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Home modified by Bastian Goldluecke

Bastian Goldluecke — Sun, 20 Jul 2014 15:29:02 -0000

--- v45
+++ v46
@@ -1,3 +1,10 @@
+### PLEASE NOTE ###
+
+Documentation on the SourceForge web page is outdated and only valid up to and including release 5. Please visit the [new project web page](http://cocolib.net) for the most recent information.
+
+
+
+


 ### Description

 COCOLIB is a C++/CUDA library for variational image analysis and continuous global optimization, focusing on the minimization of functionals of the type

Home modified by Bastian Goldluecke

Bastian Goldluecke — Tue, 18 Jun 2013 09:58:20 -0000

--- v44
+++ v45
@@ -40,6 +40,7 @@

 [[download_button]]

+

 Note: since it produces an about 30% smaller file than bzip2, the tarball was compressed with lzma2. Please untar using

 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Home modified by Bastian Goldluecke

Bastian Goldluecke — Tue, 18 Jun 2013 09:57:00 -0000

--- v43
+++ v44
@@ -39,6 +39,12 @@
 Current release tarball:

 [[download_button]]
+
+Note: since it produces an about 30% smaller file than bzip2, the tarball was compressed with lzma2. Please untar using
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    tar -xvJf cocolib-release-5.tar.xz
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Home modified by Bastian Goldluecke

Bastian Goldluecke — Thu, 13 Jun 2013 14:33:05 -0000

--- v42
+++ v43
@@ -1,10 +1,14 @@
 ### Description

-COCOLIB is a library for continuous global optimization, focusing on the minimization of functionals of the type
+COCOLIB is a C++/CUDA library for variational image analysis and continuous global optimization, focusing on the minimization of functionals of the type

 E(u) = J(u) + F(u)

 where u is an image (i.e. vector-valued function on an interval), J is a convex and closed regularizer, and F a closed and usually also convex data term.
+
+
+Since release 5, COCOLIB also contains a growing toolkit for light field analysis based on variational methods, which implements many methods from our research papers and has ready-to-use scripts for benchmarking on the HCI light field data base. For further information, see the [light field suite documentation](Light field suite).
+

 The complete overview can be found on the [project home page](https://sourceforge.net/p/cocolib/home/Home/).

@@ -13,21 +17,16 @@
 


 ### Requirements and support

-All algorithms are implemented in C++ using CUDA, and have been tested on Ubuntu 11.04 and later as well as Linux Mint 12. The code is provided as a service to the image processing community and "as is", without any support beyond the documentation presented here. If you run into a problem, please ask in the forum for help. If I find the time, I will try to be of assistance.
+All algorithms are implemented in C++ using CUDA. They have been tested on Ubuntu 13.04 and above, Fedora Core 19 and Mac OS X, but should compile and run on many other Debian flavors as well. The code is provided as a service to the image processing community and "as is", without any support beyond the documentation presented here. If you run into a problem, please ask in the forum or via e-Mail for help. If I find the time, I will try to be of assistance.

-You will need to install the following manually on your system:
-
-- CUDA capable graphics card
-- CUDA toolkit (SDK is not necessary), compiler 'nvcc' must be in shell search path
-
+You will need a CUDA capable graphics card in your system, which supports at least revision 1.3. 

 The following pre-requisites can usually be installed via the package management system:

-- Latest nVidia driver, needs to fit to CUDA toolkit
+- Latest nVidia driver and CUDA toolkit
 - Qt library (with development support, Ubuntu package 'libqt4-dev')
 - GNU scientific library (with development support, Ubuntu package 'libgsl0-dev')
 - For better segmentation data terms, you need the ANN "approximate nearest neighbour" library (Ubuntu: 'libann-dev'). If you don't have it, support for it will be disabled by the configure script.
-- Another optional dependency (which makes some stuff faster) is the CUDPP library, available on google code. It has to be installed manually, but an install script is provided (see below).

 See below for further instructions and install scripts for Ubuntu.
@@ -68,7 +67,12 @@
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-If everything works fine, the library *libcocolib.a* will be built in the folder *cocolib*, and the command line tool *coco-ip* will be built in the folder *examples*.
+If everything works fine, the following stuff will be built:
+
+- the library *libcocolib.a* in the folder *cocolib*,
+- the command line tool *coco-ip* in the folder *examples*,
+- the command line tool *lightfields* in the folder *lightfields*,
+- some auxiliary stuff in the folder *tools*.

 In case there were errors, please check out the [common build problems](Troubleshooting) here, before reporting the problem on the forum.

