import cv2

 import numpy as np

 import random

 def nms(dets, thresh):

     print(dets)

     x1 = dets[:, 0]

     y1 = dets[:, 1]

     x2 = dets[:, 2]

     y2 = dets[:, 3]

     score = dets[:, 4]

     #  x1=3,x2=5,习惯上计算x方向长度就

     # 是x=3、4、5这三个像素，即5-3+1=3，而不是5-3=2，所以需要加1

     areas = (x2 - x1 + 1) * (y2 - y1 + 1) #所有box面积

     order = score.argsort()[::-1] #得到score降序排列的坐标索引

     print("order",order)

     keep = []

     while order.size > 0:

         i = order[0] #最大得分box的坐标索引

         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:]])

         #最高得分的boax与其他box的公共部分(交集)

         w = np.maximum(0.0, xx2 - xx1 + 1)

         h = np.maximum(0.0, yy2 - yy1 + 1) #求高和宽，并使数值合法化

         inter = w * h #其他所有box的面积

         ovr = inter / (areas[i] + areas[order[1:]] - inter)  #IOU:交并比

         #ovr小表示两个box交集少，可能是另一个物体的框，故需要保留

         inds = np.where(ovr <= thresh)[0]

         order = order[inds + 1]  #iou小于阈值的框

     return keep

 img = cv2.imread("E:\jupyterProject\Pytorch\cs75.jpg")

 img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

 dets=np.array([[500,250,1200,700,0.8], [400,200,1000,500,0.5],

                [800,150,1300,600,0.6]], np.float)

 # 设置thread为0.3

 rtn_box=nms(dets, 0.3) #0.3为faster-rcnn中配置文件的默认值

 cls_dets=dets[rtn_box, :]

 print("nms box:", cls_dets)

 img_cp1=img.copy()

 for box in cls_dets.tolist():

     x1,y1,x2,y2,score=int(box[0]),int(box[1]),int(box[2]),int(box[3]),box[-1]

     y_text=int(random.uniform(y1, y2))

     cv2.rectangle(img_cp1, (x1,y1), (x2, y2), (0, 255, 255), 2)

     cv2.putText(img_cp1, "CAR:"+str(score), (x1,y1), 2,1, (255, 0, 255))

 cv2.imshow("nms_img",img_cp1)

 # cv2.destroyAllWindows()#消除所有显示的图框

 cv2.imwrite("newcs75.jpg",img_cp1)

 cv2.waitKey(0)