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import os
from dataset import IQADataset
def download_dataset(remote_tar_file, dataset_root):
import tarfile
import wget
def bar_custom(current, total, width=80):
output = f"[*] Downloading: {current / total * 100:.1f}% [{current / 10**6:.0f} MB / {total / 10**6:.0f} MB]"
return output
local_tar_file = os.path.join(dataset_root, os.path.basename(remote_tar_file))
wget.download(remote_tar_file, out=local_tar_file, bar=bar_custom)
with tarfile.open(local_tar_file) as z:
z.extractall(dataset_root)
print(f"\n[*] Downloading finished, deleting the .tar file.")
os.remove(local_tar_file)
def prepare_dataset(name, dataset_root, attributes, download):
score_synthesis_datasets = ["A57", "CIDIQ_MOS100", "CIDIQ_MOS50", "CSIQ", "LIVE", "LIVE_MD", "MDID2013", "MDID2016", "SDIVL", "MDIVL", "TID2008", "TID2013", "VCLFER", "KADID-10k", "Toyama", "PDAP-HDDS"]
score_authentic_datasets = ["LIVE_Challenge", "CID2013", "KonIQ-10k", "SPAQ"]
nonscore_synthesis_datasets = ["Waterloo_Exploration"]
nonscore_authentic_datasets = []
available_datasets = score_synthesis_datasets + score_authentic_datasets + nonscore_synthesis_datasets + nonscore_authentic_datasets
if name in score_synthesis_datasets:
avail_attributes = ["dis_img_path", "dis_type", "ref_img_path", "score"]
elif name in score_authentic_datasets:
avail_attributes = ["dis_img_path", "dis_type", "score"]
elif name in nonscore_synthesis_datasets:
avail_attributes = ["dis_img_path", "dis_type", "ref_img_path"]
elif name in nonscore_authentic_datasets:
avail_attributes = ["dis_img_path", "dis_type"]
else:
raise NotImplementedError(f"Dataset '{name}' is not supported. Currently supported datasets are: {available_datasets}.")
if attributes is not None:
assert type(attributes) == list
for attr in attributes:
if attr not in avail_attributes:
raise KeyError(f"[!] Attribute: {attr} is not available in {name}.")
else:
attributes = avail_attributes
if not os.path.exists(dataset_root):
os.makedirs(dataset_root)
dataset_dir = os.path.join(dataset_root, name)
if not os.path.exists(dataset_dir):
if download is True:
remote_tar_file = f"http://ivc.uwaterloo.ca/database/IQADataset/{name}.tar"
print(f"[*] Cannnot find dataset '{name}'' in '{dataset_dir}', downloading it from '{remote_tar_file}'")
download_dataset(remote_tar_file, dataset_root)
else:
raise FileNotFoundError(f"[!] Cannnot find dataset '{name}' in '{dataset_dir}', try setting 'download=True' or download it manually.")
return attributes
def load_dataset(name, dataset_root="data", attributes=None, download=True):
csv_file = os.path.join("csv", name) + ".txt"
attributes = prepare_dataset(name, dataset_root, attributes, download)
return IQADataset(csv_file, name, dataset_root, attributes)
def load_dataset_pytorch(name, dataset_root="data", attributes=None, download=True, transform=None):
from torchvision import transforms
from dataset_pytorch import IQADatasetPyTorch
if transform is None:
transform = transforms.ToTensor()
csv_file = os.path.join("csv", name) + ".txt"
attributes = prepare_dataset(name, dataset_root, attributes, download)
return IQADatasetPyTorch(csv_file, name, dataset_root, attributes, transform)
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