the classes ``1`` or ``3``.
>>> aug = iaa.BlendAlphaSegMapClassIds(
>>> [1, 2, 3, 4],
>>> nb_sample_classes=2,
>>> foreground=iaa.GaussianBlur(3.0))
Create an augmenter that randomly picks ``2`` classes from the
list ``[1, 2, 3, 4]`` and blurs the image content wherever these classes
appear in the segmentation map. Note that as the sampling of class ids
happens *with replacement*, it is not guaranteed to sample two *unique*
class ids.
>>> aug = iaa.Sometimes(0.2,
>>> iaa.BlendAlphaSegMapClassIds(
>>> 2,
>>> background=iaa.TotalDropout(1.0)))
Create an augmenter that zeros for roughly every fifth image all
image pixels that do *not* belong to class id ``2`` (note that the
`background` branch was used, not the `foreground` branch).
Example use case: Human body landmark detection where both the
landmarks/keypoints and the body segmentation map are known. Train the
model to detect landmarks and sometimes remove all non-body information
to force the model to become more independent of the background.
"""
# Added in 0.4.0.
def __init__(self,
class_ids,
foreground=None, background=None,
nb_sample_classes=None,
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
super(BlendAlphaSegMapClassIds, self).__init__(
SegMapClassIdsMaskGen(
class_ids=class_ids,
nb_sample_classes=nb_sample_classes
),
foreground=foreground,
background=background,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic)
[docs]class BlendAlphaBoundingBoxes(BlendAlphaMask):
"""Blend images from two branches based on areas enclosed in bounding boxes.
This class generates masks that are ``1.0`` within bounding boxes of given
labels. A mask pixel will be set to ``1.0`` if *at least* one bounding box
covers the area and has one of the requested labels.
This class is a thin wrapper around
:class:`~imgaug.augmenters.blend.BlendAlphaMask` together with
:class:`~imgaug.augmenters.blend.BoundingBoxesMaskGen`.
.. note::
Avoid using augmenters as children that affect pixel locations (e.g.
horizontal flips). See
:class:`~imgaug.augmenters.blend.BlendAlphaMask` for details.
.. note::
This class will produce an ``AssertionError`` if there are no
bounding boxes in a batch.
Added in 0.4.0.
**Supported dtypes**:
See :class:`~imgaug.augmenters.blend.BlendAlphaMask`.
Parameters
----------
labels : None or str or list of str or imgaug.parameters.StochasticParameter
See :class:`~imgaug.augmenters.blend.BoundingBoxesMaskGen`.
foreground : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the foreground branch.
High alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the foreground branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
background : None or imgaug.augmenters.meta.Augmenter or iterable of imgaug.augmenters.meta.Augmenter, optional
Augmenter(s) that make up the background branch.
Low alpha values will show this branch's results.
* If ``None``, then the input images will be reused as the output
of the background branch.
* If ``Augmenter``, then that augmenter will be used as the branch.
* If iterable of ``Augmenter``, then that iterable will be
converted into a ``Sequential`` and used as the augmenter.
nb_sample_labels : None or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional
See :class:`~imgaug.augmenters.blend.BoundingBoxesMaskGen`.
seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
name : None or str, optional
See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
Old name for parameter `seed`.
Its usage will not yet cause a deprecation warning,
but it is still recommended to use `seed` now.
Outdated since 0.4.0.
deterministic : bool, optional
Deprecated since 0.4.0.
See method ``to_deterministic()`` for an alternative and for
details about what the "deterministic mode" actually does.
Examples
--------
>>> import imgaug.augmenters as iaa
>>> aug = iaa.BlendAlphaBoundingBoxes("person",
>>> foreground=iaa.Grayscale(1.0))
Create an augmenter that removes color within bounding boxes having the
label ``person``.
>>> aug = iaa.BlendAlphaBoundingBoxes(["person", "car"],
>>> foreground=iaa.AddToHue((-255, 255)))
Create an augmenter that randomizes the hue within bounding boxes that
have the label ``person`` or ``car``.
>>> aug = iaa.BlendAlphaBoundingBoxes(["person", "car"],
>>> foreground=iaa.AddToHue((-255, 255)),
>>> nb_sample_labels=1)
Create an augmenter that randomizes the hue within bounding boxes that
have either the label ``person`` or ``car``. Only one label is picked per
image. Note that the sampling happens with replacement, so if
``nb_sample_classes`` would be ``>1``, it could still lead to only one
*unique* label being sampled.
>>> aug = iaa.BlendAlphaBoundingBoxes(None,
>>> background=iaa.Multiply(0.0))
Create an augmenter that zeros all pixels (``Multiply(0.0)``)
that are *not* (``background`` branch) within bounding boxes of
*any* (``None``) label. In other words, all pixels outside of bounding
boxes become black.
Note that we don't use ``TotalDropout`` here, because by default it will
also remove all coordinate-based augmentables, which will break the
blending of such inputs.
"""
# Added in 0.4.0.
def __init__(self,
labels,
foreground=None, background=None,
nb_sample_labels=None,
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
super(BlendAlphaBoundingBoxes, self).__init__(
BoundingBoxesMaskGen(
labels=labels,
nb_sample_labels=nb_sample_labels
),
foreground=foreground,
background=background,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic)
[docs]@six.add_metaclass(ABCMeta)
class IBatchwiseMaskGenerator(object):
"""Interface for classes generating masks for batches.
Child classes are supposed to receive a batch and generate an iterable
of masks, one per row (i.e. image), matching the row shape (i.e. image
shape). This is used in :class:`~imgaug.augmenters.blend.BlendAlphaMask`.
Added in 0.4.0.
"""
# Added in 0.4.0.
[docs] def draw_masks(self, batch, random_state=None):
"""Generate a mask with given shape.
Parameters
----------
batch : imgaug.augmentables.batches._BatchInAugmentation
Shape of the mask to sample.
random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
A seed or random number generator to use during the sampling
process. If ``None``, the global RNG will be used.
See also :func:`~imgaug.augmenters.meta.Augmenter.__init__`
for a similar parameter with more details.
Returns
-------
iterable of ndarray
Masks, one per row in the batch.
Each mask must be a ``float32`` array in interval ``[0.0, 1.0]``.
It must either have the same shape as the row (i.e. the image)
or shape ``(H, W)`` if all channels are supposed to have the
same mask.
"""
[docs]class StochasticParameterMaskGen(IBatchwiseMaskGenerator):
"""Mask generator that queries stochastic parameters for mask values.
This class receives batches for which to generate masks, iterates over
the batch rows (i.e. images) and generates one mask per row.
For a row with shape ``(H, W, C)`` (= image shape), it generates
either a ``(H, W)`` mask (if ``per_channel`` is false-like) or a
``(H, W, C)`` mask (if ``per_channel`` is true-like).
The ``per_channel`` is sampled per batch for each row/image.
Added in 0.4.0.
