Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. See http://dlib.net for the main project documentation and API reference.
Go into the examples folder and type:
mkdir build; cd build; cmake .. ; cmake --build .That will build all the examples. If you have a CPU that supports AVX instructions then turn them on like this:
mkdir build; cd build; cmake .. -DUSE_AVX_INSTRUCTIONS=1; cmake --build .Doing so will make some things run faster.
The examples folder has a CMake tutorial that tells you what to do. There are also additional instructions on the dlib web site.
Before you can run the Python example programs you must compile dlib. Type:
python setup.py installor type
python setup.py install --yes USE_AVX_INSTRUCTIONSif you have a CPU that supports AVX instructions, since this makes some things run faster. Note that you need to have boost-python installed to compile the Python API.
Type the following to compile and run the dlib unit test suite:
cd dlib/test
mkdir build
cd build
cmake ..
cmake --build . --config Release
./dtest --runallNote that on windows your compiler might put the test executable in a subfolder called Release. If that's the case then you have to go to that folder before running the test.
This library is licensed under the Boost Software License, which can be found in dlib/LICENSE.txt. The long and short of the license is that you can use dlib however you like, even in closed source commercial software.
This research is based in part upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) under contract number 2014-14071600010. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the U.S. Government.