Mercurial > hg > orthanc-book
annotate Sphinx/source/plugins/python.rst @ 378:16dc3561b41e
Filtering and returning metadata using Python
| author | Sebastien Jodogne <s.jodogne@gmail.com> |
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| date | Wed, 08 Apr 2020 08:49:33 +0200 |
| parents | 766fe39fdf35 |
| children | c9fe3d0d0fa1 |
| rev | line source |
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1 .. _python-plugin: |
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4 Python plugin for Orthanc |
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5 ========================= |
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6 |
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7 .. contents:: |
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8 |
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10 Overview |
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11 -------- |
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12 |
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13 This plugin can be used to write :ref:`Orthanc plugins |
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14 <creating-plugins>` using the `Python programming language |
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15 <https://en.wikipedia.org/wiki/Python_(programming_language)>`__ |
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16 instead of the more complex C/C++ programming languages. |
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17 |
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18 Python plugins have access to more features and a more consistent SDK |
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19 than :ref:`Lua scripts <lua>`. The Python API is automatically |
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20 generated from the `Orthanc plugin SDK in C |
| 345 | 21 <https://hg.orthanc-server.com/orthanc/file/Orthanc-1.5.7/Plugins/Include/orthanc/OrthancCPlugin.h>`__ |
| 22 using the `Clang <https://en.wikipedia.org/wiki/Clang>`__ compiler | |
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23 front-end. |
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24 |
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25 As of initial release 1.0 of the plugin, the coverage of the C SDK is |
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26 about 75% (105 functions are automatically wrapped in Python out of a |
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27 total of 139 functions in the Orthanc SDK 1.5.7). |
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28 |
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29 |
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30 Source code |
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31 ----------- |
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32 |
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33 * Link to the `official releases of this plugin |
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34 <https://www.orthanc-server.com/browse.php?path=/plugin-python>`__. |
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35 |
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36 * Link to the `code repository |
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37 <https://hg.orthanc-server.com/orthanc-python/>`__. |
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38 |
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39 |
| 353 | 40 Licensing |
| 41 --------- | |
| 42 | |
| 43 Pay attention to the fact that this plugin is licensed under the terms | |
| 44 of the `AGPL license | |
| 45 <https://en.wikipedia.org/wiki/GNU_Affero_General_Public_License>`__. | |
| 46 | |
| 47 This has an important consequence: If you distribute Orthanc to | |
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48 clients together with one Python script, or if you put an Orthanc |
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49 server equipped with one Python script on a Web portal, you **must** |
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50 disclose the source code of your Python script to the Orthanc |
| 354 | 51 community under the terms of the AGPL license. |
| 52 | |
| 53 We suggest you to put the source code of your Python scripts on the | |
| 54 dedicated `"OrthancContributed" repository on GitHub | |
| 55 <https://github.com/jodogne/OrthancContributed/tree/master/Plugins>`__, | |
| 56 and/or to send it to the `Orthanc Users | |
| 57 <https://groups.google.com/forum/#!forum/orthanc-users>`__ discussion | |
| 58 group. | |
| 353 | 59 |
| 60 Check out the :ref:`FAQ about licensing <licensing>` for more context. | |
| 61 | |
| 62 | |
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63 Usage |
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64 ----- |
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65 |
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66 Docker |
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67 ...... |
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68 |
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69 .. highlight:: json |
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70 |
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71 The most direct way of starting Orthanc together with the Python |
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72 plugin is through :ref:`Docker <docker>`. Let's create the file |
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73 ``/tmp/hello.py`` that contains the following basic Python script:: |
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74 |
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75 print('Hello world!') |
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76 |
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77 .. highlight:: json |
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78 |
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79 Let's also create the file ``/tmp/orthanc.json`` that contains the |
