OpenCV is used for computer vision. It is an open-source library. Through its features, it helps machines to recognize objects or faces. It has numerous use cases like identifying objects, used in CCTV footage analysis, tracking camera movements, face recognition, image and video analysis, and more. CV is the abbreviation for computer vision. This feature helps computers in understanding digital media such as videos. It allows the computer to understand the content of the produced images. 

OpenCV is widely used for image recognition and identification. It understands the picture by extracting any available descriptions, objects, three-dimensional models, etc. Earlier it was written using C or C++ languages. Later, it got updated with the Python programming language that allows better computer vision with the help of its extensive library support. OpenCV is constantly being updated as per the requirements. 

The two main features that CV follows while image recognition is Object Classification and identification. In classification, developers train the model with a specific dataset of particular objects. When any new entity is given as an input, the model will try to classify them based on the trained data. In identification, the model is trained in a way where it can identify the instances of the objects. 

Unlike human eyes, machines require some memory to recognize the object. To achieve image recognition using OpenCV is done by training the model with the required datasets. Machines convert these objects' info into numbers and store it in their memory. Conversion of an image into numbers is done with the help of pixel values. Pixel is the smallest unit of the graphics or the image represented and displayed on the device's digital display. 

Picture intensities of specific locations of the images are represented with the help of numbers. The two popular ways of finding the images are RGB and Grayscale. As the name suggests, Grayscale images are images that contain only black and white colors. Here the pixel value is determined based on the level of the darkness. Contrast measurement of intensity is achieved by selecting the strongest and weakest intensity. Black is considered the weakest contrast, while white is the strongest.

RGB indicates red, green, and blue colors. A new color is formed by mixing these three colors. These colors have specific values. The image is processed by categorizing them in terms of RGB. All the pixel values of these colors are put into the array for the machine to interpret them. Thus, based on the interpretation, the computer can read the image. OpenCV is free to use as it is free of cost.

It is faster. With the help of Python libraries, you can explore more of its features. As OpenCV is written in C, it is portable and can be run on any device compatible with the C language. You can read the images using OpenCV. You can perform various operations on it. You can load the image as the input using the read() function. On execution of the read command to load the image, if it returns a matrix, it is because of the unsupported, missing, or invalid files. 

Learn more on OpenCV concepts and their features and functions by enrolling in Great Learning Academy’s free OpenCV courses. Learn OpenCV and get free OpenCV certificates on successfully completing the registered courses.