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Image warping using correspondences between sparse control points.
tf.contrib.image.sparse_image_warp(
image, source_control_point_locations, dest_control_point_locations,
interpolation_order=2, regularization_weight=0.0, num_boundary_points=0,
name='sparse_image_warp'
)
Apply a non-linear warp to the image, where the warp is specified by
the source and destination locations of a (potentially small) number of
control points. First, we use a polyharmonic spline
(tf.contrib.image.interpolate_spline
) to interpolate the displacements
between the corresponding control points to a dense flow field.
Then, we warp the image using this dense flow field
(tf.contrib.image.dense_image_warp
).
Let t index our control points. For regularization_weight=0, we have: warped_image[b, dest_control_point_locations[b, t, 0], dest_control_point_locations[b, t, 1], :] = image[b, source_control_point_locations[b, t, 0], source_control_point_locations[b, t, 1], :].
For regularization_weight > 0, this condition is met approximately, since
regularized interpolation trades off smoothness of the interpolant vs.
reconstruction of the interpolant at the control points.
See tf.contrib.image.interpolate_spline
for further documentation of the
interpolation_order and regularization_weight arguments.
Args | |
---|---|
image
|
[batch, height, width, channels] float Tensor
|
source_control_point_locations
|
[batch, num_control_points, 2] float
Tensor
|
dest_control_point_locations
|
[batch, num_control_points, 2] float
Tensor
|
interpolation_order
|
polynomial order used by the spline interpolation |
regularization_weight
|
weight on smoothness regularizer in interpolation |
num_boundary_points
|
How many zero-flow boundary points to include at each image edge.Usage: num_boundary_points=0: don't add zero-flow points num_boundary_points=1: 4 corners of the image num_boundary_points=2: 4 corners and one in the middle of each edge (8 points total) num_boundary_points=n: 4 corners and n-1 along each edge |
name
|
A name for the operation (optional).
Note that image and offsets can be of type tf.half, tf.float32, or tf.float64, and do not necessarily have to be the same type. |
Returns | |
---|---|
warped_image
|
[batch, height, width, channels] float Tensor with same
type as input image.
|
flow_field
|
[batch, height, width, 2] float Tensor containing the dense
flow field produced by the interpolation.
|