models/01_YoloV5/01_Pytorch/processing.py [91:108]:
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        x1 = bboxes[:, 0]
        y1 = bboxes[:, 1]
        x2 = bboxes[:, 2]
        y2 = bboxes[:, 3]
        areas = (x2 - x1 + 1) * (y2 - y1 + 1) 
        order = scores.flatten().argsort()[::-1]
        keep = []
        while order.size > 0:
            i = order[0]
            keep.append(i)
            xx1 = np.maximum(x1[i], x1[order[1:]])
            yy1 = np.maximum(y1[i], y1[order[1:]])
            xx2 = np.minimum(x2[i], x2[order[1:]])
            yy2 = np.minimum(y2[i], y2[order[1:]])
            w = np.maximum(0.0, xx2 - xx1 + 1)
            h = np.maximum(0.0, yy2 - yy1 + 1)
            inter = w * h
            iou = inter / (areas[i] + areas[order[1:]] - inter)
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models/02_YoloV4/01_Pytorch/utils.py [41:58]:
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    x1 = bboxes[:, 0]
    y1 = bboxes[:, 1]
    x2 = bboxes[:, 2]
    y2 = bboxes[:, 3]
    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
    order = scores.flatten().argsort()[::-1]
    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)
        xx1 = np.maximum(x1[i], x1[order[1:]])
        yy1 = np.maximum(y1[i], y1[order[1:]])
        xx2 = np.minimum(x2[i], x2[order[1:]])
        yy2 = np.minimum(y2[i], y2[order[1:]])
        w = np.maximum(0.0, xx2 - xx1 + 1)
        h = np.maximum(0.0, yy2 - yy1 + 1)
        inter = w * h
        iou = inter / (areas[i] + areas[order[1:]] - inter)
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