Image warping using correspondences between sparse control points.
img_sparse_image_warp( image, source_control_point_locations, dest_control_point_locations, interpolation_order = 2, regularization_weight = 0, num_boundary_points = 0, name = "sparse_image_warp" )
| 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). |
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.
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.