from __future__ import print_function, division, absolute_import
import copy
import numpy as np
import skimage.draw
import skimage.measure
from .. import imgaug as ia
from .utils import normalize_shape, project_coords
# TODO functions: square(), to_aspect_ratio(), contains_point()
[docs]class BoundingBox(object):
"""Class representing bounding boxes.
Each bounding box is parameterized by its top left and bottom right
corners. Both are given as x and y-coordinates. The corners are intended
to lie inside the bounding box area. As a result, a bounding box that lies
completely inside the image but has maximum extensions would have
coordinates ``(0.0, 0.0)`` and ``(W - epsilon, H - epsilon)``. Note that
coordinates are saved internally as floats.
Parameters
----------
x1 : number
X-coordinate of the top left of the bounding box.
y1 : number
Y-coordinate of the top left of the bounding box.
x2 : number
X-coordinate of the bottom right of the bounding box.
y2 : number
Y-coordinate of the bottom right of the bounding box.
label : None or str, optional
Label of the bounding box, e.g. a string representing the class.
"""
def __init__(self, x1, y1, x2, y2, label=None):
"""Create a new BoundingBox instance."""
if x1 > x2:
x2, x1 = x1, x2
if y1 > y2:
y2, y1 = y1, y2
self.x1 = x1
self.y1 = y1
self.x2 = x2
self.y2 = y2
self.label = label
@property
def x1_int(self):
"""Get the x-coordinate of the top left corner as an integer.
Returns
-------
int
X-coordinate of the top left corner, rounded to the closest
integer.
"""
# use numpy's round to have consistent behaviour between python
# versions
return int(np.round(self.x1))
@property
def y1_int(self):
"""Get the y-coordinate of the top left corner as an integer.
Returns
-------
int
Y-coordinate of the top left corner, rounded to the closest
integer.
"""
# use numpy's round to have consistent behaviour between python
# versions
return int(np.round(self.y1))
@property
def x2_int(self):
"""Get the x-coordinate of the bottom left corner as an integer.
Returns
-------
int
X-coordinate of the bottom left corner, rounded to the closest
integer.
"""
# use numpy's round to have consistent behaviour between python
# versions
return int(np.round(self.x2))
@property
def y2_int(self):
"""Get the y-coordinate of the bottom left corner as an integer.
Returns
-------
int
Y-coordinate of the bottom left corner, rounded to the closest
integer.
"""
# use numpy's round to have consistent behaviour between python
# versions
return int(np.round(self.y2))
@property
def height(self):
"""Estimate the height of the bounding box.
Returns
-------
number
Height of the bounding box.
"""
return self.y2 - self.y1
@property
def width(self):
"""Estimate the width of the bounding box.
Returns
-------
number
Width of the bounding box.
"""
return self.x2 - self.x1
@property
def center_x(self):
"""Estimate the x-coordinate of the center point of the bounding box.
Returns
-------
number
X-coordinate of the center point of the bounding box.
"""
return self.x1 + self.width/2
@property
def center_y(self):
"""Estimate the y-coordinate of the center point of the bounding box.
Returns
-------
number
Y-coordinate of the center point of the bounding box.
"""
return self.y1 + self.height/2
@property
def area(self):
"""Estimate the area of the bounding box.
Returns
-------
number
Area of the bounding box, i.e. ``height * width``.
"""
return self.height * self.width
# TODO add test for tuple of number
[docs] def contains(self, other):
"""Estimate whether the bounding box contains a given point.
Parameters
----------
other : tuple of number or imgaug.augmentables.kps.Keypoint
Point to check for.
Returns
-------
bool
``True`` if the point is contained in the bounding box,
``False`` otherwise.
"""
if isinstance(other, tuple):
x, y = other
else:
x, y = other.x, other.y
return self.x1 <= x <= self.x2 and self.y1 <= y <= self.y2
# TODO add tests for ndarray inputs
[docs] def project(self, from_shape, to_shape):
"""Project the bounding box onto a differently shaped image.
