tf_ThinPlateSpline
TensorFlow implementation of Thin Plate Spline (TPS) Spatial Transformer Network (STN). The old version implementation based on STN paper is TPS_STN. This implmentation, however, can work with non-fixed control points. Each solution of TPS system is dynamically calculated every feed-forward step.
Usage :
V = ThinPlateSpline(U, coord, vector, out_size)
V = ThinPlateSpline2(U, source, target, out_size)
Args :
U : float Tensor [num_batch, height, width, num_channels].
Input Tensor.
coord : float Tensor [num_batch, num_point, 2]
Relative coordinates of the control points.
vector : float Tensor [num_batch, num_point, 2]
The vector on the control points.
out_size: tuple of two integers [height, width]
The size of the output of the network (height, width)
For version 2.
source : float Tensor [num_batch, num_point, 2]
Relative coordinates of the source points.
target : float Tensor [num_batch, num_point, 2]
Relative coordinates of the target points.
Example
Version 1
# test.py
p = np.array([
[-0.5, -0.5],
[0.5, -0.5],
[-0.5, 0.5],
[0.5, 0.5]])
v = np.array([
[0.2, 0.2],
[0.4, 0.4],
[0.6, 0.6],
[0.8, 0.8]])
p = tf.constant(p.reshape([1, 4, 2]), dtype=tf.float32)
v = tf.constant(v.reshape([1, 4, 2]), dtype=tf.float32)
t_img = tf.constant(img.reshape(shape), dtype=tf.float32)
t_img = ThinPlateSpline(t_img, p, v, out_size)
original.png
transformed.png
Version 2
# test2.py
s_ = np.array([ # source position
[-0.5, -0.5],
[0.5, -0.5],
[-0.5, 0.5],
[0.5, 0.5]])
t_ = np.array([ # target position
[-0.3, -0.3],
[0.3, -0.3],
[-0.3, 0.3],
[0.3, 0.3]])
s = tf.constant(s_.reshape([1, 4, 2]), dtype=tf.float32)
t = tf.constant(t_.reshape([1, 4, 2]), dtype=tf.float32)
t_img = tf.constant(img.reshape(shape), dtype=tf.float32)
t_img = ThinPlateSpline2(t_img, s, t, out_size) original.png
transformed2.png