Python object_detection.utils.dataset_util.recursive_parse_xml_to_dict() Examples
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Example #1
Source File: check_imagenet_data.py From MBMD with MIT License | 5 votes |
def main(_): data_dir = FLAGS.data_dir #writer = tf.python_io.TFRecordWriter(FLAGS.output_path) label_map_dict = label_map_util.get_label_map_dict(FLAGS.label_map_path) # logging.info('Reading from Imagenet dataset.') examples_list = dataset_util.read_examples_list(FLAGS.data_list_path) for idx, example in enumerate(examples_list): if idx % 100 == 0: print('On image %d of %d'%(idx, len(examples_list))) # logging.info('On image %d of %d', idx, len(examples_list)) path = os.path.join(FLAGS.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'] if not data.has_key('object'): continue dict_to_tf_example(data, FLAGS.data_dir, label_map_dict, FLAGS.ignore_difficult_instances) # if tf_example is not None: # writer.write(tf_example.SerializeToString()) #writer.close()
Example #2
Source File: create_pascal_tf_record.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 5 votes |
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()
Example #3
Source File: create_pascal_tf_record.py From container_detection with GNU General Public License v3.0 | 5 votes |
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 = ['cont_train', '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', 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()
Example #4
Source File: build2_tf_record.py From train_ssd_mobilenet with MIT License | 5 votes |
def create_tf_record(output_record_file, label_map_dict, jpegfilenames): '''Creates a TFRecord file from examples. Args: output_record_file: Path to where output file is saved. label_map_dict: The label map dictionary. jpegfilenames: Examples to parse and save to tf record. ''' writer = tf.python_io.TFRecordWriter(output_record_file) for idx, example in enumerate(jpegfilenames): if idx % 100 == 0: logging.info('On image %d of %d', idx, len(jpegfilenames)) xmlfile = os.path.join(ANNOTATIONS_DIR, example + '.xml') if not os.path.exists(xmlfile): logging.warning('Could not find %s, ignoring example.', xmlfile) continue with tf.gfile.GFile(xmlfile, '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, label_map_dict) writer.write(tf_example.SerializeToString()) writer.close()
Example #5
Source File: create_pascal_tf_record.py From moveo_ros with MIT License | 5 votes |
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()
Example #6
Source File: create_pet_tf_record.py From moveo_ros with MIT License | 5 votes |
def create_tf_record(output_filename, label_map_dict, annotations_dir, image_dir, examples): """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. """ 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)) path = os.path.join(annotations_dir, 'xmls', example + '.xml') if not os.path.exists(path): logging.warning('Could not find %s, ignoring example.', path) continue 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, label_map_dict, image_dir) writer.write(tf_example.SerializeToString()) writer.close() # TODO: Add test for pet/PASCAL main files.
Example #7
Source File: create_knots_tf_record.py From active-learning-detect with MIT License | 5 votes |
def main(_): if FLAGS.set not in SETS: raise ValueError('set must be in : {}'.format(SETS)) print("Getting data for {} set".format(FLAGS.set)) data_dir = FLAGS.data_dir writer = tf.python_io.TFRecordWriter(FLAGS.output_path) label_map_dict = label_map_util.get_label_map_dict(FLAGS.label_map_path) if FLAGS.set == 'trainval': examples_list_knot1 = get_examples_list(data_dir, 'knot_', 'train') examples_list_knot2 = get_examples_list(data_dir, 'knot_', 'val') #print(len(examples_list_knot1), len(examples_list_knot2), len(examples_list_defect1), len(examples_list_defect2)) examples_list = examples_list_knot1 + examples_list_knot2 # + examples_list_defect1 + examples_list_defect2 else: examples_list = get_examples_list(data_dir, 'knot_', FLAGS.set) print("About to parse {} examples".format(len(examples_list))) annotations_dir = os.path.join(data_dir, FLAGS.annotations_dir) for idx, example in enumerate(examples_list): if idx % 10 == 0: logging.info('On image %d of %d', idx, len(examples_list)) print('On image {0} of {1}'.format(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()
Example #8
Source File: create_pascal_tf_record.py From hands-detection with MIT License | 5 votes |
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()
Example #9
Source File: create_pet_tf_record.py From hands-detection with MIT License | 5 votes |
def create_tf_record(output_filename, label_map_dict, annotations_dir, image_dir, examples): """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. """ 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)) path = os.path.join(annotations_dir, 'xmls', example + '.xml') if not os.path.exists(path): logging.warning('Could not find %s, ignoring example.', path) continue 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, label_map_dict, image_dir) writer.write(tf_example.SerializeToString()) writer.close() # TODO: Add test for pet/PASCAL main files.
Example #10
Source File: create_pascal_tf_record.py From object_detection_kitti with Apache License 2.0 | 5 votes |
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()
Example #11
Source File: create_pet_tf_record.py From object_detection_kitti with Apache License 2.0 | 5 votes |
def create_tf_record(output_filename, label_map_dict, annotations_dir, image_dir, examples): """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. """ 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)) path = os.path.join(annotations_dir, 'xmls', example + '.xml') if not os.path.exists(path): logging.warning('Could not find %s, ignoring example.', path) continue 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, label_map_dict, image_dir) writer.write(tf_example.SerializeToString()) writer.close() # TODO: Add test for pet/PASCAL main files.
