create_pet_tf_record.py

# Copyright 2017 The TensorFlow Authors. 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 the Oxford pet dataset to TFRecord for object_detection.

See: O. M. Parkhi, A. Vedaldi, A. Zisserman, C. V. Jawahar
     Cats and Dogs
     IEEE Conference on Computer Vision and Pattern Recognition, 2012
     http://www.robots.ox.ac.uk/~vgg/data/pets/

Example usage:
    python object_detection/dataset_tools/create_pet_tf_record.py \
        --data_dir=/home/user/pet \
        --output_dir=/home/user/pet/output
"""

import hashlib
import io
import logging
import os
import random
import re

from lxml import etree
import numpy as np
import PIL.Image
import tensorflow as tf

from object_detection.utils import dataset_util
from object_detection.utils import label_map_util

flags = tf.app.flags
flags.DEFINE_string('data_dir', '', 'Root directory to raw pet dataset.')
flags.DEFINE_string('output_dir', '', 'Path to directory to output TFRecords.')
flags.DEFINE_string('label_map_path', 'data/pet_label_map.pbtxt',
                    'Path to label map proto')
flags.DEFINE_boolean('faces_only', True, 'If True, generates bounding boxes '
                     'for pet faces.  Otherwise generates bounding boxes (as '
                     'well as segmentations for full pet bodies).  Note that '
                     'in the latter case, the resulting files are much larger.')
flags.DEFINE_string('mask_type', 'png', 'How to represent instance '
                    'segmentation masks. Options are "png" or "numerical".')
FLAGS = flags.FLAGS


def get_class_name_from_filename(file_name):
  """Gets the class name from a file.

  Args:
    file_name: The file name to get the class name from.
               ie. "american_pit_bull_terrier_105.jpg"

  Returns:
    A string of the class name.
  """
  match = re.match(r'([A-Za-z_]+)(_[0-9]+\.jpg)', file_name, re.I)
  return match.groups()[0]


def dict_to_tf_example(data,
                       mask_path,
                       label_map_dict,
                       image_subdirectory,
                       ignore_difficult_instances=False,
                       faces_only=True,
                       mask_type='png'):
  """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 dataset_util.recursive_parse_xml_to_dict)
    mask_path: String path to PNG encoded mask.
    label_map_dict: A map from string label names to integers ids.
    image_subdirectory: String specifying subdirectory within the
      Pascal dataset directory holding the actual image data.
    ignore_difficult_instances: Whether to skip difficult instances in the
      dataset  (default: False).
    faces_only: If True, generates bounding boxes for pet faces.  Otherwise
      generates bounding boxes (as well as segmentations for full pet bodies).
    mask_type: 'numerical' or 'png'. 'png' is recommended because it leads to
      smaller file sizes.

  Returns:
    example: The converted tf.Example.

  Raises:
    ValueError: if the image pointed to by data['filename'] is not a valid JPEG
  """
  img_path = os.path.join(image_subdirectory, data['filename'])
  with tf.gfile.GFile(img_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()

  with tf.gfile.GFile(mask_path, 'rb') as fid:
    encoded_mask_png = fid.read()
  encoded_png_io = io.BytesIO(encoded_mask_png)
  mask = PIL.Image.open(encoded_png_io)
  if mask.format != 'PNG':
    raise ValueError('Mask format not PNG')

  mask_np = np.asarray(mask)
  nonbackground_indices_x = np.any(mask_np != 2, axis=0)
  nonbackground_indices_y = np.any(mask_np != 2, axis=1)
  nonzero_x_indices = np.where(nonbackground_indices_x)
  nonzero_y_indices = np.where(nonbackground_indices_y)

  width = int(data['size']['width'])
  height = int(data['size']['height'])

  xmins = []
  ymins = []
  xmaxs = []
  ymaxs = []
  classes = []
  classes_text = []
  truncated = []
  poses = []
  difficult_obj = []
  masks = []
  for obj in data['object']:
    difficult = bool(int(obj['difficult']))
    if ignore_difficult_instances and difficult:
      continue
    difficult_obj.append(int(difficult))

    if faces_only:
      xmin = float(obj['bndbox']['xmin'])
      xmax = float(obj['bndbox']['xmax'])
      ymin = float(obj['bndbox']['ymin'])
      ymax = float(obj['bndbox']['ymax'])
    else:
      xmin = float(np.min(nonzero_x_indices))
      xmax = float(np.max(nonzero_x_indices))
      ymin = float(np.min(nonzero_y_indices))
      ymax = float(np.max(nonzero_y_indices))

    xmins.append(xmin / width)
    ymins.append(ymin / height)
    xmaxs.append(xmax / width)
    ymaxs.append(ymax / height)
    class_name = get_class_name_from_filename(data['filename'])
    classes_text.append(class_name.encode('utf8'))
    classes.append(label_map_dict[class_name])
    truncated.append(int(obj['truncated']))
    poses.append(obj['pose'].encode('utf8'))
    if not faces_only:
      mask_remapped = (mask_np != 2).astype(np.uint8)
      masks.append(mask_remapped)

