OPENCV is a library of codes from c and C++. OPENCV Project Code supports image processing and computer algorithms. Research and development process should be supported by certain toolbox this created by OPENCV. MAC OS, windows, android and Linux supports OPENCV. Projects on OPENCV can be done by practical programmers and academic scholars.

Core Functionality of OPENCV Project CODE:

When an image pixel level is changed it should be measured and analyzed in the project. Other than this measurement of time, interoperability and discrete Fourier transform, changing brightness and base drawing are to be noted in the project.

OPENCV IOS:

It helps in conversion of videos and images. OPENCV is needed in I phone cameras in order to process video frames. UI image view and UI button is needed in the creation of IOS project. Apart form this certain frameworks are manually added they are as follows:

  • AV foundation.
  • Core graphics.
  • Assets library.
  • UI kit foundation.
  • Quartz core.
  • Core video.
  • Core image.
OpenCV-Algorithm

OpenCV-Algorithm

Super Resolution of OPENCV:

Various classes and functions which are part of super resolution framework are given below:

  • Superres:: super resolution:: next frame- usage of this class processes the next frame of input to produce results of output.
  • Superres:: create resolution :: set input – it is needed to set input frame source for super resolution algorithms.
  • Superres:: super resolution:: collect garbage – this class is used to clear all presented inner buffers.
  • Superres:: super resolution- this class defines the interface of super resolution algorithms.
  • Superres: create super resolution – BTVL1 – this class is used to make bilateral TV- L1 super resolution.

Tracking and Motion Framework on OPENCV:

Visual field tracks the video source. Dense tracking, mean shift tracking and camshaft tracking are the three types of tracking techniques.

Dense Tracking Techniques:

Two more groups of optical flow technique are obtained from this technique. They are block matching method and horen- schunk method.

Mean Shift and Can Shift Tracking:

Distribution of load density data set is found out by this method. Morphological filters helps in tracking corners and edges. This brings out structuring elements such as square, cross shape, diamond and xShape.

Visualization of OPENCV:

Widgets are displayed by 3D visualization which works with the scenes. Some of visualization classes are viz:: get window by name.

Future Scope:

More android application can be created using OPENCV project code . So projects on OPENCV are the future of computing.