OpenCV Made Easy: A Python Approach

Name: OpenCV Made Easy: A Python Approach
ISBN: 9787121362903

李立宗Publishing House of Electronics Industry2019

4.0

Word count: 787 words
Chapters: 23
ISBN: 9787121362903

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Summary

OpenCV Made Easy: A Python Approach is a beginner-oriented introduction to computer vision and image processing by Li Lizong, published by Publishing House of Electronics Industry in May 2019. The book runs about 787,000 characters across 480 pages. It is built around OpenCV for Python, following the structure of the official OpenCV documentation while filling in and clarifying details the documentation leaves vague. Unlike reference manuals that merely list API usage, this book explains both the syntax and operation of OpenCV functions and the algorithmic principles behind them, so that readers understand why each function is used as it is and can choose the right function and parameters in real image-processing tasks. The book's distinctive teaching path is this: before applying a function to a real image, it first demonstrates the process and output with small numerical and array examples, letting readers observe at the numerical level how pixels are read, written, transformed, combined and thresholded, then moving on to full-image processing. For algorithmic principles, the author uses plain language and everyday examples rather than piling up abstract formulas, lowering the barrier for readers with no background. The content progresses from easy to difficult, covering environment setup and image reading/writing, pixel and array operations, ROI and channel handling, image arithmetic and bit planes, color space conversion, geometric transformations, thresholding, image smoothing, morphological operations, image gradients and Canny edge detection, image pyramids, contour extraction and features, histograms, Fourier transforms, template matching, the Hough transform, image segmentation and foreground extraction, video reading/writing, drawing and mouse interaction, and classic algorithms including K-nearest neighbors, support vector machines, K-means clustering and face recognition. The book includes many ready-to-run Python example programs and is suitable as a university textbook or a self-study roadmap.

Editor's review

市面上的OpenCV中文书大致分两类：一类是详尽但偏“函数手册”的参考书，翻起来像查字典；另一类是项目导向的实战书，直接给出完整案例，却让读者对原理一知半解。这本书走的是第三条路——把函数放回数据里讲。它最有价值的做法，是在处理真实图像之前，先用几个元素的小数组跑一遍函数，把输入输出打印出来。像素怎么被加、被阈值化、被卷积、被映射，都能在终端里直接看到数字的变化。这种“先数值、后图像”的讲法，对刚接触图像处理的读者非常友好：抽象的概念一旦落到具体数字上，理解成本会明显下降。同时，本书对算法原理的交代相当克制而清楚，尽量避免复杂公式，改用生活化例子说明问题，例如借助加权求和、掩膜、位平面分解等章节，把图像运算的直觉建立起来。对需要选函数、调参数的实际工作来说，这种理解比记住语法更管用。内容覆盖面也足够完整：从读写图像、色彩空间、几何变换，一直延伸到轮廓、直方图、傅里叶变换、霍夫变换、图像分割、视频处理，最后落到KNN、SVM、K均值与人脸识别等经典算法，形成了一条较完整的入门路径。2019年初版约480页、78.7万字，体量适合一个学期的课程或两三个月的系统自学。需要说明的是，本书出版于2019年，示例基于当时的OpenCV版本，部分接口在更新的版本中可能有调整，读者上手时应对照官方文档核对。它更适合作为“打地基”的第一本书，而非深度学习时代的终点。

Who it's for

A good fit for

零基础或仅有少量Python基础、想系统学习图像处理的读者
计算机、自动化、电子信息等专业需要课程教材的本科生与研究生
从事机器视觉、工业检测等工作的技术人员，作为OpenCV函数与原理的案头参考
希望理解算法原理而非只抄代码的自学者与竞赛选手

