Mercurial > hg > orthanc-book
annotate Sphinx/source/plugins/python.rst @ 539:fd5c1410db5b
doc for RestApiPostAfterPlugins
author | Alain Mazy <alain@mazy.be> |
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date | Thu, 05 Nov 2020 15:09:50 +0100 |
parents | 50bdd7c7e9b9 |
children | d0332c5a2cb8 |
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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 |
528 | 21 <https://hg.orthanc-server.com/orthanc/file/Orthanc-1.8.0/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 **Note for OS X:** As indicated by `Stephen Douglas Scotti |
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142 <https://groups.google.com/g/orthanc-users/c/RnmZKFv8FaY/m/HhvOD2A2CAAJ>`__, |
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143 here is a sample invocation of CMake to force the version of Python to |
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144 be used on OS X:: |
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145 |
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146 $ cmake .. -DPYTHON_VERSION=3.8 -DSTATIC_BUILD=ON -DCMAKE_BUILD_TYPE=Release \ |
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147 -DPYTHON_LIBRARY=/usr/local/Cellar/python@3.8/3.8.5/Frameworks/Python.framework/Versions/3.8/lib/libpython3.8.dylib \ |
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148 -DPYTHON_INCLUDE_DIR=/usr/local/Cellar/python@3.8/3.8.5/Frameworks/Python.framework/Versions/3.8/include/python3.8/ |
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149 |
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150 |
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151 Microsoft Windows |
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152 ................. |
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153 |
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154 Pre-compiled binaries for Microsoft Windows `are also available |
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155 <https://www.orthanc-server.com/browse.php?path=/plugin-python>`__. |
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156 |
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157 Beware that one version of the Python plugin can only be run against |
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158 one version of the Python interpreter. This version is clearly |
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159 indicated in the filename of the precompiled binaries. |
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160 |
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161 .. highlight:: text |
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162 |
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163 You are of course free to compile the plugin from sources. You'll have |
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164 to explicitly specify the path to your Python installation while |
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165 invoking CMake. For instance:: |
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166 |
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167 C:\orthanc-python\Build> cmake .. -DPYTHON_VERSION=2.7 -DPYTHON_WINDOWS_ROOT=C:/Python27 \ |
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168 -DSTATIC_BUILD=ON -DCMAKE_BUILD_TYPE=Release -G "Visual Studio 15 2017" |
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169 |
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170 **Note about debug builds**: Usually, building Python modules such as the Python |
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171 plugin for Orthanc in debug mode (where ``_DEBUG`` is defined) leads to a module |
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172 (``.exe`` or ``.dll``) that requires a debug build of Python, and debug versions of all |
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173 the Python libraries. This is quite cumbersome, for it requires building Python |
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174 on your own or downloading additional debug files. |
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175 |
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176 Since using a debug build of Python is only necessary in very specific cases |
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177 (such as the debugging of code at the boundary between Python and an extension), |
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178 we have changed the default behavior to use the release Python library by default. |
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179 |
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180 This means that you are able to build this plugin in debug mode with the |
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181 standard Python distribution. |
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182 |
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183 In case you need to use the Python debug libraries, you can instruct the build |
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184 system to do so by setting the ``PYTHON_WINDOWS_USE_RELEASE_LIBS`` CMake option, |
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185 that is ``ON`` by default, to ``OFF``. The previous build example would then be, |
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186 should you require a full debug build:: |
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187 |
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188 C:\orthanc-python\Build> cmake .. -DPYTHON_VERSION=2.7 -DPYTHON_WINDOWS_ROOT=C:/Python27 \ |
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189 -DSTATIC_BUILD=ON -DPYTHON_WINDOWS_USE_RELEASE_LIBS=OFF \ |
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190 -DCMAKE_BUILD_TYPE=Debug -G "Visual Studio 15 2017" |
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191 |
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192 Please note that this CMake option only impacts **debug** builds under Windows, |
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193 when using (any version of) the Microsoft Visual Studio compiler. |
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194 |
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195 The precompiled binaries all use release (i.e. non-debug) versions of Python. |
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196 |
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197 |
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198 Configuration options |
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199 --------------------- |
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200 |
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201 The only two configuration options that are available for this plugin |
