基於C++完成kinect+opencv 獲得深度及黑色數據

基於C++完成kinect+opencv 獲得深度及黑色數據。本站提示廣大學習愛好者：（基於C++完成kinect+opencv 獲得深度及黑色數據）文章只能為提供參考，不一定能成為您想要的結果。以下是基於C++完成kinect+opencv 獲得深度及黑色數據正文

開辟情況 vs2010+OPENCV2.4.10

起首，下載最新的Kinect 2 SDK  http://www.microsoft.com/en-us/kinectforwindows/develop/downloads-docs.aspx

下載以後不要拔出Kinect，最好也不消拔出除鍵盤鼠標之外的其它USB裝備，然後裝置SDK，裝置完成以後拔出Kinect，會有裝置新裝備的提醒。裝置完成以後可以去“開端”那邊找到兩個新裝置的軟件，一個是可以顯示Kinect深度圖，別的一個軟件展現SDK中的各類例子法式。

進入SDK的裝置目次，可以找到sample這個文件夾，外面是四種說話編寫的例子，個中native是C++的，managed是C#的，還有別的兩種說話不熟習，我就熟習C++，橫豎只是嘗嘗的，就用C++了。

opencv+kinect .cpp

#include <opencv2\opencv.hpp> 
#include<iostream>
//windows的頭文件，必需要，否則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>
 
 
//最遠間隔(mm)
const int MAX_DISTANCE = 3500;
//比來間隔(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()
{
  //黑色圖象
  Mat image_rgb;
  //深度圖象
  Mat image_depth;
 
  //創立一個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;
  }
 
  //記載以後銜接KINECT的數目（為多銜接做預備）
  int iSensorCount;
  //取得以後KINECT的數目
  HRESULT hr = NuiGetSensorCount(&iSensorCount);
 
 
  //依照序列初始化KINETC實例，這裡就銜接了一個KINECT，所以沒有效到輪回
  hr = NuiCreateSensorByIndex(iSensorCount - 1, &m_pNuiSensor);
  //初始化，讓其可以吸收黑色和深度數據流
  hr = m_pNuiSensor->NuiInitialize(NUI_INITIALIZE_FLAG_USES_COLOR | NUI_INITIALIZE_FLAG_USES_DEPTH);
 
  //斷定能否失足
  if (FAILED(hr))
  {
    cout<<"NuiInitialize failed"<<endl;
    return hr;
  }
 
  //黑色圖象獲得下一幀事宜
  HANDLE nextColorFrameEvent = CreateEvent(NULL, TRUE, FALSE, NULL);
  //黑色圖象事宜句柄
  HANDLE colorStreamHandle = NULL;
  //深度圖象獲得下一幀事宜
  HANDLE nextDepthFrameEvent = CreateEvent(NULL, TRUE, FALSE, NULL);
  //深度圖象事宜句柄
  HANDLE depthStreamHandle = NULL;
 
  //實例翻開數據流，這裡NUI_IMAGE_TYPE_COLOR表現黑色圖象
  hr = m_pNuiSensor->NuiImageStreamOpen(NUI_IMAGE_TYPE_COLOR, NUI_IMAGE_RESOLUTION_640x480, 0,2,nextColorFrameEvent,&colorStreamHandle);
 
  if( FAILED( hr ) )//斷定能否提取准確
  {
    cout<<"Could not open color image stream video"<<endl;
    m_pNuiSensor->NuiShutdown();
    return hr;
  }
 
  //實例翻開數據流，這裡NUI_IMAGE_TYPE_DEPTH表現深度圖象
  hr = m_pNuiSensor->NuiImageStreamOpen(NUI_IMAGE_TYPE_DEPTH, NUI_IMAGE_RESOLUTION_640x480, 0,2, nextDepthFrameEvent, &depthStreamHandle);
 
  if( FAILED( hr ) )//斷定能否提取准確
  {
    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;
 
    //無窮期待新的黑色數據，比及後前往
    if (WaitForSingleObject(nextColorFrameEvent, 0) == 0)
    {
      //從適才翻開數據流的流句柄中獲得該幀數據，讀取到的數據地址存於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，它包含兩個數據對象：pitch每行字節數，pBits第一個字節地址
      //並鎖定命據，如許當我們讀數據的時刻，kinect就不會去修正它
 
 
      pTexture->LockRect(0, &lockedRect, NULL, 0);
      //確認取得的數據能否有用
      if (lockedRect.Pitch != 0)
      {
        //將數據轉換為OpenCV的Mat格局
        for (int i = 0; i < image_rgb.rows; i++)
        {
          //第i行的指針
          uchar *prt = image_rgb.ptr(i);
 
          //每一個字節代表一個色彩信息，直接應用uchar
          uchar *pBuffer = (uchar*)(lockedRect.pBits) + i * lockedRect.Pitch;
 
          for (int j = 0; j < image_rgb.cols; j++)
          {  
            prt[3 * j] = pBuffer[4 * j];//外部數據是4個字節，0-1-2是BGR，第4個如今未應用
            prt[3 * j + 1] = pBuffer[4 * j + 1];
            prt[3 * j + 2] = pBuffer[4 * j + 2];
          }
        }
 
        imshow("colorImage",image_rgb);
        //消除鎖定
        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; 
 
 
      //深度圖象的處置
      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;
          //這裡須要轉換，由於每一個深度數據是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，將數據歸一化處置，對深度間隔在300mm-3500mm規模內的像素，映照到【0—255】內，
            //超越規模的，都去做是邊沿像素
            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);
 
 
 
        //接上去是對齊部門，將遠景摳出來
 
        //寄存深度點的參數
        NUI_DEPTH_IMAGE_POINT* depthPoints = new NUI_DEPTH_IMAGE_POINT[640 * 480];
        if (ColorToDepthLockRect.Pitch != 0) 
        { 
          HRESULT hrState = S_OK; 
           
          //一個能在分歧空間坐標改變的類（包含：深度，黑色，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; 
          } 
 
          //主要的一步：從色彩空間映照到深度空間。參數解釋：
          //【參數1】：黑色圖象的類型
          //【參數2】：黑色圖象的分辯率
          //【參數3】：深度圖象的分辯率
          //【參數4】：深度圖象的個數
          //【參數5】：深度像素點數
          //【參數6】：取內存的年夜小，個數。類型為NUI_DEPTH_IMAGE_PIXEL
          //【參數7】：寄存映照成果點的參數
          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; 
          } 
 
 
          //顯示的圖象
          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; 
              //從保留了映照坐標的數組中獲得點
              NUI_DEPTH_IMAGE_POINT depthPointAtIndex = depthPoints[index]; 
 
              //界限斷定
              if (depthPointAtIndex.x >= 0 && depthPointAtIndex.x < image_depth.cols &&
                depthPointAtIndex.y >=0 && depthPointAtIndex.y < image_depth.rows)
              {
                //深度斷定，在MIN_DISTANCE與MAX_DISTANCE之間確當成遠景，顯示出來
                //這個應用也很主要，當應用真實的深度像素點再在深度圖象中獲得深度值來斷定的時刻，會失足
                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;
}