E.g. if the bounding box is on its original image at
``x1=(10 of 100 pixels)`` and ``y1=(20 of 100 pixels)`` and is
projected onto a new image with size ``(width=200, height=200)``,
its new position will be ``(x1=20, y1=40)``.
(Analogous for ``x2``/``y2``.)
This is intended for cases where the original image is resized.
It cannot be used for more complex changes (e.g. padding, cropping).
Parameters
----------
from_shape : tuple of int or ndarray
Shape of the original image. (Before resize.)
to_shape : tuple of int or ndarray
Shape of the new image. (After resize.)
Returns
-------
imgaug.augmentables.bbs.BoundingBox
``BoundingBox`` instance with new coordinates.
"""
coords_proj = project_coords([(self.x1, self.y1), (self.x2, self.y2)],
from_shape, to_shape)
return self.copy(
x1=coords_proj[0][0],
y1=coords_proj[0][1],
x2=coords_proj[1][0],
y2=coords_proj[1][1],
label=self.label)
[docs] def extend(self, all_sides=0, top=0, right=0, bottom=0, left=0):
"""Extend the size of the bounding box along its sides.
Parameters
----------
all_sides : number, optional
Value by which to extend the bounding box size along all
sides.
top : number, optional
Value by which to extend the bounding box size along its top
side.
right : number, optional
Value by which to extend the bounding box size along its right
side.
bottom : number, optional
Value by which to extend the bounding box size along its bottom
side.
left : number, optional
Value by which to extend the bounding box size along its left
side.
Returns
-------
imgaug.BoundingBox
Extended bounding box.
"""
return BoundingBox(
x1=self.x1 - all_sides - left,
x2=self.x2 + all_sides + right,
y1=self.y1 - all_sides - top,
y2=self.y2 + all_sides + bottom
)
[docs] def intersection(self, other, default=None):
"""Compute the intersection BB between this BB and another BB.
Note that in extreme cases, the intersection can be a single point.
In that case the intersection bounding box exists and it will be
returned, but it will have a height and width of zero.
Parameters
----------
other : imgaug.augmentables.bbs.BoundingBox
Other bounding box with which to generate the intersection.
default : any, optional
Default value to return if there is no intersection.
Returns
-------
imgaug.augmentables.bbs.BoundingBox or any
Intersection bounding box of the two bounding boxes if there is
an intersection.
If there is no intersection, the default value will be returned,
which can by anything.
"""
x1_i = max(self.x1, other.x1)
y1_i = max(self.y1, other.y1)
x2_i = min(self.x2, other.x2)
y2_i = min(self.y2, other.y2)
if x1_i > x2_i or y1_i > y2_i:
return default
else:
return BoundingBox(x1=x1_i, y1=y1_i, x2=x2_i, y2=y2_i)
[docs] def union(self, other):
"""Compute the union BB between this BB and another BB.
This is equivalent to drawing a bounding box around all corner points
of both bounding boxes.
Parameters
----------
other : imgaug.augmentables.bbs.BoundingBox
Other bounding box with which to generate the union.
Returns
-------
imgaug.augmentables.bbs.BoundingBox
Union bounding box of the two bounding boxes.
"""
return BoundingBox(
x1=min(self.x1, other.x1),
y1=min(self.y1, other.y1),
x2=max(self.x2, other.x2),
y2=max(self.y2, other.y2),
)
[docs] def iou(self, other):
"""Compute the IoU between this bounding box and another one.
IoU is the intersection over union, defined as::
``area(intersection(A, B)) / area(union(A, B))``
``= area(intersection(A, B))
/ (area(A) + area(B) - area(intersection(A, B)))``
Parameters
----------
other : imgaug.augmentables.bbs.BoundingBox
Other bounding box with which to compare.
