Source code for mmpose.datasets.datasets.body.humanart21_dataset
# Copyright (c) OpenMMLab. All rights reserved.
import copy
from typing import Optional
import numpy as np
from mmpose.registry import DATASETS
from .humanart_dataset import HumanArtDataset
[docs]@DATASETS.register_module()
class HumanArt21Dataset(HumanArtDataset):
"""Human-Art dataset for pose estimation with 21 kpts.
"Human-Art: A Versatile Human-Centric Dataset
Bridging Natural and Artificial Scenes", CVPR'2023.
More details can be found in the `paper
<https://arxiv.org/abs/2303.02760>`__ .
Human-Art keypoints::
0: 'nose',
1: 'left_eye',
2: 'right_eye',
3: 'left_ear',
4: 'right_ear',
5: 'left_shoulder',
6: 'right_shoulder',
7: 'left_elbow',
8: 'right_elbow',
9: 'left_wrist',
10: 'right_wrist',
11: 'left_hip',
12: 'right_hip',
13: 'left_knee',
14: 'right_knee',
15: 'left_ankle',
16: 'right_ankle',
17: 'left_finger',
18: 'right_finger',
19: 'left_toe',
20: 'right_toe',
Args:
ann_file (str): Annotation file path. Default: ''.
bbox_file (str, optional): Detection result file path. If
``bbox_file`` is set, detected bboxes loaded from this file will
be used instead of ground-truth bboxes. This setting is only for
evaluation, i.e., ignored when ``test_mode`` is ``False``.
Default: ``None``.
data_mode (str): Specifies the mode of data samples: ``'topdown'`` or
``'bottomup'``. In ``'topdown'`` mode, each data sample contains
one instance; while in ``'bottomup'`` mode, each data sample
contains all instances in a image. Default: ``'topdown'``
metainfo (dict, optional): Meta information for dataset, such as class
information. Default: ``None``.
data_root (str, optional): The root directory for ``data_prefix`` and
``ann_file``. Default: ``None``.
data_prefix (dict, optional): Prefix for training data. Default:
``dict(img=None, ann=None)``.
filter_cfg (dict, optional): Config for filter data. Default: `None`.
indices (int or Sequence[int], optional): Support using first few
data in annotation file to facilitate training/testing on a smaller
dataset. Default: ``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.
Default: ``True``.
pipeline (list, optional): Processing pipeline. Default: [].
test_mode (bool, optional): ``test_mode=True`` means in test phase.
Default: ``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``. Default: ``False``.
max_refetch (int, optional): If ``Basedataset.prepare_data`` get a
None img. The maximum extra number of cycles to get a valid
image. Default: 1000.
"""
METAINFO: dict = dict(from_file='configs/_base_/datasets/humanart21.py')
[docs] def parse_data_info(self, raw_data_info: dict) -> Optional[dict]:
"""Parse raw COCO annotation of an instance.
Args:
raw_data_info (dict): Raw data information loaded from
``ann_file``. It should have following contents:
- ``'raw_ann_info'``: Raw annotation of an instance
- ``'raw_img_info'``: Raw information of the image that
contains the instance
Returns:
dict | None: Parsed instance annotation
"""
ann = raw_data_info['raw_ann_info']
img = raw_data_info['raw_img_info']
# filter invalid instance
if 'bbox' not in ann or 'keypoints' not in ann:
return None
img_w, img_h = img['width'], img['height']
# get bbox in shape [1, 4], formatted as xywh
x, y, w, h = ann['bbox']
x1 = np.clip(x, 0, img_w - 1)
y1 = np.clip(y, 0, img_h - 1)
x2 = np.clip(x + w, 0, img_w - 1)
y2 = np.clip(y + h, 0, img_h - 1)
bbox = np.array([x1, y1, x2, y2], dtype=np.float32).reshape(1, 4)
# keypoints in shape [1, K, 2] and keypoints_visible in [1, K]
_keypoints = np.array(
ann['keypoints_21'], dtype=np.float32).reshape(1, -1, 3)
keypoints = _keypoints[..., :2]
keypoints_visible = np.minimum(1, _keypoints[..., 2])
if 'num_keypoints' in ann:
num_keypoints = ann['num_keypoints']
else:
num_keypoints = np.count_nonzero(keypoints.max(axis=2))
data_info = {
'img_id': ann['image_id'],
'img_path': img['img_path'],
'bbox': bbox,
'bbox_score': np.ones(1, dtype=np.float32),
'num_keypoints': num_keypoints,
'keypoints': keypoints,
'keypoints_visible': keypoints_visible,
'iscrowd': ann.get('iscrowd', 0),
'segmentation': ann.get('segmentation', None),
'id': ann['id'],
'category_id': ann['category_id'],
# store the raw annotation of the instance
# it is useful for evaluation without providing ann_file
'raw_ann_info': copy.deepcopy(ann),
}
if 'crowdIndex' in img:
data_info['crowd_index'] = img['crowdIndex']
return data_info