Mercurial > hg > orthanc-book
annotate Sphinx/source/plugins/python.rst @ 352:6258b2c14e56
cont
| author | Sebastien Jodogne <s.jodogne@gmail.com> |
|---|---|
| date | Sat, 28 Mar 2020 14:29:06 +0100 |
| parents | e2863083fa30 |
| children | 0122c668f4ec |
| 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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9 Work-in-progress. |
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10 |
| 349 | 11 Being a plugin, the Python API has access to more features than |
| 350 | 12 :ref:`Lua scripts <lua>`. |
| 349 | 13 |
| 345 | 14 The Python API is automatically generated from the `Orthanc plugin SDK |
| 15 in C | |
| 16 <https://hg.orthanc-server.com/orthanc/file/Orthanc-1.5.7/Plugins/Include/orthanc/OrthancCPlugin.h>`__ | |
| 17 using the `Clang <https://en.wikipedia.org/wiki/Clang>`__ compiler | |
| 18 front-end. The coverage of the C SDK is about 75% (105 functions are | |
| 19 automatically wrapped in Python out of a total of 139 functions in C). | |
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20 |
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21 |
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22 Samples |
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23 ------- |
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24 |
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25 Extending the REST API |
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26 ...................... |
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27 |
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28 .. highlight:: python |
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29 |
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30 Here is a basic Python script that registers two new routes in the |
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31 REST API:: |
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32 |
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33 import orthanc |
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34 import pprint |
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35 |
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36 def OnRest(output, uri, **request): |
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37 pprint.pprint(request) |
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38 print('Accessing uri: %s' % uri) |
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39 output.AnswerBuffer('ok\n', 'text/plain') |
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40 |
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41 orthanc.RegisterRestCallback('/(to)(t)o', OnRest) |
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42 orthanc.RegisterRestCallback('/tata', OnRest) |
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43 |
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44 .. highlight:: json |
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45 |
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46 Here is the associated minimal configuration file for Orthanc |
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47 (provided the Python script is saved as ``rest.py``):: |
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48 |
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49 { |
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50 "Plugins" : [ "." ], |
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51 "PythonScript" : "rest.py", |
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52 "PythonVerbose" : false |
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53 } |
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54 |
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55 .. highlight:: bash |
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56 |
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57 The route can then be accessed as:: |
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58 |
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59 $ curl http://localhost:8042/toto |
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60 ok |
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61 |
| 345 | 62 |
| 63 Listening to changes | |
| 64 .................... | |
| 65 | |
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66 .. highlight:: python |
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67 |
| 345 | 68 This sample uploads a DICOM file as soon as Orthanc is started:: |
| 69 | |