Not a good fit for

已具备扎实图像处理与OpenCV经验、想直接学习深度学习目标检测的读者
期待以项目实战为主线、边做完整应用边学的读者
需要C++接口OpenCV内容的读者

Reading tips

先按章节顺序读前五章，把像素、数组、通道、色彩空间这几块基础打牢，后面章节会轻松很多。
每章示例代码务必亲手敲一遍，并打印中间数组，观察数值变化，这是本书最核心的学习方式。
读到阈值、平滑、形态学、梯度等章节时，用同一张自己的图片反复试验不同参数，建立直观感受。
动手前先核对所用OpenCV版本与书中接口是否一致，遇到差异以官方文档为准。

About the publisher

Publishing House of Electronics Industry China · Founded in 1982

The Publishing House of Electronics Industry is a comprehensive publisher distinguished by its focus on electronic information and related fields. Judging from the more than 3,000 titles in its catalogue, its publishing covers programming and design, artificial intelligence, computer science, finance and economics, photography, and social sciences, attending both to cutting-edge technology and to general readers. Representative titles include Factor Investing: Methods and Practice, x86 Assembly Language: From Real Mode to Protected Mode, The RISC-V Reader, Artificial Intelligence: Speech Recognition, Understanding and Practice, Phased Array Radar Technology, Radar Target Characteristics, and 100 Go Mistakes and How to Avoid Them, most of which centre on technical methods and engineering practice. Its authors include Shi Chuan, Li Zhong, Wang Xiaobo, David Patterson, Andrew Waterman, and Yu Zihao, among others from China and abroad. Owing to limited public information, no further publishing features are elaborated here.

Chapter 1 Getting Started with OpenCV
Chapter 2 Fundamentals of Image Processing
Chapter 3 Image Arithmetic
Chapter 4 Color Space Conversion
Chapter 5 Geometric Transformations
Chapter 6 Thresholding
Chapter 7 Image Smoothing
Chapter 8 Morphological Operations
Chapter 9 Image Gradients
Chapter 10 Canny Edge Detection
Chapter 11 Image Pyramids
Chapter 12 Image Contours
Chapter 13 Histogram Processing
Chapter 14 Fourier Transform
Chapter 15 Template Matching
Chapter 16 Hough Transform
Chapter 17 Image Segmentation and Extraction
Chapter 18 Video Processing
Chapter 19 Drawing and Interaction
Chapter 20 K-Nearest Neighbors
Chapter 21 Support Vector Machines
Chapter 22 K-Means Clustering
Chapter 23 Face Recognition

FAQ

这本书需要什么基础？

需要基本的Python语法与numpy数组概念，了解列表、切片与数组索引即可。图像处理与计算机视觉的前置知识不需要，书中会从图像的基本表示方法讲起。

初版和第2版有什么区别？

初版2019年5月出版，ISBN 9787121362903，约480页；第2版2023年6月出版，ISBN 9787121455995，内容扩展至584页、23章，补充了示意图与练习题，解答也做了重新设计。

书里的代码能直接运行吗？

示例代码均为Python编写，安装opencv-python与numpy后大多可直接运行。但由于出版时间较早，部分接口在较新的OpenCV版本中可能发生变化，需要在运行前核对相应版本的文档。

学完能做人脸识别项目吗？

最后一章涵盖人脸检测与基于LBPH、EigenFaces、Fisherfaces的人脸识别方法，足以完成传统方法的小型项目。但若面向实用级识别效果，还需进一步学习深度学习方案。

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About the author

李立宗 Author

Li Lizong is an author about whom limited public information is available. Only his name and two works are known, while details such as nationality, English name, and dates of birth and death remain unconfirmed. Judging from the works in the library, his writing centers on computer vision and OpenCV technology, covering a Python-oriented introductory tutorial as well as a case collection that ranges from beginner level to deep learning. This suggests that his content balances foundational introduction with practical application, making it suitable for readers who wish to learn image processing and computer vision with OpenCV-Python. His works are categorized in the library under computer science and software learning, and they generally emphasize hands-on use of technical tools, with a tutorial style that is likely to progress step by step. Because there is no public biographical information, no further inferences are made here; the description of his writing focus is based only on the subject matter and categorization of his works.