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