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dataset.py
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dataset.py
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import os
from PIL import Image
from torchvision import transforms
from torch.utils.data import Dataset
def load_img(filepath):
img = Image.open(filepath)
if(len(img.split())==4):
img,_,_,_ = img.split()
return img
class Kinetics(Dataset):
def __init__(self, ori_dir, cpr_dir):
super(Kinetics, self).__init__()
self.cpr_dir = cpr_dir
self.ori_dir = ori_dir
self.img_list = [img for img in os.listdir(self.cpr_dir) if img[-4:]=='.png']
self.transform = transforms.Compose([transforms.ToTensor()])
def __len__(self):
return len(self.img_list)
def __getitem__(self, idx):
img = self.img_list[idx]
ori_img = load_img(os.path.join(self.ori_dir, img))
cpr_img = load_img(os.path.join(self.cpr_dir, img))
sample = {'de': cpr_img, 'ori':ori_img}
if self.transform:
sample['de'] = self.transform(sample['de'])
sample['ori'] = self.transform(sample['ori'])
return sample