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OpenCV is an open-source machine learning library for Python that adds enhanced artificial intelligence capabilities in computer vision and image processing.

This course is a comprehensive introduction to building applications using OpenCV, including viewing, identifying, and editing image and video files using Python. You’ll start with an introduction to the library before immediately applying the framework to view images, video, webcam input, and text. You’ll then take your image captures and apply various edits, including decolorization, cropping, resizing, and captioning with layered text and objects. You’ll continue with basic machine learning functions, including edge, object, and face detection. With the basics in hand, you’ll wrap up with real-world projects to build document scanners and object trackers.

By the end of this course, you’ll have a working knowledge of OpenCV and machine learning. You’ll be prepared to build functional applications using the principles of computer vision in Python.

Mastering Computer Vision in Python with OpenCV

In this lesson, we’ll use some OpenCV functions we learned in this course to create an object tracker.
 
With this object tracker, we’ll be able to draw a tracking box in the first frame of the video, and then that box will track the object. Follow the guidelines to complete the partial code given in the “Try it out” section at the end of the lesson.


Follow step-by-step instructions to  create a simple object tracker.

Introduction to Python OpenCV

Read Images, Videos, and Webcam

Editing Images

Reading and Editing Images with OpenCV

Advanced Editings

Detection

Assessment on Advanced Editing and Detection

Real-life projects

Conclusion

Build an Object Tracker