Parameters
----------
parameter : imgaug.parameters.StochasticParameter
Stochastic parameter to draw mask samples from.
Expected to return values in interval ``[0.0, 1.0]`` (not all
stochastic parameters do that) and must be able to handle sampling
shapes ``(H, W)`` and ``(H, W, C)`` (all stochastic parameters should
do that).
per_channel : bool or float or imgaug.parameters.StochasticParameter, optional
Whether to use the same mask for all channels (``False``)
or to sample a new mask for each channel (``True``).
If this value is a float ``p``, then for ``p`` percent of all rows
(i.e. images) `per_channel` will be treated as ``True``, otherwise
as ``False``.
"""
# Added in 0.4.0.
def __init__(self, parameter, per_channel):
super(StochasticParameterMaskGen, self).__init__()
self.parameter = parameter
self.per_channel = iap.handle_probability_param(per_channel,
"per_channel")
# Added in 0.4.0.
[docs] def draw_masks(self, batch, random_state=None):
"""
See :func:`~imgaug.augmenters.blend.IBatchwiseMaskGenerator.draw_masks`.
"""
shapes = batch.get_rowwise_shapes()
random_state = iarandom.RNG(random_state)
per_channel = self.per_channel.draw_samples((len(shapes),),
random_state=random_state)
return [self._draw_mask(shape, random_state, per_channel_i)
for shape, per_channel_i
in zip(shapes, per_channel)]
# Added in 0.4.0.
def _draw_mask(self, shape, random_state, per_channel):
if len(shape) == 2 or per_channel >= 0.5:
mask = self.parameter.draw_samples(shape,
random_state=random_state)
else:
# TODO When this was wrongly sampled directly as (H,W,C) no
# test for AlphaElementwise ended up failing. That should not
# happen.
# We are guarantueed here to have (H, W, C) as shape (H, W) is
# handled by the above block.
# As the mask is not channelwise, we will just return (H, W)
# instead of (H, W, C).
mask = self.parameter.draw_samples(shape[0:2],
random_state=random_state)
# mask has no elements if height or width in shape is 0
if mask.size > 0:
assert 0 <= mask.item(0) <= 1.0, (
"Expected 'parameter' samples to be in the interval "
"[0.0, 1.0]. Got min %.4f and max %.4f." % (
np.min(mask), np.max(mask),))
return mask
[docs]class SomeColorsMaskGen(IBatchwiseMaskGenerator):
"""Generator that produces masks based on some similar colors in images.
This class receives batches for which to generate masks, iterates over
the batch rows (i.e. images) and generates one mask per row.
The mask contains high alpha values for some colors, while other colors
get low mask values. Which colors are chosen is random. How wide or
narrow the selection is (e.g. very specific blue tone or all blue-ish
colors) is determined by the hyperparameters.
The color selection method performs roughly the following steps:
1. Split the full color range of the hue in ``HSV`` into ``nb_bins``
bins (i.e. ``256/nb_bins`` different possible hue tones).
2. Shift the bins by ``rotation_deg`` degrees. (This way, the ``0th``
bin does not always start at exactly ``0deg`` of hue.)
3. Sample ``alpha`` values for each bin.
4. Repeat the ``nb_bins`` bins until there are ``256`` bins.
5. Smoothen the alpha values of neighbouring bins using a gaussian
kernel. The kernel's ``sigma`` is derived from ``smoothness``.
6. Associate all hue values in the image with the corresponding bin's
alpha value. This results in the alpha mask.
.. note::
This mask generator will produce an ``AssertionError`` for batches
that contain no images.
Added in 0.4.0.
**Supported dtypes**:
See :func:`~imgaug.augmenters.color.change_colorspaces_`.
Parameters
----------
nb_bins : int or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional
Number of bins. For ``B`` bins, each bin denotes roughly ``360/B``
degrees of colors in the hue channel. Lower values lead to a coarser
selection of colors. Expected value range is ``[2, 256]``.
* If ``int``: Exactly that value will be used for all images.
* If ``tuple`` ``(a, b)``: A random value will be uniformly sampled
per image from the discrete interval ``[a..b]``.
* If ``list``: A random value will be picked per image from that
list.
* If ``StochasticParameter``: That parameter will be queried once
per batch for ``(N,)`` values -- one per image.
smoothness : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
Strength of the 1D gaussian kernel applied to the sampled binwise
alpha values. Larger values will lead to more similar grayscaling of
neighbouring colors. Expected value range is ``[0.0, 1.0]``.
* If ``number``: Exactly that value will be used for all images.
* If ``tuple`` ``(a, b)``: A random value will be uniformly sampled
per image from the interval ``[a, b]``.
* If ``list``: A random value will be picked per image from that
list.
* If ``StochasticParameter``: That parameter will be queried once
per batch for ``(N,)`` values -- one per image.
alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
Parameter to sample binwise alpha blending factors from. Expected
value range is ``[0.0, 1.0]``. Note that the alpha values will be
smoothed between neighbouring bins. Hence, it is usually a good idea
to set this so that the probability distribution peaks are around
``0.0`` and ``1.0``, e.g. via a list ``[0.0, 1.0]`` or a ``Beta``
distribution.
It is not recommended to set this to a deterministic value, otherwise
all bins and hence all pixels in the generated mask will have the
same value.
* If ``number``: Exactly that value will be used for all bins.
* If ``tuple`` ``(a, b)``: A random value will be uniformly sampled
per bin from the interval ``[a, b]``.
* If ``list``: A random value will be picked per bin from that list.
* If ``StochasticParameter``: That parameter will be queried once
per batch for ``(N*B,)`` values -- one per image and bin.
rotation_deg : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
Rotiational shift of each bin as a fraction of ``360`` degrees.
E.g. ``0.0`` will not shift any bins, while a value of ``0.5`` will
shift by around ``180`` degrees. This shift is mainly used so that
the ``0th`` bin does not always start at ``0deg``. Expected value
range is ``[-360, 360]``. This parameter can usually be kept at the
default value.
* If ``number``: Exactly that value will be used for all images.
* If ``tuple`` ``(a, b)``: A random value will be uniformly sampled
per image from the interval ``[a, b]``.
* If ``list``: A random value will be picked per image from that
list.
* If ``StochasticParameter``: That parameter will be queried once
per batch for ``(N,)`` values -- one per image.
from_colorspace : str, optional
The source colorspace (of the input images).
See :func:`~imgaug.augmenters.color.change_colorspace_`.
