'''
Various geometric image transformations for 2D object detection, both deterministic
and probabilistic.
Copyright (C) 2018 Pierluigi Ferrari
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
'''
from __future__ import division
import numpy as np
import cv2
import random
from transformations.object_detection_2d_image_boxes_validation_utils import BoxFilter, ImageValidator
class Resize:
'''
Resizes images to a specified height and width in pixels.
'''
def __init__(self,
height,
width,
interpolation_mode=cv2.INTER_LINEAR,
box_filter=None,
labels_format={'class_id': 0, 'xmin': 1, 'ymin': 2, 'xmax': 3, 'ymax': 4}):
'''
Arguments:
height (int): The desired height of the output images in pixels.
width (int): The desired width of the output images in pixels.
interpolation_mode (int, optional): An integer that denotes a valid
OpenCV interpolation mode. For example, integers 0 through 5 are
valid interpolation modes.
box_filter (BoxFilter, optional): Only relevant if ground truth bounding boxes are given.
A `BoxFilter` object to filter out bounding boxes that don't meet the given criteria
after the transformation. Refer to the `BoxFilter` documentation for details. If `None`,
the validity of the bounding boxes is not checked.
labels_format (dict, optional): A dictionary that defines which index in the last axis of the labels
of an image contains which bounding box coordinate. The dictionary maps at least the keywords
'xmin', 'ymin', 'xmax', and 'ymax' to their respective indices within last axis of the labels array.
'''
if not (isinstance(box_filter, BoxFilter) or box_filter is None):
raise ValueError("`box_filter` must be either `None` or a `BoxFilter` object.")
self.out_height = height
self.out_width = width
self.interpolation_mode = interpolation_mode
self.box_filter = box_filter
self.labels_format = labels_format
def __call__(self, image, labels=None, return_inverter=False):
img_height, img_width = image.shape[:2]
xmin = self.labels_format['xmin']
ymin = self.labels_format['ymin']
xmax = self.labels_format['xmax']
ymax = self.labels_format['ymax']
image = cv2.resize(image,
dsize=(self.out_width, self.out_height),
interpolation=self.interpolation_mode)
if return_inverter:
def inverter(labels):
labels = np.copy(labels)
labels[:, [ymin+1, ymax+1]] = np.round(labels[:, [ymin+1, ymax+1]] * (img_height / self.out_height), decimals=0)
labels[:, [xmin+1, xmax+1]] = np.round(labels[:, [xmin+1, xmax+1]] * (img_width / self.out_width), decimals=0)
return labels
if labels is None:
if return_inverter:
return image, inverter
else:
return image
else:
labels = np.copy(labels)
labels[:, [ymin, ymax]] = np.round(labels[:, [ymin, ymax]] * (self.out_height / img_height), decimals=0)
labels[:, [xmin, xmax]] = np.round(labels[:, [xmin, xmax]] * (self.out_width / img_width), decimals=0)
if not (self.box_filter is None):
self.box_filter.labels_format = self.labels_format
labels = self.box_filter(labels=labels,
image_height=self.out_height,
image_width=self.out_width)
if return_inverter:
return image, labels, inverter
else:
return image, labels
class ResizeRandomInterp:
'''
Resizes images to a specified height and width in pixels using a radnomly
selected interpolation mode.
'''
def __init__(self,
height,
width,
interpolation_modes=[cv2.INTER_NEAREST,
cv2.INTER_LINEAR,
cv2.INTER_CUBIC,
cv2.INTER_AREA,
cv2.INTER_LANCZOS4],
box_filter=None,
labels_format={'class_id': 0, 'xmin': 1, 'ymin': 2, 'xmax': 3, 'ymax': 4}):
'''
Arguments:
height (int): The desired height of the output image in pixels.
width (int): The desired width of the output image in pixels.
interpolation_modes (list/tuple, optional): A list/tuple of integers
that represent valid OpenCV interpolation modes. For example,
integers 0 through 5 are valid interpolation modes.
box_filter (BoxFilter, optional): Only relevant if ground truth bounding boxes are given.
