| | |
| | 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 |
| |
|
| |
|
| | @DATASETS.register_module() |
| | 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_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'] |
| | |
| | |
| | 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'] |
| |
|
| | if self.return_classes: |
| | data_info['text'] = self.metainfo['thing_classes'] |
| | data_info['stuff_text'] = self.metainfo['stuff_classes'] |
| | data_info['custom_entities'] = True |
| |
|
| | 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() |
| | |
| | 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 |
| |
|
