OpenCV

OpenCV: Open Source Computer Vision Library

Open Source Computer Vision Library is an open source computer vision and machine learning software library with over 2500 optimized algorithms for detecting faces, recognizing objects, classifying human actions in videos, tracking camera movements, extracting 3D models and more.

What is OpenCV?

OpenCV (Open Source Computer Vision Library) is an open source, cross-platform library of programming functions mainly aimed at real-time computer vision and machine learning. It was officially launched in 1999 by Intel but later it was supported by Willow Garage then Itseez (which was later acquired by Intel). The library is cross-platform and free for use under the open-source Apache 2 License.

OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 80 million. It is used for commercial applications and academic research in fields including medical image analysis, stitching street view images, surveillance systems, advanced robotics, augmented reality, image correction, tracking and much more.

Some major features of OpenCV are:

• Image processing: OpenCV has a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms like filtering, edge detection, corner detection, image segmentation, morphological image processing, object detection etc.
• Video analysis: It includes object tracking algorithms, background subtraction algorithms and much more that can be used in analyzing videos.
• Camera calibration and 3D reconstruction: OpenCV has prevalent APIs for calibration, stereo vision, and point cloud generation.
• Features 2D detection framework: OpenCV offers robust feature detectors and descriptors along with the external libraries seamlessly plugged into the framework.
• ML Algorithms: A comprehensive set of ML algorithms ranging from classifiers to neural networks and clustering methods are included.
• Computational photography: OpenCV has all the latest computer vision algorithms that can enhance the quality of images and videos taken with low budget cameras.
• Cross platform: OpenCV is written natively in C++. It can take advantage of multi-core processing. OpenCV supports Windows, Linux, Mac OS, iOS, and Android.

OpenCV Features

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