枕头OSError：无法识别图像文件<_io.BytesIO对象位于0x1347afa40>

Question

我是机器学习和对象检测的新手，我正在尝试加载自己的数据集以进行对象检测。当我尝试生成tfrecords时，出现错误“ OSError：无法识别图像文件<_io.BytesIO对象位于0x1347afa40>”。我正在使用以下代码：https://github.com/wpilibsuite/DetectCoral/blob/local/mobilenet/dataset/scripts/generate_tfrecord.py，其脚本中编写了经过修改的路径和函数。

问题摘录

def create_tf_example(group, path, labels):
    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)

# The problem occurs on the line below 

    image = Image.open(encoded_jpg_io)

完整代码

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
import json
import glob
from os.path import join

from PIL import Image

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

def get_labels():
    f = None
    base = "/Users/kabirkumar/Desktop/full_data/meta.json"
    for file in glob.glob('/Users/kabirkumar/Desktop/full_data/meta.json'):
        f = file
    with open(f, 'r') as meta:
        return [label["title"] for label in json.load(meta)["classes"]]


def class_text_to_int(label, labels):
    return labels.index(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, labels):
    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)

# the problem occurs for the line below

    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'], labels))
    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

input_csvv = '/Users/kabirkumar/Desktop/Object-detection/data/train.csv'

output_tfrecordd = '/Users/kabirkumar/Desktop/Object-detection/data/train.record'


def main(input_csv, output_tfrecord):
    writer = tf.io.TFRecordWriter(output_tfrecord)
    path = '/Users/kabirkumar/Desktop/Object-detection/Filming Day 2 Video/imgtrain'
    examples = pd.read_csv(input_csv)
    grouped = split(examples, 'filename')
    labels = get_labels()



    
    for group in grouped:
        tf_example = create_tf_example(group, path, labels)
        writer.write(tf_example.SerializeToString())

    writer.close()
    print(end='.\nSuccessfully created the TFRecords: {}'.format(output_tfrecord))

main('/Users/kabirkumar/Desktop/Object-detection/data/train.csv', join('/Users/kabirkumar/Desktop/Object-detection/data/train.record'))

我修改的路径是csv输入路径，tfrecord输出路径和图像路径。