利用KINECT+OPENCV检测手势的演示程序

2011-4-10 增加结果图片，更新代码，将模板改为6个（0-5）

1，原理：读入KINECT深度数据，转换为二值图像，找到轮廓，与轮廓模板比较，找到HU矩阵最小的为匹配结果

2，基础：OPENNI, OPENCV2.2 以及http://blog.163.com/gz_ricky/blog/static/182049118201122311118325/
的例程基础上修改

3，结果：仅仅用于演示利用OPENCV+OPENNI编程，对结果精度，处理速度等没有优化，仅供参考

对0，1和5的比较比较准确

废话少说，一切都在代码中。

// KinectOpenCVTest.cpp : 定义控制台应用程序的入口点。  
 //  
  
#include "stdafx.h"  
  
  
#include <stdlib.h>  
#include <iostream>  
#include <string>  
#include <XnCppWrapper.h>  
#include <opencv2/opencv.hpp>  
  
//#include "opencv/cv.h"  
//#include "opencv/highgui.h"  
using namespace std;  
using namespace cv;  
  
#define SAMPLE_XML_PATH "http://www.cnblogs.com/Data/SamplesConfig.xml"  
  
//全局模板轮廓  
vector<vector<Point>> g_TemplateContours;  
  
//模板个数  
int g_handTNum = 6;  
  
void CheckOpenNIError( XnStatus eResult, string sStatus )  
{   
    if( eResult != XN_STATUS_OK )   
    {  
        cerr << sStatus << " Error: " << xnGetStatusString( eResult ) << endl;  
        return;       
    }  
}  
  
//载入模板的轮廓  
void init_hand_template()  
{  
    //int handTNum = 10;  
    string temp = "HandTemplate/";  
      
    int i = 0;  
  
  
    for(i=0; i<g_handTNum; i++)  
    {  
        stringstream ss;  
        ss << i << ".bmp";  
  
        string fileName = temp + ss.str();   
  
        //读入灰度图像  
        Mat src = imread(fileName, 0);  
  
        if(!src.data)  
        {  
            printf("未找到文件: %s\n", fileName);  
            continue;  
        }  
  
        vector<vector<Point>> contours;  
        vector<Vec4i> hierarchy;  
  
        findContours(src, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);  
        //findContours(src, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);  
  
        g_TemplateContours.push_back(contours[0]);  
    }  
}  
  
//模板匹配手  
int hand_template_match(Mat& hand)  
{  
    //int handTNum = 10;  
    int minId = -1;  
    double minHu = 1;  
      
    double hu;  
    int method = CV_CONTOURS_MATCH_I1;  
      
    //match_num = 0;  
  
    for(int i=0; i<g_handTNum; i++){  
  
        Mat temp(g_TemplateContours.at(i));  
        hu = matchShapes(temp, hand, method, 0);  
          
        //找到hu矩最小的模板  
        if(hu < minHu){  
            minHu = hu;  
            minId = i;  
        }  
          
        //printf("%f ", hu);  
    }  
      
    //显示匹配结果  
    int Hmatch_value = 25;//模板匹配系数  
  
    if(minHu<((double)Hmatch_value)/100)  
        return minId;  
    else  
        return -1;  
}  
  
void findHand(Mat& src, Mat& dst)  
{  
    vector<vector<Point>> contours;  
    vector<Vec4i> hierarchy;  
  
    //找到外部轮廓  
    //findContours(src, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);  
    findContours(src, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE); //CV_CHAIN_APPROX_NONE);  
    //findContours(src, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);  
  
    Mat dst_r = Mat::zeros(src.rows, src.cols, CV_8UC3);  
    dst_r.copyTo(dst);  
  
    // iterate through all the top-level contours,  
    // draw each connected component with its own random color  
    int idx = 0;  
    double maxArea = 0.0;  
    int maxId = -1;  
  
    for(unsigned int i = 0; i<contours.size(); i++)  
    {  
        Mat temp(contours.at(i));  
        double area = fabs(contourArea(temp));  
        if(area > maxArea)  
        {  
            maxId = i;  
            maxArea = area;  
        }  
    }  
  
