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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import os.path as osp | |
| from typing import Callable, List, Optional, Sequence, Union | |
| from mmdet.registry import DATASETS | |
| from .api_wrappers import COCOPanoptic | |
| from .coco import CocoDataset | |
| class CocoPanopticDataset(CocoDataset): | |
| """Coco dataset for Panoptic segmentation. | |
| The annotation format is shown as follows. The `ann` field is optional | |
| for testing. | |
| .. code-block:: none | |
| [ | |
| { | |
| 'filename': f'{image_id:012}.png', | |
| 'image_id':9 | |
| 'segments_info': | |
| [ | |
| { | |
| 'id': 8345037, (segment_id in panoptic png, | |
| convert from rgb) | |
| 'category_id': 51, | |
| 'iscrowd': 0, | |
| 'bbox': (x1, y1, w, h), | |
| 'area': 24315 | |
| }, | |
| ... | |
| ] | |
| }, | |
| ... | |
| ] | |
| Args: | |
| ann_file (str): Annotation file path. Defaults to ''. | |
| metainfo (dict, optional): Meta information for dataset, such as class | |
| information. Defaults to None. | |
| data_root (str, optional): The root directory for ``data_prefix`` and | |
| ``ann_file``. Defaults to None. | |
| data_prefix (dict, optional): Prefix for training data. Defaults to | |
| ``dict(img=None, ann=None, seg=None)``. The prefix ``seg`` which is | |
| for panoptic segmentation map must be not None. | |
| filter_cfg (dict, optional): Config for filter data. Defaults to None. | |
| indices (int or Sequence[int], optional): Support using first few | |
| data in annotation file to facilitate training/testing on a smaller | |
| dataset. Defaults to None which means using all ``data_infos``. | |
| serialize_data (bool, optional): Whether to hold memory using | |
| serialized objects, when enabled, data loader workers can use | |
| shared RAM from master process instead of making a copy. Defaults | |
| to True. | |
| pipeline (list, optional): Processing pipeline. Defaults to []. | |
| test_mode (bool, optional): ``test_mode=True`` means in test phase. | |
| Defaults to False. | |
| lazy_init (bool, optional): Whether to load annotation during | |
| instantiation. In some cases, such as visualization, only the meta | |
| information of the dataset is needed, which is not necessary to | |
| load annotation file. ``Basedataset`` can skip load annotations to | |
| save time by set ``lazy_init=False``. Defaults to False. | |
| max_refetch (int, optional): If ``Basedataset.prepare_data`` get a | |
| None img. The maximum extra number of cycles to get a valid | |
| image. Defaults to 1000. | |
| """ | |
| 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', 'banner', | |
| 'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff', | |
| 'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light', | |
| 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield', | |
| 'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow', | |
| 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile', | |
| 'wall-wood', 'water-other', 'window-blind', 'window-other', | |
| 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', | |
| 'cabinet-merged', 'table-merged', 'floor-other-merged', | |
| 'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged', | |
| 'paper-merged', 'food-other-merged', 'building-other-merged', | |
| 'rock-merged', 'wall-other-merged', 'rug-merged'), | |
| 'thing_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'), | |
| 'stuff_classes': | |
| ('banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain', | |
| 'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house', | |
| 'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield', | |
| 'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow', | |
| 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile', | |
| 'wall-wood', 'water-other', 'window-blind', 'window-other', | |
| 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', | |
| 'cabinet-merged', 'table-merged', 'floor-other-merged', | |
| 'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged', | |
| 'paper-merged', 'food-other-merged', 'building-other-merged', | |
| 'rock-merged', 'wall-other-merged', 'rug-merged'), | |
| '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), (255, 255, 128), (147, 211, 203), | |
| (150, 100, 100), (168, 171, 172), (146, 112, 198), (210, 170, 100), | |
