If you’re interested in working with images and videos and want to integrate machine learning into your computer vision applications, OpenCV is an essential library to explore.
OpenCV is a comprehensive open-source library that supports numerous programming languages, including Python, and is widely used by both individuals and commercial organizations.
In this tutorial, you will familiarize yourself with OpenCV and understand its significance.
By the end of this tutorial, you will:
- Understand what the OpenCV library is.
- Learn about its applications and its user base.
- Know how to install and import OpenCV in Python.
Let’s get started!
Tutorial Overview
This tutorial consists of four main parts:
- What is OpenCV?
- What is OpenCV Used For?
- Who Uses OpenCV?
- How to Install and Import OpenCV in Python?
What is OpenCV?
OpenCV stands for Open Source Computer Vision Library. As indicated by its name, it is an open-source library focused on computer vision and machine learning software.
Licensed under Apache 2.0, OpenCV permits users to utilize, modify, and distribute the software, making it particularly appealing for commercial use.
Written primarily in C++, OpenCV supports various operating systems such as Windows, Linux, Android, and macOS, and has interfaces in C++, Python, Java, and MATLAB.
It is primarily designed for real-time computer vision applications.
What is OpenCV Used For?
OpenCV is a vast library containing over 2,500 optimized algorithms that cater to a wide range of computer vision applications, including:
- Face detection and recognition
- Object identification
- Object tracking
- Image registration and stitching
- Augmented reality
- And much more
Throughout this series of tutorials, you will discover the specific role OpenCV plays in applying machine learning to various computer vision tasks.
Notable machine learning algorithms implemented in OpenCV include:
- K-Nearest Neighbors
- Support Vector Machines
- Decision Trees
Additionally, OpenCV provides support for several deep learning frameworks, including TensorFlow and PyTorch.
Who Uses OpenCV?
According to the OpenCV website, the library has been downloaded over 18 million times, boasting a community of more than 47,000 users.
Many established companies utilize the OpenCV library, including renowned names like Google, Yahoo, Microsoft, Intel, and Toyota.
The range of applications for OpenCV within these companies is extensive. For instance, OpenCV is used for:
- Stitching street view images
- Detecting intrusions in surveillance systems in Israel
- Monitoring mining equipment in China
- Assisting robots in navigation and object handling at Willow Garage
- Detecting drowning incidents in swimming pools in Europe
- Facilitating interactive art installations in Spain and New York
- Inspecting runways for debris in Turkey
- Checking product labels in factories worldwide
- Performing rapid face detection in Japan
These examples highlight the breadth of OpenCV’s applications across various industries.
How to Install and Import OpenCV in Python
As mentioned earlier, OpenCV is primarily written in C++, but it can be accessed from Python via binding generators, which serve as a bridge between the two languages.
To install the OpenCV library from the Python Package Index (PyPI), you can use the following command:
pip install opencv-pythonImporting OpenCV into your Python project is simple:
import cv2Throughout this tutorial series, we will frequently use the import statement to access OpenCV functionalities.
We will begin with the basics, learning how to read images and videos as NumPy arrays, display them, access pixel values, and convert between different color spaces.
Further Reading
For additional insights and resources, consider the following:
Books:
- Mastering OpenCV 4 with Python, 2019
Websites:
Summary
In this tutorial, you were introduced to the OpenCV library and its significance in the field of computer vision.
Specifically, you learned about:
- The definition and purpose of OpenCV.
- Its applications and the user base.
- How to install and import OpenCV in Python.
Feel free to reach out for any further modifications or additional questions!