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202 are the following: |
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203 |
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204 * ``PythonScript`` indicates where the Python script is located. If |
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205 this configuration option is not provided, the Python plugin is not |
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206 started. |
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207 |
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208 * ``PythonVerbose`` is a Boolean value to make the Python interpreter |
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209 verbose. |
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210 |
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211 |
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212 Samples |
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213 ------- |
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214 |
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215 Extending the REST API |
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216 ...................... |
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217 |
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218 .. highlight:: python |
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219 |
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220 Here is a basic Python script that registers two new routes in the |
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221 REST API:: |
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222 |
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223 import orthanc |
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224 import pprint |
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225 |
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226 def OnRest(output, uri, **request): |
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227 pprint.pprint(request) |
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228 print('Accessing uri: %s' % uri) |
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229 output.AnswerBuffer('ok\n', 'text/plain') |
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230 |
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231 orthanc.RegisterRestCallback('/(to)(t)o', OnRest) |
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232 orthanc.RegisterRestCallback('/tata', OnRest) |
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233 |
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234 .. highlight:: json |
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235 |
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236 Here is the associated minimal configuration file for Orthanc |
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237 (provided the Python script is saved as ``rest.py``):: |
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238 |
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239 { |
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240 "Plugins" : [ "." ], |
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241 "PythonScript" : "rest.py", |
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242 "PythonVerbose" : false |
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243 } |
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244 |
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245 .. highlight:: bash |
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246 |
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247 The route can then be accessed as:: |
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248 |
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249 $ curl http://localhost:8042/toto |
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250 ok |
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251 |
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252 |
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253 .. _python-changes: |
345 | 254 |
255 Listening to changes | |
256 .................... | |
257 | |
346
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258 .. highlight:: python |
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259 |
345 | 260 This sample uploads a DICOM file as soon as Orthanc is started:: |
261 | |
262 import orthanc | |
263 | |
264 def OnChange(changeType, level, resource): | |
265 if changeType == orthanc.ChangeType.ORTHANC_STARTED: | |
266 print('Started') | |
267 | |
268 with open('/tmp/sample.dcm', 'rb') as f: | |
269 orthanc.RestApiPost('/instances', f.read()) | |
270 | |
271 elif changeType == orthanc.ChangeType.ORTHANC_STOPPED: | |
272 print('Stopped') | |
273 | |
274 elif changeType == orthanc.ChangeType.NEW_INSTANCE: | |
275 print('A new instance was uploaded: %s' % resource) | |
276 | |
277 orthanc.RegisterOnChangeCallback(OnChange) | |
278 | |
279 | |
280 Accessing the content of a new instance | |
346
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281 ....................................... |
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282 |
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283 .. highlight:: python |
345 | 284 |
285 :: | |
286 | |
287 import orthanc | |
288 import json | |
289 import pprint | |
290 | |
291 def OnStoredInstance(dicom, instanceId): | |
292 print('Received instance %s of size %d (transfer syntax %s, SOP class UID %s)' % ( | |
293 instanceId, dicom.GetInstanceSize(), | |
294 dicom.GetInstanceMetadata('TransferSyntax'), | |
295 dicom.GetInstanceMetadata('SopClassUid'))) | |
296 | |
297 # Print the origin information | |
298 if dicom.GetInstanceOrigin() == orthanc.InstanceOrigin.DICOM_PROTOCOL: | |
299 print('This instance was received through the DICOM protocol') | |
300 elif dicom.GetInstanceOrigin() == orthanc.InstanceOrigin.REST_API: | |
301 print('This instance was received through the REST API') | |
302 | |
303 # Print the DICOM tags | |
304 pprint.pprint(json.loads(dicom.GetInstanceSimplifiedJson())) | |
305 | |
306 orthanc.RegisterOnStoredInstanceCallback(OnStoredInstance) | |
346
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307 |
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308 |
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309 Calling pydicom |
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310 ............... |
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311 |
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312 .. highlight:: python |
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313 |
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314 Here is a sample Python plugin that registers a REST callback to dump |
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315 the content of the dataset of one given DICOM instance stored in |
