def pad_to_bounding_box(image, offset_height, offset_width, target_height,
                        target_width, dynamic_shape=False):
  """Pad `image` with zeros to the specified `height` and `width`.

  Adds `offset_height` rows of zeros on top, `offset_width` columns of
  zeros on the left, and then pads the image on the bottom and right
  with zeros until it has dimensions `target_height`, `target_width`.

  This op does nothing if `offset_*` is zero and the image already has size
  `target_height` by `target_width`.

  Args:
    image: 3-D tensor with shape `[height, width, channels]`
    offset_height: Number of rows of zeros to add on top.
    offset_width: Number of columns of zeros to add on the left.
    target_height: Height of output image.
    target_width: Width of output image.
    dynamic_shape: Whether the input image has undertermined shape. If set to
      `True`, shape information will be retrieved at run time. Default to
      `False`.

  Returns:
    3-D tensor of shape `[target_height, target_width, channels]`
  Raises:
    ValueError: If the shape of `image` is incompatible with the `offset_*` or
      `target_*` arguments, and `dynamic_shape` is set to `False`.
  """
  image = ops.convert_to_tensor(image, name='image')
  _Check3DImage(image, require_static=(not dynamic_shape))
  height, width, depth = _ImageDimensions(image, dynamic_shape=dynamic_shape)

  after_padding_width = target_width - offset_width - width
  after_padding_height = target_height - offset_height - height

  if not dynamic_shape:
    if target_width < width:
      raise ValueError('target_width must be >= width')
    if target_height < height:
      raise ValueError('target_height must be >= height')

    if after_padding_width < 0:
      raise ValueError('target_width not possible given '
                       'offset_width and image width')
    if after_padding_height < 0:
      raise ValueError('target_height not possible given '
                       'offset_height and image height')

  # Do not pad on the depth dimensions.
  if (dynamic_shape or offset_width or offset_height or
      after_padding_width or after_padding_height):
    paddings = array_ops.reshape(
      array_ops.pack([offset_height, after_padding_height,
                      offset_width, after_padding_width,
                      0, 0]),
      [3, 2])
    padded = array_ops.pad(image, paddings)
    if not dynamic_shape:
      padded.set_shape([target_height, target_width, depth])
  else:
    padded = image

  return padded
Pad `image` with zeros to the specified `height` and `width`.

  Adds `offset_height` rows of zeros on top, `offset_width` columns of
  zeros on the left, and then pads the image on the bottom and right
  with zeros until it has dimensions `target_height`, `target_width`.

  This op does nothing if `offset_*` is zero and the image already has size
  `target_height` by `target_width`.

  Args:
    image: 3-D tensor with shape `[height, width, channels]`
    offset_height: Number of rows of zeros to add on top.
    offset_width: Number of columns of zeros to add on the left.
    target_height: Height of output image.
    target_width: Width of output image.
    dynamic_shape: Whether the input image has undertermined shape. If set to
      `True`, shape information will be retrieved at run time. Default to
      `False`.

  Returns:
    3-D tensor of shape `[target_height, target_width, channels]`
  Raises:
    ValueError: If the shape of `image` is incompatible with the `offset_*` or
      `target_*` arguments, and `dynamic_shape` is set to `False`.