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datasets/EndoScene.py

+#Adopted from ACSNet
+
+import torch
+from torch.utils.data import Dataset, DataLoader
+import torchvision.transforms.functional as F
+from torchvision import transforms
+import os
+from PIL import Image
+import os.path as osp
+from utils.transform import *
+
+
+# EndoScene Dataset
+class EndoScene(Dataset):
+    def __init__(self, root, data_dir, mode='train', transform=None):
+        super(EndoScene, self).__init__()
+        data_path1 = osp.join(root, data_dir)
+        #data_path2 = osp.join(root, data_dir) + '/CVC-612'
+        self.imglist = []
+        self.gtlist = []
+
+        datalist1 = os.listdir(osp.join(data_path1, 'image'))
+        for data1 in datalist1:
+            self.imglist.append(osp.join(data_path1 + '/image', data1))
+            self.gtlist.append(osp.join(data_path1 + '/gtpolyp', data1))
+
+        #datalist2 = os.listdir(osp.join(data_path2, 'image'))
+        #for data2 in datalist2:
+            #self.imglist.append(osp.join(data_path2 + '/image', data2))
+            #self.gtlist.append(osp.join(data_path2 + '/gtpolyp', data2))
+
+        if transform is None:
+            if mode == 'train':
+               transform = transforms.Compose([
+                   Resize((320, 320)),
+                   RandomHorizontalFlip(),
+                   RandomVerticalFlip(),
+                   RandomRotation(90),
+                   RandomZoom((0.9, 1.1)),
+                   #Translation(10),
+                   RandomCrop((256, 256)),
+                   #transforms.Normalize([0.485, 0.456, 0.406],
+                                 #[0.229, 0.224, 0.225])]
+                   ToTensor(),
+
+               ])
+            elif mode == 'valid' or mode == 'test':
+                transform = transforms.Compose([
+                   Resize((320, 320)),
+                   ToTensor(),
+               ])
+        self.transform = transform
+
+    def __getitem__(self, index):
+        img_path = self.imglist[index]
+        gt_path = self.gtlist[index]
+        img = Image.open(img_path).convert('RGB')
+        gt = Image.open(gt_path).convert('L')
+        data = {'image': img, 'label': gt}
+        if self.transform:
+            data = self.transform(data)
+
+        return data
+
+    def __len__(self):
+        return len(self.imglist)

datasets/__init__.py

+from .EndoScene import EndoScene
+from .kvasir_SEG import kvasir_SEG

datasets/kvasir_SEG.py

+#Adopted from ACSNet
+import os
+import os.path as osp
+from utils.transform import *
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+
+# KavSir-SEG Dataset
+class kvasir_SEG(Dataset):
+    def __init__(self, root, data2_dir, mode='train', transform=None):
+        super(kvasir_SEG, self).__init__()
+        data_path = osp.join(root, data2_dir)
+        self.imglist = []
+        self.gtlist = []
+
+        datalist = os.listdir(osp.join(data_path, 'images'))
+        for data in datalist:
+            self.imglist.append(osp.join(data_path+'/images', data))
+            self.gtlist.append(osp.join(data_path+'/masks', data))
+
+        if transform is None:
+            if mode == 'train':
+               transform = transforms.Compose([
+                   Resize((320,320 )),
+                   RandomHorizontalFlip(),
+                   RandomVerticalFlip(),
+                   RandomRotation(90),
+                   RandomZoom((0.9, 1.1)),
+                   Translation(10),
+                   RandomCrop((256, 256)),
+                   ToTensor(),
+
+               ])
+            elif mode == 'valid' or mode == 'test':
+                transform = transforms.Compose([
+                   Resize((320, 320)),
+                   ToTensor(),
+               ])
+        self.transform = transform
+
+    def __getitem__(self, index):
+        img_path = self.imglist[index]
+        gt_path = self.gtlist[index]
+        img = Image.open(img_path).convert('RGB')
+        gt = Image.open(gt_path).convert('L')
+        data = {'image': img, 'label': gt}
+        if self.transform:
+            data = self.transform(data)
+
+        return data
+
+    def __len__(self):
+        return len(self.imglist)
+
+
+class test_dataset:
+    def __init__(self, image_root, gt_root, testsize):
+        self.testsize = testsize
+        self.images = [image_root + f for f in os.listdir(image_root) if f.endswith('.jpg') or f.endswith('.png')]
+        self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.tif') or f.endswith('.png')]
+        self.images = sorted(self.images)
+        self.gts = sorted(self.gts)
+        self.transform = transforms.Compose([
+            transforms.Resize((self.testsize, self.testsize)),
+            transforms.ToTensor(),
+            #transforms.Normalize([0.485, 0.456, 0.406],
+                                 #[0.229, 0.224, 0.225])])
+        self.gt_transform = transforms.ToTensor()
+        self.size = len(self.images)
+        self.index = 0
+
+    def load_data(self):
+        image = self.rgb_loader(self.images[self.index])
+        image = self.transform(image).unsqueeze(0)
+        gt = self.binary_loader(self.gts[self.index])
+        name = self.images[self.index].split('/')[-1]
+        if name.endswith('.jpg'):
+            name = name.split('.jpg')[0] + '.png'
+        self.index += 1
+        return image, gt, name
+
+    def rgb_loader(self, path):
+        with open(path, 'rb') as f:
+            img = Image.open(f)
+            return img.convert('RGB')
+
+    def binary_loader(self, path):
+        with open(path, 'rb') as f:
+            img = Image.open(f)
+            return img.convert('L')