Resizing the Image Using OpenCV and Python

The following article will help you to resizing the images in python using OpenCV.Resizing an image means change the dimensions of the image change either width of it or height of it or both at the same time. To resize the image OpenCV provides the cv2.resize().

Resizing an image can be done in many ways:

1. Preserved Aspect Ratio (Height to width ratio) a. Downscale (Decreases the size of image) b. Upscale (Increases the size of image)

2. Do not Preserve the Aspect ratio a. Resizing only width b. Resizing only height3. Resizing in both : height and width

Step 1: Create the file

Install the geditor on your system for installing you need to enable Wi-Fi.

sudo apt-get install gedit

Create a new Python file using editor by following command:

gedit filename.py

1. Preserved Aspect Ratio a. Downscale with resize(): In below code scale_percent is holding the value of percentage by which image has to be scaled. If the value of scale_percent is <100 then the image will be scaled down. Here the scale_percent of original image is used to find the height and width of the output image.

scale_percent = 60 # percent of original size 
width = int(img.shape[1] * scale_percent / 100) 
height = int(img.shape[0] * scale_percent / 100) 
dim = (width, height) # resize image in down scale 
resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA) 
print('Resized Dimensions : ',resized.shape) 
cv2.imwrite('/root/vidhi/resize.png', resized)

Output: The original dimensions are: (99, 194, 4)

The resized Dimensions are: (59, 116, 4) b. Upscale with resize(): Here, In up scaling the value of scale_percent is providing >100. In up scalding also the scale_percent is given to find the height and width of the output image. For that code is given below:

scale_percent = 220 # percent of original size 
width = int(img.shape[1] * scale_percent / 100) 
height = int(img.shape[0] * scale_percent / 100) 
dim = (width, height) # resize image in up scale 
resized_up = cv2.resize(img, dim, interpolation = cv2.INTER_AREA) 
print('Resized Dimensions : ',resized_up.shape) 
cv2.imwrite('root/vidhi/resize_up.png', resized_up) 

Output: The original dimensions are:(99, 194, 4) The resized dimensions are: (217, 426, 4)

2. Do not Preserve the Aspect Ratio a. Resizing only width: In this code we providing specific amount of width but height is not changed it will remain as it is.

width = 440 
height = img.shape[0] # keep original height 
dim = (width, height) # resize image 
resized_w = cv2.resize(img, dim, interpolation = cv2.INTER_AREA) 
print('Resized Dimensions : ',resized_w.shape) 
cv2.imwrite('root/vidhi/resize_w.png', resized_w)

Output: The Original dimensions are: (99, 194, 4) The resized dimensions are:(99, 440, 4)

b. Resizing only height: In this code we providing specific amount of height but width is not changed it will remain as it is.

width = img.shape[1] # keep original width 
height = 440 
dim = (width, height) # resize image 
resized_h = cv2.resize(img, dim, interpolation = cv2.INTER_AREA) 
print('Resized Dimensions : ',resized_h.shape) 
cv2.imwrite'/root/vidhi/resize_h.png', resized_h) 

Output: The Original dimensions are: (99, 194, 4) The resized dimensions are: (440, 194, 4)

3. Resize to specific width and height: In following code we will providing the specific height and width.

width = 350 
height = 450 
dim = (width, height) # resize image 
resized_hw = cv2.resize(img, dim, interpolation = cv2.INTER_AREA) 
print('Resized Dimensions : ',resized_hw.shape) 
cv2.imwrite('/root/vidhi/resize_hw.png', resized_hw)

Output: The Original dimensions are: (99, 194, 4) The resized dimensions are: (450, 350, 4)

This is the combined output of all the resized Images:

This is the termial output:

This article will help you to resize the images when you want to reduce the pixels of an image. It can also use when you want to zoom the image.