Source code for

@author: Junguang Jiang
import os
from typing import Sequence, Optional, Dict, Callable
from PIL import Image
import tqdm
import numpy as np
from torch.utils import data
import torch

[docs]class SegmentationList(data.Dataset): """A generic Dataset class for domain adaptation in image segmentation Args: root (str): Root directory of dataset classes (seq[str]): The names of all the classes data_list_file (str): File to read the image list from. label_list_file (str): File to read the label list from. data_folder (str): Sub-directory of the image. label_folder (str): Sub-directory of the label. mean (seq[float]): mean BGR value. Normalize and convert to the image if not None. Default: None. id_to_train_id (dict, optional): the map between the id on the label and the actual train id. train_id_to_color (seq, optional): the map between the train id and the color. transforms (callable, optional): A function/transform that takes in (PIL Image, label) pair \ and returns a transformed version. E.g, :class:``. .. note:: In ``data_list_file``, each line is the relative path of an image. If your data_list_file has different formats, please over-ride :meth:`~SegmentationList.parse_data_file`. :: source_dir/dog_xxx.png target_dir/dog_xxy.png In ``label_list_file``, each line is the relative path of an label. If your label_list_file has different formats, please over-ride :meth:`~SegmentationList.parse_label_file`. .. warning:: When mean is not None, please do not provide Normalize and ToTensor in transforms. """ def __init__(self, root: str, classes: Sequence[str], data_list_file: str, label_list_file: str, data_folder: str, label_folder: str, id_to_train_id: Optional[Dict] = None, train_id_to_color: Optional[Sequence] = None, transforms: Optional[Callable] = None): self.root = root self.classes = classes self.data_list_file = data_list_file self.label_list_file = label_list_file self.data_folder = data_folder self.label_folder = label_folder self.ignore_label = 255 self.id_to_train_id = id_to_train_id self.train_id_to_color = np.array(train_id_to_color) self.data_list = self.parse_data_file(self.data_list_file) self.label_list = self.parse_label_file(self.label_list_file) self.transforms = transforms
[docs] 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() for line in f.readlines()] return data_list
[docs] def parse_label_file(self, file_name): """Parse file to label list Args: file_name (str): The path of data file Returns: List of label path """ with open(file_name, "r") as f: label_list = [line.strip() for line in f.readlines()] return label_list
def __len__(self): return len(self.data_list) def __getitem__(self, index): image_name = self.data_list[index] label_name = self.label_list[index] image =, self.data_folder, image_name)).convert('RGB') label =, self.label_folder, label_name)) image, label = self.transforms(image, label) # remap label if isinstance(label, torch.Tensor): label = label.numpy() label = np.asarray(label, np.int64) label_copy = self.ignore_label * np.ones(label.shape, dtype=np.int64) if self.id_to_train_id: for k, v in self.id_to_train_id.items(): label_copy[label == k] = v return image, label_copy.copy() @property def num_classes(self) -> int: """Number of classes""" return len(self.classes)
[docs] def decode_target(self, target): """ Decode label (each value is integer) into the corresponding RGB value. Args: target (numpy.array): label in shape H x W Returns: RGB label (PIL Image) in shape H x W x 3 """ target = target.copy() target[target == 255] = self.num_classes # unknown label is black on the RGB label target = self.train_id_to_color[target] return Image.fromarray(target.astype(np.uint8))
[docs] def collect_image_paths(self): """Return a list of the absolute path of all the images""" return [os.path.join(self.root, self.data_folder, image_name) for image_name in self.data_list]
@staticmethod def _save_pil_image(image, path): os.makedirs(os.path.dirname(path), exist_ok=True)
[docs] def translate(self, transform: Callable, target_root: str, color=False): """ Translate an image and save it into a specified directory Args: transform (callable): a transform function that maps (image, label) pair from one domain to another domain target_root (str): the root directory to save images and labels """ os.makedirs(target_root, exist_ok=True) for image_name, label_name in zip(tqdm.tqdm(self.data_list), self.label_list): image_path = os.path.join(target_root, self.data_folder, image_name) label_path = os.path.join(target_root, self.label_folder, label_name) if os.path.exists(image_path) and os.path.exists(label_path): continue image =, self.data_folder, image_name)).convert('RGB') label =, self.label_folder, label_name)) translated_image, translated_label = transform(image, label) self._save_pil_image(translated_image, image_path) self._save_pil_image(translated_label, label_path) if color: colored_label = self.decode_target(np.array(translated_label)) file_name, file_ext = os.path.splitext(label_name) self._save_pil_image(colored_label, os.path.join(target_root, self.label_folder, "{}_color{}".format(file_name, file_ext)))
@property def evaluate_classes(self): """The name of classes to be evaluated""" return self.classes @property def ignore_classes(self): """The name of classes to be ignored""" return list(set(self.classes) - set(self.evaluate_classes))


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