text: # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  #
  # 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
  #
  #     http://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.
  # ==============================================================================
  """Implementation of image ops."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  from tensorflow.python.compat import compat
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import gen_image_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import string_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
  
  ops.NotDifferentiable('RandomCrop')
  # TODO(b/31222613): This op may be differentiable, and there may be
  # latent bugs here.
  ops.NotDifferentiable('RGBToHSV')
  # TODO(b/31222613): This op may be differentiable, and there may be
  # latent bugs here.
  ops.NotDifferentiable('HSVToRGB')
  ops.NotDifferentiable('DrawBoundingBoxes')
  ops.NotDifferentiable('SampleDistortedBoundingBox')
  ops.NotDifferentiable('SampleDistortedBoundingBoxV2')
  # TODO(bsteiner): Implement the gradient function for extract_glimpse
  # TODO(b/31222613): This op may be differentiable, and there may be
  # latent bugs here.
  ops.NotDifferentiable('ExtractGlimpse')
  ops.NotDifferentiable('NonMaxSuppression')
  ops.NotDifferentiable('NonMaxSuppressionV2')
  ops.NotDifferentiable('NonMaxSuppressionWithOverlaps')
  
  
  # pylint: disable=invalid-name
  def _assert(cond, ex_type, msg):
    """A polymorphic assert, works with tensors and boolean expressions.
  
    If `cond` is not a tensor, behave like an ordinary assert statement, except
    that a empty list is returned. If `cond` is a tensor, return a list
    containing a single TensorFlow assert op.
  
    Args:
      cond: Something evaluates to a boolean value. May be a tensor.
      ex_type: The exception class to use.
      msg: The error message.
  
    Returns:
      A list, containing at most one assert op.
    """
    if _is_tensor(cond):
      return [control_flow_ops.Assert(cond, [msg])]
    else:
      if not cond:
        raise ex_type(msg)
      else:
        return []
  
  
  def _is_tensor(x):
    """Returns `True` if `x` is a symbolic tensor-like object.
  
    Args:
      x: A python object to check.
  
    Returns:
      `True` if `x` is a `tf.Tensor` or `tf.Variable`, otherwise `False`.
    """
    return isinstance(x, (ops.Tensor, variables.Variable))
  
  
  def _ImageDimensions(image, rank):
    """Returns the dimensions of an image tensor.
  
    Args:
      image: A rank-D Tensor. For 3-D  of shape: `[height, width, channels]`.
      rank: The expected rank of the image
  
    Returns:
      A list of corresponding to the dimensions of the
      input image.  Dimensions that are statically known are python integers,
      otherwise they are integer scalar tensors.
    """
    if image.get_shape().is_fully_defined():
      return image.get_shape().as_list()
    else:
      static_shape = image.get_shape().with_rank(rank).as_list()
      dynamic_shape = array_ops.unstack(array_ops.shape(image), rank)
      return [
          s if s is not None else d for s, d in zip(static_shape, dynamic_shape)
      ]
  
  
  def _Check3DImage(image, require_static=True):
    """Assert that we are working with properly shaped image.
  
    Args:
      image: 3-D Tensor of shape [height, width, channels]
      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:
      image_shape = image.get_shape().with_rank(3)
    except ValueError:
      raise ValueError("'image' (shape %s) must be three-dimensional." %
                       image.shape)
    if require_static and not image_shape.is_fully_defined():
      raise ValueError("'image' (shape %s) must be fully defined." % image_shape)
    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.'])
      ]
    else:
      return []
  
  
  def _Assert3DImage(image):
    """Assert that we are working with a properly shaped image.
  
    Performs the check statically if possible (i.e. if the shape
    is statically known). Otherwise adds a control dependency
    to an assert op that checks the dynamic shape.
  
    Args:
      image: 3-D Tensor of shape [height, width, channels]
  
    Raises:
      ValueError: if `image.shape` is not a 3-vector.
  
    Returns:
      If the shape of `image` could be verified statically, `image` is
      returned unchanged, otherwise there will be a control dependency
      added that asserts the correct dynamic shape.
    """
    return control_flow_ops.with_dependencies(
        _Check3DImage(image, require_static=False), image)
  
  
  def _AssertAtLeast3DImage(image):
    """Assert that we are working with a properly shaped image.
  
    Performs the check statically if possible (i.e. if the shape
    is statically known). Otherwise adds a control dependency
    to an assert op that checks the dynamic shape.
  
    Args:
      image: >= 3-D Tensor of size [*, height, width, depth]
  
    Raises:
      ValueError: if image.shape is not a [>= 3] vector.
  
    Returns:
      If the shape of `image` could be verified statically, `image` is
      returned unchanged, otherwise there will be a control dependency
      added that asserts the correct dynamic shape.
    """
    return control_flow_ops.with_dependencies(
        _CheckAtLeast3DImage(image, require_static=False), image)
  
  
  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):
    """Assert that we are working with a properly shaped grayscale image.
  
    Performs the check statically if possible (i.e. if the shape
    is statically known). Otherwise adds a control dependency
    to an assert op that checks the dynamic shape.
  
    Args:
      image: >= 2-D Tensor of size [*, 1]
  
    Raises:
      ValueError: if image.shape is not a [>= 2] vector or if
                last dimension is not size 1.
  
    Returns:
      If the shape of `image` could be verified statically, `image` is
      returned unchanged, otherwise there will be a control dependency
      added that asserts the correct dynamic shape.
    """
    return control_flow_ops.with_dependencies(
        _CheckGrayscaleImage(image, require_static=False), image)
  
  
  def _CheckGrayscaleImage(image, require_static=True):
    """Assert that we are working with properly shaped grayscale image.
  
    Args:
      image: >= 2-D Tensor of size [*, 1]
      require_static: Boolean, whether static shape is required.
  
    Raises:
      ValueError: if image.shape is not a [>= 2] vector or if
                last dimension is not size 1.
  
    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(2)
      else:
        image_shape = image.get_shape().with_rank_at_least(2)
    except ValueError:
      raise ValueError('A grayscale image must be at least two-dimensional.')
    if require_static and not image_shape.is_fully_defined():
      raise ValueError('\'image\' must be fully defined.')
    if image_shape.is_fully_defined():
      if image_shape[-1] != 1:
        raise ValueError('Last dimension of a grayscale image should be size 1.')
    if not image_shape.is_fully_defined():
      return [
          check_ops.assert_equal(
              array_ops.shape(image)[-1],
              1,
              message='Last dimension of a grayscale image should be size 1.'),
          check_ops.assert_greater_equal(
              array_ops.rank(image),
              3,
              message='A grayscale image must be at least two-dimensional.')
      ]
    else:
      return []
  
  
  def fix_image_flip_shape(image, result):
    """Set the shape to 3 dimensional if we don't know anything else.
  
    Args:
      image: original image size
      result: flipped or transformed image
  
    Returns:
      An image whose shape is at least None,None,None.
    """
  
    image_shape = image.get_shape()
    if image_shape == tensor_shape.unknown_shape():
      result.set_shape([None, None, None])
    else:
      result.set_shape(image_shape)
    return result
  
  
  @tf_export('image.random_flip_up_down')
  def random_flip_up_down(image, seed=None):
    """Randomly flips an image vertically (upside down).
  
    With a 1 in 2 chance, outputs the contents of `image` flipped along the first
    dimension, which is `height`.  Otherwise output the image as-is.
  
    Args:
      image: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
        of shape `[height, width, channels]`.
      seed: A Python integer. Used to create a random seed. See
        `tf.compat.v1.set_random_seed` for behavior.
  
    Returns:
      A tensor of the same type and shape as `image`.
    Raises:
      ValueError: if the shape of `image` not supported.
    """
    return _random_flip(image, 0, seed, 'random_flip_up_down')
  
  
  @tf_export('image.random_flip_left_right')
  def random_flip_left_right(image, seed=None):
    """Randomly flip an image horizontally (left to right).
  
    With a 1 in 2 chance, outputs the contents of `image` flipped along the
    second dimension, which is `width`.  Otherwise output the image as-is.
  
    Args:
      image: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
        of shape `[height, width, channels]`.
      seed: A Python integer. Used to create a random seed. See
        `tf.compat.v1.set_random_seed` for behavior.
  
    Returns:
      A tensor of the same type and shape as `image`.
  
    Raises:
      ValueError: if the shape of `image` not supported.
    """
    return _random_flip(image, 1, seed, 'random_flip_left_right')
  
  
  def _random_flip(image, flip_index, seed, scope_name):
    """Randomly (50% chance) flip an image along axis `flip_index`.
  
    Args:
      image: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
        of shape `[height, width, channels]`.
      flip_index: Dimension along which to flip image. Vertical: 0, Horizontal: 1
      seed: A Python integer. Used to create a random seed. See
        `tf.compat.v1.set_random_seed` for behavior.
      scope_name: Name of the scope in which the ops are added.
  
    Returns:
      A tensor of the same type and shape as `image`.
  
    Raises:
      ValueError: if the shape of `image` not supported.
    """
    with ops.name_scope(None, scope_name, [image]) as scope:
      image = ops.convert_to_tensor(image, name='image')
      image = _AssertAtLeast3DImage(image)
      shape = image.get_shape()
      if shape.ndims == 3 or shape.ndims is None:
        uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed)
        mirror_cond = math_ops.less(uniform_random, .5)
        result = control_flow_ops.cond(
            mirror_cond,
            lambda: array_ops.reverse(image, [flip_index]),
            lambda: image,
            name=scope)
        return fix_image_flip_shape(image, result)
      elif shape.ndims == 4:
        batch_size = array_ops.shape(image)[0]
        uniform_random = random_ops.random_uniform([batch_size],
                                                   0,
                                                   1.0,
                                                   seed=seed)
        flips = math_ops.round(
            array_ops.reshape(uniform_random, [batch_size, 1, 1, 1]))
        flips = math_ops.cast(flips, image.dtype)
        flipped_input = array_ops.reverse(image, [flip_index + 1])
        return flips * flipped_input + (1 - flips) * image
      else:
        raise ValueError('\'image\' must have either 3 or 4 dimensions.')
  
  
  @tf_export('image.flip_left_right')
  def flip_left_right(image):
    """Flip an image horizontally (left to right).
  
    Outputs the contents of `image` flipped along the width dimension.
  
    See also `reverse()`.
  
    Args:
      image: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
        of shape `[height, width, channels]`.
  
    Returns:
      A tensor of the same type and shape as `image`.
  
    Raises:
      ValueError: if the shape of `image` not supported.
    """
    return _flip(image, 1, 'flip_left_right')
  
  
  @tf_export('image.flip_up_down')
  def flip_up_down(image):
    """Flip an image vertically (upside down).
  
    Outputs the contents of `image` flipped along the height dimension.
  
    See also `reverse()`.
  
    Args:
      image: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
        of shape `[height, width, channels]`.
  
    Returns:
      A `Tensor` of the same type and shape as `image`.
  
    Raises:
      ValueError: if the shape of `image` not supported.
    """
    return _flip(image, 0, 'flip_up_down')
  
  
  def _flip(image, flip_index, scope_name):
    """Flip an image either horizontally or vertically.
  
    Outputs the contents of `image` flipped along the dimension `flip_index`.
  
    See also `reverse()`.
  
    Args:
      image: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
        of shape `[height, width, channels]`.
      flip_index: 0 For vertical, 1 for horizontal.
      scope_name: string, scope name.
  
    Returns:
      A `Tensor` of the same type and shape as `image`.
  
    Raises:
      ValueError: if the shape of `image` not supported.
    """
    with ops.name_scope(None, scope_name, [image]):
      image = ops.convert_to_tensor(image, name='image')
      image = _AssertAtLeast3DImage(image)
      shape = image.get_shape()
      if shape.ndims == 3 or shape.ndims is None:
        return fix_image_flip_shape(image, array_ops.reverse(image, [flip_index]))
      elif shape.ndims == 4:
        return array_ops.reverse(image, [flip_index + 1])
      else:
        raise ValueError('\'image\' must have either 3 or 4 dimensions.')
  
  
  @tf_export('image.rot90')
  def rot90(image, k=1, name=None):
    """Rotate image(s) counter-clockwise by 90 degrees.
  
    Args:
      image: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
        of shape `[height, width, channels]`.
      k: A scalar integer. The number of times the image is rotated by 90 degrees.
      name: A name for this operation (optional).
  
    Returns:
      A rotated tensor of the same type and shape as `image`.
  
    Raises:
      ValueError: if the shape of `image` not supported.
    """
    with ops.name_scope(name, 'rot90', [image, k]) as scope:
      image = ops.convert_to_tensor(image, name='image')
      image = _AssertAtLeast3DImage(image)
      k = ops.convert_to_tensor(k, dtype=dtypes.int32, name='k')
      k.get_shape().assert_has_rank(0)
      k = math_ops.mod(k, 4)
  
      shape = image.get_shape()
      if shape.ndims == 3 or shape.ndims is None:
        return _rot90_3D(image, k, scope)
      elif shape.ndims == 4:
        return _rot90_4D(image, k, scope)
      else:
        raise ValueError('\'image\' must have either 3 or 4 dimensions.')
  
  
  def _rot90_3D(image, k, name_scope):
    """Rotate image counter-clockwise by 90 degrees `k` times.
  
    Args:
      image: 3-D Tensor of shape `[height, width, channels]`.
      k: A scalar integer. The number of times the image is rotated by 90 degrees.
      name_scope: A valid TensorFlow name scope.
  
    Returns:
      A 3-D tensor of the same type and shape as `image`.
  
    """
  
    def _rot90():
      return array_ops.transpose(array_ops.reverse_v2(image, [1]), [1, 0, 2])
  
    def _rot180():
      return array_ops.reverse_v2(image, [0, 1])
  
    def _rot270():
      return array_ops.reverse_v2(array_ops.transpose(image, [1, 0, 2]), [1])
  
    cases = [(math_ops.equal(k, 1), _rot90), (math_ops.equal(k, 2), _rot180),
             (math_ops.equal(k, 3), _rot270)]
  
    result = control_flow_ops.case(
        cases, default=lambda: image, exclusive=True, name=name_scope)
    result.set_shape([None, None, image.get_shape()[2]])
    return result
  
  
  def _rot90_4D(images, k, name_scope):
    """Rotate batch of images counter-clockwise by 90 degrees `k` times.
  
    Args:
      images: 4-D Tensor of shape `[height, width, channels]`.
      k: A scalar integer. The number of times the images are rotated by 90
        degrees.
      name_scope: A valid TensorFlow name scope.
  
    Returns:
      A 4-D `Tensor` of the same type and shape as `images`.
    """
  
    def _rot90():
      return array_ops.transpose(array_ops.reverse_v2(images, [2]), [0, 2, 1, 3])
  
    def _rot180():
      return array_ops.reverse_v2(images, [1, 2])
  
    def _rot270():
      return array_ops.reverse_v2(array_ops.transpose(images, [0, 2, 1, 3]), [2])
  
    cases = [(math_ops.equal(k, 1), _rot90), (math_ops.equal(k, 2), _rot180),
             (math_ops.equal(k, 3), _rot270)]
  
    result = control_flow_ops.case(
        cases, default=lambda: images, exclusive=True, name=name_scope)
    shape = result.get_shape()
    result.set_shape([shape[0], None, None, shape[3]])
    return result
  
  
  @tf_export('image.transpose', v1=['image.transpose', 'image.transpose_image'])
  def transpose(image, name=None):
    """Transpose image(s) by swapping the height and width dimension.
  
    Args:
      image: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
        of shape `[height, width, channels]`.
      name: A name for this operation (optional).
  
    Returns:
      If `image` was 4-D, a 4-D float Tensor of shape
     `[batch, width, height, channels]`
      If `image` was 3-D, a 3-D float Tensor of shape
     `[width, height, channels]`
  
    Raises:
      ValueError: if the shape of `image` not supported.
    """
    with ops.name_scope(name, 'transpose', [image]):
      image = ops.convert_to_tensor(image, name='image')
      image = _AssertAtLeast3DImage(image)
      shape = image.get_shape()
      if shape.ndims == 3 or shape.ndims is None:
        return array_ops.transpose(image, [1, 0, 2], name=name)
      elif shape.ndims == 4:
        return array_ops.transpose(image, [0, 2, 1, 3], name=name)
      else:
        raise ValueError('\'image\' must have either 3 or 4 dimensions.')
  
  
  @tf_export('image.central_crop')
  def central_crop(image, central_fraction):
    """Crop the central region of the image(s).
  
    Remove the outer parts of an image but retain the central region of the image
    along each dimension. If we specify central_fraction = 0.5, this function
    returns the region marked with "X" in the below diagram.
  
         --------
        |        |
        |  XXXX  |
        |  XXXX  |
        |        |   where "X" is the central 50% of the image.
         --------
  
    This function works on either a single image (`image` is a 3-D Tensor), or a
    batch of images (`image` is a 4-D Tensor).
  
    Args:
      image: Either a 3-D float Tensor of shape [height, width, depth], or a 4-D
        Tensor of shape [batch_size, height, width, depth].
      central_fraction: float (0, 1], fraction of size to crop
  
    Raises:
      ValueError: if central_crop_fraction is not within (0, 1].
  