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80 following minimal :ref:`configuration for Orthanc <configuration>`:: |
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81 |
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82 { |
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83 "StorageDirectory" : "/var/lib/orthanc/db", |
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84 "RemoteAccessAllowed" : true, |
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85 "Plugins" : [ |
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86 "/usr/local/share/orthanc/plugins" |
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87 ], |
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88 "PythonScript" : "/etc/orthanc/hello.py" |
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89 } |
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90 |
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91 .. highlight:: bash |
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92 |
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93 Given these two files, Orthanc can be started as follows:: |
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94 |
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95 $ docker run -p 4242:4242 -p 8042:8042 --rm \ |
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96 -v /tmp/orthanc.json:/etc/orthanc/orthanc.json:ro \ |
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97 -v /tmp/hello.py:/etc/orthanc/hello.py:ro \ |
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98 jodogne/orthanc-python |
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99 |
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100 .. highlight:: text |
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101 |
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102 You'll see the following excerpt in the log, which indicates that the Python plugin is properly loaded:: |
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103 |
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104 W0331 15:48:12.990661 PluginsManager.cpp:269] Registering plugin 'python' (version mainline) |
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105 W0331 15:48:12.990691 PluginsManager.cpp:168] Python plugin is initializing |
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106 W0331 15:48:12.990743 PluginsManager.cpp:168] Using Python script "hello.py" from directory: /etc/orthanc |
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107 W0331 15:48:12.990819 PluginsManager.cpp:168] Program name: /usr/local/sbin/Orthanc |
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108 Hello world! |
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109 |
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110 |
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111 Compiling from source |
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112 ..................... |
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113 |
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114 .. highlight:: bash |
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115 |
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116 The procedure to compile this plugin from source is similar to that |
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117 for the :ref:`core of Orthanc <compiling>`. The following commands |
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118 should work for most UNIX-like distribution (including GNU/Linux):: |
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119 |
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120 $ mkdir Build |
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121 $ cd Build |
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122 $ cmake .. -DPYTHON_VERSION=3.7 -DSTATIC_BUILD=ON -DCMAKE_BUILD_TYPE=Release |
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123 $ make |
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124 |
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125 Before running CMake, make sure that the Python interpreter and its |
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126 associated development library are installed. On Ubuntu 18.04 LTS, you |
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127 would for instance install packages ``libpython3.7-dev`` and |
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128 ``python3.7``. |
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129 |
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130 The compilation will produce the shared library ``OrthancPython``, |
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131 that can be loaded by properly setting the ``Plugins`` |
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132 :ref:`configuration option <configuration>` of Orthanc. |
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133 |
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134 **Warning:** The shared library is only compatible with the Python |
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135 interpreter whose version corresponds to the value of the |
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136 ``PYTHON_VERSION`` argument that was given to CMake. |
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137 |
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138 |
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139 Microsoft Windows |
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140 ................. |
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141 |
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142 Pre-compiled binaries for Microsoft Windows `are also available |
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143 <https://www.orthanc-server.com/browse.php?path=/plugin-python>`__. |
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144 |
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145 Beware that one version of the Python plugin can only be run against |
