opencv Getting started with opencv What & Why OPENCV?
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OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. It was built for various purpose such as machine learning, computer vision, algorithm, mathematical operations, video capturing, image processing etc. Over the years it has become very popular among the researchers and developers as for its support in different platforms (windows, Linux, android, ios). Also it has wrapper in various renowned programming languages. Under the license agreement, it has access for businesses to utilize and modify the code.
The library contains more than 2500 optimized algorithms, which has excellent accuracy in performance and speed. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has great people and community involved as users, developers and researchers, the number is more than 47 thousand and estimated number of downloads exceeding 7 million. The library is extensively in professional companies, research groups and other groups.
Many well-established companies like Google, Yahoo, Microsoft, Intel, IBM, Sony, Honda, Toyota that employ the library, there are many startups such as Applied Minds, VideoSurf, and Zeitera, that make extensive use of OpenCV. OpenCV’s deployed uses span the range from stitching streetview images together, detecting intrusions in surveillance video in Israel, monitoring mine equipment in China, helping robots navigate and pick up objects at Willow Garage, detection of swimming pool drowning accidents in Europe, running interactive art in Spain and New York, checking runways for debris in Turkey, inspecting labels on products in factories around the world on to rapid face detection in Japan. It has C++, C, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS. OpenCV leans mostly towards real-time vision applications and takes advantage of MMX and SSE instructions when available. A full-featured CUDA and OpenCL interfaces are being actively developed right now. There are over 500 algorithms and about 10 times as many functions that compose or support those algorithms. OpenCV is written natively in C++ and has a templated interface that works seamlessly with STL containers.
Information collected from the official website
