Source code for torchvision.datasets.cityscapes
import json
import os
from collections import namedtuple
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from PIL import Image
from .utils import extract_archive, iterable_to_str, verify_str_arg
from .vision import VisionDataset
[docs]class Cityscapes(VisionDataset):
"""`Cityscapes <http://www.cityscapes-dataset.com/>`_ Dataset.
Args:
root (str or ``pathlib.Path``): Root directory of dataset where directory ``leftImg8bit``
and ``gtFine`` or ``gtCoarse`` are located.
split (string, optional): The image split to use, ``train``, ``test`` or ``val`` if mode="fine"
otherwise ``train``, ``train_extra`` or ``val``
mode (string, optional): The quality mode to use, ``fine`` or ``coarse``
target_type (string or list, optional): Type of target to use, ``instance``, ``semantic``, ``polygon``
or ``color``. Can also be a list to output a tuple with all specified target types.
transform (callable, optional): A function/transform that takes in a PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
transforms (callable, optional): A function/transform that takes input sample and its target as entry
and returns a transformed version.
Examples:
Get semantic segmentation target
.. code-block:: python
dataset = Cityscapes('./data/cityscapes', split='train', mode='fine',
target_type='semantic')
img, smnt = dataset[0]
Get multiple targets
.. code-block:: python
dataset = Cityscapes('./data/cityscapes', split='train', mode='fine',
target_type=['instance', 'color', 'polygon'])
img, (inst, col, poly) = dataset[0]
Validate on the "coarse" set
.. code-block:: python
dataset = Cityscapes('./data/cityscapes', split='val', mode='coarse',
target_type='semantic')
img, smnt = dataset[0]
"""
# Based on https://github.com/mcordts/cityscapesScripts
CityscapesClass = namedtuple(
"CityscapesClass",
["name", "id", "train_id", "category", "category_id", "has_instances", "ignore_in_eval", "color"],
)
classes = [
CityscapesClass("unlabeled", 0, 255, "void", 0, False, True, (0, 0, 0)),
CityscapesClass("ego vehicle", 1, 255, "void", 0, False, True, (0, 0, 0)),
CityscapesClass("rectification border", 2, 255, "void", 0, False, True, (0, 0, 0)),
CityscapesClass("out of roi", 3, 255, "void", 0, False, True, (0, 0, 0)),
CityscapesClass("static", 4, 255, "void", 0, False, True, (0, 0, 0)),
CityscapesClass("dynamic", 5, 255, "void", 0, False, True, (111, 74, 0)),
CityscapesClass("ground", 6, 255, "void", 0, False, True, (81, 0, 81)),
CityscapesClass("road", 7, 0, "flat", 1, False, False, (128, 64, 128)),
CityscapesClass("sidewalk", 8, 1, "flat", 1, False, False, (244, 35, 232)),
CityscapesClass("parking", 9, 255, "flat", 1, False, True, (250, 170, 160)),
CityscapesClass("rail track", 10, 255, "flat", 1, False, True, (230, 150, 140)),
CityscapesClass("building", 11, 2, "construction", 2, False, False, (70, 70, 70)),
CityscapesClass("wall", 12, 3, "construction", 2, False, False, (102, 102, 156)),
CityscapesClass("fence", 13, 4, "construction", 2, False, False, (190, 153, 153)),
CityscapesClass("guard rail", 14, 255, "construction", 2, False, True, (180, 165, 180)),
CityscapesClass("bridge", 15, 255, "construction", 2, False, True, (150, 100, 100)),
CityscapesClass("tunnel", 16, 255, "construction", 2, False, True, (150, 120, 90)),
CityscapesClass("pole", 17, 5, "object", 3, False, False, (153, 153, 153)),
CityscapesClass("polegroup", 18, 255, "object", 3, False, True, (153, 153, 153)),
CityscapesClass("traffic light", 19, 6, "object", 3, False, False, (250, 170, 30)),
CityscapesClass("traffic sign", 20, 7, "object", 3, False, False, (220, 220, 0)),
CityscapesClass("vegetation", 21, 8, "nature", 4, False, False, (107, 142, 35)),
CityscapesClass("terrain", 22, 9, "nature", 4, False, False, (152, 251, 152)),
CityscapesClass("sky", 23, 10, "sky", 5, False, False, (70, 130, 180)),
CityscapesClass("person", 24, 11, "human", 6, True, False, (220, 20, 60)),
CityscapesClass("rider", 25, 12, "human", 6, True, False, (255, 0, 0)),
CityscapesClass("car", 26, 13, "vehicle", 7, True, False, (0, 0, 142)),
CityscapesClass("truck", 27, 14, "vehicle", 7, True, False, (0, 0, 70)),
CityscapesClass("bus", 28, 15, "vehicle", 7, True, False, (0, 60, 100)),
