fix(pre_commit): 🎨 auto format pre-commit hooks
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@ -56,9 +56,6 @@ from supervision.detection.utils import (
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polygon_to_xyxy,
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scale_boxes,
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)
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from supervision.keypoints.core import KeyPoints
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from supervision.keypoints.skeletons import KnownSkeletons, Skeleton
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from supervision.keypoints.annotate import KeyPointAnnotator, SkeletonAnnotator
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from supervision.draw.color import Color, ColorPalette
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from supervision.draw.utils import (
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calculate_optimal_line_thickness,
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@ -72,7 +69,9 @@ from supervision.draw.utils import (
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)
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from supervision.geometry.core import Point, Position, Rect
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from supervision.geometry.utils import get_polygon_center
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from supervision.keypoints.annotate import KeyPointAnnotator, SkeletonAnnotator
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from supervision.keypoints.core import KeyPoints
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from supervision.keypoints.skeletons import KnownSkeletons, Skeleton
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from supervision.metrics.detection import ConfusionMatrix, MeanAveragePrecision
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from supervision.tracker.byte_tracker.core import ByteTrack
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from supervision.utils.file import list_files_with_extensions
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@ -8,7 +8,7 @@ from supervision.annotators.base import ImageType
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from supervision.annotators.utils import scene_to_annotator_img_type
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from supervision.draw.color import Color
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from supervision.keypoints.core import KeyPoints
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from supervision.keypoints.skeletons import Skeleton, KnownSkeletons
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from supervision.keypoints.skeletons import KnownSkeletons, Skeleton
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class BaseKeyPointAnnotator(ABC):
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@ -75,14 +75,11 @@ class SkeletonAnnotator(BaseKeyPointAnnotator):
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self.skeleton = skeleton
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@scene_to_annotator_img_type
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def annotate(
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self, scene: ImageType, keypoints: KeyPoints
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) -> ImageType:
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def annotate(self, scene: ImageType, keypoints: KeyPoints) -> ImageType:
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if len(keypoints) == 0:
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return scene
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if keypoints.class_id is None:
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raise ValueError(
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"KeyPoints must have class_id to annotate a skeleton")
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raise ValueError("KeyPoints must have class_id to annotate a skeleton")
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xy_all = keypoints.xy
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for xy in xy_all:
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@ -58,8 +58,7 @@ class KeyPoints:
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xy: npt.NDArray[np.float32]
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class_id: Optional[npt.NDArray[np.int_]] = None
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confidence: Optional[npt.NDArray[np.float32]] = None
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data: Dict[str, Union[npt.NDArray[Any], List]
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] = field(default_factory=dict)
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data: Dict[str, Union[npt.NDArray[Any], List]] = field(default_factory=dict)
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def __post_init__(self):
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validate_keypoints_fields(
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@ -139,8 +138,7 @@ class KeyPoints:
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xy = ultralytics_results.keypoints.xy.cpu().numpy()
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class_id = ultralytics_results.boxes.cls.cpu().numpy().astype(int)
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class_names = np.array([ultralytics_results.names[i]
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for i in class_id])
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class_names = np.array([ultralytics_results.names[i] for i in class_id])
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confidence = ultralytics_results.keypoints.conf.cpu().numpy()
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data = {CLASS_NAME_DATA_FIELD: class_names}
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@ -1,5 +1,3 @@
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from typing import List, Tuple
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@ -70,6 +68,7 @@ class KnownSkeletons:
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# Suppose you have a model returning 15 keypoints.
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skeleton = KnownSkeletons().get_skeleton(15)
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"""
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_instance = None
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_skeletons: dict[int, Skeleton] = {}
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@ -81,14 +80,14 @@ class KnownSkeletons:
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def add_skeleton(self, skeleton: Skeleton) -> None:
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len_limbs = len(skeleton.limbs)
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if len_limbs in self._skeletons:
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raise ValueError(
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f"A skeleton with {len_limbs} limbs already exists")
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raise ValueError(f"A skeleton with {len_limbs} limbs already exists")
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self._skeletons[len_limbs] = skeleton
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def get_skeleton(self, limb_count: int) -> Skeleton:
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if limb_count not in self._skeletons:
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print(
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f"Warning: No skeleton found with {limb_count} limbs. Will create one now.")
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f"Warning: No skeleton found with {limb_count} limbs. Will create one now."
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)
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self.add_skeleton(_make_sequential_skeleton(limb_count))
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return self._skeletons[limb_count]
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