Question

I am using the code that I am providing below to convert csv to tfrecord in pycharm, but I can t get the tfrecord file. I get this message:

tensorflow.python.framework.errors_impl.NotFoundError: Failed to create a 
NewWriteableFile:  : The system cannot find the file specified.
; No such file or directory

我寻找了不同的选择,但没有人给我一个解决办法,也许我不理解什么,或者我没有发现的法典有错误。

我的守则是:

    csv_input = "C:/Users/Documents/Model 
    A/MTrain_labels.csv"
    output_path = "C:/Users/Documents/Model A/"
    image_dir = "C:/Users/Documents/Model A/Base"

    from __future__ import division
    from __future__ import print_function
    from __future__ import absolute_import
    import os
    import io
    import sys
    import pandas as pd
    import tensorflow as tf
    import protos
    from PIL import Image
    from object_detection.utils import dataset_util
    from collections import namedtuple, OrderedDict
    from numpy import split

    flagsMJ.DEFINE_string( csv_input ,   ,  Path to the CSV input )
    flagsMJ.DEFINE_string( output_path ,   , Path to output TFRecord )
    flagsMJ.DEFINE_string( image_dir ,   ,  Path to images )

    def class_text_to_int(row_label):
        if row_label == "MM":
            return 1
        elif row_label == "JJ":
             return 2
        else:
             row_label = None

    def slipt(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.io.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.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()

Answer 1

错误tensorflow.python.framework.errors_impl.NotFoundErr表示,具体档案或目录可查。检查:

<>Path Strings:You ve下定义的旗帜,但也提供违约道路作为论据。消除这些论点。

flagsMJ.DEFINE_string( csv_input ,   ,  Path to CSV )

不

flagsMJ.DEFINE_string( csv_input ,   ,  C:/Users/Documents/Model A/MTrain_labels.csv )

Directory Existence: Verify that directories C:/Users/Documents/Model A/ and C:/Users/Documents/Model A/Base actually exist.
<>File Existence: Confirm MTrain_labels.csv存在于指定名录中。
<>Run as Admin:有时,书面许可可能是一个问题。经营PyCharm公司作为管理人。

www.un.org/Depts/DGACM/index_french.htm

python your_script.py --csv_input="C:/path/to/csv" --output_path="C:/path/to/output" --image_dir="C:/path/to/images"

<>:> 你的法典似乎不振。确保其与避免沙尘错误保持一致。

所有这一切,你们应当善于行。

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