CityscapesClass("caravan", 29, 255, "vehicle", 7, True, True, (0, 0, 90)),
CityscapesClass("trailer", 30, 255, "vehicle", 7, True, True, (0, 0, 110)),
CityscapesClass("train", 31, 16, "vehicle", 7, True, False, (0, 80, 100)),
CityscapesClass("motorcycle", 32, 17, "vehicle", 7, True, False, (0, 0, 230)),
CityscapesClass("bicycle", 33, 18, "vehicle", 7, True, False, (119, 11, 32)),
CityscapesClass("license plate", -1, -1, "vehicle", 7, False, True, (0, 0, 142)),
]
def __init__(
self,
root: Union[str, Path],
split: str = "train",
mode: str = "fine",
target_type: Union[List[str], str] = "instance",
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
transforms: Optional[Callable] = None,
) -> None:
super().__init__(root, transforms, transform, target_transform)
self.mode = "gtFine" if mode == "fine" else "gtCoarse"
self.images_dir = os.path.join(self.root, "leftImg8bit", split)
self.targets_dir = os.path.join(self.root, self.mode, split)
self.target_type = target_type
self.split = split
self.images = []
self.targets = []
verify_str_arg(mode, "mode", ("fine", "coarse"))
if mode == "fine":
valid_modes = ("train", "test", "val")
else:
valid_modes = ("train", "train_extra", "val")
msg = "Unknown value '{}' for argument split if mode is '{}'. Valid values are {{{}}}."
msg = msg.format(split, mode, iterable_to_str(valid_modes))
verify_str_arg(split, "split", valid_modes, msg)
if not isinstance(target_type, list):
self.target_type = [target_type]
[
verify_str_arg(value, "target_type", ("instance", "semantic", "polygon", "color"))
for value in self.target_type
]
if not os.path.isdir(self.images_dir) or not os.path.isdir(self.targets_dir):
if split == "train_extra":
image_dir_zip = os.path.join(self.root, "leftImg8bit_trainextra.zip")
else:
image_dir_zip = os.path.join(self.root, "leftImg8bit_trainvaltest.zip")
if self.mode == "gtFine":
target_dir_zip = os.path.join(self.root, f"{self.mode}_trainvaltest.zip")
elif self.mode == "gtCoarse":
target_dir_zip = os.path.join(self.root, f"{self.mode}.zip")
if os.path.isfile(image_dir_zip) and os.path.isfile(target_dir_zip):
extract_archive(from_path=image_dir_zip, to_path=self.root)
extract_archive(from_path=target_dir_zip, to_path=self.root)
else:
raise RuntimeError(
"Dataset not found or incomplete. Please make sure all required folders for the"
' specified "split" and "mode" are inside the "root" directory'
)
for city in os.listdir(self.images_dir):
img_dir = os.path.join(self.images_dir, city)
target_dir = os.path.join(self.targets_dir, city)
for file_name in os.listdir(img_dir):
target_types = []
for t in self.target_type:
target_name = "{}_{}".format(
file_name.split("_leftImg8bit")[0], self._get_target_suffix(self.mode, t)
)
target_types.append(os.path.join(target_dir, target_name))
self.images.append(os.path.join(img_dir, file_name))
self.targets.append(target_types)
[docs] def __getitem__(self, index: int) -> Tuple[Any, Any]:
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is a tuple of all target types if target_type is a list with more
than one item. Otherwise, target is a json object if target_type="polygon", else the image segmentation.
"""
image = Image.open(self.images[index]).convert("RGB")
targets: Any = []
for i, t in enumerate(self.target_type):
if t == "polygon":
target = self._load_json(self.targets[index][i])
else:
target = Image.open(self.targets[index][i]) # type: ignore[assignment]
targets.append(target)
target = tuple(targets) if len(targets) > 1 else targets[0]
if self.transforms is not None:
image, target = self.transforms(image, target)
return image, target
def __len__(self) -> int:
return len(self.images)
def extra_repr(self) -> str:
lines = ["Split: {split}", "Mode: {mode}", "Type: {target_type}"]
return "\n".join(lines).format(**self.__dict__)
def _load_json(self, path: str) -> Dict[str, Any]:
with open(path) as file:
data = json.load(file)
return data
def _get_target_suffix(self, mode: str, target_type: str) -> str:
if target_type == "instance":
return f"{mode}_instanceIds.png"
elif target_type == "semantic":
return f"{mode}_labelIds.png"
elif target_type == "color":
return f"{mode}_color.png"
else:
return f"{mode}_polygons.json"
