開發環境 vs2010+OPENCV2.4.10
首先，下載最新的Kinect 2 SDK http://www.microsoft.com/en-us/kinectforwindows/develop/downloads-docs.aspx
下載之后不要插入Kinect，最好也不用插入除了鍵盤鼠標以外的其它USB設備，然后安裝SDK，安裝完成之后插入Kinect，會有安裝新設備的提示。安裝完成之后可以去“開始”那里找到兩個新安裝的軟件，一個是可以顯示Kinect深度圖，另外一個軟件展示SDK中的各種例子程序。
進入SDK的安裝目錄，可以找到sample這個文件夾，里面是四種語言編寫的例子，其中native是C++的，managed是C#的，還有另外兩種語言不熟悉，我就熟悉C++，反正只是試試的，就用C++了。
opencv+kinect .cpp
#include <opencv2/opencv.hpp> #include<iostream>//windows的頭文件，必須要，不然NuiApi.h用不了#include <Windows.h>//Kinect for windows 的頭文件#include "NuiApi.h" using namespace std;using namespace cv; #include <d3d11.h> //最遠距離(mm)const int MAX_DISTANCE = 3500;//最近距離(mm)const int MIN_DISTANCE = 200; const LONG m_depthWidth = 640;const LONG m_depthHeight = 480;const LONG m_colorWidth = 640;const LONG m_colorHeight = 480;const LONG cBytesPerPixel = 4; int main(){ //彩色圖像 Mat image_rgb; //深度圖像 Mat image_depth; //創建一個MAT image_rgb.create(480,640,CV_8UC3); image_depth.create(480,640,CV_8UC1); //一個KINECT實例指針 INuiSensor* m_pNuiSensor = NULL; if (m_pNuiSensor != NULL) { return 0; } //記錄當前連接KINECT的數量（為多連接做準備） int iSensorCount; //獲得當前KINECT的數量 HRESULT hr = NuiGetSensorCount(&iSensorCount); //按照序列初始化KINETC實例，這里就連接了一個KINECT，所以沒有用到循環 hr = NuiCreateSensorByIndex(iSensorCount - 1, &m_pNuiSensor); //初始化，讓其可以接收彩色和深度數據流 hr = m_pNuiSensor->NuiInitialize(NUI_INITIALIZE_FLAG_USES_COLOR | NUI_INITIALIZE_FLAG_USES_DEPTH); //判斷是否出錯 if (FAILED(hr)) { cout<<"NuiInitialize failed"<<endl; return hr; } //彩色圖像獲取下一幀事件 HANDLE nextColorFrameEvent = CreateEvent(NULL, TRUE, FALSE, NULL); //彩色圖像事件句柄 HANDLE colorStreamHandle = NULL; //深度圖像獲取下一幀事件 HANDLE nextDepthFrameEvent = CreateEvent(NULL, TRUE, FALSE, NULL); //深度圖像事件句柄 HANDLE depthStreamHandle = NULL; //實例打開數據流，這里NUI_IMAGE_TYPE_COLOR表示彩色圖像 hr = m_pNuiSensor->NuiImageStreamOpen(NUI_IMAGE_TYPE_COLOR, NUI_IMAGE_RESOLUTION_640x480, 0,2,nextColorFrameEvent,&colorStreamHandle); if( FAILED( hr ) )//判斷是否提取正確 { cout<<"Could not open color image stream video"<<endl; m_pNuiSensor->NuiShutdown(); return hr; } //實例打開數據流，這里NUI_IMAGE_TYPE_DEPTH表示深度圖像 hr = m_pNuiSensor->NuiImageStreamOpen(NUI_IMAGE_TYPE_DEPTH, NUI_IMAGE_RESOLUTION_640x480, 0,2, nextDepthFrameEvent, &depthStreamHandle); if( FAILED( hr ) )//判斷是否提取正確 { cout<<"Could not open color image stream video"<<endl; m_pNuiSensor->NuiShutdown(); return hr; } cv::namedWindow("depth", CV_WINDOW_AUTOSIZE); moveWindow("depth",300,600); cv::namedWindow("colorImage",CV_WINDOW_AUTOSIZE); moveWindow("colorImage",0,200); while (1) { NUI_IMAGE_FRAME pImageFrame_rgb; NUI_IMAGE_FRAME pImageFrame_depth; //無限等待新的彩色數據，等到后返回 if (WaitForSingleObject(nextColorFrameEvent, 0) == 0) { //從剛才打開數據流的流句柄中得到該幀數據，讀取到的數據地址存于pImageFrame hr = m_pNuiSensor->NuiImageStreamGetNextFrame(colorStreamHandle, 0, &pImageFrame_rgb); if (FAILED(hr)) { cout<<"Could not get color image"<<endl; m_pNuiSensor->NuiShutdown(); return -1; } INuiFrameTexture *pTexture = pImageFrame_rgb.pFrameTexture; NUI_LOCKED_RECT lockedRect; //提取數據幀到LockedRect，它包括兩個數據對象：pitch每行字節數，pBits第一個字節地址 //并鎖定數據，這樣當我們讀數據的時候，kinect就不會去修改它 pTexture->LockRect(0, &lockedRect, NULL, 0); //確認獲得的數據是否有效 if (lockedRect.Pitch != 0) { //將數據轉換為OpenCV的Mat格式 for (int i = 0; i < image_rgb.rows; i++) { //第i行的指針 uchar *prt = image_rgb.ptr(i); //每個字節代表一個顏色信息，直接使用uchar uchar *pBuffer = (uchar*)(lockedRect.pBits) + i * lockedRect.Pitch; for (int j = 0; j < image_rgb.cols; j++) { prt[3 * j] = pBuffer[4 * j];//內部數據是4個字節，0-1-2是BGR，第4個現在未使用 prt[3 * j + 1] = pBuffer[4 * j + 1]; prt[3 * j + 2] = pBuffer[4 * j + 2]; } } imshow("colorImage",image_rgb); //解除鎖定 pTexture->UnlockRect(0); //釋放幀 m_pNuiSensor->NuiImageStreamReleaseFrame(colorStreamHandle, &pImageFrame_rgb ); } else { cout<<"Buffer length of received texture is bogus/r/n"<<endl; } BOOL nearMode; INuiFrameTexture* pColorToDepthTexture; //深度圖像的處理 if (WaitForSingleObject(nextDepthFrameEvent, INFINITE) == 0) { hr = m_pNuiSensor->NuiImageStreamGetNextFrame(depthStreamHandle, 0 , &pImageFrame_depth); if (FAILED(hr)) { cout<<"Could not get color image"<<endl; NuiShutdown(); return -1; } hr = m_pNuiSensor->NuiImageFrameGetDepthImagePixelFrameTexture( depthStreamHandle, &pImageFrame_depth, &nearMode, &pColorToDepthTexture); INuiFrameTexture *pTexture = pImageFrame_depth.pFrameTexture; NUI_LOCKED_RECT lockedRect; NUI_LOCKED_RECT ColorToDepthLockRect; pTexture->LockRect(0, &lockedRect, NULL, 0); pColorToDepthTexture->LockRect(0,&ColorToDepthLockRect,NULL,0); //歸一化 for (int i = 0; i < image_depth.rows; i++) { uchar *prt = image_depth.ptr<uchar>(i); uchar* pBuffer = (uchar*)(lockedRect.pBits) + i * lockedRect.Pitch; //這里需要轉換，因為每個深度數據是2個字節，應將BYTE轉成USHORT USHORT *pBufferRun = (USHORT*)pBuffer; for (int j = 0; j < image_depth.cols; j++) { //先向，將數據歸一化處理，對深度距離在300mm-3500mm范圍內的像素，映射到【0—255】內， //超出范圍的，都去做是邊緣像素 if (pBufferRun[j] << 3 > MAX_DISTANCE) prt[j] = 255; else if(pBufferRun[j] << 3 < MIN_DISTANCE) prt[j] = 0; else prt[j] = (BYTE)(256 * (pBufferRun[j] << 3)/ MAX_DISTANCE); } } imshow("depth", image_depth); //接下來是對齊部分，將前景摳出來 //存放深度點的參數 NUI_DEPTH_IMAGE_POINT* depthPoints = new NUI_DEPTH_IMAGE_POINT[640 * 480]; if (ColorToDepthLockRect.Pitch != 0) { HRESULT hrState = S_OK; //一個能在不同空間坐標轉變的類（包括：深度，彩色，骨骼） INuiCoordinateMapper* pMapper; //設置KINECT實例的空間坐標系 hrState = m_pNuiSensor->NuiGetCoordinateMapper(&pMapper); if (FAILED(hrState)) { return hrState; } //重要的一步：從顏色空間映射到深度空間。參數說明： //【參數1】：彩色圖像的類型 //【參數2】：彩色圖像的分辨率 //【參數3】：深度圖像的分辨率 //【參數4】：深度圖像的個數 //【參數5】：深度像素點數 //【參數6】：取內存的大小，個數。類型為NUI_DEPTH_IMAGE_PIXEL //【參數7】：存放映射結果點的參數 hrState = pMapper->MapColorFrameToDepthFrame(NUI_IMAGE_TYPE_COLOR, NUI_IMAGE_RESOLUTION_640x480, NUI_IMAGE_RESOLUTION_640x480, 640 * 480, (NUI_DEPTH_IMAGE_PIXEL*)ColorToDepthLockRect.pBits,640 * 480, depthPoints); if (FAILED(hrState)) { return hrState; } //顯示的圖像 Mat show; show.create(480,640,CV_8UC3); show = 0; for (int i = 0; i < image_rgb.rows; i++) { for (int j = 0; j < image_rgb.cols; j++) { uchar *prt_rgb = image_rgb.ptr(i); uchar *prt_show = show.ptr(i); //在內存中偏移量 long index = i * 640 + j; //從保存了映射坐標的數組中獲取點 NUI_DEPTH_IMAGE_POINT depthPointAtIndex = depthPoints[index]; //邊界判斷 if (depthPointAtIndex.x >= 0 && depthPointAtIndex.x < image_depth.cols && depthPointAtIndex.y >=0 && depthPointAtIndex.y < image_depth.rows) { //深度判斷，在MIN_DISTANCE與MAX_DISTANCE之間的當成前景，顯示出來 //這個使用也很重要，當使用真正的深度像素點再在深度圖像中獲取深度值來判斷的時候，會出錯 if (depthPointAtIndex.depth >= MIN_DISTANCE && depthPointAtIndex.depth <= MAX_DISTANCE) { prt_show[3 * j] = prt_rgb[j * 3]; prt_show[3 * j + 1] = prt_rgb[j * 3 + 1]; prt_show[3 * j + 2] = prt_rgb[j * 3 + 2]; } } } } imshow("show", show); } delete []depthPoints; pTexture->UnlockRect(0); m_pNuiSensor->NuiImageStreamReleaseFrame(depthStreamHandle, &pImageFrame_depth); } else { cout<<"Buffer length of received texture is bogus/r/n"<<endl; } } if (cvWaitKey(20) == 27) break; } return 0;} 
