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Dockerfile

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+FROM python:latest
+
+WORKDIR /usr/src/app
+
+COPY . .
+
+RUN /usr/local/bin/python -m pip install --upgrade pip
+RUN pip install -r requirements.txt
+
+CMD ["python", "./app.py"]

requirements.txt

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+numpy
+matplotlib
+tqdm
+torch
+torchmetrics
+segmentation_models_pytorch

src/medicalDataLoader.py

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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]