Returns
-------
float
IoU between the two bounding boxes.
"""
inters = self.intersection(other)
if inters is None:
return 0.0
area_union = self.area + other.area - inters.area
return inters.area / area_union if area_union > 0 else 0.0
[docs] def is_fully_within_image(self, image):
"""Estimate whether the bounding box is fully inside the image area.
Parameters
----------
image : (H,W,...) ndarray or tuple of int
Image dimensions to use.
If an ``ndarray``, its shape will be used.
If a ``tuple``, it is assumed to represent the image shape
and must contain at least two integers.
Returns
-------
bool
``True`` if the bounding box is fully inside the image area.
``False`` otherwise.
"""
shape = normalize_shape(image)
height, width = shape[0:2]
return (
self.x1 >= 0
and self.x2 < width
and self.y1 >= 0
and self.y2 < height)
[docs] def is_partly_within_image(self, image):
"""Estimate whether the BB is at least partially inside the image area.
Parameters
----------
image : (H,W,...) ndarray or tuple of int
Image dimensions to use.
If an ``ndarray``, its shape will be used.
If a ``tuple``, it is assumed to represent the image shape
and must contain at least two integers.
Returns
-------
bool
``True`` if the bounding box is at least partially inside the
image area.
``False`` otherwise.
"""
shape = normalize_shape(image)
height, width = shape[0:2]
eps = np.finfo(np.float32).eps
img_bb = BoundingBox(x1=0, x2=width-eps, y1=0, y2=height-eps)
return self.intersection(img_bb) is not None
[docs] def is_out_of_image(self, image, fully=True, partly=False):
"""Estimate whether the BB is partially/fully outside of the image area.
Parameters
----------
image : (H,W,...) ndarray or tuple of int
Image dimensions to use.
If an ``ndarray``, its shape will be used.
If a ``tuple``, it is assumed to represent the image shape and
must contain at least two integers.
fully : bool, optional
Whether to return ``True`` if the bounding box is fully outside
of the image area.
partly : bool, optional
Whether to return ``True`` if the bounding box is at least
partially outside fo the image area.
Returns
-------
bool
``True`` if the bounding box is partially/fully outside of the
image area, depending on defined parameters.
``False`` otherwise.
"""
if self.is_fully_within_image(image):
return False
elif self.is_partly_within_image(image):
return partly
return fully
[docs] @ia.deprecated(alt_func="BoundingBox.clip_out_of_image()",
comment="clip_out_of_image() has the exactly same "
"interface.")
def cut_out_of_image(self, *args, **kwargs):
return self.clip_out_of_image(*args, **kwargs)
[docs] def clip_out_of_image(self, image):
"""Clip off all parts of the BB box that are outside of the image.
Parameters
----------
image : (H,W,...) ndarray or tuple of int
Image dimensions to use for the clipping of the bounding box.
If an ``ndarray``, its shape will be used.
If a ``tuple``, it is assumed to represent the image shape and
must contain at least two integers.
Returns
-------
imgaug.augmentables.bbs.BoundingBox
Bounding box, clipped to fall within the image dimensions.
"""
shape = normalize_shape(image)
height, width = shape[0:2]
assert height > 0, (
"Expected image with height>0, got shape %s." % (image.shape,))
assert width > 0, (
"Expected image with width>0, got shape %s." % (image.shape,))
eps = np.finfo(np.float32).eps
x1 = np.clip(self.x1, 0, width - eps)
x2 = np.clip(self.x2, 0, width - eps)
y1 = np.clip(self.y1, 0, height - eps)
y2 = np.clip(self.y2, 0, height - eps)
return self.copy(
x1=x1,
y1=y1,
x2=x2,
y2=y2,
label=self.label
)
# TODO convert this to x/y params?
[docs] def shift(self, top=None, right=None, bottom=None, left=None):
"""Move this bounding box along the x/y-axis.
