renaming iou_with_jaccard(...) to box_iou_batch_with_jaccard(...) and moving it to supervision.__init__.py

supervision/detection/utils.py

@@ -1326,76 +1326,3 @@ def spread_out_boxes(
 
     return pad_boxes(xyxy_padded, px=-1)
 
-
-def _jaccard(box_a: List[float], box_b: List[float], is_crowd: bool) -> float:
-    """
-    Calculate the Jaccard index (intersection over union) between two bounding boxes.
-    If a gt object is marked as "iscrowd", a dt is allowed to match any subregion
-    of the gt. Choosing gt' in the crowd gt that best matches the dt can be done using
-    gt'=intersect(dt,gt). Since by definition union(gt',dt)=dt, computing
-    iou(gt,dt,iscrowd) = iou(gt',dt) = area(intersect(gt,dt)) / area(dt)
-
-    Args:
-        box_a (List[float]): Box coordinates in the format [x, y, width, height].
-        box_b (List[float]): Box coordinates in the format [x, y, width, height].
-        iscrowd (bool): Flag indicating if the second box is a crowd region or not.
-
-    Returns:
-        float: Jaccard index between the two bounding boxes.
-    """
-    # Smallest number to avoid division by zero
-    EPS = np.spacing(1)
-
-    xa, ya, x2a, y2a = box_a[0], box_a[1], box_a[0] + box_a[2], box_a[1] + box_a[3]
-    xb, yb, x2b, y2b = box_b[0], box_b[1], box_b[0] + box_b[2], box_b[1] + box_b[3]
-
-    # Innermost left x
-    xi = max(xa, xb)
-    # Innermost right x
-    x2i = min(x2a, x2b)
-    # Same for y
-    yi = max(ya, yb)
-    y2i = min(y2a, y2b)
-
-    # Calculate areas
-    Aa = max(x2a - xa, 0.0) * max(y2a - ya, 0.0)
-    Ab = max(x2b - xb, 0.0) * max(y2b - yb, 0.0)
-    Ai = max(x2i - xi, 0.0) * max(y2i - yi, 0.0)
-
-    if is_crowd:
-        return Ai / (Aa + EPS)
-
-    return Ai / (Aa + Ab - Ai + EPS)
-
-
-def iou_with_jaccard(
-    boxes_true: List[List[float]],
-    boxes_detection: List[List[float]],
-    is_crowd: List[bool],
-) -> np.ndarray:
-    """
-    Calculate the intersection over union (IoU) between detection bounding boxes (dt)
-    and ground-truth bounding boxes (gt).
-    Reference: https://github.com/rafaelpadilla/review_object_detection_metrics
-
-    Args:
-        boxes_true (List[List[float]]): List of ground-truth bounding boxes in the \
-            format [x, y, width, height].
-        boxes_detection (List[List[float]]): List of detection bounding boxes in the \
-            format [x, y, width, height].
-        is_crowd (List[bool]): List indicating if each ground-truth bounding box \
-            is a crowd region or not.
-
-    Returns:
-        np.ndarray: Array of IoU values of shape (len(dt), len(gt)).
-    """
-    assert len(is_crowd) == len(boxes_true), (
-        "iou(iscrowd=) must have the same length as boxes_true"
-    )
-    if len(boxes_detection) == 0 or len(boxes_true) == 0:
-        return np.array([])
-    ious = np.zeros((len(boxes_detection), len(boxes_true)), dtype=np.float64)
-    for g_idx, g in enumerate(boxes_true):
-        for d_idx, d in enumerate(boxes_detection):
-            ious[d_idx, g_idx] = _jaccard(d, g, is_crowd[g_idx])
-    return ious