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| from __future__ import print_function, division | |
| import os | |
| import torch | |
| import pandas as pd | |
| from skimage import io, transform | |
| import numpy as np | |
| from torch.utils.data import Dataset, DataLoader | |
| from torchvision import transforms, utils | |
| from PIL import Image, ImageOps | |
| from random import random, randint | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| def make_dataset(root, mode): | |
| assert mode in ['train','val', 'test'] | |
| items = [] | |
| if mode == 'train': | |
| train_img_path = os.path.join(root, 'train', 'Img') | |
| train_mask_path = os.path.join(root, 'train', 'GT') | |
| images = os.listdir(train_img_path) | |
| labels = os.listdir(train_mask_path) | |
| images.sort() | |
| labels.sort() | |
| for it_im, it_gt in zip(images, labels): | |
| item = (os.path.join(train_img_path, it_im), os.path.join(train_mask_path, it_gt)) | |
| items.append(item) | |
| elif mode == 'val': | |
| val_img_path = os.path.join(root, 'val', 'Img') | |
| val_mask_path = os.path.join(root, 'val', 'GT') | |
| images = os.listdir(val_img_path) | |
| labels = os.listdir(val_mask_path) | |
| images.sort() | |
| labels.sort() | |
| for it_im, it_gt in zip(images, labels): | |
| item = (os.path.join(val_img_path, it_im), os.path.join(val_mask_path, it_gt)) | |
| items.append(item) | |
| else: | |
| test_img_path = os.path.join(root, 'test', 'Img') | |
| test_mask_path = os.path.join(root, 'test', 'GT') | |
| images = os.listdir(test_img_path) | |
| labels = os.listdir(test_mask_path) | |
| images.sort() | |
| labels.sort() | |
| for it_im, it_gt in zip(images, labels): | |
| item = (os.path.join(test_img_path, it_im), os.path.join(test_mask_path, it_gt)) | |
| items.append(item) | |
| return items | |
| class MedicalImageDataset(Dataset): | |
| """Face Landmarks dataset.""" | |
| def __init__(self, mode, root_dir, transform=None, mask_transform=None, augment=False, equalize=False): | |
| """ | |
| Args: | |
| root_dir (string): Directory with all the images. | |
| transform (callable, optional): Optional transform to be applied | |
| on a sample. | |
| """ | |
| self.root_dir = root_dir | |
| self.transform = transform | |
| self.mask_transform = mask_transform | |
| self.imgs = make_dataset(root_dir, mode) | |
| self.augmentation = augment | |
| self.equalize = equalize | |
| self.mode = mode | |
| def __len__(self): | |
| return len(self.imgs) | |
| def augment(self, img, mask): | |
| if random() > 0.5: | |
| img = ImageOps.flip(img) | |
| mask = ImageOps.flip(mask) | |
| if random() > 0.5: | |
| img = ImageOps.mirror(img) | |
| mask = ImageOps.mirror(mask) | |
| if random() > 0.5: | |
| angle = random() * 60 - 30 | |
| img = img.rotate(angle) | |
| mask = mask.rotate(angle) | |
| return img, mask | |
| def __getitem__(self, index): | |
| img_path, mask_path = self.imgs[index] | |
| img = Image.open(img_path) | |
| mask = Image.open(mask_path).convert('L') | |
| if self.equalize: | |
| img = ImageOps.equalize(img) | |
| if self.augmentation: | |
| img, mask = self.augment(img, mask) | |
| if self.transform: | |
| img = self.transform(img) | |
| mask = self.mask_transform(mask) | |
| return [img, mask, img_path] | |