"""
# Added in 0.4.0.
# TODO colorlib.CSPACE_RGB produces 'has no attribute' error?
def __init__(self, nb_bins=(5, 15), smoothness=(0.1, 0.3),
alpha=[0.0, 1.0], rotation_deg=(0, 360),
from_colorspace="RGB"):
# pylint: disable=dangerous-default-value
super(SomeColorsMaskGen, self).__init__()
self.nb_bins = iap.handle_discrete_param(
nb_bins, "nb_bins", value_range=(1, 256),
tuple_to_uniform=True, list_to_choice=True)
self.smoothness = iap.handle_continuous_param(
smoothness, "smoothness", value_range=(0.0, 1.0),
tuple_to_uniform=True, list_to_choice=True)
self.alpha = iap.handle_continuous_param(
alpha, "alpha", value_range=(0.0, 1.0),
tuple_to_uniform=True, list_to_choice=True)
self.rotation_deg = iap.handle_continuous_param(
rotation_deg, "rotation_deg", value_range=(-360, 360),
tuple_to_uniform=True, list_to_choice=True)
self.from_colorspace = from_colorspace
self.sigma_max = 10.0
# Added in 0.4.0.
[docs] def draw_masks(self, batch, random_state=None):
"""
See :func:`~imgaug.augmenters.blend.IBatchwiseMaskGenerator.draw_masks`.
"""
assert batch.images is not None, (
"Can only generate masks for batches that contain images, but "
"got a batch without images.")
random_state = iarandom.RNG(random_state)
samples = self._draw_samples(batch, random_state=random_state)
return [self._draw_mask(image, i, samples)
for i, image
in enumerate(batch.images)]
# Added in 0.4.0.
def _draw_mask(self, image, image_idx, samples):
return self.generate_mask(
image,
samples[0][image_idx],
samples[1][image_idx] * self.sigma_max,
samples[2][image_idx],
self.from_colorspace)
# Added in 0.4.0.
def _draw_samples(self, batch, random_state):
nb_rows = batch.nb_rows
nb_bins = self.nb_bins.draw_samples((nb_rows,),
random_state=random_state)
smoothness = self.smoothness.draw_samples((nb_rows,),
random_state=random_state)
alpha = self.alpha.draw_samples((np.sum(nb_bins),),
random_state=random_state)
rotation_deg = self.rotation_deg.draw_samples(
(nb_rows,), random_state=random_state)
nb_bins = np.clip(nb_bins, 1, 256)
smoothness = np.clip(smoothness, 0.0, 1.0)
alpha = np.clip(alpha, 0.0, 1.0)
rotation_bins = np.mod(
np.round(rotation_deg * (256/360)).astype(np.int32),
256)
binwise_alphas = _split_1d_array_to_list(alpha, nb_bins)
return binwise_alphas, smoothness, rotation_bins
[docs] @classmethod
def generate_mask(cls, image, binwise_alphas, sigma,
rotation_bins, from_colorspace):
"""Generate a colorwise alpha mask for a single image.
Added in 0.4.0.
Parameters
----------
image : ndarray
Image for which to generate the mask. Must have shape ``(H,W,3)``
in colorspace `from_colorspace`.
binwise_alphas : ndarray
Alpha values of shape ``(B,)`` with ``B`` in ``[1, 256]``
and values in interval ``[0.0, 1.0]``. Will be upscaled to
256 bins by simple repetition. Each bin represents ``1/256`` th
of the hue.
sigma : float
Sigma of the 1D gaussian kernel applied to the upscaled binwise
alpha value array.
rotation_bins : int
By how much to rotate the 256 bin alpha array. The rotation is
given in number of bins.
from_colorspace : str
Colorspace of the input image. One of
``imgaug.augmenters.color.CSPACE_*``.
Returns
-------
ndarray
``float32`` mask array of shape ``(H, W)`` with values in
``[0.0, 1.0]``
"""
# import has to be deferred, otherwise python 2.7 fails
from . import color as colorlib
image_hsv = colorlib.change_colorspace_(
np.copy(image),
to_colorspace=colorlib.CSPACE_HSV,
from_colorspace=from_colorspace)
if 0 in image_hsv.shape[0:2]:
return np.zeros(image_hsv.shape[0:2], dtype=np.float32)
binwise_alphas = cls._upscale_to_256_alpha_bins(binwise_alphas)
binwise_alphas = cls._rotate_alpha_bins(binwise_alphas, rotation_bins)
binwise_alphas_smooth = cls._smoothen_alphas(binwise_alphas, sigma)
mask = cls._generate_pixelwise_alpha_mask(image_hsv,
binwise_alphas_smooth)
return mask
# Added in 0.4.0.
@classmethod
def _upscale_to_256_alpha_bins(cls, alphas):
# repeat alphas bins so that B sampled bins become 256 bins
nb_bins = len(alphas)
nb_repeats_per_bin = int(np.ceil(256/nb_bins))
alphas = np.repeat(alphas, (nb_repeats_per_bin,))
alphas = alphas[0:256]
return alphas
# Added in 0.4.0.
@classmethod
def _rotate_alpha_bins(cls, alphas, rotation_bins):
# e.g. for offset 2: abcdef -> cdefab
# note: offset here is expected to be in [0, 256]
if rotation_bins > 0:
alphas = np.roll(alphas, -rotation_bins)
return alphas
# Added in 0.4.0.
@classmethod
def _smoothen_alphas(cls, alphas, sigma):
if sigma <= 0.0+1e-2:
return alphas
ksize = max(int(sigma * 2.5), 3)
ksize_y, ksize_x = (1, ksize)
if ksize_x % 2 == 0:
ksize_x += 1
# we fake here cv2.BORDER_WRAP, because GaussianBlur does not
# support that mode, i.e. we want:
# cdefgh|abcdefgh|abcdefg
alphas = np.concatenate([
alphas[-ksize_x:],
alphas,
alphas[:ksize_x],
])
alphas = cv2.GaussianBlur(
_normalize_cv2_input_arr_(alphas[np.newaxis, :]),
ksize=(ksize_x, ksize_y),
sigmaX=sigma, sigmaY=sigma,
borderType=cv2.BORDER_REPLICATE
)[0, :]
# revert fake BORDER_WRAP
alphas = alphas[ksize_x:-ksize_x]
return alphas
# Added in 0.4.0.
@classmethod
def _generate_pixelwise_alpha_mask(cls, image_hsv, hue_to_alpha):
hue = image_hsv[:, :, 0]
table = hue_to_alpha * 255
table = np.clip(np.round(table), 0, 255).astype(np.uint8)
mask = ia.apply_lut(hue, table)
return mask.astype(np.float32) / 255.0
# Added in 0.4.0.
class _LinearGradientMaskGen(IBatchwiseMaskGenerator):
# Added in 0.4.0.
def __init__(self, axis, min_value=0.0, max_value=1.0,
start_at=0.0, end_at=1.0):
self.axis = axis
self.min_value = iap.handle_continuous_param(
min_value, "min_value", value_range=(0.0, 1.0),
tuple_to_uniform=True, list_to_choice=True)
self.max_value = iap.handle_continuous_param(
max_value, "max_value", value_range=(0.0, 1.0),
tuple_to_uniform=True, list_to_choice=True)
self.start_at = iap.handle_continuous_param(
start_at, "start_at", value_range=(0.0, 1.0),
tuple_to_uniform=True, list_to_choice=True)
self.end_at = iap.handle_continuous_param(
end_at, "end_at", value_range=(0.0, 1.0),
tuple_to_uniform=True, list_to_choice=True)
def draw_masks(self, batch, random_state=None):
"""
See :func:`~imgaug.augmenters.blend.IBatchwiseMaskGenerator.draw_masks`.
