This code was developped by Aurelien Lucchi during his doctoral studies in the computer vision laboratory run by Prof. Pascal Fua at EPFL.

Contributors:

• Aurelien Lucchi (github @alucchi)
• Yunpeng Li
• Pol Monsó Purtí (github @quimnuss)

This tutorial will help you compile the code and run it on your own system. It is intended for people with a Linux system but windows or mac users should also be able to compile the code.

Step-by-step instructions:

1. Copy the source code from the web.

2. Make sure you have cmake installed on your system (cmake is a tool to create makefiles).

3. Compile third-party libraries :

   UNIX

   • OpenCv (Install package opencv-dev in Ubuntu or download from http://opencv.org). Versions 3 and 2.4 work.
   • zlib. If you have problems with zlib, we recommend using the one shipped as a 3rdparty within opencv. Set ZLIB_INCLUDE_DIR to 3rdparty/zlib and ZLIB_LIBRARY to build/3rdpartz/lib/zlib.lib
   • ITK : Download from the ITK web site and set review flag to ON (this can be done by using advanced mode with ccmake and settting variable Module_ITKReview to ON). Tested version 4.7.
   • SLIC: Go to lib/slic. Type ccmake .., change the OpenCV_DIR and then type make to build the slic library.
   • Go to lib/libDAI-0.2.4 and type make. Note that libDAI requires the following dependencies for Ubuntu: sudo apt-get install g++ make doxygen graphviz libboost-dev libboost-graph-dev libboost-program-options-dev

   WINDOWS

   We recommend using MSBuild to build. For example "C:\Windows\Microsoft.NET\Framework64\v4.0.30319\MSBuild.exe" opencv.sln /p:Configuration=Release /m

   • OpenCV: download archive from web site. Then create a Visual Studio solution with cmake (or cmake-gui). You might have to change the options to vuils a static library. Then compile with MSBuild.exe opencv.sln. Versions 3 and 2.4 work.
   • zlib

   If you have problems with zlib, we recommend using the one shipped as a 3rdparty within opencv. Set ZLIB_INCLUDE_DIR to 3rdparty/zlib and ZLIB_LIBRARY to build/3rdparty/lib/Release/zlib.lib. In OpenCV 2.4 you may have to copy the generated zconf.h from build/3rdparty/include or sources/build/3rdparty/zlib to 3rdparty/zlib, or include both paths.

   • SLIC: Go to lib/slic and type cmake .; make
   • ITK: Download from the ITK web site and set review flag to ON (can be done by using advanced mode with ccmake and settting variable Module_ITKReview to ON). Tested version 4.7.
   • Go to lib/libDAI-0.2.4 and type cmake and MSBuild.exe dai.sln. Note that libDAI requires boost.
   • Add dirent.h to core/
   • [OPTIONAL] Install 7-zip to compress intermediary results.

4. Main code

   Edit the variables in the "THIRD-PARTY LIBRARIES" section in CMakeLists_common.txt (you can also use the ccmake (UNIX) or cmake-gui (WINDOWS) interface). You might want to turn off some of the dependencies. Look at the USE_??? flags.

   Go to the superpixels_public directory and compile with

   UNIX

   mkdir build ccmake ..

   Once configured and generated run make

   WINDOWS

   mkdir build cmake-gui ..

   Once configured and generated run MSBuild.exe sliceMe.sln or open the sliceMe.sln with Visual Studio.

5. Edit the config file

   This program relies on a configuration file that contains all the parameters needed for training or applying the algorithm to a new dataset. Below is a description of each parameter.

   • trainingDir: Input volume (TIF cube).
   • maskTrainingDir: Mask volume (TIF cube or series of png images containing black and white voxels only)
   • outputModel: Output model file (default=model.txt)
   • testDir: Test input volume (TIF cube).
   • maskTestDir: Test mask volume (TIF cube containing black and white voxels only).
   • stepForOutputFiles: output files and corresponding scores are computed every stepForOutputFiles iterations. Reasonable values are 10 for default training algorithm and 100 for stochastic optimization (-w 9 option described below).
   • featureTypes: Type of features extracted from the image/cube and used to train a model. The default value is featureTypes=33.
   • nMaxIterations: Maximum number of iterations. This number should be around 500-1000 iterations depending on how much training data is provided to the algorithm.

   An example configuration file named config.txt with a set of default parameters is provided in the root directory or on the biomed web.

6. Running the code

   Look at howto.pdf

• Multithreading

For ssvm, edit svm_struct_globals.h and change NTHREADS