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# Licensed under the Apache License, Version 2.0 (the "License");
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"""Bounding Box List operations."""

import tensorflow as tf

from official.vision.beta.ops import sampling_ops
from official.vision.beta.projects.centernet.ops import box_list


def _copy_extra_fields(boxlist_to_copy_to, boxlist_to_copy_from):
  """Copies the extra fields of boxlist_to_copy_from to boxlist_to_copy_to.

  Args:
    boxlist_to_copy_to: BoxList to which extra fields are copied.
    boxlist_to_copy_from: BoxList from which fields are copied.

  Returns:
    boxlist_to_copy_to with extra fields.
  """
  for field in boxlist_to_copy_from.get_extra_fields():
    boxlist_to_copy_to.add_field(field, boxlist_to_copy_from.get_field(field))
  return boxlist_to_copy_to


def scale(boxlist, y_scale, x_scale):
  """scale box coordinates in x and y dimensions.

  Args:
    boxlist: BoxList holding N boxes
    y_scale: (float) scalar tensor
    x_scale: (float) scalar tensor

  Returns:
    boxlist: BoxList holding N boxes
  """
  with tf.name_scope('Scale'):
    y_scale = tf.cast(y_scale, tf.float32)
    x_scale = tf.cast(x_scale, tf.float32)
    y_min, x_min, y_max, x_max = tf.split(
        value=boxlist.get(), num_or_size_splits=4, axis=1)
    y_min = y_scale * y_min
    y_max = y_scale * y_max
    x_min = x_scale * x_min
    x_max = x_scale * x_max
    scaled_boxlist = box_list.BoxList(
        tf.concat([y_min, x_min, y_max, x_max], 1))
    return _copy_extra_fields(scaled_boxlist, boxlist)


def area(boxlist):
  """Computes area of boxes.

  Args:
    boxlist: BoxList holding N boxes

  Returns:
    a tensor with shape [N] representing box areas.
  """
  with tf.name_scope('Area'):
    y_min, x_min, y_max, x_max = tf.split(
        value=boxlist.get(), num_or_size_splits=4, axis=1)
    return tf.squeeze((y_max - y_min) * (x_max - x_min), [1])


def change_coordinate_frame(boxlist, window):
  """Change coordinate frame of the boxlist to be relative to window's frame.

  Given a window of the form [ymin, xmin, ymax, xmax],
  changes bounding box coordinates from boxlist to be relative to this window
  (e.g., the min corner maps to (0,0) and the max corner maps to (1,1)).

  An example use case is data augmentation: where we are given groundtruth
  boxes (boxlist) and would like to randomly crop the image to some
  window (window). In this case we need to change the coordinate frame of
  each groundtruth box to be relative to this new window.

  Args:
    boxlist: A BoxList object holding N boxes.
    window: A rank 1 tensor [4].

  Returns:
    Returns a BoxList object with N boxes.
  """
  with tf.name_scope('ChangeCoordinateFrame'):
    win_height = window[2] - window[0]
    win_width = window[3] - window[1]
    boxlist_new = scale(box_list.BoxList(
        boxlist.get() - [window[0], window[1], window[0], window[1]]),
                        1.0 / win_height, 1.0 / win_width)
    boxlist_new = _copy_extra_fields(boxlist_new, boxlist)
    return boxlist_new


def matmul_gather_on_zeroth_axis(params, indices):
  """Matrix multiplication based implementation of tf.gather on zeroth axis.

  Args:
    params: A float32 Tensor. The tensor from which to gather values.
      Must be at least rank 1.
    indices: A Tensor. Must be one of the following types: int32, int64.
      Must be in range [0, params.shape[0])

  Returns:
    A Tensor. Has the same type as params. Values from params gathered
    from indices given by indices, with shape indices.shape + params.shape[1:].
  """
  with tf.name_scope('MatMulGather'):
    params_shape = sampling_ops.combined_static_and_dynamic_shape(params)
    indices_shape = sampling_ops.combined_static_and_dynamic_shape(indices)
    params2d = tf.reshape(params, [params_shape[0], -1])
    indicator_matrix = tf.one_hot(indices, params_shape[0])
    gathered_result_flattened = tf.matmul(indicator_matrix, params2d)
    return tf.reshape(gathered_result_flattened,
                      tf.stack(indices_shape + params_shape[1:]))


def gather(boxlist, indices, fields=None, use_static_shapes=False):
  """Gather boxes from BoxList according to indices and return new BoxList.

  By default, `gather` returns boxes corresponding to the input index list, as
  well as all additional fields stored in the boxlist (indexing into the
  first dimension).  However one can optionally only gather from a
  subset of fields.

  Args:
    boxlist: BoxList holding N boxes
    indices: a rank-1 tensor of type int32 / int64
    fields: (optional) list of fields to also gather from.  If None (default),
      all fields are gathered from.  Pass an empty fields list to only gather
      the box coordinates.
    use_static_shapes: Whether to use an implementation with static shape
      gurantees.

  Returns:
    subboxlist: a BoxList corresponding to the subset of the input BoxList
    specified by indices

  Raises:
    ValueError: if specified field is not contained in boxlist or if the
      indices are not of type int32
  """
  with tf.name_scope('Gather'):
    if len(indices.shape.as_list()) != 1:
      raise ValueError('indices should have rank 1')
    if indices.dtype != tf.int32 and indices.dtype != tf.int64:
      raise ValueError('indices should be an int32 / int64 tensor')
    gather_op = tf.gather
    if use_static_shapes:
      gather_op = matmul_gather_on_zeroth_axis
    subboxlist = box_list.BoxList(gather_op(boxlist.get(), indices))
    if fields is None:
      fields = boxlist.get_extra_fields()
    fields += ['boxes']
    for field in fields:
      if not boxlist.has_field(field):
        raise ValueError('boxlist must contain all specified fields')
      subfieldlist = gather_op(boxlist.get_field(field), indices)
      subboxlist.add_field(field, subfieldlist)
    return subboxlist


def prune_completely_outside_window(boxlist, window):
  """Prunes bounding boxes that fall completely outside of the given window.

  The function clip_to_window prunes bounding boxes that fall
  completely outside the window, but also clips any bounding boxes that
  partially overflow. This function does not clip partially overflowing boxes.

  Args:
    boxlist: a BoxList holding M_in boxes.
    window: a float tensor of shape [4] representing [ymin, xmin, ymax, xmax]
      of the window

  Returns:
    pruned_boxlist: a new BoxList with all bounding boxes partially or fully in
      the window.
    valid_indices: a tensor with shape [M_out] indexing the valid bounding boxes
     in the input tensor.
  """
  with tf.name_scope('PruneCompleteleyOutsideWindow'):
    y_min, x_min, y_max, x_max = tf.split(
        value=boxlist.get(), num_or_size_splits=4, axis=1)
    win_y_min, win_x_min, win_y_max, win_x_max = tf.unstack(window)
    coordinate_violations = tf.concat([
        tf.greater_equal(y_min, win_y_max), tf.greater_equal(x_min, win_x_max),
        tf.less_equal(y_max, win_y_min), tf.less_equal(x_max, win_x_min)
    ], 1)
    valid_indices = tf.reshape(
        tf.where(tf.logical_not(tf.reduce_any(coordinate_violations, 1))), [-1])
    return gather(boxlist, valid_indices), valid_indices


def clip_to_window(boxlist, window, filter_nonoverlapping=True):
  """Clip bounding boxes to a window.

  This op clips any input bounding boxes (represented by bounding box
  corners) to a window, optionally filtering out boxes that do not
  overlap at all with the window.

  Args:
    boxlist: BoxList holding M_in boxes
    window: a tensor of shape [4] representing the [y_min, x_min, y_max, x_max]
      window to which the op should clip boxes.
    filter_nonoverlapping: whether to filter out boxes that do not overlap at
      all with the window.

  Returns:
    a BoxList holding M_out boxes where M_out <= M_in
  """

  with tf.name_scope('ClipToWindow'):
    y_min, x_min, y_max, x_max = tf.split(
        value=boxlist.get(), num_or_size_splits=4, axis=1)
    win_y_min = window[0]
    win_x_min = window[1]
    win_y_max = window[2]
    win_x_max = window[3]
    y_min_clipped = tf.maximum(tf.minimum(y_min, win_y_max), win_y_min)
    y_max_clipped = tf.maximum(tf.minimum(y_max, win_y_max), win_y_min)
    x_min_clipped = tf.maximum(tf.minimum(x_min, win_x_max), win_x_min)
    x_max_clipped = tf.maximum(tf.minimum(x_max, win_x_max), win_x_min)
    clipped = box_list.BoxList(
        tf.concat([y_min_clipped, x_min_clipped, y_max_clipped, x_max_clipped],
                  1))
    clipped = _copy_extra_fields(clipped, boxlist)
    if filter_nonoverlapping:
      areas = area(clipped)
      nonzero_area_indices = tf.cast(
          tf.reshape(tf.where(tf.greater(areas, 0.0)), [-1]), tf.int32)
      clipped = gather(clipped, nonzero_area_indices)
    return clipped


def height_width(boxlist):
  """Computes height and width of boxes in boxlist.