    //for( ; idx >= 0; idx = hierarchy[idx][0] )  
    //{  
    //  //Scalar color( rand()&255, rand()&255, rand()&255 );  
    //  //drawContours(dst, contours, idx, color, CV_FILLED, 8, hierarchy );  
  
    //  double area = contourArea(contours.at(idx));  
    //  if(area > maxArea)  
    //  {  
    //      maxId = idx;  
    //      maxArea = area;  
    //  }  
    //}  
  
    //显示最大轮廓外形，以及最佳匹配的模板ID  
    if(contours.size() > 0)  
    {  
        Scalar color(0, 255, 255 );  
        drawContours(dst, contours, maxId, color);  
  
        Mat hand(contours.at(maxId));  
        int value = hand_template_match(hand);  
  
        if(value >= 0)  
        {  
            Scalar templateColor(255, 0, 255 );  
            drawContours(dst, g_TemplateContours, value, templateColor);  
  
            printf("Match %d \r\n", value);  
  
            stringstream ss;  
            ss << "Match " << value;  
            string text = ss.str();  
            putText(dst, text, Point(300, 30), FONT_HERSHEY_SIMPLEX, 1.0, templateColor);  
        }  
    }  
}  
  
  
int HandDetect()  
{  
    init_hand_template();  
  
    XnStatus eResult = XN_STATUS_OK;    
  
    // 1. initial val  
    xn::DepthMetaData m_DepthMD;  
    xn::ImageMetaData m_ImageMD;  
  
    // for opencv Mat  
    Mat  m_depth16u( 480,640,CV_16UC1);  
    Mat  m_rgb8u( 480,640,CV_8UC3);  
    Mat  m_DepthShow( 480,640,CV_8UC1);  
    Mat  m_ImageShow( 480,640,CV_8UC3);  
  
    Mat  m_DepthThreshShow( 480,640,CV_8UC1);  
    Mat  m_HandShow( 480,640,CV_8UC3);  
  
    //cvNamedWindow("depth");  
    //cvNamedWindow("image");  
    //cvNamedWindow("depthThresh");  
  
    char key=0;  
  
    // 2. initial context   
    xn::Context mContext;   
  
    eResult = mContext.Init();   
  
    //xn::EnumerationErrors errors;  
    //eResult = mContext.InitFromXmlFile(SAMPLE_XML_PATH, &errors);  
  
    CheckOpenNIError( eResult, "initialize context" );    
      
    //Set mirror  
    mContext.SetGlobalMirror(!mContext.GetGlobalMirror());  
  
    // 3. create depth generator    
    xn::DepthGenerator mDepthGenerator;    
    eResult = mDepthGenerator.Create( mContext );   
    CheckOpenNIError( eResult, "Create depth generator" );    
  
    // 4. create image generator   
    xn::ImageGenerator mImageGenerator;  
    eResult = mImageGenerator.Create( mContext );   
    CheckOpenNIError( eResult, "Create image generator" );  
  
    // 5. set map mode    
    XnMapOutputMode mapMode;   
    mapMode.nXRes = 640;    
    mapMode.nYRes = 480;   
    mapMode.nFPS = 30;   
    eResult = mDepthGenerator.SetMapOutputMode( mapMode );    
    eResult = mImageGenerator.SetMapOutputMode( mapMode );    
  
    //由于 Kinect 的深度摄像机和彩色摄像机是在不同的位置，而且镜头本身的参数也不完全相同，所以两个摄像机所取得的画面会有些微的差异  
    //将深度摄像机的视角调整到RGB摄像机位置  
    // 6. correct view port    
    mDepthGenerator.GetAlternativeViewPointCap().SetViewPoint( mImageGenerator );   
  
    // 7. start generate data    
    eResult = mContext.StartGeneratingAll();    
  