@@ -90,13 +94,10 @@
         ./install_dependencies_ubuntu.sh
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-- In order to install the cuDPP library, use

-        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-        ./install_cudpp.sh
-        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+- Note: With release 5, the library does no longer depend on cuDPP, which has not received upgrades for a while does not seem to compile correctly anymore.

-- You still need to install the CUDA toolkit manually, and put 'nvcc' in your shell search path. Other than that, all pre-requisites are installed.
+

WikiPage Home modified by Bastian Goldluecke

Bastian Goldluecke — Fri, 17 Aug 2012 14:20:26 -0000

--- v41
+++ v42
@@ -46,7 +46,7 @@
 Latest development version via *subversion*:
 
 ~~~~~~~~~~~~~~~~~~~
-svn checkout https://svn.code.sf.net/p/cocolib/code/trunk cocolib-dev
+svn checkout svn://svn.code.sf.net/p/cocolib/code/trunk cocolib-dev
 ~~~~~~~~~~~~~~~~~~~

WikiPage Home modified by Bastian Goldluecke

Bastian Goldluecke — Fri, 17 Aug 2012 14:19:50 -0000

--- v40
+++ v41
@@ -27,7 +27,7 @@
 - Qt library (with development support, Ubuntu package 'libqt4-dev')
 - GNU scientific library (with development support, Ubuntu package 'libgsl0-dev')
 - For better segmentation data terms, you need the ANN "approximate nearest neighbour" library (Ubuntu: 'libann-dev'). If you don't have it, support for it will be disabled by the configure script.
-- Another optional dependency (which makes some stuff faster) is the CUDPP library, available on google code. It has to be installed manually.
+- Another optional dependency (which makes some stuff faster) is the CUDPP library, available on google code. It has to be installed manually, but an install script is provided (see below).
 
 
 See below for further instructions and install scripts for Ubuntu.
@@ -90,7 +90,13 @@
         ./install_dependencies_ubuntu.sh
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-- You still need to install the toolkit manually, and put 'nvcc' in your shell search path. Other than that, all pre-requisites are installed.
+- In order to install the cuDPP library, use
+
+        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+        ./install_cudpp.sh
+        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+- You still need to install the CUDA toolkit manually, and put 'nvcc' in your shell search path. Other than that, all pre-requisites are installed.
 
 
 


@@ -110,14 +116,14 @@
         ./test_cocolib.sh
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The results are automatically compared to reference results provided with the library.
+The results are automatically compared to reference results provided with the library. Note that there might be slight differences due to rounding errors and different compiler optimizations - if results differ, check if yours looks reasonable close and everything is probably ok.
 
 
 
 


 ### Getting started with the tool: running the examples
 
-In order to test if the command line tool was built correctly, try to
+In order to experiment with the command line tool,
 
 - change to the "examples" subdirectory.
 - View an example configuration file with

WikiPage Home modified by Bastian Goldluecke

Bastian Goldluecke — Thu, 26 Apr 2012 13:34:19 -0000

--- v39 
+++ v40 
@@ -55,45 +55,63 @@
 - Download and unpack the desired release of the library
 - Change to library base directory
 - Make sure pre-requisites are installed (see below)
-- Run
-
-        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+- To test for the pre-requisites and create the makefile, run
+
+        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
         ./configure.sh
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-  to test for the pre-requisites and create the makefile.
-
-- Then, type
+- Then, to build the library, type
 
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
         make
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-   to build the library.
 
 If everything works fine, the library *libcocolib.a* will be built in the folder *cocolib*, and the command line tool *coco-ip* will be built in the folder *examples*.
 
 In case there were errors, please check out the [common build problems](Troubleshooting) here, before reporting the problem on the forum.
 
 
 
 ### Installing pre-requisites
 
 - For Ubuntu-based systems, there are install scripts for some of the pre-requisites in the library base directory.
 
 - In order to install the newest nVidia graphics card driver, use
 
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
         ./install_nvidia_driver_ubuntu.sh
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 - In order to install all dependencies available as apt-packages, use
 
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
         ./install_dependencies_ubuntu.sh
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 - You still need to install the toolkit manually, and put 'nvcc' in your shell search path. Other than that, all pre-requisites are installed.
+
+
+


+### Testing the library
+
+In order to test whether the library was built successfully, run the test script:
+
+        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+        cd examples
+        ./test_cocolib_quick.sh
+        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This is the short test script, which only checks fast algorithms (should take around 1-2 minutes). To run all tests (15 minutes), run
+
+        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+        cd examples
+        ./test_cocolib.sh
+        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The results are automatically compared to reference results provided with the library.
+

