OpenCV, an open-source computer vision library, provides hundreds of computer vision and image processing algorithms. It is designed for efficient image-related computational tasks emphasizing computer vision. This library is widely used to perform real-time operations on images and videos. In addition, it also offers image processing functionalities as a part of its modules. This library is written in C and C++ high-level languages and runs on Linux and Windows.
In addition, OpenCV provides Python wrappers. To elaborate further, the use of OpenCV is made possible in other high-level languages through binding wrappers. Since the OpenCV library is written in C++ Language, the wrappers act as a bridge between C++ and Python, hence allowing the users to call C++ functions using Python.
This article serves as a guide related to the OpenCV image library and its applications. In addition, it also covers the primary difference between image processing and computer vision to provide a clear perception of different OpenCV modules.
Image Processing and Computer Vision
Computer Vision is an Artificial Intelligence (AI) driven field of work which works on enabling the computers and systems to derive useful data from different visual inputs, for instance, digital images and videos. Computer vision helps computers provide recommendations based on that information. Computer Vision requires a hefty amount of data to operate efficiently. The tasks are accomplished via two fundamental technologies. The first one is Deep Learning and the second type is Convolutional Neural Network (CNN). It is widely used for security and biometrics, namely, face recognition, and in the medical industry as well for improved x-ray and MRI scans.
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On the other hand, image processing is a process of transforming any image into a digital form and performing certain operations to acquire useful information. There are 5 categories of image processing, known as visualization, recognition, sharpening and restoration, pattern recognition, and retrieval. Some prominent examples for the above types would be:
Image processing is a vast ten step process. It is widely used for medical image retrieval and traffic sensing technologies to sense traffic in particular areas or situations.
OpenCV Image Library
A general misconception that’s common among people is that image processing and computer vision tasks are relatively easier. This perception arises from the fact that humans observe the surroundings and understand the visual streams through nerves and neurons. However, for computers, there is no sensory support similar to Retina to sense and perceive images with depth, especially 3D images. The images are transmitted in the form of a grid of numbers. Hence, it makes it quite difficult for computers to process those numbers as anything.
This is exactly where the OpenCV library comes in. OpenCV is a widely used library that’s employed primarily for computer vision tasks. It is open-source and widely available. Furthermore, it is easier to set up and install on any platform. OpenCV is based on the C Language which enables the library to utilize the capability of multiple processors to efficiently perform the tasks. In addition, OpenCV now supports several languages like Java, Matlab, and Python.
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The goal is to provide an easy-to-use computer vision library that would enable people to complete complex tasks efficiently. Therefore, it is equipped with fairly sophisticated features and modules. These modules target various fields such as medical imaging, security, user interface, video, and robotics.
Modules of OpenCV
OpenCV library provides a huge variety of computer vision models. Some of the widely used modules are listed below:
- Core Module: It consists of modules most commonly used for defining the image data structure.
- Image Processing Module: This module consists of major linear and non-linear image processing operations.
- Video Analysis Module: A module with video analysis functionalities such as motion detection and object tracking
- Camera Calibration Module: It consists of multiple-view algorithms used for camera calibration and object estimation.
- 2D Features Framework Module: This module consists of 2D computer vision features including detectors and descriptors.
- Object Detection Module: A module dedicated to the detection of objects in an image or a video. It consists of predefined libraries for eyes, faces, cars, etc.
- High-level GUI Module: This module consists of UI functionalities for computer vision applications.
- Video I/O Module: A module designed to capture video details. Information consists of video interfaces, codecs, etc.
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Applications of OpenCV Image Library
OpenCV is widely used in various image processing and computer vision tasks. Some of the common tasks include face detection, face recognition, object detection, surveillance, object counting, segmentation, classification, and video processing. In addition to these supported features, there are several real-world applications of openCV in different fields. These include (but are not limited to):
And that is a wrap! In conclusion, OpenCV is an essential library for computer vision tasks. In this article, we have covered its basic features, major modules, and widely used applications. Also, OpenCV provides Microsoft Azure Cloud and Azure KinectFusion support thanks to its partnership with Microsoft Azure AI.
We hope that this detailed guide on clustering will assist you in understanding OpenCV, its features, and its applications.
Happy AI coding :)