Illustration of transforms

Note

Try on collab 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 collab, 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)

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.

augmenter = v2.AugMix()
imgs = [augmenter(orig_img) for _ in range(4)]
plot([orig_img] + imgs)

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)