Metadata-Version: 2.1
Name: scikit-ipp
Version: 1.2.0
Summary: Image processing library powered by Intel(R) IPP
Home-page: http://github.com/IntelPython/scikit-ipp
Author: Intel Corporation
Maintainer: Intel Corp.
Maintainer-email: scripting@intel.com
License: BSD
Download-URL: http://github.com/IntelPython/scikit-ipp
Platform: Windows
Platform: Linux
Platform: Mac OS-X
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: ipp
Requires-Dist: intel-openmp
Requires-Dist: numpy==1.19.5

# scikit-IPP (skipp)
`scikit-ipp` is optimization of open-source image processing library [scikit-image](https://scikit-image.org/) by using Intel® Integrated Performance Primitives (Intel® IPP) library.

`scikit-ipp` is a standalone package, provided scikit-image-like API to some of Intel® IPP functions.

- [Documentation](https://intelpython.github.io/scikit-ipp/)
- [Source Code](https://github.com/IntelPython/scikit-ipp)
- [About Intel® IPP](https://software.intel.com/en-us/intel-ipp)


# Getting started
`scikit-ipp` is easily built from source with the majority of the necessary prerequisites available on conda.  The instructions below detail how to gather the prerequisites, setting one's build environment, and finally building and installing the completed package.  `scikit-ipp` can be built for all three major platforms (Windows, Linux, macOS).

The build-process (using setup.py) happens in 2 stages:
1. Running cython on C and Cython sources
2. Compiling and linking


# Building scikit-ipp using conda-build
The easiest way to build `scikit-ipp` is using the conda-build with the provided recipe.

## Prerequisites
* Python version >= 3.6
* conda-build version >= 3
* C compiler

### Building `scikit-ipp`
````
cd <checkout-dir>
conda build -c intel conda-recipe
````

This will build the conda package and tell you where to find it (```.../scikit-ipp*.tar.bz2```).

## Installing the built scikit-ipp conda package
```
conda install <path-to-conda-package-as-built-above>
```
To actually use your `scikit-ipp`, dependent packages need to be installed. To ensure, do

Linux or Windows:
```
conda install -c intel numpy ipp
```
# Building documentation for scikit-ipp
## Prerequisites for creating documentation
* sphinx >= 3.0
* sphinx_rtd_theme >= 0.4
* sphinx-gallery >= 0.3.1
* matplotlib > = 3.0.1

## Building documentation
1. Install scikit-ipp into your python environment
2. ```cd doc && make html```
3. The documentation will be in ```doc/_build/html```

# Examples
Introductory examples for `scikit-ipp` [link](examples/scikit-ipp_examples.ipynb)