| 70 import orthanc | |
| 71 | |
| 72 def OnChange(changeType, level, resource): | |
| 73 if changeType == orthanc.ChangeType.ORTHANC_STARTED: | |
| 74 print('Started') | |
| 75 | |
| 76 with open('/tmp/sample.dcm', 'rb') as f: | |
| 77 orthanc.RestApiPost('/instances', f.read()) | |
| 78 | |
| 79 elif changeType == orthanc.ChangeType.ORTHANC_STOPPED: | |
| 80 print('Stopped') | |
| 81 | |
| 82 elif changeType == orthanc.ChangeType.NEW_INSTANCE: | |
| 83 print('A new instance was uploaded: %s' % resource) | |
| 84 | |
| 85 orthanc.RegisterOnChangeCallback(OnChange) | |
| 86 | |
| 87 | |
| 88 Accessing the content of a new instance | |
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89 ....................................... |
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90 |
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91 .. highlight:: python |
| 345 | 92 |
| 93 :: | |
| 94 | |
| 95 import orthanc | |
| 96 import json | |
| 97 import pprint | |
| 98 | |
| 99 def OnStoredInstance(dicom, instanceId): | |
| 100 print('Received instance %s of size %d (transfer syntax %s, SOP class UID %s)' % ( | |
| 101 instanceId, dicom.GetInstanceSize(), | |
| 102 dicom.GetInstanceMetadata('TransferSyntax'), | |
| 103 dicom.GetInstanceMetadata('SopClassUid'))) | |
| 104 | |
| 105 # Print the origin information | |
| 106 if dicom.GetInstanceOrigin() == orthanc.InstanceOrigin.DICOM_PROTOCOL: | |
| 107 print('This instance was received through the DICOM protocol') | |
| 108 elif dicom.GetInstanceOrigin() == orthanc.InstanceOrigin.REST_API: | |
| 109 print('This instance was received through the REST API') | |
| 110 | |
| 111 # Print the DICOM tags | |
| 112 pprint.pprint(json.loads(dicom.GetInstanceSimplifiedJson())) | |
| 113 | |
| 114 orthanc.RegisterOnStoredInstanceCallback(OnStoredInstance) | |
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115 |
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116 |
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117 Calling pydicom |
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118 ............... |
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119 |
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120 .. highlight:: python |
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121 |
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122 Here is a sample Python plugin that registers a REST callback to dump |
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123 the content of the dataset of one given DICOM instance stored in |
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124 Orthanc, using `pydicom <https://pydicom.github.io/>`__:: |
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125 |
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126 import io |
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127 import orthanc |
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128 import pydicom |
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129 |
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130 def DecodeInstance(output, uri, **request): |
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131 if request['method'] == 'GET': |
| 347 | 132 # Retrieve the instance ID from the regular expression (*) |
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133 instanceId = request['groups'][0] |
| 347 | 134 # Get the content of the DICOM file |
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135 f = orthanc.GetDicomForInstance(instanceId) |
| 347 | 136 # Parse it using pydicom |
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137 dicom = pydicom.dcmread(io.BytesIO(f)) |
| 347 | 138 # Return a string representation the dataset to the caller |
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139 output.AnswerBuffer(str(dicom), 'text/plain') |
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140 else: |
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141 output.SendMethodNotAllowed('GET') |
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142 |
| 347 | 143 orthanc.RegisterRestCallback('/pydicom/(.*)', DecodeInstance) # (*) |
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144 |
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145 .. highlight:: bash |
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146 |
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147 This can be called as follows:: |
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148 |
