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"""
Simply load images from a folder or nested folders (does not have any split).
"""
import logging
from pathlib import Path
import omegaconf
import torch
from ..utils.image import ImagePreprocessor, load_image
from .base_dataset import BaseDataset
class ImageFolder(BaseDataset, torch.utils.data.Dataset):
default_conf = {
"glob": ["*.jpg", "*.png", "*.jpeg", "*.JPG", "*.PNG"],
"images": "???",
"root_folder": "/",
"preprocessing": ImagePreprocessor.default_conf,
}
def _init(self, conf):
self.root = conf.root_folder
if isinstance(conf.images, str):
if not Path(conf.images).is_dir():
with open(conf.images, "r") as f:
self.images = f.read().rstrip("\n").split("\n")
logging.info(f"Found {len(self.images)} images in list file.")
else:
self.images = []
glob = [conf.glob] if isinstance(conf.glob, str) else conf.glob
for g in glob:
self.images += list(Path(conf.images).glob("**/" + g))
if len(self.images) == 0:
raise ValueError(
f"Could not find any image in folder: {conf.images}."
)
self.images = [i.relative_to(conf.images) for i in self.images]
self.root = conf.images
logging.info(f"Found {len(self.images)} images in folder.")
elif isinstance(conf.images, omegaconf.listconfig.ListConfig):
self.images = conf.images.to_container()
else:
raise ValueError(conf.images)
self.preprocessor = ImagePreprocessor(conf.preprocessing)
def get_dataset(self, split):
return self
def __getitem__(self, idx):
path = self.images[idx]
img = load_image(path)
data = {"name": str(path), **self.preprocessor(img)}
return data
def __len__(self):
return len(self.images)
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