def batch_displacement_warp2d(imgs, vector_fields):
"""
warp images by free form transformation
Parameters
----------
imgs : tf.Tensor
images to be warped
[n_batch, xlen, ylen, n_channel]
vector_fields : tf.Tensor
[n_batch, 2, xlen, ylen]
Returns
-------
output : tf.Tensor
warped imagees
[n_batch, xlen, ylen, n_channel]
"""
n_batch = tf.shape(imgs)[0]
xlen = tf.shape(imgs)[1]
ylen = tf.shape(imgs)[2]
grids = batch_mgrid(n_batch, xlen, ylen)
T_g = grids + vector_fields
output = batch_warp2d(imgs, T_g)
return output
def batch_affine_warp2d(imgs, theta):
"""
affine transforms 2d images
Parameters
----------
imgs : tf.Tensor
images to be warped
[n_batch, xlen, ylen, n_channel]
theta : tf.Tensor
parameters of affine transformation
[n_batch, 6]
Returns
-------
output : tf.Tensor
warped images
[n_batch, xlen, ylen, n_channel]
"""
n_batch = tf.shape(imgs)[0]
xlen = tf.shape(imgs)[1]
ylen = tf.shape(imgs)[2]
theta = tf.reshape(theta, [-1, 2, 3])
matrix = tf.slice(theta, [0, 0, 0], [-1, -1, 2])
t = tf.slice(theta, [0, 0, 2], [-1, -1, -1])
grids = batch_mgrid(n_batch, xlen, ylen)
coords = tf.reshape(grids, [n_batch, 2, -1])
T_g = tf.batch_matmul(matrix, coords) + t
T_g = tf.reshape(T_g, [n_batch, 2, xlen, ylen])
output = batch_warp2d(imgs, T_g)
return output
def batch_displacement_warp2d(imgs,
vector_fields,
vector_fields_in_pixel_space=False):
"""
warp images by free form transformation
Parameters
----------
imgs : tf.Tensor
images to be warped
[n_batch, xlen, ylen, n_channel]
vector_fields : tf.Tensor
[n_batch, xlen, ylen, 2]
vector_fields_in_pixel_space: boolean
If vector_fields_in_pixel_space, it means that the displacements
in the vector field are expressed in pixels.
Therefore, they will be rescaled from imagen domain
to [-1.,1.][-1.,1.] to make it compatible with the convention used
by the warper.
Returns
-------
output : tf.Tensor
warped imagees
[n_batch, xlen, ylen, n_channel]
"""
# Transpose the vector field to [n_batch, 2, xlen, ylen]
vector_fields_transposed = tf.transpose(vector_fields, [0, 3, 1, 2])
n_batch = tf.shape(imgs)[0]
xlen = tf.shape(imgs)[1]
ylen = tf.shape(imgs)[2]
grids = batch_mgrid(n_batch, xlen, ylen)
if vector_fields_in_pixel_space:
# Scale the vector field from image domain to [-1.,1.][-1.,1.]
vector_fields_transposed_rescaled = tf.stack(
[(2. * vector_fields_transposed[:, 0, :, :]) /
(tf.to_float(xlen) - 1.),
(2. * vector_fields_transposed[:, 1, :, :]) /
(tf.to_float(ylen) - 1.)], 1)
T_g = grids + vector_fields_transposed_rescaled
else:
T_g = grids + vector_fields_transposed
return batch_warp2d(imgs, T_g)
