Image thresholding is a fundamental technique in image processing that is widely used for various applications such as image segmentation, noise reduction, and edge detection. In this tutorial, we will explore how to perform image thresholding using OpenCV, a popular computer vision library.

What is Image Thresholding?

Image thresholding is the process of converting a grayscale image into a binary image by assigning a threshold value to each pixel. Pixels with intensity values above the threshold are set to one (or white), and pixels with intensity values below the threshold are set to zero (or black).

OpenCV Thresholding Methods

OpenCV provides several methods for thresholding images. The most commonly used methods are:

  • Binary Thresholding: Converts an image to binary by setting pixels above the threshold to white and pixels below the threshold to black.
  • Otsu's Thresholding: Automatically determines the optimal threshold value using the Otsu's method.
  • Adaptive Thresholding: Uses a different threshold value for each pixel based on the local pixel neighborhood.

Example Code

Here's an example of how to perform binary thresholding using OpenCV:

import cv2
import numpy as np

# Load an image
image = cv2.imread('path/to/image.jpg')

# Convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Apply binary thresholding
_, thresh = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY)

# Display the original and thresholded images
cv2.imshow('Original Image', image)
cv2.imshow('Thresholded Image', thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()

Further Reading

For more information on image thresholding and OpenCV, please refer to the following resources:

Image Thresholding Example