Sara (सार) is a Sanskrit word meaning essence.

Sara focuses on:

1. having an easy-to-use and simple API,
2. having easy-to-understand and efficient implementations of computer vision algorithms,
3. rigorous testing.

The design of Sara libraries is driven by the KISS principle.

Try it and feel free to become involved with the development of the libraries.

I dedicate lots of patience and love to maintain Sara and make it evolve as much as my time and energy allow it.

Sara is licensed with the Mozilla Public License version 2.0.

Tested Compilers:

• Visual Studio 2013
• gcc 4.8, 4.9
• clang 3.5, 3.6

Sara loves C++11!

Sara constantly uses move semantics, type deduction with the auto keyword, lambda functions, curly-brace initialization styles.

I am waiting a little bit before migrating my code to C++14.

I don't have much time to maintain the documentation. I'd love your help.

What I can easily do is to keep up-to-date the reference documentation here. There is also some more friendly documentation on at the readthedocs.org but it is not up-to-date.

Honestly you will be much better off consulting the examples folder and the test folder.

The codes are generally short and carefully so they should help you to get up to speed with the library usage.

I started writing Sara in 2009, when I started my PhD at the IMAGINE lab in Ecole des Ponts, ParisTech.

Historically, I started writing DO-CV before openCV came up with a new C++ API (In late 2015, a computer vision researcher was shocked when I told him that I don't like openCV and told me patronizingly that the openCV C++ API was actually released in 2007. Well I did not know about computer vision yet. Anyways so what?)

I used openCV for the first time during my research internship at Siemens. That was in 2008 (quite some time, now that I think of it!). I was very frustrated with it. After a while, I started writing the library as a hobby to have a more easy-to-use library and also to gain a better mastery of the C++ language. Now, the library keeps evolving and can be reused for serious applications in the industry.

Today openCV has evolved a lot. Despite that openCV has yet to convince me to use it, API-wise. Besides, not everybody in the industry uses openCV.

I like my library and it is still alive, lightweight, tested since 2009!

To build the libraries, run:

1. Install the following packages:

   • On Debian-based distributions:

     sudo apt-get install -qq \
       cmake \
       doxygen \
       libjpeg8-dev \
       libpng12-dev \
       libtiff5-dev \
       libavcodec-ffmpeg-dev \
       libavformat-ffmpeg-dev \
       libavutil-ffmpeg-dev \
       qtbase5-dev
     
     # To install Python bindings.
     sudo apt-get install -qq \
       boost-python-dev \
       python3-dev
     

   • On Red Hat-based distributions:

     sudo yum install -y
       cmake \
       doxygen \
       libjpeg-devel \
       libpng-devel \
       libtiff-devel \
       ffmpeg \
       ffmpeg-devel \
       qt-devel
     
     # To install Python bindings.
     sudo apt-get install -qq \
       boost-python-devel \
       python3-devel
     

2. Build the library:

   mkdir build
   cd build
   cmake .. \
     -DCMAKE_BUILD_TYPE=Release \
     -DSARA_BUILD_SHARED_LIBS=ON \
     -DSARA_BUILD_SAMPLES=ON \
     -DSARA_BUILD_TESTS=ON
   make  -j`nproc`  # to build with all your CPU cores.
   

3. Run the tests to make sure everything is alright.

   ctest --output-on-failure
   

4. Create DEB and RPM package.

   make package
   

5. Deploy by install the Debian package with Ubuntu Software Center, or type:

   # Debian-based distros:
   sudo dpkg -i libDO-Sara-shared-{version}.deb
   
   # Red Hat-based distros:
   sudo rpm -i libDO-Sara-shared-{version}.deb