OpenCV and Python

OpenCV（Open Source Machine Vision）是一个开源库，包含数百种用于图像和视频分析的 C 和 C++ 优化算法，自 1999 年推出以来一直被研发界广泛使用。视力捐赠者被接受为基本的发展工具。OpenCV 最初由英特尔开发，用于开发机器视觉研究并增强使用重型处理器的应用程序。OpenCV 的主要优势在于它的执行速度，尤其是在实时应用程序中，当然还有它的开源和免费特性。该培训包旨在使机器视觉研究人员社区更加熟悉这个有价值的库，它将通过一系列不同的示例为您以逐步和实用的方式开发其应用程序做好准备。

Python 和 OpenCV 的结合，除了其强大而令人印象深刻的功能外，对于刚开始从事图像处理和编码的人来说也很容易学习。本课程的培训从介绍、安装和加载图像以非常快速和简单的方式逐步开始，然后继续对图像应用主要的常用操作，应用数学运算和几何变换，以及各种应用过滤器和如何实现 他们的构造将在图像上表现出来。此外，边缘检测、形态变换、直方图和指向几种模式匹配方法的最广泛使用的方法的使用，这些方法是任何图像处理程序最重要的部分和目标，将得到很好的解释。

Published 05/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 21 lectures (3h) | Size: 1.19 GB

Learn how can work with openCV in python

What you’ll learn
Installing Python, OpenCV, Numpy and Visual Studio Code
Learning Numpy
Using OpenCV in Python
Learning Main Methods in OpenCV

Requirements
Python

Description
OpenCV (Open Source Machine Vision) is an open-source library of more than hundreds of optimized algorithms in C and C ++ for image and video analysis, which has been widely used by the research and development community since its introduction in 1999. Car vision donors are accepted as a basic development tool. OpenCV was originally developed by Intel to develop research into machine vision and to enhance applications that use heavy-duty processors. The main advantage of OpenCV is its speed of execution, especially in real-time applications, and of course its open source and free nature. This training package is an attempt to make the community of machine vision researchers more familiar with this valuable library, which will prepare you for the development of its applications in a step-by-step and practical manner with a diverse set of examples.

The combination of Python and OpenCV, in addition to its vast and impressive capabilities, is also easy to learn for people who are just starting out in image processing and coding. The training of this course starts step by step with the introduction, installation, and loading of images in a very fast and easy way and then continues by applying the main common operations on images, applying mathematical operations and geometric transformations, and various application filters and how to implement Their construction will be expressed on the image. Also, the use of the most widely used methods for edge detection, morphological transformations, histograms, and pointing to several pattern matching methods, which are the most important parts and goals of any image processing program, will be well and easily explained.

Who this course is for
All Level