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# Copyright 2017 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.
# ==============================================================================
"""3D->2D projector model as used in PTN (NIPS16)."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from nets import perspective_transform
def model(voxels, transform_matrix, params, is_training):
"""Model transforming the 3D voxels into 2D projections.
Args:
voxels: A tensor of size [batch, depth, height, width, channel]
representing the input of projection layer (tf.float32).
transform_matrix: A tensor of size [batch, 16] representing
the flattened 4-by-4 matrix for transformation (tf.float32).
params: Model parameters (dict).
is_training: Set to True if while training (boolean).
Returns:
A transformed tensor (tf.float32)
"""
del is_training # Doesn't make a difference for projector
# Rearrangement (batch, z, y, x, channel) --> (batch, y, z, x, channel).
# By the standard, projection happens along z-axis but the voxels
# are stored in a different way. So we need to switch the y and z
# axis for transformation operation.
voxels = tf.transpose(voxels, [0, 2, 1, 3, 4])
z_near = params.focal_length
z_far = params.focal_length + params.focal_range
transformed_voxels = perspective_transform.transformer(
voxels, transform_matrix, [params.vox_size] * 3, z_near, z_far)
views = tf.reduce_max(transformed_voxels, [1])
views = tf.reverse(views, [1])
return views
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