Illustration of transforms
Note
Try on Colab or go to the end to download the full example code.
This example illustrates some of the various transforms available in the torchvision.transforms.v2 module.
from PIL import Image
from pathlib import Path
import matplotlib.pyplot as plt
import torch
from torchvision.transforms import v2
plt.rcParams["savefig.bbox"] = 'tight'
# if you change the seed, make sure that the randomly-applied transforms
# properly show that the image can be both transformed and *not* transformed!
torch.manual_seed(0)
# If you're trying to run that on Colab, you can download the assets and the
# helpers from https://github.com/pytorch/vision/tree/main/gallery/
from helpers import plot
orig_img = Image.open(Path('../assets') / 'astronaut.jpg')
Geometric Transforms
Geometric image transformation refers to the process of altering the geometric properties of an image, such as its shape, size, orientation, or position. It involves applying mathematical operations to the image pixels or coordinates to achieve the desired transformation.
Pad
The Pad transform
(see also pad())
pads all image borders with some pixel values.
padded_imgs = [v2.Pad(padding=padding)(orig_img) for padding in (3, 10, 30, 50)]
plot([orig_img] + padded_imgs)
Resize
The Resize transform
(see also resize())
resizes an image.
resized_imgs = [v2.Resize(size=size)(orig_img) for size in (30, 50, 100, orig_img.size)]
plot([orig_img] + resized_imgs)
CenterCrop
The CenterCrop transform
(see also center_crop())
crops the given image at the center.
center_crops = [v2.CenterCrop(size=size)(orig_img) for size in (30, 50, 100, orig_img.size)]
plot([orig_img] + center_crops)
FiveCrop
The FiveCrop transform
(see also five_crop())
crops the given image into four corners and the central crop.
(top_left, top_right, bottom_left, bottom_right, center) = v2.FiveCrop(size=(100, 100))(orig_img)
plot([orig_img] + [top_left, top_right, bottom_left, bottom_right, center])
RandomPerspective
The RandomPerspective transform
(see also perspective())
performs random perspective transform on an image.
perspective_transformer = v2.RandomPerspective(distortion_scale=0.6, p=1.0)
perspective_imgs = [perspective_transformer(orig_img) for _ in range(4)]
plot([orig_img] + perspective_imgs)
RandomRotation
The RandomRotation transform
(see also rotate())
rotates an image with random angle.
rotater = v2.RandomRotation(degrees=(0, 180))
rotated_imgs = [rotater(orig_img) for _ in range(4)]
plot([orig_img] + rotated_imgs)
RandomAffine
The RandomAffine transform
(see also affine())
performs random affine transform on an image.
affine_transfomer = v2.RandomAffine(degrees=(30, 70), translate=(0.1, 0.3), scale=(0.5, 0.75))
affine_imgs = [affine_transfomer(orig_img) for _ in range(4)]
plot([orig_img] + affine_imgs)
ElasticTransform
The ElasticTransform transform
(see also elastic_transform())
Randomly transforms the morphology of objects in images and produces a
see-through-water-like effect.
elastic_transformer = v2.ElasticTransform(alpha=250.0)
transformed_imgs = [elastic_transformer(orig_img) for _ in range(2)]
plot([orig_img] + transformed_imgs)
RandomCrop
The RandomCrop transform
(see also crop())
crops an image at a random location.
cropper = v2.RandomCrop(size=(128, 128))
crops = [cropper(orig_img) for _ in range(4)]
plot([orig_img] + crops)
RandomResizedCrop
The RandomResizedCrop transform
(see also resized_crop())
crops an image at a random location, and then resizes the crop to a given
size.
resize_cropper = v2.RandomResizedCrop(size=(32, 32))
resized_crops = [resize_cropper(orig_img) for _ in range(4)]
plot([orig_img] + resized_crops)
Photometric Transforms
Photometric image transformation refers to the process of modifying the photometric properties of an image, such as its brightness, contrast, color, or tone. These transformations are applied to change the visual appearance of an image while preserving its geometric structure.
Except Grayscale, the following transforms are random,
which means that the same transform
instance will produce different result each time it transforms a given image.
