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Co-authored-by: dingwenchao <12962189468@163.com> Co-authored-by: 丁文超 <dingwenchao@dingwenchaodeMacBook-Pro.local> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
import torch
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def adjust_bboxes_to_image_border(boxes, image_shape, threshold=20):
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'''Adjust bounding boxes to stick to image border if they are within a certain threshold.
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Args:
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boxes: (n, 4)
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image_shape: (height, width)
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threshold: pixel threshold
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Returns:
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adjusted_boxes: adjusted bounding boxes
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'''
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# Image dimensions
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h, w = image_shape
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# Adjust boxes
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boxes[:, 0] = torch.where(boxes[:, 0] < threshold, 0, boxes[:, 0]) # x1
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boxes[:, 1] = torch.where(boxes[:, 1] < threshold, 0, boxes[:, 1]) # y1
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boxes[:, 2] = torch.where(boxes[:, 2] > w - threshold, w, boxes[:, 2]) # x2
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boxes[:, 3] = torch.where(boxes[:, 3] > h - threshold, h, boxes[:, 3]) # y2
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return boxes
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def bbox_iou(box1, boxes, iou_thres=0.9, image_shape=(640, 640), raw_output=False):
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'''Compute the Intersection-Over-Union of a bounding box with respect to an array of other bounding boxes.
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Args:
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box1: (4, )
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boxes: (n, 4)
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Returns:
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high_iou_indices: Indices of boxes with IoU > thres
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'''
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boxes = adjust_bboxes_to_image_border(boxes, image_shape)
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# obtain coordinates for intersections
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x1 = torch.max(box1[0], boxes[:, 0])
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y1 = torch.max(box1[1], boxes[:, 1])
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x2 = torch.min(box1[2], boxes[:, 2])
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y2 = torch.min(box1[3], boxes[:, 3])
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# compute the area of intersection
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intersection = (x2 - x1).clamp(0) * (y2 - y1).clamp(0)
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# compute the area of both individual boxes
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box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
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box2_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
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# compute the area of union
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union = box1_area + box2_area - intersection
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# compute the IoU
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iou = intersection / union # Should be shape (n, )
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if raw_output:
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if iou.numel() == 0:
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return 0
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return iou
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# get indices of boxes with IoU > thres
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high_iou_indices = torch.nonzero(iou > iou_thres).flatten()
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return high_iou_indices
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