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146 one version of the Python interpreter. This version is clearly |
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147 indicated in the name of the folder. |
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148 |
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149 As of release 1.0, the Orthanc project only provides pre-compiled |
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150 binaries for Microsoft Windows 32bit and Python 2.7. Even though this |
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151 version of Python is not supported anymore, it can still run on all |
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152 the versions of Microsoft Windows that have been released for more |
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153 than 10 years. |
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154 |
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155 .. highlight:: text |
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156 |
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157 You are of course free to compile the plugin from sources if you need |
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158 a more recent version. You'll have to explicitly specify the path to |
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159 your Python installation while invoking CMake. For instance:: |
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160 |
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161 C:\orthanc-python\Build> cmake .. -DPYTHON_VERSION=2.7 -DPYTHON_WINDOWS_ROOT=C:/Python27 \ |
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162 -DSTATIC_BUILD=ON -DCMAKE_BUILD_TYPE=Release -G "Visual Studio 15 2017" |
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163 |
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164 |
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165 Configuration options |
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166 --------------------- |
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167 |
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168 The only two configuration options that are available for this plugin |
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169 are the following: |
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170 |
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171 * ``PythonScript`` indicates where the Python script is located. If |
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172 this configuration option is not provided, the Python plugin is not |
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173 started. |
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174 |
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175 * ``PythonVerbose`` is a Boolean value to make the Python interpreter |
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176 verbose. |
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177 |
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343
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178 |
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179 Samples |
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180 ------- |
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181 |
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182 Extending the REST API |
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183 ...................... |
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184 |
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185 .. highlight:: python |
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186 |
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187 Here is a basic Python script that registers two new routes in the |
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188 REST API:: |
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189 |
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190 import orthanc |
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191 import pprint |
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192 |
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193 def OnRest(output, uri, **request): |
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194 pprint.pprint(request) |
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195 print('Accessing uri: %s' % uri) |
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196 output.AnswerBuffer('ok\n', 'text/plain') |
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197 |
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198 orthanc.RegisterRestCallback('/(to)(t)o', OnRest) |
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199 orthanc.RegisterRestCallback('/tata', OnRest) |
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200 |
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201 .. highlight:: json |
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202 |
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203 Here is the associated minimal configuration file for Orthanc |
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204 (provided the Python script is saved as ``rest.py``):: |
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205 |
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206 { |
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207 "Plugins" : [ "." ], |
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208 "PythonScript" : "rest.py", |
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209 "PythonVerbose" : false |
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210 } |
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211 |
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212 .. highlight:: bash |
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213 |
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214 The route can then be accessed as:: |
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215 |
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216 $ curl http://localhost:8042/toto |
