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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.
# *--------------------------------------------------------------------------------------------*/
import tensorflow as tf
def check_fill_and_interpolation(fill_mode, interpolation, fill_value, function_name=None):
"""
Function checking fill mode and value and interpolation method for a given augmentation function.
Raise an error if parameter value is not allowed
Args:
fill_mode (str): fill mode method in tensorflow keras ("wrap", "nearest"...)
interpolation (str): interpolation method. Support "nearest" and "bilinear"
fill_value (float): pixel value in fill mode
function_name (str): augmentation function name
Returns:
"""
if fill_mode not in ("reflect", "wrap", "constant", "nearest"):
raise ValueError(
f"Argument `fill_mode` of function `{function_name}`: supported values are 'reflect', "
f"'wrap', 'constant' and 'nearest'. Received {fill_mode}")
if interpolation not in ("nearest", "bilinear"):
raise ValueError(
f"Argument `interpolation` of function `{function_name}`: supported values "
f"are 'nearest' and 'bilinear'. Received {interpolation}")
if type(fill_value) not in (int, float) or fill_value < -1.:
raise ValueError(
f"Argument `fill_value` of function `{function_name}`: expecting float values "
f"greater than or equal to -1. Received {fill_value}")
def generate_coordinates(tensor_shape):
"""
Create a list of indices for each dimension of the tensor
Args:
tensor_shape (tuple): tuple of 4 elements for all dimensions including batch
Returns:
a tf.Tensor with the generated coordinates
"""
indices = [tf.range(tensor_shape[0]),tf.range(tensor_shape[1]),tf.range(tensor_shape[2]),tf.range(tensor_shape[3])]
# Use tf.meshgrid to generate the grid of coordinates
coordinates = tf.stack(tf.meshgrid(indices[0],indices[1],indices[2],indices[3],indexing='ij'), axis=-1) # INT32
coordinates = tf.reshape(coordinates,[-1,tensor_shape[1]*tensor_shape[2]*tensor_shape[3],4]) # (batch, width*height*channel, 2) INT32
coordinates = tf.cast(coordinates,tf.float32)
return coordinates # shape: (batch, width*height*channel, 4) FLOAT32
def image_projective_transform(images, output_shape, fill_value, transforms, fill_mode, interpolation):
"""
This function is here because tf.raw_ops.ImageProjectiveTransformV3() is not compatible with XLA_GPU compilation while this function works on GPU.
Definition :
If one row of transforms is [a0, a1, a2, b0, b1, b2, c0, c1], then it maps the output point (x, y) to a transformed input point
(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k), where k = c0 x + c1 y + 1.
If the transformed point lays outside of the input image, the output pixel is set to fill_value.
The function returns the transformed image.
Args:
images (tf.Tensor): batch of input images
output_shape (tuple): shape of the output. Not used so far
fill_value (float): filled pixel value
transforms (np.array): transformation matrix to be applied on image
fill_mode: method for filling when image is augmented ("wrap", "reflect"...)
