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Image Binarization with OpenCV

This section explains how to convert a grayscale image into a binary image using OpenCV.

Image binarization is commonly used for: - Document processing - Edge and shape detection - Image segmentation - Preprocessing for OCR and classical vision algorithms

1. Implementation Principle​

OpenCV provides the cv2.threshold() function to perform image binarization.

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

Parameters: - src: input grayscale image

  • thresh: threshold value
  • maxval: value assigned to pixels above the threshold
  • type: thresholding method

2. Implementation Effect​

Navigate to the OpenCV working directory:

cd ~/opencv

Run the image binarization script:

python3 09.image_binarize.py
note

Select the image window and press q to exit the program.

Image Binarization Result

3. Implementation Code​

import cv2

def binarize_image(input_path, output_path, threshold):
    image = cv2.imread(input_path, cv2.IMREAD_GRAYSCALE)

    if image is None:
        print("Error: Unable to open image file.")
        return

    _, binary_image = cv2.threshold(
        image,
        threshold,
        255,
        cv2.THRESH_BINARY
    )

    if cv2.imwrite(output_path, binary_image):
        print(f"Image saved to {output_path}")
        cv2.imshow('Image Preview', cv2.imread(output_path))
        cv2.waitKey(0)
        cv2.destroyAllWindows()
    else:
        print("Error: Unable to save image file.")

binarize_image(
    '/home/jetson/opencv/images/hemihex_logo.png',
    '/home/jetson/opencv/images/hemihex_logo_binarize.png',
    127
)

4. Code Explanation​

  • cv2.imread(..., cv2.IMREAD_GRAYSCALE) loads the image in grayscale\
  • cv2.threshold() applies binary thresholding
  • cv2.imwrite() saves the output image
  • Display functions preview the result

Maintained by HemiHex for OpenCV-based image processing workflows.