Enhanced-U-Net/utils/data_loader.py

# Adopted from PRAnet
import os
from PIL import Image
import torch.utils.data as data
import torchvision.transforms as transforms

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')