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inspecting Python API
author Sebastien Jodogne <s.jodogne@gmail.com>
date Thu, 02 Apr 2020 08:39:41 +0200
parents 234de55ed125
children cb1d9a5bb043 847996394688
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1 .. _python-plugin:
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2
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3
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4 Python plugin for Orthanc
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5 =========================
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6
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7 .. contents::
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8
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9 Overview
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10 --------
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11
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12 This plugin can be used to write :ref:`Orthanc plugins
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13 <creating-plugins>` using the `Python programming language
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14 <https://en.wikipedia.org/wiki/Python_(programming_language)>`__
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15 instead of the more complex C/C++ programming languages.
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16
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17 Python plugins have access to more features and a more consistent SDK
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18 than :ref:`Lua scripts <lua>`. The Python API is automatically
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19 generated from the `Orthanc plugin SDK in C
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20 <https://hg.orthanc-server.com/orthanc/file/Orthanc-1.5.7/Plugins/Include/orthanc/OrthancCPlugin.h>`__
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21 using the `Clang <https://en.wikipedia.org/wiki/Clang>`__ compiler
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22 front-end.
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23
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24 As of initial release 1.0 of the plugin, the coverage of the C SDK is
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25 about 75% (105 functions are automatically wrapped in Python out of a
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26 total of 139 functions in the Orthanc SDK 1.5.7).
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27
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28 Licensing
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29 ---------
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30
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31 Pay attention to the fact that this plugin is licensed under the terms
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32 of the `AGPL license
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33 <https://en.wikipedia.org/wiki/GNU_Affero_General_Public_License>`__.
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34
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35 This has an important consequence: If you distribute Orthanc to
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36 clients together with one Python script, or if you put an Orthanc
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37 server equipped with one Python script on a Web portal, you **must**
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38 disclose the source code of your Python script to the Orthanc
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39 community under the terms of the AGPL license.
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40
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41 We suggest you to put the source code of your Python scripts on the
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42 dedicated `"OrthancContributed" repository on GitHub
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43 <https://github.com/jodogne/OrthancContributed/tree/master/Plugins>`__,
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44 and/or to send it to the `Orthanc Users
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45 <https://groups.google.com/forum/#!forum/orthanc-users>`__ discussion
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46 group.
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47
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48 Check out the :ref:`FAQ about licensing <licensing>` for more context.
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49
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50
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51 Usage
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52 -----
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53
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54 Docker
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55 ......
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56
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57 .. highlight:: json
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58
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59 The most direct way of starting Orthanc together with the Python
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60 plugin is through :ref:`Docker <docker>`. Let's create the file
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61 ``/tmp/hello.py`` that contains the following basic Python script::
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62
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63 print('Hello world!')
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64
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65 .. highlight:: json
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66
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67 Let's also create the file ``/tmp/orthanc.json`` that contains the
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68 following minimal :ref:`configuration for Orthanc <configuration>`::
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69
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70 {
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71 "StorageDirectory" : "/var/lib/orthanc/db",
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72 "RemoteAccessAllowed" : true,
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73 "Plugins" : [
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74 "/usr/local/share/orthanc/plugins"
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75 ],
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76 "PythonScript" : "/etc/orthanc/hello.py"
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77 }
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78
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79 .. highlight:: bash
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80
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81 Given these two files, Orthanc can be started as follows::
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82
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83 $ docker run -p 4242:4242 -p 8042:8042 --rm \
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84 -v /tmp/orthanc.json:/etc/orthanc/orthanc.json:ro \
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85 -v /tmp/hello.py:/etc/orthanc/hello.py:ro \
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86 jodogne/orthanc-python
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87
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88 .. highlight:: text
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89
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90 You'll see the following excerpt in the log, which indicates that the Python plugin is properly loaded::
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91
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92 W0331 15:48:12.990661 PluginsManager.cpp:269] Registering plugin 'python' (version mainline)
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93 W0331 15:48:12.990691 PluginsManager.cpp:168] Python plugin is initializing
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94 W0331 15:48:12.990743 PluginsManager.cpp:168] Using Python script "hello.py" from directory: /etc/orthanc
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95 W0331 15:48:12.990819 PluginsManager.cpp:168] Program name: /usr/local/sbin/Orthanc
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96 Hello world!
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97
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98
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99 Compiling from source
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100 .....................
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101
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102 .. highlight:: bash
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103
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104 The procedure to compile this plugin from source is similar to that
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105 for the :ref:`core of Orthanc <compiling>`. The following commands
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106 should work for most UNIX-like distribution (including GNU/Linux)::
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107
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108 $ mkdir Build
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109 $ cd Build
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110 $ cmake .. -DPYTHON_VERSION=3.7 -DSTATIC_BUILD=ON -DCMAKE_BUILD_TYPE=Release
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111 $ make
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112
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113 Before running CMake, make sure that the Python interpreter and its
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114 associated development library are installed. On Ubuntu 18.04 LTS, you
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115 would for instance install packages ``libpython3.7-dev`` and
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116 ``python3.7``.
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117
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118 The compilation will produce the shared library ``OrthancPython``,
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119 that can be loaded by properly setting the ``Plugins``
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120 :ref:`configuration option <configuration>` of Orthanc.
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121
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122 **Warning:** The shared library is only compatible with the Python
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123 interpreter whose version corresponds to the value of the
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124 ``PYTHON_VERSION`` argument that was given to CMake.
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125
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126
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127 Microsoft Windows
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128 .................
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129
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130 Pre-compiled binaries for Microsoft Windows `are also available
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131 <https://www.orthanc-server.com/browse.php?path=/plugin-python>`__.
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132
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133 Beware that one version of the Python plugin can only be run against
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134 one version of the Python interpreter. This version is clearly
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135 indicated in the name of the folder.
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136
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137 As of release 1.0, the Orthanc project only provides pre-compiled
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138 binaries for Microsoft Windows 32bit and Python 2.7. Even though this
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139 version of Python is not supported anymore, it can still run on all
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140 the versions of Microsoft Windows that have been released for more
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141 than 10 years.
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142
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143 .. highlight:: text
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144
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145 You are of course free to compile the plugin from sources if you need
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146 a more recent version. You'll have to explicitly specify the path to
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147 your Python installation while invoking CMake. For instance::
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148
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149 C:\orthanc-python\Build> cmake .. -DPYTHON_VERSION=2.7 -DPYTHON_WINDOWS_ROOT=C:/Python27 \
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150 -DSTATIC_BUILD=ON -DCMAKE_BUILD_TYPE=Release -G "Visual Studio 15 2017"
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151
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152
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153 Configuration options
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154 ---------------------
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155
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156 The only two configuration options that are available for this plugin
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157 are the following:
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158
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159 * ``PythonScript`` indicates where the Python script is located. If
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160 this configuration option is not provided, the Python plugin is not
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161 started.
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162
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163 * ``PythonVerbose`` is a Boolean value to make the Python interpreter
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164 verbose.
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165
343
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166
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167 Samples
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168 -------
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169
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170 Extending the REST API
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171 ......................
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172
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173 .. highlight:: python
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174
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175 Here is a basic Python script that registers two new routes in the
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176 REST API::
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177
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178 import orthanc
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179 import pprint
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180
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181 def OnRest(output, uri, **request):
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182 pprint.pprint(request)
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183 print('Accessing uri: %s' % uri)
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184 output.AnswerBuffer('ok\n', 'text/plain')
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185
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186 orthanc.RegisterRestCallback('/(to)(t)o', OnRest)
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187 orthanc.RegisterRestCallback('/tata', OnRest)
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188
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189 .. highlight:: json
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190
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191 Here is the associated minimal configuration file for Orthanc
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192 (provided the Python script is saved as ``rest.py``)::
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193
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194 {
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195 "Plugins" : [ "." ],
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196 "PythonScript" : "rest.py",
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197 "PythonVerbose" : false
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198 }
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199
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200 .. highlight:: bash
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201
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202 The route can then be accessed as::
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203
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204 $ curl http://localhost:8042/toto
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205 ok
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206
345
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207
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208 Listening to changes
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209 ....................
