Mercurial > hg > orthanc-book
annotate Sphinx/source/plugins/python.rst @ 591:2397b0e12bc8
solution to avoid deadlock in Python OnChange()
author | Sebastien Jodogne <s.jodogne@gmail.com> |
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date | Thu, 07 Jan 2021 14:53:36 +0100 |
parents | 404aca6160f0 |
children | 4038ae299b18 |
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 |
568 | 21 <https://hg.orthanc-server.com/orthanc/file/Orthanc-1.8.2/OrthancServer/Plugins/Include/orthanc/OrthancCPlugin.h>`__ |
345 | 22 using the `Clang <https://en.wikipedia.org/wiki/Clang>`__ compiler |
364
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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 | |
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280 |
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281 .. warning:: |
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282 Your callback function will be called synchronously with |
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283 the core of Orthanc. This implies that deadlocks might emerge if |
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284 you call other core primitives of Orthanc in your callback (such |
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285 deadlocks are particular visible in the presence of other plugins |
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286 or Lua scripts). It is thus strongly advised to avoid any call to |
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287 the REST API of Orthanc in the callback. If you have to call other |
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288 primitives of Orthanc, you should make these calls in a separate |
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289 thread, passing the pending events to be processed through a |
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290 message queue. |
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291 |
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292 Here is the template of a possible solution to avoid such deadlocks by |
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293 relying on the multithreading primitives of Python:: |
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294 |
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295 import orthanc |
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296 import threading |
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297 |
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298 def OnChange(changeType, level, resource): |
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299 # One can safely invoke the "orthanc" module in this function |
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300 orthanc.LogWarning("Hello world") |
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301 |
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302 def _OnChange(changeType, level, resource): |
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303 # Invoke the actual "OnChange()" function in a separate thread |
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304 t = threading.Timer(0, function = OnChange, args = (changeType, level, resource)) |
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305 t.start() |
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306 |
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307 orthanc.RegisterOnChangeCallback(_OnChange) |
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308 |
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309 |
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310 |
345 | 311 Accessing the content of a new instance |
346
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312 ....................................... |
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313 |
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314 .. highlight:: python |
345 | 315 |
316 :: | |
317 | |
318 import orthanc | |
319 import json | |
320 import pprint | |
321 | |
322 def OnStoredInstance(dicom, instanceId): | |
323 print('Received instance %s of size %d (transfer syntax %s, SOP class UID %s)' % ( | |
324 instanceId, dicom.GetInstanceSize(), | |
325 dicom.GetInstanceMetadata('TransferSyntax'), | |
326 dicom.GetInstanceMetadata('SopClassUid'))) | |
327 | |
328 # Print the origin information | |
329 if dicom.GetInstanceOrigin() == orthanc.InstanceOrigin.DICOM_PROTOCOL: | |
330 print('This instance was received through the DICOM protocol') | |
331 elif dicom.GetInstanceOrigin() == orthanc.InstanceOrigin.REST_API: | |
332 print('This instance was received through the REST API') | |
333 | |
334 # Print the DICOM tags | |
335 pprint.pprint(json.loads(dicom.GetInstanceSimplifiedJson())) | |
336 | |
337 orthanc.RegisterOnStoredInstanceCallback(OnStoredInstance) | |
346
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338 |
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339 |
590
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340 .. warning:: |
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341 Your callback function will be called synchronously with |
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342 the core of Orthanc. This implies that deadlocks might emerge if |
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343 you call other core primitives of Orthanc in your callback (such |
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344 deadlocks are particular visible in the presence of other plugins |
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345 or Lua scripts). It is thus strongly advised to avoid any call to |
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346 the REST API of Orthanc in the callback. If you have to call other |
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347 primitives of Orthanc, you should make these calls in a separate |
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348 thread, passing the pending events to be processed through a |
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349 message queue. |
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350 |
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351 |
346
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352 Calling pydicom |
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353 ............... |
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354 |
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355 .. highlight:: python |
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356 |
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357 Here is a sample Python plugin that registers a REST callback to dump |
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358 the content of the dataset of one given DICOM instance stored in |
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359 Orthanc, using `pydicom <https://pydicom.github.io/>`__:: |
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360 |
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361 import io |
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362 import orthanc |
