OpenCV image resize

Open Source Computer Vision (OpenCV) is an open-sourceA software or a library whose code is made available to the public. computer vision and machine learning software library which provides a wide range of functions and algorithms to process and analyze images and videos.

Note: learn more about OpenCV.

Image matrix

An image is typically represented as a two-dimensional matrix, where each element corresponds to a pixel value within the image. These pixel values store information regarding the color intensity of each pixel.

In the case of grayscale images, each pixel value ranges from 0 to 2550 \textnormal{ to } 255. For example, a greyscaled image of size 4×34 \times 3 pixels will be:

[

 [135, 150, 120, 100], 

 [50, 80, 200, 90], 

 [200, 180, 220, 75]

]
Image matrix of a grayscaled image

However, for colored images, each pixel value is represented by an RGB (Red, Green, Blue) matrix in the form [R, G, B]. The R, G, and B values individually range from 0 to 2550 \textnormal{ to } 255, indicating the intensity of the respective color channels.

For example, a colored image of dimensions 2×2×32 \times 2 \times 3 pixels might have an image matrix like the following:

[

 [[255, 0, 146], [0, 255, 45]], 

 [[0, 0, 255], [255, 255, 69]]

]
Image matrix of a colored image
Image with RGB pixels
Image with RGB pixels

Resizing an image

Resizing an image means changing the dimensions of the image while retaining its original form. Python library OpenCV can do this job efficiently using the resize() method. Its syntax is as follows:

resize(x,y) where:

  • x is the image matrix of the image being resized.

  • y is a tuple containing the new dimensions of the resultant resized image.

Example code for image resizing

The following image has the dimensions of 586×546586 \times 546:

Orignial image (represented by `image.jpg` in the code)
Orignial image (represented by `image.jpg` in the code)

The code below will resize this image and change its dimensions from 586×546586 \times 546 to 600×300600 \times 300:

import cv2

# Load the image
image = cv2.imread('image.jpg')

# Define the new dimensions for the resized image
new_width = 600
new_height = 300

# Resize the image
resized_image = cv2.resize(image, (new_width, new_height))

# Display the resized image
cv2.imshow('Original Image', image)
cv2.imshow('Resized Image', resized_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
Python code to resize the image

Note: You can drag the image windows to have a better comparison.

Code explanation

  • Line 1: Imports cv2 library.

  • Line 4: imread() method loads the image and stores its matrix in image variable.

  • Lines 7–9: Defines new dimensions.

  • Line 11: Uses resize() method of cv2 library and stores the resized image matrix in the resized_image variable.

  • Line 14: Displays the original image with the label Original Image.

  • Line 15: Displays the resized image with the label resized_image.

  • Line 16: waitKey(0) waits for the user to press any key to close all the windows.

  • Line 17: distroyAllWindows() function closes every opened window.

The resultant resized image will be as follow:

Resized image with 600x300 dimensions
Resized image with 600x300 dimensions

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