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210
346
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211 .. highlight:: python
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212
345
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213 This sample uploads a DICOM file as soon as Orthanc is started::
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214
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215 import orthanc
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216
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217 def OnChange(changeType, level, resource):
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218 if changeType == orthanc.ChangeType.ORTHANC_STARTED:
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219 print('Started')
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220
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221 with open('/tmp/sample.dcm', 'rb') as f:
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222 orthanc.RestApiPost('/instances', f.read())
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223
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224 elif changeType == orthanc.ChangeType.ORTHANC_STOPPED:
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225 print('Stopped')
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226
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227 elif changeType == orthanc.ChangeType.NEW_INSTANCE:
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228 print('A new instance was uploaded: %s' % resource)
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229
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230 orthanc.RegisterOnChangeCallback(OnChange)
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231
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232
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233 Accessing the content of a new instance
346
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234 .......................................
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235
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236 .. highlight:: python
345
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237
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238 ::
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239
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240 import orthanc
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241 import json
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242 import pprint
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243
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244 def OnStoredInstance(dicom, instanceId):
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245 print('Received instance %s of size %d (transfer syntax %s, SOP class UID %s)' % (
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246 instanceId, dicom.GetInstanceSize(),
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247 dicom.GetInstanceMetadata('TransferSyntax'),
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248 dicom.GetInstanceMetadata('SopClassUid')))
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249
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250 # Print the origin information
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251 if dicom.GetInstanceOrigin() == orthanc.InstanceOrigin.DICOM_PROTOCOL:
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252 print('This instance was received through the DICOM protocol')
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253 elif dicom.GetInstanceOrigin() == orthanc.InstanceOrigin.REST_API:
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254 print('This instance was received through the REST API')
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255
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256 # Print the DICOM tags
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257 pprint.pprint(json.loads(dicom.GetInstanceSimplifiedJson()))
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258
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259 orthanc.RegisterOnStoredInstanceCallback(OnStoredInstance)
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260
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261
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262 Calling pydicom
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263 ...............
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264
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265 .. highlight:: python
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266
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267 Here is a sample Python plugin that registers a REST callback to dump
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268 the content of the dataset of one given DICOM instance stored in
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269 Orthanc, using `pydicom <https://pydicom.github.io/>`__::
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270
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271 import io
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272 import orthanc
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273 import pydicom
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274
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275 def DecodeInstance(output, uri, **request):
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276 if request['method'] == 'GET':
347
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277 # Retrieve the instance ID from the regular expression (*)
346
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278 instanceId = request['groups'][0]
347
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279 # Get the content of the DICOM file
346
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280 f = orthanc.GetDicomForInstance(instanceId)
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281 # Parse it using pydicom
346
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282 dicom = pydicom.dcmread(io.BytesIO(f))
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283 # Return a string representation the dataset to the caller
346
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284 output.AnswerBuffer(str(dicom), 'text/plain')
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285 else:
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286 output.SendMethodNotAllowed('GET')
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287
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288 orthanc.RegisterRestCallback('/pydicom/(.*)', DecodeInstance) # (*)
346
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289
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290 .. highlight:: bash
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291
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292 This can be called as follows::
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293
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294 $ curl http://localhost:8042/pydicom/19816330-cb02e1cf-df3a8fe8-bf510623-ccefe9f5
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295
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296
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297 Auto-routing studies
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298 ....................
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299
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300 .. highlight:: python
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301
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302 Here is a sample Python plugin that routes any :ref:`stable study
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303 <lua-callbacks>` to a modality named ``samples`` (as declared in the
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304 ``DicomModalities`` configuration option)::
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305
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306 import orthanc
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307
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308 def OnChange(changeType, level, resourceId):
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309 if changeType == orthanc.ChangeType.STABLE_STUDY:
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310 print('Stable study: %s' % resourceId)
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311 orthanc.RestApiPost('/modalities/sample/store', resourceId)
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312
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313 orthanc.RegisterOnChangeCallback(OnChange)
348
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314
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315
352
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316 Rendering a thumbnail using PIL/Pillow
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317 ......................................
