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Source code for torchvision.datasets.cityscapes

import json
import os
from collections import namedtuple
import zipfile
from typing import Any, Callable, Dict, List, Optional, Union, Tuple

from .utils import extract_archive, verify_str_arg, iterable_to_str
from .vision import VisionDataset
from PIL import Image


[docs]class Cityscapes(VisionDataset): """`Cityscapes <http://www.cityscapes-dataset.com/>`_ Dataset. Args: root (string): 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: str, 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(Cityscapes, self).__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{}'.format('_trainextra.zip')) else: image_dir_zip = os.path.join(self.root, 'leftImg8bit{}'.format('_trainvaltest.zip')) if self.mode == 'gtFine': target_dir_zip = os.path.join(self.root, '{}{}'.format(self.mode, '_trainvaltest.zip')) elif self.mode == 'gtCoarse': target_dir_zip = os.path.join(self.root, '{}{}'.format(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]) 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, 'r') as file: data = json.load(file) return data def _get_target_suffix(self, mode: str, target_type: str) -> str: if target_type == 'instance': return '{}_instanceIds.png'.format(mode) elif target_type == 'semantic': return '{}_labelIds.png'.format(mode) elif target_type == 'color': return '{}_color.png'.format(mode) else: return '{}_polygons.json'.format(mode)

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