python transforms_PyTorch教程【六】Transforms的使用

1、Transforms的使用

from PIL import Image

from torch.utils.tensorboard import SummaryWriter

from torchvision import transforms

# python的用法->tensor数据类型

# 通过transforms.ToTensor去看两个问题

# 绝对路径：D:\leran_pytorch\dataset\train\ants\0013035.jpg

# 相对路径：dataset/train/ants/0013035.jpg

img_path = "dataset/train/ants/0013035.jpg"

img = Image.open(img_path)

writer = SummaryWriter("logs")

# 1、transforms该如何使用(python)

# 2、为什么我们需要Tensor数据类型

tensor_trans = transforms.ToTensor()

tensor_img = tensor_trans(img)

writer.add_image("Tensor_img", tensor_img)

writer.close()

2、常见的Transforms

from PIL import Image

from torch.utils.tensorboard import SummaryWriter

from torchvision import transforms

writer = SummaryWriter("logs")

img = Image.open("images/6a00d8341c630a53ef00e553d0beb18834-800wi.jpg")

print(img)

# ToTensor

trans_totensor = transforms.ToTensor()

img_tensor = trans_totensor(img)

writer.add_image("ToTensor", img_tensor)

# Normalize

print(img_tensor[0][0][0])

trans_norm = transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])

img_norm = trans_norm(img_tensor)

print(img_norm[0][0][0])

writer.add_image("Normalize", img_norm, 2)

# Resize

print(img.size)

trans_resize = transforms.Resize((512, 512))

# img PIL -> resize -> img_resize PIL

img_resize = trans_resize(img)

# img_resize PIL->totensor ->img_resize tensor

img_resize = trans_totensor(img_resize)

writer.add_image("Resize", img_resize, 0)

print(img_resize)

# Compose - resize - 2

trans_resize_2 = transforms.Resize(512)

# PIL -> PIL ->tensor

trans_compose = pose([trans_resize_2, trans_totensor])

img_resize_2 = trans_compose(img)

writer.add_image("Resize", img_resize_2, 1)

# RandomCrop

trans_random = transforms.RandomCrop(512)

trans_compose_2 = pose([trans_random, [trans_totensor]])

for i in range(10):

img_crop = trans_compose_2(img)

writer.add_image("RandomCrop", img_crop, i)

writer.close()

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