WikiPage Home modified by Bastian Goldluecke

Bastian Goldluecke — Thu, 26 Apr 2012 13:29:59 -0000

--- v38 
+++ v39 
@@ -26,74 +26,74 @@
 - Latest nVidia driver, needs to fit to CUDA toolkit
 - Qt library (with development support, Ubuntu package 'libqt4-dev')
 - GNU scientific library (with development support, Ubuntu package 'libgsl0-dev')
-- For better segmentation data terms, you need the ANN "approximate nearest neighbour" library (Ubuntu: 'libann-dev'). If you don't have it, you can disable it by uncommenting the line "#define USE_ANN" in the source, but it is not recommended. 
-
-The below for more instructions and install scripts for Ubuntu.
-
-
-
-


+- For better segmentation data terms, you need the ANN "approximate nearest neighbour" library (Ubuntu: 'libann-dev'). If you don't have it, support for it will be disabled by the configure script.
+- Another optional dependency (which makes some stuff faster) is the CUDPP library, available on google code. It has to be installed manually.
+
+
+See below for further instructions and install scripts for Ubuntu.
+
+
+
+


 ### Downloading the library
 
 Current release tarball:
 
 [[download_button]]
 
 
 


 Latest development version via *subversion*:
 
 ~~~~~~~~~~~~~~~~~~~
 svn checkout https://svn.code.sf.net/p/cocolib/code/trunk cocolib-dev
 ~~~~~~~~~~~~~~~~~~~
 
 


 ### Building the library and examples
 
 - Download and unpack the desired release of the library
 - Change to library base directory
 - Make sure pre-requisites are installed (see below)
 - Run
 
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
         ./configure.sh
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
   to test for the pre-requisites and create the makefile.
 
 - Then, type
 
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
         make
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
    to build the library.
 
 If everything works fine, the library *libcocolib.a* will be built in the folder *cocolib*, and the command line tool *coco-ip* will be built in the folder *examples*.
 
 In case there were errors, please check out the [common build problems](Troubleshooting) here, before reporting the problem on the forum.
 
 
 
 ### Installing pre-requisites
 
 - For Ubuntu-based systems, there are install scripts for some of the pre-requisites in the library base directory.
 
 - In order to install the newest nVidia graphics card driver, use
 
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
         ./install_nvidia_driver_ubuntu.sh
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 - In order to install all dependencies available as apt-packages, use
 
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
         ./install_dependencies_ubuntu.sh
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 - You still need to install the toolkit manually, and put 'nvcc' in your shell search path. Other than that, all pre-requisites are installed.
-
-- An optional dependency (which makes some stuff faster) is the CUDPP library, available on google code. It has to be installed manually.

WikiPage Home modified by Bastian Goldluecke

Bastian Goldluecke — Thu, 26 Apr 2012 13:28:22 -0000

--- v37 
+++ v38 
@@ -52,14 +52,22 @@
 
 - Download and unpack the desired release of the library
 - Change to library base directory
-- In a shell, type
-
-        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-        qmake-qt4 cocolib.pro
+- Make sure pre-requisites are installed (see below)
+- Run
+
+        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+        ./configure.sh
+        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+  to test for the pre-requisites and create the makefile.
+
+- Then, type
+
+        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
         make
         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-to build the library
+   to build the library.
 
 If everything works fine, the library *libcocolib.a* will be built in the folder *cocolib*, and the command line tool *coco-ip* will be built in the folder *examples*.

WikiPage Home modified by Bastian Goldluecke

Bastian Goldluecke — Thu, 26 Apr 2012 13:27:02 -0000

--- v36 
+++ v37 
@@ -18,11 +18,12 @@
 You will need to install the following manually on your system:
 
 - CUDA capable graphics card
-- Latest nVidia driver, CUDA toolkit (SDK is not necessary), compiler 'nvcc' must be in shell search path
-
-
+- CUDA toolkit (SDK is not necessary), compiler 'nvcc' must be in shell search path
+
+
 The following pre-requisites can usually be installed via the package management system:
 
+- Latest nVidia driver, needs to fit to CUDA toolkit
 - Qt library (with development support, Ubuntu package 'libqt4-dev')
 - GNU scientific library (with development support, Ubuntu package 'libgsl0-dev')
 - For better segmentation data terms, you need the ANN "approximate nearest neighbour" library (Ubuntu: 'libann-dev'). If you don't have it, you can disable it by uncommenting the line "#define USE_ANN" in the source, but it is not recommended.