Data analysis problems might be solved in different ways with different approaches, so it is nice to have a way to write your code quickly and use as many out-of-the-box solutions as possible to find the optimal one to your particular problem. When it comes to computer vision, the OpenCV library is very much to the point.
OpenCV is a cross-platform and open-source software library for computer vision and machine learning. It provides a common infrastructure for real-time computer vision based on optimized code. The library boasts its C++, Python, Java and MATLAB/OCTAVE interfaces as well as compatibility with Windows, Linux, Android, and Mac OS. OpenCV makes it easy to build powerful computer vision applications from scratch—no need to reinvent the wheel. Also, the great advantage of OpenCV is a vast community where software engineers can get help for coding and other platform-related questions.
OpenCV provides not only the implementation of various image processing algorithms but also the implementation of machine learning algorithms and—although it does not provide for training the model—it is capable of reusing models created by using other machine learning frameworks like Keras or TensorFlow. Most common OpenCV applications encompass human action classification, object tracking, facial recognition, model extraction, and image database searches.
In conclusion, it is clear that OpenCV is a great way to find quick solutions to computer vision problems and to write cross-platform applications in different programming languages.
At our company we are using OpenCV to an increasing extent, discovering its new powerful features and conforming them to solve our specific computer vision problems. We will try to share the most interesting ones with our readers in our future blog articles.