Brief of OpenCV — Python
What Is OpenCV?
OpenCV [OpenCV] is an open source (see http://opensource.org) computer vision library available from http://SourceForge.net/projects/opencvlibrary. Th e library is written in C and C++ and runs under Linux, Windows and Mac OS X. Th ere is active development on interfaces for Python, Ruby, Matlab, and other languages. OpenCV was designed for computational effi ciency and with a strong focus on realtime applications. OpenCV is written in optimized C and can take advantage of multicore processors. If you desire further automatic optimization on Intel architectures [Intel], you can buy Intel’s Integrated Performance Primitives (IPP) libraries [IPP], which consist of low-level optimized routines in many diff erent algorithmic areas. OpenCV automatically uses the appropriate IPP library at runtime if that library is installed. One of OpenCV’s goals is to provide a simple-to-use computer vision infrastructure that helps people build fairly sophisticated vision applications quickly. Th e OpenCV library contains over 500 functions that span many areas in vision, including factory product inspection, medical imaging, security, user interface, camera calibration, stereo vision, and robotics. Because computer vision and machine learning oft en go hand-inhand, OpenCV also contains a full, general-purpose Machine Learning Library (MLL). Th is sublibrary is focused on statistical pattern recognition and clustering. Th e MLL is highly useful for the vision tasks that are at the core of OpenCV’s mission, but it is general enough to be used for any machine learning problem.
Who Uses OpenCV?
Most computer scientists and practical programmers are aware of some facet of the role that computer vision plays. But few people are aware of all the ways in which computer vision is used. For example, most people are somewhat aware of its use in surveillance, and many also know that it is increasingly being used for images and video on the Web. A few have seen some use of computer vision in game interfaces. Yet few people realize that most aerial and street-map images (such as in Google’s Street View) make heavy use of camera calibration and image stitching techniques. Some are aware of niche applications in safety monitoring, unmanned fl ying vehicles, or biomedical analysis. But few are aware how pervasive machine vision has become in manufacturing: virtually everything that is mass-produced has been automatically inspected at some point using computer vision. Th e open source license for OpenCV has been structured such that you can build a commercial product using all or part of OpenCV. You are under no obligation to opensource your product or to return improvements to the public domain, though we hope you will. In part because of these liberal licensing terms, there is a large user community that includes people from major companies (IBM, Microsoft , Intel, SONY, Siemens, and Google, to name only a few) and research centers (such as Stanford, MIT, CMU, Cambridge, and INRIA). Th ere is a Yahoo groups forum where users can post questions and discussion at http://groups.yahoo.com/group/OpenCV; it has about 20,000 members. OpenCV is popular around the world, with large user communities in China, Japan, Russia, Europe, and Israel.
Will be continued about using open cv…………
Github- https://github.com/Meghashyamt
My Website https://meghashyamt.000webhostapp.com/
Linkedin https://www.linkedin.com/in/meghashyam-thiruveedula-59097843/