A `BoxFilter` object to filter out bounding boxes that don't meet the given criteria
after the transformation. Refer to the `BoxFilter` documentation for details. If `None`,
the validity of the bounding boxes is not checked.
labels_format (dict, optional): A dictionary that defines which index in the last axis of the labels
of an image contains which bounding box coordinate. The dictionary maps at least the keywords
'xmin', 'ymin', 'xmax', and 'ymax' to their respective indices within last axis of the labels array.
'''
if not (isinstance(interpolation_modes, (list, tuple))):
raise ValueError("`interpolation_mode` must be a list or tuple.")
self.height = height
self.width = width
self.interpolation_modes = interpolation_modes
self.box_filter = box_filter
self.labels_format = labels_format
self.resize = Resize(height=self.height,
width=self.width,
box_filter=self.box_filter,
labels_format=self.labels_format)
def __call__(self, image, labels=None, return_inverter=False):
self.resize.interpolation_mode = np.random.choice(self.interpolation_modes)
self.resize.labels_format = self.labels_format
return self.resize(image, labels, return_inverter)
class Flip:
'''
Flips images horizontally or vertically.
'''
def __init__(self,
dim='horizontal',
labels_format={'class_id': 0, 'xmin': 1, 'ymin': 2, 'xmax': 3, 'ymax': 4}):
'''
Arguments:
dim (str, optional): Can be either of 'horizontal' and 'vertical'.
If 'horizontal', images will be flipped horizontally, i.e. along
the vertical axis. If 'horizontal', images will be flipped vertically,
i.e. along the horizontal axis.
labels_format (dict, optional): A dictionary that defines which index in the last axis of the labels
of an image contains which bounding box coordinate. The dictionary maps at least the keywords
'xmin', 'ymin', 'xmax', and 'ymax' to their respective indices within last axis of the labels array.
'''
if not (dim in {'horizontal', 'vertical'}): raise ValueError("`dim` can be one of 'horizontal' and 'vertical'.")
self.dim = dim
self.labels_format = labels_format
def __call__(self, image, labels=None, return_inverter=False):
img_height, img_width = image.shape[:2]
xmin = self.labels_format['xmin']
ymin = self.labels_format['ymin']
xmax = self.labels_format['xmax']
ymax = self.labels_format['ymax']
if self.dim == 'horizontal':
image = image[:,::-1]
if labels is None:
return image
else:
labels = np.copy(labels)
labels[:, [xmin, xmax]] = img_width - labels[:, [xmax, xmin]]
return image, labels
else:
image = image[::-1]
if labels is None:
return image
else:
labels = np.copy(labels)
labels[:, [ymin, ymax]] = img_height - labels[:, [ymax, ymin]]
return image, labels
class RandomFlip:
'''
Randomly flips images horizontally or vertically. The randomness only refers
to whether or not the image will be flipped.
'''
def __init__(self,
dim='horizontal',
prob=0.5,
labels_format={'class_id': 0, 'xmin': 1, 'ymin': 2, 'xmax': 3, 'ymax': 4}):
'''
Arguments:
dim (str, optional): Can be either of 'horizontal' and 'vertical'.
If 'horizontal', images will be flipped horizontally, i.e. along
the vertical axis. If 'horizontal', images will be flipped vertically,
i.e. along the horizontal axis.
prob (float, optional): `(1 - prob)` determines the probability with which the original,
unaltered image is returned.
labels_format (dict, optional): A dictionary that defines which index in the last axis of the labels
of an image contains which bounding box coordinate. The dictionary maps at least the keywords
'xmin', 'ymin', 'xmax', and 'ymax' to their respective indices within last axis of the labels array.
'''
self.dim = dim
self.prob = prob
self.labels_format = labels_format
self.flip = Flip(dim=self.dim, labels_format=self.labels_format)
def __call__(self, image, labels=None):
p = np.random.uniform(0,1)
if p >= (1.0-self.prob):
self.flip.labels_format = self.labels_format
return self.flip(image, labels)
elif labels is None:
return image
else:
return image, labels
class Translate:
'''
Translates images horizontally and/or vertically.
