44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
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from PIL import Image
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from torch.utils.tensorboard import SummaryWriter
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from torchvision import transforms
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writer = SummaryWriter("logs")
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img = Image.open(r"images/7759525_1363d24e88.jpg")
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# TeTensor的使用
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trans_totensor = transforms.ToTensor()
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img_tensor = trans_totensor(img)
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writer.add_image("ToTensor", img_tensor)
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# Normalize的使用
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print(img_tensor[0][0][0])
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trans_norm = transforms.Normalize([1, 3, 5], [3, 2, 1])
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img_norm = trans_norm(img_tensor)
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print(img_norm[0][0][0])
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writer.add_image("Normalize", img_norm,2)
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# Resize的使用
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print(img.size)
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trans_resize = transforms.Resize((512, 512))
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# img PIL -> resize -> img_resize PIL
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img_resize = trans_resize(img) # 返回的是PIL
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# img_resize PIL -> ToTensor -> img_resize Tensor
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img_resize = trans_totensor(img_resize) # PIL转换为Tensor
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writer.add_image("Resize", img_resize, 0)
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# Compose的使用 compose = ToTensor + Normalize
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trans_resize_2 = transforms.Resize(512)
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# transforms.Compose([trans_resize_2, trans_totensor])是一个类，可以直接调用
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# 用于将多个transforms组合起来使用 达到 ToTensor + Normalize 的效果
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trans_compose = transforms.Compose([trans_resize_2, trans_totensor])
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img_resize_2 = trans_compose(img)
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writer.add_image("Raszie", img_resize_2, 1)
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# RandomCrop的使用
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trans_random = transforms.RandomCrop(256, 128)
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trans_compose2 = transforms.Compose([trans_random, trans_totensor])
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for i in range(10):
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img_crop = trans_compose2(img)
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writer.add_image("RandomCrop", img_crop, i)
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writer.close()
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