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363 import pydicom |
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364 |
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365 def DecodeInstance(output, uri, **request): |
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366 if request['method'] == 'GET': |
347 | 367 # Retrieve the instance ID from the regular expression (*) |
346
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368 instanceId = request['groups'][0] |
347 | 369 # Get the content of the DICOM file |
346
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370 f = orthanc.GetDicomForInstance(instanceId) |
347 | 371 # Parse it using pydicom |
346
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372 dicom = pydicom.dcmread(io.BytesIO(f)) |
347 | 373 # Return a string representation the dataset to the caller |
346
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374 output.AnswerBuffer(str(dicom), 'text/plain') |
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375 else: |
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376 output.SendMethodNotAllowed('GET') |
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377 |
347 | 378 orthanc.RegisterRestCallback('/pydicom/(.*)', DecodeInstance) # (*) |
346
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379 |
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380 .. highlight:: bash |
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381 |
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382 This can be called as follows:: |
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383 |
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384 $ curl http://localhost:8042/pydicom/19816330-cb02e1cf-df3a8fe8-bf510623-ccefe9f5 |
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385 |
347 | 386 |
387 Auto-routing studies | |
388 .................... | |
389 | |
390 .. highlight:: python | |
391 | |
392 Here is a sample Python plugin that routes any :ref:`stable study | |
393 <lua-callbacks>` to a modality named ``samples`` (as declared in the | |
394 ``DicomModalities`` configuration option):: | |
395 | |
396 import orthanc | |
397 | |
398 def OnChange(changeType, level, resourceId): | |
399 if changeType == orthanc.ChangeType.STABLE_STUDY: | |
400 print('Stable study: %s' % resourceId) | |
401 orthanc.RestApiPost('/modalities/sample/store', resourceId) | |
402 | |
403 orthanc.RegisterOnChangeCallback(OnChange) | |
348 | 404 |
405 | |
539 | 406 Note that, if you want to use an orthanc plugin to transfer the study, |
407 you should use the ``RestApiPostAfterPlugins()`` method:: | |
408 | |
409 import orthanc | |
410 | |
411 def OnChange(changeType, level, resourceId): | |
412 if changeType == orthanc.ChangeType.STABLE_STUDY: | |
413 print('Stable study: %s' % resourceId) | |
414 orthanc.RestApiPostAfterPlugins('/dicom-web/servers/sample/store', resourceId) | |
415 | |
416 orthanc.RegisterOnChangeCallback(OnChange) | |
417 | |
418 | |
352 | 419 Rendering a thumbnail using PIL/Pillow |
420 ...................................... | |
348 | 421 |
422 .. highlight:: python | |
423 | |
424 :: | |
425 | |
426 from PIL import Image | |
427 import io | |
428 import orthanc | |
429 | |
430 def DecodeInstance(output, uri, **request): | |
431 if request['method'] == 'GET': | |
432 # Retrieve the instance ID from the regular expression (*) | |
433 instanceId = request['groups'][0] | |
434 | |
435 # Render the instance, then open it in Python using PIL/Pillow | |
436 png = orthanc.RestApiGet('/instances/%s/rendered' % instanceId) | |
437 image = Image.open(io.BytesIO(png)) | |
438 | |
439 # Downsize the image as a 64x64 thumbnail | |
440 image.thumbnail((64, 64), Image.ANTIALIAS) | |
441 | |
442 # Save the thumbnail as JPEG, then send the buffer to the caller | |
443 jpeg = io.BytesIO() | |
444 image.save(jpeg, format = "JPEG", quality = 80) | |
445 jpeg.seek(0) | |
446 output.AnswerBuffer(jpeg.read(), 'text/plain') | |
447 | |
448 else: | |
449 output.SendMethodNotAllowed('GET') | |
450 | |
451 orthanc.RegisterRestCallback('/pydicom/(.*)', DecodeInstance) # (*) | |
351 | 452 |
453 | |
369
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454 .. _python-introspection: |
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455 |
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456 Inspecting the available API |
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457 ............................ |
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458 |
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459 .. highlight:: python |
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460 |
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461 Thanks to Python's introspection primitives, it is possible to inspect |
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462 the API of the ``orthanc`` module in order to dump all the available |
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463 features:: |
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464 |
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465 import inspect |
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466 import numbers |
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467 import orthanc |
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468 |
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469 # Loop over the members of the "orthanc" module |
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470 for (name, obj) in inspect.getmembers(orthanc): |
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471 if inspect.isroutine(obj): |
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472 print('Function %s():\n Documentation: %s\n' % (name, inspect.getdoc(obj))) |
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473 |
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474 elif inspect.isclass(obj): |
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475 print('Class %s:\n Documentation: %s' % (name, inspect.getdoc(obj))) |
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476 |
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477 # Loop over the members of the class |
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478 for (subname, subobj) in inspect.getmembers(obj): |
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479 if isinstance(subobj, numbers.Number): |
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480 print(' - Enumeration value %s: %s' % (subname, subobj)) |
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481 elif (not subname.startswith('_') and |
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482 inspect.ismethoddescriptor(subobj)): |
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483 print(' - Method %s(): %s' % (subname, inspect.getdoc(subobj))) |
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484 print('') |
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485 |
373
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486 |
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487 .. _python-scheduler: |