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217 ok |
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218 |
| 345 | 219 |
| 220 Listening to changes | |
| 221 .................... | |
| 222 | |
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223 .. highlight:: python |
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224 |
| 345 | 225 This sample uploads a DICOM file as soon as Orthanc is started:: |
| 226 | |
| 227 import orthanc | |
| 228 | |
| 229 def OnChange(changeType, level, resource): | |
| 230 if changeType == orthanc.ChangeType.ORTHANC_STARTED: | |
| 231 print('Started') | |
| 232 | |
| 233 with open('/tmp/sample.dcm', 'rb') as f: | |
| 234 orthanc.RestApiPost('/instances', f.read()) | |
| 235 | |
| 236 elif changeType == orthanc.ChangeType.ORTHANC_STOPPED: | |
| 237 print('Stopped') | |
| 238 | |
| 239 elif changeType == orthanc.ChangeType.NEW_INSTANCE: | |
| 240 print('A new instance was uploaded: %s' % resource) | |
| 241 | |
| 242 orthanc.RegisterOnChangeCallback(OnChange) | |
| 243 | |
| 244 | |
| 245 Accessing the content of a new instance | |
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246 ....................................... |
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247 |
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248 .. highlight:: python |
| 345 | 249 |
| 250 :: | |
| 251 | |
| 252 import orthanc | |
| 253 import json | |
| 254 import pprint | |
| 255 | |
| 256 def OnStoredInstance(dicom, instanceId): | |
| 257 print('Received instance %s of size %d (transfer syntax %s, SOP class UID %s)' % ( | |
| 258 instanceId, dicom.GetInstanceSize(), | |
| 259 dicom.GetInstanceMetadata('TransferSyntax'), | |
| 260 dicom.GetInstanceMetadata('SopClassUid'))) | |
| 261 | |
| 262 # Print the origin information | |
| 263 if dicom.GetInstanceOrigin() == orthanc.InstanceOrigin.DICOM_PROTOCOL: | |
| 264 print('This instance was received through the DICOM protocol') | |
| 265 elif dicom.GetInstanceOrigin() == orthanc.InstanceOrigin.REST_API: | |
| 266 print('This instance was received through the REST API') | |
| 267 | |
| 268 # Print the DICOM tags | |
| 269 pprint.pprint(json.loads(dicom.GetInstanceSimplifiedJson())) | |
| 270 | |
| 271 orthanc.RegisterOnStoredInstanceCallback(OnStoredInstance) | |
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272 |
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273 |
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274 Calling pydicom |
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275 ............... |
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276 |
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277 .. highlight:: python |
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278 |
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279 Here is a sample Python plugin that registers a REST callback to dump |
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280 the content of the dataset of one given DICOM instance stored in |
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281 Orthanc, using `pydicom <https://pydicom.github.io/>`__:: |
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282 |
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283 import io |
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284 import orthanc |
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285 import pydicom |
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286 |
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287 def DecodeInstance(output, uri, **request): |
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288 if request['method'] == 'GET': |
| 347 | 289 # Retrieve the instance ID from the regular expression (*) |
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290 instanceId = request['groups'][0] |
| 347 | 291 # Get the content of the DICOM file |
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292 f = orthanc.GetDicomForInstance(instanceId) |
| 347 | 293 # Parse it using pydicom |
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294 dicom = pydicom.dcmread(io.BytesIO(f)) |
| 347 | 295 # Return a string representation the dataset to the caller |
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296 output.AnswerBuffer(str(dicom), 'text/plain') |
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297 else: |
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298 output.SendMethodNotAllowed('GET') |
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299 |
| 347 | 300 orthanc.RegisterRestCallback('/pydicom/(.*)', DecodeInstance) # (*) |
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346
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301 |
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302 .. highlight:: bash |
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303 |
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304 This can be called as follows:: |
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305 |
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306 $ curl http://localhost:8042/pydicom/19816330-cb02e1cf-df3a8fe8-bf510623-ccefe9f5 |
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307 |
| 347 | 308 |
| 309 Auto-routing studies | |
| 310 .................... | |
| 311 | |
| 312 .. highlight:: python | |
| 313 | |
| 314 Here is a sample Python plugin that routes any :ref:`stable study | |
| 315 <lua-callbacks>` to a modality named ``samples`` (as declared in the | |
| 316 ``DicomModalities`` configuration option):: | |
| 317 | |
| 318 import orthanc | |
| 319 | |
| 320 def OnChange(changeType, level, resourceId): | |
| 321 if changeType == orthanc.ChangeType.STABLE_STUDY: | |
| 322 print('Stable study: %s' % resourceId) | |
| 323 orthanc.RestApiPost('/modalities/sample/store', resourceId) | |
| 324 | |
| 325 orthanc.RegisterOnChangeCallback(OnChange) | |
| 348 | 326 |
| 327 | |
| 352 | 328 Rendering a thumbnail using PIL/Pillow |
| 329 ...................................... | |
| 348 | 330 |
