text: print("\n\n\n")
  import sys
  print(sys.path)
  import os
  print("Path at terminal when executing this file")
  print(os.getcwd() + "\n")
  
  print("This file path, relative to os.getcwd()")
  print(__file__ + "\n")
  
  print("This file full path (following symlinks)")
  full_path = os.path.realpath(__file__)
  print(full_path + "\n")
  
  print("This file directory and name")
  path, filename = os.path.split(full_path)
  print(path + ' --> ' + filename + "\n")
  
  print("This file directory only")
  print(os.path.dirname(full_path))
  
  
  # Copyright 2020 Google Research. 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.
  # ==============================================================================
  r"""Convert PASCAL dataset to TFRecord.
  
  Example usage:
      python create_pascal_tfrecord.py  --data_dir=/tmp/VOCdevkit  \
          --year=VOC2012  --output_path=/tmp/pascal
  """
  import hashlib
  import io
  import json
  import os
  
  from absl import app
  from absl import flags
  from absl import logging
  
  from lxml import etree
  import PIL.Image
  import tensorflow as tf
  
  from dataset import tfrecord_util
  
  FLAGS = flags.FLAGS
  
  SETS = ['train', 'val', 'trainval', 'test']
  YEARS = ['VOC2007', 'VOC2012', 'merged']
  
  pascal_label_map_dict = {
      'background': 0,
      'aeroplane': 1,
      'bicycle': 2,
      'bird': 3,
      'boat': 4,
      'bottle': 5,
      'bus': 6,
      'car': 7,
      'cat': 8,
      'chair': 9,
      'cow': 10,
      'diningtable': 11,
      'dog': 12,
      'horse': 13,
      'motorbike': 14,
      'person': 15,
      'pottedplant': 16,
      'sheep': 17,
      'sofa': 18,
      'train': 19,
      'tvmonitor': 20,
  }
  
  
  def define_flags():
    """Define the flags."""
    flags.DEFINE_string('data_dir', '',
                        'Root directory to raw PASCAL VOC dataset.')
    flags.DEFINE_string('set', 'train', 'Convert training set, validation set or '
                        'merged set.')
    flags.DEFINE_string('annotations_dir', 'Annotations',
                        '(Relative) path to annotations directory.')
    flags.DEFINE_string('year', 'VOC2007', 'Desired challenge year.')
    flags.DEFINE_string('output_path', '', 'Path to output TFRecord and json.')
    flags.DEFINE_string('label_map_json_path', None,
                        'Path to label map json file with a dictionary.')
    flags.DEFINE_boolean('ignore_difficult_instances', False, 'Whether to ignore '
                         'difficult instances')
    flags.DEFINE_integer('num_shards', 100, 'Number of shards for output file.')
    flags.DEFINE_integer('num_images', None, 'Max number of imags to process.')
  
  
  class UniqueId(object):
    """Class to get the unique {image/ann}_id each time calling the functions."""
  
    def __init__(self):
      self.image_id = 0
      self.ann_id = 0
  
    def get_image_id(self):
      self.image_id += 1
      return self.image_id
  
    def get_ann_id(self):
      self.ann_id += 1
      return self.ann_id
  
  
  def dict_to_tf_example(data,
                         images_dir,
                         label_map_dict,
                         unique_id,
                         ignore_difficult_instances=False,
                         ann_json_dict=None):
    """Convert XML derived dict to tf.Example proto.
  
    Notice that this function normalizes the bounding box coordinates provided
    by the raw data.
  
    Args:
      data: dict holding PASCAL XML fields for a single image (obtained by running
        tfrecord_util.recursive_parse_xml_to_dict)
      images_dir: Path to the directory holding raw images.
      label_map_dict: A map from string label names to integers ids.
      unique_id: UniqueId object to get the unique {image/ann}_id for the image
        and the annotations.
      ignore_difficult_instances: Whether to skip difficult instances in the
        dataset  (default: False).
      ann_json_dict: annotation json dictionary.
  
    Returns:
      example: The converted tf.Example.
  
    Raises:
      ValueError: if the image pointed to by data['filename'] is not a valid JPEG
    """
    full_path = os.path.join(images_dir, data['filename'])
    with tf.io.gfile.GFile(full_path, 'rb') as fid:
      encoded_jpg = fid.read()
    encoded_jpg_io = io.BytesIO(encoded_jpg)
    image = PIL.Image.open(encoded_jpg_io)
    if image.format != 'JPEG':
      raise ValueError('Image format not JPEG')
    key = hashlib.sha256(encoded_jpg).hexdigest()
  
    image_id = unique_id.get_image_id()
  
    width = int(data['size']['width'])
    height = int(data['size']['height'])
    if ann_json_dict:
      image = {
          'file_name': data['filename'],
          'height': height,
          'width': width,
          'id': image_id,
      }
      ann_json_dict['images'].append(image)
  
    xmin = []
    ymin = []
    xmax = []
    ymax = []
    area = []
    classes = []
    classes_text = []
    truncated = []
    poses = []
    difficult_obj = []
    if 'object' in data:
      for obj in data['object']:
        difficult = bool(int(obj['difficult']))
        if ignore_difficult_instances and difficult:
          continue
  
        difficult_obj.append(int(difficult))
  
        xmin.append(float(obj['bndbox']['xmin']) / width)
        ymin.append(float(obj['bndbox']['ymin']) / height)
        xmax.append(float(obj['bndbox']['xmax']) / width)
        ymax.append(float(obj['bndbox']['ymax']) / height)
        area.append((xmax[-1] - xmin[-1]) * (ymax[-1] - ymin[-1]))
        classes_text.append(obj['name'].encode('utf8'))
        classes.append(label_map_dict[obj['name']])
        truncated.append(int(obj['truncated']))
        poses.append(obj['pose'].encode('utf8'))
  
