Transforms 示例¶
此示例说明了 torchvision.transforms.v2 模块中可用的各种转换。
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')
几何变换¶
几何图像变换是指改变图像几何属性(如形状、大小、方向或位置)的过程。它涉及对图像像素或坐标应用数学运算,以实现所需的变换。
填充¶
Pad
转换(另请参阅 pad()
)用一些像素值填充所有图像边界。
padded_imgs = [v2.Pad(padding=padding)(orig_img) for padding in (3, 10, 30, 50)]
plot([orig_img] + padded_imgs)
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调整大小¶
Resize
转换(另请参阅 resize()
)调整图像大小。
resized_imgs = [v2.Resize(size=size)(orig_img) for size in (30, 50, 100, orig_img.size)]
plot([orig_img] + resized_imgs)
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中心裁剪¶
CenterCrop
转换(另请参阅 center_crop()
)在中心裁剪给定的图像。
center_crops = [v2.CenterCrop(size=size)(orig_img) for size in (30, 50, 100, orig_img.size)]
plot([orig_img] + center_crops)
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五点裁剪¶
FiveCrop
转换(另请参阅 five_crop()
)将给定的图像裁剪成四个角和中心裁剪。
(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])
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随机透视¶
RandomPerspective
转换(另请参阅 perspective()
)对图像执行随机透视变换。
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)
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随机旋转¶
RandomRotation
转换(另请参阅 rotate()
)以随机角度旋转图像。
rotater = v2.RandomRotation(degrees=(0, 180))
rotated_imgs = [rotater(orig_img) for _ in range(4)]
plot([orig_img] + rotated_imgs)
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随机仿射¶
RandomAffine
转换(另请参阅 affine()
)对图像执行随机仿射变换。
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)
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弹性变换¶
ElasticTransform
变换(另请参阅 elastic_transform()
)随机变换图像中物体的形态,并产生类似水波纹的效果。
elastic_transformer = v2.ElasticTransform(alpha=250.0)
transformed_imgs = [elastic_transformer(orig_img) for _ in range(2)]
plot([orig_img] + transformed_imgs)
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随机裁剪¶
RandomCrop
变换(另请参阅 crop()
)在随机位置裁剪图像。
cropper = v2.RandomCrop(size=(128, 128))
crops = [cropper(orig_img) for _ in range(4)]
plot([orig_img] + crops)
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随机调整大小裁剪¶
RandomResizedCrop
变换(另请参阅 resized_crop()
)在随机位置裁剪图像,然后将裁剪后的图像调整为给定大小。
resize_cropper = v2.RandomResizedCrop(size=(32, 32))
resized_crops = [resize_cropper(orig_img) for _ in range(4)]
plot([orig_img] + resized_crops)
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光度变换¶
光度图像变换是指修改图像的光度属性(如亮度、对比度、颜色或色调)的过程。这些变换用于改变图像的视觉外观,同时保留其几何结构。
除了 Grayscale
之外,以下变换都是随机的,这意味着即使使用相同的变换实例,每次变换给定图像时也会产生不同的结果。
灰度¶
Grayscale
变换(另请参阅 to_grayscale()
)将图像转换为灰度
gray_img = v2.Grayscale()(orig_img)
plot([orig_img, gray_img], cmap='gray')
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颜色抖动¶
ColorJitter
变换随机更改图像的亮度、对比度、饱和度、色调和其他属性。
jitter = v2.ColorJitter(brightness=.5, hue=.3)
jittered_imgs = [jitter(orig_img) for _ in range(4)]
plot([orig_img] + jittered_imgs)
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高斯模糊¶
GaussianBlur
变换(另请参阅 gaussian_blur()
)对图像执行高斯模糊变换。
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)
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随机反色¶
RandomInvert
变换(另请参阅 invert()
)随机反转给定图像的颜色。
inverter = v2.RandomInvert()
invertered_imgs = [inverter(orig_img) for _ in range(4)]
plot([orig_img] + invertered_imgs)
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随机色调分离¶
RandomPosterize
变换(另请参阅 posterize()
)通过减少每个颜色通道的位数来随机对图像进行色调分离。
posterizer = v2.RandomPosterize(bits=2)
posterized_imgs = [posterizer(orig_img) for _ in range(4)]
plot([orig_img] + posterized_imgs)
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随机日光化¶
RandomSolarize
变换(另请参阅 solarize()
)通过反转阈值以上的所有像素值来随机对图像进行日光化。
solarizer = v2.RandomSolarize(threshold=192.0)
solarized_imgs = [solarizer(orig_img) for _ in range(4)]
plot([orig_img] + solarized_imgs)
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随机调整锐度¶
RandomAdjustSharpness
变换(另请参阅 adjust_sharpness()
)随机调整给定图像的锐度。
sharpness_adjuster = v2.RandomAdjustSharpness(sharpness_factor=2)
sharpened_imgs = [sharpness_adjuster(orig_img) for _ in range(4)]
plot([orig_img] + sharpened_imgs)
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随机自动对比度¶
RandomAutocontrast
变换(另请参阅 autocontrast()
)随机将自动对比度应用于给定的图像。
autocontraster = v2.RandomAutocontrast()
autocontrasted_imgs = [autocontraster(orig_img) for _ in range(4)]
plot([orig_img] + autocontrasted_imgs)
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随机均衡化¶
RandomEqualize
变换(另请参阅 equalize()
)随机均衡化给定图像的直方图。
equalizer = v2.RandomEqualize()
equalized_imgs = [equalizer(orig_img) for _ in range(4)]
plot([orig_img] + equalized_imgs)
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JPEG¶
JPEG
变换(另请参阅 jpeg()
)以随机压缩程度将 JPEG 压缩应用于给定的图像。
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增强变换¶
以下变换是多种变换的组合,可以是几何变换、光度变换或两者兼有。
AutoAugment¶
AutoAugment
变换根据给定的自动增强策略自动增强数据。有关可用策略,请参阅 AutoAugmentPolicy
。
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)
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RandAugment¶
RandAugment
是 AutoAugment 的替代版本。
augmenter = v2.RandAugment()
imgs = [augmenter(orig_img) for _ in range(4)]
plot([orig_img] + imgs)
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TrivialAugmentWide¶
TrivialAugmentWide
是 AutoAugment 的替代实现。但是,它不是多次变换图像,而是仅使用来自给定列表的随机变换和随机强度数变换图像一次。
augmenter = v2.TrivialAugmentWide()
imgs = [augmenter(orig_img) for _ in range(4)]
plot([orig_img] + imgs)
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AugMix¶
AugMix
变换在图像的增强版本之间进行插值。
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随机应用变换¶
以下变换以给定的概率 p
随机应用。也就是说,给定 p = 0.5
,即使使用相同的变换实例调用,也有 50% 的几率返回原始图像,50% 的几率返回变换后的图像!
随机水平翻转¶
RandomHorizontalFlip
变换(另请参阅 hflip()
)以给定的概率对图像执行水平翻转。
hflipper = v2.RandomHorizontalFlip(p=0.5)
transformed_imgs = [hflipper(orig_img) for _ in range(4)]
plot([orig_img] + transformed_imgs)
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随机垂直翻转¶
RandomVerticalFlip
变换(另请参阅 vflip()
)以给定的概率对图像执行垂直翻转。
vflipper = v2.RandomVerticalFlip(p=0.5)
transformed_imgs = [vflipper(orig_img) for _ in range(4)]
plot([orig_img] + transformed_imgs)
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随机应用¶
RandomApply
变换以给定的概率随机应用变换列表。
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)
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脚本总运行时间:(0 分 6.437 秒)