人脸旋转 《转》
Posted wainiwann
tags:
篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了人脸旋转 《转》相关的知识，希望对你有一定的参考价值。
近期接触了一下四元数，目的是要获取人脸朝向，看到这篇文章才发现了一些线索，所以记录一下。
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更新一下进展：
自从发现了下面的 pitch roll yaw 对照，基本上也算是了解了个大概了。
然后就又发现了下面的文章：
原贴地址：https://www.amobbs.com/thread-5504669-1-1.html
四元数转换成欧拉角，pitch角范围问题？
用下面的语句将四元数[w x y z]转换成欧拉角roll，pitch，yaw
Roll = atan2(2 * (w * z + x * y) , 1 - 2 * (z * z + x * x)); Pitch = asin(2 * (w * x - y * z)); Yaw = atan2(2 * (w * y + z * x) , 1 - 2 * (x * x + y * y));
Roll和Pitch的范围是-180到180度，Pitch用asin计算范围只有-90到90度。
最大的问题时当Pitch（超过）90度，也就是说88度89度90度，然后递减又是89度88度87度，这个90度的分解处，roll和yaw角会有一个180度的转变（假定原来roll和yaw都是0，在pitch角90度的分解处会突然变成180度）
请问这个该如何解决?
感谢~~~
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从上面的文章大致找到了获取角度的处理，然而获取角度之后大概应该也就知道朝向的问题了。有很多的突破！
(3)人脸旋转
获取人脸跟踪结果后，我们除了得到面部关键顶点还可以直接获取人脸朝向（Get3DPose函数）。下图是人脸朝向的定义：
|
Angle |
Value |
|
Pitch angle 0=neutral |
-90 = looking down towards the floor +90 = looking up towards the ceiling Face Tracking tracks when the user’s head pitch is less than 20 degrees, but works best when less than 10 degrees. |
|
Roll angle 0 = neutral |
-90 = horizontal parallel with right shoulder of subject +90 = horizontal parallel with left shoulder of the subject Face Tracking tracks when the user’s head roll is less than 90 degrees, but works best when less than 45 degrees. |
|
Yaw angle 0 = neutral |
-90 = turned towards the right shoulder of the subject +90 = turned towards the left shoulder of the subject Face Tracking tracks when the user’s head yaw is less than 45 degrees, but works best when less than 30 degrees |
自然如果你做简单的人头旋转头部动作识别，肯定得用到这块。如果你做人脸标表情、动作或者三维建模，也少不了使用这个旋转角度对人脸进行一些仿射变换。很显然，Roll方向上的人脸旋转可以消除掉(我在我的人脸识别中就这样)。如果你使用OpenCV进行放射旋转，要熟悉矩阵旋转公式，一旦人脸旋转，那么所有的顶点坐标都要跟着旋转，如果扣除人脸区域，那么相应的人脸顶点坐标也要减去x轴和y轴的数值。还有需要考虑一些临界情况，比如人脸从左侧移除，人脸从上方、下方走出，会不会导致程序崩溃？此时的人脸数据不可以使用。
之后还有动画单元等概念，也是可以通过函数直接获取结果，这部分我未进行研究，如果做表情或者人脸动画，则需要深入研究下。
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原贴地址：http://brightguo.com/kinect-face-tracking/
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// win32_KinectFaceTracking.cpp : 定义控制台应用程序的入口点。 /**************************************************** 程序用途：KinectFace Tracking简单例子 开发环境：VisualStudio 2010 win32程序 OpenCV2.4.4 显示界面库 Kinect SDK v1.6 驱动版本 Windows 7 操作系统 开发人员：箫鸣 开发时间：2013-3-11~ 2013-3-12 联系方式：weibo.com/guoming0000 guoming0000@sina.com www.ilovecode.cn 备注：另有配套相关博客文章： Kinect Face Tracking SDK[Kinect人脸跟踪] ******************************************************/ #include "stdafx.h" #include <windows.h> #include <opencv2opencv.hpp> #include <mmsystem.h> #include <assert.h> //#include <strsafe.h> #include "NuiApi.h" using namespace cv; using namespace std; //---------------------------------------------------- #define _WINDOWS #include <FaceTrackLib.h> //显示网状人脸,初始化人脸模型 HRESULT VisualizeFaceModel(IFTImage* pColorImg, IFTModel* pModel, FT_CAMERA_CONFIG const* pCameraConfig, FLOAT const* pSUCoef, FLOAT zoomFactor, POINT viewOffset, IFTResult* pAAMRlt, UINT32 color);//pColorImg为图像缓冲区，pModel三维人脸模型 //---图像大小等参数-------------------------------------------- #define COLOR_WIDTH 640 #define COLOR_HIGHT 480 #define DEPTH_WIDTH 320 #define DEPTH_HIGHT 240 #define SKELETON_WIDTH 640 #define SKELETON_HIGHT 480 #define CHANNEL 3 BYTE DepthBuf[DEPTH_WIDTH*DEPTH_HIGHT*CHANNEL]; //---人脸跟踪用到的变量------------------------------------------ IFTImage* pColorFrame,*pColorDisplay; //彩色图像数据，pColorDisplay是用于处理的深度数据 IFTImage* pDepthFrame; //深度图像数据 FT_VECTOR3D m_hint3D[2]; //头和肩膀中心的坐标 //----各种内核事件和句柄----------------------------------------------------------------- HANDLE m_hNextColorFrameEvent; HANDLE m_hNextDepthFrameEvent; HANDLE m_hNextSkeletonEvent; HANDLE m_pColorStreamHandle;//保存图像数据流的句柄，用以提取数据 HANDLE m_pDepthStreamHandle; HANDLE m_hEvNuiProcessStop;//用于结束的事件对象 //----------------------------------------------------------------------------------- //获取彩色图像数据，并进行显示 int DrawColor(HANDLE h) { const NUI_IMAGE_FRAME * pImageFrame = NULL; HRESULT hr = NuiImageStreamGetNextFrame( h, 0, &pImageFrame ); if( FAILED( hr ) ) { cout<<"Get Color Image Frame Failed"<<endl; return -1; } INuiFrameTexture * pTexture = pImageFrame->pFrameTexture; NUI_LOCKED_RECT LockedRect; pTexture->LockRect( 0, &LockedRect, NULL, 0 );//提取数据帧到LockedRect中，包括两个数据对象：pitch表示每行字节数，pBits第一个字节的地址 if( LockedRect.Pitch != 0 )//如果每行字节数不为0 { BYTE * pBuffer = (BYTE*) LockedRect.pBits;//pBuffer指向数据帧的第一个字节的地址 //该函数的作用是在LockedRect第一个字节开始的地址复制min(pColorFrame->GetBufferSize(), UINT(pTexture->BufferLen())）个字节到pColorFrame->GetBuffer()所指的缓冲区 memcpy(pColorFrame->GetBuffer(), PBYTE(LockedRect.pBits), //PBYTE表示无符号单字节数值 min(pColorFrame->GetBufferSize(), UINT(pTexture->BufferLen())));//GetBuffer()它的作用是返回一个可写的缓冲指针 //OpenCV显示彩色视频 Mat temp(COLOR_HIGHT,COLOR_WIDTH,CV_8UC4,pBuffer); imshow("ColorVideo",temp); int c = waitKey(1);//按下ESC结束 //如果在视频界面按下ESC,q,Q都会导致整个程序退出 if( c == 27 || c == \'q\' || c == \'Q\' ) { SetEvent(m_hEvNuiProcessStop); } } NuiImageStreamReleaseFrame( h, pImageFrame ); return 0; } //获取深度图像数据，并进行显示 