    Returns:
      3-D / 4-D float Tensor, as per the input.
    """
    with ops.name_scope(None, 'central_crop', [image]):
      image = ops.convert_to_tensor(image, name='image')
      if central_fraction <= 0.0 or central_fraction > 1.0:
        raise ValueError('central_fraction must be within (0, 1]')
      if central_fraction == 1.0:
        return image
  
      _AssertAtLeast3DImage(image)
      rank = image.get_shape().ndims
      if rank != 3 and rank != 4:
        raise ValueError('`image` should either be a Tensor with rank = 3 or '
                         'rank = 4. Had rank = {}.'.format(rank))
  
      # Helper method to return the `idx`-th dimension of `tensor`, along with
      # a boolean signifying if the dimension is dynamic.
      def _get_dim(tensor, idx):
        static_shape = tensor.get_shape().dims[idx].value
        if static_shape is not None:
          return static_shape, False
        return array_ops.shape(tensor)[idx], True
  
      # Get the height, width, depth (and batch size, if the image is a 4-D
      # tensor).
      if rank == 3:
        img_h, dynamic_h = _get_dim(image, 0)
        img_w, dynamic_w = _get_dim(image, 1)
        img_d = image.get_shape()[2]
      else:
        img_bs = image.get_shape()[0]
        img_h, dynamic_h = _get_dim(image, 1)
        img_w, dynamic_w = _get_dim(image, 2)
        img_d = image.get_shape()[3]
  
      # Compute the bounding boxes for the crop. The type and value of the
      # bounding boxes depend on the `image` tensor's rank and whether / not the
      # dimensions are statically defined.
      if dynamic_h:
        img_hd = math_ops.cast(img_h, dtypes.float64)
        bbox_h_start = math_ops.cast((img_hd - img_hd * central_fraction) / 2,
                                     dtypes.int32)
      else:
        img_hd = float(img_h)
        bbox_h_start = int((img_hd - img_hd * central_fraction) / 2)
  
      if dynamic_w:
        img_wd = math_ops.cast(img_w, dtypes.float64)
        bbox_w_start = math_ops.cast((img_wd - img_wd * central_fraction) / 2,
                                     dtypes.int32)
      else:
        img_wd = float(img_w)
        bbox_w_start = int((img_wd - img_wd * central_fraction) / 2)
  
      bbox_h_size = img_h - bbox_h_start * 2
      bbox_w_size = img_w - bbox_w_start * 2
  
      if rank == 3:
        bbox_begin = array_ops.stack([bbox_h_start, bbox_w_start, 0])
        bbox_size = array_ops.stack([bbox_h_size, bbox_w_size, -1])
      else:
        bbox_begin = array_ops.stack([0, bbox_h_start, bbox_w_start, 0])
        bbox_size = array_ops.stack([-1, bbox_h_size, bbox_w_size, -1])
  
      image = array_ops.slice(image, bbox_begin, bbox_size)
  
      # Reshape the `image` tensor to the desired size.
      if rank == 3:
        image.set_shape([
            None if dynamic_h else bbox_h_size,
            None if dynamic_w else bbox_w_size, img_d
        ])
      else:
        image.set_shape([
            img_bs, None if dynamic_h else bbox_h_size,
            None if dynamic_w else bbox_w_size, img_d
        ])
      return image
  
  
  @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')
      assert_ops += _assert(after_padding_height >= 0, ValueError,
                            'height must be <= target - offset')
      image = control_flow_ops.with_dependencies(assert_ops, image)
  
      # Do not pad on the depth dimensions.
      paddings = array_ops.reshape(
          array_ops.stack([
              0, 0, offset_height, after_padding_height, offset_width,
              after_padding_width, 0, 0
          ]), [4, 2])
      padded = array_ops.pad(image, paddings)
  
      padded_shape = [
          None if _is_tensor(i) else i
          for i in [batch, target_height, target_width, depth]
      ]
      padded.set_shape(padded_shape)
  
      if not is_batch:
        padded = array_ops.squeeze(padded, axis=[0])
  
      return padded
  
  
  @tf_export('image.crop_to_bounding_box')
  def crop_to_bounding_box(image, offset_height, offset_width, target_height,
                           target_width):
    """Crops an image to a specified bounding box.
  
    This op cuts a rectangular part out of `image`. The top-left corner of the
    returned image is at `offset_height, offset_width` in `image`, and its
    lower-right corner is at
    `offset_height + target_height, offset_width + target_width`.
  
    Args:
      image: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
        of shape `[height, width, channels]`.
      offset_height: Vertical coordinate of the top-left corner of the result in
        the input.
      offset_width: Horizontal coordinate of the top-left corner of the result in
        the input.
      target_height: Height of the result.
      target_width: Width of the result.
  
    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, or either `target_height` or `target_width` is not positive.
    """
    with ops.name_scope(None, 'crop_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)
  
      assert_ops += _assert(offset_width >= 0, ValueError,
                            'offset_width must be >= 0.')
      assert_ops += _assert(offset_height >= 0, ValueError,
                            'offset_height must be >= 0.')
      assert_ops += _assert(target_width > 0, ValueError,
                            'target_width must be > 0.')
      assert_ops += _assert(target_height > 0, ValueError,
                            'target_height must be > 0.')
      assert_ops += _assert(width >= (target_width + offset_width), ValueError,
                            'width must be >= target + offset.')
      assert_ops += _assert(height >= (target_height + offset_height), ValueError,
                            'height must be >= target + offset.')
      image = control_flow_ops.with_dependencies(assert_ops, image)
  
      cropped = array_ops.slice(
          image, array_ops.stack([0, offset_height, offset_width, 0]),
          array_ops.stack([-1, target_height, target_width, -1]))
  
      cropped_shape = [
          None if _is_tensor(i) else i
          for i in [batch, target_height, target_width, depth]
      ]
      cropped.set_shape(cropped_shape)
  
      if not is_batch:
        cropped = array_ops.squeeze(cropped, axis=[0])
  
      return cropped
  
  
  @tf_export('image.resize_with_crop_or_pad',
             v1=['image.resize_with_crop_or_pad',
                 'image.resize_image_with_crop_or_pad'])
  def resize_image_with_crop_or_pad(image, target_height, target_width):
    """Crops and/or pads an image to a target width and height.
  
    Resizes an image to a target width and height by either centrally
    cropping the image or padding it evenly with zeros.
  
    If `width` or `height` is greater than the specified `target_width` or
    `target_height` respectively, this op centrally crops along that dimension.
    If `width` or `height` is smaller than the specified `target_width` or
    `target_height` respectively, this op centrally pads with 0 along that
    dimension.
  
    Args:
      image: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
        of shape `[height, width, channels]`.
      target_height: Target height.
      target_width: Target width.
  
    Raises:
      ValueError: if `target_height` or `target_width` are zero or negative.
  
    Returns:
      Cropped and/or padded image.
      If `images` was 4-D, a 4-D float Tensor of shape
      `[batch, new_height, new_width, channels]`.
      If `images` was 3-D, a 3-D float Tensor of shape
      `[new_height, new_width, channels]`.
    """
    with ops.name_scope(None, 'resize_image_with_crop_or_pad', [image]):
      image = ops.convert_to_tensor(image, name='image')
      image_shape = image.get_shape()
      is_batch = True
      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)
      assert_ops += _assert(target_width > 0, ValueError,
                            'target_width must be > 0.')
      assert_ops += _assert(target_height > 0, ValueError,
                            'target_height must be > 0.')
  
      image = control_flow_ops.with_dependencies(assert_ops, image)
      # `crop_to_bounding_box` and `pad_to_bounding_box` have their own checks.
      # Make sure our checks come first, so that error messages are clearer.
      if _is_tensor(target_height):
        target_height = control_flow_ops.with_dependencies(
            assert_ops, target_height)
      if _is_tensor(target_width):
        target_width = control_flow_ops.with_dependencies(assert_ops,
                                                          target_width)
  
      def max_(x, y):
        if _is_tensor(x) or _is_tensor(y):
          return math_ops.maximum(x, y)
        else:
          return max(x, y)
  
      def min_(x, y):
        if _is_tensor(x) or _is_tensor(y):
          return math_ops.minimum(x, y)
        else:
          return min(x, y)
  
      def equal_(x, y):
        if _is_tensor(x) or _is_tensor(y):
          return math_ops.equal(x, y)
        else:
          return x == y
  
      _, height, width, _ = _ImageDimensions(image, rank=4)
      width_diff = target_width - width
      offset_crop_width = max_(-width_diff // 2, 0)
      offset_pad_width = max_(width_diff // 2, 0)
  
      height_diff = target_height - height
      offset_crop_height = max_(-height_diff // 2, 0)
      offset_pad_height = max_(height_diff // 2, 0)
  
      # Maybe crop if needed.
      cropped = crop_to_bounding_box(image, offset_crop_height, offset_crop_width,
                                     min_(target_height, height),
                                     min_(target_width, width))
  
      # Maybe pad if needed.
      resized = pad_to_bounding_box(cropped, offset_pad_height, offset_pad_width,
                                    target_height, target_width)
  
      # In theory all the checks below are redundant.
      if resized.get_shape().ndims is None:
        raise ValueError('resized contains no shape.')
  
      _, resized_height, resized_width, _ = _ImageDimensions(resized, rank=4)
  
      assert_ops = []
      assert_ops += _assert(
          equal_(resized_height, target_height), ValueError,
          'resized height is not correct.')
      assert_ops += _assert(
          equal_(resized_width, target_width), ValueError,
          'resized width is not correct.')
  
      resized = control_flow_ops.with_dependencies(assert_ops, resized)
  
      if not is_batch:
        resized = array_ops.squeeze(resized, axis=[0])
  
      return resized
  
  
  @tf_export(v1=['image.ResizeMethod'])
  class ResizeMethodV1(object):
    BILINEAR = 0
    NEAREST_NEIGHBOR = 1
    BICUBIC = 2
    AREA = 3
  
  
  @tf_export('image.ResizeMethod', v1=[])
  class ResizeMethod(object):
    BILINEAR = 'bilinear'
    NEAREST_NEIGHBOR = 'nearest'
    BICUBIC = 'bicubic'
    AREA = 'area'
    LANCZOS3 = 'lanczos3'
    LANCZOS5 = 'lanczos5'
    GAUSSIAN = 'gaussian'
    MITCHELLCUBIC = 'mitchellcubic'
  
  
  def _resize_images_common(images, resizer_fn, size, preserve_aspect_ratio, name,
                            skip_resize_if_same):
    """Core functionality for v1 and v2 resize functions."""
    with ops.name_scope(name, 'resize', [images, size]):
      images = ops.convert_to_tensor(images, name='images')
      if images.get_shape().ndims is None:
        raise ValueError('\'images\' contains no shape.')
      # TODO(shlens): Migrate this functionality to the underlying Op's.
      is_batch = True
      if images.get_shape().ndims == 3:
        is_batch = False
        images = array_ops.expand_dims(images, 0)
      elif images.get_shape().ndims != 4:
        raise ValueError('\'images\' must have either 3 or 4 dimensions.')
  
      _, height, width, _ = images.get_shape().as_list()
  
      try:
        size = ops.convert_to_tensor(size, dtypes.int32, name='size')
      except (TypeError, ValueError):
        raise ValueError('\'size\' must be a 1-D int32 Tensor')
      if not size.get_shape().is_compatible_with([2]):
        raise ValueError('\'size\' must be a 1-D Tensor of 2 elements: '
                         'new_height, new_width')
      size_const_as_shape = tensor_util.constant_value_as_shape(size)
      new_height_const = size_const_as_shape.dims[0].value
      new_width_const = size_const_as_shape.dims[1].value
  
      if preserve_aspect_ratio:
        # Get the current shapes of the image, even if dynamic.
        _, current_height, current_width, _ = _ImageDimensions(images, rank=4)
  
        # do the computation to find the right scale and height/width.
        scale_factor_height = (
            math_ops.cast(new_height_const, dtypes.float32) /
            math_ops.cast(current_height, dtypes.float32))
        scale_factor_width = (
            math_ops.cast(new_width_const, dtypes.float32) /
            math_ops.cast(current_width, dtypes.float32))
        scale_factor = math_ops.minimum(scale_factor_height, scale_factor_width)
        scaled_height_const = math_ops.cast(
            math_ops.round(scale_factor *
                           math_ops.cast(current_height, dtypes.float32)),
            dtypes.int32)
        scaled_width_const = math_ops.cast(
            math_ops.round(scale_factor *
                           math_ops.cast(current_width, dtypes.float32)),
            dtypes.int32)
  
        # NOTE: Reset the size and other constants used later.
        size = ops.convert_to_tensor([scaled_height_const, scaled_width_const],
                                     dtypes.int32,
                                     name='size')
        size_const_as_shape = tensor_util.constant_value_as_shape(size)
        new_height_const = size_const_as_shape.dims[0].value
        new_width_const = size_const_as_shape.dims[1].value
  
      # If we can determine that the height and width will be unmodified by this
      # transformation, we avoid performing the resize.
      if skip_resize_if_same and all(
          x is not None
          for x in [new_width_const, width, new_height_const, height]) and (
              width == new_width_const and height == new_height_const):
        if not is_batch:
          images = array_ops.squeeze(images, axis=[0])
        return images
  
      images = resizer_fn(images, size)
  
      # NOTE(mrry): The shape functions for the resize ops cannot unpack
      # the packed values in `new_size`, so set the shape here.
      images.set_shape([None, new_height_const, new_width_const, None])
  
      if not is_batch:
        images = array_ops.squeeze(images, axis=[0])
      return images
  
  
  @tf_export(v1=['image.resize_images', 'image.resize'])
  def resize_images(images,
                    size,
                    method=ResizeMethodV1.BILINEAR,
                    align_corners=False,
                    preserve_aspect_ratio=False,
                    name=None):
    """Resize `images` to `size` using the specified `method`.
  
    Resized images will be distorted if their original aspect ratio is not
    the same as `size`.  To avoid distortions see
    `tf.compat.v1.image.resize_image_with_pad`.
  
    `method` can be one of:
  
    *   <b>`ResizeMethod.BILINEAR`</b>: [Bilinear interpolation.](
      https://en.wikipedia.org/wiki/Bilinear_interpolation)
    *   <b>`ResizeMethod.NEAREST_NEIGHBOR`</b>: [Nearest neighbor interpolation.](
      https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
    *   <b>`ResizeMethod.BICUBIC`</b>: [Bicubic interpolation.](
      https://en.wikipedia.org/wiki/Bicubic_interpolation)
    *   <b>`ResizeMethod.AREA`</b>: Area interpolation.
  
    The return value has the same type as `images` if `method` is
    `ResizeMethod.NEAREST_NEIGHBOR`. It will also have the same type as `images`
    if the size of `images` can be statically determined to be the same as `size`,
    because `images` is returned in this case. Otherwise, the return value has
    type `float32`.
  
    Args:
      images: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
        of shape `[height, width, channels]`.
      size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The new
        size for the images.
      method: ResizeMethod.  Defaults to `ResizeMethod.BILINEAR`.
      align_corners: bool.  If True, the centers of the 4 corner pixels of the
        input and output tensors are aligned, preserving the values at the corner
        pixels. Defaults to `False`.
      preserve_aspect_ratio: Whether to preserve the aspect ratio. If this is set,
        then `images` will be resized to a size that fits in `size` while
        preserving the aspect ratio of the original image. Scales up the image if
        `size` is bigger than the current size of the `image`. Defaults to False.
      name: A name for this operation (optional).
  
    Raises:
      ValueError: if the shape of `images` is incompatible with the
        shape arguments to this function
      ValueError: if `size` has invalid shape or type.
      ValueError: if an unsupported resize method is specified.
  
    Returns:
      If `images` was 4-D, a 4-D float Tensor of shape
      `[batch, new_height, new_width, channels]`.
      If `images` was 3-D, a 3-D float Tensor of shape
      `[new_height, new_width, channels]`.
    """
  
    def resize_fn(images_t, new_size):
      """Legacy resize core function, passed to _resize_images_common."""
      if method == ResizeMethodV1.BILINEAR or method == ResizeMethod.BILINEAR:
        return gen_image_ops.resize_bilinear(
            images_t, new_size, align_corners=align_corners)
      elif (method == ResizeMethodV1.NEAREST_NEIGHBOR or
            method == ResizeMethod.NEAREST_NEIGHBOR):
        return gen_image_ops.resize_nearest_neighbor(
            images_t, new_size, align_corners=align_corners)
      elif method == ResizeMethodV1.BICUBIC or method == ResizeMethod.BICUBIC:
        return gen_image_ops.resize_bicubic(
            images_t, new_size, align_corners=align_corners)
      elif method == ResizeMethodV1.AREA or method == ResizeMethod.AREA:
        return gen_image_ops.resize_area(
            images_t, new_size, align_corners=align_corners)
      else:
        raise ValueError('Resize method is not implemented.')
  
    return _resize_images_common(
        images,
        resize_fn,
        size,
        preserve_aspect_ratio=preserve_aspect_ratio,
        name=name,
        skip_resize_if_same=True)
  
  
  @tf_export('image.resize', v1=[])
  def resize_images_v2(images,
                       size,
                       method=ResizeMethod.BILINEAR,
                       preserve_aspect_ratio=False,
                       antialias=False,
                       name=None):
    """Resize `images` to `size` using the specified `method`.
  