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149 $ curl http://localhost:8042/pydicom/19816330-cb02e1cf-df3a8fe8-bf510623-ccefe9f5 |
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150 |
| 347 | 151 |
| 152 Auto-routing studies | |
| 153 .................... | |
| 154 | |
| 155 .. highlight:: python | |
| 156 | |
| 157 Here is a sample Python plugin that routes any :ref:`stable study | |
| 158 <lua-callbacks>` to a modality named ``samples`` (as declared in the | |
| 159 ``DicomModalities`` configuration option):: | |
| 160 | |
| 161 import orthanc | |
| 162 | |
| 163 def OnChange(changeType, level, resourceId): | |
| 164 if changeType == orthanc.ChangeType.STABLE_STUDY: | |
| 165 print('Stable study: %s' % resourceId) | |
| 166 orthanc.RestApiPost('/modalities/sample/store', resourceId) | |
| 167 | |
| 168 orthanc.RegisterOnChangeCallback(OnChange) | |
| 348 | 169 |
| 170 | |
| 352 | 171 Rendering a thumbnail using PIL/Pillow |
| 172 ...................................... | |
| 348 | 173 |
| 174 .. highlight:: python | |
| 175 | |
| 176 :: | |
| 177 | |
| 178 from PIL import Image | |
| 179 import io | |
| 180 import orthanc | |
| 181 | |
| 182 def DecodeInstance(output, uri, **request): | |
| 183 if request['method'] == 'GET': | |
| 184 # Retrieve the instance ID from the regular expression (*) | |
| 185 instanceId = request['groups'][0] | |
| 186 | |
| 187 # Render the instance, then open it in Python using PIL/Pillow | |
| 188 png = orthanc.RestApiGet('/instances/%s/rendered' % instanceId) | |
| 189 image = Image.open(io.BytesIO(png)) | |
| 190 | |
| 191 # Downsize the image as a 64x64 thumbnail | |
| 192 image.thumbnail((64, 64), Image.ANTIALIAS) | |
| 193 | |
| 194 # Save the thumbnail as JPEG, then send the buffer to the caller | |
| 195 jpeg = io.BytesIO() | |
| 196 image.save(jpeg, format = "JPEG", quality = 80) | |
| 197 jpeg.seek(0) | |
| 198 output.AnswerBuffer(jpeg.read(), 'text/plain') | |
| 199 | |
| 200 else: | |
| 201 output.SendMethodNotAllowed('GET') | |
| 202 | |
| 203 orthanc.RegisterRestCallback('/pydicom/(.*)', DecodeInstance) # (*) | |
| 351 | 204 |
| 205 | |
| 206 Performance and concurrency | |
| 207 --------------------------- | |
| 208 | |
| 209 .. highlight:: python | |
| 210 | |
| 211 Let us consider the following sample Python script that makes a | |
| 212 CPU-intensive computation on a REST callback:: | |
| 213 | |
| 214 import math | |
| 215 import orthanc | |
| 216 import time | |
| 217 | |
| 218 # CPU-intensive computation taking about 4 seconds | |
| 219 def SlowComputation(): | |
| 220 start = time.time() | |
| 221 for i in range(1000): | |
| 222 for j in range(30000): | |
| 223 math.sqrt(float(j)) | |
| 224 end = time.time() | |
| 225 duration = (end - start) | |
| 226 return 'computation done in %.03f seconds\n' % duration | |
| 227 | |
| 228 def OnRest(output, uri, **request): | |
| 229 answer = SlowComputation() | |
| 230 output.AnswerBuffer(answer, 'text/plain') | |
| 231 | |
| 232 orthanc.RegisterRestCallback('/computation', OnRest) | |
| 233 | |
| 234 | |
| 235 .. highlight:: text | |
| 236 | |
| 237 Calling this REST route from the command-line returns the time that is | |
| 238 needed to compute 30 million times a squared root on your CPU:: | |
| 239 | |
| 240 $ curl http://localhost:8042/computation | |
| 241 computation done in 4.208 seconds | |
| 242 | |
| 243 Now, let us call this route three times concurrently (we use bash):: | |
| 244 | |
| 245 $ (curl http://localhost:8042/computation & curl http://localhost:8042/computation & curl http://localhost:8042/computation ) | |
| 246 computation done in 11.262 seconds | |
| 247 computation done in 12.457 seconds | |
| 248 computation done in 13.360 seconds | |
| 249 | |
| 250 As can be seen, the computation time has tripled. This means that the | |
| 251 computations were not distributed across the available CPU cores. | |
| 252 This might seem surprising, as Orthanc is a threaded server (in | |
| 253 Orthanc, a pool of C++ threads serves concurrent requests). | |
| 254 | |
| 255 The explanation is that the Python interpreter (`CPython | |
| 256 <https://en.wikipedia.org/wiki/CPython>`__ actually) is built on the | |
| 257 top of a so-called `Global Interpreter Lock (GIL) | |
| 258 <https://en.wikipedia.org/wiki/Global_interpreter_lock>`__. The GIL is | |
| 259 basically a mutex that protects all the calls to the Python | |
| 260 interpreter. If multiple C++ threads from Orthanc call a Python | |
| 261 callback, only one can proceed at any given time. | |
