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