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94 lines
3.7 KiB
94 lines
3.7 KiB
import json
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from pathlib import Path
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from typing import Any, Callable, Optional, Tuple, Union
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import PIL.Image
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from .utils import download_and_extract_archive, verify_str_arg
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from .vision import VisionDataset
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class Food101(VisionDataset):
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"""`The Food-101 Data Set <https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/>`_.
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The Food-101 is a challenging data set of 101 food categories with 101,000 images.
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For each class, 250 manually reviewed test images are provided as well as 750 training images.
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On purpose, the training images were not cleaned, and thus still contain some amount of noise.
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This comes mostly in the form of intense colors and sometimes wrong labels. All images were
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rescaled to have a maximum side length of 512 pixels.
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Args:
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root (str or ``pathlib.Path``): Root directory of the dataset.
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split (string, optional): The dataset split, supports ``"train"`` (default) and ``"test"``.
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transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
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version. E.g, ``transforms.RandomCrop``.
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target_transform (callable, optional): A function/transform that takes in the target and transforms it.
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download (bool, optional): If True, downloads the dataset from the internet and
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puts it in root directory. If dataset is already downloaded, it is not
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downloaded again. Default is False.
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"""
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_URL = "http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz"
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_MD5 = "85eeb15f3717b99a5da872d97d918f87"
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def __init__(
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self,
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root: Union[str, Path],
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split: str = "train",
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transform: Optional[Callable] = None,
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target_transform: Optional[Callable] = None,
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download: bool = False,
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) -> None:
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super().__init__(root, transform=transform, target_transform=target_transform)
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self._split = verify_str_arg(split, "split", ("train", "test"))
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self._base_folder = Path(self.root) / "food-101"
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self._meta_folder = self._base_folder / "meta"
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self._images_folder = self._base_folder / "images"
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if download:
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self._download()
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if not self._check_exists():
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raise RuntimeError("Dataset not found. You can use download=True to download it")
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self._labels = []
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self._image_files = []
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with open(self._meta_folder / f"{split}.json") as f:
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metadata = json.loads(f.read())
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self.classes = sorted(metadata.keys())
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self.class_to_idx = dict(zip(self.classes, range(len(self.classes))))
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for class_label, im_rel_paths in metadata.items():
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self._labels += [self.class_to_idx[class_label]] * len(im_rel_paths)
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self._image_files += [
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self._images_folder.joinpath(*f"{im_rel_path}.jpg".split("/")) for im_rel_path in im_rel_paths
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]
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def __len__(self) -> int:
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return len(self._image_files)
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def __getitem__(self, idx: int) -> Tuple[Any, Any]:
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image_file, label = self._image_files[idx], self._labels[idx]
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image = PIL.Image.open(image_file).convert("RGB")
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if self.transform:
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image = self.transform(image)
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if self.target_transform:
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label = self.target_transform(label)
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return image, label
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def extra_repr(self) -> str:
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return f"split={self._split}"
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def _check_exists(self) -> bool:
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return all(folder.exists() and folder.is_dir() for folder in (self._meta_folder, self._images_folder))
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def _download(self) -> None:
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if self._check_exists():
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return
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download_and_extract_archive(self._URL, download_root=self.root, md5=self._MD5)
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