{"id":23005,"date":"2024-04-09T10:52:00","date_gmt":"2024-04-09T05:22:00","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/opencv-tutorial-in-python\/"},"modified":"2024-09-02T17:01:32","modified_gmt":"2024-09-02T11:31:32","slug":"opencv-tutorial-in-python","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/opencv-tutorial-in-python\/","title":{"rendered":"OpenCV Tutorial: A Guide to Learn OpenCV in Python"},"content":{"rendered":"\n

OpenCV is a widely acclaimed open-source library that provides an extensive suite of computer vision and image processing<\/a> functions.

It offers a vast array of tools and techniques that empower developers to build cutting-edge applications in fields like robotics, augmented reality, facial recognition<\/a>, object detection, and much more. With its powerful capabilities and user-friendly interface, OpenCV has become a go-to choice for developers worldwide.<\/p>\n\n\n\n

In this tutorial, we will start from the basics and gradually delve into more advanced topics. We will cover everything you need to know to become proficient in OpenCV, regardless of your prior experience with computer vision<\/a>.

Whether you're a beginner taking your first steps or an experienced developer looking to deepen your understanding, this guide will provide clear explanations, practical examples, and hands-on exercises to sharpen your skills.<\/p>\n\n\n\n

What is OpenCV?<\/strong><\/h2>\n\n\n\n

OpenCV is an open-source software library for computer vision and machine learning.

The OpenCV full form is Open Source Computer Vision Library. It was created to provide a shared infrastructure for applications for computer vision and to speed up the use of machine perception in consumer products. OpenCV, as a BSD-licensed software, makes it simple for companies to use and change the code. <\/p>\n\n\n\n

Gary Bradsky invented OpenCV in 1999 and soon the first release came in 2000. This library is based on optimised C \/ C++ and supports Java and Python along with C++ through an interface. <\/p>\n\n\n\n

The library has more than 2500 optimised algorithms, including an extensive collection of computer vision and machine learning algorithms<\/a>, both classic and state-of-the-art.<\/p>\n\n\n\n

Using OpenCV it becomes easy to do complex tasks such as identify and recognise faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D object models, generate 3D point clouds from stereo cameras, stitch images together to generate an entire scene with a high resolution image and many more.<\/p>\n\n\n\n

Let's get started!<\/p>\n\n\n\n

\n
\n
\n
<\/div>\n Transform Healthcare with AI<\/span>\n <\/div>\n

\n AI in Healthcare Program<\/a>\n <\/p>\n

Transform healthcare with AI. Apply now for Johns Hopkins AI in Healthcare Program and enhance patient outcomes with cutting-edge skills<\/p>\n

\n
\n
<\/div>\n Duration: 10 weeks<\/span>\n <\/div>\n
\n
<\/div>\n 8+ Case Studies<\/span>\n <\/div>\n <\/div>\n \n Discover the Program\n
<\/div>\n <\/a>\n <\/div>\n <\/div>\n\n\n\n

What is Computer Vision?<\/strong><\/h2>\n\n\n\n

The term Computer Vision (CV) is used and heard very often in artificial intelligence (AI) and deep learning (DL) applications. The term essentially means giving a computer the ability to see the world as we humans do.<\/p>\n\n\n\n

    \n
  • Computer Vision is a field of study which enables computers to replicate the human visual system. As already mentioned above, It\u2019s a subset of artificial intelligence which collects information from digital images or videos and processes them to define the attributes. <\/li>\n\n\n\n
  • The entire process involves image acquiring, screening, analysing, identifying and extracting information. This extensive processing helps computers to understand any visual content and act on it accordingly. <\/li>\n\n\n\n
  • Computer vision projects translate digital visual content into explicit descriptions to gather multi-dimensional data. This data is then turned into a computer-readable language to aid the decision-making process. <\/li>\n\n\n\n
  • The main objective of this branch of artificial intelligence is to teach machines to collect information from pixels. <\/li>\n<\/ul>\n\n\n\n

    How does a computer read an image?<\/strong>
    <\/h2>\n\n\n\n

    A digital image is an image composed of picture elements, also known as pixels, each with finite, discrete quantities of numeric representation for its intensity or grey level. So the computer sees an image as numerical values of these pixels and in order to recognise a certain image, it has to recognise the patterns and regularities in this numerical data.<\/p>\n\n\n\n

    Here is a hypothetical example of how pixels form an image. The darker pixels are represented by a number closer to the zero and lighter pixels are represented by numbers approaching one. All other colours are represented by the numbers between 0 and 1. <\/p>\n\n\n\n

    But usually, you will find that for any colour image, there are 3 primary channels \u2013 Red, green and blue and the value of each channel varies from 0-255.

    In more simpler terms we can say that a digital image is actually formed by the combination of three basic colour channels  Red, green, and blue whereas for a grayscale image we have only one channel whose values also vary from 0-255.<\/p>\n\n\n\n

    OpenCV Tutorial Video<\/strong><\/h2>\n\n\n\n
    \n