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写在前面的话

OpenCV是计算机视觉中经典的专用库，其支持多语言、跨平台，功能强大。OpenCV-Python为OpenCV提供了Python接口，使得使用者在Python中能够调用C/C++，在保证易读性和运行效率的前提下，实现所需的功能。

OpenCV-Python Tutorials是官方提供的文档，其内容全面、简单易懂，使得初学者能够快速上手使用。2014年段力辉在当时已翻译过OpenCV3.0，但时隔五年，如今的OpenCV4.1中许多函数和内容已经有所更新，因此有必要对该官方文档再进行一次翻译。

翻译过程中难免有所疏漏，如发现错误，希望大家指出，谢谢支持。

OpenCV-Python Tutorials官方文档：https://docs.opencv.org/4.1.2/d6/d00/tutorial_py_root.html

目录

OpenCV中文文档

Introduction to OpenCV | OpenCV简介

Learn how to setup OpenCV-Python on your computer!

OpenCV-Python Tutorials

1.1 OpenCV-Python教程简介

Getting Started with OpenCV-Python

1.2 在Windows中安装OpenCV-Python

Set Up OpenCV-Python in Windows

1.3 在Fedora中安装OpenCV-Python

Set Up OpenCV-Python in Fedora

1.4 在Ubuntu中安装OpenCV-Python

Set Up OpenCV-Python in Ubuntu

2 Gui Features in OpenCV | OpenCV中的GUI特性

Here you will learn how to display and save images and videos, control mouse events and create trackbar.

2.1 图像入门

2.2 视频入门

2.3 OpenCV中的绘图功能

2.4 鼠标作为画笔

2.5 轨迹栏作为调色板

3 Core Operations | 核心操作

In this section you will learn basic operations on image like pixel editing, geometric transformations, code optimization, some mathematical tools etc.

3.1 图像的基本操作

3.2 图像上的算法运算

3.3 性能衡量和提升技术

4 Image Processing in OpenCV | OpenCV中的图像处理

In this section you will learn different image processing functions inside OpenCV.

4.1 改变颜色空间

4.2 图像几何变换

4.3 图像阈值

4.4 图像平滑

4.5 形态转换

4.6 图像梯度

4.7 Canny边缘检测

4.8 图像金字塔

4.9.1 OpenCV中的轮廓

4.9.2 轮廓特征

4.9.3 轮廓属性

4.9.4 轮廓：更多属性

4.9.5 轮廓分层

4.10.1 直方图-1：查找，绘制，分析

4.10.2 直方图-2：直方图均衡

4.10.3 直方图3：二维直方图

4.10.4 直方图-4：直方图反投影

4.11 傅里叶变换

4.12 模板匹配

4.13 霍夫线变换

4.14 霍夫圈变换

4.15 图像分割与分水岭算法

4.16 交互式前景提取使用GrabCut算法

5 Feature Detection and Description | 特征检测与描述

In this section you will learn about feature detectors and descriptors

5.1 理解特征

5.2 哈里斯角检测

5.3 Shi-Tomasi拐角探测器和良好的跟踪功能

5.4 SIFT（尺度不变特征变换）简介

5.5 SURF简介（加速的强大功能）

5.6 用于角点检测的FAST算法

5.7 BRIEF（二进制的鲁棒独立基本特征）

5.8 ORB（定向快速和旋转简要）

5.9 特征匹配

5.10 特征匹配+单应性查找对象

6 Video analysis (video module) | 视频分析

In this section you will learn different techniques to work with videos like object tracking etc.

6.1 如何使用背景分离方法

6.2 Meanshift和Camshift

6.3 光流

7 相机校准和3D重建

In this section we will learn about camera calibration, stereo imaging etc.

7.1 相机校准

7.2 姿态估计

7.3 对极几何

7.4 立体图像的深度图

8 Machine Learning | 机器学习

In this section you will learn different image processing functions inside OpenCV.

8.1 K-Nearest Neighbour

Learn to use kNN for classification Plus learn about handwritten digit recognition using kNN

8.1.1 Understanding k-Nearest Neighbour | 理解KNN

Get a basic understanding of what kNN is

8.1.2 OCR of Hand-written Data using kNN | 使用OCR手写数据集运行 KNN

Now let’s use kNN in OpenCV for digit recognition OCR

8.2 Support Vector Machines (SVM)

Understand concepts of SVM

8.2.1 Understanding SVM | 理解SVM

Get a basic understanding of what SVM is

8.2.2 OCR of Hand-written Data using SVM | 使用OCR手写数据集运行SVM

Let’s use SVM functionalities in OpenCV

8.3 K-Means Clustering

Learn to use K-Means Clustering to group data to a number of clusters. Plus learn to do color quantization using K-Means Clustering

8.3.1 Understanding K-Means Clustering | 理解K均值聚类

Read to get an intuitive understanding of K-Means Clustering

8.3.2 K-Means Clustering in OpenCV | OpenCV中的K均值

Now let’s try K-Means functions in OpenCV

9 计算摄影学

In this section you will learn different computational photography techniques like image denoising etc.

9.1 图像去噪

9.2 图像修补

9.3 高动态范围

10 目标检测

In this section you will learn object detection techniques like face detection etc.

10.1 级联分类器

10.2 级联分类器训练

OpenCV-Python Binding

In this section, we will see how OpenCV-Python bindings are generated

11.1 OpenCV-Python Bindings

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