5 Best Ways to Resize an Image in OpenCV Using Python

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💡 Problem Formulation: Resizing images is a common preprocessing step in many computer vision applications. When dealing with images in OpenCV using Python, you might have a high-resolution image and you need to scale it down or up for various reasons such as reducing the computational cost or fitting a particular display size. For example, you may have an input image of 4000×3000 pixels and you want to resize it to be 1000×750 pixels.

Method 1: Resize with a Specific Width and Height

The most direct method to resize an image is to specify the exact dimensions you want. OpenCV provides the cv2.resize() function which takes an image and new dimensions as mandatory arguments. This can be useful when you know the exact size the output image needs to be.

Here’s an example:

import cv2
# Load the image
image = cv2.imread('image.jpg')
# Resize the image
resized_image = cv2.resize(image, (1000, 750))
# Save the resized image
cv2.imwrite('resized_image.jpg', resized_image)

Output: An image resized to 1000×750 pixels.

This code snippet loads an image using cv2.imread(), then it resizes the image to 1000×750 pixels using cv2.resize(). The resized image is then saved with cv2.imwrite().

Method 2: Resizing While Maintaining Aspect Ratio

To maintain the aspect ratio of the image when resizing, you need to calculate the ratio of the new dimensions to the old dimensions and use that ratio to adjust the width or height accordingly. This prevents distortion of the image.

Here’s an example:

import cv2
# Load the image
image = cv2.imread('image.jpg')
# Calculate the ratio
h, w = image.shape[:2]
new_width = 500
ratio = new_width / w
new_height = int(h * ratio)
# Resize the image
resized_image = cv2.resize(image, (new_width, new_height))
# Save the resized image
cv2.imwrite('resized_image.jpg', resized_image)

Output: An image resized to a new width of 500 pixels with the aspect ratio maintained.

After loading the image, we get the current width and height, calculate the new height while maintaining the aspect ratio, and then resize the image with cv2.resize().

Method 3: Resizing Using Interpolation Methods

OpenCV provides different interpolation methods for resizing images. Beyond the default interpolation method, you can specify others like INTER_LINEAR, INTER_CUBIC, or INTER_NEAREST to achieve different effects or optimize for different use cases.

Here’s an example:

import cv2
# Load the image
image = cv2.imread('image.jpg')
# Resize the image with a different interpolation - cubic
resized_image = cv2.resize(image, (1000, 750), interpolation=cv2.INTER_CUBIC)
# Save the resized image
cv2.imwrite('resized_image_cubic.jpg', resized_image)

Output: An image resized to 1000×750 pixels using cubic interpolation.

This snippet shows how to resize an image using cubic interpolation by setting the interpolation parameter in the cv2.resize() function.

Method 4: Resizing by Scaling Factors

Instead of specifying the target size of an image, OpenCV also allows you to use scaling factors for both the X and Y axes. This method is helpful when you need to reduce or increase the size of an image by a certain percentage.

Here’s an example:

import cv2
# Load the image
image = cv2.imread('image.jpg')
# Scale the image
fx, fy = 0.5, 0.5  # scale factors
resized_image = cv2.resize(image, None, fx=fx, fy=fy)
# Save the resized image
cv2.imwrite('resized_image_scale.jpg', resized_image)

Output: An image resized to 50% of its original dimensions.

The line with cv2.resize() uses fx and fy to scale the width and height of the image by 50%, respectively; None in the dimension argument tells OpenCV to derive the dimensions from the scale factors.

Bonus One-Liner Method 5: Resizing with Python Imaging Library (PIL)

While OpenCV is powerful, for a simple resize operation you might want to consider PIL, the Python Imaging Library. It offers a more Pythonic API and can be easier for some straightforward, non-intensive tasks.

Here’s an example:

from PIL import Image
# Open an image file
with Image.open('image.jpg') as img:
    # Resize the image
    img_resized = img.resize((1000, 750))
    # Save it back to disk
    img_resized.save('resized_image_PIL.jpg')

Output: An image resized to 1000×750 pixels using the Python Imaging Library.

This one-liner opens the image, resizes it using the .resize() method from PIL, and saves it. Note that this doesn’t maintain the aspect ratio.

Summary/Discussion

  • Method 1: Resize with Specific Dimensions. Strong for precision. Weak if the aspect ratio needs to be maintained without additional calculations.
  • Method 2: Maintaining Aspect Ratio. Ideal for consistent display. Requires initial aspect ratio calculation.
  • Method 3: Use of Interpolation Methods. Offers trade-offs between speed and quality. Advanced method for nuanced resizing needs.
  • Method 4: Resizing by Scaling Factors. Good for relative resizing operations. Risk of losing image quality if not cautious with factors.
  • Bonus Method 5: Python Imaging Library. More Pythonic syntax. Simple for quick tasks but less feature-rich than OpenCV.