'''
def __init__(self,
dy,
dx,
clip_boxes=True,
box_filter=None,
background=(0,0,0),
labels_format={'class_id': 0, 'xmin': 1, 'ymin': 2, 'xmax': 3, 'ymax': 4}):
'''
Arguments:
dy (float): The fraction of the image height by which to translate images along the
vertical axis. Positive values translate images downwards, negative values
translate images upwards.
dx (float): The fraction of the image width by which to translate images along the
horizontal axis. Positive values translate images to the right, negative values
translate images to the left.
clip_boxes (bool, optional): Only relevant if ground truth bounding boxes are given.
If `True`, any ground truth bounding boxes will be clipped to lie entirely within the
image after the translation.
box_filter (BoxFilter, optional): Only relevant if ground truth bounding boxes are given.
A `BoxFilter` object to filter out bounding boxes that don't meet the given criteria
after the transformation. Refer to the `BoxFilter` documentation for details. If `None`,
the validity of the bounding boxes is not checked.
background (list/tuple, optional): A 3-tuple specifying the RGB color value of the
background pixels of the translated images.
labels_format (dict, optional): A dictionary that defines which index in the last axis of the labels
of an image contains which bounding box coordinate. The dictionary maps at least the keywords
'xmin', 'ymin', 'xmax', and 'ymax' to their respective indices within last axis of the labels array.
'''
if not (isinstance(box_filter, BoxFilter) or box_filter is None):
raise ValueError("`box_filter` must be either `None` or a `BoxFilter` object.")
self.dy_rel = dy
self.dx_rel = dx
self.clip_boxes = clip_boxes
self.box_filter = box_filter
self.background = background
self.labels_format = labels_format
def __call__(self, image, labels=None):
img_height, img_width = image.shape[:2]
# Compute the translation matrix.
dy_abs = int(round(img_height * self.dy_rel))
dx_abs = int(round(img_width * self.dx_rel))
M = np.float32([[1, 0, dx_abs],
[0, 1, dy_abs]])
# Translate the image.
image = cv2.warpAffine(image,
M=M,
dsize=(img_width, img_height),
borderMode=cv2.BORDER_CONSTANT,
borderValue=self.background)
if labels is None:
return image
else:
xmin = self.labels_format['xmin']
ymin = self.labels_format['ymin']
xmax = self.labels_format['xmax']
ymax = self.labels_format['ymax']
labels = np.copy(labels)
# Translate the box coordinates to the translated image's coordinate system.
labels[:,[xmin,xmax]] += dx_abs
labels[:,[ymin,ymax]] += dy_abs
# Compute all valid boxes for this patch.
if not (self.box_filter is None):
self.box_filter.labels_format = self.labels_format
labels = self.box_filter(labels=labels,
image_height=img_height,
image_width=img_width)
if self.clip_boxes:
labels[:,[ymin,ymax]] = np.clip(labels[:,[ymin,ymax]], a_min=0, a_max=img_height-1)
labels[:,[xmin,xmax]] = np.clip(labels[:,[xmin,xmax]], a_min=0, a_max=img_width-1)
return image, labels
class RandomTranslate:
'''
Randomly translates images horizontally and/or vertically.
'''
def __init__(self,
dy_minmax=(0.03,0.3),
dx_minmax=(0.03,0.3),
prob=0.5,
clip_boxes=True,
box_filter=None,
image_validator=None,
n_trials_max=3,
background=(0,0,0),
labels_format={'class_id': 0, 'xmin': 1, 'ymin': 2, 'xmax': 3, 'ymax': 4}):
'''
Arguments:
dy_minmax (list/tuple, optional): A 2-tuple `(min, max)` of non-negative floats that
determines the minimum and maximum relative translation of images along the vertical
axis both upward and downward. That is, images will be randomly translated by at least
`min` and at most `max` either upward or downward. For example, if `dy_minmax == (0.05,0.3)`,
an image of size `(100,100)` will be translated by at least 5 and at most 30 pixels
either upward or downward. The translation direction is chosen randomly.
dx_minmax (list/tuple, optional): A 2-tuple `(min, max)` of non-negative floats that
determines the minimum and maximum relative translation of images along the horizontal
axis both to the left and right. That is, images will be randomly translated by at least
`min` and at most `max` either left or right. For example, if `dx_minmax == (0.05,0.3)`,
an image of size `(100,100)` will be translated by at least 5 and at most 30 pixels
either left or right. The translation direction is chosen randomly.
prob (float, optional): `(1 - prob)` determines the probability with which the original,
unaltered image is returned.
clip_boxes (bool, optional): Only relevant if ground truth bounding boxes are given.
