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from ..data_aug import cityscapes_like_image_train_aug, cityscapes_like_image_test_aug, cityscapes_like_label_aug
from .common_dataset import CommonDataset
from ..ab_dataset import ABDataset
from ..dataset_split import train_val_test_split
import numpy as np
from typing import Dict, List, Optional
from torchvision.transforms import Compose, Lambda
import os
from ..registery import dataset_register
@dataset_register(
name='BaiduPerson',
classes=[
'person', 'background'
],
task_type='Semantic Segmentation',
object_type='Person',
# class_aliases=[['automobile', 'car']],
class_aliases=[],
shift_type=None
)
class BaiduPerson(ABDataset):
def create_dataset(self, root_dir: str, split: str, transform: Optional[Compose],
classes: List[str], ignore_classes: List[str], idx_map: Optional[Dict[int, int]]):
if transform is None:
x_transform = cityscapes_like_image_train_aug() if split == 'train' else cityscapes_like_image_test_aug()
y_transform = cityscapes_like_label_aug()
self.transform = x_transform
else:
x_transform = transform
y_transform = cityscapes_like_label_aug()
images_path, labels_path = [], []
for p in os.listdir(os.path.join(root_dir, 'images')):
images_path += [os.path.join(root_dir, 'images', p)]
labels_path += [os.path.join(root_dir, 'profiles', p.split('.')[0] + '-profile.jpg')]
idx_map_in_y_transform = {i: i for i in range(len(classes))}
# dataset.targets = np.asarray(dataset.targets)
if len(ignore_classes) > 0:
for ignore_class in ignore_classes:
# dataset.data = dataset.data[dataset.targets != classes.index(ignore_class)]
# dataset.targets = dataset.targets[dataset.targets != classes.index(ignore_class)]
idx_map_in_y_transform[ignore_class] = 255
if idx_map is not None:
# note: the code below seems correct but has bug!
# for old_idx, new_idx in idx_map.items():
# dataset.targets[dataset.targets == old_idx] = new_idx
# for ti, t in enumerate(dataset.targets):
# dataset.targets[ti] = idx_map[t]
for k, v in idx_map.items():
idx_map_in_y_transform[k] = v
def map_class(x):
x[x > 1] = 1 # 0: background; 1: person
for k, v in idx_map_in_y_transform.items():
x[x == k] = v
return x
y_transform = Compose([
*y_transform.transforms,
Lambda(lambda x: map_class(x))
])
dataset = CommonDataset(images_path, labels_path, x_transform=x_transform, y_transform=y_transform)
dataset = train_val_test_split(dataset, split)
return dataset
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