definition
transforms.Resize is a PyTorch transform that resizes an image to a given size. It is commonly used as a preprocessing step in computer vision tasks such as object detection and image classification.
The transforms.Resize class takes in one required argument, size, which can be a single integer or a tuple of two integers. If size is a single integer, the image is resized to have the same aspect ratio as the original image but with the shorter side set to size. If size is a tuple of two integers, the image is resized to have the dimensions (size[0], size[1]).
Here’s an example of how to use transforms.Resize in PyTorch:
import torch
import torchvision.transforms as transforms
from PIL import Image
# Load the image
img = Image.open('example_image.jpg')
# Create a Resize transform with a target size of 256x256
resize = transforms.Resize((256, 256))
# Apply the resize transform to the image
img_resized = resize(img)
# Show the original and resized images side by side
img.show()
img_resized.show()In this example, we first load an example image using the PIL library. Then, we create a Resize transform object with a target size of 256×256.
Next, we apply the resize transform to the image using the __call__() method, which resizes the image to the target size.
Finally, we display the original and resized images side by side using the show() method of the PIL library.
parameter–interpolation
The transforms.Resize class in PyTorch has an optional parameter called interpolation, which specifies the method used for resizing the image. The default value of interpolation is PIL.Image.BILINEAR, which corresponds to the bilinear interpolation method.
Bilinear interpolation is a commonly used method for image resizing that computes new pixel values based on weighted averages of surrounding pixels. When resizing an image, bilinear interpolation considers the four nearest pixels to the current pixel location and computes a weighted average based on their intensity values. This method is effective in preserving the overall structure and details of the original image, but it can also introduce some artifacts, such as blurring or jagged edges, if the image is resized too much.
In addition to PIL.Image.BILINEAR, transforms.Resize supports several other interpolation methods that can be specified using the interpolation parameter. Here are the supported methods:
-
PIL.Image.NEAREST: uses the nearest neighbor interpolation method, which simply selects the nearest pixel to the current pixel location. -
PIL.Image.BOX: uses the box interpolation method, which computes the average intensity value of all pixels within a square region around the current pixel location. -
PIL.Image.BICUBIC: uses the bicubic interpolation method, which computes new pixel values based on a cubic function that takes into account more surrounding pixels than bilinear interpolation.
Here’s an example of how to use transforms.Resize with different interpolation methods:
import torch
import torchvision.transforms as transforms
from PIL import Image
# Load the image
img = Image.open('example_image.jpg')
# Create Resize transforms with different interpolation methods
resize_bilinear = transforms.Resize((256, 256))
resize_nearest = transforms.Resize((256, 256), interpolation=Image.NEAREST)
resize_bicubic = transforms.Resize((256, 256), interpolation=Image.BICUBIC)
# Apply the resize transforms to the image
img_resized_bilinear = resize_bilinear(img)
img_resized_nearest = resize_nearest(img)
img_resized_bicubic = resize_bicubic(img)
# Show the original and resized images side by side
img.show()
img_resized_bilinear.show()
img_resized_nearest.show()
img_resized_bicubic.show()In this example, we create three different Resize transforms with different interpolation methods: bilinear, nearest neighbor, and bicubic. We then apply these transforms to the original image and display the resulting images side by side. This allows us to compare the different interpolation methods and see how they affect the quality of the resized images.
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