115 lines
4.5 KiB
Python
115 lines
4.5 KiB
Python
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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 check_integrity, download_and_extract_archive, download_url, verify_str_arg
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from .vision import VisionDataset
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class Flowers102(VisionDataset):
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"""`Oxford 102 Flower <https://www.robots.ox.ac.uk/~vgg/data/flowers/102/>`_ Dataset.
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.. warning::
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This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format.
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Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The
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flowers were chosen to be flowers commonly occurring in the United Kingdom. Each class consists of
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between 40 and 258 images.
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The images have large scale, pose and light variations. In addition, there are categories that
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have large variations within the category, and several very similar categories.
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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), ``"val"``, or ``"test"``.
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transform (callable, optional): A function/transform that takes in a PIL image and returns a
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transformed 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.
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"""
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_download_url_prefix = "https://www.robots.ox.ac.uk/~vgg/data/flowers/102/"
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_file_dict = { # filename, md5
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"image": ("102flowers.tgz", "52808999861908f626f3c1f4e79d11fa"),
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"label": ("imagelabels.mat", "e0620be6f572b9609742df49c70aed4d"),
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"setid": ("setid.mat", "a5357ecc9cb78c4bef273ce3793fc85c"),
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}
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_splits_map = {"train": "trnid", "val": "valid", "test": "tstid"}
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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", "val", "test"))
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self._base_folder = Path(self.root) / "flowers-102"
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self._images_folder = self._base_folder / "jpg"
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if download:
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self.download()
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if not self._check_integrity():
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raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")
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from scipy.io import loadmat
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set_ids = loadmat(self._base_folder / self._file_dict["setid"][0], squeeze_me=True)
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image_ids = set_ids[self._splits_map[self._split]].tolist()
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labels = loadmat(self._base_folder / self._file_dict["label"][0], squeeze_me=True)
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image_id_to_label = dict(enumerate((labels["labels"] - 1).tolist(), 1))
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self._labels = []
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self._image_files = []
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for image_id in image_ids:
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self._labels.append(image_id_to_label[image_id])
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self._image_files.append(self._images_folder / f"image_{image_id:05d}.jpg")
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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_integrity(self):
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if not (self._images_folder.exists() and self._images_folder.is_dir()):
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return False
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for id in ["label", "setid"]:
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filename, md5 = self._file_dict[id]
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if not check_integrity(str(self._base_folder / filename), md5):
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return False
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return True
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def download(self):
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if self._check_integrity():
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return
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download_and_extract_archive(
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f"{self._download_url_prefix}{self._file_dict['image'][0]}",
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str(self._base_folder),
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md5=self._file_dict["image"][1],
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)
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for id in ["label", "setid"]:
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filename, md5 = self._file_dict[id]
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download_url(self._download_url_prefix + filename, str(self._base_folder), md5=md5)
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