Learning OpenCV, 2nd Edition

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Overview

What Is OpenCV?

OpenCV [OpenCV] is an open source (see http://opensource.org) computer vision library available from

http://opencv.org. The library is written in C and C++

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and runs under Linux, Windows, Mac OS X, iOS,

and Android. Interfaces are available for Python, Java, Ruby, Matlab, and other languages.

OpenCV was designed for computational efficiency with a strong focus on real-time applications:

optimizations were made at all levels, from algorithms to multicore and CPU instructions. For example,

OpenCV supports optimizations for SSE, MMX, AVX, NEON, OpenMP, and TBB. If you desire further

optimization on Intel architectures [Intel] for basic image processing, you can buy Intel’s Integrated

Performance Primitives (IPP) libraries [IPP], which consist of low-level optimized routines in many

different algorithmic areas. OpenCV automatically uses the appropriate instructions from IPP at runtime.

The GPU module also provides CUDA-accelerated versions of many routines (for Nvidia GPUs) and

OpenCL-optimized ones (for generic GPUs).

One of OpenCV’s goals is to provide a simple-to-use computer vision infrastructure that helps people build

fairly sophisticated vision applications quickly. The OpenCV library contains over 500 functions that span

many areas, including factory product inspection, medical imaging, security, user interface, camera

calibration, stereo vision, and robotics. Because computer vision and machine learning often go hand-in-

hand, OpenCV also contains a full, general-purpose Machine Learning Library (MLL). This sub-library is

focused on statistical pattern recognition and clustering. The MLL is highly useful for the vision tasks that

are at the core of OpenCV’s mission, but it is general enough to be used for any machine learning problem.

Who Uses OpenCV?

Most computer scientists and practical programmers are aware of some facet of the role that computer

vision plays. But few people are aware of all the ways in which computer vision is used. For example, most

people are somewhat aware of its use in surveillance, and many also know that it is increasingly being used

for images and video on the Web. A few have seen some use of computer vision in game interfaces. Yet

few people realize that most aerial and street-map images (such as in Google’s Street View) make heavy

use of camera calibration and image stitching techniques. Some are aware of niche applications in safety

monitoring, unmanned aerial vehicles, or biomedical analysis. But few are aware how pervasive machine

vision has become in manufacturing: virtually everything that is mass-produced has been automatically

inspected at some point using computer vision.

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The legacy C interface is still supported, and will remain so for the foreseeable future.