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Image Scaling with OpenCV

This section explains how to resize images using OpenCV in Python.

Image scaling is commonly used for:

  • Data preprocessing
  • Input normalization for models
  • Display optimization
  • Reducing computational cost

1. Implementation Principle​

OpenCV provides the cv2.resize() function to adjust image dimensions.

Key points:

  • Target size can be specified directly
  • The function returns a resized image
  • Aspect ratio must be handled explicitly if required

2. Implementation Effect​

Navigate to the OpenCV working directory:

cd ~/opencv

Run the image scaling script:

python3 04.image_resize.py
note

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

Image Scaling Result

3. Implementation Code​

import cv2

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

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

    resized_image = cv2.resize(image, size)

    if cv2.imwrite(output_path, resized_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.")

resize_image(
    '/home/jetson/opencv/images/hemihex_logo.png',
    '/home/jetson/opencv/images/hemihex_logo_resize.png',
    (500, 100)
)

4. Code Explanation​

  • cv2.imread() loads the source image\
  • cv2.resize() resizes the image to (width, height)\
  • cv2.imwrite() saves the resized image\
  • Display functions preview the result

Maintained by HemiHex for OpenCV-based image processing workflows.