Parameters
----------
top : None or int, optional
Amount of pixels by which to shift this object *from* the
top (towards the bottom).
right : None or int, optional
Amount of pixels by which to shift this object *from* the
right (towards the left).
bottom : None or int, optional
Amount of pixels by which to shift this object *from* the
bottom (towards the top).
left : None or int, optional
Amount of pixels by which to shift this object *from* the
left (towards the right).
Returns
-------
imgaug.augmentables.bbs.BoundingBox
Shifted bounding box.
"""
top = top if top is not None else 0
right = right if right is not None else 0
bottom = bottom if bottom is not None else 0
left = left if left is not None else 0
return self.copy(
x1=self.x1+left-right,
x2=self.x2+left-right,
y1=self.y1+top-bottom,
y2=self.y2+top-bottom
)
# TODO add explicit test for zero-sized BBs (worked when tested by hand)
[docs] def draw_on_image(self, image, color=(0, 255, 0), alpha=1.0, size=1,
copy=True, raise_if_out_of_image=False, thickness=None):
"""Draw the bounding box on an image.
Parameters
----------
image : (H,W,C) ndarray
The image onto which to draw the bounding box.
Currently expected to be ``uint8``.
color : iterable of int, optional
The color to use, corresponding to the channel layout of the
image. Usually RGB.
alpha : float, optional
The transparency of the drawn bounding box, where ``1.0`` denotes
no transparency and ``0.0`` is invisible.
size : int, optional
The thickness of the bounding box in pixels. If the value is
larger than ``1``, then additional pixels will be added around
the bounding box (i.e. extension towards the outside).
copy : bool, optional
Whether to copy the input image or change it in-place.
raise_if_out_of_image : bool, optional
Whether to raise an error if the bounding box is fully outside of
the image. If set to ``False``, no error will be raised and only
the parts inside the image will be drawn.
thickness : None or int, optional
Deprecated.
Returns
-------
(H,W,C) ndarray(uint8)
Image with bounding box drawn on it.
"""
if thickness is not None:
ia.warn_deprecated(
"Usage of argument 'thickness' in BoundingBox.draw_on_image() "
"is deprecated. The argument was renamed to 'size'.")
size = thickness
if raise_if_out_of_image and self.is_out_of_image(image):
raise Exception(
"Cannot draw bounding box x1=%.8f, y1=%.8f, x2=%.8f, y2=%.8f "
"on image with shape %s." % (
self.x1, self.y1, self.x2, self.y2, image.shape))
result = np.copy(image) if copy else image
if isinstance(color, (tuple, list)):
color = np.uint8(color)
for i in range(size):
y1, y2, x1, x2 = self.y1_int, self.y2_int, self.x1_int, self.x2_int
# When y values get into the range (H-0.5, H), the *_int functions
# round them to H. That is technically sensible, but in the case
# of drawing means that the border lies just barely outside of
# the image, making the border disappear, even though the BB is
# fully inside the image. Here we correct for that because of
# beauty reasons. Same is the case for x coordinates.
if self.is_fully_within_image(image):
y1 = np.clip(y1, 0, image.shape[0]-1)
y2 = np.clip(y2, 0, image.shape[0]-1)
x1 = np.clip(x1, 0, image.shape[1]-1)
x2 = np.clip(x2, 0, image.shape[1]-1)
y = [y1-i, y1-i, y2+i, y2+i]
x = [x1-i, x2+i, x2+i, x1-i]
rr, cc = skimage.draw.polygon_perimeter(y, x, shape=result.shape)
if alpha >= 0.99:
result[rr, cc, :] = color
else:
if ia.is_float_array(result):
# TODO use blend_alpha here
result[rr, cc, :] = (
(1 - alpha) * result[rr, cc, :]
+ alpha * color)
result = np.clip(result, 0, 255)
else:
input_dtype = result.dtype
result = result.astype(np.float32)
result[rr, cc, :] = (
(1 - alpha) * result[rr, cc, :]
+ alpha * color)
result = np.clip(result, 0, 255).astype(input_dtype)
return result
# TODO add tests for pad and pad_max
# TODO also add to_heatmap
# TODO add this to BoundingBoxesOnImage
[docs] def to_keypoints(self):
"""Convert the BB's corners to keypoints (clockwise, from top left).