interpolation: interpolation method such as "nearest" or "bilinear"
Returns:
images after transformation (tf.Tensor)
"""
# Definition of a0, a1, a2, b0, b1, b2, c0 and c1 variables, shape: (batch, ) FLOAT32
(a0, a1, a2, b0, b1, b2, c0, c1) = (transforms[:,0][...,None],
transforms[:,1][...,None],
transforms[:,2][...,None],
transforms[:,3][...,None],
transforms[:,4][...,None],
transforms[:,5][...,None],
transforms[:,6][...,None],
transforms[:,7][...,None])
# Get the shape of the input batch of images
im_shape = tf.shape(images) # shape: (4,) INT32
# Creation of the Tensor containing the coordinates of each pixel in the batch of images
init_coordinates = generate_coordinates(im_shape) # shape: (batch, width*height*channel, 4) FLOAT32
b = init_coordinates[:,:,0] # shape: (batch, width*height*channel) FLOAT32
x = init_coordinates[:,:,1] # shape: (batch, width*height*channel) FLOAT32
y = init_coordinates[:,:,2] # shape: (batch, width*height*channel) FLOAT32
c = init_coordinates[:,:,3] # shape: (batch, width*height*channel) FLOAT32
k = c1*x + c0*y + 1 # shape: (batch, width*height*channel) FLOAT32
(x_prime, y_prime) = ((b1 * x + b0 * y + b2) / k, (a1 * x + a0 * y + a2) / k) # tuple of shape: (batch, width*height*channel) FLOAT32
if fill_mode=='reflect'.upper() or fill_mode=='wrap'.upper():
x_prime = tf.math.floormod(x_prime,tf.cast(im_shape[1]-1,tf.float32)) # shape: (batch, width*height*channel) FLOAT32
y_prime = tf.math.floormod(y_prime,tf.cast(im_shape[2]-1,tf.float32)) # shape: (batch, width*height*channel) FLOAT32
trans_coordinates = tf.stack([b,x_prime,y_prime,c],axis=-1) # shape: (batch, width*height*channel, 4) FLOAT32
trans_coordinates = tf.cast(trans_coordinates,tf.int32) # shape: (batch, width*height*channel, 4) INT32
trans_coordinates = tf.reshape(trans_coordinates,[-1,4]) # shape: (batch*width*height*channel, 4) INT32
ll_x = trans_coordinates[:,1]>=0 # shape: (batch*width*height*channel) BOOL
ul_x = trans_coordinates[:,1]<=(im_shape[1]-1) # shape: (batch*width*height*channel) BOOL
ll_y = trans_coordinates[:,2]>=0 # shape: (batch*width*height*channel) BOOL
ul_y = trans_coordinates[:,2]<=(im_shape[2]-1) # shape: (batch*width*height*channel) BOOL
xbmask = tf.logical_and(ll_x,ul_x) # shape: (batch*width*height*channel) BOOL
ybmask = tf.logical_and(ll_y,ul_y) # shape: (batch*width*height*channel) BOOL
bmask = tf.logical_and(xbmask,ybmask) # shape: (batch*width*height*channel) BOOL
# Create a mask for the out of bound coordinates fill the final images with fill_values
mask = tf.cast(bmask,dtype=trans_coordinates.dtype) # shape: (batch*width*height*channel) INT32
trans_coordinates *= mask[...,None] # shape: (batch*width*height*channel, 4) INT32
mask = tf.cast(bmask,dtype=images.dtype) # shape: (batch*width*height*channel) IMAGES_DTYPE
mask = tf.reshape(mask,im_shape) # shape: (batch, width, height, channel) IMAGES_DTYPE
fill_mask = (1-mask)*tf.cast(fill_value,dtype=images.dtype) # shape: (batch, width, height, channel) IMAGES_DTYPE
# Gather pixels that are located in the original Tensor with the help of the transformed coordinates to form the new Tensor
transformed_image = tf.gather_nd(images,trans_coordinates) # shape: (batch*width*height*channel) FLOAT32
transformed_image = tf.reshape(transformed_image,im_shape) # shape: (batch, width, height, channel) FLOAT32
transformed_image = transformed_image*mask + fill_mask # shape: (batch, width, height, channel) FLOAT32
return transformed_image
def transform_images(
images,
transforms,
fill_mode='reflect',
fill_value=0.0,
interpolation='bilinear'):
"""
The function returns the transformed images.
