Mercurial > hg > orthanc-book
annotate Sphinx/source/plugins/python.rst @ 472:03dfb896ca4b
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author | Sebastien Jodogne <s.jodogne@gmail.com> |
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date | Tue, 04 Aug 2020 20:53:35 +0200 |
parents | a735476a0e6e 46949efa5818 |
children | 933e28b6a64d |
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 |
462 | 21 <https://hg.orthanc-server.com/orthanc/file/Orthanc-1.7.2/OrthancServer/Plugins/Include/orthanc/OrthancCPlugin.h>`__ |
345 | 22 using the `Clang <https://en.wikipedia.org/wiki/Clang>`__ compiler |
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23 front-end. |
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24 |
462 | 25 As of release 2.0 of the plugin, the coverage of the C SDK is about |
26 75% (119 functions are automatically wrapped in Python out of a total | |
27 of 157 functions in the Orthanc SDK 1.7.2). | |
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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 |
405 | 111 `Here <https://bitbucket.org/osimis/orthanc-setup-samples/src/master/docker/python/>`__ is a full example |
112 of a more complex setup using the :ref:`osimis/orthanc <docker-osimis>` images. | |
113 | |
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114 Compiling from source |
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115 ..................... |
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116 |
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117 .. highlight:: bash |
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118 |
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119 The procedure to compile this plugin from source is similar to that |
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120 for the :ref:`core of Orthanc <compiling>`. The following commands |
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121 should work for most UNIX-like distribution (including GNU/Linux):: |
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122 |
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123 $ mkdir Build |
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124 $ cd Build |
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125 $ cmake .. -DPYTHON_VERSION=3.7 -DSTATIC_BUILD=ON -DCMAKE_BUILD_TYPE=Release |
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126 $ make |
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127 |
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128 Before running CMake, make sure that the Python interpreter and its |
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129 associated development library are installed. On Ubuntu 18.04 LTS, you |
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130 would for instance install packages ``libpython3.7-dev`` and |
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131 ``python3.7``. |
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132 |
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133 The compilation will produce the shared library ``OrthancPython``, |
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134 that can be loaded by properly setting the ``Plugins`` |
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135 :ref:`configuration option <configuration>` of Orthanc. |
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136 |
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137 **Warning:** The shared library is only compatible with the Python |
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138 interpreter whose version corresponds to the value of the |
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139 ``PYTHON_VERSION`` argument that was given to CMake. |
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140 |
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141 |
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142 Microsoft Windows |
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143 ................. |
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144 |
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145 Pre-compiled binaries for Microsoft Windows `are also available |
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146 <https://www.orthanc-server.com/browse.php?path=/plugin-python>`__. |
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147 |
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148 Beware that one version of the Python plugin can only be run against |
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149 one version of the Python interpreter. This version is clearly |
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150 indicated in the filename of the precompiled binaries. |
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151 |
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152 .. highlight:: text |
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153 |
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154 You are of course free to compile the plugin from sources. You'll have |
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155 to explicitly specify the path to your Python installation while |
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156 invoking CMake. For instance:: |
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157 |
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158 C:\orthanc-python\Build> cmake .. -DPYTHON_VERSION=2.7 -DPYTHON_WINDOWS_ROOT=C:/Python27 \ |
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159 -DSTATIC_BUILD=ON -DCMAKE_BUILD_TYPE=Release -G "Visual Studio 15 2017" |
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160 |
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161 **Note about debug builds**: Usually, building Python modules such as the Python |
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162 plugin for Orthanc in debug mode (where ``_DEBUG`` is defined) leads to a module |
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163 (``.exe`` or ``.dll``) that requires a debug build of Python, and debug versions of all |
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164 the Python libraries. This is quite cumbersome, for it requires building Python |
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165 on your own or downloading additional debug files. |
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166 |
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167 Since using a debug build of Python is only necessary in very specific cases |
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168 (such as the debugging of code at the boundary between Python and an extension), |
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169 we have changed the default behavior to use the release Python library by default. |
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170 |
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171 This means that you are able to build this plugin in debug mode with the |
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172 standard Python distribution. |