Returns
-------
list of imgaug.augmentables.kps.Keypoint
Corners of the bounding box as keypoints.
"""
# TODO get rid of this deferred import
from imgaug.augmentables.kps import Keypoint
return [
Keypoint(x=self.x1, y=self.y1),
Keypoint(x=self.x2, y=self.y1),
Keypoint(x=self.x2, y=self.y2),
Keypoint(x=self.x1, y=self.y2)
]
[docs] def copy(self, x1=None, y1=None, x2=None, y2=None, label=None):
"""Create a shallow copy of this BoundingBox instance.
Parameters
----------
x1 : None or number
If not ``None``, then the ``x1`` coordinate of the copied object
will be set to this value.
y1 : None or number
If not ``None``, then the ``y1`` coordinate of the copied object
will be set to this value.
x2 : None or number
If not ``None``, then the ``x2`` coordinate of the copied object
will be set to this value.
y2 : None or number
If not ``None``, then the ``y2`` coordinate of the copied object
will be set to this value.
label : None or string
If not ``None``, then the ``label`` of the copied object
will be set to this value.
Returns
-------
imgaug.augmentables.bbs.BoundingBox
Shallow copy.
"""
return BoundingBox(
x1=self.x1 if x1 is None else x1,
x2=self.x2 if x2 is None else x2,
y1=self.y1 if y1 is None else y1,
y2=self.y2 if y2 is None else y2,
label=copy.deepcopy(self.label) if label is None else label
)
[docs] def deepcopy(self, x1=None, y1=None, x2=None, y2=None, label=None):
"""
Create a deep copy of the BoundingBox object.
Parameters
----------
x1 : None or number
If not ``None``, then the ``x1`` coordinate of the copied object
will be set to this value.
y1 : None or number
If not ``None``, then the ``y1`` coordinate of the copied object
will be set to this value.
x2 : None or number
If not ``None``, then the ``x2`` coordinate of the copied object
will be set to this value.
y2 : None or number
If not ``None``, then the ``y2`` coordinate of the copied object
will be set to this value.
label : None or string
If not ``None``, then the ``label`` of the copied object
will be set to this value.
Returns
-------
imgaug.augmentables.bbs.BoundingBox
Deep copy.
"""
# TODO write specific copy routine with deepcopy for label and remove
# the deepcopy from copy()
return self.copy(x1=x1, y1=y1, x2=x2, y2=y2, label=label)
def __repr__(self):
return self.__str__()
def __str__(self):
return "BoundingBox(x1=%.4f, y1=%.4f, x2=%.4f, y2=%.4f, label=%s)" % (
self.x1, self.y1, self.x2, self.y2, self.label)
[docs]class BoundingBoxesOnImage(object):
"""Container for the list of all bounding boxes on a single image.
Parameters
----------
bounding_boxes : list of imgaug.augmentables.bbs.BoundingBox
List of bounding boxes on the image.
shape : tuple of int
The shape of the image on which the bounding boxes are placed.
Examples
--------
>>> import numpy as np
>>> from imgaug.augmentables.bbs import BoundingBox, BoundingBoxesOnImage
>>>
>>> image = np.zeros((100, 100))
>>> bbs = [
>>> BoundingBox(x1=10, y1=20, x2=20, y2=30),
>>> BoundingBox(x1=25, y1=50, x2=30, y2=70)
>>> ]
>>> bbs_oi = BoundingBoxesOnImage(bbs, shape=image.shape)
"""
def __init__(self, bounding_boxes, shape):
self.bounding_boxes = bounding_boxes
self.shape = normalize_shape(shape)
# TODO remove this? here it is image height, but in BoundingBox it is
# bounding box height
@property
def height(self):
"""Get the height of the image on which the bounding boxes fall.
