Free online courseOpenCV Python: Classical Computer Vision Theory and Algorithms

Course Description

Turn Python into a practical computer vision toolkit by learning how classical algorithms work and how to implement them with OpenCV. This free online course is designed to help you move from simply calling functions to understanding what happens under the hood, so you can debug pipelines, choose the right parameters, and get results that hold up on real images and video.

You’ll begin by setting up OpenCV in a clean development workflow and then quickly start working with images as NumPy arrays: reading, writing, slicing regions of interest, inspecting pixels, and handling common color representations. From there, you’ll gain confidence manipulating visual data in multiple color spaces, drawing overlays, building small interactive tools with the mouse and trackbars, and blending or compositing imagery in a way that mirrors real application needs.

As the course progresses, you’ll develop an intuition for core image processing operations. You’ll learn why padding matters, how convolution changes an image, and how different smoothing approaches behave when noise, edges, and fine textures are present. You’ll also explore thresholding strategies for reliable segmentation, then extend those ideas with morphology, gradients, and edge detection—skills that are fundamental to tasks like document cleanup, object isolation, and measurement.

Beyond preprocessing, the course connects classical vision to higher-level understanding. You’ll work with histograms and backprojection, template matching, the Fourier domain, and popular geometric detection methods such as Hough transforms. You’ll also dive into segmentation approaches that help separate touching objects and refine foreground selection, building the ability to tackle messy, real-world scenes.

To round out a complete classical CV foundation, you’ll study interest points, feature descriptors, and matching workflows, including robust alignment with homography. Finally, you’ll reach applied topics such as tracking, optical flow, camera calibration, pose estimation, epipolar geometry, and depth from stereo. Practice questions throughout reinforce key concepts and terminology, making this course a strong step toward computer vision projects, interviews, or further machine learning study.