Transforms

transforms实战¶

In [105]:

from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
%matplotlib inline
# 加载原始图片
img = Image.open("./figures/lenna.jpg") 
print(img.size)
plt.imshow(img)

from PIL import Image from torchvision import transforms import matplotlib.pyplot as plt %matplotlib inline # 加载原始图片 img = Image.open("./figures/lenna.jpg") print(img.size) plt.imshow(img)

(316, 316)

Out[105]:

<matplotlib.image.AxesImage at 0x157bbb7b4f0>

transforms.CenterCrop(size)¶

In [106]:

# 对给定图片进行沿中心切割
# 对图片沿中心放大切割，超出图片大小的部分填0
img_centercrop1 = transforms.CenterCrop((500,500))(img)
print(img_centercrop1.size)
# 对图片沿中心缩小切割，超出期望大小的部分剔除
img_centercrop2 = transforms.CenterCrop((224,224))(img)
print(img_centercrop2.size)
plt.subplot(1,3,1),plt.imshow(img),plt.title("Original")
plt.subplot(1,3,2),plt.imshow(img_centercrop1),plt.title("500 * 500")
plt.subplot(1,3,3),plt.imshow(img_centercrop2),plt.title("224 * 224")
plt.show()

# 对给定图片进行沿中心切割 # 对图片沿中心放大切割，超出图片大小的部分填0 img_centercrop1 = transforms.CenterCrop((500,500))(img) print(img_centercrop1.size) # 对图片沿中心缩小切割，超出期望大小的部分剔除 img_centercrop2 = transforms.CenterCrop((224,224))(img) print(img_centercrop2.size) plt.subplot(1,3,1),plt.imshow(img),plt.title("Original") plt.subplot(1,3,2),plt.imshow(img_centercrop1),plt.title("500 * 500") plt.subplot(1,3,3),plt.imshow(img_centercrop2),plt.title("224 * 224") plt.show()

(500, 500)
(224, 224)

transforms.ColorJitter(brightness=0, contrast=0, saturation=0, hue=0)¶

In [107]:

# 对图片的亮度，对比度，饱和度，色调进行改变
img_CJ = transforms.ColorJitter(brightness=1,contrast=0.5,saturation=0.5,hue=0.5)(img)
print(img_CJ.size)
plt.imshow(img_CJ)

# 对图片的亮度，对比度，饱和度，色调进行改变 img_CJ = transforms.ColorJitter(brightness=1,contrast=0.5,saturation=0.5,hue=0.5)(img) print(img_CJ.size) plt.imshow(img_CJ)

(316, 316)

Out[107]:

<matplotlib.image.AxesImage at 0x157bbcb6400>

transforms.Grayscale(num_output_channels)¶

In [108]:

img_grey_c3 = transforms.Grayscale(num_output_channels=3)(img)
img_grey_c1 = transforms.Grayscale(num_output_channels=1)(img)
plt.subplot(1,2,1),plt.imshow(img_grey_c3),plt.title("channels=3")
plt.subplot(1,2,2),plt.imshow(img_grey_c1),plt.title("channels=1")
plt.show()

img_grey_c3 = transforms.Grayscale(num_output_channels=3)(img) img_grey_c1 = transforms.Grayscale(num_output_channels=1)(img) plt.subplot(1,2,1),plt.imshow(img_grey_c3),plt.title("channels=3") plt.subplot(1,2,2),plt.imshow(img_grey_c1),plt.title("channels=1") plt.show()

transforms.Resize¶

In [109]:

# 等比缩放
img_resize = transforms.Resize(224)(img)
print(img_resize.size)
plt.imshow(img_resize)

# 等比缩放 img_resize = transforms.Resize(224)(img) print(img_resize.size) plt.imshow(img_resize)

(224, 224)

Out[109]:

<matplotlib.image.AxesImage at 0x157bbdb0580>

transforms.Scale¶

In [110]:

# 等比缩放 不推荐使用此转换以支持调整大小
img_scale = transforms.Scale(224)(img)
print(img_scale.size)
plt.imshow(img_scale)

# 等比缩放 不推荐使用此转换以支持调整大小 img_scale = transforms.Scale(224)(img) print(img_scale.size) plt.imshow(img_scale)

(224, 224)

Out[110]:

<matplotlib.image.AxesImage at 0x157bbe06df0>

transforms.RandomCrop¶

In [111]:

# 随机裁剪成指定大小
# 设立随机种子
import torch
torch.manual_seed(31)
# 随机裁剪
img_randowm_crop1 = transforms.RandomCrop(224)(img)
img_randowm_crop2 = transforms.RandomCrop(224)(img)
print(img_randowm_crop1.size)
plt.subplot(1,2,1),plt.imshow(img_randowm_crop1)
plt.subplot(1,2,2),plt.imshow(img_randowm_crop2)
plt.show()

# 随机裁剪成指定大小 # 设立随机种子 import torch torch.manual_seed(31) # 随机裁剪 img_randowm_crop1 = transforms.RandomCrop(224)(img) img_randowm_crop2 = transforms.RandomCrop(224)(img) print(img_randowm_crop1.size) plt.subplot(1,2,1),plt.imshow(img_randowm_crop1) plt.subplot(1,2,2),plt.imshow(img_randowm_crop2) plt.show()

(224, 224)

transforms.RandomHorizontalFlip¶

In [112]:

# 随机左右旋转
# 设立随机种子，可能不旋转
import torch
torch.manual_seed(31)

img_random_H = transforms.RandomHorizontalFlip()(img)
print(img_random_H.size)
plt.imshow(img_random_H)

# 随机左右旋转 # 设立随机种子，可能不旋转 import torch torch.manual_seed(31) img_random_H = transforms.RandomHorizontalFlip()(img) print(img_random_H.size) plt.imshow(img_random_H)

(316, 316)

Out[112]:

<matplotlib.image.AxesImage at 0x157bbf138b0>

transforms.RandomVerticalFlip¶

In [113]:

# 随机垂直方向旋转
img_random_V = transforms.RandomVerticalFlip()(img)
print(img_random_V.size)
plt.imshow(img_random_V)

# 随机垂直方向旋转 img_random_V = transforms.RandomVerticalFlip()(img) print(img_random_V.size) plt.imshow(img_random_V)

(316, 316)

Out[113]:

<matplotlib.image.AxesImage at 0x157bbf67af0>

transforms.RandomResizedCrop¶

In [114]:

# 随机裁剪成指定大小
img_random_resizecrop = transforms.RandomResizedCrop(224,scale=(0.5,0.5))(img)
print(img_random_resizecrop.size)
plt.imshow(img_random_resizecrop)

# 随机裁剪成指定大小 img_random_resizecrop = transforms.RandomResizedCrop(224,scale=(0.5,0.5))(img) print(img_random_resizecrop.size) plt.imshow(img_random_resizecrop)

(224, 224)

Out[114]:

<matplotlib.image.AxesImage at 0x157bcf976a0>

对图片进行组合变化 tranforms.Compose()¶

In [115]:

# 对一张图片的操作可能是多种的，我们使用transforms.Compose()将他们组装起来
transformer = transforms.Compose([
    transforms.Resize(256),
    transforms.transforms.RandomResizedCrop((224), scale = (0.5,1.0)),
    transforms.RandomVerticalFlip(),
])
img_transform = transformer(img)
plt.imshow(img_transform)

# 对一张图片的操作可能是多种的，我们使用transforms.Compose()将他们组装起来 transformer = transforms.Compose([ transforms.Resize(256), transforms.transforms.RandomResizedCrop((224), scale = (0.5,1.0)), transforms.RandomVerticalFlip(), ]) img_transform = transformer(img) plt.imshow(img_transform)

Out[115]:

<matplotlib.image.AxesImage at 0x157bcff0fa0>

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最后更新: November 30, 2023
创建日期: November 30, 2023