    Resized images will be distorted if their original aspect ratio is not
    the same as `size`.  To avoid distortions see
    `tf.image.resize_with_pad`.
  
    When 'antialias' is true, the sampling filter will anti-alias the input image
    as well as interpolate.   When downsampling an image with [anti-aliasing](
    https://en.wikipedia.org/wiki/Spatial_anti-aliasing) the sampling filter
    kernel is scaled in order to properly anti-alias the input image signal.
    'antialias' has no effect when upsampling an image.
  
    *   <b>`bilinear`</b>: [Bilinear interpolation.](
      https://en.wikipedia.org/wiki/Bilinear_interpolation) If 'antialias' is
      true, becomes a hat/tent filter function with radius 1 when downsampling.
    *   <b>`lanczos3`</b>:  [Lanczos kernel](
      https://en.wikipedia.org/wiki/Lanczos_resampling) with radius 3.
      High-quality practical filter but may have some ringing especially on
      synthetic images.
    *   <b>`lanczos5`</b>: [Lanczos kernel] (
      https://en.wikipedia.org/wiki/Lanczos_resampling) with radius 5.
      Very-high-quality filter but may have stronger ringing.
    *   <b>`bicubic`</b>: [Cubic interpolant](
      https://en.wikipedia.org/wiki/Bicubic_interpolation) of Keys. Equivalent to
      Catmull-Rom kernel. Reasonably good quality and faster than Lanczos3Kernel,
      particularly when upsampling.
    *   <b>`gaussian`</b>: [Gaussian kernel](
      https://en.wikipedia.org/wiki/Gaussian_filter) with radius 3,
      sigma = 1.5 / 3.0.
    *   <b>`nearest`</b>: [Nearest neighbor interpolation.](
      https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
      'antialias' has no effect when used with nearest neighbor interpolation.
    *   <b>`area`</b>: Anti-aliased resampling with area interpolation.
      'antialias' has no effect when used with area interpolation; it
      always anti-aliases.
    *   <b>`mitchellcubic`</b>: Mitchell-Netravali Cubic non-interpolating filter.
      For synthetic images (especially those lacking proper prefiltering), less
      ringing than Keys cubic kernel but less sharp.
  
    Note that near image edges the filtering kernel may be partially outside the
    image boundaries. For these pixels, only input pixels inside the image will be
    included in the filter sum, and the output value will be appropriately
    normalized.
  
    The return value has the same type as `images` if `method` is
    `ResizeMethod.NEAREST_NEIGHBOR`. Otherwise, the return value has type
    `float32`.
  
    Args:
      images: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
        of shape `[height, width, channels]`.
      size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The new
        size for the images.
      method: ResizeMethod.  Defaults to `bilinear`.
      preserve_aspect_ratio: Whether to preserve the aspect ratio. If this is set,
        then `images` will be resized to a size that fits in `size` while
        preserving the aspect ratio of the original image. Scales up the image if
        `size` is bigger than the current size of the `image`. Defaults to False.
      antialias: Whether to use an anti-aliasing filter when downsampling an
        image.
      name: A name for this operation (optional).
  
    Raises:
      ValueError: if the shape of `images` is incompatible with the
        shape arguments to this function
      ValueError: if `size` has invalid shape or type.
      ValueError: if an unsupported resize method is specified.
  
    Returns:
      If `images` was 4-D, a 4-D float Tensor of shape
      `[batch, new_height, new_width, channels]`.
      If `images` was 3-D, a 3-D float Tensor of shape
      `[new_height, new_width, channels]`.
    """
  
    def resize_fn(images_t, new_size):
      """Resize core function, passed to _resize_images_common."""
      scale_and_translate_methods = [
          ResizeMethod.LANCZOS3, ResizeMethod.LANCZOS5, ResizeMethod.GAUSSIAN,
          ResizeMethod.MITCHELLCUBIC
      ]
  
      def resize_with_scale_and_translate(method):
        scale = (
            math_ops.cast(new_size, dtype=dtypes.float32) /
            math_ops.cast(array_ops.shape(images_t)[1:3], dtype=dtypes.float32))
        return gen_image_ops.scale_and_translate(
            images_t,
            new_size,
            scale,
            array_ops.zeros([2]),
            kernel_type=method,
            antialias=antialias)
  
      if method == ResizeMethod.BILINEAR:
        if antialias:
          return resize_with_scale_and_translate('triangle')
        else:
          return gen_image_ops.resize_bilinear(
              images_t, new_size, half_pixel_centers=True)
      elif method == ResizeMethod.NEAREST_NEIGHBOR:
        return gen_image_ops.resize_nearest_neighbor(
            images_t, new_size, half_pixel_centers=True)
      elif method == ResizeMethod.BICUBIC:
        if antialias:
          return resize_with_scale_and_translate('keyscubic')
        else:
          return gen_image_ops.resize_bicubic(
              images_t, new_size, half_pixel_centers=True)
      elif method == ResizeMethod.AREA:
        return gen_image_ops.resize_area(images_t, new_size)
      elif method in scale_and_translate_methods:
        return resize_with_scale_and_translate(method)
      else:
        raise ValueError('Resize method is not implemented.')
  
    return _resize_images_common(
        images,
        resize_fn,
        size,
        preserve_aspect_ratio=preserve_aspect_ratio,
        name=name,
        skip_resize_if_same=False)
  
  
  def _resize_image_with_pad_common(image, target_height, target_width,
                                    resize_fn):
    """Core functionality for v1 and v2 resize_image_with_pad functions."""
    with ops.name_scope(None, 'resize_image_with_pad', [image]):
      image = ops.convert_to_tensor(image, name='image')
      image_shape = image.get_shape()
      is_batch = True
      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)
      assert_ops += _assert(target_width > 0, ValueError,
                            'target_width must be > 0.')
      assert_ops += _assert(target_height > 0, ValueError,
                            'target_height must be > 0.')
  
      image = control_flow_ops.with_dependencies(assert_ops, image)
  
      def max_(x, y):
        if _is_tensor(x) or _is_tensor(y):
          return math_ops.maximum(x, y)
        else:
          return max(x, y)
  
      _, height, width, _ = _ImageDimensions(image, rank=4)
  
      # convert values to float, to ease divisions
      f_height = math_ops.cast(height, dtype=dtypes.float64)
      f_width = math_ops.cast(width, dtype=dtypes.float64)
      f_target_height = math_ops.cast(target_height, dtype=dtypes.float64)
      f_target_width = math_ops.cast(target_width, dtype=dtypes.float64)
  
      # Find the ratio by which the image must be adjusted
      # to fit within the target
      ratio = max_(f_width / f_target_width, f_height / f_target_height)
      resized_height_float = f_height / ratio
      resized_width_float = f_width / ratio
      resized_height = math_ops.cast(
          math_ops.floor(resized_height_float), dtype=dtypes.int32)
      resized_width = math_ops.cast(
          math_ops.floor(resized_width_float), dtype=dtypes.int32)
  
      padding_height = (f_target_height - resized_height_float) / 2
      padding_width = (f_target_width - resized_width_float) / 2
      f_padding_height = math_ops.floor(padding_height)
      f_padding_width = math_ops.floor(padding_width)
      p_height = max_(0, math_ops.cast(f_padding_height, dtype=dtypes.int32))
      p_width = max_(0, math_ops.cast(f_padding_width, dtype=dtypes.int32))
  
      # Resize first, then pad to meet requested dimensions
      resized = resize_fn(image, [resized_height, resized_width])
  
      padded = pad_to_bounding_box(resized, p_height, p_width, target_height,
                                   target_width)
  
      if padded.get_shape().ndims is None:
        raise ValueError('padded contains no shape.')
  
      _ImageDimensions(padded, rank=4)
  
      if not is_batch:
        padded = array_ops.squeeze(padded, axis=[0])
  
      return padded
  
  
  @tf_export(v1=['image.resize_image_with_pad'])
  def resize_image_with_pad_v1(image,
                               target_height,
                               target_width,
                               method=ResizeMethodV1.BILINEAR,
                               align_corners=False):
    """Resizes and pads an image to a target width and height.
  
    Resizes an image to a target width and height by keeping
    the aspect ratio the same without distortion. If the target
    dimensions don't match the image dimensions, the image
    is resized and then padded with zeroes to match requested
    dimensions.
  
    Args:
      image: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
        of shape `[height, width, channels]`.
      target_height: Target height.
      target_width: Target width.
      method: Method to use for resizing image. See `resize_images()`
      align_corners: bool.  If True, the centers of the 4 corner pixels of the
        input and output tensors are aligned, preserving the values at the corner
        pixels. Defaults to `False`.
  
    Raises:
      ValueError: if `target_height` or `target_width` are zero or negative.
  
    Returns:
      Resized and padded image.
      If `images` was 4-D, a 4-D float Tensor of shape
      `[batch, new_height, new_width, channels]`.
      If `images` was 3-D, a 3-D float Tensor of shape
      `[new_height, new_width, channels]`.
    """
  
    def _resize_fn(im, new_size):
      return resize_images(im, new_size, method, align_corners=align_corners)
  
    return _resize_image_with_pad_common(image, target_height, target_width,
                                         _resize_fn)
  
  
  @tf_export('image.resize_with_pad', v1=[])
  def resize_image_with_pad_v2(image,
                               target_height,
                               target_width,
                               method=ResizeMethod.BILINEAR,
                               antialias=False):
    """Resizes and pads an image to a target width and height.
  
    Resizes an image to a target width and height by keeping
    the aspect ratio the same without distortion. If the target
    dimensions don't match the image dimensions, the image
    is resized and then padded with zeroes to match requested
    dimensions.
  
    Args:
      image: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
        of shape `[height, width, channels]`.
      target_height: Target height.
      target_width: Target width.
      method: Method to use for resizing image. See `image.resize()`
      antialias: Whether to use anti-aliasing when resizing. See 'image.resize()'.
  
    Raises:
      ValueError: if `target_height` or `target_width` are zero or negative.
  
    Returns:
      Resized and padded image.
      If `images` was 4-D, a 4-D float Tensor of shape
      `[batch, new_height, new_width, channels]`.
      If `images` was 3-D, a 3-D float Tensor of shape
      `[new_height, new_width, channels]`.
    """
  
    def _resize_fn(im, new_size):
      return resize_images_v2(im, new_size, method, antialias=antialias)
  
    return _resize_image_with_pad_common(image, target_height, target_width,
                                         _resize_fn)
  
  
  @tf_export('image.per_image_standardization')
  def per_image_standardization(image):
    """Linearly scales each image in `image` to have mean 0 and variance 1.
  
    For each 3-D image `x` in `image`, computes `(x - mean) / adjusted_stddev`,
    where
  
    - `mean` is the average of all values in `x`
    - `adjusted_stddev = max(stddev, 1.0/sqrt(N))` is capped away from 0 to
      protect against division by 0 when handling uniform images
      - `N` is the number of elements in `x`
      - `stddev` is the standard deviation of all values in `x`
  
    Args:
      image: An n-D Tensor with at least 3 dimensions, the last 3 of which are the
        dimensions of each image.
  
    Returns:
      A `Tensor` with same shape and dtype as `image`.
  
    Raises:
      ValueError: if the shape of 'image' is incompatible with this function.
    """
    with ops.name_scope(None, 'per_image_standardization', [image]) as scope:
      image = ops.convert_to_tensor(image, name='image')
      image = _AssertAtLeast3DImage(image)
  
      # Remember original dtype to so we can convert back if needed
      orig_dtype = image.dtype
      if orig_dtype not in [dtypes.float16, dtypes.float32]:
        image = convert_image_dtype(image, dtypes.float32)
  
      num_pixels = math_ops.reduce_prod(array_ops.shape(image)[-3:])
      image_mean = math_ops.reduce_mean(image, axis=[-1, -2, -3], keepdims=True)
  
      # Apply a minimum normalization that protects us against uniform images.
      stddev = math_ops.reduce_std(image, axis=[-1, -2, -3], keepdims=True)
      min_stddev = math_ops.rsqrt(math_ops.cast(num_pixels, image.dtype))
      adjusted_stddev = math_ops.maximum(stddev, min_stddev)
  
      image -= image_mean
      image = math_ops.div(image, adjusted_stddev, name=scope)
      return convert_image_dtype(image, orig_dtype, saturate=True)
  
  
  @tf_export('image.random_brightness')
  def random_brightness(image, max_delta, seed=None):
    """Adjust the brightness of images by a random factor.
  
    Equivalent to `adjust_brightness()` using a `delta` randomly picked in the
    interval `[-max_delta, max_delta)`.
  
    Args:
      image: An image or images to adjust.
      max_delta: float, must be non-negative.
      seed: A Python integer. Used to create a random seed. See
        `tf.compat.v1.set_random_seed` for behavior.
  
    Returns:
      The brightness-adjusted image(s).
  
    Raises:
      ValueError: if `max_delta` is negative.
    """
    if max_delta < 0:
      raise ValueError('max_delta must be non-negative.')
  
    delta = random_ops.random_uniform([], -max_delta, max_delta, seed=seed)
    return adjust_brightness(image, delta)
  
  
  @tf_export('image.random_contrast')
  def random_contrast(image, lower, upper, seed=None):
    """Adjust the contrast of an image or images by a random factor.
  
    Equivalent to `adjust_contrast()` but uses a `contrast_factor` randomly
    picked in the interval `[lower, upper]`.
  
    Args:
      image: An image tensor with 3 or more dimensions.
      lower: float.  Lower bound for the random contrast factor.
      upper: float.  Upper bound for the random contrast factor.
      seed: A Python integer. Used to create a random seed. See
        `tf.compat.v1.set_random_seed` for behavior.
  
    Returns:
      The contrast-adjusted image(s).
  
    Raises:
      ValueError: if `upper <= lower` or if `lower < 0`.
    """
    if upper <= lower:
      raise ValueError('upper must be > lower.')
  
    if lower < 0:
      raise ValueError('lower must be non-negative.')
  
    # Generate an a float in [lower, upper]
    contrast_factor = random_ops.random_uniform([], lower, upper, seed=seed)
    return adjust_contrast(image, contrast_factor)
  
  
  @tf_export('image.adjust_brightness')
  def adjust_brightness(image, delta):
    """Adjust the brightness of RGB or Grayscale images.
  
    This is a convenience method that converts RGB images to float
    representation, adjusts their brightness, and then converts them back to the
    original data type. If several adjustments are chained, it is advisable to
    minimize the number of redundant conversions.
  
    The value `delta` is added to all components of the tensor `image`. `image` is
    converted to `float` and scaled appropriately if it is in fixed-point
    representation, and `delta` is converted to the same data type. For regular
    images, `delta` should be in the range `[0,1)`, as it is added to the image in
    floating point representation, where pixel values are in the `[0,1)` range.
  
    Args:
      image: RGB image or images to adjust.
      delta: A scalar. Amount to add to the pixel values.
  
    Returns:
      A brightness-adjusted tensor of the same shape and type as `image`.
    """
    with ops.name_scope(None, 'adjust_brightness', [image, delta]) as name:
      image = ops.convert_to_tensor(image, name='image')
      # Remember original dtype to so we can convert back if needed
      orig_dtype = image.dtype
  
      if orig_dtype in [dtypes.float16, dtypes.float32]:
        flt_image = image
      else:
        flt_image = convert_image_dtype(image, dtypes.float32)
  
      adjusted = math_ops.add(
          flt_image, math_ops.cast(delta, flt_image.dtype), name=name)
  
      return convert_image_dtype(adjusted, orig_dtype, saturate=True)
  
  
  @tf_export('image.adjust_contrast')
  def adjust_contrast(images, contrast_factor):
    """Adjust contrast of RGB or grayscale images.
  
    This is a convenience method that converts RGB images to float
    representation, adjusts their contrast, and then converts them back to the
    original data type. If several adjustments are chained, it is advisable to
    minimize the number of redundant conversions.
  
    `images` is a tensor of at least 3 dimensions.  The last 3 dimensions are
    interpreted as `[height, width, channels]`.  The other dimensions only
    represent a collection of images, such as `[batch, height, width, channels].`
  
    Contrast is adjusted independently for each channel of each image.
  
    For each channel, this Op computes the mean of the image pixels in the
    channel and then adjusts each component `x` of each pixel to
    `(x - mean) * contrast_factor + mean`.
  
    Args:
      images: Images to adjust.  At least 3-D.
      contrast_factor: A float multiplier for adjusting contrast.
  
    Returns:
      The contrast-adjusted image or images.
    """
    with ops.name_scope(None, 'adjust_contrast',
                        [images, contrast_factor]) as name:
      images = ops.convert_to_tensor(images, name='images')
      # Remember original dtype to so we can convert back if needed
      orig_dtype = images.dtype
  
      if orig_dtype in (dtypes.float16, dtypes.float32):
        flt_images = images
      else:
        flt_images = convert_image_dtype(images, dtypes.float32)
  
      adjusted = gen_image_ops.adjust_contrastv2(
          flt_images, contrast_factor=contrast_factor, name=name)
  
      return convert_image_dtype(adjusted, orig_dtype, saturate=True)
  
  
  @tf_export('image.adjust_gamma')
  def adjust_gamma(image, gamma=1, gain=1):
    """Performs Gamma Correction on the input image.
    Also known as Power Law Transform. This function converts the
    input images at first to float representation, then transforms them
    pixelwise according to the equation `Out = gain * In**gamma`,
    and then converts the back to the original data type.
  