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173 |
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174 In case you need to use the Python debug libraries, you can instruct the build |
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175 system to do so by setting the ``PYTHON_WINDOWS_USE_RELEASE_LIBS`` CMake option, |
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176 that is ``ON`` by default, to ``OFF``. The previous build example would then be, |
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177 should you require a full debug build:: |
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178 |
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179 C:\orthanc-python\Build> cmake .. -DPYTHON_VERSION=2.7 -DPYTHON_WINDOWS_ROOT=C:/Python27 \ |
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180 -DSTATIC_BUILD=ON -DPYTHON_WINDOWS_USE_RELEASE_LIBS=OFF \ |
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181 -DCMAKE_BUILD_TYPE=Debug -G "Visual Studio 15 2017" |
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182 |
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183 Please note that this CMake option only impacts **debug** builds under Windows, |
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184 when using (any version of) the Microsoft Visual Studio compiler. |
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185 |
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186 The precompiled binaries all use release (i.e. non-debug) versions of Python. |
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187 |
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188 |
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189 Configuration options |
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190 --------------------- |
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191 |
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192 The only two configuration options that are available for this plugin |
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193 are the following: |
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194 |
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195 * ``PythonScript`` indicates where the Python script is located. If |
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196 this configuration option is not provided, the Python plugin is not |
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197 started. |
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198 |
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199 * ``PythonVerbose`` is a Boolean value to make the Python interpreter |
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200 verbose. |
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201 |
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202 |
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203 Samples |
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204 ------- |
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205 |
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206 Extending the REST API |
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207 ...................... |
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208 |
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209 .. highlight:: python |
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210 |
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211 Here is a basic Python script that registers two new routes in the |
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212 REST API:: |
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213 |
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214 import orthanc |
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215 import pprint |
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216 |
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217 def OnRest(output, uri, **request): |
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218 pprint.pprint(request) |
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219 print('Accessing uri: %s' % uri) |
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220 output.AnswerBuffer('ok\n', 'text/plain') |
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221 |
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222 orthanc.RegisterRestCallback('/(to)(t)o', OnRest) |
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223 orthanc.RegisterRestCallback('/tata', OnRest) |
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224 |
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225 .. highlight:: json |
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226 |
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227 Here is the associated minimal configuration file for Orthanc |
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228 (provided the Python script is saved as ``rest.py``):: |
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229 |
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230 { |
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231 "Plugins" : [ "." ], |
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232 "PythonScript" : "rest.py", |
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233 "PythonVerbose" : false |
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234 } |
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235 |
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236 .. highlight:: bash |
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237 |
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238 The route can then be accessed as:: |
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239 |
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240 $ curl http://localhost:8042/toto |
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241 ok |
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242 |
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243 |
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244 .. _python-changes: |
345 | 245 |
246 Listening to changes | |
247 .................... | |
248 | |
346
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249 .. highlight:: python |
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250 |
345 | 251 This sample uploads a DICOM file as soon as Orthanc is started:: |
252 | |
253 import orthanc | |
254 | |
255 def OnChange(changeType, level, resource): | |
256 if changeType == orthanc.ChangeType.ORTHANC_STARTED: | |
257 print('Started') | |
258 | |
259 with open('/tmp/sample.dcm', 'rb') as f: | |
260 orthanc.RestApiPost('/instances', f.read()) | |
261 | |
262 elif changeType == orthanc.ChangeType.ORTHANC_STOPPED: | |
263 print('Stopped') | |
264 | |
265 elif changeType == orthanc.ChangeType.NEW_INSTANCE: | |
266 print('A new instance was uploaded: %s' % resource) | |
267 | |
268 orthanc.RegisterOnChangeCallback(OnChange) | |
269 | |
270 | |
271 Accessing the content of a new instance | |
346
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272 ....................................... |
