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"""🕺 High-Resolution Network: Deep high-resolution representation learning for
human pose estimation. Requires an object detector.
"""
from typing import Any, Dict, Optional
from peekingduck.pipeline.nodes.abstract_node import AbstractNode
from peekingduck.pipeline.nodes.model.hrnetv1 import hrnet_model
[docs]class Node(AbstractNode):
"""Initializes and uses HRNet model to infer poses from detected bboxes.
Note that HRNet must be used in conjunction with an object detector applied
prior.
The HRNet applied to human pose estimation uses the representation head,
called HRNetV1.
The HRNet node is capable of detecting single human figures simultaneously
per inference, with 17 keypoints estimated for each detected human figure.
The keypoint indices table can be found
:ref:`here <whole-body-keypoint-ids>`.
Inputs:
|img_data|
|bboxes_data|
Outputs:
|keypoints_data|
|keypoint_scores_data|
|keypoint_conns_data|
Configs:
weights_parent_dir (:obj:`Optional[str]`): **default = null**. |br|
Change the parent directory where weights will be stored by
replacing ``null`` with an absolute path to the desired directory.
resolution (:obj:`Dict[str, int]`):
**default = { height: 192, width: 256 }**. |br|
Resolution of input array to HRNet model.
score_threshold (:obj:`float`): **[0, 1], default = 0.1**. |br|
Threshold to determine if detection should be returned
References:
Deep High-Resolution Representation Learning for Visual Recognition:
https://arxiv.org/abs/1908.07919
"""
def __init__(self, config: Dict[str, Any] = None, **kwargs: Any) -> None:
super().__init__(config, node_path=__name__, **kwargs)
self.model = hrnet_model.HRNetModel(self.config)
def run(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Reads the bbox input and returns the poses and pose bbox of the
specified objects chosen to be detected.
"""
keypoints, keypoint_scores, keypoint_conns = self.model.predict(
inputs["img"], inputs["bboxes"]
)
outputs = {
"keypoints": keypoints,
"keypoint_scores": keypoint_scores,
"keypoint_conns": keypoint_conns,
}
return outputs
def _get_config_types(self) -> Dict[str, Any]:
"""Returns dictionary mapping the node's config keys to respective types."""
return {
"resolution": Dict[str, int],
"resolution.height": int,
"resolution.width": int,
"score_threshold": float,
"weights_parent_dir": Optional[str],
}