If `True`, any ground truth bounding boxes will be clipped to lie entirely within the
image after the translation.
box_filter (BoxFilter, optional): Only relevant if ground truth bounding boxes are given.
A `BoxFilter` object to filter out bounding boxes that don't meet the given criteria
after the transformation. Refer to the `BoxFilter` documentation for details. If `None`,
the validity of the bounding boxes is not checked.
image_validator (ImageValidator, optional): Only relevant if ground truth bounding boxes are given.
An `ImageValidator` object to determine whether a translated image is valid. If `None`,
any outcome is valid.
n_trials_max (int, optional): Only relevant if ground truth bounding boxes are given.
Determines the maxmial number of trials to produce a valid image. If no valid image could
be produced in `n_trials_max` trials, returns the unaltered input image.
background (list/tuple, optional): A 3-tuple specifying the RGB color value of the
background pixels of the translated images.
labels_format (dict, optional): A dictionary that defines which index in the last axis of the labels
of an image contains which bounding box coordinate. The dictionary maps at least the keywords
'xmin', 'ymin', 'xmax', and 'ymax' to their respective indices within last axis of the labels array.
'''
if dy_minmax[0] > dy_minmax[1]:
raise ValueError("It must be `dy_minmax[0] <= dy_minmax[1]`.")
if dx_minmax[0] > dx_minmax[1]:
raise ValueError("It must be `dx_minmax[0] <= dx_minmax[1]`.")
if dy_minmax[0] < 0 or dx_minmax[0] < 0:
raise ValueError("It must be `dy_minmax[0] >= 0` and `dx_minmax[0] >= 0`.")
if not (isinstance(image_validator, ImageValidator) or image_validator is None):
raise ValueError("`image_validator` must be either `None` or an `ImageValidator` object.")
self.dy_minmax = dy_minmax
self.dx_minmax = dx_minmax
self.prob = prob
self.clip_boxes = clip_boxes
self.box_filter = box_filter
self.image_validator = image_validator
self.n_trials_max = n_trials_max
self.background = background
self.labels_format = labels_format
self.translate = Translate(dy=0,
dx=0,
clip_boxes=self.clip_boxes,
box_filter=self.box_filter,
background=self.background,
labels_format=self.labels_format)
def __call__(self, image, labels=None):
p = np.random.uniform(0,1)
if p >= (1.0-self.prob):
img_height, img_width = image.shape[:2]
xmin = self.labels_format['xmin']
ymin = self.labels_format['ymin']
xmax = self.labels_format['xmax']
ymax = self.labels_format['ymax']
# Override the preset labels format.
if not self.image_validator is None:
self.image_validator.labels_format = self.labels_format
self.translate.labels_format = self.labels_format
for _ in range(max(1, self.n_trials_max)):
# Pick the relative amount by which to translate.
dy_abs = np.random.uniform(self.dy_minmax[0], self.dy_minmax[1])
dx_abs = np.random.uniform(self.dx_minmax[0], self.dx_minmax[1])
# Pick the direction in which to translate.
dy = np.random.choice([-dy_abs, dy_abs])
dx = np.random.choice([-dx_abs, dx_abs])
self.translate.dy_rel = dy
self.translate.dx_rel = dx
if (labels is None) or (self.image_validator is None):
# We either don't have any boxes or if we do, we will accept any outcome as valid.
return self.translate(image, labels)
else:
# Translate the box coordinates to the translated image's coordinate system.
new_labels = np.copy(labels)
new_labels[:, [ymin, ymax]] += int(round(img_height * dy))
new_labels[:, [xmin, xmax]] += int(round(img_width * dx))
# Check if the patch is valid.
if self.image_validator(labels=new_labels,
image_height=img_height,
image_width=img_width):
return self.translate(image, labels)
# If all attempts failed, return the unaltered input image.
if labels is None:
return image
else:
return image, labels
elif labels is None:
return image
else:
return image, labels
class Scale:
'''
Scales images, i.e. zooms in or out.
'''
def __init__(self,
factor,
clip_boxes=True,
box_filter=None,
background=(0,0,0),
labels_format={'class_id': 0, 'xmin': 1, 'ymin': 2, 'xmax': 3, 'ymax': 4}):
'''
Arguments:
factor (float): The fraction of the image size by which to scale images. Must be positive.
clip_boxes (bool, optional): Only relevant if ground truth bounding boxes are given.
If `True`, any ground truth bounding boxes will be clipped to lie entirely within the
image after the translation.
box_filter (BoxFilter, optional): Only relevant if ground truth bounding boxes are given.
A `BoxFilter` object to filter out bounding boxes that don't meet the given criteria
after the transformation. Refer to the `BoxFilter` documentation for details. If `None`,
the validity of the bounding boxes is not checked.
background (list/tuple, optional): A 3-tuple specifying the RGB color value of the potential
background pixels of the scaled images.
labels_format (dict, optional): A dictionary that defines which index in the last axis of the labels
of an image contains which bounding box coordinate. The dictionary maps at least the keywords
'xmin', 'ymin', 'xmax', and 'ymax' to their respective indices within last axis of the labels array.
'''
if factor <= 0:
raise ValueError("It must be `factor > 0`.")
if not (isinstance(box_filter, BoxFilter) or box_filter is None):
raise ValueError("`box_filter` must be either `None` or a `BoxFilter` object.")
self.factor = factor
self.clip_boxes = clip_boxes
self.box_filter = box_filter
self.background = background
self.labels_format = labels_format
def __call__(self, image, labels=None):
img_height, img_width = image.shape[:2]
# Compute the rotation matrix.
M = cv2.getRotationMatrix2D(center=(img_width / 2, img_height / 2),
angle=0,
scale=self.factor)
# Scale the image.
image = cv2.warpAffine(image,
M=M,
dsize=(img_width, img_height),
borderMode=cv2.BORDER_CONSTANT,
borderValue=self.background)
if labels is None:
return image
else:
xmin = self.labels_format['xmin']
ymin = self.labels_format['ymin']
xmax = self.labels_format['xmax']
ymax = self.labels_format['ymax']
labels = np.copy(labels)
# Scale the bounding boxes accordingly.
# Transform two opposite corner points of the rectangular boxes using the rotation matrix `M`.
toplefts = np.array([labels[:,xmin], labels[:,ymin], np.ones(labels.shape[0])])
bottomrights = np.array([labels[:,xmax], labels[:,ymax], np.ones(labels.shape[0])])
new_toplefts = (np.dot(M, toplefts)).T
new_bottomrights = (np.dot(M, bottomrights)).T
labels[:,[xmin,ymin]] = np.round(new_toplefts, decimals=0).astype(np.int)
labels[:,[xmax,ymax]] = np.round(new_bottomrights, decimals=0).astype(np.int)
# Compute all valid boxes for this patch.
if not (self.box_filter is None):
self.box_filter.labels_format = self.labels_format
labels = self.box_filter(labels=labels,
image_height=img_height,
image_width=img_width)
if self.clip_boxes:
labels[:,[ymin,ymax]] = np.clip(labels[:,[ymin,ymax]], a_min=0, a_max=img_height-1)
labels[:,[xmin,xmax]] = np.clip(labels[:,[xmin,xmax]], a_min=0, a_max=img_width-1)
return image, labels
class RandomScale:
'''
Randomly scales images.
'''
def __init__(self,
min_factor=0.5,
max_factor=1.5,
prob=0.5,
clip_boxes=True,
box_filter=None,
image_validator=None,
n_trials_max=3,
background=(0,0,0),
labels_format={'class_id': 0, 'xmin': 1, 'ymin': 2, 'xmax': 3, 'ymax': 4}):
'''
Arguments:
min_factor (float, optional): The minimum fraction of the image size by which to scale images.
Must be positive.
max_factor (float, optional): The maximum fraction of the image size by which to scale images.
Must be positive.
prob (float, optional): `(1 - prob)` determines the probability with which the original,
unaltered image is returned.
clip_boxes (bool, optional): Only relevant if ground truth bounding boxes are given.
