https://github.com/tum-vision/afs
Automatic Feature Selection
https://github.com/tum-vision/afs
Last synced: 6 months ago
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Automatic Feature Selection
- Host: GitHub
- URL: https://github.com/tum-vision/afs
- Owner: tum-vision
- License: gpl-3.0
- Created: 2015-03-02T09:49:50.000Z (about 10 years ago)
- Default Branch: master
- Last Pushed: 2017-04-03T19:27:26.000Z (about 8 years ago)
- Last Synced: 2024-11-15T03:33:31.979Z (7 months ago)
- Language: C++
- Size: 2.24 MB
- Stars: 31
- Watchers: 21
- Forks: 20
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
Automatic Feature Selection is a code framework for the feature selection method introduced in
>Caner Hazirbas, Julia Diebold, Daniel Cremers,
>**Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation**,
>*In Scale Space and Variational Methods in Computer Vision*, 2015.
## Framework Structure
--AFS
|
-- build ; build directories for CMake and QtCreator projects
|
-- cuda ; cuda source/header files
|
-- doc ; code documentation and related conference paper
|
-- html ; html documentation (double-click on **index.html**)
|
-- latex ; PDF documentation
|
-- include ; header files
|
-- misc ; miscellaneous source files and mrmr
|
-- mrmr ; folder to store mrmr executable file
|
-- src ; source files
|
-- AFS.pro ; QtCreator Project File
|
-- CMakeLists.txt ; CMake Project File
## How To Build
### Required Hardware/Software
To be able to compile and run the code you need a computer with
* Ubuntu OS (12.04/14.04)
* NVidia GPU with CUDA support
You need to install the following libraries (I recommend you to download and compile the libraries from source):
* [OpenCV 2.4.10](http://opencv.org/downloads.html)
* [Boost 1.54.0](http://www.boost.org/users/history/version_1_54_0.html)
Along with these libraries, you need to install CUDA drivers on your machine:
* [CUDA 6.5 Production Release](https://developer.nvidia.com/cuda-downloads)
Once libraries are installed, you can download the source from github:
git clone https://github.com/tum-vision/afs.git
This framework requires [mrmr](http://penglab.janelia.org/proj/mRMR/) method to be compiled. Please download the [source files](http://penglab.janelia.org/proj/mRMR/mrmr_c_src.zip) inside the folder *misc/mrmr/* and compile the code with **make** command.
### Build from CMake
1. Please export the following paths in your **.bashrc** **if you built libraries from source**.
* OpenCV 2.4.10
```
export OPENCV_DIR=/OpenCV/2.4.10/share/OpenCV
export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/OpenCV/2.4.10/lib/pkgconfig
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/OpenCV/2.4.10/lib
```
* Boost 1.54.0
```
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Boost/1.54/build/lib
```
2. Change the following lines in CMakeLists.txt
* **Line 13** : Set path to the **OpenCVConfig.cmake**
* **Line 23** : Set **CUDA Compute Capability** in arch=compute_??,code=sm_??
3. Run console in AFS/build/CMake and type:
```
cmake ../../
make
make install
make clean (to clean the project)
```
### Build from QtCreator(qmake)
1. Set *INCLUDEPATH* and *LIBS* path in **AFS.pro** (qmake project file) for :
* OpenCV 2.4.10: **Lines 71, 73**
* Boost 1.54.0 : **Lines 89, 91**
* CUDA 6.5 : **Lines 104, 106**
2. Set CUDA installation directory(**CUDA_DIR**) and CUDA compute capability(**CUDA_ARCH**) in **Lines 101, 102**
3. You can use build/QtCreator/Debug and build/QtCreator/Release folders to compile the project with QtCreator
Both builds will copy the executable file (*AFS*) into the main project folder.
You can run the executable as **./AFS**.