CBSE Class 10th AI (Artificial Intelleigence) | 21. OpenCV by Abraham | Learn Smarter
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21. OpenCV

OpenCV is a powerful and widely used open-source library designed for image and video processing, integral to computer vision applications. The chapter covers how to install OpenCV in Python, manage images, and utilize various image processing techniques. It also delves into face detection and real-time video capture, highlighting applications in several fields such as healthcare and automotive.

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Sections

  • 21

    Opencv

    OpenCV is a powerful, open-source library for computer vision applications, allowing computers to interpret visual data like humans.

    Learning Practice

  • 21.1

    What Is Opencv?

    OpenCV is an open-source library that provides tools for image processing, face detection, and real-time visual data analysis.

    Learning Practice

  • 21.2

    Installing Opencv In Python

    This section covers the installation process to set up OpenCV for Python.

    Learning Practice

  • 21.3

    Working With Images

    This section introduces how to read and display images using OpenCV, as well as explaining how images are represented in matrices.

    Learning Practice

  • 21.3.1

    Reading An Image

    This section covers the basics of reading an image using OpenCV, including key functions and their purposes.

    Learning Practice

  • 21.3.2

    Image As A Matrix

    This section introduces the concept of images being represented as matrices, highlighting the differences between grayscale and color images.

    Learning Practice

  • 21.4

    Image Processing With Opencv

    This section covers basic image processing techniques using OpenCV, including converting images to grayscale, resizing, blurring, and drawing shapes.

    Learning Practice

  • 21.4.1

    Converting To Grayscale

    This section explains how to convert color images to grayscale using OpenCV's cvtColor function.

    Learning Practice

  • 21.4.2

    Resizing An Image

    This section covers how to resize images using OpenCV, an essential technique for image processing.

    Learning Practice

  • 21.4.3

    Blurring An Image

    This section covers the method of blurring an image using OpenCV's GaussianBlur function, a vital image processing technique.

    Learning Practice

  • 21.4.4

    Drawing On Images

    This section covers how to draw basic shapes, such as rectangles and circles, on images using OpenCV.

    Learning Practice

  • 21.5

    Face Detection Using Opencv

    This section introduces face detection using OpenCV and its Haar Cascade Classifier model.

    Learning Practice

  • 21.5.1

    Load Haar Cascade Classifier

    This section describes how to load a pre-trained Haar Cascade Classifier in OpenCV for face detection.

    Learning Practice

  • 21.5.2

    Detect Faces In An Image

    The section covers the process of detecting faces in images using the OpenCV library's Haar Cascade model.

    Learning Practice

  • 21.5.3

    Display Detected Faces

    This section explains how to display detected faces in an image using OpenCV.

    Learning Practice

  • 21.6

    Using Webcam With Opencv

    This section discusses how to capture live video from a webcam using OpenCV, outlining the key code needed for successful implementation.

    Learning Practice

  • 21.7

    Applications Of Opencv

    OpenCV has diverse applications across various fields, including healthcare, automotive, retail, security, and education.

    Learning Practice

References

Chapter_21_OpenC.pdf

Class Notes

Memorization

What we have learnt

  • OpenCV is crucial for compu...
  • Images can be manipulated t...
  • Real-time video processing ...

Final Test

Revision Tests