谈谈NITE 2的第一个程序UserViewer

    我觉得学习一个新的API，最好的办法是学习它提供的Samples。今天根据自己的了解，分享我自己对新的NITE 2的了解，首先看看NITE2 中提供的UserViewer，将它提供的工程文件夹导入到VS2010中，运行，直接看运行结果：

     当人体出现在Kinect面前时，就能获取身体深度图像，当做出POSE_CROSSED_HANDS（“双手抱胸”）的动作时，就开始骨骼跟踪，最多获取全身15个主要关节点坐标的3D信息。现在开始代码的了解。首先看主函数main()：

#include "Viewer.h"

int main(int argc, char** argv)
{
    openni::Status rc = openni::STATUS_OK;

    // 调用SampleViewer类构造函数，初始化
    SampleViewer sampleViewer("叶梅树的骨骼跟踪");

    // 调用SampleViewer的init进行初始化跟踪
    rc = sampleViewer.Init(argc, argv);
    if (rc != openni::STATUS_OK)
    {
        return 1;
    }
    // 调用Run函数开始循环跟踪
    sampleViewer.Run();
}

    现在看看SampleViewer类怎么初始化，怎么获取人体深度信息，怎么开始实时跟踪人体骨骼信息的：

/************************************************************************/
/* 类初始化，并创建m_pUserTracker对象，就是人体跟踪器                                   */
/************************************************************************/
SampleViewer::SampleViewer(const char* strSampleName)
{
    ms_self = this;
    strncpy(m_strSampleName, strSampleName, ONI_MAX_STR);
    m_pUserTracker = new nite::UserTracker;
}
...
openni::Status SampleViewer::Init(int argc, char **argv)
{
    m_pTexMap = NULL;

    // OpenNI初始化
    openni::Status rc = openni::OpenNI::initialize();
17      
    // 只有一台体感设备时，初始化为openni::ANY_DEVICE。
    const char* deviceUri = openni::ANY_DEVICE;
    for (int i = 1; i < argc-1; ++i)
    {
        if (strcmp(argv[i], "-device") == 0)
        {
            deviceUri = argv[i+1];
            break;
        }
    }

    // 打开体感设备
    openni::Status rc = m_device.open(deviceUri);
    if (rc != openni::STATUS_OK)
    {
        printf("Open Device failed:\n%s\n", openni::OpenNI::getExtendedError());
        return rc;
    }

    /************************************************************************/
    /*NITE初始化时其实是调用NiteCAPI.h底层函数 niteInitialize().
    static Status initialize()
    {
    return (Status)niteInitialize();
    }                                                                     */
    /************************************************************************/
    nite::NiTE::initialize();

    /************************************************************************/
    /* m_pUserTracker->create(&m_device)创建开始跟踪“指定打开的体感设备的人体信息”，
       函数实现为（并且调用NiteCAPI.h底层函数niteInitializeUserTrackerByDevice()）：
       Status create(openni::Device* pDevice = NULL)
       {
       if (pDevice == NULL)
       {
       return (Status)niteInitializeUserTracker(&m_UserTrackerHandle);
       // Pick a device
       }
       return (Status)niteInitializeUserTrackerByDevice(pDevice, &m_UserTrackerHandle);
       }
    /************************************************************************/
    if (m_pUserTracker->create(&m_device) != nite::STATUS_OK)
    {
        return openni::STATUS_ERROR;
    }

    return InitOpenGL(argc, argv);

}
openni::Status SampleViewer::Run()    //Does not return
{
    // 开始循环跟踪人体深度数据和人体骨骼坐标了。回调函数为Display()函数。
    glutMainLoop();

    return openni::STATUS_OK;
}

    现在看看回调函数Display()函数的前半部分：主要是定位到人体深度图像，并做像素转换处理，显示出来。

void SampleViewer::Display()
{
    nite::UserTrackerFrameRef userTrackerFrame;
    openni::VideoFrameRef depthFrame;
    nite::Status rc = m_pUserTracker->readFrame(&userTrackerFrame);
    if (rc != nite::STATUS_OK)
    {
        printf("GetNextData failed\n");
        return;
    }

    depthFrame = userTrackerFrame.getDepthFrame();

