深度学习知识点：IOU的计算及代码实现

        if BBGT.size > 0:
            # 计算IoU
            # BBGT是真实坐标，bb是预测坐标
            ixmin = np.maximum(BBGT[:, 0], bb[0])
            iymin = np.maximum(BBGT[:, 1], bb[1])
            ixmax = np.minimum(BBGT[:, 2], bb[2])
            iymax = np.minimum(BBGT[:, 3], bb[3])
            iw = np.maximum(ixmax - ixmin + 1., 0.)
            ih = np.maximum(iymax - iymin + 1., 0.)

            # interesting
            inters = iw * ih

            # union
            # (bb[2] - bb[0]) 是预测框的width，(bb[3] - bb[1])是预测框的height
            # (BBGT[:, 2] - BBGT[:, 0]) 是真实框的width
            # (BBGT[:, 3] - BBGT[:, 1]) 是真实框的height
            uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
                   (BBGT[:, 2] - BBGT[:, 0] + 1.) *
                   (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)

            overlaps = inters / uni
            # 检测到的目标框可能预若干个真实目标框都有交集，选择其中交集最大的
            ovmax = np.max(overlaps)