利用OpenCV检测手掌(palm)和拳头(fist)

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：

[1] groupRectangles的说明文档

[2] palm.xml和fist.xml的下载地址

[3] 人脸和眼睛检测的opencv示例代码

groupRectangles

Groups the object candidate rectangles.

**C++:**voidgroupRectangles(vector& **rectList**, int **groupThreshold**, double **eps**=0.2)

**C++:**voidgroupRectangles(vector& **rectList**, vector& **weights**, int**groupThreshold**, double **eps**=0.2)

**Python:**cv2.groupRectangles(rectList, groupThreshold[, eps])→ rectList, weights

Parameters:	* **rectList** – Input/output vector of rectangles. Output vector includes retained and grouped rectangles. (The Python list is not modified in place.) * **groupThreshold** – Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it. * **eps** – Relative difference between sides of the rectangles to merge them into a group.

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 byeps. Wheneps=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 togroupThresholdrectangles 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
原文链接: https://www.cnblogs.com/lxy2017/p/4044392.html

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