Posted on Mon 20 June 2016

Setting up computer vision env

Well, If this is a blog on some of the cool stuff in Computer vision, I think i must mention about the api’s that one can use, or how to setup your system.

So yes, there is a myraid choice available to start developing. I personally like OpenCV because of its’ enormous community support along with its multiplatform facilities.

Apart from this, there is MATLAB. Whoaaa… this is like a computing powerhouse. With so many easy to use functionality, and simply clicking your way into outputs, MATLAB has one hell of a pricetag.

Apart from these, there are :

  • SimpleCV : This is actually computer vision made easy.There is no need to spend endless time in figuring out data structures, Matrices, what color format to select ( 8UC1, 32FC1,etc)
  • VxL Libraries (I like them too.. I’ll use them the day I make a robot :P )
  • VLFeat : This may come out very handy !! It contains some of the finest implementations in vision as well as image understanding algorithms
  • ImageJ : A java library for primarily image processing
  • CCV ( Haven’t used it yet, so .. what do i know )

Anyway, moving on to the installation, there are plenty of online resources that can be used for downloading and setting up OpenCV.

For compiling opencv c++:

  • Use CMAKE. It’s much simpler to compile using cmake

You can use this boiler plate file if incase .

# Filename : CMakeLists.txt
cmake_minimum_required(VERSION 2.8)
project( project_name )
find_package( OpenCV REQUIRED )
add_executable( project_name project_name.cpp )
target_link_libraries( project_name ${OpenCV_LIBS} )

simply execute :

cmake .
make 
./project_name

Incase you’re using python,

python filename.py <args>

To be continued


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