注意看哦，有两个版本的。
理论基础
说实话，讲理论基础实在不是我的强项，但是还是得硬着头皮来讲，希望我的讲解不至于晦涩难懂。
非极大值抑制，简称为NMS算法。是一种获取局部最大值的有效方法。在3领域中，假设一个行向量的长度为w，从左向右，由第一个到第w个和其3领域中的数值进行比对。
如果某个i大于i+1并且小于i-1，则其为一个绝不最大值，同时也就意味着i+1不是一个局部最大值，所以将i移动2个步长，从i+2开始继续向后进行比较判断。如果某个i不满足上述条件，则将i+1，继续对i+1进行比对。当比对到最后一个w时，直接将w设置为局部最大值。算法流程如下图所示。
应用范围
非极大值抑制NMS在目标检测，定位等领域是一种被广泛使用的方法。对于目标具体位置定位过程，不管是使用sw(sliding Window)还是ss(selective search)方法，都会产生好多的候选区域。实际看到的情形就是好多区域的交叉重叠，难以满足实际的应用。如下图所示。
针对该问题有3种传统的解决思路。
第一种，选取好多矩形框的交集，即公共区域作为最后的目标区域。
第二种，选取好多矩形框的并集，即所有矩形框的最小外截矩作为目标区域。当然这里也不是只要相交就直接取并集，需要相交的框满足交集占最小框的面积达到一定比例(也就是阈值)才合并。
第三种,也就是本文的NMS，简单的说，对于有相交的就选取其中置信度最高的一个作为最后结果，对于没相交的就直接保留下来，作为最后结果。
总体来说，3种处理思路都各有千秋，不能一概评论哪种好坏。各种顶会论文也会选择不同的处理方法。
#include #include #include #include // 新版本写在下面文件中： #include //#include "opencv2/features2d/features2d.hpp" #include using namespace std; using namespace cv; void nms( const std::vector & srcRects, std::vector & resRects, float thresh ) { resRects.clear(); const size_t size = srcRects.size(); if (!size) { return; } // Sort the bounding boxes by the bottom - right y - coordinate of the bounding box std::multimap idxs; for (size_t i = 0; i < size; ++i) { idxs.insert(std::pair (srcRects[i].br().y, i)); } // keep looping while some indexes still remain in the indexes list while (idxs.size() > 0) { // grab the last rectangle auto lastElem = --std::end(idxs); const cv::Rect& rect1 = srcRects[lastElem->second]; resRects.push_back(rect1); idxs.erase(lastElem); for (auto pos = std::begin(idxs); pos != std::end(idxs); ) { // grab the current rectangle const cv::Rect& rect2 = srcRects[pos->second]; float intArea = (rect1 & rect2).area(); float unionArea = rect1.area() + rect2.area() - intArea; float overlap = intArea / unionArea; // if there is sufficient overlap, suppress the current bounding box if (overlap > thresh) { pos = idxs.erase(pos); } else { ++pos; } } } } / * * * main * * */ int main(int argc, char* argv[]) { std::vector srcRects; /* // Test 1 srcRects.push_back(cv::Rect(cv::Point(114, 60), cv::Point(178, 124))); srcRects.push_back(cv::Rect(cv::Point(120, 60), cv::Point(184, 124))); srcRects.push_back(cv::Rect(cv::Point(114, 66), cv::Point(178, 130)));*/ /* // Test 2 srcRects.push_back(cv::Rect(cv::Point(12, 84), cv::Point(140, 212))); srcRects.push_back(cv::Rect(cv::Point(24, 84), cv::Point(152, 212))); srcRects.push_back(cv::Rect(cv::Point(12, 96), cv::Point(140, 224))); srcRects.push_back(cv::Rect(cv::Point(36, 84), cv::Point(164, 212))); srcRects.push_back(cv::Rect(cv::Point(24, 96), cv::Point(152, 224))); srcRects.push_back(cv::Rect(cv::Point(24, 108), cv::Point(152, 236)));*/ // Test 3 srcRects.push_back(cv::Rect(cv::Point(12, 30), cv::Point(76, 94))); srcRects.push_back(cv::Rect(cv::Point(12, 36), cv::Point(76, 100))); srcRects.push_back(cv::Rect(cv::Point(72, 36), cv::Point(200, 164))); srcRects.push_back(cv::Rect(cv::Point(84, 48), cv::Point(212, 176))); cv::Size size(0, 0); for (const auto& r : srcRects) { size.width = std::max(size.width, r.x + r.width); size.height = std::max(size.height, r.y + r.height); } cv::Mat img = cv::Mat(2 * size.height, 2 * size.width, CV_8UC3, cv::Scalar(0, 0, 0)); cv::Mat imgCopy = img.clone(); for (auto r : srcRects) { cv::rectangle(img, r, cv::Scalar(0, 0, 255), 2); } cv::namedWindow("before", cv::WINDOW_NORMAL); cv::imshow("before", img); cv::waitKey(1); std::vector resRects; nms(srcRects, resRects, 0.3f); for (auto r : resRects) { cv::rectangle(imgCopy, r, cv::Scalar(0, 255, 0), 2); } cv::namedWindow("after", cv::WINDOW_NORMAL); cv::imshow("after", imgCopy); cv::waitKey(0); return 0; } 实验结果：
