plot_transforms_illustrations.py

"""
==========================
Illustration of transforms
==========================

.. note::
    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_transforms_illustrations.ipynb>`_
    or :ref:`go to the end <sphx_glr_download_auto_examples_transforms_plot_transforms_illustrations.py>` to download the full example code.

This example illustrates some of the various transforms available in :ref:`the
torchvision.transforms.v2 module <transforms>`.
"""
# %%

# sphinx_gallery_thumbnail_path = "../../gallery/assets/transforms_thumbnail.png"

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 :class:`~torchvision.transforms.Pad` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.Resize` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.CenterCrop` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.FiveCrop` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.RandomPerspective` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.RandomRotation` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.RandomAffine` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.ElasticTransform` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.RandomCrop` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.RandomResizedCrop` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.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 :class:`~torchvision.transforms.Grayscale` transform
# (see also :func:`~torchvision.transforms.functional.to_grayscale`)
# converts an image to grayscale
gray_img = v2.Grayscale()(orig_img)
plot([orig_img, gray_img], cmap='gray')

# %%
# ColorJitter
# ~~~~~~~~~~~
# The :class:`~torchvision.transforms.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 :class:`~torchvision.transforms.GaussianBlur` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.RandomInvert` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.RandomPosterize` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.RandomSolarize` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.RandomAdjustSharpness` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.RandomAutocontrast` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.RandomEqualize` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.v2.JPEG` transform
# (see also :func:`~torchvision.transforms.v2.functional.jpeg`)
# applies JPEG compression to the given image with random
# degree of compression.
jpeg = v2.JPEG((5, 50))
jpeg_imgs = [jpeg(orig_img) for _ in range(4)]
plot([orig_img] + jpeg_imgs)

# %%
# Augmentation Transforms
# -----------------------
# The following transforms are combinations of multiple transforms,
# either geometric or photometric, or both.
#
# AutoAugment
# ~~~~~~~~~~~
# The :class:`~torchvision.transforms.AutoAugment` transform
# automatically augments data based on a given auto-augmentation policy.
# See :class:`~torchvision.transforms.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 :class:`~torchvision.transforms.RandAugment` is an alternate version of AutoAugment.
augmenter = v2.RandAugment()
imgs = [augmenter(orig_img) for _ in range(4)]
plot([orig_img] + imgs)

# %%
# TrivialAugmentWide
# ~~~~~~~~~~~~~~~~~~
# The :class:`~torchvision.transforms.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 :class:`~torchvision.transforms.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 :class:`~torchvision.transforms.RandomHorizontalFlip` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.RandomVerticalFlip` transform
# (see also :func:`~torchvision.transforms.functional.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 :class:`~torchvision.transforms.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)