    if (m_pTexMap == NULL)
    {
        // Texture map init
        m_nTexMapX = MIN_CHUNKS_SIZE(depthFrame.getVideoMode().getResolutionX(), TEXTURE_SIZE);
        m_nTexMapY = MIN_CHUNKS_SIZE(depthFrame.getVideoMode().getResolutionY(), TEXTURE_SIZE);
        m_pTexMap = new openni::RGB888Pixel[m_nTexMapX * m_nTexMapY];
    }

    const nite::UserMap& userLabels = userTrackerFrame.getUserMap();

    glClear (GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);

    glMatrixMode(GL_PROJECTION);
    glPushMatrix();
    glLoadIdentity();
    glOrtho(0, GL_WIN_SIZE_X, GL_WIN_SIZE_Y, 0, -1.0, 1.0);

    if (depthFrame.isValid() && g_drawDepth)
    {
        const openni::DepthPixel* pDepth = (const openni::DepthPixel*)depthFrame.getData();
        int width = depthFrame.getWidth();
        int height = depthFrame.getHeight();
        // Calculate the accumulative histogram (the yellow display...)
        memset(m_pDepthHist, 0, MAX_DEPTH*sizeof(float));
        int restOfRow = depthFrame.getStrideInBytes() / sizeof(openni::DepthPixel) - width;

        unsigned int nNumberOfPoints = 0;
        for (int y = 0; y < height; ++y)
        {
            for (int x = 0; x < width; ++x, ++pDepth)
            {
                if (*pDepth != 0)
                {
                    m_pDepthHist[*pDepth]++;
                    nNumberOfPoints++;
                }
            }
            pDepth += restOfRow;
        }
        for (int nIndex=1; nIndex<MAX_DEPTH; nIndex++)
        {
            m_pDepthHist[nIndex] += m_pDepthHist[nIndex-1];
        }
        if (nNumberOfPoints)
        {
            for (int nIndex=1; nIndex<MAX_DEPTH; nIndex++)
            {
                m_pDepthHist[nIndex] = (unsigned int)(256 * (1.0f - (m_pDepthHist[nIndex] / nNumberOfPoints)));
            }
        }
    }

    memset(m_pTexMap, 0, m_nTexMapX*m_nTexMapY*sizeof(openni::RGB888Pixel));

    float factor[3] = {1, 1, 1};
    // check if we need to draw depth frame to texture
    if (depthFrame.isValid() && g_drawDepth)
    {
        const nite::UserId* pLabels = userLabels.getPixels();

        const openni::DepthPixel* pDepthRow = (const openni::DepthPixel*)depthFrame.getData();
        openni::RGB888Pixel* pTexRow = m_pTexMap + depthFrame.getCropOriginY() * m_nTexMapX;
        int rowSize = depthFrame.getStrideInBytes() / sizeof(openni::DepthPixel);

        for (int y = 0; y < depthFrame.getHeight(); ++y)
        {
            const openni::DepthPixel* pDepth = pDepthRow;
            openni::RGB888Pixel* pTex = pTexRow + depthFrame.getCropOriginX();

            for (int x = 0; x < depthFrame.getWidth(); ++x, ++pDepth, ++pTex, ++pLabels)
            {
                if (*pDepth != 0)
                {
                    if (*pLabels == 0)
                    {
                        if (!g_drawBackground)
                        {
                            factor[0] = factor[1] = factor[2] = 0;

                        }
                        else
                        {
                            factor[0] = Colors[colorCount][0];
                            factor[1] = Colors[colorCount][1];
                            factor[2] = Colors[colorCount][2];
                        }
                    }
                    else
                    {
                        factor[0] = Colors[*pLabels % colorCount][0];
                        factor[1] = Colors[*pLabels % colorCount][1];
                        factor[2] = Colors[*pLabels % colorCount][2];
                    }
//                    // Add debug lines - every 10cm
//                     else if ((*pDepth / 10) % 10 == 0)
//                     {
//                         factor[0] = factor[2] = 0;
//                     }

                    int nHistValue = m_pDepthHist[*pDepth];
                    pTex->r = nHistValue*factor[0];
                    pTex->g = nHistValue*factor[1];
                    pTex->b = nHistValue*factor[2];

                    factor[0] = factor[1] = factor[2] = 1;
                }
            }

            pDepthRow += rowSize;
            pTexRow += m_nTexMapX;
        }
    }

    glTexParameteri(GL_TEXTURE_2D, GL_GENERATE_MIPMAP_SGIS, GL_TRUE);
    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR_MIPMAP_LINEAR);
    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR);
    glTexImage2D(GL_TEXTURE_2D, 0, GL_RGB, m_nTexMapX, m_nTexMapY, 0, GL_RGB, GL_UNSIGNED_BYTE, m_pTexMap);

