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Binarization

Introduction​

We previously learned about grayscale image transformation. Compared to color images, grayscale images have the advantage of containing less information, with single-channel values from 0 to 255 enabling faster processing speeds. Binarization in this section further processes grayscale images: values below a certain threshold are converted to 0 (pure black), and values above the threshold are converted to 255 (pure white). This more clearly preserves the main contours of the image.

Experiment Objective​

Perform binarization processing on an image.

Experiment Explanation​

The OpenCV Python library provides the threshold() function for threshold processing, thereby implementing image binarization.

threshold() Usage​

retval, img = cv2.threshold(src, thresh, maxval, type)

Threshold processing. Returns retval as the threshold value used during processing; returns img as the image after threshold processing.

  • src: Original image.
  • thresh: Threshold value, typically between 125 and 150.
  • maxval: Maximum value used in threshold processing, typically 255.
  • type: Processing type.
    • cv2.THRESH_BINARY: Binary threshold processing.
    • cv2.THRESH_BINARY_INV: Inverse binary threshold processing.
    • cv2.THRESH_TOZERO: Below-threshold zero processing.
    • cv2.THRESH_TOZERO_INV: Above-threshold zero processing.
    • cv2.THRESH_TRUNC: Truncation threshold processing.

This experiment uses cv2.THRESH_BINARY binary threshold processing to achieve image binarization. With the threshold thresh = 127 and the maximum value maxval = 255, pixels in the image with values < 127 will be changed to 0, and those > 127 will be changed to 255, turning it into a purely black-and-white image.

The code flow is as follows:


The reference code is as follows:

'''
Experiment Name: Image Binarization
Experiment Platform: WalnutPi 1B
'''

import cv2

img = cv2.imread("lenna.jpg",0) # Read the image and convert to grayscale
cv2.imshow('lenna', img) # Display the image

# Binarize the image
retval, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY) 
cv2.imshow('binary', img) # Display the image

cv2.waitKey() # Wait for any keyboard key to be pressed
cv2.destroyAllWindows() # Close the window

Experiment Results​

Run the above code on the WalnutPi; the experiment results are as shown below (multiple windows may overlap — use the mouse to drag them apart):

binary