import logging
import os
from os.path import join
from typing import Any, Callable, List, Optional, Tuple
from PIL import Image
from torchvision.transforms.functional import to_tensor
from .base import ImageDatasetBase
log = logging.getLogger(__name__)
[docs]
class StreetHazards(ImageDatasetBase):
"""
Benchmark Dataset for Anomaly Segmentation.
From the paper *Scaling Out-of-Distribution Detection for Real-World Settings*
.. image:: https://github.com/hendrycks/anomaly-seg/raw/master/streethazards.gif
:width: 800px
:alt: Street Hazards Dataset Example
:align: center
:see Paper: `ArXiv <https://arxiv.org/pdf/1911.11132>`__
:see Website: `GitHub <https://github.com/hendrycks/anomaly-seg>`__
"""
classes: List[str] = [
"unlabeled",
"building",
"fence",
"other",
"pedestrian",
"pole",
"road line",
"road",
"sidewalk",
"vegetation",
"car",
"wall",
"traffic sign",
] #: class index to name mapping
subset_list = ["test", "train", "validation"]
root_dir_name = "streethazards"
base_folders = {
"test": "test/images/",
"train": "train/images/training/",
"validation": "train/images/validation/",
}
url_list = {
"test": "https://people.eecs.berkeley.edu/~hendrycks/streethazards_test.tar",
"train": "https://people.eecs.berkeley.edu/~hendrycks/streethazards_train.tar",
"validation": "https://people.eecs.berkeley.edu/~hendrycks/streethazards_train.tar",
}
filename_list = {
"test": ("streethazards_test.tar", "8c547c1346b00c21b2483887110bfea7"),
"train": ("streethazards_train.tar", "cd2d1a8649848afb85b5059d227d2090"),
"validation": ("streethazards_train.tar", "cd2d1a8649848afb85b5059d227d2090"),
}
def __init__(
self,
root: str,
subset: str,
transform: Optional[Callable[[Tuple], Tuple]] = None,
download: bool = False,
) -> None:
"""
:param root: root path for dataset
:param subset: one of ``train``, ``test``, ``validation``
:param transform: transformations to apply to images and masks, will get tuple as argument
:param download: if dataset should be downloaded automatically
"""
root = join(root, self.root_dir_name)
super(ImageDatasetBase, self).__init__(root, transform=transform)
self.base_folder = self.base_folders[subset]
self.url = self.url_list[subset]
self.filename, self.tgz_md5 = self.filename_list[subset]
if download:
self.download()
if subset not in self.subset_list:
raise ValueError(f"Invalid subset: {subset}")
if not self._check_integrity():
raise RuntimeError(
"Dataset not found or corrupted." + " You can use download=True to download it"
)
self.basedir = os.path.join(self.root, self.base_folder)
self.files = self._get_file_list(self.basedir)
def _get_file_list(self, root) -> List[str]:
"""
Recursively get all files in the root directory
:param root: root directory for the search
"""
current_files = [os.path.join(root, entry) for entry in os.listdir(root)]
all_files = []
for path in current_files:
if os.path.isdir(path):
all_files += self._get_file_list(path)
else:
all_files.append(path)
return all_files
def __getitem__(self, index: int) -> Tuple[Any, Any]:
"""
:param index: index
:returns: (image, target) where target is the annotation of the image.
"""
file, target = self.files[index], self.files[index].replace("images", "annotations")
# to return a PIL Image
img = Image.open(file)
target = to_tensor(Image.open(target)).squeeze(0)
target = (target * 255).long() - 1 # labels to integer
target[target >= 13] = -1 # negative labels for outliers
if self.transform is not None:
img, target = self.transform(img, target)
return img, target