If `True`, any ground truth bounding boxes will be clipped to lie entirely within the
image after the translation.
box_filter (BoxFilter, optional): Only relevant if ground truth bounding boxes are given.
A `BoxFilter` object to filter out bounding boxes that don't meet the given criteria
after the transformation. Refer to the `BoxFilter` documentation for details. If `None`,
the validity of the bounding boxes is not checked.
image_validator (ImageValidator, optional): Only relevant if ground truth bounding boxes are given.
An `ImageValidator` object to determine whether a scaled image is valid. If `None`,
any outcome is valid.
n_trials_max (int, optional): Only relevant if ground truth bounding boxes are given.
Determines the maxmial number of trials to produce a valid image. If no valid image could
be produced in `n_trials_max` trials, returns the unaltered input image.
background (list/tuple, optional): A 3-tuple specifying the RGB color value of the potential
background pixels of the scaled images.
labels_format (dict, optional): A dictionary that defines which index in the last axis of the labels
of an image contains which bounding box coordinate. The dictionary maps at least the keywords
'xmin', 'ymin', 'xmax', and 'ymax' to their respective indices within last axis of the labels array.
'''
if not (0 < min_factor <= max_factor):
raise ValueError("It must be `0 < min_factor <= max_factor`.")
if not (isinstance(image_validator, ImageValidator) or image_validator is None):
raise ValueError("`image_validator` must be either `None` or an `ImageValidator` object.")
self.min_factor = min_factor
self.max_factor = max_factor
self.prob = prob
self.clip_boxes = clip_boxes
self.box_filter = box_filter
self.image_validator = image_validator
self.n_trials_max = n_trials_max
self.background = background
self.labels_format = labels_format
self.scale = Scale(factor=1.0,
clip_boxes=self.clip_boxes,
box_filter=self.box_filter,
background=self.background,
labels_format=self.labels_format)
def __call__(self, image, labels=None):
p = np.random.uniform(0,1)
if p >= (1.0-self.prob):
img_height, img_width = image.shape[:2]
xmin = self.labels_format['xmin']
ymin = self.labels_format['ymin']
xmax = self.labels_format['xmax']
ymax = self.labels_format['ymax']
# Override the preset labels format.
if not self.image_validator is None:
self.image_validator.labels_format = self.labels_format
self.scale.labels_format = self.labels_format
for _ in range(max(1, self.n_trials_max)):
# Pick a scaling factor.
factor = np.random.uniform(self.min_factor, self.max_factor)
self.scale.factor = factor
if (labels is None) or (self.image_validator is None):
# We either don't have any boxes or if we do, we will accept any outcome as valid.
return self.scale(image, labels)
else:
# Scale the bounding boxes accordingly.
# Transform two opposite corner points of the rectangular boxes using the rotation matrix `M`.
toplefts = np.array([labels[:,xmin], labels[:,ymin], np.ones(labels.shape[0])])
bottomrights = np.array([labels[:,xmax], labels[:,ymax], np.ones(labels.shape[0])])
# Compute the rotation matrix.
M = cv2.getRotationMatrix2D(center=(img_width / 2, img_height / 2),
angle=0,
scale=factor)
new_toplefts = (np.dot(M, toplefts)).T
new_bottomrights = (np.dot(M, bottomrights)).T
new_labels = np.copy(labels)
new_labels[:,[xmin,ymin]] = np.around(new_toplefts, decimals=0).astype(np.int)
new_labels[:,[xmax,ymax]] = np.around(new_bottomrights, decimals=0).astype(np.int)
# Check if the patch is valid.
if self.image_validator(labels=new_labels,
image_height=img_height,
image_width=img_width):
return self.scale(image, labels)
# If all attempts failed, return the unaltered input image.
if labels is None:
return image
else:
return image, labels
elif labels is None:
return image
else:
return image, labels
class Rotate:
'''
Rotates images counter-clockwise by 90, 180, or 270 degrees.
'''
def __init__(self,
angle,
labels_format={'class_id': 0, 'xmin': 1, 'ymin': 2, 'xmax': 3, 'ymax': 4}):
'''
Arguments:
angle (int): The angle in degrees by which to rotate the images counter-clockwise.
Only 90, 180, and 270 are valid values.
labels_format (dict, optional): A dictionary that defines which index in the last axis of the labels
of an image contains which bounding box coordinate. The dictionary maps at least the keywords
'xmin', 'ymin', 'xmax', and 'ymax' to their respective indices within last axis of the labels array.
'''
if not angle in {90, 180, 270}:
raise ValueError("`angle` must be in the set {90, 180, 270}.")
self.angle = angle
self.labels_format = labels_format
def __call__(self, image, labels=None):
img_height, img_width = image.shape[:2]
# Compute the rotation matrix.
M = cv2.getRotationMatrix2D(center=(img_width / 2, img_height / 2),
angle=self.angle,
scale=1)
# Get the sine and cosine from the rotation matrix.
cos_angle = np.abs(M[0, 0])
sin_angle = np.abs(M[0, 1])
# Compute the new bounding dimensions of the image.
img_width_new = int(img_height * sin_angle + img_width * cos_angle)
img_height_new = int(img_height * cos_angle + img_width * sin_angle)
# Adjust the rotation matrix to take into account the translation.
M[1, 2] += (img_height_new - img_height) / 2
M[0, 2] += (img_width_new - img_width) / 2
# Rotate the image.
image = cv2.warpAffine(image,
M=M,
dsize=(img_width_new, img_height_new))
if labels is None:
return image
else:
xmin = self.labels_format['xmin']
ymin = self.labels_format['ymin']
xmax = self.labels_format['xmax']
ymax = self.labels_format['ymax']
labels = np.copy(labels)
# Rotate the bounding boxes accordingly.
# Transform two opposite corner points of the rectangular boxes using the rotation matrix `M`.
toplefts = np.array([labels[:,xmin], labels[:,ymin], np.ones(labels.shape[0])])
bottomrights = np.array([labels[:,xmax], labels[:,ymax], np.ones(labels.shape[0])])
new_toplefts = (np.dot(M, toplefts)).T
new_bottomrights = (np.dot(M, bottomrights)).T
labels[:,[xmin,ymin]] = np.round(new_toplefts, decimals=0).astype(np.int)
labels[:,[xmax,ymax]] = np.round(new_bottomrights, decimals=0).astype(np.int)
if self.angle == 90:
# ymin and ymax were switched by the rotation.
labels[:,[ymax,ymin]] = labels[:,[ymin,ymax]]
elif self.angle == 180:
# ymin and ymax were switched by the rotation,
# and also xmin and xmax were switched.
labels[:,[ymax,ymin]] = labels[:,[ymin,ymax]]
labels[:,[xmax,xmin]] = labels[:,[xmin,xmax]]
elif self.angle == 270:
# xmin and xmax were switched by the rotation.
labels[:,[xmax,xmin]] = labels[:,[xmin,xmax]]
return image, labels
class RandomRotate:
'''
Randomly rotates images counter-clockwise.
'''
def __init__(self,
angles=[90, 180, 270],
prob=0.5,
labels_format={'class_id': 0, 'xmin': 1, 'ymin': 2, 'xmax': 3, 'ymax': 4}):
'''
Arguments:
angle (list): The list of angles in degrees from which one is randomly selected to rotate
the images counter-clockwise. Only 90, 180, and 270 are valid values.
prob (float, optional): `(1 - prob)` determines the probability with which the original,
unaltered image is returned.
labels_format (dict, optional): A dictionary that defines which index in the last axis of the labels
of an image contains which bounding box coordinate. The dictionary maps at least the keywords
'xmin', 'ymin', 'xmax', and 'ymax' to their respective indices within last axis of the labels array.
'''
for angle in angles:
if not angle in {90, 180, 270}:
raise ValueError("`angles` can only contain the values 90, 180, and 270.")
self.angles = angles
self.prob = prob
self.labels_format = labels_format
self.rotate = Rotate(angle=90, labels_format=self.labels_format)
def __call__(self, image, labels=None):
p = np.random.uniform(0,1)
if p >= (1.0-self.prob):
# Pick a rotation angle.
self.rotate.angle = random.choice(self.angles)
self.rotate.labels_format = self.labels_format
return self.rotate(image, labels)
elif labels is None:
return image
else:
return image, labels
