环境 ：win10 + tensorflow
前期准备：完成object detection api 的配置 环境配置
（一）图片准备
下载labelimg 有很多种方法这里 列出几种
1.去github 上下载附上链接 https://github.com/tzutalin/labelImg/releases
labelimg下载
下载后直接解压运行labelimg.exe
2.有pycharm 的同学直接“File”-“setting”-“project interpreter”-点击“+”号
输入框中输入 “labelimg”![在这里插入图片描述](https://img-blog.csdnimg.cn/20190812133923856.PNG
点击“install package”
安装成功后打开D:\anaconda\envs\tensorflow\Lib\site-packages\labelImg（找到你使用相对应环境中的site-packages文件夹 因为我用了多个环境这里用的是tensorflow ）中的labelimg.py 运行 后 可看到
点击“open”打开要使用的图片的路径-点击

一直到自己的图片打完为止
找到自己打完标签后的xml文件路径
（二）xml 转换csv

# -*- coding: utf-8 -*-


import os

import glob

import pandas as pd

import xml.etree.ElementTree as ET

# 用于改变当前工作目录到指定的路径。
os.chdir(r'C:\Users\Administrator\Desktop\实验手册\soursexml')

# 图片路径
path = r'C:\Users\Administrator\Desktop\实验手册\soursexml'

def xml_to_csv(path):
    counter = 0;

    xml_list = []

    for xml_file in glob.glob(path + '/*.xml'):

        counter = counter + 1;

        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)
        print(xml_list)

    column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax']

    xml_df = pd.DataFrame(xml_list, columns=column_name)

    print(xml_df)

    print('执行', counter, '次！')

    return xml_df


def main():
    image_path = path

    xml_df = xml_to_csv(image_path)

    #    xml_df.to_csv('arthritis_test.csv', index=None)

    xml_df.to_csv('arthritis_test.csv', index=None)

    print('Successfully converted xml to csv.')


main()

改变path 和os.chdir 到自己的使用 labelimg 生成的xml文件目录
运行程序后会得到一个csv文件

（）
（三）生成tfrecord文件

"""

Usage:

  # From tensorflow/models/

  # Create train data:

  python generate_tfrecord.py --csv_input=data/tv_vehicle_labels.csv  --output_path=train.record

  # Create test data:

  python generate_tfrecord.py --csv_input=data/test_labels.csv  --output_path=test.record

"""

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

os.chdir(r'D:\anaconda\envs\tensorflow\download\models-master\research\object_detection')

# 这里改自己的object_detection的路径下


flags = tf.app.flags

flags.DEFINE_string('csv_input', '', 'Path to the CSV input')

flags.DEFINE_string('output_path', '', 'Path to output TFRecord')

FLAGS = flags.FLAGS


# TO-DO replace this with label map

# 注意将对应的label改成自己的类别！！！！！！！！！！

def class_text_to_int(row_label):
    if row_label == 'daisy':

        return 1

    # elif row_label == 'vehicle':
    #
    #     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(os.getcwd(), 'images')

    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.app.run()

运行