Function pad_to_bounding_box

tensorflow/python/ops/image_ops_impl.py:730–809

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

(image, offset_height, offset_width, target_height,
                        target_width)

@tf_export('image.pad_to_bounding_box')
def pad_to_bounding_box(image, offset_height, offset_width, target_height,
                        target_width):
  """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: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
      of 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.

  Returns:
    If `image` was 4-D, a 4-D float Tensor of shape
    `[batch, target_height, target_width, channels]`
    If `image` was 3-D, a 3-D float Tensor of shape
    `[target_height, target_width, channels]`

  Raises:
    ValueError: If the shape of `image` is incompatible with the `offset_*` or
      `target_*` arguments, or either `offset_height` or `offset_width` is
      negative.
  """
  with ops.name_scope(None, 'pad_to_bounding_box', [image]):
    image = ops.convert_to_tensor(image, name='image')

    is_batch = True
    image_shape = image.get_shape()
    if image_shape.ndims == 3:
      is_batch = False
      image = array_ops.expand_dims(image, 0)
    elif image_shape.ndims is None:
      is_batch = False
      image = array_ops.expand_dims(image, 0)
      image.set_shape([None] * 4)
    elif image_shape.ndims != 4:
      raise ValueError('\'image\' must have either 3 or 4 dimensions.')

    assert_ops = _CheckAtLeast3DImage(image, require_static=False)
    batch, height, width, depth = _ImageDimensions(image, rank=4)

    after_padding_width = target_width - offset_width - width

    after_padding_height = target_height - offset_height - height

    assert_ops += _assert(offset_height >= 0, ValueError,
                          'offset_height must be >= 0')
    assert_ops += _assert(offset_width >= 0, ValueError,
                          'offset_width must be >= 0')
    assert_ops += _assert(after_padding_width >= 0, ValueError,
                          'width must be <= target - offset')