Transforms使用

from PIL import Image

from torchvision import transforms

from torch.utils.tensorboard import SummaryWriter

# 创建一个 TensorBoard 日志记录器

writer = SummaryWriter("Logs")

# 打开图像

img = Image.open("data/train/ants_image/0013035.jpg")

print(img)  # 打印图像信息

# 将图像转换为张量

tensor_trans = transforms.ToTensor()

img_tensor = tensor_trans(img)

writer.add_image("ToTensor", img_tensor)  # 将张量图像添加到 TensorBoard

# 打印张量的第一个像素值

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

# 归一化转换

trans_norm = transforms.Normalize([6, 3, 2], [9, 3, 5])

img_norm = trans_norm(img_tensor)

print(img_norm[0][0][0])  # 打印归一化后的第一个像素值

writer.add_image("Normalize", img_norm, 1)  # 将归一化图像添加到 TensorBoard

# 打印原始图像大小

print(img.size)

# 调整图像大小

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

img_resize = trans_resize(img)  # 将 PIL 图像调整大小

img_resize = tensor_trans(img_resize)  # 将调整后的图像转换为张量

writer.add_image("Resize", img_resize, 0)  # 将调整大小的图像添加到 TensorBoard

print(img_resize)  # 打印调整大小后的张量

# 使用 Compose 进行多步转换

trans_resize_2 = transforms.Resize(512)  # 调整图像的短边到 512

trans_compose = transforms.Compose([trans_resize_2, tensor_trans])  # 组合转换

img_resize_2 = trans_compose(img)  # 应用组合转换

writer.add_image("Resize", img_resize_2, 1)  # 将组合转换后的图像添加到 TensorBoard

# 关闭 TensorBoard 日志记录器

writer.close()