| 331 .. highlight:: python | |
| 332 | |
| 333 :: | |
| 334 | |
| 335 from PIL import Image | |
| 336 import io | |
| 337 import orthanc | |
| 338 | |
| 339 def DecodeInstance(output, uri, **request): | |
| 340 if request['method'] == 'GET': | |
| 341 # Retrieve the instance ID from the regular expression (*) | |
| 342 instanceId = request['groups'][0] | |
| 343 | |
| 344 # Render the instance, then open it in Python using PIL/Pillow | |
| 345 png = orthanc.RestApiGet('/instances/%s/rendered' % instanceId) | |
| 346 image = Image.open(io.BytesIO(png)) | |
| 347 | |
| 348 # Downsize the image as a 64x64 thumbnail | |
| 349 image.thumbnail((64, 64), Image.ANTIALIAS) | |
| 350 | |
| 351 # Save the thumbnail as JPEG, then send the buffer to the caller | |
| 352 jpeg = io.BytesIO() | |
| 353 image.save(jpeg, format = "JPEG", quality = 80) | |
| 354 jpeg.seek(0) | |
| 355 output.AnswerBuffer(jpeg.read(), 'text/plain') | |
| 356 | |
| 357 else: | |
| 358 output.SendMethodNotAllowed('GET') | |
| 359 | |
| 360 orthanc.RegisterRestCallback('/pydicom/(.*)', DecodeInstance) # (*) | |
| 351 | 361 |
| 362 | |
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369
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363 .. _python-introspection: |
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364 |
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365 Inspecting the available API |
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366 ............................ |
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367 |
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368 .. highlight:: python |
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369 |
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370 Thanks to Python's introspection primitives, it is possible to inspect |
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371 the API of the ``orthanc`` module in order to dump all the available |
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372 features:: |
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373 |
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374 import inspect |
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375 import numbers |
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376 import orthanc |
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377 |
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378 # Loop over the members of the "orthanc" module |
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379 for (name, obj) in inspect.getmembers(orthanc): |
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380 if inspect.isroutine(obj): |
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381 print('Function %s():\n Documentation: %s\n' % (name, inspect.getdoc(obj))) |
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382 |
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383 elif inspect.isclass(obj): |
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384 print('Class %s:\n Documentation: %s' % (name, inspect.getdoc(obj))) |
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385 |
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386 # Loop over the members of the class |
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387 for (subname, subobj) in inspect.getmembers(obj): |
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388 if isinstance(subobj, numbers.Number): |
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389 print(' - Enumeration value %s: %s' % (subname, subobj)) |
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390 elif (not subname.startswith('_') and |
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391 inspect.ismethoddescriptor(subobj)): |
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392 print(' - Method %s(): %s' % (subname, inspect.getdoc(subobj))) |
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393 print('') |
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394 |
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395 |
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396 .. _python-scheduler: |
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397 |
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398 Scheduling a task for periodic execution |
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399 ........................................ |
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400 |
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401 .. highlight:: python |
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402 |
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403 The following Python script will periodically (every second) run the |
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404 function ``Hello()`` thanks to the ``threading`` module:: |
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405 |
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406 import orthanc |
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407 import threading |
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408 |
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409 TIMER = None |
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410 |
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411 def Hello(): |
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412 global TIMER |
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413 TIMER = None |
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414 orthanc.LogWarning("In Hello()") |
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415 # Do stuff... |
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416 TIMER = threading.Timer(1, Hello) # Re-schedule after 1 second |
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417 TIMER.start() |
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418 |
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419 def OnChange(changeType, level, resource): |
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420 if changeType == orthanc.ChangeType.ORTHANC_STARTED: |
