Synopsis "Digital Image Processing with OpenCV and Python"
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 workflow Work confidently with pixels, arrays, BGR/RGB ordering, image arithmetic, masks, and color spaces Improve images using histograms, contrast enhancement, convolution, denoising, edge detection, and sharpening Apply affine and perspective transformations, image warping, pyramids, and Fourier-domain filtering Segment images using thresholding, morphology, contours, watershed, and GrabCut Detect, describe, and match visual features with classical computer-vision methods Process video, detect motion, track objects, calibrate cameras, estimate depth, and understand 3D reconstruction Explore CNNs, deep-learning object detection, and OpenCV's DNN workflow Bring the techniques together in complete document-scanner and real-time object-counter projects Across 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.