Added in 0.4.0.
"""
random_state = iarandom.RNG(random_state)
shapes = batch.get_rowwise_shapes()
samples = self._draw_samples(len(shapes), random_state=random_state)
return [self._draw_mask(shape, i, samples)
for i, shape
in enumerate(shapes)]
# Added in 0.4.0.
def _draw_mask(self, shape, image_idx, samples):
return self.generate_mask(
shape,
samples[0][image_idx],
samples[1][image_idx],
samples[2][image_idx],
samples[3][image_idx])
# Added in 0.4.0.
def _draw_samples(self, nb_rows, random_state):
min_value = self.min_value.draw_samples((nb_rows,),
random_state=random_state)
max_value = self.max_value.draw_samples((nb_rows,),
random_state=random_state)
start_at = self.start_at.draw_samples(
(nb_rows,), random_state=random_state)
end_at = self.end_at.draw_samples(
(nb_rows,), random_state=random_state)
return min_value, max_value, start_at, end_at
@classmethod
@abstractmethod
def generate_mask(cls, shape, min_value, max_value, start_at, end_at):
"""Generate a horizontal gradient mask.
Added in 0.4.0.
Parameters
----------
shape : tuple of int
Shape of the image. The mask will have the same height and
width.
min_value : number
Minimum value of the gradient in interval ``[0.0, 1.0]``.
max_value : number
Maximum value of the gradient in interval ``[0.0, 1.0]``.
start_at : number
Position on the x-axis where the linear gradient starts, given as
a fraction of the axis size. Interval is ``[0.0, 1.0]``.
end_at : number
Position on the x-axis where the linear gradient ends, given as
a fraction of the axis size. Interval is ``[0.0, 1.0]``.
Returns
-------
ndarray
``float32`` mask array with same height and width as the image.
Values are in ``[0.0, 1.0]``.
"""
# Added in 0.4.0.
@classmethod
def _generate_mask(cls, shape, axis, min_value, max_value, start_at,
end_at):
height, width = shape[0:2]
axis_size = shape[axis]
min_value = min(max(min_value, 0.0), 1.0)
max_value = min(max(max_value, 0.0), 1.0)
start_at_px = min(max(int(start_at * axis_size), 0), axis_size)
end_at_px = min(max(int(end_at * axis_size), 0), axis_size)
inverted = False
if end_at_px < start_at_px:
inverted = True
start_at_px, end_at_px = end_at_px, start_at_px
before_grad = np.full((start_at_px,), min_value,
dtype=np.float32)
grad = np.linspace(start=min_value,
stop=max_value,
num=end_at_px - start_at_px,
dtype=np.float32)
after_grad = np.full((axis_size - end_at_px,), max_value,
dtype=np.float32)
mask = np.concatenate((
before_grad,
grad,
after_grad
), axis=0)
if inverted:
mask = 1.0 - mask
if axis == 0:
mask = mask[:, np.newaxis]
mask = np.tile(mask, (1, width))
else:
mask = mask[np.newaxis, :]
mask = np.tile(mask, (height, 1))
return mask
[docs]class HorizontalLinearGradientMaskGen(_LinearGradientMaskGen):
"""Generator that produces horizontal linear gradient masks.
This class receives batches and produces for each row (i.e. image)
a horizontal linear gradient that matches the row's shape (i.e. image
shape). The gradient increases linearly from a minimum value to a
maximum value along the x-axis. The start and end points (i.e. where the
minimum value starts to increase and where it reaches the maximum)
may be defines as fractions of the width. E.g. for width ``100`` and
``start=0.25``, ``end=0.75``, the gradient would have its minimum
in interval ``[0px, 25px]`` and its maximum in interval ``[75px, 100px]``.
Note that this has nothing to do with a *derivative* along the x-axis.
Added in 0.4.0.
Parameters
----------
min_value : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
Minimum value that the mask will have up to the start point of the
linear gradient.
Note that `min_value` is allowed to be larger than `max_value`,
in which case the gradient will start at the (higher) `min_value`
and decrease towards the (lower) `max_value`.
* If ``number``: Exactly that value will be used for all images.
* If ``tuple`` ``(a, b)``: A random value will be uniformly sampled
per image from the interval ``[a, b]``.
* If ``list``: A random value will be picked per image from that list.
* If ``StochasticParameter``: That parameter will be queried once
per batch for ``(N,)`` values -- one per image.
max_value : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
Maximum value that the mask will have at the end of the
linear gradient.
Datatypes are analogous to `min_value`.
start_at : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
Position on the x-axis where the linear gradient starts, given as a
fraction of the axis size. Interval is ``[0.0, 1.0]``, where ``0.0``
is at the left of the image.
If ``end_at < start_at`` the gradient will be inverted.
Datatypes are analogous to `min_value`.
end_at : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
Position on the x-axis where the linear gradient ends, given as a
fraction of the axis size. Interval is ``[0.0, 1.0]``, where ``0.0``
is at the right of the image.
Datatypes are analogous to `min_value`.
"""
# Added in 0.4.0.
def __init__(self, min_value=(0.0, 0.2), max_value=(0.8, 1.0),
start_at=(0.0, 0.2), end_at=(0.8, 1.0)):
super(HorizontalLinearGradientMaskGen, self).__init__(
axis=1,
min_value=min_value,
max_value=max_value,
start_at=start_at,
end_at=end_at)
[docs] @classmethod
def generate_mask(cls, shape, min_value, max_value, start_at, end_at):
"""Generate a linear horizontal gradient mask.
Added in 0.4.0.
Parameters
----------
shape : tuple of int
Shape of the image. The mask will have the same height and
width.
min_value : number
Minimum value of the gradient in interval ``[0.0, 1.0]``.
max_value : number
Maximum value of the gradient in interval ``[0.0, 1.0]``.
start_at : number
Position on the x-axis where the linear gradient starts, given as
a fraction of the axis size. Interval is ``[0.0, 1.0]``.
end_at : number
Position on the x-axis where the linear gradient ends, given as
a fraction of the axis size. Interval is ``[0.0, 1.0]``.
Returns
-------
ndarray
``float32`` mask array with same height and width as the image.
Values are in ``[0.0, 1.0]``.
"""
return cls._generate_mask(
axis=1,
shape=shape,
min_value=min_value,
max_value=max_value,
start_at=start_at,
end_at=end_at)
[docs]class VerticalLinearGradientMaskGen(_LinearGradientMaskGen):
"""Generator that produces vertical linear gradient masks.
See :class:`~imgaug.augmenters.blend.HorizontalLinearGradientMaskGen`
for details.
Added in 0.4.0.
