2.1 选出 p < 2 的集合，作为一个 cluster，并计算加权曼哈顿距离 wp。 

2.2 在该 cluster 中挑选出最小的 wp 作为 bi 的 wp。 

confluence_process(prediction, conf_thres=0.1, wp_thres=0.6)

# Detections matrix nx6 (xyxy, conf, cls)
if multi_label:
    i, j = (x[:, 5:] > conf_thres).nonzero().t()
    x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
else:  # best class only
    conf, j = x[:, 5:].max(1, keepdim=True)
    x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]

confluence(prediction, class_num, wp_thres=0.6)

index = np.arange(0, len(prediction), 1).reshape(-1,1)
infos = np.concatenate((prediction, index), 1)

for c in range(class_num):       
    pcs = infos[infos[:, 5] == c]             
    while (len(pcs)):                      
        n = len(pcs)       
        xs = pcs[:, [0, 2]]
        ys = pcs[:, [1, 3]]             
        ps = []        
        # 遍历 pcs，计算每一个box 和其余 box 的 p 值，然后聚类成簇，再根据 wp 挑出 best
        confluence_min = 10000
        best = None
        for i, pc in enumerate(pcs):

index_other = [j for j in range(n) if j!= i]
x_t = xs[i]
x_t = np.tile(x_t, (n-1, 1))
x_other = xs[index_other]
x_all = np.concatenate((x_t, x_other), 1)
.
.
.
# wp
wp = p / pc[4]
wp = wp[p < 2]

if (len(wp) == 0):
    value = 0
else:
    value = wp.min()

# select the bbox which has the smallest wp as the best bbox
if (value < confluence_min):
   confluence_min = value
   best = i  

keep.append(int(pcs[best][6])) 
if (len(ps) > 0):               
    p = ps[best]
    index_ = np.where(p < wp_thres)[0]
    index_ = [i if i < best else i +1 for i in index_]
else:
    index_ = []
    
# delect the bboxes whose Manhattan Distance is below the predefined MD
index_eff = [j for j in range(n) if (j != best and j not in index_)]            
pcs = pcs[index_eff]