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Source code for tllib.vision.datasets.segmentation.cityscapes

"""
@author: Junguang Jiang
@contact: JiangJunguang1123@outlook.com
"""
import os
from .segmentation_list import SegmentationList
from .._util import download as download_data


[docs]class Cityscapes(SegmentationList): """`Cityscapes <https://www.cityscapes-dataset.com/>`_ is a real-world semantic segmentation dataset collected in driving scenarios. Args: root (str): Root directory of dataset split (str, optional): The dataset split, supports ``train``, or ``val``. data_folder (str, optional): Sub-directory of the image. Default: 'leftImg8bit'. label_folder (str, optional): Sub-directory of the label. Default: 'gtFine'. mean (seq[float]): mean BGR value. Normalize the image if not None. Default: None. transforms (callable, optional): A function/transform that takes in (PIL image, label) pair \ and returns a transformed version. E.g, :class:`~tllib.vision.transforms.segmentation.Resize`. .. note:: You need to download Cityscapes manually. Ensure that there exist following files in the `root` directory before you using this class. :: leftImg8bit/ train/ val/ test/ gtFine/ train/ val/ test/ """ CLASSES = ['road', 'sidewalk', 'building', 'wall', 'fence', 'pole', 'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky', 'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle'] ID_TO_TRAIN_ID = { 7: 0, 8: 1, 11: 2, 12: 3, 13: 4, 17: 5, 19: 6, 20: 7, 21: 8, 22: 9, 23: 10, 24: 11, 25: 12, 26: 13, 27: 14, 28: 15, 31: 16, 32: 17, 33: 18 } TRAIN_ID_TO_COLOR = [(128, 64, 128), (244, 35, 232), (70, 70, 70), (102, 102, 156), (190, 153, 153), (153, 153, 153), (250, 170, 30), (220, 220, 0), (107, 142, 35), (152, 251, 152), (70, 130, 180), (220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70), (0, 60, 100), (0, 80, 100), (0, 0, 230), (119, 11, 32), [0, 0, 0]] download_list = [ ("image_list", "image_list.zip", "https://cloud.tsinghua.edu.cn/f/08745e798b16483db4bf/?dl=1"), ] EVALUATE_CLASSES = CLASSES def __init__(self, root, split='train', data_folder='leftImg8bit', label_folder='gtFine', **kwargs): assert split in ['train', 'val'] # download meta information from Internet list(map(lambda args: download_data(root, *args), self.download_list)) data_list_file = os.path.join(root, "image_list", "{}.txt".format(split)) self.split = split super(Cityscapes, self).__init__(root, Cityscapes.CLASSES, data_list_file, data_list_file, os.path.join(data_folder, split), os.path.join(label_folder, split), id_to_train_id=Cityscapes.ID_TO_TRAIN_ID, train_id_to_color=Cityscapes.TRAIN_ID_TO_COLOR, **kwargs) def parse_label_file(self, label_list_file): with open(label_list_file, "r") as f: label_list = [line.strip().replace("leftImg8bit", "gtFine_labelIds") for line in f.readlines()] return label_list
[docs]class FoggyCityscapes(Cityscapes): """`Foggy Cityscapes <https://www.cityscapes-dataset.com/>`_ is a real-world semantic segmentation dataset collected in foggy driving scenarios. Args: root (str): Root directory of dataset split (str, optional): The dataset split, supports ``train``, or ``val``. data_folder (str, optional): Sub-directory of the image. Default: 'leftImg8bit'. label_folder (str, optional): Sub-directory of the label. Default: 'gtFine'. beta (float, optional): The parameter for foggy. Choices includes: 0.005, 0.01, 0.02. Default: 0.02 mean (seq[float]): mean BGR value. Normalize the image if not None. Default: None. transforms (callable, optional): A function/transform that takes in (PIL image, label) pair \ and returns a transformed version. E.g, :class:`~tllib.vision.transforms.segmentation.Resize`. .. note:: You need to download Cityscapes manually. Ensure that there exist following files in the `root` directory before you using this class. :: leftImg8bit_foggy/ train/ val/ test/ gtFine/ train/ val/ test/ """ def __init__(self, root, split='train', data_folder='leftImg8bit_foggy', label_folder='gtFine', beta=0.02, **kwargs): assert beta in [0.02, 0.01, 0.005] self.beta = beta super(FoggyCityscapes, self).__init__(root, split, data_folder, label_folder, **kwargs) def parse_data_file(self, file_name): """Parse file to image list Args: file_name (str): The path of data file Returns: List of image path """ with open(file_name, "r") as f: data_list = [line.strip().replace("leftImg8bit", "leftImg8bit_foggy_beta_{}".format(self.beta)) for line in f.readlines()] return data_list

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