Mercurial > hg > orthanc-book
annotate Sphinx/source/plugins/python.rst @ 375:766fe39fdf35
typo
author | Sebastien Jodogne <s.jodogne@gmail.com> |
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date | Mon, 06 Apr 2020 09:25:24 +0200 |
parents | f5bbdabb04d9 |
children | ffe62e6c086f 16dc3561b41e |
rev | line source |
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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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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 |
345 | 21 <https://hg.orthanc-server.com/orthanc/file/Orthanc-1.5.7/Plugins/Include/orthanc/OrthancCPlugin.h>`__ |
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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28 |
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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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38 |
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39 |
353 | 40 Licensing |
41 --------- | |
42 | |
43 Pay attention to the fact that this plugin is licensed under the terms | |
44 of the `AGPL license | |
45 <https://en.wikipedia.org/wiki/GNU_Affero_General_Public_License>`__. | |
46 | |
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 |
354 | 51 community under the terms of the AGPL license. |
52 | |
53 We suggest you to put the source code of your Python scripts on the | |
54 dedicated `"OrthancContributed" repository on GitHub | |
55 <https://github.com/jodogne/OrthancContributed/tree/master/Plugins>`__, | |
56 and/or to send it to the `Orthanc Users | |
57 <https://groups.google.com/forum/#!forum/orthanc-users>`__ discussion | |
58 group. | |
353 | 59 |
60 Check out the :ref:`FAQ about licensing <licensing>` for more context. | |
61 | |
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 | 219 |
220 Listening to changes | |
221 .................... | |
222 | |
346
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223 .. highlight:: python |
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224 |
345 | 225 This sample uploads a DICOM file as soon as Orthanc is started:: |
226 | |
227 import orthanc | |
228 | |
229 def OnChange(changeType, level, resource): | |
230 if changeType == orthanc.ChangeType.ORTHANC_STARTED: | |
231 print('Started') | |
232 | |
233 with open('/tmp/sample.dcm', 'rb') as f: | |
234 orthanc.RestApiPost('/instances', f.read()) | |
235 | |
236 elif changeType == orthanc.ChangeType.ORTHANC_STOPPED: | |
237 print('Stopped') | |
238 | |
239 elif changeType == orthanc.ChangeType.NEW_INSTANCE: | |
240 print('A new instance was uploaded: %s' % resource) | |
241 | |
242 orthanc.RegisterOnChangeCallback(OnChange) | |
243 | |
244 | |
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 | 249 |
250 :: | |
251 | |
252 import orthanc | |
253 import json | |
254 import pprint | |
255 | |
256 def OnStoredInstance(dicom, instanceId): | |
257 print('Received instance %s of size %d (transfer syntax %s, SOP class UID %s)' % ( | |
258 instanceId, dicom.GetInstanceSize(), | |
259 dicom.GetInstanceMetadata('TransferSyntax'), | |
260 dicom.GetInstanceMetadata('SopClassUid'))) | |
261 | |
262 # Print the origin information | |
263 if dicom.GetInstanceOrigin() == orthanc.InstanceOrigin.DICOM_PROTOCOL: | |
264 print('This instance was received through the DICOM protocol') | |
265 elif dicom.GetInstanceOrigin() == orthanc.InstanceOrigin.REST_API: | |
266 print('This instance was received through the REST API') | |
267 | |
268 # Print the DICOM tags | |
269 pprint.pprint(json.loads(dicom.GetInstanceSimplifiedJson())) | |
270 | |
271 orthanc.RegisterOnStoredInstanceCallback(OnStoredInstance) | |
346
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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 | 289 # Retrieve the instance ID from the regular expression (*) |
346
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290 instanceId = request['groups'][0] |
347 | 291 # Get the content of the DICOM file |
346
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292 f = orthanc.GetDicomForInstance(instanceId) |
347 | 293 # Parse it using pydicom |
346
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294 dicom = pydicom.dcmread(io.BytesIO(f)) |
347 | 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 | 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 | 308 |
309 Auto-routing studies | |
310 .................... | |
311 | |
312 .. highlight:: python | |
313 | |
314 Here is a sample Python plugin that routes any :ref:`stable study | |
315 <lua-callbacks>` to a modality named ``samples`` (as declared in the | |
316 ``DicomModalities`` configuration option):: | |
317 | |
318 import orthanc | |
319 | |