| 262 | |
| 263 .. highlight:: python | |
| 264 | |
| 265 The solution is to use the `multiprocessing primitives | |
| 266 <https://docs.python.org/3/library/multiprocessing.html>`__ of Python. | |
| 267 The "master" Python interpreter that is initially started by the | |
| 268 Orthanc plugin, can start several `children processes | |
| 269 <https://en.wikipedia.org/wiki/Process_(computing)>`__, each of these | |
| 270 processes running a separate Python interpreter. This allows to | |
| 271 offload intensive computations from the "master" Python interpreter of | |
| 272 Orthanc onto those "slave" interpreters. The ``multiprocessing`` | |
| 273 library is actually quite straightforward to use:: | |
| 274 | |
| 275 import math | |
| 276 import multiprocessing | |
| 277 import orthanc | |
| 278 import signal | |
| 279 import time | |
| 280 | |
| 281 # CPU-intensive computation taking about 4 seconds | |
| 282 # (same code as above) | |
| 283 def SlowComputation(): | |
| 284 start = time.time() | |
| 285 for i in range(1000): | |
| 286 for j in range(30000): | |
| 287 math.sqrt(float(j)) | |
| 288 end = time.time() | |
| 289 duration = (end - start) | |
| 290 return 'computation done in %.03f seconds\n' % duration | |
| 291 | |
| 292 # Ignore CTRL+C in the slave processes | |
| 293 def Initializer(): | |
| 294 signal.signal(signal.SIGINT, signal.SIG_IGN) | |
| 295 | |
| 296 # Create a pool of 4 slave Python interpreters | |
| 297 POOL = multiprocessing.Pool(4, initializer = Initializer) | |
| 298 | |
| 299 def OnRest(output, uri, **request): | |
| 300 # Offload the call to "SlowComputation" onto one slave process. | |
| 301 # The GIL is unlocked until the slave sends its answer back. | |
| 302 answer = POOL.apply(SlowComputation) | |
| 303 output.AnswerBuffer(answer, 'text/plain') | |
| 304 | |
| 305 orthanc.RegisterRestCallback('/computation', OnRest) | |
| 306 | |
| 307 .. highlight:: text | |
| 308 | |
| 309 Here is now the result of calling this route three times concurrently:: | |
| 310 | |
| 311 $ (curl http://localhost:8042/computation & curl http://localhost:8042/computation & curl http://localhost:8042/computation ) | |
| 312 computation done in 4.211 seconds | |
| 313 computation done in 4.215 seconds | |
| 314 computation done in 4.225 seconds | |
| 315 | |
| 316 As can be seen, the calls to the Python computation now fully run in | |
| 317 parallel (the time is cut down from 12 seconds to 4 seconds, the same | |
| 318 as for one isolated request). | |
| 319 | |
| 320 Note also how the ``multiprocessing`` library allows to make a fine | |
| 321 control over the computational resources that are available to the | |
| 322 Python script: The number of "slave" interpreters can be easily | |
| 323 changed in the constructor of the ``multiprocessing.Pool`` object, and | |
| 324 are fully independent of the threads used by the Orthanc server. | |
| 325 | |
| 352 | 326 .. highlight:: python |
| 327 | |
| 328 Very importantly, pay attention to the fact that only the "master" | |
| 329 Python interpreter has access to the Orthanc SDK. For instance, here | |
| 330 is how you would parse a DICOM file in a slave process:: | |
| 331 | |
| 332 import pydicom | |
| 333 import io | |
| 334 | |
| 335 def OffloadedDicomParsing(dicom): | |
| 336 # No access to the "orthanc" library here, as we are in the slave process | |
| 337 dataset = pydicom.dcmread(io.BytesIO(dicom)) | |
| 338 return str(dataset) | |
| 339 | |
| 340 def OnRest(output, uri, **request): | |
| 341 # The call to "orthanc.RestApiGet()" is only possible in the master process | |
| 342 dicom = orthanc.RestApiGet('/instances/19816330-cb02e1cf-df3a8fe8-bf510623-ccefe9f5/file') | |
| 343 answer = POOL.apply(OffloadedDicomParsing, args = (dicom, )) | |
| 344 output.AnswerBuffer(answer, 'text/plain') | |
| 345 | |
| 346 Communication primitives such as ``multiprocessing.Queue`` are | |
| 347 available to exchange messages from the "slave" Python interpreters to | |
| 348 the "master" Python interpreter if further calls to the Orthanc SDK | |
| 349 are required. | |
| 350 | |
| 351 | 351 Obviously, an in-depth discussion about the ``multiprocessing`` |
| 352 library is out of the scope of this document. There are many | |
| 353 references available on Internet. Also, note that ``multithreading`` | |
| 354 is not useful here, as Python multithreading is also limited by the | |
| 355 GIL, and is more targeted at dealing with costly I/O operations. |