In this article, we are going to discuss How to adjust the saturation of an image in PyTorch.
adjust_saturation() method
Saturation is basically used to adjust the intensity of the color of the given Image, we can adjust the saturation of an image by using the adjust_saturation() method of torchvision.transforms module. The method accepts a batch of tensor images, PIL images, and tensor images as input. With tensor, we provide shapes in [C, H, W], where C represents the number of channels and H, and W represents the height and width respectively. This method returns Saturation adjusted image. The below syntax is used to adjust the saturation of an image.
Syntax: torchvision.transforms.functional.adjust_saturation(img, saturation_factor)
Parameter:
- img: This is our input image for which we adjust it’s saturation.
- saturation_factor: This parameter is used to define how much saturation is to be adjusted. 0 and 1 will give a black-and-white and original image respectively.
Return: This method returns a Saturation adjusted image.
The below image is used for demonstration:
Example 1
In this example, we are adjusting the saturation of an image.
Python3
# import required library import torch import torchvision import torchvision.transforms as T import torchvision.transforms.functional as F from torchvision.io import read_image # read the image from computer pic = read_image('img.png') # adjust the saturation of the input image pic = F.adjust_saturation(pic, 10) pic = T.ToPILImage()(pic) # display result pic.show() |
Output:
Example 2
In this example, we are adjusting the saturation of an image when saturation_factor=0. it gives us a black and white image.
Python3
# import required library import torch import torchvision import torchvision.transforms as T import torchvision.transforms.functional as F from torchvision.io import read_image # read the image from computer pic = read_image('img.png') # adjust the saturation of the input image # saturation_factor = 0 pic = F.adjust_saturation(pic, 0) pic = T.ToPILImage()(pic) # display result pic.show() |
Output:
Example 3
In this example, we are adjusting the saturation of an image when saturation_factor=1. it gives us the original image.
Python3
# import required library import torch import torchvision import torchvision.transforms as T import torchvision.transforms.functional as F from torchvision.io import read_image # read the image from computer pic = read_image('img.png') # adjust the saturation of the input image # saturation_factor = 1 pic = F.adjust_saturation(pic, 1) pic = T.ToPILImage()(pic) # display result pic.show() |
Output:
