The Open Source Computer Vision Library, or OpenCV if you prefer, houses over 2500 algorithms, extensive documentation and sample code for real-time computer vision.
OpenCV focuses mainly towards real-time image processing, as such, if it finds Intel's Integrated Performance Primitives on the system, it will use these commercial optimized routines to accelerate itself.
OpenCV library supports:
- Real-time capture.
- Video file import.
- Object detection.
- Basic image treatment: brightness, contrast, threshold.
- Blob detection
OpenCV can accomplish numerous different tasks including basic image processing, such as filtering, morphology, geometrical transformations, histograms, and color space transformations. It can also perform advanced image processing like inpainting, watershed & meanshift segmentation etc. OpenCV can also undertake more complex tasks such as contour processing and computational geometry, various feature detectors and descriptors (these can range from simple Harris detector to Hough transform, SURF, or MSER) object tracking, optical flow, object detection using cascades of boosted haar classifiers, camera calibration, and machine learning tools (data clustering and statistical classifiers).
The application is cross-platform and works on Windows, Mac OS X, Linux, Android and iOS.
Extended dnn module, documentation improvements, some other new functionality and bug fixes.