320 def OnChange(changeType, level, resourceId): | |
321 if changeType == orthanc.ChangeType.STABLE_STUDY: | |
322 print('Stable study: %s' % resourceId) | |
323 orthanc.RestApiPost('/modalities/sample/store', resourceId) | |
324 | |
325 orthanc.RegisterOnChangeCallback(OnChange) | |
348 | 326 |
327 | |
352 | 328 Rendering a thumbnail using PIL/Pillow |
329 ...................................... | |
348 | 330 |
331 .. highlight:: python | |
332 | |
333 :: | |
334 | |
335 from PIL import Image | |
336 import io | |
337 import orthanc | |
338 | |
339 def DecodeInstance(output, uri, **request): | |
340 if request['method'] == 'GET': | |
341 # Retrieve the instance ID from the regular expression (*) | |
342 instanceId = request['groups'][0] | |
343 | |
344 # Render the instance, then open it in Python using PIL/Pillow | |
345 png = orthanc.RestApiGet('/instances/%s/rendered' % instanceId) | |
346 image = Image.open(io.BytesIO(png)) | |
347 | |
348 # Downsize the image as a 64x64 thumbnail | |
349 image.thumbnail((64, 64), Image.ANTIALIAS) | |
350 | |
351 # Save the thumbnail as JPEG, then send the buffer to the caller | |
352 jpeg = io.BytesIO() | |
353 image.save(jpeg, format = "JPEG", quality = 80) | |
354 jpeg.seek(0) | |
355 output.AnswerBuffer(jpeg.read(), 'text/plain') | |
356 | |
357 else: | |
358 output.SendMethodNotAllowed('GET') | |
359 | |
360 orthanc.RegisterRestCallback('/pydicom/(.*)', DecodeInstance) # (*) | |
351 | 361 |
362 | |
369
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363 .. _python-introspection: |
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364 |
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365 Inspecting the available API |
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366 ............................ |
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367 |
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368 .. highlight:: python |
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369 |
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370 Thanks to Python's introspection primitives, it is possible to inspect |
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371 the API of the ``orthanc`` module in order to dump all the available |
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372 features:: |
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373 |
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374 import inspect |
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375 import numbers |
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376 import orthanc |
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377 |
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378 # Loop over the members of the "orthanc" module |
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379 for (name, obj) in inspect.getmembers(orthanc): |
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380 if inspect.isroutine(obj): |
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381 print('Function %s():\n Documentation: %s\n' % (name, inspect.getdoc(obj))) |
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382 |
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383 elif inspect.isclass(obj): |
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384 print('Class %s:\n Documentation: %s' % (name, inspect.getdoc(obj))) |
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385 |
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386 # Loop over the members of the class |
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387 for (subname, subobj) in inspect.getmembers(obj): |
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388 if isinstance(subobj, numbers.Number): |
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389 print(' - Enumeration value %s: %s' % (subname, subobj)) |
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390 elif (not subname.startswith('_') and |
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391 inspect.ismethoddescriptor(subobj)): |
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392 print(' - Method %s(): %s' % (subname, inspect.getdoc(subobj))) |
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393 print('') |
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394 |
373
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395 |
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396 .. _python-scheduler: |
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397 |
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398 Scheduling a task for periodic execution |
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399 ........................................ |
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400 |
