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Python and OpenCV 新手教程2017

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新手教程

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Alexander Mordvintsev & Abid K 编写对于python快速入门opencv的教程，详细的从安装环境到具体的代码，执行，运行结果都有详细的配图。

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OpenCV-Python Tutorials

Documentation

Release 1

Alexander Mordvintsev & Abid K

Nov 05, 2017

新手入门教程

Contents

1 OpenCV-Python Tutorials 3

1.1 Introduction to OpenCV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.2 Gui Features in OpenCV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

1.3 Core Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

1.4 Image Processing in OpenCV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

1.5 Feature Detection and Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

1.6 Video Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

1.7 Camera Calibration and 3D Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207

1.8 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225

1.9 Computational Photography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250

1.10 Object Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259

1.11 OpenCV-Python Bindings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264

2 Indices and tables 269

OpenCV-Python Tutorials Documentation, Release 1

Contents:

Contents 1

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OpenCV with Python

2017-06 美国最新OpenCV with Python。150RMB于亚马逊购买所得。国内还没的卖。亚马逊读者评价，5星。 == I've been following this book's development on Github for a while now. I'm super excited it's finally out! It has some great stuff in there that everyone should know (classification, regression, decision trees, SVMs, etc.) and beyond. It uses OpenCV, some Scikit learn and Keras. Python only! === 5.0 颗星，最多 5 颗星The Best Guidance for ML and CV! 留言者Haotian Zhang于 2017年10月12日 - 已在美国亚马逊上发表 Micheal's book is intended for starters, undergraduates & PhD students, researchers, primarily in the machine learning or related area.This book will be an essential reference for practitioners of modern machine learning. It covers the basic concepts such as Decision Tree, SVMs, Classification and etc. and the powerful modern computing methods that build on those concepts. I've been following this book on Git for quite a long time and I even forked the original code. It uses the Python Jupyter Notebook to describe concepts in each chapter and also uses the OpenCV, Python Libraries like Scikit-learn and Machine Learning Framework Keras. Both the book and code are quite helpful towards my study. A must-buy for anyone interested in machine learning or curious about how to extract useful knowledge from Computer Vision.

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