Returns
-------
int
Image height.
"""
return self.shape[0]
# TODO remove this? here it is image width, but in BoundingBox it is
# bounding box width
@property
def width(self):
"""Get the width of the image on which the bounding boxes fall.
Returns
-------
int
Image width.
"""
return self.shape[1]
@property
def empty(self):
"""Determine whether this instance contains zero bounding boxes.
Returns
-------
bool
True if this object contains zero bounding boxes.
"""
return len(self.bounding_boxes) == 0
[docs] def on(self, image):
"""Project bounding boxes from one image (shape) to a another one.
Parameters
----------
image : ndarray or tuple of int
New image onto which the bounding boxes are to be projected.
May also simply be that new image's shape tuple.
Returns
-------
imgaug.augmentables.bbs.BoundingBoxesOnImage
Object containing the same bounding boxes after projection to
the new image shape.
"""
shape = normalize_shape(image)
if shape[0:2] == self.shape[0:2]:
return self.deepcopy()
bounding_boxes = [bb.project(self.shape, shape)
for bb in self.bounding_boxes]
return BoundingBoxesOnImage(bounding_boxes, shape)
[docs] @classmethod
def from_xyxy_array(cls, xyxy, shape):
"""Convert an ``(N,4) ndarray`` to a ``BoundingBoxesOnImage`` instance.
This is the inverse of
:func:`imgaug.BoundingBoxesOnImage.to_xyxy_array`.
Parameters
----------
xyxy : (N,4) ndarray
Array containing the corner coordinates (top-left, bottom-right)
of ``N`` bounding boxes in the form ``(x1, y1, x2, y2)``.
Should usually be of dtype ``float32``.
shape : tuple of int
Shape of the image on which the bounding boxes are placed.
Should usually be ``(H, W, C)`` or ``(H, W)``.
Returns
-------
imgaug.augmentables.bbs.BoundingBoxesOnImage
Object containing a list of :class:`BoundingBox` instances
derived from the provided corner coordinates.
"""
assert xyxy.shape[1] == 4, (
"Expected input array of shape (N, 4), got shape %s." % (
xyxy.shape,))
boxes = [BoundingBox(*row) for row in xyxy]
return cls(boxes, shape)
[docs] def to_xyxy_array(self, dtype=np.float32):
"""Convert the ``BoundingBoxesOnImage`` object to an ``(N,4) ndarray``.
This is the inverse of
:func:`imgaug.BoundingBoxesOnImage.from_xyxy_array`.
Parameters
----------
dtype : numpy.dtype, optional
Desired output datatype of the ndarray.
Returns
-------
ndarray
``(N,4) ndarray``, where ``N`` denotes the number of bounding
boxes and ``4`` denotes the top-left and bottom-right bounding
box corner coordinates in form ``(x1, y1, x2, y2)``.
"""
xyxy_array = np.zeros((len(self.bounding_boxes), 4), dtype=np.float32)
for i, box in enumerate(self.bounding_boxes):
xyxy_array[i] = [box.x1, box.y1, box.x2, box.y2]
return xyxy_array.astype(dtype)
[docs] def draw_on_image(self, image, color=(0, 255, 0), alpha=1.0, size=1,
copy=True, raise_if_out_of_image=False, thickness=None):
"""Draw all bounding boxes onto a given image.
Parameters
----------
image : (H,W,3) ndarray
The image onto which to draw the bounding boxes.
This image should usually have the same shape as set in
``BoundingBoxesOnImage.shape``.
color : int or list of int or tuple of int or (3,) ndarray, optional
The RGB color of all bounding boxes.
