| import pandas as pd |
| import numpy as np |
| from torch.utils.data import Dataset |
| import os |
| from PIL import Image |
|
|
| class Thyroid_Datasets(Dataset): |
| def __init__( |
| self, |
| path_to_images, |
| fold, |
| PRED_LABEL, |
| transform=None, |
| sample=0, |
| finding="any"): |
| self.transform = transform |
| self.path_to_images = path_to_images |
| self.PRED_LABEL = PRED_LABEL |
| self.df = pd.read_csv("%s/CSV/%s.csv" % (path_to_images,fold)) |
| print("%s/CSV/%s.csv " % (path_to_images,fold), "num of images: %s" % len(self.df)) |
|
|
| if(sample > 0 and sample < len(self.df)): |
| self.df = self.df.sample(sample) |
| self.df = self.df.dropna(subset = ['Path']) |
| self.df = self.df.set_index("Path") |
|
|
|
|
| def __len__(self): |
| return len(self.df) |
| |
|
|
| def __getitem__(self, idx): |
| X = self.df.index[idx] |
| if str(X) is not None: |
| image = Image.open(os.path.join(self.path_to_images,str(X))) |
| image = image.convert('RGB') |
| label = self.df["label".strip()].iloc[idx].astype('int') |
| subg = self.df["subtype".strip()].iloc[idx] |
| if self.transform: |
| image = self.transform(image) |
|
|
| return (image, label, subg) |
|
|
| class Multi_Thyroid_Datasets(Dataset): |
| def __init__( |
| self, |
| path_to_images, |
| fold, |
| PRED_LABEL, |
| transform=None, |
| sample=0, |
| finding="any"): |
| self.transform = transform |
| self.path_to_images = path_to_images |
| self.PRED_LABEL = PRED_LABEL |
| self.df = pd.read_csv("%s/CSV/%s.csv" % (path_to_images,fold)) |
| print("%s/CSV/%s.csv " % (path_to_images,fold), "num of images: %s" % len(self.df)) |
|
|
| if(sample > 0 and sample < len(self.df)): |
| self.df = self.df.sample(sample) |
| self.df = self.df.dropna(subset = ['Path']) |
| self.df = self.df.set_index("Path") |
|
|
|
|
| def __len__(self): |
| return len(self.df) |
| |
|
|
| def __getitem__(self, idx): |
| X = self.df.index[idx] |
| if str(X) is not None: |
| image = Image.open(os.path.join(self.path_to_images,str(X))) |
| image = image.convert('RGB') |
| label = self.df["label".strip()].iloc[idx].astype('int') |
| subg = self.df["subtype".strip()].iloc[idx] |
| if self.transform: |
| image = self.transform(image) |
|
|
| return (image, label, subg) |
| return (image, label, subg) |
