目标检测的非最大值抑制-NMS

非极大抑制，是在对象检测中用的较为频繁的方法，当在一个对象区域，框出了很多框，那么如下图：

上图来自这里
目的就是为了在这些框中找到最适合的那个框，主要就是通过迭代的形式，不断的以最大得分的框去与其他框做iou操作，并过滤那些iou较大（即交集较大）的框
按照github上R-CNN的matlab代码，改成py的，具体如下：

def iou(xminNp,yminNp,xmaxNp,ymaxNp,areas,lastInd,beforeInd,threshold):    #将lastInd指向的box，与之前的所有存活的box指向坐标做比较    xminNpTmp = np.maximum(xminNp[lastInd], xminNp[beforeInd])    yminNpTmp = np.maximum(yminNp[lastInd], yminNp[beforeInd])    xmaxNpTmp = np.maximum(xmaxNp[lastInd], xmaxNp[beforeInd])    ymaxNpTmp = np.maximum(ymaxNp[lastInd], ymaxNp[beforeInd])    #计算lastInd指向的box，与存活box交集的，所有width，height    w = np.maximum(0.0,xmaxNpTmp-xminNpTmp)    h = np.maximum(0.0,ymaxNpTmp-yminNpTmp)    #计算存活box与last指向box的交集面积    inter = w*h    iouValue = inter/(areas[beforeInd]+areas[lastInd]-inter)        indexOutput = [item[0] for item in zip(beforeInd,iouValue) if item[1] <= threshold ]    return indexOutputdef nms(boxes,threshold):    '''    boxes:n by 5的矩阵，n表示box个数，每一行分别为[xmin,ymin,xmax,ymax,score]    '''    assert isinstance(boxes,numpy.ndarray),'boxes must numpy object'    assert boxes.shape[1] == 5,'the column Dimension should be 5'    xminNp = boxes[:,0]    yminNp = boxes[:,1]    xmaxNp = boxes[:,2]    ymaxNp = boxes[:,3]    scores = boxes[:,4]    #计算每个box的面积    areas = (xmaxNp-xminNp)*(ymaxNp-yminNp)    #对每个box的得分按升序排序    scoresSorted = sorted(list(enumerate(scores)),key = lambda item:item[1])    #提取排序后数据的原索引    index = [ item[0] for item in scoresSorted ]    pick = []    while index:        #将当前index中最后一个加入pick        lastInd = index[-1]        pick.append(lastInd)        #计算最后一个box与之前所有box的iou        index = iou(xminNp,yminNp,xmaxNp,ymaxNp,areas,lastInd,index[:-1],threshold)    return pick[:-1]if __name__ == '__main__':    nms(boxes,threshold)

参考资料：
[] 非极大抑制。http://www.cnblogs.com/liekkas0626/p/5219244.html