AI & ML · Ebook

OpenCV: Practical Computer Vision

by Shriira Press

A comprehensive, self-contained guide to OpenCV, the library that, more than any other, is how computer vision actually gets done in code. Where the companion Computer Vision book explains why vision works — the inverse problem, classical features, CNNs, the task landscape — this book teaches how to build vision systems: reading and processing images as arrays, filtering and edges, contours and morphology, geometric transforms, feature detection and matching, classical object detection, video and motion analysis, camera geometry and 3D, and running deep-learning models through OpenCV's DNN module. It blends intuition, the concepts behind the API, and runnable code (cv2 in Python).

Contents

1. 1Preface
2. 2Chapter 1 — What Is OpenCV?
3. 3Chapter 2 — Images as Arrays: I/O, Pixels, and NumPy
4. 4Chapter 3 — Drawing, Color Spaces, and Basic Operations
5. 5Chapter 4 — Filtering and Convolution
6. 6Chapter 5 — Edges, Gradients, and Contours
7. 7Chapter 6 — Morphology, Thresholding, and Segmentation
8. 8Chapter 7 — Geometric Transformations
9. 9Chapter 8 — Feature Detection and Matching
10. 10Chapter 9 — Classical Object Detection
11. 11Chapter 10 — Video Analysis and Motion
12. 12Chapter 11 — Camera, Geometry, and 3D
13. 13Chapter 12 — The DNN Module: Deep Learning in OpenCV
14. 14Chapter 13 — Pipelines, Performance, and the Profession
15. 15Appendix A — Glossary and API Quick Reference
16. 16Appendix B — Further Reading and Resources