机器学习常见数据格式转换（xml_to_csv，csv_to_tfrecord）

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labelimg（目标检测标注工具）的安装与使用教程
在上述文章中，笔者曾写到过，标注完后，生成的xml文件就可以方便后续的xml_to_csv以及转换为tfrecord文件了。
不过当时并未提及到如何进行转换，这篇文章给出具体操作。

xml_to_csv

代码如下：

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

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 main():
    print(os.getcwd())
    # 结果为E:\python_code\crack\models_trainning
    # ToDo 根据自己实际目录修改
    # image_path = os.path.join(os.getcwd(), 'dataset/crack/test')  # 根据自己实际目录修改，或者使用下面的路径
    image_path = 'E:/python_code/crack/models_trainning/dataset/crack/test'
    print(image_path)
    xml_df = xml_to_csv(image_path)
    xml_df.to_csv('./dataset/crack/train/crack_test.csv', index=None)  # 根据自己实际目录修改
    print('Successfully converted xml to csv.')

main()

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    这里需要注意的是，这里的话我们只需要修改路径，就不需要在终端运行（每次需要先去该目录下）了，对于不玩linux的同学比较友好。

print(os.getcwd())
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结果为E:\python_code\crack\models_trainning

image_path = os.path.join(os.getcwd(), 'dataset/crack/test')
image_path = 'E:/python_code/crack/models_trainning/dataset/crack/test'
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以上两种图片路径方法都可以，一个采用的是os.path.join()进行路径拼接。

xml_df.to_csv('./dataset/crack/train/crack_test.csv', index=None) 
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保存为csv的路径可以随意写
结果如下

csv_to_tfrecord

# -*- coding: utf-8-*-
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 tensorflow.compat.v1 as tf
from PIL import Image
from research.object_detection.utils import dataset_util
from collections import namedtuple, OrderedDict

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


# 将分类名称转成ID号
def class_text_to_int(row_label):
    if row_label == 'crack':
        return 1
    # elif row_label == 'car':
    #     return 2
    # elif row_label == 'person':
    #     return 3
    # elif row_label == 'kite':
    #     return 4
    else:
        print('NONE: ' + row_label)
        # 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):
    print(os.path.join(path, '{}'.format(group.filename)))
    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 + '.jpg').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(csv_input, output_path, imgPath):
    writer = tf.python_io.TFRecordWriter(output_path)
    path = imgPath
    examples = pd.read_csv(csv_input)
    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 TFRecords: {}'.format(output_path))


if __name__ == '__main__':
    # ToDo 修改相应目录
    imgPath = r'E:\python_code\crack\models_trainning\dataset\crack\test'
    output_path = 'dataset/crack/test/crack_test.record'
    csv_input = 'dataset/crack/test/crack_test.csv'
    main(csv_input, output_path, imgPath)

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如xml_to_csv类似，只要把路径改好即可
imgPath是图片所在文件夹路径
output_path是tfrecord生成的路径
csv_iinput是使用的csv的路径
    当然，你可能会出现下面报错，起初笔者还以为是编码问题，可是始终未能解决。后来仔细检查发现，是自己路径搞错了，因此大家出现这个错误的时候，检查一下路径先。

总结

    这是笔者之前想要训练 模型的时候搞得，当时查了好久，光我上面 说的那个简单到离谱的utf-8的问题，当时都困扰我好久了。希望能帮助大家
    这篇文章是笔者第一次使用markdown编辑器写的，现在对它的感觉还行！！😀😀