# 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.
"""This module implements the chamfer distance."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

from tensorflow_graphics.util import export_api
from tensorflow_graphics.util import shape
from tensorflow_graphics.util import type_alias


def evaluate(point_set_a: type_alias.TensorLike,
             point_set_b: type_alias.TensorLike,
             name: str = "chamfer_distance_evaluate") -> tf.Tensor:
  """Computes the Chamfer distance for the given two point sets.

  Note:
    This is a symmetric version of the Chamfer distance, calculated as the sum
    of the average minimum distance from point_set_a to point_set_b and vice
    versa.
    The average minimum distance from one point set to another is calculated as
    the average of the distances between the points in the first set and their
    closest point in the second set, and is thus not symmetrical.

  Note:
    This function returns the exact Chamfer distance and not an approximation.

  Note:
    In the following, A1 to An are optional batch dimensions, which must be
    broadcast compatible.

  Args:
    point_set_a: A tensor of shape `[A1, ..., An, N, D]`, where the last axis
      represents points in a D dimensional space.
    point_set_b: A tensor of shape `[A1, ..., An, M, D]`, where the last axis
      represents points in a D dimensional space.
    name: A name for this op. Defaults to "chamfer_distance_evaluate".

  Returns:
    A tensor of shape `[A1, ..., An]` storing the chamfer distance between the
    two point sets.

  Raises:
    ValueError: if the shape of `point_set_a`, `point_set_b` is not supported.
  """
  with tf.name_scope(name):
    point_set_a = tf.convert_to_tensor(value=point_set_a)
    point_set_b = tf.convert_to_tensor(value=point_set_b)

    shape.compare_batch_dimensions(
        tensors=(point_set_a, point_set_b),
        tensor_names=("point_set_a", "point_set_b"),
        last_axes=-3,
        broadcast_compatible=True)
    # Verify that the last axis of the tensors has the same dimension.
    dimension = point_set_a.shape.as_list()[-1]
    shape.check_static(
        tensor=point_set_b,
        tensor_name="point_set_b",
        has_dim_equals=(-1, dimension))

    # Create N x M matrix where the entry i,j corresponds to ai - bj (vector of
    # dimension D).
    difference = (
        tf.expand_dims(point_set_a, axis=-2) -
        tf.expand_dims(point_set_b, axis=-3))
    # Calculate the square distances between each two points: |ai - bj|^2.
    square_distances = tf.einsum("...i,...i->...", difference, difference)

    minimum_square_distance_a_to_b = tf.reduce_min(
        input_tensor=square_distances, axis=-1)
    minimum_square_distance_b_to_a = tf.reduce_min(
        input_tensor=square_distances, axis=-2)

    return (
        tf.reduce_mean(input_tensor=minimum_square_distance_a_to_b, axis=-1) +
        tf.reduce_mean(input_tensor=minimum_square_distance_b_to_a, axis=-1))


# API contains all public functions and classes.
__all__ = export_api.get_functions_and_classes()
