class str, model_config: str, model_checkpoint: str, input_buffer: str, output_buffer: Union[str, List[str]], enable_key: Optional[Union[str, int]] = None, enable: bool = True, device: str = 'cuda:0', class_ids: Optional[List[int]] = None, labels: Optional[List[str]] = None, bbox_thr: float = 0.5, smooth: bool = False, smooth_filter_cfg: str = 'configs/_base_/filters/')[源代码]

Perform top-down pose estimation using MMPose model.

The node should be placed after an object detection node.

  • name (str) – The node name (also thread name)

  • model_cfg (str) – The model config file

  • model_checkpoint (str) – The model checkpoint file

  • input_buffer (str) – The name of the input buffer

  • output_buffer (str|list) – The name(s) of the output buffer(s)

  • enable_key (str|int, optional) – Set a hot-key to toggle enable/disable of the node. If an int value is given, it will be treated as an ascii code of a key. Please note: (1) If enable_key is set, the bypass() method need to be overridden to define the node behavior when disabled; (2) Some hot-keys are reserved for particular use. For example: ‘q’, ‘Q’ and 27 are used for exiting. Default: None

  • enable (bool) – Default enable/disable status. Default: True

  • device (str) – Specify the device to hold model weights and inference the model. Default: 'cuda:0'

  • class_ids (list[int], optional) – Specify the object category indices to apply pose estimation. If both class_ids and labels are given, labels will be ignored. If neither is given, pose estimation will be applied for all objects. Default: None

  • labels (list[str], optional) – Specify the object category names to apply pose estimation. See also class_ids. Default: None

  • bbox_thr (float) – Set a threshold to filter out objects with low bbox scores. Default: 0.5

  • smooth (bool) – If set to True, a Smoother will be used to refine the pose estimation result. Default: True

  • smooth_filter_cfg (str) – The filter config path to build the smoother. Only valid when smooth==True. By default, OneEuro filter will be used.

>>> cfg = dict(
...     type='TopDownPoseEstimatorNode',
...     name='human pose estimator',
...     model_config='configs/wholebody/2d_kpt_sview_rgb_img/'
...     'topdown_heatmap/coco-wholebody/'
...     '',
...     model_checkpoint=''
...     'top_down/vipnas/vipnas_mbv3_coco_wholebody_256x192_dark'
...     '-e2158108_20211205.pth',
...     labels=['person'],
...     smooth=True,
...     input_buffer='det_result',
...     output_buffer='human_pose')
>>> from import NODES
>>> node =
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