A collection of examples of value-based methods for Python image processing

Image processing is one of the important tasks in the field of computer vision, and image valueization is one of the common methods. This article will show you some examples of value methods for image processing using Python to help you understand and apply these techniques.

Valueization methods convert images from continuous pixel values ​​into discrete representations, typically using gray levels or colors to represent pixels. This transformation can help us reduce the complexity of the image and highlight features of interest. Below are several common value methods and their Python implementation examples.

1. Binarization

Binarization is a method of converting image pixel values ​​into a binary representation. Typically, we set pixels whose pixel value is greater than a certain threshold to white, and set pixels whose pixel value is less than or equal to the threshold to black. This method is commonly used for tasks such as image segmentation and object recognition.

Here is an example code for binarization using the OpenCV library:

import cv2

def binarization(image, threshold):
    gray = cv2.cvtColor(image, cv2.<