    Args:
      image : RGB image or images to adjust.
      gamma : A scalar or tensor. Non negative real number.
      gain  : A scalar or tensor. The constant multiplier.
    Returns:
      A Tensor. A Gamma-adjusted tensor of the same shape and type as `image`.
    Raises:
      ValueError: If gamma is negative.
    Notes:
      For gamma greater than 1, the histogram will shift towards left and
      the output image will be darker than the input image.
      For gamma less than 1, the histogram will shift towards right and
      the output image will be brighter than the input image.
    References:
      [1] http://en.wikipedia.org/wiki/Gamma_correction
    """
  
    with ops.name_scope(None, 'adjust_gamma', [image, gamma, gain]) as name:
      image = ops.convert_to_tensor(image, name='image')
      # Remember original dtype to so we can convert back if needed
      orig_dtype = image.dtype
  
      if orig_dtype in [dtypes.float16, dtypes.float32]:
        flt_image = image
      else:
        flt_image = convert_image_dtype(image, dtypes.float32)
  
      assert_op = _assert(gamma >= 0, ValueError,
                          'Gamma should be a non-negative real number.')
      if assert_op:
        gamma = control_flow_ops.with_dependencies(assert_op, gamma)
  
      # According to the definition of gamma correction.
      adjusted_img = gain * flt_image**gamma
  
      return convert_image_dtype(adjusted_img, orig_dtype, saturate=True)
  
  
  @tf_export('image.convert_image_dtype')
  def convert_image_dtype(image, dtype, saturate=False, name=None):
    """Convert `image` to `dtype`, scaling its values if needed.
  
    Images that are represented using floating point values are expected to have
    values in the range [0,1). Image data stored in integer data types are
    expected to have values in the range `[0,MAX]`, where `MAX` is the largest
    positive representable number for the data type.
  
    This op converts between data types, scaling the values appropriately before
    casting.
  
    Note that converting from floating point inputs to integer types may lead to
    over/underflow problems. Set saturate to `True` to avoid such problem in
    problematic conversions. If enabled, saturation will clip the output into the
    allowed range before performing a potentially dangerous cast (and only before
    performing such a cast, i.e., when casting from a floating point to an integer
    type, and when casting from a signed to an unsigned type; `saturate` has no
    effect on casts between floats, or on casts that increase the type's range).
  
    Args:
      image: An image.
      dtype: A `DType` to convert `image` to.
      saturate: If `True`, clip the input before casting (if necessary).
      name: A name for this operation (optional).
  
    Returns:
      `image`, converted to `dtype`.
    """
    image = ops.convert_to_tensor(image, name='image')
    if dtype == image.dtype:
      return array_ops.identity(image, name=name)
  
    with ops.name_scope(name, 'convert_image', [image]) as name:
      # Both integer: use integer multiplication in the larger range
      if image.dtype.is_integer and dtype.is_integer:
        scale_in = image.dtype.max
        scale_out = dtype.max
        if scale_in > scale_out:
          # Scaling down, scale first, then cast. The scaling factor will
          # cause in.max to be mapped to above out.max but below out.max+1,
          # so that the output is safely in the supported range.
          scale = (scale_in + 1) // (scale_out + 1)
          scaled = math_ops.div(image, scale)
  
          if saturate:
            return math_ops.saturate_cast(scaled, dtype, name=name)
          else:
            return math_ops.cast(scaled, dtype, name=name)
        else:
          # Scaling up, cast first, then scale. The scale will not map in.max to
          # out.max, but converting back and forth should result in no change.
          if saturate:
            cast = math_ops.saturate_cast(image, dtype)
          else:
            cast = math_ops.cast(image, dtype)
          scale = (scale_out + 1) // (scale_in + 1)
          return math_ops.multiply(cast, scale, name=name)
      elif image.dtype.is_floating and dtype.is_floating:
        # Both float: Just cast, no possible overflows in the allowed ranges.
        # Note: We're ignoring float overflows. If your image dynamic range
        # exceeds float range, you're on your own.
        return math_ops.cast(image, dtype, name=name)
      else:
        if image.dtype.is_integer:
          # Converting to float: first cast, then scale. No saturation possible.
          cast = math_ops.cast(image, dtype)
          scale = 1. / image.dtype.max
          return math_ops.multiply(cast, scale, name=name)
        else:
          # Converting from float: first scale, then cast
          scale = dtype.max + 0.5  # avoid rounding problems in the cast
          scaled = math_ops.multiply(image, scale)
          if saturate:
            return math_ops.saturate_cast(scaled, dtype, name=name)
          else:
            return math_ops.cast(scaled, dtype, name=name)
  
  
  @tf_export('image.rgb_to_grayscale')
  def rgb_to_grayscale(images, name=None):
    """Converts one or more images from RGB to Grayscale.
  
    Outputs a tensor of the same `DType` and rank as `images`.  The size of the
    last dimension of the output is 1, containing the Grayscale value of the
    pixels.
  
    Args:
      images: The RGB tensor to convert. Last dimension must have size 3 and
        should contain RGB values.
      name: A name for the operation (optional).
  
    Returns:
      The converted grayscale image(s).
    """
    with ops.name_scope(name, 'rgb_to_grayscale', [images]) as name:
      images = ops.convert_to_tensor(images, name='images')
      # Remember original dtype to so we can convert back if needed
      orig_dtype = images.dtype
      flt_image = convert_image_dtype(images, dtypes.float32)
  
      # Reference for converting between RGB and grayscale.
      # https://en.wikipedia.org/wiki/Luma_%28video%29
      rgb_weights = [0.2989, 0.5870, 0.1140]
      gray_float = math_ops.tensordot(flt_image, rgb_weights, [-1, -1])
      gray_float = array_ops.expand_dims(gray_float, -1)
      return convert_image_dtype(gray_float, orig_dtype, name=name)
  
  
  @tf_export('image.grayscale_to_rgb')
  def grayscale_to_rgb(images, name=None):
    """Converts one or more images from Grayscale to RGB.
  
    Outputs a tensor of the same `DType` and rank as `images`.  The size of the
    last dimension of the output is 3, containing the RGB value of the pixels.
    The input images' last dimension must be size 1.
  
    Args:
      images: The Grayscale tensor to convert. Last dimension must be size 1.
      name: A name for the operation (optional).
  
    Returns:
      The converted grayscale image(s).
    """
    with ops.name_scope(name, 'grayscale_to_rgb', [images]) as name:
      images = _AssertGrayscaleImage(images)
  
      images = ops.convert_to_tensor(images, name='images')
      rank_1 = array_ops.expand_dims(array_ops.rank(images) - 1, 0)
      shape_list = ([array_ops.ones(rank_1, dtype=dtypes.int32)] +
                    [array_ops.expand_dims(3, 0)])
      multiples = array_ops.concat(shape_list, 0)
      rgb = array_ops.tile(images, multiples, name=name)
      rgb.set_shape(images.get_shape()[:-1].concatenate([3]))
      return rgb
  
  
  # pylint: disable=invalid-name
  @tf_export('image.random_hue')
  def random_hue(image, max_delta, seed=None):
    """Adjust the hue of RGB images by a random factor.
  
    Equivalent to `adjust_hue()` but uses a `delta` randomly
    picked in the interval `[-max_delta, max_delta]`.
  
    `max_delta` must be in the interval `[0, 0.5]`.
  
    Args:
      image: RGB image or images. Size of the last dimension must be 3.
      max_delta: float.  Maximum value for the random delta.
      seed: An operation-specific seed. It will be used in conjunction with the
        graph-level seed to determine the real seeds that will be used in this
        operation. Please see the documentation of set_random_seed for its
        interaction with the graph-level random seed.
  
    Returns:
      Adjusted image(s), same shape and DType as `image`.
  
    Raises:
      ValueError: if `max_delta` is invalid.
    """
    if max_delta > 0.5:
      raise ValueError('max_delta must be <= 0.5.')
  
    if max_delta < 0:
      raise ValueError('max_delta must be non-negative.')
  
    delta = random_ops.random_uniform([], -max_delta, max_delta, seed=seed)
    return adjust_hue(image, delta)
  
  
  @tf_export('image.adjust_hue')
  def adjust_hue(image, delta, name=None):
    """Adjust hue of RGB images.
  
    This is a convenience method that converts an RGB image to float
    representation, converts it to HSV, add an offset to the hue channel, converts
    back to RGB and then back to the original data type. If several adjustments
    are chained it is advisable to minimize the number of redundant conversions.
  
    `image` is an RGB image.  The image hue is adjusted by converting the
    image(s) to HSV and rotating the hue channel (H) by
    `delta`.  The image is then converted back to RGB.
  
    `delta` must be in the interval `[-1, 1]`.
  
    Args:
      image: RGB image or images. Size of the last dimension must be 3.
      delta: float.  How much to add to the hue channel.
      name: A name for this operation (optional).
  
    Returns:
      Adjusted image(s), same shape and DType as `image`.
    """
    with ops.name_scope(name, 'adjust_hue', [image]) as name:
      image = ops.convert_to_tensor(image, name='image')
      # Remember original dtype to so we can convert back if needed
      orig_dtype = image.dtype
      if orig_dtype in (dtypes.float16, dtypes.float32):
        flt_image = image
      else:
        flt_image = convert_image_dtype(image, dtypes.float32)
  
      rgb_altered = gen_image_ops.adjust_hue(flt_image, delta)
  
      return convert_image_dtype(rgb_altered, orig_dtype)
  
  
  # pylint: disable=invalid-name
  @tf_export('image.random_jpeg_quality')
  def random_jpeg_quality(image, min_jpeg_quality, max_jpeg_quality, seed=None):
    """Randomly changes jpeg encoding quality for inducing jpeg noise.
  
    `min_jpeg_quality` must be in the interval `[0, 100]` and less than
    `max_jpeg_quality`.
    `max_jpeg_quality` must be in the interval `[0, 100]`.
  
    Args:
      image: RGB image or images. Size of the last dimension must be 3.
      min_jpeg_quality: Minimum jpeg encoding quality to use.
      max_jpeg_quality: Maximum jpeg encoding quality to use.
      seed: An operation-specific seed. It will be used in conjunction with the
        graph-level seed to determine the real seeds that will be used in this
        operation. Please see the documentation of set_random_seed for its
        interaction with the graph-level random seed.
  
    Returns:
      Adjusted image(s), same shape and DType as `image`.
  
    Raises:
      ValueError: if `min_jpeg_quality` or `max_jpeg_quality` is invalid.
    """
    if (min_jpeg_quality < 0 or max_jpeg_quality < 0 or min_jpeg_quality > 100 or
        max_jpeg_quality > 100):
      raise ValueError('jpeg encoding range must be between 0 and 100.')
  
    if min_jpeg_quality >= max_jpeg_quality:
      raise ValueError('`min_jpeg_quality` must be less than `max_jpeg_quality`.')
  
    if compat.forward_compatible(2019, 4, 4):
      jpeg_quality = random_ops.random_uniform([],
                                               min_jpeg_quality,
                                               max_jpeg_quality,
                                               seed=seed,
                                               dtype=dtypes.int32)
    else:
      np.random.seed(seed)
      jpeg_quality = np.random.randint(min_jpeg_quality, max_jpeg_quality)
    return adjust_jpeg_quality(image, jpeg_quality)
  
  
  @tf_export('image.adjust_jpeg_quality')
  def adjust_jpeg_quality(image, jpeg_quality, name=None):
    """Adjust jpeg encoding quality of an RGB image.
  
    This is a convenience method that adjusts jpeg encoding quality of an
    RGB image.
  
    `image` is an RGB image.  The image's encoding quality is adjusted
    to `jpeg_quality`.
    `jpeg_quality` must be in the interval `[0, 100]`.
  
    Args:
      image: RGB image or images. Size of the last dimension must be 3.
      jpeg_quality: Python int or Tensor of type int32.  jpeg encoding quality.
      name: A name for this operation (optional).
  
    Returns:
      Adjusted image(s), same shape and DType as `image`.
    """
    with ops.name_scope(name, 'adjust_jpeg_quality', [image]) as name:
      image = ops.convert_to_tensor(image, name='image')
      # Remember original dtype to so we can convert back if needed
      orig_dtype = image.dtype
      # Convert to uint8
      image = convert_image_dtype(image, dtypes.uint8)
      # Encode image to jpeg with given jpeg quality
      if compat.forward_compatible(2019, 4, 4):
        if not _is_tensor(jpeg_quality):
          # If jpeg_quality is a int (not tensor).
          jpeg_quality = ops.convert_to_tensor(jpeg_quality, dtype=dtypes.int32)
        image = gen_image_ops.encode_jpeg_variable_quality(image, jpeg_quality)
      else:
        image = gen_image_ops.encode_jpeg(image, quality=jpeg_quality)
  
      # Decode jpeg image
      image = gen_image_ops.decode_jpeg(image)
      # Convert back to original dtype and return
      return convert_image_dtype(image, orig_dtype)
  
  
  @tf_export('image.random_saturation')
  def random_saturation(image, lower, upper, seed=None):
    """Adjust the saturation of RGB images by a random factor.
  
    Equivalent to `adjust_saturation()` but uses a `saturation_factor` randomly
    picked in the interval `[lower, upper]`.
  
    Args:
      image: RGB image or images. Size of the last dimension must be 3.
      lower: float.  Lower bound for the random saturation factor.
      upper: float.  Upper bound for the random saturation factor.
      seed: An operation-specific seed. It will be used in conjunction with the
        graph-level seed to determine the real seeds that will be used in this
        operation. Please see the documentation of set_random_seed for its
        interaction with the graph-level random seed.
  
    Returns:
      Adjusted image(s), same shape and DType as `image`.
  
    Raises:
      ValueError: if `upper <= lower` or if `lower < 0`.
    """
    if upper <= lower:
      raise ValueError('upper must be > lower.')
  
    if lower < 0:
      raise ValueError('lower must be non-negative.')
  
    # Pick a float in [lower, upper]
    saturation_factor = random_ops.random_uniform([], lower, upper, seed=seed)
    return adjust_saturation(image, saturation_factor)
  
  
  @tf_export('image.adjust_saturation')
  def adjust_saturation(image, saturation_factor, name=None):
    """Adjust saturation of RGB images.
  
    This is a convenience method that converts RGB images to float
    representation, converts them to HSV, add an offset to the saturation channel,
    converts back to RGB and then back to the original data type. If several
    adjustments are chained it is advisable to minimize the number of redundant
    conversions.
  
    `image` is an RGB image or images.  The image saturation is adjusted by
    converting the images to HSV and multiplying the saturation (S) channel by
    `saturation_factor` and clipping. The images are then converted back to RGB.
  
    Args:
      image: RGB image or images. Size of the last dimension must be 3.
      saturation_factor: float. Factor to multiply the saturation by.
      name: A name for this operation (optional).
  
    Returns:
      Adjusted image(s), same shape and DType as `image`.
    """
    with ops.name_scope(name, 'adjust_saturation', [image]) as name:
      image = ops.convert_to_tensor(image, name='image')
      # Remember original dtype to so we can convert back if needed
      orig_dtype = image.dtype
      if orig_dtype in (dtypes.float16, dtypes.float32):
        flt_image = image
      else:
        flt_image = convert_image_dtype(image, dtypes.float32)
  
      adjusted = gen_image_ops.adjust_saturation(flt_image, saturation_factor)
  
      return convert_image_dtype(adjusted, orig_dtype)
  
  
  @tf_export('io.is_jpeg', 'image.is_jpeg', v1=['io.is_jpeg', 'image.is_jpeg'])
  def is_jpeg(contents, name=None):
    r"""Convenience function to check if the 'contents' encodes a JPEG image.
  
    Args:
      contents: 0-D `string`. The encoded image bytes.
      name: A name for the operation (optional)
  
    Returns:
       A scalar boolean tensor indicating if 'contents' may be a JPEG image.
       is_jpeg is susceptible to false positives.
    """
    # Normal JPEGs start with \xff\xd8\xff\xe0
    # JPEG with EXIF starts with \xff\xd8\xff\xe1
    # Use \xff\xd8\xff to cover both.
    with ops.name_scope(name, 'is_jpeg'):
      substr = string_ops.substr(contents, 0, 3)
      return math_ops.equal(substr, b'\xff\xd8\xff', name=name)
  
  
  def _is_png(contents, name=None):
    r"""Convenience function to check if the 'contents' encodes a PNG image.
  
