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| from torch.utils.data import Dataset, DataLoader | |
| from pathlib import Path | |
| from typing import List, Tuple, TypeVar, Optional | |
| import pandas as pd | |
| import os | |
| from PIL import Image | |
| from torch import Tensor | |
| from torchvision import transforms | |
| import torch | |
| PathLike = TypeVar("PathLike", str, Path, None) | |
| # can be found at https://data.lhncbc.nlm.nih.gov/public/Tuberculosis-Chest-X-ray-Datasets/Montgomery-County-CXR-Set/MontgomerySet/index.html | |
| class MonDataset(Dataset): | |
| def __init__(self, base_path:PathLike, | |
| csv_path:PathLike, | |
| csv_name:str, | |
| img_size:int=128, | |
| labels:List[str] =('right lung', 'left lung', ), | |
| **kwargs) -> None: | |
| self.df = pd.read_csv(os.path.join(csv_path, csv_name)) | |
| self.base_path = Path(base_path) | |
| self.labels = labels | |
| self.img_size = img_size | |
| def load_image(self, fname: str) -> Tensor: | |
| img = Image.open(self.base_path /fname).convert('L').resize((self.img_size, self.img_size)) | |
| img = transforms.ToTensor()(img).float() | |
| return img | |
| def load_labels(self, fnames: List[str]) -> Tensor: | |
| labels = [] | |
| for fname in fnames: | |
| label = Image.open(self.base_path /fname).convert('L').resize((self.img_size, self.img_size)) | |
| # convert to tensor | |
| label = transforms.ToTensor()(label).float() | |
| # make binary | |
| label = (label > .5).float() | |
| labels.append(label) | |
| # append all labels and merge | |
| label = torch.stack(labels).sum(0) | |
| # lungs have no overlap (right?) | |
| if (label > 1).sum()>0: | |
| print("overlapping lungs!", fnames) | |
| label = (label > .5) | |
| return label | |
| def __getitem__(self, index) -> Tuple[Tensor, Tensor]: | |
| i = self.df.index[index] | |
| img = self.load_image(self.df.loc[i, "scan"]) | |
| fnames = [ self.df.loc[i, l] for l in self.labels] | |
| labels = self.load_labels(fnames) | |
| return img, labels | |
| def __len__(self) -> int: | |
| return len(self.df) | |