Parameters
----------
min_value : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
Minimum value that the mask will have up to the start point of the
linear gradient.
Note that `min_value` is allowed to be larger than `max_value`,
in which case the gradient will start at the (higher) `min_value`
and decrease towards the (lower) `max_value`.
* If ``number``: Exactly that value will be used for all images.
* If ``tuple`` ``(a, b)``: A random value will be uniformly sampled
per image from the interval ``[a, b]``.
* If ``list``: A random value will be picked per image from that list.
* If ``StochasticParameter``: That parameter will be queried once
per batch for ``(N,)`` values -- one per image.
max_value : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
Maximum value that the mask will have at the end of the
linear gradient.
Datatypes are analogous to `min_value`.
start_at : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
Position on the y-axis where the linear gradient starts, given as a
fraction of the axis size. Interval is ``[0.0, 1.0]``, where ``0.0``
is at the top of the image.
If ``end_at < start_at`` the gradient will be inverted.
Datatypes are analogous to `min_value`.
end_at : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
Position on the x-axis where the linear gradient ends, given as a
fraction of the axis size. Interval is ``[0.0, 1.0]``, where ``1.0``
is at the bottom of the image.
Datatypes are analogous to `min_value`.
"""
# Added in 0.4.0.
def __init__(self, min_value=(0.0, 0.2), max_value=(0.8, 1.0),
start_at=(0.0, 0.2), end_at=(0.8, 1.0)):
super(VerticalLinearGradientMaskGen, self).__init__(
axis=0,
min_value=min_value,
max_value=max_value,
start_at=start_at,
end_at=end_at)
[docs] @classmethod
def generate_mask(cls, shape, min_value, max_value, start_at, end_at):
"""Generate a linear horizontal gradient mask.
Added in 0.4.0.
Parameters
----------
shape : tuple of int
Shape of the image. The mask will have the same height and
width.
min_value : number
Minimum value of the gradient in interval ``[0.0, 1.0]``.
max_value : number
Maximum value of the gradient in interval ``[0.0, 1.0]``.
start_at : number
Position on the x-axis where the linear gradient starts, given as
a fraction of the axis size. Interval is ``[0.0, 1.0]``.
end_at : number
Position on the x-axis where the linear gradient ends, given as
a fraction of the axis size. Interval is ``[0.0, 1.0]``.
Returns
-------
ndarray
``float32`` mask array with same height and width as the image.
Values are in ``[0.0, 1.0]``.
"""
return cls._generate_mask(
axis=0,
shape=shape,
min_value=min_value,
max_value=max_value,
start_at=start_at,
end_at=end_at)
[docs]class RegularGridMaskGen(IBatchwiseMaskGenerator):
"""Generate masks following a regular grid pattern.
This mask generator splits each image into a grid-like pattern of
``H`` rows and ``W`` columns. Each cell is then filled with an alpha
value, sampled randomly per cell.
The difference to :class:`CheckerboardMaskGen` is that this mask generator
samples random alpha values per cell, while in the checkerboard the
alpha values follow a fixed pattern.
Added in 0.4.0.
Parameters
----------
nb_rows : int or tuple of int or list of int or imgaug.parameters.StochasticParameter
Number of rows of the regular grid.
* If ``int``: Exactly that value will be used for all images.
* If ``tuple`` ``(a, b)``: A random value will be uniformly sampled
per image from the discrete interval ``[a..b]``.
* If ``list``: A random value will be picked per image from that
list.
* If ``StochasticParameter``: That parameter will be queried once
per batch for ``(N,)`` values -- one per image.
nb_cols : int or tuple of int or list of int or imgaug.parameters.StochasticParameter
Number of columns of the checkerboard. Analogous to `nb_rows`.
alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
Alpha value of each cell.
* If ``number``: Exactly that value will be used for all images.
* If ``tuple`` ``(a, b)``: A random value will be uniformly sampled
per image from the interval ``[a, b]``.
* If ``list``: A random value will be picked per image from that list.
* If ``StochasticParameter``: That parameter will be queried once
per batch for ``(N,)`` values -- one per image.
"""
# Added in 0.4.0.
def __init__(self, nb_rows, nb_cols, alpha=[0.0, 1.0]):
# pylint: disable=dangerous-default-value
self.nb_rows = iap.handle_discrete_param(
nb_rows, "nb_rows", value_range=(1, None),
tuple_to_uniform=True, list_to_choice=True,
allow_floats=False)
self.nb_cols = iap.handle_discrete_param(
nb_cols, "nb_cols", value_range=(1, None),
tuple_to_uniform=True, list_to_choice=True,
allow_floats=False)
self.alpha = iap.handle_continuous_param(
alpha, "alpha", value_range=(0.0, 1.0),
tuple_to_uniform=True, list_to_choice=True)
[docs] def draw_masks(self, batch, random_state=None):
"""
See :func:`~imgaug.augmenters.blend.IBatchwiseMaskGenerator.draw_masks`.
Added in 0.4.0.
"""
random_state = iarandom.RNG(random_state)
shapes = batch.get_rowwise_shapes()
nb_rows, nb_cols, alpha = self._draw_samples(len(shapes),
random_state=random_state)
return [self.generate_mask(shape, nb_rows_i, nb_cols_i, alpha_i)
for shape, nb_rows_i, nb_cols_i, alpha_i
in zip(shapes, nb_rows, nb_cols, alpha)]
# Added in 0.4.0.
def _draw_samples(self, nb_images, random_state):
nb_rows = self.nb_rows.draw_samples((nb_images,),
random_state=random_state)
nb_cols = self.nb_cols.draw_samples((nb_images,),
random_state=random_state)
nb_alphas_per_img = nb_rows * nb_cols
alpha_raw = self.alpha.draw_samples(
(np.sum(nb_alphas_per_img),),
random_state=random_state)
alpha = _split_1d_array_to_list(alpha_raw, nb_alphas_per_img)
return nb_rows, nb_cols, alpha
[docs] @classmethod
def generate_mask(cls, shape, nb_rows, nb_cols, alphas):
"""Generate a mask following a checkerboard pattern.
Added in 0.4.0.
Parameters
----------
shape : tuple of int
Height and width of the output mask.
nb_rows : int
Number of rows of the checkerboard pattern.
nb_cols : int
Number of columns of the checkerboard pattern.
alphas : ndarray
1D or 2D array containing for each cell the alpha value, i.e.
``nb_rows*nb_cols`` values.
Returns
-------
ndarray
``float32`` mask array with same height and width as
``segmap.shape``. Values are in ``[0.0, 1.0]``.
