非极大值抑制---NMS

目标检测中常用到NMS，在faster R-CNN中，每一个bounding box都有一个打分，NMS实现逻辑是：

1，按打分最高到最低将BBox排序 ，例如：A B C D E F

2，A的分数最高，保留。从B-E与A分别求重叠率IoU，假设B、D与A的IoU大于阈值，那么B和D可以认为是重复标记去除

3，余下C E F，重复前面两步。

源码如下：

#coding:utf-8  
import numpy as np  
  
def py_cpu_nms(dets, thresh):  
    """Pure Python NMS baseline."""  
    x1 = dets[:, 0]  
    y1 = dets[:, 1]  
    x2 = dets[:, 2]  
    y2 = dets[:, 3]  
    scores = dets[:, 4]  #bbox打分
  
    areas = (x2 - x1 + 1) * (y2 - y1 + 1)  
#打分从大到小排列，取index  
    order = scores.argsort()[::-1]  
#keep为最后保留的边框  
    keep = []  
    while order.size > 0:  
#order[0]是当前分数最大的窗口，肯定保留  
        i = order[0]  
        keep.append(i)  
#计算窗口i与其他所有窗口的交叠部分的面积
        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:]])  
  
        w = np.maximum(0.0, xx2 - xx1 + 1)  
        h = np.maximum(0.0, yy2 - yy1 + 1)  
        inter = w * h  
#交/并得到iou值  
        ovr = inter / (areas[i] + areas[order[1:]] - inter)  
#inds为所有与窗口i的iou值小于threshold值的窗口的index，其他窗口此次都被窗口i吸收  
        inds = np.where(ovr <= thresh)[0]  
#order里面只保留与窗口i交叠面积小于threshold的那些窗口，由于ovr长度比order长度少1(不包含i)，所以inds+1对应到保留的窗口
        order = order[inds + 1]  
  
    return keep

下面是我从网上copy下来的简单实现代码：

import cv2
import numpy as np
import random
 
img=np.zeros((300,400), np.uint8)
dets=np.array([[83,54,165,163,0.8], [67,48,118,132,0.5], [91,38,192,171,0.6]], np.float)
 
img_cp=img.copy()
for box in dets.tolist():    #显示待测试框及置信度
    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_cp, (x1,y1), (x2, y2), (255, 255, 255), 2)
    cv2.putText(img_cp, str(score), (x2-30, y_text), 2,1, (255, 255, 0))
cv2.imshow("ori_img", img_cp)
 
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_cp=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_cp, (x1,y1), (x2, y2), (255, 255, 255), 2)
    cv2.putText(img_cp, str(score), (x2-30, y_text), 2,1, (255, 255, 0))