348
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318
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319 .. highlight:: python
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320
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321 ::
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322
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323 from PIL import Image
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324 import io
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325 import orthanc
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326
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327 def DecodeInstance(output, uri, **request):
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328 if request['method'] == 'GET':
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329 # Retrieve the instance ID from the regular expression (*)
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330 instanceId = request['groups'][0]
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331
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332 # Render the instance, then open it in Python using PIL/Pillow
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333 png = orthanc.RestApiGet('/instances/%s/rendered' % instanceId)
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334 image = Image.open(io.BytesIO(png))
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335
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336 # Downsize the image as a 64x64 thumbnail
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337 image.thumbnail((64, 64), Image.ANTIALIAS)
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338
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339 # Save the thumbnail as JPEG, then send the buffer to the caller
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340 jpeg = io.BytesIO()
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341 image.save(jpeg, format = "JPEG", quality = 80)
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342 jpeg.seek(0)
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343 output.AnswerBuffer(jpeg.read(), 'text/plain')
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344
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345 else:
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346 output.SendMethodNotAllowed('GET')
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347
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348 orthanc.RegisterRestCallback('/pydicom/(.*)', DecodeInstance) # (*)
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349
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350
369
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351 .. _python-introspection:
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352
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353 Inspecting the available API
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354 ............................
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355
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356 .. highlight:: python
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357
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358 Thanks to Python's introspection primitives, it is possible to inspect
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359 the API of the ``orthanc`` module in order to dump all the available
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360 features::
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361
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362 import inspect
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363 import numbers
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364 import orthanc
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365
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366 # Loop over the members of the "orthanc" module
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367 for (name, obj) in inspect.getmembers(orthanc):
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368 if inspect.isroutine(obj):
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369 print('Function %s():\n Documentation: %s\n' % (name, inspect.getdoc(obj)))
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370
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371 elif inspect.isclass(obj):
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372 print('Class %s:\n Documentation: %s' % (name, inspect.getdoc(obj)))
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373
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374 # Loop over the members of the class
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375 for (subname, subobj) in inspect.getmembers(obj):
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376 if isinstance(subobj, numbers.Number):
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377 print(' - Enumeration value %s: %s' % (subname, subobj))
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378 elif (not subname.startswith('_') and
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379 inspect.ismethoddescriptor(subobj)):
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380 print(' - Method %s(): %s' % (subname, inspect.getdoc(subobj)))
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381 print('')
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382
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383
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384
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385
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386
351
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387 Performance and concurrency
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388 ---------------------------
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389
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390 .. highlight:: python
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391
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392 Let us consider the following sample Python script that makes a
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393 CPU-intensive computation on a REST callback::
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394
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395 import math
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396 import orthanc
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397 import time
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398
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399 # CPU-intensive computation taking about 4 seconds
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400 def SlowComputation():
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401 start = time.time()
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402 for i in range(1000):
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403 for j in range(30000):
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404 math.sqrt(float(j))
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405 end = time.time()
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406 duration = (end - start)
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407 return 'computation done in %.03f seconds\n' % duration
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408
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409 def OnRest(output, uri, **request):
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410 answer = SlowComputation()
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411 output.AnswerBuffer(answer, 'text/plain')
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412
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413 orthanc.RegisterRestCallback('/computation', OnRest)
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414
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415
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416 .. highlight:: text
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417
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418 Calling this REST route from the command-line returns the time that is
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419 needed to compute 30 million times a squared root on your CPU::
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420
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421 $ curl http://localhost:8042/computation
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422 computation done in 4.208 seconds
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423
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424 Now, let us call this route three times concurrently (we use bash)::
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425
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426 $ (curl http://localhost:8042/computation & curl http://localhost:8042/computation & curl http://localhost:8042/computation )
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427 computation done in 11.262 seconds
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428 computation done in 12.457 seconds
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429 computation done in 13.360 seconds
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430
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431 As can be seen, the computation time has tripled. This means that the
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432 computations were not distributed across the available CPU cores.
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433 This might seem surprising, as Orthanc is a threaded server (in
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434 Orthanc, a pool of C++ threads serves concurrent requests).
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435
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436 The explanation is that the Python interpreter (`CPython
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437 <https://en.wikipedia.org/wiki/CPython>`__ actually) is built on the
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438 top of a so-called `Global Interpreter Lock (GIL)
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439 <https://en.wikipedia.org/wiki/Global_interpreter_lock>`__. The GIL is
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440 basically a mutex that protects all the calls to the Python
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441 interpreter. If multiple C++ threads from Orthanc call a Python
353
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442 callback, only one can proceed at any given time. Note however that
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443 the GIL only applies to the Python script: The baseline REST API of
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444 Orthanc is not affected by the GIL.
