Image Resizing using OpenCV | Python

Last Updated : 30 Oct, 2025

OpenCV provides the cv2.resize() function, which allows you to resize images efficiently. By selecting different interpolation methods, you can control the balance between image quality and resizing speed.

Syntax:

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

Parameters:

src: Source/input image.
dsize: Desired size (width, height). Order is width first, then height.
dst(Optional): Output image, rarely used explicitly.
fx(optional): Scale factor along the horizontal axis.
fy(optional): Scale factor along the vertical axis.
interpolation(optional): Interpolation method to use.

Return Value: Returns a resized image as a NumPy array, which can be displayed, saved, or further processed.

Note: Use either dsize or fx/fy for scaling, dsize: when you know exact width & height and fx/fy: when you want to scale by a factor. Do not set both together (unless dsize=None)

Interpolation Methods

Interpolation is the method used to decide pixel colors when an image is resized. Below are some methods:

Method	When to Use	Description
cv2.INTER_AREA	Shrinking	Minimizes distortion while downscaling.
cv2.INTER_LINEAR	General resizing	Balances speed and quality
cv2.INTER_CUBIC	Enlarging	Higher quality for upscaling
cv2.INTER_NEAREST	Fast resizing	Quick but lower quality

Example: Resizing images using OpenCV

Output

👁 imageResizing_output

Output representing different way to resize image

Explanation:

cv2.imread(): Loads the image in BGR format.
cv2.resize(): Resizes the image using either scale factors (fx, fy) or exact dimensions (dsize). INTER_AREA: Downscaling, INTER_CUBIC: Upscaling (smooth results) and INTER_LINEAR: General resizing
cv2.cvtColor(): Converts image from BGR to RGB for correct Matplotlib display.
plt.subplot(): Arranges multiple images in a 2×2 grid.
plt.title(): Adds a title for each image.
plt.axis("off"): Hides axis lines and labels.
plt.tight_layout(): Adjusts spacing between images.