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# Copyright (c) Tencent Inc. All rights reserved.
import os.path as osp
from typing import List, Union
from mmengine.fileio import get_local_path, join_path
from mmengine.utils import is_abs
from mmdet.datasets.coco import CocoDataset
from mmyolo.registry import DATASETS
from .utils import RobustBatchShapePolicyDataset
@DATASETS.register_module()
class YOLOv5MixedGroundingDataset(RobustBatchShapePolicyDataset, CocoDataset):
"""Mixed grounding dataset."""
METAINFO = {
'classes': ('object',),
'palette': [(220, 20, 60)]}
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)
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
# print(len(data_list))
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 = {}
img_path = None
img_prefix = self.data_prefix.get('img', None)
if isinstance(img_prefix, str):
img_path = osp.join(img_prefix, img_info['file_name'])
elif isinstance(img_prefix, (list, tuple)):
for prefix in img_prefix:
candidate_img_path = osp.join(prefix, img_info['file_name'])
if osp.exists(candidate_img_path):
img_path = candidate_img_path
break
assert img_path is not None, (
f'Image path {img_info["file_name"]} not found in'
f'{img_prefix}')
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'] = float(img_info['height'])
data_info['width'] = float(img_info['width'])
cat2id = {}
texts = []
for ann in ann_info:
cat_name = ' '.join([img_info['caption'][t[0]:t[1]]
for t in ann['tokens_positive']])
if cat_name not in cat2id:
cat2id[cat_name] = len(cat2id)
texts.append([cat_name])
data_info['texts'] = texts
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, float(img_info['width'])) - max(x1, 0))
inter_h = max(0,
min(y1 + h, float(img_info['height'])) - max(y1, 0))
if inter_w * inter_h == 0:
continue
if ann['area'] <= 0 or w < 1 or h < 1:
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
cat_name = ' '.join([img_info['caption'][t[0]:t[1]]
for t in ann['tokens_positive']])
instance['bbox_label'] = cat2id[cat_name]
if ann.get('segmentation', None):
instance['mask'] = ann['segmentation']
instances.append(instance)
# NOTE: for detection task, we set `is_detection` to 1
data_info['is_detection'] = 1
data_info['instances'] = instances
# print(data_info['texts'])
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)
valid_data_infos = []
for i, data_info in enumerate(self.data_list):
img_id = data_info['img_id']
width = int(data_info['width'])
height = int(data_info['height'])
if filter_empty_gt and img_id not in ids_with_ann:
continue
if min(width, height) >= min_size:
valid_data_infos.append(data_info)
return valid_data_infos
def _join_prefix(self):
"""Join ``self.data_root`` with ``self.data_prefix`` and
``self.ann_file``.
"""
# Automatically join annotation file path with `self.root` if
# `self.ann_file` is not an absolute path.
if self.ann_file and not is_abs(self.ann_file) and self.data_root:
self.ann_file = join_path(self.data_root, self.ann_file)
# Automatically join data directory with `self.root` if path value in
# `self.data_prefix` is not an absolute path.
for data_key, prefix in self.data_prefix.items():
if isinstance(prefix, (list, tuple)):
abs_prefix = []
for p in prefix:
if not is_abs(p) and self.data_root:
abs_prefix.append(join_path(self.data_root, p))
else:
abs_prefix.append(p)
self.data_prefix[data_key] = abs_prefix
elif isinstance(prefix, str):
if not is_abs(prefix) and self.data_root:
self.data_prefix[data_key] = join_path(
self.data_root, prefix)
else:
self.data_prefix[data_key] = prefix
else:
raise TypeError('prefix should be a string, tuple or list,'
f'but got {type(prefix)}')
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