"""
from . import size as sizelib
height, width = shape[0:2]
if 0 in (height, width):
return np.zeros((height, width), dtype=np.float32)
nb_rows = min(max(nb_rows, 1), height)
nb_cols = min(max(nb_cols, 1), width)
cell_height = int(height / nb_rows)
cell_width = int(width / nb_cols)
# If there are more alpha values than nb_rows*nb_cols we reduce the
# number of alpha values.
alphas = alphas.flat[0:nb_rows*nb_cols]
assert alphas.size == nb_rows*nb_cols, (
"Expected `alphas` to not contain less values than "
"`nb_rows * nb_cols` (both clipped to [1, height] and "
"[1, width] respectively). Got %d alpha values vs %d expected "
"values (nb_rows=%d, nb_cols=%d) for requested mask shape %s." % (
alphas.size, nb_rows * nb_cols, nb_rows, nb_cols,
(height, width)))
mask = alphas.astype(np.float32).reshape((nb_rows, nb_cols))
mask = np.repeat(mask, cell_height, axis=0)
mask = np.repeat(mask, cell_width, axis=1)
# if mask is too small, reflection pad it on all sides
missing_height = height - mask.shape[0]
missing_width = width - mask.shape[1]
top = int(np.floor(missing_height / 2))
bottom = int(np.ceil(missing_height / 2))
left = int(np.floor(missing_width / 2))
right = int(np.ceil(missing_width / 2))
mask = sizelib.pad(mask,
top=top, right=right, bottom=bottom, left=left,
mode="reflect")
return mask
[docs]class CheckerboardMaskGen(IBatchwiseMaskGenerator):
"""Generate masks following a checkerboard-like pattern.
This mask generator splits each image into a regular grid of
``H`` rows and ``W`` columns. Each cell is then filled with either
``1.0`` or ``0.0``. The cell at the top-left is always ``1.0``. Its right
and bottom neighbour cells are ``0.0``. The 4-neighbours of any cell always
have a value opposite to the cell's value (``0.0`` vs. ``1.0``).
Added in 0.4.0.
Parameters
----------
nb_rows : int or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional
Number of rows of the checkerboard.
* If ``int``: Exactly that value will be used for all images.
* If ``tuple`` ``(a, b)``: A random value will be uniformly sampled
per image from the discrete interval ``[a..b]``.
* If ``list``: A random value will be picked per image from that
list.
* If ``StochasticParameter``: That parameter will be queried once
per batch for ``(N,)`` values -- one per image.
nb_cols : int or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional
Number of columns of the checkerboard. Analogous to `nb_rows`.
"""
def __init__(self, nb_rows, nb_cols):
self.grid = RegularGridMaskGen(nb_rows=nb_rows,
nb_cols=nb_cols,
alpha=1)
@property
def nb_rows(self):
"""Get the number of rows of the checkerboard grid.
Added in 0.4.0.
Returns
-------
int
The number of rows.
"""
return self.grid.nb_rows
@property
def nb_cols(self):
"""Get the number of columns of the checkerboard grid.
Added in 0.4.0.
Returns
-------
int
The number of columns.
"""
return self.grid.nb_cols
[docs] def draw_masks(self, batch, random_state=None):
"""
See :func:`~imgaug.augmenters.blend.IBatchwiseMaskGenerator.draw_masks`.
Added in 0.4.0.
"""
# pylint: disable=protected-access
random_state = iarandom.RNG(random_state)
shapes = batch.get_rowwise_shapes()
nb_rows, nb_cols, _alpha = self.grid._draw_samples(
len(shapes), random_state=random_state)
return [self.generate_mask(shape, nb_rows_i, nb_cols_i)
for shape, nb_rows_i, nb_cols_i
in zip(shapes, nb_rows, nb_cols)]
[docs] @classmethod
def generate_mask(cls, shape, nb_rows, nb_cols):
"""Generate a mask following a checkerboard pattern.
Added in 0.4.0.
Parameters
----------
shape : tuple of int
Height and width of the output mask.
nb_rows : int
Number of rows of the checkerboard pattern.
nb_cols : int
Number of columns of the checkerboard pattern.
Returns
-------
ndarray
``float32`` mask array with same height and width as
``segmap.shape``. Values are in ``[0.0, 1.0]``.
"""
height, width = shape[0:2]
if 0 in (height, width):
return np.zeros((height, width), dtype=np.float32)
nb_rows = min(max(nb_rows, 1), height)
nb_cols = min(max(nb_cols, 1), width)
alphas = np.full((nb_cols,), 1.0, dtype=np.float32)
alphas[::2] = 0.0
alphas = np.tile(alphas[np.newaxis, :], (nb_rows, 1))
alphas[::2, :] = 1.0 - alphas[::2, :]
return RegularGridMaskGen.generate_mask(shape, nb_rows, nb_cols, alphas)
[docs]class SegMapClassIdsMaskGen(IBatchwiseMaskGenerator):
"""Generator that produces masks highlighting segmentation map classes.
This class produces for each segmentation map in a batch a mask in which
the locations of a set of provided classes are highlighted (i.e. ``1.0``).
The classes may be provided as a fixed list of class ids or a stochastic
parameter from which class ids will be sampled.
The produced masks are initially of the same height and width as the
segmentation map arrays and later upscaled to the image height and width.
.. note::
Segmentation maps can have multiple channels. If that is the case
then for each position ``(x, y)`` it is sufficient that any class id
in any channel matches one of the desired class ids.
.. note::
This class will produce an ``AssertionError`` if there are no
segmentation maps in a batch.
Added in 0.4.0.
Parameters
----------
class_ids : int or tuple of int or list of int or imgaug.parameters.StochasticParameter
Segmentation map classes to mark in the produced mask.
If `nb_sample_classes` is ``None`` then this is expected to be either
a single ``int`` (always mark this one class id) or a ``list`` of
``int`` s (always mark these class ids).
If `nb_sample_classes` is set, then this parameter will be treated
as a stochastic parameter with the following valid types:
* If ``int``: Exactly that class id will be used for all
segmentation maps.
* If ``tuple`` ``(a, b)``: ``N`` random values will be uniformly
sampled per segmentation map from the discrete interval
``[a..b]`` and used as the class ids.
* If ``list``: ``N`` random values will be picked per segmentation
map from that list and used as the class ids.
* If ``StochasticParameter``: That parameter will be queried once
per batch for ``(sum(N),)`` values.
``N`` denotes the number of classes to sample per segmentation
map (derived from `nb_sample_classes`) and ``sum(N)`` denotes the
sum of ``N`` s over all segmentation maps.
nb_sample_classes : None or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional
Number of class ids to sample (with replacement) per segmentation map.
As sampling happens with replacement, fewer *unique* class ids may be
sampled.
* If ``None``: `class_ids` is expected to be a fixed value of
class ids to be used for all segmentation maps.
* If ``int``: Exactly that many class ids will be sampled for all
segmentation maps.
* If ``tuple`` ``(a, b)``: A random value will be uniformly
sampled per segmentation map from the discrete interval
``[a..b]``.
* If ``list`` or ``int``: A random value will be picked per
segmentation map from that list.
* If ``StochasticParameter``: That parameter will be queried once
per batch for ``(B,)`` values, where ``B`` is the number of
segmentation maps.