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273 |
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274 .. highlight:: python |
345 | 275 |
276 :: | |
277 | |
278 import orthanc | |
279 import json | |
280 import pprint | |
281 | |
282 def OnStoredInstance(dicom, instanceId): | |
283 print('Received instance %s of size %d (transfer syntax %s, SOP class UID %s)' % ( | |
284 instanceId, dicom.GetInstanceSize(), | |
285 dicom.GetInstanceMetadata('TransferSyntax'), | |
286 dicom.GetInstanceMetadata('SopClassUid'))) | |
287 | |
288 # Print the origin information | |
289 if dicom.GetInstanceOrigin() == orthanc.InstanceOrigin.DICOM_PROTOCOL: | |
290 print('This instance was received through the DICOM protocol') | |
291 elif dicom.GetInstanceOrigin() == orthanc.InstanceOrigin.REST_API: | |
292 print('This instance was received through the REST API') | |
293 | |
294 # Print the DICOM tags | |
295 pprint.pprint(json.loads(dicom.GetInstanceSimplifiedJson())) | |
296 | |
297 orthanc.RegisterOnStoredInstanceCallback(OnStoredInstance) | |
346
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298 |
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299 |
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300 Calling pydicom |
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301 ............... |
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302 |
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303 .. highlight:: python |
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304 |
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305 Here is a sample Python plugin that registers a REST callback to dump |
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306 the content of the dataset of one given DICOM instance stored in |
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307 Orthanc, using `pydicom <https://pydicom.github.io/>`__:: |
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308 |
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309 import io |
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310 import orthanc |
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311 import pydicom |
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312 |
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313 def DecodeInstance(output, uri, **request): |
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314 if request['method'] == 'GET': |
347 | 315 # Retrieve the instance ID from the regular expression (*) |
346
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316 instanceId = request['groups'][0] |
347 | 317 # Get the content of the DICOM file |
346
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318 f = orthanc.GetDicomForInstance(instanceId) |
347 | 319 # Parse it using pydicom |
346
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320 dicom = pydicom.dcmread(io.BytesIO(f)) |
347 | 321 # Return a string representation the dataset to the caller |
346
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322 output.AnswerBuffer(str(dicom), 'text/plain') |
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323 else: |
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324 output.SendMethodNotAllowed('GET') |
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325 |
347 | 326 orthanc.RegisterRestCallback('/pydicom/(.*)', DecodeInstance) # (*) |
346
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327 |
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328 .. highlight:: bash |
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329 |
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330 This can be called as follows:: |
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331 |
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332 $ curl http://localhost:8042/pydicom/19816330-cb02e1cf-df3a8fe8-bf510623-ccefe9f5 |
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333 |
347 | 334 |
335 Auto-routing studies | |
336 .................... | |
337 | |
338 .. highlight:: python | |
339 | |
340 Here is a sample Python plugin that routes any :ref:`stable study | |
341 <lua-callbacks>` to a modality named ``samples`` (as declared in the | |
342 ``DicomModalities`` configuration option):: | |
343 | |
344 import orthanc | |
345 | |
346 def OnChange(changeType, level, resourceId): | |
347 if changeType == orthanc.ChangeType.STABLE_STUDY: | |
348 print('Stable study: %s' % resourceId) | |
349 orthanc.RestApiPost('/modalities/sample/store', resourceId) | |
350 | |
351 orthanc.RegisterOnChangeCallback(OnChange) | |
348 | 352 |
353 | |
352 | 354 Rendering a thumbnail using PIL/Pillow |
355 ...................................... | |
348 | 356 |
357 .. highlight:: python | |
358 | |
359 :: | |
360 | |
361 from PIL import Image | |
362 import io | |
363 import orthanc | |
364 | |
365 def DecodeInstance(output, uri, **request): | |
366 if request['method'] == 'GET': | |
367 # Retrieve the instance ID from the regular expression (*) | |
368 instanceId = request['groups'][0] | |
369 | |
370 # Render the instance, then open it in Python using PIL/Pillow | |
371 png = orthanc.RestApiGet('/instances/%s/rendered' % instanceId) | |
372 image = Image.open(io.BytesIO(png)) | |
373 | |
374 # Downsize the image as a 64x64 thumbnail | |
375 image.thumbnail((64, 64), Image.ANTIALIAS) | |
376 | |
377 # Save the thumbnail as JPEG, then send the buffer to the caller | |
378 jpeg = io.BytesIO() | |
379 image.save(jpeg, format = "JPEG", quality = 80) | |
380 jpeg.seek(0) | |
381 output.AnswerBuffer(jpeg.read(), 'text/plain') | |
382 | |
383 else: | |
384 output.SendMethodNotAllowed('GET') | |
385 | |
386 orthanc.RegisterRestCallback('/pydicom/(.*)', DecodeInstance) # (*) | |
351 | 387 |
388 | |
369
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389 .. _python-introspection: |
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390 |
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391 Inspecting the available API |
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392 ............................ |
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393 |
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394 .. highlight:: python |
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395 |
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396 Thanks to Python's introspection primitives, it is possible to inspect |
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397 the API of the ``orthanc`` module in order to dump all the available |
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398 features:: |
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399 |
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400 import inspect |
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401 import numbers |
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402 import orthanc |
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403 |
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404 # Loop over the members of the "orthanc" module |
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405 for (name, obj) in inspect.getmembers(orthanc): |
