Package: diplib
Python bindings to DIPlib 3 (a.k.a. PyDIP)IntroductionThe purpose of theDIPlibproject is to provide a one-stop library and
development environment for quantitative image analysis, be it applied
to microscopy, radiology, astronomy, or anything in between.As opposed to all other image processing/analysis libraries and packages out
there,DIPlibfocuses on quantification. The first priority is precision,
all other principles have a lower priority. Our principles are:Precision:We implement the most precise known methods, and output often defaults to
floating-point samples. The purpose of these algorithms is quantification,
not approximation.Ease of useAlthough our Python bindings are not much more than a thin wrapper of the
C++ library functionality, the image analysis functionality is always easy
to use. For example, the user does not, in general, need to be aware of
the data type of the image to use these algorithms effectively.EfficiencyWe implement the most efficient known algorithms, as long as they don't
compromise precision. Ease-of-use features might also incur a slight overhead
in execution times. The library can be used in high-throughput quantitative analysis
pipelines, but is not designed for real-time video processing.Besides an extensive collection of image processing and analysis algorithms,
this package containsDIPviewer, an interactive multi-dimensional image viewer,
andDIPjavaio, an interface to theOME Bio-Formatslibrary.
The package is compatible withNumPyand any image processing package that uses
aNumPy-compatible way of representing images.SeetheDIPlibwebsitefor more information.Note!We consider the Python bindings (PyDIP) to be in development. We aim at
not making breaking changes, but will sometimes do so when we feel it significantly
improves the usability of the module. These changes will always be highlighted in
the change logs and the release notification on theDIPlibwebsite.
We recommend that you pin your project to use a specific version of the package
on PyPI, and carefully read the change logs before upgrading.InstallationTo install, simply typepip install diplibWindows users might need to install theMicrosoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019.To read images through theBio-Formatslibrary, you will need to download it
separately:python -m diplib download_bioformatsNote:Thediplibpackage on PyPI vendors theOpenMPlibrary for some platforms
(libomp.dylibon macOS,libgomp.soon Linux). It is possible, though rare, for another package to vendor
an incompatibleOpenMPlibrary, and for the combined use to cause Python to crash.
See for examplethis issue. If you happen to run into this problem,
pleaselet us know!.
You can find more information about the simultaneous use of multipleOpenMPlibrarieson this page.UsageThe interface only has automatically generated docstrings that show the names
of each of the parameters. Use theDIPlibreference to learn how to use each
function. Get started by readingthe User Manual.These Jupyter notebooks give a short introduction:01_pydip_basics.ipynb02_filtering.ipynb03_numpy_interaction.ipynb04_tensor_images.ipynbLicenseCopyright 2014-2023 Cris Luengo and contributorsCopyright 1995-2014 Delft University of TechnologyLicensed under the Apache License, Version 2.0 (the "License");
you may not use this library except in compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0(or see theLICENSE.txtfile in this distribution)Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.