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316 Orthanc, using `pydicom <https://pydicom.github.io/>`__:: |
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317 |
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318 import io |
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319 import orthanc |
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320 import pydicom |
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321 |
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322 def DecodeInstance(output, uri, **request): |
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323 if request['method'] == 'GET': |
347 | 324 # Retrieve the instance ID from the regular expression (*) |
346
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325 instanceId = request['groups'][0] |
347 | 326 # Get the content of the DICOM file |
346
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327 f = orthanc.GetDicomForInstance(instanceId) |
347 | 328 # Parse it using pydicom |
346
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329 dicom = pydicom.dcmread(io.BytesIO(f)) |
347 | 330 # Return a string representation the dataset to the caller |
346
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331 output.AnswerBuffer(str(dicom), 'text/plain') |
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332 else: |
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333 output.SendMethodNotAllowed('GET') |
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334 |
347 | 335 orthanc.RegisterRestCallback('/pydicom/(.*)', DecodeInstance) # (*) |
346
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336 |
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337 .. highlight:: bash |
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338 |
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339 This can be called as follows:: |
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340 |
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341 $ curl http://localhost:8042/pydicom/19816330-cb02e1cf-df3a8fe8-bf510623-ccefe9f5 |
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342 |
347 | 343 |
344 Auto-routing studies | |
345 .................... | |
346 | |
347 .. highlight:: python | |
348 | |
349 Here is a sample Python plugin that routes any :ref:`stable study | |
350 <lua-callbacks>` to a modality named ``samples`` (as declared in the | |
351 ``DicomModalities`` configuration option):: | |
352 | |
353 import orthanc | |
354 | |
355 def OnChange(changeType, level, resourceId): | |
356 if changeType == orthanc.ChangeType.STABLE_STUDY: | |
357 print('Stable study: %s' % resourceId) | |
358 orthanc.RestApiPost('/modalities/sample/store', resourceId) | |
359 | |
360 orthanc.RegisterOnChangeCallback(OnChange) | |
348 | 361 |
362 | |
539 | 363 Note that, if you want to use an orthanc plugin to transfer the study, |
364 you should use the ``RestApiPostAfterPlugins()`` method:: | |
365 | |
366 import orthanc | |
367 | |
368 def OnChange(changeType, level, resourceId): | |
369 if changeType == orthanc.ChangeType.STABLE_STUDY: | |
370 print('Stable study: %s' % resourceId) | |
371 orthanc.RestApiPostAfterPlugins('/dicom-web/servers/sample/store', resourceId) | |
372 | |
373 orthanc.RegisterOnChangeCallback(OnChange) | |
374 | |
375 | |
352 | 376 Rendering a thumbnail using PIL/Pillow |
377 ...................................... | |
348 | 378 |
379 .. highlight:: python | |
380 | |
381 :: | |
382 | |
383 from PIL import Image | |
384 import io | |
385 import orthanc | |
386 | |
387 def DecodeInstance(output, uri, **request): | |
388 if request['method'] == 'GET': | |
389 # Retrieve the instance ID from the regular expression (*) | |
390 instanceId = request['groups'][0] | |
391 | |
392 # Render the instance, then open it in Python using PIL/Pillow | |
393 png = orthanc.RestApiGet('/instances/%s/rendered' % instanceId) | |
394 image = Image.open(io.BytesIO(png)) | |
395 | |
396 # Downsize the image as a 64x64 thumbnail | |
397 image.thumbnail((64, 64), Image.ANTIALIAS) | |
398 | |
399 # Save the thumbnail as JPEG, then send the buffer to the caller | |
400 jpeg = io.BytesIO() | |
401 image.save(jpeg, format = "JPEG", quality = 80) | |
402 jpeg.seek(0) | |
403 output.AnswerBuffer(jpeg.read(), 'text/plain') | |
404 | |
405 else: | |
406 output.SendMethodNotAllowed('GET') | |
407 | |
408 orthanc.RegisterRestCallback('/pydicom/(.*)', DecodeInstance) # (*) | |
351 | 409 |
410 | |
369
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411 .. _python-introspection: |
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412 |
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413 Inspecting the available API |
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414 ............................ |
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415 |
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416 .. highlight:: python |
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417 |
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418 Thanks to Python's introspection primitives, it is possible to inspect |
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419 the API of the ``orthanc`` module in order to dump all the available |
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420 features:: |
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421 |
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422 import inspect |
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423 import numbers |
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424 import orthanc |
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425 |
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426 # Loop over the members of the "orthanc" module |
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427 for (name, obj) in inspect.getmembers(orthanc): |
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428 if inspect.isroutine(obj): |
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429 print('Function %s():\n Documentation: %s\n' % (name, inspect.getdoc(obj))) |
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430 |
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431 elif inspect.isclass(obj): |
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432 print('Class %s:\n Documentation: %s' % (name, inspect.getdoc(obj))) |
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433 |
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434 # Loop over the members of the class |
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435 for (subname, subobj) in inspect.getmembers(obj): |
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436 if isinstance(subobj, numbers.Number): |
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437 print(' - Enumeration value %s: %s' % (subname, subobj)) |