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421 orthanc.LogWarning("Starting the scheduler") |
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422 Hello() |
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423 |
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424 elif changeType == orthanc.ChangeType.ORTHANC_STOPPED: |
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425 if TIMER != None: |
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426 orthanc.LogWarning("Stopping the scheduler") |
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427 TIMER.cancel() |
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428 |
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429 orthanc.RegisterOnChangeCallback(OnChange) |
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430 |
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431 |
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432 .. _python-metadata: |
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433 |
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434 Filtering and returning metadata |
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435 ................................ |
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436 |
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437 Besides the main DICOM tags, Orthanc associates some metadata to each |
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438 resource it stores (this includes the date of last update, the |
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439 transfer syntax, the remote AET...). People are often interested in |
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440 getting such metadata while calling the ``/tools/find`` route in the |
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441 :ref:`REST API <rest-find>`, or even in filtering this metadata the |
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442 same way they look for DICOM tags. |
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443 |
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444 This feature is not built in the core of Orthanc, as metadata is not |
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445 indexed in the Orthanc database, contrarily to the main DICOM |
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446 tags. Filtering metadata requires a linear search over all the |
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447 matching resources, which induces a cost in the performance. |
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448 |
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449 .. highlight:: python |
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450 |
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451 Nevertheless, here is a full sample Python script that overwrites the |
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452 ``/tools/find`` route in order to give access to metadata:: |
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453 |
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454 import json |
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455 import orthanc |
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456 import re |
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457 |
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458 # Get the path in the REST API to the given resource that was returned |
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459 # by a call to "/tools/find" |
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460 def GetPath(resource): |
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461 if resource['Type'] == 'Patient': |
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462 return '/patients/%s' % resource['ID'] |
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463 elif resource['Type'] == 'Study': |
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464 return '/studies/%s' % resource['ID'] |
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465 elif resource['Type'] == 'Series': |
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466 return '/series/%s' % resource['ID'] |
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467 elif resource['Type'] == 'Instance': |
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468 return '/instances/%s' % resource['ID'] |
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469 else: |
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470 raise Exception('Unknown resource level') |
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471 |
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472 def FindWithMetadata(output, uri, **request): |
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473 # The "/tools/find" route expects a POST method |
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474 if request['method'] != 'POST': |
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475 output.SendMethodNotAllowed('POST') |
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476 else: |
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477 # Parse the query provided by the user, and backup the "Expand" field |
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478 query = json.loads(request['body']) |
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479 |
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480 if 'Expand' in query: |
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481 originalExpand = query['Expand'] |
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482 else: |
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483 originalExpand = False |
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484 |
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485 # Call the core "/tools/find" route |
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486 query['Expand'] = True |
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487 answers = orthanc.RestApiPost('/tools/find', json.dumps(query)) |