"""
# Added in 0.4.0.
def __init__(self, class_ids, nb_sample_classes=None):
if nb_sample_classes is None:
if ia.is_single_integer(class_ids):
class_ids = [class_ids]
assert isinstance(class_ids, list), (
"Expected `class_ids` to be a single integer or a list of "
"integers if `nb_sample_classes` is None. Got type `%s`. "
"Set `nb_sample_classes` to e.g. an integer to enable "
"stochastic parameters for `class_ids`." % (
type(class_ids).__name__,))
self.class_ids = class_ids
self.nb_sample_classes = None
else:
self.class_ids = iap.handle_discrete_param(
class_ids, "class_ids", value_range=(0, None),
tuple_to_uniform=True, list_to_choice=True,
allow_floats=False)
self.nb_sample_classes = iap.handle_discrete_param(
nb_sample_classes, "nb_sample_classes", value_range=(0, None),
tuple_to_uniform=True, list_to_choice=True,
allow_floats=False)
[docs] def draw_masks(self, batch, random_state=None):
"""
See :func:`~imgaug.augmenters.blend.IBatchwiseMaskGenerator.draw_masks`.
Added in 0.4.0.
"""
assert batch.segmentation_maps is not None, (
"Can only generate masks for batches that contain segmentation "
"maps, but got a batch without them.")
random_state = iarandom.RNG(random_state)
class_ids = self._draw_samples(batch.nb_rows,
random_state=random_state)
return [self.generate_mask(segmap, class_ids_i)
for segmap, class_ids_i
in zip(batch.segmentation_maps, class_ids)]
# Added in 0.4.0.
def _draw_samples(self, nb_rows, random_state):
nb_sample_classes = self.nb_sample_classes
if nb_sample_classes is None:
assert isinstance(self.class_ids, list), (
"Expected list got %s." % (type(self.class_ids).__name__,))
return [self.class_ids] * nb_rows
nb_sample_classes = nb_sample_classes.draw_samples(
(nb_rows,), random_state=random_state)
nb_sample_classes = np.clip(nb_sample_classes, 0, None)
class_ids_raw = self.class_ids.draw_samples(
(np.sum(nb_sample_classes),),
random_state=random_state)
class_ids = _split_1d_array_to_list(class_ids_raw, nb_sample_classes)
return class_ids
# TODO this could be simplified to something like:
# segmap.keep_only_classes(class_ids).draw_mask()
[docs] @classmethod
def generate_mask(cls, segmap, class_ids):
"""Generate a mask of where the segmentation map has the given classes.
Added in 0.4.0.
Parameters
----------
segmap : imgaug.augmentables.segmap.SegmentationMapsOnImage
The segmentation map for which to generate the mask.
class_ids : iterable of int
IDs of the classes to set to ``1.0``.
For an ``(x, y)`` position, it is enough that *any* channel
at the given location to have one of these class ids to be marked
as ``1.0``.
Returns
-------
ndarray
``float32`` mask array with same height and width as
``segmap.shape``. Values are in ``[0.0, 1.0]``.
"""
mask = np.zeros(segmap.arr.shape[0:2], dtype=bool)
for class_id in class_ids:
# note that segmap has shape (H,W,C), so we max() along C
mask_i = np.any(segmap.arr == class_id, axis=2)
mask = np.logical_or(mask, mask_i)
mask = mask.astype(np.float32)
mask = ia.imresize_single_image(mask, segmap.shape[0:2])
return mask
[docs]class BoundingBoxesMaskGen(IBatchwiseMaskGenerator):
"""Generator that produces masks highlighting bounding boxes.
This class produces for each row (i.e. image + bounding boxes) in a batch
a mask in which the inner areas of bounding box rectangles with given
labels are marked (i.e. set to ``1.0``). The labels may be provided as a
fixed list of strings or a stochastic parameter from which labels will be
sampled. If no labels are provided, all bounding boxes will be marked.
A pixel will be set to ``1.0`` if *at least* one bounding box at that
location has one of the requested labels, even if there is *also* one
bounding box at that location with a not requested label.
.. note::
This class will produce an ``AssertionError`` if there are no
bounding boxes in a batch.
Added in 0.4.0.
Parameters
----------
labels : None or str or list of str or imgaug.parameters.StochasticParameter
Labels of bounding boxes to select for.
If `nb_sample_labels` is ``None`` then this is expected to be either
also ``None`` (select all BBs) or a single ``str`` (select BBs with
this one label) or a ``list`` of ``str`` s (always select BBs with
these labels).
If `nb_sample_labels` is set, then this parameter will be treated
as a stochastic parameter with the following valid types:
* If ``None``: Ignore the sampling count and always use all
bounding boxes.
* If ``str``: Exactly that label will be used for all
images.
* If ``list`` of ``str``: ``N`` random values will be picked per
image from that list and used as the labels.
* If ``StochasticParameter``: That parameter will be queried once
per batch for ``(sum(N),)`` values.
``N`` denotes the number of labels to sample per segmentation
map (derived from `nb_sample_labels`) and ``sum(N)`` denotes the
sum of ``N`` s over all images.
nb_sample_labels : None or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional
Number of labels to sample (with replacement) per image.
As sampling happens with replacement, fewer *unique* labels may be
sampled.
* If ``None``: `labels` is expected to also be ``None`` or a fixed
value of labels to be used for all images.
* If ``int``: Exactly that many labels will be sampled for all
images.
* If ``tuple`` ``(a, b)``: A random value will be uniformly
sampled per image from the discrete interval ``[a..b]``.
* If ``list``: A random value will be picked per image from
that list.
* If ``StochasticParameter``: That parameter will be queried once
per batch for ``(B,)`` values, where ``B`` is the number of
images.
"""
# Added in 0.4.0.
def __init__(self, labels=None, nb_sample_labels=None):
if labels is None:
self.labels = None
self.nb_sample_labels = None
elif nb_sample_labels is None:
if ia.is_string(labels):
labels = [labels]
assert isinstance(labels, list), (
"Expected `labels` a single string or a list of "
"strings if `nb_sample_labels` is None. Got type `%s`. "
"Set `nb_sample_labels` to e.g. an integer to enable "
"stochastic parameters for `labels`." % (
type(labels).__name__,))
self.labels = labels
self.nb_sample_labels = None
else:
self.labels = iap.handle_categorical_string_param(labels, "labels")
self.nb_sample_labels = iap.handle_discrete_param(
nb_sample_labels, "nb_sample_labels", value_range=(0, None),
tuple_to_uniform=True, list_to_choice=True,
allow_floats=False)
[docs] def draw_masks(self, batch, random_state=None):
"""
See :func:`~imgaug.augmenters.blend.IBatchwiseMaskGenerator.draw_masks`.
Added in 0.4.0.