    Args:
      contents: 0-D `string`. The encoded image bytes.
      name: A name for the operation (optional)
  
    Returns:
       A scalar boolean tensor indicating if 'contents' may be a PNG image.
       is_png is susceptible to false positives.
    """
    with ops.name_scope(name, 'is_png'):
      substr = string_ops.substr(contents, 0, 3)
      return math_ops.equal(substr, b'\211PN', name=name)
  
  
  tf_export(
      'io.decode_and_crop_jpeg',
      'image.decode_and_crop_jpeg',
      v1=['io.decode_and_crop_jpeg', 'image.decode_and_crop_jpeg'])(
          gen_image_ops.decode_and_crop_jpeg)
  
  tf_export(
      'io.decode_bmp',
      'image.decode_bmp',
      v1=['io.decode_bmp', 'image.decode_bmp'])(
          gen_image_ops.decode_bmp)
  tf_export(
      'io.decode_gif',
      'image.decode_gif',
      v1=['io.decode_gif', 'image.decode_gif'])(
          gen_image_ops.decode_gif)
  tf_export(
      'io.decode_jpeg',
      'image.decode_jpeg',
      v1=['io.decode_jpeg', 'image.decode_jpeg'])(
          gen_image_ops.decode_jpeg)
  tf_export(
      'io.decode_png',
      'image.decode_png',
      v1=['io.decode_png', 'image.decode_png'])(
          gen_image_ops.decode_png)
  
  tf_export(
      'io.encode_jpeg',
      'image.encode_jpeg',
      v1=['io.encode_jpeg', 'image.encode_jpeg'])(
          gen_image_ops.encode_jpeg)
  tf_export(
      'io.extract_jpeg_shape',
      'image.extract_jpeg_shape',
      v1=['io.extract_jpeg_shape', 'image.extract_jpeg_shape'])(
          gen_image_ops.extract_jpeg_shape)
  
  
  @tf_export(
      'io.decode_image',
      'image.decode_image',
      v1=['io.decode_image', 'image.decode_image'])
  def decode_image(contents,
                   channels=None,
                   dtype=dtypes.uint8,
                   name=None,
                   expand_animations=True):
    """Function for `decode_bmp`, `decode_gif`, `decode_jpeg`, and `decode_png`.
  
    Detects whether an image is a BMP, GIF, JPEG, or PNG, and performs the
    appropriate operation to convert the input bytes `string` into a `Tensor`
    of type `dtype`.
  
    Note: `decode_gif` returns a 4-D array `[num_frames, height, width, 3]`, as
    opposed to `decode_bmp`, `decode_jpeg` and `decode_png`, which return 3-D
    arrays `[height, width, num_channels]`. Make sure to take this into account
    when constructing your graph if you are intermixing GIF files with BMP, JPEG,
    and/or PNG files. Alternately, set the `expand_animations` argument of this
    function to `False`, in which case the op will return 3-dimensional tensors
    and will truncate animated GIF files to the first frame.
  
    Args:
      contents: 0-D `string`. The encoded image bytes.
      channels: An optional `int`. Defaults to `0`. Number of color channels for
        the decoded image.
      dtype: The desired DType of the returned `Tensor`.
      name: A name for the operation (optional)
      expand_animations: Controls the shape of the returned op's output.
        If `True`, the returned op will produce a 3-D tensor for PNG, JPEG, and
        BMP files; and a 4-D tensor for all GIFs, whether animated or not.
        If, `False`, the returned op will produce a 3-D tensor for all file
        types and will truncate animated GIFs to the first frame.
  
    Returns:
      `Tensor` with type `dtype` and a 3- or 4-dimensional shape, depending on
      the file type and the value of the `expand_animations` parameter.
  
    Raises:
      ValueError: On incorrect number of channels.
    """
    with ops.name_scope(name, 'decode_image'):
      if channels not in (None, 0, 1, 3, 4):
        raise ValueError('channels must be in (None, 0, 1, 3, 4)')
      substr = string_ops.substr(contents, 0, 3)
  
      def _bmp():
        """Decodes a BMP image."""
        signature = string_ops.substr(contents, 0, 2)
        # Create assert op to check that bytes are BMP decodable
        is_bmp = math_ops.equal(signature, 'BM', name='is_bmp')
        decode_msg = 'Unable to decode bytes as JPEG, PNG, GIF, or BMP'
        assert_decode = control_flow_ops.Assert(is_bmp, [decode_msg])
        bmp_channels = 0 if channels is None else channels
        good_channels = math_ops.not_equal(bmp_channels, 1, name='check_channels')
        channels_msg = 'Channels must be in (None, 0, 3) when decoding BMP images'
        assert_channels = control_flow_ops.Assert(good_channels, [channels_msg])
        with ops.control_dependencies([assert_decode, assert_channels]):
          return convert_image_dtype(gen_image_ops.decode_bmp(contents), dtype)
  
      def _gif():
        """Decodes a GIF image."""
        # Create assert to make sure that channels is not set to 1
        # Already checked above that channels is in (None, 0, 1, 3)
        gif_channels = 0 if channels is None else channels
        good_channels = math_ops.logical_and(
            math_ops.not_equal(gif_channels, 1, name='check_gif_channels'),
            math_ops.not_equal(gif_channels, 4, name='check_gif_channels'))
        channels_msg = 'Channels must be in (None, 0, 3) when decoding GIF images'
        assert_channels = control_flow_ops.Assert(good_channels, [channels_msg])
        with ops.control_dependencies([assert_channels]):
          result = convert_image_dtype(gen_image_ops.decode_gif(contents), dtype)
          if not expand_animations:
            # For now we decode animated GIFs fully and toss out all but the
            # first frame when expand_animations is False
            result = array_ops.gather(result, 0)
          return result
  
      def check_gif():
        # Create assert op to check that bytes are GIF decodable
        is_gif = math_ops.equal(substr, b'\x47\x49\x46', name='is_gif')
        return control_flow_ops.cond(is_gif, _gif, _bmp, name='cond_gif')
  
      def _png():
        """Decodes a PNG image."""
        return convert_image_dtype(
            gen_image_ops.decode_png(
                contents,
                channels,
                dtype=dtypes.uint8 if dtype == dtypes.uint8 else dtypes.uint16),
            dtype)
  
      def check_png():
        """Checks if an image is PNG."""
        return control_flow_ops.cond(
            _is_png(contents), _png, check_gif, name='cond_png')
  
      def _jpeg():
        """Decodes a jpeg image."""
        jpeg_channels = 0 if channels is None else channels
        good_channels = math_ops.not_equal(
            jpeg_channels, 4, name='check_jpeg_channels')
        channels_msg = ('Channels must be in (None, 0, 1, 3) when decoding JPEG '
                        'images')
        assert_channels = control_flow_ops.Assert(good_channels, [channels_msg])
        with ops.control_dependencies([assert_channels]):
          return convert_image_dtype(
              gen_image_ops.decode_jpeg(contents, channels), dtype)
  
      # Decode normal JPEG images (start with \xff\xd8\xff\xe0)
      # as well as JPEG images with EXIF data (start with \xff\xd8\xff\xe1).
      return control_flow_ops.cond(
          is_jpeg(contents), _jpeg, check_png, name='cond_jpeg')
  
  
  @tf_export('image.total_variation')
  def total_variation(images, name=None):
    """Calculate and return the total variation for one or more images.
  
    The total variation is the sum of the absolute differences for neighboring
    pixel-values in the input images. This measures how much noise is in the
    images.
  
    This can be used as a loss-function during optimization so as to suppress
    noise in images. If you have a batch of images, then you should calculate
    the scalar loss-value as the sum:
    `loss = tf.reduce_sum(tf.image.total_variation(images))`
  
    This implements the anisotropic 2-D version of the formula described here:
  
    https://en.wikipedia.org/wiki/Total_variation_denoising
  
    Args:
      images: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
        of shape `[height, width, channels]`.
      name: A name for the operation (optional).
  
    Raises:
      ValueError: if images.shape is not a 3-D or 4-D vector.
  
    Returns:
      The total variation of `images`.
  
      If `images` was 4-D, return a 1-D float Tensor of shape `[batch]` with the
      total variation for each image in the batch.
      If `images` was 3-D, return a scalar float with the total variation for
      that image.
    """
  
    with ops.name_scope(name, 'total_variation'):
      ndims = images.get_shape().ndims
  
      if ndims == 3:
        # The input is a single image with shape [height, width, channels].
  
        # Calculate the difference of neighboring pixel-values.
        # The images are shifted one pixel along the height and width by slicing.
        pixel_dif1 = images[1:, :, :] - images[:-1, :, :]
        pixel_dif2 = images[:, 1:, :] - images[:, :-1, :]
  
        # Sum for all axis. (None is an alias for all axis.)
        sum_axis = None
      elif ndims == 4:
        # The input is a batch of images with shape:
        # [batch, height, width, channels].
  
        # Calculate the difference of neighboring pixel-values.
        # The images are shifted one pixel along the height and width by slicing.
        pixel_dif1 = images[:, 1:, :, :] - images[:, :-1, :, :]
        pixel_dif2 = images[:, :, 1:, :] - images[:, :, :-1, :]
  
        # Only sum for the last 3 axis.
        # This results in a 1-D tensor with the total variation for each image.
        sum_axis = [1, 2, 3]
      else:
        raise ValueError('\'images\' must be either 3 or 4-dimensional.')
  
      # Calculate the total variation by taking the absolute value of the
      # pixel-differences and summing over the appropriate axis.
      tot_var = (
          math_ops.reduce_sum(math_ops.abs(pixel_dif1), axis=sum_axis) +
          math_ops.reduce_sum(math_ops.abs(pixel_dif2), axis=sum_axis))
  
    return tot_var
  
  
  @tf_export('image.sample_distorted_bounding_box', v1=[])
  def sample_distorted_bounding_box_v2(image_size,
                                       bounding_boxes,
                                       seed=0,
                                       min_object_covered=0.1,
                                       aspect_ratio_range=None,
                                       area_range=None,
                                       max_attempts=None,
                                       use_image_if_no_bounding_boxes=None,
                                       name=None):
    """Generate a single randomly distorted bounding box for an image.
  
    Bounding box annotations are often supplied in addition to ground-truth labels
    in image recognition or object localization tasks. A common technique for
    training such a system is to randomly distort an image while preserving
    its content, i.e. *data augmentation*. This Op outputs a randomly distorted
    localization of an object, i.e. bounding box, given an `image_size`,
    `bounding_boxes` and a series of constraints.
  
    The output of this Op is a single bounding box that may be used to crop the
    original image. The output is returned as 3 tensors: `begin`, `size` and
    `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
    image. The latter may be supplied to `tf.image.draw_bounding_boxes` to
    visualize what the bounding box looks like.
  
    Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`.
    The bounding box coordinates are floats in `[0.0, 1.0]` relative to the width
    and height of the underlying image.
  
    For example,
  
    ```python
        # Generate a single distorted bounding box.
        begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
            tf.shape(image),
            bounding_boxes=bounding_boxes,
            min_object_covered=0.1)
  
        # Draw the bounding box in an image summary.
        image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
                                                      bbox_for_draw)
        tf.compat.v1.summary.image('images_with_box', image_with_box)
  
        # Employ the bounding box to distort the image.
        distorted_image = tf.slice(image, begin, size)
    ```
  
    Note that if no bounding box information is available, setting
    `use_image_if_no_bounding_boxes = true` will assume there is a single implicit
    bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
    false and no bounding boxes are supplied, an error is raised.
  
    Args:
      image_size: A `Tensor`. Must be one of the following types: `uint8`, `int8`,
        `int16`, `int32`, `int64`. 1-D, containing `[height, width, channels]`.
      bounding_boxes: A `Tensor` of type `float32`. 3-D with shape `[batch, N, 4]`
        describing the N bounding boxes associated with the image.
      seed: An optional `int`. Defaults to `0`. If `seed` is set to non-zero, the
        random number generator is seeded by the given `seed`.  Otherwise, it is
        seeded by a random seed.
      min_object_covered: A Tensor of type `float32`. Defaults to `0.1`. The
        cropped area of the image must contain at least this fraction of any
        bounding box supplied. The value of this parameter should be non-negative.
        In the case of 0, the cropped area does not need to overlap any of the
        bounding boxes supplied.
      aspect_ratio_range: An optional list of `floats`. Defaults to `[0.75,
        1.33]`. The cropped area of the image must have an aspect `ratio = width /
        height` within this range.
      area_range: An optional list of `floats`. Defaults to `[0.05, 1]`. The
        cropped area of the image must contain a fraction of the supplied image
        within this range.
      max_attempts: An optional `int`. Defaults to `100`. Number of attempts at
        generating a cropped region of the image of the specified constraints.
        After `max_attempts` failures, return the entire image.
      use_image_if_no_bounding_boxes: An optional `bool`. Defaults to `False`.
        Controls behavior if no bounding boxes supplied. If true, assume an
        implicit bounding box covering the whole input. If false, raise an error.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of `Tensor` objects (begin, size, bboxes).
  
      begin: A `Tensor`. Has the same type as `image_size`. 1-D, containing
      `[offset_height, offset_width, 0]`. Provide as input to
        `tf.slice`.
      size: A `Tensor`. Has the same type as `image_size`. 1-D, containing
      `[target_height, target_width, -1]`. Provide as input to
        `tf.slice`.
      bboxes: A `Tensor` of type `float32`. 3-D with shape `[1, 1, 4]` containing
      the distorted bounding box.
      Provide as input to `tf.image.draw_bounding_boxes`.
    """
    seed1, seed2 = random_seed.get_seed(seed) if seed else (0, 0)
    return sample_distorted_bounding_box(image_size, bounding_boxes, seed1, seed2,
                                         min_object_covered, aspect_ratio_range,
                                         area_range, max_attempts,
                                         use_image_if_no_bounding_boxes, name)
  
  
  @tf_export(v1=['image.sample_distorted_bounding_box'])
  @deprecation.deprecated(
      date=None,
      instructions='`seed2` arg is deprecated.'
      'Use sample_distorted_bounding_box_v2 instead.')
  def sample_distorted_bounding_box(image_size,
                                    bounding_boxes,
                                    seed=None,
                                    seed2=None,
                                    min_object_covered=0.1,
                                    aspect_ratio_range=None,
                                    area_range=None,
                                    max_attempts=None,
                                    use_image_if_no_bounding_boxes=None,
                                    name=None):
    """Generate a single randomly distorted bounding box for an image.
  
    Bounding box annotations are often supplied in addition to ground-truth labels
    in image recognition or object localization tasks. A common technique for
    training such a system is to randomly distort an image while preserving
    its content, i.e. *data augmentation*. This Op outputs a randomly distorted
    localization of an object, i.e. bounding box, given an `image_size`,
    `bounding_boxes` and a series of constraints.
  
    The output of this Op is a single bounding box that may be used to crop the
    original image. The output is returned as 3 tensors: `begin`, `size` and
    `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
    image. The latter may be supplied to `tf.image.draw_bounding_boxes` to
    visualize what the bounding box looks like.
  
    Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`.
    The
    bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
    height of the underlying image.
  
    For example,
  
    ```python
        # Generate a single distorted bounding box.
        begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
            tf.shape(image),
            bounding_boxes=bounding_boxes,
            min_object_covered=0.1)
  
        # Draw the bounding box in an image summary.
        image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
                                                      bbox_for_draw)
        tf.compat.v1.summary.image('images_with_box', image_with_box)
  
        # Employ the bounding box to distort the image.
        distorted_image = tf.slice(image, begin, size)
    ```
  
    Note that if no bounding box information is available, setting
    `use_image_if_no_bounding_boxes = True` will assume there is a single implicit
    bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
    false and no bounding boxes are supplied, an error is raised.
  
    Args:
      image_size: A `Tensor`. Must be one of the following types: `uint8`, `int8`,
        `int16`, `int32`, `int64`. 1-D, containing `[height, width, channels]`.
      bounding_boxes: A `Tensor` of type `float32`. 3-D with shape `[batch, N, 4]`
        describing the N bounding boxes associated with the image.
      seed: An optional `int`. Defaults to `0`. If either `seed` or `seed2` are
        set to non-zero, the random number generator is seeded by the given
        `seed`.  Otherwise, it is seeded by a random seed.
      seed2: An optional `int`. Defaults to `0`. A second seed to avoid seed
        collision.
      min_object_covered: A Tensor of type `float32`. Defaults to `0.1`. The
        cropped area of the image must contain at least this fraction of any
        bounding box supplied. The value of this parameter should be non-negative.
        In the case of 0, the cropped area does not need to overlap any of the
        bounding boxes supplied.
      aspect_ratio_range: An optional list of `floats`. Defaults to `[0.75,
        1.33]`. The cropped area of the image must have an aspect ratio = width /
        height within this range.
      area_range: An optional list of `floats`. Defaults to `[0.05, 1]`. The
        cropped area of the image must contain a fraction of the supplied image
        within this range.
      max_attempts: An optional `int`. Defaults to `100`. Number of attempts at
        generating a cropped region of the image of the specified constraints.
        After `max_attempts` failures, return the entire image.
      use_image_if_no_bounding_boxes: An optional `bool`. Defaults to `False`.
        Controls behavior if no bounding boxes supplied. If true, assume an
        implicit bounding box covering the whole input. If false, raise an error.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of `Tensor` objects (begin, size, bboxes).
  
      begin: A `Tensor`. Has the same type as `image_size`. 1-D, containing
      `[offset_height, offset_width, 0]`. Provide as input to
        `tf.slice`.
      size: A `Tensor`. Has the same type as `image_size`. 1-D, containing
      `[target_height, target_width, -1]`. Provide as input to
        `tf.slice`.
      bboxes: A `Tensor` of type `float32`. 3-D with shape `[1, 1, 4]` containing
      the distorted bounding box.
        Provide as input to `tf.image.draw_bounding_boxes`.
    """
    with ops.name_scope(name, 'sample_distorted_bounding_box'):
      return gen_image_ops.sample_distorted_bounding_box_v2(
          image_size,
          bounding_boxes,
          seed=seed,
          seed2=seed2,
          min_object_covered=min_object_covered,
          aspect_ratio_range=aspect_ratio_range,
          area_range=area_range,
          max_attempts=max_attempts,
          use_image_if_no_bounding_boxes=use_image_if_no_bounding_boxes,
          name=name)
  
  
  @tf_export('image.non_max_suppression')
  def non_max_suppression(boxes,
                          scores,
                          max_output_size,
                          iou_threshold=0.5,
                          score_threshold=float('-inf'),
                          name=None):
    """Greedily selects a subset of bounding boxes in descending order of score.
  
