| # 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 | |
| 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 |