Example #12
Source File: create_dataset.py From object_detection_kitti with Apache License 2.0 | 5 votes |
def xml_to_dict(path): with tf.gfile.GFile(path, 'r') as fid: xml_str = fid.read() xml = etree.fromstring(xml_str) return dataset_util.recursive_parse_xml_to_dict(xml)['annotation']
Example #13
Source File: create_imagenet_tf_record.py From MBMD with MIT License | 5 votes |
def main(_): data_dir = FLAGS.data_dir writer = tf.python_io.TFRecordWriter(FLAGS.output_path) label_map_dict = label_map_util.get_label_map_dict(FLAGS.label_map_path) # logging.info('Reading from Imagenet dataset.') examples_list = dataset_util.read_examples_list(FLAGS.data_list_path) for idx, example in enumerate(examples_list): if idx % 100 == 0: print('On image %d of %d'%(idx, len(examples_list))) # logging.info('On image %d of %d', idx, len(examples_list)) path = os.path.join(FLAGS.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'] if not data.has_key('object'): continue tf_example = dict_to_tf_example(data, FLAGS.data_dir, label_map_dict, FLAGS.ignore_difficult_instances) if tf_example is not None: writer.write(tf_example.SerializeToString()) writer.close()
Example #14
Source File: create_pet_tf_record.py From DOTA_models with Apache License 2.0 | 5 votes |
def create_tf_record(output_filename, label_map_dict, annotations_dir, image_dir, examples): """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. """ 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)) path = os.path.join(annotations_dir, 'xmls', example + '.xml') if not os.path.exists(path): logging.warning('Could not find %s, ignoring example.', path) continue 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, label_map_dict, image_dir) writer.write(tf_example.SerializeToString()) writer.close() # TODO: Add test for pet/PASCAL main files.
Example #15
Source File: create_pascal_tf_record.py From MBMD with MIT License | 5 votes |
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()
Example #16
Source File: create_pet_tf_record.py From MBMD with MIT License | 5 votes |
def create_tf_record(output_filename, label_map_dict, annotations_dir, image_dir, examples): """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. """ 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)) path = os.path.join(annotations_dir, 'xmls', example + '.xml') if not os.path.exists(path): logging.warning('Could not find %s, ignoring example.', path) continue 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, label_map_dict, image_dir) writer.write(tf_example.SerializeToString()) writer.close() # TODO: Add test for pet/PASCAL main files.
Example #17
Source File: create_pascal_tf_record.py From Elphas with Apache License 2.0 | 5 votes |
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()
Example #18
Source File: create_pascal_tf_record.py From object_detection_with_tensorflow with MIT License | 5 votes |
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()
Example #19
Source File: create_pet_tf_record.py From object_detection_with_tensorflow with MIT License | 5 votes |
def create_tf_record(output_filename, label_map_dict, annotations_dir, image_dir, examples): """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. """ 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)) path = os.path.join(annotations_dir, 'xmls', example + '.xml') if not os.path.exists(path): logging.warning('Could not find %s, ignoring example.', path) continue 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, label_map_dict, image_dir) writer.write(tf_example.SerializeToString()) writer.close() # TODO: Add test for pet/PASCAL main files.
Example #20
Source File: create_pascal_tf_record.py From object_detection_with_tensorflow with MIT License | 5 votes |
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()
Example #21
Source File: create_pascal_tf_record.py From MAX-Object-Detector with Apache License 2.0 | 5 votes |
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()
Example #22
Source File: create_pascal_tf_record.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
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()
Example #23
Source File: create_pascal_tf_record.py From models with Apache License 2.0 | 5 votes |
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()
Example #24
Source File: create_pet_tf_record.py From mtl-ssl with Apache License 2.0 | 5 votes |
def create_tf_record(output_filename, label_map_dict, annotations_dir, image_dir, examples): """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. """ 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)) path = os.path.join(annotations_dir, 'xmls', example + '.xml') if not os.path.exists(path): logging.warning('Could not find %s, ignoring example.', path) continue 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, label_map_dict, image_dir) writer.write(tf_example.SerializeToString()) writer.close() # TODO: Add test for pet/PASCAL main files.
Example #25
Source File: create_pascal_tf_record.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
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()
Example #26
Source File: create_pascal_tf_record.py From ros_tensorflow with Apache License 2.0 | 5 votes |
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()
Example #27
Source File: create_pascal_tf_record.py From DOTA_models with Apache License 2.0 | 5 votes |
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()
Example #28
Source File: create_pascal_tf_record.py From object_detector_app with MIT License | 5 votes |
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()
Example #29
Source File: create_pet_tf_record.py From object_detector_app with MIT License | 5 votes |
def create_tf_record(output_filename, label_map_dict, annotations_dir, image_dir, examples): """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. """ 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)) path = os.path.join(annotations_dir, 'xmls', example + '.xml') if not os.path.exists(path): logging.warning('Could not find %s, ignoring example.', path) continue 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, label_map_dict, image_dir) writer.write(tf_example.SerializeToString()) writer.close() # TODO: Add test for pet/PASCAL main files.
Example #30
Source File: create_pascal_tf_record.py From vehicle_counting_tensorflow with MIT License | 5 votes |
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()