# Copyright (c) OpenMMLab. All rights reserved. import copy import os.path as osp from typing import List, Union from mmdet.registry import DATASETS from .api_wrappers import COCO from .base_det_dataset import BaseDetDataset @DATASETS.register_module() class CocoDataset(BaseDetDataset): """Dataset for COCO.""" METAINFO = { 'classes': ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'), # palette is a list of color tuples, which is used for visualization. 'palette': [(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230), (106, 0, 228), (0, 60, 100), (0, 80, 100), (0, 0, 70), (0, 0, 192), (250, 170, 30), (100, 170, 30), (220, 220, 0), (175, 116, 175), (250, 0, 30), (165, 42, 42), (255, 77, 255), (0, 226, 252), (182, 182, 255), (0, 82, 0), (120, 166, 157), (110, 76, 0), (174, 57, 255), (199, 100, 0), (72, 0, 118), (255, 179, 240), (0, 125, 92), (209, 0, 151), (188, 208, 182), (0, 220, 176), (255, 99, 164), (92, 0, 73), (133, 129, 255), (78, 180, 255), (0, 228, 0), (174, 255, 243), (45, 89, 255), (134, 134, 103), (145, 148, 174), (255, 208, 186), (197, 226, 255), (171, 134, 1), (109, 63, 54), (207, 138, 255), (151, 0, 95), (9, 80, 61), (84, 105, 51), (74, 65, 105), (166, 196, 102), (208, 195, 210), (255, 109, 65), (0, 143, 149), (179, 0, 194), (209, 99, 106), (5, 121, 0), (227, 255, 205), (147, 186, 208), (153, 69, 1), (3, 95, 161), (163, 255, 0), (119, 0, 170), (0, 182, 199), (0, 165, 120), (183, 130, 88), (95, 32, 0), (130, 114, 135), (110, 129, 133), (166, 74, 118), (219, 142, 185), (79, 210, 114), (178, 90, 62), (65, 70, 15), (127, 167, 115), (59, 105, 106), (142, 108, 45), (196, 172, 0), (95, 54, 80), (128, 76, 255), (201, 57, 1), (246, 0, 122), (191, 162, 208)] } 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 self.file_client.get_local_path(self.ann_file) 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