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Open Source Computer Vision Library is the full name for OpenCV, a programming functions library and open source toolkit for cross-platform use in real-time computer vision image processing and OpenCV tracking. Developed near the turn of the 21st century, it was initially purposed for three dimensional (3-D) display walls and ray tracing. Making use of creative coding, OpenCV can offer a framework to developers of performance-optimized vision-based code in a C or C++ interface initially, though available in several languages, and is adaptable for remote usage on hand-held devices. It is capable of video file real-time capture, basic video configurations, object detection, and motion and color tracking, among other functions. OpenCV is capable of camera calibrations as it can find and track camera calibrations and set stereo correspondence on video cameras.
The CalcGlobalOrientation function for OpenCV tracking calculates motion orientation of a specified region in conjunction with a second CalcMotionGradient command, and creates a motion history and timestamp to track motion direction, returning results in degrees and recording subsequent shifts. The final result would be a sum of the original orientation and the angles of shift. Reading and writing of image files and forcing of them to a three-channel color image, files can be modified, directly and indirectly accessed, and converted to grayscale images or color byte-images.
The optical flow of images can be directed by means of block matching tracking, and every pixel calculated and instructed in flow. Allocation and releasing of images for one-channel byte images or three-channel float images to set a region of interest or clone an image are possible. OpenCV allows the capture of frame images from a video sequence from a file from several cameras simultaneously by grabbing one image from each and then retrieving from them all, to create and edit new video flows.
Facial OpenCV tracking is done by means of its Camshift functions. This function implements an object tracking algorithm, finds the object center, creates a color histogram, calculates facial probability, then shifts location of the face’s rectangle in each video frame and makes adjustments by calculating size and angle. It concentrates the brightest pixels over the centered face and uses scale for adapting to smaller faces in subsequent frames if the image is retreating.
OpenCV tracking abilities are used in many applications. From facial recognition to gesture recognition, mobile robotics, human-computer interaction programs and stereopsis, which creates stereo vision depth perception by means of using two cameras, making use of object, color, and motion tracking. OpenCV also has statistical machine learning libraries containing decision tree learning modules, expectation-maximization tracking algorithms, gradient boosting trees, and artificial neural networks functioning modules.