#include
#include
#include
#include
// 新版本写在下面文件中： #include
//#include "opencv2/features2d/features2d.hpp" #include
using namespace std; using namespace cv; static void sort(int n, const vector
x, vector
indices) { // 排序函数，排序后进行交换的是indices中的数据 // n：排序总数// x：带排序数// indices：初始为0~n-1数目 int i, j; for (i = 0; i < n; i++) for (j = i + 1; j < n; j++) { if (x[indices[j]] > x[indices[i]]) { //float x_tmp = x[i]; int index_tmp = indices[i]; //x[i] = x[j]; indices[i] = indices[j]; //x[j] = x_tmp; indices[j] = index_tmp; } } } int nonMaximumSuppression(int numBoxes, const vector
points,const vector
oppositePoints, const vector
score, float overlapThreshold,int& numBoxesOut, vector
& pointsOut, vector
& oppositePointsOut, vector
scoreOut) { // 实现检测出的矩形窗口的非极大值抑制nms // numBoxes：窗口数目// points：窗口左上角坐标点// oppositePoints：窗口右下角坐标点// score：窗口得分 // overlapThreshold：重叠阈值控制// numBoxesOut：输出窗口数目// pointsOut：输出窗口左上角坐标点 // oppositePoints：输出窗口右下角坐标点// scoreOut：输出窗口得分 int i, j, index; vector
box_area(numBoxes); // 定义窗口面积变量并分配空间 vector
indices(numBoxes); // 定义窗口索引并分配空间 vector
is_suppressed(numBoxes); // 定义是否抑制表标志并分配空间 // 初始化indices、is_supperssed、box_area信息 for (i = 0; i < numBoxes; i++) { indices[i] = i; is_suppressed[i] = 0; box_area[i] = (float)( (oppositePoints[i].x - points[i].x + 1) *(oppositePoints[i].y - points[i].y + 1)); } // 对输入窗口按照分数比值进行排序，排序后的编号放在indices中 sort(numBoxes, score, indices); for (i = 0; i < numBoxes; i++) // 循环所有窗口 { if (!is_suppressed[indices[i]]) // 判断窗口是否被抑制 { for (j = i + 1; j < numBoxes; j++) // 循环当前窗口之后的窗口 { if (!is_suppressed[indices[j]]) // 判断窗口是否被抑制 { int x1max = max(points[indices[i]].x, points[indices[j]].x); // 求两个窗口左上角x坐标最大值 int x2min = min(oppositePoints[indices[i]].x, oppositePoints[indices[j]].x); // 求两个窗口右下角x坐标最小值 int y1max = max(points[indices[i]].y, points[indices[j]].y); // 求两个窗口左上角y坐标最大值 int y2min = min(oppositePoints[indices[i]].y, oppositePoints[indices[j]].y); // 求两个窗口右下角y坐标最小值 int overlapWidth = x2min - x1max + 1; // 计算两矩形重叠的宽度 int overlapHeight = y2min - y1max + 1; // 计算两矩形重叠的高度 if (overlapWidth > 0 && overlapHeight > 0) { float overlapPart = (overlapWidth * overlapHeight) / box_area[indices[j]]; // 计算重叠的比率 if (overlapPart > overlapThreshold) // 判断重叠比率是否超过重叠阈值 { is_suppressed[indices[j]] = 1; // 将窗口j标记为抑制 } } } } } } numBoxesOut = 0; // 初始化输出窗口数目0 for (i = 0; i < numBoxes; i++) { if (!is_suppressed[i]) numBoxesOut++; // 统计输出窗口数目 } index = 0; for (i = 0; i < numBoxes; i++) // 遍历所有输入窗口 { if (!is_suppressed[indices[i]]) // 将未发生抑制的窗口信息保存到输出信息中 { pointsOut.push_back(Point(points[indices[i]].x,points[indices[i]].y)); oppositePointsOut.push_back(Point(oppositePoints[indices[i]].x,oppositePoints[indices[i]].y)); scoreOut.push_back(score[indices[i]]); index++; } } return true; } int main() { Mat image=Mat::zeros(600,600,CV_8UC3); int numBoxes=4; vector
points(numBoxes); vector
oppositePoints(numBoxes); vector
score(numBoxes); points[0]=Point(200,200);oppositePoints[0]=Point(400,400);score[0]=0.99; points[1]=Point(220,220);oppositePoints[1]=Point(420,420);score[1]=0.9; points[2]=Point(100,100);oppositePoints[2]=Point(150,150);score[2]=0.82; points[3]=Point(200,240);oppositePoints[3]=Point(400,440);score[3]=0.5; float overlapThreshold=0.8; int numBoxesOut; vector
pointsOut; vector
oppositePointsOut; vector
scoreOut; nonMaximumSuppression( numBoxes,points,oppositePoints,score,overlapThreshold,numBoxesOut,pointsOut,oppositePointsOut,scoreOut); for (int i=0;i
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