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Ashp116 2025-07-12 14:54:26 -04:00
parent 2378d8bdca
commit d2ffd04aed
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1 changed files with 16 additions and 10 deletions
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26
supervision/detection/utils.py
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@ -48,23 +48,29 @@ def polygon_to_mask(polygon: np.ndarray, resolution_wh: Tuple[int, int]) -> np.n
return mask
def box_iou(box_true: Union[List[float], np.ndarray], box_detection: Union[List[float], np.ndarray]) -> float:
def box_iou(
box_true: Union[List[float], np.ndarray],
box_detection: Union[List[float], np.ndarray]
) -> float:
"""
Compute the Intersection over Union (IoU) between two bounding boxes.
Both `box_true` and `box_detection` should be in (x_min, y_min, x_max, y_max) format.
Both `box_true` and `box_detection` should be in (x_min, y_min, x_max, y_max)
format.
Note:
Use `box_iou` when computing IoU between two individual boxes.
For comparing multiple boxes (arrays of boxes), use `box_iou_batch` for better performance.
For comparing multiple boxes (arrays of boxes), use `box_iou_batch` for better
performance.
Args:
box_true (Union[List[float], np.ndarray]): A single bounding box represented as [x_min, y_min, x_max, y_max].
box_detection (Union[List[float], np.ndarray]): A single bounding box represented as [x_min, y_min, x_max, y_max].
box_true (Union[List[float], np.ndarray]): A single bounding box represented as
[x_min, y_min, x_max, y_max].
box_detection (Union[List[float], np.ndarray]): A single bounding box represented as
[x_min, y_min, x_max, y_max].
Returns:
float: The IoU value between the two boxes.
Ranges from 0.0 (no overlap) to 1.0 (perfect overlap).
float: IoU score between the two boxes.
"""
box_true = np.array(box_true)
box_detection = np.array(box_detection)
@ -814,7 +820,7 @@ def move_oriented_boxes(
])
offset = np.array([5, 5])
sv.move_oriented_boxes(xyxy=xyxy, offset=offset)
sv.move_oriented_boxes(xyxy=xyxyxyxy, offset=offset)
# array([
# [
# [25, 15],