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UPDATE: Code review fix and docs updated

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Ashp116 2025-07-12 14:50:43 -04:00
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6
docs/detection/utils.md
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@ -5,6 +5,12 @@ status: new
# Detection Utils
<div class="md-typeset">
<h2><a href="#supervision.detection.utils.box_iou">box_iou</a></h2>
</div>
:::supervision.detection.utils.box_iou
<div class="md-typeset">
<h2><a href="#supervision.detection.utils.box_iou_batch">box_iou_batch</a></h2>
</div>

31
supervision/detection/utils.py
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@ -48,32 +48,41 @@ def polygon_to_mask(polygon: np.ndarray, resolution_wh: Tuple[int, int]) -> np.n
return mask
def box_iou(box1: np.ndarray, box2: 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.
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.
Args:
box1 (np.ndarray): A bounding box represented as [x1, y1, x2, y2].
box2 (np.ndarray): A bounding box represented as [x1, y1, x2, y2].
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 box1 and box2.
float: The IoU value between the two boxes.
Ranges from 0.0 (no overlap) to 1.0 (perfect overlap).
"""
inter_x1 = max(box1[0], box2[0])
inter_y1 = max(box1[1], box2[1])
inter_x2 = min(box1[2], box2[2])
inter_y2 = min(box1[3], box2[3])
box_true = np.array(box_true)
box_detection = np.array(box_detection)
inter_x1 = max(box_true[0], box_detection[0])
inter_y1 = max(box_true[1], box_detection[1])
inter_x2 = min(box_true[2], box_detection[2])
inter_y2 = min(box_true[3], box_detection[3])
inter_w = max(0, inter_x2 - inter_x1)
inter_h = max(0, inter_y2 - inter_y1)
inter_area = inter_w * inter_h
area1 = (box1[2] - box1[0]) * (box1[3] - box1[1])
area2 = (box2[2] - box2[0]) * (box2[3] - box2[1])
area_true = (box_true[2] - box_true[0]) * (box_true[3] - box_true[1])
area_detection = (box_detection[2] - box_detection[0]) * (box_detection[3] - box_detection[1])
union_area = area1 + area2 - inter_area
union_area = area_true + area_detection - inter_area
return inter_area / union_area + 1e-6