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from typing import List
from inference.core.entities.responses.inference import Keypoint
from inference.core.exceptions import ModelArtefactError
def superset_keypoints_count(keypoints_metadata={}) -> int:
"""Returns the number of keypoints in the superset."""
max_keypoints = 0
for keypoints in keypoints_metadata.values():
if len(keypoints) > max_keypoints:
max_keypoints = len(keypoints)
return max_keypoints
def model_keypoints_to_response(
keypoints_metadata: dict,
keypoints: List[float],
predicted_object_class_id: int,
keypoint_confidence_threshold: float,
) -> List[Keypoint]:
if keypoints_metadata is None:
raise ModelArtefactError("Keypoints metadata not available.")
keypoint_id2name = keypoints_metadata[predicted_object_class_id]
results = []
for keypoint_id in range(len(keypoints) // 3):
if keypoint_id >= len(keypoint_id2name):
# Ultralytics only supports single class keypoint detection, so points might be padded with zeros
break
confidence = keypoints[3 * keypoint_id + 2]
if confidence < keypoint_confidence_threshold:
continue
keypoint = Keypoint(
x=keypoints[3 * keypoint_id],
y=keypoints[3 * keypoint_id + 1],
confidence=confidence,
class_id=keypoint_id,
class_name=keypoint_id2name[keypoint_id],
)
results.append(keypoint)
return results
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