    // Display the OpenGL texture map
    glColor4f(1,1,1,1);

    glEnable(GL_TEXTURE_2D);
    glBegin(GL_QUADS);

    g_nXRes = depthFrame.getVideoMode().getResolutionX();
    g_nYRes = depthFrame.getVideoMode().getResolutionY();

    // upper left
    glTexCoord2f(0, 0);
    glVertex2f(0, 0);
    // upper right
    glTexCoord2f((float)g_nXRes/(float)m_nTexMapX, 0);
    glVertex2f(GL_WIN_SIZE_X, 0);
    // bottom right
    glTexCoord2f((float)g_nXRes/(float)m_nTexMapX, (float)g_nYRes/(float)m_nTexMapY);
    glVertex2f(GL_WIN_SIZE_X, GL_WIN_SIZE_Y);
    // bottom left
    glTexCoord2f(0, (float)g_nYRes/(float)m_nTexMapY);
    glVertex2f(0, GL_WIN_SIZE_Y);

    glEnd();
    glDisable(GL_TEXTURE_2D);

处理完之后，开始跟踪人体骨骼坐标：

// 读取人体跟踪器中的所有人
    const nite::Array<nite::UserData>& users = userTrackerFrame.getUsers();
    for (int i = 0; i < users.getSize(); ++i)
    {
        const nite::UserData& user = users[i];

        updateUserState(user, userTrackerFrame.getTimestamp());
        if (user.isNew())
        {
            // 如果是新出现在Kinect，开始跟踪人体骨骼
            m_pUserTracker->startSkeletonTracking(user.getId());
            // 开始检测该人做的姿势是不是(POSE_CROSSED_HANDS)
            m_pUserTracker->startPoseDetection(user.getId(), nite::POSE_CROSSED_HANDS);
        }
        else if (!user.isLost())
        {
            // 如果没有丢失跟踪的人，就“画出”相关信息
            if (g_drawStatusLabel)
            {
                DrawStatusLabel(m_pUserTracker, user);
            }
            if (g_drawCenterOfMass)
            {
                DrawCenterOfMass(m_pUserTracker, user);
            }
            if (g_drawBoundingBox)
            {
                DrawBoundingBox(user);
            }

            // 当人体骨骼跟踪状态处于"SKELETON_TRACKED"就“画出”人体主要骨骼点的坐标和他们之间的连线。
            if (users[i].getSkeleton().getState() == nite::SKELETON_TRACKED && g_drawSkeleton)
            {
                DrawSkeleton(m_pUserTracker, user);
            }
        }

        if (m_poseUser == 0 || m_poseUser == user.getId())
        {
            const nite::PoseData& pose = user.getPose(nite::POSE_CROSSED_HANDS);

            if (pose.isEntered())
            {
                // Start timer
                sprintf(g_generalMessage, "In exit pose. Keep it for %d second%s to exit\n", g_poseTimeoutToExit/1000, g_poseTimeoutToExit/1000 == 1 ? "" : "s");
                printf("Counting down %d second to exit\n", g_poseTimeoutToExit/1000);
                m_poseUser = user.getId();
                m_poseTime = userTrackerFrame.getTimestamp();
            }
            else if (pose.isExited())
            {
                memset(g_generalMessage, 0, sizeof(g_generalMessage));
                printf("Count-down interrupted\n");
                m_poseTime = 0;
                m_poseUser = 0;
            }
            else if (pose.isHeld())
            {
                // tick
                if (userTrackerFrame.getTimestamp() - m_poseTime > g_poseTimeoutToExit * 1000)
                {
                    printf("Count down complete. Exit...\n");
                    Finalize();
                    exit(2);
                }
            }
        }
    }

    if (g_drawFrameId)
    {
        DrawFrameId(userTrackerFrame.getFrameIndex());
    }

    if (g_generalMessage[0] != '\0')
    {
        char *msg = g_generalMessage;
        glColor3f(1.0f, 0.0f, 0.0f);
        glRasterPos2i(100, 20);
        glPrintString(GLUT_BITMAP_HELVETICA_18, msg);
    }



