Computer Vision for Machine Learning using OpenCV

Introduction:

OpenCV is Open Source Computer Vision Library in Python that can be used to perform various operations on images and videos such as face recognition, motion detection etc.

It is widely used in the software industry for object detection using Machine Learning.

Use case 1 (Face Recognition):

In below image OpenCV is being used for face recognition. The use of Haar Cascade in python programs along with it helps in simplifying the programming process.

Use case 2 (Video Processing):

One may also perform these operations on a video, as the video frames are merely images. Below is a video being played using OpenCV. 

The frame rate had to be taken into account to do this. For example if the frame rate is 30 frames per second, one has to enter 33 in the waitKey command as approximately 33 milliseconds elapse between each frame.

Conclusion:

OpenCV can be used for object recognition and for image and video processing. Therefore, applications exist virtually everywhere. For example, surveillance (persons), shopping (items to be sold), motion detection (wildlife flora and fauna footage), augmented reality (recognizing real world objects for what they are so as to make virtual objects interact with them, creating a seamless composite view), etc.