  feature_dict = {
      'image/height': dataset_util.int64_feature(height),
      'image/width': dataset_util.int64_feature(width),
      'image/filename': dataset_util.bytes_feature(
          data['filename'].encode('utf8')),
      'image/source_id': dataset_util.bytes_feature(
          data['filename'].encode('utf8')),
      'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),
      'image/encoded': dataset_util.bytes_feature(encoded_jpg),
      'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
      'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
      'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
      'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
      'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
      'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
      'image/object/class/label': dataset_util.int64_list_feature(classes),
      'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
      'image/object/truncated': dataset_util.int64_list_feature(truncated),
      'image/object/view': dataset_util.bytes_list_feature(poses),
  }
  if not faces_only:
    if mask_type == 'numerical':
      mask_stack = np.stack(masks).astype(np.float32)
      masks_flattened = np.reshape(mask_stack, [-1])
      feature_dict['image/object/mask'] = (
          dataset_util.float_list_feature(masks_flattened.tolist()))
    elif mask_type == 'png':
      encoded_mask_png_list = []
      for mask in masks:
        img = PIL.Image.fromarray(mask)
        output = io.BytesIO()
        img.save(output, format='PNG')
        encoded_mask_png_list.append(output.getvalue())
      feature_dict['image/object/mask'] = (
          dataset_util.bytes_list_feature(encoded_mask_png_list))

  example = tf.train.Example(features=tf.train.Features(feature=feature_dict))
  return example


def create_tf_record(output_filename,
                     label_map_dict,
                     annotations_dir,
                     image_dir,
                     examples,
                     faces_only=True,
                     mask_type='png'):
  """Creates a TFRecord file from examples.

  Args:
    output_filename: Path to where output file is saved.
    label_map_dict: The label map dictionary.
    annotations_dir: Directory where annotation files are stored.
    image_dir: Directory where image files are stored.
    examples: Examples to parse and save to tf record.
    faces_only: If True, generates bounding boxes for pet faces.  Otherwise
      generates bounding boxes (as well as segmentations for full pet bodies).
    mask_type: 'numerical' or 'png'. 'png' is recommended because it leads to
      smaller file sizes.
  """
  writer = tf.python_io.TFRecordWriter(output_filename)
  for idx, example in enumerate(examples):
    if idx % 100 == 0:
      logging.info('On image %d of %d', idx, len(examples))
    xml_path = os.path.join(annotations_dir, 'xmls', example + '.xml')
    mask_path = os.path.join(annotations_dir, 'trimaps', example + '.png')

    if not os.path.exists(xml_path):
      logging.warning('Could not find %s, ignoring example.', xml_path)
      continue
    with tf.gfile.GFile(xml_path, 'r') as fid:
      xml_str = fid.read()
    xml = etree.fromstring(xml_str)
    data = dataset_util.recursive_parse_xml_to_dict(xml)['annotation']

    try:
      tf_example = dict_to_tf_example(
          data,
          mask_path,
          label_map_dict,
          image_dir,
          faces_only=faces_only,
          mask_type=mask_type)
      writer.write(tf_example.SerializeToString())
    except ValueError:
      logging.warning('Invalid example: %s, ignoring.', xml_path)

  writer.close()


# TODO: Add test for pet/PASCAL main files.
def main(_):
  data_dir = FLAGS.data_dir
  label_map_dict = label_map_util.get_label_map_dict(FLAGS.label_map_path)

  logging.info('Reading from Pet dataset.')
  image_dir = os.path.join(data_dir, 'images')
  annotations_dir = os.path.join(data_dir, 'annotations')
  examples_path = os.path.join(annotations_dir, 'trainval.txt')
  examples_list = dataset_util.read_examples_list(examples_path)

  # Test images are not included in the downloaded data set, so we shall perform
  # our own split.
  random.seed(42)
  random.shuffle(examples_list)
  num_examples = len(examples_list)
  num_train = int(0.7 * num_examples)
  train_examples = examples_list[:num_train]
  val_examples = examples_list[num_train:]
  logging.info('%d training and %d validation examples.',
               len(train_examples), len(val_examples))

  train_output_path = os.path.join(FLAGS.output_dir, 'pet_train.record')
  val_output_path = os.path.join(FLAGS.output_dir, 'pet_val.record')
  if FLAGS.faces_only:
    train_output_path = os.path.join(FLAGS.output_dir,
                                     'pet_train_with_masks.record')
    val_output_path = os.path.join(FLAGS.output_dir,
                                   'pet_val_with_masks.record')
  create_tf_record(
      train_output_path,
      label_map_dict,
      annotations_dir,
      image_dir,
      train_examples,
      faces_only=FLAGS.faces_only,
      mask_type=FLAGS.mask_type)
  create_tf_record(
      val_output_path,
      label_map_dict,
      annotations_dir,
      image_dir,
      val_examples,
      faces_only=FLAGS.faces_only,
      mask_type=FLAGS.mask_type)


if __name__ == '__main__':
  tf.app.run()