351
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445
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446 .. highlight:: python
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447
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448 The solution is to use the `multiprocessing primitives
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449 <https://docs.python.org/3/library/multiprocessing.html>`__ of Python.
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450 The "master" Python interpreter that is initially started by the
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451 Orthanc plugin, can start several `children processes
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452 <https://en.wikipedia.org/wiki/Process_(computing)>`__, each of these
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453 processes running a separate Python interpreter. This allows to
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454 offload intensive computations from the "master" Python interpreter of
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455 Orthanc onto those "slave" interpreters. The ``multiprocessing``
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456 library is actually quite straightforward to use::
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457
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458 import math
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459 import multiprocessing
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460 import orthanc
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461 import signal
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462 import time
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463
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464 # CPU-intensive computation taking about 4 seconds
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465 # (same code as above)
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466 def SlowComputation():
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467 start = time.time()
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468 for i in range(1000):
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469 for j in range(30000):
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470 math.sqrt(float(j))
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471 end = time.time()
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472 duration = (end - start)
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473 return 'computation done in %.03f seconds\n' % duration
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474
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475 # Ignore CTRL+C in the slave processes
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476 def Initializer():
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477 signal.signal(signal.SIGINT, signal.SIG_IGN)
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478
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479 # Create a pool of 4 slave Python interpreters
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480 POOL = multiprocessing.Pool(4, initializer = Initializer)
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481
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482 def OnRest(output, uri, **request):
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483 # Offload the call to "SlowComputation" onto one slave process.
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484 # The GIL is unlocked until the slave sends its answer back.
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485 answer = POOL.apply(SlowComputation)
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486 output.AnswerBuffer(answer, 'text/plain')
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487
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488 orthanc.RegisterRestCallback('/computation', OnRest)
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489
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490 .. highlight:: text
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491
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492 Here is now the result of calling this route three times concurrently::
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493
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494 $ (curl http://localhost:8042/computation & curl http://localhost:8042/computation & curl http://localhost:8042/computation )
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495 computation done in 4.211 seconds
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496 computation done in 4.215 seconds
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497 computation done in 4.225 seconds
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498
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499 As can be seen, the calls to the Python computation now fully run in
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500 parallel (the time is cut down from 12 seconds to 4 seconds, the same
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501 as for one isolated request).
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502
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503 Note also how the ``multiprocessing`` library allows to make a fine
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504 control over the computational resources that are available to the
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505 Python script: The number of "slave" interpreters can be easily
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506 changed in the constructor of the ``multiprocessing.Pool`` object, and
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507 are fully independent of the threads used by the Orthanc server.
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508
352
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509 .. highlight:: python
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510
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511 Very importantly, pay attention to the fact that only the "master"
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512 Python interpreter has access to the Orthanc SDK. For instance, here
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513 is how you would parse a DICOM file in a slave process::
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514
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515 import pydicom
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516 import io
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517
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518 def OffloadedDicomParsing(dicom):
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519 # No access to the "orthanc" library here, as we are in the slave process
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520 dataset = pydicom.dcmread(io.BytesIO(dicom))
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521 return str(dataset)
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522
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523 def OnRest(output, uri, **request):
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524 # The call to "orthanc.RestApiGet()" is only possible in the master process
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525 dicom = orthanc.RestApiGet('/instances/19816330-cb02e1cf-df3a8fe8-bf510623-ccefe9f5/file')
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526 answer = POOL.apply(OffloadedDicomParsing, args = (dicom, ))
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527 output.AnswerBuffer(answer, 'text/plain')
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528
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529 Communication primitives such as ``multiprocessing.Queue`` are
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530 available to exchange messages from the "slave" Python interpreters to
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531 the "master" Python interpreter if further calls to the Orthanc SDK
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532 are required.
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533
351
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534 Obviously, an in-depth discussion about the ``multiprocessing``
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535 library is out of the scope of this document. There are many
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536 references available on Internet. Also, note that ``multithreading``
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537 is not useful here, as Python multithreading is also limited by the
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538 GIL, and is more targeted at dealing with costly I/O operations.