| OpenCV 4.3.0 Open Source Computer Vision

The Core Functionality (core module)

Here you will learn the about the basic building blocks of the library. A must read and know for understanding how to manipulate the images on a pixel level.

- Mat - The Basic Image Container

  Compatibility: > OpenCV 2.0

  Author: Bernát Gábor

  You will learn how to store images in the memory and how to print out their content to the console.

- How to scan images, lookup tables and time measurement with OpenCV

  Compatibility: > OpenCV 2.0

  Author: Bernát Gábor

  You'll find out how to scan images (go through each of the image pixels) with OpenCV. Bonus: time measurement with OpenCV.

- Mask operations on matrices

  Languages: C++, Java, Python

  Compatibility: > OpenCV 2.0

  Author: Bernát Gábor

  You'll find out how to scan images with neighbor access and use the cv::filter2D function to apply kernel filters on images.

- Operations with images

  Languages: C++, Java, Python

  Compatibility: > OpenCV 2.0

  Reading/writing images from file, accessing pixels, primitive operations, visualizing images.

- Adding (blending) two images using OpenCV

  Languages: C++, Java, Python

  Compatibility: > OpenCV 2.0

  Author: Ana Huamán

  We will learn how to blend two images!

- Changing the contrast and brightness of an image!

  Languages: C++, Java, Python

  Compatibility: > OpenCV 2.0

  Author: Ana Huamán

  We will learn how to change our image appearance!

- Discrete Fourier Transform

  Languages: C++, Java, Python

  Compatibility: > OpenCV 2.0

  Author: Bernát Gábor

  You will see how and why use the Discrete Fourier transformation with OpenCV.

- File Input and Output using XML and YAML files

  Compatibility: > OpenCV 2.0

  Author: Bernát Gábor

  You will see how to use the cv::FileStorage data structure of OpenCV to write and read data to XML or YAML file format.

- How to use the OpenCV parallel_for_ to parallelize your code

  Compatibility: >= OpenCV 2.4.3

  You will see how to use the OpenCV parallel_for_ to easily parallelize your code.