Computer Vision is an artificial intelligence(AI) field that provides human-like intelligence to computers to extract useful information from images and videos. With the help of deep learning, computer vision enables computers to perform image classification, object tracking, and other activities. One of the most popular computer vision libraries that are covered in this tutorial is OpenCV.
OpenCV is a computer vision library that is written in C++. With the help of different wrappers, you can use OpenCV in Python, MATLAB, and Java. The best thing about OpenCV is that it supports several cutting-edge computer vision algorithms that make it easier to perform activities such as object identification, tracking of moving objects, facial recognition, removing red eyes from the photos, etc.
OpenCV runs on all Windows, macOS, Linux, and Android, making it ideal for all sorts of computer vision and image processing applications. In addition, it works well with NumPy, Matplotlib, SciPy, and other Python libraries. Apart from object detection and facial recognition, you can perform different tasks using this library. For example, you can help robots follow a line, detect intrusions, inspect runways for rubble, stitch images to produce street view, and others.
It is interesting to note that companies of all sizes ranging from startups to well-established use OpenCV. For example, some well-known organizations that employ OpenCV are Google, Sony, Microsoft, IBM, Intel, etc. In a nutshell, OpenCV is the best library if you want to build computer vision applications.
Following are the topics that are covered in the OpenCV tutorial: