# Python bindings to DIPlib 3 (a.k.a. PyDIP)
## Introduction
The purpose of the *DIPlib* project 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, *DIPlib* focuses on quantification. The first priority is precision,
all other principles have a lower priority. Our principles are:
1. **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.
2. **Ease of use**
Although 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.
3. **Efficiency**
We 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 contains *DIPviewer*, an interactive multi-dimensional image viewer,
and *DIPjavaio*, an interface to the
[*OME Bio-Formats*](https://www.openmicroscopy.org/bio-formats/) library.
The package is compatible with *NumPy* and any image processing package that uses
a *NumPy*-compatible way of representing images.
See [the *DIPlib* website](https://diplib.org/) for 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 the *DIPlib* website.
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.
## Installation
To install, simply type
pip install diplib
Windows users might need to install the
[Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019](https://support.microsoft.com/en-us/help/2977003/the-latest-supported-visual-c-downloads).
To read images through the *Bio-Formats* library, you will need to download it
separately:
python -m diplib download_bioformats
**Note:** The `diplib` package on PyPI vendors the *OpenMP* library for some platforms
(`libomp.dylib` on macOS, `libgomp.so` on Linux). It is possible, though rare, for another package to vendor
an incompatible *OpenMP* library, and for the combined use to cause Python to crash.
See for example [this issue](https://github.com/DIPlib/diplib/issues/130). If you happen to run into this problem,
please [let us know!](https://github.com/DIPlib/diplib/issues/new/choose).
You can find more information about the simultaneous use of multiple *OpenMP* libraries
[on this page](https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md).
## Usage
The interface only has automatically generated docstrings that show the names
of each of the parameters. Use the *DIPlib* reference to learn how to use each
function. Get started by reading [the User Manual](https://diplib.org/diplib-docs/pydip_user_manual.html).
These Jupyter notebooks give a short introduction:
- [01_pydip_basics.ipynb](https://github.com/DIPlib/diplib/blob/master/examples/python/01_pydip_basics.ipynb)
- [02_filtering.ipynb](https://github.com/DIPlib/diplib/blob/master/examples/python/02_filtering.ipynb)
- [03_numpy_interaction.ipynb](https://github.com/DIPlib/diplib/blob/master/examples/python/03_numpy_interaction.ipynb)
- [04_tensor_images.ipynb](https://github.com/DIPlib/diplib/blob/master/examples/python/04_tensor_images.ipynb)
## License
Copyright 2014-2024 Cris Luengo and contributors
Copyright 1995-2014 Delft University of Technology
Licensed 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 at
http://www.apache.org/licenses/LICENSE-2.0
(or see the [`LICENSE.txt`](LICENSE.txt) file 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.
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"description": "# Python bindings to DIPlib 3 (a.k.a. PyDIP)\n\n## Introduction\n\nThe purpose of the *DIPlib* project is to provide a one-stop library and\ndevelopment environment for quantitative image analysis, be it applied\nto microscopy, radiology, astronomy, or anything in between.\n\nAs opposed to all other image processing/analysis libraries and packages out\nthere, *DIPlib* focuses on quantification. The first priority is precision,\nall other principles have a lower priority. Our principles are:\n\n1. **Precision:**\n\n We implement the most precise known methods, and output often defaults to\n floating-point samples. The purpose of these algorithms is quantification,\n not approximation.\n\n2. **Ease of use**\n\n Although our Python bindings are not much more than a thin wrapper of the\n C++ library functionality, the image analysis functionality is always easy\n to use. For example, the user does not, in general, need to be aware of\n the data type of the image to use these algorithms effectively.\n\n3. **Efficiency**\n\n We implement the most efficient known algorithms, as long as they don't\n compromise precision. Ease-of-use features might also incur a slight overhead\n in execution times. The library can be used in high-throughput quantitative analysis\n pipelines, but is not designed for real-time video processing.\n\nBesides an extensive collection of image processing and analysis algorithms,\nthis package contains *DIPviewer*, an interactive multi-dimensional image viewer,\nand *DIPjavaio*, an interface to the\n[*OME Bio-Formats*](https://www.openmicroscopy.org/bio-formats/) library.\nThe package is compatible with *NumPy* and any image processing package that uses\na *NumPy*-compatible way of representing images.\n\nSee [the *DIPlib* website](https://diplib.org/) for more information.\n\n**Note!** We consider the Python bindings (*PyDIP*) to be in development. We aim at\nnot making breaking changes, but will sometimes do so when we feel it significantly\nimproves the usability of the module. These changes will always be highlighted in\nthe change logs and the release notification on the *DIPlib* website.\nWe recommend that you pin your project to use a specific version of the package\non PyPI, and carefully read the change logs before upgrading.\n\n## Installation\n\nTo install, simply type\n\n pip install diplib\n\nWindows users might need to install the\n[Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019](https://support.microsoft.com/en-us/help/2977003/the-latest-supported-visual-c-downloads).\n\nTo read images through the *Bio-Formats* library, you will need to download it\nseparately:\n\n python -m diplib download_bioformats\n\n**Note:** The `diplib` package on PyPI vendors the *OpenMP* library for some platforms\n(`libomp.dylib` on macOS, `libgomp.so` on Linux). It is possible, though rare, for another package to vendor\nan incompatible *OpenMP* library, and for the combined use to cause Python to crash.\nSee for example [this issue](https://github.com/DIPlib/diplib/issues/130). If you happen to run into this problem,\nplease [let us know!](https://github.com/DIPlib/diplib/issues/new/choose).\nYou can find more information about the simultaneous use of multiple *OpenMP* libraries\n[on this page](https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md).\n\n## Usage\n\nThe interface only has automatically generated docstrings that show the names\nof each of the parameters. Use the *DIPlib* reference to learn how to use each\nfunction. Get started by reading [the User Manual](https://diplib.org/diplib-docs/pydip_user_manual.html).\n\nThese Jupyter notebooks give a short introduction:\n\n- [01_pydip_basics.ipynb](https://github.com/DIPlib/diplib/blob/master/examples/python/01_pydip_basics.ipynb)\n- [02_filtering.ipynb](https://github.com/DIPlib/diplib/blob/master/examples/python/02_filtering.ipynb)\n- [03_numpy_interaction.ipynb](https://github.com/DIPlib/diplib/blob/master/examples/python/03_numpy_interaction.ipynb)\n- [04_tensor_images.ipynb](https://github.com/DIPlib/diplib/blob/master/examples/python/04_tensor_images.ipynb)\n\n## License\n\nCopyright 2014-2024 Cris Luengo and contributors \nCopyright 1995-2014 Delft University of Technology\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this library except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0 \n (or see the [`LICENSE.txt`](LICENSE.txt) file in this distribution)\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n",
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