numpy框架下给定两个框的x1, y1, z1, w1, h1, d1,x2, y2, z2, w2, h2, d2计算两者的ciou，写出对应的python函数,用一个函数

以下是numpy框架下计算两个框的ciou的函数：

import numpy as np

def ciou(x1, y1, z1, w1, h1, d1, x2, y2, z2, w2, h2, d2):
    """
    计算两个框的ciou
    
    参数：
    x1, y1, z1, w1, h1, d1: 第一个框的左上角坐标和宽度、高度、深度
    x2, y2, z2, w2, h2, d2: 第二个框的左上角坐标和宽度、高度、深度
    
    返回值：
    两个框的ciou
    """
    # 计算两个框的面积和宽度、高度、深度的最小值和最大值
    area1 = w1 * h1 * d1
    area2 = w2 * h2 * d2
    max_xy = np.minimum((x1 + w1, y1 + h1, z1 + d1), (x2 + w2, y2 + h2, z2 + d2))
    min_xy = np.maximum((x1, y1, z1), (x2, y2, z2))
    inter_whd = np.clip(max_xy - min_xy, a_min=0, a_max=None)
    inter_area = inter_whd[0] * inter_whd[1] * inter_whd[2]
    union_area = area1 + area2 - inter_area

    # 计算两个框的中心点坐标和宽度、高度、深度的差值
    center1 = np.array((x1 + w1 / 2, y1 + h1 / 2, z1 + d1 / 2))
    center2 = np.array((x2 + w2 / 2, y2 + h2 / 2, z2 + d2 / 2))
    delta_center = center1 - center2
    w1_, h1_, d1_ = w1, h1, d1
    w2_, h2_, d2_ = w2, h2, d2
    if inter_area > 0:
        # 计算相交的框的左上角和右下角坐标
        max_xy_i = np.maximum((x1, y1, z1), (x2, y2, z2))
        min_xy_i = np.minimum((x1 + w1, y1 + h1, z1 + d1), (x2 + w2, y2 + h2, z2 + d2))
        # 计算相交框的宽度、高度、深度的最小值
        inter_whd_i = np.clip(max_xy_i - min_xy_i, a_min=0, a_max=None)
        inter_diagonal = np.linalg.norm(inter_whd_i)
        # 计算相交框的中心点坐标和宽度、高度、深度的差值
        center_i = (max_xy_i + min_xy_i) / 2
        delta_center_i = np.linalg.norm(center1 - center_i, ord=2, axis=-1)
        # 计算相交框的最小闭合矩形对角线长度
        w_i, h_i, d_i = min_xy_i - max_xy_i
        v_i = w_i * h_i * d_i
        alpha = 4 / (np.pi ** 2) * np.arctan(w_i * h_i * d_i / (4 * v_i - inter_area))
        # 计算调整后的宽度、高度、深度
        w1_ = w1 * np.exp(alpha * (delta_center_i / inter_diagonal - 1))
        h1_ = h1 * np.exp(alpha * (delta_center_i / inter_diagonal - 1))
        d1_ = d1 * np.exp(alpha * (delta_center_i / inter_diagonal - 1))
        w2_ = w2 * np.exp(alpha * (delta_center_i / inter_diagonal - 1))
        h2_ = h2 * np.exp(alpha * (delta_center_i / inter_diagonal - 1))
        d2_ = d2 * np.exp(alpha * (delta_center_i / inter_diagonal - 1))

    # 计算ciou
    w_intersection = inter_whd[0]
    h_intersection = inter_whd[1]
    d_intersection = inter_whd[2]
    v_intersection = w_intersection * h_intersection * d_intersection
    v_union = w1_ * h1_ * d1_ + w2_ * h2_ * d2_ - v_intersection
    iou = v_intersection / v_union
    rho = delta_center ** 2 / (w1_ ** 2 + h1_ ** 2 + d1_ ** 2)
    c_iou = iou - rho.sum() / rho.shape[-1] * (1 - iou)
    return c_iou