friday-dlib 19.7.0

# dlib C++ library [](https://travis-ci.org/davisking/dlib)

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](http://dlib.net) for the main project documentation and API reference.

## Compiling dlib C++ example programs

Go into the examples folder and type:

`bash 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:

`bash mkdir build; cd build; cmake .. -DUSE_AVX_INSTRUCTIONS=1; cmake --build . `

Doing so will make some things run faster.

## Compiling your own C++ programs that use dlib

The examples folder has a [CMake tutorial](https://github.com/davisking/dlib/blob/master/examples/CMakeLists.txt) that tells you what to do. There are also additional instructions on the [dlib web site](http://dlib.net/compile.html).

## Compiling dlib Python API

Before you can run the Python example programs you must compile dlib. Type:

`bash python setup.py install `

or type

`bash python setup.py install --yes USE_AVX_INSTRUCTIONS `

if 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.

## Running the unit test suite

Type the following to compile and run the dlib unit test suite:

`bash cd dlib/test mkdir build cd build cmake .. cmake --build . --config Release ./dtest --runall `

Note 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](https://github.com/davisking/dlib/blob/master/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.

## dlib sponsors

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.

Version: 19.7 Date: Sun Sep 17 08:28:45 EDT 2017 Mercurial Revision ID: 6ee27f33d90c