| (92, 136, 89), (218, 88, 184), (241, 129, 0), (217, 17, 255), | |
| (124, 74, 181), (70, 70, 70), (255, 228, 255), (154, 208, 0), | |
| (193, 0, 92), (76, 91, 113), (255, 180, 195), (106, 154, 176), | |
| (230, 150, 140), (60, 143, 255), (128, 64, 128), (92, 82, 55), | |
| (254, 212, 124), (73, 77, 174), (255, 160, 98), (255, 255, 255), | |
| (104, 84, 109), (169, 164, 131), (225, 199, 255), (137, 54, 74), | |
| (135, 158, 223), (7, 246, 231), (107, 255, 200), (58, 41, 149), | |
| (183, 121, 142), (255, 73, 97), (107, 142, 35), (190, 153, 153), | |
| (146, 139, 141), (70, 130, 180), (134, 199, 156), (209, 226, 140), | |
| (96, 36, 108), (96, 96, 96), (64, 170, 64), (152, 251, 152), | |
| (208, 229, 228), (206, 186, 171), (152, 161, 64), (116, 112, 0), | |
| (0, 114, 143), (102, 102, 156), (250, 141, 255)] | |
| } | |
| COCOAPI = COCOPanoptic | |
| # ann_id is not unique in coco panoptic dataset. | |
| ANN_ID_UNIQUE = False | |
| def __init__(self, | |
| ann_file: str = '', | |
| metainfo: Optional[dict] = None, | |
| data_root: Optional[str] = None, | |
| data_prefix: dict = dict(img=None, ann=None, seg=None), | |
| filter_cfg: Optional[dict] = None, | |
| indices: Optional[Union[int, Sequence[int]]] = None, | |
| serialize_data: bool = True, | |
| pipeline: List[Union[dict, Callable]] = [], | |
| test_mode: bool = False, | |
| lazy_init: bool = False, | |
| max_refetch: int = 1000, | |
| backend_args: dict = None, | |
| **kwargs) -> None: | |
| super().__init__( | |
| ann_file=ann_file, | |
| metainfo=metainfo, | |
| data_root=data_root, | |
| data_prefix=data_prefix, | |
| filter_cfg=filter_cfg, | |
| indices=indices, | |
| serialize_data=serialize_data, | |
| pipeline=pipeline, | |
| test_mode=test_mode, | |
| lazy_init=lazy_init, | |
| max_refetch=max_refetch, | |
| backend_args=backend_args, | |
| **kwargs) | |
| def parse_data_info(self, raw_data_info: dict) -> dict: | |
| """Parse raw annotation to target format. | |
| Args: | |
| raw_data_info (dict): Raw data information load from ``ann_file``. | |
| Returns: | |
| dict: Parsed annotation. | |
| """ | |
| img_info = raw_data_info['raw_img_info'] | |
| ann_info = raw_data_info['raw_ann_info'] | |
| # filter out unmatched annotations which have | |
| # same segment_id but belong to other image | |
| ann_info = [ | |
| ann for ann in ann_info if ann['image_id'] == img_info['img_id'] | |
| ] | |
| data_info = {} | |
| 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'].replace('jpg', 'png')) | |
| 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 = [] | |
| segments_info = [] | |
| for ann in ann_info: | |
| instance = {} | |
| x1, y1, w, h = ann['bbox'] | |
| if ann['area'] <= 0 or w < 1 or h < 1: | |
| continue | |
| bbox = [x1, y1, x1 + w, y1 + h] | |
| category_id = ann['category_id'] | |
| contiguous_cat_id = self.cat2label[category_id] | |
| is_thing = self.coco.load_cats(ids=category_id)[0]['isthing'] | |
| if is_thing: | |
| is_crowd = ann.get('iscrowd', False) | |
| instance['bbox'] = bbox | |
| instance['bbox_label'] = contiguous_cat_id | |
| if not is_crowd: | |
| instance['ignore_flag'] = 0 | |
| else: | |
| instance['ignore_flag'] = 1 | |
| is_thing = False | |
| segment_info = { | |
| 'id': ann['id'], | |
| 'category': contiguous_cat_id, | |
| 'is_thing': is_thing | |
| } | |
| segments_info.append(segment_info) | |
| if len(instance) > 0 and is_thing: | |
| instances.append(instance) | |
| data_info['instances'] = instances | |
| data_info['segments_info'] = segments_info | |
| return data_info | |
| def filter_data(self) -> List[dict]: | |
| """Filter images too small or without ground truth. | |
| Returns: | |
| List[dict]: ``self.data_list`` after filtering. | |
| """ | |
| 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) | |
| ids_with_ann = set() | |
| # check whether images have legal thing annotations. | |
| for data_info in self.data_list: | |
| for segment_info in data_info['segments_info']: | |
| if not segment_info['is_thing']: | |
| continue | |
| ids_with_ann.add(data_info['img_id']) | |
| valid_data_list = [] | |
| for data_info in 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_with_ann: | |
| continue | |
| if min(width, height) >= min_size: | |
| valid_data_list.append(data_info) | |
| return valid_data_list | |