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438 elif (not subname.startswith('_') and |
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439 inspect.ismethoddescriptor(subobj)): |
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440 print(' - Method %s(): %s' % (subname, inspect.getdoc(subobj))) |
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441 print('') |
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442 |
373
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443 |
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444 .. _python-scheduler: |
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445 |
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446 Scheduling a task for periodic execution |
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447 ........................................ |
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448 |
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449 .. highlight:: python |
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450 |
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451 The following Python script will periodically (every second) run the |
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452 function ``Hello()`` thanks to the ``threading`` module:: |
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453 |
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454 import orthanc |
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455 import threading |
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456 |
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457 TIMER = None |
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458 |
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459 def Hello(): |
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460 global TIMER |
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461 TIMER = None |
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462 orthanc.LogWarning("In Hello()") |
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463 # Do stuff... |
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464 TIMER = threading.Timer(1, Hello) # Re-schedule after 1 second |
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465 TIMER.start() |
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466 |
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467 def OnChange(changeType, level, resource): |
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468 if changeType == orthanc.ChangeType.ORTHANC_STARTED: |
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469 orthanc.LogWarning("Starting the scheduler") |
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470 Hello() |
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471 |
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472 elif changeType == orthanc.ChangeType.ORTHANC_STOPPED: |
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473 if TIMER != None: |
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474 orthanc.LogWarning("Stopping the scheduler") |
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475 TIMER.cancel() |
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476 |
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477 orthanc.RegisterOnChangeCallback(OnChange) |
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478 |
378
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479 |
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480 .. _python-metadata: |
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481 |
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482 Filtering and returning metadata |
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483 ................................ |
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484 |
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485 Besides the main DICOM tags, Orthanc associates some metadata to each |
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486 resource it stores (this includes the date of last update, the |
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487 transfer syntax, the remote AET...). People are often interested in |
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488 getting such metadata while calling the ``/tools/find`` route in the |
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489 :ref:`REST API <rest-find>`, or even in filtering this metadata the |
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490 same way they look for DICOM tags. |
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491 |
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492 This feature is not built in the core of Orthanc, as metadata is not |
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493 indexed in the Orthanc database, contrarily to the main DICOM |
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494 tags. Filtering metadata requires a linear search over all the |
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495 matching resources, which induces a cost in the performance. |
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496 |
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497 .. highlight:: python |
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498 |
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499 Nevertheless, here is a full sample Python script that overwrites the |
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500 ``/tools/find`` route in order to give access to metadata:: |
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501 |
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502 import json |
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503 import orthanc |
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504 import re |
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505 |
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506 # Get the path in the REST API to the given resource that was returned |
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507 # by a call to "/tools/find" |
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508 def GetPath(resource): |
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509 if resource['Type'] == 'Patient': |
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510 return '/patients/%s' % resource['ID'] |
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511 elif resource['Type'] == 'Study': |
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512 return '/studies/%s' % resource['ID'] |
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513 elif resource['Type'] == 'Series': |
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514 return '/series/%s' % resource['ID'] |
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515 elif resource['Type'] == 'Instance': |
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516 return '/instances/%s' % resource['ID'] |