        if ann_json_dict:
          abs_xmin = int(obj['bndbox']['xmin'])
          abs_ymin = int(obj['bndbox']['ymin'])
          abs_xmax = int(obj['bndbox']['xmax'])
          abs_ymax = int(obj['bndbox']['ymax'])
          abs_width = abs_xmax - abs_xmin
          abs_height = abs_ymax - abs_ymin
          ann = {
              'area': abs_width * abs_height,
              'iscrowd': 0,
              'image_id': image_id,
              'bbox': [abs_xmin, abs_ymin, abs_width, abs_height],
              'category_id': label_map_dict[obj['name']],
              'id': unique_id.get_ann_id(),
              'ignore': 0,
              'segmentation': [],
          }
          ann_json_dict['annotations'].append(ann)
  
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/height':
                    tfrecord_util.int64_feature(height),
                'image/width':
                    tfrecord_util.int64_feature(width),
                'image/filename':
                    tfrecord_util.bytes_feature(data['filename'].encode('utf8')),
                'image/source_id':
                    tfrecord_util.bytes_feature(str(image_id).encode('utf8')),
                'image/key/sha256':
                    tfrecord_util.bytes_feature(key.encode('utf8')),
                'image/encoded':
                    tfrecord_util.bytes_feature(encoded_jpg),
                'image/format':
                    tfrecord_util.bytes_feature('jpeg'.encode('utf8')),
                'image/object/bbox/xmin':
                    tfrecord_util.float_list_feature(xmin),
                'image/object/bbox/xmax':
                    tfrecord_util.float_list_feature(xmax),
                'image/object/bbox/ymin':
                    tfrecord_util.float_list_feature(ymin),
                'image/object/bbox/ymax':
                    tfrecord_util.float_list_feature(ymax),
                'image/object/area':
                    tfrecord_util.float_list_feature(area),
                'image/object/class/text':
                    tfrecord_util.bytes_list_feature(classes_text),
                'image/object/class/label':
                    tfrecord_util.int64_list_feature(classes),
                'image/object/difficult':
                    tfrecord_util.int64_list_feature(difficult_obj),
                'image/object/truncated':
                    tfrecord_util.int64_list_feature(truncated),
                'image/object/view':
                    tfrecord_util.bytes_list_feature(poses),
            }))
    return example
  
  
  def main(_):
    if FLAGS.set not in SETS:
      raise ValueError('set must be in : {}'.format(SETS))
    if FLAGS.year not in YEARS:
      raise ValueError('year must be in : {}'.format(YEARS))
    if not FLAGS.output_path:
      raise ValueError('output_path cannot be empty.')
  
    data_dir = FLAGS.data_dir
    years = ['VOC2007', 'VOC2012']
    if FLAGS.year != 'merged':
      years = [FLAGS.year]
  
    output_dir = os.path.dirname(FLAGS.output_path)
    if not tf.io.gfile.exists(output_dir):
      tf.io.gfile.makedirs(output_dir)
    logging.info('Writing to output directory: %s', output_dir)
  
    writers = [
        tf.io.TFRecordWriter(FLAGS.output_path + '-%05d-of-%05d.tfrecord' %
                             (i, FLAGS.num_shards))
        for i in range(FLAGS.num_shards)
    ]
  
    if FLAGS.label_map_json_path:
      with tf.io.gfile.GFile(FLAGS.label_map_json_path, 'rb') as f:
        label_map_dict = json.load(f)
    else:
      label_map_dict = pascal_label_map_dict
  
    ann_json_dict = {
        'images': [],
        'type': 'instances',
        'annotations': [],
        'categories': []
    }
  
    unique_id = UniqueId()
    for year in years:
      example_class = list(label_map_dict.keys())[1]
      examples_path = os.path.join(data_dir, year, 'ImageSets', 'Main',
                                   example_class + '_' + FLAGS.set + '.txt')
      examples_list = tfrecord_util.read_examples_list(examples_path)
      annotations_dir = os.path.join(data_dir, year, FLAGS.annotations_dir)
  
      for class_name, class_id in label_map_dict.items():
        cls = {'supercategory': 'none', 'id': class_id, 'name': class_name}
        ann_json_dict['categories'].append(cls)
  
      logging.info('Reading from PASCAL %s dataset.', year)
      for idx, example in enumerate(examples_list):
        if FLAGS.num_images and idx >= FLAGS.num_images:
          break
        if idx % 100 == 0:
          logging.info('On image %d of %d', idx, len(examples_list))
        path = os.path.join(annotations_dir, example + '.xml')
        with tf.io.gfile.GFile(path, 'r') as fid:
          xml_str = fid.read()
        xml = etree.fromstring(xml_str)
        data = tfrecord_util.recursive_parse_xml_to_dict(xml)['annotation']
  
        img_dir = os.path.join(FLAGS.data_dir, data['folder'], 'JPEGImages')
  
        tf_example = dict_to_tf_example(
            data,
            img_dir,
            label_map_dict,
            unique_id,
            FLAGS.ignore_difficult_instances,
            ann_json_dict=ann_json_dict)
        writers[idx % FLAGS.num_shards].write(tf_example.SerializeToString())
  
    for writer in writers:
      writer.close()
  
    json_file_path = os.path.join(
        os.path.dirname(FLAGS.output_path),
        'json_' + os.path.basename(FLAGS.output_path) + '.json')
    with tf.io.gfile.GFile(json_file_path, 'w') as f:
      json.dump(ann_json_dict, f)
  
  
  if __name__ == '__main__':
    define_flags()
    app.run(main)
  