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488 |
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489 Scheduling a task for periodic execution |
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490 ........................................ |
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491 |
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492 .. highlight:: python |
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493 |
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494 The following Python script will periodically (every second) run the |
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495 function ``Hello()`` thanks to the ``threading`` module:: |
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496 |
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497 import orthanc |
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498 import threading |
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499 |
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500 TIMER = None |
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501 |
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502 def Hello(): |
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503 global TIMER |
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504 TIMER = None |
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505 orthanc.LogWarning("In Hello()") |
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506 # Do stuff... |
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507 TIMER = threading.Timer(1, Hello) # Re-schedule after 1 second |
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508 TIMER.start() |
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509 |
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510 def OnChange(changeType, level, resource): |
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511 if changeType == orthanc.ChangeType.ORTHANC_STARTED: |
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512 orthanc.LogWarning("Starting the scheduler") |
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513 Hello() |
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514 |
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515 elif changeType == orthanc.ChangeType.ORTHANC_STOPPED: |
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516 if TIMER != None: |
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517 orthanc.LogWarning("Stopping the scheduler") |
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518 TIMER.cancel() |
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519 |
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520 orthanc.RegisterOnChangeCallback(OnChange) |
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521 |
378
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522 |
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523 .. _python-metadata: |
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524 |
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525 Filtering and returning metadata |
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526 ................................ |
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527 |
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528 Besides the main DICOM tags, Orthanc associates some metadata to each |
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529 resource it stores (this includes the date of last update, the |
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530 transfer syntax, the remote AET...). People are often interested in |
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531 getting such metadata while calling the ``/tools/find`` route in the |
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532 :ref:`REST API <rest-find>`, or even in filtering this metadata the |
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533 same way they look for DICOM tags. |
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534 |
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535 This feature is not built in the core of Orthanc, as metadata is not |
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536 indexed in the Orthanc database, contrarily to the main DICOM |
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537 tags. Filtering metadata requires a linear search over all the |
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538 matching resources, which induces a cost in the performance. |
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539 |
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540 .. highlight:: python |
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541 |
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542 Nevertheless, here is a full sample Python script that overwrites the |
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543 ``/tools/find`` route in order to give access to metadata:: |
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544 |
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545 import json |
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546 import orthanc |
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547 import re |
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548 |
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549 # Get the path in the REST API to the given resource that was returned |
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550 # by a call to "/tools/find" |
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551 def GetPath(resource): |
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552 if resource['Type'] == 'Patient': |
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553 return '/patients/%s' % resource['ID'] |
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554 elif resource['Type'] == 'Study': |
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555 return '/studies/%s' % resource['ID'] |
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556 elif resource['Type'] == 'Series': |
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557 return '/series/%s' % resource['ID'] |
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558 elif resource['Type'] == 'Instance': |
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559 return '/instances/%s' % resource['ID'] |
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560 else: |
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561 raise Exception('Unknown resource level') |
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562 |
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563 def FindWithMetadata(output, uri, **request): |
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564 # The "/tools/find" route expects a POST method |
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565 if request['method'] != 'POST': |
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566 output.SendMethodNotAllowed('POST') |
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567 else: |
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568 # Parse the query provided by the user, and backup the "Expand" field |