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517 else: |
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518 raise Exception('Unknown resource level') |
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519 |
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520 def FindWithMetadata(output, uri, **request): |
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521 # The "/tools/find" route expects a POST method |
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522 if request['method'] != 'POST': |
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523 output.SendMethodNotAllowed('POST') |
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524 else: |
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525 # Parse the query provided by the user, and backup the "Expand" field |
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526 query = json.loads(request['body']) |
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527 |
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528 if 'Expand' in query: |
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529 originalExpand = query['Expand'] |
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530 else: |
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531 originalExpand = False |
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532 |
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533 # Call the core "/tools/find" route |
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534 query['Expand'] = True |
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535 answers = orthanc.RestApiPost('/tools/find', json.dumps(query)) |
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536 |
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537 # Loop over the matching resources |
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538 filteredAnswers = [] |
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539 for answer in json.loads(answers): |
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540 try: |
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541 # Read the metadata that is associated with the resource |
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542 metadata = json.loads(orthanc.RestApiGet('%s/metadata?expand' % GetPath(answer))) |
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543 |
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544 # Check whether the metadata matches the regular expressions |
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545 # that were provided in the "Metadata" field of the user request |
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546 isMetadataMatch = True |
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547 if 'Metadata' in query: |
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548 for (name, pattern) in query['Metadata'].items(): |
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549 if name in metadata: |
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550 value = metadata[name] |
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551 else: |
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552 value = '' |
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553 |
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554 if re.match(pattern, value) == None: |
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555 isMetadataMatch = False |
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556 break |
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557 |
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558 # If all the metadata matches the provided regular |
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559 # expressions, add the resource to the filtered answers |
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560 if isMetadataMatch: |
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561 if originalExpand: |
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562 answer['Metadata'] = metadata |
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563 filteredAnswers.append(answer) |
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564 else: |
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565 filteredAnswers.append(answer['ID']) |
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566 except: |
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567 # The resource was deleted since the call to "/tools/find" |
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568 pass |
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569 |
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570 # Return the filtered answers in the JSON format |
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571 output.AnswerBuffer(json.dumps(filteredAnswers, indent = 3), 'application/json') |
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572 |
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573 orthanc.RegisterRestCallback('/tools/find', FindWithMetadata) |
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574 |
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575 |
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576 **Warning:** In the sample above, the filtering of the metadata is |
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577 done using Python's `library for regular expressions |
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578 <https://docs.python.org/3/library/re.html>`__. It is evidently |
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579 possible to adapt this script in order to use the DICOM conventions |
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580 about `attribute matching |
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581 <http://dicom.nema.org/medical/dicom/2019e/output/chtml/part04/sect_C.2.2.2.html>`__. |
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582 |
469
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583 .. highlight:: bash |
378
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584 |
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585 Here is a sample call to retrieve all the studies that were last |
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586 updated in 2019 thanks to this Python script:: |
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587 |
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588 $ curl http://localhost:8042/tools/find -d '{"Level":"Study","Query":{},"Expand":true,"Metadata":{"LastUpdate":"^2019.*$"}}' |
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589 |
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590 |
469
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591 .. _python-paging: |
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592 |
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593 Implementing basic paging |
