a generic C++ library for image analysis
C++ Jupyter Notebook HTML Python C CMake Other
Latest commit 6fcc71f Jul 8, 2016 @ukoethe committed on GitHub Merge pull request #380 from ukoethe/eigenvalue_fix
Added missing error message on non-matching strides, generalized color normalization.

README.md

VIGRA Computer Vision Library

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            Copyright 1998-2013 by Ullrich Koethe

This file is part of the VIGRA computer vision library.
You may use, modify, and distribute this software according
to the terms stated in the LICENSE.txt file included in
the VIGRA distribution.

The VIGRA Website is
    http://ukoethe.github.io/vigra/                      
Please direct questions, bug reports, and contributions to        
    ullrich.koethe@iwr.uni-heidelberg.de    or                    
    vigra@informatik.uni-hamburg.de                               


THIS SOFTWARE IS PROVIDED AS IS AND WITHOUT ANY EXPRESS OR
IMPLIED WARRANTIES, INCLUDING, WITHOUT LIMITATION, THE IMPLIED
WARRANTIES OF MERCHANTIBILITY AND FITNESS FOR A PARTICULAR PURPOSE.

Installation

Installation instructions can be found in the file $VIGRA_PATH/doc/vigra/Installation.html If the documentation has not yet been generated (e.g. when you build from a development snapshot), you find these instructions in $VIGRA_PATH/docsrc/installation.dxx or online at http://ukoethe.github.io/vigra/doc-release/vigra/Installation.html

Documentation

If you downloaded an official release, the documentation can be found in $VIGRA_PATH/doc/vigra/, the start file is $VIGRA_PATH/doc/vigra/index.html or online at http://ukoethe.github.io/vigra/#documentation.

When you use the development version from github, you can generate documentation by make doc.

Download

VIGRA can be downloaded at http://ukoethe.github.io/vigra/#download. The official development repository is at https://github.com/ukoethe/vigra

What is VIGRA

VIGRA is a computer vision library that puts its main emphasis on flexible algorithms, because algorithms represent the principal know-how of this field. The library was consequently built using generic programming as introduced by Stepanov and Musser and exemplified in the C++ Standard Template Library. By writing a few adapters (image iterators and accessors) you can use VIGRA's algorithms on top of your data structures, within your environment. Alternatively, you can also use the data structures provided within VIGRA, which can be easily adapted to a wide range of applications. VIGRA's flexibility comes almost for free: Since the design uses compile-time polymorphism (templates), performance of the compiled program approaches that of a traditional, hand tuned, inflexible, solution.