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569 query = json.loads(request['body']) |
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570 |
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571 if 'Expand' in query: |
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572 originalExpand = query['Expand'] |
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573 else: |
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574 originalExpand = False |
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575 |
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576 # Call the core "/tools/find" route |
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577 query['Expand'] = True |
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578 answers = orthanc.RestApiPost('/tools/find', json.dumps(query)) |
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579 |
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580 # Loop over the matching resources |
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581 filteredAnswers = [] |
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582 for answer in json.loads(answers): |
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583 try: |
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584 # Read the metadata that is associated with the resource |
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585 metadata = json.loads(orthanc.RestApiGet('%s/metadata?expand' % GetPath(answer))) |
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586 |
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587 # Check whether the metadata matches the regular expressions |
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588 # that were provided in the "Metadata" field of the user request |
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589 isMetadataMatch = True |
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590 if 'Metadata' in query: |
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591 for (name, pattern) in query['Metadata'].items(): |
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592 if name in metadata: |
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593 value = metadata[name] |
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594 else: |
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595 value = '' |
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596 |
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597 if re.match(pattern, value) == None: |
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598 isMetadataMatch = False |
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599 break |
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600 |
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601 # If all the metadata matches the provided regular |
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602 # expressions, add the resource to the filtered answers |
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603 if isMetadataMatch: |
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604 if originalExpand: |
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605 answer['Metadata'] = metadata |
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606 filteredAnswers.append(answer) |
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607 else: |
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608 filteredAnswers.append(answer['ID']) |
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609 except: |
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610 # The resource was deleted since the call to "/tools/find" |
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611 pass |
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612 |
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613 # Return the filtered answers in the JSON format |
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614 output.AnswerBuffer(json.dumps(filteredAnswers, indent = 3), 'application/json') |
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615 |
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616 orthanc.RegisterRestCallback('/tools/find', FindWithMetadata) |
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617 |
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618 |
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619 **Warning:** In the sample above, the filtering of the metadata is |
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620 done using Python's `library for regular expressions |
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621 <https://docs.python.org/3/library/re.html>`__. It is evidently |
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622 possible to adapt this script in order to use the DICOM conventions |
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623 about `attribute matching |
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624 <http://dicom.nema.org/medical/dicom/2019e/output/chtml/part04/sect_C.2.2.2.html>`__. |
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625 |
469
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626 .. highlight:: bash |
378
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627 |
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628 Here is a sample call to retrieve all the studies that were last |
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629 updated in 2019 thanks to this Python script:: |
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630 |
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631 $ curl http://localhost:8042/tools/find -d '{"Level":"Study","Query":{},"Expand":true,"Metadata":{"LastUpdate":"^2019.*$"}}' |
369
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632 |
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633 |
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634 .. _python-paging: |
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635 |
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636 Implementing basic paging |
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637 ......................... |
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638 |
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639 .. highlight:: python |
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640 |
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641 As explained in the FAQ, the :ref:`Orthanc Explorer interface is |
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642 low-level <improving-interface>`, and is not adapted for |
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643 end-users. One common need is to implement paging of studies, which |
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644 calls for server-side sorting of studies. This can be done using the |
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645 following sample Python plugin that registers a new route |
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646 ``/sort-studies`` in the REST API of Orthanc:: |
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647 |