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406 if inspect.isroutine(obj): |
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407 print('Function %s():\n Documentation: %s\n' % (name, inspect.getdoc(obj))) |
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408 |
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409 elif inspect.isclass(obj): |
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410 print('Class %s:\n Documentation: %s' % (name, inspect.getdoc(obj))) |
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411 |
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412 # Loop over the members of the class |
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413 for (subname, subobj) in inspect.getmembers(obj): |
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414 if isinstance(subobj, numbers.Number): |
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415 print(' - Enumeration value %s: %s' % (subname, subobj)) |
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416 elif (not subname.startswith('_') and |
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417 inspect.ismethoddescriptor(subobj)): |
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418 print(' - Method %s(): %s' % (subname, inspect.getdoc(subobj))) |
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419 print('') |
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420 |
373
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421 |
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422 .. _python-scheduler: |
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423 |
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424 Scheduling a task for periodic execution |
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425 ........................................ |
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426 |
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427 .. highlight:: python |
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428 |
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429 The following Python script will periodically (every second) run the |
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430 function ``Hello()`` thanks to the ``threading`` module:: |
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431 |
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432 import orthanc |
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433 import threading |
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434 |
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435 TIMER = None |
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436 |
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437 def Hello(): |
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438 global TIMER |
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439 TIMER = None |
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440 orthanc.LogWarning("In Hello()") |
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441 # Do stuff... |
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442 TIMER = threading.Timer(1, Hello) # Re-schedule after 1 second |
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443 TIMER.start() |
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444 |
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445 def OnChange(changeType, level, resource): |
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446 if changeType == orthanc.ChangeType.ORTHANC_STARTED: |
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447 orthanc.LogWarning("Starting the scheduler") |
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448 Hello() |
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449 |
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450 elif changeType == orthanc.ChangeType.ORTHANC_STOPPED: |
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451 if TIMER != None: |
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452 orthanc.LogWarning("Stopping the scheduler") |
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453 TIMER.cancel() |
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454 |
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455 orthanc.RegisterOnChangeCallback(OnChange) |
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456 |
378
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457 |
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458 .. _python-metadata: |
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459 |
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460 Filtering and returning metadata |
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461 ................................ |
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462 |
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463 Besides the main DICOM tags, Orthanc associates some metadata to each |
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464 resource it stores (this includes the date of last update, the |
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465 transfer syntax, the remote AET...). People are often interested in |
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466 getting such metadata while calling the ``/tools/find`` route in the |
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467 :ref:`REST API <rest-find>`, or even in filtering this metadata the |
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468 same way they look for DICOM tags. |
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469 |
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470 This feature is not built in the core of Orthanc, as metadata is not |
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471 indexed in the Orthanc database, contrarily to the main DICOM |
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472 tags. Filtering metadata requires a linear search over all the |
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473 matching resources, which induces a cost in the performance. |
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474 |
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475 .. highlight:: python |
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476 |
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477 Nevertheless, here is a full sample Python script that overwrites the |
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478 ``/tools/find`` route in order to give access to metadata:: |
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479 |
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480 import json |
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481 import orthanc |
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482 import re |
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483 |
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484 # Get the path in the REST API to the given resource that was returned |
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485 # by a call to "/tools/find" |
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486 def GetPath(resource): |
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487 if resource['Type'] == 'Patient': |
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488 return '/patients/%s' % resource['ID'] |
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489 elif resource['Type'] == 'Study': |
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490 return '/studies/%s' % resource['ID'] |
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491 elif resource['Type'] == 'Series': |
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492 return '/series/%s' % resource['ID'] |
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493 elif resource['Type'] == 'Instance': |
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494 return '/instances/%s' % resource['ID'] |
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495 else: |
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496 raise Exception('Unknown resource level') |
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497 |
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498 def FindWithMetadata(output, uri, **request): |
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499 # The "/tools/find" route expects a POST method |
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500 if request['method'] != 'POST': |
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501 output.SendMethodNotAllowed('POST') |
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502 else: |
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503 # Parse the query provided by the user, and backup the "Expand" field |
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504 query = json.loads(request['body']) |
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505 |
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506 if 'Expand' in query: |
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507 originalExpand = query['Expand'] |
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508 else: |
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509 originalExpand = False |
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510 |
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511 # Call the core "/tools/find" route |
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512 query['Expand'] = True |
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513 answers = orthanc.RestApiPost('/tools/find', json.dumps(query)) |
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514 |
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515 # Loop over the matching resources |
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516 filteredAnswers = [] |
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517 for answer in json.loads(answers): |
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518 try: |
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519 # Read the metadata that is associated with the resource |
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520 metadata = json.loads(orthanc.RestApiGet('%s/metadata?expand' % GetPath(answer))) |
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521 |
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522 # Check whether the metadata matches the regular expressions |
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523 # that were provided in the "Metadata" field of the user request |
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524 isMetadataMatch = True |
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525 if 'Metadata' in query: |
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526 for (name, pattern) in query['Metadata'].items(): |
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527 if name in metadata: |
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528 value = metadata[name] |
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529 else: |
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530 value = '' |
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531 |
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532 if re.match(pattern, value) == None: |
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533 isMetadataMatch = False |
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534 break |
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535 |
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536 # If all the metadata matches the provided regular |
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537 # expressions, add the resource to the filtered answers |
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538 if isMetadataMatch: |
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539 if originalExpand: |
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540 answer['Metadata'] = metadata |
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541 filteredAnswers.append(answer) |
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542 else: |
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543 filteredAnswers.append(answer['ID']) |
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544 except: |
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545 # The resource was deleted since the call to "/tools/find" |
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546 pass |
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547 |
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548 # Return the filtered answers in the JSON format |
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549 output.AnswerBuffer(json.dumps(filteredAnswers, indent = 3), 'application/json') |
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550 |
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551 orthanc.RegisterRestCallback('/tools/find', FindWithMetadata) |
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552 |
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553 |
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554 **Warning:** In the sample above, the filtering of the metadata is |
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555 done using Python's `library for regular expressions |
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556 <https://docs.python.org/3/library/re.html>`__. It is evidently |
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557 possible to adapt this script in order to use the DICOM conventions |
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558 about `attribute matching |
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559 <http://dicom.nema.org/medical/dicom/2019e/output/chtml/part04/sect_C.2.2.2.html>`__. |
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560 |
469
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561 .. highlight:: bash |
378
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562 |
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563 Here is a sample call to retrieve all the studies that were last |
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564 updated in 2019 thanks to this Python script:: |
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565 |
