code: # Copyright 2015 Paul Balanca. 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.
  # ==============================================================================
  """Provides data for the Pascal VOC Dataset (images + annotations).
  """
  import os
  
  import tensorflow as tf
  import dataset_utils
  
  slim = tf.contrib.slim
  
  VOC_LABELS = {
      'none': (0, 'Background'),
      'aeroplane': (1, 'Vehicle'),
      'bicycle': (2, 'Vehicle'),
      'bird': (3, 'Animal'),
      'boat': (4, 'Vehicle'),
      'bottle': (5, 'Indoor'),
      'bus': (6, 'Vehicle'),
      'car': (7, 'Vehicle'),
      'cat': (8, 'Animal'),
      'chair': (9, 'Indoor'),
      'cow': (10, 'Animal'),
      'diningtable': (11, 'Indoor'),
      'dog': (12, 'Animal'),
      'horse': (13, 'Animal'),
      'motorbike': (14, 'Vehicle'),
      'person': (15, 'Person'),
      'pottedplant': (16, 'Indoor'),
      'sheep': (17, 'Animal'),
      'sofa': (18, 'Indoor'),
      'train': (19, 'Vehicle'),
      'tvmonitor': (20, 'Indoor'),
  }
  
  VOC_HUMAN_LIGHT_LABELS = {
      'none' : (0, 'Background'),
      'human_red' : (1, 'TrafficLight'),
      'human_geeen' : (2, 'TrafficLight')
  }
  
  def label_dict(dataname='pascalvoc'):
      if dataname == 'humanlight':
          return VOC_HUMAN_LIGHT_LABELS
      
      return VOC_LABELS
  
  def get_split(split_name, dataset_dir, file_pattern, reader,
                split_to_sizes, items_to_descriptions, num_classes):
      """Gets a dataset tuple with instructions for reading Pascal VOC dataset.
  
      Args:
        split_name: A train/test split name.
        dataset_dir: The base directory of the dataset sources.
        file_pattern: The file pattern to use when matching the dataset sources.
          It is assumed that the pattern contains a '%s' string so that the split
          name can be inserted.
        reader: The TensorFlow reader type.
  
      Returns:
        A `Dataset` namedtuple.
  
      Raises:
          ValueError: if `split_name` is not a valid train/test split.
      """
      if split_name not in split_to_sizes:
          raise ValueError('split name %s was not recognized.' % split_name)
      file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
  
      # Allowing None in the signature so that dataset_factory can use the default.
      if reader is None:
          reader = tf.TFRecordReader
      # Features in Pascal VOC TFRecords.
      keys_to_features = {
          'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
          'image/format': tf.FixedLenFeature((), tf.string, default_value='jpeg'),
          'image/height': tf.FixedLenFeature([1], tf.int64),
          'image/width': tf.FixedLenFeature([1], tf.int64),
          'image/channels': tf.FixedLenFeature([1], tf.int64),
          'image/shape': tf.FixedLenFeature([3], tf.int64),
          'image/object/bbox/xmin': tf.VarLenFeature(dtype=tf.float32),
          'image/object/bbox/ymin': tf.VarLenFeature(dtype=tf.float32),
          'image/object/bbox/xmax': tf.VarLenFeature(dtype=tf.float32),
          'image/object/bbox/ymax': tf.VarLenFeature(dtype=tf.float32),
          'image/object/bbox/label': tf.VarLenFeature(dtype=tf.int64),
          'image/object/bbox/difficult': tf.VarLenFeature(dtype=tf.int64),
          'image/object/bbox/truncated': tf.VarLenFeature(dtype=tf.int64),
      }
      items_to_handlers = {
          'image': slim.tfexample_decoder.Image('image/encoded', 'image/format'),
          'shape': slim.tfexample_decoder.Tensor('image/shape'),
          'object/bbox': slim.tfexample_decoder.BoundingBox(
                  ['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/'),
          'object/label': slim.tfexample_decoder.Tensor('image/object/bbox/label'),
          'object/difficult': slim.tfexample_decoder.Tensor('image/object/bbox/difficult'),
          'object/truncated': slim.tfexample_decoder.Tensor('image/object/bbox/truncated'),
      }
      decoder = slim.tfexample_decoder.TFExampleDecoder(
          keys_to_features, items_to_handlers)
  
      labels_to_names = None
      if dataset_utils.has_labels(dataset_dir):
          labels_to_names = dataset_utils.read_label_file(dataset_dir)
      # else:
      #     labels_to_names = create_readable_names_for_imagenet_labels()
      #     dataset_utils.write_label_file(labels_to_names, dataset_dir)
  
      return slim.dataset.Dataset(
              data_sources=file_pattern,
              reader=reader,
              decoder=decoder,
              num_samples=split_to_sizes[split_name],
              items_to_descriptions=items_to_descriptions,
              num_classes=num_classes,
              labels_to_names=labels_to_names)
  
apis: tensorflow.FixedLenFeature tensorflow.VarLenFeature
api_extract: [(80, 'os.path.join', 'os.path.join', (['dataset_dir', '(file_pattern % split_name)'], {}), False, 'import os\n'), (114, 'dataset_utils.has_labels', 'dataset_utils.has_labels', (['dataset_dir'], {}), False, 'import dataset_utils\n'), (87, 'tensorflow.FixedLenFeature', 'tf.FixedLenFeature', (['()', 'tf.string'], {'default_value': '""""""'}), True, 'import tensorflow as tf\n'), (88, 'tensorflow.FixedLenFeature', 'tf.FixedLenFeature', (['()', 'tf.string'], {'default_value': '"""jpeg"""'}), True, 'import tensorflow as tf\n'), (89, 'tensorflow.FixedLenFeature', 'tf.FixedLenFeature', (['[1]', 'tf.int64'], {}), True, 'import tensorflow as tf\n'), (90, 'tensorflow.FixedLenFeature', 'tf.FixedLenFeature', (['[1]', 'tf.int64'], {}), True, 'import tensorflow as tf\n'), (91, 'tensorflow.FixedLenFeature', 'tf.FixedLenFeature', (['[1]', 'tf.int64'], {}), True, 'import tensorflow as tf\n'), (92, 'tensorflow.FixedLenFeature', 'tf.FixedLenFeature', (['[3]', 'tf.int64'], {}), True, 'import tensorflow as tf\n'), (93, 'tensorflow.VarLenFeature', 'tf.VarLenFeature', ([], {'dtype': 'tf.float32'}), True, 'import tensorflow as tf\n'), (94, 'tensorflow.VarLenFeature', 'tf.VarLenFeature', ([], {'dtype': 'tf.float32'}), True, 'import tensorflow as tf\n'), (95, 'tensorflow.VarLenFeature', 'tf.VarLenFeature', ([], {'dtype': 'tf.float32'}), True, 'import tensorflow as tf\n'), (96, 'tensorflow.VarLenFeature', 'tf.VarLenFeature', ([], {'dtype': 'tf.float32'}), True, 'import tensorflow as tf\n'), (97, 'tensorflow.VarLenFeature', 'tf.VarLenFeature', ([], {'dtype': 'tf.int64'}), True, 'import tensorflow as tf\n'), (98, 'tensorflow.VarLenFeature', 'tf.VarLenFeature', ([], {'dtype': 'tf.int64'}), True, 'import tensorflow as tf\n'), (99, 'tensorflow.VarLenFeature', 'tf.VarLenFeature', ([], {'dtype': 'tf.int64'}), True, 'import tensorflow as tf\n'), (115, 'dataset_utils.read_label_file', 'dataset_utils.read_label_file', (['dataset_dir'], {}), False, 'import dataset_utils\n')]