Build an Example proto for an example.

  Args:
    filename: string, path to an image file, e.g., '/path/to/example.JPG'
    image_buffer: string, JPEG encoding of RGB image
    label: integer, identifier for the ground truth for the network
    text: string, unique human-readable, e.g. 'dog'
    height: integer, image height in pixels
    width: integer, image width in pixels
  Returns:
    Example proto

def _convert_to_example(filename, image_buffer, label, text, height, width):
  """Build an Example proto for an example.

  Args:
    filename: string, path to an image file, e.g., '/path/to/example.JPG'
    image_buffer: string, JPEG encoding of RGB image
    label: integer, identifier for the ground truth for the network
    text: string, unique human-readable, e.g. 'dog'
    height: integer, image height in pixels
    width: integer, image width in pixels
  Returns:
    Example proto
  """

  colorspace = 'RGB'
  channels = 3
  image_format = 'JPEG'

  example = tf.train.Example(features=tf.train.Features(feature={
      'image/height': _int64_feature(height),
      'image/width': _int64_feature(width),
      'image/colorspace': _bytes_feature(tf.compat.as_bytes(colorspace)),
      'image/channels': _int64_feature(channels),
      'image/class/label': _int64_feature(label),
      'image/class/text': _bytes_feature(tf.compat.as_bytes(text)),
      'image/format': _bytes_feature(tf.compat.as_bytes(image_format)),
      'image/filename': _bytes_feature(tf.compat.as_bytes(os.path.basename(filename))),
      'image/encoded': _bytes_feature(tf.compat.as_bytes(image_buffer))}))
  return example