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WarpST_one.py
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WarpST_one.py
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import tensorflow as tf
from bicubic_interp import bicubic_interp_2d
from keras import backend as K
import random
def WarpST_one(x,inputs, name):
out_size = (256,256,1)
V = x
U = inputs
# U, V, out_size = x[0], x[1], x[2]
"""Deformable Transformer Layer with bicubic interpolation
U : tf.float, [num_batch, height, width, num_channels].
Input tensor to warp
V : tf.float, [num_batch, height, width, 2]
Warp map. It is interpolated to out_size.
out_size: a tuple of two ints
The size of the output of the network (height, width)
----------
References :
https://github.com/daviddao/spatial-transformer-tensorflow/blob/master/spatial_transformer.py
"""
def _repeat(x, n_repeats):
with tf.variable_scope('_repeat'+str(random.random())):
rep = tf.transpose(
tf.expand_dims(tf.ones(shape=tf.stack([n_repeats, ])), 1), [1, 0])
rep = tf.cast(rep, 'int32')
x = tf.matmul(tf.reshape(x, (-1, 1)), rep)
return tf.reshape(x, [-1])
def _interpolate(im, x, y, out_size):
with tf.variable_scope('_interpolate'+str(random.random())):
# constants
num_batch = tf.shape(im)[0]
# height = tf.shape(im)[1]
# width = tf.shape(im)[2]
height = 256
width = 256
channels = tf.shape(im)[3]
x = tf.cast(x, 'float32')
y = tf.cast(y, 'float32')
height_f = tf.cast(height, 'float32')
width_f = tf.cast(width, 'float32')
out_height = out_size[0]
out_width = out_size[1]
zero = tf.zeros([], dtype='int32')
max_y = tf.cast(tf.shape(im)[1] - 1, 'int32')
max_x = tf.cast(tf.shape(im)[2] - 1, 'int32')
# scale indices from [-1, 1] to [0, width/height]
# x = (x - 0.5)* 4.0
# y = (y - 0.5)* 4.0
# do sampling
x0 = tf.cast(tf.floor(x), 'int32')
x1 = tf.add(x0 ,1)
y0 = tf.cast(tf.floor(y), 'int32')
y1 = tf.add(y0, 1)
x0 = tf.clip_by_value(x0, zero, max_x)
x1 = tf.clip_by_value(x1, zero, max_x)
y0 = tf.clip_by_value(y0, zero, max_y)
y1 = tf.clip_by_value(y1, zero, max_y)
dim2 = width
dim1 = tf.multiply(width, height)
base = _repeat(tf.range(tf.shape(im)[0])*dim1, out_height*out_width)
base_y0 = tf.add(base , tf.multiply(y0,dim2))
base_y1 = tf.add(base , tf.multiply(y1,dim2))
idx_a = tf.add(base_y0 , x0)
idx_b = tf.add(base_y1 , x0)
idx_c = tf.add(base_y0 , x1)
idx_d = tf.add(base_y1 , x1)
# use indices to lookup pixels in the flat image and restore
# channels dim
im_flat = tf.reshape(im, tf.stack([-1, channels]))
im_flat = tf.cast(im_flat, 'float32')
Ia = tf.gather(im_flat, idx_a)
Ib = tf.gather(im_flat, idx_b)
Ic = tf.gather(im_flat, idx_c)
Id = tf.gather(im_flat, idx_d)
# and finally calculate interpolated values
x0_f = tf.cast(x0, 'float32')
x1_f = tf.cast(x1, 'float32')
y0_f = tf.cast(y0, 'float32')
y1_f = tf.cast(y1, 'float32')
wa = tf.expand_dims((tf.multiply((x1_f-x) , (y1_f-y))), 1)
wb = tf.expand_dims((tf.multiply((x1_f-x) , (y-y0_f))), 1)
wc = tf.expand_dims((tf.multiply((x-x0_f) , (y1_f-y))), 1)
wd = tf.expand_dims((tf.multiply((x-x0_f) , (y-y0_f))), 1)
output = tf.add_n([tf.multiply(wa,Ia), tf.multiply(wb,Ib), tf.multiply(wc,Ic), tf.multiply(wd,Id)])
return output
def _meshgrid(height, width):
with tf.variable_scope('_meshgrid'+str(random.random())):
# This should be equivalent to:
# x_t, y_t = np.meshgrid(np.linspace(-1, 1, width),
# np.linspace(-1, 1, height))
# ones = np.ones(np.prod(x_t.shape))
# grid = np.vstack([x_t.flatten(), y_t.flatten(), ones])
# x_t = tf.matmul(tf.ones(shape=tf.stack([height, 1])),
# tf.transpose(tf.expand_dims(tf.linspace(-1.0, 1.0, width), 1), [1, 0]))
# y_t = tf.matmul(tf.expand_dims(tf.linspace(-1.0, 1.0, height), 1),
# tf.ones(shape=tf.stack([1, width])))
x_t = tf.matmul(tf.ones(shape=tf.stack([height, 1])),
tf.transpose(tf.expand_dims(tf.linspace(0.0, 255.0, width), 1), [1, 0]))
y_t = tf.matmul(tf.expand_dims(tf.linspace(0.0, 255.0, height), 1),
tf.ones(shape=tf.stack([1, width])))
x_t_flat = tf.reshape(x_t, (1, -1))
y_t_flat = tf.reshape(y_t, (1, -1))
grid = tf.concat([x_t_flat, y_t_flat], 0)
return grid
def _transform(V, U, out_size):
with tf.variable_scope('_transform'+str(random.random())):
# num_batch = tf.shape(U)[0]
height = 256 #tf.shape(U)[1]
width = 256 #tf.shape(U)[2]
# num_channels = tf.shape(U)[3]
# grid of (x_t, y_t, 1), eq (1) in ref [1]
# height_f = tf.cast(height, 'float32')
# width_f = tf.cast(width, 'float32')
out_height = out_size[0]
out_width = out_size[1]
grid = _meshgrid(out_height, out_width) # [2, h*w]
grid = tf.reshape(grid, [-1]) # [2*h*w]
grid = tf.tile(grid, tf.stack([tf.shape(U)[0]])) # [n*2*h*w]
grid = tf.reshape(grid, tf.stack([tf.shape(U)[0], 2, -1])) # [n, 2, h*w]
# transform (x, y)^T -> (x+vx, x+vy)^T
# V = bicubic_interp_2d(V, out_size)
V = tf.transpose(V, [0, 3, 1, 2]) # [n, 2, h, w]
V = tf.reshape(V, [tf.shape(U)[0], 2, -1]) # [n, 2, h*w]
T_g = tf.add(V, grid) # [n, 2, h*w]
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])
input_transformed = _interpolate(
U, x_s_flat, y_s_flat, out_size)
output = tf.reshape(
input_transformed,
tf.stack([tf.shape(U)[0], out_height, out_width, 1]))
return output
# name='DeformableTransformer'
with tf.variable_scope(name):
output = _transform(V, U, out_size)
return output
def jitter_diff(jit):
# second one reduce the first one become the difference of the jitter
#input size is [batch, height, channel]
jit1 = tf.slice(jit,[0,5,0],[-1,243,-1])
jit2 = tf.slice(jit,[0,6,0],[-1,243,-1])
dif = tf.subtract(jit1, jit2)
return dif