# 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 raw PASCAL dataset to TFRecord for object_detection. Example usage: python object_detection/dataset_tools/create_pascal_tf_record.py \ --data_dir=/home/user/VOCdevkit \ --year=VOC2012 \ --output_path=/home/user/pascal.record """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import hashlib import io import logging import os from lxml import etree 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 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') flags.DEFINE_string('label_map_path', 'data/pascal_label_map.pbtxt', 'Path to label map proto') flags.DEFINE_boolean('ignore_difficult_instances', False, 'Whether to ignore ' 'difficult instances') FLAGS = flags.FLAGS SETS = ['train', 'val', 'trainval', 'test'] YEARS = ['VOC2007', 'VOC2012', 'merged'] def dict_to_tf_example(data, dataset_directory, label_map_dict, ignore_difficult_instances=False, image_subdirectory='JPEGImages'): """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) dataset_directory: Path to root directory holding PASCAL dataset label_map_dict: A map from string label names to integers ids. ignore_difficult_instances: Whether to skip difficult instances in the dataset (default: False). image_subdirectory: String specifying subdirectory within the PASCAL dataset directory holding the actual image data. 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(data['folder'], image_subdirectory, data['filename']) full_path = os.path.join(dataset_directory, img_path) with tf.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() width = int(data['size']['width']) height = int(data['size']['height']) xmin = [] ymin = [] xmax = [] ymax = [] 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) 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')) example = tf.train.Example(features=tf.train.Features(feature={ '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(xmin), 'image/object/bbox/xmax': dataset_util.float_list_feature(xmax), 'image/object/bbox/ymin': dataset_util.float_list_feature(ymin), 'image/object/bbox/ymax': dataset_util.float_list_feature(ymax), '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), })) 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)) data_dir = FLAGS.data_dir years = ['VOC2007', 'VOC2012'] if FLAGS.year != 'merged': years = [FLAGS.year] writer = tf.python_io.TFRecordWriter(FLAGS.output_path) label_map_dict = label_map_util.get_label_map_dict(FLAGS.label_map_path) for year in years: logging.info('Reading from PASCAL %s dataset.', year) examples_path = os.path.join(data_dir, year, 'ImageSets', 'Main', 'aeroplane_' + FLAGS.set + '.txt') annotations_dir = os.path.join(data_dir, year, FLAGS.annotations_dir) examples_list = dataset_util.read_examples_list(examples_path) for idx, example in enumerate(examples_list): 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.gfile.GFile(path, 'r') as fid: xml_str = fid.read() xml = etree.fromstring(xml_str) data = dataset_util.recursive_parse_xml_to_dict(xml)['annotation'] tf_example = dict_to_tf_example(data, FLAGS.data_dir, label_map_dict, FLAGS.ignore_difficult_instances) writer.write(tf_example.SerializeToString()) writer.close() if __name__ == '__main__': tf.app.run()