from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
%matplotlib inline
# 加载原始图片
img = Image.open("./lenna.jpg")
print(img.size)
plt.imshow(img)
(316, 316)
<matplotlib.image.AxesImage at 0x157bbb7b4f0>
# 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)
# 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)
<matplotlib.image.AxesImage at 0x157bbcb6400>
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()
# 等比缩放
img_resize = transforms.Resize(224)(img)
print(img_resize.size)
plt.imshow(img_resize)
(224, 224)
<matplotlib.image.AxesImage at 0x157bbdb0580>
# 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)
<matplotlib.image.AxesImage at 0x157bbe06df0>
# 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)
# 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)
<matplotlib.image.AxesImage at 0x157bbf138b0>
# 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)
<matplotlib.image.AxesImage at 0x157bbf67af0>
# 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)
<matplotlib.image.AxesImage at 0x157bcf976a0>
# 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)
<matplotlib.image.AxesImage at 0x157bcff0fa0>
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》