我正在尝试将XML文件转换为CSV。最后，我需要TFRecord格式的图像和注释来训练我的自定义SSD MobileNet V2 320x320型号。
以下是我在这个过程中使用的完整python脚本：
""" Sample TensorFlow XML-to-TFRecord converter
usage: generate_tfrecord.py [-h] [-x XML_DIR] [-l LABELS_PATH] [-o OUTPUT_PATH] [-i IMAGE_DIR] [-c CSV_PATH]
optional arguments:
-h, --help show this help message and exit
-x XML_DIR, --xml_dir XML_DIR
Path to the folder where the input .xml files are stored.
-l LABELS_PATH, --labels_path LABELS_PATH
Path to the labels (.pbtxt) file.
-o OUTPUT_PATH, --output_path OUTPUT_PATH
Path of output TFRecord (.record) file.
-i IMAGE_DIR, --image_dir IMAGE_DIR
Path to the folder where the input image files are stored. Defaults to the same directory as XML_DIR.
-c CSV_PATH, --csv_path CSV_PATH
Path of output .csv file. If none provided, then no file will be written.
"""
import os
import glob
import pandas as pd
import io
import xml.etree.ElementTree as ET
import argparse
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1)
import tensorflow.compat.v1 as tf
from PIL import Image
from object_detection.utils import dataset_util, label_map_util
from collections import namedtuple
# Initiate argument parser
parser = argparse.ArgumentParser(
description="Sample TensorFlow XML-to-TFRecord converter")
parser.add_argument("-x",
"--xml_dir",
help="Path to the folder where the input .xml files are stored.",
type=str)
parser.add_argument("-l",
"--labels_path",
help="Path to the labels (.pbtxt) file.", type=str)
parser.add_argument("-o",
"--output_path",
help="Path of output TFRecord (.record) file.", type=str)
parser.add_argument("-i",
"--image_dir",
help="Path to the folder where the input image files are stored. "
"Defaults to the same directory as XML_DIR.",
type=str, default=None)
parser.add_argument("-c",
"--csv_path",
help="Path of output .csv file. If none provided, then no file will be "
"written.",
type=str, default=None)
args = parser.parse_args()
if args.image_dir is None:
args.image_dir = args.xml_dir
label_map = label_map_util.load_labelmap(args.labels_path)
label_map_dict = label_map_util.get_label_map_dict(label_map)
def xml_to_csv(path):
"""Iterates through all .xml files (generated by labelImg) in a given directory and combines
them in a single Pandas dataframe.
Parameters:
----------
path : str
The path containing the .xml files
Returns
-------
Pandas DataFrame
The produced dataframe
"""
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 class_text_to_int(row_label):
return label_map_dict[row_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):
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(args.output_path)
path = os.path.join(args.image_dir)
examples = xml_to_csv(args.xml_dir)
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 TFRecord file: {}'.format(args.output_path))
if args.csv_path is not None:
examples.to_csv(args.csv_path, index=None)
print('Successfully created the CSV file: {}'.format(args.csv_path))
if __name__ == '__main__':
tf.app.run()
下面是我的XML文件示例：
<annotation>
<folder></folder>
<filename>20210401_135713_jpg.rf.c3433a47627088aa68da9693635476d7.jpg</filename>
<path>20210401_135713_jpg.rf.c3433a47627088aa68da9693635476d7.jpg</path>
<source>
<database>roboflow.ai</database>
</source>
<size>
<width>800</width>
<height>600</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>orange</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<occluded>0</occluded>
<bndbox>
<xmin>167</xmin>
<xmax>278</xmax>
<ymin>162</ymin>
<ymax>264</ymax>
</bndbox>
</object>
</annotation>
我得到以下错误：
Traceback (most recent call last):
File "Tensorflowscriptsgenerate_tfrecord.py", line 168, in <module>
tf.app.run()
File "C:UsersaDocumentsCapstoneCuisine_VisionDetectioncuisinevisionlibsite-packagestensorflowpythonplatformapp.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "C:UsersaDocumentsCapstoneCuisine_VisionDetectioncuisinevisionlibsite-packagesabslapp.py", line 303, in run
_run_main(main, args)
File "C:UsersaDocumentsCapstoneCuisine_VisionDetectioncuisinevisionlibsite-packagesabslapp.py", line 251, in _run_main
sys.exit(main(argv))
File "Tensorflowscriptsgenerate_tfrecord.py", line 155, in main
examples = xml_to_csv(args.xml_dir)
File "Tensorflowscriptsgenerate_tfrecord.py", line 88, in xml_to_csv
int(member[4][0].text),
IndexError: child index out of range
Traceback (most recent call last):
File "Tensorflowscriptsgenerate_tfrecord.py", line 168, in <module>
tf.app.run()
File "C:UsersaDocumentsCapstoneCuisine_VisionDetectioncuisinevisionlibsite-packagestensorflowpythonplatformapp.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "C:UsersaDocumentsCapstoneCuisine_VisionDetectioncuisinevisionlibsite-packagesabslapp.py", line 303, in run
_run_main(main, args)
File "C:UsersaDocumentsCapstoneCuisine_VisionDetectioncuisinevisionlibsite-packagesabslapp.py", line 251, in _run_main
sys.exit(main(argv))
File "Tensorflowscriptsgenerate_tfrecord.py", line 155, in main
examples = xml_to_csv(args.xml_dir)
File "Tensorflowscriptsgenerate_tfrecord.py", line 88, in xml_to_csv
int(member[4][0].text),
IndexError: child index out of range
有人能想出解决这个问题的办法吗？非常感谢。
您只需要在xml_to_csv(路径(的嵌套for循环中进行索引：
之前：
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)
)
之后：
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[5][0].text),
int(member[5][1].text),
int(member[5][2].text),
int(member[5][3].text)
)
因为它查找对象的bndbox，它的顺序是第6位，但您从0开始计数，所以您得到5。
我认为下面是您想要的
import xml.etree.ElementTree as ET
xml = '''<annotation>
<folder></folder>
<filename>20210401_135713_jpg.rf.c3433a47627088aa68da9693635476d7.jpg</filename>
<path>20210401_135713_jpg.rf.c3433a47627088aa68da9693635476d7.jpg</path>
<source>
<database>roboflow.ai</database>
</source>
<size>
<width>800</width>
<height>600</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>orange</name>
<pose>Unspecified</pose>
<truncated>11</truncated>
<difficult>12</difficult>
<occluded>13</occluded>
<bndbox>
<xmin>167</xmin>
<xmax>278</xmax>
<ymin>162</ymin>
<ymax>264</ymax>
</bndbox>
</object>
</annotation>'''
xml_list = []
root = ET.fromstring(xml)
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,
member[1].text,
int(member[2].text),
int(member[3].text),
int(member[4].text)
)
xml_list.append(value)
print(xml_list)
输出
[('20210401_135713_jpg.rf.c3433a47627088aa68da9693635476d7.jpg', 800, 600, 'orange', 'Unspecified', 11, 12, 13)]