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import os
import pandas as pd
from PIL import Image
from torch.utils.data import Dataset
class IQADatasetPyTorch(Dataset):
def __init__(self, csv_file, name, dataset_root, attributes, transform):
self.df = pd.read_csv(csv_file, dtype=str)
self.name = name
self.dataset_root = dataset_root
self.attributes = attributes
self.transform = transform
self.length = len(self.df)
def __str__(self):
return f"IQADataset ({self.name}), attributes: {self.attributes}"
def __len__(self):
return self.length
def __getitem__(self, idx):
sample = {}
for attr in self.attributes:
sample[attr] = self.df[attr][idx]
if attr == "dis_img_path":
sample["dis_img"] = self.transform(Image.open(os.path.join(self.dataset_root, self.df[attr][idx])))
elif attr == "ref_img_path":
sample["ref_img"] = self.transform(Image.open(os.path.join(self.dataset_root, self.df[attr][idx])))
elif attr == "score":
sample[attr] = float(self.df[attr][idx])
else:
pass
return sample
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