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401 .. highlight:: python |
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402 |
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403 The following Python script will periodically (every second) run the |
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404 function ``Hello()`` thanks to the ``threading`` module:: |
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405 |
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406 import orthanc |
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407 import threading |
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408 |
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409 TIMER = None |
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410 |
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411 def Hello(): |
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412 global TIMER |
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413 TIMER = None |
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414 orthanc.LogWarning("In Hello()") |
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415 # Do stuff... |
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416 TIMER = threading.Timer(1, Hello) # Re-schedule after 1 second |
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417 TIMER.start() |
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418 |
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419 def OnChange(changeType, level, resource): |
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420 if changeType == orthanc.ChangeType.ORTHANC_STARTED: |
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421 orthanc.LogWarning("Starting the scheduler") |
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422 Hello() |
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423 |
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424 elif changeType == orthanc.ChangeType.ORTHANC_STOPPED: |
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425 if TIMER != None: |
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426 orthanc.LogWarning("Stopping the scheduler") |
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427 TIMER.cancel() |
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428 |
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429 orthanc.RegisterOnChangeCallback(OnChange) |
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430 |
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431 |
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432 |
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433 |
351 | 434 Performance and concurrency |
435 --------------------------- | |
436 | |
437 .. highlight:: python | |
438 | |
439 Let us consider the following sample Python script that makes a | |
440 CPU-intensive computation on a REST callback:: | |
441 | |
442 import math | |
443 import orthanc | |
444 import time | |
445 | |
446 # CPU-intensive computation taking about 4 seconds | |
447 def SlowComputation(): | |
448 start = time.time() | |
449 for i in range(1000): | |
450 for j in range(30000): | |
451 math.sqrt(float(j)) | |
452 end = time.time() | |
453 duration = (end - start) | |
454 return 'computation done in %.03f seconds\n' % duration | |
455 | |
456 def OnRest(output, uri, **request): | |
457 answer = SlowComputation() | |
458 output.AnswerBuffer(answer, 'text/plain') | |
459 | |
460 orthanc.RegisterRestCallback('/computation', OnRest) | |
461 | |
462 | |
463 .. highlight:: text | |
464 | |
465 Calling this REST route from the command-line returns the time that is | |
466 needed to compute 30 million times a squared root on your CPU:: | |
467 | |
468 $ curl http://localhost:8042/computation | |
469 computation done in 4.208 seconds | |
470 | |
471 Now, let us call this route three times concurrently (we use bash):: | |
472 | |
473 $ (curl http://localhost:8042/computation & curl http://localhost:8042/computation & curl http://localhost:8042/computation ) | |
474 computation done in 11.262 seconds | |
475 computation done in 12.457 seconds | |
476 computation done in 13.360 seconds | |
477 | |
478 As can be seen, the computation time has tripled. This means that the | |
479 computations were not distributed across the available CPU cores. | |
480 This might seem surprising, as Orthanc is a threaded server (in | |
481 Orthanc, a pool of C++ threads serves concurrent requests). | |
482 | |
483 The explanation is that the Python interpreter (`CPython | |
484 <https://en.wikipedia.org/wiki/CPython>`__ actually) is built on the | |
485 top of a so-called `Global Interpreter Lock (GIL) | |
486 <https://en.wikipedia.org/wiki/Global_interpreter_lock>`__. The GIL is | |
