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243 lines
8.7 KiB
243 lines
8.7 KiB
5 months ago
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import os
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import os.path
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from pathlib import Path
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from typing import Any, Callable, List, Optional, Tuple, Union
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from PIL import 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 Caltech101(VisionDataset):
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"""`Caltech 101 <https://data.caltech.edu/records/20086>`_ 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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Args:
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root (str or ``pathlib.Path``): Root directory of dataset where directory
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``caltech101`` exists or will be saved to if download is set to True.
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target_type (string or list, optional): Type of target to use, ``category`` or
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``annotation``. Can also be a list to output a tuple with all specified
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target types. ``category`` represents the target class, and
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``annotation`` is a list of points from a hand-generated outline.
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Defaults to ``category``.
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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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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.. warning::
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To download the dataset `gdown <https://github.com/wkentaro/gdown>`_ is required.
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"""
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def __init__(
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self,
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root: Union[str, Path],
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target_type: Union[List[str], str] = "category",
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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__(os.path.join(root, "caltech101"), transform=transform, target_transform=target_transform)
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os.makedirs(self.root, exist_ok=True)
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if isinstance(target_type, str):
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target_type = [target_type]
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self.target_type = [verify_str_arg(t, "target_type", ("category", "annotation")) for t in target_type]
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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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self.categories = sorted(os.listdir(os.path.join(self.root, "101_ObjectCategories")))
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self.categories.remove("BACKGROUND_Google") # this is not a real class
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# For some reason, the category names in "101_ObjectCategories" and
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# "Annotations" do not always match. This is a manual map between the
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# two. Defaults to using same name, since most names are fine.
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name_map = {
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"Faces": "Faces_2",
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"Faces_easy": "Faces_3",
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"Motorbikes": "Motorbikes_16",
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"airplanes": "Airplanes_Side_2",
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}
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self.annotation_categories = list(map(lambda x: name_map[x] if x in name_map else x, self.categories))
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self.index: List[int] = []
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self.y = []
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for (i, c) in enumerate(self.categories):
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n = len(os.listdir(os.path.join(self.root, "101_ObjectCategories", c)))
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self.index.extend(range(1, n + 1))
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self.y.extend(n * [i])
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def __getitem__(self, index: int) -> Tuple[Any, Any]:
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"""
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Args:
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index (int): Index
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Returns:
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tuple: (image, target) where the type of target specified by target_type.
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"""
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import scipy.io
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img = Image.open(
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os.path.join(
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self.root,
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"101_ObjectCategories",
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self.categories[self.y[index]],
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f"image_{self.index[index]:04d}.jpg",
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)
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)
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target: Any = []
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for t in self.target_type:
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if t == "category":
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target.append(self.y[index])
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elif t == "annotation":
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data = scipy.io.loadmat(
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os.path.join(
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self.root,
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"Annotations",
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self.annotation_categories[self.y[index]],
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f"annotation_{self.index[index]:04d}.mat",
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)
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)
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target.append(data["obj_contour"])
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target = tuple(target) if len(target) > 1 else target[0]
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if self.transform is not None:
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img = self.transform(img)
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if self.target_transform is not None:
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target = self.target_transform(target)
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return img, target
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def _check_integrity(self) -> bool:
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# can be more robust and check hash of files
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return os.path.exists(os.path.join(self.root, "101_ObjectCategories"))
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def __len__(self) -> int:
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return len(self.index)
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def download(self) -> None:
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if self._check_integrity():
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print("Files already downloaded and verified")
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return
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download_and_extract_archive(
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"https://drive.google.com/file/d/137RyRjvTBkBiIfeYBNZBtViDHQ6_Ewsp",
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self.root,
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filename="101_ObjectCategories.tar.gz",
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md5="b224c7392d521a49829488ab0f1120d9",
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)
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download_and_extract_archive(
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"https://drive.google.com/file/d/175kQy3UsZ0wUEHZjqkUDdNVssr7bgh_m",
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self.root,
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filename="Annotations.tar",
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md5="6f83eeb1f24d99cab4eb377263132c91",
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)
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def extra_repr(self) -> str:
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return "Target type: {target_type}".format(**self.__dict__)
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class Caltech256(VisionDataset):
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"""`Caltech 256 <https://data.caltech.edu/records/20087>`_ Dataset.
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Args:
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root (str or ``pathlib.Path``): Root directory of dataset where directory
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``caltech256`` exists or will be saved to if download is set to True.
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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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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def __init__(
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self,
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root: str,
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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__(os.path.join(root, "caltech256"), transform=transform, target_transform=target_transform)
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os.makedirs(self.root, exist_ok=True)
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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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self.categories = sorted(os.listdir(os.path.join(self.root, "256_ObjectCategories")))
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self.index: List[int] = []
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self.y = []
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for (i, c) in enumerate(self.categories):
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n = len(
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[
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item
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for item in os.listdir(os.path.join(self.root, "256_ObjectCategories", c))
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if item.endswith(".jpg")
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]
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)
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self.index.extend(range(1, n + 1))
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self.y.extend(n * [i])
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def __getitem__(self, index: int) -> Tuple[Any, Any]:
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"""
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Args:
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index (int): Index
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Returns:
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tuple: (image, target) where target is index of the target class.
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"""
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img = Image.open(
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os.path.join(
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self.root,
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"256_ObjectCategories",
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self.categories[self.y[index]],
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f"{self.y[index] + 1:03d}_{self.index[index]:04d}.jpg",
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)
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)
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target = self.y[index]
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if self.transform is not None:
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img = self.transform(img)
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if self.target_transform is not None:
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target = self.target_transform(target)
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return img, target
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def _check_integrity(self) -> bool:
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# can be more robust and check hash of files
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return os.path.exists(os.path.join(self.root, "256_ObjectCategories"))
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def __len__(self) -> int:
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return len(self.index)
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def download(self) -> None:
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if self._check_integrity():
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print("Files already downloaded and verified")
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return
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download_and_extract_archive(
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"https://drive.google.com/file/d/1r6o0pSROcV1_VwT4oSjA2FBUSCWGuxLK",
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self.root,
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filename="256_ObjectCategories.tar",
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md5="67b4f42ca05d46448c6bb8ecd2220f6d",
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)
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