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# Ultralytics YOLO 🚀, AGPL-3.0 license
import torch
def adjust_bboxes_to_image_border(boxes, image_shape, threshold=20):
"""
Adjust bounding boxes to stick to image border if they are within a certain threshold.
Args:
boxes (torch.Tensor): (n, 4)
image_shape (tuple): (height, width)
threshold (int): pixel threshold
Returns:
adjusted_boxes (torch.Tensor): adjusted bounding boxes
"""
# Image dimensions
h, w = image_shape
# Adjust boxes
boxes[boxes[:, 0] < threshold, 0] = 0 # x1
boxes[boxes[:, 1] < threshold, 1] = 0 # y1
boxes[boxes[:, 2] > w - threshold, 2] = w # x2
boxes[boxes[:, 3] > h - threshold, 3] = h # y2
return boxes
def bbox_iou(box1, boxes, iou_thres=0.9, image_shape=(640, 640), raw_output=False):
"""
Compute the Intersection-Over-Union of a bounding box with respect to an array of other bounding boxes.
Args:
box1 (torch.Tensor): (4, )
boxes (torch.Tensor): (n, 4)
iou_thres (float): IoU threshold
image_shape (tuple): (height, width)
raw_output (bool): If True, return the raw IoU values instead of the indices
Returns:
high_iou_indices (torch.Tensor): Indices of boxes with IoU > thres
"""
boxes = adjust_bboxes_to_image_border(boxes, image_shape)
# Obtain coordinates for intersections
x1 = torch.max(box1[0], boxes[:, 0])
y1 = torch.max(box1[1], boxes[:, 1])
x2 = torch.min(box1[2], boxes[:, 2])
y2 = torch.min(box1[3], boxes[:, 3])
# Compute the area of intersection
intersection = (x2 - x1).clamp(0) * (y2 - y1).clamp(0)
# Compute the area of both individual boxes
box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
box2_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
# Compute the area of union
union = box1_area + box2_area - intersection
# Compute the IoU
iou = intersection / union # Should be shape (n, )
if raw_output:
return 0 if iou.numel() == 0 else iou
# return indices of boxes with IoU > thres
return torch.nonzero(iou > iou_thres).flatten()
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