You can pretty much accomplish any Computer Vision task you can think of with OpenCV. In real-life situations, you’ll need to combine many blocks to attain the required outcome. So all you have to do now is figure out which modules and functions to employ to acquire what you want.

Data structures

One of the best aspects of OpenCV is that it has several built-in primitives for image processing. If you’re starting from scratch, you’ll need to define concepts like a picture, a point, a rectangle, and so on. Almost any Computer Vision algorithm relies on them. All of these fundamental structures are included with OpenCV out of the box, and they are found in the core module. Another benefit is that these structures have already been performance and memory-optimized, so you don’t have to worry about the implementation specifics.

The imgcodecs system controls jpeg image writing and reading. With a single command, you may save an input picture as a jpg or png file when you operate on it and generate an output image.

When working with cameras, you’ll be dealing with a lot of video data. The video module is responsible for all aspects of video file input and output. You can record video from a camera or read video files in a variety of formats with ease. You may also save a group of frames as a movie file by adjusting parameters like frames per second and frame size.

Image operations

There are a lot of fundamental image processing procedures that you will utilize over and over again when writing a Computer Vision program. The imgproc module contains the majority of these functions. Morphological operations, transformations, filtering, color conversions, histograms, shape and motion analysis, and other operations are all possible. An additional package, ximgproc, provides complex image processing methods including structured forests for edge identification, domain transform filters, adaptive manifold filters, and others.


In the field of computer vision, 3D reconstruction is a hot issue. Using the appropriate methods, we can recreate a 3D scene from a series of 2D photos. OpenCV includes methods for determining the connection between objects in 2D pictures and calculating their 3D coordinates. All of this is handled by a module named calib3d. This module may also deal with camera calibration, which is necessary for estimating the camera’s parameters. These are essentially the internal settings of any camera that uses them to convert a collected scene into an image.

Video analysis

Analyzing the movements between subsequent frames in a video, monitoring distinct objects in a video, constructing video surveillance programs, and so on are all examples of video analysis jobs. All of this may be handled by the video module of OpenCV. Video stabilization is dealt with via a module called videostab. Video stabilization is a crucial feature of most video cameras. It’s difficult to maintain your hands stable while filming films with the camera in your hands. If you look at the video as is, it will appear choppy and shaky. Video stabilization methods are used by all current devices to process videos before they are delivered to the end-user.

Face recognition

Face recognition is the process of recognizing a person in a photograph. That’s not the same as face detection, which involves locating a face inside an image. So, if you want to create a viable biometric system that can recognize the person in front of the camera, you must first run a face detector to determine the position of the face, and then a face recognizer to determine who that person is. Face recognition is dealt with by a module named face.

Machine learning (ML)

To accomplish varied tasks, Computer Vision employs a variety of machine learning methods. OpenCV features a module named ml that contains a number of machine learning techniques. K-Nearest Neighbors, Support Vector Machines, Neural Networks, Bayes Classifier, Support Vector Machines, Decision Trees, and more techniques are among them. It also offers a flann module that contains nearest neighbor search techniques for a huge quantity of data. ML techniques are widely utilized in the development of systems for object identification, picture categorization, face detection, and other similar tasks.


OpenCV is a comprehensive library for computer vision problems. You should create your apps and use OpenCV as much as possible.

OpenCV is always introducing new modules for the most up-to-date machine learning methods; check out their Github source and familiarize yourself with the implementation. You may even volunteer at the library, which is a terrific opportunity to learn and meet new people.