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# * Copyright (c) 2022-2023 STMicroelectronics.
# * All rights reserved.
# *
# * This software is licensed under terms that can be found in the LICENSE file in
# * the root directory of this software component.
# * If no LICENSE file comes with this software, it is provided AS-IS.
# *--------------------------------------------------------------------------------------------*/
"""
References:
----------
Some of the code in this package is from or was inspired by:
Keras Image Preprocessing Layers
The Tensorflow Authors
Copyright (c) 2019
Link to the source code:
https://github.com/keras-team/keras/blob/v2.12.0/keras/layers/preprocessing/image_preprocessing.py#L394-L495
"""
import math
import tensorflow as tf
from common.data_augmentation import check_dataaug_argument, apply_change_rate
from common.data_augmentation import \
check_fill_and_interpolation, transform_images, \
get_flip_matrix, get_translation_matrix, get_rotation_matrix, \
get_shear_matrix, get_zoom_matrix
#------------------------- Random flip -------------------------
def random_flip(images, mode=None, change_rate=0.5):
"""
This function randomly flips input images horizontally, vertically, or both.
Setting `change_rate` to 0.5 usually gives good results (don't set
it to 1.0, otherwise all the images will be flipped).
Args:
images:
Input RGB or grayscale images with shape
[batch_size, width, height, channels].
mode:
A string representing the flip axis. Either "horizontal",
"vertical" or "horizontal_and_vertical".
change_rate:
A float in the interval [0, 1] representing the number of
changed images versus the total number of input images average
ratio. For example, if `change_rate` is set to 0.25, 25% of
the input images will get changed on average (75% won't get
changed). If it is set to 0.0, no images are changed. If it is
set to 1.0, all the images are changed.
Returns:
The flipped images.
"""
if mode not in ("horizontal", "vertical", "horizontal_and_vertical"):
raise ValueError(
"Argument `mode` of function `random_flip`: supported values are 'horizontal', "
"'vertical' and 'horizontal_and_vertical'. Received {}".format(mode))
images_shape = tf.shape(images)
batch_size = images_shape[0]
image_width = images_shape[1]
image_height = images_shape[2]
matrix = get_flip_matrix(batch_size, image_width, image_height, mode)
flipped_images = transform_images(images, matrix)
return apply_change_rate(images, flipped_images, change_rate)
#--------------------------------- Random translation ---------------------
def random_translation(
images,
width_factor=None,
height_factor=None,
fill_mode='reflect',
interpolation='bilinear',
fill_value=0.0,
change_rate=1.0):
""""
This function randomly translates input images.
Args:
images:
Input RGB or grayscale images with shape
[batch_size, width, height, channels].
width_factor:
A float or a tuple of 2 floats, specifies the range of values
the horizontal shift factors are sampled from (one per image).
If a scalar value v is used, it is equivalent to the tuple (-v, v).
A negative factor means shifting the image left, while a positive
factor means shifting the image right.
For example, `width_factor`=(-0.2, 0.3) results in an output shifted
left by up to 20% or shifted right by up to 30%.
height_factor:
A float or a tuple of 2 floats, specifies the range of values
the vertical shift factors are sampled from (one per image).
If a scalar value v is used, it is equivalent to the tuple (-v, v).
A negative factor means shifting the image up, while a positive
factor means shifting the image down.
For example, `height_factor`=(-0.2, 0.3) results in an output shifted
up by up to 20% or shifted down by up to 30%.
fill_mode:
Points outside the boundaries of the input are filled according
to the given mode. One of {'constant', 'reflect', 'wrap', 'nearest'}.
See Tensorflow documentation at https://tensorflow.org
for more details.
interpolation:
A string, the interpolation method. Supported values: 'nearest', 'bilinear'.
change_rate:
A float in the interval [0, 1] representing the number of
changed images versus the total number of input images average
ratio. For example, if `change_rate` is set to 0.25, 25% of
the input images will get changed on average (75% won't get
changed). If it is set to 0.0, no images are changed. If it is
set to 1.0, all the images are changed.
Returns:
The translated images.
"""
check_dataaug_argument(width_factor, "width_factor", function_name="random_translation", data_type=float)
if isinstance(width_factor, (tuple, list)):
width_lower = width_factor[0]
width_upper = width_factor[1]
else:
width_lower = -width_factor
width_upper = width_factor
check_dataaug_argument(height_factor, "height_factor", function_name="random_translation", data_type=float)
if isinstance(height_factor, (tuple, list)):
height_lower = height_factor[0]
height_upper = height_factor[1]
else:
height_lower = -height_factor
height_upper = height_factor
check_fill_and_interpolation(fill_mode, interpolation, fill_value, function_name="random_translation")
image_shape = tf.shape(images)
batch_size = image_shape[0]
width = tf.cast(image_shape[1], tf.float32)
height = tf.cast(image_shape[2], tf.float32)
translation_width = tf.random.uniform(
[batch_size, 1], minval=width_lower, maxval=width_upper, dtype=tf.float32)
translation_height = tf.random.uniform(
[batch_size, 1], minval=height_lower, maxval=height_upper, dtype=tf.float32)
translations = tf.cast(
tf.concat([translation_width * width, translation_height * height], axis=1),
dtype=tf.float32)
translation_matrix = get_translation_matrix(translations)
translated_images = transform_images(
images,
translation_matrix,
interpolation=interpolation,
fill_mode=fill_mode,
fill_value=fill_value)
return apply_change_rate(images, translated_images, change_rate)
#------------------ Random rotation ----------------
def random_rotation(
images,
factor=None,
fill_mode='reflect',
interpolation='bilinear',
fill_value=0.0,
change_rate=1.0):
"""
This function randomly rotates input images clock-wise and counter clock-wise.
Args:
images:
Input RGB or grayscale images with shape
[batch_size, width, height, channels].
factor:
A float or a tuple of 2 floats, specifies the range of values the
rotation angles are sampled from (one per image). If a scalar
value v is used, it is equivalent to the tuple (-v, v).
Rotation angles are in gradients (fractions of 2*pi). A positive
angle means rotating counter clock-wise, while a negative angle
means rotating clock-wise.
For example, `factor`=(-0.2, 0.3) results in an output rotated by
a random amount in the range [-20% * 2pi, 30% * 2pi].
fill_mode:
Points outside the boundaries of the input are filled according
to the given mode. One of {'constant', 'reflect', 'wrap', 'nearest'}.
See Tensorflow documentation at https://tensorflow.org
for more details.
interpolation:
A string, the interpolation method. Supported values: 'nearest', 'bilinear'.
change_rate:
A float in the interval [0, 1] representing the number of
changed images versus the total number of input images average
ratio. For example, if `change_rate` is set to 0.25, 25% of
the input images will get changed on average (75% won't get
changed). If it is set to 0.0, no images are changed. If it is
set to 1.0, all the images are changed.
Returns:
The rotated images.
"""
check_dataaug_argument(factor, "factor", function_name="random_rotation", data_type=float)
if not isinstance(factor, (tuple, list)):
factor = (-factor, factor)
check_fill_and_interpolation(fill_mode, interpolation, fill_value, function_name="random_rotation")
image_shape = tf.shape(images)
batch_size = image_shape[0]
width = tf.cast(image_shape[1], tf.float32)
height = tf.cast(image_shape[2], tf.float32)
min_angle = factor[0] * 2. * math.pi
max_angle = factor[1] * 2. * math.pi
angles = tf.random.uniform([batch_size], minval=min_angle, maxval=max_angle)
rotation_matrix = get_rotation_matrix(angles, width, height)
rotated_images = transform_images(
images,
rotation_matrix,
fill_mode=fill_mode,
fill_value=fill_value,
interpolation=interpolation)
return apply_change_rate(images, rotated_images, change_rate)
#------------------ Random shear ----------------
def random_shear(
images,
factor=None,
axis='xy',
fill_mode='reflect',
interpolation='bilinear',
fill_value=0.0,
change_rate=1.0):
"""
This function randomly shears input images.
Args:
images:
Input RGB or grayscale images with shape
[batch_size, width, height, channels].
factor:
A float or a tuple of 2 floats, specifies the range of values
the shear angles are sampled from (one per image). If a scalar
value v is used, it is equivalent to the tuple (-v, v). Angles
are in radians (fractions of 2*pi).
For example, factor=(-0.349, 0.785) results in an output sheared
by a random angle in the range [-20 degrees, +45 degrees].
axis:
The shear axis:
'xy': shear along both axis
'x': shear along the x axis only
'y': shear along the y axis only
fill_mode:
Points outside the boundaries of the input are filled according
to the given mode. One of {'constant', 'reflect', 'wrap', 'nearest'}.
See Tensorflow documentation at https://tensorflow.org
for more details.
interpolation:
A string, the interpolation method. Supported values: 'nearest', 'bilinear'.
change_rate:
A float in the interval [0, 1] representing the number of
changed images versus the total number of input images average
ratio. For example, if `change_rate` is set to 0.25, 25% of
the input images will get changed on average (75% won't get
changed). If it is set to 0.0, no images are changed. If it is
set to 1.0, all the images are changed.
Returns:
The sheared images.
"""
if axis == 'x':
function_name = "random_shear_x"
elif axis == 'y':
function_name = "random_shear_y"
else:
function_name = "random_shear"
check_dataaug_argument(factor, "factor", function_name=function_name, data_type=float)
if not isinstance(factor, (tuple, list)):
factor = (-factor, factor)
check_fill_and_interpolation(fill_mode, interpolation, fill_value, function_name=function_name)
batch_size = tf.shape(images)[0]
min_angle = factor[0] * 2. * math.pi
max_angle = factor[1] * 2. * math.pi
angles = tf.random.uniform([batch_size], minval=min_angle, maxval=max_angle)
shear_matrix = get_shear_matrix(angles, axis=axis)
sheared_images = transform_images(
images,
shear_matrix,
fill_mode=fill_mode,
fill_value=fill_value,
interpolation=interpolation)
return apply_change_rate(images, sheared_images, change_rate)
#--------------------------------- Random zoom ---------------------
def random_zoom(
images,
width_factor=None,
height_factor=None,
fill_mode='reflect',
interpolation='bilinear',
fill_value=0.0,
change_rate=1.0):
"""
This function randomly zooms in/out on each axis of input images.
If `width_factor` and `height_factor` are both set, the images are zoomed
in or out on each axis independently, which may result in noticeable distortion.
If you want to avoid distortion, only set `width_factor` and the mages will be
zoomed by the same amount in both directions.
Args:
images:
Input RGB or grayscale images with shape
[batch_size, width, height, channels].
width_factor:
A float or a tuple of 2 floats, specifies the range of values horizontal
zoom factors are sampled from (one per image). If a scalar value v is used,
it is equivalent to the tuple (-v, v). Factors are fractions of the width
of the image. A positive factor means zooming out, while a negative factor
means zooming in.
For example, width_factor=(0.2, 0.3) results in an output zoomed out by
a random amount in the range [+20%, +30%]. width_factor=(-0.3, -0.2) results
in an output zoomed in by a random amount in the range [+20%, +30%].
height_factor:
A float or a tuple of 2 floats, specifies the range of values vertical
zoom factors are sampled from (one per image). If a scalar value v is used,
it is equivalent to the tuple (-v, v). Factors are fractions of the height
of the image. A positive value means zooming out, while a negative value
means zooming in.
For example, height_factor=(0.2, 0.3) results in an output zoomed out
between 20% to 30%. height_factor=(-0.3, -0.2) results in an output zoomed
in between 20% to 30%.
If `height_factor` is not set, it defaults to None. In this case, images
images will be zoomed by the same amounts in both directions and no image
distortion will occur.
fill_mode:
Points outside the boundaries of the input are filled according
to the given mode. One of {'constant', 'reflect', 'wrap', 'nearest'}.
See Tensorflow documentation at https://tensorflow.org
for more details.
interpolation:
A string, the interpolation method. Supported values: 'nearest', 'bilinear'.
change_rate:
A float in the interval [0, 1] representing the number of
changed images versus the total number of input images average
ratio. For example, if `change_rate` is set to 0.25, 25% of
the input images will get changed on average (75% won't get
changed). If it is set to 0.0, no images are changed. If it is
set to 1.0, all the images are changed.
Returns:
The zoomed images.
"""
check_dataaug_argument(width_factor, "width_factor", function_name="random_zoom", data_type=float)
if isinstance(width_factor, (tuple, list)):
width_lower = width_factor[0]
width_upper = width_factor[1]
else:
width_lower = -width_factor
width_upper = width_factor
if height_factor is not None:
check_dataaug_argument(height_factor, "height_factor", function_name="random_zoom", data_type=float)
if isinstance(height_factor, (tuple, list)):
height_lower = height_factor[0]
height_upper = height_factor[1]
else:
height_lower = -height_factor
height_upper = height_factor
if abs(height_lower) > 1.0 or abs(height_upper) > 1.0:
raise ValueError(
"Argument `height_factor` of function `random_zoom`: expecting float "
"values in the interval [-1.0, 1.0]. Received: {}".format(height_factor))
check_fill_and_interpolation(fill_mode, interpolation, fill_value, function_name="random_zoom")
image_shape = tf.shape(images)
batch_size = image_shape[0]
width = tf.cast(image_shape[1], tf.float32)
height = tf.cast(image_shape[2], tf.float32)
zoom_width = tf.random.uniform(
[batch_size, 1], minval=1.0 + width_lower, maxval=1.0 + width_upper, dtype=tf.float32)
if height_factor is not None:
zoom_height = tf.random.uniform(
[batch_size, 1], minval=1.0 + height_lower, maxval=1.0 + height_upper, dtype=tf.float32)
else:
zoom_height = zoom_width
zooms = tf.cast(tf.concat([zoom_width, zoom_height], axis=1), dtype=tf.float32)
zoom_matrix = get_zoom_matrix(zooms, width, height)
zoomed_images = transform_images(
images,
zoom_matrix,
fill_mode=fill_mode,
fill_value=fill_value,
interpolation=interpolation)
return apply_change_rate(images, zoomed_images, change_rate)
#--------------------------------- Random bounded crop ---------------------
def random_bounded_crop(
images,
width_factor=None,
height_factor=None,
fill_mode='reflect',
interpolation='bilinear',
fill_value=0.0,
change_rate=1.0):
"""
This function randomly crops or dezoom on each axis of the input images.
If `width_factor` and `height_factor` are both set, the images are zoomed
in or out on each axis independently, which may result in noticeable distortion.
If you want to avoid distortion, only set `width_factor` and the mages will be
zoomed by the same amount in both directions.
Args:
images:
Input RGB or grayscale images with shape
[batch_size, width, height, channels].
width_factor:
A float or a tuple of 2 floats, specifies the range of values horizontal
zoom factors are sampled from (one per image). If a scalar value v is used,
it is equivalent to the tuple (-v, v). Factors are fractions of the width
of the image. A positive factor means zooming out, while a negative factor
means zooming in.
For example, width_factor=(0.2, 0.3) results in an output zoomed out by
a random amount in the range [+20%, +30%]. width_factor=(-0.3, -0.2) results
in an output zoomed in by a random amount in the range [+20%, +30%].
height_factor:
A float or a tuple of 2 floats, specifies the range of values vertical
zoom factors are sampled from (one per image). If a scalar value v is used,
it is equivalent to the tuple (-v, v). Factors are fractions of the height
of the image. A positive value means zooming out, while a negative value
means zooming in.
For example, height_factor=(0.2, 0.3) results in an output zoomed out
between 20% to 30%. height_factor=(-0.3, -0.2) results in an output zoomed
in between 20% to 30%.
If `height_factor` is not set, it defaults to None. In this case, images
images will be zoomed by the same amounts in both directions and no image
distortion will occur.
fill_mode:
Points outside the boundaries of the input are filled according
to the given mode. One of {'constant', 'reflect', 'wrap', 'nearest'}.
See Tensorflow documentation at https://tensorflow.org
for more details.
interpolation:
A string, the interpolation method. Supported values: 'nearest', 'bilinear'.
change_rate:
A float in the interval [0, 1] representing the number of
changed images versus the total number of input images average
ratio. For example, if `change_rate` is set to 0.25, 25% of
the input images will get changed on average (75% won't get
changed). If it is set to 0.0, no images are changed. If it is
set to 1.0, all the images are changed.
Returns:
The zoomed images.
"""
check_dataaug_argument(width_factor, "width_factor", function_name="random_bounded_crop", data_type=float)
if isinstance(width_factor, (tuple, list)):
width_lower = width_factor[0]
width_upper = width_factor[1]
else:
width_lower = -width_factor
width_upper = width_factor
if height_factor is not None:
check_dataaug_argument(height_factor, "height_factor", function_name="random_bounded_crop", data_type=float)
if isinstance(height_factor, (tuple, list)):
height_lower = height_factor[0]
height_upper = height_factor[1]
else:
height_lower = -height_factor
height_upper = height_factor
if abs(height_lower) > 1.0 or abs(height_upper) > 1.0:
raise ValueError(
"Argument `height_factor` of function `random_bounded_crop`: expecting float "
"values in the interval [-1.0, 1.0]. Received: {}".format(height_factor))
check_fill_and_interpolation(fill_mode, interpolation, fill_value, function_name="random_bounded_crop")
image_shape = tf.shape(images)
batch_size = image_shape[0]
width = tf.cast(image_shape[1], tf.float32)
height = tf.cast(image_shape[2], tf.float32)
zoom_width = tf.random.uniform(
[batch_size, 1], minval=1.0 + width_lower, maxval=1.0 + width_upper, dtype=tf.float32)
if height_factor is not None:
zoom_height = tf.random.uniform(
[batch_size, 1], minval=1.0 + height_lower, maxval=1.0 + height_upper, dtype=tf.float32)
else:
zoom_height = zoom_width
zoom_factor_w = 1-zoom_width
zoom_factor_h = 1-zoom_height
zoom_factor_w *= tf.cast(zoom_factor_w>=0,tf.float32)
zoom_factor_h *= tf.cast(zoom_factor_h>=0,tf.float32)
translation_width = tf.random.uniform(
[batch_size, 1], minval=-1, maxval=1, dtype=tf.float32)
translation_height = tf.random.uniform(
[batch_size, 1], minval=-1, maxval=1, dtype=tf.float32)
translation_width *= zoom_factor_w/2
translation_height *= zoom_factor_h/2
zooms = tf.cast(tf.concat([zoom_width, zoom_height], axis=1), dtype=tf.float32) # shape : (batch, 2)
translations = tf.cast(
tf.concat([translation_width * width, translation_height * height], axis=1),
dtype=tf.float32)
zoom_matrix = get_zoom_matrix(zooms, width, height)
translation_matrix = get_translation_matrix(translations)
translated_images = transform_images(
images,
translation_matrix,
fill_mode=fill_mode,
fill_value=fill_value,
interpolation=interpolation)
zoomed_images = transform_images(
translated_images,
zoom_matrix,
fill_mode=fill_mode,
fill_value=fill_value,
interpolation=interpolation)
return apply_change_rate(images, zoomed_images, change_rate)
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