我正在尝试通过Tensorflw进行物体检测机器学习;我正在看一段尼古拉斯·雷诺特的视频。这是一段一年前的视频，在运行trainning模块将我的xml和jpg文件转换为记录文件时，我遇到了这个错误。尝试了很多事情，现在陷入了下一步该怎么做的困境。

import os
import glob
import pandas as pd
import io
import xml.etree.ElementTree as ET
import argparse

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'    # Suppress TensorFlow logging (1)
import tensorflow.compat.v1 as tf
from PIL import Image
from object_detection.utils import dataset_util, label_map_util
from collections import namedtuple

# Initiate argument parser
parser = argparse.ArgumentParser(
    description="Sample TensorFlow XML-to-TFRecord converter")
parser.add_argument("-x",
                    "--xml_dir",
                    help="Path to the folder where the input .xml files are stored.",
                    type=str)
parser.add_argument("-l",
                    "--labels_path",
                    help="Path to the labels (.pbtxt) file.", type=str)
parser.add_argument("-o",
                    "--output_path",
                    help="Path of output TFRecord (.record) file.", type=str)
parser.add_argument("-i",
                    "--image_dir",
                    help="Path to the folder where the input image files are stored. "
                         "Defaults to the same directory as XML_DIR.",
                    type=str, default=None)
parser.add_argument("-c",
                    "--csv_path",
                    help="Path of output .csv file. If none provided, then no file will be "
                         "written.",
                    type=str, default=None)

args = parser.parse_args()

if args.image_dir is None:
    args.image_dir = args.xml_dir

label_map = label_map_util.load_labelmap(args.labels_path)
label_map_dict = label_map_util.get_label_map_dict(label_map)

def xml_to_csv(path):
    xml_list = []
    for xml_file in glob.glob(path + '/*.xml'):
        tree = ET.parse(xml_file)
        root = tree.getroot()
        for member in root.findall('object'):
            value = (root.find('filename').text,
                     int(root.find('size')[0].text),
                     int(root.find('size')[1].text),
                     member[0].text,
                     int(member[4][0].text),
                     int(member[4][1].text),
                     int(member[4][2].text),
                     int(member[4][3].text)
                     )
            xml_list.append(value)
    column_name = ['filename', 'width', 'height',
                   'class', 'xmin', 'ymin', 'xmax', 'ymax']
    xml_df = pd.DataFrame(xml_list, columns=column_name)
    return xml_df

def class_text_to_int(row_label):
    return label_map_dict[row_label]

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(args.output_path)
    path = os.path.join(args.image_dir)
    examples = xml_to_csv(args.xml_dir)
    grouped = split(examples, 'filename')
    for group in grouped:
        tf_example = create_tf_example(group, path)
        writer.write(tf_example.SerializeToString())
    writer.close()
    print('Successfully created the TFRecord file: {}'.format(args.output_path))
    if args.csv_path is not None:
        examples.to_csv(args.csv_path, index=None)
        print('Successfully created the CSV file: {}'.format(args.csv_path))

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

这就是我所面对的错误

Traceback (most recent call last):
  File "C:\Users\Kanna\OneDrive\Desktop\Files\Codes\RealTimeObjectDetection\Tensorflow\scripts\generate_tfrecord.py", line 62, in <module>
    label_map_dict = label_map_util.get_label_map_dict(label_map)
  File "C:\Users\Kanna\anaconda3\lib\site-packages\object_detection\utils\label_map_util.py", line 164, in get_label_map_dict
    label_map = load_labelmap(label_map_path)
  File "C:\Users\Kanna\anaconda3\lib\site-packages\object_detection\utils\label_map_util.py", line 133, in load_labelmap
    label_map_string = fid.read()
  File "C:\Users\Kanna\anaconda3\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 114, in read
    self._preread_check()
  File "C:\Users\Kanna\anaconda3\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 76, in _preread_check
    self._read_buf = _pywrap_file_io.BufferedInputStream(
TypeError: __init__(): incompatible constructor arguments. The following argument types are supported:
    1. tensorflow.python.lib.io._pywrap_file_io.BufferedInputStream(filename: str, buffer_size: int, token: tensorflow.python.lib.io._pywrap_file_io.TransactionToken = None)

Invoked with: item {
  name: "Hello"
  id: 1
}
item {
  name: "ILoveYou"
  id: 2
}
item {
  name: "no"
  id: 3
}
item {
  name: "yes"
  id: 4
}
item {
  name: "thanks"
  id: 5
}
, 524288
Traceback (most recent call last):
  File "C:\Users\Kanna\OneDrive\Desktop\Files\Codes\RealTimeObjectDetection\Tensorflow\scripts\generate_tfrecord.py", line 62, in <module>
    label_map_dict = label_map_util.get_label_map_dict(label_map)
  File "C:\Users\Kanna\anaconda3\lib\site-packages\object_detection\utils\label_map_util.py", line 164, in get_label_map_dict
    label_map = load_labelmap(label_map_path)
  File "C:\Users\Kanna\anaconda3\lib\site-packages\object_detection\utils\label_map_util.py", line 133, in load_labelmap
    label_map_string = fid.read()
  File "C:\Users\Kanna\anaconda3\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 114, in read
    self._preread_check()
  File "C:\Users\Kanna\anaconda3\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 76, in _preread_check
    self._read_buf = _pywrap_file_io.BufferedInputStream(
TypeError: __init__(): incompatible constructor arguments. The following argument types are supported:
    1. tensorflow.python.lib.io._pywrap_file_io.BufferedInputStream(filename: str, buffer_size: int, token: tensorflow.python.lib.io._pywrap_file_io.TransactionToken = None)

Invoked with: item {
  name: "Hello"
  id: 1
}
item {
  name: "ILoveYou"
  id: 2
}
item {
  name: "no"
  id: 3
}
item {
  name: "yes"
  id: 4
}
item {
  name: "thanks"
  id: 5
}
, 524288