我正在尝试使用tensorflow 2.0生成tfrecord文件，首先我已经正确生成了文件，但是当我尝试再次生成它们时，python控制台显示以下错误：
Traceback (most recent call last):
File "generate_tfrecordv2.py", line 106, in <module>
tf.compat.v1.app.run()
File "C:\Users\LUIS\AppData\Roaming\Python\Python37\site-packages\tensorflow_c ore\python\platform\app.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "C:\Users\LUIS\AppData\Roaming\Python\Python37\site-packages\absl\app.py" , line 299, in run
_run_main(main, args)
File "C:\Users\LUIS\AppData\Roaming\Python\Python37\site-packages\absl\app.py" , line 250, in _run_main
sys.exit(main(argv))
File "generate_tfrecordv2.py", line 95, in main
grouped = split(examples, 'filename')
File "generate_tfrecordv2.py", line 45, in split
gb = df.groupby(group)
File "D:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py", line 7632, in groupby
observed=observed, **kwargs)
File "D:\ProgramData\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.p y", line 2110, in
groupby return klass(obj, by, **kwds)
File "D:\ProgramData\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.p y", line 360, in __init__
mutated=self.mutated)
File "D:\ProgramData\Anaconda3\lib\site-packages\pandas\core\groupby\grouper.p y", line 578, in _get_grouper
raise KeyError(gpr) KeyError: 'filename'
CSV文件内容就是这样
filename;width;height;class;xmin;ymin;xmax;ymax
19219.jpg;800;600;person;49;49;377;559
19219.jpg;800;600;person;431;131;644;592
您能告诉我这是什么错误吗？我确实使用的命令是这样的：
python generate_tfrecord.py --csv_input=train_labels.csv --image_dir=train --output_path=train.record
这是我的xml示例：
<annotation>
<folder>data_modified</folder>
<filename>1_245</filename>
<path>C:\material\dataset\test\1_245.jpg</path>
<source>
<database>Unknown</database>
</source>
<size>
<width>800</width>
<height>600</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>person</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>279</xmin>
<ymin>116</ymin>
<xmax>423</xmax>
<ymax>415</ymax>
</bndbox>
</object>
</annotation>
我确实更改了generate_tfrecords.py并重新生成xml_to_csv，但是它不起作用
"""
Usage:
# From tensorflow/models/
# Create train data:
python generate_tfrecord.py --csv_input=data/train_labels.csv --output_path=train.record
# Create test data:
python generate_tfrecord.py --csv_input=data/test_labels.csv --output_path=test.record
"""
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
from PIL import Image
#from object_detection.utils import dataset_util
#lc inicio
import dataset_util
#lc fin
from collections import namedtuple, OrderedDict
#lc inicio
#flags = tf.app.flags
flags = tf.compat.v1.flags
#lc fin
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 == 'person':
return 1
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.compat.v1.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)
writer = tf.compat.v1.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))
if __name__ == '__main__':
tf.compat.v1.app.run()
答案 0 :(得分：0)
我遇到了同样的错误，因为我使用了RectLabel，并且直接从那里导出了CSV文件。
CSV必须首先具有以下行：
文件名，宽度，高度，类，xmin，ymin，xmax，ymax
annotations.csv示例：
filename,width,height,class,xmin,ymin,xmax,ymax
8.jpg,1280,720,label1,427,82,848,578
9.jpg,1280,720,label1,426,87,845,585
28.jpg,1280,720,label1,435,100,847,563
14.jpg,352,640,label2,103,215,276,398
15.jpg,352,640,label2,106,215,279,399
29.jpg,352,640,label2,61,197,270,405
17.jpg,1280,720,label1,471,178,875,671
我使该文件运行以下脚本： https://gist.github.com/iKhushPatel/ed1f837656b155d9b94d45b42e00f5e4
并遵循本教程： https://towardsdatascience.com/custom-object-detection-using-tensorflow-from-scratch-e61da2e10087