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488 |
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489 # Loop over the matching resources |
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490 filteredAnswers = [] |
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491 for answer in json.loads(answers): |
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492 try: |
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493 # Read the metadata that is associated with the resource |
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494 metadata = json.loads(orthanc.RestApiGet('%s/metadata?expand' % GetPath(answer))) |
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495 |
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496 # Check whether the metadata matches the regular expressions |
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497 # that were provided in the "Metadata" field of the user request |
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498 isMetadataMatch = True |
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499 if 'Metadata' in query: |
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500 for (name, pattern) in query['Metadata'].items(): |
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501 if name in metadata: |
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502 value = metadata[name] |
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503 else: |
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504 value = '' |
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505 |
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506 if re.match(pattern, value) == None: |
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507 isMetadataMatch = False |
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508 break |
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509 |
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510 # If all the metadata matches the provided regular |
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511 # expressions, add the resource to the filtered answers |
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512 if isMetadataMatch: |
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513 if originalExpand: |
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514 answer['Metadata'] = metadata |
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515 filteredAnswers.append(answer) |
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516 else: |
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517 filteredAnswers.append(answer['ID']) |
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518 except: |
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519 # The resource was deleted since the call to "/tools/find" |
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520 pass |
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521 |
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522 # Return the filtered answers in the JSON format |
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523 output.AnswerBuffer(json.dumps(filteredAnswers, indent = 3), 'application/json') |
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524 |
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525 orthanc.RegisterRestCallback('/tools/find', FindWithMetadata) |
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526 |
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527 |
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528 **Warning:** In the sample above, the filtering of the metadata is |
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529 done using Python's `library for regular expressions |
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530 <https://docs.python.org/3/library/re.html>`__. It is evidently |
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531 possible to adapt this script in order to use the DICOM conventions |
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532 about `attribute matching |
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533 <http://dicom.nema.org/medical/dicom/2019e/output/chtml/part04/sect_C.2.2.2.html>`__. |
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534 |
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535 .. highlight:: python |
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536 |
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537 Here is a sample call to retrieve all the studies that were last |
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538 updated in 2019 thanks to this Python script:: |
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539 |
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540 $ curl http://localhost:8042/tools/find -d '{"Level":"Study","Query":{},"Expand":true,"Metadata":{"LastUpdate":"^2019.*$"}}' |
|
369
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541 |
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542 |
| 351 | 543 Performance and concurrency |
| 544 --------------------------- | |
| 545 | |
| 546 .. highlight:: python | |
| 547 | |
| 548 Let us consider the following sample Python script that makes a | |
| 549 CPU-intensive computation on a REST callback:: | |
| 550 | |
| 551 import math | |
| 552 import orthanc | |
| 553 import time | |
| 554 | |
| 555 # CPU-intensive computation taking about 4 seconds | |
| 556 def SlowComputation(): | |
| 557 start = time.time() | |
| 558 for i in range(1000): | |
| 559 for j in range(30000): | |
| 560 math.sqrt(float(j)) | |
| 561 end = time.time() | |
| 562 duration = (end - start) | |
| 563 return 'computation done in %.03f seconds\n' % duration | |
| 564 | |
| 565 def OnRest(output, uri, **request): | |
| 566 answer = SlowComputation() | |
| 567 output.AnswerBuffer(answer, 'text/plain') | |
| 568 | |
| 569 orthanc.RegisterRestCallback('/computation', OnRest) | |
| 570 | |
| 571 | |
| 572 .. highlight:: text | |
| 573 | |
| 574 Calling this REST route from the command-line returns the time that is | |