    Prunes away boxes that have high intersection-over-union (IOU) overlap
    with previously selected boxes.  Bounding boxes are supplied as
    `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the coordinates of any
    diagonal pair of box corners and the coordinates can be provided as normalized
    (i.e., lying in the interval `[0, 1]`) or absolute.  Note that this algorithm
    is agnostic to where the origin is in the coordinate system.  Note that this
    algorithm is invariant to orthogonal transformations and translations
    of the coordinate system; thus translating or reflections of the coordinate
    system result in the same boxes being selected by the algorithm.
    The output of this operation is a set of integers indexing into the input
    collection of bounding boxes representing the selected boxes.  The bounding
    box coordinates corresponding to the selected indices can then be obtained
    using the `tf.gather` operation.  For example:
      ```python
      selected_indices = tf.image.non_max_suppression(
          boxes, scores, max_output_size, iou_threshold)
      selected_boxes = tf.gather(boxes, selected_indices)
      ```
  
    Args:
      boxes: A 2-D float `Tensor` of shape `[num_boxes, 4]`.
      scores: A 1-D float `Tensor` of shape `[num_boxes]` representing a single
        score corresponding to each box (each row of boxes).
      max_output_size: A scalar integer `Tensor` representing the maximum number
        of boxes to be selected by non max suppression.
      iou_threshold: A float representing the threshold for deciding whether boxes
        overlap too much with respect to IOU.
      score_threshold: A float representing the threshold for deciding when to
        remove boxes based on score.
      name: A name for the operation (optional).
  
    Returns:
      selected_indices: A 1-D integer `Tensor` of shape `[M]` representing the
        selected indices from the boxes tensor, where `M <= max_output_size`.
    """
    with ops.name_scope(name, 'non_max_suppression'):
      iou_threshold = ops.convert_to_tensor(iou_threshold, name='iou_threshold')
      score_threshold = ops.convert_to_tensor(
          score_threshold, name='score_threshold')
      return gen_image_ops.non_max_suppression_v3(boxes, scores, max_output_size,
                                                  iou_threshold, score_threshold)
  
  
  @tf_export('image.non_max_suppression_padded')
  def non_max_suppression_padded(boxes,
                                 scores,
                                 max_output_size,
                                 iou_threshold=0.5,
                                 score_threshold=float('-inf'),
                                 pad_to_max_output_size=False,
                                 name=None):
    """Greedily selects a subset of bounding boxes in descending order of score.
  
    Performs algorithmically equivalent operation to tf.image.non_max_suppression,
    with the addition of an optional parameter which zero-pads the output to
    be of size `max_output_size`.
    The output of this operation is a tuple containing the set of integers
    indexing into the input collection of bounding boxes representing the selected
    boxes and the number of valid indices in the index set.  The bounding box
    coordinates corresponding to the selected indices can then be obtained using
    the `tf.slice` and `tf.gather` operations.  For example:
      ```python
      selected_indices_padded, num_valid = tf.image.non_max_suppression_padded(
          boxes, scores, max_output_size, iou_threshold,
          score_threshold, pad_to_max_output_size=True)
      selected_indices = tf.slice(
          selected_indices_padded, tf.constant([0]), num_valid)
      selected_boxes = tf.gather(boxes, selected_indices)
      ```
  
    Args:
      boxes: A 2-D float `Tensor` of shape `[num_boxes, 4]`.
      scores: A 1-D float `Tensor` of shape `[num_boxes]` representing a single
        score corresponding to each box (each row of boxes).
      max_output_size: A scalar integer `Tensor` representing the maximum number
        of boxes to be selected by non max suppression.
      iou_threshold: A float representing the threshold for deciding whether boxes
        overlap too much with respect to IOU.
      score_threshold: A float representing the threshold for deciding when to
        remove boxes based on score.
      pad_to_max_output_size: bool.  If True, size of `selected_indices` output is
        padded to `max_output_size`.
      name: A name for the operation (optional).
  
    Returns:
      selected_indices: A 1-D integer `Tensor` of shape `[M]` representing the
        selected indices from the boxes tensor, where `M <= max_output_size`.
      valid_outputs: A scalar integer `Tensor` denoting how many elements in
      `selected_indices` are valid.  Valid elements occur first, then padding.
    """
    with ops.name_scope(name, 'non_max_suppression_padded'):
      iou_threshold = ops.convert_to_tensor(iou_threshold, name='iou_threshold')
      score_threshold = ops.convert_to_tensor(
          score_threshold, name='score_threshold')
      if compat.forward_compatible(2018, 8, 7) or pad_to_max_output_size:
        return gen_image_ops.non_max_suppression_v4(boxes, scores,
                                                    max_output_size,
                                                    iou_threshold,
                                                    score_threshold,
                                                    pad_to_max_output_size)
      else:
        return gen_image_ops.non_max_suppression_v3(boxes, scores,
                                                    max_output_size,
                                                    iou_threshold,
                                                    score_threshold)
  
  
  @tf_export('image.non_max_suppression_overlaps')
  def non_max_suppression_with_overlaps(overlaps,
                                        scores,
                                        max_output_size,
                                        overlap_threshold=0.5,
                                        score_threshold=float('-inf'),
                                        name=None):
    """Greedily selects a subset of bounding boxes in descending order of score.
  
    Prunes away boxes that have high overlap with previously selected boxes.
    N-by-n overlap values are supplied as square matrix.
    The output of this operation is a set of integers indexing into the input
    collection of bounding boxes representing the selected boxes.  The bounding
    box coordinates corresponding to the selected indices can then be obtained
    using the `tf.gather` operation.  For example:
      ```python
      selected_indices = tf.image.non_max_suppression_overlaps(
          overlaps, scores, max_output_size, iou_threshold)
      selected_boxes = tf.gather(boxes, selected_indices)
      ```
  
    Args:
      overlaps: A 2-D float `Tensor` of shape `[num_boxes, num_boxes]`.
      scores: A 1-D float `Tensor` of shape `[num_boxes]` representing a single
        score corresponding to each box (each row of boxes).
      max_output_size: A scalar integer `Tensor` representing the maximum number
        of boxes to be selected by non max suppression.
      overlap_threshold: A float representing the threshold for deciding whether
        boxes overlap too much with respect to the provided overlap values.
      score_threshold: A float representing the threshold for deciding when to
        remove boxes based on score.
      name: A name for the operation (optional).
  
    Returns:
      selected_indices: A 1-D integer `Tensor` of shape `[M]` representing the
        selected indices from the overlaps tensor, where `M <= max_output_size`.
    """
    with ops.name_scope(name, 'non_max_suppression_overlaps'):
      overlap_threshold = ops.convert_to_tensor(
          overlap_threshold, name='overlap_threshold')
      # pylint: disable=protected-access
      return gen_image_ops.non_max_suppression_with_overlaps(
          overlaps, scores, max_output_size, overlap_threshold, score_threshold)
      # pylint: enable=protected-access
  
  
  _rgb_to_yiq_kernel = [[0.299, 0.59590059, 0.2115],
                        [0.587, -0.27455667, -0.52273617],
                        [0.114, -0.32134392, 0.31119955]]
  
  
  @tf_export('image.rgb_to_yiq')
  def rgb_to_yiq(images):
    """Converts one or more images from RGB to YIQ.
  
    Outputs a tensor of the same shape as the `images` tensor, containing the YIQ
    value of the pixels.
    The output is only well defined if the value in images are in [0,1].
  
    Args:
      images: 2-D or higher rank. Image data to convert. Last dimension must be
        size 3.
  
    Returns:
      images: tensor with the same shape as `images`.
    """
    images = ops.convert_to_tensor(images, name='images')
    kernel = ops.convert_to_tensor(
        _rgb_to_yiq_kernel, dtype=images.dtype, name='kernel')
    ndims = images.get_shape().ndims
    return math_ops.tensordot(images, kernel, axes=[[ndims - 1], [0]])
  
  
  _yiq_to_rgb_kernel = [[1, 1, 1], [0.95598634, -0.27201283, -1.10674021],
                        [0.6208248, -0.64720424, 1.70423049]]
  
  
  @tf_export('image.yiq_to_rgb')
  def yiq_to_rgb(images):
    """Converts one or more images from YIQ to RGB.
  
    Outputs a tensor of the same shape as the `images` tensor, containing the RGB
    value of the pixels.
    The output is only well defined if the Y value in images are in [0,1],
    I value are in [-0.5957,0.5957] and Q value are in [-0.5226,0.5226].
  
    Args:
      images: 2-D or higher rank. Image data to convert. Last dimension must be
        size 3.
  
    Returns:
      images: tensor with the same shape as `images`.
    """
    images = ops.convert_to_tensor(images, name='images')
    kernel = ops.convert_to_tensor(
        _yiq_to_rgb_kernel, dtype=images.dtype, name='kernel')
    ndims = images.get_shape().ndims
    return math_ops.tensordot(images, kernel, axes=[[ndims - 1], [0]])
  
  
  _rgb_to_yuv_kernel = [[0.299, -0.14714119, 0.61497538],
                        [0.587, -0.28886916, -0.51496512],
                        [0.114, 0.43601035, -0.10001026]]
  
  
  @tf_export('image.rgb_to_yuv')
  def rgb_to_yuv(images):
    """Converts one or more images from RGB to YUV.
  
    Outputs a tensor of the same shape as the `images` tensor, containing the YUV
    value of the pixels.
    The output is only well defined if the value in images are in [0,1].
  
    Args:
      images: 2-D or higher rank. Image data to convert. Last dimension must be
        size 3.
  
    Returns:
      images: tensor with the same shape as `images`.
    """
    images = ops.convert_to_tensor(images, name='images')
    kernel = ops.convert_to_tensor(
        _rgb_to_yuv_kernel, dtype=images.dtype, name='kernel')
    ndims = images.get_shape().ndims
    return math_ops.tensordot(images, kernel, axes=[[ndims - 1], [0]])
  
  
  _yuv_to_rgb_kernel = [[1, 1, 1], [0, -0.394642334, 2.03206185],
                        [1.13988303, -0.58062185, 0]]
  
  
  @tf_export('image.yuv_to_rgb')
  def yuv_to_rgb(images):
    """Converts one or more images from YUV to RGB.
  
    Outputs a tensor of the same shape as the `images` tensor, containing the RGB
    value of the pixels.
    The output is only well defined if the Y value in images are in [0,1],
    U and V value are in [-0.5,0.5].
  
    Args:
      images: 2-D or higher rank. Image data to convert. Last dimension must be
        size 3.
  
    Returns:
      images: tensor with the same shape as `images`.
    """
    images = ops.convert_to_tensor(images, name='images')
    kernel = ops.convert_to_tensor(
        _yuv_to_rgb_kernel, dtype=images.dtype, name='kernel')
    ndims = images.get_shape().ndims
    return math_ops.tensordot(images, kernel, axes=[[ndims - 1], [0]])
  
  
  def _verify_compatible_image_shapes(img1, img2):
    """Checks if two image tensors are compatible for applying SSIM or PSNR.
  
    This function checks if two sets of images have ranks at least 3, and if the
    last three dimensions match.
  
    Args:
      img1: Tensor containing the first image batch.
      img2: Tensor containing the second image batch.
  
    Returns:
      A tuple containing: the first tensor shape, the second tensor shape, and a
      list of control_flow_ops.Assert() ops implementing the checks.
  
    Raises:
      ValueError: When static shape check fails.
    """
    shape1 = img1.get_shape().with_rank_at_least(3)
    shape2 = img2.get_shape().with_rank_at_least(3)
    shape1[-3:].assert_is_compatible_with(shape2[-3:])
  
    if shape1.ndims is not None and shape2.ndims is not None:
      for dim1, dim2 in zip(
          reversed(shape1.dims[:-3]), reversed(shape2.dims[:-3])):
        if not (dim1 == 1 or dim2 == 1 or dim1.is_compatible_with(dim2)):
          raise ValueError('Two images are not compatible: %s and %s' %
                           (shape1, shape2))
  
    # Now assign shape tensors.
    shape1, shape2 = array_ops.shape_n([img1, img2])
  
    # TODO(sjhwang): Check if shape1[:-3] and shape2[:-3] are broadcastable.
    checks = []
    checks.append(
        control_flow_ops.Assert(
            math_ops.greater_equal(array_ops.size(shape1), 3), [shape1, shape2],
            summarize=10))
    checks.append(
        control_flow_ops.Assert(
            math_ops.reduce_all(math_ops.equal(shape1[-3:], shape2[-3:])),
            [shape1, shape2],
            summarize=10))
    return shape1, shape2, checks
  
  
  @tf_export('image.psnr')
  def psnr(a, b, max_val, name=None):
    """Returns the Peak Signal-to-Noise Ratio between a and b.
  
    This is intended to be used on signals (or images). Produces a PSNR value for
    each image in batch.
  
    The last three dimensions of input are expected to be [height, width, depth].
  
    Example:
  
    ```python
        # Read images from file.
        im1 = tf.decode_png('path/to/im1.png')
        im2 = tf.decode_png('path/to/im2.png')
        # Compute PSNR over tf.uint8 Tensors.
        psnr1 = tf.image.psnr(im1, im2, max_val=255)
  
        # Compute PSNR over tf.float32 Tensors.
        im1 = tf.image.convert_image_dtype(im1, tf.float32)
        im2 = tf.image.convert_image_dtype(im2, tf.float32)
        psnr2 = tf.image.psnr(im1, im2, max_val=1.0)
        # psnr1 and psnr2 both have type tf.float32 and are almost equal.
    ```
  
    Arguments:
      a: First set of images.
      b: Second set of images.
      max_val: The dynamic range of the images (i.e., the difference between the
        maximum the and minimum allowed values).
      name: Namespace to embed the computation in.
  
    Returns:
      The scalar PSNR between a and b. The returned tensor has type `tf.float32`
      and shape [batch_size, 1].
    """
    with ops.name_scope(name, 'PSNR', [a, b]):
      # Need to convert the images to float32.  Scale max_val accordingly so that
      # PSNR is computed correctly.
      max_val = math_ops.cast(max_val, a.dtype)
      max_val = convert_image_dtype(max_val, dtypes.float32)
      a = convert_image_dtype(a, dtypes.float32)
      b = convert_image_dtype(b, dtypes.float32)
      mse = math_ops.reduce_mean(math_ops.squared_difference(a, b), [-3, -2, -1])
      psnr_val = math_ops.subtract(
          20 * math_ops.log(max_val) / math_ops.log(10.0),
          np.float32(10 / np.log(10)) * math_ops.log(mse),
          name='psnr')
  
      _, _, checks = _verify_compatible_image_shapes(a, b)
      with ops.control_dependencies(checks):
        return array_ops.identity(psnr_val)
  
  
  def _ssim_helper(x, y, reducer, max_val, compensation=1.0, k1=0.01, k2=0.03):
    r"""Helper function for computing SSIM.
  
    SSIM estimates covariances with weighted sums.  The default parameters
    use a biased estimate of the covariance:
    Suppose `reducer` is a weighted sum, then the mean estimators are
      \mu_x = \sum_i w_i x_i,
      \mu_y = \sum_i w_i y_i,
    where w_i's are the weighted-sum weights, and covariance estimator is
      cov_{xy} = \sum_i w_i (x_i - \mu_x) (y_i - \mu_y)
    with assumption \sum_i w_i = 1. This covariance estimator is biased, since
      E[cov_{xy}] = (1 - \sum_i w_i ^ 2) Cov(X, Y).
    For SSIM measure with unbiased covariance estimators, pass as `compensation`
    argument (1 - \sum_i w_i ^ 2).
  
    Arguments:
      x: First set of images.
      y: Second set of images.
      reducer: Function that computes 'local' averages from set of images. For
        non-convolutional version, this is usually tf.reduce_mean(x, [1, 2]), and
        for convolutional version, this is usually tf.nn.avg_pool2d or
        tf.nn.conv2d with weighted-sum kernel.
      max_val: The dynamic range (i.e., the difference between the maximum
        possible allowed value and the minimum allowed value).
      compensation: Compensation factor. See above.
      k1: Default value 0.01
      k2: Default value 0.03 (SSIM is less sensitivity to K2 for lower values, so
        it would be better if we taken the values in range of 0< K2 <0.4).
  
