Free online courseOpenCV Python: Classical Computer Vision Theory and Algorithms

Course Description

Learn classical computer vision with OpenCV and Python through a practical, theory-backed approach designed for real projects in AI and machine learning. This free course guides you from setting up OpenCV in a modern development workflow to confidently working with images, video, and pixel-level operations.

You will build strong foundations in color spaces, drawing and interaction tools, trackbars, blending, resizing, overlays, and geometric transforms such as translation, rotation, affine, and perspective mapping. From there, deepen your understanding with histograms, equalization techniques, padding, convolution, and widely used denoising and smoothing filters.

Progress into robust image analysis with thresholding methods, morphological processing, gradients, and edge detection, then explore higher-level techniques including pyramid blending, contour analysis, Fourier transforms, template matching, and Hough-based shape detection. The course also introduces segmentation strategies such as watershed and graph-cut approaches for separating objects from complex scenes.

For feature-based vision, study corner detectors and keypoint pipelines, moving through modern descriptors and matching workflows, including homography-based alignment. Finally, connect vision to geometry and motion with object tracking methods, optical flow, camera calibration, pose estimation, epipolar geometry, and stereo depth estimation, helping you bridge from classical algorithms to practical perception systems.