If a single ``int`` ``C``, then that is equivalent to ``(C,C,C)``.
alpha : float, optional
Alpha/transparency of the bounding box.
size : int, optional
Thickness in pixels.
copy : bool, optional
Whether to copy the image before drawing the bounding boxes.
raise_if_out_of_image : bool, optional
Whether to raise an exception if any bounding box is outside of the
image.
thickness : None or int, optional
Deprecated.
Returns
-------
(H,W,3) ndarray
Image with drawn bounding boxes.
"""
image = np.copy(image) if copy else image
for bb in self.bounding_boxes:
image = bb.draw_on_image(
image,
color=color,
alpha=alpha,
size=size,
copy=False,
raise_if_out_of_image=raise_if_out_of_image,
thickness=thickness
)
return image
[docs] def remove_out_of_image(self, fully=True, partly=False):
"""Remove all BBs that are fully/partially outside of the image.
Parameters
----------
fully : bool, optional
Whether to remove bounding boxes that are fully outside of the
image.
partly : bool, optional
Whether to remove bounding boxes that are partially outside of
the image.
Returns
-------
imgaug.augmentables.bbs.BoundingBoxesOnImage
Reduced set of bounding boxes, with those that were
fully/partially outside of the image being removed.
"""
bbs_clean = [
bb
for bb
in self.bounding_boxes
if not bb.is_out_of_image(self.shape, fully=fully, partly=partly)]
return BoundingBoxesOnImage(bbs_clean, shape=self.shape)
[docs] @ia.deprecated(alt_func="BoundingBoxesOnImage.clip_out_of_image()",
comment="clip_out_of_image() has the exactly same "
"interface.")
def cut_out_of_image(self):
return self.clip_out_of_image()
[docs] def clip_out_of_image(self):
"""Clip off all parts from all BBs that are outside of the image.
Returns
-------
imgaug.augmentables.bbs.BoundingBoxesOnImage
Bounding boxes, clipped to fall within the image dimensions.
"""
bbs_cut = [
bb.clip_out_of_image(self.shape)
for bb
in self.bounding_boxes
if bb.is_partly_within_image(self.shape)]
return BoundingBoxesOnImage(bbs_cut, shape=self.shape)
[docs] def shift(self, top=None, right=None, bottom=None, left=None):
"""Move all all BBs along the x/y-axis.
Parameters
----------
top : None or int, optional
Amount of pixels by which to shift all objects *from* the
top (towards the bottom).
right : None or int, optional
Amount of pixels by which to shift all objects *from* the
right (towads the left).
bottom : None or int, optional
Amount of pixels by which to shift all objects *from* the
bottom (towards the top).
left : None or int, optional
Amount of pixels by which to shift all objects *from* the
left (towards the right).
Returns
-------
imgaug.augmentables.bbs.BoundingBoxesOnImage
Shifted bounding boxes.
"""
bbs_new = [
bb.shift(top=top, right=right, bottom=bottom, left=left)
for bb
in self.bounding_boxes]
return BoundingBoxesOnImage(bbs_new, shape=self.shape)
[docs] def copy(self):
"""Create a shallow copy of the ``BoundingBoxesOnImage`` instance.
Returns
-------
imgaug.augmentables.bbs.BoundingBoxesOnImage
Shallow copy.
"""
return copy.copy(self)
[docs] def deepcopy(self):
"""Create a deep copy of the ``BoundingBoxesOnImage`` object.
Returns
-------
imgaug.augmentables.bbs.BoundingBoxesOnImage
Deep copy.
"""
# Manual copy is far faster than deepcopy for BoundingBoxesOnImage,
# so use manual copy here too
bbs = [bb.deepcopy() for bb in self.bounding_boxes]
return BoundingBoxesOnImage(bbs, tuple(self.shape))
def __repr__(self):
return self.__str__()
def __str__(self):
return (
"BoundingBoxesOnImage(%s, shape=%s)"
% (str(self.bounding_boxes), self.shape))