Learning OpenCV 5 Computer Vision with Python, Fourth Edition: Tackle tools, techniques, and algorithms for computer vision and machine learning

Computer vision starts with a simple question: what can software understand from an image or video? This fourth edition of Learning OpenCV 5 Computer Vision with Python takes that question into practical territory, using Python 3 and OpenCV 5 to build applications that work with pictures, camera feeds, moving objects, faces, and 3D scenes.

The chapters progress from setting up the tools to developing increasingly capable vision systems. Along the way, readers encounter the image-processing fundamentals behind detection, recognition, tracking, and augmented reality—not just a collection of functions, but techniques applied to concrete computer vision tasks.

From pixels to practical applications 💻

Early chapters establish the workflow: configure OpenCV, handle files and cameras, display images and video, and process visual data. Topics including color models, edge and contour detection, depth estimation, and segmentation help explain how raw pixels can be prepared and interpreted.

Find, recognize, and follow visual information

The book then turns to faces, image descriptors, and custom object detectors. Readers explore methods for identifying visual features, matching images, classifying objects, and tracking motion through video. This progression shows how separate computer vision techniques can work together in an application.

Build toward 3D, machine learning, and scale

Later chapters extend the work to camera models, augmented reality, 3D reconstruction and navigation, and neural networks. The final topics consider OpenCV applications at scale, broadening the focus from local experiments to larger deployment contexts.

Who will find it useful?

This guide is aimed at Python programmers interested in computer vision and machine learning, and at developers who want to work with OpenCV 5. Its hands-on emphasis makes it especially relevant to readers who learn by connecting concepts to implemented examples. The supplied copy is an Early Access preview; draft material may be updated before the completed edition is published.