# How to perform random affine transformation of an image in PyTorch

• Last Updated : 13 May, 2022

In this article, we will cover how to perform the random affine transformation of an image in PyTorch.

## RandomAffine() method

RandomAffine() method accepts PIL Image and Tensor Image. The tensor image is a PyTorch tensor with [C, H, W] shape, where C represents the number of channels and  H, W represents the height and width respectively. This method returns the affine transformed image of the input image. The below syntax is used to perform the affine transformation of an image in PyTorch.

Syntax: torchvision.transforms.RandomAffine(degree)

Parameters:

• degree: This is our desired range of degree. It’s a sequence like (min, max).

Return: This method returns the affine transformed image of the input image.

The below image is used for demonstration: ### Example 1:

The following example is to understand how to perform the random affine transformation of an image in PyTorch whereas, the desired range of degree is (50,60).

## Python3

 `# import required libraries``import` `torch``from` `PIL ``import` `Image``import` `torchvision.transforms as transforms` `# Read input image from computer``img ``=` `Image.``open``(``'a.jpg'``)` `# define an transform``transform ``=` `transforms.RandomAffine((``50``, ``60``))` `# apply the above transform on image``img ``=` `transform(img)` `# display image after apply transform``img.show()`

Output: ### Example 2:

The following example is to know how to perform the random affine transformation of an image in PyTorch whereas, the desired range of degrees is (30).

## Python3

 `# import required libraries``import` `torch``from` `PIL ``import` `Image``import` `torchvision.transforms as transforms` `# Read input image from computer``img ``=` `Image.``open``(``'a.jpg'``)` `# define an transform``transform ``=` `transforms.RandomAffine((``30``))` `# apply the above transform on image``img ``=` `transform(img)` `# display image after apply transform``img.show()`

Output: My Personal Notes arrow_drop_up