def ST_CPAB_transformer(U, theta, out_size):
""" Spatial transformer using CPAB transformations
Arguments:
U: 4D-`Tensor` [n_batch, height, width, n_channels]. Input images to
transform.
theta: `Matrix` [n_batch, d]. Parameters for the transformation. Each
row specify a transformation for each input image. The number d is
determined by tessalation. See transformer/setup_CPAB_transformer.py
for more information.
out_size: `list` where out_size[0] is the output height and out_size[1]
is the output width of each interpolated image.
Output:
V: 4D-`Tensor` [n_batch, out_size[0], out_size[1], n_channels]. Tensor
with transformed images.
"""
with tf.name_scope('ST_CPAB_transformer'):
# Create grid of points
out_height = out_size[0]
out_width = out_size[1]
grid = tf_meshgrid(out_height, out_width)
# Transform grid
T_g = tf_CPAB_transformer(grid[:2], theta)
# Slice and reshape
x_s = tf.slice(T_g, [0, 0, 0], [-1, 1, -1])
y_s = tf.slice(T_g, [0, 1, 0], [-1, 1, -1])
x_s_flat = tf.reshape(x_s, [-1])
y_s_flat = tf.reshape(y_s, [-1])
# Interpolate values
V = tf_interpolate(U, x_s_flat, y_s_flat, out_size)
return V
def ST_TPS_transformer(U, theta, out_size, tps_size=[4, 4]):
#GUY
tps_size = [8, 8]
""" Spatial transformer using thin plate spline transformations
Arguments:
U: 4D-`Tensor` [n_batch, height, width, n_channels]. Input images to
transform.
theta: `Matrix` [n_batch, 2*tps_size[0]*tps_size[1]]. Parameters for
the transformation. Each row specify a transformation for each
input image.
out_size: `list` where out_size[0] is the output height and out_size[1]
is the output width of each interpolated image.
tps_size: `list` where tps_size[0] is the number of points in the x
direction and tps_size[1] is the number of points in the y direction.
This should be set to match the dimension of theta.
Output:
V: 4D-`Tensor` [n_batch, out_size[0], out_size[1], n_channels]. Tensor
with transformed images.
"""
with tf.name_scope('ST_TPS_transformer'):
theta = tf.reshape(theta, (-1, tps_size[0] * tps_size[1], 2))
# Create grid of points
out_height = out_size[0]
out_width = out_size[1]
grid = tf_meshgrid(out_height, out_width)
# Call transformer
T_g = tf_TPS_transformer(grid, theta)
# Slice and reshape
x_s = tf.slice(T_g, [0, 0, 0], [-1, 1, -1])
y_s = tf.slice(T_g, [0, 1, 0], [-1, 1, -1])
x_s_flat = tf.reshape(x_s, [-1])
y_s_flat = tf.reshape(y_s, [-1])
# Interpolate values
V = tf_interpolate(U, x_s_flat, y_s_flat, out_size)
return V
def ST_Homografy_transformer(U, theta, out_size):
""" Spatial transformer using homografy transformations
Arguments:
U: 4D-`Tensor` [n_batch, height, width, n_channels]. Input images to
transform.
theta: `Matrix` [n_batch, 9]. Parameters for the transformation. Each
row specify a transformation for each input image.
out_size: `list` where out_size[0] is the output height and out_size[1]
is the output width of each interpolated image.
Output:
V: 4D-`Tensor` [n_batch, out_size[0], out_size[1], n_channels]. Tensor
with transformed images.
"""
with tf.name_scope('ST_Homografy_transformer'):
# Reshape theta
theta = tf.reshape(theta, (-1, 3, 3))
# Make it a valid homografy
theta = theta / tf.norm(theta, axis=[1, 2], keep_dims=True)
# Create grid of points
out_height = out_size[0]
out_width = out_size[1]
grid = tf_meshgrid(out_height, out_width)
# Call transformer
T_g = tf_Homografy_transformer(grid, theta)
# Slice and reshape
x_s = tf.slice(T_g, [0, 0, 0], [-1, 1, -1])
y_s = tf.slice(T_g, [0, 1, 0], [-1, 1, -1])
x_s_flat = tf.reshape(x_s, [-1])
y_s_flat = tf.reshape(y_s, [-1])
# Interpolate values
V = tf_interpolate(U, x_s_flat, y_s_flat, out_size)
return V