old_contents: # Copyright 2022 The KerasCV Authors
  #
  # 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
  #
  #     https://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.
  """Utility functions for preprocessing demos."""
  import matplotlib.pyplot as plt
  import numpy as np
  import tensorflow as tf
  import tensorflow_datasets as tfds
  
  from keras_cv import bounding_box
  
  
  def preprocess_voc(inputs, format, image_size):
      """mapping function to create batched image and bbox coordinates"""
      inputs["image"] = tf.image.resize(inputs["image"], image_size)
      inputs["objects"]["bbox"] = bounding_box.convert_format(
          inputs["objects"]["bbox"],
          images=inputs["image"],
          source="rel_yxyx",
          target=format,
      )
      return {"images": inputs["image"], "bounding_boxes": inputs["objects"]["bbox"]}
  
  
  def load_voc_dataset(
      bounding_box_format,
      name="voc/2007",
      batch_size=9,
      image_size=(224, 224),
  ):
  
      dataset = tfds.load(name, split=tfds.Split.TRAIN, shuffle_files=True)
      dataset = dataset.map(
          lambda x: preprocess_voc(x, format=bounding_box_format, image_size=image_size),
          num_parallel_calls=tf.data.AUTOTUNE,
      )
      dataset = dataset.padded_batch(
          batch_size, padding_values={"images": None, "bounding_boxes": -1.0}
      )
      return dataset
  
  
  def visualize_data(data, bounding_box_format):
      data = next(iter(data))
      images = data["images"]
      bounding_boxes = data["bounding_boxes"]
      output_images = visualize_bounding_boxes(
          images, bounding_boxes, bounding_box_format
      ).numpy()
      gallery_show(output_images)
  
  
  def visualize_bounding_boxes(image, bounding_boxes, bounding_box_format):
      color = np.array([[255.0, 0.0, 0.0]])
      bounding_boxes = bounding_box.convert_format(
          bounding_boxes,
          source=bounding_box_format,
          target="rel_yxyx",
          images=image,
      )
      return tf.image.draw_bounding_boxes(image, bounding_boxes, color, name=None)
  
  
  def gallery_show(images):
      images = images.astype(int)
      for i in range(9):
          image = images[i]
          plt.subplot(3, 3, i + 1)
          plt.imshow(image.astype("uint8"))
          plt.axis("off")
      plt.show()
  
new_contents: # Copyright 2022 The KerasCV Authors
  #
  # 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
  #
  #     https://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.
  """Utility functions for preprocessing demos."""
  import matplotlib.pyplot as plt
  import numpy as np
  import tensorflow as tf
  import tensorflow_datasets as tfds
  
  from keras_cv import bounding_box
  
  
  def preprocess_voc(inputs, format, image_size):
      """mapping function to create batched image and bbox coordinates"""
      inputs["image"] = tf.image.resize(inputs["image"], image_size)
      inputs["objects"]["bbox"] = bounding_box.convert_format(
          inputs["objects"]["bbox"],
          images=inputs["image"],
          source="rel_yxyx",
          target=format,
      )
      inputs["objects"]["bbox"] = bounding_box.add_class_id(inputs["objects"]["bbox"])
      return {"images": inputs["image"], "bounding_boxes": inputs["objects"]["bbox"]}
  
  
  def load_voc_dataset(
      bounding_box_format,
      name="voc/2007",
      batch_size=9,
      image_size=(224, 224),
  ):
  
      dataset = tfds.load(name, split=tfds.Split.TRAIN, shuffle_files=True)
      dataset = dataset.map(
          lambda x: preprocess_voc(x, format=bounding_box_format, image_size=image_size),
          num_parallel_calls=tf.data.AUTOTUNE,
      )
      dataset = dataset.padded_batch(
          batch_size, padding_values={"images": None, "bounding_boxes": -1.0}
      )
      return dataset
  
  
  def visualize_data(data, bounding_box_format):
      data = next(iter(data))
      images = data["images"]
      bounding_boxes = data["bounding_boxes"]
      output_images = visualize_bounding_boxes(
          images, bounding_boxes, bounding_box_format
      ).numpy()
      gallery_show(output_images)
  
  
  def visualize_bounding_boxes(image, bounding_boxes, bounding_box_format):
      color = np.array([[255.0, 0.0, 0.0]])
      bounding_boxes = bounding_boxes[..., :4]
      bounding_boxes = bounding_box.convert_format(
          bounding_boxes,
          source=bounding_box_format,
          target="rel_yxyx",
          images=image,
      )
      return tf.image.draw_bounding_boxes(image, bounding_boxes, color, name=None)
  
  
  def gallery_show(images):
      images = images.astype(int)
      for i in range(9):
          image = images[i]
          plt.subplot(3, 3, i + 1)
          plt.imshow(image.astype("uint8"))
          plt.axis("off")
      plt.show()
  
subject: update demo scripts (#722)