This marks the beginning of my computer vision explorations. In an effort to document my journey for future reference or to aid others in their pursuits, I will be storing the majority of my notes here. Initially, I will focus on fundamental computer vision concepts such as loading a standard image, converting it to grayscale, applying a Gaussian blur, obtaining the threshold image, and ultimately identifying the image's contours.
I will use a few images as an example and display the image in a 2 by 2 format. Where we are going to use openCV for most if not all methods for the images.
Input picture here!!! - Arty Chan
An image is composed of either 3 or 4 matrices that are formatted in RGB or RGBA. As I intend to use openCV, the library will default to using only 3 matrices, which represent Red, Green, and Blue. It is essential that the three matrices are identical in size to accurately portray the image. Within the image's dimensions, each element or cell is assigned an integer value between 0 and 255, which ultimately corresponds to a particular color. By combining and multiplying the three matrices, the image generates a color composition. Upon zooming out, a pet image will display a varied quantity of integers.