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594 ......................... |
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595 |
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596 .. highlight:: python |
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597 |
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598 As explained in the FAQ, the :ref:`Orthanc Explorer interface is |
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599 low-level <improving-interface>`, and is not adapted for |
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600 end-users. One common need is to implement paging of studies, which |
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601 calls for server-side sorting of studies. This can be done using the |
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602 following sample Python plugin that registers a new route |
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603 ``/sort-studies`` in the REST API of Orthanc:: |
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604 |
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605 import json |
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606 import orthanc |
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607 |
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608 def GetStudyDate(study): |
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609 if 'StudyDate' in study['MainDicomTags']: |
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610 return study['MainDicomTags']['StudyDate'] |
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611 else: |
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612 return '' |
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613 |
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614 def SortStudiesByDate(output, uri, **request): |
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615 if request['method'] == 'GET': |
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616 # Retrieve all the studies |
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617 studies = json.loads(orthanc.RestApiGet('/studies?expand')) |
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618 |
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619 # Sort the studies according to the "StudyDate" DICOM tag |
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620 studies = sorted(studies, key = GetStudyDate) |
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621 |
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622 # Read the limit/offset arguments provided by the user |
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623 offset = 0 |
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624 if 'offset' in request['get']: |
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625 offset = int(request['get']['offset']) |
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626 |
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627 limit = 0 |
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628 if 'limit' in request['get']: |
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629 limit = int(request['get']['limit']) |
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630 |
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631 # Truncate the list of studies |
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632 if limit == 0: |
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633 studies = studies[offset : ] |
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634 else: |
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635 studies = studies[offset : offset + limit] |
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636 |
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637 # Return the truncated list of studies |
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638 output.AnswerBuffer(json.dumps(studies), 'application/json') |
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639 else: |
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640 output.SendMethodNotAllowed('GET') |
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641 |
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642 orthanc.RegisterRestCallback('/sort-studies', SortStudiesByDate) |
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643 |
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644 |
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645 .. highlight:: bash |
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646 |
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647 Here is a sample call to this new REST route, that could be issued by |
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648 any JavaScript framework (the ``json_pp`` command-line pretty-prints a |
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649 JSON file):: |
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650 |
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651 $ curl http://localhost:8042/sort-studies | json_pp |
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652 |
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653 This route also implement paging (i.e. it can limit and offset the |
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654 returned studies):: |
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655 |
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656 $ curl 'http://localhost:8042/sort-studies?offset=2&limit=2' | json_pp |
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657 |
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658 Obviously, this basic sample can be improved in many ways. To improve |
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659 performance, one could for instance cache the result of |
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660 ``/studies?expand`` in memory by :ref:`listening to changes |
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661 <python-changes>` in the list of studies |
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662 (cf. ``orthanc.ChangeType.NEW_STUDY`` and |
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663 ``orthanc.ChangeType.DELETED``). |
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664 |
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665 |
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666 |
351 | 667 Performance and concurrency |
668 --------------------------- | |
669 | |
471
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670 **Important:** This section only applies to UNIX-like systems. The |
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671 ``multiprocessing`` package will not work on Microsoft Windows as the |
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672 latter OS has a different model for `forking processes |
