Resize Image - ( Image Basics with OpenCV )

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Syntax

cv2.resize(src, dsize[, dst[, fx[, fy[, interpolation]]]])

Example

import cv2

# read image
img = cv2.imread('cat.jpg')

# resize image
resized_img = cv2.resize(img, (500, 500))

# display image
cv2.imshow('Resized Image', resized_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output

The output of the above code will show the resized image in a window.

Explanation

Resizing an image is a common image processing operation that involves changing the dimensions of an image without modifying its content. In OpenCV, the cv2.resize() function can be used to resize an image.

The cv2.resize() function takes in the source image (src) and the desired size of the output image (dsize) as its input arguments. It also allows the user to specify the scaling factors (fx and fy) to be applied to the width and height of the image. Finally, the user can also choose the interpolation method to be used during the resizing operation.

In the above example, we use the cv2.imread() function to read an image from a file. We then use the cv2.resize() function to resize the image to the desired dimensions of 500 x 500 pixels. Finally, we use the cv2.imshow() function to display the resized image.

Use

Resizing an image is often required in computer vision and image processing applications. It can be used to change the size of an image to fit a specific output target such as a display screen, a machine learning model, or a web platform.

Important Points

• The cv2.resize() function is used to resize an image in OpenCV
• It takes in the source image and the desired size of the output image as input arguments
• The function also allows the user to specify the scaling factors to be applied to the width and height of the image
• The user can also choose the interpolation method to be used during the resizing operation

Summary

In conclusion, the cv2.resize() function in OpenCV is a useful tool for resizing images. It can be used to change the dimensions of an image to fit the desired output target. By providing the desired output size, the user can obtain an image that is compatible with various applications such as computer vision, machine learning, and web development.