mmpose.datasets.datasets.body.mpii_trb_dataset 源代码

# 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

[文档]@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 <>`__ . 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/') def _load_annotations(self) -> Tuple[List[dict], List[dict]]: """Load data from annotations in MPII-TRB format.""" assert exists(self.ann_file), 'Annotation file does not exist' with get_local_path(self.ann_file) as local_path: with open(local_path) as anno_file: = json.load(anno_file) imgid2info = {img['id']: img for img in['images']} instance_list = [] image_list = [] used_img_ids = set() # mpii-trb bbox scales are normalized with factor 200. pixel_std = 200. for ann in['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
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