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673 <https://en.wikipedia.org/wiki/Fork_(system_call)>`__. |
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674 |
351 | 675 .. highlight:: python |
676 | |
677 Let us consider the following sample Python script that makes a | |
678 CPU-intensive computation on a REST callback:: | |
679 | |
680 import math | |
681 import orthanc | |
682 import time | |
683 | |
684 # CPU-intensive computation taking about 4 seconds | |
685 def SlowComputation(): | |
686 start = time.time() | |
687 for i in range(1000): | |
688 for j in range(30000): | |
689 math.sqrt(float(j)) | |
690 end = time.time() | |
691 duration = (end - start) | |
692 return 'computation done in %.03f seconds\n' % duration | |
693 | |
694 def OnRest(output, uri, **request): | |
695 answer = SlowComputation() | |
696 output.AnswerBuffer(answer, 'text/plain') | |
697 | |
698 orthanc.RegisterRestCallback('/computation', OnRest) | |
699 | |
700 | |
701 .. highlight:: text | |
702 | |
703 Calling this REST route from the command-line returns the time that is | |
704 needed to compute 30 million times a squared root on your CPU:: | |
705 | |
706 $ curl http://localhost:8042/computation | |
707 computation done in 4.208 seconds | |
708 | |
709 Now, let us call this route three times concurrently (we use bash):: | |
710 | |
711 $ (curl http://localhost:8042/computation & curl http://localhost:8042/computation & curl http://localhost:8042/computation ) | |
712 computation done in 11.262 seconds | |
713 computation done in 12.457 seconds | |
714 computation done in 13.360 seconds | |
715 | |
716 As can be seen, the computation time has tripled. This means that the | |
717 computations were not distributed across the available CPU cores. | |
718 This might seem surprising, as Orthanc is a threaded server (in | |
719 Orthanc, a pool of C++ threads serves concurrent requests). | |
720 | |
721 The explanation is that the Python interpreter (`CPython | |
722 <https://en.wikipedia.org/wiki/CPython>`__ actually) is built on the | |
723 top of a so-called `Global Interpreter Lock (GIL) | |
724 <https://en.wikipedia.org/wiki/Global_interpreter_lock>`__. The GIL is | |
725 basically a mutex that protects all the calls to the Python | |
726 interpreter. If multiple C++ threads from Orthanc call a Python | |
353 | 727 callback, only one can proceed at any given time. Note however that |
728 the GIL only applies to the Python script: The baseline REST API of | |
729 Orthanc is not affected by the GIL. | |
351 | 730 |
731 .. highlight:: python | |
732 | |
733 The solution is to use the `multiprocessing primitives | |
734 <https://docs.python.org/3/library/multiprocessing.html>`__ of Python. | |
735 The "master" Python interpreter that is initially started by the | |
736 Orthanc plugin, can start several `children processes | |
737 <https://en.wikipedia.org/wiki/Process_(computing)>`__, each of these | |
738 processes running a separate Python interpreter. This allows to | |
739 offload intensive computations from the "master" Python interpreter of | |
740 Orthanc onto those "slave" interpreters. The ``multiprocessing`` | |
741 library is actually quite straightforward to use:: | |
742 | |
743 import math | |
744 import multiprocessing | |
745 import orthanc | |
746 import signal | |
747 import time | |
748 | |
749 # CPU-intensive computation taking about 4 seconds | |
750 # (same code as above) | |
751 def SlowComputation(): | |
752 start = time.time() | |
753 for i in range(1000): | |
754 for j in range(30000): | |
755 math.sqrt(float(j)) | |
756 end = time.time() | |
757 duration = (end - start) | |
758 return 'computation done in %.03f seconds\n' % duration | |
759 | |
760 # Ignore CTRL+C in the slave processes | |
761 def Initializer(): | |
762 signal.signal(signal.SIGINT, signal.SIG_IGN) | |
763 | |
764 # Create a pool of 4 slave Python interpreters | |
765 POOL = multiprocessing.Pool(4, initializer = Initializer) | |
766 | |
767 def OnRest(output, uri, **request): | |
768 # Offload the call to "SlowComputation" onto one slave process. | |
769 # The GIL is unlocked until the slave sends its answer back. | |
770 answer = POOL.apply(SlowComputation) | |
771 output.AnswerBuffer(answer, 'text/plain') | |
772 | |
773 orthanc.RegisterRestCallback('/computation', OnRest) | |
774 | |
775 .. highlight:: text | |
776 | |
777 Here is now the result of calling this route three times concurrently:: | |
778 | |
779 $ (curl http://localhost:8042/computation & curl http://localhost:8042/computation & curl http://localhost:8042/computation ) | |
780 computation done in 4.211 seconds | |
781 computation done in 4.215 seconds | |
782 computation done in 4.225 seconds | |
783 | |
784 As can be seen, the calls to the Python computation now fully run in | |
785 parallel (the time is cut down from 12 seconds to 4 seconds, the same | |
786 as for one isolated request). | |
787 | |
788 Note also how the ``multiprocessing`` library allows to make a fine | |
789 control over the computational resources that are available to the | |
790 Python script: The number of "slave" interpreters can be easily | |
791 changed in the constructor of the ``multiprocessing.Pool`` object, and | |
792 are fully independent of the threads used by the Orthanc server. | |
793 | |
352 | 794 .. highlight:: python |
795 | |
796 Very importantly, pay attention to the fact that only the "master" | |
797 Python interpreter has access to the Orthanc SDK. For instance, here | |
798 is how you would parse a DICOM file in a slave process:: | |
799 | |
800 import pydicom | |
801 import io | |
802 | |
803 def OffloadedDicomParsing(dicom): | |
804 # No access to the "orthanc" library here, as we are in the slave process | |
805 dataset = pydicom.dcmread(io.BytesIO(dicom)) | |
806 return str(dataset) | |
807 | |
808 def OnRest(output, uri, **request): | |
809 # The call to "orthanc.RestApiGet()" is only possible in the master process | |
810 dicom = orthanc.RestApiGet('/instances/19816330-cb02e1cf-df3a8fe8-bf510623-ccefe9f5/file') | |
811 answer = POOL.apply(OffloadedDicomParsing, args = (dicom, )) | |
812 output.AnswerBuffer(answer, 'text/plain') | |
813 | |
814 Communication primitives such as ``multiprocessing.Queue`` are | |
815 available to exchange messages from the "slave" Python interpreters to | |
816 the "master" Python interpreter if further calls to the Orthanc SDK | |
817 are required. | |
818 | |
351 | 819 Obviously, an in-depth discussion about the ``multiprocessing`` |
820 library is out of the scope of this document. There are many | |
375 | 821 references available on Internet. Also, note that ``threading`` is not |
822 useful here, as Python multithreading is also limited by the GIL, and | |
823 is more targeted at dealing with costly I/O operations or with the | |
824 :ref:`scheduling of commands <python-scheduler>`. |