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566 $ curl http://localhost:8042/tools/find -d '{"Level":"Study","Query":{},"Expand":true,"Metadata":{"LastUpdate":"^2019.*$"}}' |
369
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567 |
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568 |
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569 .. _python-paging: |
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570 |
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571 Implementing basic paging |
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572 ......................... |
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573 |
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574 .. highlight:: python |
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575 |
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576 As explained in the FAQ, the :ref:`Orthanc Explorer interface is |
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577 low-level <improving-interface>`, and is not adapted for |
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578 end-users. One common need is to implement paging of studies, which |
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579 calls for server-side sorting of studies. This can be done using the |
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580 following sample Python plugin that registers a new route |
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581 ``/sort-studies`` in the REST API of Orthanc:: |
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582 |
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583 import json |
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584 import orthanc |
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585 |
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586 def GetStudyDate(study): |
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587 if 'StudyDate' in study['MainDicomTags']: |
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588 return study['MainDicomTags']['StudyDate'] |
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589 else: |
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590 return '' |
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591 |
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592 def SortStudiesByDate(output, uri, **request): |
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593 if request['method'] == 'GET': |
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594 # Retrieve all the studies |
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595 studies = json.loads(orthanc.RestApiGet('/studies?expand')) |
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596 |
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597 # Sort the studies according to the "StudyDate" DICOM tag |
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598 studies = sorted(studies, key = GetStudyDate) |
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599 |
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600 # Read the limit/offset arguments provided by the user |
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601 offset = 0 |
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602 if 'offset' in request['get']: |
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603 offset = int(request['get']['offset']) |
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604 |
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605 limit = 0 |
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606 if 'limit' in request['get']: |
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607 limit = int(request['get']['limit']) |
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608 |
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609 # Truncate the list of studies |
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610 if limit == 0: |
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611 studies = studies[offset : ] |
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612 else: |
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613 studies = studies[offset : offset + limit] |
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614 |
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615 # Return the truncated list of studies |
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616 output.AnswerBuffer(json.dumps(studies), 'application/json') |
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617 else: |
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618 output.SendMethodNotAllowed('GET') |
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619 |
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620 orthanc.RegisterRestCallback('/sort-studies', SortStudiesByDate) |
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621 |
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622 |
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623 .. highlight:: bash |
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624 |
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625 Here is a sample call to this new REST route, that could be issued by |
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626 any JavaScript framework (the ``json_pp`` command-line pretty-prints a |
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627 JSON file):: |
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628 |
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629 $ curl http://localhost:8042/sort-studies | json_pp |
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630 |
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631 This route also implement paging (i.e. it can limit and offset the |
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632 returned studies):: |
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633 |
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634 $ curl 'http://localhost:8042/sort-studies?offset=2&limit=2' | json_pp |
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635 |
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636 Obviously, this basic sample can be improved in many ways. To improve |
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637 performance, one could for instance cache the result of |
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638 ``/studies?expand`` in memory by :ref:`listening to changes |
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639 <python-changes>` in the list of studies |
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640 (cf. ``orthanc.ChangeType.NEW_STUDY`` and |
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641 ``orthanc.ChangeType.DELETED``). |
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642 |
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643 |
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644 |
351 | 645 Performance and concurrency |
646 --------------------------- | |
647 | |
471
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648 **Important:** This section only applies to UNIX-like systems. The |
