RandomResizedCrop() Method in Python PyTorch

Last Updated : 03 Jun, 2022

In this article, we are going to discuss RandomResizedCrop() method in Pytorch using Python.

RandomResizedCrop() method

RandomResizedCrop() method of torchvision.transforms module is used to crop a random area of the image and resized this image to the given size. This method accepts both PIL Image and Tensor Image. The tensor image is a PyTorch tensor with [C, H, W] shape, where C represents a number of channels and H, W represents height and width respectively. This method returns a randomly cropped image.

Syntax: torchvision.transforms.RandomResizedCrop(size, scale, ratio)

Parameters:

size: Desired crop size of the image.

scale: This parameter is used to define the upper and lower bounds for the random area.

ratio: This parameter is used to define upper and lower bounds for the random aspect ratio.

Return: This method will returns the randomly cropped image of given input size.

The below image is used for demonstration:

Example 1:

In this example, we are transforming the image with a height of 300 and a width of 600.

Python3

# import required libraries 
import torch 
import torchvision.transforms as transforms 
from PIL import Image 
  
# Read image 
image = Image.open('pic.png') 
  
# create an transform for crop the image 
# 300px height and 600px wide 
transform = transforms.RandomResizedCrop(size=(300, 600)) 
  
# use above created transform to crop 
# the image 
image_crop = transform(image) 
  
# display result 
image_crop.show() 

Output:

RandomResizedCrop() Method in Python PyTorch

Example 2:

In this example, we crop an image at a random location with the expected scale of 0.2 to 0.8.

Python3

# import required libraries 
import torch 
import torchvision.transforms as transforms 
from PIL import Image 
  
# Read image 
image = Image.open('a.png') 
  
# create an transform for crop the image 
transform = transforms.RandomResizedCrop(size=(300, 600),  
                                         scale=(0.2, 0.8)) 
  
# use above created transform to crop 
# the image 
image_crop = transform(image) 
  
# display result 
image_crop.show() 

Output:

Example 3:

In this example, we crop an image at a random location with the expected ratio of 0.5 to 1.08.

Python3

# import required libraries 
import torch 
import torchvision.transforms as transforms 
from PIL import Image 
  
# Read image 
image = Image.open('a.png') 
  
# create an transform for crop the image 
transform = transforms.RandomResizedCrop( 
    size=(300, 600), scale=(0.2, 0.8), ratio=(0.5, 1.08)) 
  
# use above created transform to crop 
# the image 
image_crop = transform(image) 
  
# display result 
image_crop.show() 