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648 import json |
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649 import orthanc |
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650 |
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651 def GetStudyDate(study): |
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652 if 'StudyDate' in study['MainDicomTags']: |
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653 return study['MainDicomTags']['StudyDate'] |
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654 else: |
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655 return '' |
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656 |
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657 def SortStudiesByDate(output, uri, **request): |
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658 if request['method'] == 'GET': |
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659 # Retrieve all the studies |
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660 studies = json.loads(orthanc.RestApiGet('/studies?expand')) |
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661 |
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662 # Sort the studies according to the "StudyDate" DICOM tag |
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663 studies = sorted(studies, key = GetStudyDate) |
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664 |
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665 # Read the limit/offset arguments provided by the user |
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666 offset = 0 |
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667 if 'offset' in request['get']: |
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668 offset = int(request['get']['offset']) |
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669 |
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670 limit = 0 |
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671 if 'limit' in request['get']: |
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672 limit = int(request['get']['limit']) |
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673 |
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674 # Truncate the list of studies |
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675 if limit == 0: |
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676 studies = studies[offset : ] |
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677 else: |
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678 studies = studies[offset : offset + limit] |
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679 |
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680 # Return the truncated list of studies |
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681 output.AnswerBuffer(json.dumps(studies), 'application/json') |
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682 else: |
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683 output.SendMethodNotAllowed('GET') |
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684 |
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685 orthanc.RegisterRestCallback('/sort-studies', SortStudiesByDate) |
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686 |
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687 |
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688 .. highlight:: bash |
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689 |
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690 Here is a sample call to this new REST route, that could be issued by |
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691 any JavaScript framework (the ``json_pp`` command-line pretty-prints a |
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692 JSON file):: |
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693 |
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694 $ curl http://localhost:8042/sort-studies | json_pp |
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695 |
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696 This route also implement paging (i.e. it can limit and offset the |
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697 returned studies):: |
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698 |
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699 $ curl 'http://localhost:8042/sort-studies?offset=2&limit=2' | json_pp |
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700 |
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701 Obviously, this basic sample can be improved in many ways. To improve |
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702 performance, one could for instance cache the result of |
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703 ``/studies?expand`` in memory by :ref:`listening to changes |
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704 <python-changes>` in the list of studies |
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705 (cf. ``orthanc.ChangeType.NEW_STUDY`` and |
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706 ``orthanc.ChangeType.DELETED``). |
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707 |
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708 |
551
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709 .. _python_excel: |
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710 |
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711 Creating a Microsoft Excel report |
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712 ................................. |
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713 |
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714 .. highlight:: python |
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715 |
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716 As Orthanc plugins have access to any installed Python module, it is |
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717 very easy to implement a server-side plugin that generates a report in |
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718 the Microsoft Excel ``.xls`` format. Here is a working example:: |
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719 |
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720 import StringIO |
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721 import json |
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722 import orthanc |
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723 import xlwt |
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724 |
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725 def CreateExcelReport(output, uri, **request): |
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726 if request['method'] != 'GET' : |
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Python plugin: excel generation example
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727 output.SendMethodNotAllowed('GET') |
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Python plugin: excel generation example
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728 else: |
