// YeNite2SimpleUsingOpenCV.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"
#include <iostream>// OpenCV 头文件
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>#include <OpenNI.h>
#include <NiTE.h>using namespace std;
using namespace openni;
using namespace nite;int main( int argc, char **argv )
{// 初始化OpenNI
    OpenNI::initialize();// 打开Kinect设备
    Device  mDevice;mDevice.open( ANY_DEVICE );// 创建深度数据流
    VideoStream mDepthStream;mDepthStream.create( mDevice, SENSOR_DEPTH );// 设置VideoMode模式
    VideoMode mDepthMode;mDepthMode.setResolution( 640, 480 );mDepthMode.setFps( 30 );mDepthMode.setPixelFormat( PIXEL_FORMAT_DEPTH_1_MM );mDepthStream.setVideoMode(mDepthMode);// 同样的设置彩色数据流
    VideoStream mColorStream;mColorStream.create( mDevice, SENSOR_COLOR );// 设置VideoMode模式
    VideoMode mColorMode;mColorMode.setResolution( 640, 480 );mColorMode.setFps( 30 );mColorMode.setPixelFormat( PIXEL_FORMAT_RGB888 );mColorStream.setVideoMode( mColorMode);// 设置深度图像映射到彩色图像
    mDevice.setImageRegistrationMode( IMAGE_REGISTRATION_DEPTH_TO_COLOR );// 为了得到骨骼数据，先初始化NiTE
    NiTE::initialize();// 创建用户跟踪器
    UserTracker mUserTracker;mUserTracker.create( &mDevice );// Control the smoothing factor of the skeleton joints. Factor should be between 0 (no smoothing at all) and 1 (no movement at all)mUserTracker.setSkeletonSmoothingFactor( 0.1f );// 创建User彩色图像显示cv::namedWindow( "User Image",  CV_WINDOW_AUTOSIZE );// 环境初始化后，开始获取深度数据流和彩色数据流
    mDepthStream.start();mColorStream.start();while( true ){// 创建OpenCV：：Mat，用于显示彩色数据图像
        cv::Mat cImageBGR;// 读取彩色图像数据帧信息流
        VideoFrameRef mColorFrame;mColorStream.readFrame( &mColorFrame );// 将彩色数据流转换为OpenCV格式，记得格式是：CV_8UC3（含R\G\B）const cv::Mat mImageRGB( mColorFrame.getHeight(), mColorFrame.getWidth(),CV_8UC3, (void*)mColorFrame.getData() );// RGB ==> BGR
        cv::cvtColor( mImageRGB, cImageBGR, CV_RGB2BGR );// 读取User用户数据帧信息流
        UserTrackerFrameRef  mUserFrame;mUserTracker.readFrame( &mUserFrame );// 得到Users信息const nite::Array<UserData>& aUsers = mUserFrame.getUsers();for( int i = 0; i < aUsers.getSize(); ++ i ){const UserData& rUser = aUsers[i];// 检查用户状态if( rUser.isNew() ){// 开始对该用户的骨骼跟踪
                mUserTracker.startSkeletonTracking( rUser.getId() );}if( rUser.isVisible() ){// 得到用户骨骼数据const Skeleton& rSkeleton = rUser.getSkeleton();// 检查骨骼状态是否为“跟踪状态”if( rSkeleton.getState() == SKELETON_TRACKED ){// 得到15个骨骼数据                    SkeletonJoint aJoints[15];aJoints[ 0] = rSkeleton.getJoint( JOINT_HEAD );aJoints[ 1] = rSkeleton.getJoint( JOINT_NECK );aJoints[ 2] = rSkeleton.getJoint( JOINT_LEFT_SHOULDER );aJoints[ 3] = rSkeleton.getJoint( JOINT_RIGHT_SHOULDER );aJoints[ 4] = rSkeleton.getJoint( JOINT_LEFT_ELBOW );aJoints[ 5] = rSkeleton.getJoint( JOINT_RIGHT_ELBOW );aJoints[ 6] = rSkeleton.getJoint( JOINT_LEFT_HAND );aJoints[ 7] = rSkeleton.getJoint( JOINT_RIGHT_HAND );aJoints[ 8] = rSkeleton.getJoint( JOINT_TORSO );aJoints[ 9] = rSkeleton.getJoint( JOINT_LEFT_HIP );aJoints[10] = rSkeleton.getJoint( JOINT_RIGHT_HIP );aJoints[11] = rSkeleton.getJoint( JOINT_LEFT_KNEE );aJoints[12] = rSkeleton.getJoint( JOINT_RIGHT_KNEE );aJoints[13] = rSkeleton.getJoint( JOINT_LEFT_FOOT );aJoints[14] = rSkeleton.getJoint( JOINT_RIGHT_FOOT );// 将骨骼3D坐标转换为深度坐标下骨骼位置坐标，并保存在数组中cv::Point2f aPoint[15];for( int  s = 0; s < 15; ++ s ){const Point3f& rPos = aJoints[s].getPosition();mUserTracker.convertJointCoordinatesToDepth( rPos.x, rPos.y, rPos.z,&(aPoint[s].x), &(aPoint[s].y) );}// 在彩色图像中画出骨骼间的连接线cv::line( cImageBGR, aPoint[ 0], aPoint[ 1], cv::Scalar( 255, 0, 0 ), 3 );cv::line( cImageBGR, aPoint[ 1], aPoint[ 2], cv::Scalar( 255, 0, 0 ), 3 );cv::line( cImageBGR, aPoint[ 1], aPoint[ 3], cv::Scalar( 255, 0, 0 ), 3 );cv::line( cImageBGR, aPoint[ 2], aPoint[ 4], cv::Scalar( 255, 0, 0 ), 3 );cv::line( cImageBGR, aPoint[ 3], aPoint[ 5], cv::Scalar( 255, 0, 0 ), 3 );cv::line( cImageBGR, aPoint[ 4], aPoint[ 6], cv::Scalar( 255, 0, 0 ), 3 );cv::line( cImageBGR, aPoint[ 5], aPoint[ 7], cv::Scalar( 255, 0, 0 ), 3 );cv::line( cImageBGR, aPoint[ 1], aPoint[ 8], cv::Scalar( 255, 0, 0 ), 3 );cv::line( cImageBGR, aPoint[ 8], aPoint[ 9], cv::Scalar( 255, 0, 0 ), 3 );cv::line( cImageBGR, aPoint[ 8], aPoint[10], cv::Scalar( 255, 0, 0 ), 3 );cv::line( cImageBGR, aPoint[ 9], aPoint[11], cv::Scalar( 255, 0, 0 ), 3 );cv::line( cImageBGR, aPoint[10], aPoint[12], cv::Scalar( 255, 0, 0 ), 3 );cv::line( cImageBGR, aPoint[11], aPoint[13], cv::Scalar( 255, 0, 0 ), 3 );cv::line( cImageBGR, aPoint[12], aPoint[14], cv::Scalar( 255, 0, 0 ), 3 );// 同样的在彩色图像中骨骼位置上画“圆”for( int  s = 0; s < 15; ++ s ){if( aJoints[s].getPositionConfidence() > 0.5 )cv::circle( cImageBGR, aPoint[s], 3, cv::Scalar( 0, 0, 255 ), 2 );elsecv::circle( cImageBGR, aPoint[s], 3, cv::Scalar( 0, 255, 0 ), 2 );}}}}// 显示图像cv::imshow( "User Image", cImageBGR );// 按键“q”退出循环if( cv::waitKey( 1 ) == 'q' )break;}// 先销毁User跟踪器
    mUserTracker.destroy();// 销毁彩色数据流和深度数据流
    mColorStream.destroy();mDepthStream.destroy();// 关闭Kinect设备
    mDevice.close();// 关闭NITE和OpenNI环境
    NiTE::shutdown();OpenNI::shutdown();return 0;
}