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Image Grayscale Conversion with OpenCV

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

Grayscale conversion is commonly used for: - Reducing computational complexity - Feature extraction - Preprocessing for computer vision algorithms

1. Implementation Principle​

OpenCV provides the cv2.cvtColor() function to convert images between different color spaces.

For grayscale conversion, the following flag is used:

cv2.COLOR_BGR2GRAY

This converts a BGR color image into a single-channel grayscale image.

2. Implementation Effect​

Navigate to the OpenCV working directory:

cd ~/opencv

Run the grayscale conversion script:

python3 08.image_grayscale.py
note

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

Image Grayscale Result

3. Implementation Code​

import cv2

def grayscale_image(input_path, output_path):
    image = cv2.imread(input_path)

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

    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    if cv2.imwrite(output_path, gray_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.")

grayscale_image(
    '/home/jetson/opencv/images/hemihex_logo.png',
    '/home/jetson/opencv/images/hemihex_logo_grayscale.png'
)

4. Code Explanation​

  • cv2.imread() loads the source image
  • cv2.cvtColor() converts the image to grayscale
  • cv2.imwrite() saves the grayscale image
  • Display functions preview the result

Summary​

  • Grayscale images use a single intensity channel
  • Conversion reduces data size and complexity
  • Implemented using cv2.cvtColor()
  • Common preprocessing step in vision pipelines

Maintained by HemiHex for OpenCV-based image processing workflows.