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729 # Create an Excel writer |
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730 excel = xlwt.Workbook() |
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731 sheet = excel.add_sheet('Studies') |
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Python plugin: excel generation example
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732 |
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733 # Loop over the studies stored in Orthanc |
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734 row = 0 |
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735 studies = orthanc.RestApiGet('/studies?expand') |
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736 for study in json.loads(studies): |
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737 sheet.write(row, 0, study['PatientMainDicomTags'].get('PatientID')) |
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738 sheet.write(row, 1, study['PatientMainDicomTags'].get('PatientName')) |
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739 sheet.write(row, 2, study['MainDicomTags'].get('StudyDescription')) |
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740 row += 1 |
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741 |
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742 # Serialize the Excel workbook to a string, and return it to the caller |
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743 # https://stackoverflow.com/a/15649139/881731 |
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744 b = StringIO.StringIO() |
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745 excel.save(b) |
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Python plugin: excel generation example
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746 output.AnswerBuffer(b.getvalue(), 'application/vnd.ms-excel') |
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747 |
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748 orthanc.RegisterRestCallback('/report.xls', CreateExcelReport) |
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Python plugin: excel generation example
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749 |
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750 If opening the ``http://localhost:8042/report.xls`` URI, this Python |
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751 will generate a workbook with one sheet that contains the list of |
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752 studies, with the patient ID, the patient name and the study |
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Python plugin: excel generation example
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753 description. |
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754 |
469
46949efa5818
Implementing basic paging using a Python script
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755 |
556
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756 .. _python_authorization: |
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757 |
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758 Forbid or allow access to REST resources (authorization) |
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759 ........................................................ |
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760 |
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761 .. highlight:: python |
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762 |
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763 The following Python script installs a callback that is triggered |
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764 whenever the HTTP server of Orthanc is accessed:: |
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765 |
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766 import orthanc |
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767 import pprint |
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768 |
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769 def Filter(uri, **request): |
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770 print('User trying to access URI: %s' % uri) |
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771 pprint.pprint(request) |
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772 return True # False to forbid access |
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773 |
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774 orthanc.RegisterIncomingHttpRequestFilter(Filter) |
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775 |
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776 If access is not granted, the ``Filter`` callback must return |
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777 ``False``. As a consequence, the HTTP status code would be set to |
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778 ``403 Forbidden``. If access is granted, the ``Filter`` must return |
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779 ``true``. The ``request`` argument contains more information about the |
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780 request (such as the HTTP headers, the IP address of the caller and |
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781 the GET arguments). |
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782 |
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783 Note that this is similar to the ``IncomingHttpRequestFilter()`` |
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784 callback that is available in :ref:`Lua scripts <lua-filter-rest>`. |
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785 |
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786 Thanks to Python, it is extremely easy to call remote Web services for |
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787 authorization. Here is an example using the ``requests`` library:: |
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788 |
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789 import json |
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790 import orthanc |
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791 import requests |
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792 |
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793 def Filter(uri, **request): |
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794 body = { |
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795 'uri' : uri, |
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796 'headers' : request['headers'] |
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797 } |
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798 r = requests.post('http://localhost:8000/authorize', |
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799 data = json.dumps(body)) |
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800 return r.json() ['granted'] # Must be a Boolean |
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801 |
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802 orthanc.RegisterIncomingHttpRequestFilter(Filter) |
