Note
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Illustration of transforms¶
This example illustrates the various transforms available in the torchvision.transforms module.
# sphinx_gallery_thumbnail_path = "../../gallery/assets/transforms_thumbnail.png"
from PIL import Image
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import torch
import torchvision.transforms as T
plt.rcParams["savefig.bbox"] = 'tight'
orig_img = Image.open(Path('assets') / 'astronaut.jpg')
# 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)
def plot(imgs, with_orig=True, row_title=None, **imshow_kwargs):
if not isinstance(imgs[0], list):
# Make a 2d grid even if there's just 1 row
imgs = [imgs]
num_rows = len(imgs)
num_cols = len(imgs[0]) + with_orig
fig, axs = plt.subplots(nrows=num_rows, ncols=num_cols, squeeze=False)
for row_idx, row in enumerate(imgs):
row = [orig_img] + row if with_orig else row
for col_idx, img in enumerate(row):
ax = axs[row_idx, col_idx]
ax.imshow(np.asarray(img), **imshow_kwargs)
ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
if with_orig:
axs[0, 0].set(title='Original image')
axs[0, 0].title.set_size(8)
if row_title is not None:
for row_idx in range(num_rows):
axs[row_idx, 0].set(ylabel=row_title[row_idx])
plt.tight_layout()
Pad¶
The Pad
transform
(see also pad()
)
fills image borders with some pixel values.
padded_imgs = [T.Pad(padding=padding)(orig_img) for padding in (3, 10, 30, 50)]
plot(padded_imgs)
Resize¶
The Resize
transform
(see also resize()
)
resizes an image.
resized_imgs = [T.Resize(size=size)(orig_img) for size in (30, 50, 100, orig_img.size)]
plot(resized_imgs)
CenterCrop¶
The CenterCrop
transform
(see also center_crop()
)
crops the given image at the center.
center_crops = [T.CenterCrop(size=size)(orig_img) for size in (30, 50, 100, orig_img.size)]
plot(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) = T.FiveCrop(size=(100, 100))(orig_img)
plot([top_left, top_right, bottom_left, bottom_right, center])
Grayscale¶
The Grayscale
transform
(see also to_grayscale()
)
converts an image to grayscale
gray_img = T.Grayscale()(orig_img)
plot([gray_img], cmap='gray')
Random transforms¶
The following transforms are random, which means that the same transfomer instance will produce different result each time it transforms a given image.
ColorJitter¶
The ColorJitter
transform
randomly changes the brightness, saturation, and other properties of an image.
jitter = T.ColorJitter(brightness=.5, hue=.3)
jitted_imgs = [jitter(orig_img) for _ in range(4)]
plot(jitted_imgs)
GaussianBlur¶
The GaussianBlur
transform
(see also gaussian_blur()
)
performs gaussian blur transform on an image.
blurrer = T.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5))
blurred_imgs = [blurrer(orig_img) for _ in range(4)]
plot(blurred_imgs)
RandomPerspective¶
The RandomPerspective
transform
(see also perspective()
)
performs random perspective transform on an image.
perspective_transformer = T.RandomPerspective(distortion_scale=0.6, p=1.0)
perspective_imgs = [perspective_transformer(orig_img) for _ in range(4)]
plot(perspective_imgs)
RandomRotation¶
The RandomRotation
transform
(see also rotate()
)
rotates an image with random angle.
rotater = T.RandomRotation(degrees=(0, 180))
rotated_imgs = [rotater(orig_img) for _ in range(4)]
plot(rotated_imgs)
RandomAffine¶
The RandomAffine
transform
(see also affine()
)
performs random affine transform on an image.
affine_transfomer = T.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(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 = T.ElasticTransform(alpha=250.0)
transformed_imgs = [elastic_transformer(orig_img) for _ in range(2)]
plot(transformed_imgs)
RandomCrop¶
The RandomCrop
transform
(see also crop()
)
crops an image at a random location.
