What is OpenCV?
OpenCV is a non-proprietary machine learning software library that was designed to offer computer vision applications with a frequent infrastructure wherein they can expedite the application of machine perception in economically available products. It is consist of over 2500 revamped algorithms that can be consumed in distributing a plethora of machine learning and computer vision competences which includes human action classification, objects tracking, facial recognition, model extraction, 3D production and database image searches. Sweetened with a BSD license, the on-premise software capacitates IT professionals and businesses to employ and customize codes based on their own likings and desires.
In addition, the free-to-use application which adopts MATLAB, Python, Java and C++ interfaces rely on immediate vision applications in making the most of SSE and MMX instructions when required. Powered with a capability to work splendidly on STL containers, the C++-written program services multiple government agencies, research groups and critically acclaimed companies such as Microsoft, Intel, IBM, Sony Honda, Toyota, and Google. Apart from the aforementioned corporations, OpenCV is also being exhausted in various countries like China, Japan, Spain, Israel, the United States and several countries in Europe in speeding up their daily operations.
The Japanese take advantage of OpenCV in examining labels on in products around the world by rapid face detection to ensure its quality while Israel appropriates the program in detecting disturbances in surveillance videos and combining street view photos together. Spain and New York, on the other hand, guarantee smooth interactive transactions with OpenCV while China and Turkey spent it on tracking mine equipment and monitoring runways for debris. Additionally, the program is also applicable in apprehending swimming pool drowning catastrophes in diversified European countries.
Energized with an OpenCL adequacy, the open-source program has the potentiality to do multi-core processing thus assuring computational efficiency. The world-renowned program which has an estimated number of more than 14 million downloads systematically makes use of the necessary Intel Integrated Primitives libraries in further optimization with the Intel framework. Moreover, OpenCV is also loaded with more than 500 functions that range from an assorted area of vision such as user interface, security, factory product inspection, stereo vision, camera calibration, robotics and medical imaging.
Among the primary ins and outs behind the design of the library is to give a boost to commercial users in robotics, monitoring, human-computer interface, and biometrics by granting them with a free and open substructure where the collective endeavors of the vision society can be centralized and enhanced. In addition, OpenCV likewise assists businesses in creating methodical vision applications at a fast rate thanks to its extensive support which vary from display and storage of movies and single images to periodic input support. The platform is also imperative in progressing vision-based commercial applications as it cobbles together compact and high-caliber codes for optimum performance.
On a related note, OpenCV is also indispensable in the dissemination of vision apprehension as it indulges developers with a streamlined infrastructure wherein they can construct, transfer and read complex codes as well as access polish codes for basic vision communications. Furthermore, the open-source platform also enables researchers to accumulate demos and research projects.
The Open CV is divided into six modules including CVAUX, Highgui, CXCORE, ML and CV module. The CV module is equipped with basic image processing and higher-level computer vision breakthroughs that grant allocates users with the knack to do image analysis such as contour retrieval, histograms, morphology and feature selection. In addition, the CV module which is innervated with motion analysis, object tracking, object and face detection and camera calibration elements are also invigorated with the aptness to sort out geometrical and color space transformations as well as instigate structural analysis on planar subdivisions and shape descriptors.
The CXCORE, on the other hand, is mobilized with principal data type definitions, linear algebra and statistics approach, error handlers and persistence and graphics functions. The CXCORE is also arrayed with a substantial data structure such as matrix algebra, elemental operations on dense arrays, RNG, DFT, DCT, 2D graphics, XML and YAML data persistence and heterogeneous data structures that include growing sequences, sparse matrices, and graphs. Besides the CXCORE, OpenCV also sports an ML module which is a machine learning library that is made up of a multiplex of clustering tools and statistical classifiers.
Meanwhile, HighGUI is inclusive of input and output routines, a multi-platform windowing faculty and a graphic user interface which percolates as a hub for hoarding and loading images and image or video acquisition. It is also fortified with a multi-platform windowing stature and simple graphic user interface amenities that are expended in accessing all OpenCV visual mock-ups. Another module of OpenCV is the CVAUX which contains extinct and exploratory codes and the most transparent face recognition interfaces.
Aside from face recognition, other responsibilities of the CVAUX is camera calibration, 3D vision, stereo calibration, bundle adjustment, face details detection and tracking, textures, shape matching and skeletons, stereo correspondence, trifocal sensor and graph cliques. Concurrently, CVCAM is furnished with video access interfaces that are affable through DirectX on Windows 32-bit computers.
OpenCV is also buttressed with numerous benefits which include its knack to grant users with various processing methods such as image processing or low-level processing and computer vision or high-level processing. While low-level processing can be made use of in creating a complete solution with the basic elements in the library, the high-level method is ideal in aiding users to resolve elaborate problems in the computer vision. In connection with the low-level processing, the image processing procedure sanctions users to scale down the noise involved in basic affairs such as image pre-processing.
Image processing refers to the involvement of the fluctuating nature of an image such as human interpretation improvement, image data processing for transmission and storage and demonstration for separate machine perception. The main goals of the low-level image processing are to facilitate users in restoring, enhancing and understanding images. Restoration is a process that involves reconstructing a corrupted image to a clean original while enhancement modifies an image to make it clearer and more straightforward to human observers. Understanding, meanwhile, is the ability to mimic the human visual system in culling a meaning from an image.