| 575 needed to compute 30 million times a squared root on your CPU:: | |
| 576 | |
| 577 $ curl http://localhost:8042/computation | |
| 578 computation done in 4.208 seconds | |
| 579 | |
| 580 Now, let us call this route three times concurrently (we use bash):: | |
| 581 | |
| 582 $ (curl http://localhost:8042/computation & curl http://localhost:8042/computation & curl http://localhost:8042/computation ) | |
| 583 computation done in 11.262 seconds | |
| 584 computation done in 12.457 seconds | |
| 585 computation done in 13.360 seconds | |
| 586 | |
| 587 As can be seen, the computation time has tripled. This means that the | |
| 588 computations were not distributed across the available CPU cores. | |
| 589 This might seem surprising, as Orthanc is a threaded server (in | |
| 590 Orthanc, a pool of C++ threads serves concurrent requests). | |
| 591 | |
| 592 The explanation is that the Python interpreter (`CPython | |
| 593 <https://en.wikipedia.org/wiki/CPython>`__ actually) is built on the | |
| 594 top of a so-called `Global Interpreter Lock (GIL) | |
| 595 <https://en.wikipedia.org/wiki/Global_interpreter_lock>`__. The GIL is | |
| 596 basically a mutex that protects all the calls to the Python | |
| 597 interpreter. If multiple C++ threads from Orthanc call a Python | |
| 353 | 598 callback, only one can proceed at any given time. Note however that |
| 599 the GIL only applies to the Python script: The baseline REST API of | |
| 600 Orthanc is not affected by the GIL. | |
| 351 | 601 |
| 602 .. highlight:: python | |
| 603 | |
| 604 The solution is to use the `multiprocessing primitives | |
| 605 <https://docs.python.org/3/library/multiprocessing.html>`__ of Python. | |
| 606 The "master" Python interpreter that is initially started by the | |
| 607 Orthanc plugin, can start several `children processes | |
| 608 <https://en.wikipedia.org/wiki/Process_(computing)>`__, each of these | |
| 609 processes running a separate Python interpreter. This allows to | |
| 610 offload intensive computations from the "master" Python interpreter of | |
| 611 Orthanc onto those "slave" interpreters. The ``multiprocessing`` | |
| 612 library is actually quite straightforward to use:: | |
| 613 | |
| 614 import math | |
| 615 import multiprocessing | |
| 616 import orthanc | |
| 617 import signal | |
| 618 import time | |
| 619 | |
| 620 # CPU-intensive computation taking about 4 seconds | |
| 621 # (same code as above) | |
| 622 def SlowComputation(): | |
| 623 start = time.time() | |
| 624 for i in range(1000): | |
| 625 for j in range(30000): | |
| 626 math.sqrt(float(j)) | |
| 627 end = time.time() | |
| 628 duration = (end - start) | |
| 629 return 'computation done in %.03f seconds\n' % duration | |
| 630 | |
| 631 # Ignore CTRL+C in the slave processes | |
| 632 def Initializer(): | |
| 633 signal.signal(signal.SIGINT, signal.SIG_IGN) | |
| 634 | |
| 635 # Create a pool of 4 slave Python interpreters | |
| 636 POOL = multiprocessing.Pool(4, initializer = Initializer) | |
| 637 | |
| 638 def OnRest(output, uri, **request): | |
| 639 # Offload the call to "SlowComputation" onto one slave process. | |
| 640 # The GIL is unlocked until the slave sends its answer back. | |
| 641 answer = POOL.apply(SlowComputation) | |
| 642 output.AnswerBuffer(answer, 'text/plain') | |
| 643 | |
| 644 orthanc.RegisterRestCallback('/computation', OnRest) | |
| 645 | |
| 646 .. highlight:: text | |
| 647 | |
| 648 Here is now the result of calling this route three times concurrently:: | |
| 649 | |
| 650 $ (curl http://localhost:8042/computation & curl http://localhost:8042/computation & curl http://localhost:8042/computation ) | |
| 651 computation done in 4.211 seconds | |
| 652 computation done in 4.215 seconds | |
| 653 computation done in 4.225 seconds | |
| 654 | |
| 655 As can be seen, the calls to the Python computation now fully run in | |
| 656 parallel (the time is cut down from 12 seconds to 4 seconds, the same | |
| 657 as for one isolated request). | |
| 658 | |
| 659 Note also how the ``multiprocessing`` library allows to make a fine | |
| 660 control over the computational resources that are available to the | |
| 661 Python script: The number of "slave" interpreters can be easily | |
| 662 changed in the constructor of the ``multiprocessing.Pool`` object, and | |
| 663 are fully independent of the threads used by the Orthanc server. | |
| 664 | |
| 352 | 665 .. highlight:: python |
| 666 | |
| 667 Very importantly, pay attention to the fact that only the "master" | |
| 668 Python interpreter has access to the Orthanc SDK. For instance, here | |
| 669 is how you would parse a DICOM file in a slave process:: | |
| 670 | |
| 671 import pydicom | |
| 672 import io | |
| 673 | |
| 674 def OffloadedDicomParsing(dicom): | |
| 675 # No access to the "orthanc" library here, as we are in the slave process | |
| 676 dataset = pydicom.dcmread(io.BytesIO(dicom)) | |
| 677 return str(dataset) | |
| 678 | |
| 679 def OnRest(output, uri, **request): | |
| 680 # The call to "orthanc.RestApiGet()" is only possible in the master process | |
| 681 dicom = orthanc.RestApiGet('/instances/19816330-cb02e1cf-df3a8fe8-bf510623-ccefe9f5/file') | |
| 682 answer = POOL.apply(OffloadedDicomParsing, args = (dicom, )) | |
| 683 output.AnswerBuffer(answer, 'text/plain') | |
| 684 | |
| 685 Communication primitives such as ``multiprocessing.Queue`` are | |
| 686 available to exchange messages from the "slave" Python interpreters to | |
| 687 the "master" Python interpreter if further calls to the Orthanc SDK | |
| 688 are required. | |
| 689 | |
| 351 | 690 Obviously, an in-depth discussion about the ``multiprocessing`` |
| 691 library is out of the scope of this document. There are many | |
| 375 | 692 references available on Internet. Also, note that ``threading`` is not |
| 693 useful here, as Python multithreading is also limited by the GIL, and | |
| 694 is more targeted at dealing with costly I/O operations or with the | |
| 695 :ref:`scheduling of commands <python-scheduler>`. |