  Args:
    boxlist: BoxList holding N boxes

  Returns:
    Height: A tensor with shape [N] representing box heights.
    Width: A tensor with shape [N] representing box widths.
  """
  with tf.name_scope('HeightWidth'):
    y_min, x_min, y_max, x_max = tf.split(
        value=boxlist.get(), num_or_size_splits=4, axis=1)
    return tf.squeeze(y_max - y_min, [1]), tf.squeeze(x_max - x_min, [1])


def prune_small_boxes(boxlist, min_side):
  """Prunes small boxes in the boxlist which have a side smaller than min_side.

  Args:
    boxlist: BoxList holding N boxes.
    min_side: Minimum width AND height of box to survive pruning.

  Returns:
    A pruned boxlist.
  """
  with tf.name_scope('PruneSmallBoxes'):
    height, width = height_width(boxlist)
    is_valid = tf.logical_and(tf.greater_equal(width, min_side),
                              tf.greater_equal(height, min_side))
    return gather(boxlist, tf.reshape(tf.where(is_valid), [-1]))


def assert_or_prune_invalid_boxes(boxes):
  """Makes sure boxes have valid sizes (ymax >= ymin, xmax >= xmin).

  When the hardware supports assertions, the function raises an error when
  boxes have an invalid size. If assertions are not supported (e.g. on TPU),
  boxes with invalid sizes are filtered out.

  Args:
    boxes: float tensor of shape [num_boxes, 4]

  Returns:
    boxes: float tensor of shape [num_valid_boxes, 4] with invalid boxes
      filtered out.

  Raises:
    tf.errors.InvalidArgumentError: When we detect boxes with invalid size.
      This is not supported on TPUs.
  """

  ymin, xmin, ymax, xmax = tf.split(
      boxes, num_or_size_splits=4, axis=1)

  height_check = tf.Assert(tf.reduce_all(ymax >= ymin), [ymin, ymax])
  width_check = tf.Assert(tf.reduce_all(xmax >= xmin), [xmin, xmax])

  with tf.control_dependencies([height_check, width_check]):
    boxes_tensor = tf.concat([ymin, xmin, ymax, xmax], axis=1)
    boxlist = box_list.BoxList(boxes_tensor)
    boxlist = prune_small_boxes(boxlist, 0)

  return boxlist.get()


def to_absolute_coordinates(boxlist,
                            height,
                            width,
                            check_range=True,
                            maximum_normalized_coordinate=1.1):
  """Converts normalized box coordinates to absolute pixel coordinates.

  This function raises an assertion failed error when the maximum box coordinate
  value is larger than maximum_normalized_coordinate (in which case coordinates
  are already absolute).

  Args:
    boxlist: BoxList with coordinates in range [0, 1].
    height: Maximum value for height of absolute box coordinates.
    width: Maximum value for width of absolute box coordinates.
    check_range: If True, checks if the coordinates are normalized or not.
    maximum_normalized_coordinate: Maximum coordinate value to be considered
      as normalized, default to 1.1.

  Returns:
    boxlist with absolute coordinates in terms of the image size.

  """
  with tf.name_scope('ToAbsoluteCoordinates'):
    height = tf.cast(height, tf.float32)
    width = tf.cast(width, tf.float32)

    # Ensure range of input boxes is correct.
    if check_range:
      box_maximum = tf.reduce_max(boxlist.get())
      max_assert = tf.Assert(
          tf.greater_equal(maximum_normalized_coordinate, box_maximum),
          ['maximum box coordinate value is larger '
           'than %f: ' % maximum_normalized_coordinate, box_maximum])
      with tf.control_dependencies([max_assert]):
        width = tf.identity(width)

    return scale(boxlist, height, width)