    // 8. read data    
    eResult = mContext.WaitNoneUpdateAll();    
    while( (key!=27) && !(eResult = mContext.WaitNoneUpdateAll( ))  )   
    {    
        // 9a. get the depth map    
        mDepthGenerator.GetMetaData(m_DepthMD);  
        memcpy(m_depth16u.data,m_DepthMD.Data(), 640*480*2);  
  
        // 9b. get the image map    
        mImageGenerator.GetMetaData(m_ImageMD);  
        memcpy(m_rgb8u.data,m_ImageMD.Data(),640*480*3);  
  
        //将未知深度转为白色，便于在OPENCV中分析  
        XnDepthPixel* pDepth = (XnDepthPixel*)m_depth16u.data;  
  
        for (XnUInt y = 0; y < m_DepthMD.YRes(); ++y)  
        {  
            for (XnUInt x = 0; x < m_DepthMD.XRes(); ++x, ++pDepth)  
            {  
                if (*pDepth == 0)  
                {  
                    *pDepth = 0xFFFF;  
                }  
            }  
        }  
  
  
        //由于OpenNI获得的深度图片是16位无符号整数，而OpenCV显示的是8位的，所以要作转换。  
  
        //将距离转换为灰度值（0-2550mm 转换到 0-255），例如1000毫米转换为 1000×255/2550 = 100  
        //m_depth16u.convertTo(m_DepthShow,CV_8U, 255/2096.0);  
        m_depth16u.convertTo(m_DepthShow,CV_8U, 255/2550.0);  
  
        //可以考虑根据数据缩减图像大小到有效范围  
  
        //在此对灰度图像进行处理，平滑和去噪声  
        //medianBlur(m_DepthShow, m_DepthThreshShow, 3);  
        //m_DepthThreshShow.copyTo(m_DepthShow);  
        //medianBlur(m_DepthThreshShow, m_DepthShow, 3);  
        blur(m_DepthShow, m_DepthThreshShow, Size(3, 3));  
        //m_DepthThreshShow.copyTo(m_DepthShow);  
        blur(m_DepthThreshShow, m_DepthShow, Size(3, 3));  
  
        Mat  pyrTemp( 240,320,CV_8UC1);  
        pyrDown(m_DepthShow, pyrTemp);  
        pyrUp(pyrTemp, m_DepthShow);  
  
        //dilate(m_DepthShow, m_DepthThreshShow, Mat(), Point(-1,-1), 3);  
        //erode(m_DepthThreshShow, m_DepthShow, Mat(), Point(-1,-1), 3);  
  
        //for(int i = 0; i < m_depth16u.rows; i++)  
        //  for(int j = 0; j < m_depth16u.cols; j++)  
        //  {  
        //      if(m_depth16u.at<unsigned short>(i,j) < 1)  
        //          m_depth16u.at<unsigned short>(i,j) == 0xFFFF;  
  
        //      //m_depth16u.at<double>(i,j)=1./(i+j+1);  
        //  }  
  
        //RGB和BGR在内存对应的位置序列不同，所以也要转换。  
        cvtColor(m_rgb8u,m_ImageShow,CV_RGB2BGR);  
  
        //imshow("depth", m_DepthShow);  
        //imshow("image", m_ImageShow);  
  
        double  thd_max = 0xFFFF;  
        double  thd_val = 100.0;  
  
        //反转黑白图像，以便找到最大外部轮廓  
        //threshold(m_DepthShow, m_DepthThreshShow, thd_val, thd_max, CV_THRESH_BINARY);  
        threshold(m_DepthShow, m_DepthThreshShow, thd_val, thd_max, CV_THRESH_BINARY_INV);  
        imshow("depthThresh", m_DepthThreshShow);  
  
        findHand(m_DepthThreshShow, m_HandShow);  
        imshow( "Hand", m_HandShow );  
  
        key=cvWaitKey(20);  
    }  
  
    // 10. stop    
    mContext.StopGeneratingAll();  
    mContext.Shutdown();    
  
    return 0;  
}  
  
int _tmain(int argc, _TCHAR* argv[])  
{  
    HandDetect();  
}  

trackback: http://blog.csdn.net/firefight/archive/2011/04/06/6304050.aspx