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optical_flow_warp_old.py
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optical_flow_warp_old.py
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import tensorflow as tf
from tensorflow.python.platform import app
import numpy as np
def transformer_old(U, flo, out_size, name='SpatialTransformer', **kwargs):
"""Backward warping layer
Implements a backward warping layer described in
"Unsupervised Deep Learning for Optical Flow Estimation, Zhe Ren et al"
Parameters
----------
U : float
The output of a convolutional net should have the
shape [num_batch, height, width, num_channels].
flo: float
The optical flow used to do the backward warping.
shape is [num_batch, height, width, 2]
out_size: tuple of two ints
The size of the output of the network (height, width)
"""
def _repeat(x, n_repeats):
with tf.variable_scope('_repeat'):
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'):
# constants
num_batch = tf.shape(im)[0]
height = tf.shape(im)[1]
width = tf.shape(im)[2]
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 + 1.0) * (width_f - 1) / 2.0
y = (y + 1.0) * (height_f - 1) / 2.0
# do sampling
x0 = tf.cast(tf.floor(x), 'int32')
x1 = x0 + 1
y0 = tf.cast(tf.floor(y), 'int32')
y1 = y0 + 1
x0_c = tf.clip_by_value(x0, zero, max_x)
x1_c = tf.clip_by_value(x1, zero, max_x)
y0_c = tf.clip_by_value(y0, zero, max_y)
y1_c = tf.clip_by_value(y1, zero, max_y)
dim2 = width
dim1 = width * height
base = _repeat(tf.range(num_batch) * dim1, out_height * out_width)
base_y0 = base + y0_c * dim2
base_y1 = base + y1_c * dim2
idx_a = base_y0 + x0_c
idx_b = base_y1 + x0_c
idx_c = base_y0 + x1_c
idx_d = base_y1 + x1_c
# 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(((x1_f - x) * (y1_f - y)), 1)
wb = tf.expand_dims(((x1_f - x) * (y - y0_f)), 1)
wc = tf.expand_dims(((x - x0_f) * (y1_f - y)), 1)
wd = tf.expand_dims(((x - x0_f) * (y - y0_f)), 1)
output = tf.add_n([wa * Ia, wb * Ib, wc * Ic, wd * Id])
return output
def _meshgrid(height, width):
with tf.variable_scope('_meshgrid'):
# 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])))
return x_t, y_t
def _transform(flo, input_dim, out_size):
with tf.variable_scope('_transform'):
num_batch = tf.shape(input_dim)[0]
height = tf.shape(input_dim)[1]
width = tf.shape(input_dim)[2]
num_channels = tf.shape(input_dim)[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]
x_t, y_t = _meshgrid(out_height, out_width)
x_t = tf.expand_dims(x_t, 0)
x_t = tf.tile(x_t, [num_batch, 1, 1])
y_t = tf.expand_dims(y_t, 0)
y_t = tf.tile(y_t, [num_batch, 1, 1])
x_s = x_t + flo[:, :, :, 0] / (
(tf.cast(out_width, tf.float32) - 1.0) / 2.0)
y_s = y_t + flo[:, :, :, 1] / (
(tf.cast(out_height, tf.float32) - 1.0) / 2.0)
x_s_flat = tf.reshape(x_s, [-1])
y_s_flat = tf.reshape(y_s, [-1])
input_transformed = _interpolate(input_dim, x_s_flat, y_s_flat,
out_size)
output = tf.reshape(
input_transformed,
tf.stack([num_batch, out_height, out_width, num_channels]))
return output
with tf.variable_scope(name):
output = _transform(flo, U, out_size)
return output
def main(unused_argv):
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True, log_device_placement=False))
image = tf.constant(
[1, 2, 3, 4, 5, 6, 7, 8, 9], shape=[1, 3, 3, 1], dtype="float32")
flo = np.zeros((1, 3, 3, 2))
flo[0, 1, 1, 0] = 1.0
#flo[0, 1, 1, 1] = 1.0
flo = tf.constant(flo, dtype="float32")
image2 = transformer_old(image, flo, [3, 3])
print(image2.eval(session=sess))
if __name__ == '__main__':
app.run()