Function _CheckAtLeast3DImage

tensorflow/python/ops/image_ops_impl.py:200–239

Assert that we are working with properly shaped image. Args: image: >= 3-D Tensor of size [*, height, width, depth] require_static: If `True`, requires that all dimensions of `image` are known and non-zero. Raises: ValueError: if image.shape is not a [>= 3] vector. Returns

(image, require_static=True)

def _CheckAtLeast3DImage(image, require_static=True):
  """Assert that we are working with properly shaped image.

  Args:
    image: >= 3-D Tensor of size [*, height, width, depth]
    require_static: If `True`, requires that all dimensions of `image` are known
      and non-zero.

  Raises:
    ValueError: if image.shape is not a [>= 3] vector.

  Returns:
    An empty list, if `image` has fully defined dimensions. Otherwise, a list
    containing an assert op is returned.
  """
  try:
    if image.get_shape().ndims is None:
      image_shape = image.get_shape().with_rank(3)
    else:
      image_shape = image.get_shape().with_rank_at_least(3)
  except ValueError:
    raise ValueError("'image' must be at least three-dimensional.")
  if require_static and not image_shape.is_fully_defined():
    raise ValueError('\'image\' must be fully defined.')
  if any(x == 0 for x in image_shape):
    raise ValueError('all dims of \'image.shape\' must be > 0: %s' %
                     image_shape)
  if not image_shape.is_fully_defined():
    return [
        check_ops.assert_positive(
            array_ops.shape(image),
            ["all dims of 'image.shape' "
             'must be > 0.']),
        check_ops.assert_greater_equal(
            array_ops.rank(image),
            3,
            message="'image' must be at least three-dimensional.")
    ]
  else:
    return []


def _AssertGrayscaleImage(image):