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803 |
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804 .. highlight:: javascript |
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805 |
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806 This filter could be used together with the following Web service |
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807 implemented using `Node.js |
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808 <https://en.wikipedia.org/wiki/Node.js>`__:: |
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809 |
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810 const http = require('http'); |
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811 |
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812 const requestListener = function(req, res) { |
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813 let body = ''; |
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814 req.on('data', function(chunk) { |
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815 body += chunk; |
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816 }); |
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817 req.on('end', function() { |
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818 console.log(JSON.parse(body)); |
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819 var answer = { |
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820 'granted' : false // Forbid access |
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821 }; |
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822 res.writeHead(200); |
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823 res.end(JSON.stringify(answer)); |
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824 }); |
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825 } |
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826 |
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827 http.createServer(requestListener).listen(8000); |
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828 |
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829 |
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830 |
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831 |
351 | 832 Performance and concurrency |
833 --------------------------- | |
834 | |
471
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835 **Important:** This section only applies to UNIX-like systems. The |
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836 ``multiprocessing`` package will not work on Microsoft Windows as the |
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837 latter OS has a different model for `forking processes |
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838 <https://en.wikipedia.org/wiki/Fork_(system_call)>`__. |
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839 |
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840 Using slave processes |
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841 ..................... |
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842 |
351 | 843 .. highlight:: python |
844 | |
845 Let us consider the following sample Python script that makes a | |
846 CPU-intensive computation on a REST callback:: | |
847 | |
848 import math | |
849 import orthanc | |
850 import time | |
851 | |
852 # CPU-intensive computation taking about 4 seconds | |
853 def SlowComputation(): | |
854 start = time.time() | |
855 for i in range(1000): | |
856 for j in range(30000): | |
857 math.sqrt(float(j)) | |
858 end = time.time() | |
859 duration = (end - start) | |
860 return 'computation done in %.03f seconds\n' % duration | |
861 | |
862 def OnRest(output, uri, **request): | |
863 answer = SlowComputation() | |
864 output.AnswerBuffer(answer, 'text/plain') | |
865 | |
866 orthanc.RegisterRestCallback('/computation', OnRest) | |
867 | |
868 | |
869 .. highlight:: text | |
870 | |
871 Calling this REST route from the command-line returns the time that is | |
872 needed to compute 30 million times a squared root on your CPU:: | |
873 | |
874 $ curl http://localhost:8042/computation | |
875 computation done in 4.208 seconds | |
876 | |
877 Now, let us call this route three times concurrently (we use bash):: | |
878 | |
879 $ (curl http://localhost:8042/computation & curl http://localhost:8042/computation & curl http://localhost:8042/computation ) | |
880 computation done in 11.262 seconds | |
881 computation done in 12.457 seconds | |
882 computation done in 13.360 seconds | |
883 | |
884 As can be seen, the computation time has tripled. This means that the | |
885 computations were not distributed across the available CPU cores. | |
886 This might seem surprising, as Orthanc is a threaded server (in | |
887 Orthanc, a pool of C++ threads serves concurrent requests). | |
888 | |
889 The explanation is that the Python interpreter (`CPython | |
890 <https://en.wikipedia.org/wiki/CPython>`__ actually) is built on the | |
891 top of a so-called `Global Interpreter Lock (GIL) | |
892 <https://en.wikipedia.org/wiki/Global_interpreter_lock>`__. The GIL is | |
893 basically a mutex that protects all the calls to the Python | |
894 interpreter. If multiple C++ threads from Orthanc call a Python | |
353 | 895 callback, only one can proceed at any given time. Note however that |
896 the GIL only applies to the Python script: The baseline REST API of | |
897 Orthanc is not affected by the GIL. | |
351 | 898 |
899 .. highlight:: python | |
900 | |
901 The solution is to use the `multiprocessing primitives | |
902 <https://docs.python.org/3/library/multiprocessing.html>`__ of Python. | |
903 The "master" Python interpreter that is initially started by the | |
904 Orthanc plugin, can start several `children processes | |
905 <https://en.wikipedia.org/wiki/Process_(computing)>`__, each of these | |
906 processes running a separate Python interpreter. This allows to | |
907 offload intensive computations from the "master" Python interpreter of | |
908 Orthanc onto those "slave" interpreters. The ``multiprocessing`` | |
909 library is actually quite straightforward to use:: | |
910 | |
911 import math | |
912 import multiprocessing | |
913 import orthanc | |
914 import signal | |
915 import time | |
916 | |
917 # CPU-intensive computation taking about 4 seconds | |
918 # (same code as above) | |
919 def SlowComputation(): | |
920 start = time.time() | |
921 for i in range(1000): | |
922 for j in range(30000): | |
923 math.sqrt(float(j)) | |
924 end = time.time() | |
925 duration = (end - start) | |
926 return 'computation done in %.03f seconds\n' % duration | |
927 | |
928 # Ignore CTRL+C in the slave processes | |
929 def Initializer(): | |
930 signal.signal(signal.SIGINT, signal.SIG_IGN) | |
931 | |
932 # Create a pool of 4 slave Python interpreters | |
933 POOL = multiprocessing.Pool(4, initializer = Initializer) | |
934 | |
935 def OnRest(output, uri, **request): | |
936 # Offload the call to "SlowComputation" onto one slave process. | |
937 # The GIL is unlocked until the slave sends its answer back. | |
