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#Copyright 2018 Google LLC
#
# 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.
"""This module implements the Lambertian reflectance."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
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
def brdf(direction_incoming_light,
direction_outgoing_light,
surface_normal,
albedo,
name=None):
"""Evaluates the brdf of a Lambertian surface.
Note:
In the following, A1 to An are optional batch dimensions, which must be
broadcast compatible.
Note:
The gradient of this function is not smooth when the dot product of the
normal with any light is 0.0.
Args:
direction_incoming_light: A tensor of shape `[A1, ..., An, 3]`, where the
last dimension represents a normalized incoming light vector.
direction_outgoing_light: A tensor of shape `[A1, ..., An, 3]`, where the
last dimension represents a normalized outgoing light vector.
surface_normal: A tensor of shape `[A1, ..., An, 3]`, where the last
dimension represents a normalized surface normal.
albedo: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents albedo with values in [0,1].
name: A name for this op. Defaults to "lambertian_brdf".
Returns:
A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents
the amount of reflected light in any outgoing direction.
Raises:
ValueError: if the shape of `direction_incoming_light`,
`direction_outgoing_light`, `surface_normal`, `shininess` or `albedo` is not
supported.
InvalidArgumentError: if at least one element of `albedo` is outside of
[0,1].
"""
with tf.compat.v1.name_scope(name, "lambertian_brdf", [
direction_incoming_light, direction_outgoing_light, surface_normal, albedo
]):
direction_incoming_light = tf.convert_to_tensor(
value=direction_incoming_light)
direction_outgoing_light = tf.convert_to_tensor(
value=direction_outgoing_light)
surface_normal = tf.convert_to_tensor(value=surface_normal)
albedo = tf.convert_to_tensor(value=albedo)
shape.check_static(
tensor=direction_incoming_light,
tensor_name="direction_incoming_light",
has_dim_equals=(-1, 3))
shape.check_static(
tensor=direction_outgoing_light,
tensor_name="direction_outgoing_light",
has_dim_equals=(-1, 3))
shape.check_static(
tensor=surface_normal,
tensor_name="surface_normal",
has_dim_equals=(-1, 3))
shape.check_static(
tensor=albedo, tensor_name="albedo", has_dim_equals=(-1, 3))
shape.compare_batch_dimensions(
tensors=(direction_incoming_light, direction_outgoing_light,
surface_normal, albedo),
tensor_names=("direction_incoming_light", "direction_outgoing_light",
"surface_normal", "albedo"),
last_axes=-2,
broadcast_compatible=True)
direction_incoming_light = asserts.assert_normalized(
direction_incoming_light)
direction_outgoing_light = asserts.assert_normalized(
direction_outgoing_light)
surface_normal = asserts.assert_normalized(surface_normal)
albedo = asserts.assert_all_in_range(albedo, 0.0, 1.0, open_bounds=False)
# Checks whether the incoming or outgoing light point behind the surface.
dot_incoming_light_surface_normal = vector.dot(-direction_incoming_light,
surface_normal)
dot_outgoing_light_surface_normal = vector.dot(direction_outgoing_light,
surface_normal)
min_dot = tf.minimum(dot_incoming_light_surface_normal,
dot_outgoing_light_surface_normal)
common_shape = shape.get_broadcasted_shape(min_dot.shape, albedo.shape)
d_val = lambda dim: 1 if dim is None else tf.compat.v1.dimension_value(dim)
common_shape = [d_val(dim) for dim in common_shape]
condition = tf.broadcast_to(tf.greater_equal(min_dot, 0.0), common_shape)
albedo = tf.broadcast_to(albedo, common_shape)
return tf.compat.v1.where(condition, albedo / math.pi,
tf.zeros_like(albedo))
# API contains all public functions and classes.
__all__ = export_api.get_functions_and_classes()
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