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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Copyright 2019 The TensorFlow IO Authors.
Step1:  解码用于医学成像的 DICOM 文件
<table class="tfo-notebook-buttons" align="left">
  <td><a target="_blank" href="https
Step2:  安装要求的软件包，然后重新启动运行时
Step3:  解码 DICOM 图像
Step4:  解码 DICOM 元数据和使用标记
decode_dicom_data 用于解码标记信息。dicom_tags 包含有用的信息，如患者的年龄和性别，因此可以使用 dicom_tags.PatientsAge 和 dicom_tags.PatientsSex 等 DICOM 标记。tensorflow_io 借用了 pydicom dicom 软件包的标记法。
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Python Code:
#@title 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
#
# https://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.
Explanation: Copyright 2019 The TensorFlow IO Authors.
End of explanation
!curl -OL https://github.com/tensorflow/io/raw/master/docs/tutorials/dicom/dicom_00000001_000.dcm
!ls -l dicom_00000001_000.dcm
Explanation: 解码用于医学成像的 DICOM 文件
<table class="tfo-notebook-buttons" align="left">
  <td><a target="_blank" href="https://tensorflow.google.cn/io/tutorials/dicom"><img src="https://tensorflow.google.cn/images/tf_logo_32px.png">在 TensorFlow.org 上查看 </a></td>
  <td><a target="_blank" href="https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/io/tutorials/dicom.ipynb"><img src="https://tensorflow.google.cn/images/colab_logo_32px.png">在 Google Colab 中运行 </a></td>
  <td><a target="_blank" href="https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/io/tutorials/dicom.ipynb"><img src="https://tensorflow.google.cn/images/GitHub-Mark-32px.png">在 GitHub 中查看源代码</a></td>
      <td><a href="https://storage.googleapis.com/tensorflow_docs/io/docs/tutorials/dicom.ipynb">{img1下载笔记本</a></td>
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概述
本教程将介绍如何在 TensorFlow IO 中使用 tfio.image.decode_dicom_image 通过 TensorFlow 解码 DICOM 文件。
设置和用法
下载 DICOM 图像
本教程中使用的 DICOM 图像来自 NIH Chest X-Ray 数据集。
NIH Chest X-Ray 数据集包含 NIH 临床中心提供的 100,000 张胸部 X 射线检查的去标识化 PNG 图像，可通过此链接下载。
Google Cloud 还提供了可从 Cloud Storage 中获得的 DICOM 版本图像。
在本教程中，您将从 GitHub 仓库下载数据集的样本文件
注：有关数据集的更多信息，请查看以下参考资料：
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, Ronald Summers, ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases, IEEE CVPR, pp. 3462-3471, 2017
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try:
  # Use the Colab's preinstalled TensorFlow 2.x
  %tensorflow_version 2.x 
except:
  pass
!pip install tensorflow-io
Explanation: 安装要求的软件包，然后重新启动运行时
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import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import tensorflow_io as tfio
image_bytes = tf.io.read_file('dicom_00000001_000.dcm')
image = tfio.image.decode_dicom_image(image_bytes, dtype=tf.uint16)
skipped = tfio.image.decode_dicom_image(image_bytes, on_error='skip', dtype=tf.uint8)
lossy_image = tfio.image.decode_dicom_image(image_bytes, scale='auto', on_error='lossy', dtype=tf.uint8)
fig, axes = plt.subplots(1,2, figsize=(10,10))
axes[0].imshow(np.squeeze(image.numpy()), cmap='gray')
axes[0].set_title('image')
axes[1].imshow(np.squeeze(lossy_image.numpy()), cmap='gray')
axes[1].set_title('lossy image');
Explanation: 解码 DICOM 图像
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tag_id = tfio.image.dicom_tags.PatientsAge
tag_value = tfio.image.decode_dicom_data(image_bytes,tag_id)
print(tag_value)
print(f"PatientsAge : {tag_value.numpy().decode('UTF-8')}")
tag_id = tfio.image.dicom_tags.PatientsSex
tag_value = tfio.image.decode_dicom_data(image_bytes,tag_id)
print(f"PatientsSex : {tag_value.numpy().decode('UTF-8')}")
Explanation: 解码 DICOM 元数据和使用标记
decode_dicom_data 用于解码标记信息。dicom_tags 包含有用的信息，如患者的年龄和性别，因此可以使用 dicom_tags.PatientsAge 和 dicom_tags.PatientsSex 等 DICOM 标记。tensorflow_io 借用了 pydicom dicom 软件包的标记法。
End of explanation
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