int DrawDepth(HANDLE h) { const NUI_IMAGE_FRAME * pImageFrame = NULL; HRESULT hr = NuiImageStreamGetNextFrame( h, 0, &pImageFrame ); if( FAILED( hr ) ) { cout<<"Get Depth Image Frame Failed"<<endl; return -1; } INuiFrameTexture * pTexture = pImageFrame->pFrameTexture; NUI_LOCKED_RECT LockedRect; pTexture->LockRect( 0, &LockedRect, NULL, 0 ); if( LockedRect.Pitch != 0 ) { USHORT * pBuff = (USHORT*) LockedRect.pBits;//注意这里需要转换，因为每个数据是2个字节，存储的同上面的颜色信息不一样，这里是2个字节一个信息，不能再用BYTE，转化为USHORT // pDepthBuffer = pBuff; memcpy(pDepthFrame->GetBuffer(), PBYTE(LockedRect.pBits), min(pDepthFrame->GetBufferSize(), UINT(pTexture->BufferLen()))); for(int i=0;i<DEPTH_WIDTH*DEPTH_HIGHT;i++) { BYTE index = pBuff[i]&0x07;//提取ID信息 USHORT realDepth = (pBuff[i]&0xFFF8)>>3;//提取距离信息 BYTE scale = 255 - (BYTE)(256*realDepth/0x0fff);//因为提取的信息时距离信息 DepthBuf[CHANNEL*i] = DepthBuf[CHANNEL*i+1] = DepthBuf[CHANNEL*i+2] = 0; switch( index ) { case 0: DepthBuf[CHANNEL*i]=scale/2; DepthBuf[CHANNEL*i+1]=scale/2; DepthBuf[CHANNEL*i+2]=scale/2; break; case 1: DepthBuf[CHANNEL*i]=scale; break; case 2: DepthBuf[CHANNEL*i+1]=scale; break; case 3: DepthBuf[CHANNEL*i+2]=scale; break; case 4: DepthBuf[CHANNEL*i]=scale; DepthBuf[CHANNEL*i+1]=scale; break; case 5: DepthBuf[CHANNEL*i]=scale; DepthBuf[CHANNEL*i+2]=scale; break; case 6: DepthBuf[CHANNEL*i+1]=scale; DepthBuf[CHANNEL*i+2]=scale; break; case 7: DepthBuf[CHANNEL*i]=255-scale/2; DepthBuf[CHANNEL*i+1]=255-scale/2; DepthBuf[CHANNEL*i+2]=255-scale/2; break; } } Mat temp(DEPTH_HIGHT,DEPTH_WIDTH,CV_8UC3,DepthBuf); imshow("DepthVideo",temp); int c = waitKey(1);//按下ESC结束 if( c == 27 || c == \'q\' || c == \'Q\' ) { SetEvent(m_hEvNuiProcessStop); } } NuiImageStreamReleaseFrame( h, pImageFrame ); return 0; } //获取骨骼数据，并进行显示 int DrawSkeleton() { NUI_SKELETON_FRAME SkeletonFrame;//骨骼帧的定义 cv::Point pt[20]; Mat skeletonMat=Mat(SKELETON_HIGHT,SKELETON_WIDTH,CV_8UC3,Scalar(0,0,0)); //直接从kinect中提取骨骼帧 HRESULT hr = NuiSkeletonGetNextFrame( 0, &SkeletonFrame ); if( FAILED( hr ) ) { cout<<"Get Skeleton Image Frame Failed"<<endl; return -1; } bool bFoundSkeleton = false; for( int i = 0 ; i < NUI_SKELETON_COUNT ; i++ ) { if( SkeletonFrame.SkeletonData[i].eTrackingState == NUI_SKELETON_TRACKED ) { bFoundSkeleton = true; } } // 跟踪到了骨架 if( bFoundSkeleton ) { NuiTransformSmooth(&SkeletonFrame,NULL); for( int i = 0 ; i < NUI_SKELETON_COUNT ; i++ ) { if( SkeletonFrame.SkeletonData[i].eTrackingState == NUI_SKELETON_TRACKED ) { for (int j = 0; j < NUI_SKELETON_POSITION_COUNT; j++) { float