# https://blog.csdn.net/a1103688841/article/details/89711120
import numpy as np
 
boxes=np.array([[100,100,210,210,0.72],
        [250,250,420,420,0.8],
        [220,220,320,330,0.92],
        [100,100,210,210,0.72],
        [230,240,325,330,0.81],
        [220,230,315,340,0.9]]) 
 
 
def py_cpu_nms(dets, thresh):
    #dets的数据格式是dets[[xmin,ymin,xmax,ymax,scores]....]
    x1 = dets[:,0]  #取所有行的第0个数，即所有的xmin组成一个列表
    y1 = dets[:,1]
    x2 = dets[:,2]
    y2 = dets[:,3]
    areas = (y2-y1+1) * (x2-x1+1)   #每个框的面积，组成列表
    scores = dets[:,4]  # 所有的分数

    # 这边的keep用于存放，NMS后剩余的方框
    keep = []      
    # 取出分数从大到小排列的索引。.argsort()是从小到大排列，[::-1]是列表头和尾颠倒一下
    index = scores.argsort()[::-1]    
    # 上面这两句比如分数[0.72 0.8  0.92 0.72 0.81 0.9 ]    
    # 对应的索引index[  2   5    4     1    3   0  ]记住是取出索引，scores列表没变。

    # index会剔除遍历过的方框，和合并过的方框。 
    while index.size >0:
        i = index[0]       # every time the first is the biggst, and add it directly
        # keep保留的是索引值，不是具体的分数。
        keep.append(i)

        # 计算交集的左上角和右下角
        x11 = np.maximum(x1[i], x1[index[1:]])    # calculate the points of overlap 
        y11 = np.maximum(y1[i], y1[index[1:]])
        # print(y11)
        x22 = np.minimum(x2[i], x2[index[1:]])
        y22 = np.minimum(y2[i], y2[index[1:]])
        print(x11, y11, x22, y22)       
 
        w = np.maximum(0, x22-x11+1)    # the weights of overlap
        h = np.maximum(0, y22-y11+1)    # the height of overlap
       
        overlaps = w*h
        ious = overlaps / (areas[i]+areas[index[1:]] - overlaps)

        idx = np.where(ious<=thresh)[0]
        index = index[idx+1]   # because index start from 1
 
    return keep
        
 
import matplotlib.pyplot as plt
def plot_bbox(dets, c='k'):
    x1 = dets[:,0]
    y1 = dets[:,1]
    x2 = dets[:,2]
    y2 = dets[:,3]
    
    plt.plot([x1,x2], [y1,y1], c)
    plt.plot([x1,x1], [y1,y2], c)
    plt.plot([x1,x2], [y2,y2], c)
    plt.plot([x2,x2], [y1,y2], c)
    plt.title(" nms")
 
    
plt.figure(1)
ax1 = plt.subplot(1,2,1)
ax2 = plt.subplot(1,2,2)
 
plt.sca(ax1)
plot_bbox(boxes,'k')   # before nms
 
keep = py_cpu_nms(boxes, thresh=0.7)
plt.sca(ax2)
plot_bbox(boxes[keep], 'r')# after nms
plt.show()

Reference：
NMS的python实现