HWDB1.1数据集.png转换成.tfrecord

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PeppaPeppaPeppa

最新推荐文章于 2025-05-23 11:25:10 发布

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创建data_prepare文件夹，内含data_convert.py和src/tfrecord.py。data文件夹分test和train，分别存放.png图片。运行代码将.png转换为.tfrecord文件。

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1、首先建立一个data_prepare文件夹，data_prepare文件夹中包含以下三个文件
文件夹中包含以上三个文件文件夹中包含在这里插入图片描述
data_convert.py代码

# coding:utf-8
from __future__ import absolute_import
import argparse
import os
import logging
from src.tfrecord import main

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('-t', '--tensorflow-data-dir', default='data/')
    parser.add_argument('--train-shards', default=2, type=int)
    parser.add_argument('--test-shards', default=2, type=int)
    parser.add_argument('--num-threads', default=2, type=int)
    parser.add_argument('--dataset-name', default='satellite', type=str)
    return parser.parse_args()

if __name__ == '__main__':
    logging.basicConfig(level=logging.INFO)
    args = parse_args()
    args.tensorflow_dir = args.tensorflow_data_dir
    args.train_directory = os.path.join(args.tensorflow_dir, 'train')
    args.test_directory = os.path.join(args.tensorflow_dir, 'test')
    args.output_directory = args.tensorflow_dir
    args.labels_file = os.path.join(args.tensorflow_dir, 'label.txt')
    if os.path.exists(args.labels_file) is False:
        logging.warning('Can\'t find label.txt. Now create it.')
        all_entries = os.listdir(args.train_directory)
        dirnames = []
        for entry in all_entries:
            if os.path.isdir(os.path.join(args.train_directory, entry)):
                dirnames.append(entry)
        with open(args.labels_file, 'w') as f:
            for dirname in dirnames:
                f.write(dirname + '\n')
    main(args)

2、其中data文件夹中包含test 和 train两个文件夹，里面分别含有测试集和训练集.png格式的图片
在这里插入图片描述
3、其中src文件夹中包含tfrecord.py文件
![在这里插入图片描述](https://img-blog.csdnimg.cn/20190410154815514.png
tfrecord.py代码

# coding:utf-8
# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Converts image data to TFRecords file format with Example protos.
The image data set is expected to reside in JPEG files located in the
following directory structure.
  data_dir/label_0/image0.jpeg
  data_dir/label_0/image1.jpg
  ...
  data_dir/label_1/weird-image.jpeg
  data_dir/label_1/my-image.jpeg
  ...
where the sub-directory is the unique label associated with these images.
This TensorFlow script converts the training and evaluation data into
a sharded data set consisting of TFRecord files
  train_directory/train-00000-of-01024
  train_directory/train-00001-of-01024
  ...
  train_directory/train-00127-of-01024
and
  test_directory/test-00000-of-00128
  test_directory/test-00001-of-00128
  ...
  test_directory/test-00127-of-00128
where we have selected 1024 and 128 shards for each data set. Each record
within the TFRecord file is a serialized Example proto. The Example proto
contains the following fields:
  image/encoded: string containing JPEG encoded image in RGB colorspace
  image/height: integer, image height in pixels
  image/width: integer, image w