import copy
import os.path as osp
from typing import List, Union
import mmdet.datasets
from mmdet.datasets.api_wrappers import COCO
from mmdet.datasets.base_det_dataset import BaseDetDataset
from mmdet.registry import DATASETS
[docs]@DATASETS.register_module(force=True)
class CocoDataset(BaseDetDataset):
"""
Dataset for COCO. This class is similar to mmdet.datasets.CocoDataset.
Differently, we add kept_classes attribute to filter out samples.
"""
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
[docs] def __init__(self, kept_classes=(), **kwargs):
self.kept_classes = kept_classes
super().__init__(**kwargs)
[docs] 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.
"""
self.coco = self.COCOAPI(self.ann_file)
# 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)
if self.kept_classes:
self.kept_cats = self.coco.get_cat_ids(cat_names=self.kept_classes)
self.kept_labels = [self.cat2label[cat] for cat in self.kept_cats]
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
# if self.kept_classes:
# self._indices = self.generate_kept_indices()
return data_list
[docs] 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']
if self.return_classes:
data_info['text'] = self.metainfo['classes']
data_info['custom_entities'] = True
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
[docs] def filter_data(self) -> List[dict]:
"""Filter annotations according to filter_cfg.
Returns:
List[dict]: Filtered results.
"""
if self.kept_classes:
ids_in_cat = set()
for i, class_id in enumerate(self.kept_cats):
ids_in_cat |= set(self.cat_img_map[class_id])
valid_data_infos = []
for i, data_info in enumerate(self.data_list):
img_id = data_info['img_id']
if img_id in ids_in_cat:
valid_data_infos.append(data_info)
return valid_data_infos
# 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
# def generate_kept_indices(self):
#
# kept_indices = []
# for i in range(len(self.data_list)):
# data = self.data_list[i]
# instances = data['instances']
# for instance in instances:
# if instance['bbox_label'] in self.kept_labels:
# kept_indices.append(i)
# break
# return kept_indices