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import numpy as np
import torch
import torch.utils.data
from PIL import Image
import torchvision.transforms as transforms
import os
class DataLoader(torch.utils.data.Dataset):
def __init__(self, img_dir, task):
self.low_img_dir = img_dir
self.task = task
self.train_low_data_names = []
self.train_target_data_names = []
for root, dirs, names in os.walk(self.low_img_dir):
for name in names:
self.train_low_data_names.append(os.path.join(root, name))
self.train_low_data_names.sort()
self.count = len(self.train_low_data_names)
transform_list = []
transform_list += [transforms.ToTensor()]
self.transform = transforms.Compose(transform_list)
def load_images_transform(self, file):
im = Image.open(file).convert('RGB')
img_norm = self.transform(im).numpy()
img_norm = np.transpose(img_norm, (1, 2, 0))
return img_norm
def __getitem__(self, index):
low = self.load_images_transform(self.train_low_data_names[index])
low = np.asarray(low, dtype=np.float32)
low = np.transpose(low[:, :, :], (2, 0, 1))
img_name = self.train_low_data_names[index].split('\\')[-1]
return torch.from_numpy(low),img_name
def __len__(self):
return self.count
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