Tips and typical application examples for using OpenCV in Python
This article isn't specifically about Python, but rather about using OpenCV within the Python environment. It will cover the most relevant basic operations in the data processing phase of deep learning and walk you through four interesting and practical examples: 6.1 Introduction to OpenCV OpenCV is the most widely used open-source computer vision library today. Originally developed in C/C++, it supports multiple platforms including Linux, Windows, macOS, Android, and iOS. Additionally, it offers interfaces for Python, MATLAB, and Java, which has contributed to its popularity in both academic research and industrial applications. Its rich functionality, high performance, and permissive licensing have made it a go-to tool in the field of computer vision. The project was initiated by Intel in 1998. Gary Bradski, a computer vision engineer at Intel, noticed that many students were developing their own internal code or libraries for vision tasks. This inspired him to create a general-purpose library that could be easily accessed and used by researchers and developers alike. The first alpha version of OpenCV was released at CVPR in 2000, followed by several beta versions before the official release in 2006. In 2009, when Gary joined Willow Garage, OpenCV received more active development and support. Version 1.1 was released, and by 2010, OpenCV 2.0 introduced a comprehensive C++ interface. Since then, OpenCV has continued to evolve, with major updates like OpenCV 3.0 in 2015, which brought architectural improvements, more algorithms, better performance, and enhanced GPU support. Today, OpenCV is widely used in both research and commercial settings. 6.1.1 Structure of OpenCV Currently, OpenCV has two main versions: OpenCV2 and OpenCV3. While OpenCV3 offers more features and better usability, this section focuses on OpenCV2 due to its compatibility with deep learning frameworks and ease of use for beginners. The OpenCV library is divided into several modules based on functionality. Some of the most commonly used ones include: For video and more specialized visual tasks, OpenCV includes additional modules such as: From a user perspective, OpenCV3 brings more features and finer module organization compared to OpenCV2. 6.1.2 Installing and Using OpenCV Installing OpenCV on Linux is straightforward. Most distributions offer package managers that can install it directly. For example, on Ubuntu 16.04 LTS, you can run the following commands in the terminal: Alternatively, you can download the source code from the official website and compile it manually. First, install the required dependencies: Next, clone the OpenCV repository and prepare the build: On Windows, you can download the installer from the OpenCV official website. After installation, locate the `cv2.pyd` file under ` To use OpenCV in Python, simply import it with: Once imported, you’re ready to start working with images and videos using OpenCV's powerful tools. Medical Pressure Gauge,Mini-Sized Gauge Medical Device,Oxygen Cylinder Gauge,Oxygen Tank Gauge ZHOUSHAN JIAERLING METER CO.,LTD , https://www.zsjrlmeter.com
sudo apt install libopencv-dev python-opencv
sudo apt install build-essential cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev
git clone https://github.com/opencv/opencv.git
cd opencv
mkdir release
cd release
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local
make
sudo make install
https://www.lfd.uci.edu/~gohlke/pythonlibs/#opencv
import cv2