Source code for mmpose.datasets.datasets.body.mpii_trb_dataset
# Copyright (c) OpenMMLab. All rights reserved.
import json
import os.path as osp
from typing import List, Tuple
import numpy as np
from mmengine.fileio import exists, get_local_path
from mmpose.registry import DATASETS
from mmpose.structures.bbox import bbox_cs2xyxy
from ..base import BaseCocoStyleDataset
[docs]@DATASETS.register_module()
class MpiiTrbDataset(BaseCocoStyleDataset):
"""MPII-TRB Dataset dataset for pose estimation.
"TRB: A Novel Triplet Representation for Understanding 2D Human Body",
ICCV'2019. More details can be found in the `paper
<https://arxiv.org/abs/1910.11535>`__ .
MPII-TRB keypoints::
0: 'left_shoulder'
1: 'right_shoulder'
2: 'left_elbow'
3: 'right_elbow'
4: 'left_wrist'
5: 'right_wrist'
6: 'left_hip'
7: 'right_hip'
8: 'left_knee'
9: 'right_knee'
10: 'left_ankle'
11: 'right_ankle'
12: 'head'
13: 'neck'
14: 'right_neck'
15: 'left_neck'
16: 'medial_right_shoulder'
17: 'lateral_right_shoulder'
18: 'medial_right_bow'
19: 'lateral_right_bow'
20: 'medial_right_wrist'
21: 'lateral_right_wrist'
22: 'medial_left_shoulder'
23: 'lateral_left_shoulder'
24: 'medial_left_bow'
25: 'lateral_left_bow'
26: 'medial_left_wrist'
27: 'lateral_left_wrist'
28: 'medial_right_hip'
29: 'lateral_right_hip'
30: 'medial_right_knee'
31: 'lateral_right_knee'
32: 'medial_right_ankle'
33: 'lateral_right_ankle'
34: 'medial_left_hip'
35: 'lateral_left_hip'
36: 'medial_left_knee'
37: 'lateral_left_knee'
38: 'medial_left_ankle'
39: 'lateral_left_ankle'
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/mpii_trb.py')
def _load_annotations(self) -> Tuple[List[dict], List[dict]]:
"""Load data from annotations in MPII-TRB format."""
assert exists(self.ann_file), (
f'Annotation file `{self.ann_file}` does not exist')
with get_local_path(self.ann_file) as local_path:
with open(local_path) as anno_file:
self.data = json.load(anno_file)
imgid2info = {img['id']: img for img in self.data['images']}
instance_list = []
image_list = []
used_img_ids = set()
# mpii-trb bbox scales are normalized with factor 200.
pixel_std = 200.
for ann in self.data['annotations']:
img_id = ann['image_id']
# center, scale in shape [1, 2] and bbox in [1, 4]
center = np.array([ann['center']], dtype=np.float32)
scale = np.array([[ann['scale'], ann['scale']]],
dtype=np.float32) * pixel_std
bbox = bbox_cs2xyxy(center, scale)
# keypoints in shape [1, K, 2] and keypoints_visible in [1, K]
_keypoints = np.array(
ann['keypoints'], dtype=np.float32).reshape(1, -1, 3)
keypoints = _keypoints[..., :2]
keypoints_visible = np.minimum(1, _keypoints[..., 2])
img_path = osp.join(self.data_prefix['img'],
imgid2info[img_id]['file_name'])
instance_info = {
'id': ann['id'],
'img_id': img_id,
'img_path': img_path,
'bbox_center': center,
'bbox_scale': scale,
'bbox': bbox,
'bbox_score': np.ones(1, dtype=np.float32),
'num_keypoints': ann['num_joints'],
'keypoints': keypoints,
'keypoints_visible': keypoints_visible,
'iscrowd': ann['iscrowd'],
}
# val set
if 'headbox' in ann:
instance_info['headbox'] = np.array(
ann['headbox'], dtype=np.float32)
instance_list.append(instance_info)
if instance_info['img_id'] not in used_img_ids:
used_img_ids.add(instance_info['img_id'])
image_list.append({
'img_id': instance_info['img_id'],
'img_path': instance_info['img_path'],
})
instance_list = sorted(instance_list, key=lambda x: x['id'])
return instance_list, image_list