    // Swap the OpenGL display buffers
    glutSwapBuffers();

}

    其中“画出”骨骼坐标和它们之间的函数如下：

void DrawSkeleton(nite::UserTracker* pUserTracker, const nite::UserData& userData)
{
    DrawLimb(pUserTracker, userData.getSkeleton().getJoint(nite::JOINT_HEAD), userData.getSkeleton().getJoint(nite::JOINT_NECK), userData.getId() % colorCount);

    DrawLimb(pUserTracker, userData.getSkeleton().getJoint(nite::JOINT_LEFT_SHOULDER), userData.getSkeleton().getJoint(nite::JOINT_LEFT_ELBOW), userData.getId() % colorCount);
    DrawLimb(pUserTracker, userData.getSkeleton().getJoint(nite::JOINT_LEFT_ELBOW), userData.getSkeleton().getJoint(nite::JOINT_LEFT_HAND), userData.getId() % colorCount);

    DrawLimb(pUserTracker, userData.getSkeleton().getJoint(nite::JOINT_RIGHT_SHOULDER), userData.getSkeleton().getJoint(nite::JOINT_RIGHT_ELBOW), userData.getId() % colorCount);
    DrawLimb(pUserTracker, userData.getSkeleton().getJoint(nite::JOINT_RIGHT_ELBOW), userData.getSkeleton().getJoint(nite::JOINT_RIGHT_HAND), userData.getId() % colorCount);

    DrawLimb(pUserTracker, userData.getSkeleton().getJoint(nite::JOINT_LEFT_SHOULDER), userData.getSkeleton().getJoint(nite::JOINT_RIGHT_SHOULDER), userData.getId() % colorCount);

    DrawLimb(pUserTracker, userData.getSkeleton().getJoint(nite::JOINT_LEFT_SHOULDER), userData.getSkeleton().getJoint(nite::JOINT_TORSO), userData.getId() % colorCount);
    DrawLimb(pUserTracker, userData.getSkeleton().getJoint(nite::JOINT_RIGHT_SHOULDER), userData.getSkeleton().getJoint(nite::JOINT_TORSO), userData.getId() % colorCount);

    DrawLimb(pUserTracker, userData.getSkeleton().getJoint(nite::JOINT_TORSO), userData.getSkeleton().getJoint(nite::JOINT_LEFT_HIP), userData.getId() % colorCount);
    DrawLimb(pUserTracker, userData.getSkeleton().getJoint(nite::JOINT_TORSO), userData.getSkeleton().getJoint(nite::JOINT_RIGHT_HIP), userData.getId() % colorCount);

    DrawLimb(pUserTracker, userData.getSkeleton().getJoint(nite::JOINT_LEFT_HIP), userData.getSkeleton().getJoint(nite::JOINT_RIGHT_HIP), userData.getId() % colorCount);


    DrawLimb(pUserTracker, userData.getSkeleton().getJoint(nite::JOINT_LEFT_HIP), userData.getSkeleton().getJoint(nite::JOINT_LEFT_KNEE), userData.getId() % colorCount);
    DrawLimb(pUserTracker, userData.getSkeleton().getJoint(nite::JOINT_LEFT_KNEE), userData.getSkeleton().getJoint(nite::JOINT_LEFT_FOOT), userData.getId() % colorCount);

    DrawLimb(pUserTracker, userData.getSkeleton().getJoint(nite::JOINT_RIGHT_HIP), userData.getSkeleton().getJoint(nite::JOINT_RIGHT_KNEE), userData.getId() % colorCount);
    DrawLimb(pUserTracker, userData.getSkeleton().getJoint(nite::JOINT_RIGHT_KNEE), userData.getSkeleton().getJoint(nite::JOINT_RIGHT_FOOT), userData.getId() % colorCount);
}

     综上，定位跟踪人体骨骼坐标，基本包括以下几个步骤：
    1. 初始化OpenNI、NITE等；

    2. 创建 new nite::UserTracker对象；

    3. m_pUserTracker->create(&m_device)

    4. 开始跟踪人体骨骼  m_pUserTracker->startSkeletonTracking(user.getId());
    5. 开始检测该人做的姿势是不是(POSE_CROSSED_HANDS)：m_pUserTracker->startPoseDetection(user.getId(), nite::POSE_CROSSED_HANDS);

