Digital Image Processing with OpenCV and Python

By Martin Linder

Stop copying computer-vision code you don't understand. Learn what the algorithms are doing-and build with them confidently.Digital Image Processing with OpenCV and Python takes you from the fundamentals of pixels and image arrays to practical computer vision, motion analysis, 3D reconstruction, and deep-learning inference.
Designed for readers who already know basic Python but are new to image processing, it combines conceptual explanations with practical OpenCV techniques so you can understand not only which function to call, but why and when to use it.
You'll learn how to: Set up Python, OpenCV, NumPy, Matplotlib, and a practical computer-vision workflowWork confidently with pixels, arrays, BGR/RGB ordering, image arithmetic, masks, and color spacesImprove images using histograms, contrast enhancement, convolution, denoising, edge detection, and sharpeningApply affine and perspective transformations, image warping, pyramids, and Fourier-domain filteringSegment images using thresholding, morphology, contours, watershed, and GrabCutDetect, describe, and match visual features with classical computer-vision methodsProcess video, detect motion, track objects, calibrate cameras, estimate depth, and understand 3D reconstructionExplore CNNs, deep-learning object detection, and OpenCV's DNN workflowBring the techniques together in complete document-scanner and real-time object-counter projectsAcross twenty-six structured chapters, the book builds from first principles toward complete applications, making it useful for self-learners, students, programmers, engineers, and developers who want transferable computer-vision skills rather than isolated recipes.Turn Python into a practical image-analysis toolkit-get your copy and start building today.

• Format: Paperback
• Language: English
• ISBN: B0HFNKWRVP
• ISBN13: 9798193461975
• Release Date: August 2026
• Publisher: Independently Published
• Length: 204 Pages
• Weight: 1.07 lbs.
• Dimensions: 0.4" x 8.5" x 11.0"