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649 ``multiprocessing`` package will not work on Microsoft Windows as the |
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650 latter OS has a different model for `forking processes |
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651 <https://en.wikipedia.org/wiki/Fork_(system_call)>`__. |
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652 |
351 | 653 .. highlight:: python |
654 | |
655 Let us consider the following sample Python script that makes a | |
656 CPU-intensive computation on a REST callback:: | |
657 | |
658 import math | |
659 import orthanc | |
660 import time | |
661 | |
662 # CPU-intensive computation taking about 4 seconds | |
663 def SlowComputation(): | |
664 start = time.time() | |
665 for i in range(1000): | |
666 for j in range(30000): | |
667 math.sqrt(float(j)) | |
668 end = time.time() | |
669 duration = (end - start) | |
670 return 'computation done in %.03f seconds\n' % duration | |
671 | |
672 def OnRest(output, uri, **request): | |
673 answer = SlowComputation() | |
674 output.AnswerBuffer(answer, 'text/plain') | |
675 | |
676 orthanc.RegisterRestCallback('/computation', OnRest) | |
677 | |
678 | |
679 .. highlight:: text | |
680 | |
681 Calling this REST route from the command-line returns the time that is | |
682 needed to compute 30 million times a squared root on your CPU:: | |
683 | |
684 $ curl http://localhost:8042/computation | |
685 computation done in 4.208 seconds | |
686 | |
687 Now, let us call this route three times concurrently (we use bash):: | |
688 | |
689 $ (curl http://localhost:8042/computation & curl http://localhost:8042/computation & curl http://localhost:8042/computation ) | |
690 computation done in 11.262 seconds | |
691 computation done in 12.457 seconds | |
692 computation done in 13.360 seconds | |
693 | |
694 As can be seen, the computation time has tripled. This means that the | |
695 computations were not distributed across the available CPU cores. | |
696 This might seem surprising, as Orthanc is a threaded server (in | |
697 Orthanc, a pool of C++ threads serves concurrent requests). | |
698 | |
699 The explanation is that the Python interpreter (`CPython | |
700 <https://en.wikipedia.org/wiki/CPython>`__ actually) is built on the | |
701 top of a so-called `Global Interpreter Lock (GIL) | |
702 <https://en.wikipedia.org/wiki/Global_interpreter_lock>`__. The GIL is | |
703 basically a mutex that protects all the calls to the Python | |
704 interpreter. If multiple C++ threads from Orthanc call a Python | |
353 | 705 callback, only one can proceed at any given time. Note however that |
706 the GIL only applies to the Python script: The baseline REST API of | |
707 Orthanc is not affected by the GIL. | |
351 | 708 |
709 .. highlight:: python | |
710 | |
711 The solution is to use the `multiprocessing primitives | |
712 <https://docs.python.org/3/library/multiprocessing.html>`__ of Python. | |
713 The "master" Python interpreter that is initially started by the | |
714 Orthanc plugin, can start several `children processes | |
715 <https://en.wikipedia.org/wiki/Process_(computing)>`__, each of these | |
716 processes running a separate Python interpreter. This allows to | |
717 offload intensive computations from the "master" Python interpreter of | |
718 Orthanc onto those "slave" interpreters. The ``multiprocessing`` | |
719 library is actually quite straightforward to use:: | |
720 | |
721 import math | |
722 import multiprocessing | |
723 import orthanc | |
724 import signal | |
725 import time | |
726 | |
727 # CPU-intensive computation taking about 4 seconds | |
728 # (same code as above) | |
729 def SlowComputation(): | |
730 start = time.time() | |
731 for i in range(1000): | |
732 for j in range(30000): | |
733 math.sqrt(float(j)) | |
734 end = time.time() | |
735 duration = (end - start) | |
736 return 'computation done in %.03f seconds\n' % duration | |
737 | |
738 # Ignore CTRL+C in the slave processes | |
739 def Initializer(): | |
740 signal.signal(signal.SIGINT, signal.SIG_IGN) | |
741 | |
742 # Create a pool of 4 slave Python interpreters | |
743 POOL = multiprocessing.Pool(4, initializer = Initializer) | |
744 | |
745 def OnRest(output, uri, **request): | |
746 # Offload the call to "SlowComputation" onto one slave process. | |
747 # The GIL is unlocked until the slave sends its answer back. | |
748 answer = POOL.apply(SlowComputation) | |
749 output.AnswerBuffer(answer, 'text/plain') | |
750 | |
751 orthanc.RegisterRestCallback('/computation', OnRest) | |
752 | |
753 .. highlight:: text | |
754 | |
755 Here is now the result of calling this route three times concurrently:: | |
756 | |
757 $ (curl http://localhost:8042/computation & curl http://localhost:8042/computation & curl http://localhost:8042/computation ) | |
758 computation done in 4.211 seconds | |
759 computation done in 4.215 seconds | |
760 computation done in 4.225 seconds | |
761 | |
762 As can be seen, the calls to the Python computation now fully run in | |
763 parallel (the time is cut down from 12 seconds to 4 seconds, the same | |
764 as for one isolated request). | |
765 | |
766 Note also how the ``multiprocessing`` library allows to make a fine | |
767 control over the computational resources that are available to the | |
768 Python script: The number of "slave" interpreters can be easily | |
769 changed in the constructor of the ``multiprocessing.Pool`` object, and | |
770 are fully independent of the threads used by the Orthanc server. | |
771 | |
352 | 772 .. highlight:: python |
773 | |
774 Very importantly, pay attention to the fact that only the "master" | |
775 Python interpreter has access to the Orthanc SDK. For instance, here | |
776 is how you would parse a DICOM file in a slave process:: | |
777 | |
778 import pydicom | |
779 import io | |
780 | |
781 def OffloadedDicomParsing(dicom): | |
782 # No access to the "orthanc" library here, as we are in the slave process | |
783 dataset = pydicom.dcmread(io.BytesIO(dicom)) | |
784 return str(dataset) | |
785 | |
786 def OnRest(output, uri, **request): | |
787 # The call to "orthanc.RestApiGet()" is only possible in the master process | |
788 dicom = orthanc.RestApiGet('/instances/19816330-cb02e1cf-df3a8fe8-bf510623-ccefe9f5/file') | |
789 answer = POOL.apply(OffloadedDicomParsing, args = (dicom, )) | |
790 output.AnswerBuffer(answer, 'text/plain') | |
791 | |
792 Communication primitives such as ``multiprocessing.Queue`` are | |
793 available to exchange messages from the "slave" Python interpreters to | |
794 the "master" Python interpreter if further calls to the Orthanc SDK | |
795 are required. | |
796 | |
351 | 797 Obviously, an in-depth discussion about the ``multiprocessing`` |
798 library is out of the scope of this document. There are many | |
375 | 799 references available on Internet. Also, note that ``threading`` is not |
800 useful here, as Python multithreading is also limited by the GIL, and | |
801 is more targeted at dealing with costly I/O operations or with the | |
802 :ref:`scheduling of commands <python-scheduler>`. |