fx,fy; NuiTransformSkeletonToDepthImage( SkeletonFrame.SkeletonData[i].SkeletonPositions[j], &fx, &fy ); pt[j].x = (int) ( fx * SKELETON_WIDTH )/320; pt[j].y = (int) ( fy * SKELETON_HIGHT )/240; circle(skeletonMat,pt[j],5,CV_RGB(255,0,0)); } // cout<<"one people"<<endl; // cv::line(skeletonMat,pt[NUI_SKELETON_POSITION_SHOULDER_CENTER],pt[NUI_SKELETON_POSITION_SPINE],CV_RGB(0,255,0)); // cv::line(skeletonMat,pt[NUI_SKELETON_POSITION_SPINE],pt[NUI_SKELETON_POSITION_HIP_CENTER],CV_RGB(0,255,0)); cv::line(skeletonMat,pt[NUI_SKELETON_POSITION_HEAD],pt[NUI_SKELETON_POSITION_SHOULDER_CENTER],CV_RGB(0,255,0)); cv::line(skeletonMat,pt[NUI_SKELETON_POSITION_HAND_RIGHT],pt[NUI_SKELETON_POSITION_WRIST_RIGHT],CV_RGB(0,255,0)); cv::line(skeletonMat,pt[NUI_SKELETON_POSITION_WRIST_RIGHT],pt[NUI_SKELETON_POSITION_ELBOW_RIGHT],CV_RGB(0,255,0)); cv::line(skeletonMat,pt[NUI_SKELETON_POSITION_ELBOW_RIGHT],pt[NUI_SKELETON_POSITION_SHOULDER_RIGHT],CV_RGB(0,255,0)); cv::line(skeletonMat,pt[NUI_SKELETON_POSITION_SHOULDER_RIGHT],pt[NUI_SKELETON_POSITION_SHOULDER_CENTER],CV_RGB(0,255,0)); cv::line(skeletonMat,pt[NUI_SKELETON_POSITION_SHOULDER_CENTER],pt[NUI_SKELETON_POSITION_SHOULDER_LEFT],CV_RGB(0,255,0)); cv::line(skeletonMat,pt[NUI_SKELETON_POSITION_SHOULDER_LEFT],pt[NUI_SKELETON_POSITION_ELBOW_LEFT],CV_RGB(0,255,0)); cv::line(skeletonMat,pt[NUI_SKELETON_POSITION_ELBOW_LEFT],pt[NUI_SKELETON_POSITION_WRIST_LEFT],CV_RGB(0,255,0)); cv::line(skeletonMat,pt[NUI_SKELETON_POSITION_WRIST_LEFT],pt[NUI_SKELETON_POSITION_HAND_LEFT],CV_RGB(0,255,0)); m_hint3D[0].x=SkeletonFrame.SkeletonData[i].SkeletonPositions[NUI_SKELETON_POSITION_SHOULDER_CENTER].x; m_hint3D[0].y=SkeletonFrame.SkeletonData[i].SkeletonPositions[NUI_SKELETON_POSITION_SHOULDER_CENTER].y; m_hint3D[0].z=SkeletonFrame.SkeletonData[i].SkeletonPositions[NUI_SKELETON_POSITION_SHOULDER_CENTER].z; m_hint3D[1].x=SkeletonFrame.SkeletonData[i].SkeletonPositions[NUI_SKELETON_POSITION_HEAD].x; m_hint3D[1].y=SkeletonFrame.SkeletonData[i].SkeletonPositions[NUI_SKELETON_POSITION_HEAD].y; m_hint3D[1].z=SkeletonFrame.SkeletonData[i].SkeletonPositions[NUI_SKELETON_POSITION_HEAD].z; // cout<<"("<<m_hint3D[0].x<<","<<m_hint3D[0].y<<","<<m_hint3D[0].z<<")"<<endl; // cout<<"("<<m_hint3D[1].x<<","<<m_hint3D[1].y<<","<<m_hint3D[1].z<<")"<<endl<<endl; } } } imshow("SkeletonVideo",skeletonMat); waitKey(1); int c = waitKey(1);//按下ESC结束 if( c == 27 || c == \'q\' || c == \'Q\' ) { SetEvent(m_hEvNuiProcessStop); } return 0; } DWORD WINAPI KinectDataThread(LPVOID pParam)//线程函数 { HANDLE