PyTorch②---transforms结构及用法、常见的Transforms

常见的Transforms

PIL类型：用PIL读入，Image.open()
tensor类型：用transforms转，ToTensor()
narrays类型：用opencv读入，cv.imread()

常见的方法

from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms

writer=SummaryWriter("logs")
img=Image.open("images/3.jpg")
print(img)
'''ToTensor()'''
trans_totensor=transforms.ToTensor()
img_tensor=trans_totensor(img)
writer.add_image("ToTensor",img_tensor)

'''Normalize(torch.nn.Module)'''
#输入两个参数，是平均值和方差，图片是三维的。(input-mean)/std
#output[channel] = (input[channel] - mean[channel]) / std[channel]
print(img_tensor[0][0][0])
trans_norm=transforms.Normalize([1,5,8],[3,2,1])
img_norm=trans_norm(img_tensor)
print(img_norm[0][0][0])
writer.add_image("Normalize",img_norm,1)


'''Resize(torch.nn.Module)'''
print(img)
trans_size=transforms.Resize((512,512))
#img PIL->resize->img_resize PIL
img_resize=trans_size(img)
#img_resize PIL->img_resize tensor
img_resize=trans_totensor(img_resize)
print(img_resize)
writer.add_image("Resize",img_resize,1)

'''Compose:对一张图片的多种操作合并成一种'''
trans_resize_2=transforms.Resize(512)
#先进行裁剪，再变成tensor的类型
trans_compose=transforms.Compose([trans_resize_2,trans_totensor])
img_resize_2=trans_compose(img)
writer.add_image("Resize2",img_resize_2,1)

'''RanDomCrop:随机裁剪'''
trans_random=transforms.RandomCrop((200,400))#裁剪正方形的话直接（512），一个括号
#先进行裁剪，再变成tensor的类型
trans_compose_2=transforms.Compose([trans_random,trans_totensor])
for i in range(10):#随机裁剪10步
    img_crop=trans_compose_2(img)
    writer.add_image("RanDomCrop1",img_crop,i)
writer.close()