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这是一个主要由@ptrblckPytorch 论坛提供的片段，用于对某些图像进行数据增强。

任务是分割，所以我假设图像及其对应的掩码需要增加。

我想知道如何在转换后显示一些图像和对应的蒙版以了解它们的外观？

这是脚本：

import torch
from torch.utils.data.dataset import Dataset  # For custom data-sets
import torchvision.transforms as transforms
import torchvision.transforms.functional as tf
from PIL import Image
import numpy 
import glob
import matplotlib.pyplot as plt
from split_dataset import test_loader
import os

class CustomDataset(Dataset):
    def __init__(self, image_paths, target_paths, transform_images, transform_masks):   

    self.image_paths = image_paths
    self.target_paths = target_paths

    self.transform_images = transform_images
    self.transform_masks = transform_masks


    self.transformm = transforms.Compose([transforms.Lambda(lambda x: tf.rotate(x, 10)),
                                          transforms.Lambda(lambda x: tf.affine(x, angle=0,
                                      translate=(0, 0),
                                      scale=0.2,
                                      shear=0.2))
                                        ])

    self.transform = transforms.ToTensor()

    self.mapping = {
        0: 0,
        255: 1              
    }

def mask_to_class(self, mask):
    for k in self.mapping:
        mask[mask==k] = self.mapping[k]
    return mask

def __getitem__(self, index):

    image = Image.open(self.image_paths[index])
    mask = Image.open(self.target_paths[index])

    if any([img in self.image_paths[index] for img in self.transform_images]):
        print('applying special transformation')
        image = self.transformm(image) #augmentation

    if any([msk in self.target_paths[index] for msk in self.transform_masks]):
        print('applying special transformation')
        image = self.transformm(mask) #augmentation

    t_image = image.convert('L')
    t_image = self.transform(t_image) # transform to tensor for image
    mask = self.transform(mask) # transform to tensor for mask


    mask = torch.from_numpy(numpy.array(mask, dtype=numpy.uint8)) 
    mask = self.mask_to_class(mask)
    mask = mask.long()

    return t_image, mask, self.image_paths[index], self.target_paths[index] 

def __len__(self):  # return count of sample we have

    return len(self.image_paths)


image_paths = glob.glob("D:\\Neda\\Pytorch\\U-net\\my_data\\imagesResized\\*.png")
target_paths = glob.glob("D:\\Neda\\Pytorch\\U-net\\my_data\\labelsResized\\*.png")


transform_images = ['image_981.png', 'image_982.png','image_983.png', 'image_984.png', 'image_985.png',
                    'image_986.png','image_987.png','image_988.png','image_989.png','image_990.png',
                    'image_991.png']  # apply special transformation only on these images
print(transform_images)
#['image_991.png', 'image_991.png']

transform_masks = ['image_labeled_981.png', 'image_labeled_982.png','image_labeled_983.png', 'image_labeled_984.png',
                    'image_labeled_985.png', 'image_labeled_986.png','image_labeled_987.png','image_labeled_988.png',
                    'image_labeled_989.png','image_labeled_990.png',
                    'image_labeled_991.png'] 

dataset = CustomDataset(image_paths, target_paths, transform_images, transform_masks)

for transform_images in dataset:

    #print(transform_images)        
    transform_images = Image.open(os.path.join(image_paths, transform_images))
    transform_images = numpy.array(transform_images)

    transform_masks = Image.open(os.path.join(target_paths, transform_masks))
    transform_masks = numpy.array(transform_masks)


    fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=1, sharex=True, sharey=True, figsize = (6,6))

    img1 = ax1.imshow(transform_images, cmap='gray')
    ax1.axis('off')   

    img2 = ax2.imshow(transform_masks)
    ax1.axis('off')        
    plt.show()

目前它正在导致错误

path = os.fspath(path)
TypeError: expected str, bytes or os.PathLike object, not tuple

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1 回答 1

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glob.glob返回与输入匹配的路径名列表。您正在使用它，就好像它是一条路径一样。您可以采用基本路径并将其与您的图像名称连接起来。我还建议不要transform_images在 for 循环中重用变量名。我将它分别重命名为current_image和current_mask。

这是修改后的代码：

basePath = 'D:\\Neda\\Pytorch\\U-net\\my_data\\imagesResized\\'
image = Image.open(os.path.join(basePath, current_image))

[...]

targetPath = 'D:\\Neda\\Pytorch\\U-net\\my_data\\labelsResized\\'
mask = Image.open(os.path.join(targetPath, current_mask))
于 2019-03-13T11:45:43.997 回答