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comparison Sphinx/source/users/rest.rst @ 820:fa6d9c7237b4
Downloading decoded images from Python
| author | Sebastien Jodogne <s.jodogne@gmail.com> |
|---|---|
| date | Fri, 25 Feb 2022 09:34:37 +0100 |
| parents | 01fa632daae9 |
| children | e95713f90614 |
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| 819:a67ceccebf02 | 820:fa6d9c7237b4 |
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| 425 | 425 |
| 426 $ curl -H 'Accept: image/x-portable-arbitrarymap' http://localhost:8042/instances/609665c0-c5198aa2-8632476b-a00e0de0-e9075d94/image-uint16 > full-16.pam | 426 $ curl -H 'Accept: image/x-portable-arbitrarymap' http://localhost:8042/instances/609665c0-c5198aa2-8632476b-a00e0de0-e9075d94/image-uint16 > full-16.pam |
| 427 | 427 |
| 428 Users of Matlab or Octave can find related information :ref:`in the | 428 Users of Matlab or Octave can find related information :ref:`in the |
| 429 dedicated section <matlab>`. | 429 dedicated section <matlab>`. |
| 430 | |
| 431 | |
| 432 .. _download_numpy: | |
| 433 | |
| 434 Downloading decoded images from Python | |
| 435 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | |
| 436 | |
| 437 .. highlight:: python | |
| 438 | |
| 439 Starting with Orthanc 1.10.0, it is possible to immediately download | |
| 440 DICOM instances and DICOM series as numpy arrays (even if they use a | |
| 441 compressed transfer syntax). This is especially useful for the | |
| 442 integration within AI (artificial intelligence) pipelines. Here is a | |
| 443 sample call:: | |
| 444 | |
| 445 import io | |
| 446 import numpy | |
| 447 import requests | |
| 448 | |
| 449 r = requests.get('https://demo.orthanc-server.com/instances/6582b1c0-292ad5ab-ba0f088f-f7a1766f-9a29a54f/numpy') | |
| 450 r.raise_for_status() | |
| 451 | |
| 452 image = numpy.load(io.BytesIO(r.content)) | |
| 453 print(image.shape) # (1, 358, 512, 1) | |
| 454 | |
| 455 The downloaded numpy array for one single DICOM instance contains | |
| 456 floating-point values, and has a shape of ``(1, height, width, 1)`` if | |
| 457 the corresponding instance is grayscale, or ``(1, height, width, 3)`` | |
| 458 if the instance has colors (e.g. in ultrasound images). If applicable, | |
| 459 the ``Rescale Slope (0028,1053)`` and ``Rescale Intercept | |
| 460 (0028,1052)`` DICOM tags are applied to the floating-point values. | |
| 461 | |
| 462 Similarly, this feature is available at the series level:: | |
| 463 | |
| 464 import io | |
| 465 import numpy | |
| 466 import requests | |
| 467 | |
| 468 r = requests.get('https://demo.orthanc-server.com/series/dc0216d2-a406a5ad-31ef7a78-113ae9d9-29939f9e/numpy') | |
| 469 r.raise_for_status() | |
| 470 | |
| 471 image = numpy.load(io.BytesIO(r.content)) | |
| 472 print(image.shape) # (100, 256, 256, 1) | |
| 473 | |
| 474 As can be seen, in the case of a DICOM series, the first dimension of | |
| 475 the resulting numpy array corresponds to the depth of the series | |
| 476 (i.e. to its number of 2D slices). | |
| 477 | |
| 478 Some options are available for these ``/instances/.../numpy`` and | |
| 479 ``/series/.../numpy`` routes: | |
| 480 | |
| 481 * ``?compress=1`` will return a ``.npz`` compressed numpy archive | |
| 482 instead of a plain ``.npy`` numpy array. This can be used to reduce | |
| 483 the network bandwidth. In such a case, the array of interest is | |
| 484 named ``arr_0`` in the ``.npz`` archive, e.g.:: | |
| 485 | |
| 486 print(image['arr_0'].shape) | |
| 487 | |
| 488 * ``?rescale=0`` will skip the conversion to floating-point values, | |
| 489 and will not apply the rescale slope/intercept. This can be useful | |
| 490 to reduce the network bandwidth or to receive the original integer | |
| 491 values of the voxels. | |
| 492 | |
| 430 | 493 |
| 431 Downloading studies | 494 Downloading studies |
| 432 ^^^^^^^^^^^^^^^^^^^ | 495 ^^^^^^^^^^^^^^^^^^^ |
| 433 | 496 |
| 434 .. highlight:: bash | 497 .. highlight:: bash |