/
triangle.py
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/
triangle.py
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# Copyright 2020 The TensorFlow Authors
#
# 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
#
# https://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.
"""Tensorflow triangle utility functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow_graphics.math import vector
from tensorflow_graphics.util import asserts
from tensorflow_graphics.util import export_api
from tensorflow_graphics.util import shape
from tensorflow_graphics.util import type_alias
def normal(v0: type_alias.TensorLike,
v1: type_alias.TensorLike,
v2: type_alias.TensorLike,
clockwise: bool = False,
normalize: bool = True,
name: str = "triangle_normal") -> tf.Tensor:
"""Computes face normals (triangles).
Note:
In the following, A1 to An are optional batch dimensions, which must be
broadcast compatible.
Args:
v0: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the first vertex of a triangle.
v1: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the second vertex of a triangle.
v2: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the third vertex of a triangle.
clockwise: Winding order to determine front-facing triangles.
normalize: A `bool` indicating whether output normals should be normalized
by the function.
name: A name for this op. Defaults to "triangle_normal".
Returns:
A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents
a normalized vector.
Raises:
ValueError: If the shape of `v0`, `v1`, or `v2` is not supported.
"""
with tf.name_scope(name):
v0 = tf.convert_to_tensor(value=v0)
v1 = tf.convert_to_tensor(value=v1)
v2 = tf.convert_to_tensor(value=v2)
shape.check_static(tensor=v0, tensor_name="v0", has_dim_equals=(-1, 3))
shape.check_static(tensor=v1, tensor_name="v1", has_dim_equals=(-1, 3))
shape.check_static(tensor=v2, tensor_name="v2", has_dim_equals=(-1, 3))
shape.compare_batch_dimensions(
tensors=(v0, v1, v2), last_axes=-2, broadcast_compatible=True)
normal_vector = vector.cross(v1 - v0, v2 - v0, axis=-1)
normal_vector = asserts.assert_nonzero_norm(normal_vector)
if not clockwise:
normal_vector *= -1.0
if normalize:
return tf.nn.l2_normalize(normal_vector, axis=-1)
return normal_vector
def area(v0: type_alias.TensorLike,
v1: type_alias.TensorLike,
v2: type_alias.TensorLike,
name: str = "triangle_area") -> tf.Tensor:
"""Computes triangle areas.
Note: Computed triangle area = 0.5 * | e1 x e2 | where e1 and e2 are edges
of triangle. A degenerate triangle will return 0 area, whereas the normal
for a degenerate triangle is not defined.
In the following, A1 to An are optional batch dimensions, which must be
broadcast compatible.
Args:
v0: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the first vertex of a triangle.
v1: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the second vertex of a triangle.
v2: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the third vertex of a triangle.
name: A name for this op. Defaults to "triangle_area".
Returns:
A tensor of shape `[A1, ..., An, 1]`, where the last dimension represents
a normalized vector.
"""
with tf.name_scope(name):
v0 = tf.convert_to_tensor(value=v0)
v1 = tf.convert_to_tensor(value=v1)
v2 = tf.convert_to_tensor(value=v2)
shape.check_static(tensor=v0, tensor_name="v0", has_dim_equals=(-1, 3))
shape.check_static(tensor=v1, tensor_name="v1", has_dim_equals=(-1, 3))
shape.check_static(tensor=v2, tensor_name="v2", has_dim_equals=(-1, 3))
shape.compare_batch_dimensions(
tensors=(v0, v1, v2), last_axes=-2, broadcast_compatible=True)
normals = vector.cross(v1 - v0, v2 - v0, axis=-1)
return 0.5 * tf.linalg.norm(tensor=normals, axis=-1, keepdims=True)
# API contains all public functions and classes.
__all__ = export_api.get_functions_and_classes()