Grayscale
The Grayscale transform
(see also to_grayscale())
converts an image to grayscale
gray_img = v2.Grayscale()(orig_img)
plot([orig_img, gray_img], cmap='gray')
ColorJitter
The ColorJitter transform
randomly changes the brightness, contrast, saturation, hue, and other properties of an image.
jitter = v2.ColorJitter(brightness=.5, hue=.3)
jittered_imgs = [jitter(orig_img) for _ in range(4)]
plot([orig_img] + jittered_imgs)
GaussianBlur
The GaussianBlur transform
(see also gaussian_blur())
performs gaussian blur transform on an image.
blurrer = v2.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5.))
blurred_imgs = [blurrer(orig_img) for _ in range(4)]
plot([orig_img] + blurred_imgs)
RandomInvert
The RandomInvert transform
(see also invert())
randomly inverts the colors of the given image.
inverter = v2.RandomInvert()
invertered_imgs = [inverter(orig_img) for _ in range(4)]
plot([orig_img] + invertered_imgs)
RandomPosterize
The RandomPosterize transform
(see also posterize())
randomly posterizes the image by reducing the number of bits
of each color channel.
posterizer = v2.RandomPosterize(bits=2)
posterized_imgs = [posterizer(orig_img) for _ in range(4)]
plot([orig_img] + posterized_imgs)
RandomSolarize
The RandomSolarize transform
(see also solarize())
randomly solarizes the image by inverting all pixel values above
the threshold.
solarizer = v2.RandomSolarize(threshold=192.0)
solarized_imgs = [solarizer(orig_img) for _ in range(4)]
plot([orig_img] + solarized_imgs)
RandomAdjustSharpness
The RandomAdjustSharpness transform
(see also adjust_sharpness())
randomly adjusts the sharpness of the given image.
sharpness_adjuster = v2.RandomAdjustSharpness(sharpness_factor=2)
sharpened_imgs = [sharpness_adjuster(orig_img) for _ in range(4)]
plot([orig_img] + sharpened_imgs)
RandomAutocontrast
The RandomAutocontrast transform
(see also autocontrast())
randomly applies autocontrast to the given image.
autocontraster = v2.RandomAutocontrast()
autocontrasted_imgs = [autocontraster(orig_img) for _ in range(4)]
plot([orig_img] + autocontrasted_imgs)
RandomEqualize
The RandomEqualize transform
(see also equalize())
randomly equalizes the histogram of the given image.
equalizer = v2.RandomEqualize()
equalized_imgs = [equalizer(orig_img) for _ in range(4)]
plot([orig_img] + equalized_imgs)
JPEG
The JPEG transform
(see also jpeg())
applies JPEG compression to the given image with random
degree of compression.
Augmentation Transforms
The following transforms are combinations of multiple transforms, either geometric or photometric, or both.
AutoAugment
The AutoAugment transform
automatically augments data based on a given auto-augmentation policy.
See AutoAugmentPolicy for the available policies.
policies = [v2.AutoAugmentPolicy.CIFAR10, v2.AutoAugmentPolicy.IMAGENET, v2.AutoAugmentPolicy.SVHN]
augmenters = [v2.AutoAugment(policy) for policy in policies]
imgs = [
[augmenter(orig_img) for _ in range(4)]
for augmenter in augmenters
]
row_title = [str(policy).split('.')[-1] for policy in policies]
plot([[orig_img] + row for row in imgs], row_title=row_title)
RandAugment
The RandAugment is an alternate version of AutoAugment.
augmenter = v2.RandAugment()
imgs = [augmenter(orig_img) for _ in range(4)]
plot([orig_img] + imgs)
TrivialAugmentWide
The TrivialAugmentWide is an alternate implementation of AutoAugment.
However, instead of transforming an image multiple times, it transforms an image only once
using a random transform from a given list with a random strength number.
augmenter = v2.TrivialAugmentWide()
imgs = [augmenter(orig_img) for _ in range(4)]
plot([orig_img] + imgs)
AugMix
The AugMix transform interpolates between augmented versions of an image.
Randomly-applied Transforms
The following transforms are randomly-applied given a probability p. That is, given p = 0.5,
there is a 50% chance to return the original image, and a 50% chance to return the transformed image,
even when called with the same transform instance!
RandomHorizontalFlip
The RandomHorizontalFlip transform
(see also hflip())
performs horizontal flip of an image, with a given probability.
hflipper = v2.RandomHorizontalFlip(p=0.5)
transformed_imgs = [hflipper(orig_img) for _ in range(4)]
plot([orig_img] + transformed_imgs)
RandomVerticalFlip
The RandomVerticalFlip transform
(see also vflip())
performs vertical flip of an image, with a given probability.
vflipper = v2.RandomVerticalFlip(p=0.5)
transformed_imgs = [vflipper(orig_img) for _ in range(4)]
plot([orig_img] + transformed_imgs)
RandomApply
The RandomApply transform
randomly applies a list of transforms, with a given probability.
applier = v2.RandomApply(transforms=[v2.RandomCrop(size=(64, 64))], p=0.5)
transformed_imgs = [applier(orig_img) for _ in range(4)]
plot([orig_img] + transformed_imgs)
Total running time of the script: (0 minutes 6.543 seconds)