hEvents[4] = {m_hEvNuiProcessStop,m_hNextColorFrameEvent, m_hNextDepthFrameEvent,m_hNextSkeletonEvent};//内核事件 while(1) { int nEventIdx; nEventIdx=WaitForMultipleObjects(sizeof(hEvents)/sizeof(hEvents[0]), hEvents,FALSE,100); if (WAIT_OBJECT_0 == WaitForSingleObject(m_hEvNuiProcessStop, 0)) { break; } // Process signal events if (WAIT_OBJECT_0 == WaitForSingleObject(m_hNextColorFrameEvent, 0)) { DrawColor(m_pColorStreamHandle);//获取彩色图像并进行显示 } if (WAIT_OBJECT_0 == WaitForSingleObject(m_hNextDepthFrameEvent, 0)) { DrawDepth(m_pDepthStreamHandle); } if (WAIT_OBJECT_0 == WaitForSingleObject(m_hNextSkeletonEvent, 0)) { DrawSkeleton(); } //这种方式关闭程序时可能出问题 // switch(nEventIdx) // { // case 0: // break; // case 1: // DrawColor(m_pVideoStreamHandle); // case 2: // DrawDepth(m_pDepthStreamHandle); // case 3: // DrawSkeleton(); // } } CloseHandle(m_hEvNuiProcessStop); m_hEvNuiProcessStop = NULL; CloseHandle( m_hNextSkeletonEvent ); CloseHandle( m_hNextDepthFrameEvent ); CloseHandle( m_hNextColorFrameEvent ); return 0; } int main() { m_hint3D[0].x=0;//肩膀的中心坐标,三维向量 m_hint3D[0].y=0; m_hint3D[0].z=0; m_hint3D[1].x=0;//头的中心坐标，三维向量 m_hint3D[1].y=0; m_hint3D[1].z=0; //使用微软提供的API来操作Kinect之前，要进行NUI初始化 HRESULT hr = NuiInitialize(NUI_INITIALIZE_FLAG_USES_DEPTH_AND_PLAYER_INDEX|NUI_INITIALIZE_FLAG_USES_COLOR|NUI_INITIALIZE_FLAG_USES_SKELETON); if( hr != S_OK )//Kinect提供了两种处理返回值的方式，就是判断上面的函数是否执行成功。 { cout<<"NuiInitialize failed"<<endl; return hr; } //1、Color ----打开KINECT设备的彩色图信息通道 m_hNextColorFrameEvent = CreateEvent( NULL, TRUE, FALSE, NULL );//创建一个windows事件对象，创建成功则返回事件的句柄 m_pColorStreamHandle = NULL;//保存图像数据流的句柄，用以提取数据 //打开KINECT设备的彩色图信息通道，并用m_pColorStreamHandle保存该流的句柄，以便于以后读取 hr = NuiImageStreamOpen(NUI_IMAGE_TYPE_COLOR,NUI_IMAGE_RESOLUTION_640x480, 0, 2, m_hNextColorFrameEvent, &m_pColorStreamHandle); if( FAILED( hr ) ) { cout<<"Could not open image stream video"<<endl; return hr; } //2、Depth -----打开Kinect设备的深度图信息通道 m_hNextDepthFrameEvent = CreateEvent( NULL, TRUE, FALSE, NULL ); m_pDepthStreamHandle = NULL;//保存深度数据流的句柄，用以提取数据 //打开KINECT设备的深度图信息通道，并用m_pDepthStreamHandle保存该流的句柄，以便于以后读取 hr = NuiImageStreamOpen( NUI_IMAGE_TYPE_DEPTH_AND_PLAYER_INDEX, NUI_IMAGE_RESOLUTION_320x240, 0, 2, m_hNextDepthFrameEvent, &m_pDepthStreamHandle); if( FAILED( hr ) ) { cout<<"Could not open depth stream video"<<endl; return hr; } //3、Skeleton -----定义骨骼信号事件句柄，打开骨骼跟踪事件 m_hNextSkeletonEvent = CreateEvent( NULL, TRUE, FALSE, NULL ); hr = NuiSkeletonTrackingEnable( m_hNextSkeletonEvent, NUI_SKELETON_TRACKING_FLAG_ENABLE_IN_NEAR_RANGE|NUI_SKELETON_TRACKING_FLAG_ENABLE_SEATED_SUPPORT); if( FAILED( hr ) ) { cout<<"Could not open skeleton stream video"<<endl; return hr; } //4、用于结束的事件对象 m_hEvNuiProcessStop = CreateEvent(NULL,TRUE,FALSE,NULL); //5、开启一个线程---用于读取彩色、深度、骨骼数据,该线程用于调用线程函数KinectDataThread，线程函数对深度读取彩色和深度图像并进行骨骼跟踪，同时进行显示 HANDLE m_hProcesss = CreateThread(NULL, 0, KinectDataThread, 0, 0, 0); //////////////////////////////////////////////////////////////////////// //m_hint3D[0] = FT_VECTOR3D(0, 0, 0);//头中心坐标，初始化 //m_hint3D[1] = FT_VECTOR3D(0, 0, 0);//肩膀的中心坐标 pColorFrame = FTCreateImage();//彩色图像数据，数据类型为IFTImage* pDepthFrame = FTCreateImage();//深度图像数据 pColorDisplay = FTCreateImage(); //----------1、创建一个人脸跟踪实例----------------------- IFTFaceTracker* pFT = FTCreateFaceTracker();//返回数据类型为IFTFaceTracker* if(!pFT) { return -1;// Handle errors } //初始化人脸跟踪所需要的数据数据-----FT_CAMERA_CONFIG包含彩色或深度传感器的信息（长，宽，焦距） FT_CAMERA_CONFIG myCameraConfig = {COLOR_WIDTH, COLOR_HIGHT, NUI_CAMERA_COLOR_NOMINAL_FOCAL_LENGTH_IN_PIXELS}; // width, height, focal length FT_CAMERA_CONFIG depthConfig = {DEPTH_WIDTH, DEPTH_HIGHT, NUI_CAMERA_DEPTH_NOMINAL_FOCAL_LENGTH_IN_PIXELS}; //depthConfig.FocalLength = NUI_CAMERA_DEPTH_NOMINAL_FOCAL_LENGTH_IN_PIXELS; //depthConfig.Width = DEPTH_WIDTH; //depthConfig.Height = DEPTH_HIGHT;//这里一定要填，而且要填对才行！！ //IFTFaceTracker的初始化, --- 人脸跟踪主要接口IFTFaceTracker的初始化 hr = pFT->Initialize(&myCameraConfig, &depthConfig, NULL, NULL); if( FAILED(hr) ) { return -2;// Handle errors } // 2、----------创建一个实例接受3D跟踪结果---------------- IFTResult* pFTResult = NULL; hr = pFT->CreateFTResult(&pFTResult); if(FAILED(hr)) { return -11; } // prepare Image and SensorData for 640x480 RGB images if(!pColorFrame) { return -12;// Handle errors } // Attach assumes that the camera code provided by the application // is filling the buffer cameraFrameBuffer //申请内存空间 pColorDisplay->Allocate(COLOR_WIDTH, COLOR_HIGHT, FTIMAGEFORMAT_UINT8_B8G8R8X8); hr = pColorFrame->Allocate(COLOR_WIDTH, COLOR_HIGHT, FTIMAGEFORMAT_UINT8_B8G8R8X8); if (FAILED(hr)) { return hr; } hr = pDepthFrame->Allocate(DEPTH_WIDTH, DEPTH_HIGHT, FTIMAGEFORMAT_UINT16_D13P3); if (FAILED(hr)) { return hr; } //填充FT_SENSOR_DATA结构，包含用于人脸追踪所需要的所有输入数据 FT_SENSOR_DATA sensorData; POINT point; sensorData.ZoomFactor = 1.0f; point.x = 0; point.y = 0; sensorData.ViewOffset = point;//POINT(0,0) bool isTracked = false;//跟踪判断条件 //int iFaceTrackTimeCount=0; // 跟踪人脸 while ( 1 ) { sensorData.pVideoFrame = pColorFrame;//彩色图像数据 sensorData.pDepthFrame = pDepthFrame;//深度图像数据 //初始化追踪，比较耗时 