"""
assert batch.bounding_boxes is not None, (
"Can only generate masks for batches that contain bounding boxes, "
"but got a batch without them.")
random_state = iarandom.RNG(random_state)
if self.labels is None:
return [self.generate_mask(bbsoi, None)
for bbsoi in batch.bounding_boxes]
labels = self._draw_samples(batch.nb_rows, random_state=random_state)
return [self.generate_mask(bbsoi, labels_i)
for bbsoi, labels_i
in zip(batch.bounding_boxes, labels)]
# Added in 0.4.0.
def _draw_samples(self, nb_rows, random_state):
nb_sample_labels = self.nb_sample_labels
if nb_sample_labels is None:
assert isinstance(self.labels, list), (
"Expected list got %s." % (type(self.labels).__name__,))
return [self.labels] * nb_rows
nb_sample_labels = nb_sample_labels.draw_samples(
(nb_rows,), random_state=random_state)
nb_sample_labels = np.clip(nb_sample_labels, 0, None)
labels_raw = self.labels.draw_samples(
(np.sum(nb_sample_labels),),
random_state=random_state)
labels = _split_1d_array_to_list(labels_raw, nb_sample_labels)
return labels
# TODO this could be simplified to something like
# bbsoi.only_labels(labels).draw_mask()
[docs] @classmethod
def generate_mask(cls, bbsoi, labels):
"""Generate a mask of the areas of bounding boxes with given labels.
Added in 0.4.0.
Parameters
----------
bbsoi : imgaug.augmentables.bbs.BoundingBoxesOnImage
The bounding boxes for which to generate the mask.
labels : None or iterable of str
Labels of the bounding boxes to set to ``1.0``.
For an ``(x, y)`` position, it is enough that *any* bounding box
at the given location has one of the labels.
If this is ``None``, all bounding boxes will be marked.
Returns
-------
ndarray
``float32`` mask array with same height and width as
``segmap.shape``. Values are in ``[0.0, 1.0]``.
"""
labels = set(labels) if labels is not None else None
height, width = bbsoi.shape[0:2]
mask = np.zeros((height, width), dtype=np.float32)
for bb in bbsoi:
if labels is None or bb.label in labels:
x1 = min(max(int(bb.x1), 0), width)
y1 = min(max(int(bb.y1), 0), height)
x2 = min(max(int(bb.x2), 0), width)
y2 = min(max(int(bb.y2), 0), height)
if x1 < x2 and y1 < y2:
mask[y1:y2, x1:x2] = 1.0
return mask
[docs]class InvertMaskGen(IBatchwiseMaskGenerator):
"""Generator that inverts the outputs of other mask generators.
This class receives batches and calls for each row (i.e. image)
a child mask generator to produce a mask. That mask is then inverted
for ``p%`` of all rows, i.e. converted to ``1.0 - mask``.
Added in 0.4.0.
Parameters
----------
p : bool or float or imgaug.parameters.StochasticParameter, optional
Probability of inverting each mask produced by the other mask
generator.
child : IBatchwiseMaskGenerator
The other mask generator to invert.
"""
# Added in 0.4.0.
def __init__(self, p, child):
self.p = iap.handle_probability_param(p, "p")
self.child = child
[docs] def draw_masks(self, batch, random_state=None):
"""
See :func:`~imgaug.augmenters.blend.IBatchwiseMaskGenerator.draw_masks`.
Added in 0.4.0.
"""
random_state = iarandom.RNG(random_state)
masks = self.child.draw_masks(batch, random_state=random_state)
p = self.p.draw_samples(len(masks), random_state=random_state)
for mask, p_i in zip(masks, p):
if p_i >= 0.5:
mask[...] = 1.0 - mask
return masks
[docs]@ia.deprecated(alt_func="Alpha",
comment="Alpha is deprecated. "
"Use BlendAlpha instead. "
"The order of parameters is the same. "
"Parameter 'first' was renamed to 'foreground'. "
"Parameter 'second' was renamed to 'background'.")
def Alpha(factor=0, first=None, second=None, per_channel=False,
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
"""See :class:`BlendAlpha`.
Deprecated since 0.4.0.
"""
# pylint: disable=invalid-name
return BlendAlpha(
factor=factor,
foreground=first,
background=second,
per_channel=per_channel,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic)
[docs]@ia.deprecated(alt_func="AlphaElementwise",
comment="AlphaElementwise is deprecated. "
"Use BlendAlphaElementwise instead. "
"The order of parameters is the same. "
"Parameter 'first' was renamed to 'foreground'. "
"Parameter 'second' was renamed to 'background'.")
def AlphaElementwise(factor=0, first=None, second=None, per_channel=False,
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
"""See :class:`BlendAlphaElementwise`.
Deprecated since 0.4.0.
"""
# pylint: disable=invalid-name
return BlendAlphaElementwise(
factor=factor,
foreground=first,
background=second,
per_channel=per_channel,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic)
[docs]@ia.deprecated(alt_func="BlendAlphaSimplexNoise",
comment="SimplexNoiseAlpha is deprecated. "
"Use BlendAlphaSimplexNoise instead. "
"The order of parameters is the same. "
"Parameter 'first' was renamed to 'foreground'. "
"Parameter 'second' was renamed to 'background'.")
def SimplexNoiseAlpha(first=None, second=None, per_channel=False,
size_px_max=(2, 16), upscale_method=None,
iterations=(1, 3), aggregation_method="max",
sigmoid=True, sigmoid_thresh=None,
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
"""See :class:`BlendAlphaSimplexNoise`.
Deprecated since 0.4.0.
"""
# pylint: disable=invalid-name
return BlendAlphaSimplexNoise(
foreground=first,
background=second,
per_channel=per_channel,
size_px_max=size_px_max,
upscale_method=upscale_method,
iterations=iterations,
aggregation_method=aggregation_method,
sigmoid=sigmoid,
sigmoid_thresh=sigmoid_thresh,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic)
[docs]@ia.deprecated(alt_func="BlendAlphaFrequencyNoise",
comment="FrequencyNoiseAlpha is deprecated. "
"Use BlendAlphaFrequencyNoise instead. "
"The order of parameters is the same. "
"Parameter 'first' was renamed to 'foreground'. "
"Parameter 'second' was renamed to 'background'.")
def FrequencyNoiseAlpha(exponent=(-4, 4), first=None, second=None,
per_channel=False, size_px_max=(4, 16),
upscale_method=None,
iterations=(1, 3), aggregation_method=["avg", "max"],
sigmoid=0.5, sigmoid_thresh=None,
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
"""See :class:`BlendAlphaFrequencyNoise`.
Deprecated since 0.4.0.
"""
# pylint: disable=invalid-name, dangerous-default-value
return BlendAlphaFrequencyNoise(
exponent=exponent,
foreground=first,
background=second,
per_channel=per_channel,
size_px_max=size_px_max,
upscale_method=upscale_method,
iterations=iterations,
aggregation_method=aggregation_method,
sigmoid=sigmoid,
sigmoid_thresh=sigmoid_thresh,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic)