Faster RCNN 源码解读(2) -- NMS(非极大抑制)

NMS简介

非极大抑制算法应用相当广泛，其主要目的是消除多余的框，找到最佳的物体检测位置。

其实现的思想主要是将各个框的置信度进行排序，然后选择其中置信度最高的框A，将其作为标准选择其他框，同时设置一个阈值，当其他框B与A的重合程度超过阈值就将B舍弃掉，然后在剩余的框中选择置信度最大的框，重复上述操作。

定义dets矩阵与阈值

import numpy as npdets = np.array([                [204, 102, 358, 250, 0.5],                [257, 118, 380, 250, 0.7],                [280, 135, 400, 250, 0.6],                [255, 118, 360, 235, 0.7]])thresh = 0.3

其中，二维数组dets的每一维分别表示为xmin, ymin, xmax, ymax, confidence。

lib/nms/py_cpu_nms.py

对于Python代码而言，NMS实现的核心是在lib/nms/py_cpu_nms.py的代码。因此接下来就其细节进行详尽的分析:

import numpy as npdef py_cpu_nms(dets, thresh):    """Pure Python NMS baseline."""    x1 = dets[:, 0]  #xmin    y1 = dets[:, 1]  #ymin    x2 = dets[:, 2]  #xmax    y2 = dets[:, 3]  #ymax    scores = dets[:, 4]  #confidence    areas = (x2 - x1 + 1) * (y2 - y1 + 1)  #the size of bbox    order = scores.argsort()[::-1]  #sort bounding boxes by decreasing order, returning array([3, 1, 2, 0])    keep = []        # store the final bounding boxes    while order.size > 0:        i = order[0]   #the index of the bbox with highest confidence        keep.append(i)    #save it to keep        xx1 = np.maximum(x1[i], x1[order[1:]]) #array([ 257.,  280.,  255.])        yy1 = np.maximum(y1[i], y1[order[1:]]) #array([ 118.,  135.,  118.])        xx2 = np.minimum(x2[i], x2[order[1:]]) #array([ 360.,  360.,  358.])        yy2 = np.minimum(y2[i], y2[order[1:]]) #array([ 235.,  235.,  235.])        w = np.maximum(0.0, xx2 - xx1 + 1)   #array([ 104.,   81.,  104.])        h = np.maximum(0.0, yy2 - yy1 + 1)   #array([ 118.,  101.,  118.])        inter = w * h   #array([ 12272.,   8181.,  12272.])        # Cross Area / (bbox + particular area - Cross Area)        ovr = inter / (areas[i] + areas[order[1:]] - inter)        #reserve all the boundingbox whose ovr less than thresh        inds = np.where(ovr <= thresh)[0]        order = order[inds + 1]    return keep

参考资料

Faster RCNN – NMS
NMS解析