    Returns:
      A pair containing the luminance measure, and the contrast-structure measure.
    """
  
    c1 = (k1 * max_val)**2
    c2 = (k2 * max_val)**2
  
    # SSIM luminance measure is
    # (2 * mu_x * mu_y + c1) / (mu_x ** 2 + mu_y ** 2 + c1).
    mean0 = reducer(x)
    mean1 = reducer(y)
    num0 = mean0 * mean1 * 2.0
    den0 = math_ops.square(mean0) + math_ops.square(mean1)
    luminance = (num0 + c1) / (den0 + c1)
  
    # SSIM contrast-structure measure is
    #   (2 * cov_{xy} + c2) / (cov_{xx} + cov_{yy} + c2).
    # Note that `reducer` is a weighted sum with weight w_k, \sum_i w_i = 1, then
    #   cov_{xy} = \sum_i w_i (x_i - \mu_x) (y_i - \mu_y)
    #          = \sum_i w_i x_i y_i - (\sum_i w_i x_i) (\sum_j w_j y_j).
    num1 = reducer(x * y) * 2.0
    den1 = reducer(math_ops.square(x) + math_ops.square(y))
    c2 *= compensation
    cs = (num1 - num0 + c2) / (den1 - den0 + c2)
  
    # SSIM score is the product of the luminance and contrast-structure measures.
    return luminance, cs
  
  
  def _fspecial_gauss(size, sigma):
    """Function to mimic the 'fspecial' gaussian MATLAB function."""
    size = ops.convert_to_tensor(size, dtypes.int32)
    sigma = ops.convert_to_tensor(sigma)
  
    coords = math_ops.cast(math_ops.range(size), sigma.dtype)
    coords -= math_ops.cast(size - 1, sigma.dtype) / 2.0
  
    g = math_ops.square(coords)
    g *= -0.5 / math_ops.square(sigma)
  
    g = array_ops.reshape(g, shape=[1, -1]) + array_ops.reshape(g, shape=[-1, 1])
    g = array_ops.reshape(g, shape=[1, -1])  # For tf.nn.softmax().
    g = nn_ops.softmax(g)
    return array_ops.reshape(g, shape=[size, size, 1, 1])
  
  
  def _ssim_per_channel(img1,
                        img2,
                        max_val=1.0,
                        filter_size=11,
                        filter_sigma=1.5,
                        k1=0.01,
                        k2=0.03):
    """Computes SSIM index between img1 and img2 per color channel.
  
    This function matches the standard SSIM implementation from:
    Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image
    quality assessment: from error visibility to structural similarity. IEEE
    transactions on image processing.
  
    Details:
      - 11x11 Gaussian filter of width 1.5 is used.
      - k1 = 0.01, k2 = 0.03 as in the original paper.
  
    Args:
      img1: First image batch.
      img2: Second image batch.
      max_val: The dynamic range of the images (i.e., the difference between the
        maximum the and minimum allowed values).
      filter_size: Default value 11 (size of gaussian filter).
      filter_sigma: Default value 1.5 (width of gaussian filter).
      k1: Default value 0.01
      k2: Default value 0.03 (SSIM is less sensitivity to K2 for lower values, so
        it would be better if we taken the values in range of 0< K2 <0.4).
  
    Returns:
      A pair of tensors containing and channel-wise SSIM and contrast-structure
      values. The shape is [..., channels].
    """
    filter_size = constant_op.constant(filter_size, dtype=dtypes.int32)
    filter_sigma = constant_op.constant(filter_sigma, dtype=img1.dtype)
  
    shape1, shape2 = array_ops.shape_n([img1, img2])
    checks = [
        control_flow_ops.Assert(
            math_ops.reduce_all(
                math_ops.greater_equal(shape1[-3:-1], filter_size)),
            [shape1, filter_size],
            summarize=8),
        control_flow_ops.Assert(
            math_ops.reduce_all(
                math_ops.greater_equal(shape2[-3:-1], filter_size)),
            [shape2, filter_size],
            summarize=8)
    ]
  
    # Enforce the check to run before computation.
    with ops.control_dependencies(checks):
      img1 = array_ops.identity(img1)
  
    # TODO(sjhwang): Try to cache kernels and compensation factor.
    kernel = _fspecial_gauss(filter_size, filter_sigma)
    kernel = array_ops.tile(kernel, multiples=[1, 1, shape1[-1], 1])
  
    # The correct compensation factor is `1.0 - tf.reduce_sum(tf.square(kernel))`,
    # but to match MATLAB implementation of MS-SSIM, we use 1.0 instead.
    compensation = 1.0
  
    # TODO(sjhwang): Try FFT.
    # TODO(sjhwang): Gaussian kernel is separable in space. Consider applying
    #   1-by-n and n-by-1 Gaussain filters instead of an n-by-n filter.
    def reducer(x):
      shape = array_ops.shape(x)
      x = array_ops.reshape(x, shape=array_ops.concat([[-1], shape[-3:]], 0))
      y = nn.depthwise_conv2d(x, kernel, strides=[1, 1, 1, 1], padding='VALID')
      return array_ops.reshape(
          y, array_ops.concat([shape[:-3], array_ops.shape(y)[1:]], 0))
  
    luminance, cs = _ssim_helper(img1, img2, reducer, max_val, compensation, k1,
                                 k2)
  
    # Average over the second and the third from the last: height, width.
    axes = constant_op.constant([-3, -2], dtype=dtypes.int32)
    ssim_val = math_ops.reduce_mean(luminance * cs, axes)
    cs = math_ops.reduce_mean(cs, axes)
    return ssim_val, cs
  
  
  @tf_export('image.ssim')
  def ssim(img1,
           img2,
           max_val,
           filter_size=11,
           filter_sigma=1.5,
           k1=0.01,
           k2=0.03):
    """Computes SSIM index between img1 and img2.
  
    This function is based on the standard SSIM implementation from:
    Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image
    quality assessment: from error visibility to structural similarity. IEEE
    transactions on image processing.
  
    Note: The true SSIM is only defined on grayscale.  This function does not
    perform any colorspace transform.  (If input is already YUV, then it will
    compute YUV SSIM average.)
  
    Details:
      - 11x11 Gaussian filter of width 1.5 is used.
      - k1 = 0.01, k2 = 0.03 as in the original paper.
  
    The image sizes must be at least 11x11 because of the filter size.
  
    Example:
  
    ```python
        # Read images from file.
        im1 = tf.decode_png('path/to/im1.png')
        im2 = tf.decode_png('path/to/im2.png')
        # Compute SSIM over tf.uint8 Tensors.
        ssim1 = tf.image.ssim(im1, im2, max_val=255, filter_size=11,
                              filter_sigma=1.5, k1=0.01, k2=0.03)
  
        # Compute SSIM over tf.float32 Tensors.
        im1 = tf.image.convert_image_dtype(im1, tf.float32)
        im2 = tf.image.convert_image_dtype(im2, tf.float32)
        ssim2 = tf.image.ssim(im1, im2, max_val=1.0, filter_size=11,
                              filter_sigma=1.5, k1=0.01, k2=0.03)
        # ssim1 and ssim2 both have type tf.float32 and are almost equal.
    ```
  
    Args:
      img1: First image batch.
      img2: Second image batch.
      max_val: The dynamic range of the images (i.e., the difference between the
        maximum the and minimum allowed values).
      filter_size: Default value 11 (size of gaussian filter).
      filter_sigma: Default value 1.5 (width of gaussian filter).
      k1: Default value 0.01
      k2: Default value 0.03 (SSIM is less sensitivity to K2 for lower values, so
        it would be better if we taken the values in range of 0< K2 <0.4).
  
    Returns:
      A tensor containing an SSIM value for each image in batch.  Returned SSIM
      values are in range (-1, 1], when pixel values are non-negative. Returns
      a tensor with shape: broadcast(img1.shape[:-3], img2.shape[:-3]).
    """
    _, _, checks = _verify_compatible_image_shapes(img1, img2)
    with ops.control_dependencies(checks):
      img1 = array_ops.identity(img1)
  
    # Need to convert the images to float32.  Scale max_val accordingly so that
    # SSIM is computed correctly.
    max_val = math_ops.cast(max_val, img1.dtype)
    max_val = convert_image_dtype(max_val, dtypes.float32)
    img1 = convert_image_dtype(img1, dtypes.float32)
    img2 = convert_image_dtype(img2, dtypes.float32)
    ssim_per_channel, _ = _ssim_per_channel(img1, img2, max_val, filter_size,
                                            filter_sigma, k1, k2)
    # Compute average over color channels.
    return math_ops.reduce_mean(ssim_per_channel, [-1])
  
  
  # Default values obtained by Wang et al.
  _MSSSIM_WEIGHTS = (0.0448, 0.2856, 0.3001, 0.2363, 0.1333)
  
  
  @tf_export('image.ssim_multiscale')
  def ssim_multiscale(img1,
                      img2,
                      max_val,
                      power_factors=_MSSSIM_WEIGHTS,
                      filter_size=11,
                      filter_sigma=1.5,
                      k1=0.01,
                      k2=0.03):
    """Computes the MS-SSIM between img1 and img2.
  
    This function assumes that `img1` and `img2` are image batches, i.e. the last
    three dimensions are [height, width, channels].
  
    Note: The true SSIM is only defined on grayscale.  This function does not
    perform any colorspace transform.  (If input is already YUV, then it will
    compute YUV SSIM average.)
  
    Original paper: Wang, Zhou, Eero P. Simoncelli, and Alan C. Bovik. "Multiscale
    structural similarity for image quality assessment." Signals, Systems and
    Computers, 2004.
  
    Arguments:
      img1: First image batch.
      img2: Second image batch. Must have the same rank as img1.
      max_val: The dynamic range of the images (i.e., the difference between the
        maximum the and minimum allowed values).
      power_factors: Iterable of weights for each of the scales. The number of
        scales used is the length of the list. Index 0 is the unscaled
        resolution's weight and each increasing scale corresponds to the image
        being downsampled by 2.  Defaults to (0.0448, 0.2856, 0.3001, 0.2363,
        0.1333), which are the values obtained in the original paper.
      filter_size: Default value 11 (size of gaussian filter).
      filter_sigma: Default value 1.5 (width of gaussian filter).
      k1: Default value 0.01
      k2: Default value 0.03 (SSIM is less sensitivity to K2 for lower values, so
        it would be better if we taken the values in range of 0< K2 <0.4).
  
    Returns:
      A tensor containing an MS-SSIM value for each image in batch.  The values
      are in range [0, 1].  Returns a tensor with shape:
      broadcast(img1.shape[:-3], img2.shape[:-3]).
    """
    with ops.name_scope(None, 'MS-SSIM', [img1, img2]):
      # Convert to tensor if needed.
      img1 = ops.convert_to_tensor(img1, name='img1')
      img2 = ops.convert_to_tensor(img2, name='img2')
      # Shape checking.
      shape1, shape2, checks = _verify_compatible_image_shapes(img1, img2)
      with ops.control_dependencies(checks):
        img1 = array_ops.identity(img1)
  
      # Need to convert the images to float32.  Scale max_val accordingly so that
      # SSIM is computed correctly.
      max_val = math_ops.cast(max_val, img1.dtype)
      max_val = convert_image_dtype(max_val, dtypes.float32)
      img1 = convert_image_dtype(img1, dtypes.float32)
      img2 = convert_image_dtype(img2, dtypes.float32)
  
      imgs = [img1, img2]
      shapes = [shape1, shape2]
  
      # img1 and img2 are assumed to be a (multi-dimensional) batch of
      # 3-dimensional images (height, width, channels). `heads` contain the batch
      # dimensions, and `tails` contain the image dimensions.
      heads = [s[:-3] for s in shapes]
      tails = [s[-3:] for s in shapes]
  
      divisor = [1, 2, 2, 1]
      divisor_tensor = constant_op.constant(divisor[1:], dtype=dtypes.int32)
  
      def do_pad(images, remainder):
        padding = array_ops.expand_dims(remainder, -1)
        padding = array_ops.pad(padding, [[1, 0], [1, 0]])
        return [array_ops.pad(x, padding, mode='SYMMETRIC') for x in images]
  
      mcs = []
      for k in range(len(power_factors)):
        with ops.name_scope(None, 'Scale%d' % k, imgs):
          if k > 0:
            # Avg pool takes rank 4 tensors. Flatten leading dimensions.
            flat_imgs = [
                array_ops.reshape(x, array_ops.concat([[-1], t], 0))
                for x, t in zip(imgs, tails)
            ]
  
            remainder = tails[0] % divisor_tensor
            need_padding = math_ops.reduce_any(math_ops.not_equal(remainder, 0))
            # pylint: disable=cell-var-from-loop
            padded = control_flow_ops.cond(need_padding,
                                           lambda: do_pad(flat_imgs, remainder),
                                           lambda: flat_imgs)
            # pylint: enable=cell-var-from-loop
  
            downscaled = [
                nn_ops.avg_pool(
                    x, ksize=divisor, strides=divisor, padding='VALID')
                for x in padded
            ]
            tails = [x[1:] for x in array_ops.shape_n(downscaled)]
            imgs = [
                array_ops.reshape(x, array_ops.concat([h, t], 0))
                for x, h, t in zip(downscaled, heads, tails)
            ]
  
          # Overwrite previous ssim value since we only need the last one.
          ssim_per_channel, cs = _ssim_per_channel(
              *imgs,
              max_val=max_val,
              filter_size=filter_size,
              filter_sigma=filter_sigma,
              k1=k1,
              k2=k2)
          mcs.append(nn_ops.relu(cs))
  
      # Remove the cs score for the last scale. In the MS-SSIM calculation,
      # we use the l(p) at the highest scale. l(p) * cs(p) is ssim(p).
      mcs.pop()  # Remove the cs score for the last scale.
      mcs_and_ssim = array_ops.stack(
          mcs + [nn_ops.relu(ssim_per_channel)], axis=-1)
      # Take weighted geometric mean across the scale axis.
      ms_ssim = math_ops.reduce_prod(
          math_ops.pow(mcs_and_ssim, power_factors), [-1])
  
      return math_ops.reduce_mean(ms_ssim, [-1])  # Avg over color channels.
  
  
  @tf_export('image.image_gradients')
  def image_gradients(image):
    """Returns image gradients (dy, dx) for each color channel.
  
    Both output tensors have the same shape as the input: [batch_size, h, w,
    d]. The gradient values are organized so that [I(x+1, y) - I(x, y)] is in
    location (x, y). That means that dy will always have zeros in the last row,
    and dx will always have zeros in the last column.
  
    Arguments:
      image: Tensor with shape [batch_size, h, w, d].
  
    Returns:
      Pair of tensors (dy, dx) holding the vertical and horizontal image
      gradients (1-step finite difference).
  
    Raises:
      ValueError: If `image` is not a 4D tensor.
    """
    if image.get_shape().ndims != 4:
      raise ValueError('image_gradients expects a 4D tensor '
                       '[batch_size, h, w, d], not %s.', image.get_shape())
    image_shape = array_ops.shape(image)
    batch_size, height, width, depth = array_ops.unstack(image_shape)
    dy = image[:, 1:, :, :] - image[:, :-1, :, :]
    dx = image[:, :, 1:, :] - image[:, :, :-1, :]
  
    # Return tensors with same size as original image by concatenating
    # zeros. Place the gradient [I(x+1,y) - I(x,y)] on the base pixel (x, y).
    shape = array_ops.stack([batch_size, 1, width, depth])
    dy = array_ops.concat([dy, array_ops.zeros(shape, image.dtype)], 1)
    dy = array_ops.reshape(dy, image_shape)
  
    shape = array_ops.stack([batch_size, height, 1, depth])
    dx = array_ops.concat([dx, array_ops.zeros(shape, image.dtype)], 2)
    dx = array_ops.reshape(dx, image_shape)
  
    return dy, dx
  
  
  @tf_export('image.sobel_edges')
  def sobel_edges(image):
    """Returns a tensor holding Sobel edge maps.
  
    Arguments:
      image: Image tensor with shape [batch_size, h, w, d] and type float32 or
        float64.  The image(s) must be 2x2 or larger.
  
