oak2 is a cross-platform C++ implementation of Random Forests supporting local and global image classification and regression. Local prediction models can be employed for problems such as image segmentation and image synthesis, while global models enable image-level predictions.
oak2 depends on several third-party libraries:
Eigen is a header-only library and can be simply installed via:
#!bash
$ mkdir 3rdparty
$ cd 3rdparty
$ wget https://gitlab.com/libeigen/eigen/-/archive/3.3.7/eigen-3.3.7.tar.gz --progress=bar:force:noscroll
$ mkdir eigen
$ tar xf eigen-3.3.7.tar.gz -C eigen --strip-components=1
You can download and install ITK in the same 3rdparty folder via:
#!bash
$ wget https://sourceforge.net/projects/itk/files/itk/5.0/InsightToolkit-5.0.0.tar.gz
$ tar xvf InsightToolkit-5.0.0.tar.gz
$ cd InsightToolkit-5.0.0
$ mkdir build
$ cd build
$ cmake -DCMAKE_INSTALL_PREFIX=../../itk ..
$ make -j4
$ make install
Alternatively, you can check out these ITK install instructions.
You can install Boost and TBB via apt-get:
#!bash
$ sudo apt-get install libboost-all-dev libtbb-dev
Note, you might have to specify a specific version via apt-get install <package>=<version>.
oak2 comes with a CMake configuration file. From the top folder where CMakeLists.txt is located (same as this README), do the following to build all internal libraries and executables:
#!bash
$ mkdir build
$ cd build
$ export THIRD_PARTY_DIR=<folder_containing_eigen_and_itk>
$ cmake ..
$ make -j4
The command line tools are located in build/oak/apps.
Detailed instructions and examples are coming soon...
Special thanks go to Hauke Heibel who has contributed significantly to the C++ implementation.