Args:
images (tf.Tensor): batch of input images
transforms (np.array): transformation matrix to be applied on image
fill_mode: method for filling when image is augmented ("wrap", "reflect"...)
fill_value (float): filled pixel value
interpolation: interpolation method such as "nearest" or "bilinear"
Returns:
images after transformation (tf.Tensor)
"""
output_shape = tf.shape(images)[1:3]
return image_projective_transform(
images=images,
output_shape=output_shape,
fill_value=fill_value,
transforms=transforms,
fill_mode=fill_mode.upper(),
interpolation=interpolation.upper())
######### Legacy Code #########
# return tf.raw_ops.ImageProjectiveTransformV3(
# images=images,
# output_shape=output_shape,
# fill_value=fill_value,
# transforms=transforms,
# fill_mode=fill_mode.upper(),
# interpolation=interpolation.upper())
def get_flip_matrix(batch_size, width, height, mode):
"""
This function creates a batch of flipping matrices
Args:
batch_size (int): size of input batch of images
width (float): normalized image width
height (float): normailzed image height
mode (str): flipping direction, "horizontal", "vertical" or by default both
Returns:
batch of flipping matrices (tf.Tensor)
"""
if mode == "horizontal":
# Flip all the images horizontally
matrix = tf.tile([-1, 0, (width-1), 0, 1, 0, 0, 0], [batch_size])
matrix = tf.reshape(matrix, [batch_size, 8])
elif mode == "vertical":
# Flip all the images vertically
matrix = tf.tile([1, 0, 0, 0, -1, (height-1), 0, 0], [batch_size])
matrix = tf.reshape(matrix, [batch_size, 8])
else:
# Randomly flip images horizontally, vertically or both
flips = [[-1, 0, (width-1), 0, 1, 0, 0, 0],
[ 1, 0, 0, 0, -1, (height-1), 0, 0],
[-1, 0, (width-1), 0, -1, (height-1), 0, 0]]
select = tf.random.uniform([batch_size], minval=0, maxval=3, dtype=tf.int32)
matrix = tf.gather(flips, select)
return tf.cast(matrix, tf.float32)
def get_translation_matrix(translations):
"""
This function creates a batch of translation matrices given
a batch of x and y translation fractions.
Translation fractions are independent from each other
and may be different from one batch item to another.
The translation matrix is:
[[ 1, 0, -x_translation],
[ 0, 1, -y_translation],
[ 0, 1, 0 ]]
The function returns the following representation of the matrix:
[ 1, 0, -x_translation, 0, 1, -y_translation, 0, 1]
with entry [2, 2] being implicit and equal to 1.
Args:
translations (tuple): normalized translation values
Returns:
tf.Tensor with translation matrix
"""
num_translations = tf.shape(translations)[0]
matrix = tf.concat([
tf.ones((num_translations, 1), tf.float32),
tf.zeros((num_translations, 1), tf.float32),
-translations[:, 0, None],
tf.zeros((num_translations, 1), tf.float32),
tf.ones((num_translations, 1), tf.float32),
-translations[:, 1, None],
tf.zeros((num_translations, 2), tf.float32),
],
axis=1)
return matrix
def get_rotation_matrix(angles, width, height):
"""
This function creates a batch of rotation matrices given a batch of angles.
Angles are independent from each other and may be different from
one batch item to another.
The rotation matrix is:
[ cos(angle) -sin(angle), x_offset]
[ sin(angle), cos(angle), y_offset]
[ 0, 0, 1 ]
x_offset and y_offset are calculated from the angles and image dimensions.
The function returns the following representation of the matrix:
[ cos(angle), -sin(angle), x_offset, sin(angle), cos(angle), 0, 0 ]
with entry [2, 2] being implicit and equal to 1.
Args:
angles (list(float)): batch of angles fow which we compute a rotation matrix
width (float): normalized width of input images
height (float): normalized height of input images
Returns:
(tf.Tensor), rotation matrices
"""
width = tf.cast(width, tf.float32)
height = tf.cast(height, tf.float32)
num_angles = tf.shape(angles)[0]
x_offset = ((width - 1) - (tf.cos(angles) * (width - 1) - tf.sin(angles) * (height - 1))) / 2.0
y_offset = ((height - 1) - (tf.sin(angles) * (width - 1) + tf.cos(angles) * (height - 1))) / 2.0
matrix = tf.concat([
tf.cos(angles)[:, None],
-tf.sin(angles)[:, None],
x_offset[:, None],
tf.sin(angles)[:, None],
tf.cos(angles)[:, None],
y_offset[:, None],
tf.zeros((num_angles, 2), tf.float32)
],
axis=1)
return matrix
def get_shear_matrix(angles, axis):
"""
This function creates a batch of shearing matrices given a batch
of angles. Angles are independent from each other and may be different
from one batch item to another.
The shear matrix along the x axis only is:
[ 1 -sin(angle), 0 ]
[ 0, 1, 0 ]
[ 0, 0, 1 ]
The shear matrix along the y axis only is:
[ 1, 0, 0 ]
[ cos(angle), 1, 0 ]
[ 0, 0, 1 ]
The shear matrix along both x and y axis is:
[ 1 -sin(angle), 0 ]
[ 0, cos(angle), 0 ]
[ 0, 0, 1 ]
The function returns the following representation of the
shear matrix along both x and y axis:
[ 1, -sin(angle), 0, 0, cos(angle), 0, 0, 0 ]
with entry [2, 2] being implicit and equal to 1.
Representations are similar for x axis only and y axis only.
Args:
angles (list(float)): batch of angles for which we compute a shear matrix
axis (str): axis on which we shear ("x" or "y", by default both)
Returns:
(tf.Tensor): shear matrices
"""
num_angles = tf.shape(angles)[0]
x_offset = tf.zeros(num_angles)
y_offset = tf.zeros(num_angles)
if axis == 'x':
matrix = tf.concat([
tf.ones((num_angles, 1), tf.float32),
-tf.sin(angles)[:, None],
x_offset[:, None],
tf.zeros((num_angles, 1), tf.float32),
tf.ones((num_angles, 1), tf.float32),
y_offset[:, None],
tf.zeros((num_angles, 2), tf.float32)
],
axis=1)
elif axis == 'y':
matrix = tf.concat([
tf.ones((num_angles, 1), tf.float32),
tf.zeros((num_angles, 1), tf.float32),
x_offset[:, None],
tf.cos(angles)[:, None],
tf.ones((num_angles, 1), tf.float32),
y_offset[:, None],
tf.zeros((num_angles, 2), tf.float32)
],
axis=1)
else:
matrix = tf.concat([
tf.ones((num_angles, 1), tf.float32),
-tf.sin(angles)[:, None],
x_offset[:, None],
tf.zeros((num_angles, 1), tf.float32),
tf.cos(angles)[:, None],
y_offset[:, None],
tf.zeros((num_angles, 2), tf.float32)
],
axis=1)
return matrix
def get_zoom_matrix(zooms, width, height):
"""
This function creates a batch of zooming matrices.
Arguments width and height are the image dimensions.
The zoom matrix is:
[[ zoom 0, x_offset],
[ 0, zoom, y_offset],
[ 0, 1, 0 ]]
Args:
zooms (list(float)): batch of zoom values
width (float): normalized width of input images
height (float): normalized height of input images
Returns:
(tf.Tensor): batch of zoom matrices
"""
width = tf.cast(width, tf.float32)
height = tf.cast(height, tf.float32)
num_zooms = tf.shape(zooms)[0]
x_offset = ((width - 1.) / 2.0) * (1.0 - zooms[:, 0, None])
y_offset = ((height - 1.) / 2.0) * (1.0 - zooms[:, 1, None])
matrix = tf.concat([
zooms[:, 0, None],
tf.zeros((num_zooms, 1), tf.float32),
x_offset,
tf.zeros((num_zooms, 1), tf.float32),
zooms[:, 1, None],
y_offset,
tf.zeros((num_zooms, 2), tf.float32),
],
axis=-1)
return matrix
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