    Returns:
      Tensor holding edge maps for each channel. Returns a tensor with shape
      [batch_size, h, w, d, 2] where the last two dimensions hold [[dy[0], dx[0]],
      [dy[1], dx[1]], ..., [dy[d-1], dx[d-1]]] calculated using the Sobel filter.
    """
    # Define vertical and horizontal Sobel filters.
    static_image_shape = image.get_shape()
    image_shape = array_ops.shape(image)
    kernels = [[[-1, -2, -1], [0, 0, 0], [1, 2, 1]],
               [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]]
    num_kernels = len(kernels)
    kernels = np.transpose(np.asarray(kernels), (1, 2, 0))
    kernels = np.expand_dims(kernels, -2)
    kernels_tf = constant_op.constant(kernels, dtype=image.dtype)
  
    kernels_tf = array_ops.tile(
        kernels_tf, [1, 1, image_shape[-1], 1], name='sobel_filters')
  
    # Use depth-wise convolution to calculate edge maps per channel.
    pad_sizes = [[0, 0], [1, 1], [1, 1], [0, 0]]
    padded = array_ops.pad(image, pad_sizes, mode='REFLECT')
  
    # Output tensor has shape [batch_size, h, w, d * num_kernels].
    strides = [1, 1, 1, 1]
    output = nn.depthwise_conv2d(padded, kernels_tf, strides, 'VALID')
  
    # Reshape to [batch_size, h, w, d, num_kernels].
    shape = array_ops.concat([image_shape, [num_kernels]], 0)
    output = array_ops.reshape(output, shape=shape)
    output.set_shape(static_image_shape.concatenate([num_kernels]))
    return output
  
  
  def resize_bicubic(images,
                     size,
                     align_corners=False,
                     name=None,
                     half_pixel_centers=False):
    return gen_image_ops.resize_bicubic(
        images=images,
        size=size,
        align_corners=align_corners,
        half_pixel_centers=half_pixel_centers,
        name=name)
  
  
  def resize_bilinear(images,
                      size,
                      align_corners=False,
                      name=None,
                      half_pixel_centers=False):
    return gen_image_ops.resize_bilinear(
        images=images,
        size=size,
        align_corners=align_corners,
        half_pixel_centers=half_pixel_centers,
        name=name)
  
  
  def resize_nearest_neighbor(images,
                              size,
                              align_corners=False,
                              name=None,
                              half_pixel_centers=False):
    return gen_image_ops.resize_nearest_neighbor(
        images=images,
        size=size,
        align_corners=align_corners,
        half_pixel_centers=half_pixel_centers,
        name=name)
  
  
  resize_area_deprecation = deprecation.deprecated(
      date=None,
      instructions=(
          'Use `tf.image.resize(...method=ResizeMethod.AREA...)` instead.'))
  tf_export(v1=['image.resize_area'])(
      resize_area_deprecation(gen_image_ops.resize_area))
  
  resize_bicubic_deprecation = deprecation.deprecated(
      date=None,
      instructions=(
          'Use `tf.image.resize(...method=ResizeMethod.BICUBIC...)` instead.'))
  tf_export(v1=['image.resize_bicubic'])(
      resize_bicubic_deprecation(resize_bicubic))
  
  resize_bilinear_deprecation = deprecation.deprecated(
      date=None,
      instructions=(
          'Use `tf.image.resize(...method=ResizeMethod.BILINEAR...)` instead.'))
  tf_export(v1=['image.resize_bilinear'])(
      resize_bilinear_deprecation(resize_bilinear))
  
  resize_nearest_neighbor_deprecation = deprecation.deprecated(
      date=None,
      instructions=(
          'Use `tf.image.resize(...method=ResizeMethod.NEAREST_NEIGHBOR...)` '
          'instead.'))
  tf_export(v1=['image.resize_nearest_neighbor'])(
      resize_nearest_neighbor_deprecation(resize_nearest_neighbor))
  
  
  @tf_export('image.crop_and_resize', v1=[])
  def crop_and_resize_v2(image,
                         boxes,
                         box_indices,
                         crop_size,
                         method='bilinear',
                         extrapolation_value=0,
                         name=None):
    """Extracts crops from the input image tensor and resizes them.
  
    Extracts crops from the input image tensor and resizes them using bilinear
    sampling or nearest neighbor sampling (possibly with aspect ratio change) to a
    common output size specified by `crop_size`. This is more general than the
    `crop_to_bounding_box` op which extracts a fixed size slice from the input
    image and does not allow resizing or aspect ratio change.
  
    Returns a tensor with `crops` from the input `image` at positions defined at
    the bounding box locations in `boxes`. The cropped boxes are all resized (with
    bilinear or nearest neighbor interpolation) to a fixed
    `size = [crop_height, crop_width]`. The result is a 4-D tensor
    `[num_boxes, crop_height, crop_width, depth]`. The resizing is corner aligned.
    In particular, if `boxes = [[0, 0, 1, 1]]`, the method will give identical
    results to using `tf.compat.v1.image.resize_bilinear()` or
    `tf.compat.v1.image.resize_nearest_neighbor()`(depends on the `method`
    argument) with
    `align_corners=True`.
  
    Args:
      image: A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
        Both `image_height` and `image_width` need to be positive.
      boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor
        specifies the coordinates of a box in the `box_ind[i]` image and is
        specified in normalized coordinates `[y1, x1, y2, x2]`. A normalized
        coordinate value of `y` is mapped to the image coordinate at `y *
        (image_height - 1)`, so as the `[0, 1]` interval of normalized image
        height is mapped to `[0, image_height - 1]` in image height coordinates.
        We do allow `y1` > `y2`, in which case the sampled crop is an up-down
        flipped version of the original image. The width dimension is treated
        similarly. Normalized coordinates outside the `[0, 1]` range are allowed,
        in which case we use `extrapolation_value` to extrapolate the input image
        values.
      box_indices: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0,
        batch)`. The value of `box_ind[i]` specifies the image that the `i`-th box
        refers to.
      crop_size: A 1-D tensor of 2 elements, `size = [crop_height, crop_width]`.
        All cropped image patches are resized to this size. The aspect ratio of
        the image content is not preserved. Both `crop_height` and `crop_width`
        need to be positive.
      method: An optional string specifying the sampling method for resizing. It
        can be either `"bilinear"` or `"nearest"` and default to `"bilinear"`.
        Currently two sampling methods are supported: Bilinear and Nearest
          Neighbor.
      extrapolation_value: An optional `float`. Defaults to `0`. Value used for
        extrapolation, when applicable.
      name: A name for the operation (optional).
  
    Returns:
      A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.
    """
    return gen_image_ops.crop_and_resize(image, boxes, box_indices, crop_size,
                                         method, extrapolation_value, name)
  
  
  @tf_export(v1=['image.crop_and_resize'])
  @deprecation.deprecated_args(None,
                               'box_ind is deprecated, use box_indices instead',
                               'box_ind')
  def crop_and_resize_v1(  # pylint: disable=missing-docstring
      image,
      boxes,
      box_ind=None,
      crop_size=None,
      method='bilinear',
      extrapolation_value=0,
      name=None,
      box_indices=None):
    box_ind = deprecation.deprecated_argument_lookup('box_indices', box_indices,
                                                     'box_ind', box_ind)
    return gen_image_ops.crop_and_resize(image, boxes, box_ind, crop_size, method,
                                         extrapolation_value, name)
  
  
  crop_and_resize_v1.__doc__ = gen_image_ops.crop_and_resize.__doc__
  
  
  @tf_export(v1=['image.extract_glimpse'])
  def extract_glimpse(
      input,  # pylint: disable=redefined-builtin
      size,
      offsets,
      centered=True,
      normalized=True,
      uniform_noise=True,
      name=None):
    """Extracts a glimpse from the input tensor.
  
    Returns a set of windows called glimpses extracted at location
    `offsets` from the input tensor. If the windows only partially
    overlaps the inputs, the non overlapping areas will be filled with
    random noise.
  
    The result is a 4-D tensor of shape `[batch_size, glimpse_height,
    glimpse_width, channels]`. The channels and batch dimensions are the
    same as that of the input tensor. The height and width of the output
    windows are specified in the `size` parameter.
  
    The argument `normalized` and `centered` controls how the windows are built:
  
    * If the coordinates are normalized but not centered, 0.0 and 1.0
      correspond to the minimum and maximum of each height and width
      dimension.
    * If the coordinates are both normalized and centered, they range from
      -1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper
      left corner, the lower right corner is located at (1.0, 1.0) and the
      center is at (0, 0).
    * If the coordinates are not normalized they are interpreted as
      numbers of pixels.
  
    Args:
      input: A `Tensor` of type `float32`. A 4-D float tensor of shape
        `[batch_size, height, width, channels]`.
      size: A `Tensor` of type `int32`. A 1-D tensor of 2 elements containing the
        size of the glimpses to extract.  The glimpse height must be specified
        first, following by the glimpse width.
      offsets: A `Tensor` of type `float32`. A 2-D integer tensor of shape
        `[batch_size, 2]` containing the y, x locations of the center of each
        window.
      centered: An optional `bool`. Defaults to `True`. indicates if the offset
        coordinates are centered relative to the image, in which case the (0, 0)
        offset is relative to the center of the input images. If false, the (0,0)
        offset corresponds to the upper left corner of the input images.
      normalized: An optional `bool`. Defaults to `True`. indicates if the offset
        coordinates are normalized.
      uniform_noise: An optional `bool`. Defaults to `True`. indicates if the
        noise should be generated using a uniform distribution or a Gaussian
        distribution.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `float32`.
    """
    return gen_image_ops.extract_glimpse(
        input=input,
        size=size,
        offsets=offsets,
        centered=centered,
        normalized=normalized,
        uniform_noise=uniform_noise,
        name=name)
  
  
  @tf_export('image.extract_glimpse', v1=[])
  def extract_glimpse_v2(
      input,  # pylint: disable=redefined-builtin
      size,
      offsets,
      centered=True,
      normalized=True,
      noise='uniform',
      name=None):
    """Extracts a glimpse from the input tensor.
  
    Returns a set of windows called glimpses extracted at location
    `offsets` from the input tensor. If the windows only partially
    overlaps the inputs, the non overlapping areas will be filled with
    random noise.
  
    The result is a 4-D tensor of shape `[batch_size, glimpse_height,
    glimpse_width, channels]`. The channels and batch dimensions are the
    same as that of the input tensor. The height and width of the output
    windows are specified in the `size` parameter.
  
    The argument `normalized` and `centered` controls how the windows are built:
  
    * If the coordinates are normalized but not centered, 0.0 and 1.0
      correspond to the minimum and maximum of each height and width
      dimension.
    * If the coordinates are both normalized and centered, they range from
      -1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper
      left corner, the lower right corner is located at (1.0, 1.0) and the
      center is at (0, 0).
    * If the coordinates are not normalized they are interpreted as
      numbers of pixels.
  
    Args:
      input: A `Tensor` of type `float32`. A 4-D float tensor of shape
        `[batch_size, height, width, channels]`.
      size: A `Tensor` of type `int32`. A 1-D tensor of 2 elements containing the
        size of the glimpses to extract.  The glimpse height must be specified
        first, following by the glimpse width.
      offsets: A `Tensor` of type `float32`. A 2-D integer tensor of shape
        `[batch_size, 2]` containing the y, x locations of the center of each
        window.
      centered: An optional `bool`. Defaults to `True`. indicates if the offset
        coordinates are centered relative to the image, in which case the (0, 0)
        offset is relative to the center of the input images. If false, the (0,0)
        offset corresponds to the upper left corner of the input images.
      normalized: An optional `bool`. Defaults to `True`. indicates if the offset
        coordinates are normalized.
      noise: An optional `string`. Defaults to `uniform`. indicates if the noise
        should be `uniform` (uniform distribution), `gaussian` (gaussian
        distribution), or `zero` (zero padding).
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `float32`.
    """
    return gen_image_ops.extract_glimpse(
        input=input,
        size=size,
        offsets=offsets,
        centered=centered,
        normalized=normalized,
        noise=noise,
        uniform_noise=False,
        name=name)
  
  
  @tf_export('image.combined_non_max_suppression')
  def combined_non_max_suppression(boxes,
                                   scores,
                                   max_output_size_per_class,
                                   max_total_size,
                                   iou_threshold=0.5,
                                   score_threshold=float('-inf'),
                                   pad_per_class=False,
                                   clip_boxes=True,
                                   name=None):
    """Greedily selects a subset of bounding boxes in descending order of score.
  
    This operation performs non_max_suppression on the inputs per batch, across
    all classes.
    Prunes away boxes that have high intersection-over-union (IOU) overlap
    with previously selected boxes.  Bounding boxes are supplied as
    [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
    diagonal pair of box corners and the coordinates can be provided as normalized
    (i.e., lying in the interval [0, 1]) or absolute.  Note that this algorithm
    is agnostic to where the origin is in the coordinate system. Also note that
    this algorithm is invariant to orthogonal transformations and translations
    of the coordinate system; thus translating or reflections of the coordinate
    system result in the same boxes being selected by the algorithm.
    The output of this operation is the final boxes, scores and classes tensor
    returned after performing non_max_suppression.
  
    Args:
      boxes: A 4-D float `Tensor` of shape `[batch_size, num_boxes, q, 4]`. If `q`
        is 1 then same boxes are used for all classes otherwise, if `q` is equal
        to number of classes, class-specific boxes are used.
      scores: A 3-D float `Tensor` of shape `[batch_size, num_boxes, num_classes]`
        representing a single score corresponding to each box (each row of boxes).
      max_output_size_per_class: A scalar integer `Tensor` representing the
        maximum number of boxes to be selected by non max suppression per class
      max_total_size: A scalar representing maximum number of boxes retained over
        all classes.
      iou_threshold: A float representing the threshold for deciding whether boxes
        overlap too much with respect to IOU.
      score_threshold: A float representing the threshold for deciding when to
        remove boxes based on score.
      pad_per_class: If false, the output nmsed boxes, scores and classes are
        padded/clipped to `max_total_size`. If true, the output nmsed boxes,
        scores and classes are padded to be of length
        `max_size_per_class`*`num_classes`, unless it exceeds `max_total_size` in
        which case it is clipped to `max_total_size`. Defaults to false.
      clip_boxes: If true, the coordinates of output nmsed boxes will be clipped
        to [0, 1]. If false, output the box coordinates as it is. Defaults to
        true.
      name: A name for the operation (optional).
  
    Returns:
      'nmsed_boxes': A [batch_size, max_detections, 4] float32 tensor
        containing the non-max suppressed boxes.
      'nmsed_scores': A [batch_size, max_detections] float32 tensor containing
        the scores for the boxes.
      'nmsed_classes': A [batch_size, max_detections] float32 tensor
        containing the class for boxes.
      'valid_detections': A [batch_size] int32 tensor indicating the number of
        valid detections per batch item. Only the top valid_detections[i] entries
        in nms_boxes[i], nms_scores[i] and nms_class[i] are valid. The rest of the
        entries are zero paddings.
    """
    with ops.name_scope(name, 'combined_non_max_suppression'):
      iou_threshold = ops.convert_to_tensor(
          iou_threshold, dtype=dtypes.float32, name='iou_threshold')
      score_threshold = ops.convert_to_tensor(
          score_threshold, dtype=dtypes.float32, name='score_threshold')
      return gen_image_ops.combined_non_max_suppression(
          boxes, scores, max_output_size_per_class, max_total_size, iou_threshold,
          score_threshold, pad_per_class, clip_boxes)
  
  
  @tf_export('image.draw_bounding_boxes', v1=[])
  def draw_bounding_boxes_v2(images, boxes, colors, name=None):
    """Draw bounding boxes on a batch of images.
  
    Outputs a copy of `images` but draws on top of the pixels zero or more
    bounding boxes specified by the locations in `boxes`. The coordinates of the
    each bounding box in `boxes` are encoded as `[y_min, x_min, y_max, x_max]`.
    The bounding box coordinates are floats in `[0.0, 1.0]` relative to the width
    and height of the underlying image.
  
    For example, if an image is 100 x 200 pixels (height x width) and the bounding
    box is `[0.1, 0.2, 0.5, 0.9]`, the upper-left and bottom-right coordinates of
    the bounding box will be `(40, 10)` to `(180, 50)` (in (x,y) coordinates).
  
    Parts of the bounding box may fall outside the image.
  
    Args:
      images: A `Tensor`. Must be one of the following types: `float32`, `half`.
        4-D with shape `[batch, height, width, depth]`. A batch of images.
      boxes: A `Tensor` of type `float32`. 3-D with shape `[batch,
        num_bounding_boxes, 4]` containing bounding boxes.
      colors: A `Tensor` of type `float32`. 2-D. A list of RGBA colors to cycle
        through for the boxes.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `images`.
    """
    if colors is None:
      return gen_image_ops.draw_bounding_boxes(images, boxes, name)
    return gen_image_ops.draw_bounding_boxes_v2(images, boxes, colors, name)
  
  
  @tf_export(v1=['image.draw_bounding_boxes'])
  def draw_bounding_boxes(images, boxes, name=None, colors=None):
    """Draw bounding boxes on a batch of images.
  
    Outputs a copy of `images` but draws on top of the pixels zero or more
    bounding boxes specified by the locations in `boxes`. The coordinates of the
    each bounding box in `boxes` are encoded as `[y_min, x_min, y_max, x_max]`.
    The bounding box coordinates are floats in `[0.0, 1.0]` relative to the width
    and height of the underlying image.
  
    For example, if an image is 100 x 200 pixels (height x width) and the bounding
    box is `[0.1, 0.2, 0.5, 0.9]`, the upper-left and bottom-right coordinates of
    the bounding box will be `(40, 10)` to `(180, 50)` (in (x,y) coordinates).
  
    Parts of the bounding box may fall outside the image.
  
    Args:
      images: A `Tensor`. Must be one of the following types: `float32`, `half`.
        4-D with shape `[batch, height, width, depth]`. A batch of images.
      boxes: A `Tensor` of type `float32`. 3-D with shape `[batch,
        num_bounding_boxes, 4]` containing bounding boxes.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `images`.
    """
    return draw_bounding_boxes_v2(images, boxes, colors, name)
  