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# Copyright (c) OpenMMLab. All rights reserved.
# written by lzx
from mmdet.registry import DATASETS
from mmdet.datasets.api_wrappers import COCO
from .HSI import HSIDataset
@DATASETS.register_module()
class SIRSTDataset(HSIDataset):
"""Dataset for COCO."""
METAINFO = {
'classes':
('object',),
# palette is a list of color tuples, which is used for visualization.
'palette':
[(220, 20, 60),]
}
COCOAPI = COCO
# @DATASETS.register_module()
# class SIRSTDataset(CocoDataset):
# """Dataset for COCO."""
#
# METAINFO = {
# 'classes':
# ('object',),
# # palette is a list of color tuples, which is used for visualization.
# 'palette':
# [(220, 20, 60),]
# }
# COCOAPI = COCO
# # ann_id is unique in coco dataset.
# ANN_ID_UNIQUE = True
#
# def load_data_list(self) -> List[dict]:
# """Load annotations from an annotation file named as ``self.ann_file``
#
# Returns:
# List[dict]: A list of annotation.
# """ # noqa: E501
# with get_local_path(
# self.ann_file, backend_args=self.backend_args) as local_path:
# self.coco = self.COCOAPI(local_path)
# # The order of returned `cat_ids` will not
# # change with the order of the `classes`
# self.cat_ids = self.coco.get_cat_ids(
# cat_names=self.metainfo['classes'])
# self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
# self.cat_img_map = copy.deepcopy(self.coco.cat_img_map)
#
# img_ids = self.coco.get_img_ids()
# data_list = []
# total_ann_ids = []
# for img_id in img_ids:
# raw_img_info = self.coco.load_imgs([img_id])[0]
# raw_img_info['img_id'] = img_id
#
# ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
# raw_ann_info = self.coco.load_anns(ann_ids)
# total_ann_ids.extend(ann_ids)
#
# parsed_data_info = self.parse_data_info({
# 'raw_ann_info':
# raw_ann_info,
# 'raw_img_info':
# raw_img_info
# })
# data_list.append(parsed_data_info)
# if self.ANN_ID_UNIQUE:
# assert len(set(total_ann_ids)) == len(
# total_ann_ids
# ), f"Annotation ids in '{self.ann_file}' are not unique!"
#
# del self.coco
#
# return data_list
#
# def parse_data_info(self, raw_data_info: dict) -> Union[dict, List[dict]]:
# """Parse raw annotation to target format.
#
# Args:
# raw_data_info (dict): Raw data information load from ``ann_file``
#
# Returns:
# Union[dict, List[dict]]: Parsed annotation.
# """
# img_info = raw_data_info['raw_img_info']
# ann_info = raw_data_info['raw_ann_info']
#
# data_info = {}
#
# # TODO: need to change data_prefix['img'] to data_prefix['img_path']
# img_path = osp.join(self.data_prefix['img'], img_info['file_name'])
# if self.data_prefix.get('seg', None):
# seg_map_path = osp.join(
# self.data_prefix['seg'],
# img_info['file_name'].rsplit('.', 1)[0] + self.seg_map_suffix)
# else:
# seg_map_path = None
# data_info['img_path'] = img_path
# data_info['img_id'] = img_info['img_id']
# data_info['seg_map_path'] = seg_map_path
# data_info['height'] = img_info['height']
# data_info['width'] = img_info['width']
#
# instances = []
# for i, ann in enumerate(ann_info):
# instance = {}
#
# if ann.get('ignore', False):
# continue
# x1, y1, w, h = ann['bbox']
# inter_w = max(0, min(x1 + w, img_info['width']) - max(x1, 0))
# inter_h = max(0, min(y1 + h, img_info['height']) - max(y1, 0))
# if inter_w * inter_h == 0:
# continue
# if ann['area'] <= 0 or w < 1 or h < 1:
# continue
# if ann['category_id'] not in self.cat_ids:
# continue
# bbox = [x1, y1, x1 + w, y1 + h]
#
# if ann.get('iscrowd', False):
# instance['ignore_flag'] = 1
# else:
# instance['ignore_flag'] = 0
# instance['bbox'] = bbox
# instance['bbox_label'] = self.cat2label[ann['category_id']]
#
# if ann.get('segmentation', None):
# instance['mask'] = ann['segmentation']
#
# instances.append(instance)
# data_info['instances'] = instances
# return data_info
#
# 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']
# if filter_empty_gt and img_id not in ids_in_cat:
# continue
# if min(width, height) >= min_size:
# valid_data_infos.append(data_info)
#
# return valid_data_infos |