938 answer = POOL.apply(SlowComputation) | |
939 output.AnswerBuffer(answer, 'text/plain') | |
940 | |
941 orthanc.RegisterRestCallback('/computation', OnRest) | |
942 | |
943 .. highlight:: text | |
944 | |
945 Here is now the result of calling this route three times concurrently:: | |
946 | |
947 $ (curl http://localhost:8042/computation & curl http://localhost:8042/computation & curl http://localhost:8042/computation ) | |
948 computation done in 4.211 seconds | |
949 computation done in 4.215 seconds | |
950 computation done in 4.225 seconds | |
951 | |
952 As can be seen, the calls to the Python computation now fully run in | |
953 parallel (the time is cut down from 12 seconds to 4 seconds, the same | |
954 as for one isolated request). | |
955 | |
956 Note also how the ``multiprocessing`` library allows to make a fine | |
957 control over the computational resources that are available to the | |
958 Python script: The number of "slave" interpreters can be easily | |
959 changed in the constructor of the ``multiprocessing.Pool`` object, and | |
960 are fully independent of the threads used by the Orthanc server. | |
961 | |
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962 Obviously, an in-depth discussion about the ``multiprocessing`` |
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963 library is out of the scope of this document. There are many |
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964 references available on Internet. Also, note that ``threading`` is not |
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965 useful here, as Python multithreading is also limited by the GIL, and |
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966 is more targeted at dealing with costly I/O operations or with the |
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967 :ref:`scheduling of commands <python-scheduler>`. |
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968 |
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969 |
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970 Slave processes and the "orthanc" module |
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971 ........................................ |
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972 |
352 | 973 .. highlight:: python |
974 | |
555
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975 Very importantly, pay attention to the fact that **only the "master" |
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976 Python interpreter has access to the Orthanc SDK**. The "slave" |
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977 processes have no access to the ``orthanc`` module. |
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978 |
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979 You must write your Python plugin so as that all the calls to |
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980 ``orthanc`` are moved from the slaves process to the master |
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981 process. For instance, here is how you would parse a DICOM file in a |
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982 slave process:: |
352 | 983 |
984 import pydicom | |
985 import io | |
986 | |
987 def OffloadedDicomParsing(dicom): | |
988 # No access to the "orthanc" library here, as we are in the slave process | |
989 dataset = pydicom.dcmread(io.BytesIO(dicom)) | |
990 return str(dataset) | |
991 | |
992 def OnRest(output, uri, **request): | |
993 # The call to "orthanc.RestApiGet()" is only possible in the master process | |
994 dicom = orthanc.RestApiGet('/instances/19816330-cb02e1cf-df3a8fe8-bf510623-ccefe9f5/file') | |
995 answer = POOL.apply(OffloadedDicomParsing, args = (dicom, )) | |
996 output.AnswerBuffer(answer, 'text/plain') | |
997 | |
998 Communication primitives such as ``multiprocessing.Queue`` are | |
999 available to exchange messages from the "slave" Python interpreters to | |
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1000 the "master" Python interpreter for more advanced scenarios. |
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1001 |
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1002 NB: Starting with release 3.0 of the Python plugin, it is possible to |
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1003 call the REST API of Orthanc from a slave process in a more direct |
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1004 way. The function ``orthanc.GenerateRestApiAuthorizationToken()`` can |
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1005 be used to create an authorization token that provides full access to |
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1006 the REST API of Orthanc (without have to set credentials in your |
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1007 plugin). Any HTTP client library for Python, such as `requests |
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1008 <https://requests.readthedocs.io/en/master/>`__, can then be used to |
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1009 access the REST API of Orthanc. Here is a minimal example:: |
352 | 1010 |
555
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1011 import json |
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1012 import multiprocessing |
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1013 import orthanc |
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1014 import requests |
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1015 import signal |
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1016 |
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1017 TOKEN = orthanc.GenerateRestApiAuthorizationToken() |
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1018 |
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1019 def SlaveProcess(): |
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1020 r = requests.get('http://localhost:8042/instances', |
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1021 headers = { 'Authorization' : TOKEN }) |
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1022 return json.dumps(r.json()) |
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1023 |
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1024 def Initializer(): |
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1025 signal.signal(signal.SIGINT, signal.SIG_IGN) |
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1026 |
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1027 POOL = multiprocessing.Pool(4, initializer = Initializer) |
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1028 |
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1029 def OnRest(output, uri, **request): |
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1030 answer = POOL.apply(SlaveProcess) |
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1031 output.AnswerBuffer(answer, 'text/plain') |
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1032 |
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1033 orthanc.RegisterRestCallback('/computation', OnRest) |