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# Copyright (c) OpenMMLab. All rights reserved.
# Modified from https://github.com/facebookresearch/detectron2/blob/master/detectron2/data/datasets/cityscapes.py # noqa
# and https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
from typing import List
from mmdet.registry import DATASETS
from .coco import CocoDataset
@DATASETS.register_module()
class CityscapesDataset(CocoDataset):
"""Dataset for Cityscapes."""
METAINFO = {
'classes': ('person', 'rider', 'car', 'truck', 'bus', 'train',
'motorcycle', 'bicycle'),
'palette': [(220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70),
(0, 60, 100), (0, 80, 100), (0, 0, 230), (119, 11, 32)]
}
def filter_data(self) -> List[dict]:
"""Filter annotations according to filter_cfg.
Returns:
List[dict]: Filtered results.
"""
if self.test_mode:
return self.data_list
if self.filter_cfg is None:
return self.data_list
filter_empty_gt = self.filter_cfg.get('filter_empty_gt', False)
min_size = self.filter_cfg.get('min_size', 0)
# obtain images that contain annotation
ids_with_ann = set(data_info['img_id'] for data_info in self.data_list)
# obtain images that contain annotations of the required categories
ids_in_cat = set()
for i, class_id in enumerate(self.cat_ids):
ids_in_cat |= set(self.cat_img_map[class_id])
# merge the image id sets of the two conditions and use the merged set
# to filter out images if self.filter_empty_gt=True
ids_in_cat &= ids_with_ann
valid_data_infos = []
for i, data_info in enumerate(self.data_list):
img_id = data_info['img_id']
width = data_info['width']
height = data_info['height']
all_is_crowd = all([
instance['ignore_flag'] == 1
for instance in data_info['instances']
])
if filter_empty_gt and (img_id not in ids_in_cat or all_is_crowd):
continue
if min(width, height) >= min_size:
valid_data_infos.append(data_info)
return valid_data_infos
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