Невозможно создать файл записей tf из файла csv в тензорном потоке - PullRequest
Новая
       16

Невозможно создать файл записей tf из файла csv в тензорном потоке

0 голосов
/ 31 октября 2019

Я хочу обнаружить объект с маркировкой обнаруживающего объекта на изображении, поэтому я должен пометить изображения и преобразовать их в CSV-файл, а теперь пытаюсь конвертировать в файл TF-записей, но получаю сообщение об ошибке при создании файла TF-записей из CSV-файлаиспользуя скрипт ниже. Я следую этому уроку https://pythonprogramming.net/creating-tfrecord-files-tensorflow-object-detection-api-tutorial/

python generate_tfrecord.py --csv_input = images \ train_labels.csv --image_dir = images \ train --output_path = train.record 

  File "generate_tfrecord.py", line 17, in <module>
    import tensorflow as tf
  File "C:\Users\Programs\Python\Python36\lib\site-packages\tensorflow\__init__.py", line 98, in <module>
    from tensorflow_core import *
  File "C:\Users\Programs\Python\Python36\lib\site-packages\tensorflow_core\__init__.py", line 40, in <module>
    from tensorflow.python.tools import module_util as _module_util
  File "C:\Users\Programs\Python\Python36\lib\site-packages\tensorflow\__init__.py", line 50, in __getattr__
    module = self._load()
  File "C:\Users\Programs\Python\Python36\lib\site-packages\tensorflow\__init__.py", line 44, in _load
    module = _importlib.import_module(self.__name__)
  File "C:\Users\Programs\Python\Python36\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "C:\Users\Programs\Python\Python36\lib\site-packages\tensorflow_core\python\__init__.py", line 52, in <module>
    from tensorflow.core.framework.graph_pb2 import *
  File "C:\Users\Programs\Python\Python36\lib\site-packages\tensorflow_core\core\framework\graph_pb2.py", line 7, in <module>
    from google.protobuf import descriptor as _descriptor
  File "C:\Users\Programs\Python\Python36\lib\site-packages\google\protobuf\descriptor.py", line 47, in <module>
    from google.protobuf.pyext import _message
ImportError: DLL load failed: The specified procedure could not be found.

Ниже приведен код для generate_tfrecord.py

//////////////////////////////////////////// 

"""

from __future__ import division
from __future__ import print_function
from __future__ import absolute_import

import os
import io
import pandas as pd
import tensorflow as tf

from PIL import Image
from object_detection.utils import dataset_util
from collections import namedtuple, OrderedDict

flags = tf.compat.v1.flags
flags.DEFINE_string('csv_input', '', 'data\\train_labels.csv')
flags.DEFINE_string('output_path', '', 'data\\train.record')
flags.DEFINE_string('image_dir', '', 'images\\train')
FLAGS = flags.FLAGS


# TO-DO replace this with label map
def class_text_to_int(row_label):
    if row_label == 'parcel':
        return 1
    if row_label =='not':
        return 2
    else:
        None


def split(df, group):
    data = namedtuple('data', ['filename', 'object'])
    gb = df.groupby(group)
    return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]


def create_tf_example(group, path):
    with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
        encoded_jpg = fid.read()
    encoded_jpg_io = io.BytesIO(encoded_jpg)
    image = Image.open(encoded_jpg_io)
    width, height = image.size

    filename = group.filename.encode('utf8')
    image_format = b'jpg'
    xmins = []
    xmaxs = []
    ymins = []
    ymaxs = []
    classes_text = []
    classes = []

    for index, row in group.object.iterrows():
        xmins.append(row['xmin'] / width)
        xmaxs.append(row['xmax'] / width)
        ymins.append(row['ymin'] / height)
        ymaxs.append(row['ymax'] / height)
        classes_text.append(row['class'].encode('utf8'))
        classes.append(class_text_to_int(row['class']))

    tf_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(filename),
        'image/source_id': dataset_util.bytes_feature(filename),
        'image/encoded': dataset_util.bytes_feature(encoded_jpg),
        'image/format': dataset_util.bytes_feature(image_format),
        '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),
    }))
    return tf_example


def main(_):
    writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
    path = os.path.join(FLAGS.image_dir)
    examples = pd.read_csv(FLAGS.csv_input)
    grouped = split(examples, 'filename')
    for group in grouped:
        tf_example = create_tf_example(group, path)
        writer.write(tf_example.SerializeToString())

    writer.close()
    output_path = os.path.join(os.getcwd(), FLAGS.output_path)
    print('Successfully created the TFRecords: {}'.format(output_path))


if __name__ == '__main__':
    tf.compat.v1.app.run
...