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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 | |