cropper = T.RandomCrop(size=(128, 128))
crops = [cropper(orig_img) for _ in range(4)]
plot(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 = T.RandomResizedCrop(size=(32, 32))
resized_crops = [resize_cropper(orig_img) for _ in range(4)]
plot(resized_crops)
RandomInvert¶
The RandomInvert
transform
(see also invert()
)
randomly inverts the colors of the given image.
inverter = T.RandomInvert()
invertered_imgs = [inverter(orig_img) for _ in range(4)]
plot(invertered_imgs)
RandomPosterize¶
The RandomPosterize
transform
(see also posterize()
)
randomly posterizes the image by reducing the number of bits
of each color channel.
posterizer = T.RandomPosterize(bits=2)
posterized_imgs = [posterizer(orig_img) for _ in range(4)]
plot(posterized_imgs)
RandomSolarize¶
The RandomSolarize
transform
(see also solarize()
)
randomly solarizes the image by inverting all pixel values above
the threshold.
solarizer = T.RandomSolarize(threshold=192.0)
solarized_imgs = [solarizer(orig_img) for _ in range(4)]
plot(solarized_imgs)
RandomAdjustSharpness¶
The RandomAdjustSharpness
transform
(see also adjust_sharpness()
)
randomly adjusts the sharpness of the given image.
sharpness_adjuster = T.RandomAdjustSharpness(sharpness_factor=2)
sharpened_imgs = [sharpness_adjuster(orig_img) for _ in range(4)]
plot(sharpened_imgs)
RandomAutocontrast¶
The RandomAutocontrast
transform
(see also autocontrast()
)
randomly applies autocontrast to the given image.
autocontraster = T.RandomAutocontrast()
autocontrasted_imgs = [autocontraster(orig_img) for _ in range(4)]
plot(autocontrasted_imgs)
RandomEqualize¶
The RandomEqualize
transform
(see also equalize()
)
randomly equalizes the histogram of the given image.
equalizer = T.RandomEqualize()
equalized_imgs = [equalizer(orig_img) for _ in range(4)]
plot(equalized_imgs)
AutoAugment¶
The AutoAugment
transform
automatically augments data based on a given auto-augmentation policy.
See AutoAugmentPolicy
for the available policies.
policies = [T.AutoAugmentPolicy.CIFAR10, T.AutoAugmentPolicy.IMAGENET, T.AutoAugmentPolicy.SVHN]
augmenters = [T.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(imgs, row_title=row_title)
RandAugment¶
The RandAugment
transform automatically augments the data.
augmenter = T.RandAugment()
imgs = [augmenter(orig_img) for _ in range(4)]
plot(imgs)
TrivialAugmentWide¶
The TrivialAugmentWide
transform automatically augments the data.
augmenter = T.TrivialAugmentWide()
imgs = [augmenter(orig_img) for _ in range(4)]
plot(imgs)
AugMix¶
The AugMix
transform automatically augments the data.
Randomly-applied transforms¶
Some transforms are randomly-applied given a probability p
. That is, the
transformed image may actually be the same as the original one, even when
called with the same transformer instance!
RandomHorizontalFlip¶
The RandomHorizontalFlip
transform
(see also hflip()
)
performs horizontal flip of an image, with a given probability.
hflipper = T.RandomHorizontalFlip(p=0.5)
transformed_imgs = [hflipper(orig_img) for _ in range(4)]
plot(transformed_imgs)
RandomVerticalFlip¶
The RandomVerticalFlip
transform
(see also vflip()
)
performs vertical flip of an image, with a given probability.
vflipper = T.RandomVerticalFlip(p=0.5)
transformed_imgs = [vflipper(orig_img) for _ in range(4)]
plot(transformed_imgs)
RandomApply¶
The RandomApply
transform
randomly applies a list of transforms, with a given probability.
applier = T.RandomApply(transforms=[T.RandomCrop(size=(64, 64))], p=0.5)
transformed_imgs = [applier(orig_img) for _ in range(4)]
plot(transformed_imgs)
Total running time of the script: ( 0 minutes 8.586 seconds)