来自csv的Tensorflow Tfrecord，“”“ UnicodeDecodeError：'utf-8'编解码器无法在已经为UTF-8的位置解码字节0xa8”“

如何解决来自csv的Tensorflow Tfrecord，“”“ UnicodeDecodeError：'utf-8'编解码器无法在已经为UTF-8的位置解码字节0xa8”“

我一直在关注这个youtube，尝试从csv生成tfrecord文件。

Creating TFRecords - TensorFlow Object Detection API Tutorial p.4

1. 首先，我将文件从xml转换为csv。它显示成功转换。文件为csv，显示为UTF-8逗号格式。

2. 然后当我尝试运行generatetfrecord.py时显示错误：UnicodeDecodeError：'utf-8'编解码器无法将字节0xa8解码为已经为UTF-8的位置

这是generatetfrecord.py代码：

"""
Usage:
  # From tensorflow/models/
  # Create train data:
  python3 generate_tfrecord.py --csv_input=data/train_labels.csv  --output_path=data/train.record

  # Create test data:
  python3 generate_tfrecord.py --csv_input=data/test_labels.csv  --output_path=data/test.record
"""
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.app.flags
flags.DEFINE_string('csv_input','','Path to the CSV input')
flags.DEFINE_string('output_path','Path to output TFRecord')
flags.DEFINE_string('image_dir','Path to images')
FLAGS = flags.FLAGS


# TO-DO replace this with label map
def class_text_to_int(row_label):
    if row_label == 'tennisball':
        return 1
    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.io.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('utf-8')
    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.compat.v1.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()

这是我的csv的样子： in excel in notepad

我尝试了很多来自网络的解决方案，但是没有运气。有什么想法吗？