    6. 最后获取人体骨骼坐标信息，主要包括15个骨骼点：

JOINT_HEAD
JOINT_NECK
JOINT_LEFT_SHOULDER
JOINT_RIGHT_SHOULDER
JOINT_LEFT_ELBOW
JOINT_RIGHT_ELBOW
JOINT_LEFT_HAND
JOINT_RIGHT_HAND
JOINT_TORSO
JOINT_LEFT_HIP
JOINT_RIGHT_HIP
JOINT_LEFT_KNEE
JOINT_RIGHT_KNEE
JOINT_LEFT_FOOT
JOINT_RIGHT_FOOT

    最后附上一些有关跟踪骨骼信息的类型和函数：

    一：之前的NITE1我们需要做出“投降”的姿势，才能跟踪骨骼坐标，但现在多了一个姿势，“双手抱胸”。我想后面一个会更加容易获得，具体姿势类型为enum nite::PoseType：POSE_PSI和POSE_CROSSED_HANDS。

     二：骨骼的状态，通过对获取的骨骼跟踪状态，进行相关的处理。

enum

nite::SkeletonState {   nite::SKELETON_NONE, nite::SKELETON_CALIBRATING, nite::SKELETON_TRACKED, nite::SKELETON_CALIBRATION_ERROR_NOT_IN_POSE,   nite::SKELETON_CALIBRATION_ERROR_HANDS, nite::SKELETON_CALIBRATION_ERROR_HEAD, nite::SKELETON_CALIBRATION_ERROR_LEGS, nite::SKELETON_CALIBRATION_ERROR_TORSO }

     三：当然最重要的是nite::UserTrackerFrameRef，我把它叫做人体跟踪快照，对于这个类的含义的描述是这样的：

Snapshot of the User Tracker algorithm. It holds all the users identified at this time, including their position, skeleton and such, as well as the floor plane。具体是什么意思，你说的算~~~

    四： 其实封装好的底层人体跟踪函数也不多，和手的跟踪差不多，也很好理解

// UserTracker
NITE_API NiteStatus niteInitializeUserTracker(NiteUserTrackerHandle*);
NITE_API NiteStatus niteInitializeUserTrackerByDevice(void*, NiteUserTrackerHandle*);
NITE_API NiteStatus niteShutdownUserTracker(NiteUserTrackerHandle);

NITE_API NiteStatus niteStartSkeletonTracking(NiteUserTrackerHandle, NiteUserId);
NITE_API void niteStopSkeletonTracking(NiteUserTrackerHandle, NiteUserId);
NITE_API bool niteIsSkeletonTracking(NiteUserTrackerHandle, NiteUserId);

NITE_API NiteStatus niteSetSkeletonSmoothing(NiteUserTrackerHandle, float);
NITE_API NiteStatus niteGetSkeletonSmoothing(NiteUserTrackerHandle, float*);

NITE_API NiteStatus niteStartPoseDetection(NiteUserTrackerHandle, NiteUserId, NitePoseType);
NITE_API void niteStopPoseDetection(NiteUserTrackerHandle, NiteUserId, NitePoseType);
NITE_API void niteStopAllPoseDetection(NiteUserTrackerHandle, NiteUserId);

NITE_API NiteStatus niteRegisterUserTrackerCallbacks(NiteUserTrackerHandle, NiteUserTrackerCallbacks*, void*);
NITE_API void niteUnregisterUserTrackerCallbacks(NiteUserTrackerHandle, NiteUserTrackerCallbacks*);

NITE_API NiteStatus niteReadUserTrackerFrame(NiteUserTrackerHandle, NiteUserTrackerFrame**);

    总结：对于NITE2的理解，只有两个”手的跟踪“和”人体骨骼跟踪“，但对他们的封装更加简单了，我们也更加容易轻松的通过NITE2获取我们所需要的”手和人体骨骼“。对于获取信息之后如何处理，想怎么处理，已经不管NITE2的事情了。下面就让我们自己开始写自己的程序吧，相信看完之前的三篇”随笔博文“和这篇博文，

谈谈OpenNI 2的安装

谈谈OpenNI 2的第一个程序

谈谈NITE 2的第一个程序HandViewer

你也能轻松的开始使用OpenNI2 SDK 和NITE2了吧~~~