from torchvision import transforms2.ToTensor的用法
作用：将PIL图像或者numpy转换成tensor格式
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from PIL import Image
img_path = "C:\Users\hp\PycharmProjectspythonProject\Pytorch_Learning\flower_data\traindaisy\5547758_eea9edfd54_n.jpg"
img = Image.open(img_path)
tensor = transforms.ToTensor()
tensor_img = tensor(img)
writer = SummaryWriter("logs")
writer.add_image("tensor_img", tensor_img, 1)
writer.close()
3. Normalize的用法
用均值和标准差归一化张量图像，参数：mean, std；输入图像类型为tensor，输出类型为tensor
o
u
t
p
u
t
[
c
h
a
n
n
e
l
]
=
(
i
n
p
u
t
[
c
h
a
n
n
e
l
]
−
m
e
a
n
[
c
h
a
n
n
e
l
]
)
/
s
t
d
[
c
h
a
n
n
e
l
]
output[channel] = (input[channel] - mean[channel]) / std[channel]
output[channel]=(input[channel]−mean[channel])/std[channel]
# Normalize
transform_normal = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
img_normal = transform_normal(img_tensor)
print(type(img_normal))
writer.add_image("Normalize", img_normal, 1)
4. RandomCrop 的用法
随机裁剪，参数size表示随机裁剪图片的大小，输入的图像类型为PIL类型，输出的类型也为PIL类型。
下面的这个例子是随机对图像剪裁10次，每次剪裁后的图像大小为32*32
# RandomCrop
transform_randomCrop = transforms.RandomCrop((32, 32))
for i in range(0, 10):
img_randomCrop = transform_randomCrop(img_PIL)
# print(type(img_randomCrop))
img_randomCrop = transform_tensor(img_randomCrop)
writer.add_image("RandomCrop", img_randomCrop, i)
5. Resize的用法
将输入的PIL图像裁剪成指定大小，输入参数为size（int类型或者turple类型），输出为PIL图像
# Resize
transform_resize = transforms.Resize((128, 128))
img_resize = transform_resize(img_PIL)
img_resize = transform_tensor(img_resize)
writer.add_image("Resize", img_resize, 1)
6. Compose的用法
该方法将所有方法集合到一起，按照顺序依次执行，用法示例如下：
# Compose
transform_compose = transforms.Compose([transform_resize, transform_tensor])
img_resize_2 = transform_compose(img_PIL)
writer.add_image("Compose", img_resize_2, 1)
writer.close()
7. 所有代码
from torch.utils.tensorboard import SummaryWriter
from PIL import Image
from torchvision.transforms import transforms
img_path = "C:\Users\hp\PycharmProjectspythonProject\Pytorch_Learning\flower_data\traindaisy\5547758_eea9edfd54_n.jpg"
writer = SummaryWriter("logs")
img_PIL = Image.open(img_path)
# ToTensor
transform_tensor = transforms.ToTensor()
img_tensor = transform_tensor(img_PIL)
# Normalize
transform_normal = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
img_normal = transform_normal(img_tensor)
print(type(img_normal))
writer.add_image("Normalize", img_normal, 1)
# RandomCrop
transform_randomCrop = transforms.RandomCrop((32, 32))
for i in range(0, 10):
img_randomCrop = transform_randomCrop(img_PIL)
# print(type(img_randomCrop))
img_randomCrop = transform_tensor(img_randomCrop)
writer.add_image("RandomCrop", img_randomCrop, i)
# Resize
transform_resize = transforms.Resize((128, 128))
img_resize = transform_resize(img_PIL)
img_resize = transform_tensor(img_resize)
writer.add_image("Resize", img_resize, 1)
# Compose
transform_compose = transforms.Compose([transform_resize, transform_tensor])
img_resize_2 = transform_compose(img_PIL)
writer.add_image("Compose", img_resize_2, 1)
writer.close()