if(!isTracked)//为false { //会耗费较多cpu计算资源，开始跟踪 hr = pFT->StartTracking(&sensorData, NULL, m_hint3D, pFTResult);//输入为彩色图像，深度图像，人头和肩膀的三维坐标 if(SUCCEEDED(hr) && SUCCEEDED(pFTResult->GetStatus())) { isTracked = true; } else { isTracked = false; } } else { //继续追踪，很迅速，它一般使用一个已大概知晓的人脸模型，所以它的调用不会消耗多少cpu计算，pFTResult存放跟踪的结果 hr = pFT->ContinueTracking(&sensorData, m_hint3D, pFTResult); if(FAILED(hr) || FAILED (pFTResult->GetStatus())) { // 跟丢 isTracked = false; } } int bStop; if(isTracked) { IFTModel* ftModel;//三维人脸模型 HRESULT hr = pFT->GetFaceModel(&ftModel);//得到三维人脸模型 FLOAT* pSU = NULL; UINT numSU; BOOL suConverged; pFT->GetShapeUnits(NULL, &pSU, &numSU, &suConverged); POINT viewOffset = {0, 0}; pColorFrame->CopyTo(pColorDisplay,NULL,0,0);//将彩色图像pColorFrame复制到pColorDisplay中，然后对pColorDisplay进行直接处理 hr = VisualizeFaceModel(pColorDisplay, ftModel, &myCameraConfig, pSU, 1.0, viewOffset, pFTResult, 0x00FFFF00);//该函数为画网格 if(FAILED(hr)) printf("显示失败！！n"); Mat tempMat(COLOR_HIGHT,COLOR_WIDTH,CV_8UC4,pColorDisplay->GetBuffer()); imshow("faceTracking",tempMat); bStop = waitKey(1);//按下ESC结束 } else// -----------当isTracked = false时，则值显示获取到的彩色图像信息 { pColorFrame->CopyTo(pColorDisplay,NULL,0,0); Mat tempMat(COLOR_HIGHT,COLOR_WIDTH,CV_8UC4,pColorDisplay->GetBuffer()); imshow("faceTracking",tempMat); bStop = waitKey(1); } if(m_hEvNuiProcessStop!=NULL) { if( bStop == 27 || bStop == \'q\' || bStop == \'Q\' ) { SetEvent(m_hEvNuiProcessStop); if(m_hProcesss!=NULL) { WaitForSingleObject(m_hProcesss,INFINITE); CloseHandle(m_hProcesss); m_hProcesss = NULL; } break; } } else { break; } //这里也要判断是否m_hEvNuiProcessStop已经被激活了！ Sleep(16); // iFaceTrackTimeCount++; // if(iFaceTrackTimeCount>16*1000) // break; } if(m_hProcesss!=NULL) { WaitForSingleObject(m_hProcesss,INFINITE); CloseHandle(m_hProcesss); m_hProcesss = NULL; } // Clean up. pFTResult->Release(); pColorFrame->Release(); pFT->Release(); NuiShutdown(); return 0; } //显示网状人脸,初始化人脸模型 HRESULT VisualizeFaceModel(IFTImage* pColorImg, IFTModel* pModel, FT_CAMERA_CONFIG const* pCameraConfig, FLOAT const* pSUCoef, FLOAT zoomFactor, POINT viewOffset, IFTResult* pAAMRlt, UINT32 color)//zoomFactor = 1.0f viewOffset = POINT(0,0) pAAMRlt为跟踪结果 { if (!pColorImg || !pModel || !pCameraConfig || !pSUCoef || !pAAMRlt) { return E_POINTER; } HRESULT hr = S_OK; UINT vertexCount = pModel->GetVertexCount();//面部特征点的个数 FT_VECTOR2D* pPts2D = reinterpret_cast<FT_VECTOR2D*>(_malloca(sizeof(FT_VECTOR2D) * vertexCount));//二维向量 reinterpret_cast强制类型转换符 _malloca在堆栈上分配内存 //以上是关于人脸旋转 《转》的主要内容，如果未能解决你的问题，请参考以下文章