标签：des style blog http color io os ar for
思路：利用训练好的palm.xml和fist.xml文件，用OpenCV的CascadeClassifier对每一帧图像检测palm和fist，之后对多帧中检测到的palm和fist进行聚类分组，满足分组条件的区域为最终检测结果。
代码：
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
using namespace std;
using namespace cv;
/** Function Headers */
void detectAndDisplay( Mat frame );
void RestoreVectors(vector<vector<Rect>>& vecs_bank, vector<Rect>& vecAll);
/** Global variables */
String palm_cascade_name = "palm.xml";
String fist_cascade_name = "fist.xml";
CascadeClassifier palm_cascade;
CascadeClassifier fist_cascade;
string window_name = "Capture - Palm and fist detection";
/** @function main */
int main( int argc, const char** argv )
{
CvCapture* capture;
Mat frame;
//-- 1. Load the cascades
if( !palm_cascade.load( palm_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
if( !fist_cascade.load( fist_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
//-- 2. Read the video stream
capture = cvCaptureFromCAM( -1 );
if( capture )
{
while( true )
{
frame = cvQueryFrame( capture );
//-- 3. Apply the classifier to the frame
if( !frame.empty() )
{ detectAndDisplay( frame ); }
else
{ printf(" --(!) No captured frame -- Break!"); break; }
int c = waitKey(10);
if( (char)c == ‘q‘ || (char)c == ‘Q‘ || 27 == c) { break; }
}
}
cvReleaseCapture(&capture);
return 0;
}
/** @function detectAndDisplay */
void detectAndDisplay( Mat frame )
{
std::vector<Rect> faces;
std::vector<Rect> palms;
std::vector<Rect> fists;
static vector<vector<Rect>> palms_bank;
static vector<vector<Rect>> fists_bank;
const int MAX_NUM = 3;
Mat frame_gray;
cvtColor( frame, frame_gray, CV_BGR2GRAY );
equalizeHist( frame_gray, frame_gray );
//-- Palm detection
palm_cascade.detectMultiScale( frame_gray, palms, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );
palms_bank.push_back(palms);
if(palms_bank.size() > MAX_NUM)
palms_bank.erase(palms_bank.begin());
vector<Rect> palmAll;
RestoreVectors(palms_bank, palmAll);
groupRectangles(palmAll, 2);
for( size_t j = 0; j < palmAll.size(); j++ )
{
rectangle(frame, palmAll[j], Scalar(0,255,0), 2);
}
//-- Fist detection
fist_cascade.detectMultiScale( frame_gray, fists, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );
fists_bank.push_back(fists);
if(fists_bank.size() > MAX_NUM)
fists_bank.erase(fists_bank.begin());
vector<Rect> fistAll;
RestoreVectors(fists_bank, fistAll);
groupRectangles(fistAll, 2);
for( size_t j = 0; j < fistAll.size(); j++ )
{
rectangle(frame, fistAll[j], Scalar(0,0,255), 2);
}
//-- Show what you got
imshow( window_name, frame );
}
void RestoreVectors(vector<vector<Rect>>& vecs_bank, vector<Rect>& vecAll)
{
for(size_t i = 0; i < vecs_bank.size(); i++){
vecAll.insert(vecAll.end(), vecs_bank[i].begin(), vecs_bank[i].end());
}
}
参考：
代码，palm.xml和fist.xml文件，说明文档可以从这里下载：
http://download.csdn.net/detail/lichengyu/7751671
Groups the object candidate rectangles.
| Parameters: |
|
|---|
The function is a wrapper for the generic function partition() . It clusters all the input rectangles using the rectangle equivalence criteria that combines rectangles with similar sizes and similar locations. The similarity is defined by eps. When eps=0 , no clustering is done at all. If 
, all the rectangles are put in one cluster. Then, the small clusters containing less than or equal to groupThreshold rectangles are rejected. In each other cluster, the average rectangle is computed and put into the output rectangle list.
原文：http://blog.csdn.net/lichengyu/article/details/38544189
标签：des style blog http color io os ar for
原文地址：http://www.cnblogs.com/lxy2017/p/4044392.html
