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56 lines
1.7 KiB
56 lines
1.7 KiB
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Model validation metrics
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"""
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import numpy as np
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import torch
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def box_area(box):
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# box = xyxy(4,n)
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return (box[2] - box[0]) * (box[3] - box[1])
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def bbox_ioa(box1, box2, eps=1e-7):
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"""Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2
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box1: np.array of shape(nx4)
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box2: np.array of shape(mx4)
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returns: np.array of shape(nxm)
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"""
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# Get the coordinates of bounding boxes
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b1_x1, b1_y1, b1_x2, b1_y2 = box1.T
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b2_x1, b2_y1, b2_x2, b2_y2 = box2.T
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# Intersection area
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inter_area = (np.minimum(b1_x2[:, None], b2_x2) - np.maximum(b1_x1[:, None], b2_x1)).clip(0) * \
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(np.minimum(b1_y2[:, None], b2_y2) - np.maximum(b1_y1[:, None], b2_y1)).clip(0)
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# box2 area
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box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps
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# Intersection over box2 area
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return inter_area / box2_area
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def box_iou(box1, box2, eps=1e-7):
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# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
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"""
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Return intersection-over-union (Jaccard index) of boxes.
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Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
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Arguments:
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box1 (Tensor[N, 4])
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box2 (Tensor[M, 4])
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Returns:
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iou (Tensor[N, M]): the NxM matrix containing the pairwise
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IoU values for every element in boxes1 and boxes2
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"""
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# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
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(a1, a2), (b1, b2) = box1[:, None].chunk(2, 2), box2.chunk(2, 1)
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inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
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# IoU = inter / (area1 + area2 - inter)
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return inter / (box_area(box1.T)[:, None] + box_area(box2.T) - inter + eps)
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