def _convert_to_example(filename, image_buffer, label, synset, human, bbox,
                        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
    synset: string, unique WordNet ID specifying the label, e.g., 'n02323233'
    human: string, human-readable label, e.g., 'red fox, Vulpes vulpes'
    bbox: list of bounding boxes; each box is a list of integers
      specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to belong to
      the same label as the image label.
    height: integer, image height in pixels
    width: integer, image width in pixels
  Returns:
    Example proto
  """
  xmin = []
  ymin = []
  xmax = []
  ymax = []
  for b in bbox:
    assert len(b) == 4
    # pylint: disable=expression-not-assigned
    [l.append(point) for l, point in zip([xmin, ymin, xmax, ymax], b)]
    # pylint: enable=expression-not-assigned

  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(colorspace),
      'image/channels': _int64_feature(channels),
      'image/class/label': _int64_feature(label),
      'image/class/synset': _bytes_feature(synset),
      'image/class/text': _bytes_feature(human),
      'image/object/bbox/xmin': _float_feature(xmin),
      'image/object/bbox/xmax': _float_feature(xmax),
      'image/object/bbox/ymin': _float_feature(ymin),
      'image/object/bbox/ymax': _float_feature(ymax),
      'image/object/bbox/label': _int64_feature([label] * len(xmin)),
      'image/format': _bytes_feature(image_format),
      'image/filename': _bytes_feature(os.path.basename(filename)),
      'image/encoded': _bytes_feature(image_buffer)}))
  return example