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‎01-Introduction-and-Installation/README.md

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# [01: 简介与安装](http://codec.wang/opencv-python-introduction-and-installation/)
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![](http://blog.codec.wang/cv2_install_opencv-python.jpg)
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相信大部分人知道的OpenCV都是用C++来开发的，那为什么我推荐使用Python呢？<!-- more -->
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> 本教程翻译自[OpenCV官方英文教程](http://opencv-python-tutroals.readthedocs.io/en/latest/index.html)，我按照使用度和难易度翻译，重新编写了大量原创内容，将不常用和较难的部分写成番外篇，浅显易懂，很easy的辣。每节的源码、图片和练习题答案均可在引用处找到噢(⊙o⊙)
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---
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## Python照样快！
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众所周知，虽然Python语法简洁、编写高效，但相比C/C++运行慢很多。然而Python还有个重要的特性：它是一门胶水语言！Python可以很容易地扩展C/C++。OpenCV-Python就是用Python包装了C++的实现，背后实际就是C++的代码在跑，运行速度非常接近原生。
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比如我分别用Python和C++实现读入图片和调整图片的亮度对比度，结果如下：
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![](http://blog.codec.wang/cv2_python_vs_cplus_speed.jpg)
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**可以看到某些情况下Python的运行速度甚至好于C++，代码行数也直接少一半多！**
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另外，图像是矩阵数据，OpenCV-Python原生支持[Numpy](https://baike.baidu.com/item/numpy)，相当于Python中的Matlab，为矩阵运算、科学计算提供了极大的便利性。
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## 人工智能浪潮
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近些年，人工智能AI相关技术的快速发展大家有目共睹。在编程语言方面，更多人希望的是具备高效开发效率、跨平台、高度扩展性的语言，尤其是一些AI巨头优先推出支持Python语言的深度学习框架，如Facebook的[PyTorch](https://pytorch.org/)、Google的[Tensorflow](https://tensorflow.google.cn/)等，可以说Python是名副其实的“网红语言”了。
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![](http://blog.codec.wang/cv2_ai_ml_dl2.jpg)
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从[TIOBE编程语言排行榜](https://www.tiobe.com/tiobe-index/)也可以看到，Python发展迅猛，已经逼近C++的份额。这个排行榜每月更新，就不截图了，我编写时的TOP5：Java/C/C++/Python/C#。
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## 人生苦短，我用Python
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- 如果你搞科研用，果断放弃C++（Matlab？出门左拐）
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- 如果你是快速原型开发，验证方案，果断放弃C++
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- 如果你懒的配置OpenCV环境，果断放弃C++
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- 如果你的程序是在支持Python的较高硬件环境下运行，果断放弃C++
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- 如果你担心Python写不了界面，那是你的问题o_o ....
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- 除非你的程序是MFC或已经用C++编写其他模块或是嵌入式设备，那就用C++吧
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**"人生苦短，我用Python！！！"**
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## 安装
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> 本教程编写时使用的相关版本是：OpenCV 4.x，Python 3.x。
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### opencv-python
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只需终端下的一条指令：
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``` bash
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pip install opencv-python
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```
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pip是Python的包管理器，如果你还没安装Python，强烈推荐安装[Anaconda](https://www.anaconda.com/download/)，它包含了大量的科学计算包，不用后期一个个安装。
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### Anaconda安装
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进入Anaconda[官网](https://www.anaconda.com/download/)，下载最新版本的安装文件，速度比较慢的话，可以去[清华开源镜像站](https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/)。
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- Windows版是exe文件，双击直接安装，安装时记得勾选 `Add Anaconda to my PATH environment variable`，添加到环境变量。
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- Linux版是sh文件，执行`bash Anaconda3-xx.sh`，Linux版也会提示添加到环境变量，记得输yes就行。
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- MAC版是pkg文件，同样直接双击安装即可。
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### 安装测试
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Python的版本可以在终端中输入`python --version`来查看。对于OpenCV，打开Python的开发环境，输入`import cv2`，运行没有报错说明一切正常。要查看OpenCV的版本，可以：
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``` bash
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print(cv2.__version__)
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```
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> 编辑器我习惯用[Visual Studio Code](http://code.visualstudio.com/)，也可以用[PyCharm](http://www.jetbrains.com/pycharm/)/[Atom](https://atom.io/)/Jupyter Notebook(Anaconda自带)。
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### 常见问题
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1. pip识别不了：pip的目录没有添加到环境变量中，添加到用户(或系统)变量的path中。
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2. 下载速度很慢：可[到此处](https://pypi.org/search/?q=opencv-python)下载离线版。终端输入`pip install 文件名`安装。
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## 学习软件
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为了便于学习OpenCV，我编写了一款教学软件[LearnOpenCVEdu](https://github.com/codecwang/LearnOpenCVEdu)，目前只开发了一部分功能，欢迎Star支持:smiley:。
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![](http://blog.codec.wang/cv2_learn_opencv_edu_soft_screenshot.jpg)
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> 经验之谈：虽然我推荐大家使用OpenCV-Python进行图像处理，但想要深入理解OpenCV，C++是必须的，尤其是**OpenCV源码**！
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## 引用
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- [本节源码](https://github.com/codecwang/OpenCV-Python-Tutorial/tree/master/01-Introduction-and-Installation)
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### 网络资料
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- [OpenCV Docs官方文档](https://docs.opencv.org/)
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- [OpenCV源码](https://github.com/opencv/opencv)
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- [官方英文教程: OpenCV-Python Tutorials](http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html)
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- [LearnOpenCV](http://www.learnopencv.com)、[LearnOpenCV Github](https://github.com/spmallick/learnopencv)
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- [OpenCV 中文教程](http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/tutorials.html)
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### 书籍
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- [Programming Computer Vision with Python](http://programmingcomputervision.com/)、[中文书](https://www.amazon.cn/dp/B00L3Y3NEM/ref=sr_1_1?ie=UTF8&qid=1543929834&sr=8-1&keywords=Python+%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89)
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- [Practical Python and OpenCV](https://www.pyimagesearch.com/practical-python-opencv/)
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### 名校视觉研究所/课程
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- [卡内基梅隆大学](http://graphics.cs.cmu.edu/)
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- [多伦多大学](https://www.cs.toronto.edu/~guerzhoy/320/)

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- 参数1：图片的文件名
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- 如果图片放在当前文件夹下，直接写文件名就行了，如'lena.jpg'
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- 如果图片放在当前文件夹下，直接写文件名就行，如'lena.jpg'
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- 否则需要给出绝对路径，如'D:\OpenCVSamples\lena.jpg'
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## 引用
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- [本节源码](https://github.com/codecwang/OpenCV-Python-Tutorial/tree/master/02-Basic-Element-Image)
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- [Getting Started with Images](http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_gui/py_image_display/py_image_display.html)

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# 1.灰度图加载一张彩色图

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## 引用
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- [本节源码](https://github.com/codecwang/OpenCV-Python-Tutorial/tree/master/03-Open-Camera)
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- [Video Codecs by FOURCC](http://www.fourcc.org/codecs.php)
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- [Getting Started with Videos](http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_gui/py_video_display/py_video_display.html)

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- [本节源码](https://github.com/codecwang/OpenCV-Python-Tutorial/tree/master/04-Basic-Operations)
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- [Basic Operations on Images](http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_core/py_basic_ops/py_basic_ops.html#basic-ops)

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- [本节源码](https://github.com/codecwang/OpenCV-Python-Tutorial/tree/master/05-Changing-Colorspaces)
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- [Changing Colorspaces](http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_colorspaces/py_colorspaces.html)

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- [本节源码](https://github.com/codecwang/OpenCV-Python-Tutorial/tree/master/06-Image-Thresholding)
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- [Image Thresholding](http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_thresholding/py_thresholding.html)

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- [本节源码](https://github.com/codecwang/OpenCV-Python-Tutorial/tree/master/07-Image-Geometric-Transformation)
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- [Geometric Transformations of Images](http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_geometric_transformations/py_geometric_transformations.html)

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