The best way to learn about a new app is to go to the official website first.
Contents of this chapter
1 Introduction
Opencv official website
OpenCV (an open source computer vision library) is based on the BSD protocol, so it is free for academic and commercial use. It provides C++, C, Python and Java interfaces, and supports Windows, Linux, Mac OS, iOS and Android. OpenCV is dedicated to efficient computing and real-time application development. Because it is written in optimized C/C++, it can take full advantage of multi-core processing. And OpenSL is also enabled, which can take advantage of the hardware acceleration of the underlying heterogeneous computing platform.
Opencv is widely used all over the world. OpenCV has a user community of more than 47,000 people and more than 14 million downloads. OpenCV is used in everything from interactive art, mine inspections, web maps to advanced robotics.
In 1999, Gary Bradski (Gary Bradski) was working at Intel at the time. With the vision of promoting the development of the industry by providing a stable infrastructure for practitioners of computer vision and artificial intelligence, he launched OpenCV project.
Open CV (Open Source Computer Vision Library) is an open source computer vision library that provides many functions that implement computer vision algorithms very efficiently (from the most basic filtering to advanced object detection).
The Open CV library is written in C and C++ and can run on Windows, Linux, Mac OS X and other systems. At the same time, it is also actively developing interfaces for Python, Java, Matlab, and other languages , and importing libraries into Android and iOS to develop applications for mobile devices.
Open CV is cross-platform and can run on Windows, Linux, Mac OS, Android, iOS and other operating systems.
Open CV has a wide range of applications, including image stitching, image noise reduction, product quality inspection, human-computer interaction, face recognition, motion recognition, motion tracking, driverless driving, etc.
Open CV also provides machine learning modules, you can use machine learning algorithms such as normal Bayesian, K nearest neighbors, support vector machines, decision trees, random forests, artificial neural networks, etc.
Open CV has received a lot of support from Intel and Google since the project was established, especially thanks to Itseez, which did most of the early development work. Since then, the Arraiy team has joined the project and is responsible for maintaining the always open source and free OpenCV.org.
2. C++ or Python3 installation and configuration
Refer to the following
Opencv study notes (1)
Note: The follow-up explanation of this series of articles will focus on Python, and the C++ version will be supplemented after the overall content update is completed.
3. Opencv basic functions
[Python] OpenCV common operation function collection!
4. Understand from the project
The source code of the following open source projects can be obtained from github.
Computer Vision | Summary of Excellent and Practical OpenCV Open Source Projects
5. Start with the division of visual principles
This is the main line and focus of this series. Both Halcon and Opencv build applications based on requirements. Therefore, subsequent articles will be guided by application and studied one by one.
The following preview: image preprocessing related principles and technologies