运行generate_tfrecords.py文件不会生成该文件

Question

按照here的步骤，我试图使用tensorflow对象检测API创建我自己的对象检测程序（我正在使用Tensorflow 2.2.0。我使用以下命令在python中克隆了存储库：

git clone https://github.com/tensorflow/models.git 

我设法使用LabelImg程序标记了图像并生成了xml文件。我还使用上面链接中提供的步骤从xml成功创建了csv文件（下图）。但是，当运行我从object_detection目录中从generate_tfrecords.py获取的here文件时，如上述链接中提供的步骤中所述，在运行代码生成tfrecord时遇到以下问题-它只打印再见...不会生成tfrecord文件。

编辑：我得到的完整输出：

Warning! ***HDF5 library version mismatched error***
The HDF5 header files used to compile this application do not match
the version used by the HDF5 library to which this application is linked.
Data corruption or segmentation faults may occur if the application continues.
This can happen when an application was compiled by one version of HDF5 but
linked with a different version of static or shared HDF5 library.
You should recompile the application or check your shared library related
settings such as 'LD_LIBRARY_PATH'.
You can, at your own risk, disable this warning by setting the environment
variable 'HDF5_DISABLE_VERSION_CHECK' to a value of '1'.
Setting it to 2 or higher will suppress the warning messages totally.
Headers are 1.10.2, library is 1.10.5
        SUMMARY OF THE HDF5 CONFIGURATION
        =================================

General Information:
-------------------
                   HDF5 Version: 1.10.5
                  Configured on: 2019-03-04
                  Configured by: Visual Studio 15 2017 Win64
                    Host system: Windows-10.0.17763
              Uname information: Windows
                       Byte sex: little-endian
             Installation point: C:/Program Files/HDF5

Compiling Options:
------------------
                     Build Mode:
              Debugging Symbols:
                        Asserts:
                      Profiling:
             Optimization Level:

Linking Options:
----------------
                      Libraries:
  Statically Linked Executables: OFF
                        LDFLAGS: /machine:x64
                     H5_LDFLAGS:
                     AM_LDFLAGS:
                Extra libraries:
                       Archiver:
                         Ranlib:

Languages:
----------
                              C: yes
                     C Compiler: C:/Program Files (x86)/Microsoft Visual Studio/2017/Community/VC/Tools/MSVC/14.16.27023/bin/Hostx86/x64/cl.exe 19.16.27027.1
                       CPPFLAGS:
                    H5_CPPFLAGS:
                    AM_CPPFLAGS:
                         CFLAGS:  /DWIN32 /D_WINDOWS /W3
                      H5_CFLAGS:
                      AM_CFLAGS:
               Shared C Library: YES
               Static C Library: YES

                        Fortran: OFF
               Fortran Compiler:
                  Fortran Flags:
               H5 Fortran Flags:
               AM Fortran Flags:
         Shared Fortran Library: YES
         Static Fortran Library: YES

                            C++: ON
                   C++ Compiler: C:/Program Files (x86)/Microsoft Visual Studio/2017/Community/VC/Tools/MSVC/14.16.27023/bin/Hostx86/x64/cl.exe 19.16.27027.1
                      C++ Flags: /DWIN32 /D_WINDOWS /W3 /GR /EHsc
                   H5 C++ Flags:
                   AM C++ Flags:
             Shared C++ Library: YES
             Static C++ Library: YES

                            JAVA: OFF
                   JAVA Compiler:

Features:
---------
                   Parallel HDF5: OFF
Parallel Filtered Dataset Writes:
              Large Parallel I/O:
              High-level library: ON
                    Threadsafety: OFF
             Default API mapping: v110
  With deprecated public symbols: ON
          I/O filters (external):  DEFLATE DECODE ENCODE
                             MPE:
                      Direct VFD:
                         dmalloc:
  Packages w/ extra debug output:
                     API Tracing: OFF
            Using memory checker: OFF
 Memory allocation sanity checks: OFF
          Function Stack Tracing: OFF
       Strict File Format Checks: OFF
    Optimization Instrumentation:
Bye...

位于generate_tfrecords.py的我的<Path to Project>\models\research\object_detection\代码如下：

"""
Usage:
  # From tensorflow/models/
  # Create train data:
  python generate_tfrecords.py --csv_input=images\train_labels.csv --image_dir=images\train --output_path=train.record


  # Create test data:
  python generate_tfrecords.py --csv_input=images\test_labels.csv --image_dir=images\test --output_path=test.record
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import io
import os
from collections import namedtuple

import pandas as pd
import tensorflow as tf
from PIL import Image
from object_detection.utils import dataset_util

flags = tf.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 == 'a':
        return 1
    elif row_label == 'o':
        return 2
    else:
        return 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

#EDIT
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))

#EDIT
if __name__ == '__main__':
    main()

这就是目录的样子，就像我使用位于<Path to project>\models\research\的csv文件保存训练和测试数据的位置一样。>

培训和测试文件夹包含图像图像及其各自的.xml文件。

EDIT：

例如，这是我的test_labels.csv文件的外观，以防有所帮助：

非常感谢您关于为什么它不会生成tfrecords的任何帮助或建议。

按照此处给出的步骤，我试图使用tensorflow对象检测API创建我自己的对象检测程序（我正在使用Tensorflow 2.2.0。我使用......>在python中克隆了存储库，] >>

经过大量研究，我设法通过在终端中运行以下命令（来自this答案）来解决问题：

pip install h5py --upgrade --no-dependencies --force
以及将FLAGS.output_path, FLAGS.image_dir and FLAGS.csv_input更改为其绝对路径字符串。

Answer 1

经过大量研究，我设法通过在终端中运行以下命令（来自this答案）来解决问题：