# Modified from https://github.com/pytorch/vision/blob/master/torchvision/datasets/cityscapes.py import json import os from collections import namedtuple import torch import torch.utils.data as data from PIL import Image import numpy as np from torchvision.datasets import VisionDataset class Cityscapes(VisionDataset): """Cityscapes 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="gtFine" otherwise 'train', 'train_extra' or 'val' mode (string, optional): The quality mode to use, 'gtFine' or 'gtCoarse' 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. """ # 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, 255, 'vehicle', 7, False, True, (0, 0, 142)), ] _TRAIN_ID_TO_COLOR = [c.color for c in classes if (c.train_id != -1 and c.train_id != 255)] _TRAIN_ID_TO_COLOR.append([0, 0, 0]) _TRAIN_ID_TO_COLOR = np.array(_TRAIN_ID_TO_COLOR) _ID_TO_TRAIN_ID = np.array([c.train_id for c in classes]) def __init__(self, root, split='train', mode='gtFine', target_type='semantic', transform=None, target_transform=None, transforms=None): super(Cityscapes, self).__init__( root, transform=transform, target_transform=target_transform, transforms=transforms ) self.root = os.path.expanduser(root) self.mode = mode self.target_type = target_type self.images_dir = os.path.join(self.root, 'leftImg8bit', split) self.targets_dir = os.path.join(self.root, self.mode, split) self.split = split self.images = [] self.targets = [] if split not in ['train', 'test', 'val']: raise ValueError('Invalid split for mode! Please use split="train", split="test"' ' or split="val"') if not os.path.isdir(self.images_dir) or not os.path.isdir(self.targets_dir): 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): self.images.append(os.path.join(img_dir, file_name)) target_name = '{}_{}'.format(file_name.split('_leftImg8bit')[0], self._get_target_suffix(self.mode, self.target_type)) self.targets.append(os.path.join(target_dir, target_name)) @classmethod def encode_target(cls, target): if isinstance( target, torch.Tensor ): return torch.from_numpy( cls._ID_TO_TRAIN_ID[np.array(target)] ) else: return cls._ID_TO_TRAIN_ID[target] @classmethod def decode_fn(cls, target): target[target == 255] = 19 #target = target.astype('uint8') + 1 return cls._TRAIN_ID_TO_COLOR[target] def __getitem__(self, index): """ 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') target = Image.open(self.targets[index]) if self.transforms: image, target = self.transforms(image, target) target = self.encode_target(target) return image, target def __len__(self): return len(self.images) def _load_json(self, path): with open(path, 'r') as file: data = json.load(file) return data def _get_target_suffix(self, mode, target_type): 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) elif target_type == 'polygon': return '{}_polygons.json'.format(mode) elif target_type == 'depth': return '{}_disparity.png'.format(mode)