Learn how to simplify image analysis with thresholding in OpenCV! 📸✨
#ImageProcessing #PythonTutorial #coding #python #opencv
In Image processing, thresholding is a fundamental technique that is used to separate objects or regions in an image based on pixel intensity. Image thresholding is commonly used for image segmentation and feature extraction.
In Python, OpenCV is a popular library for computer vision and provides powerful tools for thresholding images.
In this tutorial, I’ll explain how different types of image thresholding techniques using OpenCV in Python work. I will cover the basics of thresholding and provide practical examples to help you understand how to apply these techniques in your projects.
- Simple Thresholding
It is the basic form of thresholding where each pixel in the image is compared to a predefined threshold value. Now, what does this actually mean ?
It compares each pixel’s intensity value to a fixed threshold value. If the pixel’s value is higher than the threshold, it is set to a maximum value (255, which represents white in grayscale). If it is lower than the threshold, it is set to 0 (which represents black). This process creates a binary image where pixels are either white or black, depending on whether they are above or below the threshold.
If the pixel value is greater than the threshold, it is set to a maximum value (often 255, white); otherwise, it is set to a minimum value (often 0, black).
Let us see an example:
import cv2
import numpy as np
# Load an image in grayscale
image = cv2.imread('girl.jpg', 0)
# Apply simple thresholding
_, thresholded = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)
# Display the original and thresholded images
cv2.imshow('Original Image', image)
cv2.imshow('Thresholded Image', thresholded)
cv2.waitKey(0)
cv2.destroyAllWindows()Read More Below
https://codemagnet.in/2024/04/15/image-thresholding-in-opencv-python-full-tutorial/