The low-level image processing sequence which is beefed up with methods such as pre-processing and image segmentation are composed of original images that are characterized by brightness value matrices. Making the low-level process a tad better than the high-level processing is that it does not require vast knowledge about the content of images. Additionally, low-level image processing is constituted by outputs and inputs. In a nutshell, the input of the process is the image while the output processing is a processed image.
On the other hand, high-level processing is a mechanism that embroils the comprehension of a collection of recognized objects as image analysis initiates the cognitive functions connected with human vision. High-level computer vision attempts to emulate human cognition and the skill to make decisions based on the data enclosed in the image. High-level data which are commonly expressed in symbolic fashions of computer vision is shored up with an aptitude to extricate and order image processing footfalls with the usage of all available knowledge to achieve a particular goal.
Likewise, OpenCV is bolstered with glaring attributes such as general computer vision and image processing algorithms, high-level computer vision modules, AI and machine-learning methods, image sampling and transformations, data persistence, data structures, and algorithms. Other components of the open-source programming software include 3D computing information methods, math routines, graphics not to mention its ability to create and analyze binary images.
Binary images are commonly used by inspection systems in scanning count parts, locating an object to grasp and in counting parts. Binary images resort to structural analysis in initiating well-regulated processes such as contour processing, connected components, ellipse fitting, polygonal approximation, line fitting, template matching and distance transform.
General computer vision and image processing algorithms empower users to test definitive computer vision algorithms on mid-level and low-level application programming interfaces without the need to code them. Among these algorithms include morphological operations, color conversion, template matching, corner detection, and multi-scale processing to name a few. In addition, general computer vision and image processing systems can also be exercised in image data manipulation drills such as setting and conversion, copying, releasing and allocating.
On the other side of the coin are high-level computer vision modules which are not only fortified with face detection, tracking and recognition potentialities but also amplified with an optical flow that utilizes camera option to determine the structure of a 3D image. Additionally, its camera calibration innards can be put to use in locating and tracking calibration patterns, stereo correspondence, basic matrix calculation and homography computation. Moreover, the high-level computer vision modules are likewise enthused with an Image and Video IO file output and a motion analysis ingredient which is crucial in tracking and motion segmentation.
Other than that, OpenCV is also brightened up with math spiels that are necessary in computer vision, image processing, image evaluation, computational geometry, statistics and linear algebra as well as 3D information computation methods that play a vital role in localization and mapping with either multiple views from one camera or with a stereo rig. Speaking of view, users are likewise granted with the skill to view transformations and image sampling with Open CV. The image sampling and transformation methods are more often advantageous in processing a club of pixels as a unit. Intensified with vector and matrix manipulation and linear algebra techniques, OpenCV gives users with a privilege to siphon image from subregions and conduct random sampling, rotating, warping, resizing and applying perspective chattels on a kaleidoscopic of interfaces.
In reference to methods, OpenCV is also supplied with graphical user interface approaches that are substantive in displaying images in a multi-platform application programming interface, applying slider controls and scrollbars and accepting user input. Users can also tap into the program’s data persistence peripheral in locking away a sundry of data types to a convenient disk interface. Data structures and algorithms, meanwhile, lets OpenCV consumers to effectively deposit, locate, save and maneuver a herculean number of graphs, trees, collections, sets queues and lists.
The Graphics hallmark of OpenCV, on the other hand, allows the application to come up with texts and draw comic, polygons and lines on images. Aside from its fun and inventive prospects, OpenCV is also pragmatic in labeling and marking thereby giving power to its users to rapidly detect images regardless of their sizes and locations.
OpenCV is not girded with enterprise pricing plans and is offered for free for personal and commercial users.
OpenCV supports a multitude of devices ranging from Windows, Linux, Mac, Android, iPhone, and iPad. The English-supported program which extends users with an on-premise deployment capacity serves a variety of customer types that scale from freelancers, small and medium businesses and large corporations. In order to configure and manufacture projects with OpenCV, product users must have a Microsoft Visual Studio Net 2005, Visual Studio 2005 Service Pack 1 Update for Windows Vista, Microsoft Redistributable and Microsoft Windows SDK .NET Framework 3.0 installed on their computers.
Both the Visual Studio 2005 Service Pack 1 Update for Windows Vista and Microsoft Windows SDK .NET Framework 3.0 can be downloaded at msn.com while users can alternatively use Microsoft Visual Studio Express in lieu of the Microsoft Visual Studio Net 2005. Moreover, using the Microsoft Windows SDK .NET Framework 3.0 is optional as some systems utilized different OS. Also, OpenCV follows a distinct coding style wherein core libraries are written in C or C++ formats.
Additionally, OpenCV files are drafted in lower case to be compatible with POSIX. Lastly, the platform also employs mixed-case style badges for class methods, types and external functions. Among those include Cv for external functions, Icv for internal functions, Cv for data structures, CV_ for external and a number of internal macros and ICV_ for internal macros.
OpenCV affords users with a comprehensive support as it offers email support and a ticketing system. It is likewise jazzed up with OpenCV books, video demonstrations, documentation, and tutorials.