487 basically a mutex that protects all the calls to the Python | |
488 interpreter. If multiple C++ threads from Orthanc call a Python | |
353 | 489 callback, only one can proceed at any given time. Note however that |
490 the GIL only applies to the Python script: The baseline REST API of | |
491 Orthanc is not affected by the GIL. | |
351 | 492 |
493 .. highlight:: python | |
494 | |
495 The solution is to use the `multiprocessing primitives | |
496 <https://docs.python.org/3/library/multiprocessing.html>`__ of Python. | |
497 The "master" Python interpreter that is initially started by the | |
498 Orthanc plugin, can start several `children processes | |
499 <https://en.wikipedia.org/wiki/Process_(computing)>`__, each of these | |
500 processes running a separate Python interpreter. This allows to | |
501 offload intensive computations from the "master" Python interpreter of | |
502 Orthanc onto those "slave" interpreters. The ``multiprocessing`` | |
503 library is actually quite straightforward to use:: | |
504 | |
505 import math | |
506 import multiprocessing | |
507 import orthanc | |
508 import signal | |
509 import time | |
510 | |
511 # CPU-intensive computation taking about 4 seconds | |
512 # (same code as above) | |
513 def SlowComputation(): | |
514 start = time.time() | |
515 for i in range(1000): | |
516 for j in range(30000): | |
517 math.sqrt(float(j)) | |
518 end = time.time() | |
519 duration = (end - start) | |
520 return 'computation done in %.03f seconds\n' % duration | |
521 | |
522 # Ignore CTRL+C in the slave processes | |
523 def Initializer(): | |
524 signal.signal(signal.SIGINT, signal.SIG_IGN) | |
525 | |
526 # Create a pool of 4 slave Python interpreters | |
527 POOL = multiprocessing.Pool(4, initializer = Initializer) | |
528 | |
529 def OnRest(output, uri, **request): | |
530 # Offload the call to "SlowComputation" onto one slave process. | |
531 # The GIL is unlocked until the slave sends its answer back. | |
532 answer = POOL.apply(SlowComputation) | |
533 output.AnswerBuffer(answer, 'text/plain') | |
534 | |
535 orthanc.RegisterRestCallback('/computation', OnRest) | |
536 | |
537 .. highlight:: text | |
538 | |
539 Here is now the result of calling this route three times concurrently:: | |
540 | |
541 $ (curl http://localhost:8042/computation & curl http://localhost:8042/computation & curl http://localhost:8042/computation ) | |
542 computation done in 4.211 seconds | |
543 computation done in 4.215 seconds | |
544 computation done in 4.225 seconds | |
545 | |
546 As can be seen, the calls to the Python computation now fully run in | |
547 parallel (the time is cut down from 12 seconds to 4 seconds, the same | |
548 as for one isolated request). | |
549 | |
550 Note also how the ``multiprocessing`` library allows to make a fine | |
551 control over the computational resources that are available to the | |
552 Python script: The number of "slave" interpreters can be easily | |
553 changed in the constructor of the ``multiprocessing.Pool`` object, and | |
554 are fully independent of the threads used by the Orthanc server. | |
555 | |
352 | 556 .. highlight:: python |
557 | |
558 Very importantly, pay attention to the fact that only the "master" | |
559 Python interpreter has access to the Orthanc SDK. For instance, here | |
560 is how you would parse a DICOM file in a slave process:: | |
561 | |
562 import pydicom | |
563 import io | |
564 | |
565 def OffloadedDicomParsing(dicom): | |
566 # No access to the "orthanc" library here, as we are in the slave process | |
567 dataset = pydicom.dcmread(io.BytesIO(dicom)) | |
568 return str(dataset) | |
569 | |
570 def OnRest(output, uri, **request): | |
571 # The call to "orthanc.RestApiGet()" is only possible in the master process | |
572 dicom = orthanc.RestApiGet('/instances/19816330-cb02e1cf-df3a8fe8-bf510623-ccefe9f5/file') | |
573 answer = POOL.apply(OffloadedDicomParsing, args = (dicom, )) | |
574 output.AnswerBuffer(answer, 'text/plain') | |
575 | |
576 Communication primitives such as ``multiprocessing.Queue`` are | |
577 available to exchange messages from the "slave" Python interpreters to | |
578 the "master" Python interpreter if further calls to the Orthanc SDK | |
579 are required. | |
580 | |
351 | 581 Obviously, an in-depth discussion about the ``multiprocessing`` |
582 library is out of the scope of this document. There are many | |
375 | 583 references available on Internet. Also, note that ``threading`` is not |
584 useful here, as Python multithreading is also limited by the GIL, and | |
585 is more targeted at dealing with costly I/O operations or with the | |
586 :ref:`scheduling of commands <python-scheduler>`. |